Opening the roof at AT&T Stadium before Monday night's Texans-Cowboys game suddenly became dangerous.

Monday Night Football: Metal fragment falls from AT&T Stadium roof after opening before Texans-Cowboys game - Yahoo Sports

The Latest News and Updates in brought to you by the team at WTRF:

AT&T is providing extra safety support for families this Cybersecurity 
Awareness Month

IntelBroker claims the breach impacts Microsoft, SAP, AT&T, Verizon, T-Mobile US, and more

Cisco confirms ongoing probe into alleged data breach • The Register

According to Nvidia, AT&T is teaming with Quantiphi to create a conversational AI platform to assist employees with software development and financial services tasks.

Nvidia highlights AI software and services at D.C. AI Summit

Realty Income, AT&T, and Opera look like undervalued income plays.

3 Dividend Stocks That Are Screaming Buys in October

AMARILLO, Texas (KAMR/KCIT) — With the growing number of scams coming into our lives through our phones and the internet AT&T give some helpful tips to stay ahead of the scams. Go to their webs…

AT&T Tips on Cybersecurity

Arkansas' regular season success predicated on performance in AT&T Stadium, needs offensive line to generate push in rushing attack.

Early ESPN Analytics Prediction for Hogs-Aggies Matchup

(Bloomberg) -- AT&T Inc. agreed to pay $13 million to settle an investigation by the US Federal Communications Commission into whether the telecommunications company failed to protect customer data that was stolen when a cloud vendor was hacked last year. Most Read from BloombergCalifornia’s ...

AT&T Agrees to Pay $13 Million FCC Fine For Cloud Data Leak

Telecom operator AT&T and its joint-venture partner TPG are in early-stage talks to merge their DirecTV satellite TV service with EchoStar owned Dish, a person familiar with the matter told Reuters on Friday. The two companies first attempted to merge back in 2002 when the U.S. Justice Department ...

US satellite-TV providers DirecTV and Dish are in talks to merge again, source says

AT&T

Authors:
(1) Hoon Kim, Beeble AI, and contributed equally to this work;
(2) Minje Jang, Beeble AI, and contributed equally to this work;
(3) Wonjun Yoon, Beeble AI, and contributed equally to this work;
(4) Jisoo Lee, Beeble AI, and contributed equally to this work;
(5) Donghyun Na, Beeble AI, and contributed equally to this work;
(6) Sanghyun Woo, New York University, and contributed equally to this work. Editor's Note: This is Part 4 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Table of Links Abstract and 1. Introduction
2. Related Work
3. SwitchLight and 3.1. Preliminaries
3.2. Problem Formulation
3.3. Architecture
3.4. Objectives
4. Multi-Masked Autoencoder Pre-training
5. Data
6. Experiments
7. Conclusion Appendix A. Implementation Details
B. User Study Interface
C. Video Demonstration
D. Additional Qualitative Results & References 3.2. Problem Formulation This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Hoon Kim, Beeble AI, and contributed equally to this work; (2) Minje Jang, Beeble AI, and contributed equally to this work; (3) Wonjun Yoon, Beeble AI, and contributed equally to this work; (4) Jisoo Lee, Beeble AI, and contributed equally to this work; (5) Donghyun Na, Beeble AI, and contributed equally to this work; (6) Sanghyun Woo, New York University, and contributed equally to this work. Authors: Authors: (1) Hoon Kim, Beeble AI, and contributed equally to this work; (2) Minje Jang, Beeble AI, and contributed equally to this work; (3) Wonjun Yoon, Beeble AI, and contributed equally to this work; (4) Jisoo Lee, Beeble AI, and contributed equally to this work; (5) Donghyun Na, Beeble AI, and contributed equally to this work; (6) Sanghyun Woo, New York University, and contributed equally to this work. Editor's Note: This is Part 4 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 4 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 4 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 4 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Table of Links Abstract and 1. Introduction 2. Related Work 3. SwitchLight and 3.1. Preliminaries 3.2. Problem Formulation 3.3. Architecture 3.4. Objectives 4. Multi-Masked Autoencoder Pre-training 5. Data 6. Experiments 7. Conclusion Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Work 2. Related Work 3. SwitchLight and 3.1. Preliminaries 3. SwitchLight and 3.1. Preliminaries 3.2. Problem Formulation 3.2. Problem Formulation 3.3. Architecture 3.3. Architecture 3.4. Objectives 3.4. Objectives 4. Multi-Masked Autoencoder Pre-training 4. Multi-Masked Autoencoder Pre-training 5. Data 5. Data 6. Experiments 6. Experiments 7. Conclusion 7. Conclusion Appendix A. Implementation Details B. User Study Interface C. Video Demonstration D. Additional Qualitative Results & References A. Implementation Details A. Implementation Details B. User Study Interface B. User Study Interface C. Video Demonstration C. Video Demonstration D. Additional Qualitative Results & References D. Additional Qualitative Results & References 3.2. Problem Formulation This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

Beeble Researchers Attempt to Manipulate Lighting on Digital Image Using AI

Table of Links Abstract and 1 Introduction 2 Background and 2.1 Transformer-Based Large Language Models 2.2 LLM Service & Autoregressive Generation 2.3 Batching Techniques for LLMs 3 Memory Challenges in LLM Serving 3.1 Memory Management in Existing Systems 4 Method and 4.1 PagedAttention 4.2 KV Cache Manager 4.3 Decoding with PagedAttention and vLLM 4.4 Application to Other Decoding Scenarios 4.5 Scheduling and Preemption 4.6 Distributed Execution 5 Implementation 6 Evaluation and 6.1 Experimental Setup 6.2 Basic Sampling 6.3 Parallel Sampling and Beam Search 6.4 Shared prefix 6.5 Chatbot 7 Ablation Studies 8 Discussion 9 Related Work 10 Conclusion, Acknowledgement and References Abstract High throughput serving of large language models (LLMs) requires batching sufficiently many requests at a time. However, existing systems struggle because the key-value cache (KV cache) memory for each request is huge and grows and shrinks dynamically. When managed inefficiently, this memory can be significantly wasted by fragmentation and redundant duplication, limiting the batch size. To address this problem, we propose PagedAttention, an attention algorithm inspired by the classical virtual memory and paging techniques in operating systems. On top of it, we build vLLM, an LLM serving system that achieves (1) near-zero waste in KV cache memory and (2) flexible sharing of KV cache within and across requests to further reduce memory usage. Our evaluations show that vLLM improves the throughput of popular LLMs by 2-4× with the same level of latency compared to the state-of-the-art systems, such as FasterTransformer and Orca. The improvement is more pronounced with longer sequences, larger models, and more complex decoding algorithms. vLLM’s source code is publicly available at https://github.com/vllm-project/vllm. 1 Introduction The emergence of large language models (LLMs) like GPT [5, 37] and PaLM [9] have enabled new applications such as programming assistants [6, 18] and universal chatbots [19, 35] that are starting to profoundly impact our work and daily routines. Many cloud companies [34, 44] are racing to provide these applications as hosted services. However, running these applications is very expensive, requiring a large number of hardware accelerators such as GPUs. According to recent estimates, processing an LLM request can be 10× more expensive than a traditional keyword query [43]. Given these high costs, increasing the throughput—and hence reducing the cost per request—of LLM serving systems is becoming more important. At the core of LLMs lies an autoregressive Transformer model [53]. This model generates words (tokens), one at a time, based on the input (prompt) and the previous sequence of the output’s tokens it has generated so far. For each request, this expensive process is repeated until the model outputs a termination token. This sequential generation process makes the workload memory-bound, underutilizing the computation power of GPUs and limiting the serving throughput. Improving the throughput is possible by batching multiple requests together. However, to process many requests in a batch, the memory space for each request should be efficiently managed. For example, Fig. 1 (left) illustrates the memory distribution for a 13B-parameter LLM on an NVIDIA A100 GPU with 40GB RAM. Approximately 65% of the memory is allocated for the model weights, which remain static during serving. Close to 30% of the memory is used to store the dynamic states of the requests. For Transformers, these states consist of the key and value tensors associated with the attention mechanism, commonly referred to as KV cache [41], which represent the context from earlier tokens to generate new output tokens in sequence. The remaining small percentage of memory is used for other data, including activations – the ephemeral tensors created when evaluating the LLM. Since the model weights are constant and the activations only occupy a small fraction of the GPU memory, the way the KV cache is managed is critical in determining the maximum batch size. When managed inefficiently, the KV cache memory can significantly limit the batch size and consequently the throughput of the LLM, as illustrated in Fig. 1 (right). In this paper, we observe that existing LLM serving systems [31, 60] fall short of managing the KV cache memory efficiently. This is mainly because they store the KV cache of a request in contiguous memory space, as most deep learning frameworks [33, 39] require tensors to be stored in contiguous memory. However, unlike the tensors in the traditional deep learning workloads, the KV cache has unique characteristics: it dynamically grows and shrinks over time as the model generates new tokens, and its lifetime and length are not known a priori. These characteristics make the existing systems’ approach significantly inefficient in two ways: First, the existing systems [31, 60] suffer from internal and external memory fragmentation. To store the KV cache of a request in contiguous space, they pre-allocate a contiguous chunk of memory with the request’s maximum length (e.g., 2048 tokens). This can result in severe internal fragmentation, since the request’s actual length can be much shorter than its maximum length (e.g., Fig. 11). Moreover, even if the actual length is known a priori, the pre-allocation is still inefficient: As the entire chunk is reserved during the request’s lifetime, other shorter requests cannot utilize any part of the chunk that is currently unused. Besides, external memory fragmentation can also be significant, since the preallocated size can be different for each request. Indeed, our profiling results in Fig. 2 show that only 20.4% - 38.2% of the KV cache memory is used to store the actual token states in the existing systems. Second, the existing systems cannot exploit the opportunities for memory sharing. LLM services often use advanced decoding algorithms, such as parallel sampling and beam search, that generate multiple outputs per request. In these scenarios, the request consists of multiple sequences that can partially share their KV cache. However, memory sharing is not possible in the existing systems because the KV cache of the sequences is stored in separate contiguous spaces. To address the above limitations, we propose PagedAttention, an attention algorithm inspired by the operating system’s (OS) solution to memory fragmentation and sharing: virtual memory with paging. PagedAttention divides the request’s KV cache into blocks, each of which can contain the attention keys and values of a fixed number of tokens. In PagedAttention, the blocks for the KV cache are not necessarily stored in contiguous space. Therefore, we can manage the KV cache in a more flexible way as in OS’s virtual memory: one can think of blocks as pages, tokens as bytes, and requests as processes. This design alleviates internal fragmentation by using relatively small blocks and allocating them on demand. Moreover, it eliminates external fragmentation as all blocks have the same size. Finally, it enables memory sharing at the granularity of a block, across the different sequences associated with the same request or even across the different requests. In this work, we build vLLM, a high-throughput distributed LLM serving engine on top of PagedAttention that achieves near-zero waste in KV cache memory. vLLM uses block-level memory management and preemptive request scheduling that are co-designed with PagedAttention. vLLM supports popular LLMs such as GPT [5], OPT [62], and LLaMA [52] with varying sizes, including the ones exceeding the memory capacity of a single GPU. Our evaluations on various models and workloads show that vLLM improves the LLM serving throughput by 2-4× compared to the state-of-the-art systems [31, 60], without affecting the model accuracy at all. The improvements are more pronounced with longer sequences, larger models, and more complex decoding algorithms (§4.3). In summary, we make the following contributions: • We identify the challenges in memory allocation in serving LLMs and quantify their impact on serving performance. • We propose PagedAttention, an attention algorithm that operates on KV cache stored in non-contiguous paged memory, which is inspired by the virtual memory and paging in OS. • We design and implement vLLM, a distributed LLM serving engine built on top of PagedAttention. • We evaluate vLLM on various scenarios and demonstrate that it substantially outperforms the previous state-of-theart solutions such as FasterTransformer [31] and Orca [60]. This paper is available on arxiv under CC BY 4.0 DEED license. Authors:
(1) Woosuk Kwon, UC Berkeley with Equal contribution;
(2) Zhuohan Li, UC Berkeley with Equal contribution;
(3) Siyuan Zhuang, UC Berkeley;
(4) Ying Sheng, UC Berkeley and Stanford University;
(5) Lianmin Zheng, UC Berkeley;
(6) Cody Hao Yu, Independent Researcher;
(7) Cody Hao Yu, Independent Researcher;
(8) Joseph E. Gonzalez, UC Berkeley;
(9) Hao Zhang, UC San Diego;
(10) Ion Stoica, UC Berkeley. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background and 2.1 Transformer-Based Large Language Models 2 Background and 2.1 Transformer-Based Large Language Models 2.2 LLM Service & Autoregressive Generation 2.2 LLM Service & Autoregressive Generation 2.3 Batching Techniques for LLMs 2.3 Batching Techniques for LLMs 3 Memory Challenges in LLM Serving 3 Memory Challenges in LLM Serving 3.1 Memory Management in Existing Systems 3.1 Memory Management in Existing Systems 4 Method and 4.1 PagedAttention 4 Method and 4.1 PagedAttention 4.2 KV Cache Manager 4.2 KV Cache Manager 4.3 Decoding with PagedAttention and vLLM 4.3 Decoding with PagedAttention and vLLM 4.4 Application to Other Decoding Scenarios 4.4 Application to Other Decoding Scenarios 4.5 Scheduling and Preemption 4.5 Scheduling and Preemption 4.6 Distributed Execution 4.6 Distributed Execution 5 Implementation 5 Implementation 6 Evaluation and 6.1 Experimental Setup 6 Evaluation and 6.1 Experimental Setup 6.2 Basic Sampling 6.2 Basic Sampling 6.3 Parallel Sampling and Beam Search 6.3 Parallel Sampling and Beam Search 6.4 Shared prefix 6.4 Shared prefix 6.5 Chatbot 6.5 Chatbot 7 Ablation Studies 7 Ablation Studies 8 Discussion 8 Discussion 9 Related Work 9 Related Work 10 Conclusion, Acknowledgement and References 10 Conclusion, Acknowledgement and References Abstract High throughput serving of large language models (LLMs) requires batching sufficiently many requests at a time. However, existing systems struggle because the key-value cache (KV cache) memory for each request is huge and grows and shrinks dynamically. When managed inefficiently, this memory can be significantly wasted by fragmentation and redundant duplication, limiting the batch size. To address this problem, we propose PagedAttention, an attention algorithm inspired by the classical virtual memory and paging techniques in operating systems. On top of it, we build vLLM, an LLM serving system that achieves (1) near-zero waste in KV cache memory and (2) flexible sharing of KV cache within and across requests to further reduce memory usage. Our evaluations show that vLLM improves the throughput of popular LLMs by 2-4× with the same level of latency compared to the state-of-the-art systems, such as FasterTransformer and Orca. The improvement is more pronounced with longer sequences, larger models, and more complex decoding algorithms. vLLM’s source code is publicly available at https://github.com/vllm-project/vllm. 1 Introduction The emergence of large language models (LLMs) like GPT [5, 37] and PaLM [9] have enabled new applications such as programming assistants [6, 18] and universal chatbots [19, 35] that are starting to profoundly impact our work and daily routines. Many cloud companies [34, 44] are racing to provide these applications as hosted services. However, running these applications is very expensive, requiring a large number of hardware accelerators such as GPUs. According to recent estimates, processing an LLM request can be 10× more expensive than a traditional keyword query [43]. Given these high costs, increasing the throughput—and hence reducing the cost per request—of LLM serving systems is becoming more important. At the core of LLMs lies an autoregressive Transformer model [53]. This model generates words (tokens), one at a time, based on the input (prompt) and the previous sequence of the output’s tokens it has generated so far. For each request, this expensive process is repeated until the model outputs a termination token. This sequential generation process makes the workload memory-bound, underutilizing the computation power of GPUs and limiting the serving throughput. Improving the throughput is possible by batching multiple requests together. However, to process many requests in a batch, the memory space for each request should be efficiently managed. For example, Fig. 1 (left) illustrates the memory distribution for a 13B-parameter LLM on an NVIDIA A100 GPU with 40GB RAM. Approximately 65% of the memory is allocated for the model weights, which remain static during serving. Close to 30% of the memory is used to store the dynamic states of the requests. For Transformers, these states consist of the key and value tensors associated with the attention mechanism, commonly referred to as KV cache [41], which represent the context from earlier tokens to generate new output tokens in sequence. The remaining small percentage of memory is used for other data, including activations – the ephemeral tensors created when evaluating the LLM. Since the model weights are constant and the activations only occupy a small fraction of the GPU memory, the way the KV cache is managed is critical in determining the maximum batch size. When managed inefficiently, the KV cache memory can significantly limit the batch size and consequently the throughput of the LLM, as illustrated in Fig. 1 (right). In this paper, we observe that existing LLM serving systems [31, 60] fall short of managing the KV cache memory efficiently. This is mainly because they store the KV cache of a request in contiguous memory space, as most deep learning frameworks [33, 39] require tensors to be stored in contiguous memory. However, unlike the tensors in the traditional deep learning workloads, the KV cache has unique characteristics: it dynamically grows and shrinks over time as the model generates new tokens, and its lifetime and length are not known a priori. These characteristics make the existing systems’ approach significantly inefficient in two ways: First, the existing systems [31, 60] suffer from internal and external memory fragmentation. To store the KV cache of a request in contiguous space, they pre-allocate a contiguous chunk of memory with the request’s maximum length (e.g., 2048 tokens). This can result in severe internal fragmentation, since the request’s actual length can be much shorter than its maximum length (e.g., Fig. 11). Moreover, even if the actual length is known a priori, the pre-allocation is still inefficient: As the entire chunk is reserved during the request’s lifetime, other shorter requests cannot utilize any part of the chunk that is currently unused. Besides, external memory fragmentation can also be significant, since the preallocated size can be different for each request. Indeed, our profiling results in Fig. 2 show that only 20.4% - 38.2% of the KV cache memory is used to store the actual token states in the existing systems. Second, the existing systems cannot exploit the opportunities for memory sharing. LLM services often use advanced decoding algorithms, such as parallel sampling and beam search, that generate multiple outputs per request. In these scenarios, the request consists of multiple sequences that can partially share their KV cache. However, memory sharing is not possible in the existing systems because the KV cache of the sequences is stored in separate contiguous spaces. To address the above limitations, we propose PagedAttention, an attention algorithm inspired by the operating system’s (OS) solution to memory fragmentation and sharing: virtual memory with paging. PagedAttention divides the request’s KV cache into blocks, each of which can contain the attention keys and values of a fixed number of tokens. In PagedAttention, the blocks for the KV cache are not necessarily stored in contiguous space. Therefore, we can manage the KV cache in a more flexible way as in OS’s virtual memory: one can think of blocks as pages, tokens as bytes, and requests as processes. This design alleviates internal fragmentation by using relatively small blocks and allocating them on demand. Moreover, it eliminates external fragmentation as all blocks have the same size. Finally, it enables memory sharing at the granularity of a block, across the different sequences associated with the same request or even across the different requests. In this work, we build vLLM, a high-throughput distributed LLM serving engine on top of PagedAttention that achieves near-zero waste in KV cache memory. vLLM uses block-level memory management and preemptive request scheduling that are co-designed with PagedAttention. vLLM supports popular LLMs such as GPT [5], OPT [62], and LLaMA [52] with varying sizes, including the ones exceeding the memory capacity of a single GPU. Our evaluations on various models and workloads show that vLLM improves the LLM serving throughput by 2-4× compared to the state-of-the-art systems [31, 60], without affecting the model accuracy at all. The improvements are more pronounced with longer sequences, larger models, and more complex decoding algorithms (§4.3). In summary, we make the following contributions: • We identify the challenges in memory allocation in serving LLMs and quantify their impact on serving performance. • We propose PagedAttention, an attention algorithm that operates on KV cache stored in non-contiguous paged memory, which is inspired by the virtual memory and paging in OS. • We design and implement vLLM, a distributed LLM serving engine built on top of PagedAttention. • We evaluate vLLM on various scenarios and demonstrate that it substantially outperforms the previous state-of-theart solutions such as FasterTransformer [31] and Orca [60]. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors: (1) Woosuk Kwon, UC Berkeley with Equal contribution; (2) Zhuohan Li, UC Berkeley with Equal contribution; (3) Siyuan Zhuang, UC Berkeley; (4) Ying Sheng, UC Berkeley and Stanford University; (5) Lianmin Zheng, UC Berkeley; (6) Cody Hao Yu, Independent Researcher; (7) Cody Hao Yu, Independent Researcher; (8) Joseph E. Gonzalez, UC Berkeley; (9) Hao Zhang, UC San Diego; (10) Ion Stoica, UC Berkeley. Authors: Authors: (1) Woosuk Kwon, UC Berkeley with Equal contribution; (2) Zhuohan Li, UC Berkeley with Equal contribution; (3) Siyuan Zhuang, UC Berkeley; (4) Ying Sheng, UC Berkeley and Stanford University; (5) Lianmin Zheng, UC Berkeley; (6) Cody Hao Yu, Independent Researcher; (7) Cody Hao Yu, Independent Researcher; (8) Joseph E. Gonzalez, UC Berkeley; (9) Hao Zhang, UC San Diego; (10) Ion Stoica, UC Berkeley.

PagedAttention: An Attention Algorithm Inspired By the Classical Virtual Memory in Operating Systems

Ethical Considerations in QAnon Authorship Attribution Research

Authors:
(1) Florian Cafiero (ORCID 0000-0002-1951-6942), Sciences Po, Medialab;
(2) Jean-Baptiste Camps (ORCID 0000-0003-0385-7037), Ecole nationale des chartes, Universite Paris, Sciences & Lettres. Table of Links Abstract and Introduction Why work on QAnon? Specificities and social impact Who is Q? The theories put to test Authorship attribution Results Discussion Corpus constitution Quotes of authors outside of the corpus have been Definition of two subcorpus: dealing with generic difference and an imbalanced dataset The genre of “Q drops”: a methodological challenge Detecting style changes: rolling stylometry Ethical statement, Acknowledgements, and References Dealing with quotations and copy/paste Quotes of authors outside of the corpus have been excluded as much as possible by close reading: in particular, quotes from Q, Wikipedia, the Stanford Encyclopedia of Philosophy, Abraham Lincoln, the Intelligence Resource Program (irp-fas), Steve Scully’s biography etc. All these quotes have been removed. Direct quotations (with or without quotation marks) and copy/paste between the writings of the different candidates can also occur. A good deal of them quote Donald, Eric or Melania T. – Q does it too. There is also a certain number of quotations from Q by the others (such as Paul F. for instance). This could lead to small biases in the constitution of idiolectal profiles. To avoid this, we then proceeded to systematically detect citation between the candidates themselves. Direct pairwise comparison being computationally too costly for a corpus of this size, we used a Locality-Sensitive Hashing (LSH) algorithm. To that end, we used the open source TextReuse package (Mullen, 2020). The corpus was tokenised into sentences, and broken word bi-grams (with skip of 1, that is, allowing for any one word to be inserted between the two words of the bigrams) were counted. For all pairs of sentences, a Jaccard similarity score was computed. Be A and B two samples considered as sets of bi-grams, the Jaccard similarity is computed as: All pairs of sentences with a Jaccard similarity score superior or equal to 0.5 (i.e., at least half of their bi-grams in common) were examined by a human expert, and quotations removed. Even for J = 1, we were sometimes confronted to false positives. Dan S. and Melania T. both use once the sentence “we are all in this together”, without directly citing each other. We thus left this passage in both their texts. Rarely used, the sentence “the American people are not stupid” nevertheless appears in different texts. It was kept in the texts studied, as other simple sentences (“thank you for your service” etc.) Other situations were trickier to address. For instance, Dan S. uses once the sentence: “the best is yet to come”. It is used five times by Q, himself quoting former President Donald Trump. This sentence could be used by anyone without directly quoting Q or Donald Trump. Yet, as its use by Dan S. starts with “As the President says. . . ”, we considered it a direct quotation and proceeded to deletion from Dan S.’s text. Yet, we did not delete it from Q’s own writing, as it is never used as an explicit quotation: the sentence could be used in another context, the person(s) writing the Qdrops with this sentence could try to impersonate Donald Trump, etc. In any of these cases, it would be legitimate to leave the information. Same thing goes for expression such as “the world is watching” or “make America great again”, used by Donald Trump. but also by Q and some of the potential candidates here. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Florian Cafiero (ORCID 0000-0002-1951-6942), Sciences Po, Medialab; (2) Jean-Baptiste Camps (ORCID 0000-0003-0385-7037), Ecole nationale des chartes, Universite Paris, Sciences & Lettres. Authors: Authors: (1) Florian Cafiero (ORCID 0000-0002-1951-6942), Sciences Po, Medialab; (2) Jean-Baptiste Camps (ORCID 0000-0003-0385-7037), Ecole nationale des chartes, Universite Paris, Sciences & Lettres. Table of Links Abstract and Introduction Abstract and Introduction Why work on QAnon? Specificities and social impact Why work on QAnon? Specificities and social impact Who is Q? The theories put to test Who is Q? The theories put to test Authorship attribution Authorship attribution Results Results Discussion Discussion Corpus constitution Corpus constitution Quotes of authors outside of the corpus have been Quotes of authors outside of the corpus have been Definition of two subcorpus: dealing with generic difference and an imbalanced dataset Definition of two subcorpus: dealing with generic difference and an imbalanced dataset The genre of “Q drops”: a methodological challenge The genre of “Q drops”: a methodological challenge Detecting style changes: rolling stylometry Detecting style changes: rolling stylometry Ethical statement, Acknowledgements, and References Ethical statement, Acknowledgements, and References Dealing with quotations and copy/paste Quotes of authors outside of the corpus have been excluded as much as possible by close reading: in particular, quotes from Q, Wikipedia, the Stanford Encyclopedia of Philosophy, Abraham Lincoln, the Intelligence Resource Program (irp-fas), Steve Scully’s biography etc. All these quotes have been removed. Direct quotations (with or without quotation marks) and copy/paste between the writings of the different candidates can also occur. A good deal of them quote Donald, Eric or Melania T. – Q does it too. There is also a certain number of quotations from Q by the others (such as Paul F. for instance). This could lead to small biases in the constitution of idiolectal profiles. To avoid this, we then proceeded to systematically detect citation between the candidates themselves. Direct pairwise comparison being computationally too costly for a corpus of this size, we used a Locality-Sensitive Hashing (LSH) algorithm. To that end, we used the open source TextReuse package (Mullen, 2020). The corpus was tokenised into sentences, and broken word bi-grams (with skip of 1, that is, allowing for any one word to be inserted between the two words of the bigrams) were counted. For all pairs of sentences, a Jaccard similarity score was computed. Be A and B two samples considered as sets of bi-grams, the Jaccard similarity is computed as: All pairs of sentences with a Jaccard similarity score superior or equal to 0.5 (i.e., at least half of their bi-grams in common) were examined by a human expert, and quotations removed. Even for J = 1, we were sometimes confronted to false positives. Dan S. and Melania T. both use once the sentence “we are all in this together”, without directly citing each other. We thus left this passage in both their texts. Rarely used, the sentence “the American people are not stupid” nevertheless appears in different texts. It was kept in the texts studied, as other simple sentences (“thank you for your service” etc.) Other situations were trickier to address. For instance, Dan S. uses once the sentence: “the best is yet to come”. It is used five times by Q, himself quoting former President Donald Trump. This sentence could be used by anyone without directly quoting Q or Donald Trump. Yet, as its use by Dan S. starts with “As the President says. . . ”, we considered it a direct quotation and proceeded to deletion from Dan S.’s text. Yet, we did not delete it from Q’s own writing, as it is never used as an explicit quotation: the sentence could be used in another context, the person(s) writing the Qdrops with this sentence could try to impersonate Donald Trump, etc. In any of these cases, it would be legitimate to leave the information. Same thing goes for expression such as “the world is watching” or “make America great again”, used by Donald Trump. but also by Q and some of the potential candidates here. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Addressing Quotations and Copy-Paste in QAnon Authorship Attribution

The Decentralized Finance (DeFi) sector envisioned lofty goals of universal enfranchisement of users and accessibility. While the goals continue to remain in focus, there are emerging cyber threats that pose serious problems to the integrity of the DeFi systems. Distributed Denial of Service (DDoS) assault is a serious cyber infiltration that can cause problems even for the most powerful, distributed protocols out there. In this age, DDoS attacks have long moved past being minor nuisances and have the potential to steal millions of dollars, ruin carefully managed reputations, and dent the efforts to move to a decentralized, trustless alternative. The DDoS attack is not only limited to the Defi industry alone. For instance, the largest DDoS attack on record is the 2024 Cloudflare attack. It lasted for just 80 seconds reaching about 5.6 terabits per second (Tbps). Other notable examples are the 2016 Mirai Dyn attack, the 2018 GitHub attack, and the 2020 AWS attack, etc. However, this article will shed light on the threat posed by DDoS attacks and their devastating potential on the DeFi economy. Expert industry insight into problem identification and solution-oriented approach will be provided by a top cybersecurity specialist. DeFi: A Prime Target for Cybercriminals The DeFi economy boasts a major incentive to the finance sector. It is a trustless, permissionless, and innovative space with the potential for bigger profits compared to the conventional finance scene. However, DeFi protocols handle a lot of money, mostly in crypto, so they appeal to hackers of all backgrounds. These online hackers study smart contracts in detail and use sophisticated automated systems to take advantage of vulnerabilities. A successful hacking attempt often results in users being unable to track and access their funds, rendering the platform useless. As a result, investors jump ship from the platform, encompassing it completely in financial ruin. Due to the negative effects of such attacks, companies often try to cover up the scale of the intrusion and keep numbers secret. However, the open-source nature of DeFI protocols means that they cannot keep everything under wraps and the secrecy often causes problems to compound further. Notably, the market capitalization and liquidity of hacked platforms often experience a major decline after losing potential millions in DDoS attacks. The psychological impact of a major hack on users cannot be understated as well – It undermines user confidence and could eventually cause a domino effect, resulting in the platform declaring bankruptcy. Maksym Ishchenko, Founder and CEO of Azerux, is a well-known cybersecurity professional specializing in these DDoS attacks and mitigation efforts to counter them. According to Maksym: DeFi's decentralized structure poses a special security risk. Because there isn't a single point of control as in centralized systems, security is very complicated yet undeniably crucial. A successful assault might have disastrous financial consequences, resulting in large losses and irreversible harm to one's image. Ischenko’s comments echo earlier concerns that the attacks themselves are becoming more and more complex and no longer limited to volume-based assaults which involve overloading a platform’s server with a large number of pointless requests, choking it entirely. Attackers now employ an organized approach using high-level tools that have a complete grasp of blockchain technology and its underlying infrastructure. Hence, efforts to limit DDoS attacks are becoming more and more difficult with time in the DeFi ecosystem. Advanced DDoS Attacks and the Threats to DeFi The exponential rise in attacks on the decentralized fintech platforms is largely attributed to DeFi’s explosive growth over the years. It has been a major target for hackers due to abundance of funds and relatively nascent technology that has weak links present in between. "Many projects rush to market with insufficient security testing," Maksym states. According to him, the Solana network is one of the prime examples of decentralized networks coming under DDoS attacks. Malicious automated transactions overloaded the network during the Grape Protocol's IDO on Raydium, resulting in a devastating 17-hour outage that impacted the whole Solana ecosystem. Solana has choked consistently over the few years of its existence but other experts believe that DDoS is not the only one to be blamed for the network’s frequent shortcomings. Another notable incident occurred on January 18, 2024, when the listing of Manta Network's token coincided with a DDoS assault of more than 135 million bogus RPC queries. “While the demand for Manta tokens remained high, the attack showed how susceptible even recently announced, well-known projects are to sophisticated DDoS operations. The network's bandwidth was overloaded by the sheer number of bogus RPC queries, which essentially stopped valid transactions." Ischenko notes that it is crucial to have robust DDoS mitigation techniques, especially at a time when new projects are being deployed on a blockchain. DeFi protocols need to take a hint here. How to Build Robust DDoS Protection for Your DeFi Platform A stellar DDoS defense system is necessary for a DeFi protocol in the long run. It is no longer a technical jargon that needs to be dealt with later on but a basic security mechanism that needs to be implemented from the ground up. The Azerux CEO believes that robust protection for your DeFi platform goes beyond DDoS risk mitigation. He proposes a multi-layered approach, championed by Azerux, consisting of: Network-level mitigation: Malware-focused traffic is a major cause of concern and needs to be filtered before it can reach the target servers. A well-planned filtering process, including strategic scrubbing centers, helps to prevent the system from becoming overwhelmed by the initial barrage of malware.


Application-level protection: Web application-focused security measures, such as Web Application Firewall (WAF) technology, are essential for protecting platforms in addition to securing servers. WAFs enable actual users to continue using the platform during an attack while providing administrators with real-time traffic analysis to differentiate between attackers and legitimate users.


Real-time monitoring and automated response: Advanced monitoring setups can detect irregularities associated with DDoS attacks in real-time. Automated response systems are crucial for scaling defenses to match the attack's intensity, ensuring the network remains operational under pressure. Adaptive defense mechanisms and proactive threat intelligence: Smart defenses that learn from previous DDoS attacks and continuously refine strategies can make networks more resilient over time. Engaging in proactive threat intelligence ensures that mitigation strategies evolve to counter emerging attack vectors effectively. Why DDoS Protection is Critical for DeFi The DeFI ecosystem is an emerging, dynamic field that has the potential to hit trillions of dollars worth of liquidity in the near future. It already holds tens of billions of dollars in place despite the dangers associated with it and it is expected to only grow with time. Cyber incursions like DDoS attacks are a serious issue and may cause entire platforms to collapse in a matter of days if not hours. The path to a successful DeFi future is to help create a secure, proactive environment with central attention to cybersecurity. The Decentralized Finance (DeFi) sector envisioned lofty goals of universal enfranchisement of users and accessibility. While the goals continue to remain in focus, there are emerging cyber threats that pose serious problems to the integrity of the DeFi systems. Distributed Denial of Service (DDoS) assault is a serious cyber infiltration that can cause problems even for the most powerful, distributed protocols out there. The Decentralized Finance (DeFi) sector envisioned lofty goals of universal enfranchisement of users and accessibility. While the goals continue to remain in focus, there are emerging cyber threats that pose serious problems to the integrity of the DeFi systems. Distributed Denial of Service (DDoS) assault is a serious cyber infiltration that can cause problems even for the most powerful, distributed protocols out there. In this age, DDoS attacks have long moved past being minor nuisances and have the potential to steal millions of dollars, ruin carefully managed reputations, and dent the efforts to move to a decentralized, trustless alternative. DDoS The DDoS attack is not only limited to the Defi industry alone. For instance, the largest DDoS attack on record is the 2024 Cloudflare attack . It lasted for just 80 seconds reaching about 5.6 terabits per second (Tbps). Other notable examples are the 2016 Mirai Dyn attack, the 2018 GitHub attack, and the 2020 AWS attack, etc. 2024 Cloudflare attack However, this article will shed light on the threat posed by DDoS attacks and their devastating potential on the DeFi economy. Expert industry insight into problem identification and solution-oriented approach will be provided by a top cybersecurity specialist. DeFi: A Prime Target for Cybercriminals DeFi: A Prime Target for Cybercriminals The DeFi economy boasts a major incentive to the finance sector. It is a trustless, permissionless, and innovative space with the potential for bigger profits compared to the conventional finance scene. However, DeFi protocols handle a lot of money, mostly in crypto, so they appeal to hackers of all backgrounds. These online hackers study smart contracts in detail and use sophisticated automated systems to take advantage of vulnerabilities. A successful hacking attempt often results in users being unable to track and access their funds, rendering the platform useless. As a result, investors jump ship from the platform, encompassing it completely in financial ruin. Due to the negative effects of such attacks, companies often try to cover up the scale of the intrusion and keep numbers secret. However, the open-source nature of DeFI protocols means that they cannot keep everything under wraps and the secrecy often causes problems to compound further. Notably, the market capitalization and liquidity of hacked platforms often experience a major decline after losing potential millions in DDoS attacks. The psychological impact of a major hack on users cannot be understated as well – It undermines user confidence and could eventually cause a domino effect, resulting in the platform declaring bankruptcy. Maksym Ishchenko , Founder and CEO of Azerux, is a well-known cybersecurity professional specializing in these DDoS attacks and mitigation efforts to counter them. Maksym Ishchenko According to Maksym: DeFi's decentralized structure poses a special security risk. Because there isn't a single point of control as in centralized systems, security is very complicated yet undeniably crucial. A successful assault might have disastrous financial consequences, resulting in large losses and irreversible harm to one's image. DeFi's decentralized structure poses a special security risk. Because there isn't a single point of control as in centralized systems, security is very complicated yet undeniably crucial. A successful assault might have disastrous financial consequences, resulting in large losses and irreversible harm to one's image. Ischenko’s comments echo earlier concerns that the attacks themselves are becoming more and more complex and no longer limited to volume-based assaults which involve overloading a platform’s server with a large number of pointless requests, choking it entirely. Attackers now employ an organized approach using high-level tools that have a complete grasp of blockchain technology and its underlying infrastructure. Hence, efforts to limit DDoS attacks are becoming more and more difficult with time in the DeFi ecosystem. Advanced DDoS Attacks and the Threats to DeFi Advanced DDoS Attacks and the Threats to DeFi The exponential rise in attacks on the decentralized fintech platforms is largely attributed to DeFi’s explosive growth over the years. It has been a major target for hackers due to abundance of funds and relatively nascent technology that has weak links present in between. "Many projects rush to market with insufficient security testing," Maksym states. According to him, the Solana network is one of the prime examples of decentralized networks coming under DDoS attacks. Malicious automated transactions overloaded the network during the Grape Protocol's IDO on Raydium, resulting in a devastating 17-hour outage that impacted the whole Solana ecosystem. Solana has choked consistently over the few years of its existence but other experts believe that DDoS is not the only one to be blamed for the network’s frequent shortcomings. 17-hour outage Another notable incident occurred on January 18, 2024, when the listing of Manta Network's token coincided with a DDoS assault of more than 135 million bogus RPC queries. “While the demand for Manta tokens remained high, the attack showed how susceptible even recently announced, well-known projects are to sophisticated DDoS operations. The network's bandwidth was overloaded by the sheer number of bogus RPC queries, which essentially stopped valid transactions." Ischenko notes that it is crucial to have robust DDoS mitigation techniques, especially at a time when new projects are being deployed on a blockchain. DeFi protocols need to take a hint here. How to Build Robust DDoS Protection for Your DeFi Platform How to Build Robust DDoS Protection for Your DeFi Platform A stellar DDoS defense system is necessary for a DeFi protocol in the long run. It is no longer a technical jargon that needs to be dealt with later on but a basic security mechanism that needs to be implemented from the ground up. The Azerux CEO believes that robust protection for your DeFi platform goes beyond DDoS risk mitigation. He proposes a multi-layered approach, championed by Azerux, consisting of: Azerux Network-level mitigation: Malware-focused traffic is a major cause of concern and needs to be filtered before it can reach the target servers. A well-planned filtering process, including strategic scrubbing centers, helps to prevent the system from becoming overwhelmed by the initial barrage of malware. Application-level protection: Web application-focused security measures, such as Web Application Firewall (WAF) technology, are essential for protecting platforms in addition to securing servers. WAFs enable actual users to continue using the platform during an attack while providing administrators with real-time traffic analysis to differentiate between attackers and legitimate users. Real-time monitoring and automated response: Advanced monitoring setups can detect irregularities associated with DDoS attacks in real-time. Automated response systems are crucial for scaling defenses to match the attack's intensity, ensuring the network remains operational under pressure. Network-level mitigation: Malware-focused traffic is a major cause of concern and needs to be filtered before it can reach the target servers. A well-planned filtering process, including strategic scrubbing centers, helps to prevent the system from becoming overwhelmed by the initial barrage of malware. Network-level mitigation: Malware-focused traffic is a major cause of concern and needs to be filtered before it can reach the target servers. A well-planned filtering process, including strategic scrubbing centers, helps to prevent the system from becoming overwhelmed by the initial barrage of malware. Network-level mitigation: Application-level protection: Web application-focused security measures, such as Web Application Firewall (WAF) technology, are essential for protecting platforms in addition to securing servers. WAFs enable actual users to continue using the platform during an attack while providing administrators with real-time traffic analysis to differentiate between attackers and legitimate users. Application-level protection: Web application-focused security measures, such as Web Application Firewall (WAF) technology, are essential for protecting platforms in addition to securing servers. WAFs enable actual users to continue using the platform during an attack while providing administrators with real-time traffic analysis to differentiate between attackers and legitimate users. Application-level protection: Real-time monitoring and automated response: Advanced monitoring setups can detect irregularities associated with DDoS attacks in real-time. Automated response systems are crucial for scaling defenses to match the attack's intensity, ensuring the network remains operational under pressure. Real-time monitoring and automated response: Advanced monitoring setups can detect irregularities associated with DDoS attacks in real-time. Automated response systems are crucial for scaling defenses to match the attack's intensity, ensuring the network remains operational under pressure. Real-time monitoring and automated response: Adaptive defense mechanisms and proactive threat intelligence: Smart defenses that learn from previous DDoS attacks and continuously refine strategies can make networks more resilient over time. Engaging in proactive threat intelligence ensures that mitigation strategies evolve to counter emerging attack vectors effectively. Adaptive defense mechanisms and proactive threat intelligence: Smart defenses that learn from previous DDoS attacks and continuously refine strategies can make networks more resilient over time. Engaging in proactive threat intelligence ensures that mitigation strategies evolve to counter emerging attack vectors effectively. Adaptive defense mechanisms and proactive threat intelligence: Why DDoS Protection is Critical for DeFi Why DDoS Protection is Critical for DeFi The DeFI ecosystem is an emerging, dynamic field that has the potential to hit trillions of dollars worth of liquidity in the near future. It already holds tens of billions of dollars in place despite the dangers associated with it and it is expected to only grow with time. Cyber incursions like DDoS attacks are a serious issue and may cause entire platforms to collapse in a matter of days if not hours. The path to a successful DeFi future is to help create a secure, proactive environment with central attention to cybersecurity.

The Battle Against DDoS Attacks in Decentralized Finance

Artist Maurizio Cattelan displayed Comedian at Art Basel Miami in 2019. It was, quite simply, a banana taped to a wall. That banana — or more precisely, the concept behind it — just sold for over $6 million at Sotheby’s auction house. The payment was made in crypto by Justin Sun, an entrepreneur most notable for founding the Tron blockchain network in 2017. The fruit was auctioned off on November 20, 2024. https://youtu.be/E7pPomFdpLY?feature=shared&embedable=true Wait — sorry, my mistake — it wasn’t the same banana that was displayed in 2019. The banana had actually been replaced because, as we all know, bananas typically don’t last for several years. Instead, Justin Sun was given a Certificate of Authenticity for the purchase, along with the right to replace the banana with a new one whenever he chooses. So, what’s the value here? Of course, most people will note the absurdity of buying a banana for $6 million. If the banana itself will rot within days, how can this work of art retain any resale value? Even the most simplistic or controversial works of art that have sold for millions — whether they’re splashes of paint or conceptual sculptures — are tangible objects that can endure and be traded. A banana that can be replaced by another banana? How is that any fun or worthwhile for a collector? This moment, no matter how absurd it may seem, is emblematic of a shift toward a full-fledged attention economy where the digital and physical worlds increasingly merge. A simple banana taped to a wall — destined to be replaced — purchased with invisible cryptocurrency, isn’t really a purchase of the banana itself. It’s what I call “proof of attention.” In this attention economy, the Certificate of Authenticity is the true token of value in this transaction. The banana itself becomes a mere prop in a broader performance art piece. The certificate, a physical document proving ownership and authenticity, is a testament to the fact that, at this specific point in time, someone valued this ephemeral concept at over $6 million. Could it be that, in the future, more moments will be “tagged” with price tags — not because of the beauty or inherent quality of the work, but because of the absurdity of the concept? Perhaps the more absurd a moment, the more attention it garners, and the more its value is derived from this attention rather than the object itself. The disposability of the artwork — its inevitable decay — steers the spotlight away from the piece itself. The value isn’t in the banana or even the art. It’s in the concept, the absurdity, and the sheer notoriety of it all. This work has indeed been crystallized as a meme. And, as we speak, it’s being traded digitally on the Solana blockchain under the ticker $BAN, with a market cap of $143.68 million. There is no actual banana on the blockchain, yet people are trading this token as a digital parallel to the physical piece. One trader reportedly bought $10,000 worth of $BAN and later sold it for over $270,000. This relationship between online bidding and real-world transactions, facilitated by cryptocurrency, is shaping how we assign value — not just to art, but to moments, memes, and cultural icons. In the physical world, transactions like this come with more friction. Perhaps Sotheby’s is one of the few institutions capable of orchestrating such a singularity event. Watch closely — more is coming. You could consider investing in $BAN. Disclaimer: This is not financial advice. Cryptocurrencies are considered high-risk investments. Artist Maurizio Cattelan displayed Comedian at Art Basel Miami in 2019. It was, quite simply, a banana taped to a wall. That banana — or more precisely, the concept behind it — just sold for over $6 million at Sotheby’s auction house. The payment was made in crypto by Justin Sun, an entrepreneur most notable for founding the Tron blockchain network in 2017. The fruit was auctioned off on November 20, 2024. Comedian Tron blockchain https://youtu.be/E7pPomFdpLY?feature=shared&embedable=true https://youtu.be/E7pPomFdpLY?feature=shared&embedable=true Wait — sorry, my mistake — it wasn’t the same banana that was displayed in 2019. The banana had actually been replaced because, as we all know, bananas typically don’t last for several years. Instead, Justin Sun was given a Certificate of Authenticity for the purchase, along with the right to replace the banana with a new one whenever he chooses. Wait — sorry, my mistake — it wasn’t the same banana that was displayed in 2019. The banana had actually been replaced because, as we all know, bananas typically don’t last for several years. Instead, Justin Sun was given a Certificate of Authenticity for the purchase, along with the right to replace the banana with a new one whenever he chooses. So, what’s the value here? Of course, most people will note the absurdity of buying a banana for $6 million. So, what’s the value here? Of course, most people will note the absurdity of buying a banana for $6 million. If the banana itself will rot within days, how can this work of art retain any resale value? Even the most simplistic or controversial works of art that have sold for millions — whether they’re splashes of paint or conceptual sculptures — are tangible objects that can endure and be traded. A banana that can be replaced by another banana? How is that any fun or worthwhile for a collector? This moment, no matter how absurd it may seem, is emblematic of a shift toward a full-fledged attention economy where the digital and physical worlds increasingly merge. A simple banana taped to a wall — destined to be replaced — purchased with invisible cryptocurrency, isn’t really a purchase of the banana itself. It’s what I call “proof of attention.” In this attention economy, the Certificate of Authenticity is the true token of value in this transaction. The banana itself becomes a mere prop in a broader performance art piece. The certificate, a physical document proving ownership and authenticity, is a testament to the fact that, at this specific point in time, someone valued this ephemeral concept at over $6 million. Certificate of Authenticity Could it be that, in the future, more moments will be “tagged” with price tags — not because of the beauty or inherent quality of the work, but because of the absurdity of the concept? Perhaps the more absurd a moment, the more attention it garners, and the more its value is derived from this attention rather than the object itself. The disposability of the artwork — its inevitable decay — steers the spotlight away from the piece itself. The value isn’t in the banana or even the art. It’s in the concept, the absurdity, and the sheer notoriety of it all. notoriety This work has indeed been crystallized as a meme. And, as we speak, it’s being traded digitally on the Solana blockchain under the ticker $BAN, with a market cap of $143.68 million. $BAN, with a market cap of $143.68 million. There is no actual banana on the blockchain, yet people are trading this token as a digital parallel to the physical piece. One trader reportedly bought $10,000 worth of $BAN and later sold it for over $270,000. This relationship between online bidding and real-world transactions, facilitated by cryptocurrency, is shaping how we assign value — not just to art, but to moments, memes, and cultural icons. In the physical world, transactions like this come with more friction. Perhaps Sotheby’s is one of the few institutions capable of orchestrating such a singularity event. Watch closely — more is coming. You could consider investing in $BAN. Disclaimer: This is not financial advice. Cryptocurrencies are considered high-risk investments. Disclaimer : This is not financial advice. Cryptocurrencies are considered high-risk investments. Disclaimer

$6.2M in Crypto for a Banana? Welcome to the Attention Economy

Banan üçün 6,2 milyon dollar kriptovalyuta? Diqqət İqtisadiyyatına xoş gəlmisiniz

¿6,2 millones de dólares en criptomonedas por un plátano? Bienvenidos a la economía de la atención

バナナ1本で620万ドル相当の暗号通貨？注目経済へようこそ

6,2 მილიონი დოლარი კრიპტოში ბანანისთვის? კეთილი იყოს თქვენი მობრძანება ყურადღების ეკონომიკაში

$6.2M ក្នុង Crypto សម្រាប់ Banana? សូមស្វាគមន៍មកកាន់ សេដ្ឋកិច្ចយកចិត្តទុកដាក់

US$ 6,2 milhões em cripto por uma banana? Bem-vindo à Economia da Atenção

$6.2M en Crypto huk Plátanopaq? Allin hamusqayki Economía de Atención nisqaman

$6.2M mu Crypto yeBanana? Kugamuchirwa kune Attention Economy

6,2 милиона долара у крипто за банану? Добродошли у Аттентион Ецономи

6,2 miljoner dollar i krypto för en banan? Välkommen till Attention Economy

$6.2M eka Crypto ya Banana? Mi amukeriwile eka Ikhonomi ya Nyingiso

Banan uchun kriptoda 6,2 million dollarmi? Diqqat iqtisodiyotiga xush kelibsiz

Ransomware and other cyberattacks on health institutions are on the rise. The growing frequency of hospital ransomware means more healthcare administrators are investing in cyberattack insurance policies or updating existing ones. Ensuring coverage against this severe risk to patient confidentiality and potentially their lives is receiving prime attention. The burning questions remain: why are hospital ransomware attacks more common globally, and what can reduce them? United Nations (UN) Involvement The issue has become so concerning that the United Nations Security Council met recently to discuss updated digital privacy and cybersecurity guidelines. Briefing Ambassador Tedros Adhanom Ghebreyesus was straightforward in describing cyberattacks, including ransomware, as “issues of life and death.” Ghebreyesus, the World Health Organization (WHO) Director-General, also said that ransomware undermines consumer trust in important healthcare systems, which can ultimately lead to patient harm. According to the UN, over 33% of health institutions were ransomware victims in 2020, with one-third of those paying the ransom. A Statista survey shows ransomware affected 72.7% of worldwide businesses in 2023, up from 62.4% in 2020, indicating a likely upsurge in current figures for hospitals and other health facilities over the past few years. Ghebreyesus also stressed the need for the Security Council to “use its mandate to strengthen global cybersecurity and ensure accountability” with global cooperation. What Is Ransomware? Ransomware is a form of malware that restricts access to a computer system and its data, generally through file encryption. Hackers will only release their devices and data after victims pay a ransom. Since the early days of ransomware, cyberattack technologies have significantly advanced. Ransomware is not new. It has been around for decades, with the first attack taking place in 1989 when a disgruntled scientist dispersed 20,000 malware-infected floppy disks among attendees at the WHO’s international AIDS conference. The malware program known as AIDS Trojan only kicked in after users activated an infected computer 90 times. At that point, it encrypted file names and displayed a message demanding a $189 ransom for their release. 2017 saw sustained and highly professional ransomware attacks on hospitals worldwide, targeting vulnerabilities in medical devices. The WannaCry strain infected 1,200 diagnostic devices in the United Kingdom alone while restricting access to hundreds of thousands of computers worldwide. The FBI concluded that the North Korean government was directly responsible for the attacks. Since then, Ryuk, a Russian ransomware gang, and SamSam, from Iran, have sparked a trend in major cyberattacks against health and academic institutions in the Western world. Many appear politically motivated, with several perpetrators stating they would not target Eastern countries in their cyberattacks. With the advent of cryptocurrency, ransomware strains, including CryptoLocker, CryptorBit, and CryptoWall, used the anonymous payment method to receive ransoms without the chance of detection. Crypto made ransomware a more convenient and popular way for cybercriminals to profit. The upsurge in ransomware has prompted many health organizations to seek insurance covering network security breaches. As more companies join, this insurance industry sector is anticipated to grow by 25% annually over the next few years. Unfortunately, this doesn’t deter hackers, as, in many instances, insurance coverage provides them with more straightforward methods of payment. Why Are Hospital Ransomware Attacks More Frequent? Since 2020, at the height of COVID-19, assisted by cryptocurrencies and sophisticated smartphones, ransomware hackers have formed increasing numbers of gangs using the dark web ransomware platform Ransomware-as-a-Service (RaaS). They work together in Big Game Hunting (BGH) scenarios, targeting major hospitals, which they see as easier prey. Recent advancements in artificial intelligence (AI) promote further sophistication in ransomware methods. Gangs use AI to aid in victim reconnaissance, often by searching the internet for personal employee details used in targeted phishing attacks, which is the starting point for many cybercrimes. More significant gangs incorporate AI to find weak points and loopholes in AI-generated cybersecurity defense systems. Once inside, advanced ransomware strains, like Russia’s Notya and WannaCry, spread like wildfire using AI-driven automation to propagate throughout infected systems without detection. Medical devices are still improving their built-in technologies to withstand this propagation, making them easier targets than many better-protected networked computer systems. Hence, medical software and hardware are a primary target for BGH attacks. Essentially, we, the hospital patients, are the targets, and ransomware gangs identify this, especially with geopolitical forces influencing them. Health care is, by design, centered around those needing treatment. The targeted institutions are playing with patients' lives and must agree to the ransomware demands, as the implications of not doing so are often life-threatening. Paying ransomware demands can be financially devastating, amounting to billions of dollars in all industries. In essence, though, hospitals are the easiest targets. What Can Reduce Hospital Ransomware Threats? In the current geopolitical climate, utilizing further government agencies, law enforcement, and military intelligence resources can combat health ransomware threats. Cyberattacks that threaten healthcare institutions and private citizens should carry the same severe federal penalties as those that occur beyond networks and computerized frameworks. Hospital administrators should directly promote collaborative efforts, especially the sharing of security technologies related to ransomware defense. As cybercrime continues evolving, more concerted efforts regarding public-private health sector partnerships are essential to protecting every citizen. Ransomware risk management must become a legal statute in every health institution instead of hospitals only taking action when an event occurs. Vigilance in expert staff hiring and elevated adherence to advanced cybercrime security integration should become integral to all hospital policies. Understanding the Ransomware Problem Initiatives like the recent United Nations meeting and future actions are only the beginning. The statistics show that ransomware attacks on hospitals, and in general, are increasing and will continue doing so until there is a greater understanding of their severity and potential consequences. The UN has highlighted the need for change and progress, but the private sector and all security sectors, government or otherwise, must understand the problem and enforce change to combat it. Ransomware and other cyberattacks on health institutions are on the rise. The growing frequency of hospital ransomware means more healthcare administrators are investing in cyberattack insurance policies or updating existing ones. Ransomware and other cyberattacks on health institutions are on the rise. The growing frequency of hospital ransomware means more healthcare administrators are investing in cyberattack insurance policies or updating existing ones. Ransomware and other cyberattacks on health institutions are on the rise. The growing frequency of hospital ransomware means more healthcare administrators are investing in cyberattack insurance policies or updating existing ones. Ensuring coverage against this severe risk to patient confidentiality and potentially their lives is receiving prime attention. The burning questions remain: why are hospital ransomware attacks more common globally, and what can reduce them? United Nations (UN) Involvement The issue has become so concerning that the United Nations Security Council met recently to discuss updated digital privacy and cybersecurity guidelines. Briefing Ambassador Tedros Adhanom Ghebreyesus was straightforward in describing cyberattacks, including ransomware, as “issues of life and death.” Ghebreyesus, the World Health Organization (WHO) Director-General, also said that ransomware undermines consumer trust in important healthcare systems, which can ultimately lead to patient harm. According to the UN, over 33% of health institutions were ransomware victims in 2020, with one-third of those paying the ransom. A Statista survey shows ransomware affected 72.7% of worldwide businesses in 2023 , up from 62.4% in 2020, indicating a likely upsurge in current figures for hospitals and other health facilities over the past few years. 72.7% of worldwide businesses in 2023 72.7% of worldwide businesses in 2023 Ghebreyesus also stressed the need for the Security Council to “use its mandate to strengthen global cybersecurity and ensure accountability” with global cooperation. What Is Ransomware? Ransomware is a form of malware that restricts access to a computer system and its data, generally through file encryption. Hackers will only release their devices and data after victims pay a ransom. Since the early days of ransomware, cyberattack technologies have significantly advanced. Ransomware is not new. It has been around for decades, with the first attack taking place in 1989 when a disgruntled scientist dispersed 20,000 malware-infected floppy disks among attendees at the WHO’s international AIDS conference. The malware program known as AIDS Trojan only kicked in after users activated an infected computer 90 times. At that point, it encrypted file names and displayed a message demanding a $189 ransom for their release. first attack taking place in 1989 first attack taking place in 1989 2017 saw sustained and highly professional ransomware attacks on hospitals worldwide, targeting vulnerabilities in medical devices. The WannaCry strain infected 1,200 diagnostic devices in the United Kingdom alone while restricting access to hundreds of thousands of computers worldwide. The FBI concluded that the North Korean government was directly responsible for the attacks. Since then, Ryuk, a Russian ransomware gang, and SamSam, from Iran, have sparked a trend in major cyberattacks against health and academic institutions in the Western world. Many appear politically motivated, with several perpetrators stating they would not target Eastern countries in their cyberattacks. With the advent of cryptocurrency, ransomware strains, including CryptoLocker, CryptorBit, and CryptoWall, used the anonymous payment method to receive ransoms without the chance of detection. Crypto made ransomware a more convenient and popular way for cybercriminals to profit. would not target Eastern countries would not target Eastern countries The upsurge in ransomware has prompted many health organizations to seek insurance covering network security breaches. As more companies join, this insurance industry sector is anticipated to grow by 25% annually over the next few years. Unfortunately, this doesn’t deter hackers, as, in many instances, insurance coverage provides them with more straightforward methods of payment. to grow by 25% annually to grow by 25% annually Why Are Hospital Ransomware Attacks More Frequent? Since 2020, at the height of COVID-19, assisted by cryptocurrencies and sophisticated smartphones, ransomware hackers have formed increasing numbers of gangs using the dark web ransomware platform Ransomware-as-a-Service (RaaS). They work together in Big Game Hunting (BGH) scenarios, targeting major hospitals, which they see as easier prey. Recent advancements in artificial intelligence (AI) promote further sophistication in ransomware methods. Gangs use AI to aid in victim reconnaissance, often by searching the internet for personal employee details used in targeted phishing attacks, which is the starting point for many cybercrimes. More significant gangs incorporate AI to find weak points and loopholes in AI-generated cybersecurity defense systems. Once inside, advanced ransomware strains, like Russia’s Notya and WannaCry, spread like wildfire using AI-driven automation to propagate throughout infected systems without detection. Medical devices are still improving their built-in technologies to withstand this propagation, making them easier targets than many better-protected networked computer systems. Hence, medical software and hardware are a primary target for BGH attacks. Essentially, we, the hospital patients, are the targets, and ransomware gangs identify this, especially with geopolitical forces influencing them. Health care is, by design, centered around those needing treatment. The targeted institutions are playing with patients' lives and must agree to the ransomware demands, as the implications of not doing so are often life-threatening. Paying ransomware demands can be financially devastating, amounting to billions of dollars in all industries. In essence, though, hospitals are the easiest targets. amounting to billions of dollars amounting to billions of dollars What Can Reduce Hospital Ransomware Threats? In the current geopolitical climate, utilizing further government agencies, law enforcement, and military intelligence resources can combat health ransomware threats. Cyberattacks that threaten healthcare institutions and private citizens should carry the same severe federal penalties as those that occur beyond networks and computerized frameworks. Hospital administrators should directly promote collaborative efforts, especially the sharing of security technologies related to ransomware defense. As cybercrime continues evolving, more concerted efforts regarding public-private health sector partnerships are essential to protecting every citizen. Ransomware risk management must become a legal statute in every health institution instead of hospitals only taking action when an event occurs. Vigilance in expert staff hiring and elevated adherence to advanced cybercrime security integration should become integral to all hospital policies. Understanding the Ransomware Problem Initiatives like the recent United Nations meeting and future actions are only the beginning. The statistics show that ransomware attacks on hospitals, and in general, are increasing and will continue doing so until there is a greater understanding of their severity and potential consequences. The UN has highlighted the need for change and progress, but the private sector and all security sectors, government or otherwise, must understand the problem and enforce change to combat it.

Why Are Hospital Ransomware Attacks Becoming More Frequent Globally? The UN Met to Discuss

HONG KONG, Hong Kong, November 28th, 2024/Chainwire/--Moongate has officially launched its native token, $MGT, marking a significant milestone in the company’s mission to reshape brand-consumer engagement and redefine the value of user attention. Moongate is the first attention asset protocol for real-world activations. Launched in 2022, Moongate Protocol is actively utilized by over 3,800 brands and creators, including Binance, OKX, Token2049, EthDenver, Animoca, Wonderfruit, ComplexCon, and Sony. The protocol has empowered thousands of brands to launch smart token campaigns, driving engagement from more than 1.7 million end users. As the digital landscape continues to evolve, the $MGT token introduces a structured, incentive-based model for brands and consumers alike.￼ Designed to support real-world brand activations, loyalty programs, and consumer engagement, $MGT allows users to earn rewards through participation in token-based campaigns while brands can leverage the protocol to interact more meaningfully with their audiences. $5.2M in Funding and Upcoming Exchange Listings for $MGT To achieve this vision and further fuel growth, Moongate successfully secured over $5.2 million in total funding. This includes investments from CMCC Global, Cogitent Ventures, and Token Bay Capital, as well as public sale proceeds from multiple Tier-1 launchpads. The fundraise reflects strong market interest in Moongate Protocol and $MGT, highlighting growing recognition of Moongate’s role in the attention economy. Following the launch of the $MGT token, it is now trading on Bitget, KuCoin, Gate.io, MEXC, and PancakeSwap. Bridging Digital and Real-World Engagement Moongate’s platform empowers brands to create and distribute smart tokens, offering a streamlined solution for businesses to connect directly with users both online and offline. Through initiatives such as NFT memberships, event tickets, and token-gated benefits, Moongate facilitates real-world utilities for brands. $MGT is at the core of this ecosystem, serving as the primary medium for rewards, user incentives, and engagement tracking. “We believe that $MGT will fundamentally shift how users and brands perceive and value engagement,” said Moongate’s CEO, Jonathan Mui. “With $MGT, users gain real benefits for their interactions, while brands can access insights and audiences that reflect genuine interest and commitment.” Key Features of the $MGT Token The launch of $MGT brings multiple features to users and brands: User Staking and Rewards: Users can stake $MGT to earn incentives, receive an allocation to exclusive launches, and participate in governance, shaping the protocol’s future development.
Campaign Creation for Brands: Brands can use $MGT to create premium smart token campaigns with expanded functionalities, including an in-depth CRM, analytics, and improved discoverability.
Currency for Attention: Brands can utilize $MGT as a reward to capture eligible users’ attention and engagement. This becomes a more efficient customer acquisition channel, enabling a new paradigm where ad-spent goes directly towards the end-user (rather than any intermediaries). For more information about $MGT tokenomics, users can visit Moongate’s Medium. Community Airdrop In line with Moongate’s commitment to rewarding early supporters, the protocol will distribute a portion of the total $MGT supply through a community airdrop. The snapshot was taken on November 15, 2024, and eligible users can now claim their $MGT tokens on claim.moongate.id. What’s Next for Moongate With the launch of $MGT, Moongate is set to expand its platform features while strengthening its partnerships with both Web2 and Web3 brands. Upcoming updates include enhanced staking features, an expanded rewards marketplace, and the release of MoonPad, a launchpad for partner token campaigns. These developments aim to deepen user participation and provide brands with powerful tools to create immersive experiences for their audiences. For more information on $MGT and Moongate’s vision for the future of attention assets, users can visit Moongate’s official website. About Moongate Moongate is an attention asset protocol disrupting the $1T+ attention economy. The protocol consists of two key layers—a utility layer for brands to issue smart token campaigns for real-world engagement, and a data layer where such on-chain engagement data are monetized at scale by key stakeholders. Moongate aims to disintermediate centralized platforms to maximize brand-consumer value. It also introduces an innovative “engage-to-earn” mechanism that promotes active consumer participation for brand rewards and a share of $MGT emission. To learn more users can visit Moongate's Official Website | Twitter | LinkedIn Contact CEO Jonathan Mui Moongate contact@moongate.id This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here HONG KONG, Hong Kong, November 28th, 2024/Chainwire/--Moongate has officially launched its native token, $MGT, marking a significant milestone in the company’s mission to reshape brand-consumer engagement and redefine the value of user attention. Moongate is the first attention asset protocol for real-world activations. Launched in 2022, Moongate Protocol is actively utilized by over 3,800 brands and creators, including Binance, OKX, Token2049, EthDenver, Animoca, Wonderfruit, ComplexCon, and Sony. The protocol has empowered thousands of brands to launch smart token campaigns, driving engagement from more than 1.7 million end users. As the digital landscape continues to evolve, the $MGT token introduces a structured, incentive-based model for brands and consumers alike.￼ Designed to support real-world brand activations, loyalty programs, and consumer engagement, $MGT allows users to earn rewards through participation in token-based campaigns while brands can leverage the protocol to interact more meaningfully with their audiences. $5.2M in Funding and Upcoming Exchange Listings for $MGT To achieve this vision and further fuel growth, Moongate successfully secured over $5.2 million in total funding. This includes investments from CMCC Global, Cogitent Ventures, and Token Bay Capital, as well as public sale proceeds from multiple Tier-1 launchpads. The fundraise reflects strong market interest in Moongate Protocol and $MGT, highlighting growing recognition of Moongate’s role in the attention economy. Following the launch of the $MGT token, it is now trading on Bitget , KuCoin , Gate.io, MEXC , and PancakeSwap . Bitget Bitget KuCoin KuCoin Gate.io, Gate.io, MEXC MEXC PancakeSwap PancakeSwap Bridging Digital and Real-World Engagement Moongate’s platform empowers brands to create and distribute smart tokens, offering a streamlined solution for businesses to connect directly with users both online and offline. Through initiatives such as NFT memberships, event tickets, and token-gated benefits, Moongate facilitates real-world utilities for brands. $MGT is at the core of this ecosystem, serving as the primary medium for rewards, user incentives, and engagement tracking. “We believe that $MGT will fundamentally shift how users and brands perceive and value engagement,” said Moongate’s CEO, Jonathan Mui. “We believe that $MGT will fundamentally shift how users and brands perceive and value engagement,” said Moongate’s CEO, Jonathan Mui. “With $MGT, users gain real benefits for their interactions, while brands can access insights and audiences that reflect genuine interest and commitment.” “With $MGT, users gain real benefits for their interactions, while brands can access insights and audiences that reflect genuine interest and commitment.” Key Features of the $MGT Token The launch of $MGT brings multiple features to users and brands: User Staking and Rewards: Users can stake $MGT to earn incentives, receive an allocation to exclusive launches, and participate in governance, shaping the protocol’s future development. Campaign Creation for Brands: Brands can use $MGT to create premium smart token campaigns with expanded functionalities, including an in-depth CRM, analytics, and improved discoverability. Currency for Attention: Brands can utilize $MGT as a reward to capture eligible users’ attention and engagement. This becomes a more efficient customer acquisition channel, enabling a new paradigm where ad-spent goes directly towards the end-user (rather than any intermediaries). User Staking and Rewards: Users can stake $MGT to earn incentives, receive an allocation to exclusive launches, and participate in governance, shaping the protocol’s future development. Campaign Creation for Brands: Brands can use $MGT to create premium smart token campaigns with expanded functionalities, including an in-depth CRM, analytics, and improved discoverability. Currency for Attention: Brands can utilize $MGT as a reward to capture eligible users’ attention and engagement. This becomes a more efficient customer acquisition channel, enabling a new paradigm where ad-spent goes directly towards the end-user (rather than any intermediaries). For more information about $MGT tokenomics, users can visit Moongate’s Medium . Moongate’s Medium Moongate’s Medium Community Airdrop In line with Moongate’s commitment to rewarding early supporters, the protocol will distribute a portion of the total $MGT supply through a community airdrop. The snapshot was taken on November 15, 2024, and eligible users can now claim their $MGT tokens on claim.moongate.id . claim.moongate.id claim.moongate.id What’s Next for Moongate With the launch of $MGT, Moongate is set to expand its platform features while strengthening its partnerships with both Web2 and Web3 brands. Upcoming updates include enhanced staking features, an expanded rewards marketplace, and the release of MoonPad, a launchpad for partner token campaigns. These developments aim to deepen user participation and provide brands with powerful tools to create immersive experiences for their audiences. For more information on $MGT and Moongate’s vision for the future of attention assets, users can visit Moongate’s official website . Moongate’s official website Moongate’s official website About Moongate Moongate is an attention asset protocol disrupting the $1T+ attention economy. The protocol consists of two key layers—a utility layer for brands to issue smart token campaigns for real-world engagement, and a data layer where such on-chain engagement data are monetized at scale by key stakeholders. Moongate Moongate Moongate aims to disintermediate centralized platforms to maximize brand-consumer value. It also introduces an innovative “engage-to-earn” mechanism that promotes active consumer participation for brand rewards and a share of $MGT emission. To learn more users can visit Moongate's Official Website | Twitter | LinkedIn Official Website Official Website Twitter Twitter LinkedIn LinkedIn Contact CEO Jonathan Mui Moongate contact@moongate.id This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Moongate Launches $MGT Token To Drive New Era of Engagement In The Attention Economy

Moongate ukax $MGT Token ukaruw uñstayi, Machaq Era de Compromiso Economía de Atención Ukan Irpañataki

Moongate spouští token $MGT, aby podpořil novou éru angažovanosti v ekonomice pozornosti

Moongate lanza el token $MGT para impulsar una nueva era de participación en la economía de la atención

Moongate julkaisee $MGT Tokenin edistääkseen sitoutumisen uutta aikakautta huomiotaloudessa

Moongate lance le jeton $MGT pour ouvrir une nouvelle ère d'engagement dans l'économie de l'attention

ムーンゲート、アテンションエコノミーにおけるエンゲージメントの新時代を推進するため$MGTトークンをローンチ

Moongate-ი გამოუშვებს $MGT ტოკენს, რათა მოახდინოს ჩართულობის ახალი ერა საყურადღებო ეკონომიკაში

Moongate dia namoaka $MGT Token hitondrana ny vanim-potoana vaovaon'ny fandraisana anjara amin'ny toe-karena

ध्यान अर्थतन्त्रमा संलग्नताको नयाँ युग चलाउन मुनगेटले $MGT टोकन सुरु गर्‍यो

Moongate wprowadza token $MGT, aby rozpocząć nową erę zaangażowania w gospodarce uwagi

Moongate lanserar $MGT-token för att driva en ny era av engagemang i uppmärksamhetsekonomin

Moongate เปิดตัวโทเค็น $MGT เพื่อขับเคลื่อนยุคใหม่ของการมีส่วนร่วมในเศรษฐกิจแห่งความสนใจ

Table of Links Abstract and 1 Introduction 2 Background & Problem Statement 2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides? 3 DreamLLM 3.1 End-to-End Interleaved generative Pretraining (I-GPT) 3.2 Model Training 4 Experiments and 4.1 Multimodal Comprehension 4.2 Text-Conditional Image Synthesis 4.3 Multimodal Joint Creation & Comprehension 5 Discussions 5.1 Synergy between creation & Comprehension? 5. 2 What is learned by DreamLLM? 6 Related Works 7 Conclusions and References A Additional Experiments B Additional Qualitative Examples C Implementation Details D Additional Related Works E Limitations, Failure Cases & Future Works 5.2 WHAT IS LEARNED BY DREAMLLM? Dream Query Attention In DREAMLLM, the conditional embedding is derived from MLLMs with some learned dream queries. Fig. 6 demonstrates a visualization of the learned cross-attention mechanism between these queries and the diffusion latent. Similar to (Hertz et al., 2023), we visualize the attention map averaged across all timestamps. It is seen that: i) The query attention is structured, disentangled, and semantically-oriented. This is evidenced by the fact that distinct queries adeptly capture different subject and background semantics. ii) Despite varying prompts, attention patterns exhibit remarkable similarity as shown in Fig. 6 (a) and (b). This contrasts with the token attentions from the original SD, which are typically text-token dependent. We postulate that this arises from the model’s causal nature, leading to a consistent semantic structure order. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors:
(1) Runpei Dong, Xi’an Jiaotong University and Internship at MEGVII;
(2) Chunrui Han, MEGVII Technology;
(3) Yuang Peng, Tsinghua University and Internship at MEGVII;
(4) Zekun Qi, Xi’an Jiaotong University and Internship at MEGVII;
(5) Zheng Ge, MEGVII Technology;
(6) Jinrong Yang, HUST and Internship at MEGVII;
(7) Liang Zhao, MEGVII Technology;
(8) Jianjian Sun, MEGVII Technology;
(9) Hongyu Zhou, MEGVII Technology;
(10) Haoran Wei, MEGVII Technology;
(11) Xiangwen Kong, MEGVII Technology;
(12) Xiangyu Zhang, MEGVII Technology and a Project leader;
(13) Kaisheng Ma, Tsinghua University and a Corresponding author;
(14) Li Yi, Tsinghua University, a Corresponding authors and Project leader. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background & Problem Statement 2 Background & Problem Statement 2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides? 2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides? 3 DreamLLM 3 DreamLLM 3.1 End-to-End Interleaved generative Pretraining (I-GPT) 3.1 End-to-End Interleaved generative Pretraining (I-GPT) 3.2 Model Training 3.2 Model Training 4 Experiments and 4.1 Multimodal Comprehension 4 Experiments and 4.1 Multimodal Comprehension 4.2 Text-Conditional Image Synthesis 4.2 Text-Conditional Image Synthesis 4.3 Multimodal Joint Creation & Comprehension 4.3 Multimodal Joint Creation & Comprehension 5 Discussions 5.1 Synergy between creation & Comprehension? 5.1 Synergy between creation & Comprehension? 5. 2 What is learned by DreamLLM? 5. 2 What is learned by DreamLLM? 6 Related Works 6 Related Works 7 Conclusions and References 7 Conclusions and References A Additional Experiments A Additional Experiments B Additional Qualitative Examples B Additional Qualitative Examples C Implementation Details C Implementation Details D Additional Related Works D Additional Related Works E Limitations, Failure Cases & Future Works E Limitations, Failure Cases & Future Works 5.2 WHAT IS LEARNED BY DREAMLLM? Dream Query Attention In DREAMLLM, the conditional embedding is derived from MLLMs with some learned dream queries. Fig. 6 demonstrates a visualization of the learned cross-attention mechanism between these queries and the diffusion latent. Similar to (Hertz et al., 2023), we visualize the attention map averaged across all timestamps. It is seen that: i) The query attention is structured, disentangled, and semantically-oriented. Dream Query Attention This is evidenced by the fact that distinct queries adeptly capture different subject and background semantics. ii) Despite varying prompts, attention patterns exhibit remarkable similarity as shown in Fig. 6 (a) and (b). This contrasts with the token attentions from the original SD, which are typically text-token dependent. We postulate that this arises from the model’s causal nature, leading to a consistent semantic structure order. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv Authors: (1) Runpei Dong, Xi’an Jiaotong University and Internship at MEGVII; (2) Chunrui Han, MEGVII Technology; (3) Yuang Peng, Tsinghua University and Internship at MEGVII; (4) Zekun Qi, Xi’an Jiaotong University and Internship at MEGVII; (5) Zheng Ge, MEGVII Technology; (6) Jinrong Yang, HUST and Internship at MEGVII; (7) Liang Zhao, MEGVII Technology; (8) Jianjian Sun, MEGVII Technology; (9) Hongyu Zhou, MEGVII Technology; (10) Haoran Wei, MEGVII Technology; (11) Xiangwen Kong, MEGVII Technology; (12) Xiangyu Zhang, MEGVII Technology and a Project leader; (13) Kaisheng Ma, Tsinghua University and a Corresponding author; (14) Li Yi, Tsinghua University, a Corresponding authors and Project leader. Authors: Authors: (1) Runpei Dong, Xi’an Jiaotong University and Internship at MEGVII; (2) Chunrui Han, MEGVII Technology; (3) Yuang Peng, Tsinghua University and Internship at MEGVII; (4) Zekun Qi, Xi’an Jiaotong University and Internship at MEGVII; (5) Zheng Ge, MEGVII Technology; (6) Jinrong Yang, HUST and Internship at MEGVII; (7) Liang Zhao, MEGVII Technology; (8) Jianjian Sun, MEGVII Technology; (9) Hongyu Zhou, MEGVII Technology; (10) Haoran Wei, MEGVII Technology; (11) Xiangwen Kong, MEGVII Technology; (12) Xiangyu Zhang, MEGVII Technology and a Project leader; (13) Kaisheng Ma, Tsinghua University and a Corresponding author; (14) Li Yi, Tsinghua University, a Corresponding authors and Project leader.

What Is Learned by DreamLLM? Dream Query Attention

Kraken’s delisting of Monero (XMR) from their European Economic Area (EEA) market reflects the growing regulatory crackdown on privacy-focused cryptocurrencies. On October 31st, Kraken will officially end Monero trading on its platform; joining other exchanges in phasing out coins that offer users the ability to transact anonymously. Kraken customers will have until December 31st to withdraw Monero from the platform. If any Monero is not withdrawn by the December 31st deadline, Kraken will automatically convert the remaining XMR into Bitcoin and distribute it to the customer’s account. “After thorough consideration, and exploration of all viable alternatives, we concluded we have no choice but to delist Monero (XMR) in the European Economic Area (EEA) due to regulatory changes.
We did not take this decision lightly and remain committed to providing our European clients with an exceptional trading experience.”- Statement from Kraken The European Union’s stringent new regulations, including the forthcoming Markets in Crypto-Assets (MiCA) framework and tighter anti-money laundering rules, target the anonymity features of privacy coins. Monero, which was designed to keep transaction details private and anonymous, is seen as an obstacle to regulators focused on financial transparency. Kraken’s decision to delist Monero aligns with the growing trend of exchanges that continue to distance themselves from assets that prioritize privacy, over traceability. In the span of less than a year, Binance has fully delisted Monero, while Kraken has removed it from Ireland, Belgium, and now the entire European market. For Monero supporters, this move represents more than just a policy shift from one exchange; it symbolizes the challenges ahead for privacy-focused cryptocurrencies, which give holders the ability to control their own personal financial data. As cryptocurrency regulation tightens, exchanges might continue to reject coins that cannot meet their transparency standards, which puts the future of privacy coins in question. In response to the news, Ricardo Spagni, the former Lead Maintainer of Monero, claimed the delisting was due to regulators’ frustrations with the untraceable nature of the coin. The ripple effects of Kraken’s decision may be felt across the crypto ecosystem, affecting not just Monero, but all coins that emphasize privacy. Kraken’s delisting of XMR highlights the divide between decentralized ideals and the demands of the regulated financial world. The battle for privacy in the crypto space isn’t over, but the path forward is becoming more difficult. Are you a Monero (XMR) holder? Will the delisting influence whether you will continue to hold Monero or not? What are your thoughts on this news? Join the conversation below! Read My Latest Posts: Odysee Goes Ad-Free: A Bold Move that Challenges the YouTube Empire
From Collapse to Comeback: Odysee Journeys to the Arweave Blockchain
Ferrari Drives into Europe: Now Accepting Bitcoin and Crypto Payments Kraken’s delisting of Monero (XMR) from their European Economic Area (EEA) market reflects the growing regulatory crackdown on privacy-focused cryptocurrencies. On October 31st, Kraken will officially end Monero trading on its platform; joining other exchanges in phasing out coins that offer users the ability to transact anonymously. officially end officially end Kraken customers will have until December 31st to withdraw Monero from the platform. If any Monero is not withdrawn by the December 31st deadline, Kraken will automatically convert the remaining XMR into Bitcoin and distribute it to the customer’s account. “After thorough consideration, and exploration of all viable alternatives, we concluded we have no choice but to delist Monero (XMR) in the European Economic Area (EEA) due to regulatory changes. We did not take this decision lightly and remain committed to providing our European clients with an exceptional trading experience.” - Statement from Kraken “After thorough consideration, and exploration of all viable alternatives, we concluded we have no choice but to delist Monero (XMR) in the European Economic Area (EEA) due to regulatory changes. “After thorough consideration, and exploration of all viable alternatives, we concluded we have no choice but to delist Monero (XMR) in the European Economic Area (EEA) due to regulatory changes. We did not take this decision lightly and remain committed to providing our European clients with an exceptional trading experience.” We did not take this decision lightly and remain committed to providing our European clients with an exceptional trading experience.” - Statement from Kraken - Statement from Kraken Statement from Kraken The European Union’s stringent new regulations, including the forthcoming Markets in Crypto-Assets (MiCA) framework and tighter anti-money laundering rules, target the anonymity features of privacy coins. Markets in Crypto-Assets (MiCA) Markets in Crypto-Assets (MiCA) Monero, which was designed to keep transaction details private and anonymous, is seen as an obstacle to regulators focused on financial transparency. Kraken’s decision to delist Monero aligns with the growing trend of exchanges that continue to distance themselves from assets that prioritize privacy, over traceability. In the span of less than a year, Binance has fully delisted Monero, while Kraken has removed it from Ireland, Belgium, and now the entire European market. For Monero supporters, this move represents more than just a policy shift from one exchange; it symbolizes the challenges ahead for privacy-focused cryptocurrencies, which give holders the ability to control their own personal financial data. As cryptocurrency regulation tightens, exchanges might continue to reject coins that cannot meet their transparency standards, which puts the future of privacy coins in question. In response to the news, Ricardo Spagni, the former Lead Maintainer of Monero, claimed the delisting was due to regulators’ frustrations with the untraceable nature of the coin. claimed claimed The ripple effects of Kraken’s decision may be felt across the crypto ecosystem, affecting not just Monero, but all coins that emphasize privacy. Kraken’s delisting of XMR highlights the divide between decentralized ideals and the demands of the regulated financial world. The battle for privacy in the crypto space isn’t over, but the path forward is becoming more difficult. Are you a Monero (XMR) holder? Are you a Monero (XMR) holder? Are you a Monero (XMR) holder? Will the delisting influence whether you will continue to hold Monero or not? Will the delisting influence whether you will continue to hold Monero or not? Will the delisting influence whether you will continue to hold Monero or not? What are your thoughts on this news? Join the conversation below! What are your thoughts on this news? Join the conversation below! What are your thoughts on this news? Join the conversation below! Read My Latest Posts: Odysee Goes Ad-Free: A Bold Move that Challenges the YouTube Empire From Collapse to Comeback: Odysee Journeys to the Arweave Blockchain Ferrari Drives into Europe: Now Accepting Bitcoin and Crypto Payments Odysee Goes Ad-Free: A Bold Move that Challenges the YouTube Empire Odysee Goes Ad-Free: A Bold Move that Challenges the YouTube Empire Odysee Goes Ad-Free: A Bold Move that Challenges the YouTube Empire From Collapse to Comeback: Odysee Journeys to the Arweave Blockchain From Collapse to Comeback: Odysee Journeys to the Arweave Blockchain From Collapse to Comeback: Odysee Journeys to the Arweave Blockchain Ferrari Drives into Europe: Now Accepting Bitcoin and Crypto Payments Ferrari Drives into Europe: Now Accepting Bitcoin and Crypto Payments Ferrari Drives into Europe: Now Accepting Bitcoin and Crypto Payments

Stories, News & Mentions about ATT

Att’s Stories on HackerNoon

Att's News & Mentions