pwc-archive/papers-with-abstracts huggingface dataset:
Understanding Brain Aging Across Populations: A Comprehensive Framework for Structural Analysis
Authors: Alphin J Thottupattu, Jayanthi Sivaswamy, Bharath Holla, Jithender Saini
Date: 2024-07-31 00:00:00
Tasks: Anatomy

Understanding distinct neurological aging patterns across various populations is vital in the context of a globally aging populace. This study seeks to unravel the structural variations in the aging brain, taking into consideration different ethnic backgrounds. MRI data from Indian, Chinese, Japanese, and Caucasian populations were analyzed using a two-pronged approach. Initially, a group analysis was performed involving tissue segmentation through FSL-FAST, examining gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). Subsequently, a continuous model-based analysis was employed, defining aging as a diffeomorphic transformation, which facilitated a detailed intra- and inter-population analysis, and examined both global anatomy and age-dependent distances of each population in comparison to the Indian population. Detailed insights into the spatial distribution of brain deformations over time were obtained, with particular focus on anatomical changes relative to a reference time point. The proposed comprehensive structural comparison framework, applied to a sample dataset encompassing four distinct populations, represents a pioneering effort to compare structural differences related to brain aging on a global scale. Subsequent studies utilizing larger datasets and applying the proposed analysis across groups based on various criteria will further advance our understanding of aging-related changes.
DOME Registry: Implementing community-wide recommendations for reporting supervised machine learning in biology
Authors: Omar Abdelghani Attafi, Damiano Clementel, Konstantinos Kyritsis, Emidio Capriotti, Gavin Farrell, Styliani-Christina Fragkouli, Leyla Jael Castro, András Hatos, Tom Lenaerts, Stanislav Mazurenko, Soroush Mozaffari, Franco Pradelli, Patrick Ruch, Castrense Savojardo, Paola Turina, Federico Zambelli, Damiano Piovesan, Alexander Miguel Monzon, Fotis Psomopoulos, Silvio C. E. Tosatto
Date: 2024-08-14 00:00:00

Supervised machine learning (ML) is used extensively in biology and deserves closer scrutiny. The DOME recommendations aim to enhance the validation and reproducibility of ML research by establishing standards for key aspects such as data handling and processing, optimization, evaluation, and model interpretability. The recommendations help to ensure that key details are reported transparently by providing a structured set of questions. Here, we introduce the DOME Registry (URL: registry.dome-ml.org), a database that allows scientists to manage and access comprehensive DOME-related information on published ML studies. The registry uses external resources like ORCID, APICURON and the Data Stewardship Wizard to streamline the annotation process and ensure comprehensive documentation. By assigning unique identifiers and DOME scores to publications, the registry fosters a standardized evaluation of ML methods. Future plans include continuing to grow the registry through community curation, improving the DOME score definition and encouraging publishers to adopt DOME standards, promoting transparency and reproducibility of ML in the life sciences.
Miscalibration of simulations: A comment on Luebbert and Pachter: 'Miscalibration of the honeybee odometer' arXiv:2405.12998v1
Authors: Geoffrey Willam Stuart
Date: 2024-08-14 00:00:00

In this commentary I review the claim by Luebbert and Pachter (arXiv:2405.12998v1) that the reported R-Squared value in Srinivasan et al. (Science, 287(5454):851-853, 2000), describing the relationship between distance to a food source and mean waggle duration of honeybee dances, was too high to be consistent with the reported means and standard deviations in the latter study. There is one serious limitation of the simulations conducted by Luebbert and Pachter, and two flaws that compromise their findings. The reported R-squared value of Srinivasan. et al. is within the expected range, as far as that can be determined given the limitations of the available data.
Performing clinical drug trials in children with a rare disease
Authors: Victoria Hedley, Rebecca Leary, Anando Sen, Anna Irvin, Emma Heslop, Volker Straub
Date: 2024-08-13 00:00:00
Tasks: Epidemiology

Over the past 50 years, the advancements in medical and health research have radically changed the epidemiology of health conditions in neonates, children, and adolescents; and clinical research has on the whole, moved forward. However, large sections of the pediatric community remain vulnerable and underserved, by clinical research. One reason for this is the fact that most pediatric diseases are also rare diseases (i.e., they fit the EU definition of a rare condition, by affecting no more than 5 in 10,000 individuals), and indeed the majority of conditions under this umbrella heading are in fact much rarer, affecting fewer than 1 in 100,000. Rare pediatric diseases incur particular challenges, both in terms of actually conducting clinical trials but also planning trials (and indeed, stimulating the preclinical research and knowledge generation necessary to embark on clinical trials in the first place). The pediatric regulation and orphan regulation (covering rare diseases) were introduced to address the complexities in research and development of medicines specifically for children and for people living with a rare disease, respectively. The regulations have been reasonably effective, particularly in areas where adult and pediatric diseases overlap, driving the development of more pediatric medicines; however, challenges still remain, often exacerbated by the rarity of the diseases. These include issues around trial planning, the need for more innovative methodologies in smaller populations, significant delays in trial start up and recruitment, recruitment issues (due to small populations and the nature of the conditions), lack of endpoints, and scarce data. This chapter will discuss some of the major challenges in delivering trials in pediatric rare diseases while also assessing current and future solutions to address these.
Integrated population model reveals human and environment driven changes in Baltic ringed seal (Pusa hispida botnica) demography and behavior
Authors: Murat Ersalman, Mervi Kunnasranta, Markus Ahola, Anja M. Carlsson, Sara Persson, Britt-Marie Bäcklin, Inari Helle, Linnea Cervin, Jarno Vanhatalo
Date: 2024-08-15 00:00:00

Integrated population models (IPMs) are a promising approach to test ecological theories and assess wildlife populations in dynamic and uncertain conditions. By combining multiple data sources into a unified model, they enable the parametrization of versatile, mechanistic models that can predict population dynamics in novel circumstances. Here, we present a Bayesian IPM for the ringed seal (Pusa hispida botnica) population inhabiting the Bothnian Bay in the Baltic Sea. Despite the availability of long-term monitoring data, traditional assessment methods have faltered due to dynamic environmental conditions, varying reproductive rates, and the recently re-introduced hunting, thus limiting the quality of information available to managers. We fit our model to census and various demographic, reproductive, and harvest data from 1988 to 2023 to provide a comprehensive assessment of past population trends, and predict population response to alternative hunting scenarios. We estimated that 20,000 to 36,000 ringed seals inhabited the Bothnian Bay in 2024, increasing at a rate of 3% to 6% per year. Reproductive rates have increased since 1988, leading to a substantial increase in the growth rate up until 2015. However, the re-introduction of hunting has since reduced the growth rate, and even minor quota increases are likely to reduce it further. Our results also support the hypothesis that a greater proportion of the population hauls out under lower ice cover circumstances, leading to higher aerial survey results in such years. In general, our study demonstrates the value of IPMs for monitoring wildlife populations under changing environments, and supporting science-based management decisions.
Fast computation of the statistical significance test for spatio-temporal receptive field estimates obtained using spike-triggered averaging of binary pseudo-random sequences
Authors: Murat Okatan
Date: 2024-08-14 00:00:00

Background: Spatio-temporal receptive fields (STRF) of visual neurons are often estimated using spike-triggered averaging of binary pseudo-random stimulus sequences. The stimuli are visual displays that contain black and white pixels that flicker randomly at a fixed frame rate without any spatial or temporal correlation. The spike train of a visual neuron, such as a retinal ganglion cell, is recorded simultaneously with the stimulus presentation. The neuron's STRF is estimated by averaging the stimulus frames that coincide with spikes at fixed latencies. Recently, an exact analytical method for determining the statistical significance of the estimated value of the STRF pixels has been developed. Application of the method on spike trains collected from individual mouse retinal ganglion cells revealed that the time required to compute the test ranged from a couple of minutes to half a day for different neurons. New method: Here, this method is accelerated by using the Normal approximation to the null distribution of STRF pixel estimates. Results: The significance threshold and computation time obtained under the approximate distribution are examined systematically as a function of various input spike trains collected from individual mouse retinal ganglion cells. Comparison with existing methods: The accuracy and the time saved by the use of the approximate distribution are examined in comparison with the exact distribution. Conclusions: For the real data analyzed here, the approximate distribution yields the same significance thresholds as the exact distribution within a much shorter computation time.
Spatial Dynamics Behavioral Analysis of Motivational Operations Using Weighted Voronoi Diagrams
Authors: Carlos Alberto Hernández-Linares, Porfirio Toledo, Brenda Zarahí Medina-Pérez, Varsovia Hernández, Martha Lorena Avendaño Garrido, Víctor Quintero, Alejandro León
Date: 2024-08-14 00:00:00

This paper presents a novel approach to the analysis of spatial behavior distribution, utilizing weighted Voronoi diagrams. The objective is to map and understand how an experimental subject moves and spends time in various areas of a given space, thus identifying the areas of greatest behavioral interest. The technique entails the partitioning of the space into a grid, the designation of generator points, and the assignment of weights based on the time the subject spends in each region. The data analyzed were derived from multiple experimental sessions in which subjects were exposed to various conditions, including food deprivation, water deprivation, and combined deprivation and no deprivation. The aforementioned conditions resulted in the formation of clearly delineated spatial patterns. Weighted Voronoi diagrams provided a comprehensive and precise representation of these areas of interest, facilitating an in-depth examination of the evolution of behavioral patterns in diverse contexts, such as under different Motivational Operations. This tool offers a valuable perspective for the dynamic study of spatial behaviors in variable experimental settings.
Accounting for the geometry of the respiratory tract in viral infections
Authors: Thomas Williams, James M. McCaw, James M. Osborne
Date: 2024-08-07 00:00:00

Increasingly, experimentalists and modellers alike have come to recognise the important role of spatial structure in infection dynamics. Almost invariably, spatial computational models of viral infections - as with in vitro experimental systems - represent the tissue as wide and flat, which is often assumed to be representative of entire affected tissue within the host. However, this assumption fails to take into account the distinctive geometry of the respiratory tract in the context of viral infections. The respiratory tract is characterised by a tubular, branching structure, and moreover is spatially heterogeneous: deeper regions of the lung are composed of far narrower airways and are associated with more severe infection. Here, we extend a typical multicellular model of viral dynamics to account for two essential features of the geometry of the respiratory tract: the tubular structure of airways, and the branching process between airway generations. We show that, with this more realistic tissue geometry, the dynamics of infection are substantially changed compared to standard computational and experimental approaches, and that the resulting model is equipped to tackle important biological phenomena that do not arise in a flat host tissue, including viral lineage dynamics, and heterogeneity in immune responses to infection in different regions of the respiratory tree. Our findings suggest aspects of viral dynamics which current in vitro systems may be insufficient to describe, and point to several features of respiratory infections which can be experimentally assessed.
Predicting the distributions of stock returns around the globe in the era of big data and learning
Authors: Jozef Barunik, Martin Hronec, Ondrej Tobek
Date: 2024-08-14 00:00:00

This paper presents a method for accurately predicting the full distribution of stock returns, given a comprehensive set of 194 stock characteristics and market variables. Such distributions, learned from rich data using a machine learning algorithm, are not constrained by restrictive model assumptions and allow the exploration of non-Gaussian, heavy-tailed data and their non-linear interactions. The method uses a two-stage quantile neural network combined with spline interpolation. The results show that the proposed approach outperforms alternative models in terms of out-of-sample losses. Furthermore, we show that the moments derived from such distributions can be useful as alternative empirical estimates in many cases, including mean estimation and forecasting. Finally, we examine the relationship between cross-sectional returns and several distributional characteristics. The results are robust to a wide range of US and international data.
The mean-variance portfolio selection based on the average and current profitability of the risky asset
Authors: Yu Li, Yuhan Wu, Shuhua Zhang
Date: 2024-08-15 00:00:00

We study the continuous-time pre-commitment mean-variance portfolio selection in a time-varying financial market. By introducing two indexes which respectively express the average profitability of the risky asset (AP) and the current profitability of the risky asset (CP), the optimal portfolio selection is represented by AP and CP. Furthermore, instead of the traditional maximum likelihood estimation (MLE) of return rate and volatility of the risky asset, we estimate AP and CP with the second-order variation of an auxiliary wealth process. We prove that the estimations of AP and CP in this paper are more accurate than that in MLE. And, the portfolio selection is implemented in various simulated and real financial markets. Numerical studies confirm the superior performance of our portfolio selection with the estimation of AP and CP under various evaluation criteria.
Portfolio and reinsurance optimization under unknown market price of risk
Authors: Claudia Ceci, Katia Colaneri
Date: 2024-08-14 00:00:00
Tasks: Stochastic Optimization

We investigate the optimal investment-reinsurance problem for insurance company with partial information on the market price of the risk. Through the use of filtering techniques we convert the original optimization problem involving different filtrations, into an equivalent stochastic control problem under the observation filtration only, the so-called separated problem. The Markovian structure of the separated problem allows us to apply a classical approach to stochastic optimization based on the Hamilton-Jacobi-Bellman equation, and to provide explicit formulas for the value function and the optimal investment-reinsurance strategy. We finally discuss some comparisons between the optimal strategies pursued by a partially informed insurer and that followed by a fully informed insurer, and we evaluate the value of information using the idea of indifference pricing. These results are also supported by numerical experiments.
The Concentration Risk Indicator: Raising the Bar for Financial Stability and Portfolio Performance Measurement
Authors: Ravi Kashyap
Date: 2024-08-14 00:00:00
Tasks: Management, Marketing

We have developed a novel risk management measure called the concentration risk indicator (CRI). The CRI has been created to address drawbacks with prevailing methodologies and to supplement existing methods. Modified and adapted from the Herfindahl-Hirschman (HH) index, the CRI can give a single numeric score that can be helpful to evaluate the extent of risks that arise from holding concentrated portfolios. We discuss how the CRI can become an indicator of financial stability at any desired aggregation unit: regional, national or international level. We show how the CRI can be easily applied to insurance risk and to any product portfolio mix. The CRI is particularly applicable to the current facet of the decentralized terrain, wherein the majority of the wealth is restricted to a small number of tokens. We calculate and report the CRI -- along with other risk metrics -- for individual assets and portfolios of crypto assets using a daily data sample from January 01, 2019 until August 10, 2022. The CRI is an example of developing metrics that can useful for sending concise yet powerful messages to the relevant audience. This tactic -- which can be described as marketing the benefits of any product or service by using concepts from multiple disciplines -- of creating new metrics goes further beyond the use of metrics to evaluate marketing efficacy. The simplicity of our metric -- and the intuitive explanations we have provided for the CRI -- makes it straightforward to properly articulate a strong -- clear and positive -- signal as part of marketing campaigns. The development -- and implementation -- of new risk management metrics will have greater impact when a wider rigorous risk management process has been established. We discuss several topics related to bringing about more improved risk management across all types of institutions and assets.
Stylized facts in Web3
Authors: Wei-Ru Chen, A. Christian Silva, Shen-Ning Tung
Date: 2024-08-14 00:00:00

This paper presents a comprehensive statistical analysis of the Web3 ecosystem, comparing various Web3 tokens with traditional financial assets across multiple time scales. We examine probability distributions, tail behaviors, and other key stylized facts of the returns for a diverse range of tokens, including decentralized exchanges, liquidity pools, and centralized exchanges. Despite functional differences, most tokens exhibit well-established empirical facts, including unconditional probability density of returns with heavy tails gradually becoming Gaussian and volatility clustering. Furthermore, we compare assets traded on centralized (CEX) and decentralized (DEX) exchanges, finding that DEXs exhibit similar stylized facts despite different trading mechanisms and often divergent long-term performance. We propose that this similarity is attributable to arbitrageurs striving to maintain similar centralized and decentralized prices. Our study contributes to a better understanding of the dynamics of Web3 tokens and the relationship between CEX and DEX markets, with important implications for risk management, pricing models, and portfolio construction in the rapidly evolving DeFi landscape. These results add to the growing body of literature on cryptocurrency markets and provide insights that can guide the development of more accurate models for DeFi markets.
LADDER: Language Driven Slice Discovery and Error Rectification
Authors: Shantanu Ghosh, Rayan Syed, Chenyu Wang, Clare B. Poynton, Shyam Visweswaran, Kayhan Batmanghelich
Date: 2024-07-31 00:00:00
Tasks: Attribute, Clustering, Image Classification, Language Modelling, Large Language Model, Slice Discovery

Error slice discovery is crucial to diagnose and mitigate model errors. Current clustering or discrete attribute-based slice discovery methods face key limitations: 1) clustering results in incoherent slices, while assigning discrete attributes to slices leads to incomplete coverage of error patterns due to missing or insufficient attributes; 2) these methods lack complex reasoning, preventing them from fully explaining model biases; 3) they fail to integrate \textit{domain knowledge}, limiting their usage in specialized fields \eg radiology. We propose\ladder (\underline{La}nguage-\underline{D}riven \underline{D}iscovery and \underline{E}rror \underline{R}ectification), to address the limitations by: (1) leveraging the flexibility of natural language to address incompleteness, (2) employing LLM's latent \textit{domain knowledge} and advanced reasoning to analyze sentences and derive testable hypotheses directly, identifying biased attributes, and form coherent error slices without clustering. Existing mitigation methods typically address only the worst-performing group, often amplifying errors in other subgroups. In contrast,\ladder generates pseudo attributes from the discovered hypotheses to mitigate errors across all biases without explicit attribute annotations or prior knowledge of bias. Rigorous evaluations on 6 datasets spanning natural and medical images -- comparing 200+ classifiers with diverse architectures, pretraining strategies, and LLMs -- show that\ladder consistently outperforms existing baselines in discovering and mitigating biases.
Exploration of LLMs, EEG, and behavioral data to measure and support attention and sleep
Authors: Akane Sano, Judith Amores, Mary Czerwinski
Date: 2024-08-01 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Sleep Quality

We explore the application of large language models (LLMs), pre-trained models with massive textual data for detecting and improving these altered states. We investigate the use of LLMs to estimate attention states, sleep stages, and sleep quality and generate sleep improvement suggestions and adaptive guided imagery scripts based on electroencephalogram (EEG) and physical activity data (e.g. waveforms, power spectrogram images, numerical features). Our results show that LLMs can estimate sleep quality based on human textual behavioral features and provide personalized sleep improvement suggestions and guided imagery scripts; however detecting attention, sleep stages, and sleep quality based on EEG and activity data requires further training data and domain-specific knowledge.