pwc-archive/papers-with-abstracts huggingface dataset:
Sparse Incremental Aggregation in Satellite Federated Learning
Authors: Nasrin Razmi, Sourav Mukherjee, Bho Matthiesen, Armin Dekorsy, Petar Popovski
Date: 2025-01-20 00:00:00
Tasks: Federated Learning

This paper studies Federated Learning (FL) in low Earth orbit (LEO) satellite constellations, where satellites are connected via intra-orbit inter-satellite links (ISLs) to their neighboring satellites. During the FL training process, satellites in each orbit forward gradients from nearby satellites, which are eventually transferred to the parameter server (PS). To enhance the efficiency of the FL training process, satellites apply in-network aggregation, referred to as incremental aggregation. In this work, the gradient sparsification methods from [1] are applied to satellite scenarios to improve bandwidth efficiency during incremental aggregation. The numerical results highlight an increase of over 4 x in bandwidth efficiency as the number of satellites in the orbital plane increases.
Terahertz Integrated Sensing Communications and Powering for 6G Wireless Networks
Authors: Hua Yan, Yunfei Chen
Date: 2025-01-22 00:00:00

The terahertz (THz) band has attracted significant interest for future wireless networks. In this paper, a THz integrated sensing communications and powering (THz-ISCAP) system, where sensing is leveraged to enhance communications and powering, is studied. For a given total amount of time, we aim to determine an optimal time allocation for sensing to improve the efficiency of communications and powering, along with an optimal power splitting ratio to balance these two functionalities. This is achieved by maximizing between communications and powering either the achievable rate or harvested energy while ensuring a minimum requirement on the other. Numerical results indicate that the optimal system performance can be achieved by jointly optimizing the sensing time allocation and the power splitting ratio. Additionally, the results reveal the effects of various factors, such as THz frequencies and antenna aperture sizes, on the system performance. This study provides some interesting results to offer a new perspective for the research on THz-ISCAP.
Communication-Efficient Distributed Kalman Filtering using ADMM
Authors: Muhammad Iqbal, Kundan Kumar, Simo Särkkä
Date: 2025-01-22 00:00:00
Tasks: Distributed Optimization

This paper addresses the problem of optimal linear filtering in a network of local estimators, commonly referred to as distributed Kalman filtering (DKF). The DKF problem is formulated within a distributed optimization framework, where coupling constraints require the exchange of local state and covariance updates between neighboring nodes to achieve consensus. To address these constraints, the problem is transformed into an unconstrained optimization form using the augmented Lagrangian method. The distributed alternating direction method of multipliers (ADMM) is then applied to derive update steps that achieve the desired performance while exchanging only the primal variables. Notably, the proposed method enhances communication efficiency by eliminating the need for dual variable exchange. We show that the design parameters depend on the maximum eigenvalue of the network's Laplacian matrix, yielding a significantly tighter bound compared to existing results. A rigorous convergence analysis is provided, proving that the state estimates converge to the true state and that the covariance matrices across all local estimators converge to a globally optimal solution. Numerical results are presented to validate the efficacy of the proposed approach.
EmoFormer: A Text-Independent Speech Emotion Recognition using a Hybrid Transformer-CNN model
Authors: Rashedul Hasan, Meher Nigar, Nursadul Mamun, Sayan Paul
Date: 2025-01-22 00:00:00
Tasks: Emotion Recognition, Speech Emotion Recognition

Speech Emotion Recognition is a crucial area of research in human-computer interaction. While significant work has been done in this field, many state-of-the-art networks struggle to accurately recognize emotions in speech when the data is both speech and speaker-independent. To address this limitation, this study proposes, EmoFormer, a hybrid model combining CNNs (CNNs) with Transformer encoders to capture emotion patterns in speech data for such independent datasets. The EmoFormer network was trained and tested using the Expressive Anechoic Recordings of Speech (EARS) dataset, recently released by META. We experimented with two feature extraction techniques: MFCCs and x-vectors. The model was evaluated on different emotion sets comprising 5, 7, 10, and 23 distinct categories. The results demonstrate that the model achieved its best performance with five emotions, attaining an accuracy of 90%, a precision of 0.92, a recall, and an F1-score of 0.91. However, performance decreased as the number of emotions increased, with an accuracy of 83% for seven emotions compared to 70% for the baseline network. This study highlights the effectiveness of combining CNNs and Transformer-based architectures for emotion recognition from speech, particularly when using MFCC features.
A Bayesian Modelling Framework with Model Comparison for Epidemics with Super-Spreading
Authors: Hannah Craddock, Simon EF Spencer, Xavier Didelot
Date: 2025-01-22 00:00:00
Tasks: Model Selection, Time Series

The transmission dynamics of an epidemic are rarely homogeneous. Super-spreading events and super-spreading individuals are two types of heterogeneous transmissibility. Inference of super-spreading is commonly carried out on secondary case data, the expected distribution of which is known as the offspring distribution. However, this data is seldom available. Here we introduce a multi-model framework fit to incidence time-series, data that is much more readily available. The framework consists of five discrete-time, stochastic, branching-process models of epidemics spread through a susceptible population. The framework includes a baseline model of homogeneous transmission, a unimodal and a bimodal model for super-spreading events, as well as a unimodal and a bimodal model for super-spreading individuals. Bayesian statistics is used to infer model parameters using Markov Chain Monte-Carlo. Model comparison is conducted by computing Bayes factors, with importance sampling used to estimate the marginal likelihood of each model. This estimator is selected for its consistency and lower variance compared to alternatives. Application to simulated data from each model identifies the correct model for the majority of simulations and accurately infers the true parameters, such as the basic reproduction number. We also apply our methods to incidence data from the 2003 SARS outbreak and the Covid-19 pandemic. Model selection consistently identifies the same model and mechanism for a given disease, even when using different time series. Our estimates are consistent with previous studies based on secondary case data. Quantifying the contribution of super-spreading to disease transmission has important implications for infectious disease management and control. Our modelling framework is disease-agnostic and implemented as an R package, with potential to be a valuable tool for public health.
EmoTech: A Multi-modal Speech Emotion Recognition Using Multi-source Low-level Information with Hybrid Recurrent Network
Authors: Shamin Bin Habib Avro, Taieba Taher, Nursadul Mamun
Date: 2025-01-22 00:00:00
Tasks: Emotion Recognition, Multimodal Emotion Recognition, Speech Emotion Recognition, Word Embeddings

Emotion recognition is a critical task in human-computer interaction, enabling more intuitive and responsive systems. This study presents a multimodal emotion recognition system that combines low-level information from audio and text, leveraging both Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory Networks (BiLSTMs). The proposed system consists of two parallel networks: an Audio Block and a Text Block. Mel Frequency Cepstral Coefficients (MFCCs) are extracted and processed by a BiLSTM network and a 2D convolutional network to capture low-level intrinsic and extrinsic features from speech. Simultaneously, a combined BiLSTM-CNN network extracts the low-level sequential nature of text from word embeddings corresponding to the available audio. This low-level information from speech and text is then concatenated and processed by several fully connected layers to classify the speech emotion. Experimental results demonstrate that the proposed EmoTech accurately recognizes emotions from combined audio and text inputs, achieving an overall accuracy of 84%. This solution outperforms previously proposed approaches for the same dataset and modalities.
Comparative Statics for the Subjective
Authors: Mark Whitmeyer
Date: 2025-01-22 00:00:00
Tasks: Sensitivity

I study robust comparative statics for risk-averse subjective expected utility (SEU) maximizers. Starting with a finite menu of actions totally ordered by sensitivity to risk, I identify the transformations of her menu that lead a decision-maker to take a lower action, regardless of her particular utility function or belief. My main results reveal that a robust decrease in the action selected is guaranteed by an intuitive steepening/tilting of the actions' payoffs and necessitates a slightly weaker such steepening. This basic pattern generalizes to a broad class of non-EU preferences.
European Energy Vision 2060: Charting Diverse Pathways for Europe's Energy Transition
Authors: Mostafa Barani, Konstantin Löffler, Pedro Crespo del Granado, Nikita Moskalenko, Evangelos Panos, Franziska M. Hoffart, Christian von Hirschhausen, Maria Kannavou, Hans Auer, Karlo Hainsch, Tatiana González Grandón, Siri Mathisen, Asgeir Tomasgard
Date: 2025-01-22 00:00:00

Europe is warming at the fastest rate of all continents, experiencing a temperature increase of about 1{\deg}C higher than the corresponding global increase. Aiming to be the first climate-neutral continent by 2050 under the European Green Deal, Europe requires an in-depth understanding of the potential energy transition pathways. In this paper, we develop four qualitative long-term scenarios covering the European energy landscape, considering key uncertainty pillars -- categorized under social, technological, economic, political, and geopolitical aspects. First, we place the scenarios in a three-dimensional space defined by Social dynamics, Innovation, and Geopolitical instabilities. These scenarios are brought to life by defining their narratives and focus areas according to their location in this three-dimensional space. The scenarios envision diverse futures and include distinct features. The EU Trinity scenario pictures how internal divisions among EU member states, in the context of global geopolitical instability, affect the EU climate targets. The REPowerEU++ scenario outlines the steps needed for a self-sufficient, independent European energy system by 2050. The Go RES scenario examines the feasibility of achieving carbon neutrality earlier than 2050 given favourable uncertain factors. The NECP Essentials scenario extends current national energy and climate plans until 2060 to assess their role in realizing climate neutrality. The scenarios are extended by incorporating policies and economic factors and detailed in a Qualitative to Quantitative (Q2Q) matrix, linking narratives to quantification. Finally, two scenarios are quantified to illustrate the quantification process. All the scenarios are in the process of being quantified and will be openly available and reusable.
Driver Behavior Soft-Sensor Based on Neurofuzzy Systems and Weighted Projection on Principal Components
Authors: Juan Manuel Escaño, Miguel A. Ridao-Olivar, Carmelina Ierardi, Adolfo J. Sánchez, Kumars Rouzbehi
Date: 2025-01-20 00:00:00

This work has as main objective the development of a soft-sensor to classify, in real time, the behaviors of drivers when they are at the controls of a vehicle. Efficient classification of drivers' behavior while driving, using only the measurements of the sensors already incorporated in the vehicles and without the need to add extra hardware (smart phones, cameras, etc.), is a challenge. The main advantage of using only the data center signals of modern vehicles is economical. The classification of the driving behavior and the warning to the driver of dangerous behaviors without the need to add extra hardware (and their software) to the vehicle, would allow the direct integration of these classifiers into the current vehicles without incurring a greater cost in the manufacture of the vehicles and therefore be an added value. In this work, the classification is obtained based only on speed, acceleration and inertial measurements which are already present in many modern vehicles. The proposed algorithm is based on a structure made by several Neurofuzzy systems with the combination of projected data in components of various Principal Component Analysis. A comparison with several types of classical classifying algorithms has been made.
Distributed Model Predictive Control Design for Multi-agent Systems via Bayesian Optimization
Authors: Hossein Nejatbakhsh Esfahani, Kai Liu, Javad Mohammadpour Velni
Date: 2025-01-22 00:00:00
Tasks: Bayesian Optimization, Distributed Optimization, Model Predictive Control

This paper introduces a new approach that leverages Multi-agent Bayesian Optimization (MABO) to design Distributed Model Predictive Control (DMPC) schemes for multi-agent systems. The primary objective is to learn optimal DMPC schemes even when local model predictive controllers rely on imperfect local models. The proposed method invokes a dual decomposition-based distributed optimization framework, incorporating an Alternating Direction Method of Multipliers (ADMM)-based MABO algorithm to enable coordinated learning of parameterized DMPC schemes. This enhances the closed-loop performance of local controllers, despite discrepancies between their models and the actual multi-agent system dynamics. In addition to the newly proposed algorithms, this work also provides rigorous proofs establishing the optimality and convergence of the underlying learning method. Finally, numerical examples are given to demonstrate the efficacy of the proposed MABO-based learning approach.
Closed-loop robust control of long-term diabetes progression via physical activity management
Authors: Pierluigi Francesco De Paola, Alessandro Borri, Fabrizio Dabbene, Pasquale Palumbo, Alessia Paglialonga
Date: 2025-01-22 00:00:00
Tasks: Management, Model Predictive Control

Large clinical evidence acknowledges the crucial role played by physical activity in delaying the progression of type-2 diabetes. However, the literature lacks control approaches that leverage exercise for type-2 diabetes control and more in general lacks a quantitative assessment of medical guidelines on the recommended amount of physical activity to be performed, mainly due to the absence of mathematical models that suitably estimate its benefits on diabetes progression. In this work, in order to provide a control-theoretical formulation of the exercise, we design a feedback law in terms of recommended physical activity, following a model predictive control approach, based on a widespread compact diabetes progression model, suitably modified to properly account for the long-term effect of the exercise. Moreover we illustrate how the proposed approach proves to show reliable robustness properties with respect to initial conditions and parameter perturbations, which may be used to reflect inter-patient variability. Results are encouraging in view of the validation of the control law on comprehensive high-dimensional models of diabetes progression, with the aim of translating the prediction of the controller into reasonable recommendations and to quantitatively support medical decision-making.
A coupled FE-BE multi-scale method for the dynamics of jointed structures
Authors: Hendrik D. Linder, Johann Gross, Malte Krack
Date: 2025-01-22 00:00:00
Tasks: Friction

The damping of built-up structures stems largely from the microscopic dry frictional interactions in the contact interfaces. The accurate prediction of friction damping has been an important scientific aim of the past several decades. Recent research indicates that very good agreement with vibration measurements is to be expected if the actual contact surface topography is sufficiently well known and finely resolved, and frictional-unilateral interactions are modeled in terms of the Coulomb-Signorini conditions. Resolving all relevant length scales in one finite element model leads to enormous or even prohibitive computation effort and regularization of the set-valued contact laws might be needed to ensure numerical stability. In this work, we propose a multi-scale approach: The stress and deformation field in the contact region is modeled using elastic half-space theory, implemented on a regular and fine grid of boundary elements (BE), so that the compliance matrix can be expressed in closed form. The vibration behavior of the remaining region is described using a relatively coarse finite element (FE) model, which is further reduced via component mode synthesis. The two models are coupled by enforcing compatibility and equilibrium conditions in the far field. The set-valued Coulomb-Signorini conditions are enforced robustly and efficiently using a projected over-relaxation scheme in conjunction with an appropriate active-set strategy. For the S4 beam benchmark, very good agreement with regard to the amplitude-dependent frequency and damping ratio of the first few modes is achieved, while the computation effort is reduced by several orders of magnitude compared to the full-FE reference. The proposed multi-scale method permits a very fine resolution of the contact surface topography without suffering from numerical instability.
A Dense-Cloud Model for Gamma-Ray Bursts to Explain Bimodality
Authors: F. Shekh-Momeni, J. Samimi
Date: 2004-03-04 00:00:00

In this model a collimated ultra-relativistic ejecta collides with an amorphous dense cloud surrounding the central engine, producing gamma-rays via synchrotron process. The ejecta is taken as a standard candle, while assuming a gaussian distribution in thickness and density of the surrounding cloud. Due to the cloud high density, the synchrotron emission would be an instantaneous phenomenon (fast cooling synchrotron radiation), so a GRB duration corresponds to the time that the ejecta takes to pass through the cloud. Fitting the model with the observed bimodal distribution of GRBs' durations, the ejecta's initial Lorentz factor, and its initial opening angle are obtained as $\Gamma_{0}\lesssim 10^{3}$, and $\zeta_{0} \approx 10^{-2}$, and the mean density and mean thickness of the surrounding cloud as $\bar{n} \sim 3 \times 10^{17} cm^{-3}$ and $\bar{L} \sim 2 \times 10^{13} cm$. The clouds maybe interpreted as the extremely amorphous envelops of Thorne-Zytkow objects. In this model the two classes of long and short duration GRBs are explained in a unique frame.
Detecting the Adversarially-Learned Injection Attacks via Knowledge Graphs
Authors: Yaojun Hao*, 1, Haotian Wang2, Qingshan Zhao1, Liping Feng1, Jian Wang1
Date: 2024-11-15 00:00:00
Proceeding: 2024 2024 11
Tasks: Knowledge Graphs, Recommendation Systems

ABSTRACT: Over the past two decades, many studies have devoted a good deal of attention to detect injection attacks in recommender systems. However, most of the studies mainly focus on detecting the heuristically-generated injection attacks, which are heuristically fabricated by hand-engineering. In practice, the adversarially-learned injection attacks have been proposed based on optimization methods and enhanced the ability in the camouflage and threat. Under the adversarially-learned injection attacks, the traditional detection models are likely to be fooled. In this paper, a detection method is proposed for the adversarially-learned injection attacks via knowledge graphs. Firstly, with the advantages of wealth information from knowledge graphs, item-pairs on the extension hops of knowledge graphs are regarded as the implicit preferences for users. Also, the item-pair popularity series and user item-pair matrix are constructed to express the user’s preferences. Secondly, the word embedding model and principal component analysis are utilized to extract the user’s initial vector representations from the item-pair popularity series and item-pair matrix, respectively. Moreover, the Variational Autoencoders with the improved R-drop regularization are used to reconstruct the embedding vectors and further identify the shilling profiles. Finally, the experiments on three real-world datasets indicate that the proposed detector has superior performance than benchmark methods when detecting the adversarially-learned injection attacks. In addition, the detector is evaluated under the heuristically-generated injection attacks and demonstrates the outstanding performance.
A Dataset for Analysing Complex Document Layouts in the Digital Humanities and Its Evaluation with Krippendorff’s Alpha
Authors: David Tschirschwitz, Franziska Klemstein, Benno Stein, Volker Rodehorst
Date: 2022-09-20 00:00:00
Proceeding: GCPR 2022 9
Tasks: Instance Segmentation, Object Detection, Point Cloud Segmentation, Segmentation, Semantic Segmentation

We introduce a new research resource in the form of a high-quality, domain-specific dataset for analysing the document layout of historical documents. The dataset provides an instance segmentation ground truth with 19 classes based on historical layout structures that stem (a) from the publication production process and the respective genres (life sciences, architecture, art, decorative arts, etc.) and, (b) from selected text registers (such as monograph, trade journal, illustrated magazine). Altogether, the dataset contains more than 52,000 instances annotated by experts. A baseline has been tested with the well-known Mask R-CNN and compared to the state-of-the-art model VSR. Inspired by evaluation practices from the field of Natural Language Processing (NLP), we have developed a new method for evaluating annotation consistency. Our method is based on Krippendorff’s alpha (K-
), a statistic for quantifying the so-called “inter-annotator-agreement”. In particular, we propose an adaptation of K-alpha
 that treats annotations as a multipartite graph for assessing the agreement of a variable number of annotators. The method is adjustable with regard to evaluation strictness, and it can be used in 2D or 3D as well as for a variety of tasks such as semantic segmentation, instance segmentation, and 3D point cloud segmentation.
Mix-of-Granularity: Optimize the Chunking Granularity for Retrieval-Augmented Generation
Authors: Zijie Zhong, Hanwen Liu, Xiaoya Cui, Xiaofan Zhang, Zengchang Qin
Date: 2024-06-01 00:00:00
Tasks: Chunking, RAG, Retrieval, Retrieval-augmented Generation

Integrating information from various reference databases is a major challenge for Retrieval-Augmented Generation (RAG) systems because each knowledge source adopts a unique data structure and follows different conventions. Retrieving from multiple knowledge sources with one fixed strategy usually leads to under-exploitation of information. To mitigate this drawback, inspired by Mix-of-Expert, we introduce Mix-of-Granularity (MoG), a method that dynamically determines the optimal granularity of a knowledge source based on input queries using a router. The router is efficiently trained with a newly proposed loss function employing soft labels. We further extend MoG to MoG-Graph (MoGG), where reference documents are pre-processed as graphs, enabling the retrieval of distantly situated snippets. Experiments demonstrate that MoG and MoGG effectively predict optimal granularity levels, significantly enhancing the performance of the RAG system in downstream tasks. The code of both MoG and MoGG are released in https://github.com/ZGChung/Mix-of-Granularity.