pwc-archive/papers-with-abstracts huggingface dataset:
Miniature UAV Empowered Reconfigurable Energy Harvesting Holographic Surfaces in THz Cooperative Networks
Authors: Yifei Song, Jalal Jalali, Filip Lemic, Natasha Devroye, Jeroen Famaey
Date: 2024-11-27 00:00:00

This paper focuses on enhancing the energy efficiency (EE) of a cooperative network featuring a `miniature' unmanned aerial vehicle (UAV) that operates at terahertz (THz) frequencies, utilizing holographic surfaces to improve the network's performance. Unlike traditional reconfigurable intelligent surfaces (RIS) that are typically used as passive relays to adjust signal reflections, this work introduces a novel concept: Energy harvesting (EH) using reconfigurable holographic surfaces (RHS) mounted on the miniature UAV. In this system, a source node facilitates the simultaneous reception of information and energy signals by the UAV, with the harvested energy from the RHS being used by the UAV to transmit data to a specific destination. The EE optimization involves adjusting non-orthogonal multiple access (NOMA) power coefficients and the UAV's flight path, considering the peculiarities of the THz channel. The optimization problem is solved in two steps. Initially, the trajectory is refined using a successive convex approximation (SCA) method, followed by the adjustment of NOMA power coefficients through a quadratic transform technique. The effectiveness of the proposed algorithm is demonstrated through simulations, showing superior results when compared to baseline methods.
Traits and tangles: An analysis of the Big Five paradigm by tangle-based clustering
Authors: Hanno von Bergen, Reinhard Diestel
Date: 2024-11-27 00:00:00

Using the recently developed mathematical theory of tangles, we re-assess the mathematical foundations for applications of the five factor model in personality tests by a new, mathematically rigorous, quantitative method. Our findings broadly confirm the validity of current tests, but also show that more detailed information can be extracted from existing data. We found that the big five traits appear at different levels of scrutiny. Some already emerge at a coarse resolution of our tools at which others cannot yet be discerned, while at a resolution where these _can_ be discerned, and distinguished, some of the former traits are no longer visible but have split into more refined traits or disintegrated altogether. We also identified traits other than the five targeted in those tests. These include more general traits combining two or more of the big five, as well as more specific traits refining some of them. All our analysis is structural and quantitative, and thus rigorous in explicitly defined mathematical terms. Since tangles, once computed, can be described concisely in terms of very few explicit statements referring only to the test questions used, our findings are also directly open to interpretation by experts in psychology. Tangle analysis can be applied similarly to other topics in psychology. Our paper is intended to serve as a first indication of what may be possible.
Ergodic optimal liquidations in DeFi
Authors: Jialun Cao, David Šiška
Date: 2024-11-29 00:00:00
Tasks: Management

We address the liquidation problem arising from the credit risk management in decentralised finance (DeFi) by formulating it as an ergodic optimal control problem. In decentralised derivatives exchanges, liquidation is triggered whenever the parties fail to maintain sufficient collateral for their open positions. Consequently, effectively managing and liquidating disposal of positions accrued through liquidations is a critical concern for decentralised derivatives exchanges. By simplifying the model (linear temporary and permanent price impacts, simplified cash balance dynamics), we derive the closed-form solutions for the optimal liquidation strategies, which balance immediate executions with the temporary and permanent price impacts, and the optimal long-term average reward. Numerical simulations further highlight the effectiveness of the proposed optimal strategy and demonstrate that the simplified model closely approximates the original market environment. Finally, we provide the method for calibrating the parameters in the model from the available data.
Capital Asset Pricing Model with Size Factor and Normalizing by Volatility Index
Authors: Abraham Atsiwo, Andrey Sarantsev
Date: 2024-11-29 00:00:00

The Capital Asset Pricing Model (CAPM) relates a well-diversified stock portfolio to a benchmark portfolio. We insert size effect in CAPM, capturing the observation that small stocks have higher risk and return than large stocks, on average. Dividing stock index returns by the Volatility Index makes them independent and normal. In this article, we combine these ideas to create a new discrete-time model, which includes volatility, relative size, and CAPM. We fit this model using real-world data, prove the long-term stability, and connect this research to Stochastic Portfolio Theory. We fill important gaps in our previous article on CAPM with the size factor.
Double Descent in Portfolio Optimization: Dance between Theoretical Sharpe Ratio and Estimation Accuracy
Authors: Yonghe Lu, Yanrong Yang, Terry Zhang
Date: 2024-11-28 00:00:00
Tasks: Inductive Bias, Portfolio Optimization

We study the relationship between model complexity and out-of-sample performance in the context of mean-variance portfolio optimization. Representing model complexity by the number of assets, we find that the performance of low-dimensional models initially improves with complexity but then declines due to overfitting. As model complexity becomes sufficiently high, the performance improves with complexity again, resulting in a double ascent Sharpe ratio curve similar to the double descent phenomenon observed in artificial intelligence. The underlying mechanisms involve an intricate interaction between the theoretical Sharpe ratio and estimation accuracy. In high-dimensional models, the theoretical Sharpe ratio approaches its upper limit, and the overfitting problem is reduced because there are more parameters than data restrictions, which allows us to choose well-behaved parameters based on inductive bias.
Neuroplasticity and Psychedelics: a comprehensive examination of classic and non-classic compounds in pre and clinical models
Authors: Claudio Agnorelli, Meg Spriggs, Kate Godfrey, Gabriela Sawicka, Bettina Bohl, Hannah Douglass, Andrea Fagiolini, Hashemi Parastoo, Robin Carhart-Harris, David Nutt, David Erritzoe
Date: 2024-11-29 00:00:00

Neuroplasticity, the ability of the nervous system to adapt throughout an organism's lifespan, offers potential as both a biomarker and treatment target for neuropsychiatric conditions. Psychedelics, a burgeoning category of drugs, are increasingly prominent in psychiatric research, prompting inquiries into their mechanisms of action. Distinguishing themselves from traditional medications, psychedelics demonstrate rapid and enduring therapeutic effects after a single or few administrations, believed to stem from their neuroplasticity-enhancing properties. This review examines how classic psychedelics (e.g., LSD, psilocybin, N,N-DMT) and non-classic psychedelics (e.g., ketamine, MDMA) influence neuroplasticity. Drawing from preclinical and clinical studies, we explore the molecular, structural, and functional changes triggered by these agents. Animal studies suggest psychedelics induce heightened sensitivity of the nervous system to environmental stimuli (meta-plasticity), re-opening developmental windows for long-term structural changes (hyper-plasticity), with implications for mood and behavior. Translating these findings to humans faces challenges due to limitations in current imaging techniques. Nonetheless, promising new directions for human research are emerging, including the employment of novel positron-emission tomography (PET) radioligands, non-invasive brain stimulation methods, and multimodal approaches. By elucidating the interplay between psychedelics and neuroplasticity, this review informs the development of targeted interventions for neuropsychiatric disorders and advances understanding of psychedelics' therapeutic potential.
MAFcounter: An efficient tool for counting the occurrences of k-mers in MAF files
Authors: Michail Patsakis, Kimonas Provatas, Ioannis Mouratidis, Ilias Georgakopoulos-Soares
Date: 2024-11-29 00:00:00

Motivation: With the rapid expansion of large-scale biological datasets, DNA and protein sequence alignments have become essential for comparative genomics and proteomics. These alignments facilitate the exploration of sequence similarity patterns, providing valuable insights into sequence conservation, evolutionary relationships and for functional analyses. Typically, sequence alignments are stored in formats such as the Multiple Alignment Format (MAF). Counting k-mer occurrences is a crucial task in many computational biology applications, but currently, there is no algorithm designed for k-mer counting in alignment files. Results: We have developed MAFcounter, the first k-mer counter dedicated to alignment files. MAFcounter is multithreaded, fast, and memory efficient, enabling k-mer counting in DNA and protein sequence alignment files. Availability: The MAFcounter package and its Python bindings are released under GPL license as a multi-platform application and are available at: https://github.com/Georgakopoulos-Soares-lab/MAFcounter
Decoding Imagined Movement in People with Multiple Sclerosis for Brain-Computer Interface Translation
Authors: John S. Russo, Thomas A. Shiels, Chin-Hsuan Sophie Lin, Sam E. John, David B. Grayden
Date: 2024-11-28 00:00:00
Tasks: Brain Computer Interface, EEG, Motor Imagery

Multiple Sclerosis (MS) is a heterogeneous autoimmune-mediated disorder affecting the central nervous system, commonly manifesting as fatigue and progressive limb impairment. This can significantly impact quality of life due to weakness or paralysis in the upper and lower limbs. A Brain-Computer Interface (BCI) aims to restore quality of life through control of an external device, such as a wheelchair. However, the limited BCI research in people with MS is insufficient. The current study aims to expand on the current MS-BCI literature by highlighting the feasibility of decoding MS imagined movement. We collected electroencephalography (EEG) data from eight participants with various symptoms of MS and ten neurotypical control participants. Participants made imagined movements of the hands and feet as directed by a go no-go protocol. Binary regularised linear discriminant analysis was used to classify imagined movement at individual time-frequency points. The frequency bands which provided the maximal accuracy, and the associated latency, were compared. In all MS participants, the classification algorithm achieved above 70% accuracy in at least one imagined movement vs. rest classification and most movement vs. movement classifications. There was no significant difference between classification of limbs with weakness or paralysis to neurotypical controls. Both the MS and control groups possessed decodable information within the alpha (7-13 Hz) and beta (16-30 Hz) bands at similar latency. This study is the first to demonstrate the feasibility of decoding imagined movements in people with MS. As an alternative to the P300 response, motor imagery-based control of a BCI may also be combined with existing motor imagery therapy to supplement MS rehabilitation. These promising results merit further long term BCI studies to investigate the effect of MS progression on classification performance.
A method for finding distribution of metabolic energy between organismal functions: application to birds' energy expenditures to counteract gravity and to support steady and short flights
Authors: Yuri K Shestopaloff
Date: 2024-11-28 00:00:00

Production of energy (metabolism) and its distribution is vital for living organisms, both at individual level - between different functions of an organism, as well as between species of communities at different organizational levels, including food chains. Here, a new general method for finding distribution of metabolic energy between different organismal functions is proposed. The method is based on earlier discoveries (in two independent studies, for multicellular and unicellular organisms) that metabolic allometric scaling is the result of natural selection guided by optimization of distribution of common resources between the species of a food chain. This distribution is established in such a way that it secures amount of resources for each species to reproduce in sufficient quantities (sufficient for preservation of a food chain), while not allowing some species acquiring too many resources to jeopardize existence of other species they prey on or share common resources with. The introduced method was applied to birds, including both steady and short flights modes. Birds' metabolism has specifics, because besides other functions, it has to compensate force of gravity during the flight. However, this parameter is difficult to find, while such data is important for ecological studies at population and individual levels. We discovered increase of fraction of metabolic power required to compensate force of gravity with mass increase, which is a principal factor, restricting maximum possible mass of flying animals. The obtained results show efficiency of a proposed method for detailed studies of animals' metabolism, physiology, and of population ecology.
Population dynamics in the global coral symbiont network under temperature variations
Authors: Maria Gabriella Cavalcante Basílio, Daniel Ratton Figueiredo
Date: 2024-11-28 00:00:00

Coral reefs are crucial to marine biodiversity and rely on a delicate symbiotic relationship between corals and zooxanthellae algae. Water temperature variations, however, disrupt this association, leading to coral bleaching events that severely affect marine ecosystems. This study presents a mathematical model for the population dynamics of coral and symbiont species considering the coral symbiont network and recurrent warming events. The model incorporates thermal tolerances of species and coupled growth dynamics (between corals and symbionts) to investigate how network structure and thermal tolerance influence the species' growth. Using real data from different ocean regions, results reveal that network connectivity plays a significant role in population growth after successive warming events, with generalist species demonstrating greater growth across all regions analyzed. The comparatively higher correlation between node degree and final population also emphasizes the impact of ecological network structure on species growth, offering valuable insights into coral reef population dynamics under climate change. This research highlights the need to consider network structure beyond species' thermal tolerances when evaluating the ecological responses of corals to environmental changes.
Multistage spatial model for informing release of Wolbachia-infected mosquitoes as disease control
Authors: Zhuolin Qu, Tong Wu
Date: 2024-11-27 00:00:00

Wolbachia is a naturally occurring bacterium that can infect Aedes mosquitoes and reduce the transmission of mosquito-borne diseases, including dengue fever, Zika, and chikungunya. Field trials have been conducted worldwide to suppress local epidemics. We introduce a novel partial differential equation model to simulate the spread of Wolbachia infection in mosquito populations. Our model incorporates the intricate Wolbachia maternal transmission cycle and detailed mosquito life stages, while also accounting for the spatial heterogeneity induced by mosquito dispersion across a two-dimensional domain. Prior modeling studies and field data indicate that a critical threshold of Wolbachia-infected mosquitoes is necessary for infection to persist among the mosquito population. Through our spatial model, we identify a threshold condition, termed the ``critical bubble'', for having a self-sustainable Wolbachia infection in the field. When releasing beyond this threshold, the model predicts a spatial wave of Wolbachia infection. We further quantify how this threshold and infection wave velocity depend on the diffusion process and other parameters. We numerically study various intervention scenarios to inform efficient Wolbachia release strategies. Our findings suggest that: (1) integrating Wolbachia release with pre-release mitigations targeting the adult mosquitoes, rather than the aquatic stages, better reduces the threshold for Wolbachia establishment; Habitats modification before the release may increase the threshold; (2) releases in the dry regions lower the threshold, though the infection waves may slow down or stall at the dry-wet interfaces due to the difference in carrying capacities; and (3) initiating releases just before the wet season further reduces the release threshold.
Per-event Uncertainty Quantification for Flow Cytometry using Calibration Beads
Authors: Prajakta Bedekar, Megan A. Catterton, Matthew DiSalvo, Gregory A. Cooksey, Anthony J. Kearsley, Paul N. Patrone
Date: 2024-11-28 00:00:00
Tasks: Decision Making, Uncertainty Quantification

Flow cytometry measurements are widely used in diagnostics and medical decision making. Incomplete understanding of sources of measurement uncertainty can make it difficult to distinguish autofluorescence and background sources from signals of interest. Moreover, established methods for modeling uncertainty overlook the fact that the apparent distribution of measurements is a convolution of the inherent the population variability (e.g., associated with calibration beads or cells) and instrument induced-effects. Such issues make it difficult, for example, to identify signals from small objects such as extracellular vesicles. To overcome such limitations, we formulate an explicit probabilistic measurement model that accounts for volume and labeling variation, background signals and fluorescence shot noise. Using raw data from routine per-event calibration measurements, we use this model to separate the aforementioned sources of uncertainty and demonstrate how such information can be used to facilitate decision-making and instrument characterization.
Comparative analysis of whole-body center-of-mass estimation methods in dynamic and static activities using marker-based systems
Authors: Jingshu Peng, Mohsen Alizadeh Noghani, Edgar Bolívar-Nieto
Date: 2024-11-27 00:00:00
Tasks: State Estimation

Accurate estimation of the whole-body center of mass (CoM) is essential for assessing human stability and postural control. However, selecting the most accurate estimation method is challenging due to the complexity of human movement, diverse nature of activities, and varying availability of equipment, such as marker-based systems and ground reaction force (GRF) sensors. This study compares three CoM estimation methods -- "Pelvis Markerset", "Whole-Body Markerset", and "Whole-Body Markerset & GRFs" -- across static activities, such as standing with eyes closed, and dynamic activities, such as picking up an object from the ground. Using the root mean square (RMS) of "external force residual" (the difference between measured ground reaction forces and estimated CoM accelerations multiplied by total body mass) as a performance metric, we found that while all methods performed similarly under static conditions, the "Pelvis Markerset" method showed 96% to 104% higher RMS external force residual values during dynamic activities compared to the two whole-body methods ($p<0.001$, Cohen's $d$:2.90-3.04). The accuracy of "Whole-Body Markerset & GRFs" (i.e., Kalman filter) was similar to "Whole-Body Markerset", suggesting that incorporating the GRFs through the presented Kalman filter does not improve the estimates from whole-body kinematics. Based on these findings, we recommend the "Whole-Body Markerset" as it performs well and does not require information from GRFs. The "Pelvis Markerset" method can be used in static activities or when markersets around the pelvis reflect whole-body kinematics. This method is not recommended for CoM state estimation in highly dynamic scenarios and when whole-body markersets are available.
A quantum inspired predictor of Parkinsons disease built on a diverse, multimodal dataset
Authors: Diya Vatsavai, Anya Iyer, Ashwin A. Nair
Date: 2024-11-25 00:00:00
Tasks: Quantum Machine Learning

Parkinsons disease, the fastest growing neurodegenerative disorder globally, has seen a 50 percent increase in cases within just two years. As speech, memory, and motor symptoms worsen over time, early diagnosis is crucial for preserving patients quality of life. While machine-learning-based detection has shown promise, relying on a single feature for classification can be error-prone due to the variability of symptoms between patients. To address this limitation we utilized the mPower database, which includes 150,000 samples across four key biomarkers: voice, gait, tapping, and demographic data. From these measurements, we extracted 64 features and trained a baseline Random Forest model to select the features above the 80th percentile. For classification, we designed a simulatable quantum support vector machine (qSVM) that detects high-dimensional patterns, leveraging recent advancements in quantum machine learning. With a novel, simulatable architecture that can be run on standard hardware rather than resource-intensive quantum computers, our model achieves an accuracy of 90 percent and an AUC of 0.98, surpassing benchmark models. By utilizing an innovative classification framework built on a diverse set of features, our model offers a pathway for accessible global Parkinsons screening.
Dynamic ETF Portfolio Optimization Using enhanced Transformer-Based Models for Covariance and Semi-Covariance Prediction(Work in Progress)
Authors: Jiahao Zhu, Hengzhi Wu
Date: 2024-11-29 00:00:00
Tasks: Management, Portfolio Optimization

This study explores the use of Transformer-based models to predict both covariance and semi-covariance matrices for ETF portfolio optimization. Traditional portfolio optimization techniques often rely on static covariance estimates or impose strict model assumptions, which may fail to capture the dynamic and non-linear nature of market fluctuations. Our approach leverages the power of Transformer models to generate adaptive, real-time predictions of asset covariances, with a focus on the semi-covariance matrix to account for downside risk. The semi-covariance matrix emphasizes negative correlations between assets, offering a more nuanced approach to risk management compared to traditional methods that treat all volatility equally. Through a series of experiments, we demonstrate that Transformer-based predictions of both covariance and semi-covariance significantly enhance portfolio performance. Our results show that portfolios optimized using the semi-covariance matrix outperform those optimized with the standard covariance matrix, particularly in volatile market conditions. Moreover, the use of the Sortino ratio, a risk-adjusted performance metric that focuses on downside risk, further validates the effectiveness of our approach in managing risk while maximizing returns. These findings have important implications for asset managers and investors, offering a dynamic, data-driven framework for portfolio construction that adapts more effectively to shifting market conditions. By integrating Transformer-based models with the semi-covariance matrix for improved risk management, this research contributes to the growing field of machine learning in finance and provides valuable insights for optimizing ETF portfolios.