pwc-archive/papers-with-abstracts huggingface dataset:
Exact analytical algorithm for solvent accessible surface area and derivatives in implicit solvent molecular simulations on GPUs
Authors: Xin Cao, Michelle H. Hummel, Yuzhang Wang, Carlos Simmerling, Evangelos A. Coutsias
Date: 2024-01-19 00:00:00
Tasks: CPU, GPU

In this paper, we present dSASA (differentiable SASA), an exact geometric method to calculate solvent accessible surface area (SASA) analytically along with atomic derivatives on GPUs. The atoms in a molecule are first assigned to tetrahedra in groups of four atoms by Delaunay tetrahedrization adapted for efficient GPU implementation and the SASA values for atoms and molecules are calculated based on the tetrahedrization information and inclusion-exclusion method. The SASA values from the numerical icosahedral-based method can be reproduced with more than 98% accuracy for both proteins and RNAs. Having been implemented on GPUs and incorporated into the software Amber, we can apply dSASA to implicit solvent molecular dynamics simulations with inclusion of this nonpolar term. The current GPU version of GB/SA simulations has been accelerated up to nearly 20-fold compared to the CPU version, outperforming LCPO, a commonly used, fast algorithm for calculating SASA, as the system size increases. While we focus on the accuracy of the SASA calculations for proteins and nucleic acids, we also demonstrate stable GB/SA MD mini-protein simulations.
FIMBA: Evaluating the Robustness of AI in Genomics via Feature Importance Adversarial Attacks
Authors: Heorhii Skovorodnikov, Hoda Alkhzaimi
Date: 2024-01-19 00:00:00
Tasks: Decision Making, Drug Discovery, Feature Importance

With the steady rise of the use of AI in bio-technical applications and the widespread adoption of genomics sequencing, an increasing amount of AI-based algorithms and tools is entering the research and production stage affecting critical decision-making streams like drug discovery and clinical outcomes. This paper demonstrates the vulnerability of AI models often utilized downstream tasks on recognized public genomics datasets. We undermine model robustness by deploying an attack that focuses on input transformation while mimicking the real data and confusing the model decision-making, ultimately yielding a pronounced deterioration in model performance. Further, we enhance our approach by generating poisoned data using a variational autoencoder-based model. Our empirical findings unequivocally demonstrate a decline in model performance, underscored by diminished accuracy and an upswing in false positives and false negatives. Furthermore, we analyze the resulting adversarial samples via spectral analysis yielding conclusions for countermeasures against such attacks.
Neural Population Decoding and Imbalanced Multi-Omic Datasets For Cancer Subtype Diagnosis
Authors: Charles Theodore Kent, Leila Bagheriye, Johan Kwisthout
Date: 2024-01-06 00:00:00
Tasks: Bayesian Inference

Recent strides in the field of neural computation has seen the adoption of Winner Take All (WTA) circuits to facilitate the unification of hierarchical Bayesian inference and spiking neural networks as a neurobiologically plausible model of information processing. Current research commonly validates the performance of these networks via classification tasks, particularly of the MNIST dataset. However, researchers have not yet reached consensus about how best to translate the stochastic responses from these networks into discrete decisions, a process known as population decoding. Despite being an often underexamined part of SNNs, in this work we show that population decoding has a significanct impact on the classification performance of WTA networks. For this purpose, we apply a WTA network to the problem of cancer subtype diagnosis from multi omic data, using datasets from The Cancer Genome Atlas (TCGA). In doing so we utilise a novel implementation of gene similarity networks, a feature encoding technique based on Kohoens self organising map algorithm. We further show that the impact of selecting certain population decoding methods is amplified when facing imbalanced datasets.
Evolving Diploid Boolean and Multi-Valued Gene Networks
Authors: Larry Bull
Date: 2023-11-19 00:00:00

Boolean networks have been widely used to explore aspects of gene regulation, traditionally with a single network. A modified form of the model to explore the effects of increasing the number of gene states has also recently been introduced. In this paper, these discrete dynamical networks are evolved as diploids within rugged fitness landscapes to explore their behaviour. Results suggest the general properties of haploid networks in similar circumstances remain for diploids. The previously proposed inherent fitness landscape smoothing properties of eukaryotic sex are shown to be exhibited in these dynamical systems, as is their propensity to change in size based upon the characteristics of the network and fitness landscape.
Exploring the role of structure in a time constrained decision task
Authors: Naomi Chaix-Eichel, Gautham Venugopal, Thomas Boraud, Nicolas P. Rougier
Date: 2024-01-19 00:00:00
Tasks: Decision Making

The structure of the basal ganglia is remarkably similar across a number of species (often described in terms of direct, indirect and hyperdirect pathways) and is deeply involved in decision making and action selection. In this article, we are interested in exploring the role of structure when solving a decision task while avoiding to make any strong assumption regarding the actual structure. To do so, we exploit the echo state network paradigm that allows to solve complex task based on a random architecture. Considering a temporal decision task, the question is whether a specific structure allows for better performance and if so, whether this structure shares some similarity with the basal ganglia. Our results highlight the advantage of having a slow (direct) and a fast (hyperdirect) pathway that allows to deal with late information during a decision making task.
Exploring General Intelligence via Gated Graph Transformer in Functional Connectivity Studies
Authors: Gang Qu, Anton Orlichenko, Junqi Wang, Gemeng Zhang, Li Xiao, Aiying Zhang, Zhengming Ding, Yu-Ping Wang
Date: 2024-01-18 00:00:00
Tasks: Functional Connectivity

Functional connectivity (FC) as derived from fMRI has emerged as a pivotal tool in elucidating the intricacies of various psychiatric disorders and delineating the neural pathways that underpin cognitive and behavioral dynamics inherent to the human brain. While Graph Neural Networks (GNNs) offer a structured approach to represent neuroimaging data, they are limited by their need for a predefined graph structure to depict associations between brain regions, a detail not solely provided by FCs. To bridge this gap, we introduce the Gated Graph Transformer (GGT) framework, designed to predict cognitive metrics based on FCs. Empirical validation on the Philadelphia Neurodevelopmental Cohort (PNC) underscores the superior predictive prowess of our model, further accentuating its potential in identifying pivotal neural connectivities that correlate with human cognitive processes.
Novel community data in ecology -- properties and prospects
Authors: Florian Hartig, Nerea Abrego, Alex Bush, Jonathan M. Chase, Gurutzeta Guillera-Arroita, Mathew A. Leibold, Otso Ovaskainen, Loïc Pellissier, Maximilian Pichler, Giovanni Poggiato, Laura Pollock, Sara Si-Moussi, Wilfried Thuiller, Duarte S. Viana, David I. Warton, Damaris Zurell, Douglas W. Yu
Date: 2024-01-19 00:00:00

New technologies for acquiring biological information such as eDNA, acoustic or optical sensors, make it possible to generate spatial community observations at unprecedented scales. The potential of these novel community data to standardize community observations at high spatial, temporal, and taxonomic resolution and at large spatial scale ('many rows and many columns') has been widely discussed, but so far, there has been little integration of these data with ecological models and theory. Here, we review these developments and highlight emerging solutions, focusing on statistical methods for analyzing novel community data, in particular joint species distribution models; the new ecological questions that can be answered with these data; and the potential implications of these developments for policy and conservation.
Ecosystem models cannot predict the consequences of conservation decisions
Authors: Larissa Lubiana Botelho, Cailan Jeynes-Smith, Sarah Vollert, Michael Bode
Date: 2024-01-19 00:00:00
Tasks: Decision Making, Management

Ecosystem models are often used to predict the consequences of management decisions in applied ecology, including fisheries management and threatened species conservation. These models are high-dimensional, parameter-rich, and nonlinear, yet limited data is available to calibrate them, and they are rarely tested or validated. Consequently, the accuracy of their forecasts, and their utility as decision-support tools is a matter of debate. In this paper, we calibrate ecosystem models to time-series data from 110 different experimental microcosm ecosystems, each containing between three and five interacting species. We then assess how often these calibrated models offer accurate and useful predictions about how the ecosystem will respond to a set of standard management interventions. Our results show that for each timeseries dataset, a large number of very different parameter sets offer equivalent, good fits. However, these calibrated ecosystem models have poor predictive accuracy when forecasting future dynamics and offer ambiguous predictions about how species in the ecosystem will respond to management interventions. Closer inspection reveals that the ecosystem models fail because calibration cannot determine the types of interactions that occur within the ecosystem. Our findings call into question claims that ecosystem modelling can support applied ecological decision-making when they are calibrated against real-world datasets.
Forecasting dengue outbreaks with uncertainty using seasonal weather patterns
Authors: Piumi Chathurangika, Sanjeewa Perera, Kushani De Silva
Date: 2024-01-18 00:00:00

Dengue is a vector-borne disease transmitted to humans by vectors of genus Aedes and is a global threat with health, social, and economic impact in many of the tropical countries including Sri Lanka. The virus transmission is significantly impacted by environmental conditions, with a notable contribution from elevated per-capita vector density. These conditions are dynamic in nature and specially having the tropical climate, Sri Lanka experiences seasonal weather patterns dominated by monsoons. In this work, we investigate the dynamic influence of environmental conditions on dengue emergence in Colombo district where dengue is extremely prevalent in Sri Lanka. A novel approach leveraging the Markov chain Monte Carlo simulations has been employed to identify seasonal patterns of dengue disease emergence, utilizing the dynamics of weather patterns governing in the region. The newly developed algorithm allows us to estimate the timing of dengue outbreaks with uncertainty, enabling accurate forecasts of upcoming disease emergence patterns for better preparedness.
Mechanisms of nearshore retention and offshore export of mussel larvae over the Agulhas Bank
Authors: Nicolas Weidberg, Francesca Porri, Charles von der Meden, Jennifer M. Jackson, Wayne Goschen, Christopher McQuaid
Date: 2024-01-17 00:00:00

Ecological connectivity is critical for population dynamics but in many benthic species it is complicated by a planktonic larval phase, whose dispersal remains poorly understood. Using a plankton pump, we examine the distribution of intertidal mussel larvae along three axes: alongshore, cross-shelf and by depth during a large scale (600 km) cruise over the Agulhas Bank off southern Africa in August/September 2010. As a general pattern, higher veliger abundances were found close to the coast. Our analyses of the nearshore flow, estimated from ADCP data and the vertical distribution of larvae, show that onshore larval retention may be mediated by active vertical swimming through the water column guided by light and wind-induced turbulence. A massive offshore export of larvae off St Francis Bay was, however, observed during an Agulhas Current meander which influenced inner shelf waters. We hypothesize that, by increasing and homogenizing flow, the Agulhas Current may erase the effects of larval vertical positioning on onshore retention and transport larvae offshore. Our study highlights the need to integrate the effects of complex, region-specific physical dynamics with the swimming behaviour of larvae in order to explain their spatial distribution, population connectivity and the consequences for population dynamics.
Fine scale depth regulation of invertebrate larvae around coastal fronts
Authors: Nicolas Weidberg, Wayne Goschen, Jennifer M. Jackson, Paula Pattrick, Christopher D. McQuaid, Francesca Porri
Date: 2024-01-18 00:00:00

Vertical migrations of zooplankters have been widely described, but their active movements through shallow, highly dynamic water columns within the inner shelf may be more complex and difficult to characterize. In this study, invertebrate larvae, currents, and hydrographic variables were sampled at different depths during and after the presence of fronts on three different cruises off the southern coast of South Africa. Internal wave dynamics were observed in the hydrographic data set but also through satellite imagery, although strong surface convergent currents were absent and thermal stratification was weak. During the first two cruises, fronts were more conspicuous and they preceded strong onshore currents at depth which developed with the rising tide. Vertical distributions of larvae changed accordingly, with higher abundances at these deep layers once the front disappeared. The third cruise was carried out during slack tides, the front was not conspicuous, deep strong onshore currents did not occur afterward and larval distributions did not change consistently through time. Overall, the vertical distributions of many larval taxa matched the vertical profiles of shoreward currents and multivariate analyses revealed that these flows structured the larval community, which was neither influenced by temperature nor chlorophyll. Thus, the ability to regulate active vertical positioning may enhance shoreward advection and determine nearshore larval distributions.
DrugAssist: A Large Language Model for Molecule Optimization
Authors: Geyan Ye, Xibao Cai, Houtim Lai, Xing Wang, Junhong Huang, Longyue Wang, Wei Liu, Xiangxiang Zeng
Date: 2023-12-28 00:00:00
Tasks: Drug Discovery, Language Modeling, Language Modelling, Large Language Model, model

Recently, the impressive performance of large language models (LLMs) on a wide range of tasks has attracted an increasing number of attempts to apply LLMs in drug discovery. However, molecule optimization, a critical task in the drug discovery pipeline, is currently an area that has seen little involvement from LLMs. Most of existing approaches focus solely on capturing the underlying patterns in chemical structures provided by the data, without taking advantage of expert feedback. These non-interactive approaches overlook the fact that the drug discovery process is actually one that requires the integration of expert experience and iterative refinement. To address this gap, we propose DrugAssist, an interactive molecule optimization model which performs optimization through human-machine dialogue by leveraging LLM's strong interactivity and generalizability. DrugAssist has achieved leading results in both single and multiple property optimization, simultaneously showcasing immense potential in transferability and iterative optimization. In addition, we publicly release a large instruction-based dataset called MolOpt-Instructions for fine-tuning language models on molecule optimization tasks. We have made our code and data publicly available at https://github.com/blazerye/DrugAssist, which we hope to pave the way for future research in LLMs' application for drug discovery.
Deep Generative Modeling for Financial Time Series with Application in VaR: A Comparative Review
Authors: Lars Ericson, Xuejun Zhu, Xusi Han, Rao Fu, Shuang Li, Steve Guo, Ping Hu
Date: 2024-01-18 00:00:00
Tasks: Decoder, Time Series, Time Series Generation

In the financial services industry, forecasting the risk factor distribution conditional on the history and the current market environment is the key to market risk modeling in general and value at risk (VaR) model in particular. As one of the most widely adopted VaR models in commercial banks, Historical simulation (HS) uses the empirical distribution of daily returns in a historical window as the forecast distribution of risk factor returns in the next day. The objectives for financial time series generation are to generate synthetic data paths with good variety, and similar distribution and dynamics to the original historical data. In this paper, we apply multiple existing deep generative methods (e.g., CGAN, CWGAN, Diffusion, and Signature WGAN) for conditional time series generation, and propose and test two new methods for conditional multi-step time series generation, namely Encoder-Decoder CGAN and Conditional TimeVAE. Furthermore, we introduce a comprehensive framework with a set of KPIs to measure the quality of the generated time series for financial modeling. The KPIs cover distribution distance, autocorrelation and backtesting. All models (HS, parametric and neural networks) are tested on both historical USD yield curve data and additional data simulated from GARCH and CIR processes. The study shows that top performing models are HS, GARCH and CWGAN models. Future research directions in this area are also discussed.
Interplay between Cryptocurrency Transactions and Online Financial Forums
Authors: Ana Fernández Vilas, Rebeca P. Díaz Redondo, Daniel Couto Cancela, Alejandro Torrado Pazos
Date: 2023-11-27 00:00:00

Cryptocurrencies are a type of digital money meant to provide security and anonymity while using cryptography techniques. Although cryptocurrencies represent a breakthrough and provide some important benefits, their usage poses some risks that are a result of the lack of supervising institutions and transparency. Because disinformation and volatility is discouraging for personal investors, cryptocurrencies emerged hand-in-hand with the proliferation of online users' communities and forums as places to share information that can alleviate users' mistrust. This research focuses on the study of the interplay between these cryptocurrency forums and fluctuations in cryptocurrency values. In particular, the most popular cryptocurrency Bitcoin (BTC) and a related active discussion community, Bitcointalk, are analyzed. This study shows that the activity of Bitcointalk forum keeps a direct relationship with the trend in the values of BTC, therefore analysis of this interaction would be a perfect base to support personal investments in a non-regulated market and, to confirm whether cryptocurrency forums show evidences to detect abnormal behaviors in BTC values as well as to predict or estimate these values. The experiment highlights that forum data can explain specific events in the financial field. It also underlines the relevance of quotes (regular mechanism to response a post) at periods: (1) when there is a high concentration of posts around certain topics; (2) when peaks in the BTC price are observed; and, (3) when the BTC price gradually shifts downwards and users intend to sell.
Stylized Facts and Market Microstructure: An In-Depth Exploration of German Bond Futures Market
Authors: Hamza Bodor, Laurent Carlier
Date: 2024-01-19 00:00:00

This paper presents an in-depth analysis of stylized facts in the context of futures on German bonds. The study examines four futures contracts on German bonds: Schatz, Bobl, Bund and Buxl, using tick-by-tick limit order book datasets. It uncovers a range of stylized facts and empirical observations, including the distribution of order sizes, patterns of order flow, and inter-arrival times of orders. The findings reveal both commonalities and unique characteristics across the different futures, thereby enriching our understanding of these markets. Furthermore, the paper introduces insightful realism metrics that can be used to benchmark market simulators. The study contributes to the literature on financial stylized facts by extending empirical observations to this class of assets, which has been relatively underexplored in existing research. This work provides valuable guidance for the development of more accurate and realistic market simulators.
Causal Layering via Conditional Entropy
Authors: Itai Feigenbaum, Devansh Arpit, Huan Wang, Shelby Heinecke, Juan Carlos Niebles, Weiran Yao, Caiming Xiong, Silvio Savarese
Date: 2024-01-19 00:00:00
Tasks: Causal Discovery

Causal discovery aims to recover information about an unobserved causal graph from the observable data it generates. Layerings are orderings of the variables which place causes before effects. In this paper, we provide ways to recover layerings of a graph by accessing the data via a conditional entropy oracle, when distributions are discrete. Our algorithms work by repeatedly removing sources or sinks from the graph. Under appropriate assumptions and conditioning, we can separate the sources or sinks from the remainder of the nodes by comparing their conditional entropy to the unconditional entropy of their noise. Our algorithms are provably correct and run in worst-case quadratic time. The main assumptions are faithfulness and injective noise, and either known noise entropies or weakly monotonically increasing noise entropies along directed paths. In addition, we require one of either a very mild extension of faithfulness, or strictly monotonically increasing noise entropies, or expanding noise injectivity to include an additional single argument in the structural functions.
Early Prediction of Geomagnetic Storms by Machine Learning Algorithms
Authors: Iris Yan
Date: 2024-01-17 00:00:00
Tasks: feature selection, Prediction

Geomagnetic storms (GS) occur when solar winds disrupt Earth's magnetosphere. GS can cause severe damages to satellites, power grids, and communication infrastructures. Estimate of direct economic impacts of a large scale GS exceeds $40 billion a day in the US. Early prediction is critical in preventing and minimizing the hazards. However, current methods either predict several hours ahead but fail to identify all types of GS, or make predictions within short time, e.g., one hour ahead of the occurrence. This work aims to predict all types of geomagnetic storms reliably and as early as possible using big data and machine learning algorithms. By fusing big data collected from multiple ground stations in the world on different aspects of solar measurements and using Random Forests regression with feature selection and downsampling on minor geomagnetic storm instances (which carry majority of the data), we are able to achieve an accuracy of 82.55% on data collected in 2021 when making early predictions three hours in advance. Given that important predictive features such as historic Kp indices are measured every 3 hours and their importance decay quickly with the amount of time in advance, an early prediction of 3 hours ahead of time is believed to be close to the practical limit.