pwc-archive/papers-with-abstracts huggingface dataset:
Are you sure? Modelling Drivers' Confidence Judgments in Left-Turn Gap Acceptance Decisions
Authors: Floor Bontje, Arkady Zgonnikov
Date: 2024-03-11 00:00:00

When a person makes a decision, it is automatically accompanied by a subjective probability judgment of the decision being correct, in other words, a confidence judgment. A better understanding of the mechanisms responsible for these confidence judgments could provide novel insights into human behavior. However, so far confidence judgments have been mostly studied in simplistic laboratory tasks while little is known about confidence in naturalistic dynamic tasks such as driving. In this study, we made a first attempt of connecting fundamental research on confidence with naturalistic driver behavior. We investigated the confidence of drivers in left-turn gap acceptance decisions in a driver simulator experiment (N=17). We found that confidence in these decisions depends on the size of the gap to the oncoming vehicle. Specifically, confidence increased with the gap size for trials in which the gap was accepted, and decreased with the gap size for rejected gaps. Similarly to more basic tasks, confidence was negatively related to the response times and correlated with action dynamics during decision execution. Finally, we found that confidence judgments can be captured with an extended dynamic drift-diffusion model. In the model, the drift rate of the evidence accumulator as well as the decision boundaries are functions of the gap size. Furthermore, we demonstrated that allowing for post-decision evidence accumulation in the model increases its ability to describe confidence judgments in rejected gap decisions. Overall, our study confirmed that principles known from fundamental confidence research extend to confidence judgments in dynamic decisions during a naturalistic task.
Lateral Control of Brain-Controlled Vehicle Based on SVM Probability Output Model
Authors: Hongguang Pan, Xinyu Yu, Yong Yang
Date: 2024-03-09 00:00:00
Tasks: EEG, SSVEP

The non-stationary characteristics of EEG signal and the individual differences of brain-computer interfaces (BCIs) lead to poor performance in the control process of the brain-controlled vehicles (BCVs). In this paper, by combining steady-state visual evoked potential (SSVEP) interactive interface, brain instructions generation module and vehicle lateral control module, a probabilistic output model based on support vector machine (SVM) is proposed for BCV lateral control to improve the driving performance. Firstly, a filter bank common spatial pattern (FBCSP) algorithm is introduced into the brain instructions generation module, which can improve the off-line decoding performance. Secondly, a sigmod-fitting SVM (SF-SVM) is trained based on the sigmod-fitting method and the lateral control module is developed, which can produce all commands in the form of probability instead of specific single command. Finally, a pre-experiment and two road-keeping experiments are conducted. In the pre-experiment, the experiment results show that, the average highest off-line accuracy among subjects is 95.64\%, while for those in the online stage, the average accuracy is only 84.44\%. In the road-keeping experiments, the task completion rate in the two designed scenes increased by 25.6\% and 20\%, respectively.
Propensity-score matching analysis in COVID-19-related studies: a method and quality systematic review
Authors: Chunhui Gu, Ruosha Li, Guoqiang Zhang
Date: 2024-03-10 00:00:00
Tasks: Articles

Objectives: To provide an overall quality assessment of the methods used for COVID-19-related studies using propensity score matching (PSM). Study Design and Setting: A systematic search was conducted in June 2021 on PubMed to identify COVID-19-related studies that use the PSM analysis between 2020 and 2021. Key information about study design and PSM analysis were extracted, such as covariates, matching algorithm, and reporting of estimated treatment effect type. Results: One-hundred-and-fifty (87.72%) cohort studies and thirteen (7.60%) case-control studies were found among 171 identified articles. Forty-five studies (26.32%) provided a reasonable justification for covariates selection. One-hundred-and-three (60.23%) and Sixty-nine (40.35%) studies did not provide the model that was used for calculating the propensity score or did not report the matching algorithm, respectively. Seventy-three (42.69%) studies reported the method(s) for checking covariates balance. Forty studies (23.39%) had a statistician co-author. All the case-control studies (n=13) did not have a statistician co-author (p=0.006) and all studies that clarified the treatment effect estimation (n=6) had a statistician co-author (p<0.001). Conclusions: The reporting quality of the PSM analysis is suboptimal in some COVID-19 epidemiological studies. Some pitfalls may undermine study findings that involve PSM analysis, such as a mismatch between PSM analysis and study design.
Effects of diffusion and advection on predator prey dynamics in an advective patchy environment
Authors: Qi Wang
Date: 2024-03-12 00:00:00

In this paper, we consider a specialist predator-prey patchy model over the closed stream network. We study the dynamics and the asymptotic profiles of positive steady states according to the mortality rate of the specialist predators, advection and diffusion rates. We verify that the specialist predators can successfully invade as long as the mortality rate is sufficiently small. On the other hand, the impacts of diffusion and advection on the asymptotic profiles of positive steady states and on the concentration of the species are given.
Predicting the Risk of Ischemic Stroke in Patients with Atrial Fibrillation using Heterogeneous Drug-protein-disease Network-based Deep Learning
Authors: Zhiheng Lyu, Jiannan Yang, Zhongzhi Xu, Weilan Wang, Weibin Cheng, Kwok-Leung Tsui, Gary Tse, Qingpeng Zhang
Date: 2024-03-12 00:00:00

We develop a deep learning model, ABioSPATH, to predict the one-year risk of ischemic stroke (IS) in atrial fibrillation (AF) patients. The model integrates drug-protein-disease pathways and real-world clinical data of AF patients to generate the IS risk and potential pathways for each patient. The model uses a multilayer network to identify the mechanism of drug action and disease comorbidity propagation pathways. The model is tested on the Electronic Health Record (EHR) data of 7859 AF patients from 43 hospitals in Hong Kong. The model outperforms all baselines across all metrics and provides valuable molecular-level insights for clinical use. The model also highlights key proteins in common pathways and potential IS risks tied to less-studied drugs. The model only requires routinely collected data, without requiring expensive biomarkers to be tested.
Optical detection of bacterial cells on stainless-steel surface with a low-magnification light microscope
Authors: Yuzhen Zhang, Zili Gao, Lili He
Date: 2024-03-12 00:00:00

A Rapid and cost-effective method for detecting bacterial cells on surfaces is critical to protect public health from various aspects, including food safety, clinical hygiene, and pharmacy quality. Herein, we first established an optical detection method based on a gold chip coating with 3-mercaptophenylboronic acid (3-MPBA) to capture bacterial cells, which allows for the detection and quantification of bacterial cells with a standard light microscope under low-magnification (10 fold) objective lens. Then, integrating the developed optical detection method with swab sampling to achieve to detect bacterial cells loading on stainless-steel surfaces. Using Salmonella enterica (SE1045) and Escherichia coli as model bacterial cells, we achieved a capture efficiency of up to 76.0 % for SE1045 cells and 81.1 % for E. coli cells at Log 3 CFU/mL upon the optimized conditions. Our assay showed good linear relationship between the concentrations of bacterial cells with the cell counting in images with the limit of detection (LOD) of Log 3 CFU/mL for both SE1045 and E. coli cells. A further increase in sensitivity in detecting E. coli cells was achieved through a heat treatment, enabling the LOD to be pushed as low as Log 2 CFU/mL. Furthermore, successful application was observed in assessing bacterial contamination on stainless-steel surface following integrating with swab collection, achieving a recovery rate of approximately 70 % suggests future prospects for evaluating the cleanliness of surfaces. The entire process was completed within around 2 hours, with a cost of merely 2 dollars per sample. Given a standard light microscope cost around 250 dollars, our developed method has shown great potential in practical industrial applications for bacterial contamination control on surfaces in low-resource settings.
Visualizing, Analyzing and Constructing L-System from Arborized 3D Model Using a Web Application
Authors: Nick van Nielen, Fons Verbeek, Lu Cao
Date: 2024-03-11 00:00:00

In biology, arborized structures are well represented and typically complex for visualization and analysis. In order to have a profound understanding of the topology of arborized 3D biological model, higher level abstraction is needed. We aim at constructing an abstraction of arborized 3D biological model to an L-system that provides a generalized formalization in a grammar to represent complex structures. The focus of this paper is to combine 3D visualization, analysis and L-system abstraction into a single web application. We designed a front-end user interface and a back-end. In the front-end, we used A-Frame and defined algorithms to generate and visualize L-systems. In the back-end, we utilized the Vascular Modelling Toolkit's (VMTK) centerline analysis methods to extract important features from the arborized 3D models, which can be applied to L-system generation. In addition, two 3D biological models: lactiferous duct and artery are used as two case studies to verify the functionality of this web application. In conclusion, our web application is able to visualize, analyse and create L-system abstractions of arborized 3D models. This in turn provides workflow-improving benefits, easy accessibility and extensibility.
A Unifying Approach for the Pricing of Debt Securities
Authors: Marie-Claude Vachon, Anne MacKay
Date: 2024-03-10 00:00:00
Tasks: Form

We propose a unifying framework for the pricing of debt securities under general time-inhomogeneous short-rate diffusion processes. The pricing of bonds, bond options, callable/putable bonds, and convertible bonds (CBs) is covered. Using continuous-time Markov chain (CTMC) approximations, we obtain closed-form matrix expressions to approximate the price of bonds and bond options under general one-dimensional short-rate processes. A simple and efficient algorithm is also developed to price callable/putable debt. The availability of a closed-form expression for the price of zero-coupon bonds allows for the perfect fit of the approximated model to the current market term structure of interest rates, regardless of the complexity of the underlying diffusion process selected. We further consider the pricing of CBs under general bi-dimensional time-inhomogeneous diffusion processes to model equity and short-rate dynamics. Credit risk is also incorporated into the model using the approach of Tsiveriotis and Fernandes (1998). Based on a two-layer CTMC method, an efficient algorithm is developed to approximate the price of convertible bonds. When conversion is only allowed at maturity, a closed-form matrix expression is obtained. Numerical experiments show the accuracy and efficiency of the method across a wide range of model parameters and short-rate models.
AI in ESG for Financial Institutions: An Industrial Survey
Authors: Jun Xu
Date: 2024-02-03 00:00:00
Tasks: Survey

The burgeoning integration of Artificial Intelligence (AI) into Environmental, Social, and Governance (ESG) initiatives within the financial sector represents a paradigm shift towards more sus-tainable and equitable financial practices. This paper surveys the industrial landscape to delineate the necessity and impact of AI in bolstering ESG frameworks. With the advent of stringent regulatory requirements and heightened stakeholder awareness, financial institutions (FIs) are increasingly compelled to adopt ESG criteria. AI emerges as a pivotal tool in navigating the complex in-terplay of financial activities and sustainability goals. Our survey categorizes AI applications across three main pillars of ESG, illustrating how AI enhances analytical capabilities, risk assessment, customer engagement, reporting accuracy and more. Further, we delve into the critical con-siderations surrounding the use of data and the development of models, underscoring the importance of data quality, privacy, and model robustness. The paper also addresses the imperative of responsible and sustainable AI, emphasizing the ethical dimensions of AI deployment in ESG-related banking processes. Conclusively, our findings suggest that while AI offers transformative potential for ESG in banking, it also poses significant challenges that necessitate careful consideration. The final part of the paper synthesizes the survey's insights, proposing a forward-looking stance on the adoption of AI in ESG practices. We conclude with recommendations with a reference architecture for future research and development, advocating for a balanced approach that leverages AI's strengths while mitigating its risks within the ESG domain.
Study of the Impact of the Big Data Era on Accounting and Auditing
Authors: Yuxiang Sun, Jingyi Li, Mengdie Lu, Zongying Guo
Date: 2024-03-11 00:00:00
Tasks: Anomaly Detection

Big data revolutionizes accounting and auditing, offering deep insights but also introducing challenges like data privacy and security. With data from IoT, social media, and transactions, traditional practices are evolving. Professionals must adapt to these changes, utilizing AI and machine learning for efficient data analysis and anomaly detection. Key to overcoming these challenges are enhanced analytics tools, continuous learning, and industry collaboration. By addressing these areas, the accounting and auditing fields can harness big data's potential while ensuring accuracy, transparency, and integrity in financial reporting. Keywords: Big Data, Accounting, Audit, Data Privacy, AI, Machine Learning, Transparency.
Capital Structure Adjustment Speed and Expected Returns: Examination of Information Asymmetry as a Moderating Role
Authors: Masoud Taherinia, Mehrdad Matin, Jamal Valipour, Kavian Abdolahi, Peyman Shouryabi, Mohammad Mahdi Barzegar
Date: 2024-03-09 00:00:00

Shareholders' expectations of stock returns and fluctuations are constantly changing due to restrictions in financial status and undesirable capital structure, which constrain managers to limit the changes in price trends in order to cover the risk instigated and infused by the unfavorable situation. The present research examines the moderating impact of information asymmetry on the relationship between capital structure adjustment and expected returns. The data from 120 companies approved in the Tehran Stock Exchange were extracted, and a hybrid data regression model was used to test the research hypotheses. Findings indicate that the capital structure adjustment speed correlates with the expected returns. Moreover, the information asymmetry positively affects the relationship between capital structure adjustment speed and expected returns.
From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing
Authors: Junyi Ye, Bhaskar Goswami, Jingyi Gu, Ajim Uddin, Guiling Wang
Date: 2024-03-11 00:00:00
Tasks: Decision Making, Portfolio Optimization

This paper comprehensively reviews the application of machine learning (ML) and AI in finance, specifically in the context of asset pricing. It starts by summarizing the traditional asset pricing models and examining their limitations in capturing the complexities of financial markets. It explores how 1) ML models, including supervised, unsupervised, semi-supervised, and reinforcement learning, provide versatile frameworks to address these complexities, and 2) the incorporation of advanced ML algorithms into traditional financial models enhances return prediction and portfolio optimization. These methods can adapt to changing market dynamics by modeling structural changes and incorporating heterogeneous data sources, such as text and images. In addition, this paper explores challenges in applying ML in asset pricing, addressing the growing demand for explainability in decision-making and mitigating overfitting in complex models. This paper aims to provide insights into novel methodologies showcasing the potential of ML to reshape the future of quantitative finance.
AutoEval Done Right: Using Synthetic Data for Model Evaluation
Authors: Pierre Boyeau, Anastasios N. Angelopoulos, Nir Yosef, Jitendra Malik, Michael I. Jordan
Date: 2024-03-09 00:00:00

The evaluation of machine learning models using human-labeled validation data can be expensive and time-consuming. AI-labeled synthetic data can be used to decrease the number of human annotations required for this purpose in a process called autoevaluation. We suggest efficient and statistically principled algorithms for this purpose that improve sample efficiency while remaining unbiased. These algorithms increase the effective human-labeled sample size by up to 50% on experiments with GPT-4.
Guiding LLMs The Right Way: Fast, Non-Invasive Constrained Generation
Authors: Luca Beurer-Kellner, Marc Fischer, Martin Vechev
Date: 2024-02-07 00:00:00

To ensure that text generated by large language models (LLMs) is in an expected format, constrained decoding proposes to enforce strict formal language constraints during generation. However, as we show in this work, not only do such methods incur performance overhead during generation, but many of them also significantly impair task accuracy, if they do not correctly align the underlying LLM sub-word vocabularies with external constraints. To address this, we present a novel decoding algorithm, DOMINO, that can enforce constraints in a fully subword-aligned fashion, while leveraging pre-computation and speculative decoding to achieve virtually no overhead and in some cases even almost 2$\times$ speedup over unconstrained decoding -- thereby outperforming existing approaches by a wide margin.
A PID-Controlled Non-Negative Tensor Factorization Model for Analyzing Missing Data in NILM
Authors: DengYu Shi
Date: 2024-03-09 00:00:00
Tasks: energy management, Imputation, Management, Non-Intrusive Load Monitoring, Tensor Decomposition

With the growing demand for energy and increased environmental awareness, Non-Intrusive Load Monitoring (NILM) has become an essential tool in smart grid and energy management. By analyzing total power load data, NILM infers the energy usage of individual appliances without the need for separate sensors, enabling real-time monitoring from a few locations. This approach helps users understand consumption patterns, enhance energy efficiency, and detect anomalies for effective energy management. However, NILM datasets often suffer from issues such as sensor failures and data loss, compromising data integrity, thereby impacting subsequent analysis and applications. Traditional imputation methods, such as linear interpolation and matrix factorization, struggle with nonlinear relationships and are sensitive to sparse data, resulting in information loss. To address these challenges, this paper proposes a Proportional-Integral-Derivative (PID) Controlled Non-Negative Latent Factorization of Tensor (PNLF) model, which dynamically adjusts parameter gradients to improve convergence, stability, and accuracy. Experimental results show that the PNLF model significantly outperforms state-of-the-art tensor completion models in both accuracy and efficiency. By addressing data loss issues, this study enhances load disaggregation precision and optimizes energy management, providing reliable data support for smart grid applications and policy formulation.
Adaptive Hyperparameter Optimization for Continual Learning Scenarios
Authors: Rudy Semola, Julio Hurtado, Vincenzo Lomonaco, Davide Bacciu
Date: 2024-03-09 00:00:00
Tasks: Continual Learning, Hyperparameter Optimization, Lifelong learning

Hyperparameter selection in continual learning scenarios is a challenging and underexplored aspect, especially in practical non-stationary environments. Traditional approaches, such as grid searches with held-out validation data from all tasks, are unrealistic for building accurate lifelong learning systems. This paper aims to explore the role of hyperparameter selection in continual learning and the necessity of continually and automatically tuning them according to the complexity of the task at hand. Hence, we propose leveraging the nature of sequence task learning to improve Hyperparameter Optimization efficiency. By using the functional analysis of variance-based techniques, we identify the most crucial hyperparameters that have an impact on performance. We demonstrate empirically that this approach, agnostic to continual scenarios and strategies, allows us to speed up hyperparameters optimization continually across tasks and exhibit robustness even in the face of varying sequential task orders. We believe that our findings can contribute to the advancement of continual learning methodologies towards more efficient, robust and adaptable models for real-world applications.
Automatic driving lane change safety prediction model based on LSTM
Authors: Wenjian Sun, Linying Pan, Jingyu Xu, Weixiang Wan, Yong Wang
Date: 2024-02-28 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Decision Making, Intent Detection, Trajectory Planning, Trajectory Prediction

Autonomous driving technology can improve traffic safety and reduce traffic accidents. In addition, it improves traffic flow, reduces congestion, saves energy and increases travel efficiency. In the relatively mature automatic driving technology, the automatic driving function is divided into several modules: perception, decision-making, planning and control, and a reasonable division of labor can improve the stability of the system. Therefore, autonomous vehicles need to have the ability to predict the trajectory of surrounding vehicles in order to make reasonable decision planning and safety measures to improve driving safety. By using deep learning method, a safety-sensitive deep learning model based on short term memory (LSTM) network is proposed. This model can alleviate the shortcomings of current automatic driving trajectory planning, and the output trajectory not only ensures high accuracy but also improves safety. The cell state simulation algorithm simulates the trackability of the trajectory generated by this model. The research results show that compared with the traditional model-based method, the trajectory prediction method based on LSTM network has obvious advantages in predicting the trajectory in the long time domain. The intention recognition module considering interactive information has higher prediction and accuracy, and the algorithm results show that the trajectory is very smooth based on the premise of safe prediction and efficient lane change. And autonomous vehicles can efficiently and safely complete lane changes.