pwc-archive/papers-with-abstracts huggingface dataset:
Completing Single-Cell DNA Methylome Profiles via Transfer Learning Together With KL-Divergence
Authors: Sanjeeva Dodlapati, Zongliang Jiang, Jiangwen  Sun
Date: 2022-07-21 00:00:00
Proceeding: Frontiers in Genetics 2022 7
Tasks: Imputation, Transfer Learning

The high level of sparsity in methylome profiles obtained using whole-genome bisulfite sequencing in the case of low biological material amount limits its value in the study of systems in which large samples are difficult to assemble, such as mammalian preimplantation embryonic development. The recently developed computational methods for addressing the sparsity by imputing missing have their limits when the required minimum data coverage or profiles of the same tissue in other modalities are not available. In this study, we explored the use of transfer learning together with Kullback-Leibler (KL) divergence to train predictive models for completing methylome profiles with very low coverage (below 2%). Transfer learning was used to leverage less sparse profiles that are typically available for different tissues for the same species, while KL divergence was employed to maximize the usage of information carried in the input data. A deep neural network was adopted to extract both DNA sequence and local methylation patterns for imputation. Our study of training models for completing methylome profiles of bovine oocytes and early embryos demonstrates the effectiveness of transfer learning and KL divergence, with individual increase of 29.98 and 29.43%, respectively, in prediction performance and 38.70% increase when the two were used together. The drastically increased data coverage (43.80–73.6%) after imputation powers downstream analyses involving methylomes that cannot be effectively done using the very low coverage profiles (0.06–1.47%) before imputation.
Induct-Learn: Short Phrase Prompting with Instruction Induction
Authors: Po-Chun Chen, Sheng-Lun Wei, Hen-Hsen Huang, Hsin-Hsi Chen
Date: 2024-12-12 00:00:00
Proceeding: Conference 2024 12

Large Language Models (LLMs) have demonstrated capability in “instruction induction,” generating instructions from demonstrations (input-output pairs). However, existing methods often rely on large datasets or numerous examples, which is impractical and costly in real-world scenarios. In this work, we propose a low-cost, task-level framework called Induct-Learn. It induces pseudo instructions from a few demonstrations and a short phrase, adding a CoT process into existing demonstrations. When encountering new problems, the learned pseudo instructions and demonstrations with the pseudo CoT process can be combined into a prompt to guide the LLM’s problem-solving process. We validate our approach on the BBH-Induct and Evals-Induct datasets, and the results show that the Induct-Learn framework outperforms state-of-the-art methods. We also exhibit cross-model adaptability and achieve superior performance at a lower cost compared to existing methods.
Physics-informed graph convolutional neural network for modeling geometry-adaptive steady-state natural convection
Authors: Jiang-Zhou Peng, Nadine Aubry, Yu-Bai Li, Mei Mei, Zhi-Hua Chen, Wei-Tao Wu
Date: 2023-08-21 00:00:00
Proceeding: International Journal of Heat and Mass Transfer 2023 8
Tasks: Prediction

This paper presents a novel deep learning-based surrogate model for steady-state natural convection problem
with variable geometry. Traditional deep learning based surrogate models are more or less limited by the
requirement for large amounts of training data, loss of accuracy due to pixelization of the original data, the low
accurate prediction near the boundaries and low geometry adaptive capability. To overcome the above chal-
lenges, the proposed natural convection prediction framework is mainly composed of a physics-informed neural
network (PINN) and a graph convolutional neural network (GCN), called natural convection prediction model
based on physics-informed graph convolutional network (NCV-PIGN). The GCN serves as the prediction module,
inferring and predicting natural convection phenomena by considering the interactions between unstructured
nodes and their neighbor; the PINN incorporates the governing equations of natural convection into the loss
function of the neural network, allowing the predictions from GCN to satisfy the constraints imposed by the
physical laws. The advantages of this framework are twofold: the operation principles of the GCN better align
with the development of the temperature field in real situations, and the embedding of physical information
strengthens the model’s understanding of the flow field, accurately describing the variations of temperature
gradients at the boundary positions while reducing the model’s reliance on training data. Finally, to validate the
superiority of the NCV-PIGN, we analyze its geometric adaptability and accuracy of prediction using single and
dual heat source cases. We compare the model’s prediction results at different sampling point quantities and
contrast them with those of purely data-driven models. The results demonstrate that the excellent geometric
adaptability and prediction capability of the proposed model can be achieved with only 20 training data and
once the fully trained the model can solve natural convection problems within 3 ms. The max and mean relative
errors in predicting the temperature field are less than 2% and 0.4% for both single and dual heat source cases.
Compared to the pure data-driven model, the proposed model has reduced the maximum error by 65.5% and the
mean error by 72%. These results validate the effectiveness of the developed NCV-PIGN model, enabling better
performance of the deep learning-based surrogate models for natural convection problems.
EDA-Aware RTL Generation with Large Language Models
Authors: Mubashir ul Islam, Humza Sami, Pierre-Emmanuel Gaillardon, Valerio Tenace
Date: 2024-11-21 00:00:00
Tasks: Code Generation

Large Language Models (LLMs) have become increasingly popular for generating RTL code. However, producing error-free RTL code in a zero-shot setting remains highly challenging for even state-of-the-art LLMs, often leading to issues that require manual, iterative refinement. This additional debugging process can dramatically increase the verification workload, underscoring the need for robust, automated correction mechanisms to ensure code correctness from the start. In this work, we introduce AIvril2, a self-verifying, LLM-agnostic agentic framework aimed at enhancing RTL code generation through iterative corrections of both syntax and functional errors. Our approach leverages a collaborative multi-agent system that incorporates feedback from error logs generated by EDA tools to automatically identify and resolve design flaws. Experimental results, conducted on the VerilogEval-Human benchmark suite, demonstrate that our framework significantly improves code quality, achieving nearly a 3.4$\times$ enhancement over prior methods. In the best-case scenario, functional pass rates of 77% for Verilog and 66% for VHDL were obtained, thus substantially improving the reliability of LLM-driven RTL code generation.
Large Language Models in Politics and Democracy: A Comprehensive Survey
Authors: Goshi Aoki
Date: 2024-12-01 00:00:00
Tasks: Decision Making

The advancement of generative AI, particularly large language models (LLMs), has a significant impact on politics and democracy, offering potential across various domains, including policymaking, political communication, analysis, and governance. This paper surveys the recent and potential applications of LLMs in politics, examining both their promises and the associated challenges. This paper examines the ways in which LLMs are being employed in legislative processes, political communication, and political analysis. Moreover, we investigate the potential of LLMs in diplomatic and national security contexts, economic and social modeling, and legal applications. While LLMs offer opportunities to enhance efficiency, inclusivity, and decision-making in political processes, they also present challenges related to bias, transparency, and accountability. The paper underscores the necessity for responsible development, ethical considerations, and governance frameworks to ensure that the integration of LLMs into politics aligns with democratic values and promotes a more just and equitable society.
Optimizing Student Ability Assessment: A Hierarchy Constraint-Aware Cognitive Diagnosis Framework for Educational Contexts
Authors: Xinjie Sun, Qi Liu, Kai Zhang, Shuanghong Shen, Fei Wang, Yan Zhuang, Zheng Zhang, Weiyin Gong, Shijin Wang, Lina Yang, Xingying Huo
Date: 2024-11-21 00:00:00
Tasks: cognitive diagnosis, Diagnostic, Fairness

Cognitive diagnosis (CD) aims to reveal students' proficiency in specific knowledge concepts. With the increasing adoption of intelligent education applications, accurately assessing students' knowledge mastery has become an urgent challenge. Although existing cognitive diagnosis frameworks enhance diagnostic accuracy by analyzing students' explicit response records, they primarily focus on individual knowledge state, failing to adequately reflect the relative ability performance of students within hierarchies. To address this, we propose the Hierarchy Constraint-Aware Cognitive Diagnosis Framework (HCD), designed to more accurately represent student ability performance within real educational contexts. Specifically, the framework introduces a hierarchy mapping layer to identify students' levels. It then employs a hierarchy convolution-enhanced attention layer for in-depth analysis of knowledge concepts performance among students at the same level, uncovering nuanced differences. A hierarchy inter-sampling attention layer captures performance differences across hierarchies, offering a comprehensive understanding of the relationships among students' knowledge state. Finally, through personalized diagnostic enhancement, the framework integrates hierarchy constraint perception features with existing models, improving the representation of both individual and group characteristics. This approach enables precise inference of students' knowledge state. Research shows that this framework not only reasonably constrains changes in students' knowledge states to align with real educational settings, but also supports the scientific rigor and fairness of educational assessments, thereby advancing the field of cognitive diagnosis.
The Global AI Vibrancy Tool
Authors: Loredana Fattorini, Nestor Maslej, Raymond Perrault, Vanessa Parli, John Etchemendy, Yoav Shoham, Katrina Ligett
Date: 2024-11-21 00:00:00

This paper presents the latest version of the Global AI Vibrancy Tool (GVT), an interactive suite of visualizations designed to facilitate the comparison of AI vibrancy across 36 countries, using 42 indicators organized into 8 pillars. The tool offers customizable features that allow users to conduct in-depth country-level comparisons and longitudinal analyses of AI-related metrics, all based on publicly available data. By providing a transparent assessment of national progress in AI, it serves the diverse needs of policymakers, industry leaders, researchers, and the general public. Using weights for indicators and pillars developed by AI Index's panel of experts and combined into an index, the Global AI Vibrancy Ranking for 2023 places the United States first by a significant margin, followed by China and the United Kingdom. The ranking also highlights the rise of smaller nations such as Singapore when evaluated on both absolute and per capita bases. The tool offers three sub-indices for evaluating Global AI Vibrancy along different dimensions: the Innovation Index, the Economic Competitiveness Index, and the Policy, Governance, and Public Engagement Index.
AI-powered Digital Framework for Personalized Economical Quality Learning at Scale
Authors: Mrzieh VatandoustMohammadieh, Mohammad Mahdi Mohajeri, Ali Keramati, Majid Nili Ahmadabadi
Date: 2024-11-20 00:00:00

The disparity in access to quality education is significant, both between developed and developing countries and within nations, regardless of their economic status. Socioeconomic barriers and rapid changes in the job market further intensify this issue, highlighting the need for innovative solutions that can deliver quality education at scale and low cost. This paper addresses these challenges by proposing an AI-powered digital learning framework grounded in Deep Learning (DL) theory. The DL theory emphasizes learner agency and redefines the role of teachers as facilitators, making it particularly suitable for scalable educational environments. We outline eight key principles derived from learning science and AI that are essential for implementing DL-based Digital Learning Environments (DLEs). Our proposed framework leverages AI for learner modelling based on Open Learner Modeling (OLM), activity suggestions, and AI-assisted support for both learners and facilitators, fostering collaborative and engaging learning experiences. Our framework provides a promising direction for scalable, high-quality education globally, offering practical solutions to some of the AI-related challenges in education.
NLP Cluster Analysis of Common Core State Standards and NAEP Item Specifications
Authors: Gregory Camilli, Larry Suter
Date: 2024-11-20 00:00:00

Camilli (2024) proposed a methodology using natural language processing (NLP) to map the relationship of a set of content standards to item specifications. This study provided evidence that NLP can be used to improve the mapping process. As part of this investigation, the nominal classifications of standards and items specifications were used to examine construct equivalence. In the current paper, we determine the strength of empirical support for the semantic distinctiveness of these classifications, which are known as "domains" for Common Core standards, and "strands" for National Assessment of Educational Progress (NAEP) item specifications. This is accomplished by separate k-means clustering for standards and specifications of their corresponding embedding vectors. We then briefly illustrate an application of these findings.
Intelligent Tutors for Adult Learners: An Analysis of Needs and Challenges
Authors: Adit Gupta, Momin Siddiqui, Glen Smith, Jenn Reddig, Christopher MacLellan
Date: 2024-11-19 00:00:00
Tasks: Lifelong learning

This work examines the sociotechnical factors that influence the adoption and usage of intelligent tutoring systems in self-directed learning contexts, focusing specifically on adult learners. The study is divided into two parts. First, we present Apprentice Tutors, a novel intelligent tutoring system designed to address the unique needs of adult learners. The platform includes adaptive problem selection, real-time feedback, and visual dashboards to support learning in college algebra topics. Second, we investigate the specific needs and experiences of adult users through a deployment study and a series of focus groups. Using thematic analysis, we identify key challenges and opportunities to improve tutor design and adoption. Based on these findings, we offer actionable design recommendations to help developers create intelligent tutoring systems that better align with the motivations and learning preferences of adult learners. This work contributes to a wider understanding of how to improve educational technologies to support lifelong learning and professional development.
The Moral Mind(s) of Large Language Models
Authors: Avner Seror
Date: 2024-11-19 00:00:00
Tasks: Benchmarking, Decision Making

As large language models (LLMs) increasingly participate in tasks with ethical and societal stakes, a critical question arises: do they exhibit an emergent "moral mind" - a consistent structure of moral preferences guiding their decisions - and to what extent is this structure shared across models? To investigate this, we applied tools from revealed preference theory to nearly 40 leading LLMs, presenting each with many structured moral dilemmas spanning five foundational dimensions of ethical reasoning. Using a probabilistic rationality test, we found that at least one model from each major provider exhibited behavior consistent with approximately stable moral preferences, acting as if guided by an underlying utility function. We then estimated these utility functions and found that most models cluster around neutral moral stances. To further characterize heterogeneity, we employed a non-parametric permutation approach, constructing a probabilistic similarity network based on revealed preference patterns. The results reveal a shared core in LLMs' moral reasoning, but also meaningful variation: some models show flexible reasoning across perspectives, while others adhere to more rigid ethical profiles. These findings provide a new empirical lens for evaluating moral consistency in LLMs and offer a framework for benchmarking ethical alignment across AI systems.
Multi-Bin Batching for Increasing LLM Inference Throughput
Authors: Ozgur Guldogan, Jackson Kunde, Kangwook Lee, Ramtin Pedarsani
Date: 2024-12-03 00:00:00
Tasks: Scheduling

As large language models (LLMs) grow in popularity for their diverse capabilities, improving the efficiency of their inference systems has become increasingly critical. Batching LLM requests is a critical step in scheduling the inference jobs on servers (e.g. GPUs), enabling the system to maximize throughput by allowing multiple requests to be processed in parallel. However, requests often have varying generation lengths, causing resource underutilization, as hardware must wait for the longest-running request in the batch to complete before moving to the next batch. We formalize this problem from a queueing-theoretic perspective, and aim to design a control policy which is throughput-optimal. We propose Multi-Bin Batching, a simple yet effective method that can provably improve LLM inference throughput by grouping requests with similar (predicted) execution times into predetermined bins. Through a combination of theoretical analysis and experiments, including real-world LLM inference scenarios, we demonstrate significant throughput gains compared to standard batching approaches.
Socio-Emotional Response Generation: A Human Evaluation Protocol for LLM-Based Conversational Systems
Authors: Lorraine Vanel, Ariel R. Ramos Vela, Alya Yacoubi, Chloé Clavel
Date: 2024-11-26 00:00:00
Tasks: Response Generation

Conversational systems are now capable of producing impressive and generally relevant responses. However, we have no visibility nor control of the socio-emotional strategies behind state-of-the-art Large Language Models (LLMs), which poses a problem in terms of their transparency and thus their trustworthiness for critical applications. Another issue is that current automated metrics are not able to properly evaluate the quality of generated responses beyond the dataset's ground truth. In this paper, we propose a neural architecture that includes an intermediate step in planning socio-emotional strategies before response generation. We compare the performance of open-source baseline LLMs to the outputs of these same models augmented with our planning module. We also contrast the outputs obtained from automated metrics and evaluation results provided by human annotators. We describe a novel evaluation protocol that includes a coarse-grained consistency evaluation, as well as a finer-grained annotation of the responses on various social and emotional criteria. Our study shows that predicting a sequence of expected strategy labels and using this sequence to generate a response yields better results than a direct end-to-end generation scheme. It also highlights the divergences and the limits of current evaluation metrics for generated content. The code for the annotation platform and the annotated data are made publicly available for the evaluation of future models.
Sensing-Aided 6G Drone Communications: Real-World Datasets and Demonstration
Authors: Gouranga Charan, Ahmed Alkhateeb
Date: 2024-12-06 00:00:00
Tasks: Beam Prediction

In the advent of next-generation wireless communication, millimeter-wave (mmWave) and terahertz (THz) technologies are pivotal for their high data rate capabilities. However, their reliance on large antenna arrays and narrow directive beams for ensuring adequate receive signal power introduces significant beam training overheads. This becomes particularly challenging in supporting highly-mobile applications such as drone communication, where the dynamic nature of drones demands frequent beam alignment to maintain connectivity. Addressing this critical bottleneck, our paper introduces a novel machine learning-based framework that leverages multi-modal sensory data, including visual and positional information, to expedite and refine mmWave/THz beam prediction. Unlike conventional approaches that solely depend on exhaustive beam training methods, our solution incorporates additional layers of contextual data to accurately predict beam directions, significantly mitigating the training overhead. Additionally, our framework is capable of predicting future beam alignments ahead of time. This feature enhances the system's responsiveness and reliability by addressing the challenges posed by the drones' mobility and the computational delays encountered in real-time processing. This capability for advanced beam tracking asserts a critical advancement in maintaining seamless connectivity for highly-mobile drones. We validate our approach through comprehensive evaluations on a unique, real-world mmWave drone communication dataset, which integrates concurrent camera visuals, practical GPS coordinates, and mmWave beam training data...
Perceptually Transparent Binaural Auralization of Simulated Sound Fields
Authors: Jens Ahrens
Date: 2024-12-06 00:00:00

Contrary to geometric acoustics-based simulations where the spatial information is available in a tangible form, it is not straightforward to auralize wave-based simulations. A variety of methods have been proposed that compute the ear signals of a virtual listener with known head-related transfer functions from sampling either the sound pressure or the particle velocity (or both) of the simulated sound field. The available perceptual evaluation results of such methods are not comprehensive so that it is unclear what number and arrangement of sampling points is required for achieving perceptually transparent auralization, i.e.~for achieving an auralization that is perceptually indistinguishable from the ground truth. This article presents a perceptual evaluation of the most common binaural auralization methods with and without intermediate ambisonic representation of volumetrically sampled sound pressure or sound pressure and particle velocity sampled on spherical or cubical surfaces. Our results confirm that perceptually transparent auralization is possible if sound pressure and particle velocity are available at 289 sampling points on a spherical surface grid. Other grid geometries require considerably more points. All tested methods are available open source in the Chalmers Auralization Toolbox that accompanies this article.