pwc-archive/papers-with-abstracts huggingface dataset:
An invariance principle based concentration result for large-scale stochastic pairwise interaction network systems
Authors: Giacomo Como, Fabio Fagnani, Sandro Zampieri
Date: 2024-10-30 00:00:00

We study stochastic pairwise interaction network systems whereby a finite population of agents, identified with the nodes of a graph, update their states in response to both individual mutations and pairwise interactions with their neighbors. The considered class of systems include the main epidemic models -such as the SIS, SIR, and SIRS models-, certain social dynamics models -such as the voter and anti-voter models-, as well as evolutionary dynamics on graphs. Since these stochastic systems fall into the class of finite-state Markov chains, they always admit stationary distributions. We analyze the asymptotic behavior of these stationary distributions in the limit as the population size grows large while the interaction network maintains certain mixing properties. Our approach relies on the use of Lyapunov-type functions to obtain concentration results on these stationary distributions. Notably, our results are not limited to fully mixed population models, as they do apply to a much broader spectrum of interaction network structures, including, e.g., Erd\"oos-R\'enyi random graphs.
DAWN: Designing Distributed Agents in a Worldwide Network
Authors: Zahra Aminiranjbar, Jianan Tang, Qiudan Wang, Shubha Pant, Mahesh Viswanathan
Date: 2024-10-11 00:00:00
Tasks: Decision Making

The rapid evolution of Large Language Models (LLMs) has transformed them from basic conversational tools into sophisticated entities capable of complex reasoning and decision-making. These advancements have led to the development of specialized LLM-based agents designed for diverse tasks such as coding and web browsing. As these agents become more capable, the need for a robust framework that facilitates global communication and collaboration among them towards advanced objectives has become increasingly critical. Distributed Agents in a Worldwide Network (DAWN) addresses this need by offering a versatile framework that integrates LLM-based agents with traditional software systems, enabling the creation of agentic applications suited for a wide range of use cases. DAWN enables distributed agents worldwide to register and be easily discovered through Gateway Agents. Collaborations among these agents are coordinated by a Principal Agent equipped with reasoning strategies. DAWN offers three operational modes: No-LLM Mode for deterministic tasks, Copilot for augmented decision-making, and LLM Agent for autonomous operations. Additionally, DAWN ensures the safety and security of agent collaborations globally through a dedicated safety, security, and compliance layer, protecting the network against attackers and adhering to stringent security and compliance standards. These features make DAWN a robust network for deploying agent-based applications across various industries.
OpenCity: A Scalable Platform to Simulate Urban Activities with Massive LLM Agents
Authors: Yuwei Yan, Qingbin Zeng, Zhiheng Zheng, Jingzhe Yuan, Jie Feng, Jun Zhang, Fengli Xu, Yong Li
Date: 2024-10-11 00:00:00

Agent-based models (ABMs) have long been employed to explore how individual behaviors aggregate into complex societal phenomena in urban space. Unlike black-box predictive models, ABMs excel at explaining the micro-macro linkages that drive such emergent behaviors. The recent rise of Large Language Models (LLMs) has led to the development of LLM agents capable of simulating urban activities with unprecedented realism. However, the extreme high computational cost of LLMs presents significant challenges for scaling up the simulations of LLM agents. To address this problem, we propose OpenCity, a scalable simulation platform optimized for both system and prompt efficiencies. Specifically, we propose a LLM request scheduler to reduce communication overhead by parallelizing requests through IO multiplexing. Besides, we deisgn a "group-and-distill" prompt optimization strategy minimizes redundancy by clustering agents with similar static attributes. Through experiments on six global cities, OpenCity achieves a 600-fold acceleration in simulation time per agent, a 70% reduction in LLM requests, and a 50% reduction in token usage. These improvements enable the simulation of 10,000 agents' daily activities in 1 hour on commodity hardware. Besides, the substantial speedup of OpenCity allows us to establish a urban simulation benchmark for LLM agents for the first time, comparing simulated urban activities with real-world data in 6 major cities around the globe. We believe our OpenCity platform provides a critical infrastructure to harness the power of LLMs for interdisciplinary studies in urban space, fostering the collective efforts of broader research communities. Code repo is available at https://anonymous.4open.science/r/Anonymous-OpenCity-42BD.
VEMOCLAP: A video emotion classification web application
Authors: Serkan Sulun, Paula Viana, Matthew E. P. Davies
Date: 2024-10-22 00:00:00
Tasks: Classification, Emotion Classification, Video Emotion Recognition

We introduce VEMOCLAP: Video EMOtion Classifier using Pretrained features, the first readily available and open-source web application that analyzes the emotional content of any user-provided video. We improve our previous work, which exploits open-source pretrained models that work on video frames and audio, and then efficiently fuse the resulting pretrained features using multi-head cross-attention. Our approach increases the state-of-the-art classification accuracy on the Ekman-6 video emotion dataset by 4.3% and offers an online application for users to run our model on their own videos or YouTube videos. We invite the readers to try our application at serkansulun.com/app.
Multiple-beam Interference Spectroscopy: Instrument Analysis and Spectrum Reconstruction
Authors: Mohamad Jouni, Daniele Picone, Mauro Dalla Mura
Date: 2024-10-28 00:00:00

Hyperspectral imaging systems based on multiple-beam interference (MBI), such as Fabry-Perot interferometry, are attracting interest due to their compact design, high throughput, and fine resolution. Unlike dispersive devices, which measure spectra directly, the desired spectra in interferometric systems are reconstructed from measured interferograms. Although the response of MBI devices is modeled by the Airy function, existing reconstruction techniques are often limited to Fourier-transform spectroscopy, which is tailored for two-beam interference (TBI). These methods impose limitations for MBI and are susceptible to non-idealities like irregular sampling and noise, highlighting the need for an in-depth numerical framework. To fill this gap, we propose a rigorous taxonomy of the TBI and MBI instrument description and propose a unified Bayesian formulation which both embeds the description of existing literature works and adds some of the real-world non-idealities of the acquisition process. Under this framework, we provide a comprehensive review of spectroscopy forward and inverse models. In the forward model, we propose a thorough analysis of the discretization of the continuous model and the ill-posedness of the problem. In the inverse model, we extend the range of existing solutions for spectrum reconstruction, framing them as an optimization problem. Specifically, we provide a progressive comparative analysis of reconstruction methods from more specific to more general scenarios, up to employing the proposed Bayesian framework with prior knowledge, such as sparsity constraints. Experiments on simulated and real data demonstrate the framework's flexibility and noise robustness. The code is available at https://github.com/mhmdjouni/inverspyctrometry.
Task-Aware Unified Source Separation
Authors: Kohei Saijo, Janek Ebbers, François G. Germain, Gordon Wichern, Jonathan Le Roux
Date: 2024-10-31 00:00:00
Tasks: Audio Source Separation, Music Source Separation, Speech Enhancement, Speech Separation

Several attempts have been made to handle multiple source separation tasks such as speech enhancement, speech separation, sound event separation, music source separation (MSS), or cinematic audio source separation (CASS) with a single model. These models are trained on large-scale data including speech, instruments, or sound events and can often successfully separate a wide range of sources. However, it is still challenging for such models to cover all separation tasks because some of them are contradictory (e.g., musical instruments are separated in MSS while they have to be grouped in CASS). To overcome this issue and support all the major separation tasks, we propose a task-aware unified source separation (TUSS) model. The model uses a variable number of learnable prompts to specify which source to separate, and changes its behavior depending on the given prompts, enabling it to handle all the major separation tasks including contradictory ones. Experimental results demonstrate that the proposed TUSS model successfully handles the five major separation tasks mentioned earlier. We also provide some audio examples, including both synthetic mixtures and real recordings, to demonstrate how flexibly the TUSS model changes its behavior at inference depending on the prompts.
Audiovisual angle and voice incongruence do not affect audiovisual verbal short-term memory in virtual reality
Authors: Cosima A. Ermert, Manuj Yadav, Jonathan Ehret, Chinthusa Mohanathasan, Andrea Bönsch, Torsten W. Kuhlen, Sabine J. Schlittmeier, Janina Fels
Date: 2024-10-30 00:00:00

Virtual reality (VR) environments are frequently used in auditory and cognitive research to imitate real-life scenarios, presumably enhancing state-of-the-art approaches with traditional computer screens. However, the effects of different display technologies on audiovisual processing remain underexplored. This study investigated how VR displayed with an head-mounted display (HMD) affects serial recall performance compared to traditional computer monitors, focusing on their effects on audiovisual processing in cognitive tasks. For that matter, we conducted two experiments with both an HMD and a computer monitor as display devices and two types of audiovisual incongruences: angle (Exp. 1) and voice (Exp. 2) incongruence. To quantify cognitive performance an audiovisual verbal serial recall (avVSR) task was developed where an embodied conversational agent (ECA) was animated to speak the target digit sequence. Even though subjective evaluations showed a higher sense of presence in the HMD condition, we found no effect of the display device on the proportion of correctly recalled digits. For the extreme conditions of angle incongruence in the computer monitor presentation the proportion of correctly recalled digits increased marginally, presumably due to raised attention, but the effect is likely too small to be meaningful. Response times were not affected by incongruences in either display device across both experiments. These findings suggest that the avVSR task is robust against angular and voice audiovisual incongruences, irrespective of the display device, at least for the conditions studied here. Hence, the study introduces the avVSR task in VR and contributes to the understanding of audiovisual integration.
Augmenting Polish Automatic Speech Recognition System With Synthetic Data
Authors: Łukasz Bondaruk, Jakub Kubiak, Mateusz Czyżnikiewicz
Date: 2024-10-30 00:00:00
Tasks: Automatic Speech Recognition, speech-recognition, Speech Recognition, Speech Synthesis

This paper presents a system developed for submission to Poleval 2024, Task 3: Polish Automatic Speech Recognition Challenge. We describe Voicebox-based speech synthesis pipeline and utilize it to augment Conformer and Whisper speech recognition models with synthetic data. We show that addition of synthetic speech to training improves achieved results significantly. We also present final results achieved by our models in the competition.
APCodec+: A Spectrum-Coding-Based High-Fidelity and High-Compression-Rate Neural Audio Codec with Staged Training Paradigm
Authors: Hui-Peng Du, Yang Ai, Rui-Chen Zheng, Zhen-Hua Ling
Date: 2024-10-30 00:00:00
Tasks: Decoder, Quantization

This paper proposes a novel neural audio codec, named APCodec+, which is an improved version of APCodec. The APCodec+ takes the audio amplitude and phase spectra as the coding object, and employs an adversarial training strategy. Innovatively, we propose a two-stage joint-individual training paradigm for APCodec+. In the joint training stage, the encoder, quantizer, decoder and discriminator are jointly trained with complete spectral loss, quantization loss, and adversarial loss. In the individual training stage, the encoder and quantizer fix their parameters and provide high-quality training data for the decoder and discriminator. The decoder and discriminator are individually trained from scratch without the quantization loss. The purpose of introducing individual training is to reduce the learning difficulty of the decoder, thereby further improving the fidelity of the decoded audio. Experimental results confirm that our proposed APCodec+ at low bitrates achieves comparable performance with baseline codecs at higher bitrates, thanks to the proposed staged training paradigm.
Retrieval-Augmented Approach for Unsupervised Anomalous Sound Detection and Captioning without Model Training
Authors: Ryoya Ogura, Tomoya Nishida, Yohei Kawaguchi
Date: 2024-10-29 00:00:00
Tasks: Retrieval

This paper proposes a method for unsupervised anomalous sound detection (UASD) and captioning the reason for detection. While there is a method that captions the difference between given normal and anomalous sound pairs, it is assumed to be trained and used separately from the UASD model. Therefore, the obtained caption can be irrelevant to the differences that the UASD model captured. In addition, it requires many caption labels representing differences between anomalous and normal sounds for model training. The proposed method employs a retrieval-augmented approach for captioning of anomalous sounds. Difference captioning in the embedding space output by the pre-trained CLAP (contrastive language-audio pre-training) model makes the anomalous sound detection results consistent with the captions and does not require training. Experiments based on subjective evaluation and a sample-wise analysis of the output captions demonstrate the effectiveness of the proposed method.
Timbre Difference Capturing in Anomalous Sound Detection
Authors: Tomoya Nishida, Harsh Purohit, Kota Dohi, Takashi Endo, Yohei Kawaguchi
Date: 2024-10-29 00:00:00

This paper proposes a framework of explaining anomalous machine sounds in the context of anomalous sound detection~(ASD). While ASD has been extensively explored, identifying how anomalous sounds differ from normal sounds is also beneficial for machine condition monitoring. However, existing sound difference captioning methods require anomalous sounds for training, which is impractical in typical machine condition monitoring settings where such sounds are unavailable. To solve this issue, we propose a new strategy for explaining anomalous differences that does not require anomalous sounds for training. Specifically, we introduce a framework that explains differences in predefined timbre attributes instead of using free-form text captions. Objective metrics of timbre attributes can be computed using timbral models developed through psycho-acoustical research, enabling the estimation of how and what timbre attributes have changed from normal sounds without training machine learning models. Additionally, to accurately determine timbre differences regardless of variations in normal training data, we developed a method that jointly conducts anomalous sound detection and timbre difference estimation based on a k-nearest neighbors method in an audio embedding space. Evaluation using the MIMII DG dataset demonstrated the effectiveness of the proposed method.
Representational learning for an anomalous sound detection system with source separation model
Authors: Seunghyeon Shin, Seokjin Lee
Date: 2024-10-29 00:00:00
Tasks: Novelty Detection, Representation Learning

The detection of anomalous sounds in machinery operation presents a significant challenge due to the difficulty in generalizing anomalous acoustic patterns. This task is typically approached as an unsupervised learning or novelty detection problem, given the complexities associated with the acquisition of comprehensive anomalous acoustic data. Conventional methodologies for training anomalous sound detection systems primarily employ auto-encoder architectures or representational learning with auxiliary tasks. However, both approaches have inherent limitations. Auto-encoder structures are constrained to utilizing only the target machine's operational sounds, while training with auxiliary tasks, although capable of incorporating diverse acoustic inputs, may yield representations that lack correlation with the characteristic acoustic signatures of anomalous conditions. We propose a training method based on the source separation model (CMGAN) that aims to isolate non-target machine sounds from a mixture of target and non-target class acoustic signals. This approach enables the effective utilization of diverse machine sounds and facilitates the training of complex neural network architectures with limited sample sizes. Our experimental results demonstrate that the proposed method yields better performance compared to both conventional auto-encoder training approaches and source separation techniques that focus on isolating target machine signals. Moreover, our experimental results demonstrate that the proposed method exhibits the potential for enhanced representation learning as the quantity of non-target data increases, even while maintaining a constant volume of target class data.
Cyber-physical WebAssembly: Secure Hardware Interfaces and Pluggable Drivers
Authors: Michiel Van Kenhove, Maximilian Seidler, Friedrich Vandenberghe, Warre Dujardin, Wouter Hennen, Arne Vogel, Merlijn Sebrechts, Tom Goethals, Filip De Turck, Bruno Volckaert
Date: 2024-10-30 00:00:00

The rapid expansion of Internet of Things (IoT), edge, and embedded devices in the past decade has introduced numerous challenges in terms of security and configuration management. Simultaneously, advances in cloud-native development practices have greatly enhanced the development experience and facilitated quicker updates, thereby enhancing application security. However, applying these advances to IoT, edge, and embedded devices remains a complex task, primarily due to the heterogeneous environments and the need to support devices with extended lifespans. WebAssembly and the WebAssembly System Interface (WASI) has emerged as a promising technology to bridge this gap. As WebAssembly becomes more popular on IoT, edge, and embedded devices, there is a growing demand for hardware interface support in WebAssembly programs. This work presents WASI proposals and proof-of-concept implementations to enable hardware interaction with I2C and USB, which are two commonly used protocols in IoT, directly from WebAssembly applications. This is achieved by running the device drivers within WebAssembly as well. A thorough evaluation of the proof of concepts shows that WASI-USB introduces a minimal overhead of at most 8% compared to native operating system USB APIs. However, the results show that runtime initialization overhead can be significant in low-latency applications.
User-Aware Multilingual Abusive Content Detection in Social Media
Authors: Mohammad Zia Ur Rehman, Somya Mehta, Kuldeep Singh, Kunal Kaushik, Nagendra Kumar
Date: 2024-10-26 00:00:00

Despite growing efforts to halt distasteful content on social media, multilingualism has added a new dimension to this problem. The scarcity of resources makes the challenge even greater when it comes to low-resource languages. This work focuses on providing a novel method for abusive content detection in multiple low-resource Indic languages. Our observation indicates that a post's tendency to attract abusive comments, as well as features such as user history and social context, significantly aid in the detection of abusive content. The proposed method first learns social and text context features in two separate modules. The integrated representation from these modules is learned and used for the final prediction. To evaluate the performance of our method against different classical and state-of-the-art methods, we have performed extensive experiments on SCIDN and MACI datasets consisting of 1.5M and 665K multilingual comments, respectively. Our proposed method outperforms state-of-the-art baseline methods with an average increase of 4.08% and 9.52% in F1-scores on SCIDN and MACI datasets, respectively.
A Multiphysics Analysis and Investigation of Soft Magnetics Effect on IPMSM: Case Study Dynamometer
Authors: Ali Amini, MohammadSadegh KhajueeZadeh, Abolfazl Vahedi
Date: 2024-10-31 00:00:00

Nowadays, Interior Permanent Magnet Synchronous Motors (IPMSMs) are taken into attention in the industry owing to their advantages. Moreover, in many cases, performing static tests is not enough, and investigating electric machines under dynamic conditions is necessary. Accordingly, by employing a dynamometer system, the dynamic behavior of the electric machine under test is investigated. Among the dynamometers, the best is the Alternating (AC) dynamometer because the basic dynamometers cannot take loads with high complexity. So, in the following study, two IPMSM with V-type and Delta-type rotor configurations are designed and suggested to employ in AC dynamometer. Any non-ideality in the electric machines of AC dynamometers, electrically and mechanically, causes errors in the measurement of the motor under test. Electrically and mechanically, the behavior of a system significantly depends on the used soft magnetics besides its physical and magnetic configuration. Accordingly, by performing a Multiphysics analysis and using the FEM tool to change the soft magnetics in the rotor and stator core, comparing the electric motors' behavior in the AC dynamometer is investigated under the same operating conditions electrically and mechanically. Finally, which soft magnetics is more satisfactory for the AC dynamometer can be seen.
Data-Driven Learning of Two-Stage Beamformers in Passive IRS-Assisted Systems with Inexact Oracles
Authors: Spyridon Pougkakiotis, Hassaan Hashmi, Dionysis Kalogerias
Date: 2024-10-31 00:00:00

We develop an efficient data-driven and model-free unsupervised learning algorithm for achieving fully passive intelligent reflective surface (IRS)-assisted optimal short/long-term beamforming in wireless communication networks. The proposed algorithm is based on a zeroth-order stochastic gradient ascent methodology, suitable for tackling two-stage stochastic nonconvex optimization problems with continuous uncertainty and unknown (or "black-box") terms present in the objective function, via the utilization of inexact evaluation oracles. We showcase that the algorithm can operate under realistic and general assumptions, and establish its convergence rate close to some stationary point of the associated two-stage (i.e., short/long-term) problem, particularly in cases where the second-stage (i.e., short-term) beamforming problem (e.g., transmit precoding) is solved inexactly using an arbitrary (inexact) oracle. The proposed algorithm is applicable on a wide variety of IRS-assisted optimal beamforming settings, while also being able to operate without (cascaded) channel model assumptions or knowledge of channel statistics, and over arbitrary IRS physical configurations; thus, no active sensing capability at the IRS(s) is needed. Our algorithm is numerically demonstrated to be very effective in a range of experiments pertaining to a well-studied MISO downlink model, including scenarios demanding physical IRS tuning (e.g., directly through varactor capacitances), even in large-scale regimes.