pwc-archive/papers-with-abstracts huggingface dataset:
Decoding moral judgement from text: a pilot study
Authors: Diana E. Gherman, Thorsten O. Zander
Date: 2024-05-28 00:00:00
Tasks: Attribute

Moral judgement is a complex human reaction that engages cognitive and emotional dimensions. While some of the morality neural correlates are known, it is currently unclear if we can detect moral violation at a single-trial level. In a pilot study, here we explore the feasibility of moral judgement decoding from text stimuli with passive brain-computer interfaces. For effective moral judgement elicitation, we use video-audio affective priming prior to text stimuli presentation and attribute the text to moral agents. Our results show that further efforts are necessary to achieve reliable classification between moral congruency vs. incongruency states. We obtain good accuracy results for neutral vs. morally-charged trials. With this research, we try to pave the way towards neuroadaptive human-computer interaction and more human-compatible large language models (LLMs)
Uncovering cognitive taskonomy through transfer learning in masked autoencoder-based fMRI reconstruction
Authors: Youzhi Qu, Junfeng Xia, Xinyao Jian, Wendu Li, Kaining Peng, Zhichao Liang, Haiyan Wu, Quanying Liu
Date: 2024-05-24 00:00:00
Tasks: Denoising, Transfer Learning

Data reconstruction is a widely used pre-training task to learn the generalized features for many downstream tasks. Although reconstruction tasks have been applied to neural signal completion and denoising, neural signal reconstruction is less studied. Here, we employ the masked autoencoder (MAE) model to reconstruct functional magnetic resonance imaging (fMRI) data, and utilize a transfer learning framework to obtain the cognitive taskonomy, a matrix to quantify the similarity between cognitive tasks. Our experimental results demonstrate that the MAE model effectively captures the temporal dynamics patterns and interactions within the brain regions, enabling robust cross-subject fMRI signal reconstruction. The cognitive taskonomy derived from the transfer learning framework reveals the relationships among cognitive tasks, highlighting subtask correlations within motor tasks and similarities between emotion, social, and gambling tasks. Our study suggests that the fMRI reconstruction with MAE model can uncover the latent representation and the obtained taskonomy offers guidance for selecting source tasks in neural decoding tasks for improving the decoding performance on target tasks.
Harnessing XGBoost for Robust Biomarker Selection of Obsessive-Compulsive Disorder (OCD) from Adolescent Brain Cognitive Development (ABCD) data
Authors: Xinyu Shen, Qimin Zhang, Huili Zheng, Weiwei Qi
Date: 2024-05-14 00:00:00

This study evaluates the performance of various supervised machine learning models in analyzing highly correlated neural signaling data from the Adolescent Brain Cognitive Development (ABCD) Study, with a focus on predicting obsessive-compulsive disorder scales. We simulated a dataset to mimic the correlation structures commonly found in imaging data and evaluated logistic regression, elastic networks, random forests, and XGBoost on their ability to handle multicollinearity and accurately identify predictive features. Our study aims to guide the selection of appropriate machine learning methods for processing neuroimaging data, highlighting models that best capture underlying signals in high feature correlations and prioritize clinically relevant features associated with Obsessive-Compulsive Disorder (OCD).
Spectral Brain Graph Neural Network for Prediction of Anxiety in Children with Autism Spectrum Disorder
Authors: Peiyu Duan, Nicha C. Dvornek, Jiyao Wang, Jeffrey Eilbott, Yuexi Du, Denis G. Sukhodolsky, James S. Duncan
Date: 2024-04-23 00:00:00
Tasks: Graph Neural Network

Children with Autism Spectrum Disorder (ASD) frequently exhibit comorbid anxiety, which contributes to impairment and requires treatment. Therefore, it is critical to investigate co-occurring autism and anxiety with functional imaging tools to understand the brain mechanisms of this comorbidity. Multidimensional Anxiety Scale for Children, 2nd edition (MASC-2) score is a common tool to evaluate the daily anxiety level in autistic children. Predicting MASC-2 score with Functional Magnetic Resonance Imaging (fMRI) data will help gain more insights into the brain functional networks of children with ASD complicated by anxiety. However, most of the current graph neural network (GNN) studies using fMRI only focus on graph operations but ignore the spectral features. In this paper, we explored the feasibility of using spectral features to predict the MASC-2 total scores. We proposed SpectBGNN, a graph-based network, which uses spectral features and integrates graph spectral filtering layers to extract hidden information. We experimented with multiple spectral analysis algorithms and compared the performance of the SpectBGNN model with CPM, GAT, and BrainGNN on a dataset consisting of 26 typically developing and 70 ASD children with 5-fold cross-validation. We showed that among all spectral analysis algorithms tested, using the Fast Fourier Transform (FFT) or Welch's Power Spectrum Density (PSD) as node features performs significantly better than correlation features, and adding the graph spectral filtering layer significantly increases the network's performance.
A data-driven approach to modeling brain activity using differential equations
Authors: Kuratov Andrey
Date: 2024-04-14 00:00:00

This research focuses on an innovative task of extracting equations from incomplete data, moving away from traditional methods used for complete solutions. The study addresses the challenge of extracting equations from data, particularly in the study of brain activity using electrophysiological data, which is often limited by insufficient information. The study provides a brief review of existing open-source equation derivation approaches in the context of modeling brain activity. The section below introduces a novel algorithm that employs incomplete data and prior domain knowledge to recover differential equations. The algorithm's practicality in real-world scenarios is demonstrated through its application on both synthetic and real datasets.
Cement Dust Exposure and Risk of Hyperglycemia and Overweight among Artisans and Residents Close to a Cement Factory in Sokoto, Nigeria
Authors: T Yahaya, KA Sani, E Oladele, E Yawa, M Musa, M Abubakar, R Sulaiman, M Bilyaminu
Date: 2024-06-29 00:00:00

The potential health risks of cement dust exposure are increasingly raising concern worldwide as the cement industry expands in response to rising cement demand. This necessitates the need to determine the nature of the risks in order to develop appropriate measures. This study determined the effects of cement dust exposure on the weight and blood glucose levels of people residing or working around a cement company in Sokoto, Nigeria. Demographic information was obtained using questionnaires from 72 participants, which included age, gender, educational level, exposure hours, occupation, and lifestyle. The blood glucose levels and body mass index (BMI) were measured using a Fine Test glucometer and a mechanical scale, respectively. The results showed that most of the people living or working around the cement company were middle-aged men (31-40; 42.06%) with a primary (33.33%) or secondary (45.83%) school education. It showed that 30 (41.69%) of the participants were overweight while 5 (6.94%) were obese. Additionally, 52.78% of the participants were diabetic while 31.94% were prediabetic. Participants that were exposed for long hours (> 15 hours per day) were the most diabetic (20% of the participants), followed by smokers (15%), and artisans (7%). It can be concluded that exposure to cement dust from the company increased the risk of overweight, obesity, and hyperglycemia among the participants. These health risks were worsened by daily long hours of exposure, smoking, and artisanal pollutant exposure. Human settlements and artisans should not be located near the cement company, and the company should minimize pollutant emissions.
Rooting behavior of pomegranate (Punica granatum L.) hardwood cuttings in relation to genotype and irrigation frequency
Authors: Kocher Omer Salih, Aram Akram Mohammed, Jamal Mahmood Faraj, Anwar Mohammed Raouf, Nawroz Abdul-razzak Tahir
Date: 2024-06-29 00:00:00

The study was conducted to determine the best irrigation frequency for rooting hardwood cuttings of some pomegranate genotypes that are cultivated in Halabja province, Kurdistan Region, Iraq. The hardwood cuttings were collected from 11 genotypes, which were 'Salakhani Trsh' (G1), 'Salakhani Mekhosh' (G2), 'Amriki' (G3), 'Twekl Sury Trsh' (G4), 'Twekl Astury Naw Spy' (G5), 'Hanara Sherina' (G6), 'Kawa Hanary Sherin' (G7), 'Kawa Hanary Trsh' (G8), 'Malesay Twekl Asture' (G9), 'Malesay Twekl Tank' (G10), and 'Sura Hanary Trsh' (G11). The genotypes were subjected to irrigation applications by 1-day, 2-day, 7-day, or 10-day frequencies. Among pomegranates, G11, G6, and G7 produced 95, 90, and 83% rooting percentages, which were significantly higher than the rest of other genotypes. The lowest rooting percentages (28, 36, 38, and 40%) were found in G1, G5, G3, and G10, respectively. The effect of irrigation frequencies on the genotypes confirmed that a 7-day frequency was the best irrigation frequency to achieve the maximum rooting percentages (93, 86, 80, 73, 53, and 40%) in G6, G9, G2, G4, G3, and G1, respectively. In contrast, the minimum rooting percentage (20%) was recorded in G3 with a 1-day frequency and in G1 with 10-day frequency. In this study, it was found that the cuttings of G11, G6, and G7 had the best ability to form roots, and irrigation with a 7-day frequency was the best for the cuttings of all the 11 pomegranate genotypes investigated.
Machine Learning Models for Dengue Forecasting in Singapore
Authors: Zi Iun Lai, Wai Kit Fung, Enquan Chew
Date: 2024-06-29 00:00:00
Tasks: State Space Models

With emerging prevalence beyond traditionally endemic regions, the global burden of dengue disease is forecasted to be one of the fastest growing. With limited direct treatment or vaccination currently available, prevention through vector control is widely believed to be the most effective form of managing outbreaks. This study examines traditional state space models (moving average, autoregressive, ARIMA, SARIMA), supervised learning techniques (XGBoost, SVM, KNN) and deep networks (LSTM, CNN, ConvLSTM) for forecasting weekly dengue cases in Singapore. Meteorological data and search engine trends were included as features for ML techniques. Forecasts using CNNs yielded lowest RMSE in weekly cases in 2019.
From Cognition to Computation: A Comparative Review of Human Attention and Transformer Architectures
Authors: Minglu Zhao, Dehong Xu, Tao Gao
Date: 2024-04-25 00:00:00

Attention is a cornerstone of human cognition that facilitates the efficient extraction of information in everyday life. Recent developments in artificial intelligence like the Transformer architecture also incorporate the idea of attention in model designs. However, despite the shared fundamental principle of selectively attending to information, human attention and the Transformer model display notable differences, particularly in their capacity constraints, attention pathways, and intentional mechanisms. Our review aims to provide a comparative analysis of these mechanisms from a cognitive-functional perspective, thereby shedding light on several open research questions. The exploration encourages interdisciplinary efforts to derive insights from human attention mechanisms in the pursuit of developing more generalized artificial intelligence.
Inconsistency of parsimony under the multispecies coalescent
Authors: Daniel Rickert, Wai-Tong Louis Fan, Matthew Hahn
Date: 2024-07-02 00:00:00

While it is known that parsimony can be statistically inconsistent under certain models of evolution due to high levels of homoplasy, the consistency of parsimony under the multispecies coalescent (MSC) is less well studied. Previous studies have shown the consistency of concatenated parsimony (parsimony applied to concatenated alignments) under the MSC for the rooted 4-taxa case under an infinite-sites model of mutation; on the other hand, other work has also established the inconsistency of concatenated parsimony for the unrooted 6-taxa case. These seemingly contradictory results suggest that concatenated parsimony may fail to be consistent for trees with more than 5 taxa, for all unrooted trees, or for some combination of the two. Here, we present a technique for computing the expected internal branch lengths of gene trees under the MSC. This technique allows us to determine the regions of the parameter space of the species tree under which concatenated parsimony fails for different numbers of taxa, for rooted or unrooted trees. We use our new approach to demonstrate that while parsimony succeeds in the unrooted 5-taxa case, there are regions of statistical inconsistency for concatenated parsimony for rooted 5+-taxa cases and unrooted 6+-taxa cases. Our results therefore suggest that parsimony is not generally dependable under the MSC.
Comparing fine-grained and coarse-grained object detection for ecology
Authors: Jess Tam, Justin Kay
Date: 2024-05-06 00:00:00
Tasks: object-detection, Object Detection

Computer vision applications are increasingly popular for wildlife monitoring tasks. While some studies focus on the monitoring of a single species, such as a particular endangered species, others monitor larger functional groups, such as predators. In our study, we used camera trap images collected in north-western New South Wales, Australia, to investigate how model results were affected by combining multiple species in single classes, and whether the addition of negative samples can improve model performance. We found that species that benefited the most from merging into a single class were mainly species that look alike morphologically, i.e. macropods. Whereas species that looked distinctively different gave mixed results when merged, e.g. merging pigs and goats together as non-native large mammals. We also found that adding negative samples improved model performance marginally in most instances, and recommend conducting a more comprehensive study to explore whether the marginal gains were random or consistent. We suggest that practitioners could classify morphologically similar species together as a functional group or higher taxonomic group to draw ecological inferences. Nevertheless, whether to merge classes or not will depend on the ecological question to be explored.
Distances between Extension Spaces of Phylogenetic Trees
Authors: Maria Alejandra Valdez Cabrera, Amy D Willis
Date: 2024-06-28 00:00:00

Phylogenetic trees summarize evolutionary relationships between organisms, and tools to analyze collections of phylogenetic trees enable contrasts between different genes' ancestry. The BHV metric space has enabled the analysis of collections of trees that share a common set of leaves, but many genes are not shared, even between closely related species. BHV extension spaces represent trees with non-identical leaf sets in a common BHV space, but limited analytical tools exist for extension spaces. We define the distance between two phylogenetic trees with non-identical leaf sets as the shortest BHV distance between their extension spaces, and develop a reduced gradient algorithm to compute this distance. We study the scalability of our algorithm and apply it to analyze gene trees spanning multiple domains of life. Our distance and algorithm offer a fully general, interpretable approach to analyzing both ancient and recent evolutionary divergence.
Automatic motion estimation with applicationsto hiPSC-CMs
Authors: Henrik Finsberg, Verena Charwat, Kevin Healy, Samuel Wall
Date: 2024-06-30 00:00:00
Tasks: Motion Estimation

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are an effective tool for studying cardiac function and disease, and hold promise for screening drug effects on human tissue. Changes to motion patterns in these cells are one of the important features to be characterized to understand how an introduced drug or disease may alter the human heart beat. However, quantifying motion accurately and efficiently from optical measurements using microscopy is currently lacking. In this work, we present a unified framework for performing motion analysis on a sequence of microscopically obtained images of tissues consisting of hiPSC-CMs. We provide validation of our developed software using a synthetic test case and show how it can be used to extract displacements and velocities in hiPSC-CM microtissues. Finally, we show how to apply the framework to quantify the effect of an inotropic compound. The described software system is distributed as a python package that is easy to install, well tested and can be integrated into any python workflow.
A mathematical model for droplet separation by surface tension using contact cantilevers -- applications to {\it{in situ}} diagnosis and treatment
Authors: Sonia Elizabeth Teodorescu
Date: 2024-05-13 00:00:00

This work provides an exact mathematical characterization of the meniscus formed by a liquid of density $\rho$ (model for tumor tissue) when probed with a cantilever device, operating by gravity (acceleration $g$) and with surface tension coefficient $\sigma$ (material-dependent for the specific choice of liquid and cantilever). The shape and extremal parameters (maximum height $\mathcal{H}$, break-off volume $\mathcal{V}$) of the meniscus formed, as functions of $\sigma, \rho$, are found by an exact analysis. Having knowledge of the explicit relationship between these parameters allows to perform in one procedure both diagnosis and treatment.
Cochlear Wave Propagation and Dynamics in the Human Base and Apex: Model-Based Estimates from Noninvasive Measurements
Authors: Samiya A Alkhairy
Date: 2024-04-10 00:00:00

Cochlear wavenumber and impedance are mechanistic variables that encode information regarding how the cochlea works - specifically wave propagation and Organ of Corti dynamics. These mechanistic variables underlie interesting features of cochlear signal processing such as its place-based wavelet analyzers, dispersivity and high-gain. Consequently, it is of interest to estimate these mechanistic variables in various species (particularly humans) and across various locations along the length of the cochlea. In this paper, we develop methods to estimate the mechanistic variables (wavenumber and impedance) from noninvasive response characteristics (such as the quality factors of psychophysical tuning curves) using an existing analytic shortwave single-partition model of the mammalian cochlea. We then apply these methods to estimate human mechanistic variables using reported values for quality factors from psychophysical tuning curves and a location-invariant ratio extrapolated from chinchilla. Our resultant estimates for human wavenumbers and impedances show that the minimum wavelength (which occurs at the peak of the traveling wave) is smaller in base than the apex. The Organ of Corti is stiffness dominated rather than mass dominated, and there is negative effective damping prior to the peak followed by positive effective damping. The effective stiffness, and positive and negative effective damping are greater in the base than the apex. The methods introduced here for estimating mechanistic variables from characteristics of invasive or noninvasive responses enable us to derive such estimates across various species and locations where the responses are describable by sharp filters. In addition to studying cochlear wave propagation and dynamics, the estimation methods developed here are also useful for auditory filter design.
Basket Options with Volatility Skew: Calibrating a Local Volatility Model by Sample Rearrangement
Authors: Nicola F. Zaugg, Lech A. Grzelak
Date: 2024-07-03 00:00:00

The pricing of derivatives tied to baskets of assets demands a sophisticated framework that aligns with the available market information to capture the intricate non-linear dependency structure among the assets. We describe the dynamics of the multivariate process of constituents with a copula model and propose an efficient method to extract the dependency structure from the market. The proposed method generates coherent sets of samples of the constituents process through systematic sampling rearrangement. These samples are then utilized to calibrate a local volatility model (LVM) of the basket process, which is used to price basket derivatives. We show that the method is capable of efficiently pricing basket options based on a large number of basket constituents, accomplishing the calibration process within a matter of seconds, and achieving near-perfect calibration to the index options of the market.
Predicting public market behavior from private equity deals
Authors: Paolo Barucca, Flaviano Morone
Date: 2024-07-01 00:00:00

We process private equity transactions to predict public market behavior with a logit model. Specifically, we estimate our model to predict quarterly returns for both the broad market and for individual sectors. Our hypothesis is that private equity investments (in aggregate) carry predictive signal about publicly traded securities. The key source of such predictive signal is the fact that, during their diligence process, private equity fund managers are privy to valuable company information that may not yet be reflected in the public markets at the time of their investment. Thus, we posit that we can discover investors' collective near-term insight via detailed analysis of the timing and nature of the deals they execute. We evaluate the accuracy of the estimated model by applying it to test data where we know the correct output value. Remarkably, our model performs consistently better than a null model simply based on return statistics, while showing a predictive accuracy of up to 70% in sectors such as Consumer Services, Communications, and Non Energy Minerals.
Exploring Sectoral Profitability in the Indian Stock Market Using Deep Learning
Authors: Jaydip Sen, Hetvi Waghela, Sneha Rakshit
Date: 2024-05-28 00:00:00
Tasks: Prediction, Stock Price Prediction

This paper explores using a deep learning Long Short-Term Memory (LSTM) model for accurate stock price prediction and its implications for portfolio design. Despite the efficient market hypothesis suggesting that predicting stock prices is impossible, recent research has shown the potential of advanced algorithms and predictive models. The study builds upon existing literature on stock price prediction methods, emphasizing the shift toward machine learning and deep learning approaches. Using historical stock prices of 180 stocks across 18 sectors listed on the NSE, India, the LSTM model predicts future prices. These predictions guide buy/sell decisions for each stock and analyze sector profitability. The study's main contributions are threefold: introducing an optimized LSTM model for robust portfolio design, utilizing LSTM predictions for buy/sell transactions, and insights into sector profitability and volatility. Results demonstrate the efficacy of the LSTM model in accurately predicting stock prices and informing investment decisions. By comparing sector profitability and prediction accuracy, the work provides valuable insights into the dynamics of the current financial markets in India.