pwc-archive/papers-with-abstracts huggingface dataset:
Ranking and Selection with Simultaneous Input Data Collection
Authors: Yuhao Wang, Enlu Zhou
Date: 2025-03-14 00:00:00

In this paper, we propose a general and novel formulation of ranking and selection with the existence of streaming input data. The collection of multiple streams of such data may consume different types of resources, and hence can be conducted simultaneously. To utilize the streaming input data, we aggregate simulation outputs generated under heterogeneous input distributions over time to form a performance estimator. By characterizing the asymptotic behavior of the performance estimators, we formulate two optimization problems to optimally allocate budgets for collecting input data and running simulations. We then develop a multi-stage simultaneous budget allocation procedure and provide its statistical guarantees such as consistency and asymptotic normality. We conduct several numerical studies to demonstrate the competitive performance of the proposed procedure.
On weak notions of no-arbitrage in a 1D general diffusion market with interest rates
Authors: Alexis Anagnostakis, David Criens, Mikhail Urusov
Date: 2025-03-18 00:00:00

We establish deterministic necessary and sufficient conditions for the no-arbitrage notions "no increasing profit" (NIP), "no strong arbitrage" (NSA) and "no unbounded profit with bounded risk" (NUPBR) in one-dimensional general diffusion markets. These are markets with one risky asset, which is modeled as a regular continuous strong Markov process that is also a semimartingale, and a riskless asset that grows exponentially at a constant rate $r\in \mathbb{R}$. All deterministic criteria are provided in terms of the scale function and the speed measure of the risky asset process. Our study reveals a variety of surprising effects. For instance, irrespective of the interest rate, NIP is not excluded by reflecting boundaries or an irregular scale function. In the case of non-zero interest rates, it is even possible that NUPBR holds in the presence of reflecting boundaries and/or skew thresholds. In the zero interest rate regime, we also identify NSA as the minimal no arbitrage notion that excludes reflecting boundaries and that forces the scale function to be continuously differentiable with strictly positive absolutely continuous derivative, meaning that it is of the same form as for a stochastic differential equation.
Beyond Propagation of Chaos: A Stochastic Algorithm for Mean Field Optimization
Authors: Chandan Tankala, Dheeraj M. Nagaraj, Anant Raj
Date: 2025-03-17 00:00:00

Gradient flow in the 2-Wasserstein space is widely used to optimize functionals over probability distributions and is typically implemented using an interacting particle system with $n$ particles. Analyzing these algorithms requires showing (a) that the finite-particle system converges and/or (b) that the resultant empirical distribution of the particles closely approximates the optimal distribution (i.e., propagation of chaos). However, establishing efficient sufficient conditions can be challenging, as the finite particle system may produce heavily dependent random variables. In this work, we study the virtual particle stochastic approximation, originally introduced for Stein Variational Gradient Descent. This method can be viewed as a form of stochastic gradient descent in the Wasserstein space and can be implemented efficiently. In popular settings, we demonstrate that our algorithm's output converges to the optimal distribution under conditions similar to those for the infinite particle limit, and it produces i.i.d. samples without the need to explicitly establish propagation of chaos bounds.
RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning Tasks
Authors: Eduard Tulchinskii, Daria Voronkova, Ilya Trofimov, Evgeny Burnaev, Serguei Barannikov
Date: 2025-03-14 00:00:00
Tasks: Dimensionality Reduction

Topological methods for comparing weighted graphs are valuable in various learning tasks but often suffer from computational inefficiency on large datasets. We introduce RTD-Lite, a scalable algorithm that efficiently compares topological features, specifically connectivity or cluster structures at arbitrary scales, of two weighted graphs with one-to-one correspondence between vertices. Using minimal spanning trees in auxiliary graphs, RTD-Lite captures topological discrepancies with $O(n^2)$ time and memory complexity. This efficiency enables its application in tasks like dimensionality reduction and neural network training. Experiments on synthetic and real-world datasets demonstrate that RTD-Lite effectively identifies topological differences while significantly reducing computation time compared to existing methods. Moreover, integrating RTD-Lite into neural network training as a loss function component enhances the preservation of topological structures in learned representations. Our code is publicly available at https://github.com/ArGintum/RTD-Lite
Optimizing High-Dimensional Oblique Splits
Authors: Chien-Ming Chi
Date: 2025-03-18 00:00:00

Orthogonal-split trees perform well, but evidence suggests oblique splits can enhance their performance. This paper explores optimizing high-dimensional $s$-sparse oblique splits from $\{(\vec{w}, \vec{w}^{\top}\boldsymbol{X}_{i}) : i\in \{1,\dots, n\}, \vec{w} \in \mathbb{R}^p, \| \vec{w} \|_{2} = 1, \| \vec{w} \|_{0} \leq s \}$ for growing oblique trees, where $ s $ is a user-defined sparsity parameter. We establish a connection between SID convergence and $s_0$-sparse oblique splits with $s_0\ge 1$, showing that the SID function class expands as $s_0$ increases, enabling the capture of more complex data-generating functions such as the $s_0$-dimensional XOR function. Thus, $s_0$ represents the unknown potential complexity of the underlying data-generating function. Learning these complex functions requires an $s$-sparse oblique tree with $s \geq s_0$ and greater computational resources. This highlights a trade-off between statistical accuracy, governed by the SID function class size depending on $s_0$, and computational cost. In contrast, previous studies have explored the problem of SID convergence using orthogonal splits with $ s_0 = s = 1 $, where runtime was less critical. Additionally, we introduce a practical framework for oblique trees that integrates optimized oblique splits alongside orthogonal splits into random forests. The proposed approach is assessed through simulations and real-data experiments, comparing its performance against various oblique tree models.
Spectrally-Corrected and Regularized QDA Classifier for Spiked Covariance Model
Authors: Wenya Luo, Hua Li, Zhidong Bai, Zhijun Liu
Date: 2025-03-17 00:00:00

Quadratic discriminant analysis (QDA) is a widely used method for classification problems, particularly preferable over Linear Discriminant Analysis (LDA) for heterogeneous data. However, QDA loses its effectiveness in high-dimensional settings, where the data dimension and sample size tend to infinity. To address this issue, we propose a novel QDA method utilizing spectral correction and regularization techniques, termed SR-QDA. The regularization parameters in our method are selected by maximizing the Fisher-discriminant ratio. We compare SR-QDA with QDA, regularized quadratic discriminant analysis (R-QDA), and several other competitors. The results indicate that SR-QDA performs exceptionally well, especially in moderate and high-dimensional situations. Empirical experiments across diverse datasets further support this conclusion.
Training Diagonal Linear Networks with Stochastic Sharpness-Aware Minimization
Authors: Gabriel Clara, Sophie Langer, Johannes Schmidt-Hieber
Date: 2025-03-14 00:00:00

We analyze the landscape and training dynamics of diagonal linear networks in a linear regression task, with the network parameters being perturbed by small isotropic normal noise. The addition of such noise may be interpreted as a stochastic form of sharpness-aware minimization (SAM) and we prove several results that relate its action on the underlying landscape and training dynamics to the sharpness of the loss. In particular, the noise changes the expected gradient to force balancing of the weight matrices at a fast rate along the descent trajectory. In the diagonal linear model, we show that this equates to minimizing the average sharpness, as well as the trace of the Hessian matrix, among all possible factorizations of the same matrix. Further, the noise forces the gradient descent iterates towards a shrinkage-thresholding of the underlying true parameter, with the noise level explicitly regulating both the shrinkage factor and the threshold.
Advanced Deep Learning Methods for Protein Structure Prediction and Design
Authors: Yichao Zhang, Ningyuan Deng, Xinyuan Song, Ziqian Bi, Tianyang Wang, Zheyu Yao, Keyu Chen, Ming Li, Qian Niu, Junyu Liu, Benji Peng, Sen Zhang, Ming Liu, Li Zhang, Xuanhe Pan, Jinlang Wang, Pohsun Feng, Yizhu Wen, Lawrence KQ Yan, Hongming Tseng, Yan Zhong, Yunze Wang, Ziyuan Qin, Bowen Jing, Junjie Yang, Jun Zhou, Chia Xin Liang, Junhao Song
Date: 2025-03-14 00:00:00
Tasks: Deep Learning, Multiple Sequence Alignment, Prediction, Protein Design, Protein Structure Prediction

After AlphaFold won the Nobel Prize, protein prediction with deep learning once again became a hot topic. We comprehensively explore advanced deep learning methods applied to protein structure prediction and design. It begins by examining recent innovations in prediction architectures, with detailed discussions on improvements such as diffusion based frameworks and novel pairwise attention modules. The text analyses key components including structure generation, evaluation metrics, multiple sequence alignment processing, and network architecture, thereby illustrating the current state of the art in computational protein modelling. Subsequent chapters focus on practical applications, presenting case studies that range from individual protein predictions to complex biomolecular interactions. Strategies for enhancing prediction accuracy and integrating deep learning techniques with experimental validation are thoroughly explored. The later sections review the industry landscape of protein design, highlighting the transformative role of artificial intelligence in biotechnology and discussing emerging market trends and future challenges. Supplementary appendices provide essential resources such as databases and open source tools, making this volume a valuable reference for researchers and students.
MicroVQA: A Multimodal Reasoning Benchmark for Microscopy-Based Scientific Research
Authors: James Burgess, Jeffrey J Nirschl, Laura Bravo-Sánchez, Alejandro Lozano, Sanket Rajan Gupte, Jesus G. Galaz-Montoya, Yuhui Zhang, Yuchang Su, Disha Bhowmik, Zachary Coman, Sarina M. Hasan, Alexandra Johannesson, William D. Leineweber, Malvika G Nair, Ridhi Yarlagadda, Connor Zuraski, Wah Chiu, Sarah Cohen, Jan N. Hansen, Manuel D Leonetti, Chad Liu, Emma Lundberg, Serena Yeung-Levy
Date: 2025-03-17 00:00:00
Proceeding: CVPR 2025 1
Tasks: Articles, Benchmarking, Multimodal Reasoning, Multiple-choice, Question Answering, scientific discovery, Visual Question Answering, Visual Question Answering (VQA)

Scientific research demands sophisticated reasoning over multimodal data, a challenge especially prevalent in biology. Despite recent advances in multimodal large language models (MLLMs) for AI-assisted research, existing multimodal reasoning benchmarks only target up to college-level difficulty, while research-level benchmarks emphasize lower-level perception, falling short of the complex multimodal reasoning needed for scientific discovery. To bridge this gap, we introduce MicroVQA, a visual-question answering (VQA) benchmark designed to assess three reasoning capabilities vital in research workflows: expert image understanding, hypothesis generation, and experiment proposal. MicroVQA consists of 1,042 multiple-choice questions (MCQs) curated by biology experts across diverse microscopy modalities, ensuring VQA samples represent real scientific practice. In constructing the benchmark, we find that standard MCQ generation methods induce language shortcuts, motivating a new two-stage pipeline: an optimized LLM prompt structures question-answer pairs into MCQs; then, an agent-based `RefineBot' updates them to remove shortcuts. Benchmarking on state-of-the-art MLLMs reveal a peak performance of 53\%; models with smaller LLMs only slightly underperform top models, suggesting that language-based reasoning is less challenging than multimodal reasoning; and tuning with scientific articles enhances performance. Expert analysis of chain-of-thought responses shows that perception errors are the most frequent, followed by knowledge errors and then overgeneralization errors. These insights highlight the challenges in multimodal scientific reasoning, showing MicroVQA is a valuable resource advancing AI-driven biomedical research. MicroVQA is available at https://huggingface.co/datasets/jmhb/microvqa, and project page at https://jmhb0.github.io/microvqa.
Causes of evolutionary divergence in prostate cancer
Authors: Emre Esenturk, Atef Sahli, Valeriia Haberland, Aleksandra Ziuboniewicz, Christopher Wirth, G. Steven Bova, Robert G Bristow, Mark N Brook, Benedikt Brors, Adam Butler, Géraldine Cancel-Tassin, Kevin CL Cheng, Colin S Cooper, Niall M Corcoran, Olivier Cussenot, Ros A Eeles, Francesco Favero, Clarissa Gerhauser, Abraham Gihawi, Etsehiwot G Girma, Vincent J Gnanapragasam, Andreas J Gruber, Anis Hamid, Vanessa M Hayes, Housheng Hansen He, Christopher M Hovens, Eddie Luidy Imada, G. Maria Jakobsdottir, Chol-hee Jung, Francesca Khani, Zsofia Kote-Jarai, Philippe Lamy, Gregory Leeman, Massimo Loda, Pavlo Lutsik, Luigi Marchionni, Ramyar Molania, Anthony T Papenfuss, Diogo Pellegrina, Bernard Pope, Lucio R Queiroz, Tobias Rausch, Jüri Reimand, Brian Robinson, Thorsten Schlomm, Karina D Sørensen, Sebastian Uhrig, Joachim Weischenfeldt, Yaobo Xu, Takafumi N Yamaguchi, Claudio Zanettini, Andy G Lynch, David C Wedge, Daniel S Brewer, Dan J Woodcock
Date: 2025-03-17 00:00:00
Tasks: Causal Inference

Cancer progression involves the sequential accumulation of genetic alterations that cumulatively shape the tumour phenotype. In prostate cancer, tumours can follow divergent evolutionary trajectories that lead to distinct subtypes, but the causes of this divergence remain unclear. While causal inference could elucidate the factors involved, conventional methods are unsuitable due to the possibility of unobserved confounders and ambiguity in the direction of causality. Here, we propose a method that circumvents these issues and apply it to genomic data from 829 prostate cancer patients. We identify several genetic alterations that drive divergence as well as others that prevent this transition, locking tumours into one trajectory. Further analysis reveals that these genetic alterations may cause each other, implying a positive-feedback loop that accelerates divergence. Our findings provide insights into how cancer subtypes emerge and offer a foundation for genomic surveillance strategies aimed at monitoring the progression of prostate cancer.
Bayesian Cox model with graph-structured variable selection priors for multi-omics biomarker identification
Authors: Tobias Østmo Hermansen, Manuela Zucknick, Zhi Zhao
Date: 2025-03-17 00:00:00
Tasks: Prognosis, Survival Prediction, Variable Selection

An important goal in cancer research is the survival prognosis of a patient based on a minimal panel of genomic and molecular markers such as genes or proteins. Purely data-driven models without any biological knowledge can produce non-interpretable results. We propose a penalized semiparametric Bayesian Cox model with graph-structured selection priors for sparse identification of multi-omics features by making use of a biologically meaningful graph via a Markov random field (MRF) prior to capturing known relationships between multi-omics features. Since the fixed graph in the MRF prior is for the prior probability distribution, it is not a hard constraint to determine variable selection, so the proposed model can verify known information and has the potential to identify new and novel biomarkers for drawing new biological knowledge. Our simulation results show that the proposed Bayesian Cox model with graph-based prior knowledge results in more trustable and stable variable selection and non-inferior survival prediction, compared to methods modeling the covariates independently without any prior knowledge. The results also indicate that the performance of the proposed model is robust to a partially correct graph in the MRF prior, meaning that in a real setting where not all the true network information between covariates is known, the graph can still be useful. The proposed model is applied to the primary invasive breast cancer patients data in The Cancer Genome Atlas project.
Computational identification of ketone metabolism as a key regulator of sleep stability and circadian dynamics via real-time metabolic profiling
Authors: Hao Huang, Kaijing Xu, Michael Lardelli
Date: 2025-03-16 00:00:00
Tasks: Causal Inference, Explainable artificial intelligence

Metabolism plays a crucial role in sleep regulation, yet its effects are challenging to track in real time. This study introduces a machine learning-based framework to analyze sleep patterns and identify how metabolic changes influence sleep at specific time points. We first established that sleep periods in Drosophila melanogaster function independently, with no causal relationship between different sleep episodes. Using gradient boosting models and explainable artificial intelligence techniques, we quantified the influence of time-dependent sleep features. Causal inference and autocorrelation analyses further confirmed that sleep states at different times are statistically independent, providing a robust foundation for exploring metabolic effects on sleep. Applying this framework to flies with altered monocarboxylate transporter 2 expression, we found that changes in ketone transport modified sleep stability and disrupted transitions between day and night sleep. In an Alzheimers disease model, metabolic interventions such as beta hydroxybutyrate supplementation and intermittent fasting selectively influenced the timing of day to night transitions rather than uniformly altering sleep duration. Autoencoder based similarity scoring and wavelet analysis reinforced that metabolic effects on sleep were highly time dependent. This study presents a novel approach to studying sleep-metabolism interactions, revealing that metabolic states exert their strongest influence at distinct time points, shaping sleep stability and circadian transitions.
Integrating Chain-of-Thought and Retrieval Augmented Generation Enhances Rare Disease Diagnosis from Clinical Notes
Authors: Da Wu, Zhanliang Wang, Quan Nguyen, Kai Wang
Date: 2025-03-15 00:00:00
Tasks: RAG, Retrieval, Retrieval-augmented Generation

Background: Several studies show that large language models (LLMs) struggle with phenotype-driven gene prioritization for rare diseases. These studies typically use Human Phenotype Ontology (HPO) terms to prompt foundation models like GPT and LLaMA to predict candidate genes. However, in real-world settings, foundation models are not optimized for domain-specific tasks like clinical diagnosis, yet inputs are unstructured clinical notes rather than standardized terms. How LLMs can be instructed to predict candidate genes or disease diagnosis from unstructured clinical notes remains a major challenge. Methods: We introduce RAG-driven CoT and CoT-driven RAG, two methods that combine Chain-of-Thought (CoT) and Retrieval Augmented Generation (RAG) to analyze clinical notes. A five-question CoT protocol mimics expert reasoning, while RAG retrieves data from sources like HPO and OMIM (Online Mendelian Inheritance in Man). We evaluated these approaches on rare disease datasets, including 5,980 Phenopacket-derived notes, 255 literature-based narratives, and 220 in-house clinical notes from Childrens Hospital of Philadelphia. Results: We found that recent foundations models, including Llama 3.3-70B-Instruct and DeepSeek-R1-Distill-Llama-70B, outperformed earlier versions such as Llama 2 and GPT-3.5. We also showed that RAG-driven CoT and CoT-driven RAG both outperform foundation models in candidate gene prioritization from clinical notes; in particular, both methods with DeepSeek backbone resulted in a top-10 gene accuracy of over 40% on Phenopacket-derived clinical notes. RAG-driven CoT works better for high-quality notes, where early retrieval can anchor the subsequent reasoning steps in domain-specific evidence, while CoT-driven RAG has advantage when processing lengthy and noisy notes.
Revealing higher-order neural representations of uncertainty with the Noise Estimation through Reinforcement-based Diffusion (NERD) model
Authors: Hojjat Azimi Asrari, Megan A. K. Peters
Date: 2025-03-18 00:00:00
Tasks: Denoising, Noise Estimation, Reinforcement Learning (RL)

Studies often aim to reveal ``first-order" representations (FORs), which encode aspects of an observer's environment, such as contents or structure. A less-common target is ``higher-order" representations (HORs), which are ``about" FORs -- e.g., their strength or uncertainty -- and which may contribute to learning. HORs about uncertainty are unlikely to be direct ``read-outs" of FOR characteristics, instead reflecting noisy estimation processes incorporating prior expectations about uncertainty, but how the brain represents such expected uncertainty distributions remains largely unexplored. Here, we study ``noise expectation" HORs using neural data from a task which may require the brain to learn about its own noise: decoded neurofeedback, wherein human subjects learn to volitionally produce target neural patterns. We develop and apply a Noise Estimation through Reinforcement-based Diffusion (NERD) model to characterize how brains may undertake this process, and show that NERD offers high explanatory power for human behavior.
Neural Constraints on Cognitive Experience and Mental Health
Authors: Bita Shariatpanahi, Erfan Nozari, Soroush Daftarian, Fahimeh Arab, Mina Kheirkhah, Felix P. Bernhard, Shiva Khodadadi, Erik J. Giltay, Kaat Hebbrecht, Stefan G. Hofmann, Tim Hahn, Hamidreza Jamalabadi
Date: 2025-03-18 00:00:00

Understanding how neural dynamics shape cognitive experiences remains a central challenge in neuroscience and psychiatry. Here, we present a novel framework leveraging state-to-output controllability from dynamical systems theory to model the interplay between cognitive perturbations, neural activity, and subjective experience. We demonstrate that large-scale fMRI signals are constrained to low-dimensional manifolds, where affective and cognitive states are naturally organized. Furthermore, we provide a theoretically robust method to estimate the controllability Gramian from steady-state neural responses, offering a direct measure of the energy required to steer cognitive outcomes. In five healthy participants viewing 2,185 emotionally evocative short videos, our analyses reveal a strong alignment between neural activations and affective ratings, with an average correlation of $r \approx 0.7$. In a clinical cohort of 255 patients with major depressive disorder, biweekly Hamilton Rating Scale trajectories over 11 weeks significantly mapped onto these manifolds, explaining approximately 20% more variance than chance ($p < 10^{-10}$, numerically better than chance in 93% reaching statistical significance in one-third of subjects). Our work bridges dynamical systems theory and clinical neuroscience, providing a principled approach to optimize mental health treatments by targeting the most efficient neural pathways for cognitive change.