pwc-archive/papers-with-abstracts huggingface dataset:
Towards Quantum Tensor Decomposition in Biomedical Applications
Authors: Myson Burch, Jiasen Zhang, Gideon Idumah, Hakan Doga, Richard Lartey, Lamis Yehia, Mingrui Yang, Murat Yildirim, Mihriban Karaayvaz, Omar Shehab, Weihong Guo, Ying Ni, Laxmi Parida, Xiaojuan Li, Aritra Bose
Date: 2025-02-18 00:00:00
Tasks: Tensor Decomposition

Tensor decomposition has emerged as a powerful framework for feature extraction in multi-modal biomedical data. In this review, we present a comprehensive analysis of tensor decomposition methods such as Tucker, CANDECOMP/PARAFAC, spiked tensor decomposition, etc. and their diverse applications across biomedical domains such as imaging, multi-omics, and spatial transcriptomics. To systematically investigate the literature, we applied a topic modeling-based approach that identifies and groups distinct thematic sub-areas in biomedicine where tensor decomposition has been used, thereby revealing key trends and research directions. We evaluated challenges related to the scalability of latent spaces along with obtaining the optimal rank of the tensor, which often hinder the extraction of meaningful features from increasingly large and complex datasets. Additionally, we discuss recent advances in quantum algorithms for tensor decomposition, exploring how quantum computing can be leveraged to address these challenges. Our study includes a preliminary resource estimation analysis for quantum computing platforms and examines the feasibility of implementing quantum-enhanced tensor decomposition methods on near-term quantum devices. Collectively, this review not only synthesizes current applications and challenges of tensor decomposition in biomedical analyses but also outlines promising quantum computing strategies to enhance its impact on deriving actionable insights from complex biomedical data.
E2CB2former: Effecitve and Explainable Transformer for CB2 Receptor Ligand Activity Prediction
Authors: Jiacheng Xie, Yingrui Ji, Linghuan Zeng, Xi Xiao, Gaofei Chen, Lijing Zhu, Joyanta Jyoti Mondal, Jiansheng Chen
Date: 2025-02-15 00:00:00
Tasks: Activity Prediction, Drug Design, Drug Discovery

Accurate prediction of CB2 receptor ligand activity is pivotal for advancing drug discovery targeting this receptor, which is implicated in inflammation, pain management, and neurodegenerative conditions. Although conventional machine learning and deep learning techniques have shown promise, their limited interpretability remains a significant barrier to rational drug design. In this work, we introduce CB2former, a framework that combines a Graph Convolutional Network with a Transformer architecture to predict CB2 receptor ligand activity. By leveraging the Transformer's self attention mechanism alongside the GCN's structural learning capability, CB2former not only enhances predictive performance but also offers insights into the molecular features underlying receptor activity. We benchmark CB2former against diverse baseline models including Random Forest, Support Vector Machine, K Nearest Neighbors, Gradient Boosting, Extreme Gradient Boosting, Multilayer Perceptron, Convolutional Neural Network, and Recurrent Neural Network and demonstrate its superior performance with an R squared of 0.685, an RMSE of 0.675, and an AUC of 0.940. Moreover, attention weight analysis reveals key molecular substructures influencing CB2 receptor activity, underscoring the model's potential as an interpretable AI tool for drug discovery. This ability to pinpoint critical molecular motifs can streamline virtual screening, guide lead optimization, and expedite therapeutic development. Overall, our results showcase the transformative potential of advanced AI approaches exemplified by CB2former in delivering both accurate predictions and actionable molecular insights, thus fostering interdisciplinary collaboration and innovation in drug discovery.
Fast Antibiotic resistance-Based gene editing of mammalian cells with CRISPR-Cas9 (FAB-CRISPR)
Authors: Petia Adarska, Eleanor Fox, Joshua Heyza, Carlo Barnaba, Jens Schmidt, Francesca Bottanelli
Date: 2025-02-18 00:00:00

Protein tagging with CRISPR-Cas9 enables the investigation of protein function in its native environment but is limited by low homology-directed repair (HDR) efficiency causing low knock-in rates. We present a detailed pipeline using HDR donor plasmids containing antibiotic resistance cassettes for rapid selection of gene-edited cells. Our protocol streamlines N- or C-terminal tagging in human cells, enabling HDR donor plasmid preparation in a single cloning step.
Theoretical Frameworks for Integrating Sustainability Factors into Institutional Investment Decision-Making
Authors: Innocentus Alhamis
Date: 2025-02-04 00:00:00
Tasks: Decision Making, Management

This paper explores key theoretical frameworks instrumental in understanding the relationship between sustainability and institutional investment decisions. The study identifies and analyzes various theories, including Behavioral Finance Theory, Modern Portfolio Theory, Risk Management Theory, and others, to explain how sustainability considerations increasingly influence investment choices. By examining these frameworks, the paper highlights how investors integrate Environmental, Social, and Governance (ESG) factors to optimize financial outcomes and align with broader societal goals.
Decentralized Annuity: A Quest for the Holy Grail of Lifetime Financial Security
Authors: Feng Runhuan, Liang Zongxia, Song Yilun
Date: 2025-02-19 00:00:00
Tasks: Fairness

This paper presents a novel framework for decentralized annuities, aiming to address the limitations of traditional pension systems such as defined contribution (DC) and defined benefit (DB) plans, while providing lifetime financial support. It sheds light on often ignored pitfalls within current retirement schemes and introduces individual rationality properties. The research delves into various fairness concepts that underpin existing plans, emphasizing that decentralized annuities, while meeting similar fairness criteria, offer enhanced flexibility for individual rationality and improved social welfare for all participants. Using theoretical models and examples, we demonstrate the potential of decentralized annuities to outperform self-managed plans (DC) and to produce effects comparable to defined benefit (DB) plans, particularly within larger participant pools. The paper concludes by exploring the managerial implications of decentralized annuities and laying the groundwork for the further advancement of equitable and sustainable decentralized annuity systems.
Dual Formulation of the Optimal Consumption problem with Multiplicative Habit Formation
Authors: Thijs Kamma, Antoon Pelsser
Date: 2025-02-19 00:00:00

This paper provides a dual formulation of the optimal consumption problem with internal multiplicative habit formation. In this problem, the agent derives utility from the ratio of consumption to the internal habit component. Due to this multiplicative specification of the habit model, the optimal consumption problem is not strictly concave and incorporates irremovable path-dependency. As a consequence, standard Lagrangian techniques fail to supply a candidate for the corresponding dual formulation. Using Fenchel's Duality Theorem, we manage to identify a candidate formulation and prove that it satisfies strong duality. On the basis of this strong duality result, we are able to derive duality relations that stipulate how the optimal primal controls depend on the optimal dual controls and vice versa. {Moreover, using the dual formulation, we develop an analytical evaluation mechanism to bound the accuracy of approximations to the optimal solutions.
Calibration and Option Pricing with Stochastic Volatility and Double Exponential Jumps
Authors: Gaetano Agazzotti, Claudio Aglieri Rinella, Jean-Philippe Aguilar, Justin Lars Kirkby
Date: 2025-02-19 00:00:00
Tasks: Articles, Econometrics

This work examines a stochastic volatility model with double-exponential jumps in the context of option pricing. The model has been considered in previous research articles, but no thorough analysis has been conducted to study its quality of calibration and pricing capabilities thus far. We provide evidence that this model outperforms challenger models possessing similar features (stochastic volatility and jumps), especially in the fit of the short term implied volatility smile, and that it is particularly tractable for the pricing of exotic options from different generations. The article utilizes Fourier pricing techniques (the PROJ method and its refinements) for different types of claims and several generations of exotics (Asian options, cliquets, barrier options, and options on realized variance), and all source codes are made publicly available to facilitate adoption and future research. The results indicate that this model is highly promising, thanks to the asymmetry of the jumps distribution allowing it to capture richer dynamics than a normal jump size distribution. The parameters all have meaningful econometrics interpretations that are important for adoption by risk-managers.
Deep Learning for VWAP Execution in Crypto Markets: Beyond the Volume Curve
Authors: Remi Genet
Date: 2025-02-19 00:00:00

Volume-Weighted Average Price (VWAP) is arguably the most prevalent benchmark for trade execution as it provides an unbiased standard for comparing performance across market participants. However, achieving VWAP is inherently challenging due to its dependence on two dynamic factors, volumes and prices. Traditional approaches typically focus on forecasting the market's volume curve, an assumption that may hold true under steady conditions but becomes suboptimal in more volatile environments or markets such as cryptocurrency where prediction error margins are higher. In this study, I propose a deep learning framework that directly optimizes the VWAP execution objective by bypassing the intermediate step of volume curve prediction. Leveraging automatic differentiation and custom loss functions, my method calibrates order allocation to minimize VWAP slippage, thereby fully addressing the complexities of the execution problem. My results demonstrate that this direct optimization approach consistently achieves lower VWAP slippage compared to conventional methods, even when utilizing a naive linear model presented in arXiv:2410.21448. They validate the observation that strategies optimized for VWAP performance tend to diverge from accurate volume curve predictions and thus underscore the advantage of directly modeling the execution objective. This research contributes a more efficient and robust framework for VWAP execution in volatile markets, illustrating the potential of deep learning in complex financial systems where direct objective optimization is crucial. Although my empirical analysis focuses on cryptocurrency markets, the underlying principles of the framework are readily applicable to other asset classes such as equities.
Performance Evaluation of Large Language Models in Statistical Programming
Authors: Xinyi Song, Kexin Xie, Lina Lee, Ruizhe Chen, Jared M. Clark, Hao He, Haoran He, Jie Min, Xinlei Zhang, Simin Zheng, Zhiyang Zhang, Xinwei Deng, Yili Hong
Date: 2025-02-18 00:00:00
Tasks: Code Generation

The programming capabilities of large language models (LLMs) have revolutionized automatic code generation and opened new avenues for automatic statistical analysis. However, the validity and quality of these generated codes need to be systematically evaluated before they can be widely adopted. Despite their growing prominence, a comprehensive evaluation of statistical code generated by LLMs remains scarce in the literature. In this paper, we assess the performance of LLMs, including two versions of ChatGPT and one version of Llama, in the domain of SAS programming for statistical analysis. Our study utilizes a set of statistical analysis tasks encompassing diverse statistical topics and datasets. Each task includes a problem description, dataset information, and human-verified SAS code. We conduct a comprehensive assessment of the quality of SAS code generated by LLMs through human expert evaluation based on correctness, effectiveness, readability, executability, and the accuracy of output results. The analysis of rating scores reveals that while LLMs demonstrate usefulness in generating syntactically correct code, they struggle with tasks requiring deep domain understanding and may produce redundant or incorrect results. This study offers valuable insights into the capabilities and limitations of LLMs in statistical programming, providing guidance for future advancements in AI-assisted coding systems for statistical analysis.
Bridging the Data Gap in AI Reliability Research and Establishing DR-AIR, a Comprehensive Data Repository for AI Reliability
Authors: Simin Zheng, Jared M. Clark, Fatemeh Salboukh, Priscila Silva, Karen da Mata, Fenglian Pan, Jie Min, Jiayi Lian, Caleb B. King, Lance Fiondella, Jian Liu, Xinwei Deng, Yili Hong
Date: 2025-02-17 00:00:00

Artificial intelligence (AI) technology and systems have been advancing rapidly. However, ensuring the reliability of these systems is crucial for fostering public confidence in their use. This necessitates the modeling and analysis of reliability data specific to AI systems. A major challenge in AI reliability research, particularly for those in academia, is the lack of readily available AI reliability data. To address this gap, this paper focuses on conducting a comprehensive review of available AI reliability data and establishing DR-AIR: a data repository for AI reliability. Specifically, we introduce key measurements and data types for assessing AI reliability, along with the methodologies used to collect these data. We also provide a detailed description of the currently available datasets with illustrative examples. Furthermore, we outline the setup of the DR-AIR repository and demonstrate its practical applications. This repository provides easy access to datasets specifically curated for AI reliability research. We believe these efforts will significantly benefit the AI research community by facilitating access to valuable reliability data and promoting collaboration across various academic domains within AI. We conclude our paper with a call to action, encouraging the research community to contribute and share AI reliability data to further advance this critical field of study.
Locally-Deployed Chain-of-Thought (CoT) Reasoning Model in Chemical Engineering: Starting from 30 Experimental Data
Authors: Tianhang Zhou, Yingchun Niu, Xingying Lan, Chunming Xu
Date: 2025-02-17 00:00:00
Tasks: Gaussian Processes, Language Modeling, Language Modelling, Large Language Model, Molecular Property Prediction, Prediction, Property Prediction

In the field of chemical engineering, traditional data-processing and prediction methods face significant challenges. Machine-learning and large-language models (LLMs) also have their respective limitations. This paper explores the application of the Chain-of-Thought (CoT) reasoning model in chemical engineering, starting from 30 experimental data points. By integrating traditional surrogate models like Gaussian processes and random forests with powerful LLMs such as DeepSeek-R1, a hierarchical architecture is proposed. Two CoT-building methods, Large Language Model-Chain of Thought (LLM-CoT) and Machine Learning-Large Language Model-Chain of Thought (ML-LLM-CoT), are studied. The LLM-CoT combines local models DeepSeek-r1:14b and Qwen2:7b with Ollama. The ML-LLM-CoT integrates a pre-trained Gaussian ML model with the LLM-based CoT framework. Our results show that during construction, ML-LLM-CoT is more efficient. It only has 2 points that require rethink and a total of 4 rethink times, while LLM-CoT has 5 points that need to be re-thought and 34 total rethink times. In predicting the solubility of 20 molecules with dissimilar structures, the number of molecules with a prediction deviation higher than 100\% for the Gaussian model, LLM-CoT, and ML-LLM-CoT is 7, 6, and 4 respectively. These results indicate that ML-LLM-CoT performs better in controlling the number of high-deviation molecules, optimizing the average deviation, and achieving a higher success rate in solubility judgment, providing a more reliable method for chemical engineering and molecular property prediction. This study breaks through the limitations of traditional methods and offers new solutions for rapid property prediction and process optimization in chemical engineering.
OGBoost: A Python Package for Ordinal Gradient Boosting
Authors: Mansour T. A. Sharabiani, Alex Bottle, Alireza S. Mahani
Date: 2025-02-19 00:00:00
Tasks: regression

This paper introduces OGBoost, a scikit-learn-compatible Python package for ordinal regression using gradient boosting. Ordinal variables (e.g., rating scales, quality assessments) lie between nominal and continuous data, necessitating specialized methods that reflect their inherent ordering. Built on a coordinate-descent approach for optimization and the latent-variable framework for ordinal regression, OGBoost performs joint optimization of a latent continuous regression function (functional gradient descent) and a threshold vector that converts the latent continuous value into discrete class probabilities (classical gradient descent). In addition to the stanadard methods for scikit-learn classifiers, the GradientBoostingOrdinal class implements a "decision_function" that returns the (scalar) value of the latent function for each observation, which can be used as a high-resolution alternative to class labels for comparing and ranking observations. The class has the option to use cross-validation for early stopping rather than a single holdout validation set, a more robust approach for small and/or imbalanced datasets. Furthermore, users can select base learners with different underlying algorithms and/or hyperparameters for use throughout the boosting iterations, resulting in a `heterogeneous' ensemble approach that can be used as a more efficient alternative to hyperparameter tuning (e.g. via grid search). We illustrate the capabilities of OGBoost through examples, using the wine quality dataset from the UCI respository. The package is available on PyPI and can be installed via "pip install ogboost".
A Pathwise Coordinate Descent Algorithm for LASSO Penalized Quantile Regression
Authors: Sanghee Kim, Sumanta Basu
Date: 2025-02-17 00:00:00
Tasks: quantile regression, regression

$\ell_1$ penalized quantile regression is used in many fields as an alternative to penalized least squares regressions for high-dimensional data analysis. Existing algorithms for penalized quantile regression either use linear programming, which does not scale well in high dimension, or an approximate coordinate descent (CD) which does not solve for exact coordinatewise minimum of the nonsmooth loss function. Further, neither approaches build fast, pathwise algorithms commonly used in high-dimensional statistics to leverage sparsity structure of the problem in large-scale data sets. To avoid the computational challenges associated with the nonsmooth quantile loss, some recent works have even advocated using smooth approximations to the exact problem. In this work, we develop a fast, pathwise coordinate descent algorithm to compute exact $\ell_1$ penalized quantile regression estimates for high-dimensional data. We derive an easy-to-compute exact solution for the coordinatewise nonsmooth loss minimization, which, to the best of our knowledge, has not been reported in the literature. We also employ a random perturbation strategy to help the algorithm avoid getting stuck along the regularization path. In simulated data sets, we show that our algorithm runs substantially faster than existing alternatives based on approximate CD and linear program, while retaining the same level of estimation accuracy.
Reverse Markov Learning: Multi-Step Generative Models for Complex Distributions
Authors: Xinwei Shen, Nicolai Meinshausen, Tong Zhang
Date: 2025-02-19 00:00:00
Tasks: Dimensionality Reduction

Learning complex distributions is a fundamental challenge in contemporary applications. Generative models, such as diffusion models, have demonstrated remarkable success in overcoming many limitations of traditional statistical methods. Shen and Meinshausen (2024) introduced engression, a generative approach based on scoring rules that maps noise (and covariates, if available) directly to data. While effective, engression struggles with highly complex distributions, such as those encountered in image data. In this work, we extend engression to improve its capability in learning complex distributions. We propose a framework that defines a general forward process transitioning from the target distribution to a known distribution (e.g., Gaussian) and then learns a reverse Markov process using multiple engression models. This reverse process reconstructs the target distribution step by step. Our approach supports general forward processes, allows for dimension reduction, and naturally discretizes the generative process. As a special case, when using a diffusion-based forward process, our framework offers a method to discretize the training and inference of diffusion models efficiently. Empirical evaluations on simulated and climate data validate our theoretical insights, demonstrating the effectiveness of our approach in capturing complex distributions.
Green LIME: Improving AI Explainability through Design of Experiments
Authors: Alexandra Stadler, Werner G. Müller, Radoslav Harman
Date: 2025-02-18 00:00:00

In artificial intelligence (AI), the complexity of many models and processes often surpasses human interpretability, making it challenging to understand why a specific prediction is made. This lack of transparency is particularly problematic in critical fields like healthcare, where trust in a model's predictions is paramount. As a result, the explainability of machine learning (ML) and other complex models has become a key area of focus. Efforts to improve model interpretability often involve experimenting with AI systems and approximating their behavior through simpler mechanisms. However, these procedures can be resource-intensive. Optimal design of experiments, which seeks to maximize the information obtained from a limited number of observations, offers promising methods for improving the efficiency of these explainability techniques. To demonstrate this potential, we explore Local Interpretable Model-agnostic Explanations (LIME), a widely used method introduced by Ribeiro, Singh, and Guestrin, 2016. LIME provides explanations by generating new data points near the instance of interest and passing them through the model. While effective, this process can be computationally expensive, especially when predictions are costly or require many samples. LIME is highly versatile and can be applied to a wide range of models and datasets. In this work, we focus on models involving tabular data, regression tasks, and linear models as interpretable local approximations. By utilizing optimal design of experiments' techniques, we reduce the number of function evaluations of the complex model, thereby reducing the computational effort of LIME by a significant amount. We consider this modified version of LIME to be energy-efficient or "green".