pwc-archive/papers-with-abstracts huggingface dataset:
Psychophysics assessment of anomalous contrast
Authors: Ernest Greene, Jack Morrison
Date: 2025-01-07 00:00:00

Study of image encoding mechanisms of the retina is made possible by the high precision control of timing and intensity of LED displays. One can provide stimulus flicker that reveals differential activation of ON and OFF retinal channels at frequencies above the flicker-fusion threshold. The light energy provided to ON and OFF channels can be balanced, such that a flickering letter will vanish into the background. Yet if the luminance balance has been produced using ultra-brief flashes, an "anomalous contrast" is produced that provides the letter with visibility. The present work contributes additional details about the conditions that will produce anomalous contrast, and discusses how the retinal circuitry can provide this visibility.
Neural encoding with affine feature response transforms
Authors: Lynn Le, Nils Kimman, Thirza Dado, Katja Seeliger, Paolo Papale, Antonio Lozano, Pieter Roelfsema, Marcel van Gerven, Yağmur Güçlütürk, Umut Güçlü
Date: 2025-01-07 00:00:00

Current linearizing encoding models that predict neural responses to sensory input typically neglect neuroscience-inspired constraints that could enhance model efficiency and interpretability. To address this, we propose a new method called affine feature response transform (AFRT), which exploits the brain's retinotopic organization. Applying AFRT to encode multi-unit activity in areas V1, V4, and IT of the macaque brain, we demonstrate that AFRT reduces redundant computations and enhances the performance of current linearizing encoding models by segmenting each neuron's receptive field into an affine retinal transform, followed by a localized feature response. Remarkably, by factorizing receptive fields into a sequential affine component with three interpretable parameters (for shifting and scaling) and response components with a small number of feature weights per response, AFRT achieves encoding with orders of magnitude fewer parameters compared to unstructured models. We show that the retinal transform of each neuron's encoding agrees well with the brain's receptive field. Together, these findings suggest that this new subset within spatial transformer network can be instrumental in neural encoding models of naturalistic stimuli.
A sub-Riemannian model of neural states in the primary motor cortex
Authors: Caterina Mazzetti, Jawad Ali, Alessandro Sarti, Giovanna Citti
Date: 2024-12-22 00:00:00

We develop a neurogeometric model for the arm area of motor cortex, which encodes complex motor primitives, ranging from simple movement features like movement direction, to short hand trajectories, termed fragments, and ultimately to more complex patterns known as neural states (Georgopoulos, Hatsopoulos, Kadmon-Harpaz et al). Based on the sub-riemannian framework introduced in 2023, we model the space of fragments as a set of short curves defined by kinematic parameters. We then introduce a geometric kernel that serves as a model for cortical connectivity and use it in a differential equation to describe cortical activity. By applying a grouping algorithm to this cortical activity model, we successfully recover the neural states observed in Kadmon-Harpaz et al, which were based on measured cortical activity. This confirms that the choice of kinematic variables and the distance metric used here are sufficient to explain the phenomena of neural state formation. The modularity of our model reflects the brain's hierarchical structure, where initial groupings in the kinematic space $\mathcal{M}$ lead to more abstract representations. This approach mimics how the brain processes stimuli at different scales, extracting both local and global properties.
Quantum Models of Consciousness from a Quantum Information Science Perspective
Authors: Lea Gassab, Onur Pusuluk, Marco Cattaneo, Özgür E. Müstecaplioğlu
Date: 2024-12-20 00:00:00

This perspective explores various quantum models of consciousness from the viewpoint of quantum information science, offering potential ideas and insights. The models under consideration can be categorized into three distinct groups based on the level at which quantum mechanics might operate within the brain: those suggesting that consciousness arises from electron delocalization within microtubules inside neurons, those proposing it emerges from the electromagnetic field surrounding the entire neural network, and those positing it originates from the interactions between individual neurons governed by neurotransmitter molecules. Our focus is particularly on the Posner model of cognition, for which we provide preliminary calculations on the preservation of entanglement of phosphate molecules within the geometric structure of Posner clusters. These findings provide valuable insights into how quantum information theory can enhance our understanding of brain functions.
MyESL: Sparse learning in molecular evolution and phylogenetic analysis
Authors: Maxwell Sanderford, Sudip Sharma, Glen Stecher, Jun Liu, Jieping Ye, Sudhir Kumar
Date: 2025-01-09 00:00:00
Tasks: Sparse Learning

Evolutionary sparse learning (ESL) uses a supervised machine learning approach, Least Absolute Shrinkage and Selection Operator (LASSO), to build models explaining the relationship between a hypothesis and the variation across genomic features (e.g., sites) in sequence alignments. ESL employs sparsity between and within the groups of genomic features (e.g., genomic loci) by using sparse-group LASSO. Although some software packages are available for performing sparse group LASSO, we found them less well-suited for processing and analyzing genome-scale data containing millions of features, such as bases. MyESL software fills the need for open-source software for conducting ESL analyses with facilities to pre-process the input hypotheses and large alignments, make LASSO flexible and computationally efficient, and post-process the output model to produce different metrics useful in functional or evolutionary genomics. MyESL can take phylogenetic trees and sequence alignments as input and transform them into numeric responses and features, respecetively. The model outputs are processed into user-friendly text and graphical files. The computational core of MyESL is written in C++, which offers model building with or without group sparsity, while the pre- and post-processing of inputs and model outputs is performed using customized functions written in Python. One of its applications in phylogenomics showcases the utility of MyESL. Our analysis of empirical genome-scale datasets shows that MyESL can build evolutionary models quickly and efficiently on a personal desktop, while other computational packages were unable due to their prohibitive requirements of computational resources and time. MyESL is available for Python environments on Linux and distributed as a standalone application for Windows and macOS. It is available from https://github.com/kumarlabgit/MyESL.
Asymmetric Variability: The Impact of Uneven Stochasticity on Competitive Dynamics
Authors: Ori Turkia, Nadav M. Shnerb
Date: 2025-01-07 00:00:00

Competition between species and genotypes is a dominant factor in a variety of ecological and evolutionary processes. Biological dynamics are typically highly stochastic, and therefore, analyzing a competitive system requires accounting for the random nature of birth and death processes (demographic stochasticity) as well as the variability of external conditions (environmental stochasticity). Recent studies have highlighted the importance of species life history, showing that differences in life history lead competing species to experience different levels of demographic stochasticity. Here, we propose a simple model of two-species competition with different life histories and derive analytical expressions for various properties (fixation probability, fixation time, absorption time, probability density) under a wide range of conditions, including migration, selection, and environmental stochasticity. These properties provide insights into the long-term outcomes of competition, such as species persistence, extinction risks, and the influence of environmental variability on the evenness of the community.
Collective inference of the truth of propositions from crowd probability judgments
Authors: Patrick Stinson, Jasper van den Bosch, Trenton Jerde, Nikolaus Kriegeskorte
Date: 2025-01-09 00:00:00
Tasks: General Knowledge

Every day, we judge the probability of propositions. When we communicate graded confidence (e.g. "I am 90% sure"), we enable others to gauge how much weight to attach to our judgment. Ideally, people should share their judgments to reach more accurate conclusions collectively. Peer-to-peer tools for collective inference could help debunk disinformation and amplify reliable information on social networks, improving democratic discourse. However, individuals fall short of the ideal of well-calibrated probability judgments, and group dynamics can amplify errors and polarize opinions. Here, we connect insights from cognitive science, structured expert judgment, and crowdsourcing to infer the truth of propositions from human probability judgments. In an online experiment, 376 participants judged the probability of each of 1,200 general-knowledge claims for which we have ground truth (451,200 ratings). Aggregating binary judgments by majority vote already exhibits the "wisdom of the crowd"--the superior accuracy of collective inferences relative to individual inferences. However, using continuous probability ratings and accounting for individual accuracy and calibration significantly improves collective inferences. Peer judgment behavior can be modeled probabilistically, and individual parameters capturing each peer's accuracy and miscalibration can be inferred jointly with the claim probabilities. This unsupervised approach can be complemented by supervised methods relying on truth labels to learn models that achieve well-calibrated collective inference. The algorithms we introduce can empower groups of collaborators and online communities to pool their distributed intelligence and jointly judge the probability of propositions with a well-calibrated sense of uncertainty.
A Framework for Building Enviromics Matrices in Mixed Models
Authors: B. A. Trevisan, V. S. Junqueira, B. M. Florencio, A. S. G. Coelho, G. E. Marcatti, R. T. Resende
Date: 2025-01-07 00:00:00

This study introduces a framework for constructing enviromics matrices in mixed models to integrate genetic and environmental data to enhance phenotypic predictions in plant breeding. Enviromics utilizes diverse data sources, such as climate and soil, to characterize genotype-by-environment (GxE) interactions. The approach employs block-diagonal structures in the design matrix to incorporate random effects from genetic and envirotypic covariates across trials. The covariance structure is modeled using the Kronecker product of the genetic relationship matrix and an identity matrix representing envirotypic effects, capturing genetic and environmental variability. This dual representation enables more accurate crop performance predictions across environments, improving selection strategies in breeding programs. The framework is compatible with existing mixed model software, including rrBLUP and BGLR, and can be extended for more complex interactions. By combining genetic relationships and environmental influences, this approach offers a powerful tool for advancing GxE studies and accelerating the development of improved crop varieties.
Integrating anatomy and electrophysiology in the healthy human heart: Insights from biventricular statistical shape analysis using universal coordinates
Authors: Lore Van Santvliet, Elena Zappon, Matthias A. F. Gsell, Franz Thaler, Maarten Blondeel, Steven Dymarkowski, Guido Claessen, Rik Willems, Martin Urschler, Bert Vandenberk, Gernot Plank, Maarten De Vos
Date: 2025-01-08 00:00:00
Tasks: Anatomy, Dimensionality Reduction

A cardiac digital twin is a virtual replica of a patient-specific heart, mimicking its anatomy and physiology. A crucial step of building a cardiac digital twin is anatomical twinning, where the computational mesh of the digital twin is tailored to the patient-specific cardiac anatomy. In a number of studies, the effect of anatomical variation on clinically relevant functional measurements like electrocardiograms (ECGs) is investigated, using computational simulations. While such a simulation environment provides researchers with a carefully controlled ground truth, the impact of anatomical differences on functional measurements in real-world patients remains understudied. In this study, we develop a biventricular statistical shape model and use it to quantify the effect of biventricular anatomy on ECG-derived and demographic features, providing novel insights for the development of digital twins of cardiac electrophysiology. To this end, a dataset comprising high-resolution cardiac CT scans from 271 healthy individuals, including athletes, is utilized. Furthermore, a novel, universal, ventricular coordinate-based method is developed to establish lightweight shape correspondence. The performance of the shape model is rigorously established, focusing on its dimensionality reduction capabilities and the training data requirements. Additionally, a comprehensive synthetic cohort is made available, featuring ready-to-use biventricular meshes with fiber structures and anatomical region annotations. These meshes are well-suited for electrophysiological simulations.
In-Sample and Out-of-Sample Sharpe Ratios for Linear Predictive Models
Authors: Antoine Jacquier, Johannes Muhle-Karbe, Joseph Mulligan
Date: 2025-01-07 00:00:00

We study how much the in-sample performance of trading strategies based on linear predictive models is reduced out-of-sample due to overfitting. More specifically, we compute the in- and out-of-sample means and variances of the corresponding PnLs and use these to derive a closed-form approximation for the corresponding Sharpe ratios. We find that the out-of-sample ``replication ratio'' diminishes for complex strategies with many assets and based on many weak rather than a few strong trading signals, and increases when more training data is used. The substantial quantitative importance of these effects is illustrated with an empirical case study for commodity futures following the methodology of Garleanu-Pedersen.
Market Making with Fads, Informed, and Uninformed Traders
Authors: Emilio Barucci, Adrien Mathieu, Leandro Sánchez-Betancourt
Date: 2025-01-07 00:00:00
Tasks: FAD

We characterise the solutions to a continuous-time optimal liquidity provision problem in a market populated by informed and uninformed traders. In our model, the asset price exhibits fads -- these are short-term deviations from the fundamental value of the asset. Conditional on the value of the fad, we model how informed traders and uninformed traders arrive in the market. The market maker knows of the two groups of traders but only observes the anonymous order arrivals. We study both, the complete information and the partial information versions of the control problem faced by the market maker. In such frameworks, we characterise the value of information, and we find the price of liquidity as a function of the proportion of informed traders in the market. Lastly, for the partial information setup, we explore how to go beyond the Kalman-Bucy filter to extract information about the fad from the market arrivals.
Multi-Hypothesis Prediction for Portfolio Optimization: A Structured Ensemble Learning Approach to Risk Diversification
Authors: Alejandro Rodriguez Dominguez, Muhammad Shahzad, Xia Hong
Date: 2025-01-07 00:00:00
Tasks: Decision Making, Diversity, Ensemble Learning, Portfolio Optimization

This work proposes a unified framework for portfolio allocation, covering both asset selection and optimization, based on a multiple-hypothesis predict-then-optimize approach. The portfolio is modeled as a structured ensemble, where each predictor corresponds to a specific asset or hypothesis. Structured ensembles formally link predictors' diversity, captured via ensemble loss decomposition, to out-of-sample risk diversification. A structured data set of predictor output is constructed with a parametric diversity control, which influences both the training process and the diversification outcomes. This data set is used as input for a supervised ensemble model, the target portfolio of which must align with the ensemble combiner rule implied by the loss. For squared loss, the arithmetic mean applies, yielding the equal-weighted portfolio as the optimal target. For asset selection, a novel method is introduced which prioritizes assets from more diverse predictor sets, even at the expense of lower average predicted returns, through a diversity-quality trade-off. This form of diversity is applied before the portfolio optimization stage and is compatible with a wide range of allocation techniques. Experiments conducted on the full S&P 500 universe and a data set of 1.300 global bonds of various types over more than two decades validate the theoretical framework. Results show that both sources of diversity effectively extend the boundaries of achievable portfolio diversification, delivering strong performance across both one-step and multi-step allocation tasks.
A mixture transition distribution approach to portfolio optimization
Authors: Riccardo De Blasis, Luca Galati, Filippo Petroni
Date: 2025-01-08 00:00:00
Tasks: Decision Making, Portfolio Optimization

Understanding the dependencies among financial assets is critical for portfolio optimization. Traditional approaches based on correlation networks often fail to capture the nonlinear and directional relationships that exist in financial markets. In this study, we construct directed and weighted financial networks using the Mixture Transition Distribution (MTD) model, offering a richer representation of asset interdependencies. We apply local assortativity measures--metrics that evaluate how assets connect based on similarities or differences--to guide portfolio selection and allocation. Using data from the Dow Jones 30, Euro Stoxx 50, and FTSE 100 indices constituents, we show that portfolios optimized with network-based assortativity measures consistently outperform the classical mean-variance framework. Notably, modalities in which assets with differing characteristics connect enhance diversification and improve Sharpe ratios. The directed nature of MTD-based networks effectively captures complex relationships, yielding portfolios with superior risk-adjusted returns. Our findings highlight the utility of network-based methodologies in financial decision-making, demonstrating their ability to refine portfolio optimization strategies. This work thus underscores the potential of leveraging advanced financial networks to achieve enhanced performance, offering valuable insights for practitioners and setting a foundation for future research.
Evaluating the resilience of ESG investments in European Markets during turmoil periods
Authors: Barbara Iannone, Pierdomenico Duttilo, Stefano Antonio Gattone
Date: 2025-01-04 00:00:00

This study investigates the resilience of Environmental, Social, and Governance (ESG) investments during periods of financial instability, comparing them with traditional equity indices across major European markets-Germany, France, and Italy. Using daily returns from October 2021 to February 2024, the analysis explores the effects of key global disruptions such as the Covid-19 pandemic and the Russia-Ukraine conflict on market performance. A mixture of two generalised normal distributions (MGND) and EGARCH-in-mean models are used to identify periods of market turmoil and assess volatility dynamics. The findings indicate that during crises, ESG investments present higher volatility in Germany and Italy than in France. Despite some regional variations, ESG portfolios demonstrate greater resilience compared to traditional ones, offering potential risk mitigation during market shocks. These results underscore the importance of integrating ESG factors into long-term investment strategies, particularly in the face of unpredictable financial turmoil.
Guiding Treatment Strategies: The Role of Adjuvant Anti-Her2 Neu Therapy and Skin/Nipple Involvement in Local Recurrence-Free Survival in Breast Cancer Patients
Authors: Joe Omatoi, Abdul M Mohammed, Dennis Trujillo
Date: 2025-01-04 00:00:00
Tasks: Causal Inference

This study explores how causal inference models, specifically the Linear Non-Gaussian Acyclic Model (LiNGAM), can extract causal relationships between demographic factors, treatments, conditions, and outcomes from observational patient data, enabling insights beyond correlation. Unlike traditional randomized controlled trials (RCTs), which establish causal relationships within narrowly defined populations, our method leverages broader observational data, improving generalizability. Using over 40 features in the Duke MRI Breast Cancer dataset, we found that Adjuvant Anti-Her2 Neu Therapy increased local recurrence-free survival by 169 days, while Skin/Nipple involvement reduced it by 351 days. These findings highlight the therapy's importance for Her2-positive patients and the need for targeted interventions for high-risk cases, informing personalized treatment strategies.
Calculating Customer Lifetime Value and Churn using Beta Geometric Negative Binomial and Gamma-Gamma Distribution in a NFT based setting
Authors: Sagarnil Das
Date: 2025-01-02 00:00:00
Tasks: Marketing

Customer Lifetime Value (CLV) is an important metric that measures the total value a customer will bring to a business over their lifetime. The Beta Geometric Negative Binomial Distribution (BGNBD) and Gamma Gamma Distribution are two models that can be used to calculate CLV, taking into account both the frequency and value of customer transactions. This article explains the BGNBD and Gamma Gamma Distribution models, and how they can be used to calculate CLV for NFT (Non-Fungible Token) transaction data in a blockchain setting. By estimating the parameters of these models using historical transaction data, businesses can gain insights into the lifetime value of their customers and make data-driven decisions about marketing and customer retention strategies.
Pressing Intensity: An Intuitive Measure for Pressing in Soccer
Authors: Joris Bekkers
Date: 2024-12-30 00:00:00
Tasks: Pitch control

Pressing is a fundamental defensive strategy in football, characterized by applying pressure on the ball owning team to regain possession. Despite its significance, existing metrics for measuring pressing often lack precision or comprehensive consideration of positional data, player movement and speed. This research introduces an innovative framework for quantifying pressing intensity, leveraging advancements in positional tracking data and components from Spearman's Pitch Control model. Our method integrates player velocities, movement directions, and reaction times to compute the time required for a defender to intercept an attacker or the ball. This time-to-intercept measure is then transformed into probabilistic values using a logistic function, enabling dynamic and intuitive analysis of pressing situations at the individual frame level. the model captures how every player's movement influences pressure on the field, offering actionable insights for coaches, analysts, and decision-makers. By providing a robust and intepretable metric, our approach facilitates the identification of pressing strategies, advanced situational analyses, and the derivation of metrics, advancing the analytical capabilities for modern football.