pwc-archive/papers-with-abstracts huggingface dataset:
Reduction of Nonlinear Distortion in Condenser Microphones Using a Simple Post-Processing Technique
Authors: Petr Honzík, Antonin Novak
Date: 2024-07-24 00:00:00

In this paper, we introduce a novel approach for effectively reducing nonlinear distortion in single back-plate condenser microphones, i.e., most MEMS microphones, studio recording condenser microphones, and laboratory measurement microphones. This simple post-processing technique can be easily integrated on an external hardware such as an analog circuit, microcontroller, audio codec, DSP unit, or within the ASIC chip in a case of MEMS microphones. It significantly reduces microphone distortion across its frequency and dynamic range. It relies on a single parameter, which can be derived from either the microphone's physical parameters or a straightforward measurement presented in this paper. An optimal estimate of this parameter achieves the best distortion reduction, whereas overestimating it never increases distortion beyond the original level. The technique was tested on a MEMS microphone. Our findings indicate that for harmonic excitation the proposed technique reduces the second harmonic by approximately 40 dB, leading to a significant reduction in the Total Harmonic Distortion (THD). The efficiency of the distortion reduction technique for more complex signals is demonstrated through two-tone and multitone experiments, where second-order intermodulation products are reduced by at least 20 dB.
Automatic Detection and Annotation of Sperm Whale Codas
Authors: Guy Gubnitsky, Yaly Mevorach, Shane Gero, David F. Gruber, Roee Diamant
Date: 2024-07-24 00:00:00

A key technology in sperm whale (Physeter macrocephalus) monitoring is the identification of sperm whale communication signals, known as codas. In this paper we present the first automatic coda detector and annotator. The main innovation in our detector is graph-based clustering, which utilizes the expected similarity between the clicks that make up the coda. Results show detection and accurate annotation at low signal-to-noise ratios, separation between codas and echolocation clicks, and discrimination between codas from simultaneously emitting whales. Using this automatic annotator, insights into the characterization of sperm whale communication are presented. The results include new types of coda signals, analyzes of the distribution of coda types among different whales and for different years, and evidence for synchronization between communicating whales in terms of coda type and coda transmission time. These results indicate a high degree of complexity in the communication system of this cetacean species. To ensure traceability, we share the implementation code of our coda detector.
The CHiME-8 DASR Challenge for Generalizable and Array Agnostic Distant Automatic Speech Recognition and Diarization
Authors: Samuele Cornell, Taejin Park, Steve Huang, Christoph Boeddeker, Xuankai Chang, Matthew Maciejewski, Matthew Wiesner, Paola Garcia, Shinji Watanabe
Date: 2024-07-23 00:00:00
Tasks: Automatic Speech Recognition, Distant Speech Recognition, speech-recognition, Speech Recognition, Text Normalization

This paper presents the CHiME-8 DASR challenge which carries on from the previous edition CHiME-7 DASR (C7DASR) and the past CHiME-6 challenge. It focuses on joint multi-channel distant speech recognition (DASR) and diarization with one or more, possibly heterogeneous, devices. The main goal is to spur research towards meeting transcription approaches that can generalize across arbitrary number of speakers, diverse settings (formal vs. informal conversations), meeting duration, wide-variety of acoustic scenarios and different recording configurations. Novelties with respect to C7DASR include: i) the addition of NOTSOFAR-1, an additional office/corporate meeting scenario, ii) a manually corrected Mixer 6 development set, iii) a new track in which we allow the use of large-language models (LLM) iv) a jury award mechanism to encourage participants to explore also more practical and innovative solutions. To lower the entry barrier for participants, we provide a standalone toolkit for downloading and preparing such datasets as well as performing text normalization and scoring their submissions. Furthermore, this year we also provide two baseline systems, one directly inherited from C7DASR and based on ESPnet and another one developed on NeMo and based on NeMo team submission in last year C7DASR. Baseline system results suggest that the addition of the NOTSOFAR-1 scenario significantly increases the task's difficulty due to its high number of speakers and very short duration.
Automated Code-centric Software Vulnerability Assessment: How Far Are We? An Empirical Study in C/C++
Authors: Anh The Nguyen, Triet Huynh Minh Le, M. Ali Babar
Date: 2024-07-24 00:00:00
Tasks: Multi-Task Learning

Background: The C and C++ languages hold significant importance in Software Engineering research because of their widespread use in practice. Numerous studies have utilized Machine Learning (ML) and Deep Learning (DL) techniques to detect software vulnerabilities (SVs) in the source code written in these languages. However, the application of these techniques in function-level SV assessment has been largely unexplored. SV assessment is increasingly crucial as it provides detailed information on the exploitability, impacts, and severity of security defects, thereby aiding in their prioritization and remediation. Aims: We conduct the first empirical study to investigate and compare the performance of ML and DL models, many of which have been used for SV detection, for function-level SV assessment in C/C++. Method: Using 9,993 vulnerable C/C++ functions, we evaluated the performance of six multi-class ML models and five multi-class DL models for the SV assessment at the function level based on the Common Vulnerability Scoring System (CVSS). We further explore multi-task learning, which can leverage common vulnerable code to predict all SV assessment outputs simultaneously in a single model, and compare the effectiveness and efficiency of this model type with those of the original multi-class models. Results: We show that ML has matching or even better performance compared to the multi-class DL models for function-level SV assessment with significantly less training time. Employing multi-task learning allows the DL models to perform significantly better, with an average of 8-22% increase in Matthews Correlation Coefficient (MCC). Conclusions: We distill the practices of using data-driven techniques for function-level SV assessment in C/C++, including the use of multi-task DL to balance efficiency and effectiveness. This can establish a strong foundation for future work in this area.
Mapping the Technological Future: A Topic, Sentiment, and Emotion Analysis in Social Media Discourse
Authors: Alina Landowska, Maciej Skorski, Krzysztof Rajda
Date: 2024-07-20 00:00:00
Tasks: Emotion Recognition

People worldwide are currently confronted with a number of technological challenges, which act as a potent source of uncertainty. The uncertainty arising from the volatility and unpredictability of technology (such as AI) and its potential consequences is widely discussed on social media. This study uses BERTopic modelling along with sentiment and emotion analysis on 1.5 million tweets from 2021 to 2023 to identify anticipated tech-driven futures and capture the emotions communicated by 400 key opinion leaders (KOLs). Findings indicate positive sentiment significantly outweighs negative, with a prevailing dominance of positive anticipatory emotions. Specifically, the 'Hope' score is approximately 10.33\% higher than the median 'Anxiety' score. KOLs emphasize 'Optimism' and benefits over 'Pessimism' and challenges. The study emphasizes the important role KOLs play in shaping future visions through anticipatory discourse and emotional tone during times of technological uncertainty.
Optimal Assignment and Motion Control in Two-Class Continuum Swarms
Authors: Max Emerick, Stacy Patterson, Bassam Bamieh
Date: 2024-07-25 00:00:00

We consider optimal swarm control problems where two different classes of agents are present. Continuum idealizations of large-scale swarms are used where the dynamics describe the evolution of the spatially-distributed densities of each agent class. The problem formulation we adopt is motivated by applications where agents of one class are assigned to agents of the other class, which we refer to as demand and resource agents respectively. Assignments have costs related to the distances between mutually assigned agents, and the overall cost of an assignment is quantified by a Wasserstein distance between the densities of the two agent classes. When agents can move, the assignment cost can decrease at the expense of a physical motion cost, and this tradeoff sets up a nonlinear, infinite-dimensional optimal control problem. We show that in one spatial dimension, this problem can be converted to an infinite-dimensional, but decoupled, linear-quadratic (LQ) tracking problem when expressed in terms of the respective quantile functions. Solutions are given in the general one-dimensional case, as well as in the special cases of constant and periodically time-varying demands.
Identification of a time-varying SIR Model for Covid-19
Authors: Walter HMendes aselein, Diego Eckhard
Date: 2024-07-25 00:00:00

Throughout human history, epidemics have been a constant presence. Understanding their dynamics is essential to predict scenarios and make substantiated decisions. Mathematical models are powerful tools to describe an epidemic behavior. Among the most used, the compartmental ones stand out, dividing population into classes with well-defined characteristics. One of the most known is the $SIR$ model, based on a set of differential equations describing the rates of change of three categories over time. These equations take into account parameters such as the disease transmission rate and the recovery rate, which both change over time. However, classical models use constant parameters and can not describe the behavior of a disease over long periods. In this work, it is proposed a $SIR$ model with time-varying transmission rate parameter with a method to estimate this parameter based on an optimization problem, which minimizes the sum of the squares of the errors between the model and historical data. Additionally, based on the infection rates determined by the algorithm, the model's ability to predict disease activity in future scenarios was also investigated. Epidemic data released by the government of the State of Rio Grande do Sul in Brazil was used to evaluate the models, where the models shown a very good forecasting ability, resulting in errors for predicting the total number of accumulated infected persons of 0.13% for 7 days ahead and 0.6% for 14 days ahead.
Control Informed Design of the IAC Autonomous Racecar for Operation at the Dynamic Envelope
Authors: Qilun Zhu, Matthias Schmid, Robert Prucka, Ashley Boncimino, Chris Paredis
Date: 2024-07-25 00:00:00
Tasks: Autonomous Driving

This article introduces the hardware-software co-design of the control system for an autonomy-enabled formula-style high-speed racecar that will be utilized as the deployment platform for high-level autonomy in the first ever head-to-head driverless race called the Indy Autonomous Challenge. The embedded control system needs to facilitate autonomous functionality, including perception, localization, and by-wire actuation, at high speeds and dynamic limits of the vehicle. Rapid maneuvering during the race, however, excites transient dynamics of the vehicle and the actuators. Compared to current autonomous driving focused on highway cruising and urban traffic, transient vehicle control imposes new challenges to the algorithm and system design. The presented work introduces the cascaded control structure employed by the IAC prototype to fully exploit the time scale separation between different control tasks. It is demonstrated by example way how the model-based control strategies and simulation are utilized to inform the decisions in the actuation, computation, perception, and software pipeline design decisions for the first-of-its-kind IAC racecar.
Satellite Internet of Things Research Report
Authors: Zhang Shutao
Date: 2024-07-25 00:00:00
Tasks: Cloud Computing, Edge-computing

Satellite IoT is an emerging technology that combines the advantages of satellite communication and IoT, providing global coverage, high reliability, and flexible networking. It has a wide range of applications in various fields, including smart agriculture, smart transportation, smart cities, environmental monitoring, and emergency response. With the continuous development of satellite communication, IoT, edge computing, cloud computing, AI, and ML technology, satellite IoT will play an increasingly important role in the future, supporting the digital transformation and sustainable development of society.
Robust Iterative Learning for Collaborative Road Profile Estimation and Active Suspension Control in Connected Vehicles
Authors: Harsh Modi, Mohammad R Hajidavalloo, Zhaojian Li, Minghui Zheng
Date: 2024-07-24 00:00:00

This paper presents the development of a new collaborative road profile estimation and active suspension control framework in connected vehicles, where participating vehicles iteratively refine the road profile estimation and enhance suspension control performance through an iterative learning scheme. Specifically, we develop a robust iterative learning approach to tackle the heterogeneity and model uncertainties in participating vehicles, which are important for practical implementations. In addition, the framework can be adopted as an add-on system to augment existing suspension control schemes. Comprehensive numerical studies are performed to evaluate and validate the proposed framework.
Competitive Perimeter Defense in Tree Environments
Authors: Richard L. Frost, Shaunak D. Bopardikar
Date: 2024-07-24 00:00:00

We consider a perimeter defense problem in a rooted full tree graph environment in which a single defending vehicle seeks to defend a set of specified vertices, termed as the perimeter from mobile intruders that enter the environment through the tree's leaves. We adopt the technique of competitive analysis to characterize the performance of an online algorithm for the defending vehicle. We first derive fundamental limits on the performance of any online algorithm relative to that of an optimal offline algorithm. Specifically, we give three fundamental conditions for finite, 2, and 3/2 competitive ratios in terms of the environment parameters. We then design and analyze three classes of online algorithms that have provably finite competitiveness under varying environmental parameter regimes. Finally, we give a numerical visualization of these regimes to better show the comparative strengths and weaknesses of each algorithm.
From Data to Predictive Control: A Framework for Stochastic Linear Systems with Output Measurements
Authors: Haldun Balim, Andrea Carron, Melanie N. Zeilinger, Johannes Köhler
Date: 2024-07-24 00:00:00

We introduce data to predictive control, D2PC, a framework to facilitate the design of robust and predictive controllers from data. The proposed framework is designed for discrete-time stochastic linear systems with output measurements and provides a principled design of a predictive controller based on data. The framework starts with a parameter identification method based on the Expectation-Maximization algorithm, which incorporates pre-defined structural constraints. Additionally, we provide an asymptotically correct method to quantify uncertainty in parameter estimates. Next, we develop a strategy to synthesize robust dynamic output-feedback controllers tailored to the derived uncertainty characterization. Finally, we introduce a predictive control scheme that guarantees recursive feasibility and satisfaction of chance constraints. This framework marks a significant advancement in integrating data into robust and predictive control schemes. We demonstrate the efficacy of D2PC through a numerical example involving a 10-dimensional spring-mass-damper system.
An MDP-Based Approach for Distribution System Control with PV Generation and Battery Storage
Authors: Robert Sosnowski, Marcin Baszynski, Charalambos Konstantinou
Date: 2024-07-24 00:00:00
Tasks: Decision Making

This paper proposes a decision-making approach for the control of distribution systems with distributed energy resources (DERs) equipped with photovoltaic (PV) units and battery energy storage systems (BESS). The objective is to minimize the total operational cost of the distribution system while satisfying the system operating constraints. The method is based on the discrete-time finite-horizon Markov Decision Process (MDP) framework. Different aspects of the distribution system operation are considered, such as the possibilities of curtailment of PV generation, managing battery storage, reactive power injection, load shedding, and providing a flexibility service for the transmission system. The model is tested for the IEEE 33-bus system with two added DERs and the study cases involve various unexpected events. The experimental results show that this method enables the attainment of relatively low total cost values compared to the reference deterministic approach. The benefits of applying this approach are particularly evident when there is a significant difference between the predicted and actual PV power generation.
MoULDyS: Monitoring of Autonomous Systems in the Presence of Uncertainties
Authors: Bineet Ghosh, Étienne André
Date: 2024-07-24 00:00:00

We introduce MoULDyS, that implements efficient offline and online monitoring algorithms of black-box cyber-physical systems w.r.t. safety properties. MoULDyS takes as input an uncertain log (with noisy and missing samples), as well as a bounding model in the form of an uncertain linear system; this latter model plays the role of an over-approximation so as to reduce the number of false alarms. MoULDyS is Python-based and available under the GNU General Public License v3.0 (gpl-3.0). We further provide easy-to-use scripts to recreate the results of two case studies introduced in an earlier work.
Towards Indirect Data-Driven Predictive Control for Heating Phase of Thermoforming Process
Authors: Hadi Hosseinionari, Mohammad Bajelani, Klaske van Heusden, Abbas S. Milani, Rudolf Seethaler
Date: 2024-07-24 00:00:00
Tasks: Model Predictive Control

Shaping thermoplastic sheets into three-dimensional products is challenging since overheating results in failed manufactured parts and wasted material. To this end, we propose an indirect data-driven predictive control approach using Model Predictive Control (MPC) capable of handling temperature constraints and heating-power saturation while delivering enhanced precision, overshoot control, and settling times compared to state-of-the-art methods. We employ a Non-linear Auto-Regressive with Exogenous inputs (NARX) model to define a linear control-oriented model at each operating point. Using a high-fidelity simulator, several simulation studies have been conducted to evaluate the proposed method's robustness and performance under parametric uncertainty, indicating overshoot and average steady-state error less than $2^\circ \mathrm{C}$ and $0.7^\circ \mathrm{C}$ ($7^\circ \mathrm{C}$ and $2^\circ \mathrm{C}$) for the nominal (worst-case) scenario. Finally, we applied the proposed method to a lab-scale thermoforming platform, resulting in a close response to the simulation analysis with overshoot and average steady-state error metrics less than $5.3^\circ \mathrm{C}$ and $1^\circ \mathrm{C}$, respectively.
Learning Networked Dynamical System Models with Weak Form and Graph Neural Networks
Authors: Yin Yu, Daning Huang, Seho Park, Herschel C. Pangborn
Date: 2024-07-23 00:00:00
Tasks: Form

This paper presents a sequence of two approaches for the data-driven control-oriented modeling of networked systems, i.e., the systems that involve many interacting dynamical components. First, a novel deep learning approach named the weak Latent Dynamics Model (wLDM) is developed for learning generic nonlinear dynamics with control. Leveraging the weak form, the wLDM enables more numerically stable and computationally efficient training as well as more accurate prediction, when compared to conventional methods such as neural ordinary differential equations. Building upon the wLDM framework, we propose the weak Graph Koopman Bilinear Form (wGKBF) model, which integrates geometric deep learning and Koopman theory to learn latent space dynamics for networked systems, especially for the challenging cases having multiple timescales. The effectiveness of the wLDM framework and wGKBF model are demonstrated on three example systems of increasing complexity - a controlled double pendulum, the stiff Brusselator dynamics, and an electrified aircraft energy system. These numerical examples show that the wLDM and wGKBF achieve superior predictive accuracy and training efficiency as compared to baseline models. Parametric studies provide insights into the effects of hyperparameters in the weak form. The proposed framework shows the capability to efficiently capture control-dependent dynamics in these systems, including stiff dynamics and multi-physics interactions, offering a promising direction for learning control-oriented models of complex networked systems.
Correlating Stroke Risk with Non-Invasive Tracing of Brain Blood Dynamic via a Portable Speckle Contrast Optical Spectroscopy Laser Device
Authors: Yu Xi Huang, Simon Mahler, Aidin Abedi, Julian Michael Tyszka, Yu Tung Lo, Patrick D. Lyden, Jonathan Russin, Charles Liu, Changhuei Yang
Date: 2024-07-23 00:00:00

Stroke poses a significant global health threat, with millions affected annually, leading to substantial morbidity and mortality. Current stroke risk assessment for the general population relies on markers such as demographics, blood tests, and comorbidities. A minimally invasive, clinically scalable, and cost-effective way to directly measure cerebral blood flow presents an opportunity. This opportunity has potential to positively impact effective stroke risk assessment prevention and intervention. Physiological changes in the cerebral vascular system, particularly in response to carbon dioxide level changes and oxygen deprivation, such as during breath-holding, can offer insights into stroke risk assessment. However, existing methods for measuring cerebral perfusion reserve, such as blood flow and blood volume changes, are limited by either invasiveness or impracticality. Here, we propose a transcranial approach using speckle contrast optical spectroscopy (SCOS) to non-invasively monitor regional changes in brain blood flow and volume during breath-holding. Our study, conducted on 50 individuals classified into two groups (low-risk and higher-risk for stroke), shows significant differences in blood dynamic changes during breath-holding between the two groups, providing physiological insights for stroke risk assessment using a non-invasive quantification paradigm. Given its cost-effectiveness, scalability, portability, and simplicity, this laser-centric tool has significant potential in enhancing the pre-screening of stroke and mitigating strokes in the general population through early diagnosis and intervention.