pwc-archive/papers-with-abstracts huggingface dataset:
Agent-Based Simulation of a Perpetual Futures Market
Authors: Ramshreyas Rao
Date: 2025-01-16 00:00:00

I introduce an agent-based model of a Perpetual Futures market with heterogeneous agents trading via a central limit order book. Perpetual Futures (henceforth Perps) are financial derivatives introduced by the economist Robert Shiller, designed to peg their price to that of the underlying Spot market. This paper extends the limit order book model of Chiarella et al. (2002) by taking their agent and orderbook parameters, designed for a simple stock exchange, and applying it to the more complex environment of a Perp market with long and short traders who exhibit both positional and basis-trading behaviors. I find that despite the simplicity of the agent behavior, the simulation is able to reproduce the most salient feature of a Perp market, the pegging of the Perp price to the underlying Spot price. In contrast to fundamental simulations of stock markets which aim to reproduce empirically observed stylized facts such as the leptokurtosis and heteroscedasticity of returns, volatility clustering and others, in derivatives markets many of these features are provided exogenously by the underlying Spot price signal. This is especially true of Perps since the derivative is designed to mimic the price of the Spot market. Therefore, this paper will focus exclusively on analyzing how market and agent parameters such as order lifetime, trading horizon and spread affect the premiums at which Perps trade with respect to the underlying Spot market. I show that this simulation provides a simple and robust environment for exploring the dynamics of Perpetual Futures markets and their microstructure in this regard. Lastly, I explore the ability of the model to reproduce the effects of biasing long traders to trade positionally and short traders to basis-trade, which was the original intention behind the market design, and is a tendency observed empirically in real Perp markets.
Automated Market Makers: Toward More Profitable Liquidity Provisioning Strategies
Authors: Thanos Drossos, Daniel Kirste, Niclas Kannengießer, Ali Sunyaev
Date: 2025-01-14 00:00:00
Tasks: Position

To trade tokens in cryptoeconomic systems, automated market makers (AMMs) typically rely on liquidity providers (LPs) that deposit tokens in exchange for rewards. To profit from such rewards, LPs must use effective liquidity provisioning strategies. However, LPs lack guidance for developing such strategies, which often leads them to financial losses. We developed a measurement model based on impermanent loss to analyze the influences of key parameters (i.e., liquidity pool type, position duration, position range size, and position size) of liquidity provisioning strategies on LPs' returns. To reveal the influences of those key parameters on LPs' profits, we used the measurement model to analyze 700 days of historical liquidity provision data of Uniswap v3. By uncovering the influences of key parameters of liquidity provisioning strategies on profitability, this work supports LPs in developing more profitable strategies.
A Modern Paradigm for Algorithmic Trading
Authors: James B. Glattfelder, Thomas Houweling, Richard B. Olsen
Date: 2025-01-10 00:00:00
Tasks: Algorithmic Trading

We introduce a novel framework for developing fully-automated trading model algorithms. Unlike the traditional approach, which is grounded in analytical complexity favored by most quantitative analysts, we propose a paradigm shift that embraces real-world complexity. This approach leverages key concepts relating to self-organization, emergence, complex systems theory, scaling laws, and utilizes an event-based reframing of time. In closing, we describe an example algorithm that incorporates the outlined elements, called the Delta Engine.
Optimal Execution Strategies Incorporating Internal Liquidity Through Market Making
Authors: Yusuke Morimoto
Date: 2024-12-28 00:00:00

This paper introduces a new algorithmic execution model that integrates interbank limit and market orders with internal liquidity generated through market making. Based on the Cartea et al.\cite{cartea2015algorithmic} framework, we incorporate market impact in interbank orders while excluding it for internal market-making transactions. Our model aims to optimize the balance between interbank and internal liquidity, reducing market impact and improving execution efficiency.
Generative Modeling: A Review
Authors: Maria Nareklishvili, Nick Polson, Vadim Sokolov
Date: 2024-12-24 00:00:00
Tasks: Bayesian Inference, Dimensionality Reduction, feature selection

Generative methods (Gen-AI) are reviewed with a particular goal of solving tasks in machine learning and Bayesian inference. Generative models require one to simulate a large training dataset and to use deep neural networks to solve a supervised learning problem. To do this, we require high-dimensional regression methods and tools for dimensionality reduction (a.k.a. feature selection). The main advantage of Gen-AI methods is their ability to be model-free and to use deep neural networks to estimate conditional densities or posterior quintiles of interest. To illustrate generative methods , we analyze the well-known Ebola data set. Finally, we conclude with directions for future research.
Global Optimization by Basin-Hopping and the Lowest Energy Structures of Lennard-Jones Clusters Containing up to 110 Atoms
Authors: David Wales, Jonathan Doye
Date: 1998-03-27 00:00:00
Tasks: global-optimization

We describe a global optimization technique using `basin-hopping' in which the potential energy surface is transformed into a collection of interpenetrating staircases. This method has been designed to exploit the features which recent work suggests must be present in an energy landscape for efficient relaxation to the global minimum. The transformation associates any point in configuration space with the local minimum obtained by a geometry optimization started from that point, effectively removing transition state regions from the problem. However, unlike other methods based upon hypersurface deformation, this transformation does not change the global minimum. The lowest known structures are located for all Lennard-Jones clusters up to 110 atoms, including a number that have never been found before in unbiased searches.
A Novel Task-Driven Method with Evolvable Interactive Agents Using Event Trees for Enhanced Emergency Decision Support
Authors: Xingyu Xiao, Peng Chen, Ben Qi, Jingang Liang, Jiejuan Tong, Haitao Wang
Date: 2024-12-24 00:00:00
Tasks: Decision Making

As climate change and other global challenges increase the likelihood of unforeseen emergencies, the limitations of human-driven strategies in critical situations become more pronounced. Inadequate pre-established emergency plans can lead operators to become overwhelmed during complex systems malfunctions. This study addresses the urgent need for agile decision-making in response to various unforeseen incidents through a novel approach, EvoTaskTree (a task-driven method with evolvable interactive agents using event trees for emergency decision support). This advanced approach integrates two types of agents powered by large language models (LLMs): task executors, responsible for executing critical procedures, and task validators, ensuring the efficacy of those actions. By leveraging insights from event tree analysis, our framework encompasses three crucial tasks: initiating event subevent analysis, event tree header event analysis, and decision recommendations. The agents learn from both successful and unsuccessful responses from these tasks. Finally, we use nuclear power plants as a demonstration of a safety-critical system. Our findings indicate that the designed agents are not only effective but also outperform existing approaches, achieving an impressive accuracy rate of up to 100 % in processing previously unencoun32 tered incident scenarios. This paper demonstrates that EvoTaskTree significantly enhances the rapid formulation of emergency decision-making.
A Multimodal Social Agent
Authors: Athina Bikaki, Ioannis A. Kakadiaris
Date: 2024-12-11 00:00:00
Tasks: Common Sense Reasoning, Decision Making, Question Answering, Visual Question Answering

In recent years, large language models (LLMs) have demonstrated remarkable progress in common-sense reasoning tasks. This ability is fundamental to understanding social dynamics, interactions, and communication. However, the potential of integrating computers with these social capabilities is still relatively unexplored. However, the potential of integrating computers with these social capabilities is still relatively unexplored. This paper introduces MuSA, a multimodal LLM-based agent that analyzes text-rich social content tailored to address selected human-centric content analysis tasks, such as question answering, visual question answering, title generation, and categorization. It uses planning, reasoning, acting, optimizing, criticizing, and refining strategies to complete a task. Our approach demonstrates that MuSA can automate and improve social content analysis, helping decision-making processes across various applications. We have evaluated our agent's capabilities in question answering, title generation, and content categorization tasks. MuSA performs substantially better than our baselines.
FewVS: A Vision-Semantics Integration Framework for Few-Shot Image Classification
Authors: Zhuoling Li, Yong Wang, Kaitong Li
Date: 2024-10-28 00:00:00
Proceeding: Proceedings of the 32nd ACM International Conference on Multimedia 2024 10
Tasks: Few-Shot Image Classification, Few-Shot Learning, image-classification, Image Classification

Some recent methods address few-shot image classification by extracting semantic information from class names and devising mechanisms for aligning vision and semantics to integrate information from both modalities. However, class names provide only limited information, which is insufficient to capture the visual details in images. As a result, such vision-semantics alignment is inherently biased, leading to suboptimal integration outcomes. In this paper, we avoid such biased vision-semantics alignment by introducing CLIP, a natural bridge between vision and semantics, and enforcing unbiased vision-vision alignment as a proxy task. Specifically, we align features encoded from the few-shot encoder and CLIP's vision encoder on the same image. This alignment is accomplished through a linear projection layer, with a training objective formulated using optimal transport-based assignment prediction. Thanks to...
Spectrum Prediction via Graph Structure Learning
Authors: Dong Yang, Yue Wang, Zhipeng Cai, Yingshu Li
Date: 2024-10-07 00:00:00
Proceeding: 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall) 2024 10
Tasks: Graph structure learning, Prediction, Time Series Prediction

With the rapid development of machine learning technologies, data-driven spectrum prediction enables intelligent dynamic spectrum access to alleviate the bottleneck of spectrum resource scarcity and congestion. However, spectrum prediction still faces some key challenges, including how to exploit the implicit but crucial multi-band correlations in wideband spectrum data, and how to capture the temporal dynamics across different bands. Due to the ignorance of such crucial features inherent from spectrum occupancy patterns, existing learning-based spectrum prediction methods unfortunately suffer from inaccurate prediction performance. To fill this gap, this paper develops a novel model of graph convolutional regression neural network (GCRNN), by introducing efficient graph structure learning (GSL-GCRNN) for dynamic multi-band spectrum prediction. The proposed GSL-GCRNN model is designed to adaptively learn both the multi-band and temporal correlations in dynamic wideband spectrum scenarios. Empowered by the graph structure estimator, graph convolutional networks are fueled to effectively extract the correlations in the frequency domain, followed by gated recurrent unit networks to further extract the temporal correlations of each band. It is worth noting that the graph structure estimator further enables to learn the multi-band correlations across different time periods on-the-fly, enhancing the accuracy of wideband spectrum prediction in dynamic environments. Simulation results verify that our GSLGCRNN approach outperforms the benchmark methods.
A Multi-Scale Feature Extraction and Fusion Deep Learning Method for Classification of Wheat Diseases
Authors: Sajjad Saleem, Adil Hussain, Nabila Majeed, Zahid Akhtar, Kamran Siddique
Date: 2025-01-17 00:00:00
Tasks: Classification, Image Segmentation, Semantic Segmentation

Wheat is an important source of dietary fiber and protein that is negatively impacted by a number of risks to its growth. The difficulty of identifying and classifying wheat diseases is discussed with an emphasis on wheat loose smut, leaf rust, and crown and root rot. Addressing conditions like crown and root rot, this study introduces an innovative approach that integrates multi-scale feature extraction with advanced image segmentation techniques to enhance classification accuracy. The proposed method uses neural network models Xception, Inception V3, and ResNet 50 to train on a large wheat disease classification dataset 2020 in conjunction with an ensemble of machine vision classifiers, including voting and stacking. The study shows that the suggested methodology has a superior accuracy of 99.75% in the classification of wheat diseases when compared to current state-of-the-art approaches. A deep learning ensemble model Xception showed the highest accuracy.
Association between dietary inflammatory index and Stroke in the US population: evidence from NHANES 1999–2018
Authors: Yukang Mao1, 2†, Jiayi Weng1†, Qiyang Xie2†, Lida Wu3, Yanling Xuan4, Jun Zhang1* and Jun Han5*
Date: 2024-02-02 00:00:00
Proceeding: BMC Public Health 2024 2
Tasks: Nutrition, regression

Background There is an increasing awareness that diet-related inflammation may have an impact on the stroke.
Herein, our goal was to decipher the association of dietary inflammatory index (DII) with stroke in the US general
population.
Methods We collected the cross-sectional data of 44,019 participants of the National Health and Nutrition
Examination Survey (NHANES) 1999–2018. The association of DII with stroke was estimated using weighted
multivariate logistic regression, with its nonlinearity being examined by restricted cubic spline (RCS) regression. The
least absolute shrinkage and selection operator (LASSO) regression was applied for identifying key stroke-related
dietary factors, which was then included in the establishment of a risk prediction nomogram model, with the receiver
operating characteristic (ROC) curve being built to evaluate its discriminatory power for stroke.
Results After confounder adjustment, the adjusted odds ratios (ORs) with 95% confidence intervals (CIs) for stroke
across higher DII quartiles were 1.19 (0.94–1.54), 1.46 (1.16–1.84), and 1.87 (1.53–2.29) compared to the lowest quartile,
respectively. The RCS curve showed a nonlinear and positive association between DII and stroke. The nomogram
model based on key dietary factors identified by LASSO regression displayed a considerable predicative value for
stroke, with an area under the curve (AUC) of 79.8% (78.2–80.1%).
Conclusions Our study determined a nonlinear and positive association between DII and stroke in the US general
population. Given the intrinsic limitations of cross-sectional study design, it is necessary to conduct more research to
ensure the causality of such association.
Keywords NHANES, DII, Stroke, Cross-sectional study, LASSO
Enhancing the De-identification of Personally Identifiable Information in Educational Data
Authors: Y. Shen, Z. Ji, J. Lin, K. R. Koedginer
Date: 2025-01-14 00:00:00
Tasks: De-identification

Protecting Personally Identifiable Information (PII), such as names, is a critical requirement in learning technologies to safeguard student and teacher privacy and maintain trust. Accurate PII detection is an essential step toward anonymizing sensitive information while preserving the utility of educational data. Motivated by recent advancements in artificial intelligence, our study investigates the GPT-4o-mini model as a cost-effective and efficient solution for PII detection tasks. We explore both prompting and fine-tuning approaches and compare GPT-4o-mini's performance against established frameworks, including Microsoft Presidio and Azure AI Language. Our evaluation on two public datasets, CRAPII and TSCC, demonstrates that the fine-tuned GPT-4o-mini model achieves superior performance, with a recall of 0.9589 on CRAPII. Additionally, fine-tuned GPT-4o-mini significantly improves precision scores (a threefold increase) while reducing computational costs to nearly one-tenth of those associated with Azure AI Language. Furthermore, our bias analysis reveals that the fine-tuned GPT-4o-mini model consistently delivers accurate results across diverse cultural backgrounds and genders. The generalizability analysis using the TSCC dataset further highlights its robustness, achieving a recall of 0.9895 with minimal additional training data from TSCC. These results emphasize the potential of fine-tuned GPT-4o-mini as an accurate and cost-effective tool for PII detection in educational data. It offers robust privacy protection while preserving the data's utility for research and pedagogical analysis. Our code is available on GitHub: https://github.com/AnonJD/PrivacyAI
3rd Workshop on Maritime Computer Vision (MaCVi) 2025: Challenge Results
Authors: Benjamin Kiefer, Lojze Žust, Jon Muhovič, Matej Kristan, Janez Perš, Matija Teršek, Uma Mudenagudi Chaitra Desai, Arnold Wiliem, Marten Kreis, Nikhil Akalwadi, Yitong Quan, Zhiqiang Zhong, Zhe Zhang, Sujie Liu, Xuran Chen, Yang Yang, Matej Fabijanić, Fausto Ferreira, Seongju Lee, Junseok Lee, Kyoobin Lee, Shanliang Yao, Runwei Guan, Xiaoyu Huang, Yi Ni, Himanshu Kumar, Yuan Feng, Yi-Ching Cheng, Chia-Ming Lee, Jannik Sheikh, Andreas Michel, Wolfgang Gross, Martin Weinmann, Josip Šarić, Yipeng Lin, Xiang Yang, Nan Jiang, Yutang Lu, Fei Feng, Ali Awad, Evan Lucas, Ashraf Saleem, Ching-Heng Cheng, Yu-Fan Lin, Tzu-Yu Lin, Chih-Chung Hsu
Date: 2025-01-17 00:00:00

The 3rd Workshop on Maritime Computer Vision (MaCVi) 2025 addresses maritime computer vision for Unmanned Surface Vehicles (USV) and underwater. This report offers a comprehensive overview of the findings from the challenges. We provide both statistical and qualitative analyses, evaluating trends from over 700 submissions. All datasets, evaluation code, and the leaderboard are available to the public at https://macvi.org/workshop/macvi25.
Agent4Edu: Generating Learner Response Data by Generative Agents for Intelligent Education Systems
Authors: Weibo Gao, Qi Liu, Linan Yue, Fangzhou Yao, Rui Lv, Zheng Zhang, Hao Wang, Zhenya Huang
Date: 2025-01-17 00:00:00
Tasks: Response Generation

Personalized learning represents a promising educational strategy within intelligent educational systems, aiming to enhance learners' practice efficiency. However, the discrepancy between offline metrics and online performance significantly impedes their progress. To address this challenge, we introduce Agent4Edu, a novel personalized learning simulator leveraging recent advancements in human intelligence through large language models (LLMs). Agent4Edu features LLM-powered generative agents equipped with learner profile, memory, and action modules tailored to personalized learning algorithms. The learner profiles are initialized using real-world response data, capturing practice styles and cognitive factors. Inspired by human psychology theory, the memory module records practice facts and high-level summaries, integrating reflection mechanisms. The action module supports various behaviors, including exercise understanding, analysis, and response generation. Each agent can interact with personalized learning algorithms, such as computerized adaptive testing, enabling a multifaceted evaluation and enhancement of customized services. Through a comprehensive assessment, we explore the strengths and weaknesses of Agent4Edu, emphasizing the consistency and discrepancies in responses between agents and human learners. The code, data, and appendix are publicly available at https://github.com/bigdata-ustc/Agent4Edu.
Large language models for automated scholarly paper review: A survey
Authors: Zhenzhen Zhuang, jiandong chen, Hongfeng Xu, Yuwen Jiang, Jialiang Lin
Date: 2025-01-17 00:00:00
Tasks: Survey

Large language models (LLMs) have significantly impacted human society, influencing various domains. Among them, academia is not simply a domain affected by LLMs, but it is also the pivotal force in the development of LLMs. In academic publication, this phenomenon is represented during the incorporation of LLMs into the peer review mechanism for reviewing manuscripts. LLMs hold transformative potential for the full-scale implementation of automated scholarly paper review (ASPR), but they also pose new issues and challenges that need to be addressed. In this survey paper, we aim to provide a holistic view of ASPR in the era of LLMs. We begin with a survey to find out which LLMs are used to conduct ASPR. Then, we review what ASPR-related technological bottlenecks have been solved with the incorporation of LLM technology. After that, we move on to explore new methods, new datasets, new source code, and new online systems that come with LLMs for ASPR. Furthermore, we summarize the performance and issues of LLMs in ASPR, and investigate the attitudes and reactions of publishers and academia to ASPR. Lastly, we discuss the challenges and future directions associated with the development of LLMs for ASPR. This survey serves as an inspirational reference for the researchers and can promote the progress of ASPR for its actual implementation.
Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models
Authors: Pit Neitemeier, Björn Deiseroth, Constantin Eichenberg, Lukas Balles
Date: 2025-01-17 00:00:00
Tasks: Language Modelling, Word Embeddings

Tokenization is a fundamental step in natural language processing, breaking text into units that computational models can process. While learned subword tokenizers have become the de-facto standard, they present challenges such as large vocabularies, limited adaptability to new domains or languages, and sensitivity to spelling errors and variations. To overcome these limitations, we investigate a hierarchical architecture for autoregressive language modelling that combines character-level and word-level processing. It employs a lightweight character-level encoder to convert character sequences into word embeddings, which are then processed by a word-level backbone model and decoded back into characters via a compact character-level decoder. This method retains the sequence compression benefits of word-level tokenization without relying on a rigid, predefined vocabulary. We demonstrate, at scales up to 7 billion parameters, that hierarchical transformers match the downstream task performance of subword-tokenizer-based models while exhibiting significantly greater robustness to input perturbations. Additionally, during continued pretraining on an out-of-domain language, our model trains almost twice as fast, achieves superior performance on the target language, and retains more of its previously learned knowledge. Hierarchical transformers pave the way for NLP systems that are more robust, flexible, and generalizable across languages and domains.