pwc-archive/papers-with-abstracts huggingface dataset:
A reaction network model of microscale liquid-liquid phase separation reveals effects of spatial dimension
Authors: Jinyoung Kim, Sean D. Lawley, Jinsu Kim
Date: 2024-08-27 00:00:00

Proteins can form droplets via liquid-liquid phase separation (LLPS) in cells. Recent experiments demonstrate that LLPS is qualitatively different on two-dimensional (2d) surfaces compared to three-dimensional (3d) solutions. In this paper, we use mathematical modeling to investigate the causes of the discrepancies between LLPS in 2d versus 3d. We model the number of proteins and droplets inducing LLPS by continuous-time Markov chains and use chemical reaction network theory to analyze the model. To reflect the influence of space dimension, droplet formation and dissociation rates are determined using the first hitting times of diffusing proteins. We first show that our stochastic model reproduces the appropriate phase diagram and is consistent with the relevant thermodynamic constraints. After further analyzing the model, we find that it predicts that the space dimension induces qualitatively different features of LLPS which are consistent with recent experiments. While it has been claimed that the differences between 2d and 3d LLPS stems mainly from different diffusion coefficients, our analysis is independent of the diffusion coefficients of the proteins since we use the stationary model behavior. Therefore, our results give new hypotheses about how space dimension affects LLPS.
A novel method to separate circadian from non-circadian masking effects in order to enhance daily circadian timing and amplitude estimation from core body temperature
Authors: Phuc D Nguyen, Claire Dunbar, Hannah Scott, Bastien Lechat, Jack Manners, Gorica Micic, Nicole Lovato, Amy C Reynolds, Leon Lack, Robert Adams, Danny Eckert, Andrew Vakulin, Peter G Catcheside
Date: 2024-08-27 00:00:00
Tasks: Additive models

Circadian disruption contributes to adverse effects on sleep, performance, and health. One accepted method to track continuous daily changes in circadian timing is to measure core body temperature (CBT), and establish daily, circadian-related CBT minimum time (Tmin). This method typically applies cosine-model fits to measured CBT data, which may not adequately account for substantial wake metabolic activity and sleep effects on CBT that confound and mask circadian effects, and thus estimates of the circadian-related Tmin. This study introduced a novel physiology-grounded analytic approach to separate circadian from non-circadian effects on CBT, which we compared against traditional cosine-based methods. The dataset comprised 33 healthy participants attending a 39-hour in-laboratory study with an initial overnight sleep followed by an extended wake period. CBT data were collected at 30-second intervals via ingestible capsules. Our design captured CBT during both the baseline sleep period and during extended wake period (without sleep) and allowed us to model the influence of circadian and non-circadian effects of sleep, wake, and activity on CBT using physiology-guided generalized additive models. Model fits and estimated Tmin inferred from extended wake without sleep were compared with traditional cosine-based models fits. Compared to the traditional cosine model, the new model exhibited superior fits to CBT (Pearson R 0.90 [95%CI; [0.83 - 0.96] versus 0.81 [0.55-0.93]). The difference between estimated vs measured circadian Tmin, derived from the day without sleep, was better fit with our method (0.2 [-0.5,0.3] hours) versus previous methods (1.4 [1.1 to 1.7] hours). This new method provides superior demasking of non-circadian influences compared to traditional cosine methods, including the removal of a sleep-related bias towards an earlier estimate of circadian Tmin.
Consistent diffusion matrix estimation from population time series
Authors: Aden Forrow
Date: 2024-08-26 00:00:00
Tasks: Time Series

Progress on modern scientific questions regularly depends on using large-scale datasets to understand complex dynamical systems. An especially challenging case that has grown to prominence with advances in single-cell sequencing technologies is learning the behavior of individuals from population snapshots. In the absence of individual-level time series, standard stochastic differential equation models are often nonidentifiable because intrinsic diffusion cannot be distinguished from measurement noise. Despite the difficulty, accurately recovering diffusion terms is required to answer even basic questions about the system's behavior. We show how to combine population-level time series with velocity measurements to build a provably consistent estimator of the diffusion matrix.
Novel Methods for Analyzing Cellular Interactions in Deep Learning-Based Image Cytometry: Spatial Interaction Potential and Co-Localization Index
Authors: Toru Nagasaka, Kimihiro Yamashita, Mitsugu Fujita
Date: 2024-08-14 00:00:00
Tasks: Deep Learning, Diversity

The study presents a novel approach for quantifying cellular interactions in digital pathology using deep learning-based image cytometry. Traditional methods struggle with the diversity and heterogeneity of cells within tissues. To address this, we introduce the Spatial Interaction Potential (SIP) and the Co-Localization Index (CLI), leveraging deep learning classification probabilities. SIP assesses the potential for cell-to-cell interactions, similar to an electric field, while CLI incorporates distances between cells, accounting for dynamic cell movements. Our approach enhances traditional methods, providing a more sophisticated analysis of cellular interactions. We validate SIP and CLI through simulations and apply them to colorectal cancer specimens, demonstrating strong correlations with actual biological data. This innovative method offers significant improvements in understanding cellular interactions and has potential applications in various fields of digital pathology.
Trading with Time Series Causal Discovery: An Empirical Study
Authors: Ruijie Tang
Date: 2024-08-28 00:00:00
Tasks: Causal Discovery, Time Series

This study investigates the application of causal discovery algorithms in equity markets, with a focus on their potential to build investment strategies. An investment strategy was developed based on the causal structures identified by these algorithms. The performance of the strategy is evaluated based on the profitability and effectiveness in stock markets. The results indicate that causal discovery algorithms can successfully uncover actionable causal relationships in large markets, leading to profitable investment outcomes. However, the research also identifies a critical challenge: the computational complexity and scalability of these algorithms when dealing with large datasets. This challenge presents practical limitations for their application in real-world market analysis.
Impact of Climate transition on Credit portfolio's loss with stochastic collateral
Authors: Lionel Sopgoui
Date: 2024-08-12 00:00:00

The aim of this work is to propose an end-by-end modeling framework to evaluate the risk measures of a bank's portfolio of collateralized loans in an economy subject to the climate transition. The economy, organized in sectors, is driven by a multidimensional Ornstein-Uhlenbeck (OU) productivity process while the climate transition is declined thanks to continuous deterministic carbon price and intensities processes. We thus derive the dynamics of macroeconomic variables for each scenario. By considering that a firm defaults if it is over-indebted, we define each loan's loss at default as the difference between Exposure at Default (EAD) and the liquidated collateral, which will help us to define the Loss Given Default (LGD). We consider two types of collateral. First, if it is a financial asset (invoices, cash, or investments), we model the later by the continuous time version of the discounted cash flows methodology, where the cash flows growth is driven by the instantaneous output growth, the instantaneous growth of a carbon price function, and an arithmetic Brownian motion. Secondly, for physical asset (real estate, business equipment, or inventory), we focus on the example of a property in housing market. As in (Sopgoui 2024), a building price is the difference between the price of an equivalent efficient building following an exponential OU as well as the actualized renovation costs and the actualized future energy costs due to the inefficiency of the building, optimally determined by the carbon price process. Finally, we obtain expressions for risk measures of a portfolio of collateralized loans as a function of key climate transition parameters, such as carbon pricing and building energy efficiency. Banks will use these risk measures , depending on the climate transition scenarios, to define operating expenses, client fees, economic, and regulatory capital.
A novel k-generation propagation model for cyber risk and its application to cyber insurance
Authors: Na Ren, Xin Zhang
Date: 2024-08-26 00:00:00

The frequent occurrence of cyber risks and their serious economic consequences have created a growth market for cyber insurance. The calculation of aggregate losses, an essential step in insurance pricing, has attracted considerable attention in recent years. This research develops a path-based k-generation risk contagion model in a tree-shaped network structure that incorporates the impact of the origin contagion location and the heterogeneity of security levels on contagion probability and local loss, distinguishing it from most existing models. Furthermore, we discuss the properties of k-generation risk contagion among multi-paths using the concept of d-separation in Bayesian network (BN), and derive explicit expressions for the mean and variance of local loss on a single path. By combining these results, we compute the mean and variance values for aggregate loss across the entire network until time $t$, which is crucial for accurate cyber insurance pricing. Finally, through numerical calculations and relevant probability properties, we have obtained several findings that are valuable to risk managers and insurers.
Artificial Neural Network and Deep Learning: Fundamentals and Theory
Authors: M. M. Hammad
Date: 2024-08-12 00:00:00
Tasks: Bayesian Optimization, Deep Learning, Descriptive, Gaussian Processes, Miscellaneous

"Artificial Neural Network and Deep Learning: Fundamentals and Theory" offers a comprehensive exploration of the foundational principles and advanced methodologies in neural networks and deep learning. This book begins with essential concepts in descriptive statistics and probability theory, laying a solid groundwork for understanding data and probability distributions. As the reader progresses, they are introduced to matrix calculus and gradient optimization, crucial for training and fine-tuning neural networks. The book delves into multilayer feed-forward neural networks, explaining their architecture, training processes, and the backpropagation algorithm. Key challenges in neural network optimization, such as activation function saturation, vanishing and exploding gradients, and weight initialization, are thoroughly discussed. The text covers various learning rate schedules and adaptive algorithms, providing strategies to optimize the training process. Techniques for generalization and hyperparameter tuning, including Bayesian optimization and Gaussian processes, are also presented to enhance model performance and prevent overfitting. Advanced activation functions are explored in detail, categorized into sigmoid-based, ReLU-based, ELU-based, miscellaneous, non-standard, and combined types. Each activation function is examined for its properties and applications, offering readers a deep understanding of their impact on neural network behavior. The final chapter introduces complex-valued neural networks, discussing complex numbers, functions, and visualizations, as well as complex calculus and backpropagation algorithms. This book equips readers with the knowledge and skills necessary to design, and optimize advanced neural network models, contributing to the ongoing advancements in artificial intelligence.
Using large language models to estimate features of multi-word expressions: Concreteness, valence, arousal
Authors: Gonzalo Martínez, Juan Diego Molero, Sandra González, Javier Conde, Marc Brysbaert, Pedro Reviriego
Date: 2024-08-16 00:00:00

This study investigates the potential of large language models (LLMs) to provide accurate estimates of concreteness, valence and arousal for multi-word expressions. Unlike previous artificial intelligence (AI) methods, LLMs can capture the nuanced meanings of multi-word expressions. We systematically evaluated ChatGPT-4o's ability to predict concreteness, valence and arousal. In Study 1, ChatGPT-4o showed strong correlations with human concreteness ratings (r = .8) for multi-word expressions. In Study 2, these findings were repeated for valence and arousal ratings of individual words, matching or outperforming previous AI models. Study 3 extended the prevalence and arousal analysis to multi-word expressions and showed promising results despite the lack of large-scale human benchmarks. These findings highlight the potential of LLMs for generating valuable psycholinguistic data related to multiword expressions. To help researchers with stimulus selection, we provide datasets with AI norms of concreteness, valence and arousal for 126,397 English single words and 63,680 multi-word expressions
Ranking evaluation metrics from a group-theoretic perspective
Authors: Chiara Balestra, Andreas Mayr, Emmanuel Müller
Date: 2024-08-14 00:00:00
Tasks: Recommendation Systems

Confronted with the challenge of identifying the most suitable metric to validate the merits of newly proposed models, the decision-making process is anything but straightforward. Given that comparing rankings introduces its own set of formidable challenges and the likely absence of a universal metric applicable to all scenarios, the scenario does not get any better. Furthermore, metrics designed for specific contexts, such as for Recommender Systems, sometimes extend to other domains without a comprehensive grasp of their underlying mechanisms, resulting in unforeseen outcomes and potential misuses. Complicating matters further, distinct metrics may emphasize different aspects of rankings, frequently leading to seemingly contradictory comparisons of model results and hindering the trustworthiness of evaluations. We unveil these aspects in the domain of ranking evaluation metrics. Firstly, we show instances resulting in inconsistent evaluations, sources of potential mistrust in commonly used metrics; by quantifying the frequency of such disagreements, we prove that these are common in rankings. Afterward, we conceptualize rankings using the mathematical formalism of symmetric groups detaching from possible domains where the metrics have been created; through this approach, we can rigorously and formally establish essential mathematical properties for ranking evaluation metrics, essential for a deeper comprehension of the source of inconsistent evaluations. We conclude with a discussion, connecting our theoretical analysis to the practical applications, highlighting which properties are important in each domain where rankings are commonly evaluated. In conclusion, our analysis sheds light on ranking evaluation metrics, highlighting that inconsistent evaluations should not be seen as a source of mistrust but as the need to carefully choose how to evaluate our models in the future.
A Survey on Symbolic Knowledge Distillation of Large Language Models
Authors: Kamal Acharya, Alvaro Velasquez, Houbing Herbert Song
Date: 2024-07-12 00:00:00
Tasks: Knowledge Distillation, Survey

This survey paper delves into the emerging and critical area of symbolic knowledge distillation in Large Language Models (LLMs). As LLMs like Generative Pre-trained Transformer-3 (GPT-3) and Bidirectional Encoder Representations from Transformers (BERT) continue to expand in scale and complexity, the challenge of effectively harnessing their extensive knowledge becomes paramount. This survey concentrates on the process of distilling the intricate, often implicit knowledge contained within these models into a more symbolic, explicit form. This transformation is crucial for enhancing the interpretability, efficiency, and applicability of LLMs. We categorize the existing research based on methodologies and applications, focusing on how symbolic knowledge distillation can be used to improve the transparency and functionality of smaller, more efficient Artificial Intelligence (AI) models. The survey discusses the core challenges, including maintaining the depth of knowledge in a comprehensible format, and explores the various approaches and techniques that have been developed in this field. We identify gaps in current research and potential opportunities for future advancements. This survey aims to provide a comprehensive overview of symbolic knowledge distillation in LLMs, spotlighting its significance in the progression towards more accessible and efficient AI systems.
Evolvable Psychology Informed Neural Network for Memory Behavior Modeling
Authors: Xiaoxuan Shen, Zhihai Hu, Qirong Chen, Shengyingjie Liu, Ruxia Liang, Jianwen Sun
Date: 2024-08-23 00:00:00
Tasks: regression

Memory behavior modeling is a core issue in cognitive psychology and education. Classical psychological theories typically use memory equations to describe memory behavior, which exhibits insufficient accuracy and controversy, while data-driven memory modeling methods often require large amounts of training data and lack interpretability. Knowledge-informed neural network models have shown excellent performance in fields like physics, but there have been few attempts in the domain of behavior modeling. This paper proposed a psychology theory informed neural networks for memory behavior modeling named PsyINN, where it constructs a framework that combines neural network with differentiating sparse regression, achieving joint optimization. Specifically, to address the controversies and ambiguity of descriptors in memory equations, a descriptor evolution method based on differentiating operators is proposed to achieve precise characterization of descriptors and the evolution of memory theoretical equations. Additionally, a buffering mechanism for the sparse regression and a multi-module alternating iterative optimization method are proposed, effectively mitigating gradient instability and local optima issues. On four large-scale real-world memory behavior datasets, the proposed method surpasses the state-of-the-art methods in prediction accuracy. Ablation study demonstrates the effectiveness of the proposed refinements, and application experiments showcase its potential in inspiring psychological research.
Recent advances in Meta-model of Optimal Prognosis
Authors: Thomas Most, Johannes Will
Date: 2024-08-23 00:00:00
Tasks: model, Prognosis

In real case applications within the virtual prototyping process, it is not always possible to reduce the complexity of the physical models and to obtain numerical models which can be solved quickly. Usually, every single numerical simulation takes hours or even days. Although the progresses in numerical methods and high performance computing, in such cases, it is not possible to explore various model configurations, hence efficient surrogate models are required. Generally the available meta-model techniques show several advantages and disadvantages depending on the investigated problem. In this paper we present an automatic approach for the selection of the optimal suitable meta-model for the actual problem. Together with an automatic reduction of the variable space using advanced filter techniques an efficient approximation is enabled also for high dimensional problems.
FA-Yolov9: Improved Yolov9 Based on Feature Attention Module
Authors: Tho-Quang Nguyen, Huu-Loc Tran, Tuan-Khoa Tran, Huu-Phong Phan-Nguyen, Tien-Huy Nguyen
Date: 2024-07-07 00:00:00
Proceeding: International Conference on Multimedia Analysis and Pattern Recognition (MAPR) 2024 7

In educational environments, controlling students’ behaviours and gestures is a significant challenge. With the advancements in deep learning technology, utilizing videos and images for detecting behaviours, events, and objects has gained considerable attention. This research introduces a novel method to enhance the detection capability of the YOLOv9 model by integrating the Feature Extraction (FA) Module for detecting student behaviours in educational settings. With the incorporation of FA, an attention mechanism, the model can fine-tune feature representations and prioritize crucial multi-spatial and channel features. Our results highlight the effectiveness of employing attention mechanisms within deep learning frameworks for recognizing subtle behaviours. This research not only contributes to the advancement of computer vision techniques but also holds promise for practical applications in educational settings, facilitating efficient and comprehensive monitoring of student engagement and behaviour. Through detailed experiments on benchmark datasets, we demonstrate the effectiveness of our fine-tuned FA-YOLOv9 model in accurately detecting diverse student behaviours. Specifically, we achieved SOTA results of 77.8% mAP50 compared to other YOLO models.
Air Quality Prediction - A Study Using Neural Network Based Approach
Authors: Raunaq Singh Suri, Ajay Kumar Jain, Nishant Raj Kapoor, Aman Kumar, Harish Chandra Arora, Krishna Kumar, Hashem Jahangir
Date: 2022-12-26 00:00:00
Proceeding: Journal of Soft Computing in Civil Engineering 2022 12
Tasks: GPR

India is the 7th largest country by area and 2nd most populated country in the world. The reports prepared by IQAir revels
that India is 3rd most polluted country after Bangladesh and Pakistan, on the basis of fine particulates (PM2.5)
concentration for the year 2020. In this article, the quality of air in six Indian cities is predicted using data-driven
Artificial Neural Network. The data was taken from the 'Kaggle' online source. For six Indian cities, 6139 data sets
for ten contaminants (PM2.5, PM10, NO, NO2, NH3, CO, SO2, O3, C6H6 and C7H8) were chosen. The datasets were collected
throughout the last five years, from 2016 to 2020, and were used to develop the predictive model. Two machine learning
model are proposing in this study namely Artificial Intelligence (AI) and Gaussian Process Regression (GPR)
The R-value of ANN and GPR models are 0.9611 and 0.9843 sequentially. The other performance indices such as
RMSE, MAPE, MAE of the GPR model are 21.4079, 7.8945% and 13.5884, respectively. The developed model is
quite useful to update citizens about the predicted air quality of the urban spaces and protect them from getting affected by
the poor ambient air quality. It can also be used to find the proper abatement strategies as well as operational measures.