pwc-archive/papers-with-abstracts huggingface dataset:
A quality control analysis of the resting state hypothesis via permutation entropy on EEG recordings
Authors: Alessio Perinelli, Leonardo Ricci
Date: 2024-05-16 00:00:00
Tasks: EEG

The analysis of electrophysiological recordings of the human brain in resting state is a key experimental technique in neuroscience. Resting state is indeed the default condition to characterize brain dynamics. Its successful implementation relies both on the capacity of subjects to comply with the requirement of staying awake while not performing any cognitive task, and on the capacity of the experimenter to validate that compliance. Here we propose a novel approach, based on permutation entropy, to provide a quality control of the resting state condition by evaluating its stability during a recording. We combine the calculation of permutation entropy with a method for the estimation of its uncertainty out of a single time series, thus enabling a statistically robust assessment of resting state stationarity. The approach is showcased on electroencephalographic data recorded from young and elderly subjects and considering both eyes-closed and eyes-opened resting state conditions. Besides showing the reliability of the approach, the results showed higher instability in elderly subjects that hint at a qualitative difference between the two age groups with regard to the distribution of unstable activity within the brain. The method is therefore a tool that provides insights on the issue of resting state stability of interest for neuroscience experiments. The method can be applied to other kinds of electrophysiological data like, for example, magnetoencephalographic recordings. In addition, provided that suitable hardware and software processing units are used, its implementation, which consists here of a posteriori analysis, can be translated into a real time one.
Zero-shot counting with a dual-stream neural network model
Authors: Jessica A. F. Thompson, Hannah Sheahan, Tsvetomira Dumbalska, Julian Sandbrink, Manuela Piazza, Christopher Summerfield
Date: 2024-05-16 00:00:00
Tasks: Object Recognition, Zero-Shot Counting

Deep neural networks have provided a computational framework for understanding object recognition, grounded in the neurophysiology of the primate ventral stream, but fail to account for how we process relational aspects of a scene. For example, deep neural networks fail at problems that involve enumerating the number of elements in an array, a problem that in humans relies on parietal cortex. Here, we build a 'dual-stream' neural network model which, equipped with both dorsal and ventral streams, can generalise its counting ability to wholly novel items ('zero-shot' counting). In doing so, it forms spatial response fields and lognormal number codes that resemble those observed in macaque posterior parietal cortex. We use the dual-stream network to make successful predictions about behavioural studies of the human gaze during similar counting tasks.
A Test of the Thermodynamics of Evolution
Authors: Daniel Sadasivan, Cole Cantu, Cecilia Marsh, Andrew Graham
Date: 2024-05-20 00:00:00

Recent research has extended methods from the fields of thermodynamics and statistical mechanics into other disciplines. Most notably, one recent work creates a unified theoretical framework to understand evolutionary biology, machine learning, and thermodynamics. We present simulations of biological evolution used to test this framework. The test simulates organisms whose behavior is determined by specific parameters that play the role of genes. These genes are passed on to new simulated organisms with the capacity to mutate, allowing adaption of the organisms to the environment. With this simulation, we are able to test the the framework in question. The results of our simulation are consistent with the work being tested, providing evidence for it.
Extinctions as a vestige of instability: the geometry of stability and feasibility
Authors: Stav Marcus, Ari M. Turner, Guy Bunin
Date: 2024-05-18 00:00:00

Species coexistence is a complex, multifaceted problem. At an equilibrium, coexistence requires two conditions: stability under small perturbations; and feasibility, meaning all species abundances are positive. Which of these two conditions is more restrictive has been debated for many years, with many works focusing on statistical arguments for systems with many species. Within the framework of the Lotka-Volterra equations, we examine the geometry of the region of coexistence in the space of interaction strengths, for symmetric competitive interactions and any finite number of species. We consider what happens when starting at a point within the coexistence region, and changing the interaction strengths continuously until one of the two conditions breaks. We find that coexistence generically breaks through the loss of feasibility, as the abundance of one species reaches zero. An exception to this rule - where stability breaks before feasibility - happens only at isolated points, or more generally on a lower dimensional subset of the boundary. The reason behind this is that as a stability boundary is approached, some of the abundances generally diverge towards minus infinity, and so go extinct at some earlier point, breaking the feasibility condition first. These results define a new sense in which feasibility is a more restrictive condition than stability, and show that these two requirements are closely interrelated. We then show how our results affect the changes in the set of coexisting species when interaction strengths are changed: a system of coexisting species loses a species by its abundance continuously going to zero, and this new fixed point is unique. As parameters are further changed, multiple alternative equilibria may be found. Finally, we discuss the extent to which our results apply to asymmetric interactions.
Revisiting the exclusion principle in epidemiology at its ultimate limit
Authors: Nir Gavish
Date: 2024-05-16 00:00:00
Tasks: Epidemiology

The competitive exclusion principle in epidemiology implies that when competing strains of a pathogen provide complete protection for each other, the strain with the largest reproduction number outcompetes the other strains and drives them to extinction. The introduction of various trade-off mechanisms may facilitate the coexistence of competing strains, especially when their respective basic reproduction numbers are close so that the competition between the strains is weak. Yet, one may expect that a substantial competitive advantage of one of the strains will eventually outbalance trade-off mechanisms driving less competitive strains to extinction. The literature, however, lacks a rigorous validation of this statement. In this work, we challenge the validity of the exclusion principle at an ultimate limit in which one strain has a vast competitive advantage over the other strains. We show that when one strain is significantly more transmissible than the others, and under broad conditions, an epidemic system with two strains has a stable endemic equilibrium in which both strains coexist with comparable prevalence. Thus, the competitive exclusion principle does not unconditionally hold beyond the established case of complete immunity.
Extraction of Human DNA from Soil: Protocol Adaptations
Authors: Wera M Schmerer
Date: 2024-05-21 00:00:00

PCR-based analysis of DNA is utilized in a wide variety of fields, including Forensic Science. Aside from the more common ample sources, material analyzed here can refer to specimen excavated from a soil environment, or a sampling of the soil itself to recover DNA leached into the soil from decomposing human remains or from body fluids intermingled with the soil in an outdoor crime scene. The common problematic of these types of sample is the presence of humic acids, which are a component of any soil environment, and when the co-extracted with the DNA, lead to inhibition of enzyme-based procedures including PCR. While a variety of methods exist for the extraction of DNA from excavated skeletal remains, protocols for extraction of DNA from the soil directly are usually targeting soil microorganism. To address the need for methodology suitable for extraction of human DNA from soil, a selection of three published protocols were adapted for this purpose, to be tested and evaluated using standardized samples. The resulting protocols are presented here.
MicroBundlePillarTrack: A Python package for automated segmentation, tracking, and analysis of pillar deflection in cardiac microbundles
Authors: Hiba Kobeissi, Xining Gao, Samuel J. DePalma, Jourdan K. Ewoldt, Miranda C. Wang, Shoshana L. Das, Javiera Jilberto, David Nordsletten, Brendon M. Baker, Christopher S. Chen, Emma Lejeune
Date: 2024-05-17 00:00:00
Tasks: Optical Flow Estimation

Movies of human induced pluripotent stem cell (hiPSC)-derived engineered cardiac tissue (microbundles) contain abundant information about structural and functional maturity. However, extracting these data in a reproducible and high-throughput manner remains a major challenge. Furthermore, it is not straightforward to make direct quantitative comparisons across the multiple in vitro experimental platforms employed to fabricate these tissues. Here, we present "MicroBundlePillarTrack," an open-source optical flow-based package developed in Python to track the deflection of pillars in cardiac microbundles grown on experimental platforms with two different pillar designs ("Type 1" and "Type 2" design). Our software is able to automatically segment the pillars, track their displacements, and output time-dependent metrics for contractility analysis, including beating amplitude and rate, contractile force, and tissue stress. Because this software is fully automated, it will allow for both faster and more reproducible analyses of larger datasets and it will enable more reliable cross-platform comparisons as compared to existing approaches that require manual steps and are tailored to a specific experimental platform. To complement this open-source software, we share a dataset of 1,540 brightfield example movies on which we have tested our software. Through sharing this data and software, our goal is to directly enable quantitative comparisons across labs, and facilitate future collective progress via the biomedical engineering open-source data and software ecosystem.
Delivery strategies to improve piglets exposure to oral antibiotics
Authors: Noslen Hernández, Béatrice B. Roques, Marlène Z. Lacroix, Didier Concordet
Date: 2024-05-17 00:00:00

The widespread practice of delivering antibiotics through drinking water to livestock leads to considerable variability in exposure levels among animals, raising concerns regarding disease outbreaks and the emergence of antibiotic resistance. This variability is primarily driven by three pivotal factors: fluctuations in drug concentration within water pipes, variances in drinking behavior among animals, and differences in individual pharmacokinetic parameters. This article introduces an approach aimed at improving medication distribution by customizing it according to the drinking patterns of pigs, without escalating the medication dose. As examples, we demonstrate that incorporating the drinking behavior into the delivery of amoxicillin results in an increase in the percentage of piglets reaching an AUC/MIC ratio greater than 25h. Specifically, with Pasteurella multocida, the percentage rises from 30% to at least 60%, while with Actinobacillus pleuropneumoniae, it increases from 20% to more than 70%.
No winners: Performance of lung cancer prediction models depends on screening-detected, incidental, and biopsied pulmonary nodule use cases
Authors: Thomas Z. Li, Kaiwen Xu, Aravind Krishnan, Riqiang Gao, Michael N. Kammer, Sanja Antic, David Xiao, Michael Knight, Yency Martinez, Rafael Paez, Robert J. Lentz, Stephen Deppen, Eric L. Grogan, Thomas A. Lasko, Kim L. Sandler, Fabien Maldonado, Bennett A. Landman
Date: 2024-05-16 00:00:00
Tasks: Lung Cancer Diagnosis

Statistical models for predicting lung cancer have the potential to facilitate earlier diagnosis of malignancy and avoid invasive workup of benign disease. Many models have been published, but comparative studies of their utility in different clinical settings in which patients would arguably most benefit are scarce. This study retrospectively evaluated promising predictive models for lung cancer prediction in three clinical settings: lung cancer screening with low-dose computed tomography, incidentally detected pulmonary nodules, and nodules deemed suspicious enough to warrant a biopsy. We leveraged 9 cohorts (n=898, 896, 882, 219, 364, 117, 131, 115, 373) from multiple institutions to assess the area under the receiver operating characteristic curve (AUC) of validated models including logistic regressions on clinical variables and radiologist nodule characterizations, artificial intelligence on chest CTs, longitudinal imaging AI, and multi-modal approaches. We implemented each model from their published literature, re-training the models if necessary, and curated each cohort from primary data sources. We observed that model performance varied greatly across clinical use cases. No single predictive model emerged as a clear winner across all cohorts, but certain models excelled in specific clinical contexts. Single timepoint chest CT AI performed well in lung screening, but struggled to generalize to other clinical settings. Longitudinal imaging and multimodal models demonstrated comparatively promising performance on incidentally-detected nodules. However, when applied to nodules that underwent biopsy, all models underperformed. These results underscore the strengths and limitations of 8 validated predictive models and highlight promising directions towards personalized, noninvasive lung cancer diagnosis.
Hypergraph: A Unified and Uniform Definition with Application to Chemical Hypergraph and More
Authors: Daniel T. Chang
Date: 2024-05-14 00:00:00

The conventional definition of hypergraph has two major issues: (1) there is not a standard definition of directed hypergraph and (2) there is not a formal definition of nested hypergraph. To resolve these issues, we propose a new definition of hypergraph that unifies the concepts of undirected, directed and nested hypergraphs, and that is uniform in using hyperedge as a single construct for representing high-order correlations among things, i.e., nodes and hyperedges. Specifically, we define a hyperedge to be a simple hyperedge, a nesting hyperedge, or a directed hyperedge. With this new definition, a hypergraph is nested if it has nesting hyperedge(s), and is directed if it has directed hyperedge(s). Otherwise, a hypergraph is a simple hypergraph. The uniformity and power of this new definition, with visualization, should facilitate the use of hypergraph for representing (hierarchical) high-order correlations in general and chemical systems in particular. Graph has been widely used as a mathematical structure for machine learning on molecular structures and 3D molecular geometries. However, graph has a major limitation: it can represent only pairwise correlations between nodes. Hypergraph extends graph with high-order correlations among nodes. This extension is significant or essential for machine learning on chemical systems. For molecules, this is significant as it allows the direct, explicit representation of multicenter bonds and molecular substructures. For chemical reactions, this is essential since most chemical reactions involve multiple participants. We propose the use of chemical hypergraph, a multilevel hypergraph with simple, nesting and directed hyperedges, as a single mathematical structure for representing chemical systems. We apply the new definition of hypergraph to chemical hypergraph and, as simplified versions, molecular hypergraph and chemical reaction hypergraph.
Unraveling the Autism spectrum heterogeneity: Insights from ABIDE I Database using data/model-driven permutation testing approaches
Authors: F. J. Alcaide, I. A. Illan, J. Ramirez, J. M. Gorriz
Date: 2024-04-22 00:00:00

Autism Spectrum Condition (ASC) is a neurodevelopmental condition characterized by impairments in communication, social interaction and restricted or repetitive behaviors. Extensive research has been conducted to identify distinctions between individuals with ASC and neurotypical individuals. However, limited attention has been given to comprehensively evaluating how variations in image acquisition protocols across different centers influence these observed differences. This analysis focuses on structural magnetic resonance imaging (sMRI) data from the Autism Brain Imaging Data Exchange I (ABIDE I) database, evaluating subjects' condition and individual centers to identify disparities between ASC and control groups. Statistical analysis, employing permutation tests, utilizes two distinct statistical mapping methods: Statistical Agnostic Mapping (SAM) and Statistical Parametric Mapping (SPM). Results reveal the absence of statistically significant differences in any brain region, attributed to factors such as limited sample sizes within certain centers, noise effects and the problem of multicentrism in a heterogeneous condition such as autism. This study indicates limitations in using the ABIDE I database to detect structural differences in the brain between neurotypical individuals and those diagnosed with ASC. Furthermore, results from the SAM mapping method show greater consistency with existing literature.
Classification of colorectal primer carcinoma from normal colon with mid-infrared spectra
Authors: B. Borkovits, E. Kontsek, A. Pesti, P. Gordon, S. Gergely, I. Csabai, A. Kiss, P. Pollner
Date: 2024-03-22 00:00:00

In this project, we used formalin-fixed paraffin-embedded (FFPE) tissue samples to measure thousands of spectra per tissue core with Fourier transform mid-infrared spectroscopy using an FT-IR imaging system. These cores varied between normal colon (NC) and colorectal primer carcinoma (CRC) tissues. We created a database to manage all the multivariate data obtained from the measurements. Then, we applied classifier algorithms to identify the tissue based on its yielded spectra. For classification, we used the random forest, a support vector machine, XGBoost, and linear discriminant analysis methods, as well as three deep neural networks. We compared two data manipulation techniques using these models and then applied filtering. In the end, we compared model performances via the sum of ranking differences (SRD).
Exploiting Distributional Value Functions for Financial Market Valuation, Enhanced Feature Creation and Improvement of Trading Algorithms
Authors: Colin D. Grab
Date: 2024-05-19 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning

While research of reinforcement learning applied to financial markets predominantly concentrates on finding optimal behaviours, it is worth to realize that the reinforcement learning returns $G_t$ and state value functions themselves are of interest and play a pivotal role in the evaluation of assets. Instead of focussing on the more complex task of finding optimal decision rules, this paper studies and applies the power of distributional state value functions in the context of financial market valuation and machine learning based trading algorithms. Accurate and trustworthy estimates of the distributions of $G_t$ provide a competitive edge leading to better informed decisions and more optimal behaviour. Herein, ideas from predictive knowledge and deep reinforcement learning are combined to introduce a novel family of models called CDG-Model, resulting in a highly flexible framework and intuitive approach with minimal assumptions regarding underlying distributions. The models allow seamless integration of typical financial modelling pitfalls like transaction costs, slippage and other possible costs or benefits into the model calculation. They can be applied to any kind of trading strategy or asset class. The frameworks introduced provide concrete business value through their potential in market valuation of single assets and portfolios, in the comparison of strategies as well as in the improvement of market timing. They can positively impact the performance and enhance the learning process of existing or new trading algorithms. They are of interest from a scientific point-of-view and open up multiple areas of future research. Initial implementations and tests were performed on real market data. While the results are promising, applying a robust statistical framework to evaluate the models in general remains a challenge and further investigations are needed.
Deep Penalty Methods: A Class of Deep Learning Algorithms for Solving High Dimensional Optimal Stopping Problems
Authors: Yunfei Peng, Pengyu Wei, Wei Wei
Date: 2024-05-18 00:00:00
Tasks: Computational Efficiency

We propose a deep learning algorithm for high dimensional optimal stopping problems. Our method is inspired by the penalty method for solving free boundary PDEs. Within our approach, the penalized PDE is approximated using the Deep BSDE framework proposed by \cite{weinan2017deep}, which leads us to coin the term "Deep Penalty Method (DPM)" to refer to our algorithm. We show that the error of the DPM can be bounded by the loss function and $O(\frac{1}{\lambda})+O(\lambda h) +O(\sqrt{h})$, where $h$ is the step size in time and $\lambda$ is the penalty parameter. This finding emphasizes the need for careful consideration when selecting the penalization parameter and suggests that the discretization error converges at a rate of order $\frac{1}{2}$. We validate the efficacy of the DPM through numerical tests conducted on a high-dimensional optimal stopping model in the area of American option pricing. The numerical tests confirm both the accuracy and the computational efficiency of our proposed algorithm.
Data-generating process and time-series asset pricing
Authors: Shuxin Guo, Qiang Liu
Date: 2024-05-17 00:00:00
Tasks: Time Series

We study the data-generating processes for factors expressed in return differences, which the literature on time-series asset pricing seems to have overlooked. For the factors' data-generating processes or long-short zero-cost portfolios, a meaningful definition of returns is impossible; further, the compounded market factor (MF) significantly underestimates the return difference between the market and the risk-free rate compounded separately. Surprisingly, if MF were treated coercively as periodic-rebalancing long-short (i.e., the same as size and value), Fama-French three-factor (FF3) would be economically unattractive for lacking compounding and irrelevant for suffering from the small "size of an effect." Otherwise, FF3 might be misspecified if MF were buy-and-hold long-short. Finally, we show that OLS with net returns for single-index models leads to inflated alphas, exaggerated t-values, and overestimated Sharpe ratios (SR); worse, net returns may lead to pathological alphas and SRs. We propose defining factors (and SRs) with non-difference compound returns.
Is the annualized compounded return of Medallion over 35%?
Authors: Shuxin Guo, Qiang Liu
Date: 2024-05-17 00:00:00

It is a challenge to estimate fund performance by compounded returns. Arguably, it is incorrect to use yearly returns directly for compounding, with reported annualized return of above 60% for Medallion for the 31 years up to 2018. We propose an estimation based on fund sizes and trading profits and obtain a compounded return of 31.8% before fees. Alternatively, we suggest using the manager's wealth as a proxy and arriving at a compounded growth rate of 25.6% for Simons for the 33 years up to 2020. We conclude that the annualized compounded return of Medallion before fees is probably under 35%. Our findings have implications for correctly estimating fund performance.