pwc-archive/papers-with-abstracts huggingface dataset:
Exploring Efficient-tuning Methods in Self-supervised Speech Models
Authors: Zih-Ching Chen, Chin-Lun Fu, Chih-Ying Liu, Shang-Wen Li, Hung-Yi Lee
Date: 2022-10-10 00:00:00
Tasks: Self-Supervised Learning

In this study, we aim to explore efficient tuning methods for speech self-supervised learning. Recent studies show that self-supervised learning (SSL) can learn powerful representations for different speech tasks. However, fine-tuning pre-trained models for each downstream task is parameter-inefficient since SSL models are notoriously large with millions of parameters. Adapters are lightweight modules commonly used in NLP to solve this problem. In downstream tasks, the parameters of SSL models are frozen, and only the adapters are trained. Given the lack of studies generally exploring the effectiveness of adapters for self-supervised speech tasks, we intend to fill this gap by adding various adapter modules in pre-trained speech SSL models. We show that the performance parity can be achieved with over 90% parameter reduction, and discussed the pros and cons of efficient tuning techniques. This is the first comprehensive investigation of various adapter types across speech tasks.
Text-to-Audio Grounding Based Novel Metric for Evaluating Audio Caption Similarity
Authors: Swapnil Bhosale, Rupayan Chakraborty, Sunil Kumar Kopparapu
Date: 2022-10-03 00:00:00
Tasks: Audio captioning, Image Captioning, TAG, Text Generation

Automatic Audio Captioning (AAC) refers to the task of translating an audio sample into a natural language (NL) text that describes the audio events, source of the events and their relationships. Unlike NL text generation tasks, which rely on metrics like BLEU, ROUGE, METEOR based on lexical semantics for evaluation, the AAC evaluation metric requires an ability to map NL text (phrases) that correspond to similar sounds in addition lexical semantics. Current metrics used for evaluation of AAC tasks lack an understanding of the perceived properties of sound represented by text. In this paper, wepropose a novel metric based on Text-to-Audio Grounding (TAG), which is, useful for evaluating cross modal tasks like AAC. Experiments on publicly available AAC data-set shows our evaluation metric to perform better compared to existing metrics used in NL text and image captioning literature.
Pinning control of networks: dimensionality reduction through simultaneous block-diagonalization of matrices
Authors: Shirin Panahi, Matteo Lodi, Marco Storace, Francesco Sorrentino
Date: 2022-10-12 00:00:00
Tasks: Dimensionality Reduction

In this paper, we study the network pinning control problem in the presence of two different types of coupling: (i) node-to-node coupling among the network nodes and (ii) input-to-node coupling from the source node to the `pinned nodes'. Previous work has mainly focused on the case that (i) and (ii) are of the same type. We decouple the stability analysis of the target synchronous solution into subproblems of the lowest dimension by using the techniques of simultaneous block diagonalization (SBD) of matrices. Interestingly, we obtain two different types of blocks, driven and undriven. The overall dimension of the driven blocks is equal to the dimension of an appropriately defined controllable subspace, while all the remaining undriven blocks are scalar. Our main result is a decomposition of the stability problem into four independent sets of equations, which we call quotient controllable, quotient uncontrollable, redundant controllable, and redundant uncontrollable. Our analysis shows that the number and location of the pinned nodes affect the number and the dimension of each set of equations. We also observe that in a large variety of complex networks, stability of the target synchronous solution is de facto only determined by a single quotient controllable block.
Deep Koopman Learning of Nonlinear Time-Varying Systems
Authors: Wenjian Hao, Bowen Huang, Wei Pan, Di wu, Shaoshuai Mou
Date: 2022-10-12 00:00:00
Tasks: Computational Efficiency

This paper presents a data-driven approach to approximate the dynamics of a nonlinear time-varying system (NTVS) by a linear time-varying system (LTVS), which is resulted from the Koopman operator and deep neural networks. Analysis of the approximation error between states of the NTVS and the resulting LTVS is presented. Simulations on a representative NTVS show that the proposed method achieves small approximation errors, even when the system changes rapidly. Furthermore, simulations in an example of quadcopters demonstrate the computational efficiency of the proposed approach.
On the Internal Stability of Diffusively Coupled Multi-Agent Systems and the Dangers of Cancel Culture
Authors: Gal Barkai, Leonid Mirkin, Daniel Zelazo
Date: 2022-10-12 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

We study internal stability in the context of diffusively-coupled control architectures, common in multi-agent systems (i.e. the celebrated consensus protocol), for linear time-invariant agents. We derive a condition under which the system can not be stabilized by any controller from that class. In the finite-dimensional case the condition states that diffusive controllers cannot stabilize agents that share common unstable dynamics, directions included. This class always contains the group of homogeneous unstable agents, like integrators. We argue that the underlying reason is intrinsic cancellations of unstable agent dynamics by such controllers, even static ones, where directional properties play a key role. The intrinsic lack of internal stability explains the notorious behavior of some distributed control protocols when affected by measurement noise or exogenous disturbances.
Approximation-free Prescribed Performance Control with Prescribed Input Constraints
Authors: Pankaj K Mishra, Pushpak Jagtap
Date: 2022-10-12 00:00:00

This paper considers the tracking control problem for an unknown nonlinear system with time-varying bounded disturbance subjected to a prescribed performance and input constraints. When performance and input constraints are specified simultaneously for such a problem, a trade-off is inevitable. Consequently, a feasibility condition for prescribing performance and input constraints is devised to address such difficulties of arbitrary prescription. In addition, an approximation-free controller with low complexity is proposed, which ensures that the constraints are never violated, provided that the feasibility condition holds. Finally, simulation results corroborate the effectiveness of the proposed controller.
Road Slope Prediction and Vehicle Dynamics Control for Autonomous Vehicles
Authors: Gautam Shetty, Sabir Hossain, Chuan Hu, Xianke Lin
Date: 2022-10-11 00:00:00
Tasks: Autonomous Vehicles

Autonomous vehicles can enhance overall performance and implement safety measures in ways that are impossible with conventional automobiles. These functions are executed through vehicle control systems, which have been the subject of considerable research. Autonomous cars have a distinct advantage as they possess various perception sensors that can predict road surface conditions and other phenomena ahead of time. Many modern automotive control systems treat the road slope as a constant and do not account for changes in the road profile in their vehicle models. As a result, vehicle states may be miscalculated, which, in the worst-case scenario, may result in accidents. This is particularly true for high center-of-gravity vehicles like trailers and delivery trucks. With the help of perception sensors in autonomous vehicles, a road slope estimation system can be developed to aid these control systems in making informed decisions regarding the vehicle's state. The current review is divided into three logical steps that can be discussed in the following manner: the first section describes and reviews the individual steps for developing a road slope estimation system. The second one provides a detailed review of previous investigations that implemented different methods that employ this prediction system to improve overall vehicle performance. Finally, a roll control system is presented as an innovative idea that builds on the whole discussion. A rollover prevention system with prediction abilities is presented because (1) it proves to be a critical safety feature, especially for heavy vehicles like buses, trucks, delivery trailers, etc., and (2) not enough research has been conducted on technologies that integrate a roll stability controller with a slope estimation system.
Digital twins of nonlinear dynamical systems
Authors: Ling-Wei Kong, Yang Weng, Bryan Glaz, Mulugeta Haile, Ying-Cheng Lai
Date: 2022-10-05 00:00:00

We articulate the design imperatives for machine-learning based digital twins for nonlinear dynamical systems subject to external driving, which can be used to monitor the ``health'' of the target system and anticipate its future collapse. We demonstrate that, with single or parallel reservoir computing configurations, the digital twins are capable of challenging forecasting and monitoring tasks. Employing prototypical systems from climate, optics and ecology, we show that the digital twins can extrapolate the dynamics of the target system to certain parameter regimes never experienced before, make continual forecasting/monitoring with sparse real-time updates under non-stationary external driving, infer hidden variables and accurately predict their dynamical evolution, adapt to different forms of external driving, and extrapolate the global bifurcation behaviors to systems of some different sizes. These features make our digital twins appealing in significant applications such as monitoring the health of critical systems and forecasting their potential collapse induced by environmental changes.
Creating New Chaotic Signals with Reservoir Computers
Authors: Thomas L. Carroll
Date: 2022-09-09 00:00:00

While there have been many publications on potential applications of chaos to fields such as communications, radar, sonar, random signal generation, channel equalization and others, designing continuous chaotic systems is still an unsolved problem. There are a number of well known chaotic systems used for applications, but if any application is to become widely used, some way of generating many different chaotic signals is necessary. This work shows that one may use a reservoir computer to create a set of chaotic signals that are correlated but easily distinguishable from one chaotic signal with desirable properties. The ability to distinguish the new signals is demonstrated with a simple communications example.
Spatio-temporal Pattern Formation due to Host-Circuit Interplay in Gene Expression Dynamics
Authors: Priya Chakraborty, Ushasi Roy, Mohit K. Jolly, Sayantari Ghosh
Date: 2022-10-12 00:00:00

Biological systems are majorly dependent on their property of bistability in order to exhibit nongenetic heterogeneity in terms of cellular morphology and physiology. Spatial patterns of phenotypically heterogeneous cells, arising due to underlying bistability, may play significant role in phenomena like biofilm development, adaptation, cell motility etc. While nonlinear positive feedback regulation, like cooperative heterodimer formation are the usual reason behind bistability, similar dynamics can also occur as a consequence of host-circuit interaction. In this paper, we have investigated the pattern formation by a motif with non-cooperative positive feedback, that imposes a metabolic burden on its host due to its expression. In a cellular array set inside diffusible environment, we investigate spatio-temporal diffusion in one dimension as well as in two dimension in the context of various initial conditions respectively. Moreover, the number of cells exhibiting the same steady state, as well as their spatial distribution has been quantified in terms of connected component analysis. The effect of diffusion coefficient variation has been studied in terms of stability of related states and time evolution of patterns.
Estimating Option Pricing Models Using a Characteristic Function-Based Linear State Space Representation
Authors: H. Peter Boswijk, Roger J. A. Laeven, Evgenii Vladimirov
Date: 2022-10-12 00:00:00

We develop a novel filtering and estimation procedure for parametric option pricing models driven by general affine jump-diffusions. Our procedure is based on the comparison between an option-implied, model-free representation of the conditional log-characteristic function and the model-implied conditional log-characteristic function, which is functionally affine in the model's state vector. We formally derive an associated linear state space representation and establish the asymptotic properties of the corresponding measurement errors. The state space representation allows us to use a suitably modified Kalman filtering technique to learn about the latent state vector and a quasi-maximum likelihood estimator of the model parameters, which brings important computational advantages. We analyze the finite-sample behavior of our procedure in Monte Carlo simulations. The applicability of our procedure is illustrated in two case studies that analyze S&P 500 option prices and the impact of exogenous state variables capturing Covid-19 reproduction and economic policy uncertainty.
TaskMix: Data Augmentation for Meta-Learning of Spoken Intent Understanding
Authors: Surya Kant Sahu
Date: 2022-09-26 00:00:00
Tasks: Data Augmentation, Diversity, intent-classification, Intent Classification, Meta-Learning, Multi-Task Learning

Meta-Learning has emerged as a research direction to better transfer knowledge from related tasks to unseen but related tasks. However, Meta-Learning requires many training tasks to learn representations that transfer well to unseen tasks; otherwise, it leads to overfitting, and the performance degenerates to worse than Multi-task Learning. We show that a state-of-the-art data augmentation method worsens this problem of overfitting when the task diversity is low. We propose a simple method, TaskMix, which synthesizes new tasks by linearly interpolating existing tasks. We compare TaskMix against many baselines on an in-house multilingual intent classification dataset of N-Best ASR hypotheses derived from real-life human-machine telephony utterances and two datasets derived from MTOP. We show that TaskMix outperforms baselines, alleviates overfitting when task diversity is low, and does not degrade performance even when it is high.
On Advances, Challenges and Potentials of Remote Sensing Image Analysis in Marine Debris and Suspected Plastics Monitoring
Authors: Oktay Karakuş
Date: 2022-10-12 00:00:00
Tasks: Earth Observation

Marine plastic pollution is an emerging environmental problem since it pollutes the ocean, air and food whilst endangering the ocean wildlife via the ingestion and entanglements. During the last decade, an enormous effort has been spent on finding possible solutions to marine plastic pollution. Remote sensing imagery sits in a crucial place for these efforts since it provides informative earth observation products, and the current technology offers further essential development. Despite the advances in the last decade, there is still a way to go for marine plastic monitoring research where challenges are rarely highlighted. This paper contributes to the literature with a critical review and aims to highlight literature milestones in marine debris and suspected plastics (MD&SP) monitoring by promoting the computational imaging methodology behind these approaches along with detailed discussions on challenges and potential future research directions.
Transmission of high-definition video signals underwater using surface electromagnetic waves
Authors: Igor I. Smolyaninov, Quirino Balzano, Mark Barry
Date: 2022-10-02 00:00:00
Tasks: Vocal Bursts Intensity Prediction

A portable radio communication system operating in the 30 MHz band and capable of transmitting high-definition live underwater video images is presented. The system operation is based on launching electromagnetic surface waves propagating along water-air interface using specially designed surface wave antennas. Since the propagation length of the surface electromagnetic waves far exceeds the skin depth of bulk radio waves at the same frequency, this technique is useful for video communication underwater over distances of several meters. Also, this system appears to be efficient at communicating through the water-air interface.
Low-Latency Analog-to-Analog Signal Processing using PC Hardware and USRPs
Authors: Maximilian Engelhardt, Carsten Andrich, Alexander Ihlow, Sebastian Giehl, Giovanni Del Galdo
Date: 2022-10-12 00:00:00
Tasks: Scheduling

In this paper, we implement a low-latency rapid-prototyping platform for signal processing based on software-defined radios (SDRs) and off-the-shelf PC hardware. This platform allows to evaluate a wide variety of algorithms in real-time environments, supporting new developments in the fields of classical, AI-based, and hybrid signal processing. To accomplish this, the streaming protocol of the used USRP X310 devices is implemented using the Data Plane Development Kit (DPDK), which allows to handle network communication in userspace only. This bypasses the kernel and thus avoids the latencies caused by interrupt handling, scheduling, and context switches. It allows signal processing to be performed on isolated processor cores that are protected from interrupts to a great extent. To validate our approach, linear time-invariant channel emulation has been implemented. For this, an analog-to-analog latency of 31 microseconds was achieved, demonstrating that our PC-based approach enables the implementation of rapid-prototyping systems with low latency.
On Secure Uplink Transmission in Hybrid RF-FSO Cooperative Satellite-Aerial-Terrestrial Networks
Authors: Yuanyuan Ma, Tiejun Lv, Gaofeng Pan, Yunfei Chen, Mohamed-Slim Alouini
Date: 2022-10-12 00:00:00

This work investigates the secrecy outage performance of the uplink transmission of a radio-frequency (RF)-free-space optical (FSO) hybrid cooperative satellite-aerial-terrestrial network (SATN). Specifically, in the considered cooperative SATN, a terrestrial source (S) transmits its information to a satellite receiver (D) via the help of a cache-enabled aerial relay (R) terminal with the most popular content caching scheme, while a group of eavesdropping aerial terminals (Eves) trying to overhear the transmitted confidential information. Moreover, RF and FSO transmissions are employed over S-R and R-D links, respectively. Considering the randomness of R, D, and Eves, and employing a stochastic geometry framework, the secrecy outage performance of the cooperative uplink transmission in the considered SATN is investigated and a closed-form analytical expression for the end-to-end secrecy outage probability is derived. Finally, Monte-Carlo simulations are shown to verify the accuracy of our analysis.
Cramer-Rao Lower Bound Optimization for Hidden Moving Target Sensing via Multi-IRS-Aided Radar
Authors: Zahra Esmaeilbeig, Kumar Vijay Mishra, Arian Eamaz, Mojtaba Soltanalian
Date: 2022-10-11 00:00:00

Intelligent reflecting surface (IRS) is a rapidly emerging paradigm to enable non-line-of-sight (NLoS) wireless transmission. In this paper, we focus on IRS-aided radar estimation performance of a moving hidden or NLoS target. Unlike prior works that employ a single IRS, we investigate this problem using multiple IRS platforms and assess the estimation performance by deriving the associated Cramer-Rao lower bound (CRLB). We then design Doppler-aware IRS phase shifts by minimizing the scalar A-optimality measure of the joint parameter CRLB matrix. The resulting optimization problem is non-convex, and is thus tackled via an alternating optimization framework. Numerical results demonstrate that the deployment of multiple IRS platforms with our proposed optimized phase shifts leads to a higher estimation accuracy compared to non-IRS and single-IRS alternatives.
Smart Cup: An impedance sensing based fluid intake monitoring system for beverages classification and freshness detection
Authors: Mengxi Liu, Sizhen Bian, Bo Zhou, Agnes Grünerbl, Paul Lukowicz
Date: 2022-10-08 00:00:00

This paper presents a novel beverage intake monitoring system that can accurately recognize beverage kinds and freshness. By mounting carbon electrodes on the commercial cup, the system measures the electrochemical impedance spectrum of the fluid in the cup. We studied the frequency sensitivity of the electrochemical impedance spectrum regarding distinct beverages and the importance of features like amplitude, phase, and real and imaginary components for beverage classification. The results show that features from a low-frequency domain (100 Hz to 1000 Hz) provide more meaningful information for beverage classification than the higher frequency domain. Twenty beverages, including carbonated drinks and juices, were classified with nearly perfect accuracy using a supervised machine learning approach. The same performance was also observed in the freshness recognition, where four different kinds of milk and fruit juice were studied.
Indoor Localization with Robust Global Channel Charting: A Time-Distance-Based Approach
Authors: Maximilian Stahlke, George Yammine, Tobias Feigl, Bjoern M. Eskofier, Christopher Mutschler
Date: 2022-10-07 00:00:00
Tasks: Indoor Localization

Fingerprinting-based positioning significantly improves the indoor localization performance in non-line-of-sight-dominated areas. However, its deployment and maintenance is cost-intensive as it needs ground-truth reference systems for both the initial training and the adaption to environmental changes. In contrast, channel charting (CC) works without explicit reference information and only requires the spatial correlations of channel state information (CSI). While CC has shown promising results in modelling the geometry of the radio environment, a deeper insight into CC for localization using multi-anchor large-bandwidth measurements is still pending. We contribute a novel distance metric for time-synchronized single-input/single-output CSIs that approaches a linear correlation to the Euclidean distance. This allows to learn the environment's global geometry without annotations. To efficiently optimize the global channel chart we approximate the metric with a Siamese neural network. This enables full CC-assisted fingerprinting and positioning only using a linear transformation from the chart to the real-world coordinates. We compare our approach to the state-of-the-art of CC on two different real-world data sets recorded with a 5G and UWB radio setup. Our approach outperforms others with localization accuracies of 0.69m for the UWB and 1.4m for the 5G setup. We show that CC-assisted fingerprinting enables highly accurate localization and reduces (or eliminates) the need for annotated training data.
ECG for high-throughput screening of multiple diseases: Proof-of-concept using multi-diagnosis deep learning from population-based datasets
Authors: Weijie Sun, Sunil Vasu Kalmady, Amir Salimi, Nariman Sepehrvand, Eric Ly, Abram Hindle, Russell Greiner, Padma Kaul
Date: 2022-10-06 00:00:00
Tasks: Diagnostic

Electrocardiogram (ECG) abnormalities are linked to cardiovascular diseases, but may also occur in other non-cardiovascular conditions such as mental, neurological, metabolic and infectious conditions. However, most of the recent success of deep learning (DL) based diagnostic predictions in selected patient cohorts have been limited to a small set of cardiac diseases. In this study, we use a population-based dataset of >250,000 patients with >1000 medical conditions and >2 million ECGs to identify a wide range of diseases that could be accurately diagnosed from the patient's first in-hospital ECG. Our DL models uncovered 128 diseases and 68 disease categories with strong discriminative performance.