pwc-archive/papers-with-abstracts huggingface dataset:
MiniMax-Speech: Intrinsic Zero-Shot Text-to-Speech with a Learnable Speaker Encoder
Authors: BoWen Zhang, Congchao Guo, Geng Yang, Hang Yu, Haozhe Zhang, Heidi Lei, Jialong Mai, Junjie Yan, Kaiyue Yang, Mingqi Yang, Peikai Huang, Ruiyang Jin, Sitan Jiang, Weihua Cheng, Yawei Li, Yichen Xiao, Yiying Zhou, Yongmao Zhang, Yuan Lu, Yucen He
Date: 2025-05-12 00:00:00
Tasks: text-to-speech, Text to Speech, Voice Cloning

We introduce MiniMax-Speech, an autoregressive Transformer-based Text-to-Speech (TTS) model that generates high-quality speech. A key innovation is our learnable speaker encoder, which extracts timbre features from a reference audio without requiring its transcription. This enables MiniMax-Speech to produce highly expressive speech with timbre consistent with the reference in a zero-shot manner, while also supporting one-shot voice cloning with exceptionally high similarity to the reference voice. In addition, the overall quality of the synthesized audio is enhanced through the proposed Flow-VAE. Our model supports 32 languages and demonstrates excellent performance across multiple objective and subjective evaluations metrics. Notably, it achieves state-of-the-art (SOTA) results on objective voice cloning metrics (Word Error Rate and Speaker Similarity) and has secured the top position on the public TTS Arena leaderboard. Another key strength of MiniMax-Speech, granted by the robust and disentangled representations from the speaker encoder, is its extensibility without modifying the base model, enabling various applications such as: arbitrary voice emotion control via LoRA; text to voice (T2V) by synthesizing timbre features directly from text description; and professional voice cloning (PVC) by fine-tuning timbre features with additional data. We encourage readers to visit https://minimax-ai.github.io/tts_tech_report for more examples.
RADE: A Neural Codec for Transmitting Speech over HF Radio Channels
Authors: David Rowe, Jean-Marc Valin
Date: 2025-05-10 00:00:00
Tasks: Decoder

Speech compression is commonly used to send voice over radio channels in applications such as mobile telephony and two-way push-to-talk (PTT) radio. In classical systems, the speech codec is combined with forward error correction, modulation and radio hardware. In this paper we describe an autoencoder that replaces many of the traditional signal processing elements with a neural network. The encoder takes a vocoder feature set (short term spectrum, pitch, voicing), and produces discrete time, but continuously valued quadrature amplitude modulation (QAM) symbols. We use orthogonal frequency domain multiplexing (OFDM) to send and receive these symbols over high frequency (HF) radio channels. The decoder converts received QAM symbols to vocoder features suitable for synthesis. The autoencoder has been trained to be robust to additive Gaussian noise and multipath channel impairments while simultaneously maintaining a Peak To Average Power Ratio (PAPR) of less than 1~dB. Over simulated and real world HF radio channels we have achieved output speech intelligibility that clearly surpasses existing analog and digital radio systems over a range of SNRs.
Tri-MTL: A Triple Multitask Learning Approach for Respiratory Disease Diagnosis
Authors: June-Woo Kim, SangHoon Lee, Miika Toikkanen, Daehwan Hwang, Kyunghoon Kim
Date: 2025-05-06 00:00:00
Tasks: Diagnostic, Lung Sound Classification, Sound Classification

Auscultation remains a cornerstone of clinical practice, essential for both initial evaluation and continuous monitoring. Clinicians listen to the lung sounds and make a diagnosis by combining the patient's medical history and test results. Given this strong association, multitask learning (MTL) can offer a compelling framework to simultaneously model these relationships, integrating respiratory sound patterns with disease manifestations. While MTL has shown considerable promise in medical applications, a significant research gap remains in understanding the complex interplay between respiratory sounds, disease manifestations, and patient metadata attributes. This study investigates how integrating MTL with cutting-edge deep learning architectures can enhance both respiratory sound classification and disease diagnosis. Specifically, we extend recent findings regarding the beneficial impact of metadata on respiratory sound classification by evaluating its effectiveness within an MTL framework. Our comprehensive experiments reveal significant improvements in both lung sound classification and diagnostic performance when the stethoscope information is incorporated into the MTL architecture.
AKD : Adversarial Knowledge Distillation For Large Language Models Alignment on Coding tasks
Authors: Ilyas Oulkadda, Julien Perez
Date: 2025-05-05 00:00:00
Tasks: Code Completion, Code Generation, Knowledge Distillation, Models Alignment

The widespread adoption of Large Language Models (LLMs) for code generation, exemplified by GitHub Copilot\footnote{A coding extension powered by a Code-LLM to assist in code completion tasks} surpassing a million users, highlights the transformative potential of these tools in improving developer productivity. However, this rapid growth also underscores critical concerns regarding the quality, safety, and reliability of the code they generate. As Code-LLMs evolve, they face significant challenges, including the diminishing returns of model scaling and the scarcity of new, high-quality training data. To address these issues, this paper introduces Adversarial Knowledge Distillation (AKD), a novel approach that leverages adversarially generated synthetic datasets to distill the capabilities of larger models into smaller, more efficient ones. By systematically stress-testing and refining the reasoning capabilities of Code-LLMs, AKD provides a framework for enhancing model robustness, reliability, and security while improving their parameter-efficiency. We believe this work represents a critical step toward ensuring dependable automated code generation within the constraints of existing data and the cost-efficiency of model execution.
Load-independent Metrics for Benchmarking Force Controllers
Authors: Victor Shime, Elisa G. Vergamini, Cícero Zanette, Leonardo F. Dos Santos, Lucca Maitan, Andrea Calanca, Thiago Boaventura
Date: 2025-05-13 00:00:00
Tasks: Benchmarking

Torque-controlled actuators are critical components in mechatronic systems that closely interact with their environment, such as legged robots, collaborative manipulators, and exoskeletons. The performance and stability of these actuators depend not only on controller design and system dynamics but also significantly on load characteristics, which may include interactions with humans or unstructured environments. This load dependence highlights the need for frameworks that properly assess and compare torque controllers independent of specific loading conditions. In this short paper, we concisely present a modeling approach that captures the impact of load on the closed-loop dynamics of torque-controlled systems. Based on this model, we propose new methods and quantitative metrics, including the Passivity Index Interval, which blends passivity and small-gain theory to offer a less conservative measure of coupled stability than passivity alone. These metrics can be used alongside traditional control performance indicators, such as settling time and bandwidth, to provide a more comprehensive characterization of torque-controlled systems. We demonstrate the application of the proposed metrics through experimental comparisons of linear actuator force controllers.
Joint Communication Scheduling and Resource Allocation for Distributed Edge Learning: Seamless Integration in Next-Generation Wireless Networks
Authors: Paul Zheng, Navid Keshtiarast, Pradyumna Kumar Bishoyi, Yao Zhu, Yulin Hu, Marina Petrova, Anke Schmeink
Date: 2025-05-13 00:00:00
Tasks: Scheduling

Distributed edge learning (DL) is considered a cornerstone of intelligence enablers, since it allows for collaborative training without the necessity for local clients to share raw data with other parties, thereby preserving privacy and security. Integrating DL into the 6G networks requires a coexistence design with existing services such as high-bandwidth (HB) traffic like eMBB. Current designs in the literature mainly focus on communication round-wise designs that assume a rigid resource allocation throughout each communication round (CR). However, rigid resource allocation within a CR is a highly inefficient and inaccurate representation of the system's realistic behavior. This is due to the heterogeneous nature of the system, as clients inherently may need to access the network at different times. This work zooms into one arbitrary CR, and demonstrates the importance of considering a time-dependent resource sharing design with HB traffic. We first formulate a time-step-wise optimization problem to minimize the consumed time by DL within the CR while constrained by a DL energy budget. Due to its intractability, a session-based optimization problem is formulated assuming a CR lasts less than a large-scale coherence time. Some scheduling properties of such multi-server joint communication scheduling and resource allocation framework have been established. An iterative algorithm has been designed to solve such non-convex and non-block-separable-constrained problems. Simulation results confirm the importance of the efficient and accurate integration design proposed in this work.
Robust Indoor Localization via Conformal Methods and Variational Bayesian Adaptive Filtering
Authors: Zhiyi Zhou, Dongzhuo Liu, Songtao Guo, Yuanyuan Yang
Date: 2025-05-13 00:00:00
Tasks: Conformal Prediction, Indoor Localization, Outlier Detection

Indoor localization is critical for IoT applications, yet challenges such as non-Gaussian noise, environmental interference, and measurement outliers hinder the robustness of traditional methods. Existing approaches, including Kalman filtering and its variants, often rely on Gaussian assumptions or static thresholds, limiting adaptability in dynamic environments. This paper proposes a hierarchical robust framework integrating Variational Bayesian (VB) parameter learning, Huber M-estimation, and Conformal Outlier Detection (COD) to address these limitations. First, VB inference jointly estimates state and noise parameters, adapting to time-varying uncertainties. Second, Huber-based robust filtering suppresses mild outliers while preserving Gaussian efficiency. Third, COD provides statistical guarantees for outlier detection via dynamically calibrated thresholds, ensuring a user-controlled false alarm rate. Theoretically, we prove the Semi-positive Definiteness of Huber-based Kalman filtering covariance and the coverage of sliding window conformal prediction. Experiments on geomagnetic fingerprint datasets demonstrate significant improvements: fingerprint matching accuracy increases from 81.25% to 93.75%, and positioning errors decrease from 0.62-6.87 m to 0.03-0.35 m. Comparative studies further validate the framework's robustness, showing consistent performance gains under non-Gaussian noise and outlier conditions.
A High-Efficiency Reconfigurable Bidirectional Array Antenna Based on Transmit-Reflect Switchable Metasurface
Authors: Fan Qin, Jinyang Bi, Jiao Ma, Chao Gu, Hailin Zhang, Wenchi Cheng, Steven Gao
Date: 2025-05-13 00:00:00

This paper proposes a reconfigurable bidirectional array antenna with high-efficiency radiations and flexible beam-switching capability by employing a novel transmit-reflect switchable metasurface (TRSM). To realize the electromagnetic (EM) wave transmitted or reflected manipulation, a dedicated transmit-reflect switch layer (TRSL) with periodically soldered PIN diodes is introduced between two transmitted metasurfaces. By switching ON/OFF the embedded diodes, the TRSL performs as a mesh-type ground layer or polarization-grid layer, exhibiting a reflect or transmit property to the incident wave respectively. Further, utilizing the above TRSM configuration in conjunction with a microstrip feed antenna, bidirectional radiations are obtained at the same frequency and polarization. To further reduce the number of PIN diodes and control complexity, an enhanced TRSM using a single diode to control two unit cells is also investigated, resulting in half PIN diodes reduction. Since the bidirectional beam-switching is achieved by only controlling PIN diodes integrated in the ground plane instead of directly acting on the radiation element, which reduces insertion loss and avoids phase quantization errors, the proposed antenna can maintain a high aperture efficiency. To verify this concept, a prototype was designed, fabricated, and measured, demonstrating a successful realization of backward and forward patterns with peak gains of 22.3 and 22.1 dBi, and aperture efficiencies of 47.2% and 43.8%. The 3-dB gain bandwidths of reflected and transmitted modes are 13.7% and 12.3%. This antenna has the advantages of high gain, high aperture efficiency, simple configuration, cost-effectiveness, and flexible and digital beam control.
Max-Min Fairness in Stacked Intelligent Metasurface-Aided Rate Splitting Networks
Authors: Abdullah Quran, Shimaa Naser, Maryam Tariq, Omar Alhussein, Sami Muhaidat
Date: 2025-05-13 00:00:00
Tasks: Fairness

This paper investigates a downlink multiuser multiple-input single-output system that integrates rate-splitting multiple access (RSMA) with a stacked intelligent metasurface (SIM) to enable wave-domain beamforming. Unlike conventional digital beamforming, the proposed system leverages the programmable phase shifts of the SIM to perform beamforming entirely in the wave domain. In contrast to existing literature, this work introduces a fairness-centric SIM-RSMA design that shifts the emphasis from maximizing sum-rate to ensuring fair allocation of resources. In particular, we formulate a max-min rate optimization problem that jointly optimizes transmit power coefficients at the base station and SIM phase shifts. Given the non-convex nature of this problem, we develop an alternating optimization framework, where the power allocation is optimized through successive convex approximation and SIM beamforming is optimized using the Riemannian conjugate gradient method. Simulation results indicate that combining SIM with RSMA yields superior max-min performance compared to its integration with space division multiple access or non-orthogonal multiple access.
The Quadrature Gaussian Sum Filter and Smoother for Wiener Systems
Authors: Angel L. Cedeño, Rodrigo A. González, Juan C. Agüero
Date: 2025-05-13 00:00:00
Tasks: Computational Efficiency, State Estimation

Block-Oriented Nonlinear (BONL) models, particularly Wiener models, are widely used for their computational efficiency and practicality in modeling nonlinear behaviors in physical systems. Filtering and smoothing methods for Wiener systems, such as particle filters and Kalman-based techniques, often struggle with computational feasibility or accuracy. This work addresses these challenges by introducing a novel Gaussian Sum Filter for Wiener system state estimation that is built on a Gauss-Legendre quadrature approximation of the likelihood function associated with the output signal. In addition to filtering, a two-filter smoothing strategy is proposed, enabling accurate computation of smoothed state distributions at single and consecutive time instants. Numerical examples demonstrate the superiority of the proposed method in balancing accuracy and computational efficiency compared to traditional approaches, highlighting its benefits in control, state estimation and system identification, for Wiener systems.
A Practical Approach to Generating First-Order Rician Channel Statistics in a RC plus CATR Chamber at mmWave
Authors: Alejandro Antón Ruiz, Samar Hosseinzadegan, John Kvarnstrand, Klas Arvidsson, Andrés Alayón Glazunov
Date: 2025-05-13 00:00:00

This paper explores a novel hybrid configuration integrating a Reverberation Chamber (RC) with a Compact Antenna Test Range (CATR) to achieve a controllable Rician K-factor. The focus is testing directive antennas in the lower FR2 frequency bands (24.25-29.5 GHz) for 5G and beyond wireless applications. The study meticulously evaluates 39 unique configurations, using a stationary horn antenna for consistent reference K-factor characterization, and considers variables like absorbers and CATR polarization. Results demonstrate that the K-factor can be effectively adjusted within the hybrid setup, maintaining substantial margins above the noise level across all configurations. Sample independence is confirmed for at least 600 samples in all cases. The Bootstrap Anderson-Darling goodness-of-fit test verifies that the data align with Rician or Rayleigh distributions. Analysis of total received power, stirred and unstirred power and frequency-dependent modeling reveals that power variables are inversely related to frequency, while the K-factor remains frequency-independent. The hybrid RC-CATR system achieves a wide range of frequency-averaged K-factors from -9.2 dB to 40.8 dB, with an average granularity of 1.3 dB. Notably, configurations using co-polarized CATR signals yield large K-factors, reduced system losses, and improved frequency stability, underscoring the system's efficacy for millimeter-wave over-the-air testing. This research offers a cost-efficient and repeatable method for generating complex Rician fading channels at mmWave frequencies, crucial for the effective OTA testing of advanced wireless devices.
On the Use of CVRP to Diagnose Faulty Elements in Antenna Arrays
Authors: Alejandro Antón Ruiz, John Kvarnstrand, Klas Arvidsson, Andrés Alayón Glazunov
Date: 2025-05-13 00:00:00

This paper investigates the application of Constrained-View Radiated Power (CVRP) for diagnosing phased array element failures, specifically focusing on on-off element failure. CVRP, similar to Partial Radiated Power (PRP), considers a specific Field-of-View (FoV) but normalizes it by the FoV area. The study explores CVRP's effectiveness in detecting failures in a 2x8 cosine element array under beam-steering conditions, accounting for random and depointing errors, angular resolution, and pattern rotation. Results indicate that CVRP can detect on-off failures based on angular resolution and error severity, under the assumption of reduced Total Radiated Power (TRP) with element failures. Additionally, CVRP is effective with partial far-field patterns, making it suitable for near-field, indirect far-field, and far-field measurement systems without requiring phase acquisition in the latter two.
Non-Blocking Robustness Analysis in Discrete Event Systems
Authors: Md Nur-A-Adam Dony
Date: 2025-05-13 00:00:00
Tasks: Blocking

This paper presents a mathematical framework for characterizing state blocking in discrete event systems (DES) under transition deletions. We introduce a path-based analysis approach that determines whether systems maintain non-blocking properties when transitions are removed. Through formal analysis and case studies, we establish three key contributions: a mathematical characterization of transition-induced blocking with necessary and sufficient conditions, a definition of robust deviations that preserve non-blocking properties, and an algorithm for identifying critical transitions and analyzing system behavior under deletions. Our algorithm reduces computational complexity by leveraging minimal blocking sets, achieving significant reduction in computational requirements. We demonstrate the framework's effectiveness through manufacturing system and autonomous vehicle case studies, showing substantial improvements in identifying critical transitions and predicting potential blocking scenarios across different application domains.
Fault Detection Method for Power Conversion Circuits Using Thermal Image and Convolutional Autoencoder
Authors: Noboru Katayama, Rintaro Ishida
Date: 2025-05-13 00:00:00
Tasks: Anomaly Detection, Fault Detection, Image Augmentation

A fault detection method for power conversion circuits using thermal images and a convolutional autoencoder is presented. The autoencoder is trained on thermal images captured from a commercial power module at randomly varied load currents and augmented image2 generated through image processing techniques such as resizing, rotation, perspective transformation, and bright and contrast adjustment. Since the autoencoder is trained to output images identical to input only for normal samples, it reconstructs images similar to normal ones even when the input images containing faults. A small heater is attached to the circuit board to simulate a fault on a power module, and then thermal images were captured from different angles and positions, as well as various load currents to test the trained autoencoder model. The areas under the curve (AUC) were obtained to evaluate the proposed method. The results show the autoencoder model can detect anomalies with 100% accuracy under given conditions. The influence of hyperparameters such as the number of convolutional layers and image augmentation conditions on anomaly detection accuracy was also investigated.
Integrating Koopman theory and Lyapunov stability for enhanced model predictive control in nonlinear systems
Authors: Md Nur-A-Adam Dony
Date: 2025-05-13 00:00:00
Tasks: Model Predictive Control

This paper delves into the challenges posed by the increasing complexity of modern control systems, specifically focusing on bilinear systems, a prevalent subclass of non-linear systems characterized by state dynamics influenced by the interaction of state and control variables. Traditional control strategies, such as PID controllers, often fall short in adequately addressing the intricacies of such systems due to their predictive limitations. To bridge this gap, we introduce Model Predictive Control (MPC), a sophisticated technique that utilizes system models to forecast future behaviors, allowing for the computation of an optimal control sequence by minimizing deviations and control efforts. The Koopman operator emerges as a pivotal tool in this framework by providing a means to linearize the nonlinear dynamics of bilinear systems. By integrating the principles of Lyapunov theory with the linearizing capabilities of the Koopman operator into the MPC framework, we give rise to Koopman Lyapunov-based Model Predictive Control (Koopman LMPC). This approach not only retains MPC's predictive capabilities but also harnesses the Koopman operator's ability to transform complex nonlinear behaviors into a linear framework, thereby enhancing the robustness and applicability of LMPC. With the stability assurances from Lyapunov theory, Koopman LMPC presents a robust solution to effectively control and stabilize bilinear systems. The paper underscores the efficacy of Koopman LMPC, emphasizing its significance in achieving optimal performance and system stability, marking it as a promising approach for the future of advanced control systems.