pwc-archive/papers-with-abstracts huggingface dataset:
Arboreal networks and their underlying trees
Authors: Katharina T. Huber, Darren Overman
Date: 2025-03-28 00:00:00

Horizontal gene transfer (HGT) is an important process in bacterial evolution. Current phylogeny-based approaches to capture it cannot however appropriately account for the fact that HGT can occur between bacteria living in different ecological niches. Due to the fact that arboreal networks are a type of multiple-rooted phylogenetic network that can be thought of as a forest of rooted phylogenetic trees along with a set of additional arcs each joining two different trees in the forest, understanding the combinatorial structure of such networks might therefore pave the way to extending current phylogeny-based HGT-inference methods in this direction. A central question in this context is, how can we construct an arboreal network? Answering this question is strongly informed by finding ways to \textit{encode} an arboreal network, that is, breaking up the network into simpler combinatorial structures that, in a well defined sense uniquely determine the network. In the form of triplets, trinets and quarnets such encodings are known for certain types of single-rooted phylogenetic networks. By studying the underlying tree of an arboreal network, we compliment them here with an answer for arboreal networks.
Describing the Persistence Landscape for Introducing Microbes into Complex Communities
Authors: Jason E. McDermott, William C. Nelson, Amy E. Zimmerman, Winston Anthony, Devin Coleman-Derr, Joshua Elmore, Tara Nitka, Ryan S. McClure, Pubudu P. Handakumbura, Adam Guss, Travis J. Wheeler, Robert G. Egbert
Date: 2025-03-28 00:00:00

The introduction of non-native organisms into complex microbiome communities holds enormous potential to benefit society. However, microbiome engineering faces several challenges including successful establishment of the organism into the community, its persistence in the microbiome to serve a specified purpose, and constraint of the organism and its activity to the intended environment. A theoretical framework is needed to represent the complex interactions that drive these dynamics. Building on the concept of the community functional landscape, we define the persistence landscape as the metabolic, genetic, and broader functional composition and ecological context of the target microbiome that can be used to predict the environmental fitness of an introduced organism. Here, we discuss critical aspects of persistence landscapes that impact interactions between an introduced organism and the target microbiome, including the communitys genetic and metabolic complementation potential, cellular defense strategies, spatial and temporal dynamics, and the introduced organisms ability to compete for resources to survive. Finally, we highlight important knowledge gaps in the fields of microbial ecology and microbiome engineering that limit characterization and engineering of persistence landscapes. As a model for understanding microbiome structure and interaction in the context of microbiome engineering, the persistence landscape model should enable development of novel containment approaches while improving controlled colonization of a complex microbiome community to address pressing challenges in human health, agronomy, and biomanufacturing.
Coupling plankton and cholera dynamics: insights into outbreak prediction and practical disease management
Authors: Biplab Maity, Swarnendu Banerjee, Abhishek Senapati, Jon Pitchford, Joydev Chattopadhyay
Date: 2025-03-27 00:00:00
Tasks: Epidemiology, Management

Despite extensive control efforts over the centuries, cholera remains a globally significant health issue. Seasonal emergence of cholera cases has been reported, particularly in the Bengal delta region, which is often synchronized with plankton blooms. This phenomenon has been widely attributed to the commensal interaction between Vibrio cholerae and zooplankton in aquatic environments. Understanding the role of plankton dynamics in cholera epidemiology is therefore crucial for effective policy-making. To this end, we propose and analyze a novel compartment-based transmission model that integrates phytoplankton-zooplankton interactions into a human-bacteria cholera model. We show that zooplankton-mediated transmission can lead to counterintuitive outcomes, such as an outbreak with a delayed and lower peak still resulting in a larger overall outbreak size. Such outbreaks are prolonged by maintaining infections at lower levels during the post-peak phase, thereby intensifying epidemic overshoot and promoting the inter-epidemic persistence of pathogens. Furthermore, our analysis reveals that the timing of filtration-like interventions can be strategically guided by ecological indicators, such as phytoplankton blooms. Our study underscores the importance of incorporating ecological aspects in epidemiological research to gain a deeper understanding of disease dynamics.
Anti-pathogenic property of thermophile-fermented compost as a feed additive and its in vivo external diagnostic imaging in a fish model
Authors: Hirokuni Miyamoto, Shunsuke Ito, Kenta Suzuki, Singo Tamachi, Shion Yamada, Takayuki Nagatsuka, Takashi Satoh, Motoaki Udagawa, Hisashi Miyamoto, Hiroshi Ohno, Jun Kikuchi
Date: 2025-03-29 00:00:00
Tasks: Diagnostic

Fermentative recycling of organic matter is important for a sustainable society, but the functionality of fermented products needs to be adequately evaluated. Here, we clarify the antipathogenic properties for fish of a compost-type feed additive fermented by thermophilic Bacillaceae using non-edible marine resources as raw materials. After prior administration of the compost extract to seabream as a fish model for 70 days, the mortality rate after 28 days of exposure to the fish pathogen Edwardsiella reached a maximum of 20%, although the rate was 60% without prior administration. Under such conditions, the serum complement activity of seabream increased, and the recovery time after anesthesia treatment was also fasten. Furthermore, the differences in the degree of smoothness and glossiness of the fish body surface depending on the administration were statistically shown by imaging techniques to evaluate the texture and color tone of field photographs. These results suggest that thermophile-fermented compost is effective as a functional feed additive against fish disease infection, and that such conditions can be estimated by body surface analysis. This study provides a new perspective for the natural symbiosis industry, as well as for the utilization of non-invasive diagnosis to efficiently estimate the quality of its production activities
Association in Facial Phenotype, Gene, Disease: A Dataset for Explainable Rare Genetic Diseases Diagnosis
Authors: Jie Song, Mengqiao He, Shumin Ren, Bairong Shen
Date: 2025-03-25 00:00:00
Tasks: Diagnostic

Many rare genetic diseases exhibit recognizable facial phenotypes, which are often used as diagnostic clues. However, current facial phenotype diagnostic models, which are trained on image datasets, have high accuracy but often suffer from an inability to explain their predictions, which reduces physicians' confidence in the model output.In this paper, we constructed a dataset, called FGDD, which was collected from 509 publications and contains 1147 data records, in which each data record represents a patient group and contains patient information, variation information, and facial phenotype information. To verify the availability of the dataset, we evaluated the performance of commonly used classification algorithms on the dataset and analyzed the explainability from global and local perspectives. FGDD aims to support the training of disease diagnostic models, provide explainable results, and increase physicians' confidence with solid evidence. It also allows us to explore the complex relationship between genes, diseases, and facial phenotypes, to gain a deeper understanding of the pathogenesis and clinical manifestations of rare genetic diseases.
A Novel Framework for Comparing Combination Therapy Outcomes Using Mechanistic Graph Models
Authors: Dipayan Sengupta, Saumya Panda
Date: 2025-03-12 00:00:00

Background: Predicting the efficacy of combination therapies is a critical challenge in clinical decision-making, particularly for diseases requiring multi-drug regimens. Traditional evidence synthesis methods, such as component network meta-analysis (cNMA), often face parameter explosion and limited interpretability, especially when modeling interaction effects between components. Objective: This article introduces a general Efficacy Comparison Framework (ECF), a mechanistically grounded system for predicting combination therapy outcomes. ECF integrates biological pathway-based abstractions with expert knowledge, optimized with quasi-rules derived from clinical trial data to overcome the limitations of traditional methods. Methods: ECF employs a disease pathogenesis graph to encode domain knowledge, reducing the parameter space through mechanistic functions and sparse network structures. Optimization may be performed using a loss function inspired by the Thurstone-Mosteller model, focusing on pairwise regimen comparisons. A pilot study was conducted for acne vulgaris to evaluate ECF's ability in both tested and untested comparisons. Results: In the acne vulgaris case study, the ECF-based model achieved 76% accuracy in predicting both tested and untested regimen outcomes, demonstrating statistically comparable performance across clinical trial data and expert dermatologist consensus (p = 0.977). The agreement between ECF and expert predictions was within the range of inter-expert agreement. Discussion: ECF aligns with recent advancements in network science and synergy prediction, leveraging principles of complementary targeting and biological plausibility. Its use of disease pathogenesis graphs offers a more interpretable and scalable alternative to existing models reliant on chemical similarity or protein-protein interaction (PPI) topology.
The logarithmic relationship between mean arterial pressure and heart rate
Authors: Maximilian Nguyen
Date: 2025-03-29 00:00:00

The classic heuristic formula for calculating the mean arterial blood pressure (MAP) in the human cardiac cycle using 1/3 of the systolic pressure (P_S) plus 2/3 of the diastolic pressure (P_D) importantly neglects the nonlinear effect of heart rate (HR) on blood pressure. Regression analysis on blood pressure data from patients experiencing a variety of heart rates captured a logarithmic relationship between mean arterial pressure and heart rate, F_S=0.086 *ln(HR-41.2)+0.079, where F_S is the MAP form factor corresponding to the fraction of time the cardiac cycle spends in ventricular systole. The logarithmic relationship is qualitatively distinct from the linear and exponential relationships previously suggested in the literature yet better aligns with the known human physiology. This heart rate-dependent formula for the mean arterial pressure recapitulates the classic intuition as a special case (HR=60bpm implies F_S =1/3). In addition, the model suggests several other MAP heuristics: HR=45bpm implies F_S = 1/5, HR=50bpm implies F_S = 1/4, HR=175bpm implies F_S = 1/2. A calculator for the MAP can be found at https://mngu265.shinyapps.io/map\_calculator/
Mathematical foundations of information economics
Authors: N. S. Gonchar
Date: 2025-03-31 00:00:00

The state of economic theory and accumulated facts from the different branches of the economic science require to analyze the concept of the description of economy systems. The economic reality generates the problems the solution of that is only possible by a new paradigm of the description of economy system. The classical mathematical economics is based on a notion of the rational consumer choice generated by a certain preference relation on some set of goods a consumer wanted and the concept of maximization of the firm profit. The sense of the notion of the ratio- nal consumer choice is that it is determined by a certain utility function, defining the choice of a consumer by maximization of it on a certain budget set of goods. More- over, choices of consumers are independent. In the reality choices of consumers are not independent because they depend on the firms supply. Except the firms supply, the consumer choice is also determined by information about the state of the economy system that the consumer has and respectively eval- uates at the moment of the choice. In turn, the firms supply is made on the basis of needs of the consumers and their buying power. By information about the state of the economy system we understand a certain information about the equilibrium price vector and productive processes realized in the economy system under the equilibrium price vector.
Pool Value Replication (CPM) and Impermanent Loss Hedging
Authors: Agustin Muñoz Gonzalez, Juan Ignacio Sequeira, Ariel Dembling
Date: 2025-03-27 00:00:00

This work analytically characterizes impermanent loss for automated market makers (AMMs) in decentralized markets such as Uniswap or Balancer (CPMM). We derive a static replication formula for the pool's value using a combination of European calls and puts. Furthermore, we establish a result guaranteeing hedging coverage for all final prices within a predefined interval. These theoretical results motivate a numerical example where we illustrate the strangle strategy using real cryptocurrency options data from Deribit, one of the most liquid markets available.
A cost of capital approach to determining the LGD discount rate
Authors: Janette Larney, Arno Botha, Gerrit Lodewicus Grobler, Helgard Raubenheimer
Date: 2025-03-31 00:00:00

Loss Given Default (LGD) is a key risk parameter in determining a bank's regulatory capital. During LGD-estimation, realised recovery cash flows are to be discounted at an appropriate rate. Regulatory guidance mandates that this rate should allow for the time value of money, as well as include a risk premium that reflects the "undiversifiable risk" within these recoveries. Having extensively reviewed earlier methods of determining this rate, we propose a new approach that is inspired by the cost of capital approach from the Solvency II regulatory regime. Our method involves estimating a market-consistent price for a portfolio of defaulted loans, from which an associated discount rate may be inferred. We apply this method to mortgage and personal loans data from a large South African bank. The results reveal the main drivers of the discount rate to be the mean and variance of these recoveries, as well as the bank's cost of capital in excess of the risk-free rate. Our method therefore produces a discount rate that reflects both the undiversifiable risk of recovery recoveries and the time value of money, thereby satisfying regulatory requirements. This work can subsequently enhance the LGD-component within the modelling of both regulatory and economic capital.
Bridging Language Models and Financial Analysis
Authors: Alejandro Lopez-Lira, Jihoon Kwon, Sangwoon Yoon, Jy-yong Sohn, Chanyeol Choi
Date: 2025-03-14 00:00:00
Tasks: Financial Analysis

The rapid advancements in Large Language Models (LLMs) have unlocked transformative possibilities in natural language processing, particularly within the financial sector. Financial data is often embedded in intricate relationships across textual content, numerical tables, and visual charts, posing challenges that traditional methods struggle to address effectively. However, the emergence of LLMs offers new pathways for processing and analyzing this multifaceted data with increased efficiency and insight. Despite the fast pace of innovation in LLM research, there remains a significant gap in their practical adoption within the finance industry, where cautious integration and long-term validation are prioritized. This disparity has led to a slower implementation of emerging LLM techniques, despite their immense potential in financial applications. As a result, many of the latest advancements in LLM technology remain underexplored or not fully utilized in this domain. This survey seeks to bridge this gap by providing a comprehensive overview of recent developments in LLM research and examining their applicability to the financial sector. Building on previous survey literature, we highlight several novel LLM methodologies, exploring their distinctive capabilities and their potential relevance to financial data analysis. By synthesizing insights from a broad range of studies, this paper aims to serve as a valuable resource for researchers and practitioners, offering direction on promising research avenues and outlining future opportunities for advancing LLM applications in finance.
Enhancing Predictive Accuracy in Tennis: Integrating Fuzzy Logic and CV-GRNN for Dynamic Match Outcome and Player Momentum Analysis
Authors: Kechen Li, Jiaming Liu, Zhenyu Wu, Tianbo Ji
Date: 2025-03-25 00:00:00

The predictive analysis of match outcomes and player momentum in professional tennis has long been a subject of scholarly debate. In this paper, we introduce a novel approach to game prediction by combining a multi-level fuzzy evaluation model with a CV-GRNN model. We first identify critical statistical indicators via Principal Component Analysis and then develop a two-tier fuzzy model based on the Wimbledon data. In addition, the results of Pearson Correlation Coefficient indicate that the momentum indicators, such as Player Win Streak and Score Difference, have a strong correlation among them, revealing insightful trends among players transitioning between losing and winning streaks. Subsequently, we refine the CV-GRNN model by incorporating 15 statistically significant indicators, resulting in an increase in accuracy to 86.64% and a decrease in MSE by 49.21%. This consequently strengthens the methodological framework for predicting tennis match outcomes, emphasizing its practical utility and potential for adaptation in various athletic contexts.
Binary AddiVortes: (Bayesian) Additive Voronoi Tessellations for Binary Classification with an application to Predicting Home Mortgage Application Outcomes
Authors: Adam J. Stone, Emmanuel Ogundimu, John Paul Gosling
Date: 2025-03-18 00:00:00
Tasks: Binary Classification, Data Augmentation, Decision Making, regression

The Additive Voronoi Tessellations (AddiVortes) model is a multivariate regression model that uses multiple Voronoi tessellations to partition the covariate space for an additive ensemble model. In this paper, the AddiVortes framework is extended to binary classification by incorporating a probit model with a latent variable formulation. Specifically, we utilise a data augmentation technique, where a latent variable is introduced and the binary response is determined via thresholding. In most cases, the AddiVortes model outperforms random forests, BART and other leading black-box regression models when compared using a range of metrics. A comprehensive analysis is conducted using AddiVortes to predict an individual's likelihood of being approved for a home mortgage, based on a range of covariates. This evaluation highlights the model's effectiveness in capturing complex relationships within the data and its potential for improving decision-making in mortgage approval processes.
March Madness Tournament Predictions Model: A Mathematical Modeling Approach
Authors: Christian McIver, Karla Avalos, Nikhil Nayak
Date: 2025-03-17 00:00:00

This paper proposes a model to predict the outcome of the March Madness tournament based on historical NCAA basketball data since 2013. The framework of this project is a simplification of the FiveThrityEight NCAA March Madness prediction model, where the only four predictors of interest are Adjusted Offensive Efficiency (ADJOE), Adjusted Defensive Efficiency (ADJDE), Power Rating, and Two-Point Shooting Percentage Allowed. A logistic regression was utilized with the aforementioned metrics to generate a probability of a particular team winning each game. Then, a tournament simulation is developed and compared to real-world March Madness brackets to determine the accuracy of the model. Accuracies of performance were calculated using a naive approach and a Spearman rank correlation coefficient.
Enhancing Weakly Supervised Video Grounding via Diverse Inference Strategies for Boundary and Prediction Selection
Authors: Sunoh Kim, Daeho Um
Date: 2025-03-29 00:00:00
Tasks: Prediction, Video Grounding

Weakly supervised video grounding aims to localize temporal boundaries relevant to a given query without explicit ground-truth temporal boundaries. While existing methods primarily use Gaussian-based proposals, they overlook the importance of (1) boundary prediction and (2) top-1 prediction selection during inference. In their boundary prediction, boundaries are simply set at half a standard deviation away from a Gaussian mean on both sides, which may not accurately capture the optimal boundaries. In the top-1 prediction process, these existing methods rely heavily on intersections with other proposals, without considering the varying quality of each proposal. To address these issues, we explore various inference strategies by introducing (1) novel boundary prediction methods to capture diverse boundaries from multiple Gaussians and (2) new selection methods that take proposal quality into account. Extensive experiments on the ActivityNet Captions and Charades-STA datasets validate the effectiveness of our inference strategies, demonstrating performance improvements without requiring additional training.
Real-time Video Prediction With Fast Video Interpolation Model and Prediction Training
Authors: Shota Hirose, Kazuki Kotoyori, Kasidis Arunruangsirilert, Fangzheng Lin, Heming Sun, Jiro Katto
Date: 2025-03-29 00:00:00
Tasks: Prediction, Video Prediction

Transmission latency significantly affects users' quality of experience in real-time interaction and actuation. As latency is principally inevitable, video prediction can be utilized to mitigate the latency and ultimately enable zero-latency transmission. However, most of the existing video prediction methods are computationally expensive and impractical for real-time applications. In this work, we therefore propose real-time video prediction towards the zero-latency interaction over networks, called IFRVP (Intermediate Feature Refinement Video Prediction). Firstly, we propose three training methods for video prediction that extend frame interpolation models, where we utilize a simple convolution-only frame interpolation network based on IFRNet. Secondly, we introduce ELAN-based residual blocks into the prediction models to improve both inference speed and accuracy. Our evaluations show that our proposed models perform efficiently and achieve the best trade-off between prediction accuracy and computational speed among the existing video prediction methods. A demonstration movie is also provided at http://bit.ly/IFRVPDemo.