pwc-archive/papers-with-abstracts huggingface dataset:
Generalized non-autonomous Cohen-Grossberg neural network model
Authors: Ahmed Elmwafy, José J. Oliveira, César M. Silva
Date: 2023-09-04 00:00:00
Tasks: model

In the present paper, we investigate both the global exponential stability and the existence of a periodic solution of a general differential equation with unbounded distributed delays. The main stability criterion depends on the dominance of the non-delay terms over the delay terms. The criterion for the existence of a periodic solution is obtained with the application of the coincide degree theorem. We use the main results to get criteria for the existence and global exponential stability of periodic solutions of a generalized higher-order periodic Cohen-Grossberg neural network model with discrete-time varying delays and infinite distributed delays. Additionally, we provide a comparison with the results in the literature and a numerical simulation to illustrate the effectiveness of some of our results.
Characterizing Pulmonary Fibrosis Patterns in Post-COVID-19 Patients through Machine Learning Algorithms
Authors: John Martin, Hayder A. Albaqer, Fadhil G. Al-Amran, Habeeb W. Shubber, Salman Rawaf, Maitham G. Yousif
Date: 2023-09-21 00:00:00
Tasks: Decision Making

The COVID-19 pandemic has left a lasting impact on global healthcare systems, with increasing evidence of pulmonary fibrosis emerging as a post-infection complication. This study presents a comprehensive analysis of pulmonary fibrosis patterns in post-COVID-19 patients from South and Central Iraq, employing advanced machine learning algorithms. Data were collected from 390 patients, and their medical records were systematically analyzed. Our findings reveal distinct patterns of pulmonary fibrosis in this cohort, shedding light on the heterogeneous nature of post-COVID-19 lung complications. Machine learning models demonstrated robust predictive capabilities, offering valuable insights into the characterization of fibrotic changes. The identification of specific patterns contributes to early diagnosis and personalized treatment strategies for affected individuals. This research underscores the importance of data-driven approaches in understanding post-COVID-19 complications, particularly in regions with unique demographic and healthcare characteristics. It emphasizes the potential for machine learning to enhance clinical decision-making and improve patient care in the aftermath of the pandemic. Further investigations are warranted to validate these findings and explore additional factors influencing pulmonary fibrosis in post-COVID-19 patients.
Gene Expression Patterns of CsZCD and Apocarotenoid Accumulation during Saffron Stigma Development
Authors: Zohreh Shams
Date: 2023-09-15 00:00:00

Crocus sativus L., otherwise known as saffron, is a highly prized plant due to its unique triploid capability and elongated stigmas, contributing to its status as the costly spice globally. The color and taste properties of saffron are linked to carotenoid elements including cis- and trans-crocin, picrocrocin, and safranal. In the research carried out, we dedicated our attention to the gene CsZCD, an important player in the formation of apocarotenoids. Through the application of real-time polymerase chain reaction to RNA purified from saffron stigmas at various growth phases, it was determined that the peak expression of the CsZCD gene coincided with the red stage, which is associated with the highest concentration of apocarotenoids. The data showed a 2.69-fold enhancement in CsZCD gene expression during the red phase, whereas a 0.90-fold and 0.69-fold reduction was noted at the stages characterized by orange and yellow hues, respectively. A noteworthy observation was that CsZCD's expression was three times that of the CsTUB gene. Additionally, relative to CsTUB, CsLYC displayed 0.7-fold and 0.3-times expression. Our investigation provides insight into the governance of CsZCD during stigma maturation and its possible influence on the fluctuation in apocarotenoid content. These discoveries carry significance for the industrial production of saffron spice and underscore the importance of additional studies on pivotal genes participating in the synthesis of apocarotenoids.
Differentiable Phylogenetics via Hyperbolic Embeddings with Dodonaphy
Authors: Matthew Macaulay, Mathieu Fourment
Date: 2023-09-21 00:00:00
Tasks: Decoder

Motivation: Navigating the high dimensional space of discrete trees for phylogenetics presents a challenging problem for tree optimisation. To address this, hyperbolic embeddings of trees offer a promising approach to encoding trees efficiently in continuous spaces. However, they require a differentiable tree decoder to optimise the phylogenetic likelihood. We present soft-NJ, a differentiable version of neighbour-joining that enables gradient-based optimisation over the space of trees. Results: We illustrate the potential for differentiable optimisation over tree space for maximum likelihood inference. We then perform variational Bayesian phylogenetics by optimising embedding distributions in hyperbolic space. We compare the performance of this approximation technique on eight benchmark datasets to state-of-art methods. However, geometric frustrations of the embedding locations produce local optima that pose a challenge for optimisation. Availability: Dodonaphy is freely available on the web at www.https://github.com/mattapow/dodonaphy. It includes an implementation of soft-NJ.
Distinguishing mature and immature trees allows to estimate forest carbon uptake from stand structure
Authors: Samuel M. Fischer, Xugao Wang, Andreas Huth
Date: 2023-09-20 00:00:00

Relating forest productivity to local variations in forest structure has been a long-standing challenge. Previous studies often focused on the connection between forest structure and stand-level photosynthesis (GPP). However, biomass production (NPP) and net ecosystem exchange (NEE) are also subject to respiration and other carbon losses, which vary with local conditions and life history traits. Here, we use a simulation approach to study how these losses impact forest productivity and reveal themselves in forest structure. We fit the process-based forest model Formind to a 25ha inventory of an old-growth temperate forest in China and classify trees as "mature" (full-grown) or "immature" based on their intrinsic carbon use efficiency. Our results reveal a strong negative connection between the stand-level carbon use efficiency and the prevalence of mature trees: GPP increases with the total basal area, whereas NPP and NEE are driven by the basal area of immature trees. Accordingly, the basal area entropy - a structural proxy for the prevalence of immature trees - correlated well with NPP and NEE and had higher predictive power than other structural characteristics such as Shannon diversity and height standard deviation. Our results were robust across spatial scales (0.04-1ha) and yield promising hypotheses field studies and new theoretical work.
Migration feedback induces emergent ecotypes and abrupt transitions in evolving populations
Authors: Casey O. Barkan, Shenshen Wang
Date: 2023-09-19 00:00:00

We explore the connection between migration patterns and emergent behaviors of evolving populations in spatially heterogeneous environments. Despite extensive studies in ecologically and medically important systems, a unifying framework that clarifies this connection and makes concrete predictions remains much needed. Using a simple evolutionary model on a network of interconnected habitats with distinct fitness landscapes, we demonstrate a fundamental connection between migration feedback, emergent ecotypes, and an unusual form of discontinuous critical transition. We show how migration feedback generates spatially non-local niches in which emergent ecotypes can specialize. Rugged fitness landscapes lead to a complex, yet understandable, phase diagram in which different ecotypes coexist under different migration patterns. The discontinuous transitions are distinct from the standard first-order phase transitions in statistical physics. They arise due to simultaneous transcritical bifurcations and exhibit a "fine structure" due to symmetry breaking between intra- and inter-ecotype interactions. We suggest feasible experiments to test our predictions.
Characterizing the non-monotonic behavior of mutual information along biochemical reaction cascades
Authors: Raymond Fan, Andreas Hilfinger
Date: 2023-09-18 00:00:00

Cells sense environmental signals and transmit information intracellularly through changes in the abundance of molecular components. Such molecular abundances can be measured in single cells and exhibit significant heterogeneity in clonal populations even in identical environments. Experimentally observed joint probability distributions can then be used to quantify the covariability and mutual information between molecular abundances along signaling cascades. However, because stationary state abundances along stochastic biochemical reaction cascades are not conditionally independent, their mutual information is not constrained by the data processing inequality. Here, we report the conditions under which the mutual information between stationary state abundances increases along a cascade of biochemical reactions. This non-monotonic behavior can be intuitively understood in terms of noise propagation and time-averaging stochastic fluctuations that are short-lived compared to an extrinsic signal. Our results re-emphasize that mutual information measurements of stationary state distributions of cellular components may be of limited utility for characterizing cellular signaling processes because they do not measure information transfer.
Dynamical network stability analysis of multiple biological ages provides a framework for understanding the aging process
Authors: Glen Pridham, Andrew D. Rutenberg
Date: 2023-09-17 00:00:00

Widespread interest in non-destructive biomarkers of aging has led to a curse of plenty: a multitude of biological ages that each proffers a 'true' health-adjusted age of an individual. While each measure provides salient information on the aging process, they are each univariate, in contrast to the "hallmark" and "pillar" theories of aging which are explicitly multidimensional, multicausal and multiscale. Fortunately, multiple biological ages can be systematically combined into a multidimensional network representation. The interaction network between these biological ages permits analysis of the multidimensional effects of aging, as well as quantification of causal influences during both natural aging and, potentially, after anti-aging intervention. The behaviour of the system as a whole can then be explored using dynamical network stability analysis which identifies new, efficient biomarkers that quantify long term resilience scores on the timescale between measurements (years). We demonstrate this approach using a set of 8 biological ages from the longitudinal Swedish Adoption/Twin Study of Aging (SATSA). After extracting an interaction network between these biological ages, we observed that physiological age, a proxy for cardiometabolic health, serves as a central node in the network, implicating it as a key vulnerability for slow, age-related decline. We furthermore show that while the system as a whole is stable, there is a weakly stable direction along which recovery is slow - on the timescale of a human lifespan. This slow direction provides an aging biomarker which correlates strongly with chronological age and predicts longitudinal decline in health - suggesting that it estimates an important driver of age-related changes.
Derivatives Sensitivities Computation under Heston Model on GPU
Authors: Pierre-Antoine Arsaguet, Paul Bilokon
Date: 2023-09-19 00:00:00
Proceeding: SSRN 2023 9
Tasks: CPU, GPU, model

This report investigates the computation of option Greeks for European and Asian options under the Heston stochastic volatility model on GPU. We first implemented the exact simulation method proposed by Broadie and Kaya and used it as a baseline for precision and speed. We then proposed a novel method for computing Greeks using the Milstein discretisation method on GPU. Our results show that the proposed method provides a speed-up up to 200x compared to the exact simulation implementation and that it can be used for both European and Asian options. However, the accuracy of the GPU method for estimating Rho is inferior to the CPU method. Overall, our study demonstrates the potential of GPU for computing derivatives sensitivies with numerical methods.
Comparing effects of price limit and circuit breaker in stock exchanges by an agent-based model
Authors: Takanobu Mizuta, Isao Yagi
Date: 2023-09-19 00:00:00

The prevention of rapidly and steeply falling market prices is vital to avoid financial crisis. To this end, some stock exchanges implement a price limit or a circuit breaker, and there has been intensive investigation into which regulation best prevents rapid and large variations in price. In this study, we examine this question using an artificial market model that is an agent-based model for a financial market. Our findings show that the price limit and the circuit breaker basically have the same effect when the parameters, limit price range and limit time range, are the same. However, the price limit is less effective when limit the time range is smaller than the cancel time range. With the price limit, many sell orders are accumulated around the lower limit price, and when the lower limit price is changed before the accumulated sell orders are cancelled, it leads to the accumulation of sell orders of various prices. These accumulated sell orders essentially act as a wall against buy orders, thereby preventing price from rising. Caution should be taken in the sense that these results pertain to a limited situation. Specifically, our finding that the circuit breaker is better than the price limit should be adapted only in cases where the reason for falling prices is erroneous orders and when individual stocks are regulated.
Doubly Robust Mean-CVaR Portfolio
Authors: Kei Nakagawa, Masaya Abe, Seiichi Kuroki
Date: 2023-09-20 00:00:00
Tasks: Portfolio Optimization

In this study, we address the challenge of portfolio optimization, a critical aspect of managing investment risks and maximizing returns. The mean-CVaR portfolio is considered a promising method due to today's unstable financial market crises like the COVID-19 pandemic. It incorporates expected returns into the CVaR, which considers the expected value of losses exceeding a specified probability level. However, the instability associated with the input parameter changes and estimation errors can deteriorate portfolio performance. Therefore in this study, we propose a Doubly Robust mean-CVaR Portfolio refined approach to the mean-CVaR portfolio optimization. Our method can solve the instability problem to simultaneously optimize the multiple levels of CVaRs and define uncertainty sets for the mean parameter to perform robust optimization. Theoretically, the proposed method can be formulated as a second-order cone programming problem which is the same formulation as traditional mean-variance portfolio optimization. In addition, we derive an estimation error bound of the proposed method for the finite-sample case. Finally, experiments with benchmark and real market data show that our proposed method exhibits better performance compared to existing portfolio optimization strategies.
Multidimensional well-being of US households at a fine spatial scale using fused household surveys: fusionACS
Authors: Kevin Ummel, Miguel Poblete-Cazenave, Karthik Akkiraju, Nick Graetz, Hero Ashman, Cora Kingdon, Steven Herrera Tenorio, Aaryaman "Sunny" Singhal, Daniel Aldana Cohen, Narasimha D. Rao
Date: 2023-09-15 00:00:00
Tasks: Survey

Social science often relies on surveys of households and individuals. Dozens of such surveys are regularly administered by the U.S. government. However, they field independent, unconnected samples with specialized questions, limiting research questions to those that can be answered by a single survey. The fusionACS project seeks to integrate data from multiple U.S. household surveys by statistically "fusing" variables from "donor" surveys onto American Community Survey (ACS) microdata. This results in an integrated microdataset of household attributes and well-being dimensions that can be analyzed to address research questions in ways that are not currently possible. The presented data comprise the fusion onto the ACS of select donor variables from the Residential Energy Consumption Survey (RECS) of 2015, the National Household Transportation Survey (NHTS) of 2017, the American Housing Survey (AHS) of 2019, and the Consumer Expenditure Survey - Interview (CEI) for the years 2015-2019. The underlying statistical techniques are included in an open-source $R$ package, fusionModel, that provides generic tools for the creation, analysis, and validation of fused microdata.
Towards LLM-based Autograding for Short Textual Answers
Authors: Johannes Schneider, Bernd Schenk, Christina Niklaus
Date: 2023-09-09 00:00:00
Tasks: Decision Making, Language Modeling, Language Modelling, Large Language Model

Grading exams is an important, labor-intensive, subjective, repetitive, and frequently challenging task. The feasibility of autograding textual responses has greatly increased thanks to the availability of large language models (LLMs) such as ChatGPT and the substantial influx of data brought about by digitalization. However, entrusting AI models with decision-making roles raises ethical considerations, mainly stemming from potential biases and issues related to generating false information. Thus, in this manuscript, we provide an evaluation of a large language model for the purpose of autograding, while also highlighting how LLMs can support educators in validating their grading procedures. Our evaluation is targeted towards automatic short textual answers grading (ASAG), spanning various languages and examinations from two distinct courses. Our findings suggest that while "out-of-the-box" LLMs provide a valuable tool to provide a complementary perspective, their readiness for independent automated grading remains a work in progress, necessitating human oversight.
MBTI Personality Prediction Using Machine Learning and SMOTE for Balancing Data Based on Statement Sentences
Authors: Gregorius Ryan, Pricillia Katarina, Derwin Suhartono
Date: 2023-04-03 00:00:00
Proceeding: MDPI 2023 4

The rise of social media as a platform for self-expression and self-understanding has led to increased interest in using the Myers–Briggs Type Indicator (MBTI) to explore human personalities. Despite this, there needs to be more research on how other word-embedding techniques, machine learning algorithms, and imbalanced data-handling techniques can improve the results of MBTI personality-type predictions. Our research aimed to investigate the efficacy of these techniques by utilizing the Word2Vec model to obtain a vector representation of words in the corpus data. We implemented several machine learning approaches, including logistic regression, linear support vector classification, stochastic gradient descent, random forest, the extreme gradient boosting classifier, and the cat boosting classifier. In addition, we used the synthetic minority oversampling technique (SMOTE) to address the issue of imbalanced data. The results showed that our approach could achieve a relatively high F1 score (between 0.7383 and 0.8282), depending on the chosen model for predicting and classifying MBTI personality. Furthermore, we found that using SMOTE could improve the selected models’ performance (F1 score between 0.7553 and 0.8337), proving that the machine learning approach integrated with Word2Vec and SMOTE could predict and classify MBTI personality well, thus enhancing the understanding of MBTI.
Reinforcement Learning in Financial Markets: A Study on Dynamic Model Weight Assignment
Authors: Akash Deep
Date: 2023-08-15 00:00:00
Proceeding: International Journal of Computer Science and Telecommunications 2023 8
Tasks: Management, regression, reinforcement-learning, Reinforcement Learning, Sentiment Analysis

This study innovates in financial market forecasting by employing reinforcement learning within a diverse ensemble of models, including Random Forest Regression, LSTM networks, linear regression, and sentiment analysis. Through dynamic, performance-based weight adjustments, our approach shows marked improvement over static strategies. Real-world data testing evidences enhanced prediction accuracy, hinting at potentially more profitable trading decisions. This work underscores the untapped potential of reinforcement learning for optimal ensemble model management in the ever-changing financial landscape.
DrugChat: Towards Enabling ChatGPT-Like Capabilities on Drug Molecule Graphs
Authors: Youwei Liang, Ruiyi Zhang, Li Zhang, Pengtao Xie
Date: 2023-05-18 00:00:00
Tasks: Drug Discovery, Graph Neural Network, Language Modelling, Large Language Model

A ChatGPT-like system for drug compounds could be a game-changer in pharmaceutical research, accelerating drug discovery, enhancing our understanding of structure-activity relationships, guiding lead optimization, aiding drug repurposing, reducing the failure rate, and streamlining clinical trials. In this work, we make an initial attempt towards enabling ChatGPT-like capabilities on drug molecule graphs, by developing a prototype system DrugChat. DrugChat works in a similar way as ChatGPT. Users upload a compound molecule graph and ask various questions about this compound. DrugChat will answer these questions in a multi-turn, interactive manner. The DrugChat system consists of a graph neural network (GNN), a large language model (LLM), and an adaptor. The GNN takes a compound molecule graph as input and learns a representation for this graph. The adaptor transforms the graph representation produced by the GNN into another representation that is acceptable to the LLM. The LLM takes the compound representation transformed by the adaptor and users' questions about this compound as inputs and generates answers. All these components are trained end-to-end. To train DrugChat, we collected instruction tuning datasets which contain 10,834 drug compounds and 143,517 question-answer pairs. The code and data is available at \url{https://github.com/UCSD-AI4H/drugchat}