pwc-archive/papers-with-abstracts huggingface dataset:
Energy efficiency analysis as a function of the working voltages in supercapacitors
Authors: Jose Quintana, Alejandro Ramos, Moises Diaz, Ignacio Nuez
Date: 2024-09-25 00:00:00

Supercapacitors are increasingly used as energy storage elements. Unlike batteries, their state of charge has a considerable influence on their voltage in normal operation, allowing them to work from zero to their maximum voltage. In this work, a theoretical and practical analysis is proposed of the energy efficiency of these devices according to their working voltages. To this end, several supercapacitors were subjected to charge and discharge cycles until the measurements of current and voltage stabilized. At this point their energy efficiency was calculated. These charge-discharge cycles were carried out: i) without rest between charging and discharging; and ii) with a rest of several minutes between the two stages. Using the information obtained from the tests, the energy efficiency is shown plotted against the minimum and maximum working voltages. By consulting the data and the graphs, the ideal working voltages to optimize the energy efficiency of these devices can be obtained.
Near-Field Multipath MIMO Channel Model for Imperfect Surface Reflection
Authors: Mohamadreza Delbari, George C. Alexandropoulos, Robert Schober, H. Vincent Poor, Vahid Jamali
Date: 2024-09-25 00:00:00

Near-field (NF) communications is receiving renewed attention in the context of passive reconfigurable intelligent surfaces (RISs) due to their potentially extremely large dimensions. Although line-of-sight (LOS) links are expected to be dominant in NF scenarios, it is not a priori obvious whether or not the impact of non-LOS components can be neglected. Furthermore, despite being weaker than the LOS link, non-LOS links may be required to achieve multiplexing gains in multi-user multiple-input multiple-output (MIMO) scenarios. In this paper, we develop a generalized statistical NF model for RIS-assisted MIMO systems that extends the widely adopted point-scattering model to account for imperfect reflections at large surfaces like walls, ceilings, and the ground. Our simulation results confirm the accuracy of the proposed model and reveal that in various practical scenarios, the impact of non-LOS components is indeed non-negligible, and thus, needs to be carefully taken into consideration.
Incorporating Spatial Cues in Modular Speaker Diarization for Multi-channel Multi-party Meetings
Authors: Ruoyu Wang, Shutong Niu, Gaobin Yang, Jun Du, Shuangqing Qian, Tian Gao, Jia Pan
Date: 2024-09-25 00:00:00
Tasks: Clustering, speaker-diarization, Speaker Diarization, Speech Separation

Although fully end-to-end speaker diarization systems have made significant progress in recent years, modular systems often achieve superior results in real-world scenarios due to their greater adaptability and robustness. Historically, modular speaker diarization methods have seldom discussed how to leverage spatial cues from multi-channel speech. This paper proposes a three-stage modular system to enhance single-channel neural speaker diarization systems and recognition performance by utilizing spatial cues from multi-channel speech to provide more accurate initialization for each stage of neural speaker diarization (NSD) decoding: (1) Overlap detection and continuous speech separation (CSS) on multi-channel speech are used to obtain cleaner single speaker speech segments for clustering, followed by the first NSD decoding pass. (2) The results from the first pass initialize a complex Angular Central Gaussian Mixture Model (cACGMM) to estimate speaker-wise masks on multi-channel speech, and through Overlap-add and Mask-to-VAD, achieve initialization with lower speaker error (SpkErr), followed by the second NSD decoding pass. (3) The second decoding results are used for guided source separation (GSS), recognizing and filtering short segments containing less one word to obtain cleaner speech segments, followed by re-clustering and the final NSD decoding pass. We presented the progressively explored evaluation results from the CHiME-8 NOTSOFAR-1 (Natural Office Talkers in Settings Of Far-field Audio Recordings) challenge, demonstrating the effectiveness of our system and its contribution to improving recognition performance. Our final system achieved the first place in the challenge.
F-BIAS: Towards a distributed national core facility for Bioimage Analysis
Authors: Mélodie Ambroset, Marie Anselmet, Clément Benedetti, Arthur Meslin, Aurélien Maillot, Christian Rouvière, Guillaume Maucort, Marine Breuilly, Marvin Albert, Gaëlle Letort, Antonio Trullo, Volker Bäcker, Jérôme Mutterer, Fabrice Cordelières, Thierry Pécot, Minh-Son Phan, Stéphane Rigaud, Magali Feyeux, Perrine Paul-Gilloteaux, Anne-Sophie Macé, Jean-Yves Tinevez
Date: 2024-09-23 00:00:00

We discuss in this article the creation and organization of a national core facility for bioimage analysis, based on a distributed team. F-BIAS federates bioimage analysts across France and relies on them to deliver services to the researchers of this territory. The main challenge in implementing this structure is to provide significant scientific value to its members, thereby encouraging their active participation and persuading their respective host teams to support their involvement. F-BIAS accomplished this by creating a professional network that mitigates the negative effects of isolation experienced by its members, who are often the sole bioimage analyst within their local teams, and fosters the development of their technical skills. In a second phase we capitalized on F-BIAS to create a virtual, remotely-operating core facility for bioimage analysis, offering consultations and collaborative project services to the scientific community of France. The services are organized so that they also contribute to the technical proficiency of the analysts. To promote the creation of similar structures, we present and discuss here the organization of this nationally distributed bioimage analysis service core, highlighting successes and challenges.
DP$^2$-FedSAM: Enhancing Differentially Private Federated Learning Through Personalized Sharpness-Aware Minimization
Authors: Zhenxiao Zhang, Yuanxiong Guo, Yanmin Gong
Date: 2024-09-20 00:00:00
Tasks: Federated Learning, Personalized Federated Learning

Federated learning (FL) is a distributed machine learning approach that allows multiple clients to collaboratively train a model without sharing their raw data. To prevent sensitive information from being inferred through the model updates shared in FL, differentially private federated learning (DPFL) has been proposed. DPFL ensures formal and rigorous privacy protection in FL by clipping and adding random noise to the shared model updates. However, the existing DPFL methods often result in severe model utility degradation, especially in settings with data heterogeneity. To enhance model utility, we propose a novel DPFL method named DP$^2$-FedSAM: Differentially Private and Personalized Federated Learning with Sharpness-Aware Minimization. DP$^2$-FedSAM leverages personalized partial model-sharing and sharpness-aware minimization optimizer to mitigate the adverse impact of noise addition and clipping, thereby significantly improving model utility without sacrificing privacy. From a theoretical perspective, we provide a rigorous theoretical analysis of the privacy and convergence guarantees of our proposed method. To evaluate the effectiveness of DP$^2$-FedSAM, we conduct extensive evaluations based on common benchmark datasets. Our results verify that our method improves the privacy-utility trade-off compared to the existing DPFL methods, particularly in heterogeneous data settings.
Geometry-Induced Competitive Release in a Meta-Population Model of Range Expansions in Disordered Environments
Authors: Jimmy Gonzalez Nuñez, Daniel A. Beller
Date: 2024-09-23 00:00:00

Rare evolutionary events, such as the rise to prominence of deleterious mutations, can have drastic impacts on the evolution of growing populations. Heterogeneous environments may reduce the influence of selection on evolutionary outcomes through various mechanisms, including pinning of genetic lineages and of the population fronts. These effects play significant roles in enabling competitive release of otherwise trapped mutations. In this work we show that environments containing random arrangements of "hotspot" patches, where locally abundant resources enhance growth rates equally for all sub-populations, give rise to massively enriched deleterious mutant clones. We derive a geometrical optics description of mutant bubbles, which result from interactions with hotspots, that successfully predicts the observed increase in mutant survival. This prediction requires no fitting parameters and holds well in scenarios of rare mutations and of adaptation from standing variation. In addition, we find that the influence of environmental noise in shaping the fate of rare mutations is maximal near a percolation transition of overlapping discs, beyond which mutant survival decreases.
Inequality Sensitive Optimal Treatment Assignment
Authors: Eduardo Zambrano
Date: 2024-09-23 00:00:00

The egalitarian equivalent, $ee$, of a societal distribution of outcomes with mean $m$ is the outcome level such that the evaluator is indifferent between the distribution of outcomes and a society in which everyone obtains an outcome of $ee$. For an inequality averse evaluator, $ee < m$. In this paper, I extend the optimal treatment choice framework in Manski (2024) to the case where the welfare evaluation is made using egalitarian equivalent measures, and derive optimal treatment rules for the Bayesian, maximin and minimax regret inequality averse evaluators. I illustrate how the methodology operates in the context of the JobCorps education and training program for disadvantaged youth (Schochet, Burghardt, and McConnell 2008) and in Meager (2022)'s Bayesian meta analysis of the microcredit literature.
Impact of complex spatial population structure on early and long-term adaptation in rugged fitness landscapes
Authors: Richard Servajean, Arthur Alexandre, Anne-Florence Bitbol
Date: 2024-09-25 00:00:00

We investigate the exploration of rugged fitness landscapes by spatially structured populations with demes on the nodes of a graph, connected by migrations. In the rare migration regime, we find that finite structures can adapt more efficiently than very large ones, especially in high-dimensional fitness landscapes. Furthermore, we show that, in most landscapes, migration asymmetries associated with some suppression of natural selection allow the population to reach higher fitness peaks first. In this sense, suppression of selection can make early adaptation more efficient. However, the time it takes to reach the first fitness peak is then increased. We also find that suppression of selection tends to enhance finite-size effects. We extend our study to frequent migrations, suggesting that our conclusions hold in this regime. We then investigate the impact of spatial structure with rare migrations on long-term evolution by studying the steady state of the population. For this, we define an effective population size for the steady-state distribution. We find that suppression of selection is associated to reduced steady-state effective population sizes, and reduced average steady-state fitnesses.
Complex-Phase, Data-Driven Identification of Grid-Forming Inverter Dynamics
Authors: Anna Büttner, Hans Würfel, Sebastian Liemann, Johannes Schiffer, Frank Hellmann
Date: 2024-09-25 00:00:00
Tasks: Form

The increasing integration of renewable energy sources (RESs) into power systems requires the deployment of grid-forming inverters to ensure a stable operation. Accurate modeling of these devices is necessary. In this paper, a system identification approach to obtain low-dimensional models of grid-forming inverters is presented. The proposed approach is based on a Hammerstein-Wiener parametrization of the normal-form model. The normal-form is a gray-box model that utilizes complex frequency and phase to capture non-linear inverter dynamics. The model is validated on two well-known control strategies: droop-control and dispatchable virtual oscillators. Simulations and hardware-in-the-loop experiments demonstrate that the normal-form accurately models inverter dynamics across various operating conditions. The approach shows great potential for enhancing the modeling of RES-dominated power systems, especially when component models are unavailable or computationally expensive.
A Critical Review of Safe Reinforcement Learning Techniques in Smart Grid Applications
Authors: Van-Hai Bui, Srijita Das, Akhtar Hussain, Guilherme Vieira Hollweg, Wencong Su
Date: 2024-09-24 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Safe Reinforcement Learning

The high penetration of distributed energy resources (DERs) in modern smart power systems introduces unforeseen uncertainties for the electricity sector, leading to increased complexity and difficulty in the operation and control of power systems. As a cutting-edge machine learning technology, deep reinforcement learning (DRL) has been widely implemented in recent years to handle the uncertainty in power systems. However, in critical infrastructures such as power systems, safety issues always receive top priority, while DRL may not always meet the safety requirements of power system operators. The concept of safe reinforcement learning (safe RL) is emerging as a potential solution to overcome the shortcomings of conventional DRL in the operation and control of power systems. This study provides a rigorous review of the latest research efforts focused on safe RL to derive power system control policies while accounting for the unique safety requirements of power grids. Furthermore, this study highlights various safe RL algorithms applied in diverse applications within the power system sector, from single grid-connected power converters, residential smart homes, and buildings to large power distribution networks. For all methods outlined, a discussion on their bottlenecks, research challenges, and potential opportunities in the operation and control of power system applications is also presented. This review aims to support research in the area of safe RL algorithms, embracing smart power system operation with safety constraints amid high uncertainty from DERs.
Performance Boundary Analyses for Statistical Multi-QoS Framework Over 6G SAGINs
Authors: Jingqing Wang, Wenchi Cheng, Wei zhang
Date: 2024-09-25 00:00:00

To enable the cost-effective universal access and the enhancement of current communication services, the space-air-ground integrated networks (SAGINs) have recently been developed due to its exceptional 3D coverage and the ability to guarantee rigorous and multidimensional demands for quality-of-service (QoS) provisioning, including delay and reliability across vast distances. In response to the complex, heterogeneous, and dynamic serving scenarios and stringent performance expectations for 6G SAGINs, it is crucial to undertake modeling, assurance, and analysis of the key technologies, aligned with the diverse demands for QoS provisioning in the non-asymptotic regime, i.e., when implementing finite blocklength coding (FBC) as a new dimension for error-rate bounded QoS metric. However, how to design new statistical QoS-driven performance modeling approaches that accurately delineate the complex and dynamic behaviors of networks, particularly in terms of constraining both delay and error rate, persists as a significant challenge for implementing mURLLC within 6G SAGINs in the finite blocklength regime. To overcome these difficulties, in this paper we propose to develop a set of analytical modeling frameworks for 6G SAGIN in supporting statistical delay and error-rate bounded QoS in the finite blocklength regime. First we establish the SAGIN system architecture model. Second, the aggregate interference and decoding error probability functions are modeled and examined through using Laplace transform. Third, we introduce modeling techniques aimed at defining the$\epsilon$-effective capacity function as a crucial metric for facilitating statistical QoS standards with respect to delay and error-rate. To validate the effectiveness of the developed performance modeling schemes, we have executed a series of simulations over SAGINs.
Spectral Graph Theoretic Methods for Enhancing Network Robustness in Robot Localization
Authors: Neelkamal Somisetty, Harsha Nagarajan, Swaroop Darbha
Date: 2024-09-23 00:00:00
Tasks: Simultaneous Localization and Mapping, valid

This paper addresses the optimization of edge-weighted networks by maximizing algebraic connectivity to enhance network robustness. Motivated by the need for precise robot position estimation in cooperative localization and pose-graph sparsification in Simultaneous Localization and Mapping (SLAM), the algebraic connectivity maximization problem is formulated as a Mixed Integer Semi-Definite Program (MISDP), which is NP-hard. Leveraging spectral graph theoretic methods, specifically Cheeger's inequality, this work introduces novel "Cheeger cuts" to strengthen and efficiently solve medium-scale MISDPs. Further, a new Mixed Integer Linear Program (MILP) is developed for efficiently computing Cheeger cuts, implemented within an outer-approximation algorithm for solving the MISDP. A greedy k-opt heuristic is also presented, producing high-quality solutions that serve as valid lower bounds for Cheeger cuts. Comprehensive numerical analyses demonstrate the efficacy of strengthened cuts via substantial improvements in run times on synthetic and realistic robot localization datasets.
MT2KD: Towards A General-Purpose Encoder for Speech, Speaker, and Audio Events
Authors: Xiaoyu Yang, Qiujia Li, Chao Zhang, Phil Woodland
Date: 2024-09-25 00:00:00
Tasks: Audio Tagging, Automatic Speech Recognition, Automatic Speech Recognition (ASR), Knowledge Distillation, Multi-Task Learning, Speaker Verification, speech-recognition, Speech Recognition

With the advances in deep learning, the performance of end-to-end (E2E) single-task models for speech and audio processing has been constantly improving. However, it is still challenging to build a general-purpose model with high performance on multiple tasks, since different speech and audio processing tasks usually require different training data, input features, or model architectures to achieve optimal performance. In this work, MT2KD, a novel two-stage multi-task learning framework is proposed to build a general-purpose speech and audio encoder that jointly performs three fundamental tasks: automatic speech recognition (ASR), audio tagging (AT) and speaker verification (SV). In the first stage, multi-teacher knowledge distillation (KD) is applied to align the feature spaces of three single-task high-performance teacher encoders into a single student encoder using the same unlabelled data. In the second stage, multi-task supervised fine-tuning is carried out by initialising the model from the first stage and training on the separate labelled data of each single task. Experiments demonstrate that the proposed multi-task training pipeline significantly outperforms a baseline model trained with multi-task learning from scratch. The final system achieves good performance on ASR, AT and SV: with less than 4% relative word-error-rate increase on ASR, only 1.9 lower mean averaged precision on AT and 0.23% absolute higher equal error rate on SV compared to the best-performing single-task encoders, using only a 66M total model parameters.
Intelligent Transformation: General Intelligence Theory
Authors: Alexander Ngu, Amaya Odilon Kosso, Amaya Ngu
Date: 2024-07-31 00:00:00
Proceeding: International Journal of Intelligence Science, 2024 7
Tasks: Language Modeling, Language Modelling, Relation

This paper aims to formalize a general definition of intelligence beyond human intelligence. We accomplish this by re-imagining the concept of equality as a fundamental abstraction for relation. We discover that the concept of equality = limits the sensitivity of our mathematics to abstract relationships. We propose a new relation principle that does not rely on the concept of equality but is consistent with existing mathematical abstractions. In essence, this paper proposes a conceptual framework for general interaction and argues that this framework is also an abstraction that satisfies the definition of Intelligence. Hence we define intelligence as a formalization of generality, represented by the abstraction ∆∞Ο, where each symbol represents the concepts infinitesimal, infinite, and finite respectively. In essence, this paper proposes a General Language Model (GLM), where the abstraction ∆∞Ο represents the foundational relationship of the model. This relation is colloquially termed 'The theory of everything'.
Youtube Video Download Using Python Project Report.
Authors: Kamal Acharya
Date: 2024-08-05 00:00:00
Proceeding: Authorea 2024 8

In order to form a clear sketch of this project, here's a brief introduction of the
features and scope of YouTube Video Downloader. This project consists of three modules which are
inter-linked to each other. Each of the above modules has certain specific task to perform. Tkinter is
the standard GUI library for Python. Python when combined with Tkinter provides a fast and easy way
to create GUI applications. Tkinter provides a powerful object- oriented interface to the Tk GUI toolkit.
Tkinter provides various controls, such as buttons, labels and text boxes used in a GUI application.
These controls are commonly called widgets.Pytube library makes the video downloading very easy.
Create the object of the YouTube module by passing the link as the parameter. Then, get the
appropriate extension and resolution of the video. You can set the name of the file as your convenience,
in another case original name will be kept. After that, download the file using the download function
which has one parameter which is the location where to download the file. Python, this task is very
easy. Few lines of code will download the video from YouTube for you.
Teacher record management system project report.
Authors: Kamal Acharya
Date: 2024-08-02 00:00:00
Proceeding: Authorea 2024 8
Tasks: Management, Navigate

The Teachers Record Management System is a comprehensive solution
designed to simplify the process of managing teacher records. In today's fast-paced world, it is
imperative for schools and individuals to have access to accurate and up-to-date information
about teachers. This system addresses the needs of both individuals and schools, offering an
efficient and effective way to manage teacher records.
This web-based technology was developed with the aim of providing an easy-to-use platform
that allows users to search and manage teacher records with ease. The system is designed to be
user-friendly, making it accessible to individuals with varying levels of computer literacy. The
interface is intuitive and easy to navigate, making it possible for users to quickly find the
information they need.
POST OFFICE MANAGEMENT SYSTEM PROJECT REPORT.
Authors: Kamal Acharya
Date: 2024-08-02 00:00:00
Proceeding: Authorea 2024 8
Tasks: Management

Speedy and efficient information processing is crucial to our socially and highly
developed technology. Computer can help the intolerable burden of handling the ever
increasing amount or information with government department, public services and
business concerns expected to contain because of their ability to analyze information
as well as to retain, update and reproduce it because of their versality to present it in a
variety of forms. This may also to some extent lead to problems occurring due to
information explosion.