pwc-archive/papers-with-abstracts huggingface dataset:
evoBPE: Evolutionary Protein Sequence Tokenization
Authors: Burak Suyunu, Özdeniz Dolu, Arzucan Özgür
Date: 2025-03-11 00:00:00
Tasks: Protein Function Prediction

Recent advancements in computational biology have drawn compelling parallels between protein sequences and linguistic structures, highlighting the need for sophisticated tokenization methods that capture the intricate evolutionary dynamics of protein sequences. Current subword tokenization techniques, primarily developed for natural language processing, often fail to represent protein sequences' complex structural and functional properties adequately. This study introduces evoBPE, a novel tokenization approach that integrates evolutionary mutation patterns into sequence segmentation, addressing critical limitations in existing methods. By leveraging established substitution matrices, evoBPE transcends traditional frequency-based tokenization strategies. The method generates candidate token pairs through biologically informed mutations, evaluating them based on pairwise alignment scores and frequency thresholds. Extensive experiments on human protein sequences show that evoBPE performs better across multiple dimensions. Domain conservation analysis reveals that evoBPE consistently outperforms standard Byte-Pair Encoding, particularly as vocabulary size increases. Furthermore, embedding similarity analysis using ESM-2 suggests that mutation-based token replacements preserve biological sequence properties more effectively than arbitrary substitutions. The research contributes to protein sequence representation by introducing a mutation-aware tokenization method that better captures evolutionary nuances. By bridging computational linguistics and molecular biology, evoBPE opens new possibilities for machine learning applications in protein function prediction, structural modeling, and evolutionary analysis.
Towards species' classification of the \textit{Anastrepha pseudoparallela} group
Authors: Gabriel R. Palma, Rocío Alaiz, Alexandre S. Araújo, Marcoandre Savaris, Roberto A. Zucchi, Charles Markham, Rafael A. Moral
Date: 2025-03-11 00:00:00
Tasks: Transfer Learning

Identifying \textit{Anastrepha} species from the \textit{pseudoparallela} group is problematic due to morphological similarities among species and a broad geographic variation. This group comprises $31$ species of fruit flies and pests affecting passion fruit crops. Identifying these species utilises the morphological characteristics of the specimens' wings and aculeus tips. Considering the importance of identifying these species of this group and challenges due to the low data availability and class imbalance, this paper contributes to automating the classification of the species \textit{Anastrepha chiclayae} Greene, \textit{Anastrepha consobrina} (Loew), \textit{Anastrepha curitibana} Ara\'ujo, Norrbom \& Savaris, \textit{Anastrepha curitis} Stone, and \textit{Anastrepha pseudoparallela} (Loew) using deep Learning methods and designing new features based on wing structures. We explored transfer learning solutions and proposed a new set of features based on each wing's structure. We used the dual annealing algorithm to optimise the hyperparameters of Deep Neural Networks, Random Forests, Decision Trees, and Support Vector Machines combined with autoencoders and SMOTE algorithms to account for class imbalance. We tested our approach with a dataset of $127$ high-quality images belonging to a total of $5$ species and used three-fold cross validation for training, tuning and testing, encompassing six permutations of those to assess the performance of the learning algorithms. Our findings demonstrate that our novel approach, which combines feature extraction and machine learning techniques, can improve the species classification accuracy for rare \textit{Anastrepha pseudoparallela} group specimens, with the SMOTE and Random Forests algorithms leading to the average performance of $0.72$ in terms of the mean of the individual accuracies considering all species.
Visual Attention Graph
Authors: Kai-Fu Yang, Yong-Jie Li
Date: 2025-03-11 00:00:00
Tasks: Age Classification

Visual attention plays a critical role when our visual system executes active visual tasks by interacting with the physical scene. However, how to encode the visual object relationship in the psychological world of our brain deserves to be explored. In the field of computer vision, predicting visual fixations or scanpaths is a usual way to explore the visual attention and behaviors of human observers when viewing a scene. Most existing methods encode visual attention using individual fixations or scanpaths based on the raw gaze shift data collected from human observers. This may not capture the common attention pattern well, because without considering the semantic information of the viewed scene, raw gaze shift data alone contain high inter- and intra-observer variability. To address this issue, we propose a new attention representation, called Attention Graph, to simultaneously code the visual saliency and scanpath in a graph-based representation and better reveal the common attention behavior of human observers. In the attention graph, the semantic-based scanpath is defined by the path on the graph, while saliency of objects can be obtained by computing fixation density on each node. Systemic experiments demonstrate that the proposed attention graph combined with our new evaluation metrics provides a better benchmark for evaluating attention prediction methods. Meanwhile, extra experiments demonstrate the promising potentials of the proposed attention graph in assessing human cognitive states, such as autism spectrum disorder screening and age classification.
IA generativa aplicada a la detección del cáncer a través de Resonancia Magnética
Authors: Virginia del Campo, Iker Malaina
Date: 2025-03-11 00:00:00

Cognitive delegation to artificial intelligence (AI) systems is transforming scientific research by enabling the automation of analytical processes and the discovery of new patterns in large datasets. This study examines the ability of AI to complement and expand knowledge in the analysis of breast cancer using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Building on a previous study, we assess the extent to which AI can generate novel approaches and successfully solve them. For this purpose, AI models, specifically ChatGPT-4o, were used for data preprocessing, hypothesis generation, and the application of clustering techniques, predictive modeling, and correlation network analysis. The results obtained were compared with manually computed outcomes, revealing limitations in process transparency and the accuracy of certain calculations. However, as AI reduces errors and improves reasoning capabilities, an important question arises regarding the future of scientific research: could automation replace the human role in science? This study seeks to open the debate on the methodological and ethical implications of a science dominated by artificial intelligence.
In silico clinical trials in drug development: a systematic review
Authors: Bohua Chen, Lucia Chantal Schneider, Christian Roever, Emmanuelle Comets, Markus Christian Elze, Andrew Hooker, Joanna IntHout, Anne-Sophie Jannot, Daria Julkowska, Yanis Mimouni, Marina Savelieva, Nigel Stallard, Moreno Ursino, Marc Vandemeulebroecke, Sebastian Weber, Martin Posch, Sarah Zohar, Tim Friede
Date: 2025-03-11 00:00:00
Tasks: Articles

In the context of clinical research, computational models have received increasing attention over the past decades. In this systematic review, we aimed to provide an overview of the role of so-called in silico clinical trials (ISCTs) in medical applications. Exemplary for the broad field of clinical medicine, we focused on in silico (IS) methods applied in drug development, sometimes also referred to as model informed drug development (MIDD). We searched PubMed and ClinicalTrials.gov for published articles and registered clinical trials related to ISCTs. We identified 202 articles and 48 trials, and of these, 76 articles and 19 trials were directly linked to drug development. We extracted information from all 202 articles and 48 clinical trials and conducted a more detailed review of the methods used in the 76 articles that are connected to drug development. Regarding application, most articles and trials focused on cancer and imaging related research while rare and pediatric diseases were only addressed in 18 and 4 studies, respectively. While some models were informed combining mechanistic knowledge with clinical or preclinical (in-vivo or in-vitro) data, the majority of models were fully data-driven, illustrating that clinical data is a crucial part in the process of generating synthetic data in ISCTs. Regarding reproducibility, a more detailed analysis revealed that only 24% (18 out of 76) of the articles provided an open-source implementation of the applied models, and in only 20% of the articles the generated synthetic data were publicly available. Despite the widely raised interest, we also found that it is still uncommon for ISCTs to be part of a registered clinical trial and their application is restricted to specific diseases leaving potential benefits of ISCTs not fully exploited.
Quantifying Circadian Desynchrony in ICU Patients and Its Association with Delirium
Authors: Yuanfang Ren, Andrea E. Davidson, Jiaqing Zhang, Miguel Contreras, Ayush K. Patel, Michelle Gumz, Tezcan Ozrazgat-Baslanti, Parisa Rashidi, Azra Bihorac
Date: 2025-03-11 00:00:00

Background: Circadian desynchrony characterized by the misalignment between an individual's internal biological rhythms and external environmental cues, significantly affects various physiological processes and health outcomes. Quantifying circadian desynchrony often requires prolonged and frequent monitoring, and currently, an easy tool for this purpose is missing. Additionally, its association with the incidence of delirium has not been clearly explored. Methods: A prospective observational study was carried out in intensive care units (ICU) of a tertiary hospital. Circadian transcriptomics of blood monocytes from 86 individuals were collected on two consecutive days, although a second sample could not be obtained from all participants. Using two public datasets comprised of healthy volunteers, we replicated a model for determining internal circadian time. We developed an approach to quantify circadian desynchrony by comparing internal circadian time and external blood collection time. We applied the model and quantified circadian desynchrony index among ICU patients, and investigated its association with the incidence of delirium. Results: The replicated model for determining internal circadian time achieved comparable high accuracy. The quantified circadian desynchrony index was significantly higher among critically ill ICU patients compared to healthy subjects, with values of 10.03 hours vs 2.50-2.95 hours (p < 0.001). Most ICU patients had a circadian desynchrony index greater than 9 hours. Additionally, the index was lower in patients whose blood samples were drawn after 3pm, with values of 5.00 hours compared to 10.01-10.90 hours in other groups (p < 0.001)...
Antibiotic Resistance Microbiology Dataset (ARMD): A De-identified Resource for Studying Antimicrobial Resistance Using Electronic Health Records
Authors: Fateme Nateghi Haredasht, Fatemeh Amrollahi, Manoj Maddali, Nicholas Marshall, Stephen P. Ma, Lauren N. Cooper, Richard J. Medford, Sanjat Kanjilal, Niaz Banaei, Stanley Deresinski, Mary K. Goldstein, Steven M. Asch, Amy Chang, Jonathan H. Chen
Date: 2025-03-08 00:00:00
Tasks: Causal Inference, Decision Making, De-identification

The Antibiotic Resistance Microbiology Dataset (ARMD) is a de-identified resource derived from electronic health records (EHR) that facilitates research into antimicrobial resistance (AMR). ARMD encompasses data from adult patients, focusing on microbiological cultures, antibiotic susceptibilities, and associated clinical and demographic features. Key attributes include organism identification, susceptibility patterns for 55 antibiotics, implied susceptibility rules, and de-identified patient information. This dataset supports studies on antimicrobial stewardship, causal inference, and clinical decision-making. ARMD is designed to be reusable and interoperable, promoting collaboration and innovation in combating AMR. This paper describes the dataset's acquisition, structure, and utility while detailing its de-identification process.
Multimodal Stock Price Prediction: A Case Study of the Russian Securities Market
Authors: Kasymkhan Khubiev, Mikhail Semenov
Date: 2025-03-05 00:00:00
Tasks: Articles, Large Language Model, Stock Price Prediction, Time Series

Classical asset price forecasting methods primarily rely on numerical data, such as price time series, trading volumes, limit order book data, and technical analysis indicators. However, the news flow plays a significant role in price formation, making the development of multimodal approaches that combine textual and numerical data for improved prediction accuracy highly relevant. This paper addresses the problem of forecasting financial asset prices using the multimodal approach that combines candlestick time series and textual news flow data. A unique dataset was collected for the study, which includes time series for 176 Russian stocks traded on the Moscow Exchange and 79,555 financial news articles in Russian. For processing textual data, pre-trained models RuBERT and Vikhr-Qwen2.5-0.5b-Instruct (a large language model) were used, while time series and vectorized text data were processed using an LSTM recurrent neural network. The experiments compared models based on a single modality (time series only) and two modalities, as well as various methods for aggregating text vector representations. Prediction quality was estimated using two key metrics: Accuracy (direction of price movement prediction: up or down) and Mean Absolute Percentage Error (MAPE), which measures the deviation of the predicted price from the true price. The experiments showed that incorporating textual modality reduced the MAPE value by 55%. The resulting multimodal dataset holds value for the further adaptation of language models in the financial sector. Future research directions include optimizing textual modality parameters, such as the time window, sentiment, and chronological order of news messages.
Randomization in Optimal Execution Games
Authors: Steven Campbell, Marcel Nutz
Date: 2025-03-11 00:00:00

We study optimal execution in markets with transient price impact in a competitive setting with $N$ traders. Motivated by prior negative results on the existence of pure Nash equilibria, we consider randomized strategies for the traders and whether allowing such strategies can restore the existence of equilibria. We show that given a randomized strategy, there is a non-randomized strategy with strictly lower expected execution cost, and moreover this de-randomization can be achieved by a simple averaging procedure. As a consequence, Nash equilibria cannot contain randomized strategies, and non-existence of pure equilibria implies non-existence of randomized equilibria. Separately, we also establish uniqueness of equilibria. Both results hold in a general transaction cost model given by a strictly positive definite impact decay kernel and a convex trading cost.
Modeling Stock Return Distributions and Pricing Options
Authors: Xinxin Jiang
Date: 2025-03-11 00:00:00

This paper provides evidence that stock returns, after truncation, might be modeled by a special type of continuous mixtures or normals, so-called $q$-Gaussians. Negative binomial distributions might model the counts for extreme returns. A generalized jump-diffusion model is proposed, and an explicit option pricing formula is obtained.
Existence of Optimal Contracts for Principal-Agent Problem with Drift Control and Quadratic Effort Cost
Authors: Xinfu Chen, Shuaijie Qian, Guan Qiao
Date: 2025-03-11 00:00:00

The existence of optimal contracts of the principal-agent problem is a long-standing problem. According to the general framework in Cvitani\'c et al. [2], this existence can be derived from the existence of a classical solution to a degenerated fully nonlinear parabolic partial differential equation problem. In this work we consider the simple case with drift control and quadratic cost function, then prove the existence of classical solution to that PDE.
Long-range dependent mortality modeling with cointegration
Authors: Mei Choi Chiu, Ling Wang, Hoi Ying Wong
Date: 2025-03-12 00:00:00

Empirical studies with publicly available life tables identify long-range dependence (LRD) in national mortality data. Although the longevity market is supposed to benchmark against the national force of mortality, insurers are more concerned about the forces of mortality associated with their own portfolios than the national ones. Recent advances on mortality modeling make use of fractional Brownian motion (fBm) to capture LRD. A theoretically flexible approach even considers mixed fBm (mfBm). Using Volterra processes, we prove that the direct use of mfBm encounters the identification problem so that insurers hardly detect the LRD effect from their portfolios. Cointegration techniques can effectively bring the LRD information within the national force of mortality to the mortality models for insurers' experienced portfolios. Under the open-loop equilibrium control framework, the explicit and unique equilibrium longevity hedging strategy is derived for cointegrated forces of mortality with LRD. Using the derived hedging strategy, our numerical examples show that the accuracy of estimating cointegration is crucial for hedging against the longevity exposure of insurers with LRD national force of mortality.
Liquidity-adjusted Return and Volatility, and Autoregressive Models
Authors: Qi Deng, Zhong-guo Zhou
Date: 2025-03-02 00:00:00
Tasks: Sensitivity

We construct liquidity-adjusted return and volatility using purposely designed liquidity metrics (liquidity jump and liquidity diffusion) that incorporate additional liquidity information. Based on these measures, we introduce a liquidity-adjusted ARMA-GARCH framework to address the limitations of traditional ARMA-GARCH models, which are not effectively in modeling illiquid assets with high liquidity variability, such as cryptocurrencies. We demonstrate that the liquidity-adjusted model improves model fit for cryptocurrencies, with greater volatility sensitivity to past shocks and reduced volatility persistence of erratic past volatility. Our model is validated by the empirical evidence that the liquidity-adjusted mean-variance (LAMV) portfolios outperform the traditional mean-variance (TMV) portfolios.
Matrix H-theory approach to stock market fluctuations
Authors: Luan M. T. de Moraes, Antônio M. S. Macedo, Raydonal Ospina, Giovani L. Vasconcelos
Date: 2025-03-06 00:00:00

We introduce matrix H theory, a framework for analyzing collective behavior arising from multivariate stochastic processes with hierarchical structure. The theory models the joint distribution of the multiple variables (the measured signal) as a compound of a large-scale multivariate distribution with the distribution of a slowly fluctuating background. The background is characterized by a hierarchical stochastic evolution of internal degrees of freedom, representing the correlations between stocks at different time scales. As in its univariate version, the matrix H-theory formalism also has two universality classes: Wishart and inverse Wishart, enabling a concise description of both the background and the signal probability distributions in terms of Meijer G-functions with matrix argument. Empirical analysis of daily returns of stocks within the S&P500 demonstrates the effectiveness of matrix H theory in describing fluctuations in stock markets. These findings contribute to a deeper understanding of multivariate hierarchical processes and offer potential for developing more informed portfolio strategies in financial markets.
Detecting Crypto Pump-and-Dump Schemes: A Thresholding-Based Approach to Handling Market Noise
Authors: Mahya Karbalaii
Date: 2025-02-27 00:00:00
Tasks: Anomaly Detection

We propose a simple yet robust unsupervised model to detect pump-and-dump events on tokens listed on the Poloniex Exchange platform. By combining threshold-based criteria with exponentially weighted moving averages (EWMA) and volatility measures, our approach effectively distinguishes genuine anomalies from minor trading fluctuations, even for tokens with low liquidity and prolonged inactivity. These characteristics present a unique challenge, as standard anomaly-detection methods often over-flag negligible volume spikes. Our framework overcomes this issue by tailoring both price and volume thresholds to the specific trading patterns observed, resulting in a model that balances high true-positive detection with minimal noise.
Liquidity Competition Between Brokers and an Informed Trader
Authors: Ryan Donnelly, Zi Li
Date: 2025-03-11 00:00:00

We study a multi-agent setting in which brokers transact with an informed trader. Through a sequential Stackelberg-type game, brokers manage trading costs and adverse selection with an informed trader. In particular, supplying liquidity to the informed traders allows the brokers to speculate based on the flow information. They simultaneously attempt to minimize inventory risk and trading costs with the lit market based on the informed order flow, also known as the internalization-externalization strategy. We solve in closed form for the trading strategy that the informed trader uses with each broker and propose a system of equations which classify the equilibrium strategies of the brokers. By solving these equations numerically we may study the resulting strategies in equilibrium. Finally, we formulate a competitive game between brokers in order to determine the liquidity prices subject to precommitment supplied to the informed trader and provide a numerical example in which the resulting equilibrium is not Pareto efficient.
A Deep Bayesian Nonparametric Framework for Robust Mutual Information Estimation
Authors: Forough Fazeliasl, Michael Minyi Zhang, Bei Jiang, Linglong Kong
Date: 2025-03-11 00:00:00
Tasks: Image Generation, Mutual Information Estimation

Mutual Information (MI) is a crucial measure for capturing dependencies between variables, but exact computation is challenging in high dimensions with intractable likelihoods, impacting accuracy and robustness. One idea is to use an auxiliary neural network to train an MI estimator; however, methods based on the empirical distribution function (EDF) can introduce sharp fluctuations in the MI loss due to poor out-of-sample performance, destabilizing convergence. We present a Bayesian nonparametric (BNP) solution for training an MI estimator by constructing the MI loss with a finite representation of the Dirichlet process posterior to incorporate regularization in the training process. With this regularization, the MI loss integrates both prior knowledge and empirical data to reduce the loss sensitivity to fluctuations and outliers in the sample data, especially in small sample settings like mini-batches. This approach addresses the challenge of balancing accuracy and low variance by effectively reducing variance, leading to stabilized and robust MI loss gradients during training and enhancing the convergence of the MI approximation while offering stronger theoretical guarantees for convergence. We explore the application of our estimator in maximizing MI between the data space and the latent space of a variational autoencoder. Experimental results demonstrate significant improvements in convergence over EDF-based methods, with applications across synthetic and real datasets, notably in 3D CT image generation, yielding enhanced structure discovery and reduced overfitting in data synthesis. While this paper focuses on generative models in application, the proposed estimator is not restricted to this setting and can be applied more broadly in various BNP learning procedures.