Here's where the Big Four stand on hybrid work in 2025. The world's four largest consulting and accounting firms collectively employ 1.5 million staff and take up prime commercial real estate in hundreds of major cities worldwide. But all four firms are bucking the trend toward stricter return-to-office policies being followed by other corporate powerhouses. Companies including JPMorgan, Dell, AT&T, and Amazon have reversed their stances on remote ...

The Big Four are sticking with hybrid work. Here are the RTO policies of Deloitte, KPMG, EY, and PwC.

Employees can log into work from the comfort of their couch while raking it in as a nurse practitioner, sales associate or therapist.

Here are the top 10 side hustles that still allow you to work from home and earn up to $56 an hour—as firms like AT&T ban it | Fortune

In what spells more bad news for avid advocates of remote work, telecom giant AT&T has joined other high-profile RTO enforcers such as Amazon in mandating a five-days-a-week return to the office, starting January.

15 Companies Now Hiring Remote Jobs For 2025

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

AT&T

Breaking Down Jamba: How Mixing Attention and State Spaces Makes a Smarter LLM

Consumer behaviors are constantly changing, especially in the auto industry. Businesses that can enhance customer experiences while implementing proactive fraud prevention measures to help mitigate rising delinquencies and fight against synthetic identity fraud will be better positioned to attract and retain customers. But what exactly does this mean? For dealers, high-touch customer experiences can make it easier to identify their most interested, qualified customers. 54% of consumers stated they would buy from dealerships with a preferred experience, even if the price was not the lowest. 72% of consumers said they were more likely to visit dealerships if the buying process was improved. 54% of consumers 54% of consumers 72% of consumers 72% of consumers However, most buyers favor a combination of in-dealership and online options. In 2023, 50% of consumers completed all car buying steps at the dealership while 43% of consumers used online and in-person options. Only 7% of vehicle buyers bought completely online. For auto lenders, certain lender types are more susceptible to fraud and delinquencies than others, which is why knowing the customers is critical, especially when it comes to understanding what they can really afford. Over 40% of Baby Boomers use captive financing more than other generations while 29% of Gen Z consumers opened an auto loan with a credit union, which is the highest percentage of any generation. Consumer auto trends in 2025 are showing that debt for auto loans and leases has increased by almost 15% in the last 10 years. Auto loans and leases make up 35.9% of the total non-mortgage consumer debt with the share of debt having increased by 14.7% from 31.3% in 2014. Student loans comprise 29%, which indicates a 21.3% decrease in the share of debt from 37.1% in 2014, while credit cards account for 23.9% or a 9.6% increase in the share of debt from 21.8% in 2014. These trends show that auto is the fastest-growing category in non-mortgage consumer debt with the total outstanding balances for auto loans and leases amounting to $1.7 trillion. Delinquencies are rising as well, highlighting the financial strain on consumers. As of November 2024, 1.5% of all auto loans and leases are 60+ days past due. Some generations are feeling more of the pressure than others, with 1.7% of Millennial auto loans and 2.3% of Gen Z auto loans being over 60 days delinquent. 1.5% of all auto loans 1.5% of all auto loans Currently, the year over year percentage of deep subprime and subprime borrowers with auto loans and leases is increasing the most compared to other score bands. 13.2% of accounts are deep subprime borrowers and 8.1% are subprime borrowers, which have both grown by 4.8% year over year. And as financial pressure is felt by more people, fewer cars are being sold. In fact, auto loan and lease originations dropped by almost 2% year over year by September 2024, which is a $9.2 billion decrease. However, interest rates and higher prices are partly to blame for slower sales. From 2016 to 2024, the average new vehicle price increased by 34% while the average vehicle interest rate (60-month new car loans) rose by 56%. Fraud is a major factor in the auto industry as well with synthetic identities (Syn ID) having increased by 59% annually since 2020. In 2023, Syn ID fraud surged by 98%, resulting in $7.9 billion in losses. Auto loan credit applications with a high risk of Syn ID rose from ~5% in 2019 to +8% in 2023. Auto loans and leases with a Syn ID risk have a delinquency rate up to five times higher than the portfolio average. That is why auto dealers and lenders that are proactive at preventing fraud losses and improving customer satisfaction will gain a competitive edge in the industry.

Attracting Buyers in the Auto Industry with Seamless Customer Experience

Hey there! I'm Dominik Tomicevic, the founder and CEO of Memgraph. Back in 2011, I was one of just four people worldwide to receive the Microsoft Imagine Cup Grant, personally awarded by Bill Gates, for developing game-changing technologies in data processing. In 2016, I co-founded Memgraph, an open-source graph database designed for high-performance, real-time connected data processing. Our mission is to deliver knowledge graphs with unprecedented integration and ease of use, setting a new benchmark for knowledge graph solutions. Today, Memgraph boasts an open-source community of over 150,000 members and serves a portfolio of Global 2000 customers, including NASA, Cedars-Sinai, and Capitec Bank. On a personal note, I love making coffee and cocktails, and I nerd out about aviation. An ideal combo, right? In this post, I’ll answer the ten questions for startup founders from HackerNoon Founder and CEO David Smooke. 1. What is your company in 2–5 words? Open-source, in-memory graph database. 2. Why is now the time for your company to exist? The world runs on connected data, but existing databases are too slow, too clunky, or too expensive for real-time. With AI, fraud detection, and recommendation systems exploding in complexity, businesses need a graph database that can handle streaming data without burning through cloud budgets. That’s where we come in. 3. What do you love about your team, and why are you the ones to solve this problem? We have a team of engineers obsessed with performance, optimizations, and making complex things simple. No fluff, no bureaucracy, just a relentless focus on building a graph database that’s better than anything on the market. We come from backgrounds in engineering, mathematics, and distributed systems, so we know how to push hardware and algorithms to their limits. 4. If you weren’t building your startup, what would you be doing? I would be either making cocktails on the beach and be a top barmen or be a pilot for one of the commercial aviators. 5. At the moment, how do you measure success? What are your metrics? Active deployments to see who is using us in production, query performance to see how fast we can get, developer adoption, open-source contributions, and seeing more engineers adopting Memgraph and solving their challenges. 6. In a few sentences, what do you offer to whom? We offer a fast, real-time graph database for engineers building fraud detection, recommendation engines, network analysis, and AI-powered applications using Memgraph as a context engine in GraphRAG. If you need to process connected data in milliseconds, we’re your best bet. Whether you’re a startup, a bank, or a research lab, we help you analyze complex relationships without overpaying for slow, legacy graph databases. 7. What’s most exciting about your traction to date? Amazing customers and wild use cases. We’re powering AI, GraphRAG, and real-time context problems at some of the most innovative organizations out there—NASA, Cedars-Sinai, and others who can’t afford slow databases. When top-tier teams in space exploration and healthcare trust Memgraph to crunch their most complex graph problems, you know you’re onto something big. 8. Where do you think your growth will be next year? Next year is all about building on top of what this year is all about - and that is GraphRAG and making life easier for developers building AI with graph technology. We’re going deep on merging graphs, GenAI, and automating the messy parts—like figuring out which search methods and algorithms to run for each question. That’s where our Reasoning Graph comes in. Instead of just storing data, Memgraph will actively help decide how to answer, applying context, constraints, and the right retrieval strategies on the fly. how 9. Tell us about your first paying customer and revenue expectations over the next year. Our first customer is a Swiss company named Saporo. They use graphs to reduce identity attack surface by finding and addressing the most dangerous access (mis)configurations. We have several other customers dealing with cybersecurity and althought last year was very successful for us, as Graph RAG use cases are becoming more popular we anticipate further revenue growth. 10. What’s your biggest threat? Our biggest challenge is also our biggest opportunity—the adoption of graph technology and awareness about your data being inherently interconnected which represents an enormous opportunity. While many organizations are still unfamiliar with graph technology, those who explore it quickly see its transformative power in analyzing complex relationships and making smarter business decisions. This is why education and advocacy are at the heart of what we do. By helping more people understand the value of connected data, we are not just overcoming obstacles—we are shaping the future of data-driven decision-making and empowering businesses to unlock new possibilities. This startup founder interview template is based on HackerNoon Founder & CEO David Smooke’s ten questions for startup founders. This startup founder interview template is based on HackerNoon Founder & CEO David Smooke’s ten questions for startup founders. HackerNoon Founder & CEO David Smooke ten questions for startup founders

This CEO Got Bill Gates’ Attention—Now He’s Fueling AI with Memgraph’s Speedy Graph Tech

Learn to Detect & Prevent JSD Attacks With This Guide

Author:
(1) Tiffany N. Younger, The Graduate Center, City University of New York, New York, NY 10016, USA (tyounger@hunter.cuny.edu). Author: Author: (1) Tiffany N. Younger, The Graduate Center, City University of New York, New York, NY 10016, USA (tyounger@hunter.cuny.edu). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2. Early Life 2. Early Life 3. Whiteness as an Institution 3. Whiteness as an Institution 4. The Triple Threat 4. The Triple Threat 5. The Academic Plantation Field 5. The Academic Plantation Field 6. The Future Is Black Women 6. The Future Is Black Women 7. Imagination as a Tool 7. Imagination as a Tool 8. Imagination through Research 8. Imagination through Research 9. Imagination through Play 9. Imagination through Play 10. Collaboration as Imagination 10. Collaboration as Imagination 11. Conclusions and References 11. Conclusions and References 5. The Academic Plantation Field I entered academia in 2017, right after the Trump election, to disrupt the violence that was about to come. I was a first time mother of a six month year old Black girl. I thought academia would be the safest place to be, given the focus on academic freedom and speech. What I learned about academia shocked me, but it put into perspective what I was witnessing in the field of social service work. Academia is the belly of the beast. The foundation and construction of whiteness, patriarchy, and coloniality are conceptualized and maintained through academic research, pedagogy, and practices. Black feminist scholars such as Crenshaw (1991), Collins (2000), Hooks (1994), and Cooper (2014, 2018) all speak about the interlocking nature of these systems of oppression within academia in their scholarship. My time in academia has been eye-opening and liberating. I have spent the last seven years navigating the beast of academia, trying to figure out if there is space for me as a Black woman scholar. While in academia, I realized my mere existence was a broken policy shaped by the systems and structures of US academic institutions that curate, teach, and practice dominant epistemologies. My humanity does not protect whiteness and is deemed a threat to the institution. Throughout my seven years in academic training, I have personally been the subject of multiple complaints from students about my pedagogy, attire, and overall demeanor. I have spent almost every semester in some form of mediation mandated by the University to resolve student conflicts with me. As an adjunct faculty member, I have been taped without permission, and audio has been used by university lawyers and administrators without my knowledge. I am not perfect; I acknowledge I have areas I am challenged in, but academia has not aided in my growth as a professor. Academia has served as a place where I feel like I am being put on a psychological and emotional wiping post without any meaningful, tangible outcome. For example, when I interrogate the complaints against me and summons to what I like to call “dean court”, they have almost always been about perceived unsafety regarding feelings or the directness and candor in my deconstruction of power. One academic year, I had to hire an attorney to support me in navigating the academic institutional jungle, and I was summoned to multiple hearings without an actual material resolution. White Supremacist values continue to oppress, subjugate, surveil, exploit, police, and justify violence against Black women (Collins 2000; Rodgers 2021; Spencer and Perlow 2018). Rodgers (2021) affirms how the harm of Black women in academia shows up as overt and subtle experiences of discrimination, including the silencing of Black women’s voices and positionality. Institutional power has tried to silence me multiple times. In most of the mediations I attended, when I asked the goal of the meeting, the answer was almost always to obtain a resolution. When I asked for examples of possible resolutions, I was told that the goal was to provide space for the student who was almost always white presenting to address upset, unsafety, and hurt feelings with my pedagogy. I am a professor, not a clinician. My job is not to fix feelings it is to educate. Somehow, in the school of social work where I carry out most of my teaching, we have created a space where future social work practitioners and clinicians will hold their feelings as facts and weaponize them to hold Black women hostage. We are not your mammy; get somebody else to do it! Today, in 2024, I am deeply concerned with the field of social work and academia overall. We have reached what I hope to be the peak of extreme violence and pure savagery. I am witnessing firsthand what my aunties and mentors told me all along would happen. In 2024, I am witnessing the killing of thousands of children across the world, and even acknowledging it in this paper may be breaking an institutional policy at one of the many institutions I am affiliated with. There is this silent yet very loud gag order within academia to say nothing about what we are witnessing right before our eyes in 2024. Academic freedom has been eliminated. Institutional policies have been weaponized to dismiss violence, which protects imperialism. Yet, if we acknowledge the extreme violence and trauma, we may risk our careers and livelihoods. We have entered what I am now calling “the savagery of whiteness”. This phrase relates to the way whiteness engages in extreme violence and demands that we all stand by and “mind our business”. Whiteness is currently operating like a dysfunctional family that protects the abusive actions of an adult against children rather than protecting the children. Whiteness is upset that Black women will not agree to watch death without protest. Opinions and feelings have been turned into facts and used to target, dox, and harm young people on college campuses. I have witnessed faculty call for the arrest of students ON campus due to protests because they have different opinions. Whiteness does not know how to engage in difference. Whiteness is a tyrant. I have witnessed and been a victim of online exchanges, which resulted in a person contacting my employer to “tell on me”. If this is not the end of times, I hope it is the beginning. If this is the beginning of something new (which I hope), what does accountability look like when academic institutions are responsible for producing and helping legitimate feelings and opinions as fact? Whiteness does not have an answer because whiteness is too busy denying that it exists. Gem # 5. Align yourself with multiple institutions so that when one decides to engage in extreme harm, leaving is an option. Use your degree as a shield. It is ok to have many (small) jobs. We often have five to six jobs at one institution. One institution should not have 100% control over your life. They can provide you with benefits. But the operation of the institution should not be contingent on your dehumanization. Gem # 5. Align yourself with multiple institutions so that when one decides to engage in extreme harm, leaving is an option. Use your degree as a shield. It is ok to have many (small) jobs. We often have five to six jobs at one institution. One institution should not have 100% control over your life. They can provide you with benefits. But the operation of the institution should not be contingent on your dehumanization. Gem # 5. Align yourself with multiple institutions so that when one decides to engage in extreme harm, leaving is an option. Use your degree as a shield. It is ok to have many (small) jobs. We often have five to six jobs at one institution. One institution should not have 100% control over your life. They can provide you with benefits. But the operation of the institution should not be contingent on your dehumanization. Gem # 5. Align yourself with multiple institutions so that when one decides to engage in extreme harm, leaving is an option. Use your degree as a shield. It is ok to have many (small) jobs. We often have five to six jobs at one institution. One institution should not have 100% control over your life. They can provide you with benefits. But the operation of the institution should not be contingent on your dehumanization. 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

When Academia Attacks: Protecting Black Womanhood from Institutional Harm

Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Methodology 2 Methodology 3 Hardware 3 Hardware 4 Software 4 Software 5 Network 5 Network 6 Consensus 6 Consensus 7 Cryptocurrency Economics 7 Cryptocurrency Economics 8 Client API 8 Client API 9 Governance 9 Governance 10 Geography 10 Geography 11 Case Studies 11 Case Studies 12 Discussion and References 12 Discussion and References A. Decentralization and Policymaking A. Decentralization and Policymaking B. Software Testing B. Software Testing C. Brief Evaluations per Layer C. Brief Evaluations per Layer D. Measuring decentralization D. Measuring decentralization E. Fault Tolerance and Decentralization E. Fault Tolerance and Decentralization 5 Network Blockchain nodes communicate over a peer-to-peer (P2P) network, the decentralization properties of which have been of great interest to researchers [134,4,82,76]. Systems often implement a message diffusion mechanism [78] via a gossip protocol that avoids full graph connectivity [54]. Users typically use the Internet to access the P2P overlay, though some efforts try to introduce an independent infrastructure [47]. Here we explore networking aspects which present single points of failure, in terms of topology and bootstrapping. A notable research question that arises organically from our analysis, and touches upon both following subsections, is creating a P2P network that is both permissionless and Byzantine resilient, e.g., as explored in [49,124]. Topology. The first networking aspect of interest is the network’s topology. Evaluating a real-world network’s clustering properties is a well-known problem, traditionally done by generating random graphs and comparing the expected with the observed values [63,135,1]. In blockchain systems, every node maintains a list of peer connections. Crucially, message provenance is not typically provided, so no party can know the network origin of an incoming message. Therefore, the resource of interest are “bridges” (single nodes or small cluster of nodes) between the network graph’s components and the relevant parties are the bridges’ owners or operators. Here, a component is a single node or a cluster of tightly interconnected nodes. Topology Topology A distributed network, in the tradition of Baran [15], is key in maintaining safety and liveness. Under the CAP theorem [27], any networked system can satisfy at most two of the following properties: i) a consistent data copy; ii) data availability; iii) network partition tolerance. Ledger systems are no exception. Each node needs to maintain at least one connection to an honest party to receive all messages and avoid eclipse attacks [87]. If some parties cannot communicate with the rest of the network, either they halt and any transactions they submit are dropped (violating liveness) or they produce separate ledger versions (violating safety).[9] By preventing communication between a node and the rest of the network, an attacker reduces the honest computational power and isolates that node, making a 51% or a double spending attack (against the isolated node) easier to deploy. Additionally, an adversary can violate liveness by blocking the node’s transactions from reaching the rest of the network. Third, an adversary that controls all of a node’s connections can link transactions to the specific node, correlate the user’s addresses, and associate them with real-world information, such as an IP address, thus compromising privacy[10]. This also holds on a macroscopic level, i.e., a node that acts as a central communication hub between two clusters can obtain information and even deanonymize some participants (violating privacy). Node Bootstrapping and Peer Discovery. Joining a ledger’s network and synchronizing with it is the so-called “bootstrapping” process. All real-world ledgers rely on an initial (trusted) setup, the first (“genesis”) block.[11] Obtaining the correct genesis block is done in an out-of-band, typically secure mechanism, since it is often well-known and easy to validate from various sources. Node Bootstrapping and Peer Discovery. Node Bootstrapping and Peer Discovery. Initially, the node needs to connect to some peers and receive all available chains. Therefore, the resource here is bootstrapping nodes and the relevant parties are these nodes’ operators. Then, using the ledger protocol’s chain resolution mechanism, it decides which chain to adopt. There are two points of interest here. First, it should be guaranteed that the node connects to at least one honest peer, to retrieve all available information and avoid getting eclipsed. As explained above, if nodes gets eclipsed, the system’s liveness, safety and privacy properties may get compromised. However, connecting to honest peers is not straightforward, given that the node has no knowledge about the network participants, a standard problem in P2P networks [57]. Blockchain systems predominantly use either hardcoded peer lists or DNS seeding [120], although both techniques are censorship-prone. Notably, [120] showed that more secure alternatives, such as ZMap [61], cannot be feasibly used in existing blockchain systems. Second, even if the node connects to some honest peers, catching up by using only genesis (“bootstrapping from genesis”) is not always feasible. Especially in PoS systems, an attacker can effortlessly assemble an arbitrarily-long, seemingly correct chain (violating safety) [81,31,118]. To counter such attacks, some ledgers employ checkpoints [107,52,50], which are often issued centrally and are either hardcoded or received from the peers. Other solutions do exist, e.g., analyzing block density and relying on key erasures [12] or using VDFs [24,53], but enforcing and/or relaxing such assumptions still poses an interesting research problem. Authors:
(1) Christina Ovezik, University of Edinburgh (c.ovezik@ed.ac.uk);
(2) Dimitris Karakostas, University of Edinburgh (dkarakos@ed.ac.uk);
(3) Aggelos Kiayias, University of Edinburgh and IOG (akiayias@ed.ac.uk). Authors: Authors: (1) Christina Ovezik, University of Edinburgh (c.ovezik@ed.ac.uk); (2) Dimitris Karakostas, University of Edinburgh (dkarakos@ed.ac.uk); (3) Aggelos Kiayias, University of Edinburgh and IOG (akiayias@ed.ac.uk). 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 available on arxiv [9] In longest-chain protocols, like Bitcoin, miners keep producing blocks in isolation, ending up with different ledger versions. In BFT-style protocols, like Algorand, the ledger cannot be updated if a large number of block producers become unreachable and thus do not adopt new transactions. [10] Note that protecting privacy in the network layer can be a particularly challenging problem, even in the setting where no single adversary controls all of a node’s connections. [11] Some proposals rely on computational assumptions instead of a trusted setup [80], but their real-world performance and applicability is still untested.

Eclipse Attacks, Network Clusters & DNS Seeding

Singapore, March 21st, 2025 - In an extraordinary atmosphere at the unique Circa Amsterdam venue, the Web3 Amsterdam Conference took place on March 13th and 14th in the Dutch capital, where Bitunix exchange captured the spotlight as the Title Sponsor of the event. Singapore, March 21st, 2025 - In an extraordinary atmosphere at the unique Circa Amsterdam venue, the Web3 Amsterdam Conference took place on March 13th and 14th in the Dutch capital, where Bitunix exchange captured the spotlight as the Title Sponsor of the event. The booth of the world’s fastest-growing cryptocurrency exchange attracted significant attention, with visitors eager to learn more about Bitunix’s services. Many were particularly curious about the platform’s unique features, fees, liquidity, and transparency, with Bitunix representatives providing detailed answers without hesitation. cryptocurrency exchange attracted significant attention https://x.com/BitunixOfficial/status/1900152594623844655?embedable=true https://x.com/BitunixOfficial/status/1900152594623844655?embedable=true During her presentation at the event, Andrea Soto, from Bitunix, highlighted the exchange's most advanced features, including the exclusive Multiwindow feature which allows traders to use 16 windows at once on one screen. Additionally, she introduced Bitunix's exclusive TradingView feature for mobile, a one-of-a-kind function globally. 16 windows These features were a major draw at the Bitunix booth, with many visitors asking about them after her speech. Soto also emphasized that Bitunix prioritizes security, noting: “We provide safety for payments and wallets, as well as Proof of Reserve, so every trader using our exchange can be confident that 100% of the assets they hold are fully backed by the funds we have on the exchange.” “We provide safety for payments and wallets, as well as Proof of Reserve, so every trader using our exchange can be confident that 100% of the assets they hold are fully backed by the funds we have on the exchange.” “We provide safety for payments and wallets, as well as Proof of Reserve, so every trader using our exchange can be confident that 100% of the assets they hold are fully backed by the funds we have on the exchange.” Bitunix KOLs Discuss Market Trends and Memecoins on Stage Sonny Verecuo, Bitunix Business Development Representative, moderated a panel on Memecoin Trading Strategies with notable Bitunix KOLs, including Argiris Trader, Chento Trades, and Greg from USACryptoNoticias. Argiris Trader Chento Trades USACryptoNoticias While the panelists offered their perspectives on the memecoin market, Argiris notably criticized the growing popularity of memecoins, stating that they were draining liquidity away from altcoins. Greg from USACryptoNoticias also criticized memecoins, not favoring them as investment options. These remarks helped clarify that these panelists, such as USACryptoNoticias, do not trade with memecoins, as they focus mostly on other crypto strategies. In addition, USACryptoNoticias actively engaged with the conference audience by trading and performing live analysis both days at the Bitunix booth for his community. Aurolo, another influential crypto figure in the Spanish and LATAM region, also participated in these activities, providing valuable insights to attendees. Aurolo Crypto Million Announces IMO Token Launch on Bitunix with an Airdrop for Web3 Amsterdam Attendees Crypto Million, a well-known Bitunix KOL and one of the largest crypto influencers in Switzerland and the French region, delivered a speech in which he made a major announcement: the launch of the IMO token on Bitunix. During his speech, he gave attendees the exciting opportunity to receive free tokens in an airdrop, further highlighting Bitunix’s commitment to engaging its user base and expanding its offerings. Crypto Million IMO token The event provided a prime networking opportunity, allowing attendees to explore the latest industry trends, engage with expert speakers, and connect with leading professionals. As part of the partnership, Bitunix also hosted workshops and discussions, strengthening ties with developers, investors, and blockchain enthusiasts. Recent data from a 2024 European Central Bank study shows that 9% of Eurozone residents now hold cryptocurrencies, up from 4% in 2022, reinforcing the growing popularity of crypto in Europe and the significance of Bitunix’s presence at Web3 Amsterdam. study This conference was a good opportunity for all interested parties to get to know the services and advantages that Bitunix offers. The exchange will also be part of Paris Blockchain Week and Token2049 in Dubai. Bitunix has built a reputation for actively participating in such conferences, striving to advance global crypto adoption while also educating users about its platform and services. About Bitunix Bitunix is a global cryptocurrency derivatives exchange founded in 2021, committed to offering simple, secure, transparent, and cost-effective trading services to its users. Bitunix specializes in both spot trading and perpetual futures, with over 700 trading pairs and leverage of up to 125x. cryptocurrency derivatives exchange cryptocurrency derivatives exchange With features such as top-tier liquidity, 24/7 customer support, and a strong commitment to regulatory compliance, Bitunix remains at the forefront of providing a reliable trading experience for the global crypto community. Bitunix has attracted more than 2,000,000 users from over 100 countries, facilitating a daily trading volume exceeding $5 billion on its platform. Website | Telegram | X | LinkedIn Website Website Telegram Telegram X X LinkedIn LinkedIn

Bitunix Becomes the Center of Attention at Web3 Amsterdam, Dominating as Title Sponsor

Bitunix става център на вниманието в Web3 Amsterdam, доминиращ като титулен спонсор

Bitunix se convierte en el centro de atención en Web3 Amsterdam, dominando como patrocinador principal

Bitunix Vin Sant Atansyon nan Web3 Amstèdam, Domine kòm Sponsor Tit

Bitunix Menjadi Pusat Perhatian di Web3 Amsterdam, Mendominasi sebagai Sponsor Utama

Bitunix diventa il centro dell'attenzione al Web3 Amsterdam, dominando come sponsor principale

Bitunix הופך למרכז תשומת הלב ב-Web3 אמסטרדם, שולט כחסות כותרת

Bitunix が Web3 Amsterdam で注目の的となり、タイトルスポンサーとして存在感を発揮

Bitunix Web3 Амстердамда көңүл буруунун борбору болуп, титулдук демөөрчү катары үстөмдүк кылат

Bitunix kļūst par Web3 Amsterdamas uzmanības centru, dominējot kā titula sponsors

Bitunix wordt middelpunt van de belangstelling op Web3 Amsterdam en domineert als titelsponsor

Bitunix Inova Nzvimbo Yekutarisisa paWeb3 Amsterdam, Inotonga seChinyorwa Sponsor

Bitunix bëhet qendra e vëmendjes në Web3 Amsterdam, duke dominuar si sponsor i titullit

Frankfurt am Main, Germany, March 17th, 2025/CyberNewsWire/--Cyberattacks are no longer an abstract threat - they dominate risk planning for companies worldwide. The latest Link11 European Cyber Report shows an alarming trend: the number of DDoS attacks has more than doubled, and they are shorter, more targeted, and more technically sophisticated. Link11 Link11 Organizations that do not continuously evolve their security strategies face significant financial losses and long-term reputational damage. The numbers speak for themselves: 137% more DDoS attacks on the Link11 network compared to last year.
A new scale has been reached: The largest attack measured to date was 1.4 terabits per second (Tbps).
Attacks are shorter and highly effective: Two-thirds of all attacks peaked in just 10 to 60 seconds.
Multi-vector attacks are setting new standards: The combination of different attack vectors makes defense more difficult and requires more precise protection. 137% more DDoS attacks on the Link11 network compared to last year. A new scale has been reached: The largest attack measured to date was 1.4 terabits per second (Tbps). Attacks are shorter and highly effective: Two-thirds of all attacks peaked in just 10 to 60 seconds. Multi-vector attacks are setting new standards: The combination of different attack vectors makes defense more difficult and requires more precise protection. Why organizations should act now The Allianz Risk Barometer 2025 highlights that while digital transformation presents new opportunities, it also expands the attack surface for cyber threats. Cybercriminals are leveraging powerful botnets and increasingly sophisticated attack techniques, accelerating the speed and impact of DDoS attacks. The Allianz Risk Barometer 2025 The Allianz Risk Barometer 2025 A recent case demonstrates how these evolving threats are testing the resilience of organizations. Multi-vector DDoS: When Network Load Meets Application Attacks A four-day attack combined Layer 3/4 and Layer 7 techniques, putting both infrastructure and web applications under massive pressure. Link11 recorded a total of 120 million requests, resulting in more than a million WAF logs - a load that quickly overwhelmed conventional defenses. The attackers' strategic approach was particularly striking: Layer 3/4 attacks: Massive data streams overwhelm the network infrastructure.
Layer 7 attacks: APIs and web applications were deliberately crippled with complex queries.
Dynamic attack patterns: Attacks were launched in waves to test the response times of defenses. Layer 3/4 attacks: Massive data streams overwhelm the network infrastructure. Layer 7 attacks: APIs and web applications were deliberately crippled with complex queries. Dynamic attack patterns: Attacks were launched in waves to test the response times of defenses. Organizations that do not continuously adapt their IT security strategy risk becoming victims of targeted attacks. Web applications and APIs are particularly targeted by cybercriminals because they often handle sensitive data and control critical business processes. Modern Security Architecture is The Key to Resilience The incident underscores the growing limitations of traditional DDoS defenses, emphasizing the need for more adaptive mitigation strategies. Enterprises are increasingly turning to AI-powered systems for real-time threat detection and attack prevention. Additionally, Web Application and API (WAAP) protection is gaining importance as attackers continue to exploit this critical attack vector. Combining advanced protection solutions: AI-based attack detection for early detection of suspicious patterns
Bot management to block automated attacks
Adaptive WAF systems that adapt in real time AI-based attack detection for early detection of suspicious patterns Bot management to block automated attacks Adaptive WAF systems that adapt in real time A holistic security strategy combines advanced DDoS mitigation, continuous monitoring, and adaptive protection mechanisms. "The increasing number of DDoS attacks shows that cybercriminals continue to rely on this proven method. However, the shortened attack time does not mean that the threat is decreasing – on the contrary: companies need to react faster and further optimize their defense mechanisms," said Jens-Philipp Jung, CEO of Link11. "The increasing number of DDoS attacks shows that cybercriminals continue to rely on this proven method. However, the shortened attack time does not mean that the threat is decreasing – on the contrary: companies need to react faster and further optimize their defense mechanisms," said Jens-Philipp Jung, CEO of Link11. The full European Cyber Report 2025 can be downloaded here. here here About Link11 Link11 is a specialized global IT security provider and protects infrastructures and web applications from cyberattacks. Its cloud-based IT security solutions help companies worldwide to strengthen the cyber resilience of their networks and critical applications and avoid business disruption. Link11 is a BSI-qualified provider for DDoS protection of critical infrastructure. With ISO 27001 certification, the company meets the highest standards in data security. Link11 Link11 Contact Corporate Communications Lisa Froehlich Link11 GmbH l.froehlich@link11.com +49 16098088442 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

European Cyber Report 2025: 137% More DDoS attacks Than Last Year - What Companies Need To Know

የአውሮፓ የሳይበር ሪፖርት 2025፡ ካለፈው ዓመት 137% ተጨማሪ የ DDoS ጥቃቶች - ኩባንያዎች ማወቅ ያለባቸው

Informe Cibernético Europeo 2025: Un 137 % más de ataques DDoS que el año pasado: Lo que las empresas deben saber

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รายงานไซเบอร์ของยุโรปประจำปี 2025: การโจมตี DDoS เพิ่มขึ้น 137% เมื่อเทียบกับปีที่แล้ว - สิ่งที่บริษัทต่างๆ จำเป็นต้องรู้

Author:
(1) Thierry Boy de la Tour, Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG 38000 Grenoble, France. Table of Links Abstract and 1 Introduction 2 Basic Definitions and Notations 2.1 Sets 2.2 Sequences 2.3 Signatures and Algebras and 2.4 Categories 3 Monographs and their Morphisms 4 Limits and Colimits 5 Drawing Monographs 6 Graph Structures and Typed Monographs 7 Submonographs and Partial Morphisms 8 Algebraic Transformations of Monographs 9 Attributed Typed Monographs 10 Conclusion and References 9 Attributed Typed Monographs The notion of E-graph has been designed in [2] in order to obtain an adhesive category of graphs with attributed nodes and edges. This follows from a line of studies on Typed Attributed Graph Transformations, see [21, 22, 23]. The attributes are taken in a data type algebra and may be of different sorts (booleans, integers, strings, etc.). In the case of E-graphs only the nodes of sort values represent such attributes. But they are also typed by E-graphs, and in the type E-graphs each node of sort values represent a sort of the data type algebra. This should recall the constructions of Section 6 that we now use in order to generalize the notion of typed attributed graphs given in [2]. The idea is similarly to impose that the edges typed by a sort of a data type algebra are the elements of the corresponding carrier set. Theorem 9.4 generalizes[3] [2, Theorem 11.3] that establishes an isomorphism between the category of attributed E-graphs typed by an attributed E-graph AT G and the category of algebras of a signature denoted AGSIGpAT Gq. In particular Theorem 11.3 of [2] requires the hypothesis that AGSIGpAT Gq should be well-structured, which means that if there is an operator name of ST whose domain sort is s then s is not a sort of the data type signature Σ. Obviously this is equivalent to requiring that only nodes of T can be considered as sorts of Σ and is linked to the fact that only values nodes of E-graphs are supposed to hold attributes. Since we are not restricted to E-graphs there is no need to require that attributes should only be nodes. This has an interesting consequence: The approach adopted in [2, Definition 11.5] is to restrict the morphisms used in span rules to a class of monomorphisms that are extensions of Σ-isomorphisms to pΓ `Σq-homomorphisms. It is then possible to show [2, Theorem 11.11] that categories of typed attributed E-graphs are adhesive HLR categories (a notion that generalizes Definition 4.13, see [24]) w.r.t. this class of monomorphisms. A similar result holds on categories of ATMs. For the sake of simplicity, and since rule-based graph transformations are unlikely to modify attributes such as booleans, integers or strings (and if they do they should probably not be considered as graph transformations), we will only consider morphisms that leave the data type algebra unchanged, element by element. This leaves the possibility to transform the edges whose sort is in Γ but not in Σ. The proof that the categories ATMpT, Aq are adhesive will only be sketched below. The key point is the following lemma. Hence the property of stabilization characterizes the difference between morphisms in Monogrz\T and morphisms in ATM(T, A). Besides, it is well-known how pushouts and pullbacks in Monogrz\T can be constructed from those in Monogr, and we have seen that these can be constructed from those in Sets. Theorem 9.8. ATM(T, A) is adhesive. This result does not mean that all edges that are not attributes can be freely transformed. Their adjacencies to or from attributes may impose constraints that only few morphisms are able to satisfy. Example 9.9. Let Σ be the signature with no operation name and one sort s, and A be the Σ-algebra defined by As “ ta, bu. We consider the type monograph T “ tpe, sq,ps, equ. A monograph typed by T has any number (but at least one) of edges typed by e that must be adjacent either to a or b, and two edges typed by s, namely a and b, that must be adjacent to either the same edge x typed by e, which yields two classes of monographs (to which may be added any number of edges typed by e and adjacent to either a or b), or a and b are adjacent to y and z respectively, and we get four more classes: The function y, z → x is a morphism from these last two monographs to the two monographs above (respectively). There are no other morphisms between monographs from distinct classes. We therefore see that in the category ATM(T, A) it is possible to add or remove edges typed by e to which a or b are not adjacent, but there is no way to remove the edges y and z (because this would require a rule with a left morphism from an ATM without y and z to an ATM with y and z, and there is no such morphism), though they are not attributes. Besides, we see that this category has no initial object, no terminal object, no products nor coproducts. This paper is available on arxiv under CC BY 4.0 DEED license. [3] Our proof is also much shorter than the 6 pages taken by the corresponding result on attributed typed E-graphs. This is due partly to our use of AT (Definition 6.7) and of Theorem 6.8, but also to the simplicity of monographs compared to the 5 sorts and 6 operator names of E-graphs. Author: (1) Thierry Boy de la Tour, Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG 38000 Grenoble, France. Author: Author: (1) Thierry Boy de la Tour, Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG 38000 Grenoble, France. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Basic Definitions and Notations 2.1 Sets 2.1 Sets 2.2 Sequences 2.2 Sequences 2.3 Signatures and Algebras and 2.4 Categories 2.3 Signatures and Algebras and 2.4 Categories 3 Monographs and their Morphisms 3 Monographs and their Morphisms 4 Limits and Colimits 4 Limits and Colimits 5 Drawing Monographs 5 Drawing Monographs 6 Graph Structures and Typed Monographs 6 Graph Structures and Typed Monographs 7 Submonographs and Partial Morphisms 7 Submonographs and Partial Morphisms 8 Algebraic Transformations of Monographs 8 Algebraic Transformations of Monographs 9 Attributed Typed Monographs 9 Attributed Typed Monographs 10 Conclusion and References 10 Conclusion and References 9 Attributed Typed Monographs The notion of E-graph has been designed in [2] in order to obtain an adhesive category of graphs with attributed nodes and edges. This follows from a line of studies on Typed Attributed Graph Transformations, see [21, 22, 23]. The attributes are taken in a data type algebra and may be of different sorts (booleans, integers, strings, etc.). In the case of E-graphs only the nodes of sort values represent such attributes. But they are also typed by E-graphs, and in the type E-graphs each node of sort values represent a sort of the data type algebra. This should recall the constructions of Section 6 that we now use in order to generalize the notion of typed attributed graphs given in [2]. The idea is similarly to impose that the edges typed by a sort of a data type algebra are the elements of the corresponding carrier set. Theorem 9.4 generalizes[3] [2, Theorem 11.3] that establishes an isomorphism between the category of attributed E-graphs typed by an attributed E-graph AT G and the category of algebras of a signature denoted AGSIGpAT Gq. In particular Theorem 11.3 of [2] requires the hypothesis that AGSIGpAT Gq should be well-structured, which means that if there is an operator name of ST whose domain sort is s then s is not a sort of the data type signature Σ. Obviously this is equivalent to requiring that only nodes of T can be considered as sorts of Σ and is linked to the fact that only values nodes of E-graphs are supposed to hold attributes. Since we are not restricted to E-graphs there is no need to require that attributes should only be nodes. This has an interesting consequence: The approach adopted in [2, Definition 11.5] is to restrict the morphisms used in span rules to a class of monomorphisms that are extensions of Σ-isomorphisms to pΓ `Σq-homomorphisms. It is then possible to show [2, Theorem 11.11] that categories of typed attributed E-graphs are adhesive HLR categories (a notion that generalizes Definition 4.13, see [24]) w.r.t. this class of monomorphisms. A similar result holds on categories of ATMs. For the sake of simplicity, and since rule-based graph transformations are unlikely to modify attributes such as booleans, integers or strings (and if they do they should probably not be considered as graph transformations), we will only consider morphisms that leave the data type algebra unchanged, element by element. This leaves the possibility to transform the edges whose sort is in Γ but not in Σ. The proof that the categories ATMpT, Aq are adhesive will only be sketched below. The key point is the following lemma. Hence the property of stabilization characterizes the difference between morphisms in Monogrz \T and morphisms in ATM (T, A). Besides, it is well-known how pushouts and pullbacks in Monogrz \T can be constructed from those in Monogr , and we have seen that these can be constructed from those in Sets. Monogrz ATM Monogrz Monogr Theorem 9.8. ATM (T, A) is adhesive. Theorem 9.8. ATM This result does not mean that all edges that are not attributes can be freely transformed. Their adjacencies to or from attributes may impose constraints that only few morphisms are able to satisfy. Example 9.9. Let Σ be the signature with no operation name and one sort s, and A be the Σ-algebra defined by As “ ta, bu. We consider the type monograph T “ tpe, sq,ps, equ. A monograph typed by T has any number (but at least one) of edges typed by e that must be adjacent either to a or b, and two edges typed by s, namely a and b, that must be adjacent to either the same edge x typed by e, which yields two classes of monographs Example 9.9. Let Σ be the signature with no operation name and one sort s, and A be the Σ-algebra defined by As “ ta, bu. We consider the type monograph T “ tpe, sq,ps, equ. A monograph typed by T has any number (but at least one) of edges typed by e that must be adjacent either to a or b, and two edges typed by s, namely a and b, that must be adjacent to either the same edge x typed by e, which yields two classes of monographs (to which may be added any number of edges typed by e and adjacent to either a or b), or a and b are adjacent to y and z respectively, and we get four more classes: (to which may be added any number of edges typed by e and adjacent to either a or b), or a and b are adjacent to y and z respectively, and we get four more classes: The function y, z → x is a morphism from these last two monographs to the two monographs above (respectively). There are no other morphisms between monographs from distinct classes. We therefore see that in the category ATM(T, A) it is possible to add or remove edges typed by e to which a or b are not adjacent, but there is no way to remove the edges y and z (because this would require a rule with a left morphism from an ATM without y and z to an ATM with y and z, and there is no such morphism), though they are not attributes. The function y, z → x is a morphism from these last two monographs to the two monographs above (respectively). There are no other morphisms between monographs from distinct classes. We therefore see that in the category ATM(T, A) it is possible to add or remove edges typed by e to which a or b are not adjacent, but there is no way to remove the edges y and z (because this would require a rule with a left morphism from an ATM without y and z to an ATM with y and z, and there is no such morphism), though they are not attributes. Besides, we see that this category has no initial object, no terminal object, no products nor coproducts. Besides, we see that this category has no initial object, no terminal object, no products nor coproducts. 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 [3] Our proof is also much shorter than the 6 pages taken by the corresponding result on attributed typed E-graphs. This is due partly to our use of AT (Definition 6.7) and of Theorem 6.8, but also to the simplicity of monographs compared to the 5 sorts and 6 operator names of E-graphs.

Making Sense of Attributed Typed Monographs

Authors:
(1) Albert Gu, Machine Learning Department, Carnegie Mellon University;
(2)Tri Dao, Department of Computer Science, Princeton University agu@cs.cmu.edu, tri@tridao.me. Table of Links Abstract and 1. Introduction 2 State Space Models 3 Selective State Space Models and 3.1 Motivation: Selection as a Means of Compression 3.2 Improving SSMs with Selection 3.3 Efficient Implementation of Selective SSMs 3.4 A Simplifed SSM Architecture 3.5 Properties of Selection Mechanisms 3.6 Additional Model Details 4 Empirical Evaluation and 4.1 Synthetic Tasks 4.2 Language Modeling 4.3 DNA Modeling 4.4 Audio Modeling and Generation 4.5 Speed and Memory Benchmarks 4.6 Model Ablations 5 Discussion 6 Conclusion, Acknowledgments and References A Discussion: Selection Mechanism B Related Work and B.1 S4 Variants and Derivatives B.2 SSM Architectures B.3 Relationship to RNNs B.4 Linear Attention and B.5 Long Context Models C Mechanics of Selective SSMs D Hardware-aware Algorithm For Selective SSMs E Experimental Details and Additional Results and E.1 Synthetic Tasks E.2 Language Modeling E.3 DNA Modeling E.4 Audio Details E.5 Efficiency Benchmark B.4 Linear Attention The Linear Attention (LA) (Katharopoulos et al. 2020) framework is an important result popularizing kernel attention and showing how it relates to recurrent autoregressive models. Many variants have proposed alternative kernels and other modifications. Random Feature Attention (RFA) (H. Peng et al. 2021) chooses the kernel feature map to approximate softmax attention (i.e. the exp feature map) using the random Fourier feature approximation of Gaussian kernels (Rahimi and Recht 2007). Performer (Choromanski et al. 2021) finds an approximation to the exponential kernel involving only positive features, which also allows the softmax normalization term. TransNormer (Qin, Han, W. Sun, D. Li, et al. 2022) showed that the LA denominator term can be unstable and proposed replacing it with a LayerNorm. cosFormer (Qin, W. Sun, et al. 2022) augments RFA with a cosine reweighting mechanism that incorporates positional information to emphasize locality. Linear Randomized Attention (Zheng, C. Wang, and L. Kong 2022) generalize RFA from the perspective of importance sampling, and generalize it to provide better estimates of the full softmax kernel (rather than just the exp-transformed numerator). Aside from kernel attention, many other variants of efficient attention exist; the survey Tay, Dehghani, Bahri, et al. (2022) offers an extensive categorization of many of these. B.5 Long Context Models Long context has become a popular subject, and several recent models have claimed to scale to longer and longer sequences. However, these are often from a computational standpoint and have not been extensively validated. These include: In contrast, we believe this work presents one of the first approaches to meaningfully demonstrate increasing performance with longer context. C Mechanics of Selective SSMs The discretization step size is where we observe that the parameter can be viewed as a learnable bias and folded into the linear projection. Now applying the zero-order hold (ZOH) discretization formulas: Thus the final discrete recurrence (2a) is as desired. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Albert Gu, Machine Learning Department, Carnegie Mellon University; (2)Tri Dao, Department of Computer Science, Princeton University agu@cs.cmu.edu, tri@tridao.me. Authors: Authors: (1) Albert Gu, Machine Learning Department, Carnegie Mellon University; (2)Tri Dao, Department of Computer Science, Princeton University agu@cs.cmu.edu, tri@tridao.me. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 State Space Models 2 State Space Models 3 Selective State Space Models and 3.1 Motivation: Selection as a Means of Compression 3 Selective State Space Models and 3.1 Motivation: Selection as a Means of Compression 3.2 Improving SSMs with Selection 3.2 Improving SSMs with Selection 3.3 Efficient Implementation of Selective SSMs 3.3 Efficient Implementation of Selective SSMs 3.4 A Simplifed SSM Architecture 3.4 A Simplifed SSM Architecture 3.5 Properties of Selection Mechanisms 3.5 Properties of Selection Mechanisms 3.6 Additional Model Details 3.6 Additional Model Details 4 Empirical Evaluation and 4.1 Synthetic Tasks 4 Empirical Evaluation and 4.1 Synthetic Tasks 4.2 Language Modeling 4.2 Language Modeling 4.3 DNA Modeling 4.3 DNA Modeling 4.4 Audio Modeling and Generation 4.4 Audio Modeling and Generation 4.5 Speed and Memory Benchmarks 4.5 Speed and Memory Benchmarks 4.6 Model Ablations 4.6 Model Ablations 5 Discussion 5 Discussion 6 Conclusion, Acknowledgments and References 6 Conclusion, Acknowledgments and References A Discussion: Selection Mechanism A Discussion: Selection Mechanism B Related Work and B.1 S4 Variants and Derivatives B Related Work and B.1 S4 Variants and Derivatives B.2 SSM Architectures B.2 SSM Architectures B.3 Relationship to RNNs B.3 Relationship to RNNs B.4 Linear Attention and B.5 Long Context Models B.4 Linear Attention and B.5 Long Context Models C Mechanics of Selective SSMs C Mechanics of Selective SSMs D Hardware-aware Algorithm For Selective SSMs D Hardware-aware Algorithm For Selective SSMs E Experimental Details and Additional Results and E.1 Synthetic Tasks E Experimental Details and Additional Results and E.1 Synthetic Tasks E.2 Language Modeling E.2 Language Modeling E.3 DNA Modeling E.3 DNA Modeling E.4 Audio Details E.4 Audio Details E.5 Efficiency Benchmark E.5 Efficiency Benchmark B.4 Linear Attention The Linear Attention (LA) (Katharopoulos et al. 2020) framework is an important result popularizing kernel attention and showing how it relates to recurrent autoregressive models. Many variants have proposed alternative kernels and other modifications. Random Feature Attention (RFA) (H. Peng et al. 2021) chooses the kernel feature map to approximate softmax attention (i.e. the exp feature map) using the random Fourier feature approximation of Gaussian kernels (Rahimi and Recht 2007). Performer (Choromanski et al. 2021) finds an approximation to the exponential kernel involving only positive features, which also allows the softmax normalization term. TransNormer (Qin, Han, W. Sun, D. Li, et al. 2022) showed that the LA denominator term can be unstable and proposed replacing it with a LayerNorm. cosFormer (Qin, W. Sun, et al. 2022) augments RFA with a cosine reweighting mechanism that incorporates positional information to emphasize locality. Linear Randomized Attention (Zheng, C. Wang, and L. Kong 2022) generalize RFA from the perspective of importance sampling, and generalize it to provide better estimates of the full softmax kernel (rather than just the exp-transformed numerator). Aside from kernel attention, many other variants of efficient attention exist; the survey Tay, Dehghani, Bahri, et al. (2022) offers an extensive categorization of many of these. B.5 Long Context Models Long context has become a popular subject, and several recent models have claimed to scale to longer and longer sequences. However, these are often from a computational standpoint and have not been extensively validated. These include: In contrast, we believe this work presents one of the first approaches to meaningfully demonstrate increasing performance with longer context. C Mechanics of Selective SSMs The discretization step size is where we observe that the parameter can be viewed as a learnable bias and folded into the linear projection. Now applying the zero-order hold (ZOH) discretization formulas: Thus the final discrete recurrence (2a) is as desired. 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

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