pwc-archive/papers-with-abstracts huggingface dataset:
SurfGNN: A robust surface-based prediction model with interpretability for coactivation maps of spatial and cortical features
Authors: Zhuoshuo Li, Jiong Zhang, Youbing Zeng, Jiaying Lin, Dan Zhang, Jianjia Zhang, Duan Xu, HoSung Kim, Bingguang Liu, Mengting Liu
Date: 2024-11-05 00:00:00
Tasks: Graph Neural Network, Prediction

Current brain surface-based prediction models often overlook the variability of regional attributes at the cortical feature level. While graph neural networks (GNNs) excel at capturing regional differences, they encounter challenges when dealing with complex, high-density graph structures. In this work, we consider the cortical surface mesh as a sparse graph and propose an interpretable prediction model-Surface Graph Neural Network (SurfGNN). SurfGNN employs topology-sampling learning (TSL) and region-specific learning (RSL) structures to manage individual cortical features at both lower and higher scales of the surface mesh, effectively tackling the challenges posed by the overly abundant mesh nodes and addressing the issue of heterogeneity in cortical regions. Building on this, a novel score-weighted fusion (SWF) method is implemented to merge nodal representations associated with each cortical feature for prediction. We apply our model to a neonatal brain age prediction task using a dataset of harmonized MR images from 481 subjects (503 scans). SurfGNN outperforms all existing state-of-the-art methods, demonstrating an improvement of at least 9.0% and achieving a mean absolute error (MAE) of 0.827+0.056 in postmenstrual weeks. Furthermore, it generates feature-level activation maps, indicating its capability to identify robust regional variations in different morphometric contributions for prediction.
Low-Rank + Sparse Decomposition (LR+SD) for EEG Artifact Removal
Authors: Jerome Gilles, Travis Meyer, Pamela K. Douglas
Date: 2024-10-31 00:00:00
Tasks: EEG, EEG Artifact Removal

Concurrent EEG-fMRI recordings are advantageous over serial recordings, as they offer the ability to explore the relationship between both signals without the compounded effects of nonstationarity in the brain. Nonetheless, analysis of simultaneous recordings is challenging given that a number of noise sources are introduced into the EEG signal even after MR gradient artifact removal with balistocardiogram artifact being highly prominent. Here, we present an algorithm for automatically removing residual noise sources from the EEG signal in a single process using low rank + sparse decomposition (LR+SD). We apply this method to both experimental and simulated EEG data, where in the latter case the true EEG signature is known. The experimental data consisted of EEG data collected concurrently with fMRI (EEG-fMRI) as well as alone outside the scanning environment while subjects viewed Gabor flashes, a perceptual task known to produce event related power diminutions in the alpha spectral band. On the simulated data, the LR+SD method was able to recover the pure EEG signal and separate it from artifact with up to three EEG sources. On the experimental data, LR+SD was able to recover the diminution in alpha spectral power that follows light flashes in concurrent EEG-fMRI data, which was not detectable prior to artifact removal. At the group level, we found that the signal-to-noise ratio was increased ~34\% following LR+SD cleaning, as compared independent component analysis (ICA) in concurrently collected EEG-fMRI data. We anticipate that this method will be highly useful for analyzing simultaneously collected EEG-fMRI data, and downstream for exploring the coupling between these two modalities.
Neurophysiological Analysis in Motor and Sensory Cortices for Improving Motor Imagination
Authors: Si-Hyun Kim, Sung-Jin Kim, Dae-Hyeok Lee
Date: 2024-10-31 00:00:00
Tasks: Brain Computer Interface, EEG, Motor Imagery

Brain-computer interface (BCI) enables direct communication between the brain and external devices by decoding neural signals, offering potential solutions for individuals with motor impairments. This study explores the neural signatures of motor execution (ME) and motor imagery (MI) tasks using EEG signals, focusing on four conditions categorized as sense-related (hot and cold) and motor-related (pull and push) conditions. We conducted scalp topography analysis to examine activation patterns in the sensorimotor cortex, revealing distinct regional differences: sense--related conditions primarily activated the posterior region of the sensorimotor cortex, while motor--related conditions activated the anterior region of the sensorimotor cortex. These spatial distinctions align with neurophysiological principles, suggesting condition-specific functional subdivisions within the sensorimotor cortex. We further evaluated the performances of three neural network models-EEGNet, ShallowConvNet, and DeepConvNet-demonstrating that ME tasks achieved higher classification accuracies compared to MI tasks. Specifically, in sense-related conditions, the highest accuracy was observed in the cold condition. In motor-related conditions, the pull condition showed the highest performance, with DeepConvNet yielding the highest results. These findings provide insights into optimizing BCI applications by leveraging specific condition-induced neural activations.
Two pathways to resolve relational inconsistencies
Authors: Tomer Barak, Yonatan Loewenstein
Date: 2024-10-30 00:00:00
Tasks: Relational Reasoning

When individuals encounter observations that violate their expectations, when will they adjust their expectations and when will they maintain them despite these observations? For example, when individuals expect objects of type A to be smaller than objects B, but observe the opposite, when will they adjust their expectation about the relationship between the two objects (to A being larger than B)? Naively, one would predict that the larger the violation, the greater the adaptation. However, experiments reveal that when violations are extreme, individuals are more likely to hold on to their prior expectations rather than adjust them. To address this puzzle, we tested the adaptation of artificial neural networks (ANNs) capable of relational learning and found a similar phenomenon: Standard learning dynamics dictates that small violations would lead to adjustments of expected relations while larger ones would be resolved using a different mechanism -- a change in object representation that bypasses the need for adaptation of the relational expectations. These results suggest that the experimentally-observed stability of prior expectations when facing large expectation violations is a natural consequence of learning dynamics and does not require any additional mechanisms. We conclude by discussing the effect of intermediate adaptation steps on this stability.
Review of a Heaviside step sequence function and the recursive Heaviside step sequence function for modeling human mental state
Authors: Changsoo Shin
Date: 2024-10-26 00:00:00

In this paper, we define a novel recursive Heaviside step sequence function and demonstrate its applicability to modeling human mental states such as thought processes, memory recall, and forgetfulness. By extending the traditional Heaviside step function, which typically represents binary transitions, into a recursive sequence framework, we introduce a dynamic model that better captures the complexities of cognitive states. Furthermore, the recursive Heaviside step sequence function approximates solutions to a multidimensional inviscid advection equation, offering a unique mathematical perspective on the evolution of mental states over time. This continuous model, combined with the recursive delta sequence function, provides a comprehensive approach to exploring how memories and thoughts emerge, evolve, and fade. Through this approach, we propose that mental states can be expressed as time series functions, and the selection of the parameter N reflects individual variability in mental processing, influenced by external environments and internal experiences. We also discuss the implications of this framework for understanding human cognition and potential limitations due to modern technological constraints in replicating such processes.
On the Non-Oxygenic Origins of Thylakoids
Authors: Luc Cornet
Date: 2024-11-11 00:00:00

Thylakoid membranes are the site of oxygenic photosynthesis, one of the most important biochemical processes on earth. The ancestral state of these membranes is represented today in Gloeobacterales, where they are lacking and photosynthesis instead takes place in the cytoplasmic membrane. The evolutionary transition from this ancestral state to the modern thylakoid membranes was a major advantage, as it increased surface area devoted to the photosystems and thus photosynthetic efficiency. However, how this transition occurred remains a significant and surprisingly understudied biological question. With a highly synchronized process involving numerous assembly factors, the biogenesis of thylakoid membranes suggests the existence of intermediate evolutionary states during the emergence of this compartment. Here, I propose a non-oxygenic origin of thylakoid membranes, where these intermediate states were initially dedicated to alternative electron flows. This hypothesis addresses the paradox of cyanobacterial diversification in an euxinic environment, which is toxic to photosystem II.
GenoHoption: Bridging Gene Network Graphs and Single-Cell Foundation Models
Authors: Jiabei Cheng, Jiachen Li, Kaiyuan Yang, Hongbin Shen, Ye Yuan
Date: 2024-11-10 00:00:00

The remarkable success of foundation models has sparked growing interest in their application to single-cell biology. Models like Geneformer and scGPT promise to serve as versatile tools in this specialized field. However, representing a cell as a sequence of genes remains an open question since the order of genes is interchangeable. Injecting the gene network graph offers gene relative positions and compact data representation but poses a dilemma: limited receptive fields without in-layer message passing or parameter explosion with message passing in each layer. To pave the way forward, we propose GenoHoption, a new computational framework for single-cell sequencing data that effortlessly combines the strengths of these foundation models with explicit relationships in gene networks. We also introduce a constraint that lightens the model by focusing on learning the predefined graph structure while ensuring further hops are deducted to expand the receptive field. Empirical studies show that our model improves by an average of 1.27% on cell-type annotation and 3.86% on perturbation prediction. Furthermore, our method significantly decreases computational overhead and exhibits few-shot potential. GenoHoption can function as an efficient and expressive bridge, connecting existing single-cell foundation models to gene network graphs.
Wind Tunnel Study of the Forces Due to Drafting in Dolphin Mother-Calf pairs
Authors: D Weihs, M Ringel
Date: 2024-11-09 00:00:00

Cetacean Calves keep up with their mothers while rapidly swimming, by a hydrodynamical effect called drafting. This has been observed in the wild and enclosed areas, and has been mathematically analyzed in the past, but no quantitative measures of the actual forces involved have been made. We built wind tunnel models of Mother-Calf pairs and present force measures for various geometries and relative placements.We show that under certain configurations, the drafting forces are large enough to carry the calf along effortlessly.
Validation of an LLM-based Multi-Agent Framework for Protein Engineering in Dry Lab and Wet Lab
Authors: Zan Chen, Yungeng Liu, Yu Guang Wang, Yiqing Shen
Date: 2024-11-09 00:00:00
Tasks: Protein Design, Protein Folding, scientific discovery

Recent advancements in Large Language Models (LLMs) have enhanced efficiency across various domains, including protein engineering, where they offer promising opportunities for dry lab and wet lab experiment workflow automation. Previous work, namely TourSynbio-Agent, integrates a protein-specialized multimodal LLM (i.e. TourSynbio-7B) with domain-specific deep learning (DL) models to streamline both computational and experimental protein engineering tasks. While initial validation demonstrated TourSynbio-7B's fundamental protein property understanding, the practical effectiveness of the complete TourSynbio-Agent framework in real-world applications remained unexplored. This study presents a comprehensive validation of TourSynbio-Agent through five diverse case studies spanning both computational (dry lab) and experimental (wet lab) protein engineering. In three computational case studies, we evaluate the TourSynbio-Agent's capabilities in mutation prediction, protein folding, and protein design. Additionally, two wet-lab validations demonstrate TourSynbio-Agent's practical utility: engineering P450 proteins with up to 70% improved selectivity for steroid 19-hydroxylation, and developing reductases with 3.7x enhanced catalytic efficiency for alcohol conversion. Our findings from the five case studies establish that TourSynbio-Agent can effectively automate complex protein engineering workflows through an intuitive conversational interface, potentially accelerating scientific discovery in protein engineering.
TourSynbio-Search: A Large Language Model Driven Agent Framework for Unified Search Method for Protein Engineering
Authors: Yungeng Liu, Zan Chen, Yu Guang Wang, Yiqing Shen
Date: 2024-11-09 00:00:00
Tasks: Information Retrieval, Language Modeling, Language Modelling, Large Language Model, Multimodal Large Language Model, Natural Language Queries, Retrieval

The exponential growth in protein-related databases and scientific literature, combined with increasing demands for efficient biological information retrieval, has created an urgent need for unified and accessible search methods in protein engineering research. We present TourSynbio-Search, a novel bioinformatics search agent framework powered by the TourSynbio-7B protein multimodal large language model (LLM), designed to address the growing challenges of information retrieval across rapidly expanding protein databases and corresponding online research literature. The agent's dual-module architecture consists of PaperSearch and ProteinSearch components, enabling comprehensive exploration of both scientific literature and protein data across multiple biological databases. At its core, TourSynbio-Search employs an intelligent agent system that interprets natural language queries, optimizes search parameters, and executes search operations across major platforms including UniProt, PDB, ArXiv, and BioRxiv. The agent's ability to process intuitive natural language queries reduces technical barriers, allowing researchers to efficiently access and analyze complex biological data without requiring extensive bioinformatics expertise. Through detailed case studies in literature retrieval and protein structure visualization, we demonstrate TourSynbio-Search's effectiveness in streamlining biological information retrieval and enhancing research productivity. This framework represents an advancement in bridging the accessibility gap between complex biological databases and researchers, potentially accelerating progress in protein engineering applications. Our codes are available at: https://github.com/tsynbio/Toursynbio-Search
Identifying Virulence Determinants In Pathogenic Mycobacteria Via Changes In Host Cell Mitochondrial Morphology
Authors: Shannon Quinn, Amr Abbadi, Seyed Alireza Vaezi, Russell K. Karls, Frederick D. Quinn
Date: 2024-11-09 00:00:00

The goal of this study is to develop a computational model of the progression of changes in mitochondrial phenotype resulting from infection with pathogenic mycobacteria. This ultimately will enable a large-scale virulence screen of mutant bacterial libraries. Mycobacterium tuberculosis (Mtb) is an intracellular pathogen, but only a small number of its genes have been studied for roles in intracellular host cell survival and replication. Mitochondria are the powerhouse of the host cell and play critical roles in cell survival when attacked by certain pathogens. When Mtb bacteria invade host cells, they induce changes in mitochondrial morphology, making mitochondria a novel target for image processing and machine learning to determine virulence associations of genes in Mtb and potentially other related intracellular pathogens. By hypothesizing mitochondria as an instance of a dynamic and interconnected graph, we demonstrate a statistical approach for quantitatively recognizing novel mitochondrial phenotypes induced by invading pathogens.
Multiphysics continuum mechanics models to advance glaucoma research: State-of-the-art and future perspectives
Authors: Daniel Sebastia-Saez, Jinyuan Luo, Mengqi Qin, Tao Chen, Cynthia Yu-Wai-Man
Date: 2024-11-10 00:00:00

This review provides a broad vision on the use of mechanistic mathematical models based on continuum mechanics to tackle current challenges in glaucoma research. At present, the advent of Artificial Intelligence and data-based models have resulted in significant progress in drug candidate selection, target identification and drug delivery. Mechanistic models (not based on data gathered beforehand, but on fundamental physical principles), and specifically those based on continuum mechanics, have the potential to contribute to a better understanding of glaucoma, too. So far, these models have expanded our understanding of ocular fluid dynamics, including descriptions of fluid flow profiles within the anterior chamber of the eye under glaucomatous conditions. With the emergence of models with complex multiphysics capabilities (not only on fluid dynamics, but also diffusion, heat transfer and fluid-structure interaction), there are now even greater possibilities for the application of mechanistic models to help with current challenges in glaucoma, such as the design of drug-releasing implants, novel glaucoma drainage devices, minimally invasive glaucoma surgery procedures, and laser peripheral iridotomy.
Liquidity Jump, Liquidity Diffusion, and Crypto Wash Trading
Authors: Qi Deng, Zhong-guo Zhou
Date: 2024-10-28 00:00:00

We develop a new framework to detect wash trading in crypto assets through real-time liquidity fluctuation. We propose that short-term price jumps in crypto assets results from wash trading-induced liquidity fluctuation, and construct two complementary liquidity measures, liquidity jump (size of fluctuation) and liquidity diffusion (volatility of fluctuation), to capture the behavioral signature of wash trading. Using US stocks as a benchmark, we demonstrate that joint elevation in both liquidity metrics indicates wash trading in crypto assets. A simulated regulatory treatment that removes likely wash trades confirms this dynamic: it reduces liquidity diffusion significantly while leaving liquidity jump largely unaffected. These findings align with a theoretical model in which manipulative traders amplify both the level and variance of price pressure, whereas passive investors affect only the level. Our model offers practical tools for investors to assess market quality and for regulators to monitor manipulation risk on crypto exchanges without oversight.
The relationship between general equilibrium models with infinite-lived agents and overlapping generations models, and some applications
Authors: Ngoc-Sang Pham
Date: 2024-11-12 00:00:00

We prove that a two-cycle equilibrium in a general equilibrium model with infinitely-lived agents also constitutes an equilibrium in an overlapping generations (OLG) model. Conversely, an equilibrium in an OLG model that satisfies additional conditions is part of an equilibrium in a general equilibrium model with infinitely-lived agents. Applying this result, we demonstrate that equilibrium indeterminacy and rational asset price bubbles may arise in both types of models.
Sensitivity Analysis of emissions Markets: A Discrete-Time Radner Equilibrium Approach
Authors: Stéphane Crépey, Mekonnen Tadese, Gauthier Vermandel
Date: 2024-11-09 00:00:00
Tasks: Decision Making, Sensitivity

Emissions markets play a crucial role in reducing pollution by encouraging firms to minimize costs. However, their structure heavily relies on the decisions of policymakers, on the future economic activities, and on the availability of abatement technologies. This study examines how changes in regulatory standards, firms' abatement costs, and emissions levels affect allowance prices and firms' efforts to reduce emissions. This is done in a Radner equilibrium framework encompassing inter-temporal decision-making, uncertainty, and a comprehensive assessment of the market dynamics and outcomes. The results of this research have the potential to assist policymakers in enhancing the structure and efficiency of emissions trading systems, through an in-depth comprehension of the reactions of market stakeholders towards different market situations.
Do LLM Personas Dream of Bull Markets? Comparing Human and AI Investment Strategies Through the Lens of the Five-Factor Model
Authors: Harris Borman, Anna Leontjeva, Luiz Pizzato, Max Kun Jiang, Dan Jermyn
Date: 2024-10-28 00:00:00
Tasks: Decision Making

Large Language Models (LLMs) have demonstrated the ability to adopt a personality and behave in a human-like manner. There is a large body of research that investigates the behavioural impacts of personality in less obvious areas such as investment attitudes or creative decision making. In this study, we investigated whether an LLM persona with a specific Big Five personality profile would perform an investment task similarly to a human with the same personality traits. We used a simulated investment task to determine if these results could be generalised into actual behaviours. In this simulated environment, our results show these personas produced meaningful behavioural differences in all assessed categories, with these behaviours generally being consistent with expectations derived from human research. We found that LLMs are able to generalise traits into expected behaviours in three areas: learning style, impulsivity and risk appetite while environmental attitudes could not be accurately represented. In addition, we showed that LLMs produce behaviour that is more reflective of human behaviour in a simulation environment compared to a survey environment.
An Empirical Implementation of the Shadow Riskless Rate
Authors: Davide Lauria, Jiho Park, Yuan Hu, W. Brent Lindquist, Svetlozar T. Rachev, Frank J. Fabozzi
Date: 2024-11-11 00:00:00
Tasks: Descriptive

We address the problem of asset pricing in a market where there is no risky asset. Previous work developed a theoretical model for a shadow riskless rate (SRR) for such a market in terms of the drift component of the state-price deflator for that asset universe. Assuming asset prices are modeled by correlated geometric Brownian motion, in this work we develop a computational approach to estimate the SRR from empirical datasets. The approach employs: principal component analysis to model the effects of the individual Brownian motions; singular value decomposition to capture the abrupt changes in condition number of the linear system whose solution provides the SRR values; and a regularization to control the rate of change of the condition number. Among other uses (e.g., for option pricing, developing a term structure of interest rate), the SRR can be employed as an investment discriminator between asset classes. We apply the computational procedure to markets consisting of groups of stocks, varying asset type and number. The theoretical and computational analysis provides not only the drift, but also the total volatility of the state-price deflator. We investigate the time trajectory of these two descriptive components of the state-price deflator for the empirical datasets.