pwc-archive/papers-with-abstracts huggingface dataset:
Generalizability of PRS313 for breast cancer risk amongst non-Europeans in a Los Angeles biobank
Authors: Helen Shang, Yi Ding, Vidhya Venkateswaran, Kristin Boulier, Nikhita Kathuria-Prakash, Parisa Boodaghi Malidarreh, Jacob M. Luber, Bogdan Pasaniuc
Date: 2023-05-06 00:00:00

Polygenic risk scores (PRS) summarize the combined effect of common risk variants and are associated with breast cancer risk in patients without identifiable monogenic risk factors. One of the most well-validated PRSs in breast cancer to date is PRS313, which was developed from a Northern European biobank but has shown attenuated performance in non-European ancestries. We further investigate the generalizability of the PRS313 for American women of European (EA), African (AFR), Asian (EAA), and Latinx (HL) ancestry within one institution with a singular EHR system, genotyping platform, and quality control process. We found that the PRS313 achieved overlapping Areas under the ROC Curve (AUCs) in females of Lantix (AUC, 0.68; 95 CI, 0.65-0.71) and European ancestry (AUC, 0.70; 95 CI, 0.69-0.71) but lower AUCs for the AFR and EAA populations (AFR: AUC, 0.61; 95 CI, 0.56-0.65; EAA: AUC, 0.64; 95 CI, 0.60-0.680). While PRS313 is associated with Hormone Positive (HR+) disease in European Americans (OR, 1.42; 95 CI, 1.16-1.64), for Latinx females, it may be instead associated with Human Epidermal Growth Factor Receptor 2 (HER2+) disease (OR, 2.52; 95 CI, 1.35-4.70) although due to small numbers, additional studies are needed. In summary, we found that PRS313 was significantly associated with breast cancer but with attenuated accuracy in women of African and Asian descent within a singular health system in Los Angeles. Our work further highlights the need for additional validation in diverse cohorts prior to clinical implementation of polygenic risk scores.
Structure-Function Dynamics Hybrid Modeling: RNA Degradation
Authors: Hua Zheng, Wei Xie, Paul Whitford, Ailun Wang, Chunsheng Fang, Wandi Xu
Date: 2023-05-06 00:00:00
Tasks: Drug Discovery

RNA structure and functional dynamics play fundamental roles in controlling biological systems. Molecular dynamics simulation, which can characterize interactions at an atomistic level, can advance the understanding on new drug discovery, manufacturing, and delivery mechanisms. However, it is computationally unattainable to support the development of a digital twin for enzymatic reaction network mechanism learning, and end-to-end bioprocess design and control. Thus, we create a hybrid ("mechanistic + machine learning") model characterizing the interdependence of RNA structure and functional dynamics from atomistic to macroscopic levels. To assess the proposed modeling strategy, in this paper, we consider RNA degradation which is a critical process in cellular biology that affects gene expression. The empirical study on RNA lifetime prediction demonstrates the promising performance of the proposed multi-scale bioprocess hybrid modeling strategy.
Grid Cells, Border Cells and Discrete Complex Analysis
Authors: Y. Dabaghian
Date: 2023-05-07 00:00:00

We propose a mechanism enabling the appearance of border cells -- neurons firing at the boundaries of the navigated enclosures. The approach is based on the recent discovery of discrete complex analysis on a triangular lattice, which allows constructing discrete epitomes of complex-analytic functions and making use of their inherent ability to attain maximal values at the boundaries of generic lattice domains. As it turns out, certain elements of the discrete-complex framework readily appear in the oscillatory models of grid cells. We demonstrate that these models can extend further, producing cells that increase their activity towards the frontiers of the navigated environments. We also construct a network model of neurons with border-bound firing that conforms with the oscillatory models.
A Computational Model of Children's Learning and Use of Probabilities Across Different Ages
Authors: Zilong Wang, Thomas R. Shultz, Ardvan S. Nobandegani
Date: 2023-05-06 00:00:00

Recent empirical work has shown that human children are adept at learning and reasoning with probabilities. Here, we model a recent experiment investigating the development of school-age children's non-symbolic probability reasoning ability using the Neural Probability Learner and Sampler (NPLS) system. We demonstrate that NPLS can accurately simulate children's probability judgments at different ages, tasks and difficulty levels to discriminate two probabilistic choices through accurate probability learning and sampling. An extension of NPLS using a skewed heuristic distribution can also model children's tendency to wrongly select the outcome with more favorable items but less likely to draw the favorable ones when the probabilistic choices are similar. We discuss the roles of two model parameters that can be adjusted to simulate the probability matching versus probability maximization phenomena in children, and why frequency biases children's probabilistic judgments.
Historical constraints on the evolution of efficient color naming
Authors: Colin R. Twomey, David H. Brainard, Joshua B. Plotkin
Date: 2023-05-07 00:00:00

Color naming in natural languages is not arbitrary: it reflects efficient partitions of perceptual color space modulated by the relative needs to communicate about different colors. These psychophysical and communicative constraints help explain why languages around the world have remarkably similar, but not identical, mappings of colors to color terms. Languages converge on a small set of efficient representations. But languages also evolve, and the number of terms in a color vocabulary may change over time. Here we show that history, i.e. the existence of an antecedent color vocabulary, acts as a non-adaptive constraint that biases the choice of efficient solution as a language transitions from a vocabulary of size n to n+1 terms. Moreover, as vocabularies evolve to include more terms they explore a smaller fraction of all possible efficient vocabularies compared to equally-sized vocabularies constructed de novo. This path dependence on the cultural evolution of color naming presents an opportunity. Historical constraints can be used to reconstruct ancestral color vocabularies, allowing us to answer long-standing questions about the evolutionary sequences of color words, and enabling us to draw inferences from phylogenetic patterns of language change.
Predicting the Price Movement of Cryptocurrencies Using Linear Law-based Transformation
Authors: Marcell T. Kurbucz, Péter Pósfay, Antal Jakovác
Date: 2023-04-27 00:00:00

The aim of this paper is to investigate the effect of a novel method called linear law-based feature space transformation (LLT) on the accuracy of intraday price movement prediction of cryptocurrencies. To do this, the 1-minute interval price data of Bitcoin, Ethereum, Binance Coin, and Ripple between 1 January 2019 and 22 October 2022 were collected from the Binance cryptocurrency exchange. Then, 14-hour nonoverlapping time windows were applied to sample the price data. The classification was based on the first 12 hours, and the two classes were determined based on whether the closing price rose or fell after the next 2 hours. These price data were first transformed with the LLT, then they were classified by traditional machine learning algorithms with 10-fold cross-validation. Based on the results, LLT greatly increased the accuracy for all cryptocurrencies, which emphasizes the potential of the LLT algorithm in predicting price movements.
Financial Hedging and Risk Compression, A journey from linear regression to neural network
Authors: Ali Shirazi, Fereshteh Sadeghi Naieni Fard
Date: 2023-04-11 00:00:00
Tasks: regression

Finding the hedge ratios for a portfolio and risk compression is the same mathematical problem. Traditionally, regression is used for this purpose. However, regression has its own limitations. For example, in a regression model, we can't use highly correlated independent variables due to multicollinearity issue and instability in the results. A regression model cannot also consider the cost of hedging in the hedge ratios estimation. We have introduced several methods that address the linear regression limitation while achieving better performance. These models, in general, fall into two categories: Regularization Techniques and Common Factor Analyses. In regularization techniques, we minimize the variance of hedged portfolio profit and loss (PnL) and the hedge ratio sizes, which helps reduce the cost of hedging. The regularization techniques methods could also consider the cost of hedging as a function of the cost of funding, market condition, and liquidity. In common factor analyses, we first map variables into common factors and then find the hedge ratios so that the hedged portfolio doesn't have any exposure to the factors. We can use linear or nonlinear factors construction. We are introducing a modified beta variational autoencoder that constructs common factors nonlinearly to compute hedges. Finally, we introduce a comparison method and generate numerical results for an example.
Why Students Trade? The Analysis of Young Investors behavior
Authors: Jones Pontoh
Date: 2023-05-08 00:00:00
Tasks: Decision Making, Descriptive

Interestingly the numbers of young traders in Jakarta Stock Exchange had been increasing in recent years. Even in the middle of the global crisis caused by covid19 pandemic, in December 2021 according to KSEI, Individual investors were dominated by young investors. Data presented by KSEI showed that 60 percent of the investors listed in Indonesian Stock Exchange were young investors. Other data shows that 28 percent of the investors listed were shockingly students. It was interesting to study the behavior of young and Rookie investors at the branch of stock market in Manado State University. Basically, they varied in how to make decision to trade on the stock exchange. The problems were discussed by qualitative approach. Descriptive analysis was conducted prior to interviews. Data will be collected through data observation techniques and interviews. The study succeeded in investigating the investment behavior of young or Rookie investors at Manado State University in accordance with investment decision making and the perception of behavioral control. The perception of behavioral control greatly influenced investors decision making. Students were greatly influenced by lecturer, friends and more experienced investors. The results of the interview provide information that before they determine their behavior, first they do stock analysis, both technical and fundamental analysis. These facts shows that students investors were well literate.
A greedy algorithm for habit formation under multiplicative utility
Authors: Snezhana Kirusheva, Thomas S. Salisbury
Date: 2023-05-08 00:00:00

We consider the problem of optimizing lifetime consumption under a habit formation model, both with and without an exogenous pension. Unlike much of the existing literature, we apply a power utility to the ratio of consumption to habit, rather than to their difference. The martingale/duality method becomes intractable in this setting, so we develop a greedy version of this method that is solvable using Monte Carlo simulation. We investigate the behaviour of the greedy solution, and explore what parameter values make the greedy solution a good approximation to the optimal one.
Study on the Identification of Financial Risk Path Under the Digital Transformation of Enterprise Based on DEMATEL-ISM-MICMAC
Authors: Jie Dong
Date: 2023-05-07 00:00:00
Tasks: Management

Digital transformation challenges financial management while reducing costs and increasing efficiency for enterprises in various countries. Identifying the transmission paths of enterprise financial risks in the context of digital transformation is an urgent problem to be solved. This paper constructs a system of influencing factors of corporate financial risks in the new era through literature research. It proposes a path identification method of financial risks in the context of the digital transformation of enterprises based on DEMATEL-ISM-MICMAC. This paper explores the intrinsic association among the influencing factors of corporate financial risks, identifies the key influencing factors, sorts out the hierarchical structure of the influencing factor system, and analyses the dependency and driving relationships among the factors in this system. The results show that: (1) The political and economic environment being not optimistic will limit the enterprise's operating ability, thus directly leading to the change of the enterprise's asset and liability structure and working capital stock. (2) The enterprise's unreasonable talent training and incentive mechanism will limit the enterprise's technological innovation ability and cause a shortage of digitally literate financial talents, which eventually leads to the vulnerability of the enterprise's financial management. This study provides a theoretical reference for enterprises to develop risk management strategies and ideas for future academic research in digital finance.
Estimating the impact of supply chain network contagion on financial stability
Authors: Zlata Tabachová, Christian Diem, András Borsos, Csaba Burger, Stefan Thurner
Date: 2023-05-04 00:00:00

Realistic credit risk assessment, the estimation of losses from counterparty's failure, is central for the financial stability. Credit risk models focus on the financial conditions of borrowers and only marginally consider other risks from the real economy, supply chains in particular. Recent pandemics, geopolitical instabilities, and natural disasters demonstrated that supply chain shocks do contribute to large financial losses. Based on a unique nation-wide micro-dataset, containing practically all supply chain relations of all Hungarian firms, together with their bank loans, we estimate how firm-failures affect the supply chain network, leading to potentially additional firm defaults and additional financial losses. Within a multi-layer network framework we define a financial systemic risk index (FSRI) for every firm, quantifying these expected financial losses caused by its own- and all the secondary defaulting loans caused by supply chain network (SCN) shock propagation. We find a small fraction of firms carrying substantial financial systemic risk, affecting up to 16% of the banking system's overall equity. These losses are predominantly caused by SCN contagion. For every bank we calculate the expected loss (EL), value at risk (VaR) and expected shortfall (ES), with and without accounting for SCN contagion. We find that SCN contagion amplifies the EL, VaR, and ES by a factor of 4.3, 4.5, and 3.2, respectively. These findings indicate that for a more complete picture of financial stability and realistic credit risk assessment, SCN contagion needs to be considered. This newly quantified contagion channel is of potential relevance for regulators' future systemic risk assessments.
Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022
Authors: Cheng Zhang, Nilam Nur Amir Sjarif, Roslina Ibrahim
Date: 2023-04-21 00:00:00
Tasks: Deep Learning, Model Selection, Time Series, Time Series Forecasting

Accurately predicting the prices of financial time series is essential and challenging for the financial sector. Owing to recent advancements in deep learning techniques, deep learning models are gradually replacing traditional statistical and machine learning models as the first choice for price forecasting tasks. This shift in model selection has led to a notable rise in research related to applying deep learning models to price forecasting, resulting in a rapid accumulation of new knowledge. Therefore, we conducted a literature review of relevant studies over the past three years with a view to aiding researchers and practitioners in the field. This review delves deeply into deep learning-based forecasting models, presenting information on model architectures, practical applications, and their respective advantages and disadvantages. In particular, detailed information is provided on advanced models for price forecasting, such as Transformers, generative adversarial networks (GANs), graph neural networks (GNNs), and deep quantum neural networks (DQNNs). The present contribution also includes potential directions for future research, such as examining the effectiveness of deep learning models with complex structures for price forecasting, extending from point prediction to interval prediction using deep learning models, scrutinising the reliability and validity of decomposition ensembles, and exploring the influence of data volume on model performance.
An Empirical Study of Capital Asset Pricing Model based on Chinese A-share Trading Data
Authors: Kai Ren
Date: 2023-04-01 00:00:00
Tasks: regression

This paper presents an empirical analysis of the capital asset pricing model using trading data for the Chinese A-share market from 2000 to 2019. Firstly, the standard CAPM is tested using a Fama-MacBetch regression and although the results successfully test the three core hypotheses, the resulting beta risk does not have a significant impact on returns. Secondly, the Fama-French three-factor model, which uses a combination of market, size and value factors to price capital assets, is analysed, showing that it is able to capture most of the variation in A-share market returns, with an adjusted R-squared greater than 0.88 for the 25 portfolios constructed. Finally, the paper takes into account the "shell value contamination" problem caused by IPO regulation in the Chinese stock market, and minimises its impact by excluding stocks in the lowest 30% of the market capitalisation, which allows some of the anomalous results in the three-factor model to be effectively corrected. Although this paper does not present a more innovative approach, it is unique in its data selection and presents a detailed presentation of the data processing and regression analysis process, which 1) illustrates the applicability of capital asset pricing models in the Chinese market; and 2) provides a set of open source materials for the basic learning of capital asset pricing models.
Working Memory Capacity of ChatGPT: An Empirical Study
Authors: Dongyu Gong, Xingchen Wan, Dingmin Wang
Date: 2023-04-30 00:00:00
Tasks: Benchmarking, Language Modeling, Language Modelling, Large Language Model

Working memory is a critical aspect of both human intelligence and artificial intelligence, serving as a workspace for the temporary storage and manipulation of information. In this paper, we systematically assess the working memory capacity of ChatGPT, a large language model developed by OpenAI, by examining its performance in verbal and spatial n-back tasks under various conditions. Our experiments reveal that ChatGPT has a working memory capacity limit strikingly similar to that of humans. Furthermore, we investigate the impact of different instruction strategies on ChatGPT's performance and observe that the fundamental patterns of a capacity limit persist. From our empirical findings, we propose that n-back tasks may serve as tools for benchmarking the working memory capacity of large language models and hold potential for informing future efforts aimed at enhancing AI working memory.
MARS: Mask Attention Refinement with Sequential Quadtree Nodes for Car Damage Instance Segmentation
Authors: Teerapong Panboonyuen, Naphat Nithisopa, Panin Pienroj, Laphonchai Jirachuphun, Chaiwasut Watthanasirikrit, Naruepon Pornwiriyakul
Date: 2023-05-01 00:00:00
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation

Evaluating car damages from misfortune is critical to the car insurance industry. However, the accuracy is still insufficient for real-world applications since the deep learning network is not designed for car damage images as inputs, and its segmented masks are still very coarse. This paper presents MARS (Mask Attention Refinement with Sequential quadtree nodes) for car damage instance segmentation. Our MARS represents self-attention mechanisms to draw global dependencies between the sequential quadtree nodes layer and quadtree transformer to recalibrate channel weights and predict highly accurate instance masks. Our extensive experiments demonstrate that MARS outperforms state-of-the-art (SOTA) instance segmentation methods on three popular benchmarks such as Mask R-CNN [9], PointRend [13], and Mask Transfiner [12], by a large margin of +1.3 maskAP-based R50-FPN backbone and +2.3 maskAP-based R101-FPN backbone on Thai car-damage dataset. Our demos are available at https://github.com/kaopanboonyuen/MARS.
Score-based Transport Modeling for Mean-Field Fokker-Planck Equations
Authors: Jianfeng Lu, Yue Wu, Yang Xiang
Date: 2023-04-21 00:00:00

We use the score-based transport modeling method to solve the mean-field Fokker-Planck equations, which we call MSBTM. We establish an upper bound on the time derivative of the Kullback-Leibler (KL) divergence to MSBTM numerical estimation from the exact solution, thus validates the MSBTM approach. Besides, we provide an error analysis for the algorithm. In numerical experiments, we study two types of mean-field Fokker-Planck equation and their corresponding dynamics of particles in interacting systems. The MSBTM algorithm is numerically validated through qualitative and quantitative comparison between the MSBTM solutions, the results of integrating the associated stochastic differential equation and the analytical solutions if available.
MSVQ: Self-Supervised Learning with Multiple Sample Views and Queues
Authors: Chen Peng, Xianzhong Long, Yun Li
Date: 2023-05-09 00:00:00
Tasks: Contrastive Learning, Representation Learning, Self-Supervised Learning

Self-supervised methods based on contrastive learning have achieved great success in unsupervised visual representation learning. However, most methods under this framework suffer from the problem of false negative samples. Inspired by the mean shift for self-supervised learning, we propose a new simple framework, namely Multiple Sample Views and Queues (MSVQ). We jointly construct three soft labels on-the-fly by utilizing two complementary and symmetric approaches: multiple augmented positive views and two momentum encoders that generate various semantic features for negative samples. Two teacher networks perform similarity relationship calculations with negative samples and then transfer this knowledge to the student network. Let the student network mimic the similarity relationships between the samples, thus giving the student network a more flexible ability to identify false negative samples in the dataset. The classification results on four benchmark image datasets demonstrate the high effectiveness and efficiency of our approach compared to some classical methods. Source code and pretrained models are available \href{https://github.com/pc-cp/MSVQ}{here}.