Laura Bratton is a reporter for Yahoo Finance. Follow her on Bluesky @laurabratton.bsky.social.

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Laura is a science news writer, covering a wide variety of subjects, but she is particularly fascinated by all things aquatic, paleontology, nanotechnology, and exploring how science influences daily life.

Extremely rare ‘Einstein ring’ discovered close to Earth | Popular Science

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Many people have discovered ChatGPT’s competencies in code generation. But, how well does it actually perform, and are there alternatives? In this post, I take a look at some scenarios where AI can and the degree to which it can be useful. Finally, I do my best to evaluate its performance. help with coding Intro ChatGPT has been everywhere in the last few months. Reading and hearing about how people use it and ‘force’ it to make mistakes has been fun for a week or two, but it's getting tiring. For some reason, my LinkedIn feed still has at least one "Look how ChatGPT failed"-post every day, and I don't know what people are trying to prove with that. At the same time, I am perplexed by software developers claiming that they are using ChatGPT to do their work. Statements like "This week, I didn't write a single line of code and only used ChatGPT" even made it to podcasts where people discussed which jobs AI might replace in the future. For everyone who has seriously worked as a software developer, it is evident that AI cannot replace software developers soon. Yes, ChatGPT can generate code for a given problem description, but it is not good at solving problems, which is the main task of a software engineer. AI can support specific tasks, though, and I am looking at some examples in this post. AI Pair Programming Several services implement smart code completion, also called AI pair programming. The term is quite misleading and was probably invented by someone without much experience with pair programming. While pair programming is specifically beneficial when two people work together to discuss how something should be implemented, AI [pair programming](https://Pair programming - Wikipedia  Wikipedia https://en.wikipedia.org › wiki › Pair_programming) tools instead help with the next step. As soon as you decide what to implement and where to put the code, it suggests a solution. The two most popular ones are and GitHub Copilot is based on , a model for translating natural language to code based on GPT-3. According to a paper by OpenAI researchers, the model generated working code for given Python docstrings for more than 70% of the attempts. That's quite impressive but still also emphasizes that it requires basic coding skills (i.e., writing a docstring) to retrieve good results. Like ChatGPT, CodeX can also be used for free. GitHub Copilot Tabnine. OpenAI Codex on the OpenAI website I have used Copilot for private projects for quite a while and liked it a lot. In the beginning (when it was still in technical preview), it mainly copied lines from GitHub code. Since then, it has improved and evolved a lot. Especially for code that implements generic tasks (like calculations or conversions), there is a high chance that Copilot suggests a complete function based on a doc string. For example, when you write it would suggest the complete function, including a return value. def reverse(self, x:int) -> int... Copilot also works well for writing tests. Just add the name "test" to a given function name and it does the job. In contrast, Tabnine was a massive disappointment for me, though, and felt like a dumb auto-completion suggesting mostly useless or wrong code. While Copilot uses one large model, Tabnine is based on several smaller models that solve individual problems. This is probably also why it is more convenient to use Tabnine in local mode and train it with your own code (which is not possible with Copilot). Even with a lot of context and well-named variables, the suggestions I received from Tabnine were often far from what you would expect. Using local mode, it worked well in large repositories with a lot of similar code. But this is far too easy compared to the problems I would expect such a model to solve. It was not smart but felt more like a tool for automatic copy and pasting. There might be other use cases and circumstances where Tabnine works well (for example, in huge company code bases where everything is written in a similar style and language) but as someone who is working on many smaller projects, it just didn't work for me. Asking AI to write Code As mentioned in the intro, one of the primary use cases of ChatGPT seems to be code generation. Of course, that was already possible with CodeX, but the boom of ChatGPT made many more people aware of what is possible. Compared to the plugins mentioned before, this approach aims to translate textual problem descriptions into code instead of being integrated into an existing code base. Doing this task is challenging for a model as it involves understanding a problem and generating suitable, functional code that solves this problem. In the past, many models failed to do the former reliably, and even with more specialized models, it still is a considerable challenge that is not solved by ChatGPT. However, let's have a look at the code ChatGPT generates. Asking ChatGPT to generate a weather app I asked ChatGPT to write a Streamlit application that fetches weather data for Dortmund from a free API and displays it in a chart. This is what I got: import streamlit as st
import requests
import pandas as pd
import plotly.express as px

# Set up OpenWeatherMap API key
api_key = "YOUR_API_KEY_HERE"

# Define function to get weather data from OpenWeatherMap API
def get_weather_data():
    url = f"https://api.openweathermap.org/data/2.5/onecall?lat=51.5134&lon=7.4814&exclude=hourly,minutely&appid={api_key}&units=metric"
    response = requests.get(url)
    data = response.json()
    return data

# Get weather data
data = get_weather_data()

# Convert weather data to a Pandas DataFrame
df = pd.DataFrame(data["daily"])

# Format the date column
df["dt"] = pd.to_datetime(df["dt"], unit="s")
df["date"] = df["dt"].dt.date
df = df.set_index("date")

# Create a line chart using Plotly Express
chart = px.line(df, x=df.index, y="temp.day", title="Dortmund Weather Forecast")
chart.update_xaxes(title="Date")
chart.update_yaxes(title="Temperature (°C)")

# Display the chart in the Streamlit app
st.plotly_chart(chart) In general, that's not too bad. ChatGPT also gave some instructions on how to get the API key. Unfortunately, it didn't tell me I also have to install , but as I asked for a Streamlit app, it's OK to assume I know how to install libraries. plotly Nevertheless, there are two issues: The temperature value is not parsed correctly. It is saved as a dictionary and accessing it with will throw an error. A quick and easy fix would be to replace this with . I assume that's a good example of how the model has learned a lot of different ways how to handle the reply of the API but isn't successful in drawing a connection between parsing the data and accessing it for the chart again. y="temp.day" y=df['temp'].str['day'] The app only works if there is a valid API key and throws an error if there is none. The missing error handling is unfortunate as it displays error messages in the front end. However, it could quickly be fixed by either catching the error and displaying a more user-friendly error message (preferred) or implementing the API call so that the error is not displayed in the UI. Apart from these issues, the code looks fine and runs after fixing the first issue. Also, while the number of comments might be too ambitious in general, it's helpful to understand the generated code. Writing tests with ChatGPT Next, I asked ChatGPT to write a unit test for the application. import unittest
from app import get_weather_data

class TestWeatherApp(unittest.TestCase):

    def test_weather_data(self):
        # Call the function to get weather data
        data = get_weather_data()

        # Check that the data contains the expected keys
        self.assertIn("current", data)
        self.assertIn("hourly", data)
        self.assertIn("daily", data)

        # Check that the daily data contains the expected keys
        daily_data = data["daily"][0]
        self.assertIn("dt", daily_data)
        self.assertIn("temp", daily_data)
        self.assertIn("feels_like", daily_data)
        self.assertIn("pressure", daily_data)
        self.assertIn("humidity", daily_data)
        self.assertIn("dew_point", daily_data)
        self.assertIn("wind_speed", daily_data)
        self.assertIn("wind_deg", daily_data)
        self.assertIn("weather", daily_data)
        self.assertIn("clouds", daily_data)
        self.assertIn("pop", daily_data)
        self.assertIn("uvi", daily_data) This one is interesting. First of all, the test calls get_weather_data(), but the API key is defined outside the function. Therefore, it will run without an API key and will always fail. So the test is useless. Apart from that, this is not a unit test but an integration test. It tests if the (external) API returns the correct data. To implement a unit test, expected data should be mocked, so I asked ChatGPT to fix this. This is not a unit test but an integration test. Can you fix this? import unittest
from unittest.mock import patch
from app import get_weather_data

class TestWeatherApp(unittest.TestCase):

    @patch('requests.get')
    def test_get_weather_data(self, mock_get):
        # Set up test data
        expected_data = {'daily': [
            {'dt': 1645808400,
            'temp': {'day': 5.5, 'min': 2.15, 'max': 7.9, 'night': 3.4, 'eve': 7.9, 'morn': 2.15},
            'feels_like': {'day': 0.3, 'night': -3.04, 'eve': 4.06, 'morn': -4.01},
            'pressure': 1028, 'humidity': 51, 'dew_point': -2.57, 'wind_speed': 2.81, 'wind_deg': 194,
            'weather': [{'id': 801, 'main': 'Clouds', 'description': 'few clouds', 'icon': '02d'}],
            'clouds': 16, 'pop': 0, 'uvi': 2.21}],
            'timezone_offset': 3600}

        # Set up mock response from requests.get
        mock_response = type(
                'MockResponse',
                (object,),
                {'json': lambda self: expected_data}
            )
        mock_get.return_value = mock_response

        # Call the function to get weather data
        data = get_weather_data()

        # Check that the function returns the expected data
        self.assertEqual(data, expected_data) This looks much better, but still, I am missing testing for potential error cases, so I asked again: Shouldn't the unittest also check if the app crashes in case something unusual happens. For example, if the API is down? ChatGPT added this part to the code generated earlier: @patch('requests.get')
    def test_get_weather_data_error(self, mock_get):
        # Set up mock response from requests.get to raise an exception
        mock_get.side_effect = Exception("Connection error")

        # Call the function to get weather data
        with self.assertRaises(Exception):
            data = get_weather_data() This addition looks reasonable. However, as the original app does not handle any error cases, this test doesn't make much sense. Testing if the app doesn't crash when we know it will crash leads nowhere, so I stopped asking. Overall, the results presented by ChatGPT were OK. They certainly help with prototyping or giving a template to start from. However, the code still needs to be made production-ready. ChatGPT would certainly help with that, but improving the code step by step by asking the right questions is not an efficient way of working. Outlook Last year, Deepmind's AlphaCode took part in competitive programming contests and The code and some comments by a human competitor can be found . This has been quite a breakthrough as it was the first time an AI coding model proved problem-solving skills. It will be interesting to see how these models improve and find their way into software development. performed like a median human competitor. here Also published . here

The Current Reality and Future Possibilities of AI-Powered Coding

Discovering Web3 Gaming with Long Do and Laura Cullell

The tech workforce in the US is not growing at an optimal pace. The number of schools offering computer science is not enough which is impacting the access of computer science to young women and students from marginalized communities. The truth is that there are over in the US but only graduated into the tech workforce last year. 410,020 open computing jobs 71,226 computer science students This is because only in the US offer computer science which deeply impacts its access to young women and students from marginalized communities. 47% of public high schools To address this pressing issue, we have launched #MyStartInTech, a campaign dedicated to drawing attention to this cause and in turn, help widen access to computer science in schools. As part of this campaign, we present the #MyStartInTech interview series where some of the esteemed tech entrepreneurs and professionals in the industry share details about their journey in tech. With this, we hope to draw attention to the infinite opportunities that will lay open if young women and students are given the opportunity to study computer science. In this interview, Laura Fernández, CEO and Co-Founder, , the first campus for Data Science, Product Management, and UX/UI Design for women, by women - talks about how she got her start in the tech world. allWomen Laura is an expert in tech and innovative learning, as well as community building. She also dedicates time to mentoring young entrepreneurs and enjoys curating tech content for brands. Let’s find out what she has to say! What is the importance of technology to you now and how does it impact your life? I think we all see the importance of technology to our daily lives from picking up our phones in the morning to check the news and social media, contacting our friends, doing a guided workout or meditation through an app, or playing some new music while we get ready for work. Then, of course, there's all of the technology that helps us facilitate our work-life like team organization and collaboration tools. Moreover, there's all the technology that's built specifically for women like period tracking apps, female-based online communities, and more that support the female experience in meaningful ways. This technology is everywhere and it's only becoming more inclusive as we make the tech industry more diverse. How important is it to increase access to computer science and technology to underrepresented communities and young women today? We know that our world is becoming increasingly more digital; we feel the effects of it every day. But only the people who know how to use these technologies will be able to benefit from the prosperity of them. That means having a leg-up in terms of job opportunities across all sectors as technology increasingly penetrates every industry. It's also really important for underrepresented communities and young women to have access to technology and computer science now so that they can help create it in the future. If not, our algorithms will continue to reflect our biased society by being biased themselves. Why do you think access to computer literacy in school is important? Computer literacy from a young age is really important because it sets people up for success. If we allow more people to have access to computers and technology earlier on in life, we'll see this reflected back in our future technology. It will be more meaningful and intuitive, built by people who really understand how it works and its potential. Moreover, the more we give diverse groups of people this opportunity to become computer literate at a younger age, the brighter our technology will be in terms of helping more people in valuable ways. What do you think about .Tech Domains taking up this cause? We think this is a super meaningful cause, and we really applaud your mission and your work. What we say here at allWomen is that it all starts with culture. Our culture tells us that women, particularly women of color, don't build technology - men do. But if we start changing who has access to technology at a younger age, we believe this culture will change along with it! Learn more about #MyStartInTech campaign and lend your support at . https://startin.tech/

#MyStartInTech: An Interview With Laura Fernández, allWomen.Tech

Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Data 2.1 Data Sources 2.1 Data Sources 2.2 SS and SI Categories 2.2 SS and SI Categories 3 Methods 3.1 Lexicon Creation and Expansion 3.1 Lexicon Creation and Expansion 3.2 Annotations 3.2 Annotations 3.3 System Description 3.3 System Description 4 Results 4.1 Demographics and 4.2 System Performance 4.1 Demographics and 4.2 System Performance 5 Discussion 5 Discussion 5.1 Limitations 5.1 Limitations 6 Conclusion, Reproducibility, Funding, Acknowledgments, Author Contributions, and References 6 Conclusion, Reproducibility, Funding, Acknowledgments, Author Contributions, and References SUPPLEMENTARY SUPPLEMENTARY Guidelines for Annotating Social Support and Social Isolation in Clinical Notes Guidelines for Annotating Social Support and Social Isolation in Clinical Notes Other Supervised Models Other Supervised Models 6 CONCLUSION We offer two open-source NLP systems with different approaches, as well as a manual annotation guideline for identifying SS and SI. The rule-based approach and the LLM approach each have strengths and limitations in performing this challenging task of creating a portable system to identify fine and coarse-grained categories of SS and SI in psychiatric patients. REPRODUCIBILITY All code for rule- and LLM-based systems are available on GitHub. Link: https://github.com/CornellMHILab/Social Support Social Isolation Extraction. Link The annotation guidelines and lexicons are available in the supplementary files. FUNDING This study was funded in part by grants from the National Institutes of Health (R01 MH119177, R01 MH121907, and R01 MH121922). ACKNOWLEDGEMENTS This work was supported in part through the computational and data resources and staff expertise provided by Scientific Computing and Data at the Icahn School of Medicine at Mount Sinai Health System and supported by the Clinical and Translational Science Awards (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences. Research reported in this publication was also supported by the Office of Research Infrastructure of the National Institutes of Health under award number S10OD026880 and S10OD030463. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. AUTHOR CONTRIBUTIONS BGP and LAL worked on planning, data collection, data annotation, data extraction, software development, writing, and editing. PR worked on LLM implementation and editing. VV helped in data annotation, writing, and editing. MMS, BF, and GH helped in data annotation. JP and PA helped in planning, advising, and editing. MW, JJM, and MO helped in initial category identification, lexicon development, advising, and editing. JASR, ER, JMB, IL, GNN, AT, and AWC helped in advising and editing. JP and AWC provided resources and funding. REFERENCES [1] World Health Organization. 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[42] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research, 21(1):5485–5551, 2020. [43] Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. Scaling instruction-finetuned language models. arXiv preprint arXiv:2210.11416, 2022. [44] Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35:27730–27744, 2022. [45] Wenxuan Zhou, Sheng Zhang, Hoifung Poon, and Muhao Chen. Context-faithful prompting for large language models. arXiv preprint arXiv:2303.11315, 2023. [46] Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, and Colin A Raffel. Few-shot parameter-efficient fine-tuning is better and cheaper than incontext learning. Advances in Neural Information Processing Systems, 35:1950–1965, 2022. 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) Braja Gopal Patra, Weill Cornell Medicine, New York, NY, USA and co-first authors;
(2) Lauren A. Lepow, Icahn School of Medicine at Mount Sinai, New York, NY, USA and co-first authors;
(3) Praneet Kasi Reddy Jagadeesh Kumar. Weill Cornell Medicine, New York, NY, USA;
(4) Veer Vekaria, Weill Cornell Medicine, New York, NY, USA;
(5) Mohit Manoj Sharma, Weill Cornell Medicine, New York, NY, USA;
(6) Prakash Adekkanattu, Weill Cornell Medicine, New York, NY, USA;
(7) Brian Fennessy, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(8) Gavin Hynes, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(9) Isotta Landi, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(10) Jorge A. Sanchez-Ruiz, Mayo Clinic, Rochester, MN, USA;
(11) Euijung Ryu, Mayo Clinic, Rochester, MN, USA;
(12) Joanna M. Biernacka, Mayo Clinic, Rochester, MN, USA;
(13) Girish N. Nadkarni, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(14) Ardesheer Talati, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA and New York State Psychiatric Institute, New York, NY, USA;
(15) Myrna Weissman, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA and New York State Psychiatric Institute, New York, NY, USA;
(16) Mark Olfson, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA, New York State Psychiatric Institute, New York, NY, USA, and Columbia University Irving Medical Center, New York, NY, USA;
(17) J. John Mann, Columbia University Irving Medical Center, New York, NY, USA;
(18) Alexander W. Charney, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(19) Jyotishman Pathak, Weill Cornell Medicine, New York, NY, USA. Authors: Authors: (1) Braja Gopal Patra, Weill Cornell Medicine, New York, NY, USA and co-first authors; (2) Lauren A. Lepow, Icahn School of Medicine at Mount Sinai, New York, NY, USA and co-first authors; (3) Praneet Kasi Reddy Jagadeesh Kumar. Weill Cornell Medicine, New York, NY, USA; (4) Veer Vekaria, Weill Cornell Medicine, New York, NY, USA; (5) Mohit Manoj Sharma, Weill Cornell Medicine, New York, NY, USA; (6) Prakash Adekkanattu, Weill Cornell Medicine, New York, NY, USA; (7) Brian Fennessy, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (8) Gavin Hynes, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (9) Isotta Landi, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (10) Jorge A. Sanchez-Ruiz, Mayo Clinic, Rochester, MN, USA; (11) Euijung Ryu, Mayo Clinic, Rochester, MN, USA; (12) Joanna M. Biernacka, Mayo Clinic, Rochester, MN, USA; (13) Girish N. Nadkarni, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (14) Ardesheer Talati, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA and New York State Psychiatric Institute, New York, NY, USA; (15) Myrna Weissman, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA and New York State Psychiatric Institute, New York, NY, USA; (16) Mark Olfson, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA, New York State Psychiatric Institute, New York, NY, USA, and Columbia University Irving Medical Center, New York, NY, USA; (17) J. John Mann, Columbia University Irving Medical Center, New York, NY, USA; (18) Alexander W. Charney, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (19) Jyotishman Pathak, Weill Cornell Medicine, New York, NY, USA.

Open-Source NLP Systems for Identifying Social Support and Isolation in Psychiatric Notes

Big data has evolved into a cornerstone of business success, especially for startups navigating competitive landscapes. Harnessing its power can enable deeper customer insights, more efficient operations, and data-driven decision-making that fuels sustainable growth. This guide explores practical strategies for startups to collect, analyze, and apply big data insights effectively in 2025. Why Big Data is Essential for Startups Startups often operate with limited resources, making precise decision-making crucial. Big data empowers these businesses to identify opportunities, mitigate risks, and allocate resources efficiently. From understanding customer behavior to enhancing financial stability, leveraging big data transforms challenges into growth opportunities. Strategies for Using Big Data to Drive Growth 1. Start with Clear Objectives Before diving into data collection, define what you want to achieve. Whether it’s improving customer retention, optimizing marketing campaigns, or managing financial health, having clear objectives ensures your data efforts remain focused. Actionable Steps: Identify key performance indicators (KPIs) that align with your business goals.
Break objectives into measurable milestones, such as increasing customer lifetime value by 20% in six months. 2. Collect Data from Diverse Sources The value of big data lies in its diversity. Startups should collect data from multiple channels to gain a holistic view of their operations and market dynamics. Key Sources: Customer Data: Gather insights from website analytics, social media interactions, and purchase histories.
Market Trends: Monitor industry reports, competitor activities, and consumer sentiment analysis.
Financial Data: Use tools like credit score tracking to monitor financial metrics. This can help you manage cash flow, assess loan eligibility, and build credibility with investors. “Diverse data sources ensure your insights are well-rounded and actionable,” says Laura Bennett, Data Strategist at Insight Edge. 3. Leverage Cutting-Edge Analytics Tools Big data is only as useful as the tools you use to analyze it. Advanced analytics platforms enable startups to uncover patterns, predict trends, and make informed decisions. Tools to Explore: Google Analytics: Monitor website traffic and customer behavior.
Tableau: Visualize complex data sets for easier interpretation.
Snowflake: Manage and query large-scale data with scalability.
LeadsNavi: Identify and track potential leads based on website visitor behavior, giving startups real-time insights into which prospects are most engaged. By understanding how visitors interact with your website, you can fine-tune marketing strategies, prioritize high-value leads, and personalize outreach for maximum conversion. “The right tools amplify the impact of your data efforts,” explains James Carter, Analytics Lead at DataScale Systems. 4. Enhance Customer Understanding with Predictive Analytics Predictive analytics uses historical data and machine learning to forecast future outcomes. Startups can use these insights to anticipate customer needs, personalize experiences, and improve satisfaction. Examples: Behavior-Based Personalization: Tailor email campaigns based on past purchases or browsing habits.
Customer Churn Prediction: Identify at-risk customers and implement retention strategies. “Predictive analytics transforms raw data into actionable foresight,” notes Sarah Lopez, CX Specialist at GrowthPath Solutions. 5. Optimize Operations Through Data-Driven Insights Operational inefficiencies can drain resources, especially for startups. Big data helps identify bottlenecks, streamline processes, and reduce costs. Key Applications: Inventory Management: Use historical sales data to predict demand and avoid overstocking or stockouts.
Employee Productivity: Analyze workflows to identify areas for improvement and automation. 6. Strengthen Financial Planning with Data Insights Financial stability is critical for startup growth. Big data provides clarity on cash flow, expense tracking, and profitability trends, enabling better financial planning. Practical Use Case: Leverage SoFi's free credit score monitoring service to keep track of your credit health. Strong credit scores can improve access to loans and investments, which are vital for scaling your business. 7. Monitor and Adapt in Real-Time In today’s fast-paced business environment, static strategies no longer suffice. Real-time data monitoring enables startups to pivot quickly and stay ahead of market changes. Tools for Real-Time Monitoring: Power BI: Offers dashboards to track KPIs in real-time.
Zapier: Automates notifications when critical data thresholds are met. “Agility is the new currency of business success,” emphasizes Emily Rogers, Co-Founder of Agile Growth Labs. Best Practices for Implementing a Big Data Strategy 1. Ensure Data Accuracy and Integrity Clean, accurate data is the foundation of effective decision-making. Regularly audit your data for errors, redundancies, and outdated information. 2. Focus on Data Security and Compliance Data breaches can undermine customer trust and derail growth. Use encryption, access controls, and compliance tools to protect sensitive information. 3. Foster a Data-Driven Culture Encourage all team members to embrace data in their roles. Provide training and resources to help employees interpret and apply data insights effectively. Conclusion: Unlock Growth with Big Data Big data isn’t just for large enterprises—it’s a game-changer for startups looking to scale effectively. By setting clear objectives, leveraging the right tools, and applying insights strategically, you can turn data into a competitive advantage. Integrate tools like SoFi's free credit score monitoring service to strengthen financial health and enhance your analytics strategy. Remember, the key to growth lies in not just collecting data but actively using it to refine every aspect of your business. Start unlocking the potential of big data today to fuel your startup’s growth in 2025 and beyond. Big data has evolved into a cornerstone of business success, especially for startups navigating competitive landscapes. Harnessing its power can enable deeper customer insights, more efficient operations, and data-driven decision-making that fuels sustainable growth. This guide explores practical strategies for startups to collect, analyze, and apply big data insights effectively in 2025. Why Big Data is Essential for Startups Startups often operate with limited resources, making precise decision-making crucial. Big data empowers these businesses to identify opportunities, mitigate risks, and allocate resources efficiently. From understanding customer behavior to enhancing financial stability, leveraging big data transforms challenges into growth opportunities. Strategies for Using Big Data to Drive Growth 1. Start with Clear Objectives Before diving into data collection, define what you want to achieve. Whether it’s improving customer retention, optimizing marketing campaigns, or managing financial health, having clear objectives ensures your data efforts remain focused. Actionable Steps: Actionable Steps: Identify key performance indicators (KPIs) that align with your business goals. Break objectives into measurable milestones, such as increasing customer lifetime value by 20% in six months. Identify key performance indicators (KPIs) that align with your business goals. Break objectives into measurable milestones, such as increasing customer lifetime value by 20% in six months. 2. Collect Data from Diverse Sources The value of big data lies in its diversity. Startups should collect data from multiple channels to gain a holistic view of their operations and market dynamics. Key Sources: Customer Data: Gather insights from website analytics, social media interactions, and purchase histories. Market Trends: Monitor industry reports, competitor activities, and consumer sentiment analysis. Financial Data: Use tools like credit score tracking to monitor financial metrics. This can help you manage cash flow, assess loan eligibility, and build credibility with investors. Customer Data : Gather insights from website analytics, social media interactions, and purchase histories. Customer Data Market Trends : Monitor industry reports, competitor activities, and consumer sentiment analysis. Market Trends Financial Data : Use tools like credit score tracking to monitor financial metrics. This can help you manage cash flow, assess loan eligibility, and build credibility with investors. Financial Data credit score tracking credit score tracking “Diverse data sources ensure your insights are well-rounded and actionable,” says Laura Bennett , Data Strategist at Insight Edge. “Diverse data sources ensure your insights are well-rounded and actionable,” Laura Bennett 3. Leverage Cutting-Edge Analytics Tools Big data is only as useful as the tools you use to analyze it. Advanced analytics platforms enable startups to uncover patterns, predict trends, and make informed decisions. Tools to Explore: Google Analytics: Monitor website traffic and customer behavior. Tableau: Visualize complex data sets for easier interpretation. Snowflake: Manage and query large-scale data with scalability. LeadsNavi: Identify and track potential leads based on website visitor behavior, giving startups real-time insights into which prospects are most engaged. By understanding how visitors interact with your website, you can fine-tune marketing strategies, prioritize high-value leads, and personalize outreach for maximum conversion. Google Analytics : Monitor website traffic and customer behavior. Google Analytics Tableau : Visualize complex data sets for easier interpretation. Tableau Snowflake : Manage and query large-scale data with scalability. Snowflake LeadsNavi : Identify and track potential leads based on website visitor behavior, giving startups real-time insights into which prospects are most engaged. By understanding how visitors interact with your website, you can fine-tune marketing strategies, prioritize high-value leads, and personalize outreach for maximum conversion. LeadsNavi “The right tools amplify the impact of your data efforts,” explains James Carter, Analytics Lead at DataScale Systems. “The right tools amplify the impact of your data efforts,” James Carter, 4. Enhance Customer Understanding with Predictive Analytics Predictive analytics uses historical data and machine learning to forecast future outcomes. Startups can use these insights to anticipate customer needs, personalize experiences, and improve satisfaction. Examples: Examples: Behavior-Based Personalization: Tailor email campaigns based on past purchases or browsing habits. Customer Churn Prediction: Identify at-risk customers and implement retention strategies. Behavior-Based Personalization : Tailor email campaigns based on past purchases or browsing habits. Behavior-Based Personalization Customer Churn Prediction : Identify at-risk customers and implement retention strategies. Customer Churn Prediction “Predictive analytics transforms raw data into actionable foresight,” notes Sarah Lopez , CX Specialist at GrowthPath Solutions. “Predictive analytics transforms raw data into actionable foresight,” Sarah Lopez 5. Optimize Operations Through Data-Driven Insights Operational inefficiencies can drain resources, especially for startups. Big data helps identify bottlenecks, streamline processes, and reduce costs. Key Applications: Inventory Management: Use historical sales data to predict demand and avoid overstocking or stockouts. Employee Productivity: Analyze workflows to identify areas for improvement and automation. Inventory Management : Use historical sales data to predict demand and avoid overstocking or stockouts. Inventory Management Employee Productivity : Analyze workflows to identify areas for improvement and automation. Employee Productivity 6. Strengthen Financial Planning with Data Insights Financial stability is critical for startup growth. Big data provides clarity on cash flow, expense tracking, and profitability trends, enabling better financial planning. Practical Use Case: Leverage SoFi's free credit score monitoring service to keep track of your credit health. Strong credit scores can improve access to loans and investments, which are vital for scaling your business. Leverage SoFi's free credit score monitoring service to keep track of your credit health. Strong credit scores can improve access to loans and investments, which are vital for scaling your business. 7. Monitor and Adapt in Real-Time In today’s fast-paced business environment, static strategies no longer suffice. Real-time data monitoring enables startups to pivot quickly and stay ahead of market changes. Tools for Real-Time Monitoring: Power BI: Offers dashboards to track KPIs in real-time. Zapier: Automates notifications when critical data thresholds are met. Power BI : Offers dashboards to track KPIs in real-time. Power BI Zapier : Automates notifications when critical data thresholds are met. Zapier “Agility is the new currency of business success,” emphasizes Emily Rogers, Co-Founder of Agile Growth Labs. “Agility is the new currency of business success,” Best Practices for Implementing a Big Data Strategy 1. Ensure Data Accuracy and Integrity Clean, accurate data is the foundation of effective decision-making. Regularly audit your data for errors, redundancies, and outdated information. 2. Focus on Data Security and Compliance Data breaches can undermine customer trust and derail growth. Use encryption, access controls, and compliance tools to protect sensitive information. 3. Foster a Data-Driven Culture Encourage all team members to embrace data in their roles. Provide training and resources to help employees interpret and apply data insights effectively. Conclusion: Unlock Growth with Big Data Unlock Growth with Big Data Big data isn’t just for large enterprises—it’s a game-changer for startups looking to scale effectively. By setting clear objectives, leveraging the right tools, and applying insights strategically, you can turn data into a competitive advantage. Integrate tools like SoFi's free credit score monitoring service to strengthen financial health and enhance your analytics strategy. Remember, the key to growth lies in not just collecting data but actively using it to refine every aspect of your business. Start unlocking the potential of big data today to fuel your startup’s growth in 2025 and beyond.

 Leveraging Big Data for Startup Growth in 2025

 Jach’a yatiyawinak aprovechañax Qalltañ Jiltawi 2025 maran

Aprovechar el Big Data para el crecimiento de las empresas emergentes en 2025

Tirer parti du Big Data pour la croissance des startups en 2025

 Iskorištavanje velikih podataka za rast startupa 2025

2025 年のスタートアップの成長に向けたビッグデータの活用

 2025-жылы стартаптын өсүшү үчүн чоң маалыматтарды колдонуу

Aproveitando Big Data para o crescimento de startups em 2025

 Folosirea Big Data pentru creșterea startup-urilor în 2025

Использование больших данных для роста стартапов в 2025 году

2025 ஆம் ஆண்டில் தொடக்க நிறுவனங்களின் வளர்ச்சிக்கு பெரிய தரவைப் பயன்படுத்துதல்

 Истифодаи маълумоти калон барои рушди стартап дар соли 2025

 ኣብ 2025 ንዕብየት ስታርትኣፕ ዝኸውን ቢግ ዳታ ምጥቃም

Table of Links Abstract and 1. Introduction 2 Research Settings 3 Methods 3.1 Instruments and 3.2 Quantitative Analysis 3.3 Thematic Analysis 4 Results 4.1 Quantitative Results 4.2 Qualitative Results 5 Discussion and 5.1 Answering the research questions 5.2 Threats to validity and 5.3 Limitations and generalizability 6 Conclusion, Acknowledgments, and References 6 CONCLUSION This preliminary study mapped the psychological aspects of pair programming, analyzed ways of increasing students' intrinsic motivation and improving the pair programming practice in educational settings, and tapped into the psychology of pairing. Pilot-navigator roles are more motivating than both members coding simultaneously. The areas for improvement of pairing include task complexity, pairing constellations, feedback, soft and social skills practice, psychological aspects, role-specific insights, and following the “perfect programmer’s” traits. With further advancement of theoretical foundations around the psychology of pairing in software engineering and the development of artificially intelligent pairing programs, the further research should orient toward the experimental study of the cooperation between humans and artificial intelligence. ACKNOWLEDGMENTS This work was supported by an internal grant funding scheme (F4/61/2023) administered by the Prague University of Economics and Business. REFERENCES [1] Valový, Marcel. “Experimental Pair Programming: A Study Design and Preliminary Results.” In Proceedings of the 33rd Psychology of Programming Interest Group (PPIG’22), 2022: 107-112 [2] Valový, Marcel. “Effects of Pilot, Navigator, and Solo Programming Roles on Motivation.” New Perspectives in Software Engineering: Proceedings of the 11th International Conference on Software Process Improvement (CIMPS 2022). Cham: Springer International Publishing, 2022: 84-98. [3] Arisholm, Erik, Hans Gallis, Tore Dybå, and Dag IK Sjøberg. "Evaluating pair programming with respect to system complexity and programmer expertise." IEEE Transactions on Software Engineering 33.2 (2007): 65-86. [4] Bird, Cindy M. "How I stopped dreading and learned to love transcription." Qualitative inquiry 11, no. 2 (2005): 226-248. [5] Braun, Virginia, and Victoria Clarke. "Using thematic analysis in psychology." Qualitative research in psychology 3, no. 2 (2006): 77-101. [6] Feldt, Robert, Lefteris Angelis, Richard Torkar, and Maria Samuelsson. "Links between the personalities, views and attitudes of software engineers." Information and Software Technology 52, no. 6 (2010): 611-624. [7] Graziotin, Daniel, Per Lenberg, Robert Feldt, and Stefan Wagner. "Psychometrics in behavioral software engineering: A methodological introduction with guidelines." ACM Transactions on Software Engineering and Methodology (TOSEM) 31, no. 1 (2021): 1-36. [8] Guest, Greg, Arwen Bunce, and Laura Johnson. "How many interviews are enough? An experiment with data saturation and variability." Field methods 18, no. 1 (2006): 59-82. [9] Hannay, Jo E., Erik Arisholm, Harald Engvik, and Dag IK Sjøberg. "Effects of personality on pair programming." IEEE Transactions on Software Engineering 36, no. 1 (2009): 61-80. [10] Hannay, Jo E., Tore Dybå, Erik Arisholm, and Dag IK Sjøberg. "The effectiveness of pair programming: A meta-analysis." Information and software technology 51.7 (2009): 1110-1122. [11] Katira, Neha, Laurie Williams, Eric Wiebe, Carol Miller, Suzanne Balik, Ed Gehringer. "On understanding compatibility of student pair programmers." Proceedings of the 35th SIGCSE technical symposium on Computer science education. 2004. [12] Latham, Gary P. Work motivation: History, theory, research, and practice. Sage, 2012. [13] Lenberg, Per, Robert Feldt, and Lars Göran Wallgren. "Behavioral software engineering: A definition and systematic literature review." Journal of Systems and software 107 (2015): 15-37. [14] Sach, Rien. The Impact of Feedback on the Motivation of Software Engineers. Open University (United Kingdom), 2013. [15] Ryan, Richard M. "Control and information in the intrapersonal sphere: An extension of cognitive evaluation theory." Journal of personality and social psychology 43.3 (1982): 450. [16] Ryan, Richard M., and Edward L. Deci. "Self-determination theory." Basic psychological needs in motivation, development, and wellness (2017). [17] Self-Determination Theory. 2022. Intrinsic Motivation Inventory. (January 2023). Retrieved January 12, 2023 from http://selfdeterminationtheory.org/intrinsic-motivation-inventory/ [18] Vanhanen, Jari, and Casper Lassenius. "Effects of pair programming at the development team level: an experiment." In International Symposium on Empirical Software Engineering, (2005)., pp. 10-pp. IEEE, 2005. [19] Williams, Laurie, and Robert R. Kessler. Pair programming illuminated. Addison-Wesley Professional, 2003. [20] Williams, Laurie, Lucas Layman, Jason Osborne, Neha Katira. "Examining the compatibility of student pair programmers." AGILE 2006 (AGILE'06). IEEE, 2006 Author:
(1) Marcel Valový, Department of Information Technologies, Prague, Czech Republic (marcel.valovy@vse.cz). This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Research Settings 2 Research Settings 3 Methods 3 Methods 3.1 Instruments and 3.2 Quantitative Analysis 3.1 Instruments and 3.2 Quantitative Analysis 3.3 Thematic Analysis 3.3 Thematic Analysis 4 Results 4 Results 4.1 Quantitative Results 4.1 Quantitative Results 4.2 Qualitative Results 4.2 Qualitative Results 5 Discussion and 5.1 Answering the research questions 5 Discussion and 5.1 Answering the research questions 5.2 Threats to validity and 5.3 Limitations and generalizability 5.2 Threats to validity and 5.3 Limitations and generalizability 6 Conclusion, Acknowledgments, and References 6 Conclusion, Acknowledgments, and References 6 CONCLUSION This preliminary study mapped the psychological aspects of pair programming, analyzed ways of increasing students' intrinsic motivation and improving the pair programming practice in educational settings, and tapped into the psychology of pairing. Pilot-navigator roles are more motivating than both members coding simultaneously. The areas for improvement of pairing include task complexity, pairing constellations, feedback, soft and social skills practice, psychological aspects, role-specific insights, and following the “perfect programmer’s” traits. With further advancement of theoretical foundations around the psychology of pairing in software engineering and the development of artificially intelligent pairing programs, the further research should orient toward the experimental study of the cooperation between humans and artificial intelligence. ACKNOWLEDGMENTS This work was supported by an internal grant funding scheme (F4/61/2023) administered by the Prague University of Economics and Business. REFERENCES [1] Valový, Marcel. “Experimental Pair Programming: A Study Design and Preliminary Results.” In Proceedings of the 33rd Psychology of Programming Interest Group (PPIG’22), 2022: 107-112 [2] Valový, Marcel. “Effects of Pilot, Navigator, and Solo Programming Roles on Motivation.” New Perspectives in Software Engineering: Proceedings of the 11th International Conference on Software Process Improvement (CIMPS 2022). Cham: Springer International Publishing, 2022: 84-98. [3] Arisholm, Erik, Hans Gallis, Tore Dybå, and Dag IK Sjøberg. "Evaluating pair programming with respect to system complexity and programmer expertise." IEEE Transactions on Software Engineering 33.2 (2007): 65-86. [4] Bird, Cindy M. "How I stopped dreading and learned to love transcription." Qualitative inquiry 11, no. 2 (2005): 226-248. [5] Braun, Virginia, and Victoria Clarke. "Using thematic analysis in psychology." Qualitative research in psychology 3, no. 2 (2006): 77-101. [6] Feldt, Robert, Lefteris Angelis, Richard Torkar, and Maria Samuelsson. "Links between the personalities, views and attitudes of software engineers." Information and Software Technology 52, no. 6 (2010): 611-624. [7] Graziotin, Daniel, Per Lenberg, Robert Feldt, and Stefan Wagner. "Psychometrics in behavioral software engineering: A methodological introduction with guidelines." ACM Transactions on Software Engineering and Methodology (TOSEM) 31, no. 1 (2021): 1-36. [8] Guest, Greg, Arwen Bunce, and Laura Johnson. "How many interviews are enough? An experiment with data saturation and variability." Field methods 18, no. 1 (2006): 59-82. [9] Hannay, Jo E., Erik Arisholm, Harald Engvik, and Dag IK Sjøberg. "Effects of personality on pair programming." IEEE Transactions on Software Engineering 36, no. 1 (2009): 61-80. [10] Hannay, Jo E., Tore Dybå, Erik Arisholm, and Dag IK Sjøberg. "The effectiveness of pair programming: A meta-analysis." Information and software technology 51.7 (2009): 1110-1122. [11] Katira, Neha, Laurie Williams, Eric Wiebe, Carol Miller, Suzanne Balik, Ed Gehringer. "On understanding compatibility of student pair programmers." Proceedings of the 35th SIGCSE technical symposium on Computer science education. 2004. [12] Latham, Gary P. Work motivation: History, theory, research, and practice. Sage, 2012. [13] Lenberg, Per, Robert Feldt, and Lars Göran Wallgren. "Behavioral software engineering: A definition and systematic literature review." Journal of Systems and software 107 (2015): 15-37. [14] Sach, Rien. The Impact of Feedback on the Motivation of Software Engineers. Open University (United Kingdom), 2013. [15] Ryan, Richard M. "Control and information in the intrapersonal sphere: An extension of cognitive evaluation theory." Journal of personality and social psychology 43.3 (1982): 450. [16] Ryan, Richard M., and Edward L. Deci. "Self-determination theory." Basic psychological needs in motivation, development, and wellness (2017). [17] Self-Determination Theory. 2022. Intrinsic Motivation Inventory. (January 2023). Retrieved January 12, 2023 from http://selfdeterminationtheory.org/intrinsic-motivation-inventory/ [18] Vanhanen, Jari, and Casper Lassenius. "Effects of pair programming at the development team level: an experiment." In International Symposium on Empirical Software Engineering, (2005)., pp. 10-pp. IEEE, 2005. [19] Williams, Laurie, and Robert R. Kessler. Pair programming illuminated. Addison-Wesley Professional, 2003. [20] Williams, Laurie, Lucas Layman, Jason Osborne, Neha Katira. "Examining the compatibility of student pair programmers." AGILE 2006 (AGILE'06). IEEE, 2006 Author: (1) Marcel Valový, Department of Information Technologies, Prague, Czech Republic (marcel.valovy@vse.cz). Author: Author: (1) Marcel Valový, Department of Information Technologies, Prague, Czech Republic (marcel.valovy@vse.cz). 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 available on arxiv

The Psychology Behind Successful Pair Programming

Coin3D Introduces a New Standard for Interactive 3D Asset Generation

The Role of Volunteer Computing in Decentralized FaaS for Web3 Applications

Table of Links Abstract and 1 Introduction 2 Preliminaries 3 Temporal Hierarchies 4 Rating Maps 5 Optimal Imprints for TL(AT) 6 Conclusion and References Appendix A. Appendix to Section 2 & Appendix B. Appendix to Section 3 Appendix C. Appendix to Section 4 & Appendix D. Appendix to Section 5 6. Conclusion We defined temporal hierarchies using the unary temporal logic operator C 7→ TL(C). We looked at the bases G and G + when G is a group prevariety. In this case, we proved that the corresponding hierarchies are always strict and we compared them with the corresponding concatenation hierarchies. Moreover, we proved that if G has decidable separation, then membership is decidable for levels one and two in both hierarchies. Our main theorem yields a stronger result for the particular bases ST and DD = ST+. Indeed, it implies that level two in both hierarchies has decidable covering. In turn, this implies that level three has decidable membership. References [1] Mustapha Arfi. Polynomial operations on rational languages. In Proceedings of the 4th Annual Symposium on Theoretical Aspects of Computer Science, STACS’87, volume 247 of Lect. Notes Comp. Sci., pages 198–206. Springer, 1987. [2] Mustapha Arfi. Operations polynomiales et hierarchies de concatenation. Theoretical Computer Science, 91(1):71–84, 1991. [3] Christopher J. Ash. Inevitable graphs: a proof of the type II conjecture and some related decision procedures. International Journal of Algebra and Computation, 1(1):127–146, 1991. [4] Janusz A. Brzozowski and Rina S. Cohen. Dot-depth of star-free events. Journal of Computer and System Sciences, 5(1):1–16, 1971. [5] Janusz A. Brzozowski and Robert Knast. The dot-depth hierarchy of star-free languages is infinite. Journal of Computer and System Sciences, 16(1):37–55, 1978. [6] Janusz A. Brzozowski and Imre Simon. Characterizations of locally testable events. Discrete Mathematics, 4(3):243–271, 1973. [7] Laura Chaubard, Jean Eric Pin, and Howard Straubing. First order formulas with modular predicates. In ´ Proceedings of the 21th IEEE Symposium on Logic in Computer Science (LICS’06), pages 211–220, 2006. [8] Luc Dartois and Charles Paperman. Two-variable first order logic with modular predicates over words. In Proceedings of the 30th International Symposium on Theoretical Aspects of Computer Science, STACS’13, volume 20 of Leibniz International Proceedings in Informatics (LIPIcs), pages 329–340. Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik, 2013. [9] Manuel Delgado. Abelian poinlikes of a monoid. Semigroup Forum, 56(3):339–361, 1998. [10] Kousha Etessami, Moshe Y. Vardi, and Thomas Wilke. First-order logic with two variables and unary temporal logic. Information and Computation, 179(2):279–295, 2002. [11] Hans W. Kamp. Tense Logic and the Theory of Linear Order. Phd thesis, Computer Science Department, University of California at Los Angeles, USA, 1968. [12] Robert Knast. A semigroup characterization of dot-depth one languages. RAIRO - Theoretical Informatics and Applications, 17(4):321–330, 1983. [13] Andreas Krebs, Kamal Lodaya, Paritosh K. Pandya, and Howard Straubing. Two-variable logic with a between relation. In Proceedings of the 31st Annual ACM/IEEE Symposium on Logic in Computer Science, LICS’16, pages 106–115, 2016. [14] Andreas Krebs, Kamal Lodaya, Paritosh K. Pandya, and Howard Straubing. An algebraic decision procedure for two-variable logic with a between relation. In 27th EACSL Annual Conference on Computer Science Logic, CSL’18, Leibniz International Proceedings in Informatics (LIPIcs). Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik, 2018. [15] Andreas Krebs, Kamal Lodaya, Paritosh K. Pandya, and Howard Straubing. Two-variable logics with some betweenness relations: Expressiveness, satisfiability and membership. Logical Methods in Computer Science, 16(3), 2020. [16] Manfred Kufleitner and Tobias Walter. One quantifier alternation in first-order logic with modular predicates. RAIRO Theorerical Informatics and Applications, 49(1):1–22, 2015. [17] Alexis Maciel, Pierre P´eladeau, and Denis Th´erien. Programs over semigroups of dot-depth one. Theoretical Computer Science, 245(1):135–148, 2000. [18] Stuart W. Margolis and Jean-Eric Pin. Products of group languages. In ´ FCT’85. Springer, 1985. [19] Robert McNaughton and Seymour A. Papert. Counter-Free Automata. MIT Press, 1971. [20] Jean-Eric Pin. An explicit formula for the intersection of two polynomials of regular languages. In Proceedings of the 17th International Conference on Developments in Language Theory, DLT’13, volume 7907 of Lect. Notes Comp. Sci., pages 31–45. Springer, 2013. [21] Jean-Eric Pin and Pascal Weil. Polynomial closure and unambiguous product. Theory of Computing Systems, 30(4):383–422, 1997. [22] Jean-Eric Pin and Howard Straubing. Monoids of upper triangular boolean matrices. In ´ Semigroups. Structure and Universal Algebraic Problems, volume 39, pages 259–272. North-Holland, 1985. [23] Thomas Place. The amazing mixed polynomial closure and its applications to two-variable first-order logic. In Proceedings of the 37th Annual ACM/IEEE Symposium on Logic in Computer Science, LICS’22, 2022. [24] Thomas Place and Marc Zeitoun. Separation and the successor relation. In 32nd International Symposium on Theoretical Aspects of Computer Science, STACS’15, Leibniz International Proceedings in Informatics (LIPIcs), pages 662–675. Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik, 2015. [25] Thomas Place and Marc Zeitoun. The covering problem. Logical Methods in Computer Science, 14(3), 2018. [26] Thomas Place and Marc Zeitoun. Generic results for concatenation hierarchies. Theory of Computing Systems (ToCS), 63(4):849–901, 2019. Selected papers from CSR’17. [27] Thomas Place and Marc Zeitoun. Going higher in first-order quantifier alternation hierarchies on words. Journal of the ACM, 66(2):12:1–12:65, 2019. [28] Thomas Place and Marc Zeitoun. Separation and covering for group based concatenation hierarchies. In Proceedings of the 34th Annual ACM/IEEE Symposium on Logic in Computer Science, LICS’19, pages 1–13. IEEE Computer Society, 2019. [29] Thomas Place and Marc Zeitoun. Adding successor: A transfer theorem for separation and covering. ACM Transactions on Computational Logic, 21(2):9:1–9:45, 2020. [30] Thomas Place and Marc Zeitoun. A generic polynomial time approach to separation by first-order logic without quantifier alternation. In 42nd IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science, FSTTCS 2022, December 18-20, 2022, IIT Madras, Chennai, India, volume 250 of LIPIcs, pages 43:1–43:22. Schloss Dagstuhl - Leibniz-Zentrum f¨ur Informatik, 2022. [31] Thomas Place and Marc Zeitoun. All about unambiguous polynomial closure. TheoretiCS, 2(11):1–74, 2023. [32] Thomas Place and Marc Zeitoun. Group separation strikes back. In 38th Annual ACM/IEEE Symposium on Logic in Computer Science, LICS’23, pages 1–13. IEEE Computer Society, 2023. [33] Thomas Place and Marc Zeitoun. On all things star-free. Journal, to appear, 2023. [34] Thomas Place and Marc Zeitoun. A generic characterization of generalized unary temporal logic and two-variable first-order logic. In Proceedings of the 32nd EACSL Annual Conference on Computer Science Logic, CSL’24, 2024. [35] Marcel Paul Sch¨utzenberger. On finite monoids having only trivial subgroups. Information and Control, 8(2):190– 194, 1965. [36] Marcel Paul Sch¨utzenberger. Sur le produit de concat´enation non ambigu. Semigroup Forum, 13:47–75, 1976. [37] Imre Simon. Piecewise testable events. In Proceedings of the 2nd GI Conference on Automata Theory and Formal Languages, pages 214–222. Springer, 1975. [38] Howard Straubing. A generalization of the sch¨utzenberger product of finite monoids. Theoretical Computer Science, 13(2):137–150, 1981. [39] Howard Straubing. Finite semigroup varieties of the form V ∗ D. Journal of Pure and Applied Algebra, 36:53–94, 1985. [40] Denis Th´erien. Classification of finite monoids: The language approach. Theoretical Computer Science, 14(2):195– 208, 1981. [41] Gabriel Thierrin. Permutation automata. Theory of Computing Systems, 2(1):83–90, 1968. [42] Wolfgang Thomas. Classifying regular events in symbolic logic. Journal of Computer and System Sciences, 25(3):360–376, 1982. [43] Denis Therien and Thomas Wilke. Over words, two variables are as powerful as one quantifier alternation. In Proceedings of the 30th Annual ACM Symposium on Theory of Computing, STOC’98, pages 234–240, New York, NY, USA, 1998. ACM. [44] Yechezkel Zalcstein. Locally testable languages. Journal of Computer and System Sciences, 6(2):151–167, 1972. This paper is available on arxiv under CC BY 4.0 DEED license. Authors:
(1) Thomas Place;
(2) Marc Zaitoun. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Preliminaries 2 Preliminaries 3 Temporal Hierarchies 3 Temporal Hierarchies 4 Rating Maps 4 Rating Maps 5 Optimal Imprints for TL(AT) 5 Optimal Imprints for TL(AT) 6 Conclusion and References 6 Conclusion and References Appendix A. Appendix to Section 2 & Appendix B. Appendix to Section 3 Appendix A. Appendix to Section 2 & Appendix B. Appendix to Section 3 Appendix C. Appendix to Section 4 & Appendix D. Appendix to Section 5 Appendix C. Appendix to Section 4 & Appendix D. Appendix to Section 5 6. Conclusion We defined temporal hierarchies using the unary temporal logic operator C 7→ TL(C). We looked at the bases G and G + when G is a group prevariety. In this case, we proved that the corresponding hierarchies are always strict and we compared them with the corresponding concatenation hierarchies. Moreover, we proved that if G has decidable separation, then membership is decidable for levels one and two in both hierarchies. Our main theorem yields a stronger result for the particular bases ST and DD = ST+. Indeed, it implies that level two in both hierarchies has decidable covering. In turn, this implies that level three has decidable membership. References [1] Mustapha Arfi. Polynomial operations on rational languages. In Proceedings of the 4th Annual Symposium on Theoretical Aspects of Computer Science, STACS’87, volume 247 of Lect. Notes Comp. Sci., pages 198–206. Springer, 1987. [2] Mustapha Arfi. Operations polynomiales et hierarchies de concatenation. Theoretical Computer Science, 91(1):71–84, 1991. [3] Christopher J. Ash. Inevitable graphs: a proof of the type II conjecture and some related decision procedures. International Journal of Algebra and Computation, 1(1):127–146, 1991. [4] Janusz A. Brzozowski and Rina S. Cohen. Dot-depth of star-free events. Journal of Computer and System Sciences, 5(1):1–16, 1971. [5] Janusz A. Brzozowski and Robert Knast. The dot-depth hierarchy of star-free languages is infinite. Journal of Computer and System Sciences, 16(1):37–55, 1978. [6] Janusz A. Brzozowski and Imre Simon. Characterizations of locally testable events. Discrete Mathematics, 4(3):243–271, 1973. [7] Laura Chaubard, Jean Eric Pin, and Howard Straubing. First order formulas with modular predicates. In ´ Proceedings of the 21th IEEE Symposium on Logic in Computer Science (LICS’06), pages 211–220, 2006. [8] Luc Dartois and Charles Paperman. Two-variable first order logic with modular predicates over words. In Proceedings of the 30th International Symposium on Theoretical Aspects of Computer Science, STACS’13, volume 20 of Leibniz International Proceedings in Informatics (LIPIcs), pages 329–340. Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik, 2013. [9] Manuel Delgado. Abelian poinlikes of a monoid. Semigroup Forum, 56(3):339–361, 1998. [10] Kousha Etessami, Moshe Y. Vardi, and Thomas Wilke. First-order logic with two variables and unary temporal logic. Information and Computation, 179(2):279–295, 2002. [11] Hans W. Kamp. Tense Logic and the Theory of Linear Order. Phd thesis, Computer Science Department, University of California at Los Angeles, USA, 1968. [12] Robert Knast. A semigroup characterization of dot-depth one languages. RAIRO - Theoretical Informatics and Applications, 17(4):321–330, 1983. [13] Andreas Krebs, Kamal Lodaya, Paritosh K. Pandya, and Howard Straubing. Two-variable logic with a between relation. In Proceedings of the 31st Annual ACM/IEEE Symposium on Logic in Computer Science, LICS’16, pages 106–115, 2016. [14] Andreas Krebs, Kamal Lodaya, Paritosh K. Pandya, and Howard Straubing. An algebraic decision procedure for two-variable logic with a between relation. In 27th EACSL Annual Conference on Computer Science Logic, CSL’18, Leibniz International Proceedings in Informatics (LIPIcs). Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik, 2018. [15] Andreas Krebs, Kamal Lodaya, Paritosh K. Pandya, and Howard Straubing. Two-variable logics with some betweenness relations: Expressiveness, satisfiability and membership. Logical Methods in Computer Science, 16(3), 2020. [16] Manfred Kufleitner and Tobias Walter. One quantifier alternation in first-order logic with modular predicates. RAIRO Theorerical Informatics and Applications, 49(1):1–22, 2015. [17] Alexis Maciel, Pierre P´eladeau, and Denis Th´erien. Programs over semigroups of dot-depth one. Theoretical Computer Science, 245(1):135–148, 2000. [18] Stuart W. Margolis and Jean-Eric Pin. Products of group languages. In ´ FCT’85. Springer, 1985. [19] Robert McNaughton and Seymour A. Papert. Counter-Free Automata. MIT Press, 1971. [20] Jean-Eric Pin. An explicit formula for the intersection of two polynomials of regular languages. In Proceedings of the 17th International Conference on Developments in Language Theory, DLT’13, volume 7907 of Lect. Notes Comp. Sci., pages 31–45. Springer, 2013. [21] Jean-Eric Pin and Pascal Weil. Polynomial closure and unambiguous product. Theory of Computing Systems, 30(4):383–422, 1997. [22] Jean-Eric Pin and Howard Straubing. Monoids of upper triangular boolean matrices. In ´ Semigroups. Structure and Universal Algebraic Problems, volume 39, pages 259–272. North-Holland, 1985. [23] Thomas Place. The amazing mixed polynomial closure and its applications to two-variable first-order logic. In Proceedings of the 37th Annual ACM/IEEE Symposium on Logic in Computer Science, LICS’22, 2022. [24] Thomas Place and Marc Zeitoun. Separation and the successor relation. In 32nd International Symposium on Theoretical Aspects of Computer Science, STACS’15, Leibniz International Proceedings in Informatics (LIPIcs), pages 662–675. Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik, 2015. [25] Thomas Place and Marc Zeitoun. The covering problem. Logical Methods in Computer Science, 14(3), 2018. [26] Thomas Place and Marc Zeitoun. Generic results for concatenation hierarchies. Theory of Computing Systems (ToCS), 63(4):849–901, 2019. Selected papers from CSR’17. [27] Thomas Place and Marc Zeitoun. Going higher in first-order quantifier alternation hierarchies on words. Journal of the ACM, 66(2):12:1–12:65, 2019. [28] Thomas Place and Marc Zeitoun. Separation and covering for group based concatenation hierarchies. In Proceedings of the 34th Annual ACM/IEEE Symposium on Logic in Computer Science, LICS’19, pages 1–13. IEEE Computer Society, 2019. [29] Thomas Place and Marc Zeitoun. Adding successor: A transfer theorem for separation and covering. ACM Transactions on Computational Logic, 21(2):9:1–9:45, 2020. [30] Thomas Place and Marc Zeitoun. A generic polynomial time approach to separation by first-order logic without quantifier alternation. In 42nd IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science, FSTTCS 2022, December 18-20, 2022, IIT Madras, Chennai, India, volume 250 of LIPIcs, pages 43:1–43:22. Schloss Dagstuhl - Leibniz-Zentrum f¨ur Informatik, 2022. [31] Thomas Place and Marc Zeitoun. All about unambiguous polynomial closure. TheoretiCS, 2(11):1–74, 2023. [32] Thomas Place and Marc Zeitoun. Group separation strikes back. In 38th Annual ACM/IEEE Symposium on Logic in Computer Science, LICS’23, pages 1–13. IEEE Computer Society, 2023. [33] Thomas Place and Marc Zeitoun. On all things star-free. Journal, to appear, 2023. [34] Thomas Place and Marc Zeitoun. A generic characterization of generalized unary temporal logic and two-variable first-order logic. In Proceedings of the 32nd EACSL Annual Conference on Computer Science Logic, CSL’24, 2024. [35] Marcel Paul Sch¨utzenberger. On finite monoids having only trivial subgroups. Information and Control, 8(2):190– 194, 1965. [36] Marcel Paul Sch¨utzenberger. Sur le produit de concat´enation non ambigu. Semigroup Forum, 13:47–75, 1976. [37] Imre Simon. Piecewise testable events. In Proceedings of the 2nd GI Conference on Automata Theory and Formal Languages, pages 214–222. Springer, 1975. [38] Howard Straubing. A generalization of the sch¨utzenberger product of finite monoids. Theoretical Computer Science, 13(2):137–150, 1981. [39] Howard Straubing. Finite semigroup varieties of the form V ∗ D. Journal of Pure and Applied Algebra, 36:53–94, 1985. [40] Denis Th´erien. Classification of finite monoids: The language approach. Theoretical Computer Science, 14(2):195– 208, 1981. [41] Gabriel Thierrin. Permutation automata. Theory of Computing Systems, 2(1):83–90, 1968. [42] Wolfgang Thomas. Classifying regular events in symbolic logic. Journal of Computer and System Sciences, 25(3):360–376, 1982. [43] Denis Therien and Thomas Wilke. Over words, two variables are as powerful as one quantifier alternation. In Proceedings of the 30th Annual ACM Symposium on Theory of Computing, STOC’98, pages 234–240, New York, NY, USA, 1998. ACM. [44] Yechezkel Zalcstein. Locally testable languages. Journal of Computer and System Sciences, 6(2):151–167, 1972. 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) Thomas Place; (2) Marc Zaitoun. Authors: Authors: (1) Thomas Place; (2) Marc Zaitoun.

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