pwc-archive/papers-with-abstracts huggingface dataset:
Statistical Tools for Frequency Response Functions from Posture Control Experiments: Estimation of Probability of a Sample and Comparison Between Groups of Unpaired Samples
Authors: Vittorio Lippi
Date: 2025-01-28 00:00:00

The frequency response function (FRF) is an established way to describe the outcome of experiments in posture control literature. The FRF is an empirical transfer function between an input stimulus and the induced body segment sway profile, represented as a vector of complex values associated with a vector of frequencies. Having obtained an FRF from a trial with a subject, it can be useful to quantify the likelihood it belongs to a certain population, e.g., to diagnose a condition or to evaluate the human likeliness of a humanoid robot or a wearable device. In this work, a recently proposed method for FRF statistics based on confidence bands computed with bootstrap will be summarized, and, on its basis, possible ways to quantify the likelihood of FRFs belonging to a given set will be proposed. Furthermore, a statistical test to compare groups of unpaired samples is presented.
L-Sort: On-chip Spike Sorting with Efficient Median-of-Median Detection and Localization-based Clustering
Authors: Yuntao Han, Yihan Pan, Xiongfei Jiang, Cristian Sestito, Shady Agwa, Themis Prodromakis, Shiwei Wang
Date: 2025-01-27 00:00:00
Tasks: Clustering, Spike Sorting

Spike sorting is a critical process for decoding large-scale neural activity from extracellular recordings. The advancement of neural probes facilitates the recording of a high number of neurons with an increase in channel counts, arising a higher data volume and challenging the current on-chip spike sorters. This paper introduces L-Sort, a novel on-chip spike sorting solution featuring median-of-median spike detection and localization-based clustering. By combining the median-of-median approximation and the proposed incremental median calculation scheme, our detection module achieves a reduction in memory consumption. Moreover, the localization-based clustering utilizes geometric features instead of morphological features, thus eliminating the memory-consuming buffer for containing the spike waveform during feature extraction. Evaluation using Neuropixels datasets demonstrates that L-Sort achieves competitive sorting accuracy with reduced hardware resource consumption. Implementations on FPGA and ASIC (180 nm technology) demonstrate significant improvements in area and power efficiency compared to state-of-the-art designs while maintaining comparable accuracy. If normalized to 22 nm technology, our design can achieve roughly $\times 10$ area and power efficiency with similar accuracy, compared with the state-of-the-art design evaluated with the same dataset. Therefore, L-Sort is a promising solution for real-time, high-channel-count neural processing in implantable devices.
Explainable and Robust Millimeter Wave Beam Alignment for AI-Native 6G Networks
Authors: Nasir Khan, Asmaa Abdallah, Abdulkadir Celik, Ahmed M. Eltawil, Sinem Coleri
Date: 2025-01-23 00:00:00
Tasks: Beam Prediction, Outlier Detection

Integrated artificial intelligence (AI) and communication has been recognized as a key pillar of 6G and beyond networks. In line with AI-native 6G vision, explainability and robustness in AI-driven systems are critical for establishing trust and ensuring reliable performance in diverse and evolving environments. This paper addresses these challenges by developing a robust and explainable deep learning (DL)-based beam alignment engine (BAE) for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. The proposed convolutional neural network (CNN)-based BAE utilizes received signal strength indicator (RSSI) measurements over a set of wide beams to accurately predict the best narrow beam for each UE, significantly reducing the overhead associated with exhaustive codebook-based narrow beam sweeping for initial access (IA) and data transmission. To ensure transparency and resilience, the Deep k-Nearest Neighbors (DkNN) algorithm is employed to assess the internal representations of the network via nearest neighbor approach, providing human-interpretable explanations and confidence metrics for detecting out-of-distribution inputs. Experimental results demonstrate that the proposed DL-based BAE exhibits robustness to measurement noise, reduces beam training overhead by 75% compared to the exhaustive search while maintaining near-optimal performance in terms of spectral efficiency. Moreover, the proposed framework improves outlier detection robustness by up to 5x and offers clearer insights into beam prediction decisions compared to traditional softmax-based classifiers.
Assessment of the January 2025 Los Angeles County wildfires: A multi-modal analysis of impact, response, and population exposure
Authors: Seyd Teymoor Seydi
Date: 2025-01-20 00:00:00
Tasks: Management

This study presents a comprehensive analysis of four significant California wildfires: Palisades, Eaton, Kenneth, and Hurst, examining their impacts through multiple dimensions, including land cover change, jurisdictional management, structural damage, and demographic vulnerability. Using the Chebyshev-Kolmogorov-Arnold network model applied to Sentinel-2 imagery, the extent of burned areas was mapped, ranging from 315.36 to 10,960.98 hectares. Our analysis revealed that shrubland ecosystems were consistently the most affected, comprising 57.4-75.8% of burned areas across all events. The jurisdictional assessment demonstrated varying management complexities, from singular authority (98.7% in the Palisades Fire) to distributed management across multiple agencies. A structural impact analysis revealed significant disparities between urban interface fires (Eaton: 9,869 structures; Palisades: 8,436 structures) and rural events (Kenneth: 24 structures; Hurst: 17 structures). The demographic analysis showed consistent gender distributions, with 50.9% of the population identified as female and 49.1% as male. Working-age populations made up the majority of the affected populations, ranging from 53.7% to 54.1%, with notable temporal shifts in post-fire periods. The study identified strong correlations between urban interface proximity, structural damage, and population exposure. The Palisades and Eaton fires affected over 20,000 people each, compared to fewer than 500 in rural events. These findings offer valuable insights for the development of targeted wildfire management strategies, particularly in wildland urban interface zones, and emphasize the need for age- and gender-conscious approaches in emergency response planning.
EVolutionary Independent DEtermiNistiC Explanation
Authors: Vincenzo Dentamaro, Paolo Giglio, Donato Impedovo, Giuseppe Pirlo
Date: 2025-01-20 00:00:00
Tasks: Diagnostic, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI), Genre classification, Music Classification

The widespread use of artificial intelligence deep neural networks in fields such as medicine and engineering necessitates understanding their decision-making processes. Current explainability methods often produce inconsistent results and struggle to highlight essential signals influencing model inferences. This paper introduces the Evolutionary Independent Deterministic Explanation (EVIDENCE) theory, a novel approach offering a deterministic, model-independent method for extracting significant signals from black-box models. EVIDENCE theory, grounded in robust mathematical formalization, is validated through empirical tests on diverse datasets, including COVID-19 audio diagnostics, Parkinson's disease voice recordings, and the George Tzanetakis music classification dataset (GTZAN). Practical applications of EVIDENCE include improving diagnostic accuracy in healthcare and enhancing audio signal analysis. For instance, in the COVID-19 use case, EVIDENCE-filtered spectrograms fed into a frozen Residual Network with 50 layers improved precision by 32% for positive cases and increased the area under the curve (AUC) by 16% compared to baseline models. For Parkinson's disease classification, EVIDENCE achieved near-perfect precision and sensitivity, with a macro average F1-Score of 0.997. In the GTZAN, EVIDENCE maintained a high AUC of 0.996, demonstrating its efficacy in filtering relevant features for accurate genre classification. EVIDENCE outperformed other Explainable Artificial Intelligence (XAI) methods such as LIME, SHAP, and GradCAM in almost all metrics. These findings indicate that EVIDENCE not only improves classification accuracy but also provides a transparent and reproducible explanation mechanism, crucial for advancing the trustworthiness and applicability of AI systems in real-world settings.
Collaborative Channel Access and Transmission for NR Sidelink and Wi-Fi Coexistence over Unlicensed Spectrum
Authors: Zhuangzhuang Yan, Xinyu Gu, Zhenyu Liu, Liyang Lu
Date: 2025-01-20 00:00:00
Tasks: Deep Reinforcement Learning, Fairness

With the rapid development of various internet of things (IoT) applications, including industrial IoT (IIoT) and visual IoT (VIoT), the demand for direct device-to-device communication to support high data rates continues to grow. To address this demand, 5G-Advanced has introduced sidelink communication over the unlicensed spectrum (SL-U) to increase data rates. However, the primary challenge of SL-U in the unlicensed spectrum is ensuring fair coexistence with other incumbent systems, such as Wi-Fi. In this paper, we address the challenge by designing channel access mechanisms and power control strategies to mitigate interference and ensure fair coexistence. First, we propose a novel collaborative channel access (CCHA) mechanism that integrates channel access with resource allocation through collaborative interactions between base stations (BS) and SL-U users. This mechanism ensures fair coexistence with incumbent systems while improving resource utilization. Second, to further enhance the performance of the coexistence system, we develop a cooperative subgoal-based hierarchical deep reinforcement learning (C-GHDRL) algorithm framework. The framework enables SL-U users to make globally optimal decisions by leveraging cooperative operations between the BS and SL-U users, effectively overcoming the limitations of traditional optimization methods in solving joint optimization problems with nonlinear constraints. Finally, we mathematically model the joint channel access and power control problem and balance the trade-off between fairness and transmission rate in the coexistence system by defining a suitable reward function in the C-GHDRL algorithm. Simulation results demonstrate that the proposed scheme significantly enhances the performance of the coexistence system while ensuring fair coexistence between SL-U and Wi-Fi users.
SCDM: Score-Based Channel Denoising Model for Digital Semantic Communications
Authors: Hao Mo, Yaping Sun, Shumin Yao, Hao Chen, Zhiyong Chen, Xiaodong Xu, Nan Ma, Meixia Tao, Shuguang Cui
Date: 2025-01-18 00:00:00
Tasks: Denoising, SSIM

Score-based diffusion models represent a significant variant within the diffusion model family and have seen extensive application in the increasingly popular domain of generative tasks. Recent investigations have explored the denoising potential of diffusion models in semantic communications. However, in previous paradigms, noise distortion in the diffusion process does not match precisely with digital channel noise characteristics. In this work, we introduce the Score-Based Channel Denoising Model (SCDM) for Digital Semantic Communications (DSC). SCDM views the distortion of constellation symbol sequences in digital transmission as a score-based forward diffusion process. We design a tailored forward noise corruption to align digital channel noise properties in the training phase. During the inference stage, the well-trained SCDM can effectively denoise received semantic symbols under various SNR conditions, reducing the difficulty for the semantic decoder in extracting semantic information from the received noisy symbols and thereby enhancing the robustness of the reconstructed semantic information. Experimental results show that SCDM outperforms the baseline model in PSNR, SSIM, and MSE metrics, particularly at low SNR levels. Moreover, SCDM reduces storage requirements by a factor of 7.8. This efficiency in storage, combined with its robust denoising capability, makes SCDM a practical solution for DSC across diverse channel conditions.
A framework for IoT-Enabled Smart Agriculture
Authors: Nsengiyumva Wilberforce, Johnson Mwebaze
Date: 2025-01-17 00:00:00
Tasks: Weather Forecasting

Unpredictable weather patterns and a lack of timely, accurate information significantly challenge farmers in Uganda, leading to poor crop management, reduced yields, and heightened vulnerability to environmental stress. This research presents a framework for IoT-enabled smart agriculture, leveraging Raspberry Pi-based technology to provide real-time monitoring of weather and environmental conditions. The framework integrates sensors for temperature, rainfall, soil moisture, and pressure, connected via an MCP3208 analog-to-digital converter. Data is displayed on an LCD for immediate feedback and transmitted to the ThingSpeak platform for centralized storage, analysis, and remote access through a mobile app or web interface. Farmers can leverage this framework to optimize irrigation schedules and improve crop productivity through actionable insights derived from real-time and forecasted data on rainfall, temperature, pressure and soil moisture. Additionally, the system incorporates predictive weather forecasting to dynamically control sensor activity, reducing energy consumption and extending sensor lifespan. Simulated using Proteus, the proposed framework demonstrates significant potential to mitigate the impacts of unpredictable weather by reducing water consumption, improving forecasting accuracy, and boosting productivity.
Multi-Task Over-the-Air Federated Learning in Cell-Free Massive MIMO Systems
Authors: Chen Chen, Emil Björnson, Carlo Fischione
Date: 2025-01-17 00:00:00
Tasks: Federated Learning, Privacy Preserving

Wireless devices are expected to provide a wide range of AI services in 6G networks. The increasing computing capabilities of wireless devices and the surge of wireless data motivate the use of privacy-preserving federated learning (FL). In contrast to centralized learning that requires sending large amounts of raw data during uplink transmission, only local model parameters are uploaded in FL. Meanwhile, over-the-air (OtA) computation is considered as a communication-efficient solution for fast FL model aggregation by exploiting the superposition properties of wireless multi-access channels. The required communication resources in OtA FL do not scale with the number of FL devices. However, OtA FL is significantly affected by the uneven signal attenuation experienced by different FL devices. Moreover, the coexistence of multiple FL groups with different FL tasks brings about inter-group interference. These challenges cannot be well addressed by conventional cellular network architectures. Recently, Cell-free Massive MIMO (mMIMO) has emerged as a promising technology to provide uniform coverage and high rates via joint coherent transmission. In this paper, we investigate multi-task OtA FL in Cell-free mMIMO systems. We propose optimal designs of transmit coefficients and receive combining at different levels of cooperation among the access points, aiming to minimize the sum of OtA model aggregation errors across all FL groups. Numerical results demonstrate that Cell-free mMIMO significantly outperforms conventional Cellular mMIMO in term of the FL convergence performance by operating at appropriate cooperation levels.
Split-Aperture Phased Array Radar Resource Management for Tracking Tasks
Authors: Pepijn B. Cox, Wim L. van Rossum
Date: 2025-01-16 00:00:00
Tasks: Management, Scheduling

The next generation of radar systems will include advanced digital front-end technology in the apertures allowing for spatially subdividing radar tasks over the array, the so-called split-aperture phased array (SAPA) concept. The goal of this paper is to introduce radar resource management for the SAPA concept and to demonstrate the added benefit of the SAPA concept for active tracking tasks. To do so, the radar resource management problem is formulated and solved by employing the quality of service based resource allocation model (Q-RAM) framework. As active tracking tasks may be scheduled simultaneously, the resource allocation of tasks becomes dependent on the other tasks. The solution to the resource allocation problem is obtained by introducing the adaptive fast traversal algorithm combined with a three dimensional strip packing algorithm to handle task dependencies. It will be demonstrated by a simulation example that the SAPA concept can significantly increase the number of active tracks of a multifunction radar system compared to scheduling tasks sequentially.
On the challenges of detecting MCI using EEG in the wild
Authors: Aayush Mishra, David Joffe, Sankara Surendra Telidevara, David S Oakley, Anqi Liu
Date: 2025-01-15 00:00:00
Tasks: Domain Generalization, EEG, Electroencephalogram (EEG)

Recent studies have shown promising results in the detection of Mild Cognitive Impairment (MCI) using easily accessible Electroencephalogram (EEG) data which would help administer early and effective treatment for dementia patients. However, the reliability and practicality of such systems remains unclear. In this work, we investigate the potential limitations and challenges in developing a robust MCI detection method using two contrasting datasets: 1) CAUEEG, collected and annotated by expert neurologists in controlled settings and 2) GENEEG, a new dataset collected and annotated in general practice clinics, a setting where routine MCI diagnoses are typically made. We find that training on small datasets, as is done by most previous works, tends to produce high variance models that make overconfident predictions, and are unreliable in practice. Additionally, distribution shifts between datasets make cross-domain generalization challenging. Finally, we show that MCI detection using EEG may suffer from fundamental limitations because of the overlapping nature of feature distributions with control groups. We call for more effort in high-quality data collection in actionable settings (like general practice clinics) to make progress towards this salient goal of non-invasive MCI detection.
Hybrid Near-field and Far-field Localization with Holographic MIMO
Authors: Mengyuan Cao, Haobo Zhang, Yonina C. Eldar, Hongliang Zhang
Date: 2025-01-15 00:00:00

Due to its ability to precisely control wireless beams, holographic multiple-input multiple-output (HMIMO) is expected to be a promising solution to achieve high-accuracy localization. However, as the scale of HMIMO increases to improve beam control capability, the corresponding near-field (NF) region expands, indicating that users may exist in both NF and far-field (FF) regions with different electromagnetic transmission characteristics. As a result, existing methods for pure NF or FF localization are no longer applicable. We consider a hybrid NF and FF localization scenario in this paper, where a base station (BS) locates multiple users in both NF and FF regions with the aid of a reconfigurable intelligent surface (RIS), which is a low-cost implementation of HMIMO. In such a scenario, it is difficult to locate the users and optimize the RIS phase shifts because whether the location of the user is in the NF or FF region is unknown, and the channels of different users are coupled. To tackle this challenge, we propose a RIS-enabled localization method that searches the users in both NF and FF regions and tackles the coupling issue by jointly estimating all user locations. We derive the localization error bound by considering the channel coupling and propose an RIS phase shift optimization algorithm that minimizes the derived bound. Simulations show the effectiveness of the proposed method and demonstrate the performance gain compared to pure NF and FF techniques.
Advancing Brainwave-Based Biometrics: A Large-Scale, Multi-Session Evaluation
Authors: Matin Fallahi, Patricia Arias-Cabarcos, Thorsten Strufe
Date: 2025-01-14 00:00:00
Tasks: Contrastive Learning

The field of brainwave-based biometrics has gained attention for its potential to revolutionize user authentication through hands-free interaction, resistance to shoulder surfing, continuous authentication, and revocability. However, current research often relies on single-session or limited-session datasets with fewer than 55 subjects, raising concerns about generalizability and robustness. To address this gap, we conducted a large-scale study using a public brainwave dataset of 345 subjects and over 6,000 sessions (averaging 17 per subject) recorded over five years with three headsets. Our results reveal that deep learning approaches outperform classic feature extraction methods by 16.4\% in Equal Error Rates (EER) and comparing features using a simple cosine distance metric outperforms binary classifiers, which require extra negative samples for training. We also observe EER degrades over time (e.g., 7.7\% after 1 day to 19.69\% after a year). Therefore, it is necessary to reinforce the enrollment set after successful login attempts. Moreover, we demonstrate that fewer brainwave measurement sensors can be used, with an acceptable increase in EER, which is necessary for transitioning from medical-grade to affordable consumer-grade devices. Finally, we compared our findings with prior work on brainwave authentication and industrial biometric standards. While our performance is comparable or superior to prior work through the use of Supervised Contrastive Learning, standards remain unmet. However, we project that achieving industrial standards will be possible by training the feature extractor with at least 1,500 subjects. Moreover, we open-sourced our analysis code to promote further research.
Application of Machine Learning Models for Carbon Monoxide and Nitrogen Oxides Emission Prediction in Gas Turbines
Authors: Kamyar Zeinalipour, Laure Barriere, David Ghelardi, Marco Gori
Date: 2025-01-14 00:00:00

This paper addresses the environmental impacts linked to hazardous emissions from gas turbines, with a specific focus on employing various machine learning (ML) models to predict the emissions of Carbon Monoxide (CO) and Nitrogen Oxides (NOx) as part of a Predictive Emission Monitoring System (PEMS). We employ a comprehensive approach using multiple predictive models to offer insights on enhancing regulatory compliance and optimizing operational parameters to reduce environmental effects effectively. Our investigation explores a range of machine learning models including linear models, ensemble methods, and neural networks. The models we assess include Linear Regression, Support Vector Machines (SVM), Decision Trees, XGBoost, Multi-Layer Perceptron (MLP), Long Short-Term Memory networks (LSTM), Gated Recurrent Units (GRU), and K-Nearest Neighbors (KNN). This analysis provides a comparative overview of the performance of these ML models in estimating CO and NOx emissions from gas turbines, aiming to highlight the most effective techniques for this critical task. Accurate ML models for predicting gas turbine emissions help reduce environmental impact by enabling real-time adjustments and supporting effective emission control strategies, thus promoting sustainability.