pwc-archive/papers-with-abstracts huggingface dataset:
Cross-Layer Encrypted Semantic Communication Framework for Panoramic Video Transmission
Authors: Haixiao Gao, Mengying Sun, Xiaodong Xu, Bingxuan Xu, Shujun Han, Bizhu Wang, Sheng Jiang, Chen Dong, Ping Zhang
Date: 2024-11-19 00:00:00
Tasks: Semantic Communication

In this paper, we propose a cross-layer encrypted semantic communication (CLESC) framework for panoramic video transmission, incorporating feature extraction, encoding, encryption, cyclic redundancy check (CRC), and retransmission processes to achieve compatibility between semantic communication and traditional communication systems. Additionally, we propose an adaptive cross-layer transmission mechanism that dynamically adjusts CRC, channel coding, and retransmission schemes based on the importance of semantic information. This ensures that important information is prioritized under poor transmission conditions. To verify the aforementioned framework, we also design an end-to-end adaptive panoramic video semantic transmission (APVST) network that leverages a deep joint source-channel coding (Deep JSCC) structure and attention mechanism, integrated with a latitude adaptive module that facilitates adaptive semantic feature extraction and variable-length encoding of panoramic videos. The proposed CLESC is also applicable to the transmission of other modal data. Simulation results demonstrate that the proposed CLESC effectively achieves compatibility and adaptation between semantic communication and traditional communication systems, improving both transmission efficiency and channel adaptability. Compared to traditional cross-layer transmission schemes, the CLESC framework can reduce bandwidth consumption by 85% while showing significant advantages under low signal-to-noise ratio (SNR) conditions.
VayuBuddy: an LLM-Powered Chatbot to Democratize Air Quality Insights
Authors: Zeel B Patel, Yash Bachwana, Nitish Sharma, Sarath Guttikunda, Nipun Batra
Date: 2024-11-16 00:00:00
Tasks: Chatbot, Language Modeling, Language Modelling, Large Language Model

Nearly 6.7 million lives are lost due to air pollution every year. While policymakers are working on the mitigation strategies, public awareness can help reduce the exposure to air pollution. Air pollution data from government-installed sensors is often publicly available in raw format, but there is a non-trivial barrier for various stakeholders in deriving meaningful insights from that data. In this work, we present VayuBuddy, a Large Language Model (LLM)-powered chatbot system to reduce the barrier between the stakeholders and air quality sensor data. VayuBuddy receives the questions in natural language, analyses the structured sensory data with a LLM-generated Python code and provides answers in natural language. We use the data from Indian government air quality sensors. We benchmark the capabilities of 7 LLMs on 45 diverse question-answer pairs prepared by us. Additionally, VayuBuddy can also generate visual analysis such as line-plots, map plot, bar charts and many others from the sensory data as we demonstrate in this work.
A Library Perspective on Supervised Text Processing in Digital Libraries: An Investigation in the Biomedical Domain
Authors: Hermann Kroll, Pascal Sackhoff, Bill Matthias Thang, Maha Ksouri, Wolf-Tilo Balke
Date: 2024-11-06 00:00:00
Tasks: Knowledge Graphs, Relation Extraction, text-classification, Text Classification

Digital libraries that maintain extensive textual collections may want to further enrich their content for certain downstream applications, e.g., building knowledge graphs, semantic enrichment of documents, or implementing novel access paths. All of these applications require some text processing, either to identify relevant entities, extract semantic relationships between them, or to classify documents into some categories. However, implementing reliable, supervised workflows can become quite challenging for a digital library because suitable training data must be crafted, and reliable models must be trained. While many works focus on achieving the highest accuracy on some benchmarks, we tackle the problem from a digital library practitioner. In other words, we also consider trade-offs between accuracy and application costs, dive into training data generation through distant supervision and large language models such as ChatGPT, LLama, and Olmo, and discuss how to design final pipelines. Therefore, we focus on relation extraction and text classification, using the showcase of eight biomedical benchmarks.
From Centralized RAN to Open RAN: A Survey on the Evolution of Distributed Antenna Systems
Authors: Mahmoud A. Hasabelnaby, Mohanad Obeed, Mohammed Saif, Anas Chaaban, M. J. Hossain
Date: 2024-11-19 00:00:00
Tasks: Edge-computing

Next-generation mobile networks require evolved radio access network (RAN) architectures to meet the demands of high capacity, massive connectivity, reduced costs, and energy efficiency, and to realize communication with ultra-low latency and ultra-high reliability. {Meeting such} requirements for both mobile users and vertical industries in the next decade {requires novel solutions. One of the potential solutions that attracted significant research attention in the past 15 years} is to redesign the radio access network (RAN). In this survey, we present a comprehensive survey on distributed antenna system (DAS) architectures that address these challenges and improve network performance. We cover the transition from traditional decentralized RAN to DAS, including cloud radio-access networks (C-RAN), fog radio-access networks (F-RAN), virtualized radio-access networks (V-RAN), cell-free massive multiple-input multiple-output (CF-mMIMO), and {the most recent advances manifested in} open radio-access network (O-RAN). In the process, we discuss the benefits and limitations of these architectures, including the impact of limited-capacity fronthaul links, various cooperative uplink and downlink coding strategies, cross-layer optimization, and techniques to optimize the performance of DAS. Moreover, we review key enabling technologies for next-generation RAN systems, such as multi-access edge computing, network function virtualization, software-defined networking, and network slicing; in addition to some crucial radio access technologies, such as millimeter wave, massive multi-input multi-output, device-to-device communication, and massive machine-type communication. Last but not least, we discuss the major research challenges in DAS and identify several possible directions for future research.
OpenMS WebApps: Building User-Friendly Solutions for MS Analysis
Authors: Tom David Müller, Arslan Siraj, Axel Walter, Jihyung Kim, Samuel Wein, Johannes von Kleist, Ayesha Feroz, Matteo Pilz, Kyowon Jeong, Justin Cyril Sing, Joshua Charkow, Hannes Luc Röst, Timo Sachsenberg
Date: 2024-11-20 00:00:00

Liquid Chromatography Mass Spectrometry (LC-MS) is an indispensable analytical technique in proteomics, metabolomics, and other life sciences. While OpenMS provides advanced open-source software for MS data analysis, its complexity can be challenging for non-experts. To address this, we have developed OpenMS WebApps, a framework for creating user-friendly MS web applications based on the Streamlit Python package. OpenMS WebApps simplifies MS data analysis through an intuitive graphical user interface, interactive result visualizations, and support for both local and online execution. Key features include workspaces management, automatic generation of input widgets, and parallel execution of tools resulting in highperformance and ready-to-use solutions for online and local deployment. This framework benefits both researchers and developers: scientists can focus on their research without the burden of complex software setups, and developers can rapidly create and distribute custom WebApps with novel algorithms. Several applications built on the OpenMS WebApps template demonstrate its utility across diverse MS-related fields, enhancing the OpenMS eco-system for developers and a wider range of users. Furthermore, it integrates seamlessly with third-party software, extending benefits to developers beyond the OpenMS community.
Efficient and Physically-Consistent Modeling of Reconfigurable Electromagnetic Structures
Authors: Alexander Stutz-Tirri, Georg Schwan, Christoph Studer
Date: 2024-11-20 00:00:00

Reconfigurable electromagnetic structures (REMSs), such as reconfigurable reflectarrays (RRAs) or reconfigurable intelligent surfaces (RISs), hold significant potential to improve wireless communication and sensing systems. Even though several REMS modeling approaches have been proposed in recent years, the literature lacks models that are both computationally efficient and physically consistent. As a result, algorithms that control the reconfigurable elements of REMSs (e.g., the phase shifts of an RIS) are often built on simplistic models that are inaccurate. To enable physically accurate REMS-parameter tuning, we present a new framework for efficient and physically consistent modeling of general REMSs. Our modeling method combines a circuit-theoretic approach with a new formalism that describes a REMS's interaction with the electromagnetic (EM) waves in its far-field region. Our modeling method enables efficient computation of the entire far-field radiation pattern for arbitrary configurations of the REMS reconfigurable elements once a single full-wave EM simulation of the non-reconfigurable parts of the REMS has been performed. The predictions made by the proposed framework align with the physical laws of classical electrodynamics and model effects caused by inter-antenna coupling, non-reciprocal materials, polarization, ohmic losses, matching losses, influence of metallic housings, noise from low-noise amplifiers, and noise arising in or received by antennas. In order to validate the efficiency and accuracy of our modeling approach, we (i) compare our modeling method to EM simulations and (ii) conduct a case study involving a planar RRA that enables simultaneous multiuser beam- and null-forming using a new, computationally efficient, and physically accurate parameter tuning algorithm.
Age of Information Minimization in UAV-Assisted Covert Communication: Trajectory and Beamforming Design
Authors: Shima Salar Hosseini, Paeiz Azmi, Ali Nazari
Date: 2024-11-19 00:00:00

Unmanned aerial vehicles (UAVs) have the potential for time-sensitive applications. Due to wireless channel variation, received data may have an expiration time, particularly in critical situations such as rescue operations, natural disasters, or the military. Age of Information (AoI) is a metric that measures the freshness of received packets to specify the validity period of information. In addition, it is necessary to guarantee the privacy of confidential information transmission through air-to-ground links against eavesdroppers. This paper investigates UAV-assisted covert communication to minimize AoI in the presence of an aerial eavesdropper for the first time. However, to ensure the eavesdropper's error detection rate, UAV-enabled beamforming employs the power-domain non-orthogonal multiple access (PD-NOMA) technique to cover the covert user by a public user. PD-NOMA technique significantly improves the user's AoI, too. The joint optimization problem contains non-convex constraints and coupled optimization variables, including UAV trajectory, beamforming design, and the user's AoI which is challenging to derive a direct solution. We have developed an efficient alternating optimization technique to address the formulated optimization problem. Numerical results demonstrate the impact of the main parameters on the performance of the proposed communication system.
IoT-Based Coma Patient Monitoring System
Authors: Hailemicael Lulseged Yimer, Hailegabriel Dereje Degefa, Marco Cristani, Federico Cunico
Date: 2024-11-20 00:00:00

Continuous monitoring of coma patients is essential but challenging, especially in developing countries with limited resources, staff, and infrastructure. This paper presents a low-cost IoT-based system designed for such environments. It uses affordable hardware and robust software to monitor patients without constant internet access or extensive medical personnel. The system employs cost-effective sensors to track vital signs, including heart rate, body temperature, blood pressure, eye movement, and body position. An energy-efficient microcontroller processes data locally, synchronizing with a central server when network access is available. A locally hosted app provides on-site access to patient data, while a GSM module sends immediate alerts for critical events, even in areas with limited cellular coverage. This solution emphasizes ease of deployment, minimal maintenance, and resilience to power and network disruptions. Using open-source software and widely available hardware, it offers a scalable, adaptable system for resource-limited settings. At under $30, the system is a sustainable, cost-effective solution for continuous patient monitoring, bridging the gap until more advanced healthcare infrastructure is available.
Radiance Field Delta Video Compression in Edge-Enabled Vehicular Metaverse
Authors: Matúš Dopiriak, Eugen Šlapak, Juraj Gazda, Devendra S. Gurjar, Mohammad Abdullah Al Faruque, Marco Levorato
Date: 2024-11-01 00:00:00
Tasks: Autonomous Vehicles, Decoder, Edge-computing, SSIM, Video Compression

Connected and autonomous vehicles (CAVs) offload computationally intensive tasks to multi-access edge computing (MEC) servers via vehicle-to-infrastructure (V2I) communication, enabling applications within the vehicular metaverse, which transforms physical environment into the digital space enabling advanced analysis or predictive modeling. A core challenge is physical-to-virtual (P2V) synchronization through digital twins (DTs), reliant on MEC networks and ultra-reliable low-latency communication (URLLC). To address this, we introduce radiance field (RF) delta video compression (RFDVC), which uses RF-encoder and RF-decoder architecture using distributed RFs as DTs storing photorealistic 3D urban scenes in compressed form. This method extracts differences between CAV-frame capturing actual traffic and RF-frame capturing empty scene from the same camera pose in batches encoded and transmitted over the MEC network. Experiments show data savings up to 71% against H.264 codec and 44% against H.265 codec under different conditions as lighting changes, and rain. RFDVC also demonstrates resilience to transmission errors, achieving up to +0.29 structural similarity index measure (SSIM) improvement at block error rate (BLER) = 0.35 in non-rainy and +0.25 at BLER = 0.2 in rainy conditions, ensuring superior visual quality compared to standard video coding (VC) methods across various conditions.
Moving Horizon Estimation for Simultaneous Localization and Mapping with Robust Estimation Error Bounds
Authors: Jelena Trisovic, Alexandre Didier, Simon Muntwiler, Melanie N. Zeilinger
Date: 2024-11-20 00:00:00
Tasks: Computational Efficiency, Simultaneous Localization and Mapping

This paper presents a robust moving horizon estimation (MHE) approach with provable estimation error bounds for solving the simultaneous localization and mapping (SLAM) problem. We derive sufficient conditions to guarantee robust stability in ego-state estimates and bounded errors in landmark position estimates, even under limited landmark visibility which directly affects overall system detectability. This is achieved by decoupling the MHE updates for the ego-state and landmark positions, enabling individual landmark updates only when the required detectability conditions are met. The decoupled MHE structure also allows for parallelization of landmark updates, improving computational efficiency. We discuss the key assumptions, including ego-state detectability and Lipschitz continuity of the landmark measurement model, with respect to typical SLAM sensor configurations, and introduce a streamlined method for the range measurement model. Simulation results validate the considered method, highlighting its efficacy and robustness to noise.
Energy-based features and bi-LSTM neural network for EEG-based music and voice classification
Authors: Isaac Ariza, Ana M. Barbancho, Lorenzo J. Tardon, Isabel Barbancho
Date: 2024-11-20 00:00:00
Tasks: Binary Classification, Classification, EEG, Multi-class Classification

The human brain receives stimuli in multiple ways; among them, audio constitutes an important source of relevant stimuli for the brain regarding communication, amusement, warning, etc. In this context, the aim of this manuscript is to advance in the classification of brain responses to music of diverse genres and to sounds of different nature: speech and music. For this purpose, two different experiments have been designed to acquiere EEG signals from subjects listening to songs of different musical genres and sentences in various languages. With this, a novel scheme is proposed to characterize brain signals for their classification; this scheme is based on the construction of a feature matrix built on relations between energy measured at the different EEG channels and the usage of a bi-LSTM neural network. With the data obtained, evaluations regarding EEG-based classification between speech and music, different musical genres, and whether the subject likes the song listened to or not are carried out. The experiments unveil satisfactory performance to the proposed scheme. The results obtained for binary audio type classification attain 98.66% of success. In multi-class classification between 4 musical genres, the accuracy attained is 61.59%, and results for binary classification of musical taste rise to 96.96%.
An exploration of the effect of quantisation on energy consumption and inference time of StarCoder2
Authors: Pepijn de Reus, Ana Oprescu, Jelle Zuidema
Date: 2024-11-15 00:00:00
Tasks: Model Compression, Quantization

This study examines quantisation and pruning strategies to reduce energy consumption in code Large Language Models (LLMs) inference. Using StarCoder2, we observe increased energy demands with quantization due to lower throughput and some accuracy losses. Conversely, pruning reduces energy usage but impairs performance. The results highlight challenges and trade-offs in LLM model compression. We suggest future work on hardware-optimized quantization to enhance efficiency with minimal loss in accuracy.
Green My LLM: Studying the key factors affecting the energy consumption of code assistants
Authors: Tristan Coignion, Clément Quinton, Romain Rouvoy
Date: 2024-11-07 00:00:00
Tasks: Quantization

In recent years,Large Language Models (LLMs) have significantly improved in generating high-quality code, enabling their integration into developers' Integrated Development Environments (IDEs) as code assistants. These assistants, such as GitHub Copilot, deliver real-time code suggestions and can greatly enhance developers' productivity. However, the environmental impact of these tools, in particular their energy consumption, remains a key concern. This paper investigates the energy consumption of LLM-based code assistants by simulating developer interactions with GitHub Copilot and analyzing various configuration factors. We collected a dataset of development traces from 20 developers and conducted extensive software project development simulations to measure energy usage under different scenarios. Our findings reveal that the energy consumption and performance of code assistants are influenced by various factors, such as the number of concurrent developers, model size, quantization methods, and the use of streaming. Notably, a substantial portion of generation requests made by GitHub Copilot is either canceled or rejected by developers, indicating a potential area for reducing wasted computations. Based on these findings, we share actionable insights into optimizing configurations for different use cases, demonstrating that careful adjustments can lead to significant energy savings.
Why Anticipatory Sensing Matters in Commercial ACC Systems under Cut-In Scenarios: A Perspective from Stochastic Safety Analysis
Authors: Hao Zhang, Sixu Li, Zihao Li, Mohammad Anis, Dominique Lord, Yang Zhou
Date: 2024-11-20 00:00:00

This study presents an analytical solution for the vehicle state evolution of Adaptive Cruise Control (ACC) systems under cut-in scenarios, incorporating sensing delays and anticipation using the Lambert W function. The theoretical analysis demonstrates that the vehicle state evolution and the corresponding safety of ACC in cut-in situations are influenced by multiple factors, including the original leading vehicle's state, the initial conditions of the cut-in vehicle, subsequent cut-in maneuvers, sensing delays, and the ACC's anticipation capabilities. To quantitatively assess these influences, a series of numerical experiments were conducted to perform a stochastic safety analysis of ACC systems, accounting for embedded sensing delays and anticipation, using empirically calibrated control parameters from real-world data. The experiments revealed that the impact of sensing delays on ACC is multifaceted. Specifically, sensing delays negatively affect ACC stability, with the severity increasing as the delay lengthens. Furthermore, collision risk in cut-in scenarios becomes more significant with sensing delays, particularly when the cut-in vehicle is slower than the following vehicle and when cut-ins are aggressive. However, anticipation plays a crucial role in mitigating these risks. Even with a 0.6-second anticipation, collision risk can be reduced by 91% in highly adverse scenarios. Finally, both sensing delays and anticipation have effects that intensify with their duration. An anticipation period of 2 seconds effectively ensures safety in aggressive cut-in conditions, even in the presence of sensing delays.
Issues with Input-Space Representation in Nonlinear Data-Based Dissipativity Estimation
Authors: Ethan LoCicero, Alex Penne, Leila Bridgeman
Date: 2024-11-20 00:00:00

In data-based control, dissipativity can be a powerful tool for attaining stability guarantees for nonlinear systems if that dissipativity can be inferred from data. This work provides a tutorial on several existing methods for data-based dissipativity estimation of nonlinear systems. The interplay between the underlying assumptions of these methods and their sample complexity is investigated. It is shown that methods based on delta-covering result in an intractable trade-off between sample complexity and robustness. A new method is proposed to quantify the robustness of machine learning-based dissipativity estimation. It is shown that this method achieves a more tractable trade-off between robustness and sample complexity. Several numerical case studies demonstrate the results.