pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Inference of Asynchronous Boolean Network Models
Authors: Guy Karlebach
Date: 2025-01-05 00:00:00

Associations between phenotype and genomic and epigenomic markers are often derived by correlation. Systems Biology aims to make more robust connections and uncover broader insights by modeling the cellular mechanisms that produce a phenotype. The question of choosing the modeling methodology is of central importance. A model that does not capture biological reality closely enough will not explain the system's behavior. At the same time, highly detailed models suffer from computational limitations and are likely to overfit the data. Boolean networks strike a balance between complexity and descriptiveness and thus have received increasing interest. We previously described an algorithm for fitting Boolean networks to high-throughout experimental data that finds the optimal network with respect to the information in a given dataset. In this work, we describe a simple extension that enables the modeling of asynchronous dynamics, i.e. different reaction times for different network nodes. In addition, we present a new method for pseudo-time assignment for single-cell RNA sequencing data that is derived from the modeling procedure. Our approach greatly simplifies the construction of Boolean network models for time-series datasets, where asynchronicity often occurs. We demonstrate our methodology by integrating real data from transcriptomics experiments. These results significantly expand the applicability of the Boolean network model to experimental data.
Inverse receptive field attention for naturalistic image reconstruction from the brain
Authors: Lynn Le, Thirza Dado, Katja Seeliger, Paolo Papale, Antonio Lozano, Pieter Roelfsema, Yağmur Güçlütürk, Marcel van Gerven, Umut Güçlü
Date: 2025-01-06 00:00:00
Tasks: Image Reconstruction

Visual perception in the brain largely depends on the organization of neuronal receptive fields. Although extensive research has delineated the coding principles of receptive fields, most studies have been constrained by their foundational assumptions. Moreover, while machine learning has successfully been used to reconstruct images from brain data, this approach faces significant challenges, including inherent feature biases in the model and the complexities of brain structure and function. In this study, we introduce an inverse receptive field attention (IRFA) model, designed to reconstruct naturalistic images from neurophysiological data in an end-to-end fashion. This approach aims to elucidate the tuning properties and representational transformations within the visual cortex. The IRFA model incorporates an attention mechanism that determines the inverse receptive field for each pixel, weighting neuronal responses across the visual field and feature spaces. This method allows for an examination of the dynamics of neuronal representations across stimuli in both spatial and feature dimensions. Our results show highly accurate reconstructions of naturalistic data, independent of pre-trained models. Notably, IRF models trained on macaque V1, V4, and IT regions yield remarkably consistent spatial receptive fields across different stimuli, while the features to which neuronal representations are selective exhibit significant variation. Additionally, we propose a data-driven method to explore representational clustering within various visual areas, further providing testable hypotheses.
Asynchronous Hebbian/anti-Hebbian networks
Authors: Henrique Reis Aguiar, Matthias H. Hennig
Date: 2025-01-04 00:00:00

Lateral inhibition models coupled with Hebbian plasticity have been shown to learn factorised causal representations of input stimuli, for instance, oriented edges are learned from natural images. Currently, these models require the recurrent dynamics to settle into a stable state before weight changes can be applied, which is not only biologically implausible, but also impractical for real-time learning systems. Here, we propose a new Hebbian learning rule which is implemented using plausible biological mechanisms that have been observed experimentally. We find that this rule allows for efficient, time-continuous learning of factorised representations, very similar to the classic noncontinuous Hebbian/anti-Hebbian learning. Furthermore, we show that this rule naturally prevents catastrophic forgetting when stimuli from different distributions are shown sequentially.
The impact of motor and non-motor symptoms fluctuations on health-related quality of life in people with functional motor disorder
Authors: Martin Jirásek, Tomáš Sieger, Gabriela Chaloupková, Lucia Nováková, Petr Sojka, Mark J Edwards, Tereza Serranová
Date: 2024-12-18 00:00:00

Objective: To assess the effect of overall, between- and within-day subjectively rated fluctuations in motor and non-motor symptoms in people with functional motor disorder (FMD) on the health-related quality of life (HRQoL). Background: FMD is a complex condition characterized by fluctuating motor and non-motor symptoms that may negatively impact HRQoL. Methods: Seventy-seven patients (54 females, mean age 45.4 (SD 10.4) years) with a clinically established diagnosis of FMD, including weakness, completed symptom diaries, rating the severity of motor and non-motor symptoms (i.e., pain, fatigue, mood, cognitive difficulties) on a 10-point numerical scale three times daily for seven consecutive days. HRQoL was assessed using the SF-36 questionnaire. For the analysis, fluctuation magnitude was defined in terms of the variability in self-reported symptom scores. Results: The mental component of SF-36 was jointly predicted by the overall severity scores (t(74) = -3.61, P < 0.001) and overall general fluctuations (t(74) = -2.98, P = 0.004). The physical SF-36 was found to be related only to the overall symptom severity scores (t(74) = -7.09, P < 0.001), but not to the overall fluctuations. The assessment of the impact of different components showed that the mental component of SF-36 was significantly influenced by the combined effect of average fatigue (t(73) = -3.86, P < 0.001), between-day cognitive symptoms fluctuations (t(73) = -3.22, P = 0.002), and within-day mood fluctuations (t(73) = -2.48, P = 0.015). Conclusions: This study demonstrated the impact of self-reported symptom fluctuations across multiple motor and non-motor domains on mental but not physical HRQoL in FMD and highlighted the importance of assessing and managing fluctuations in clinical practice.
Gain modulation of actions selection without synaptic relearning
Authors: Elif Köksal-Ersöz, Pascal Chossat, Frédéric Lavigne
Date: 2024-12-18 00:00:00

Adaptation of behavior requires the brain to change goals in a changing environment. Synaptic learning has demonstrated its effectiveness in changing the probability of selecting actions based on their outcome. In the extreme case, it is vital not to repeat an action to a given goal that led to harmful punishment. The present model proposes a simple neural mechanism of gain modulation that makes possible immediate changes in the probability of selecting a goal after punishment of variable intensity. Results show how gain modulation determine the type of elementary navigation process within the state space of a network of neuronal populations of excitatory neurons regulated by inhibition. Immediately after punishment, the system can avoid the punished populations by going back or by jumping to unpunished populations. This does not require particular credit assignment at the `choice' population but only gain modulation of neurons active at the time of punishment. Gain modulation does not require statistical relearning that may lead to further errors, but can encode memories of past experiences without modification of synaptic efficacies. Therefore, gain modulation can complements synaptic plasticity.
Environmental Factors Can Have Opposite Biodiversity Influences on the Community Temporal Stability In Aquatic Ecosystems
Authors: Zihao Wen, Hang Shan, Hao Wang, Yu Cao, Liang He, Wenjing Ren, Chengjie Yin, Qingchuan Chou, Chaochao Lv, Haojie Su, Tao Tang, Qinghua Cai, Leyi Ni, Wen Xiao, Xiaolin Zhang, Kuanyi Li, Te Cao, Ming-Chih Chiu, Vincent H. Resh, Pablo Urrutia-Cordero
Date: 2025-01-06 00:00:00

1. An understanding of how biodiversity confers ecosystem stability is crucial in managing ecosystems under major environmental changes. Multiple biodiversity drivers can stabilize ecosystem functions over time. However, we know little about how local environmental conditions can influence these biodiversity drivers, and consequently how they indirectly shape the ecological stability of ecosystems. 2. We hypothesized that environmental factors can have opposite influences (i.e., not necessarily either positive or negative) on the temporal stability of communities in different environmental ranges depending on the biodiversity drivers involved. We tested this novel hypothesis by using data from a 4-year-long field study of submerged macrophyte across a water depth gradient in 8 heterogeneous bays of Erhai lake (with total sample size of 30,071 quadrats), a large lentic system in China. 3. Results indicate that a unimodal pattern of stability in temporal biomass measurements occurred along the water-depth gradient, and that multiple biodiversity drivers (the asynchrony in species dynamics, and the stability of dominant species) generally increased the temporal stability of aquatic primary producers. However, the effect of water depth either increased or decreased the stability of biomass according to the environmental conditions associated with sites along the water depth gradient. 4. Synthesis. These results reveal the influence of local environmental conditions on the biodiversity drivers of stability may help predict the functional consequences of biodiversity change across different scenarios of environmental change.
Estimating invasive rodent abundance using removal data and hierarchical models
Authors: Olivier Gimenez
Date: 2025-01-03 00:00:00
Tasks: Management

Invasive rodents pose significant ecological, economic, and public health challenges. Robust methods are needed for estimating population abundance to guide effective management. Traditional methods such as capture-recapture are often impractical for invasive species due to ethical, legal and logistical constraints. Here, I showcase the application of hierarchical multinomial N-mixture models for estimating the abundance of invasive rodents using removal data. First, I perform a simulation study which demonstrates minimal bias, as well as good precision and reliable coverage of confidence intervals across a range of sampling scenarios. I also illustrate the consequences of violating the population closure assumption, showing how between-occasion dynamics can bias inference. Second, I analyze removal data for two invasive rodent species, namely coypus (Myocastor coypus) in France and muskrats (Ondatra zibethicus) in the Netherlands. Using hierarchical multinomial N-mixture models, I examine the effects of temperature on abundance while accounting for imperfect and time-varying capture probabilities. I also show how to accommodate spatial variability using random effects, quantify uncertainty in parameter estimates, and account for violations of closure by fitting an open-population model to multi-year data. Overall, I hope to demonstrate the flexibility and utility of hierarchical models in invasive species management.
Innate behavioural mechanisms and defensive traits in ecological models of predator-prey types
Authors: Sangeeta Saha, Swadesh Pal, Roderick Melnik
Date: 2025-01-03 00:00:00

There are various examples of phenotypic plasticity in ecosystems that serve as the basis for a wide range of inducible defences against predation. These strategies include camouflage, burrowing, mimicry, evasive actions, and even counterattacks that enhance survival under fluctuating predatory threats. Additionally, the ability to exhibit plastic responses often influences ecological balances, shaping predator-prey coexistence over time. This study introduces a predator-prey model where prey species show inducible defences, providing new insights into the role of adaptive strategies in these complex interactions. The stabilizing impact of the defensive mechanism is one of several intriguing outcomes produced by the dynamics. Moreover, the predator population rises when the interference rate increases to a moderate value even in the presence of lower prey defence but decreases monotonically for stronger defence levels. Furthermore, we identify a bistable domain when the handling rate is used as a control parameter, emphasizing the critical role of initial population sizes in determining system outcomes. By considering the species diffusion in a bounded region, the study is expanded into a spatio-temporal model. The numerical simulation reveals that the Turing domain decreases as the level of protection increases. The study is subsequently extended to incorporate taxis, known as the directed movement of species toward or away from another species. Our investigation identifies the conditions under which pattern formation emerges, driven by the interplay of inducible defences, taxis as well as species diffusion. Numerical simulations demonstrate that including taxis within the spatio-temporal model exerts a stabilizing influence, thereby diminishing the potential for pattern formation in the system.
Beware of so-called 'good' correlations: a statistical reality check on individual mRNA-protein predictions
Authors: Romain-Daniel Gosselin
Date: 2025-01-05 00:00:00

Research in the life sciences often employs messenger ribonucleic acids (mRNA) quantification as a standalone approach for functional analysis. However, although the correlation between the measured levels of mRNA and proteins is positive, correlation coefficients observed empirically are incomplete, necessitating caution in making agnostic inferences. This essay provides a statistical reflection and caveat on the concept of correlation strength in the context of transcriptomics-proteomics studies. It highlights the variability in possible protein levels at given empirical correlation values, even for precise mRNA amount, and underscores the notable proportion of mRNA-protein pairs with abundances at opposite ends of their respective distributions. Cell biologists, data scientists, and biostatisticians should recognise that mRNA-protein correlation alone is insufficient to justify using a single mRNA quantification to infer the amount or function of its corresponding protein.
Genomic and pathological analyses of an asymmetric true hermaphroditism case in a female labrador retriever
Authors: Yihang Zhou
Date: 2025-01-04 00:00:00

The two main gonadal development disorders in dogs are true hermaphroditism and XX male syndrome. True hermaphroditism can be divided into two subcategories: XX sex reversal and XY sex reversal. XX Sry-negative sex reversal is more common, and it is characterized by the presence of both ovarian and testicular tissues in an animal. To date, there are 16 cases of true hermaphroditism reported in the literature, 15 of which are XX true hermaphroditism. Hermaphroditism has not been formally documented in labrador retrievers, and no case of asymmetric hermaphroditism has been reported in the literature.
Inferring and predicting Fried physical frailty phenotype deficits
Authors: Glen Pridham, Kenneth Rockwood, Andrew Rutenberg
Date: 2025-01-03 00:00:00
Tasks: Nutrition

We predict the Fried physical frailty phenotype health deficits (FPFP5: slow gait, weakness, weight loss, low activity, and exhaustion) using two measures of frailty: frailty index (FI) or frailty phenotype (FP). The FP theorizes that the FPFP5 are mutually dependent through shared etiology and positive feedbacks, so that the total number of FPFP5 deficits (NFPFP5) should be highly predictive of existing deficits. Alternatively, the FI theorizes that strong mutual dependencies exist between \emph{all} age-related health deficits, so that the FI would be more predictive. We investigated predictive models of FPFP5 using FI or NFPFP5 in the Health and Retirement Study (HRS), the English Longitudinal Study of Aging (ELSA), and the National Health and Nutrition Examination Survey (NHANES). We find that the FI, chronological age, and current deficit state are all important predictors of future FPFP5 deficits. Notably, the FI consistently out-performed NFPFP5, raising questions regarding FPFP5 causal connections and how best to measure the physical component of frailty. We discuss implications for both FPFP5 forecasting, and inference when data are missing or incomplete.
High-frequency lead-lag relationships in the Chinese stock index futures market: tick-by-tick dynamics of calendar spreads
Authors: Guanlin Li, Xiyan Chen, Yingzheng Liu
Date: 2025-01-06 00:00:00

Lead-lag relationships, integral to market dynamics, offer valuable insights into the trading behavior of high-frequency traders (HFTs) and the flow of information at a granular level. This paper investigates the lead-lag relationships between stock index futures contracts of different maturities in the Chinese financial futures market (CFFEX). Using high-frequency (tick-by-tick) data, we analyze how price movements in near-month futures contracts influence those in longer-dated contracts, such as next-month, quarterly, and semi-annual contracts. Our findings reveal a consistent pattern of price discovery, with the near-month contract leading the others by one tick, driven primarily by liquidity. Additionally, we identify a negative feedback effect of the "lead-lag spread" on the leading asset, which can predict returns of leading asset. Backtesting results demonstrate the profitability of trading based on the lead-lag spread signal, even after accounting for transaction costs. Altogether, our analysis offers valuable insights to understand and capitalize on the evolving dynamics of futures markets.
Finite Element Method for HJB in Option Pricing with Stock Borrowing Fees
Authors: Rakhymzhan Kazbek, Aidana Abdukarimova
Date: 2025-01-04 00:00:00
Tasks: CPU

In mathematical finance, many derivatives from markets with frictions can be formulated as optimal control problems in the HJB framework. Analytical optimal control can result in highly nonlinear PDEs, which might yield unstable numerical results. Accurate and convergent numerical schemes are essential to leverage the benefits of the hedging process. In this study, we apply a finite element approach with a non-uniform mesh for the task of option pricing with stock borrowing fees, leading to an HJB equation that bypasses analytical optimal control in favor of direct PDE discretization. The time integration employs the theta-scheme, with initial modifications following Rannacher`s procedure. A Newton-type algorithm is applied to address the penalty-like term at each time step. Numerical experiments are conducted, demonstrating consistency with a benchmark problem and showing a strong match. The CPU time needed to reach the desired results favors P2-FEM over FDM and linear P1-FEM, with P2-FEM displaying superior convergence. This paper presents an efficient alternative framework for the HJB problem and contributes to the literature by introducing a finite element method (FEM)-based solution for HJB applications in mathematical finance.
On consistency of optimal portfolio choice for state-dependent exponential utilities
Authors: Edoardo Berton, Marzia De Donno, Marco Maggis
Date: 2025-01-03 00:00:00

In an arbitrage-free simple market, we demonstrate that for a class of state-dependent exponential utilities, there exists a unique prediction of the random risk aversion that ensures the consistency of optimal strategies across any time horizon. Our solution aligns with the theory of forward performances, with the added distinction of identifying, among the infinite possible solutions, the one for which the profile remains optimal at all times for the market-adjusted system of preferences adopted.
Is Your Image a Good Storyteller?
Authors: Xiujie Song, Xiaoyi Pang, Haifeng Tang, Mengyue Wu, Kenny Q. Zhu
Date: 2024-12-29 00:00:00

Quantifying image complexity at the entity level is straightforward, but the assessment of semantic complexity has been largely overlooked. In fact, there are differences in semantic complexity across images. Images with richer semantics can tell vivid and engaging stories and offer a wide range of application scenarios. For example, the Cookie Theft picture is such a kind of image and is widely used to assess human language and cognitive abilities due to its higher semantic complexity. Additionally, semantically rich images can benefit the development of vision models, as images with limited semantics are becoming less challenging for them. However, such images are scarce, highlighting the need for a greater number of them. For instance, there is a need for more images like Cookie Theft to cater to people from different cultural backgrounds and eras. Assessing semantic complexity requires human experts and empirical evidence. Automatic evaluation of how semantically rich an image will be the first step of mining or generating more images with rich semantics, and benefit human cognitive assessment, Artificial Intelligence, and various other applications. In response, we propose the Image Semantic Assessment (ISA) task to address this problem. We introduce the first ISA dataset and a novel method that leverages language to solve this vision problem. Experiments on our dataset demonstrate the effectiveness of our approach. Our data and code are available at: https://github.com/xiujiesong/ISA.
ECG-guided individual identification via PPG
Authors: Riling Wei, Hanjie Chen, Kelu Yao, Chuanguang Yang, Jun Wang, Chao Li
Date: 2024-12-30 00:00:00
Tasks: Knowledge Distillation

Photoplethsmography (PPG)-based individual identification aiming at recognizing humans via intrinsic cardiovascular activities has raised extensive attention due to its high security and resistance to mimicry. However, this kind of technology witnesses unpromising results due to the limitation of low information density. To this end, electrocardiogram (ECG) signals have been introduced as a novel modality to enhance the density of input information. Specifically, a novel cross-modal knowledge distillation framework is implemented to propagate discriminate knowledge from ECG modality to PPG modality without incurring additional computational demands at the inference phase. Furthermore, to ensure efficient knowledge propagation, Contrastive Language-Image Pre-training (CLIP)-based knowledge alignment and cross-knowledge assessment modules are proposed respectively. Comprehensive experiments are conducted and results show our framework outperforms the baseline model with the improvement of 2.8% and 3.0% in terms of overall accuracy on seen- and unseen individual recognitions.
Fall Detection in Passenger Elevators using Intelligent Surveillance Camera Systems: An Application with YoloV8 Nano Model
Authors: Pinar Yozgatli, Yavuz Acar, Mehmet Tulumen, Selman Minga, Salih Selamet, Beytullah Nalbant, Mustafa Talha Toru, Berna Koca, Tevfik Keles, Mehmet Selcok
Date: 2024-12-30 00:00:00

Computer vision technology, which involves analyzing images and videos captured by cameras through deep learning algorithms, has significantly advanced the field of human fall detection. This study focuses on the application of the YoloV8 Nano model in identifying fall incidents within passenger elevators, a context that presents unique challenges due to the enclosed environment and varying lighting conditions. By training the model on a robust dataset comprising over 10,000 images across diverse elevator types, we aim to enhance the detection precision and recall rates. The model's performance, with an 85% precision and 82% recall in fall detection, underscores its potential for integration into existing elevator safety systems to enable rapid intervention.