pwc-archive/papers-with-abstracts huggingface dataset:
Enhancing Epileptic Seizure Detection with EEG Feature Embeddings
Authors: Arman Zarei, Bingzhao Zhu, Mahsa Shoaran
Date: 2023-10-28 00:00:00
Tasks: EEG, Seizure Detection, Sensitivity, Specificity

Epilepsy is one of the most prevalent brain disorders that disrupts the lives of millions worldwide. For patients with drug-resistant seizures, there exist implantable devices capable of monitoring neural activity, promptly triggering neurostimulation to regulate seizures, or alerting patients of potential episodes. Next-generation seizure detection systems heavily rely on high-accuracy machine learning-based classifiers to detect the seizure onset. Here, we propose to enhance the seizure detection performance by learning informative embeddings of the EEG signal. We empirically demonstrate, for the first time, that converting raw EEG signals to appropriate embeddings can significantly boost the performance of seizure detection algorithms. Importantly, we show that embedding features, which converts the raw EEG into an alternative representation, is beneficial for various machine learning models such as Logistic Regression, Multi-Layer Perceptron, Support Vector Machines, and Gradient Boosted Trees. The experiments were conducted on the CHB-MIT scalp EEG dataset. With the proposed EEG feature embeddings, we achieve significant improvements in sensitivity, specificity, and AUC score across multiple models. By employing this approach alongside an SVM classifier, we were able to attain state-of-the-art classification performance with a sensitivity of 100% and specificity of 99%, setting a new benchmark in the field.
Cluster-Based Cell-Free Massive MIMO Systems: A Novel Framework to Enhance Spectral Efficiency with Low Complexity
Authors: Reza Roshanghias, Reza Saadat
Date: 2023-10-28 00:00:00

The issue of diminished spectral efficiency (SE) of the downlink (DL) transmission in distributed cell-free massive MIMO (CF-mMIMO) systems poses a significant challenge in terms of user equipment (UE) performance when compared to their centralized CF-mMIMO counterparts. The primary root cause of this issue can be attributed to the reduced efficacy of distributed precoders, which are devised using local channel state information (CSI) in distributed systems. This reduced efficacy becomes particularly pronounced in terms of interference mitigation when compared to centralized precoders. To address this issue, this paper proposes a novel architectural framework for CF-mMIMO systems, referred to herein as the "cluster-based structure." Within this innovative structure, a hybrid amalgamation of centralized and distributed configurations is employed, complemented by the introduction of a unique cluster arrangement for the access points (APs) within the network. In this design, the CSI of APs within each cluster is collectively shared within a local processor unit. Consequently, by harnessing this enhanced repository of local channel information, local precoders are formulated, which facilitate more effective interference mitigation with reduced computational complexity compared to the centralized approach. This approach ultimately results in a significantly augmented SE when contrasted with the distributed architecture. The simulation results unequivocally demonstrate that within the cluster-based framework, the optimal SE for the network is attained when utilizing four clusters in conjunction with the MMSE precoding technique, leading to a notable reduction in computational complexity exceeding 85%. Importantly, this approach surpasses the SE performance of the centralized structure.
Two-stage space construction for real-time modeling of distributed parameter systems under sparse sensing
Authors: Peng Wei
Date: 2023-10-28 00:00:00

Numerous industrial processes can be defined using distributed parameter systems (DPSs). This study introduces a two-stage spatial construction approach for real-time modeling of DPSs in cases of limited sensors. Initially, a discrete space-completion approach is created to recuperate the spatiotemporal patterns of non-monitored locations under sparse sensing. The high-dimensional space construction method is employed to derive continuous spatial basis functions (SBFs). The identification and adjustment of the nonlinear temporal model are carried out via the long short-term memory (LSTM) neural network. Eventually, the amalgamation of the derived SBFs and temporal model results in a spatially continuous model. The use of a cubic B-spline surface is validated as an effective solution for optimizing space construction in the sense of least squares approximation. Experimental tests conducted on a pouch-type Li-ion battery demonstrate the efficacy of the proposed modeling technique under sparse sensing. This work highlights the promise of sparse sensors in real-time full-space modeling for large-scale battery energy storage systems.
A Generalized Statistical Model for THz wireless Channel with Random Atmospheric Absorption
Authors: Pranay Bhardwaj, S. M. Zafaruddin
Date: 2023-10-28 00:00:00

Current statistical channel models for Terahertz (THz) wireless communication primarily concentrate on the sub-THz band, mostly with $\alpha$-$\mu$ and Gaussian mixture fading distributions for short-term fading and deterministic modeling for atmospheric absorption. In this paper, we develop a generalized statistical model for signal propagation at THz frequencies considering random path-loss employing Gamma distribution for the molecular absorption coefficient, short-term fading characterized by the $\alpha$-$\eta$-$\kappa$-$\mu$ distribution, antenna misalignment errors, and transceiver hardware impairments. The proposed model can handle various propagation scenarios, including indoor and outdoor environments, backhaul/fronthaul situations, and complex urban settings. Using Fox's H-functions, we present the probability density function (PDF) and cumulative distribution function (CDF) that capture the combined statistical effects of channel impairments. We analyze the outage probability of a THz link to demonstrate the analytical tractability of the proposed generalized model. We present computer simulations to demonstrate the efficacy of the proposed model for performance assessment with the statistical effect of atmospheric absorption.
Integration of persistent Laplacian and pre-trained transformer for protein solubility changes upon mutation
Authors: JunJie Wee, Jiahui Chen, Kelin Xia, Guo-Wei Wei
Date: 2023-10-28 00:00:00

Protein mutations can significantly influence protein solubility, which results in altered protein functions and leads to various diseases. Despite of tremendous effort, machine learning prediction of protein solubility changes upon mutation remains a challenging task as indicated by the poor scores of normalized Correct Prediction Ratio (CPR). Part of the challenge stems from the fact that there is no three-dimensional (3D) structures for the wild-type and mutant proteins. This work integrates persistent Laplacians and pre-trained Transformer for the task. The Transformer, pretrained with hunderds of millions of protein sequences, embeds wild-type and mutant sequences, while persistent Laplacians track the topological invariant change and homotopic shape evolution induced by mutations in 3D protein structures, which are rendered from AlphaFold2. The resulting machine learning model was trained on an extensive data set labeled with three solubility types. Our model outperforms all existing predictive methods and improves the state-of-the-art up to 15%.
Estimating Systemic Risk within Financial Networks: A Two-Step Nonparametric Method
Authors: Weihuan Huang
Date: 2023-10-28 00:00:00

CoVaR (conditional value-at-risk) is a crucial measure for assessing financial systemic risk, which is defined as a conditional quantile of a random variable, conditioned on other random variables reaching specific quantiles. It enables the measurement of risk associated with a particular node in financial networks, taking into account the simultaneous influence of risks from multiple correlated nodes. However, estimating CoVaR presents challenges due to the unobservability of the multivariate-quantiles condition. To address the challenges, we propose a two-step nonparametric estimation approach based on Monte-Carlo simulation data. In the first step, we estimate the unobservable multivariate-quantiles using order statistics. In the second step, we employ a kernel method to estimate the conditional quantile conditional on the order statistics. We establish the consistency and asymptotic normality of the two-step estimator, along with a bandwidth selection method. The results demonstrate that, under a mild restriction on the bandwidth, the estimation error arising from the first step can be ignored. Consequently, the asymptotic results depend solely on the estimation error of the second step, as if the multivariate-quantiles in the condition were observable. Numerical experiments demonstrate the favorable performance of the two-step estimator.
Comparative Analysis of Plastid Genomes Using Pangenome Research ToolKit (PGR-TK)
Authors: Richa Jayanti, Andrew Kim, Sean Pham, Athreya Raghavan, Anish Sharma, Manoj P. Samanta
Date: 2023-10-29 00:00:00

Plastid genomes (plastomes) of angiosperms are of great interest among biologists. High-throughput sequencing is making many such genomes accessible, increasing the need for tools to perform rapid comparative analysis. This exploratory analysis investigates whether the Pangenome Research Tool Kit (PGR-TK) is suitable for analyzing plastomes. After determining the optimal parameters for this tool on plastomes, we use it to compare sequences from each of the genera - Magnolia, Solanum, Fragaria and Cotoneaster, as well as a combined set from 20 rosid genera. PGR-TK recognizes large-scale plastome structures, such as the inverted repeats, among combined sequences from distant rosid families. If the plastid genomes are rotated to the same starting point, it also correctly groups different species from the same genus together in a generated cladogram. The visual approach of PGR-TK provides insights into genome evolution without requiring gene annotations.
Forum on immune digital twins: a meeting report
Authors: Reinhard Laubenbacher, Fred Adler, Gary An, Filippo Castiglione, Stephen Eubank, Luis L. Fonseca, James Glazier, Tomas Helikar, Marti Jett-Tilton, Denise Kirschner, Paul Macklin, Borna Mehrad, Beth Moore, Virginia Pasour, Ilya Shmulevich, Amber Smith, Isabel Voigt, Thomas E. Yankeelov, Tjalf Ziemssen
Date: 2023-10-26 00:00:00

Medical digital twins are computational models of human biology relevant to a given medical condition, which can be tailored to an individual patient, thereby predicting the course of disease and individualized treatments, an important goal of personalized medicine. The immune system, which has a central role in many diseases, is highly heterogeneous between individuals, and thus poses a major challenge for this technology. If medical digital twins are to faithfully capture the characteristics of a patient's immune system, we need to answer many questions, such as: What do we need to know about the immune system to build mathematical models that reflect features of an individual? What data do we need to collect across the different scales of immune system action? What are the right modeling paradigms to properly capture immune system complexity? In February 2023, an international group of experts convened in Lake Nona, FL for two days to discuss these and other questions related to digital twins of the immune system. The group consisted of clinicians, immunologists, biologists, and mathematical modelers, representative of the interdisciplinary nature of medical digital twin development. A video recording of the entire event is available. This paper presents a synopsis of the discussions, brief descriptions of ongoing digital twin projects at different stages of progress. It also proposes a 5-year action plan for further developing this technology. The main recommendations are to identify and pursue a small number of promising use cases, to develop stimulation-specific assays of immune function in a clinical setting, and to develop a database of existing computational immune models, as well as advanced modeling technology and infrastructure.
Predicting RNA-small molecule binding sites by 3D structure
Authors: Nan Pan
Date: 2023-10-29 00:00:00
Tasks: Drug Discovery

The prediction of RNA-small molecule binding sites is crucial for the discovery of effective drugs. Various computational methods have been developed to address this challenge, using information about the structure and sequence of RNA. In this study, we introduce CplxCavity, a combination of a new algorithm and a machine learning model specifically designed to predict RNA-small molecule binding sites. CplxCavity leverages the 3D structure of RNA or RNA complexes to identify surface cavities that have the potential to bind with small molecules. Our results demonstrate that CplxCavity outperforms existing methods by accurately identifying binding sites for small molecules on RNA or RNA complexes. The introduction of CplxCavity represents a significant advancement in computational tools for studying RNA-ligand interactions, and offers promising prospects for accelerating drug discovery and the development of therapies targeting RNA.
BioImage.IO Chatbot: A Community-Driven AI Assistant for Integrative Computational Bioimaging
Authors: Wanlu Lei, Caterina Fuster-Barceló, Gabriel Reder, Arrate Muñoz-Barrutia, Wei Ouyang
Date: 2023-10-23 00:00:00
Tasks: Chatbot, Information Retrieval, Navigate, Retrieval

We present the BioImage$.$IO Chatbot, an AI assistant powered by Large Language Models and supported by a community-driven knowledge base and toolset. This chatbot is designed to cater to a wide range of user needs through a flexible extension mechanism that spans from information retrieval to AI-enhanced analysis and microscopy control. Embracing open-source principles, the chatbot is designed to evolve through community contributions. By simplifying navigation through the intricate bioimaging landscape, the BioImage$.$IO Chatbot empowers life sciences to progress by leveraging the collective expertise and innovation of its users.
Deeper Hedging: A New Agent-based Model for Effective Deep Hedging
Authors: Kang Gao, Stephen Weston, Perukrishnen Vytelingum, Namid R. Stillman, Wayne Luk, Ce Guo
Date: 2023-10-28 00:00:00

We propose the Chiarella-Heston model, a new agent-based model for improving the effectiveness of deep hedging strategies. This model includes momentum traders, fundamental traders, and volatility traders. The volatility traders participate in the market by innovatively following a Heston-style volatility signal. The proposed model generalises both the extended Chiarella model and the Heston stochastic volatility model, and is calibrated to reproduce as many empirical stylized facts as possible. According to the stylised facts distance metric, the proposed model is able to reproduce more realistic financial time series than three baseline models: the extended Chiarella model, the Heston model, and the Geometric Brownian Motion. The proposed model is further validated by the Generalized Subtracted L-divergence metric. With the proposed Chiarella-Heston model, we generate a training dataset to train a deep hedging agent for optimal hedging strategies under various transaction cost levels. The deep hedging agent employs the Deep Deterministic Policy Gradient algorithm and is trained to maximize profits and minimize risks. Our testing results reveal that the deep hedging agent, trained with data generated by our proposed model, outperforms the baseline in most transaction cost levels. Furthermore, the testing process, which is conducted using empirical data, demonstrates the effective performance of the trained deep hedging agent in a realistic trading environment.
Visibility graph analysis of crude oil futures markets: Insights from the COVID-19 pandemic and Russia-Ukraine conflict
Authors: Yan-Hong Yang, Ying-Lin Liu, Ying-Hui Shao
Date: 2023-10-29 00:00:00
Tasks: Clustering

Drawing inspiration from the significant impact of the ongoing Russia-Ukraine conflict and the recent COVID-19 pandemic on global financial markets, this study conducts a thorough analysis of three key crude oil futures markets: WTI, Brent, and Shanghai (SC). Employing the visibility graph (VG) methodology, we examine both static and dynamic characteristics using daily and high-frequency data. We identified a clear power-law decay in most VG degree distributions and highlighted the pronounced clustering tendencies within crude oil futures VGs. Our results also confirm an inverse correlation between clustering coefficient and node degree and further reveal that all VGs not only adhere to the small-world property but also exhibit intricate assortative mixing. Through the time-varying characteristics of VGs, we found that WTI and Brent demonstrate aligned behavior, while the SC market, with its unique trading mechanics, deviates. The 5-minute VGs' assortativity coefficient provides a deeper understanding of these markets' reactions to the pandemic and geopolitical events. Furthermore, the differential responses during the COVID-19 and Russia-Ukraine conflict underline the unique sensitivities of each market to global disruptions. Overall, this research offers profound insights into the structure, dynamics, and adaptability of these essential commodities markets in the face of worldwide challenges.
Optimal fees in hedge funds with first-loss compensation
Authors: Marcos Escobar-Anel, Yevhen Havrylenko, Rudi Zagst
Date: 2023-10-29 00:00:00
Tasks: Management

Hedge fund managers with the first-loss scheme charge a management fee, a performance fee and guarantee to cover a certain amount of investors' potential losses. We study how parties can choose a mutually preferred first-loss scheme in a hedge fund with the manager's first-loss deposit and investors' assets segregated. For that, we solve the manager's non-concave utility maximization problem, calculate Pareto optimal first-loss schemes and maximize a decision criterion on this set. The traditional 2% management and 20% performance fees are found to be not Pareto optimal, neither are common first-loss fee arrangements. The preferred first-loss coverage guarantee is increasing as the investor's risk-aversion or the interest rate increases. It decreases as the manager's risk-aversion or the market price of risk increases. The more risk averse the investor or the higher the interest rate, the larger is the preferred performance fee. The preferred fee schemes significantly decrease the fund's volatility.
Deep Kalman Filters Can Filter
Authors: Blanka Horvath, Anastasis Kratsios, Yannick Limmer, Xuwei Yang
Date: 2023-10-30 00:00:00

Deep Kalman filters (DKFs) are a class of neural network models that generate Gaussian probability measures from sequential data. Though DKFs are inspired by the Kalman filter, they lack concrete theoretical ties to the stochastic filtering problem, thus limiting their applicability to areas where traditional model-based filters have been used, e.g.\ model calibration for bond and option prices in mathematical finance. We address this issue in the mathematical foundations of deep learning by exhibiting a class of continuous-time DKFs which can approximately implement the conditional law of a broad class of non-Markovian and conditionally Gaussian signal processes given noisy continuous-times measurements. Our approximation results hold uniformly over sufficiently regular compact subsets of paths, where the approximation error is quantified by the worst-case 2-Wasserstein distance computed uniformly over the given compact set of paths.
Convolutional Neural Networks for Automatic Detection of Intact Adenovirus from TEM Imaging with Debris, Broken and Artefacts Particles
Authors: Olivier Rukundo, Andrea Behanova, Riccardo de Feo, Seppo Ronkko, Joni Oja, Jussi Tohka
Date: 2023-10-30 00:00:00

Regular monitoring of the primary particles and purity profiles of a drug product during development and manufacturing processes is essential for manufacturers to avoid product variability and contamination. Transmission electron microscopy (TEM) imaging helps manufacturers predict how changes affect particle characteristics and purity for virus-based gene therapy vector products and intermediates. Since intact particles can characterize efficacious products, it is beneficial to automate the detection of intact adenovirus against a non-intact-viral background mixed with debris, broken, and artefact particles. In the presence of such particles, detecting intact adenoviruses becomes more challenging. To overcome the challenge, due to such a presence, we developed a software tool for semi-automatic annotation and segmentation of adenoviruses and a software tool for automatic segmentation and detection of intact adenoviruses in TEM imaging systems. The developed semi-automatic tool exploited conventional image analysis techniques while the automatic tool was built based on convolutional neural networks and image analysis techniques. Our quantitative and qualitative evaluations showed outstanding true positive detection rates compared to false positive and negative rates where adenoviruses were nicely detected without mistaking them for real debris, broken adenoviruses, and/or staining artefacts.
IMPRESS: Evaluating the Resilience of Imperceptible Perturbations Against Unauthorized Data Usage in Diffusion-Based Generative AI
Authors: Bochuan Cao, Changjiang Li, Ting Wang, Jinyuan Jia, Bo Li, Jinghui Chen
Date: 2023-10-30 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Image Generation

Diffusion-based image generation models, such as Stable Diffusion or DALL-E 2, are able to learn from given images and generate high-quality samples following the guidance from prompts. For instance, they can be used to create artistic images that mimic the style of an artist based on his/her original artworks or to maliciously edit the original images for fake content. However, such ability also brings serious ethical issues without proper authorization from the owner of the original images. In response, several attempts have been made to protect the original images from such unauthorized data usage by adding imperceptible perturbations, which are designed to mislead the diffusion model and make it unable to properly generate new samples. In this work, we introduce a perturbation purification platform, named IMPRESS, to evaluate the effectiveness of imperceptible perturbations as a protective measure. IMPRESS is based on the key observation that imperceptible perturbations could lead to a perceptible inconsistency between the original image and the diffusion-reconstructed image, which can be used to devise a new optimization strategy for purifying the image, which may weaken the protection of the original image from unauthorized data usage (e.g., style mimicking, malicious editing). The proposed IMPRESS platform offers a comprehensive evaluation of several contemporary protection methods, and can be used as an evaluation platform for future protection methods.