pwc-archive/papers-with-abstracts huggingface dataset:
Predictive Modeling of Shading Effects on Photovoltaic Panels Using Regression Analysis
Authors: Jonathan Olivares, Tyler Depe, Kanika Sood, Rakeshkumar Mahto
Date: 2024-12-10 00:00:00
Tasks: Disaster Response, regression

Drones have become indispensable assets during human-made and natural disasters, offering damage assessment, aid delivery, and communication restoration capabilities. However, most drones rely on batteries that require frequent recharging, limiting their effectiveness in continuous missions. Photovoltaic (PV) powered drones are an ideal alternative. However, their performance degrades in variable lighting conditions. Hence, machine learning (ML) controlled PV cells present a promising solution for extending the endurance of a drone. This work evaluates five regression models, linear regression, lasso regression, ridge regression, random forest regression, and XGBoost regression, to predict shading percentages on PV panels. Accurate prediction of shading is crucial for improving the performance and efficiency of ML-controlled PV panels in varying conditions. By achieving a lower MSE and higher R2 Scores, XGBoost and random forest methods were the best-performing regression models. Notably, XGBoost showed superior performance with an R2 Score of 0.926. These findings highlight the possibility of utilizing the regression model to enhance PV-powered drones' efficiency, prolong flight time, reduce maintenance costs, and improve disaster response capabilities.
CPFI-EIT: A CNN-PINN Framework for Full-Inverse Electrical Impedance Tomography on Non-Smooth Conductivity Distributions
Authors: Yang Xuanxuan, Zhang Yangming, Chen Haofeng, Ma Gang, Wang Xiaojie
Date: 2024-12-10 00:00:00

This paper introduces a hybrid learning framework that combines convolutional neural networks (CNNs) and physics-informed neural networks (PINNs) to address the challenging problem of full-inverse electrical impedance tomography (EIT). EIT is a noninvasive imaging technique that reconstructs the spatial distribution of internal conductivity based on boundary voltage measurements from injected currents. This method has applications across medical imaging, multiphase flow detection, and tactile sensing. However, solving EIT involves a nonlinear partial differential equation (PDE) derived from Maxwell's equations, posing significant computational challenges as an ill-posed inverse problem. Existing PINN approaches primarily address semi-inverse EIT, assuming full access to internal potential data, which limits practical applications in realistic, full-inverse scenarios. Our framework employs a forward CNN-based supervised network to map differential boundary voltage measurements to a discrete potential distribution under fixed Neumann boundary conditions, while an inverse PINN-based unsupervised network enforces PDE constraints for conductivity reconstruction. Instead of traditional automatic differentiation, we introduce discrete numerical differentiation to bridge the forward and inverse networks, effectively decoupling them, enhancing modularity, and reducing computational demands. We validate our framework under realistic conditions, using a 16-electrode setup and rigorous testing on complex conductivity distributions with sharp boundaries, without Gaussian smoothing. This approach demonstrates robust flexibility and improved applicability in full-inverse EIT, establishing a practical solution for real-world imaging challenges.
A Survey on Machine and Deep Learning for Optical Communications
Authors: M. A. Amirabadi, S. A. Nezamalhosseini, M. H. Kahaei, Lawrence R. Chen
Date: 2024-12-10 00:00:00
Tasks: Deep Learning, Survey

The ever-growing complexity of optical communication systems and networks demands sophisticated methodologies to extract meaningful insights from vast amounts of heterogeneous data. Machine learning (ML) and deep learning (DL) have emerged as frontrunners in this domain, offering a transformative approach to data analysis and enabling automated self-configuration in optical communication systems. The adoption of ML and DL in optical communication is driven by the exponential increase in system and link complexity, stemming from the introduction of numerous adjustable and interdependent parameters. This is particularly evident in areas like coherent transceivers, advanced digital signal processing, optical performance monitoring, cross-layer network optimizations, and nonlinearity compensation. While the potential benefits of ML and DL are immense, the extent to which these methods can revolutionize optical communication remains largely unexplored. Additionally, many ML and DL algorithms have yet to be deployed in this field, highlighting the nascent nature of this research area.
Ensemble Machine Learning Model for Inner Speech Recognition: A Subject-Specific Investigation
Authors: Shahamat Mustavi Tasin, Muhammad E. H. Chowdhury, Shona Pedersen, Malek Chabbouh, Diala Bushnaq, Raghad Aljindi, Saidul Kabir, Anwarul Hasan
Date: 2024-12-09 00:00:00
Tasks: EEG, feature selection, speech-recognition, Speech Recognition

Inner speech recognition has gained enormous interest in recent years due to its applications in rehabilitation, developing assistive technology, and cognitive assessment. However, since language and speech productions are a complex process, for which identifying speech components has remained a challenging task. Different approaches were taken previously to reach this goal, but new approaches remain to be explored. Also, a subject-oriented analysis is necessary to understand the underlying brain dynamics during inner speech production, which can bring novel methods to neurological research. A publicly available dataset, Thinking Out Loud Dataset, has been used to develop a Machine Learning (ML)-based technique to classify inner speech using 128-channel surface EEG signals. The dataset is collected on a Spanish cohort of ten subjects while uttering four words (Arriba, Abajo, Derecha, and Izquierda) by each participant. Statistical methods were employed to detect and remove motion artifacts from the Electroencephalography (EEG) signals. A large number (191 per channel) of time-, frequency- and time-frequency-domain features were extracted. Eight feature selection algorithms are explored, and the best feature selection technique is selected for subsequent evaluations. The performance of six ML algorithms is evaluated, and an ensemble model is proposed. Deep Learning (DL) models are also explored, and the results are compared with the classical ML approach. The proposed ensemble model, by stacking the five best logistic regression models, generated an overall accuracy of 81.13% and an F1 score of 81.12% in the classification of four inner speech words using surface EEG signals. The proposed framework with the proposed ensemble of classical ML models shows promise in the classification of inner speech using surface EEG signals.
RUL forecasting for wind turbine predictive maintenance based on deep learning
Authors: Syed Shazaib Shah, Tan Daoliang, Sah Chandan Kumar
Date: 2024-12-09 00:00:00
Tasks: Feature Engineering, Scheduling

Predictive maintenance (PdM) is increasingly pursued to reduce wind farm operation and maintenance costs by accurately predicting the remaining useful life (RUL) and strategically scheduling maintenance. However, the remoteness of wind farms often renders current methodologies ineffective, as they fail to provide a sufficiently reliable advance time window for maintenance planning, limiting PdM's practicality. This study introduces a novel deep learning (DL) methodology for future RUL forecasting. By employing a multi-parametric attention-based DL approach that bypasses feature engineering, thereby minimizing the risk of human error, two models: ForeNet-2d and ForeNet-3d are proposed. These models successfully forecast the RUL for seven multifaceted wind turbine (WT) failures with a 2-week forecast window. The most precise forecast deviated by only 10 minutes from the actual RUL, while the least accurate prediction deviated by 1.8 days, with most predictions being off by only a few hours. This methodology offers a substantial time frame to access remote WTs and perform necessary maintenance, thereby enabling the practical implementation of PdM.
EEG-Based Mental Imagery Task Adaptation via Ensemble of Weight-Decomposed Low-Rank Adapters
Authors: Taveena Lotey, Aman Verma, Partha Pratim Roy
Date: 2024-12-08 00:00:00
Tasks: Domain Adaptation, EEG, EEG Signal Classification, Motor Imagery, parameter-efficient fine-tuning, Transfer Learning

Electroencephalography (EEG) is widely researched for neural decoding in Brain Computer Interfaces (BCIs) as it is non-invasive, portable, and economical. However, EEG signals suffer from inter- and intra-subject variability, leading to poor performance. Recent technological advancements have led to deep learning (DL) models that have achieved high performance in various fields. However, such large models are compute- and resource-intensive and are a bottleneck for real-time neural decoding. Data distribution shift can be handled with the help of domain adaptation techniques of transfer learning (fine-tuning) and adversarial training that requires model parameter updates according to the target domain. One such recent technique is Parameter-efficient fine-tuning (PEFT), which requires only a small fraction of the total trainable parameters compared to fine-tuning the whole model. Therefore, we explored PEFT methods for adapting EEG-based mental imagery tasks. We considered two mental imagery tasks: speech imagery and motor imagery, as both of these tasks are instrumental in post-stroke neuro-rehabilitation. We proposed a novel ensemble of weight-decomposed low-rank adaptation methods, EDoRA, for parameter-efficient mental imagery task adaptation through EEG signal classification. The performance of the proposed PEFT method is validated on two publicly available datasets, one speech imagery, and the other motor imagery dataset. In extensive experiments and analysis, the proposed method has performed better than full fine-tune and state-of-the-art PEFT methods for mental imagery EEG classification.
Bidirectional Charging Use Cases: Innovations in E-Mobility and Power-Grid Flexibility
Authors: Shangqing Wang, Juan A. Cabrera, Frank H. P. Fitzek
Date: 2024-12-05 00:00:00

This paper explores the potential of Vehicle-to-Everything (V2X) technology to enhance grid stability and support sustainable mobility in Dresden's Ostra district. By enabling electric vehicles to serve as mobile energy storage units, V2X offers grid stabilization and new business opportunities. We examine pilot projects and business use cases, focusing on Building Integrated Vehicle Energy Solutions (BIVES) and Resilient Energy Storage and Backup (RESB) as stepping stones towards full Vehicle-to-Grid (V2G) implementation. Our analysis highlights the feasibility, advantages, and challenges of implementing V2X in urban settings, underscoring its significant role in transitioning to a resilient, low-carbon urban energy system. The paper concludes with recommendations for addressing technical, regulatory, and business model challenges to accelerate V2X adoption in Dresden and beyond.
Multi-Branch Mutual-Distillation Transformer for EEG-Based Seizure Subtype Classification
Authors: Ruimin Peng, Zhenbang Du, Changming Zhao, Jingwei Luo, Wenzhong Liu, Xinxing Chen, Dongrui Wu
Date: 2024-12-04 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Knowledge Distillation

Cross-subject electroencephalogram (EEG) based seizure subtype classification is very important in precise epilepsy diagnostics. Deep learning is a promising solution, due to its ability to automatically extract latent patterns. However, it usually requires a large amount of training data, which may not always be available in clinical practice. This paper proposes Multi-Branch Mutual-Distillation (MBMD) Transformer for cross-subject EEG-based seizure subtype classification, which can be effectively trained from small labeled data. MBMD Transformer replaces all even-numbered encoder blocks of the vanilla Vision Transformer by our designed multi-branch encoder blocks. A mutual-distillation strategy is proposed to transfer knowledge between the raw EEG data and its wavelets of different frequency bands. Experiments on two public EEG datasets demonstrated that our proposed MBMD Transformer outperformed several traditional machine learning and state-of-the-art deep learning approaches. To our knowledge, this is the first work on knowledge distillation for EEG-based seizure subtype classification.
Dynamic Mean-Variance Asset Allocation in General Incomplete Markets A Nonlocal BSDE-based Feedback Control Approach
Authors: Qian Lei, Chi Seng Pun, Jingxiang Tang
Date: 2024-12-24 00:00:00

This paper studies dynamic mean-variance (MV) asset allocation problems in general incomplete markets. Besides of the conventional MV objective on portfolio's terminal wealth, our framework can accommodate running MV objectives with general (non-exponential) discounting factors while in general, any time-dependent preferences. We attempt the problem with a game-theoretic framework while decompose the equilibrium control policies into two parts: the first part is a myopic strategy characterized by a linear Volterra integral equation of the second kind and the second part reveals the hedging demand governed by a system of nonlocal backward stochastic differential equations. We manage to establish the well-posedness of the solutions to the two aforementioned equations in tailored Bananch spaces by the fixed-point theorem. It allows us to devise a numerical scheme for solving for the equilibrium control policy with guarantee and to conclude that the dynamic (equilibrium) mean-variance policy in general settings is well-defined. Our probabilistic approach allows us to consider a board range of stochastic factor models, such as the Chan--Karolyi--Longstaff--Sanders (CKLS) model. For which, we verify all technical assumptions and provide a sound numerical scheme. Numerical examples are provided to illustrate our framework.
PLD-Tree: Persistent Laplacian Decision Tree for Protein-Protein Binding Free Energy Prediction
Authors: Xingjian Xu, Jiahui Chen, Chunmei Wang
Date: 2024-12-24 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Molecular Docking

Recent advances in topology-based modeling have accelerated progress in physical modeling and molecular studies, including applications to protein-ligand binding affinity. In this work, we introduce the Persistent Laplacian Decision Tree (PLD-Tree), a novel method designed to address the challenging task of predicting protein-protein interaction (PPI) affinities. PLD-Tree focuses on protein chains at binding interfaces and employs the persistent Laplacian to capture topological invariants reflecting critical inter-protein interactions. These topological descriptors, derived from persistent homology, are further enhanced by incorporating evolutionary scale modeling (ESM) from a large language model to integrate sequence-based information. We validate PLD-Tree on two benchmark datasets-PDBbind V2020 and SKEMPI v2 demonstrating a correlation coefficient ($R_p$) of 0.83 under the sophisticated leave-out-protein-out cross-validation. Notably, our approach outperforms all reported state-of-the-art methods on these datasets. These results underscore the power of integrating machine learning techniques with topology-based descriptors for molecular docking and virtual screening, providing a robust and accurate framework for predicting protein-protein binding affinities.
Learning to engineer protein flexibility
Authors: Petr Kouba, Joan Planas-Iglesias, Jiri Damborsky, Jiri Sedlar, Stanislav Mazurenko, Josef Sivic
Date: 2024-12-24 00:00:00
Tasks: Language Modeling, Language Modelling, Protein Language Model

Generative machine learning models are increasingly being used to design novel proteins for therapeutic and biotechnological applications. However, the current methods mostly focus on the design of proteins with a fixed backbone structure, which leads to their limited ability to account for protein flexibility, one of the crucial properties for protein function. Learning to engineer protein flexibility is problematic because the available data are scarce, heterogeneous, and costly to obtain using computational as well as experimental methods. Our contributions to address this problem are three-fold. First, we comprehensively compare methods for quantifying protein flexibility and identify data relevant to learning. Second, we design and train flexibility predictors utilizing sequential or both sequential and structural information on the input. We overcome the data scarcity issue by leveraging a pre-trained protein language model. Third, we introduce a method for fine-tuning a protein inverse folding model to steer it toward desired flexibility in specified regions. We demonstrate that our method Flexpert-Design enables guidance of inverse folding models toward increased flexibility. This opens up new possibilities for protein flexibility engineering and the development of proteins with enhanced biological activities.
Old vaccines, new usages, surprisingly effective in solving the century-old problem -Inactivated African Swine Fever Virus vaccine induces safe and efficient immune protection through mucosal immunity
Authors: Yang Jinlong, Yang JingXu
Date: 2024-12-24 00:00:00

Background: African swine fever is among the most devastating viral diseases of pigs. Despite nearly a century of research, there is still no safe and effective vaccine available. The current situation is that either vaccines are safe but not effective, or they are effective but not safe.Findings: The ASF vaccine prepared using the inactivation method with propiolactone provided 98.6% protection within 100 days after three intranasal immunizations, spaced 7 days apart.Conclusions: An inactivated vaccine made from complete African swine fever virus particles using propiolactone is safe and effective for controlling ASF through mucosal immunity.
Low count of optically pumped magnetometers furnishes a reliable real-time access to sensorimotor rhythm
Authors: Nikita Fedosov, Daria Medvedeva, Oleg Shevtsov, Alexei Ossadtchi
Date: 2024-12-24 00:00:00
Tasks: Brain Computer Interface, Motor Imagery, Rhythm

This study presents an analysis of sensorimotor rhythms using an advanced, optically-pumped magnetoencephalography (OPM-MEG) system - a novel and rapidly developing technology. We conducted real-movement and motor imagery experiments with nine participants across two distinct magnetically-shielded environments: one featuring an analog active suppression system and the other a digital implementation. Our findings demonstrate that, under optimal recording conditions, OPM sensors provide highly informative signals, suitable for use in practical motor imagery brain-computer interface (BCI) applications. We further examine the feasibility of a portable, low-sensor-count OPM-based BCI under varied experimental setups, highlighting its potential for real-time control of external devices via user intentions.
The same but different: impact of animal facility sanitary status on a transgenic mouse model of Alzheimer's disease
Authors: Caroline Ismeurt-Walmsley, Patrizia Giannoni, Florence Servant, Linda-Nora Mekki, Kevin Baranger, Santiago Rivera, Philippe Marin, Benjamin Lelouvier, Sylvie Claeysen
Date: 2024-12-24 00:00:00
Tasks: Object Recognition

The gut-brain axis has emerged as a key player in the regulation of brain function and cognitive health. Gut microbiota dysbiosis has been observed in preclinical models of Alzheimer's disease and patients. Manipulating the composition of the gut microbiota enhances or delays neuropathology and cognitive deficits in mouse models. Accordingly, the health status of the animal facility may strongly influence these outcomes. In the present study, we longitudinally analysed the faecal microbiota composition and amyloid pathology of 5XFAD mice housed in a specific opportunistic pathogen-free (SOPF) and a conventional facility. The composition of the microbiota of 5XFAD mice after aging in conventional facility showed marked differences compared to WT littermates that were not observed when the mice were bred in SOPF facility. The development of amyloid pathology was also enhanced by conventional housing. We then transplanted faecal microbiota (FMT) from both sources into wild-type (WT) mice and measured memory performance, assessed in the novel object recognition test, in transplanted animals. Mice transplanted with microbiota from conventionally bred 5XFAD mice showed impaired memory performance, whereas FMT from mice housed in SOPF facility did not induce memory deficits in transplanted mice. Finally, 18 weeks of housing SOPF-born animals in a conventional facility resulted in the reappearance of specific microbiota compositions in 5XFAD vs WT mice. In conclusion, these results show a strong impact of housing conditions on microbiota-associated phenotypes and question the relevance of breeding preclinical models in specific pathogen-free (SPF) facilities.
Hyperbolic embedding of brain networks can predict the surgery outcome in temporal lobe epilepsy
Authors: Martin Guillemaud, Alice Longhena, Louis Cousyn, Valerio Frazzini, Bertrand Mathon, Vincent Navarro, Mario Chavez
Date: 2024-12-09 00:00:00

Epilepsy surgery, particularly for temporal lobe epilepsy (TLE), remains a vital treatment option for patients with drug-resistant seizures. However, accurately predicting surgical outcomes remains a significant challenge. This study introduces a novel biomarker derived from brain connectivity, analyzed using non-Euclidean network geometry, to predict the surgery outcome in TLE. Using structural and diffusion magnetic resonance imaging (MRI) data from 51 patients, we examined differences in structural connectivity networks associated to surgical outcomes. Our approach uniquely utilized hyperbolic embeddings of pre- and post-surgery brain networks, successfully distinguishing patients with favorable outcomes from those with poor outcomes. Notably, the method identified regions in the contralateral hemisphere relative to the epileptogenic zone, whose connectivity patterns emerged as a potential biomarker for favorable surgical outcomes. The prediction model achieves an area under the curve (AUC) of 0.87 and a balanced accuracy of 0.81. These results underscore the predictive capability of our model and its effectiveness in individual outcome forecasting based on structural network changes. Our findings highlight the value of non-Euclidean representation of brain networks in gaining deeper insights into connectivity alterations in epilepsy, and advancing personalized prediction of surgical outcomes in TLE.