pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning-Enabled ISAC-OTFS Pre-equalization Design for Aerial-Terrestrial Networks
Authors: WeiHao Wang, Jing Guo, Siqiang Wang, Xinyi Wang, Weijie Yuan, Zesong Fei
Date: 2024-12-06 00:00:00
Tasks: Integrated sensing and communication, ISAC, parameter estimation

Orthogonal time frequency space (OTFS) modulation has been viewed as a promising technique for integrated sensing and communication (ISAC) systems and aerial-terrestrial networks, due to its delay-Doppler domain transmission property and strong Doppler-resistance capability. However, it also suffers from high processing complexity at the receiver. In this work, we propose a novel pre-equalization based ISAC-OTFS transmission framework, where the terrestrial base station (BS) executes pre-equalization based on its estimated channel state information (CSI). In particular, the mean square error of OTFS symbol demodulation and Cramer-Rao lower bound of sensing parameter estimation are derived, and their weighted sum is utilized as the metric for optimizing the pre-equalization matrix. To address the formulated problem while taking the time-varying CSI into consideration, a deep learning enabled channel prediction-based pre-equalization framework is proposed, where a parameter-level channel prediction module is utilized to decouple OTFS channel parameters, and a low-dimensional prediction network is leveraged to correct outdated CSI. A CSI processing module is then used to initialize the input of the pre-equalization module. Finally, a residual-structured deep neural network is cascaded to execute pre-equalization. Simulation results show that under the proposed framework, the demodulation complexity at the receiver as well as the pilot overhead for channel estimation, are significantly reduced, while the symbol detection performance approaches those of conventional minimum mean square error equalization and perfect CSI.
Cell-Free Full-Duplex Communication -- An Overview
Authors: Diluka Galappaththige, Mohammadali Mohammadi, Hien Quoc Ngo, Michail Matthaiou, Chintha Tellambura
Date: 2024-12-06 00:00:00

Cell-free (CF) architecture and full-duplex (FD) communication are leading candidates for next-generation wireless networks. The CF framework removes cell boundaries in traditional cell-based systems, thereby mitigating inter-cell interference and improving coverage probability. In contrast, FD communication allows simultaneous transmission and reception on the same frequency-time resources, effectively doubling the spectral efficiency (SE). The integration of these technologies, known as CF FD communication, leverages the advantages of both approaches to enhance the spectral and energy efficiency in wireless networks. CF FD communication is particularly promising due to the low-power and cost-effective FD-enabled access points (APs), which are ideal for short-range transmissions between APs and users. Despite its potential, a comprehensive survey or tutorial on CF FD communication has been notably absent. This paper aims to address this gap in the literature. It begins with an overview of FD communication fundamentals, self-interference cancellation techniques, and CF technology principles, including their implications for current wireless networks. The discussion then moves to the integration and compatibility of CF and FD technologies, focusing on channel estimation, performance analysis, and resource allocation in CF FD massive multiple-input multiple-output (mMIMO) networks, supported by an extensive literature review and case studies.
Metrics for classes of semi-binary phylogenetic networks using $μ$-representations
Authors: Christopher Reichling, Leo van Iersel, Yukihiro Murakami
Date: 2024-12-06 00:00:00

Phylogenetic networks are useful in representing the evolutionary history of taxa. In certain scenarios, one requires a way to compare different networks. In practice, this can be rather difficult, except within specific classes of networks. In this paper, we derive metrics for the class of \emph{orchard networks} and the class of \emph{strongly reticulation-visible} networks, from variants of so-called \emph{$\mu$-representations}, which are vector representations of networks. For both network classes, we impose degree constraints on the vertices, by considering \emph{semi-binary} networks.
Comparison of Transcriptional Activation by Corticosteroids of Human MR (Ile-180) and Human MR Haplotype (Ile180Val)
Authors: Yoshinao Katsu, Jiawn Zhang, Ya Ao, Michael E. Baker
Date: 2024-12-05 00:00:00

While the classical function of human mineralocorticoid receptor (MR) is to regulate sodium and potassium homeostasis through aldosterone activation of the kidney MR, the MR also is highly expressed in the brain, where the MR is activated by cortisol in response to stress. Here, we report the half-maximal response (EC50) and fold-activation by cortisol, aldosterone and other corticosteroids of human MR rs5522, a haplotype containing valine at codon 180 instead of isoleucine found in the wild-type MR (Ile-180). MR rs5522 (Val-180) has been studied for its actions in the human brain involving coping with stress and depression. We compared the EC50 and fold-activation by corticosteroids of MR rs5522 and wild-type MR transfected into HEK293 cells with either the TAT3 promoter or the MMTV promoter. Parallel studies investigated the binding of MR antagonists, spironolactone and progesterone, to MR rs5522. In HEK293 cells with the MMTV promotor, MR rs5522 had a slightly higher EC50 compared to wild-type MR and a similar level of fold-activation for all corticosteroids. In contrast, in HEK293 cells with the TAT3 promoter, MR 5522 had a higher EC50 (lower affinity) and higher fold-activation for cortisol compared to wild-type MR (Ile-180), while compared to wild-type MR, the EC50s of MR rs5522 for aldosterone and corticosterone were slightly lower and fold-activation was higher. Spironolactone and progesterone had similar antagonist activity for MR rs5522 and MR (Ile-180) in the presence of MMTV and TAT3 promoters in HEK293 cells.
Approaches to studying virus pangenome variation graphs
Authors: Tim Downing
Date: 2024-12-06 00:00:00
Tasks: Diversity

Pangenome variation graphs (PVGs) allow for the representation of genetic diversity in a more nuanced way than traditional reference-based approaches. Here we focus on how PVGs are a powerful tool for studying genetic variation in viruses, offering insights into the complexities of viral quasispecies, mutation rates, and population dynamics. PVGs originated in human genomics and hold great promise for viral genomics. Previous work has been constrained by small sample sizes and gene-centric methods, PVGs enable a more comprehensive approach to studying viral diversity. Large viral genome collections should be used to make PVGs, which offer significant advantages: we outline accessible tools to achieve this. This spans PVG construction, PVG file formats, PVG manipulation and analysis, PVG visualisation, measuring PVG openness, and mapping reads to PVGs. Additionally, the development of PVG-specific formats for mutation representation and personalised PVGs that reflect specific research questions will further enhance PVG applications. Challenges remain, particularly in managing nested variants, optimising error detection, optimising k-mer/minimizer-based approaches for AT-rich genomes, incorporating long read sequencing data, and scalable visualisation approaches. Nevertheless, PVGs offer a new opportunities for viral population genomics, and a testing ground for tool development prior to application to larger eukaryotic genomes. These advances will enable more accurate and comprehensive detection of viral mutations, contributing to a deeper understanding of viral evolution and genotype-phenotype associations.
Improving data sharing and knowledge transfer via the Neuroelectrophysiology Analysis Ontology (NEAO)
Authors: Cristiano André Köhler, Sonja Grün, Michael Denker
Date: 2024-12-06 00:00:00
Tasks: Knowledge Graphs, Transfer Learning

Describing the processes involved in analyzing data from electrophysiology experiments to investigate the function of neural systems is inherently challenging. On the one hand, data can be analyzed by distinct methods that serve a similar purpose, such as different algorithms to estimate the spectral power content of a measured time series. On the other hand, different software codes can implement the same algorithm for the analysis while adopting different names to identify functions and parameters. Having reproducibility in mind, with these ambiguities the outcomes of the analysis are difficult to report, e.g., in the methods section of a manuscript or on a platform for scientific findings. Here, we illustrate how using an ontology to describe the analysis process can assist in improving clarity, rigour and comprehensibility by complementing, simplifying and classifying the details of the implementation. We implemented the Neuroelectrophysiology Analysis Ontology (NEAO) to define a unified vocabulary and to standardize the descriptions of the processes involved in analyzing data from neuroelectrophysiology experiments. Real-world examples demonstrate how the NEAO can be employed to annotate provenance information describing an analysis process. Based on such provenance, we detail how it can be used to query various types of information (e.g., using knowledge graphs) that enable researchers to find, understand and reuse prior analysis results.
ProtBoost: protein function prediction with Py-Boost and Graph Neural Networks -- CAFA5 top2 solution
Authors: Alexander Chervov, Anton Vakhrushev, Sergei Fironov, Loredana Martignetti
Date: 2024-12-05 00:00:00
Tasks: Protein Function Prediction

Predicting protein properties, functions and localizations are important tasks in bioinformatics. Recent progress in machine learning offers an opportunities for improving existing methods. We developed a new approach called ProtBoost, which relies on the strength of pretrained protein language models, the new Py-Boost gradient boosting method and Graph Neural Networks (GCN). The ProtBoost method was ranked second best model in the recent Critical Assessment of Functional Annotation (CAFA5) international challenge with more than 1600 participants. Py-Boost is the first gradient boosting method capable of predicting thousands of targets simultaneously, making it an ideal fit for tasks like the CAFA challange. Our GCN-based approach performs stacking of many individual models and boosts the performance significantly. Notably, it can be applied to any task where targets are arranged in a hierarchical structure, such as Gene Ontology. Additionally, we introduced new methods for leveraging the graph structure of targets and present an analysis of protein language models for protein function prediction task. ProtBoost is publicly available at: https://github.com/btbpanda/CAFA5-protein-function-prediction-2nd-place.
A biomechanical study of neck strength and impact dynamics on head and neck injury parameters
Authors: Rahid Zaman, Ashfaq Adnan
Date: 2024-12-06 00:00:00

Traumatic brain injuries (TBI) are considered a silent epidemic. It affects many people, from automobiles to sports to service members. In this study, we employed a musculoskeletal head-neck model to understand the effect of impact locations, characteristics, and neck strength on head and neck injury severity. Three types of impact forces were studied: low-velocity impact (LVI), intermediate-velocity impact (IVI), and high-velocity impact (HVI). We investigated six parameters: linear and rotational accelerations, the Generalized Acceleration Model For Brain Injury Threshold (GAMBIT), neck force, neck moment, and Neck Injury Criteria (NIC). We consider seven impact locations, three neck strengths, and three impact characteristics. We studied a total of 63 cases. It was found that the linear accelerations do not change much with different neck strengths and impact locations. The impact locations have a significant effect on head and neck injury parameters, and anterolateral impact is the most risky impact location for both head and neck. The maximum average rotational acceleration is for anterolateral eccentric impact which is 4.75 times more than the average anterior central impact. The lateral impacts generate about 10% more linear accelerations than anterior and posterior impacts. Neck forces do not vary more than 20% with impact locations and neck strength. The average head and neck injury parameters do not vary more than 10% based on neck strength. Impact characteristics have a significant role in GAMBIT and NIC. The average GAMBIT for IVI and HVI were 1.44 and 1.54 times higher than LVI. In summary, the anterolateral eccentric impact has a higher probability of head and neck injury than the other six impact locations. These findings provide objective evidence that can inform injury prevention strategies as well as aid tissue and cellular level studies.
Enhancing Fourier pricing with machine learning
Authors: Gero Junike, Hauke Stier
Date: 2024-12-06 00:00:00

Fourier pricing methods such as the Carr-Madan formula or the COS method are classic tools for pricing European options for advanced models such as the Heston model. These methods require tuning parameters such as a damping factor, a truncation range, a number of terms, etc. Estimating these tuning parameters is difficult or computationally expensive. Recently, machine learning techniques have been proposed for fast pricing: they are able to learn the functional relationship between the parameters of the Heston model and the option price. However, machine learning techniques suffer from error control and require retraining for different error tolerances. In this research, we propose to learn the tuning parameters of the Fourier methods (instead of the prices) using machine learning techniques. As a result, we obtain very fast algorithms with full error control: Our approach works with any error tolerance without retraining, as demonstrated in numerical experiments using the Heston model.
M6 Investment Challenge: The Role of Luck and Strategic Considerations
Authors: Filip Staněk
Date: 2024-11-21 00:00:00

This article investigates the influence of luck and strategic considerations on performance of teams participating in the M6 investment challenge. We find that there is insufficient evidence to suggest that the extreme Sharpe ratios observed are beyond what one would expect by chance, given the number of teams, and thus not necessarily indicative of the possibility of consistently attaining abnormal returns. Furthermore, we introduce a stylized model of the competition to derive and analyze a portfolio strategy optimized for attaining the top rank. The results demonstrate that the task of achieving the top rank is not necessarily identical to that of attaining the best investment returns in expectation. It is possible to improve one's chances of winning, even without the ability to attain abnormal returns, by choosing portfolio weights adversarially based on the current competition ranking. Empirical analysis of submitted portfolio weights aligns with this finding.
LibEvolutionEval: A Benchmark and Study for Version-Specific Code Generation
Authors: Sachit Kuhar, Wasi Uddin Ahmad, Zijian Wang, Nihal Jain, Haifeng Qian, Baishakhi Ray, Murali Krishna Ramanathan, Xiaofei Ma, Anoop Deoras
Date: 2024-11-19 00:00:00
Tasks: Code Completion, Code Generation

Recent advancements in code completion models have primarily focused on local file contexts. However, these studies do not fully capture the complexity of real-world software development, which often requires the use of rapidly-evolving public libraries. To fill the gap, we introduce LibEvolutionEval, a detailed study requiring an understanding of library evolution to perform in-line code completion accurately. LibEvolutionEval provides a version-specific code-completion task comprised of eight libraries (torch, torchvision, scipy, pil, tqdm, pyyaml, matplotlib, and pandas) as they evolve over the year along with a detailed analysis of the evolution of two popular and well-maintained public libraries: PyTorch and Matplotlib. We evaluate popular public models and find that public library evolution significantly influences model performance. We explored mitigation methods by studying how retrieved version-specific library documentation and prompting can improve the model's capability in handling these fast-evolving packages, paving a promising future path in better handling fast-evolving libraries.
Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Research
Authors: Tianyang Zhong, Zhenyuan Yang, Zhengliang Liu, Ruidong Zhang, Yiheng Liu, Haiyang Sun, Yi Pan, Yiwei Li, Yifan Zhou, Hanqi Jiang, JunHao Chen, Tianming Liu
Date: 2024-11-30 00:00:00
Tasks: Diversity

Low-resource languages serve as invaluable repositories of human history, embodying cultural evolution and intellectual diversity. Despite their significance, these languages face critical challenges, including data scarcity and technological limitations, which hinder their comprehensive study and preservation. Recent advancements in large language models (LLMs) offer transformative opportunities for addressing these challenges, enabling innovative methodologies in linguistic, historical, and cultural research. This study systematically evaluates the applications of LLMs in low-resource language research, encompassing linguistic variation, historical documentation, cultural expressions, and literary analysis. By analyzing technical frameworks, current methodologies, and ethical considerations, this paper identifies key challenges such as data accessibility, model adaptability, and cultural sensitivity. Given the cultural, historical, and linguistic richness inherent in low-resource languages, this work emphasizes interdisciplinary collaboration and the development of customized models as promising avenues for advancing research in this domain. By underscoring the potential of integrating artificial intelligence with the humanities to preserve and study humanity's linguistic and cultural heritage, this study fosters global efforts towards safeguarding intellectual diversity.
A Primer on Large Language Models and their Limitations
Authors: Sandra Johnson, David Hyland-Wood
Date: 2024-12-03 00:00:00

This paper provides a primer on Large Language Models (LLMs) and identifies their strengths, limitations, applications and research directions. It is intended to be useful to those in academia and industry who are interested in gaining an understanding of the key LLM concepts and technologies, and in utilising this knowledge in both day to day tasks and in more complex scenarios where this technology can enhance current practices and processes.
MAG-V: A Multi-Agent Framework for Synthetic Data Generation and Verification
Authors: Saptarshi Sengupta, Harsh Vashistha, Kristal Curtis, Akshay Mallipeddi, Abhinav Mathur, Joseph Ross, Liang Gou
Date: 2024-11-28 00:00:00
Tasks: Synthetic Data Generation

Extending the capabilities of Large Language Models (LLMs) with functions or tools for environment interaction has led to the emergence of the agent paradigm. In industry, training an LLM is not always feasible because of the scarcity of domain data, legal holds on proprietary customer data, rapidly changing business requirements, and the need to prototype new assistants. Agents provide an elegant solution to the above by relying on the zero-shot reasoning abilities of the underlying LLM and utilizing tools to explore and reason over customer data and respond to user requests. However, there are two concerns here: (I) acquiring large scale customer queries for agent testing is time-consuming, and (II) high reliance on the tool call sequence (or trajectory) followed by the agent to respond to user queries may lead to unexpected or incorrect behavior. To address this, we propose MAG-V, a multi-agent framework to first generate a dataset of questions that mimic customer queries; and second, reverse-engineer alternate questions from the responses for trajectory verification. Initial results indicate that our synthetic data can improve agent performance on actual customer queries. Furthermore, our trajectory verification methodology, inspired by distant supervision and using traditional machine learning (ML) models, outperforms a GPT-4o judge baseline by 11% accuracy and matches the performance of a GPT-4 judge on our constructed dataset. Overall, our approach is a step towards unifying diverse task agents into a cohesive framework for achieving an aligned objective.
Pragmatic Metacognitive Prompting Improves LLM Performance on Sarcasm Detection
Authors: Joshua Lee, Wyatt Fong, Alexander Le, Sur Shah, Kevin Han, Kevin Zhu
Date: 2024-12-04 00:00:00
Tasks: Sarcasm Detection, Sentiment Analysis

Sarcasm detection is a significant challenge in sentiment analysis due to the nuanced and context-dependent nature of verbiage. We introduce Pragmatic Metacognitive Prompting (PMP) to improve the performance of Large Language Models (LLMs) in sarcasm detection, which leverages principles from pragmatics and reflection helping LLMs interpret implied meanings, consider contextual cues, and reflect on discrepancies to identify sarcasm. Using state-of-the-art LLMs such as LLaMA-3-8B, GPT-4o, and Claude 3.5 Sonnet, PMP achieves state-of-the-art performance on GPT-4o on MUStARD and SemEval2018. This study demonstrates that integrating pragmatic reasoning and metacognitive strategies into prompting significantly enhances LLMs' ability to detect sarcasm, offering a promising direction for future research in sentiment analysis.
Personalized Federated Learning via Feature Distribution Adaptation
Authors: Connor J. McLaughlin, Lili Su
Date: 2024-11-01 00:00:00
Tasks: Federated Learning, Navigate, Personalized Federated Learning, Representation Learning

Federated learning (FL) is a distributed learning framework that leverages commonalities between distributed client datasets to train a global model. Under heterogeneous clients, however, FL can fail to produce stable training results. Personalized federated learning (PFL) seeks to address this by learning individual models tailored to each client. One approach is to decompose model training into shared representation learning and personalized classifier training. Nonetheless, previous works struggle to navigate the bias-variance trade-off in classifier learning, relying solely on limited local datasets or introducing costly techniques to improve generalization. In this work, we frame representation learning as a generative modeling task, where representations are trained with a classifier based on the global feature distribution. We then propose an algorithm, pFedFDA, that efficiently generates personalized models by adapting global generative classifiers to their local feature distributions. Through extensive computer vision benchmarks, we demonstrate that our method can adjust to complex distribution shifts with significant improvements over current state-of-the-art in data-scarce settings.
Action Recognition based Industrial Safety Violation Detection
Authors: Surya N Reddy, Vaibhav Kurrey, Mayank Nagar, Gagan Raj Gupta
Date: 2024-12-07 00:00:00
Tasks: Action Recognition, Activity Recognition, object-detection, Object Detection

Proper use of personal protective equipment (PPE) can save the lives of industry workers and it is a widely used application of computer vision in the large manufacturing industries. However, most of the applications deployed generate a lot of false alarms (violations) because they tend to generalize the requirements of PPE across the industry and tasks. The key to resolving this issue is to understand the action being performed by the worker and customize the inference for the specific PPE requirements of that action. In this paper, we propose a system that employs activity recognition models to first understand the action being performed and then use object detection techniques to check for violations. This leads to a 23% improvement in the F1-score compared to the PPE-based approach on our test dataset of 109 videos.