pwc-archive/papers-with-abstracts huggingface dataset:
Novel Class Discovery for Open Set Raga Classification
Authors: Parampreet Singh, Adwik Gupta, Vipul Arora
Date: 2024-11-27 00:00:00
Tasks: Classification, Contrastive Learning, Information Retrieval, Music Information Retrieval, Novel Class Discovery

The task of Raga classification in Indian Art Music (IAM) is constrained by the limited availability of labeled datasets, resulting in many Ragas being unrepresented during the training of machine learning models. Traditional Raga classification methods rely on supervised learning, and assume that for a test audio to be classified by a Raga classification model, it must have been represented in the training data, which limits their effectiveness in real-world scenarios where novel, unseen Ragas may appear. To address this limitation, we propose a method based on Novel Class Discovery (NCD) to detect and classify previously unseen Ragas. Our approach utilizes a feature extractor trained in a supervised manner to generate embeddings, which are then employed within a contrastive learning framework for self-supervised training, enabling the identification of previously unseen Raga classes. The results demonstrate that the proposed method can accurately detect audio samples corresponding to these novel Ragas, offering a robust solution for utilizing the vast amount of unlabeled music data available online. This approach reduces the need for manual labeling while expanding the repertoire of recognized Ragas, and other music data in Music Information Retrieval (MIR).
Optimal payoff under Bregman-Wasserstein divergence constraints
Authors: Silvana M. Pesenti, Steven Vanduffel, Yang Yang, Jing Yao
Date: 2024-11-27 00:00:00

We study optimal payoff choice for an expected utility maximizer under the constraint that their payoff is not allowed to deviate ``too much'' from a given benchmark. We solve this problem when the deviation is assessed via a Bregman-Wasserstein (BW) divergence, generated by a convex function $\phi$. Unlike the Wasserstein distance (i.e., when $\phi(x)=x^2$). The inherent asymmetry of the BW divergence makes it possible to penalize positive deviations different than negative ones. As a main contribution, we provide the optimal payoff in this setting. Numerical examples illustrate that the choice of $\phi$ allow to better align the payoff choice with the objectives of investors.
Learning Optimal Linear Block Transform by Rate Distortion Minimization
Authors: Alessandro Gnutti, Chia-Hao Kao, Wen-Hsiao Peng, Riccardo Leonardi
Date: 2024-11-27 00:00:00
Tasks: Image Compression, Video Compression

Linear block transform coding remains a fundamental component of image and video compression. Although the Discrete Cosine Transform (DCT) is widely employed in all current compression standards, its sub-optimality has sparked ongoing research into discovering more efficient alternative transforms even for fields where it represents a consolidated tool. In this paper, we introduce a novel linear block transform called the Rate Distortion Learned Transform (RDLT), a data-driven transform specifically designed to minimize the rate-distortion (RD) cost when approximating residual blocks. Our approach builds on the latest end-to-end learned compression frameworks, adopting back-propagation and stochastic gradient descent for optimization. However, unlike the nonlinear transforms used in variational autoencoder (VAE)-based methods, the goal is to create a simpler yet optimal linear block transform, ensuring practical integration into existing image and video compression standards. Differently from existing data-driven methods that design transforms based on sample covariance matrices, such as the Karhunen-Lo\`eve Transform (KLT), the proposed RDLT is directly optimized from an RD perspective. Experimental results show that this transform significantly outperforms the DCT or other existing data-driven transforms. Additionally, it is shown that when simulating the integration of our RDLT into a VVC-like image compression framework, the proposed transform brings substantial improvements. All the code used in our experiments has been made publicly available at [1].
Non-reciprocal Beyond Diagonal RIS: Sum-Rate Maximization in Full-Duplex Communications
Authors: Ziang Liu, Hongyu Li, Bruno Clerckx
Date: 2024-11-27 00:00:00

Reconfigurable intelligent surface (RIS) has been envisioned as a key technology in future wireless communication networks to enable smart radio environment. To further enhance the passive beamforming capability of RIS, beyond diagonal (BD)-RIS has been proposed considering reconfigurable interconnections among different RIS elements. BD-RIS has a unique feature that cannot be enabled by conventional diagonal RIS; it can be realized by non-reciprocal circuits and thus enables an asymmetric scattering matrix. This feature provides the capability to break the wireless channel reciprocity, and has the potential to benefit full-duplex (FD) systems. In this paper, we model the BD RIS-assisted FD systems, where the impact of BD-RIS non-reciprocity and that of structural scattering, which refers to the specular reflection generated by RIS when the RIS is turned OFF, are explicitly captured. To assess the benefits of non-reciprocal BD-RIS, we optimise the scattering matrix, precoder and combiner to maximize the DL and UL sum-rates in the FD system. To tackle this optimization problem, we propose an iterative algorithm based on block coordination descent (BCD) and penalty dual decomposition (PDD). Numerical results demonstrate surprising benefits of non-reciprocal BD-RIS that it can achieve much higher DL and UL sum-rates in the FD scenario than reciprocal BD-RIS and conventional diagonal RIS.
How chromatin interactions shed light on interpreting non-coding genomic variants: opportunities and future direc-tions
Authors: Yuheng Liang, Sedigheh Abedini, Nona Farbehi, Hamid Alinejad-Rokny
Date: 2024-11-27 00:00:00

Genomic variants, including copy number variants (CNVs) and genome-wide associa-tion study (GWAS) single nucleotide polymorphisms (SNPs), represent structural alterations that influence genomic diversity and disease susceptibility. While coding region variants have been extensively studied, non-coding and regulatory variants present significant challenges due to their potential impacts on gene regulation, which are often obscured by the complexity of the ge-nome. Chromatin interactions, which organize the genome spatially and regulate gene expression through enhancer-promoter contacts, predominantly occur in non-coding regions. Notably, more than 90% of enhancers, crucial for gene regulation, reside in these non-coding regions, underscor-ing their importance in interpreting the regulatory effects of CNVs and GWAS-associated SNPs. In this study, we integrate chromatin interaction data with CNV and GWAS data to uncover the functional implications of non-coding variants. By leveraging this integrated approach, we pro-vide new insights into how structural variants and disease-associated SNPs disrupt regulatory networks, advancing our understanding of genetic complexity. These findings offer potential av-enues for personalized medicine by elucidating disease mechanisms and guiding therapeutic strategies tailored to individual genomic profiles. This research underscores the critical role of chromatin interactions in revealing the regulatory consequences of non-coding variants, bridging the gap between genetic variation and phenotypic outcomes.
Decision making in stochastic extensive form II: Stochastic extensive forms and games
Authors: E. Emanuel Rapsch
Date: 2024-11-26 00:00:00
Tasks: Decision Making, Form

A general theory of stochastic extensive forms is developed to bridge two concepts of information flow: decision trees and refined partitions on the one side, filtrations from probability theory on the other. Instead of the traditional "nature" agent, this framework uses a single lottery draw to select a tree of a given decision forest. Each "personal" agent receives dynamic updates from an own oracle on the lottery outcome and makes partition-refining choices adapted to this information. This theory addresses a key limitation of existing approaches in extensive form theory, which struggle to model continuous-time stochastic processes, such as Brownian motion, as outcomes of "nature" decision making. Additionally, a class of stochastic extensive forms based on time-indexed action paths is constructed, encompassing a wide range of models from the literature and laying the groundwork for an approximation theory for stochastic differential games in extensive form.
Typical vs. Atypical Disfluency Classification: Introducing the IIITH-TISA Corpus and Temporal Context-Based Feature Representations
Authors: Priyanka Kommagouni, Vamshiraghusimha Narasinga, Purva Barche, Sai Akarsh C, Anil Vuppala
Date: 2024-11-26 00:00:00

Speech disfluencies in spontaneous communication can be categorized as either typical or atypical. Typical disfluencies, such as hesitations and repetitions, are natural occurrences in everyday speech, while atypical disfluencies are indicative of pathological disorders like stuttering. Distinguishing between these categories is crucial for improving voice assistants (VAs) for Persons Who Stutter (PWS), who often face premature cutoffs due to misidentification of speech termination. Accurate classification also aids in detecting stuttering early in children, preventing misdiagnosis as language development disfluency. This research introduces the IIITH-TISA dataset, the first Indian English stammer corpus, capturing atypical disfluencies. Additionally, we extend the IIITH-IED dataset with detailed annotations for typical disfluencies. We propose Perceptually Enhanced Zero-Time Windowed Cepstral Coefficients (PE-ZTWCC) combined with Shifted Delta Cepstra (SDC) as input features to a shallow Time Delay Neural Network (TDNN) classifier, capturing both local and wider temporal contexts. Our method achieves an average F1 score of 85.01% for disfluency classification, outperforming traditional features.
The more, the better? Evaluating the role of EEG preprocessing for deep learning applications
Authors: Federico Del Pup, Andrea Zanola, Louis Fabrice Tshimanga, Alessandra Bertoldo, Manfredo Atzori
Date: 2024-11-27 00:00:00
Tasks: Deep Learning, EEG, Motor Imagery

The last decade has witnessed a notable surge in deep learning applications for the analysis of electroencephalography (EEG) data, thanks to its demonstrated superiority over conventional statistical techniques. However, even deep learning models can underperform if trained with bad processed data. While preprocessing is essential to the analysis of EEG data, there is a need of research examining its precise impact on model performance. This causes uncertainty about whether and to what extent EEG data should be preprocessed in a deep learning scenario. This study aims at investigating the role of EEG preprocessing in deep learning applications, drafting guidelines for future research. It evaluates the impact of different levels of preprocessing, from raw and minimally filtered data to complex pipelines with automated artifact removal algorithms. Six classification tasks (eye blinking, motor imagery, Parkinson's and Alzheimer's disease, sleep deprivation, and first episode psychosis) and four different architectures commonly used in the EEG domain were considered for the evaluation. The analysis of 4800 different trainings revealed statistical differences between the preprocessing pipelines at the intra-task level, for each of the investigated models, and at the inter-task level, for the largest one. Raw data generally leads to underperforming models, always ranking last in averaged score. In addition, models seem to benefit more from minimal pipelines without artifact handling methods, suggesting that EEG artifacts may contribute to the performance of deep neural networks.
Deep learning-based spatio-temporal fusion for high-fidelity ultra-high-speed x-ray radiography
Authors: Songyuan Tang, Tekin Bicer, Tao Sun, Kamel Fezzaa, Samuel J. Clark
Date: 2024-11-27 00:00:00
Tasks: SSIM, Transfer Learning

Full-field ultra-high-speed (UHS) x-ray imaging experiments have been well established to characterize various processes and phenomena. However, the potential of UHS experiments through the joint acquisition of x-ray videos with distinct configurations has not been fully exploited. In this paper, we investigate the use of a deep learning-based spatio-temporal fusion (STF) framework to fuse two complementary sequences of x-ray images and reconstruct the target image sequence with high spatial resolution, high frame rate, and high fidelity. We applied a transfer learning strategy to train the model and compared the peak signal-to-noise ratio (PSNR), average absolute difference (AAD), and structural similarity (SSIM) of the proposed framework on two independent x-ray datasets with those obtained from a baseline deep learning model, a Bayesian fusion framework, and the bicubic interpolation method. The proposed framework outperformed the other methods with various configurations of the input frame separations and image noise levels. With 3 subsequent images from the low resolution (LR) sequence of a 4-time lower spatial resolution and another 2 images from the high resolution (HR) sequence of a 20-time lower frame rate, the proposed approach achieved an average PSNR of 37.57 dB and 35.15 dB, respectively. When coupled with the appropriate combination of high-speed cameras, the proposed approach will enhance the performance and therefore scientific value of the UHS x-ray imaging experiments.
Two-Timescale Digital Twin Assisted Model Interference and Retraining over Wireless Network
Authors: Jiayi Cong, Guoliang Cheng, Changsheng You, Xinyu Huang, Wen Wu
Date: 2024-11-27 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Incremental Learning

In this paper, we investigate a resource allocation and model retraining problem for dynamic wireless networks by utilizing incremental learning, in which the digital twin (DT) scheme is employed for decision making. A two-timescale framework is proposed for computation resource allocation, mobile user association, and incremental training of user models. To obtain an optimal resource allocation and incremental learning policy, we propose an efficient two-timescale scheme based on hybrid DT-physical architecture with the objective to minimize long-term system delay. Specifically, in the large-timescale, base stations will update the user association and implement incremental learning decisions based on statistical state information from the DT system. Then, in the short timescale, an effective computation resource allocation and incremental learning data generated from the DT system is designed based on deep reinforcement learning (DRL), thus reducing the network system's delay in data transmission, data computation, and model retraining steps. Simulation results demonstrate the effectiveness of the proposed two-timescale scheme compared with benchmark schemes.
Ensuring Safety in Target Pursuit Control: A CBF-Safe Reinforcement Learning Approach
Authors: Yaosheng Deng, Junjie Gao, Jiaping Xiao, Mir Feroskhan
Date: 2024-11-26 00:00:00
Tasks: Collision Avoidance, reinforcement-learning, Reinforcement Learning, Safe Reinforcement Learning

This paper addresses the target-pursuit problem, aiming to ensure each pursuer's safety regarding collision avoidance, sensing range, and input saturation. An input-constrained CBF is proposed to dynamically regulate the pursuer's control, ensuring effective target pursuit even when the target performs evasive maneuvers. To further ensure safety, two sets of CBF constraints are designed to regulate the pursuer's position, enabling it to keep the target within the sensing range while avoiding collision in complex environments with external disturbances. These three CBFs collectively form our safety filter, which filters unsafe outputs from RL by solving a Quadratic Program (QP). Finally, the safety filter, combined with a switch strategy that enhances the feasibility of solving its QP, constitutes the Control Barrier Function (CBF)-Safe Reinforcement Learning (CSRL) algorithm, whose solutions are proven to satisfy the Karush-Kuhn-Tucker (KKT) conditions for all safety constraints. Simulation results validate the effectiveness of the CSRL algorithm, demonstrating its ability to handle complex pursuit scenarios while maintaining safety and improving control performance.
Rethinking Node Representation Interpretation through Relation Coherence
Authors: Ying-Chun Lin, Jennifer Neville, Cassiano Becker, Purvanshi Metha, Nabiha Asghar, Vipul Agarwal
Date: 2024-11-01 00:00:00
Tasks: Relation

Understanding node representations in graph-based models is crucial for uncovering biases ,diagnosing errors, and building trust in model decisions. However, previous work on explainable AI for node representations has primarily emphasized explanations (reasons for model predictions) rather than interpretations (mapping representations to understandable concepts). Furthermore, the limited research that focuses on interpretation lacks validation, and thus the reliability of such methods is unclear. We address this gap by proposing a novel interpretation method-Node Coherence Rate for Representation Interpretation (NCI)-which quantifies how well different node relations are captured in node representations. We also propose a novel method (IME) to evaluate the accuracy of different interpretation methods. Our experimental results demonstrate that NCI reduces the error of the previous best approach by an average of 39%. We then apply NCI to derive insights about the node representations produced by several graph-based methods and assess their quality in unsupervised settings.
Operating System Noise in the Linux Kernel
Authors: Daniel Bristot de Oliveira, Daniel Casini, Tommaso Cucinotta
Date: 2022-06-30 00:00:00
Proceeding: IEEE Transactions on Computers ( Volume: 72, Issue: 1, 01 January 2023) 2022 6
Tasks: CPU

As modern network infrastructure moves from hardware-based to software-based using Network Function Virtualization,
a new set of requirements is raised for operating system developers. By using the real-time kernel options and advanced CPU isolation features common to the HPC use-cases, Linux is becoming a central building block for this new architecture that aims to enable a new set of low latency networked services. Tuning Linux for these applications is not an easy task, as it requires a deep understanding of the Linux execution model and the mix of user-space tooling and tracing features. This paper discusses the internal aspects of Linux that influence the Operating System Noise from a timing perspective. It also presents Linux’s osnoise tracer, an in-kernel tracer that enables the measurement of the Operating System Noise as observed by a workload, and the tracing of the sources of the noise, in an integrated manner, facilitating the analysis and debugging of the system. Finally, this paper presents a series of experiments demonstrating both Linux’s ability to deliver low OS noise (in the single-digit ms order), and the ability of the proposed tool to provide precise information about root-cause of timing-related OS noise problems.
Ultra-low Power Always-on Intelligent and Connected SNN-based System for Multimedia IoT-enabled Applications
Authors: Qi Liu, Zhixuan Zhang
Date: 2022-02-10 00:00:00
Proceeding: IEEE Internet of Things Journal 2022 2
Tasks: speech-recognition, Speech Recognition

The recent advances in artificial neural networks (ANNs) have created immense opportunities to achieve excellent results on the Internet of Things (IoT), which serve the uses of various real-time smart applications, such as in computer vision and speech recognition. However, the efficiency of ANNs comes at the expense of a huge number of computational resources. This tends to necessitate larger and wider ANNs unapplicable for embedded systems with limited hardware resources, e.g., mobile and wearable devices. To that end, biologically realistic spiking neural networks (SNNs) are first employed to build an always-on intelligent and connected integrated IoT-enabled system with ultralow power consumption, where we encode the temporal dynamic stimuli into effective, efficient, and reconstructable spike patterns to facilitate the subsequent processing. Herein, neural encoding plays a key role in faithfully describing the temporally rich patterns for downstream cognitive tasks. Therefore, a novel nonlinear piecewise latency coding approach for a fully event-driven SNN system is developed. Moreover, a surrogate postsynaptic potential kernel function is utilized to address the nondifferential nature of the spike generation scheme when using the error backpropagation learning method. The effectiveness of the proposal tandem with SNNs has been corroborated by indicative empirical results on different data sets serving cognitive tasks.
Multi-Stage Deep Transfer Learning for EmIoT-enabled Human-Computer Interaction
Authors: Rui Liu, Qi Liu, Hongxu Zhu, Hui Cao
Date: 2022-02-03 00:00:00
Proceeding: IEEE Internet of Things Journal 2022 2
Tasks: Human-Object Interaction Detection, text-to-speech, Text to Speech, Text-To-Speech Synthesis, text-to-speech translation, Transfer Learning

Emotional Internet of Things (EmIoT), which provides Internet of Things (IoT) devices cognitive and socialization capabilities, has been regarded as a future direction to improve users’ experiences. With the development of intelligent techniques, the requirement of EmIoT is not only sensing the users’ emotional states but also providing emotional feedbacks. Human–computer interaction has been studied to achieve speech interaction with IoT devices. The recent advances in neural text-to-speech (TTS) have made “human parity” synthesized speech possible for IoT-enabled human–computer interaction. Furthermore, emotion control can be achieved by using the emotional codes in a unified model, referred to as emotional TTS (or ETTS for short). Such ETTS models have achieved promising emotional expressiveness using large-scale emotion-annotated English data set; however, they are not practical in IoT environments with other mainstream languages, especially for Chinese. In fact, the limited available large-scale emotion-annotated data set is challenging the development of Chinese ETTS. To address that we propose a multistage deep transfer learning scheme to design a high-quality Chinese ETTS system under a small-scale training corpus to achieve EmIoT in Mandarin environments. In this scheme, the pretrained knowledge from the former stages corresponding to a large-scale neutral English and a medium-scale emotional English corpora is transferred to a Mandarin ETTS model. Thereby, the trained model can achieve high-quality emotional speech with limited available emotional corpus, which is able to serve various EmIoT-oriented applications. The experiments have been conducted to demonstrate the effectiveness and superiority of the proposed model as compared to other counterparts in terms of naturalness and emotional expressiveness. We refer readers to visit our demo Webpage 1 enjoy the synthesized speech samples.