pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Control for Transformer Architectures: Enhancing Generalization, Robustness and Efficiency
Authors: Kelvin Kan, Xingjian Li, Benjamin J. Zhang, Tuhin Sahai, Stanley Osher, Markos A. Katsoulakis
Date: 2025-05-16 00:00:00
Tasks: image-classification, Image Classification, Point Cloud Classification, Sentiment Analysis, Text Generation

We study Transformers through the perspective of optimal control theory, using tools from continuous-time formulations to derive actionable insights into training and architecture design. This framework improves the performance of existing Transformer models while providing desirable theoretical guarantees, including generalization and robustness. Our framework is designed to be plug-and-play, enabling seamless integration with established Transformer models and requiring only slight changes to the implementation. We conduct seven extensive experiments on tasks motivated by text generation, sentiment analysis, image classification, and point cloud classification. Experimental results show that the framework improves the test performance of the baselines, while being more parameter-efficient. On character-level text generation with nanoGPT, our framework achieves a 46% reduction in final test loss while using 42% fewer parameters. On GPT-2, our framework achieves a 5.6% reduction in final test loss, demonstrating scalability to larger models. To the best of our knowledge, this is the first work that applies optimal control theory to both the training and architecture of Transformers. It offers a new foundation for systematic, theory-driven improvements and moves beyond costly trial-and-error approaches.
Code Retrieval for MILP Instance Generation
Authors: Tianxing Yang, Huigen Ye, Hua Xu
Date: 2025-05-11 00:00:00
Tasks: Code Generation, Retrieval, Scheduling

Mixed-Integer Linear Programming (MILP) is widely used in fields such as scheduling, logistics, and planning. Enhancing the performance of MILP solvers, particularly learning-based solvers, requires substantial amounts of high-quality data. However, existing methods for MILP instance generation typically necessitate training a separate model for each problem class and are computationally intensive when generating new instances. To address these limitations, we reformulate the MILP Instance Generation task as MILP Code Generation task, enabling efficient, flexible, and interpretable instance generation through code. Since MILP instances generated from code can vary significantly in scale, we introduce MILP-EmbedSim, a new similarity metric that accurately measures the similarity between instances of varying sizes within the same problem class. Leveraging this metric, we propose MILP-Retrieval, a pipeline that retrieves generation code from library to produce MILP instances highly similar to target instance. MILP-Retrieval outperforms baselines in both MILP Code Generation and Instance Generation tasks, provides a novel perspective on MILP instance generation and opens new possibilities for learning-based solvers.
Deep Unrolled Meta-Learning for Multi-Coil and Multi-Modality MRI with Adaptive Optimization
Authors: Merham Fouladvand, Peuroly Batra
Date: 2025-05-08 00:00:00
Tasks: Meta-Learning, MRI Reconstruction, SSIM

We propose a unified deep meta-learning framework for accelerated magnetic resonance imaging (MRI) that jointly addresses multi-coil reconstruction and cross-modality synthesis. Motivated by the limitations of conventional methods in handling undersampled data and missing modalities, our approach unrolls a provably convergent optimization algorithm into a structured neural network architecture. Each phase of the network mimics a step of an adaptive forward-backward scheme with extrapolation, enabling the model to incorporate both data fidelity and nonconvex regularization in a principled manner. To enhance generalization across different acquisition settings, we integrate meta-learning, which enables the model to rapidly adapt to unseen sampling patterns and modality combinations using task-specific meta-knowledge. The proposed method is evaluated on the open source datasets, showing significant improvements in PSNR and SSIM over conventional supervised learning, especially under aggressive undersampling and domain shifts. Our results demonstrate the synergy of unrolled optimization, task-aware meta-learning, and modality fusion, offering a scalable and generalizable solution for real-world clinical MRI reconstruction.
Filtering in a hazard rate change-point model with financial and life-insurance applications
Authors: Matteo Buttarazzi, Claudia Ceci
Date: 2025-05-19 00:00:00

This paper develops a continuous-time filtering framework for estimating a hazard rate subject to an unobservable change-point. This framework arises naturally in both financial and insurance applications, where the default intensity of a firm or the mortality rate of an individual may experience a sudden jump at an unobservable time, representing, for instance, a shift in the firm's risk profile or a deterioration in an individual's health status. By employing a progressive enlargement of filtration, we integrate noisy observations of the hazard rate with default-related information. We characterise the filter, i.e. the conditional probability of the change-point given the information flow, as the unique strong solution to a stochastic differential equation driven by the innovation process enriched with the discontinuous component. A sensitivity analysis and a comparison of the filter's behaviour under various information structures are provided. Our framework further allows for the derivation of an explicit formula for the survival probability conditional on partial information. This result applies to the pricing of credit-sensitive financial instruments such as defaultable bonds, credit default swaps, and life insurance contracts. Finally, a numerical analysis illustrates how partial information leads to delayed adjustments in the estimation of the hazard rate and consequently to mispricing of credit-sensitive instruments when compared to a full-information setting.
Merton model and Poisson process with Log Normal intensity function
Authors: Masato Hisakado, Shintaro Mori
Date: 2025-05-20 00:00:00

This study considers the Merton model with temporal correlation. We show the Merton model becomes Poisson process with the log-normal distributed intensity function in the limit. We discuss the relation between this model and Hawkes process. In this model we confirm the super-normal transition when the temporal correlation is power case. The phase transition is same as seen before the limit. We apply this model to the default portfolios and find that the power decay model provides better generalization performance for the long term data.
Regularized least squares learning with heavy-tailed noise is minimax optimal
Authors: Mattes Mollenhauer, Nicole Mücke, Dimitri Meunier, Arthur Gretton
Date: 2025-05-20 00:00:00

This paper examines the performance of ridge regression in reproducing kernel Hilbert spaces in the presence of noise that exhibits a finite number of higher moments. We establish excess risk bounds consisting of subgaussian and polynomial terms based on the well known integral operator framework. The dominant subgaussian component allows to achieve convergence rates that have previously only been derived under subexponential noise - a prevalent assumption in related work from the last two decades. These rates are optimal under standard eigenvalue decay conditions, demonstrating the asymptotic robustness of regularized least squares against heavy-tailed noise. Our derivations are based on a Fuk-Nagaev inequality for Hilbert-space valued random variables.
An Asymptotic Equation Linking WAIC and WBIC in Singular Models
Authors: Naoki Hayashi, Takuro Kutsuna, Sawa Takamuku
Date: 2025-05-20 00:00:00
Tasks: Computational Efficiency, Learning Theory, Model Selection

In statistical learning, models are classified as regular or singular depending on whether the mapping from parameters to probability distributions is injective. Most models with hierarchical structures or latent variables are singular, for which conventional criteria such as the Akaike Information Criterion and the Bayesian Information Criterion are inapplicable due to the breakdown of normal approximations for the likelihood and posterior. To address this, the Widely Applicable Information Criterion (WAIC) and the Widely Applicable Bayesian Information Criterion (WBIC) have been proposed. Since WAIC and WBIC are computed using posterior distributions at different temperature settings, separate posterior sampling is generally required. In this paper, we theoretically derive an asymptotic equation that links WAIC and WBIC, despite their dependence on different posteriors. This equation yields an asymptotically unbiased expression of WAIC in terms of the posterior distribution used for WBIC. The result clarifies the structural relationship between these criteria within the framework of singular learning theory, and deepens understanding of their asymptotic behavior. This theoretical contribution provides a foundation for future developments in the computational efficiency of model selection in singular models.
Minimax Rates of Estimation for Optimal Transport Map between Infinite-Dimensional Spaces
Authors: Donlapark Ponnoprat, Masaaki Imaizumi
Date: 2025-05-19 00:00:00

We investigate the estimation of an optimal transport map between probability measures on an infinite-dimensional space and reveal its minimax optimal rate. Optimal transport theory defines distances within a space of probability measures, utilizing an optimal transport map as its key component. Estimating the optimal transport map from samples finds several applications, such as simulating dynamics between probability measures and functional data analysis. However, some transport maps on infinite-dimensional spaces require exponential-order data for estimation, which undermines their applicability. In this paper, we investigate the estimation of an optimal transport map between infinite-dimensional spaces, focusing on optimal transport maps characterized by the notion of $\gamma$-smoothness. Consequently, we show that the order of the minimax risk is polynomial rate in the sample size even in the infinite-dimensional setup. We also develop an estimator whose estimation error matches the minimax optimal rate. With these results, we obtain a class of reasonably estimable optimal transport maps on infinite-dimensional spaces and a method for their estimation. Our experiments validate the theory and practical utility of our approach with application to functional data analysis.
DrugPilot: LLM-based Parameterized Reasoning Agent for Drug Discovery
Authors: Kun Li, Zhennan Wu, Shoupeng Wang, Wenbin Hu
Date: 2025-05-20 00:00:00
Tasks: Drug Discovery

In the field of AI4Science, large-scale language models (LLMs) show great potential to parse complex scientific semantics, integrate cross-disciplinary knowledge, and assist critical task research. However, in the field of drug discovery, despite the optimization through professional data pre-training, context window expansion, and internet search, the existing LLMs are still facing challenges such as massive multi-modal and heterogeneous data processing, domain knowledge dynamic updating delay, and insufficient confidence in predicting the results of complex computational tasks. To address these challenges, we propose the DrugPilot, an LLM-based agent with parameterized reasoning for drug discovery. DrugPilot addresses key limitations of traditional end-to-end LLM prediction approaches through its parametric inference architecture. This agent system supports major phases of the drug discovery pipeline, facilitating automated planning and execution of multi-stage research tasks. To address the critical challenge of multi-modal drug data analysis (incorporating both public datasets and user-submitted data), we developed an interactive parameterized memory pool. This innovative component standardizes real-world drug data into parametric representations, simultaneously enabling efficient knowledge retrieval in multi-turn dialogue while mitigating the information loss inherent in text-based data transmission. Additionally, we created a drug instruct dataset across 8 essential drug discovery tasks for model fine-tuning and evaluation. Based on the Berkeley function calling evaluation framework, DrugPilot demonstrated the most advanced tool calling capabilities on our drug discovery tool instruction dataset, outperforming existing agents (e.g., ReAct, LoT). Specifically, it achieves task completion rates of 98.0%, 93.5%, and 64.0% on simple, multiple, and multi-turn tasks, respectively.
Multi-Ligand Simultaneous Docking Analysis of Moringa Oleifera Phytochemicals Reveals Enhanced BCL-2 Inhibition via Synergistic Action
Authors: Asmita Saha, Belaguppa Manjunath Ashwin Desai, Pronama Biswas
Date: 2025-05-17 00:00:00
Tasks: Molecular Docking

Moringa oleifera, known for its medicinal properties, contains bioactive compounds such as polyphenols and flavonoids with diverse therapeutic potentials, including anti-cancer effects. This study investigates the efficacy of M. oleifera leaf phytochemicals in inhibiting BCL-2, a critical protein involved in cancer cell survival. For the first time, multi-ligand simultaneous docking (MLSD) has been employed to understand the anti-cancer properties of M. oleifera leaf extract. Molecular docking techniques, including single-ligand and MLSD, were used to assess binding interactions with BCL-2. Single-ligand docking revealed strong binding affinities for compounds such as niazinin, alpha carotene, hesperetin, apigenin, niaziminin B, and niazimicin A, with some compounds even surpassing Venetoclax, a commercial BCL-2 inhibitor. MLSD highlighted inter-ligand interactions among apigenin, hesperetin, and niazimicin A, exhibiting a binding affinity of -14.96 kcal/mol, indicating a synergistic effect. These results shed light on the potential synergistic effects of phytochemicals when using multi-ligand simultaneous docking, underscoring the importance of considering compound interactions in the development of therapeutic strategies.
Prediction of Novel CXCR7 Inhibitors Using QSAR Modeling and Validation via Molecular Docking
Authors: Belaguppa Manjunath Ashwin Desai, Merla Sudha, Suvarna Ghosh, Pronama Biswas
Date: 2025-05-17 00:00:00
Tasks: Molecular Docking

CXCR7, a G-protein-coupled chemokine receptor, has recently emerged as a key player in cancer progression, particularly in driving angiogenesis and metastasis. Despite its significance, currently, few effective inhibitors exist for targeting this receptor. In this study aimed to address this gap by developing a QSAR model to predict potential CXCR7 inhibitors, followed by validation through molecular docking. Using the Extra Trees classifier for QSAR modeling and employing a combination of physicochemical descriptors and molecular fingerprints, compounds were classified as active or inactive with a high accuracy of 0.85. The model could efficiently screen a large dataset, identifying several promising CXCR7 inhibitors. The predicted inhibitors were further validated through molecular docking studies, revealing strong binding affinities, with the best docking score of -12.24 +- 0.49 kcal/mol. Visualization of the docked structures in both 2D and 3D confirmed the interactions between the inhibitors and the CXCR7 receptor, reinforcing their potential efficacy.
Foundation Models for AI-Enabled Biological Design
Authors: Asher Moldwin, Amarda Shehu
Date: 2025-05-16 00:00:00
Tasks: Survey

This paper surveys foundation models for AI-enabled biological design, focusing on recent developments in applying large-scale, self-supervised models to tasks such as protein engineering, small molecule design, and genomic sequence design. Though this domain is evolving rapidly, this survey presents and discusses a taxonomy of current models and methods. The focus is on challenges and solutions in adapting these models for biological applications, including biological sequence modeling architectures, controllability in generation, and multi-modal integration. The survey concludes with a discussion of open problems and future directions, offering concrete next-steps to improve the quality of biological sequence generation.
Flash Invariant Point Attention
Authors: Andrew Liu, Axel Elaldi, Nicholas T Franklin, Nathan Russell, Gurinder S Atwal, Yih-En A Ban, Olivia Viessmann
Date: 2025-05-16 00:00:00
Tasks: GPU

Invariant Point Attention (IPA) is a key algorithm for geometry-aware modeling in structural biology, central to many protein and RNA models. However, its quadratic complexity limits the input sequence length. We introduce FlashIPA, a factorized reformulation of IPA that leverages hardware-efficient FlashAttention to achieve linear scaling in GPU memory and wall-clock time with sequence length. FlashIPA matches or exceeds standard IPA performance while substantially reducing computational costs. FlashIPA extends training to previously unattainable lengths, and we demonstrate this by re-training generative models without length restrictions and generating structures of thousands of residues. FlashIPA is available at https://github.com/flagshippioneering/flash_ipa.
Mathematical models for the EP2 and EP4 signaling pathways and their crosstalk
Authors: Alessandra Cambi, Diane S Lidke, Mariya Ptashnyk, Willemijn Smit, Stefanie Sonner
Date: 2025-05-16 00:00:00

The G-protein coupled receptors EP2 and EP4 both transduce the signal of the lipid messenger Prostaglandin E2 (PGE2). Changes in the cell response to PGE2 can have important effects on immunity and development of diseases, however, a thorough understanding of the EP2-EP4 receptors' signaling pathways is lacking. Experimental data show that receptor activity (indicated by cAMP expression) has a different kinetics depending on which receptor is triggered by PGE2 and that crosstalk exists between EP2 and EP4. To better understand the underlying mechanisms and be able to predict cell responses to PGE2, we develop novel mathematical models for the cAMP signaling pathways of EP2 and EP4 and their crosstalk. Ligand binding dynamics plays a crucial role for both, the single receptor activity and their crosstalk. The mathematical models can predict the qualitative cAMP levels observed experimentally and provide possible explanations for the differences and commonalities in the signaling behavior of EP2 and EP4. As inhibition of PGE2 signaling is gaining increasing attention in tumor immunology, these mathematical models could contribute to design effective anti-tumor therapies targeting EP2 and EP4.
OmniGenBench: A Modular Platform for Reproducible Genomic Foundation Models Benchmarking
Authors: Heng Yang, Jack Cole, Yuan Li, Renzhi Chen, Geyong Min, Ke Li
Date: 2025-05-20 00:00:00
Tasks: Benchmarking

The code of nature, embedded in DNA and RNA genomes since the origin of life, holds immense potential to impact both humans and ecosystems through genome modeling. Genomic Foundation Models (GFMs) have emerged as a transformative approach to decoding the genome. As GFMs scale up and reshape the landscape of AI-driven genomics, the field faces an urgent need for rigorous and reproducible evaluation. We present OmniGenBench, a modular benchmarking platform designed to unify the data, model, benchmarking, and interpretability layers across GFMs. OmniGenBench enables standardized, one-command evaluation of any GFM across five benchmark suites, with seamless integration of over 31 open-source models. Through automated pipelines and community-extensible features, the platform addresses critical reproducibility challenges, including data transparency, model interoperability, benchmark fragmentation, and black-box interpretability. OmniGenBench aims to serve as foundational infrastructure for reproducible genomic AI research, accelerating trustworthy discovery and collaborative innovation in the era of genome-scale modeling.
In silico tool for identification of colorectal cancer from cell-free DNA biomarkers
Authors: Kartavya Mathur, Shipra Jain, Nisha Bajiya, Nishant Kumar, Gajendra P. S. Raghava
Date: 2025-05-16 00:00:00
Tasks: Diagnostic, feature selection

Colorectal cancer remains a major global health concern, with early detection being pivotal for improving patient outcomes. In this study, we leveraged high throughput methylation profiling of cellfree DNA to identify and validate diagnostic biomarkers for CRC. The GSE124600 study data were downloaded from the Gene Expression Omnibus, as the discovery cohort, comprising 142 CRC and 132 normal cfDNA methylation profiles obtained via MCTA seq. After preprocessing and filtering, 97,863 CpG sites were retained for further analysis. Differential methylation analysis using statistical tests identified 30,791 CpG sites as significantly altered in CRC samples, where p is less than 0.05. Univariate scoring enabled the selection of top ranking features, which were further refined using multiple feature selection algorithms, including Recursive Feature Elimination, Sequential Feature Selection, and SVC L1. Various machine learning models such as Logistic Regression, Support Vector Machines, Random Forest, and Multi layer Perceptron were trained and tested using independent validation datasets. The best performance was achieved with an MLP model trained on 25 features selected by RFE, reaching an AUROC of 0.89 and MCC of 0.78 on validation data. Additionally, a deep learning based convolutional neural network achieved an AUROC of 0.78. Functional annotation of the most predictive CpG sites identified several genes involved in key cellular processes, some of which were validated for differential expression in CRC using the GEPIA2 platform. Our study highlights the potential of cfDNA methylation markers combined with ML and DL models for noninvasive and accurate CRC detection, paving the way for clinically relevant diagnostic tools.
A novel model class for bowtie biological networks with universal classification properties
Authors: Charles A. Johnson, Keon Ho, Park, Enoch Yeung
Date: 2025-05-19 00:00:00

Cell sensory transcription networks are the intracellular computation structure that regulates and drives cellular activity. Activity in these networks determines the the cell's ability to adapt to changes in its environment. Resilient cells successfully identify (classify) and appropriately respond to environmental shifts. We present a model for identification and response to environmental changes in resilient bacteria. This model combines two known motifs in transcription networks: dense overlapping regulons (DORs) and single input modules (SIMs). DORs have the ability to perform cellular decision making and have a network structure similar to that of a shallow neural network, with a number of input transcription factors (TFs) mapping to a distinct set of genes. SIMs contain a master TF that simultaneously activates a number of target genes. Within most observed cell sensory transcription networks, the master transcription factor of SIMs are output genes of a DOR creating a fan-in-fan-out (bowtie) structure in the transcriptional network. We model this hybrid network motif (which we call the DOR2SIM motif) with a superposition of modular nonlinear functions to describe protein signaling in the network and basic mass action kinetics to describe the other chemical reactions in this process. We analyze this model's biological feasibility and capacity to perform classification, the first step in adaptation. We provide sufficient conditions for models of the DOR2SIM motif to classify constant (environmental) inputs. These conditions suggest that generally low monomer degradation rates as well as low expression of source node genes at equilibrium in the DOR component enable classification.