pwc-archive/papers-with-abstracts huggingface dataset:
Genus Comparisons in the Topological Analysis of RNA Structures
Authors: Nicolò Cangiotti, Stefano Grasso
Date: 2023-04-14 00:00:00

RNA folding prediction remains challenging, but can be also studied using a topological mathematical approach. In the present paper, the mathematical method to compute the topological classification of RNA structures and based on matrix field theory is shortly reviewed, as well as a computational software, McGenus, used for topological and folding predictions. Additionally, two types of analysis are performed: the prediction results from McGenus are compared with topological information extracted from experimentally-determined RNA structures, and the topology of RNA structures is investigated for biological significance, in both evolutionary and functional terms. Lastly, we advocate for more research efforts to be performed at intersection of physics-mathematics and biology, and in particular about the possible contributions that topology can provide to the study of RNA folding and structure.
A genealogical interpretation of fixed and random effects models of complex traits
Authors: Hanbin Lee
Date: 2023-04-16 00:00:00

In the analysis of complex traits, genetic effects are frequently modelled as either fixed or random effects. Such assumptions serve as a foundation of defining heritability and relatedness using genome-wide single nucleotide polymorphism (SNP) markers. In this work, I propose a genealogical framework connecting the two assumptions conditional on the ancestral recombination graph (ARG). It turns out that the reference time point in which the probability is defined determines whether the effect of a variant to behave as either a fixed or random effect. This lays a connection between the PC regression and linear mixed model (LMM) for genetic association study. The framework induces a genetic relatedness matrix (GRM) in which the elements are a function of the time to the recent common ancestor. Subsequently, a novel trait variance decomposition respect to the regions sharing a common genealogy is followed. The variance decomposition then provides a natural means to define heritability and a potential method for gene mapping.
A Method for Improving the Detection Accura-cy of MSIsensor Based on Downsampling
Authors: Ji Detao, Liu Weier
Date: 2023-04-14 00:00:00
Tasks: Prognosis

Motivation: Microsatellite instability (MSI) is a cancer biomarker associated with cancer prognosis and chemotherapy sensitivity. Since the discovery of MSI, polymerase chain reaction (PCR)-based testing has been considered the gold standard for MSI detection. However, with the decrease in sequencing costs, software that calculates MSI based on next-generation sequencing (NGS) data has been widely applied. Results: In this study, we evaluated the performance of the MSIsensor detection software, focusing on the limitations of the chi-square test algorithm in determining microsatellite stability under high-depth sequencing data. We demonstrated that the chi-square test algorithm is insufficient for accurately as-sessing microsatellite stability in this context. Furthermore, we explored the application of downsampling techniques to enhance the accuracy of MSIsensor detection. Our findings provide insight into the limita-tions of current methods and offer potential improvements for more reliable MSI detection based on NGS data.
Shu: Visualization of high dimensional biological pathways
Authors: Jorge Carrasco Muriel, Nicholas Cowie, Marjan Mansouvar, Teddy Groves, Lars Keld Nielsen
Date: 2023-04-14 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Summary: Shu is a visualization tool that integrates diverse data types into a metabolic map, with a focus on supporting multiple conditions and visualizing distributions. The goal is to provide a unified platform for handling the growing volume of multi-omics data, leveraging the metabolic maps developed by the metabolic modeling community. Additionally, shu offers a streamlined python API, based on the Grammar of Graphics, for easy integration with data pipelines. Availability and implementation: Freely available at https://github.com/biosustain/shu under MIT/Apache 2.0 license. Binaries are available in the release page of the repository and the web app is deployed at https://biosustain.github.io/shu.
Upcrossing-rate dynamics for a minimal neuron model receiving spatially distributed synaptic drive
Authors: Robert P Gowers, Magnus J E Richardson
Date: 2023-04-17 00:00:00

The spatiotemporal stochastic dynamics of the voltage as well as the upcrossing rate are derived for a model neuron comprising a long dendrite with uniformly distributed filtered excitatory and inhibitory synaptic drive. A cascade of ordinary and partial differential equations is obtained describing the evolution of first-order means and second-order spatial covariances of the voltage and its rate of change. These quantities provide an analytical form for the general, steady-state and linear response of the upcrossing rate to dynamic synaptic input. It is demonstrated that this minimal dendritic model has an unexpectedly sustained high-frequency response despite synaptic, membrane and spatial filtering.
Hierarchical network structure as the source of hierarchical dynamics (power law frequency spectra) in living and non-living systems: how state-trait continua (body plans, personalities) emerge from first principles in biophysics
Authors: Rutger Goekoop, Roy de Kleijn
Date: 2023-04-14 00:00:00
Tasks: Sociology

Living systems are hierarchical control systems that display a small world network structure, in which many smaller clusters are nested within fewer larger ones, producing a fractal-like structure with a power-law cluster size distribution (a mereology). Apart from their structure, the dynamics of living systems also shows fractal-like qualities: the timeseries of inner message passing and overt behavior contain high frequencies or states (treble) that are nested within lower frequencies or traits (bass), producing a power-law frequency spectrum that is known as a state-trait continuum in the behavioral sciences. Here, we argue that the power-law dynamics of living systems results from their power-law network structure: organisms vertically encode the deep spatiotemporal structure of their (anticipated) environments, to the effect that many small clusters near the base of the hierarchy produce high frequency signal changes and fewer larger clusters at its top produce ultra-low frequencies. Such ultra-low frequencies produce physical as well as behavioral traits (i.e. body plans and personalities). Nested-modular structure then causes higher frequencies to be embedded within lower frequencies, producing a power law state-trait continuum. At the heart of such dynamics lies the need for efficient energy dissipation through networks of coupled oscillators, which also governs the dynamics of non-living systems (e.g. earthquakes, stock market fluctuations). Since hierarchical structure produces hierarchical dynamics, the development and collapse of hierarchical structure (e.g. during maturation and disease) should leave specific traces in the dynamics of nested modular systems that may serve as early warning signs to system failure. The applications of this idea range from (bio)physics and phylogenesis to ontogenesis and clinical medicine.
Comprehensive treatment and analysis of Fishpond sediments as a source of organic fertilizers
Authors: Olufunke Oyebamiji, Peter Balogun, Emmanuel Stephen, Najeem Oladosu, Abiodun Adeoso
Date: 2023-04-15 00:00:00

Agricultural fertilizers are essential to enhance proper growth and crop yield. Chemical fertilizers endanger ecosystems, soil, plants, and animal and human lives. This has increased interest in biofertilizers which are products that contain living microorganisms or natural compounds derived from organisms such as bacteria, fungi, and algae that improve soil chemical and biological properties, stimulate plant growth and restore soil fertility. This study aimed to use natural coagulants in the management of fishpond sediment for the potential recovery of nutrients that can be utilized in crop production as organic fertilizers. Fishpond wastewater and sediment were collected, treated with natural, powdered Moringa oleifera seed coagulant, and dried. Greenhouse evaluation of the dried sediment was carried out using maize seeds on soil primed with the sediment. Growth parameters such as plant height, leaf length, root length, plant weight, and percentage yield were determined. The nitrate, total nitrogen, phosphate, and total phosphorus content of the organic fertilizer were also determined. Plant height in maize showed a 35.8-44.4 % increase in fertilized maize and a 20.3-39.3 % increase in the leaf length. There was a 38.8-66 % increase in root length and a 23.9-36.5 % increase in plant weight in fertilized maize. The nutritive component was 59.80 mg/Kg, 655.56 mg/Kg, 103.87 mg/Kg, and 426.60 mg/Kg respectively. For a period of 21 days of active growth, the growth rate of maize primed with organic fertilizer was 0.93 cm/day with a total yield of 100 % while the control was 0.68 cm/day with a total yield of 50 %. A dried biofertilizer that can improve plant growth, weight, and percentage yield without having to deal with smelly manure looks promising and seems to be a better alternative to explore in agriculture.
Back to the future: a simplified and intuitive derivation of the Lotka-Euler equation
Authors: Carlos Hernandez-Suarez
Date: 2023-04-17 00:00:00

The Lotka-Euler equation is a mathematical expression used to study population dynamics and growth, particularly in the context of demography and ecology. The growth rate $\lambda$ is the speed at which an individual produce their offspring. It is essentially a birth process, and here it is shown that by reversing the process to a death process, in which individuals die at a rate $\lambda^{-1}$, the derivation of the Lotka-Euler equation becomes more intuitive and direct, both in discrete and continuous time.
Social Learning and the Exploration-Exploitation Tradeoff
Authors: Brian Mintz, Feng Fu
Date: 2023-04-13 00:00:00

Cultures around the world show varying levels of conservatism. While maintaining traditional ideas prevents wrong ones from being embraced, it also slows or prevents adaptation to new times. Without exploration there can be no improvement, but often this effort is wasted as it fails to produce better results, making it better to exploit the best known option. This tension is known as the exploration/exploitation issue, and it occurs at the individual and group levels, whenever decisions are made. As such, it is has been investigated across many disciplines. In this work, we investigate the balance between exploration and exploitation in changing environments by thinking of exploration as mutation in a trait space with a varying fitness function. Specifically, we study how exploration rates evolves by applying adaptive dynamics to the replicator-mutator equation, under two types of fitness functions. For the first, payoffs are accrued from playing a two-player, two-action symmetric game, we consider representatives of all games in this class and find exploration rates often evolve downwards, but can also undergo neutral selection as well. Second, we study time dependent fitness with a function having a single oscillating peak. By increasing the period, we see a jump in the optimal exploration rate, which then decreases towards zero. These results establish several possible evolutionary scenarios for exploration rates, providing insight into many applications, including why we can see such diversity in rates of cultural change.
Modeling Transient Changes in Circadian Rhythms
Authors: Ziyu Zhao, Dae-Sung Hwangbo, Sumit Saurabh, Clark Rosensweig, Ravi Allada, William L. Kath, Rosemary Braun
Date: 2023-04-14 00:00:00
Tasks: Model Selection, quantile regression

The circadian clock can adapt itself to external cues, but the molecular mechanisms and regulatory networks governing circadian oscillations' transient adjustments are still largely unknown. Here we consider the specific case of circadian oscillations transiently responding to a temperature change. Using a framework motivated by Floquet theory, we model the mRNA expression level of the fat body from Drosophila melanogaster following a step change from 25C to 18C. Using the method we infer the adaptation rates of individual genes as they adapt to the new temperature. To deal with heteroskedastic noise and outliers present in the expression data we employ quantile regression and wild bootstrap for significance testing. Model selection with finite-sample corrected Akaike Information Criterion (AICc) is performed additionally for robust inference. We identify several genes with fast transition rates as potential sources of temperature-mediated responses in the circadian system of fruit flies, and the constructed network suggests that the proteasome may play important roles in governing these responses.
Canalisation and plasticity on the developmental manifold of Caenorhabditis elegans
Authors: David J. Jordan, Eric A. Miska
Date: 2023-04-14 00:00:00

How do the same mechanisms that faithfully regenerate complex developmental programs in spite of environmental and genetic perturbations also permit responsiveness to environmental signals, adaptation, and genetic evolution? Using the nematode Caenorhabditis elegans as a model, we explore the phenotypic space of growth and development in various genetic and environmental contexts. Our data are growth curves and developmental parameters obtained by automated microscopy. Using these, we show that among the traits that make up the developmental space, correlations within a particular context are predictive of correlations among different contexts. Further we find that the developmental variability of this animal can be captured on a relatively low dimensional phenoptypic manifold and that on this manifold, genetic and environmental contributions to plasticity can be deconvolved independently. Our perspective offers a new way of understanding the relationship between robustness and flexibility in complex systems, suggesting that projection and concentration of dimension can naturally align these forces as complementary rather than competing.
Optimal Investment and Consumption Strategies with General and Linear Transaction Costs under CRRA Utility
Authors: Yingting Miao, Qiang Zhang
Date: 2023-04-16 00:00:00
Tasks: Management

Transaction costs play a critical role in asset allocation and consumption strategies in portfolio management. We apply the methods of dynamic programming and singular perturbation expansion to derive the closed-form leading solutions to this problem for small transaction costs with arbitrary transaction cost structure by maximizing the expected CRRA (constant relative risk aversion) utility function for this problem. We also discuss in detail the case which consists of both fixed and proportional transaction costs.
A Natural Copula
Authors: Peter B. Lerner
Date: 2023-04-13 00:00:00

Copulas are widely used in financial economics as well as in other areas of applied mathematics. Yet, there is much arbitrariness in their choice. The author proposes "a natural copula" concept, which minimizes Wasserstein distance between distributions in some space, in which both these distributions are embedded. Transport properties and hydrodynamic interpretation are discussed with two examples of distributions of financial significance. A natural copula can be parsimoniously estimated by the methods of linear programming.
Credit Risk and Financial Performance of Commercial Banks: Evidence from Vietnam
Authors: Ha Nguyen
Date: 2023-04-17 00:00:00

Credit risk is a crucial topic in the field of financial stability, especially at this time given the profound impact of the ongoing pandemic on the world economy. This study provides insight into the impact of credit risk on the financial performance of 26 commercial banks in Vietnam for the period from 2006 to 2016. The financial performance of commercial banks is measured by return on assets (ROA), return on equity (ROE), and Net interest margin (NIM); credit risk is measured by the Non-performing loan ratio (NPLR); control variables are measured by bank-specific characteristics, including bank size (SIZE), loan loss provision ratio (LLPR), and capital adequacy ratio (CAR), and macroeconomic factors such as annual gross domestic product (GDP) growth and annual inflation rate (INF). The assumption tests show that models have autocorrelation, non-constant variance, and endogeneity. Hence, a dynamic Difference Generalized Method of Moments (dynamic Difference GMM) approach is employed to thoroughly address these problems. The empirical results show that the financial performance of commercial banks measured by ROE and NIM persists from one year to the next. Furthermore, SIZE and NPLR variables have a significant negative effect on ROA and ROE but not on NIM. There is no evidence found in support of the LLPR and CAR variables on models. The effect of GDP growth is statistically significant and positive on ROA, ROE, and NIM, whereas the INF is only found to have a significant positive impact on ROA and NIM.
Structured Multifractal Scaling of the Principal Cryptocurrencies: Examination using a Self-Explainable Machine Learning
Authors: Foued Saâdaoui
Date: 2023-04-17 00:00:00

Multifractal analysis is a forecasting technique used to study the scaling regularity properties of financial returns, to analyze the long-term memory and predictability of financial markets. In this paper, we propose a novel structural detrended multifractal fluctuation analysis (S-MF-DFA) to investigate the efficiency of the main cryptocurrencies. The new methodology generalizes the conventional approach by allowing it to proceed on the different fluctuation regimes previously determined using a change-points detection test. In this framework, the characterization of the various exogenous factors influencing the scaling behavior is performed on the basis of a single-factor model, thus creating a kind of self-explainable machine learning for price forecasting. The proposal is tested on the daily data of the three among the main cryptocurrencies in order to examine whether the digital market has experienced upheavals in recent years and whether this has in some ways led to a structured multifractal behavior. The sampled period ranges from April 2017 to December 2022. We especially detect common periods of local scaling for the three prices with a decreasing multifractality after 2018. Complementary tests on shuffled and surrogate data prove that the distribution, linear correlation, and nonlinear structure also explain at some level the structural multifractality. Finally, prediction experiments based on neural networks fed with multi-fractionally differentiated data show the interest of this new self-explained algorithm, thus giving decision-makers and investors the ability to use it for more accurate and interpretable forecasts.
Optimizing Group Utility in Itinerary Planning: A Strategic and Crowd-Aware Approach
Authors: Junhua Liu, Kwan Hui Lim, Kristin L. Wood, Menglin Li
Date: 2023-04-04 00:00:00

Itinerary recommendation is a complex sequence prediction problem with numerous real-world applications. This task becomes even more challenging when considering the optimization of multiple user queuing times and crowd levels, as well as numerous involved parameters, such as attraction popularity, queuing time, walking time, and operating hours. Existing solutions typically focus on single-person perspectives and fail to address real-world issues resulting from natural crowd behavior, like the Selfish Routing problem. In this paper, we introduce the Strategic and Crowd-Aware Itinerary Recommendation (SCAIR) algorithm, which optimizes group utility in real-world settings. We model the route recommendation strategy as a Markov Decision Process and propose a State Encoding mechanism that enables real-time planning and allocation in linear time. We evaluate our algorithm against various competitive and realistic baselines using a theme park dataset, demonstrating that SCAIR outperforms these baselines in addressing the Selfish Routing problem across four theme parks.
Addressing Variable Dependency in GNN-based SAT Solving
Authors: Zhiyuan Yan, Min Li, Zhengyuan Shi, Wenjie Zhang, Yingcong Chen, Hongce Zhang
Date: 2023-04-18 00:00:00

Boolean satisfiability problem (SAT) is fundamental to many applications. Existing works have used graph neural networks (GNNs) for (approximate) SAT solving. Typical GNN-based end-to-end SAT solvers predict SAT solutions concurrently. We show that for a group of symmetric SAT problems, the concurrent prediction is guaranteed to produce a wrong answer because it neglects the dependency among Boolean variables in SAT problems. % We propose AsymSAT, a GNN-based architecture which integrates recurrent neural networks to generate dependent predictions for variable assignments. The experiment results show that dependent variable prediction extends the solving capability of the GNN-based method as it improves the number of solved SAT instances on large test sets.
Impact Of Explainable AI On Cognitive Load: Insights From An Empirical Study
Authors: Lukas-Valentin Herm
Date: 2023-04-18 00:00:00
Tasks: Explainable artificial intelligence, Explainable Artificial Intelligence (XAI)

While the emerging research field of explainable artificial intelligence (XAI) claims to address the lack of explainability in high-performance machine learning models, in practice, XAI targets developers rather than actual end-users. Unsurprisingly, end-users are often unwilling to use XAI-based decision support systems. Similarly, there is limited interdisciplinary research on end-users' behavior during XAI explanations usage, rendering it unknown how explanations may impact cognitive load and further affect end-user performance. Therefore, we conducted an empirical study with 271 prospective physicians, measuring their cognitive load, task performance, and task time for distinct implementation-independent XAI explanation types using a COVID-19 use case. We found that these explanation types strongly influence end-users' cognitive load, task performance, and task time. Further, we contextualized a mental efficiency metric, ranking local XAI explanation types best, to provide recommendations for future applications and implications for sociotechnical XAI research.
PaTeCon: A Pattern-Based Temporal Constraint Mining Method for Conflict Detection on Knowledge Graphs
Authors: Jianhao Chen, Junyang Ren, Wentao Ding, Yuzhong Qu
Date: 2023-04-18 00:00:00
Tasks: Knowledge Graphs, Management

Temporal facts, the facts for characterizing events that hold in specific time periods, are attracting rising attention in the knowledge graph (KG) research communities. In terms of quality management, the introduction of time restrictions brings new challenges to maintaining the temporal consistency of KGs and detecting potential temporal conflicts. Previous studies rely on manually enumerated temporal constraints to detect conflicts, which are labor-intensive and may have granularity issues. We start from the common pattern of temporal facts and constraints and propose a pattern-based temporal constraint mining method, PaTeCon. PaTeCon uses automatically determined graph patterns and their relevant statistical information over the given KG instead of human experts to generate time constraints. Specifically, PaTeCon dynamically attaches class restriction to candidate constraints according to their measuring scores.We evaluate PaTeCon on two large-scale datasets based on Wikidata and Freebase respectively. The experimental results show that pattern-based automatic constraint mining is powerful in generating valuable temporal constraints.