arXiv:2410.19738v1 Announce Type: new
Abstract: This proceedings contains abstracts and position papers for the work to be presented at the fourth Logic and Practice of Programming (LPOP) Workshop. The workshop is to be held in Dallas, Texas, USA, and as a hybrid event, on October 13, 2024, in conjunction with the 40th International Conference on Logic Programming (ICLP). The focus of this workshop is integrating reasoning systems for trustworthy AI, especially including integrating diverse models of programming with rules and constraints.
The Fourth Logic and Practice of Programming Workshop: Integrating Reasoning Systems for Trustworthy AI
The Logic and Practice of Programming (LPOP) Workshop, set to take place on October 13, 2024, in Dallas, Texas, USA, is an eagerly anticipated event for professionals and researchers in the field of AI. This workshop, being held alongside the 40th International Conference on Logic Programming (ICLP), aims to bring together experts to discuss and explore the integration of reasoning systems for trustworthy AI, with a particular focus on diverse models of programming with rules and constraints.
The multi-disciplinary nature of this workshop is evident in its focus on combining reasoning systems and programming models. As AI technology continues to advance, it is crucial to ensure that these systems are trustworthy and reliable. By integrating diverse models of programming with rules and constraints, researchers aim to develop AI systems that not only make accurate predictions or decisions but also provide explanations and justifications for their actions.
The integration of reasoning systems is a crucial aspect of building trustworthy AI. Reasoning systems play a vital role in AI decision-making processes, enabling machines to process and analyze vast amounts of data, and generate logical conclusions. By combining different models of programming, such as constraint programming or logical programming, researchers can leverage the strengths of each approach to develop AI systems that are more robust and reliable.
One of the key challenges in integrating reasoning systems is the need to ensure consistency and coherency in the decision-making process. Different models of programming may have different assumptions or methodologies, leading to potential conflicts. Researchers at the LPOP Workshop aim to address these challenges by exploring techniques for integrating reasoning systems seamlessly, enabling them to work together to produce accurate and trustworthy AI systems.
Another important aspect of this workshop is the emphasis on trustworthy AI. Trust is a crucial element when it comes to adopting and utilizing AI technology in various domains. Ensuring that AI systems are transparent, explainable, and accountable is essential for building trust. By integrating reasoning systems, researchers can develop AI systems that not only make accurate predictions but also provide explanations for their actions, enabling users to understand and trust the decision-making process.
The significance of this workshop goes beyond just the AI field. The integration of reasoning systems for trustworthy AI has implications for various disciplines, including ethics, law, and policy. As AI becomes more prevalent in society, there is a growing need to address ethical and legal concerns, such as bias, fairness, and privacy. By fostering discussion and collaboration among experts from different disciplines, the LPOP Workshop aims to pave the way for the development of AI systems that are not only technically robust but also ethically and legally sound.
In conclusion, the fourth Logic and Practice of Programming Workshop is an exciting event that brings together experts from various disciplines to discuss the integration of reasoning systems for trustworthy AI. By combining diverse programming models with rules and constraints, researchers aim to develop AI systems that are more reliable, transparent, and accountable. This workshop’s multi-disciplinary nature highlights the broad impact and importance of this research, extending beyond just AI to ethics, law, and policy.
arXiv:2410.19897v1 Announce Type: new
Abstract: We present a comprehensive investigation exploring the theoretical framework of Einstein-Aether gravity theory when combined with two novel cosmological paradigms: the Barrow Agegraphic Dark Energy (BADE) and its newer variant, the New Barrow Agegraphic Dark Energy (NBADE). Our study focuses on deriving the functional relationships within Einstein-Aether gravity as they emerge from these dark energy formulations. The parameter space of our theoretical models is rigorously constrained through statistical analysis employing the Markov Chain Monte Carlo (MCMC) methodology, utilizing multiple observational datasets, incorporating measurements from cosmic chronometers (CC), Baryon Acoustic Oscillations (BAO), and the combined Pantheon+SH0ES compilation. Based on our optimized parameter sets, we conduct an extensive analysis of fundamental cosmological indicators, including cosmographic parameter evolution, dark energy equation of state parameter ($omega_{DE}$), evolution of the density parameter $Omega(z)$, dynamical characteristics in the $omega’_{DE}-omega_{DE}$ space, behavior of statefinder diagnostic pairs $(r,s^*)$ and $(r,q)$, and Om(z) diagnostic trajectories. Our analysis demonstrates that the current cosmic expansion exhibits accelerated behavior, with the dark energy component manifesting quintessence-like properties in the present epoch while trending toward phantom behavior in future evolution. We additionally evaluate the viability of both BADE and NBADE frameworks through an examination of the squared sound speed ($v_s^2$) stability criterion. The cumulative evidence suggests that these models effectively characterize contemporary cosmic evolution while offering novel perspectives on dark energy phenomenology.
Exploring the Theoretical Framework of Einstein-Aether Gravity Theory and Dark Energy Paradigms: A Roadmap for the Future
Introduction
In this comprehensive investigation, we delve into the theoretical framework of Einstein-Aether gravity theory combined with two novel cosmological paradigms: Barrow Agegraphic Dark Energy (BADE) and its newer variant, New Barrow Agegraphic Dark Energy (NBADE). Our study aims to derive the functional relationships within Einstein-Aether gravity as influenced by these dark energy formulations. By rigorously constraining the parameter space through statistical analysis and employing multiple observational datasets, including cosmic chronometers (CC), Baryon Acoustic Oscillations (BAO), and the Pantheon+SH0ES compilation, we aim to provide valuable insights into cosmography, dark energy equation of state, density parameter evolution, dynamical characteristics, and statefinder diagnostic pairs.
Optimized Parameter Sets and Cosmological Indicators
Based on our optimized parameter sets, we conduct an extensive analysis of fundamental cosmological indicators. These indicators include:
Cosmographic parameter evolution
Dark energy equation of state parameter ($omega_{DE}$)
Evolution of the density parameter $Omega(z)$
Dynamical characteristics in the $omega’_{DE}-omega_{DE}$ space
Behavior of statefinder diagnostic pairs $(r,s^*)$ and $(r,q)$
Om(z) diagnostic trajectories
Through this analysis, we aim to gain a deeper understanding of the current behavior of cosmic expansion and the nature of dark energy. Our findings suggest that the current cosmic expansion demonstrates accelerated behavior and that the dark energy component exhibits quintessence-like properties in the present epoch, trending towards phantom behavior in future evolution.
Viability Assessment of BADE and NBADE Frameworks
Furthermore, we examine the viability of both BADE and NBADE frameworks by evaluating the squared sound speed ($v_s^2$) stability criterion. This assessment will provide insights into the stability and consistency of these frameworks within the context of contemporary cosmic evolution.
Challenges and Opportunities on the Horizon
While our study presents significant progress in understanding the theoretical framework of Einstein-Aether gravity theory and its interaction with dark energy paradigms, several challenges and opportunities lie ahead:
Data Limitations: The accuracy and availability of observational datasets play a crucial role in constraining the parameter space and obtaining reliable results. Improvements in observational techniques and the acquisition of more precise data will enhance the accuracy of future analyses.
Additional Dark Energy Models: Exploring other dark energy models and their implications within the Einstein-Aether gravity framework can provide a more comprehensive understanding of dark energy phenomenology.
Validation through Future Observations: Upcoming observational missions, such as the James Webb Space Telescope (JWST) and the Euclid mission, hold tremendous potential for validating and further refining our theoretical models. Incorporating data from these missions will enhance the credibility of our findings.
Conclusion
Our study contributes to the existing knowledge of Einstein-Aether gravity theory and dark energy paradigms by presenting an in-depth analysis of the theoretical framework, optimized parameter sets, and cosmological indicators. The quintessence-like behavior of dark energy in the present epoch and its transition towards phantom behavior in the future highlight the importance of understanding and characterizing dark energy. However, future advancements in data accuracy, exploration of alternative dark energy models, and validation through upcoming observational missions will pave the way for more comprehensive and precise understanding of contemporary cosmic evolution and dark energy phenomenology.
arXiv:2410.16284v1 Announce Type: new
Abstract: The advent of 5G has driven the demand for high-quality, low-latency live streaming. However, challenges such as managing the increased data volume, ensuring synchronization across multiple streams, and maintaining consistent quality under varying network conditions persist, particularly in real-time video streaming. To address these issues, we propose a novel framework that leverages 3D virtual environments within game engines (eg. Unity 3D) to optimize multi-channel live streaming. Our approach consolidates multi-camera video data into a single stream using multiple virtual 3D canvases, significantly increasing channel amounts while reducing latency and enhancing user flexibility. For demonstration of our approach, we utilize the Unity 3D engine to integrate multiple video inputs into a single-channel stream, supporting one-to-many broadcasting, one-to-one video calling, and real-time control of video channels. By mapping video data onto a world-space canvas and capturing it via an in-world camera, we minimize redundant data transmission, achieving efficient, low-latency streaming. Our results demonstrate that this method outperforms existing multi-channel live streaming solutions in both latency reduction and user interaction. Our live video streaming system affiliated with this paper is also open-source at https://github.com/Aizierjiang/LiveStreaming.
The Evolution of Live Streaming: Enhancing Quality and User Experience with 3D Virtual Environments
As the demand for high-quality, low-latency live streaming continues to grow with the emergence of 5G technology, content providers and service providers face a range of challenges. These challenges include efficiently managing increased data volume, ensuring synchronization across multiple streams, and maintaining consistent quality under varying network conditions. Real-time video streaming, in particular, faces unique obstacles in meeting these requirements.
In order to address these challenges and optimize multi-channel live streaming, a novel framework has been proposed that leverages the power of 3D virtual environments within game engines, such as Unity 3D. This multi-disciplinary approach combines the fields of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities to create an innovative solution.
The core idea behind this framework is the consolidation of multi-camera video data into a single stream using multiple virtual 3D canvases. By mapping the video data onto a world-space canvas within the virtual environment and capturing it via an in-world camera, redundant data transmission can be minimized. This results in a significant increase in channel amounts, reduced latency, and enhanced user flexibility.
The use of game engines, such as Unity 3D, allows for seamless integration of multiple video inputs into a single-channel stream. This not only supports one-to-many broadcasting but also enables one-to-one video calling and real-time control of video channels. The integration of 3D virtual environments adds a new level of immersion and interactivity to the live streaming experience, enhancing user engagement and satisfaction.
The proposed framework offers several advancements over existing multi-channel live streaming solutions. Firstly, it effectively addresses the challenges of data volume management, synchronization, and quality consistency, ensuring a smooth streaming experience. Secondly, it significantly reduces latency, allowing for real-time interaction between the streamers and viewers. Lastly, it provides users with greater flexibility in terms of controlling and customizing video channels, resulting in a more personalized experience.
From a wider perspective, this framework exemplifies the multi-disciplinary nature of the concepts related to multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By combining knowledge and techniques from these fields, innovative solutions like this one can be developed to overcome existing challenges and push the boundaries of live streaming technology.
In conclusion, the proposed framework that leverages 3D virtual environments within game engines to optimize multi-channel live streaming represents a significant advancement in the field. Its ability to consolidate video data, reduce latency, and enhance user flexibility opens up new possibilities for high-quality, immersive live streaming experiences. As technology continues to evolve and 5G becomes more widely available, it is expected that solutions like this will become increasingly important in meeting the growing demand for real-time video streaming.
HTML tags are an essential part of creating content for the web. They allow us to structure and format our text, making it more readable and visually appealing. However, in some cases, it may be necessary to limit the use of HTML tags to maintain consistency and compatibility with specific platforms or tools. WordPress, for example, has its own set of supported HTML tags that can be used within its content editor. Therefore, when creating an article to be embedded in a WordPress post, it is crucial to use only the allowed HTML tags and exclude any others for page structure.
Potential Future Trends in the Industry
1. Artificial Intelligence (AI)
Artificial intelligence has seen significant advancements in recent years, and its potential impact on various industries, including the tech industry, is immense. AI has the ability to automate complex tasks, improve decision-making processes, and enhance overall efficiency. In the future, we can expect AI to continue revolutionizing the way we work and live.
One potential future trend related to AI is the integration of AI-powered chatbots and virtual assistants. These intelligent systems can provide personalized recommendations, answer customer inquiries, and even perform routine tasks, freeing up human resources for more complex responsibilities. The use of AI in customer service is predicted to increase, leading to improved user experiences and reduced costs for businesses.
Another area where AI is likely to make significant advancements is in healthcare. AI algorithms can analyze vast amounts of medical data to assist in the diagnosis and treatment of various diseases. This could lead to more accurate diagnoses, personalized treatment plans, and improved patient outcomes.
2. Internet of Things (IoT)
The Internet of Things refers to the network of interconnected devices and objects that can communicate and share data with each other. IoT has already transformed several sectors, such as home automation and industrial operations. However, its future potential is far from being fully realized.
One potential future trend in the IoT industry is the integration of IoT devices with artificial intelligence. AI-powered IoT systems could learn from user behavior and adapt to optimize energy consumption, enhance security, and improve overall efficiency in smart homes and buildings. This would create a more intelligent and interconnected environment, enhancing our daily lives.
Furthermore, the healthcare sector could benefit significantly from IoT advancements. Wearable devices could continuously monitor vital signs, providing real-time data to healthcare professionals. This would enable early detection of health issues, remote patient monitoring, and more proactive healthcare management.
3. Cybersecurity
As technology continues to advance, cybersecurity becomes increasingly crucial. The rise of interconnected devices and the growing amount of data being transmitted online make it imperative to invest in robust cybersecurity measures.
One potential future trend in the cybersecurity industry is the application of AI for threat detection and prevention. AI algorithms can analyze vast amounts of data, identify patterns, and detect anomalies more efficiently than human counterparts. This would enable proactive identification and mitigation of potential cyber threats, protecting businesses and individuals from attacks.
Additionally, the implementation of blockchain technology could enhance cybersecurity in various sectors. The decentralized nature of blockchain makes it inherently secure, reducing the risk of data breaches and fraudulent activities. Utilizing blockchain for storing sensitive data and conducting secure transactions could become a standard practice in the future.
Predictions and Recommendations for the Industry
Based on the potential future trends discussed above, it is clear that AI, IoT, and cybersecurity will continue to play significant roles in shaping the tech industry. To stay ahead in this rapidly evolving landscape, businesses should adapt and embrace these technologies while considering the following predictions and recommendations:
Invest in AI integration: Businesses should explore ways to integrate AI technologies into their operations, such as AI-powered chatbots or machine learning algorithms for data analysis. This can enhance productivity, improve customer experiences, and drive innovation.
Focus on IoT security: With the increasing number of IoT devices, ensuring robust security measures becomes crucial. Businesses should invest in secure IoT platforms, adopt encryption standards, and regularly update software to mitigate security risks.
Collaborate for cybersecurity: Cybersecurity threats are continuously evolving, making it essential for businesses to work together. Collaborating with cybersecurity experts, sharing threat intelligence, and participating in information-sharing networks can help organizations stay one step ahead of cybercriminals.
Embrace privacy-centric technologies: With growing concerns about data privacy, adopting technologies like blockchain for secure data storage and conducting transactions can build trust with customers. Prioritizing privacy and transparency will be crucial in gaining a competitive edge.
By embracing these predictions and recommendations, businesses can position themselves at the forefront of the industry and leverage the potential future trends to drive growth and success.
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Actually, it’s both possible
This Article was originally published before on YOZM-IT as Korean
Various way of data science
There are many programming languages in the world and software that utilizes them. And those play an important role in “Data science”.
For example, if you’re using funnel analysis to improve your product, you might want to
Compare the bounce rates of funnel stages before and after an event,
And perform a ratio test to calculate their statistical significance.
Image by the author
Meanwhile, data scientists have various career backgrounds and experiences. So They tend to use the methods they’re comfortable with, including Python, R, SAS and more.
We see this quite a bit, because in most cases, the software you use at the level of business doesn’t make much of a difference.
But what happens if you “produce different results by the software used?”
The following image shows the results of running a proportion test in R, Python, and STATA with example mentioned.
Image from the author and CAMIS project
You can see that even though we used the same values of 1000 and 123, the p-value, which indicates the significance of the proportion test, is slightly different for each method.
There are many reasons why the calculation value is different depending on the method used, such as
Different algorithms in the core logic of the programming language
Different default values of the parameters used in the function.
In the example above, if you change the value of the parameter correct in R and apply “Continuity correction” as using “correct = F” , you can see that the result is the same as in STATA.
Image from CAMIS project
Rounding
Next, I’ll introduce rounding for more general data analysis.
Image by the author
Similarly, you can see that the round changes its value depending on software.
If the fee is “0.5 billion”in some large financial transaction in business, the rounded cost could be zero or 1 billion, depending on how you calculate the rounding.
Another case could be Logistic regression, which various round can be reverse prediction.
Image from Wikipedia, edited by the author
Why is round different?
Let’s talk a little more about why this round is different.
Rounding as we usually perceive it means changing 0 ~ 4to 0, and 5 ~ 9 to 10, as shown below image.
And in decimal units, is rounding to the nearest whole number by changing .0 ~ .4999.. to 0 and .5 ~ .9999.. to 1.
However, there are a number of mathematical interpretations of when exactly 0.5 , and when it is a negative number.
Image from the Learning corner
For example, round(-23.5) should produce -23 or -24?
Both are possible, depending on the mathematical interpretation and it’s called as rounding half up and rounding half down respectively. We can take this a step further and round both positive and negative numbers closer to zero, or vice versa.
This means that round(-23.5) will round to -23, and round(23.5) will round to 23, or round to -24 and 24, respectively. These are represented by the names Rounding half toward zero, Rounding half away from zero, respectively.
Finally, there are methods called Rounding half to even and Rounding half to odd, which mean that we want to consider the nearest integers to be even and odd, respectively.
In particular, the Rounding half to even method also goes by the names Convergent rounding, Statistician’s rounding, Dutch rounding, Gaussian rounding, and Bankers’ rounding, and is one of the official standard methods according to IEEE 754.
Bankers’ rounding
Bankers’s rounding, is default method in R , so Let’s breif a little bit more.
The image below shows the result of rounding from 0.0 to 2.0.
Image from the author
While this may seem like a good idea, there is actually a problem. Because .5 is unconditionally rounded to the next integer, there is an unconditional bias towards rounding to a “+ value”.
I don’t know the exact reason for this, but one theory is that the US IRS used to use this rounding to collect taxes and was sued for unfairly profiting by collecting more taxes from people who were .5 off, so they lost the case and changed to rounding to the nearest even (or odd) number to match the .5 rounding.
This means that by modifying the rounding as shown below, we can avoid the bias that was previously occurring.
The problem with different results
In recent years, industries in various domains, including pharmaceuticals and finance, have been trying to switch from “commercial” software such as SPSS, SAS and STATA to “open source” software such as Python, R and Julia .
And as rounding mentioned earlier, diffrent result issue by software has been also raised which can create problems in terms of reproducibility, uncertainty, accuracy, and traceability.
So if you’re utilizing multiple softwares, you should be aware of why they produce different results, and how you can use them to properly
CAMIS project
Image from CAMIS project
CAMIS stands for Comparing Analysis Method Implementations in Software.
This project compares the differences in softwares (or programming languages) and make standards to produce the same results.
The core area of the project is the “statistical computation” part, so most contributions come from the data science leaders who have strong understanding with it.
But CAMIS is also an open source project, that is not restricted and maintained with various people through regular discussions, collaboration, and sharing of project progress.
Below is one of the comparisons published on the CAMIS project’s webpage, which reviews how a one sample t-test is run with each software, what the results are, and how the results are compatible with each other.
Image from CAMIS project
The CAMIS project was started by members who interested in “SAS to R” in the medical and pharmaceutical industry. So it mainly focuses on R and SAS along major statistical data analysis, but recently it’s also working on how to use Python for data science in a broader domain of the industry.
Not only clasiccal methods such as Hypothesis tests, Regression analysis, but modern methods in data science such as Bayesian statistics, Causal inference and novel implementations of existing methods (e.g. MMRM) are topic of interest in project.
Sessions are increasingly appearing at multiple data science conferences, where many researchers and contributors are encouraged to promote, contribute and utilize it as a reference.
Finally, the CAMIS project is also collaborating with academia beyond the data science industry, as similar topics have been published in The American Statistician and Drug Information Association, among others.
Image from The American Statistician
The project is also currently working with students on a thesis entitled “A comparison of MMRM methodology in SAS and R software” and is open to collaborations and suggestions on other topics.
Summary
Various software used in data science. As the domain, the libraries or software used by an organization may be dependent on a particular language, which can sometimes be mixed with personal preferred methods. (in many cases, this doesn’t vary much at the level of the business)
However, if you’re not careful, the methods you use can lead to different results.
In this article, I’ve given you some examples of and reasons for differences in the methods used by different software for calculations, and introduced the CAMIS project, a research project that aims to minimize them to ensure consistency in data analysis.
If you use different software in your data analytics work, it’s a good idea to take a look at them to understand the differences and try to find the optimal method for your purposes,
And if you work in data science in the field, I highly recommend that you take an interstate in or contribute to the CAMIS project for a global collaborative experience.
Potential Future Insights and Developments of Data Science Software Variance
The article discusses the significant role of various programming languages in data science and how different software can yield different results. We learn that even when the same values of calculation are applied across different software like R, Python, and STATA, they can produce varying results. For instance, the bounce rates and ratio tests results of an event would vary under different platforms, despite using the same data. Crucially, the article underscores the importance of the Comparing Analysis Method Implementations in Software (CAMIS) project, which aims to standardize results across various softwares.
Implications of Software Differences in Data Science
Today, different industries including pharmaceuticals and finance are transitioning from commercial to open source software such as Python, R and Julia. However, the differing results issue by software raises concerns in relation to reproducibility, uncertainty, accuracy, and traceability. This variance could trigger significant divergences in forecast modeling and data interpretation within a single organization or amongst industry competition. Resolving this discrepancy necessitates understanding why different software produce varying results and discerning how to correctly and consistently utilize their functionalities.
Potential of ‘Rounding’ in Data Science
The article mentions the role and definition of ’rounding’ in data science especially when handling extensive data sets. We learn that the process of rounding can differ based upon the mathematical interpretations used. This, too, can yield differential results across platforms and software. The concept of ‘Rounding half toward zero’, ‘Rounding half away from zero’, ‘Rounding half to even’ and ‘Rounding half to odd’ in the context of both positive and negative numbers was also introduced in the discussion. Clearly, programming languages provide more than just a tool for implementation – they offer different philosophies of approach to problem-solving in data science.
The Role of the CAMIS Project
The Comparing Analysis Method Implementations in Software (CAMIS) project is an initiative aimed at addressing differences in software used in data science. By comparing diverse software and programming languages, the project seeks to develop a standard that achieves consistent results, thereby assisting industries in confidently transitioning from commercial software to open source software. The terms of the project are not restricted and involve a collaborative, progressive effort from various contributors. A primary focus of the project is on R and SAS alongside major statistical data analysis, and it also explores the use of Python for data science across wider industry domains.
Actionable Advice
If your work involves using different software for data analytics, it is advisable to review and understand the differences and nuances of your selected tools. Moreover, strive to find the optimal methods that align with your specific industry requirements.
If you work in data science, participating in or contributing to the CAMIS project is highly beneficial for both personal growth and collaborative knowledge sharing. Apart from staying updated with the latest developments, you can also lend your expertise to this significant cause.
Utilizing rounding correctly is crucial in data science. Awareness of the different types of rounding and how different software handle this can ensure the accuracy and reliability of your results.
The more well-versed you are with your chosen programming language and software, the more effectively you can minimize and address discrepancies in your work.
