pwc-archive/papers-with-abstracts huggingface dataset:
LSTM Autoencoder-based Deep Neural Networks for Barley Genotype-to-Phenotype Prediction
Authors: Guanjin Wang, Junyu Xuan, Penghao Wang, Chengdao Li, Jie Lu
Date: 2024-07-21 00:00:00
Tasks: Decision Making, Management, Prediction

Artificial Intelligence (AI) has emerged as a key driver of precision agriculture, facilitating enhanced crop productivity, optimized resource use, farm sustainability, and informed decision-making. Also, the expansion of genome sequencing technology has greatly increased crop genomic resources, deepening our understanding of genetic variation and enhancing desirable crop traits to optimize performance in various environments. There is increasing interest in using machine learning (ML) and deep learning (DL) algorithms for genotype-to-phenotype prediction due to their excellence in capturing complex interactions within large, high-dimensional datasets. In this work, we propose a new LSTM autoencoder-based model for barley genotype-to-phenotype prediction, specifically for flowering time and grain yield estimation, which could potentially help optimize yields and management practices. Our model outperformed the other baseline methods, demonstrating its potential in handling complex high-dimensional agricultural datasets and enhancing crop phenotype prediction performance.
Hierarchical Machine Learning Classification of Parkinsonian Disorders using Saccadic Eye Movements: A Development and Validation Study
Authors: Salil B Patel, Oliver B Bredemeyer, James J FitzGerald, Chrystalina A Antoniades
Date: 2024-07-22 00:00:00

Discriminating between Parkinson's Disease (PD) and Progressive Supranuclear Palsy (PSP) is difficult due to overlapping symptoms, especially early on. Saccades (rapid conjugate eye movements between fixation points) are affected by both diseases but conventional saccade analyses exhibit group level differences only. We hypothesized analyzing entire saccade raw time series waveforms would permit superior individual level discrimination between PD, PSP, and healthy controls (HC). 13,309 saccadic eye movements from 127 participants were analyzed using a novel, calibration-free waveform analysis and hierarchical machine learning framework. Individual saccades were classified based on which trained model could reconstruct each waveform with minimum error, indicating the most likely condition. A hierarchical classifier then predicted overall status (recently diagnosed and medication-naive 'de novo' PD, 'established' PD on antiparkinsonian medication, PSP, and healthy controls) by combining each participant's saccade results. This approach substantially outperformed conventional metrics, achieving high AUROCs distinguishing de novo PD from PSP (0.92-0.97), de novo PD from HC (0.72-0.89), and PSP from HC (0.90-0.95), while the conventional model showed limited performance (AUROC range: 0.45-0.75). This calibration-free waveform analysis sets a new standard for precise saccadic classification of PD, PSP, and HC, increasing potential for clinical adoption, remote monitoring, and screening.
Hyperbolic embedding of brain networks detects regions disrupted by neurodegeneration in Alzheimer's disease
Authors: Alice Longhena, Martin Guillemaud, Fabrizio De Vico Fallani, Raffaella Lara Migliaccio, Mario Chavez
Date: 2024-07-23 00:00:00
Tasks: Anomaly Detection

Graph theoretical methods have proven valuable for investigating alterations in both anatomical and functional brain connectivity networks during Alzheimer's disease (AD). Recent studies suggest that representing brain networks in a suitable geometric space can better capture their connectivity structure. This study introduces a novel approach to characterize brain connectivity changes using low dimensional, informative representations of networks in a latent geometric space. Specifically, the networks are embedded in a polar representation of the hyperbolic plane, the hyperbolic disk. Here, we use a geometric score, entirely based on the computation of distances between nodes in the latent space, to measure the effect of a perturbation on the nodes. Precisely, the score is a local measure of distortion in the geometric neighborhood of a node following a perturbation. The method is applied to a brain network dataset of patients with AD and healthy participants, derived from diffusion weighted (DWI) and functional (fMRI) magnetic resonance imaging scans. We show that, compared with standard graph measures, our method more accurately identifies the brain regions most affected by neurodegeneration. Notably, the abnormalities detected in memory related and frontal areas are robust across multiple brain parcellation scales. Finally, our findings suggest that the geometric perturbation score could serve as a potential biomarker for characterizing the progression of the disease.
How Does a Single EEG Channel Tell Us About Brain States in Brain-Computer Interfaces ?
Authors: Zaineb Ajra, Binbin Xu, Gérard Dray, Jacky Montmain, Stéphane Perrey
Date: 2024-07-23 00:00:00
Tasks: Brain Computer Interface, EEG, Motor Imagery

Over recent decades, neuroimaging tools, particularly electroencephalography (EEG), have revolutionized our understanding of the brain and its functions. EEG is extensively used in traditional brain-computer interface (BCI) systems due to its low cost, non-invasiveness, and high temporal resolution. This makes it invaluable for identifying different brain states relevant to both medical and non-medical applications. Although this practice is widely recognized, current methods are mainly confined to lab or clinical environments because they rely on data from multiple EEG electrodes covering the entire head. Nonetheless, a significant advancement for these applications would be their adaptation for "real-world" use, using portable devices with a single-channel. In this study, we tackle this challenge through two distinct strategies: the first approach involves training models with data from multiple channels and then testing new trials on data from a single channel individually. The second method focuses on training with data from a single channel and then testing the performances of the models on data from all the other channels individually. To efficiently classify cognitive tasks from EEG data, we propose Convolutional Neural Networks (CNNs) with only a few parameters and fast learnable spectral-temporal features. We demonstrated the feasibility of these approaches on EEG data recorded during mental arithmetic and motor imagery tasks from three datasets. We achieved the highest accuracies of 100%, 91.55% and 73.45% in binary and 3-class classification on specific channels across three datasets. This study can contribute to the development of single-channel BCI and provides a robust EEG biomarker for brain states classification.
Energy-information trade-off makes the cortical critical power law the optimal coding
Authors: Tsuyoshi Tatsukawa, Jun-nosuke Teramae
Date: 2024-07-23 00:00:00
Tasks: Decoder

Stimulus responses of cortical neurons exhibit the critical power law, where the covariance eigenspectrum follows the power law with the exponent just at the edge of differentiability of the neural manifold. This criticality is conjectured to balance the expressivity and robustness of neural codes, because a non-differential fractal manifold spoils coding reliability. However, contrary to the conjecture, here we prove that the neural coding is not degraded even on the non-differentiable fractal manifold, where the coding is extremely sensitive to perturbations. Rather, we show that the trade-off between energetic cost and information always makes this critical power-law response the optimal neural coding. Direct construction of a maximum likelihood decoder of the power-law coding validates the theoretical prediction. By revealing the non-trivial nature of high-dimensional coding, the theory developed here will contribute to a deeper understanding of criticality and power laws in computation in biological and artificial neural networks.
A statistical significance test for spatio-temporal receptive field estimates obtained using spike-triggered averaging of binary pseudo-random sequences
Authors: Murat Okatan
Date: 2024-07-22 00:00:00

Spatio-temporal receptive fields (STRF) of visual neurons are often estimated using spike-triggered averaging of binary pseudo-random stimulus sequences. The spike train of a visual neuron 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. Although this is a widely used technique, an analytical method for determining the statistical significance of the estimated value of the STRF pixels seems to be lacking. Such a significance test would be useful for identifying the significant features of the STRF and investigating their relationship with experimental variables. Here, the distribution of the estimated STRF pixel values is derived for given spike trains, under the null hypothesis that spike occurrences and stimulus values are statistically independent. This distribution is then used for computing amplitude thresholds to determine the STRF pixels where the null hypothesis can be rejected at a desired two-tailed significance level. It is also proposed that the size of the receptive field may be inferred from the significant pixels. The application of the proposed method is illustrated on spike trains collected from individual mouse retinal ganglion cells.
Survey on biomarkers in human vocalizations
Authors: Aki Härmä, Bert den Brinker, Ulf Grossekathofer, Okke Ouweltjes, Srikanth Nallanthighal, Sidharth Abrol, Vibhu Sharma
Date: 2024-07-07 00:00:00
Tasks: Survey

Recent years has witnessed an increase in technologies that use speech for the sensing of the health of the talker. This survey paper proposes a general taxonomy of the technologies and a broad overview of current progress and challenges. Vocal biomarkers are often secondary measures that are approximating a signal of another sensor or identifying an underlying mental, cognitive, or physiological state. Their measurement involve disturbances and uncertainties that may be considered as noise sources and the biomarkers are coarsely qualified in terms of the various sources of noise involved in their determination. While in some proposed biomarkers the error levels seem high, there are vocal biomarkers where the errors are expected to be low and thus are more likely to qualify as candidates for adoption in healthcare applications.
When Life Gives You Lemons, Squeeze Your Way Through: Understanding Citrus Avoidance Behaviour by Free-Ranging Dogs in India
Authors: Tuhin Subhra Pal, Srijaya Nandi, Rohan Sarkar, Anindita Bhadra
Date: 2024-07-24 00:00:00

Palatability of food is driven by multiple factors like taste, smell, texture, freshness, etc. and can be very variable across species. There are classic examples of local adaptations leading to speciation, driven by food availability. Urbanization across the world is causing rapid decline of biodiversity, while also driving local adaptations in some species. Free-ranging dogs are an interesting example of adaptation to a human-dominated environment across varied habitats. They have co-existed with humans for centuries and are a perfect model system for studying local adaptations. We attempted to understand a specific aspect of their scavenging behaviour in India: citrus aversion. Pet dogs are known to avoid citrus fruits and food contaminated by them. In India, lemons are used widely in the cuisine, and discarded in the garbage. Hence, free-ranging dogs, that typically are scavengers of human leftovers, are likely to encounter lemons and lemon-contaminated food on a regular basis. We carried out a population level experiment to test response of free-ranging dogs to chicken contaminated with various parts of lemon. The dogs avoided chicken contaminated with lemon juice the most. Further, when provided with chicken dipped in three different concentrations of lemon juice, the lowest concentration was most preferred. A survey confirmed that the local people use lemon in their diet extensively and also discard these with the leftovers. People avoided giving citrus contaminated food to their pets but did not follow the same caution for free-ranging dogs. This study revealed that free-ranging dogs in West Bengal, India, are well adapted to scavenging among citrus-contaminated garbage and have their own strategies to avoid the contamination as far as possible, while maximizing their preferred food intake.
The Genomic Code: The genome instantiates a generative model of the organism
Authors: Kevin J. Mitchell, Nick Cheney
Date: 2024-07-22 00:00:00

How does the genome encode the form of the organism? What is the nature of this genomic code? Inspired by recent work in machine learning and neuroscience, we propose that the genome encodes a generative model of the organism. In this scheme, by analogy with variational autoencoders, the genome comprises a connectionist network, embodying a compressed space of latent variables, with weights that get encoded by the learning algorithm of evolution and decoded through the processes of development. The generative model analogy accounts for the complex, distributed genetic architecture of most traits and the emergent robustness and evolvability of developmental processes, while also offering a conception that lends itself to formalisation.
Chemistry-informed Machine Learning Explains Calcium-binding Proteins Fuzzy Shape for Communicating Changes in the Atomic States of Calcium Ions
Authors: Pengzhi Zhang, Jules Nde, Yossi Eliaz, Nathaniel Jennings, Piotr Cieplak, Margaret. S. Cheung
Date: 2024-07-24 00:00:00
Tasks: Explainable Models

Proteins' fuzziness are features for communicating changes in cell signaling instigated by binding with secondary messengers, such as calcium ions, associated with the coordination of muscle contraction, neurotransmitter release, and gene expression. Binding with the disordered parts of a protein, calcium ions must balance their charge states with the shape of calcium-binding proteins and their versatile pool of partners depending on the circumstances they transmit, but it is unclear whether the limited experimental data available can be used to train models to accurately predict the charges of calcium-binding protein variants. Here, we developed a chemistry-informed, machine-learning algorithm that implements a game theoretic approach to explain the output of a machine-learning model without the prerequisite of an excessively large database for high-performance prediction of atomic charges. We used the ab initio electronic structure data representing calcium ions and the structures of the disordered segments of calcium-binding peptides with surrounding water molecules to train several explainable models. Network theory was used to extract the topological features of atomic interactions in the structurally complex data dictated by the coordination chemistry of a calcium ion, a potent indicator of its charge state in protein. With our designs, we provided a framework of explainable machine learning model to annotate atomic charges of calcium ions in calcium-binding proteins with domain knowledge in response to the chemical changes in an environment based on the limited size of scientific data in a genome space.
Heterogeneous model for superdiffusive movement of dense-core vesicles in C. elegans
Authors: Anna Gavrilova, Nickolay Korabel, Victoria J. Allan, Sergei Fedotov
Date: 2024-07-25 00:00:00

Transport of dense core vesicles (DCVs) in neurons is crucial for distributing molecules like neuropeptides and growth factors. We studied the experimental trajectories of dynein-driven directed movement of DCVs in the ALA neuron C. elegans over a duration of up to 6 seconds. We analysed the DCV movement in three strains of C. elegans: 1) with normal kinesin-1 function, 2) with reduced function in kinesin light chain 2 (KLC-2), and 3) a null mutation in kinesin light chain 1 (KLC-1). We find that DCVs move superdiffusively with displacement variance $var(x) \sim t^2$ in all three strains with low reversal rates and frequent immobilization of DCVs. The distribution of DCV displacements fits a beta-binomial distribution with the mean and the variance following linear and quadratic growth patterns, respectively. We propose a simple heterogeneous random walk model to explain the observed superdiffusive retrograde transport behaviour of DCV movement. This model involves a random probability with the beta density for a DCV to resume its movement or remain in the same position.
Alleviating Non-identifiability: a High-fidelity Calibration Objective for Financial Market Simulation with Multivariate Time Series Data
Authors: ChenKai Wang, Junji Ren, Peng Yang
Date: 2024-07-23 00:00:00
Tasks: Time Series

The non-identifiability issue has been frequently reported in social simulation works, where different parameters of an agent-based simulation model yield indistinguishable simulated time series data under certain discrepancy metrics. This issue largely undermines the simulation fidelity yet lacks dedicated investigations. This paper theoretically demonstrates that incorporating multiple time series data features during the model calibration phase can exponentially alleviate non-identifiability as the number of features increases. To implement this theoretical finding, a maximization-based aggregation function is proposed based on existing discrepancy metrics to form a new calibration objective function. For verification, the task of calibrating the Financial Market Simulation (FMS), a typical yet complex social simulation, is considered. Empirical studies confirm the significant improvements in alleviating the non-identifiability of calibration tasks. Furthermore, as a model-agnostic method, it achieves much higher simulation fidelity of the chosen FMS model on both synthetic and real market data. Moreover, it is both theoretically and empirically analyzed that as long as the features are selected and not linearly correlated, they can contribute to alleviation, which demonstrates the robustness of the proposed objective. Hence, this work is expected to provide not only a rigorous understanding of non-identifiability in social simulation but also an off-the-shelf high-fidelity calibration objective function for FMS.
On Deep Learning for computing the Dynamic Initial Margin and Margin Value Adjustment
Authors: Joel P. Villarino, Álvaro Leitao
Date: 2024-07-23 00:00:00
Tasks: Dataset Generation

The present work addresses the challenge of training neural networks for Dynamic Initial Margin (DIM) computation in counterparty credit risk, a task traditionally burdened by the high costs associated with generating training datasets through nested Monte Carlo (MC) simulations. By condensing the initial market state variables into an input vector, determined through an interest rate model and a parsimonious parameterization of the current interest rate term structure, we construct a training dataset where labels are noisy but unbiased DIM samples derived from single MC paths. A multi-output neural network structure is employed to handle DIM as a time-dependent function, facilitating training across a mesh of monitoring times. The methodology offers significant advantages: it reduces the dataset generation cost to a single MC execution and parameterizes the neural network by initial market state variables, obviating the need for repeated training. Experimental results demonstrate the approach's convergence properties and robustness across different interest rate models (Vasicek and Hull-White) and portfolio complexities, validating its general applicability and efficiency in more realistic scenarios.
Estimation of bid-ask spreads in the presence of serial dependence
Authors: Xavier Brouty, Matthieu Garcin, Hugo Roccaro
Date: 2024-07-24 00:00:00

Starting from a basic model in which the dynamic of the transaction prices is a geometric Brownian motion disrupted by a microstructure white noise, corresponding to the random alternation of bids and asks, we propose moment-based estimators along with their statistical properties. We then make the model more realistic by considering serial dependence: we assume a geometric fractional Brownian motion for the price, then an Ornstein-Uhlenbeck process for the microstructure noise. In these two cases of serial dependence, we propose again consistent and asymptotically normal estimators. All our estimators are compared on simulated data with existing approaches, such as Roll, Corwin-Schultz, Abdi-Ranaldo, or Ardia-Guidotti-Kroencke estimators.
Market Making with Exogenous Competition
Authors: Robert Boyce, Martin Herdegen, Leandro Sánchez-Betancourt
Date: 2024-07-24 00:00:00

We study liquidity provision in the presence of exogenous competition. We consider a `reference market maker' who monitors her inventory and the aggregated inventory of the competing market makers. We assume that the competing market makers use a `rule of thumb' to determine their posted depths, depending linearly on their inventory. By contrast, the reference market maker optimises over her posted depths, and we assume that her fill probability depends on the difference between her posted depths and the competition's depths in an exponential way. For a linear-quadratic goal functional, we show that this model admits an approximate closed-form solution. We illustrate the features of our model and compare against alternative ways of solving the problem either via an Euler scheme or state-of-the-art reinforcement learning techniques.
The Negative Drift of a Limit Order Fill
Authors: Timothy DeLise
Date: 2024-07-23 00:00:00

Market making refers to a form of trading in financial markets characterized by passive orders which add liquidity to limit order books. Market makers are important for the proper functioning of financial markets worldwide. Given the importance, financial mathematics has endeavored to derive optimal strategies for placing limit orders in this context. This paper identifies a key discrepancy between popular model assumptions and the realities of real markets, specifically regarding the dynamics around limit order fills. Traditionally, market making models rely on an assumption of low-cost random fills, when in reality we observe a high-cost non-random fill behavior. Namely, limit order fills are caused by and coincide with adverse price movements, which create a drag on the market maker's profit and loss. We refer to this phenomenon as "the negative drift" associated with limit order fills. We describe a discrete market model and prove theoretically that the negative drift exists. We also provide a detailed empirical simulation using one of the most traded financial instruments in the world, the 10 Year US Treasury Bond futures, which also confirms its existence. To our knowledge, this is the first paper to describe and prove this phenomenon in such detail.