pwc-archive/papers-with-abstracts huggingface dataset:
The Aging Metabolite Database with Summary Statistics
Authors: Delilah Milner, Oliver Fiehn
Date: 2024-10-22 00:00:00
Tasks: Human Aging

Thousands of metabolomic papers are published each year, creating challenges for scientists to combine results and yield conclusions that span across studies. Literature databases such as the Human Metabolome Database provide summaries of metabolite detections and relevance, but it does not focus on specific processes, such as aging. Another database, MetaboAge, focuses specifically on how metabolite concentrations change with age. However, both databases can only search metabolites individually. Both databases lack an easily displayed overview of all the information provided, and they do not combine datasets to validate results across studies. This project aims to design a database from data extracted from the literature that cover studies across species, include query options, and combine data to get statistically sound results. The Aging Metabolite Database summarizes aging-related information for almost 2,700 non-unique metabolites and was created from 104 publications. A sub-database was created to focus on metabolite plasma levels in human aging and, including metadata from 27 papers. This resulted in 6,500 data points from 1,167 metabolites. 12 of these papers were selected for the summary results section of the database. Across these 12 studies, 53 common metabolites were reported and can be queried in 163 summary statistics, for example by age and sex. Summary statistics are provided for all aging and sex combinations. This information assists users to compare the results of their own age-related studies to published literature. Combining multiple datasets results in a more solid understanding of validated metabolic changes during the aging process.
Inferring Option Movements Through Residual Transactions: A Quantitative Model
Authors: Carl von Havighorst, Vincil Bishop III
Date: 2024-10-21 00:00:00
Tasks: Portfolio Optimization

This research presents a novel approach to predicting option movements by analyzing residual transactions, which are trades that deviate from standard hedging activities. Unlike traditional methods that primarily focus on open interest and trading volume, this study argues that residuals can reveal nuanced insights into institutional sentiment and strategic positioning. By examining these deviations, the model identifies early indicators of market trends, providing a refined framework for forecasting option prices. The proposed model integrates classical machine learning and regression techniques to analyze patterns in high frequency trading data, capturing complex, non linear relationships. This predictive framework allows traders to anticipate shifts in option values, enhancing strategies for better market timing, risk management, and portfolio optimization. The model's adaptability, driven by real time data processing, makes it particularly effective in fast paced trading environments, where early detection of institutional behavior is crucial for gaining a competitive edge. Overall, this research contributes to the field of options trading by offering a strategic tool that detects early market signals, optimizing trading decisions based on predictive insights derived from residual trading patterns. This approach bridges the gap between conventional metrics and the subtle behaviors of institutional players, marking a significant advancement in options market analysis.
Temporal Relational Reasoning of Large Language Models for Detecting Stock Portfolio Crashes
Authors: Kelvin J. L. Koa, Yunshan Ma, Ritchie Ng, Huanhuan Zheng, Tat-Seng Chua
Date: 2024-10-07 00:00:00
Tasks: Articles, Relational Reasoning

Stock portfolios are often exposed to rare consequential events (e.g., 2007 global financial crisis, 2020 COVID-19 stock market crash), as they do not have enough historical information to learn from. Large Language Models (LLMs) now present a possible tool to tackle this problem, as they can generalize across their large corpus of training data and perform zero-shot reasoning on new events, allowing them to detect possible portfolio crash events without requiring specific training data. However, detecting portfolio crashes is a complex problem that requires more than basic reasoning abilities. Investors need to dynamically process the impact of each new information found in the news articles, analyze the the relational network of impacts across news events and portfolio stocks, as well as understand the temporal context between impacts across time-steps, in order to obtain the overall aggregated effect on the target portfolio. In this work, we propose an algorithmic framework named Temporal Relational Reasoning (TRR). It seeks to emulate the spectrum of human cognitive capabilities used for complex problem-solving, which include brainstorming, memory, attention and reasoning. Through extensive experiments, we show that TRR is able to outperform state-of-the-art solutions on detecting stock portfolio crashes, and demonstrate how each of the proposed components help to contribute to its performance through an ablation study. Additionally, we further explore the possible applications of TRR by extending it to other related complex problems, such as the detection of possible global crisis events in Macroeconomics.
Financial Performance and Economic Implications of COFCO's Strategic Acquisition of Mengniu
Authors: Jessica Ji, David Yu
Date: 2024-10-07 00:00:00

This paper examines the merger and acquisition (M&A) process between COFCO and Mengniu Dairy, exploring the motivations behind this strategic move and identifying its key aspects. By analyzing both the financial and non-financial contributions of Mengniu Dairy to COFCO, this study provides valuable insights and references for future corporate M&A activities. The theoretical significance of this research lies in its focus on the relatively underexplored area of M&A within the dairy industry, particularly in terms of M&A contributions. Using the COFCO-Mengniu case as a model, the study broadens current research perspectives by assessing the impact of M&A from financial and non-financial standpoints, enriching the body of literature on dairy industry M&As.
Functional Clustering of Discount Functions for Behavioral Investor Profiling
Authors: Annamaria Porreca, Viviana Ventre, Roberta Martino, Salvador Cruz Rambaud, Fabrizio Maturo
Date: 2024-10-07 00:00:00
Tasks: Clustering, Decision Making, Decision Making Under Uncertainty

Classical finance models are based on the premise that investors act rationally and utilize all available information when making portfolio decisions. However, these models often fail to capture the anomalies observed in intertemporal choices and decision-making under uncertainty, particularly when accounting for individual differences in preferences and consumption patterns. Such limitations hinder traditional finance theory's ability to address key questions like: How do personal preferences shape investment choices? What drives investor behaviour? And how do individuals select their portfolios? One prominent contribution is Pompian's model of four Behavioral Investor Types (BITs), which links behavioural finance studies with Keirsey's temperament theory, highlighting the role of personality in financial decision-making. Yet, traditional parametric models struggle to capture how these distinct temperaments influence intertemporal decisions, such as how individuals evaluate trade-offs between present and future outcomes. To address this gap, the present study employs Functional Data Analysis (FDA) to specifically investigate temporal discounting behaviours revealing nuanced patterns in how different temperaments perceive and manage uncertainty over time. Our findings show heterogeneity within each temperament, suggesting that investor profiles are far more diverse than previously thought. This refined classification provides deeper insights into the role of temperament in shaping intertemporal financial decisions, offering practical implications for financial advisors to better tailor strategies to individual risk preferences and decision-making styles.
Advancements in Visual Language Models for Remote Sensing: Datasets, Capabilities, and Enhancement Techniques
Authors: Lijie Tao, Haokui Zhang, Haizhao Jing, Yu Liu, Dawei Yan, Guoting Wei, Xizhe Xue
Date: 2024-10-15 00:00:00

Recently, the remarkable success of ChatGPT has sparked a renewed wave of interest in artificial intelligence (AI), and the advancements in visual language models (VLMs) have pushed this enthusiasm to new heights. Differring from previous AI approaches that generally formulated different tasks as discriminative models, VLMs frame tasks as generative models and align language with visual information, enabling the handling of more challenging problems. The remote sensing (RS) field, a highly practical domain, has also embraced this new trend and introduced several VLM-based RS methods that have demonstrated promising performance and enormous potential. In this paper, we first review the fundamental theories related to VLM, then summarize the datasets constructed for VLMs in remote sensing and the various tasks they addressed. Finally, we categorize the improvement methods into three main parts according to the core components of VLMs and provide a detailed introduction and comparison of these methods. A project associated with this review has been created at https://github.com/taolijie11111/VLMs-in-RS-review.
Code-Driven Law NO, Normware SI!
Authors: Giovanni Sileno
Date: 2024-10-05 00:00:00

With the digitalization of society, the interest, the debates and the research efforts concerning "code", "law", "artificial intelligence", and their various relationships, have been widely increasing. Yet, most arguments primarily focus on contemporary computational methods and artifacts (inferential models constructed via machine-learning methods, rule-based systems, smart contracts), rather than attempting to identify more fundamental mechanisms. Aiming to go beyond this conceptual limitation, this paper introduces and elaborates on "normware" as an explicit additional stance -- complementary to software and hardware -- for the interpretation and the design of artificial devices. By means of a few examples, I will argue that a normware-centred perspective provides a more adequate abstraction to study and design interactions between computational systems and human institutions, and may help with the design and development of technical interventions within wider socio-technical views.
Representing Web Applications As Knowledge Graphs
Authors: Yogesh Chandrasekharuni
Date: 2024-10-06 00:00:00
Tasks: Knowledge Graphs

Traditional methods for crawling and parsing web applications predominantly rely on extracting hyperlinks from initial pages and recursively following linked resources. This approach constructs a graph where nodes represent unstructured data from web pages, and edges signify transitions between them. However, these techniques are limited in capturing the dynamic and interactive behaviors inherent to modern web applications. In contrast, the proposed method models each node as a structured representation of the application's current state, with edges reflecting user-initiated actions or transitions. This structured representation enables a more comprehensive and functional understanding of web applications, offering valuable insights for downstream tasks such as automated testing and behavior analysis.
Masked Autoencoder with Swin Transformer Network for Mitigating Electrode Shift in HD-EMG-based Gesture Recognition
Authors: Kasra Laamerad, Mehran Shabanpour, Md. Rabiul Islam, Arash Mohammadi
Date: 2024-10-07 00:00:00
Tasks: Decoder, Gesture Recognition

Multi-channel surface Electromyography (sEMG), also referred to as high-density sEMG (HD-sEMG), plays a crucial role in improving gesture recognition performance for myoelectric control. Pattern recognition models developed based on HD-sEMG, however, are vulnerable to changing recording conditions (e.g., signal variability due to electrode shift). This has resulted in significant degradation in performance across subjects, and sessions. In this context, the paper proposes the Masked Autoencoder with Swin Transformer (MAST) framework, where training is performed on a masked subset of HDsEMG channels. A combination of four masking strategies, i.e., random block masking; temporal masking; sensor-wise random masking, and; multi-scale masking, is used to learn latent representations and increase robustness against electrode shift. The masked data is then passed through MAST's three-path encoder-decoder structure, leveraging a multi-path Swin-Unet architecture that simultaneously captures time-domain, frequency-domain, and magnitude-based features of the underlying HD-sEMG signal. These augmented inputs are then used in a self-supervised pre-training fashion to improve the model's generalization capabilities. Experimental results demonstrate the superior performance of the proposed MAST framework in comparison to its counterparts.
EmoGene: Audio-Driven Emotional 3D Talking-Head Generation
Authors: Wenqing Wang, Yun Fu
Date: 2024-10-07 00:00:00
Tasks: NeRF, Talking Head Generation

Audio-driven talking-head generation is a crucial and useful technology for virtual human interaction and film-making. While recent advances have focused on improving image fidelity and lip synchronization, generating accurate emotional expressions remains underexplored. In this paper, we introduce EmoGene, a novel framework for synthesizing high-fidelity, audio-driven video portraits with accurate emotional expressions. Our approach employs a variational autoencoder (VAE)-based audio-to-motion module to generate facial landmarks, which are concatenated with emotional embedding in a motion-to-emotion module to produce emotional landmarks. These landmarks drive a Neural Radiance Fields (NeRF)-based emotion-to-video module to render realistic emotional talking-head videos. Additionally, we propose a pose sampling method to generate natural idle-state (non-speaking) videos for silent audio inputs. Extensive experiments demonstrate that EmoGene outperforms previous methods in generating high-fidelity emotional talking-head videos.
Federated brain tumor segmentation: an extensive benchmark
Authors: Matthis Manthe, Stefan Duffner, Carole Lartizien
Date: 2024-10-07 00:00:00
Tasks: Brain Tumor Segmentation, Federated Learning, Medical Image Analysis, Privacy Preserving, Tumor Segmentation

Recently, federated learning has raised increasing interest in the medical image analysis field due to its ability to aggregate multi-center data with privacy-preserving properties. A large amount of federated training schemes have been published, which we categorize into global (one final model), personalized (one model per institution) or hybrid (one model per cluster of institutions) methods. However, their applicability on the recently published Federated Brain Tumor Segmentation 2022 dataset has not been explored yet. We propose an extensive benchmark of federated learning algorithms from all three classes on this task. While standard FedAvg already performs very well, we show that some methods from each category can bring a slight performance improvement and potentially limit the final model(s) bias toward the predominant data distribution of the federation. Moreover, we provide a deeper understanding of the behaviour of federated learning on this task through alternative ways of distributing the pooled dataset among institutions, namely an Independent and Identical Distributed (IID) setup, and a limited data setup.
Zero-Shot Vision-and-Language Navigation with Collision Mitigation in Continuous Environment
Authors: Seongjun Jeong, Gi-Cheon Kang, Joochan Kim, Byoung-Tak Zhang
Date: 2024-10-07 00:00:00
Tasks: Large Language Model, Vision and Language Navigation

We propose the zero-shot Vision-and-Language Navigation with Collision Mitigation (VLN-CM), which takes these considerations. VLN-CM is composed of four modules and predicts the direction and distance of the next movement at each step. We utilize large foundation models for each modules. To select the direction, we use the Attention Spot Predictor (ASP), View Selector (VS), and Progress Monitor (PM). The ASP employs a Large Language Model (e.g. ChatGPT) to split navigation instructions into attention spots, which are objects or scenes at the location to move to (e.g. a yellow door). The VS selects from panorama images provided at 30-degree intervals the one that includes the attention spot, using CLIP similarity. We then choose the angle of the selected image as the direction to move in. The PM uses a rule-based approach to decide which attention spot to focus on next, among multiple spots derived from the instructions. If the similarity between the current attention spot and the visual observations decreases consecutively at each step, the PM determines that the agent has passed the current spot and moves on to the next one. For selecting the distance to move, we employed the Open Map Predictor (OMP). The OMP uses panorama depth information to predict an occupancy mask. We then selected a collision-free distance in the predicted direction based on the occupancy mask. We evaluated our method using the validation data of VLN-CE. Our approach showed better performance than several baseline methods, and the OPM was effective in mitigating collisions for the agent.
FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome Prediction
Authors: Siqi Li, Qiming Wu, Xin Li, Di Miao, Chuan Hong, Wenjun Gu, Yuqing Shang, Yohei Okada, Michael Hao Chen, Mengying Yan, Yilin Ning, Marcus Eng Hock Ong, Nan Liu
Date: 2024-10-07 00:00:00
Tasks: Fairness, Federated Learning, Privacy Preserving

Objective: Mitigating algorithmic disparities is a critical challenge in healthcare research, where ensuring equity and fairness is paramount. While large-scale healthcare data exist across multiple institutions, cross-institutional collaborations often face privacy constraints, highlighting the need for privacy-preserving solutions that also promote fairness. Materials and Methods: In this study, we present Fair Federated Machine Learning (FairFML), a model-agnostic solution designed to reduce algorithmic bias in cross-institutional healthcare collaborations while preserving patient privacy. As a proof of concept, we validated FairFML using a real-world clinical case study focused on reducing gender disparities in cardiac arrest outcome prediction. Results: We demonstrate that the proposed FairFML framework enhances fairness in federated learning (FL) models without compromising predictive performance. Our findings show that FairFML improves model fairness by up to 65% compared to the centralized model, while maintaining performance comparable to both local and centralized models, as measured by receiver operating characteristic analysis. Discussion and Conclusion: FairFML offers a promising and flexible solution for FL collaborations, with its adaptability allowing seamless integration with various FL frameworks and models, from traditional statistical methods to deep learning techniques. This makes FairFML a robust approach for developing fairer FL models across diverse clinical and biomedical applications.
Rules, Cases, and Reasoning: Positivist Legal Theory as a Framework for Pluralistic AI Alignment
Authors: Nicholas A. Caputo
Date: 2024-10-07 00:00:00
Tasks: Fairness

Legal theory can address two related key problems of alignment: pluralism and specification. Alignment researchers must determine how to specify what is concretely meant by vague principles like helpfulness and fairness and they must ensure that their techniques do not exclude alternative perspectives on life and values. The law faces these same problems. Leading legal theories suggest the law solves these problems through the interaction of rules and cases, where general rules promulgated by a democratic authority are given specific content through their application over time. Concrete applications allow for convergence on practical meaning while preserving space for disagreement on values. These approaches suggest improvements to existing democratic alignment processes that use AI to create cases that give content to rules, allowing for more pluralist alignment.
Military Applications of Machine Learning: A Bibliometric Perspective
Authors: José Javier Galán, Ramón Alberto Carrasco, Antonio LaTorre
Date: 2024-10-07 00:00:00

The military environment generates a large amount of data of great importance, which makes necessary the use of machine learning for its processing. Its ability to learn and predict possible scenarios by analyzing the huge volume of information generated provides automatic learning and decision support. This paper aims to present a model of a machine learning architecture applied to a military organization, carried out and supported by a bibliometric study applied to an architecture model of a nonmilitary organization. For this purpose, a bibliometric analysis up to the year 2021 was carried out, making a strategic diagram and interpreting the results. The information used has been extracted from one of the main databases widely accepted by the scientific community, ISI WoS. No direct military sources were used. This work is divided into five parts: the study of previous research related to machine learning in the military world; the explanation of our research methodology using the SciMat, Excel and VosViewer tools; the use of this methodology based on data mining, preprocessing, cluster normalization, a strategic diagram and the analysis of its results to investigate machine learning in the military context; based on these results, a conceptual architecture of the practical use of ML in the military context is drawn up; and, finally, we present the conclusions, where we will see the most important areas and the latest advances in machine learning applied, in this case, to a military environment, to analyze a large set of data, providing utility, machine learning and decision support.
Automated Quality Control System for Canned Tuna Production using Artificial Vision
Authors: Sendey Vera, Luis Chuquimarca, Wilson Galdea, Bremnen Véliz, Carlos Saldaña
Date: 2024-10-08 00:00:00
Tasks: GPU, Optical Character Recognition (OCR), Text Detection

This scientific article presents the implementation of an automated control system for detecting and classifying faults in tuna metal cans using artificial vision. The system utilizes a conveyor belt and a camera for visual recognition triggered by a photoelectric sensor. A robotic arm classifies the metal cans according to their condition. Industry 4.0 integration is achieved through an IoT system using Mosquitto, Node-RED, InfluxDB, and Grafana. The YOLOv5 model is employed to detect faults in the metal can lids and the positioning of the easy-open ring. Training with GPU on Google Colab enables OCR text detection on the labels. The results indicate efficient real-time problem identification, optimization of resources, and delivery of quality products. At the same time, the vision system contributes to autonomy in quality control tasks, freeing operators to perform other functions within the company.