pwc-archive/papers-with-abstracts huggingface dataset:
Sex Differences in 6-Year Progression of White Matter Hyperintensities in Non-Demented Older Adults: Sydney Memory and Ageing Study
Authors: Abdullah Alqarni, Wei Wen, Ben C. P. Lam, Nicole Kochan, Henry Brodaty, Perminder S. Sachdev, Jiyang Jiang
Date: 2023-07-17 00:00:00

Objectives: To examine sex differences in the associations between vascular risk factors and 6-year changes in the volume of white matter hyperintensities (WMH), and between changes in WMH volumes and changes in cognitive performance, in a cohort of non-demented older adults. Methods: WMH volumes at 3 time-points (baseline, and 2- and 6-year follow-up) were automatically quantified in participants of Sydney Memory and Ageing Study (N = 605; age range = 70-92 years; 54.78% female). Linear mixed models were applied to examine the effects of vascular risk factors and cognitive consequences of the progression of WMH, as well as the sex moderation effects in the associations. Results: Total (TWMH), periventricular (PVWMH), and deep (DWMH) WMH volumes increased by 9.47%, 7.70%, and 11.78% per year, respectively. No sex differences were found in WMH progression rates. After Bonferroni correction, increases in PVWMH volumes over time were associated with decline in global cognition, especially in visuospatial and memory domains. Men with more increases in PVWMH volumes over time had greater declines in visuospatial abilities. Moreover, higher average TWMH volumes across time-points were associated with poorer average performance in processing speed and executive function domains across time. Higher average PVWMH volumes across time-points were also associated with worse average performance in the executive function domain over time, among women but not men. Conclusion: The findings highlighted sex differences in the associations between WMH progression and cognition decline over time, suggesting sex-specific strategies in managing WMH accumulations in ageing. Keywords: Cerebral small vessel disease, white matter hyperintensities, sex differences, brain ageing, cognitive decline.
Quantitative Modelling of Diffusion-driven Pattern Formation in microRNA-regulated Gene Expression
Authors: Priya Chakraborty, Sayantari Ghosh
Date: 2023-07-18 00:00:00

MicroRNAs are extensively known for post-transcriptional gene regulation and pattern formation in the embryonic developmental stage. We explore the origin of these spatio-temporal patterns mathematically, considering three different motifs here. For three scenarios, (1) simple microRNA-based mRNA regulation with a graded response in output, (2) microRNA-based mRNA regulation resulting in bistability in the dynamics, and (3) a coordinated response of microRNA (miRNA), simultaneously regulating the mRNAs of two different pools, detailed dynamical analysis, as well as the reaction-diffusion scenario have been considered and analyzed in the steady state and for the transient dynamics further. We have observed persistent-temporal patterns, as a result of the dynamics of the motifs, that explain spatial gradients and relevant patterns formed by related proteins in development and phenotypic heterogenetic aspects in biological systems. Competitive effects of miRNA regulation have also been found to be capable to cause spatio-temporal patterns, persistent enough to direct developmental decisions. Under coordinated regulation, miRNAs are found to generate spatio-temporal patterning even from complete homogeneity in concentration of target protein, which may have impactful insights in choice of cell-fates.
Multiscale studies of delayed afterdepolarizations II: Calcium-overload-induced ventricular arrhythmias
Authors: Navneet Roshan, Rahul Pandit
Date: 2023-07-14 00:00:00

Disturbances in calcium homeostasis in a cardiac myocyte can lead to calcium-overload conditions and abnormal calcium releases, which occur primarily in the following two phases of the action potential (AP): (a) triggered or late calcium release (LCR) during the plateau phase; (b) spontaneous calcium release (SCR) during the diastolic interval (DI). Experimental and numerical studies of LCRs and SCRs have suggested that these abnormal calcium releases can lead to triggered excitations and, thence to life-threatening ventricular arrhythmias. We explore this suggestion in detail by building on our work in the previous accompanying Paper I, where we have studied abnormal calcium releases and delayed afterdepolarizations (DADs) in two state-of-the-art mathematical models for human ventricular myocytes. Here, we carry out a detailed \textit{in-silico} study of one of these models, namely, the ten Tusscher-Panfilov TP06~\cite{ten2006alternans} model. We increase the L-type Ca-channel current $I_{\rm{CaL}}$ to trigger LCRs, and calcium leak through the ryanodine receptor (RyR) to trigger SCRs, in the myocyte. We then perform multiscale simulations of coupled TP06-model myocytes in tissue in one-, two-, and three-dimensional (1D, 2D, and 3D) domains, with clumps of DAD-capable myocytes, to demonstrate how these clumps precipitate premature ventricular complexes (PVCs) that lead, in turn, to fibrillatory excitations like spiral and scroll waves. We examine possible pharmacological implications of our study for the class of ventricular arrhythmias that result from Ca\textsuperscript{2+} overload.
Multiscale studies of delayed afterdepolarizations I: A comparison of two biophysically realistic mathematical models for human ventricular myocytes
Authors: Navneet Roshan, Rahul Pandit
Date: 2023-07-14 00:00:00

Focal arrhythmias, which arise from delayed afterdepolarizations (DADs), are observed in various pathophysiological heart conditions; these can lead to sudden cardiac death. A clear understanding of the electrophysiological factors of cardiac myocytes, which lead to DADs, can suggest pharmacological targets that can eliminate DAD-induced arrhythmias. Therefore, we carry out multiscale investigations of two mathematical models for human-ventricular myocytes, namely, the ten Tusscher-Panfilov and the HuVEC15 model, at the levels of single myocytes, one- and two-dimensional (1D and 2D) tissue, and anatomically realistic bi-ventricular domains. By using continuation analysis, we uncover steady- to oscillatory-state transitions in the Ca\textsuperscript{2+} concentrations and show that they lead to DADs. We demonstrate that the Sarco/endoplasmic reticulum Ca\textsuperscript{2+}-ATPase (SERCA) pump uptake rate and the Ca\textsuperscript{2+} leak through the ryanodine-receptor (RyR) channel impact this transition significantly. We show that the frequencies and amplitudes of the DADs are key features that can be used to classify them into three types. By carrying out detailed parameter-sensitivity analyses, we identify the electrophysiological parameters, in the myocyte models, that most affect these key features. We then obtain stability (or phase) diagrams for different types of DADs. We demonstrate that the Na\textsuperscript{+}/Ca\textsuperscript{2+} exchanger plays a protective role by suppressing DADs in the TP06 model. We present tissue simulations to illustrate how arrhythmogenic premature ventricular complexes (PVCs) emerge from patches of DAD cells when we pace the tissue. We discuss the implications of our results for some DAD-induced ventricular arrhythmias, which we examine in detail in the companion Paper II.
The Impacts of Registration Regime Implementation on IPO Pricing Efficiency
Authors: Qi Deng, Linhong Zheng, Jiaqi Peng, Xu Li, Zhong-guo Zhou, Monica Hussein, Dingyi Chen, Mick Swartz
Date: 2023-07-18 00:00:00

We study the impacts of regime changes and related rule implementations on IPOs initial return for China entrepreneurial boards (ChiNext and STAR). We propose that an initial return contains the issuer fair value and an investors overreaction and examine their magnitudes and determinants. Our findings reveal an evolution of IPO pricing in response to the progression of regulation changes along four dimensions: 1) governing regulation regime, 2) listing day trading restrictions, 3) listing rules for issuers, and 4) participation requirements for investors. We find that the most efficient regulation regime in Chinese IPO pricing has four characteristics: 1) registration system, 2) no hard return caps nor trading curbs that restrict the initial return; 3) more specific listing rules for issuers, and 4) more stringent participation requirements for investors. In all contexts, we show that the registration regime governing the STAR IPOs offers the most efficient pricing.
The Great Deception: A Comprehensive Study of Execution Strategies in Corporate Share Buy-Backs
Authors: Michael Seigne, Joerg Osterrieder
Date: 2023-07-18 00:00:00
Tasks: Fairness

We delve into the intricate world of share buy-backs, a strategic corporate capital allocation tool that has gained significant prominence over the past few decades. Despite being the subject of extensive research and debate, the execution phase of these transactions remains an underexplored area. This lack of research into the execution phase is surprising, especially when compared to the extensive literature on other capital allocation decisions, such as acquisition pricing. We aim to shed light on the execution practices of share buy-backs, highlighting challenges and opportunities that arise and presenting a comparative analysis with other aspects of capital allocation. From an outsider's perspective, this paper analyses the seemingly disparate practices and aims to uncover the "dark arts" of execution. The intention is to assist corporations, investors, and regulators in better understanding the intricacies of executing share buy-backs, emphasizing the need for a cost-efficient and regulatory compliant service for corporations trading their own listed securities. We question the seemingly disproportionate fees charged to corporations for similar or even inferior outcomes, compared to retail and institutional investors. We illustrate potential inefficiencies and frictional costs in the current execution phase. The examples highlight the need for greater transparency and fairness in share buy-back executions, advocating for more equitable processes that benefit all stakeholders in the capital markets ecosystem. With the surge in regulatory attention and political pressure fueled by the rising prominence of buy-backs and evolving ESG considerations, it is crucial that we enhance understanding of this key area. This paper seeks to foster dialogue and encourage transparency, thus promoting the efficient use of resources in capital markets, and ultimately, benefiting shareholders.
Decentralized Prediction Markets and Sports Books
Authors: Hamed Amini, Maxim Bichuch, Zachary Feinstein
Date: 2023-07-17 00:00:00
Tasks: Prediction

Prediction markets allow traders to bet on potential future outcomes. These markets exist for weather, political, sports, and economic forecasting. Within this work we consider a decentralized framework for prediction markets using automated market makers (AMMs). Specifically, we construct a liquidity-based AMM structure for prediction markets that, under reasonable axioms on the underlying utility function, satisfy meaningful financial properties on the cost of betting and the resulting pricing oracle. Importantly, we study how liquidity can be pooled or withdrawn from the AMM and the resulting implications to the market behavior. In considering this decentralized framework, we additionally propose financially meaningful fees that can be collected for trading to compensate the liquidity providers for their vital market function.
Deep Reinforcement Learning for ESG financial portfolio management
Authors: Eduardo C. Garrido-Merchán, Sol Mora-Figueroa-Cruz-Guzmán, María Coronado-Vaca
Date: 2023-06-19 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Management, OpenAI Gym, reinforcement-learning, Reinforcement Learning

This paper investigates the application of Deep Reinforcement Learning (DRL) for Environment, Social, and Governance (ESG) financial portfolio management, with a specific focus on the potential benefits of ESG score-based market regulation. We leveraged an Advantage Actor-Critic (A2C) agent and conducted our experiments using environments encoded within the OpenAI Gym, adapted from the FinRL platform. The study includes a comparative analysis of DRL agent performance under standard Dow Jones Industrial Average (DJIA) market conditions and a scenario where returns are regulated in line with company ESG scores. In the ESG-regulated market, grants were proportionally allotted to portfolios based on their returns and ESG scores, while taxes were assigned to portfolios below the mean ESG score of the index. The results intriguingly reveal that the DRL agent within the ESG-regulated market outperforms the standard DJIA market setup. Furthermore, we considered the inclusion of ESG variables in the agent state space, and compared this with scenarios where such data were excluded. This comparison adds to the understanding of the role of ESG factors in portfolio management decision-making. We also analyze the behaviour of the DRL agent in IBEX 35 and NASDAQ-100 indexes. Both the A2C and Proximal Policy Optimization (PPO) algorithms were applied to these additional markets, providing a broader perspective on the generalization of our findings. This work contributes to the evolving field of ESG investing, suggesting that market regulation based on ESG scoring can potentially improve DRL-based portfolio management, with significant implications for sustainable investing strategies.
Asian Option Pricing via Laguerre Quadrature: A Diffusion Kernel Approach
Authors: P. G. Morrison
Date: 2023-06-01 00:00:00

This paper will demonstrate some new techniques for developing the theory of Asian (arithmetic average) options pricing. We discuss the basic derivation of the diffusion equations, and how various techniques from potential theory can be applied to solve these complex expressions. The Whittaker-type confluent hypergeometric functions are introduced, and we discuss how these functions are related to other systems including Mehler-Fock and modified Bessel functions. We close with a brief analysis of some index transforms and the kernels related to these integral transforms.
The Effect of COVID-19 on Cryptocurrencies and the Stock Market Volatility -- A Two-Stage DCC-EGARCH Model Analysis
Authors: Apostolos Ampountolas
Date: 2023-07-18 00:00:00

This research examines the correlations between the return volatility of cryptocurrencies, global stock market indices, and the spillover effects of the COVID-19 pandemic. For this purpose, we employed a two-stage multivariate volatility exponential GARCH (EGARCH) model with an integrated dynamic conditional correlation (DCC) approach to measure the impact on the financial portfolio returns from 2019 to 2020. Moreover, we used value-at-risk (VaR) and value-at-risk measurements based on the Cornish-Fisher expansion (CFVaR). The empirical results show significant long- and short-term spillover effects. The two-stage multivariate EGARCH model's results show that the conditional volatilities of both asset portfolios surge more after positive news and respond well to previous shocks. As a result, financial assets have low unconditional volatility and the lowest risk when there are no external interruptions. Despite the financial assets' sensitivity to shocks, they exhibit some resistance to fluctuations in market confidence. The VaR performance comparison results with the assets portfolios differ. During the COVID-19 outbreak, the Dow (DJI) index reports VaR's highest loss, followed by the S&P500. Conversely, the CFVaR reports negative risk results for the entire cryptocurrency portfolio during the pandemic, except for the Ethereum (ETH).
Is Kyle's equilibrium model stable?
Authors: Umut Cetin, Kasper Larsen
Date: 2023-07-18 00:00:00
Tasks: model

In the dynamic discrete-time trading setting of Kyle (1985), we prove that Kyle's equilibrium model is stable when there are one or two trading times. For three or more trading times, we prove that Kyle's equilibrium is not stable. These theoretical results are proven to hold irrespectively of all Kyle's input parameters.
Rethinking Intersection Over Union for Small Object Detection in Few-Shot Regime
Authors: Pierre Le Jeune, Anissa Mokraoui
Date: 2023-07-17 00:00:00
Tasks: Few-Shot Object Detection, Object, object-detection, Object Detection, Small Object Detection

In Few-Shot Object Detection (FSOD), detecting small objects is extremely difficult. The limited supervision cripples the localization capabilities of the models and a few pixels shift can dramatically reduce the Intersection over Union (IoU) between the ground truth and predicted boxes for small objects. To this end, we propose Scale-adaptive Intersection over Union (SIoU), a novel box similarity measure. SIoU changes with the objects' size, it is more lenient with small object shifts. We conducted a user study and SIoU better aligns than IoU with human judgment. Employing SIoU as an evaluation criterion helps to build more user-oriented models. SIoU can also be used as a loss function to prioritize small objects during training, outperforming existing loss functions. SIoU improves small object detection in the non-few-shot regime, but this setting is unrealistic in the industry as annotated detection datasets are often too expensive to acquire. Hence, our experiments mainly focus on the few-shot regime to demonstrate the superiority and versatility of SIoU loss. SIoU improves significantly FSOD performance on small objects in both natural (Pascal VOC and COCO datasets) and aerial images (DOTA and DIOR). In aerial imagery, small objects are critical and SIoU loss achieves new state-of-the-art FSOD on DOTA and DIOR.
DenseMP: Unsupervised Dense Pre-training for Few-shot Medical Image Segmentation
Authors: Zhaoxin Fan, Puquan Pan, Zeren Zhang, Ce Chen, Tianyang Wang, Siyang Zheng, Min Xu
Date: 2023-07-13 00:00:00
Tasks: Image Segmentation, Medical Image Analysis, Medical Image Segmentation, Segmentation, Semantic Segmentation

Few-shot medical image semantic segmentation is of paramount importance in the domain of medical image analysis. However, existing methodologies grapple with the challenge of data scarcity during the training phase, leading to over-fitting. To mitigate this issue, we introduce a novel Unsupervised Dense Few-shot Medical Image Segmentation Model Training Pipeline (DenseMP) that capitalizes on unsupervised dense pre-training. DenseMP is composed of two distinct stages: (1) segmentation-aware dense contrastive pre-training, and (2) few-shot-aware superpixel guided dense pre-training. These stages collaboratively yield a pre-trained initial model specifically designed for few-shot medical image segmentation, which can subsequently be fine-tuned on the target dataset. Our proposed pipeline significantly enhances the performance of the widely recognized few-shot segmentation model, PA-Net, achieving state-of-the-art results on the Abd-CT and Abd-MRI datasets. Code will be released after acceptance.
Fine-grained Text-Video Retrieval with Frozen Image Encoders
Authors: Zuozhuo Dai, Fangtao Shao, Qingkun Su, Zilong Dong, Siyu Zhu
Date: 2023-07-14 00:00:00
Tasks: Decoder, Retrieval, Video Retrieval

State-of-the-art text-video retrieval (TVR) methods typically utilize CLIP and cosine similarity for efficient retrieval. Meanwhile, cross attention methods, which employ a transformer decoder to compute attention between each text query and all frames in a video, offer a more comprehensive interaction between text and videos. However, these methods lack important fine-grained spatial information as they directly compute attention between text and video-level tokens. To address this issue, we propose CrossTVR, a two-stage text-video retrieval architecture. In the first stage, we leverage existing TVR methods with cosine similarity network for efficient text/video candidate selection. In the second stage, we propose a novel decoupled video text cross attention module to capture fine-grained multimodal information in spatial and temporal dimensions. Additionally, we employ the frozen CLIP model strategy in fine-grained retrieval, enabling scalability to larger pre-trained vision models like ViT-G, resulting in improved retrieval performance. Experiments on text video retrieval datasets demonstrate the effectiveness and scalability of our proposed CrossTVR compared to state-of-the-art approaches.
Source-Free Domain Adaptive Fundus Image Segmentation with Class-Balanced Mean Teacher
Authors: Longxiang Tang, Kai Li, Chunming He, Yulun Zhang, Xiu Li
Date: 2023-07-14 00:00:00
Tasks: Image Segmentation, Semantic Segmentation, Source-Free Domain Adaptation

This paper studies source-free domain adaptive fundus image segmentation which aims to adapt a pretrained fundus segmentation model to a target domain using unlabeled images. This is a challenging task because it is highly risky to adapt a model only using unlabeled data. Most existing methods tackle this task mainly by designing techniques to carefully generate pseudo labels from the model's predictions and use the pseudo labels to train the model. While often obtaining positive adaption effects, these methods suffer from two major issues. First, they tend to be fairly unstable - incorrect pseudo labels abruptly emerged may cause a catastrophic impact on the model. Second, they fail to consider the severe class imbalance of fundus images where the foreground (e.g., cup) region is usually very small. This paper aims to address these two issues by proposing the Class-Balanced Mean Teacher (CBMT) model. CBMT addresses the unstable issue by proposing a weak-strong augmented mean teacher learning scheme where only the teacher model generates pseudo labels from weakly augmented images to train a student model that takes strongly augmented images as input. The teacher is updated as the moving average of the instantly trained student, which could be noisy. This prevents the teacher model from being abruptly impacted by incorrect pseudo-labels. For the class imbalance issue, CBMT proposes a novel loss calibration approach to highlight foreground classes according to global statistics. Experiments show that CBMT well addresses these two issues and outperforms existing methods on multiple benchmarks.