pwc-archive/papers-with-abstracts huggingface dataset:
REDCHO: Robust Exact Dynamic Consensus of High Order
Authors: Rodrigo Aldana-López, Rosario Aragüés, Carlos Sagüés
Date: 2022-04-26 00:00:00
Tasks: Vocal Bursts Intensity Prediction

This article addresses the problem of average consensus in a multi-agent system when the desired consensus quantity is a time varying signal. Recently, the EDCHO protocol leveraged high order sliding modes to achieve exact consensus under a constrained set of initial conditions, limiting its applicability to static networks. In this work, we propose REDCHO, an extension of the previous protocol which is robust to mismatch in the initial conditions, making it suitable to use cases in which connection and disconnection of agents is possible. The convergence properties of the protocol are formally explored. Finally, the effectiveness and advantages of our proposal are shown with concrete simulation examples showing the benefits of REDCHO against other methods in the literature.
Linear TDOA-based Measurements for Distributed Estimation and Localized Tracking
Authors: Mohammadreza Doostmohammadian, Themistoklis Charalambous
Date: 2022-04-26 00:00:00

We propose a linear time-difference-of-arrival (TDOA) measurement model to improve \textit{distributed} estimation performance for localized target tracking. We design distributed filters over sparse (possibly large-scale) communication networks using consensus-based data-fusion techniques. The proposed distributed and localized tracking protocols considerably reduce the sensor network's required connectivity and communication rate. We, further, consider $\kappa$-redundant observability and fault-tolerant design in case of losing communication links or sensor nodes. We present the minimal conditions on the remaining sensor network (after link/node removal) such that the distributed observability is still preserved and, thus, the sensor network can track the (single) maneuvering target. The motivation is to reduce the communication load versus the processing load, as the computational units are, in general, less costly than the communication devices. We evaluate the tracking performance via simulations in MATLAB.
Masked Spectrogram Modeling using Masked Autoencoders for Learning General-purpose Audio Representation
Authors: Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Noboru Harada, Kunio Kashino
Date: 2022-04-26 00:00:00
Tasks: Contrastive Learning, Self-Supervised Learning

Recent general-purpose audio representations show state-of-the-art performance on various audio tasks. These representations are pre-trained by self-supervised learning methods that create training signals from the input. For example, typical audio contrastive learning uses temporal relationships among input sounds to create training signals, whereas some methods use a difference among input views created by data augmentations. However, these training signals do not provide information derived from the intact input sound, which we think is suboptimal for learning representation that describes the input as it is. In this paper, we seek to learn audio representations from the input itself as supervision using a pretext task of auto-encoding of masked spectrogram patches, Masked Spectrogram Modeling (MSM, a variant of Masked Image Modeling applied to audio spectrogram). To implement MSM, we use Masked Autoencoders (MAE), an image self-supervised learning method. MAE learns to efficiently encode the small number of visible patches into latent representations to carry essential information for reconstructing a large number of masked patches. While training, MAE minimizes the reconstruction error, which uses the input as training signal, consequently achieving our goal. We conducted experiments on our MSM using MAE (MSM-MAE) models under the evaluation benchmark of the HEAR 2021 NeurIPS Challenge. Our MSM-MAE models outperformed the HEAR 2021 Challenge results on seven out of 15 tasks (e.g., accuracies of 73.4% on CREMA-D and 85.8% on LibriCount), while showing top performance on other tasks where specialized models perform better. We also investigate how the design choices of MSM-MAE impact the performance and conduct qualitative analysis of visualization outcomes to gain an understanding of learned representations. We make our code available online.
Mask scalar prediction for improving robust automatic speech recognition
Authors: Arun Narayanan, James Walker, Sankaran Panchapagesan, Nathan Howard, Yuma Koizumi
Date: 2022-04-26 00:00:00
Tasks: Acoustic echo cancellation, Automatic Speech Recognition, Automatic Speech Recognition (ASR), Prediction, speech-recognition, Speech Recognition

Using neural network based acoustic frontends for improving robustness of streaming automatic speech recognition (ASR) systems is challenging because of the causality constraints and the resulting distortion that the frontend processing introduces in speech. Time-frequency masking based approaches have been shown to work well, but they need additional hyper-parameters to scale the mask to limit speech distortion. Such mask scalars are typically hand-tuned and chosen conservatively. In this work, we present a technique to predict mask scalars using an ASR-based loss in an end-to-end fashion, with minimal increase in the overall model size and complexity. We evaluate the approach on two robust ASR tasks: multichannel enhancement in the presence of speech and non-speech noise, and acoustic echo cancellation (AEC). Results show that the presented algorithm consistently improves word error rate (WER) without the need for any additional tuning over strong baselines that use hand-tuned hyper-parameters: up to 16% for multichannel enhancement in noisy conditions, and up to 7% for AEC.
Cleanformer: A multichannel array configuration-invariant neural enhancement frontend for ASR in smart speakers
Authors: Joseph Caroselli, Arun Narayanan, Nathan Howard, Tom O'Malley
Date: 2022-04-25 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This work introduces the Cleanformer, a streaming multichannel neural based enhancement frontend for automatic speech recognition (ASR). This model has a conformer-based architecture which takes as inputs a single channel each of raw and enhanced signals, and uses self-attention to derive a time-frequency mask. The enhanced input is generated by a multichannel adaptive noise cancellation algorithm known as Speech Cleaner, which makes use of noise context to derive its filter taps. The time-frequency mask is applied to the noisy input to produce enhanced output features for ASR. Detailed evaluations are presented with simulated and re-recorded datasets in speech-based and non-speech-based noise that show significant reduction in word error rate (WER) when using a large-scale state-of-the-art ASR model. It also will be shown to significantly outperform enhancement using a beamformer with ideal steering. The enhancement model is agnostic of the number of microphones and array configuration and, therefore, can be used with different microphone arrays without the need for retraining. It is demonstrated that performance improves with more microphones, up to 4, with each additional microphone providing a smaller marginal benefit. Specifically, for an SNR of -6dB, relative WER improvements of about 80\% are shown in both noise conditions.
A note on load balancing in DC microgrids
Authors: Shravan Mohan, Bharath Bhikkaji
Date: 2022-04-26 00:00:00

A problem of load balancing in isolated DC microgrids is considered in this paper. Here, a DC load is fed by multiple heterogenous DC sources, each of which is connected to the load via a boost converter. The gains of the DCC's provide for a means to control the division of load current amongst the DC sources. The primary objective of the control scheme is to minimise the total losses in the network, while maintaining the output voltage within a desired range, serving the load current demand and adhering to VI-characteristics of the power sources. Under assumptions of concavity/monotonocity/piece-wise-linearity of the VI-characteristics, the problem is solved using a convex relaxation. It is shown that the solution to the relaxed problem is tight. Thus, the resulting algorithm is guaranteed to reach global optimality in a numerically efficient manner. Simulations are provided for corroboration.
Razumikhin and Krasovskii Approaches for Safe Stabilization
Authors: Wei Ren, Raphael M. Jungers, Dimos V. Dimarogonas
Date: 2022-04-26 00:00:00

This paper studies the stabilization and safety problems of nonlinear time-delay systems. Following both Razumikhin and Krasovskii approaches, we propose novel control Lyapunov functions/functionals for the stabilization problem and novel control barrier functions/functionals for the safety problem. The proposed control Lyapunov and barrier functions/functionals extend the existing ones from the delay-free case to the time-delay case, and allow for designing the stabilizing and safety controllers in closed-form. Since analytical solutions to time-delay optimal control problems are hard to be achieved, a sliding mode control based approach is developed to merge the proposed control Lyapunov and barrier functions/functionals. Based on the sliding surface functional, a feedback control law is established to investigate the stabilization and safety objectives simultaneously. In particular, the properties of the sliding surface functional are analyzed, and further how to construct the sliding surface functional is discussed. Finally, the proposed approaches are illustrated via two numerical examples from the connected cruise control problem of automotive systems and the synchronization problem of multi-agent systems.
Cyber-Physical Vulnerability Assessment of P2P Energy Exchanges in Active Distribution Networks
Authors: Hamed Haggi, Wei Sun
Date: 2022-04-26 00:00:00
Tasks: energy trading

Owing to the decreasing costs of distributed energy resources (DERs) as well as decarbonization policies, power systems are undergoing a modernization process. The large deployment of DERs together with internet of things (IoT) devices provide a platform for peer-to-peer (P2P) energy trading in active distribution networks. However, P2P energy trading with IoT devices have driven the grid more vulnerable to cyber-physical threats. To this end, in this paper, a resilience-oriented P2P energy exchange model is developed considering three phase unbalanced distribution systems. In addition, various scenarios for vulnerability assessment of P2P energy exchanges considering adverse prosumers and consumers, who provide false information regarding the price and quantity with the goal of maximum financial benefit and system operation disruption, are considered. Techno-economic survivability analysis against these attacks are investigated on a IEEE 13-node unbalanced distribution test system. Simulation results demonstrate that adverse peers can affect the physical operation of grid, maximize their benefits, and cause financial loss of other agents.
Comparison study of the combination of the SPSA algorithm and the PSO algorithm
Authors: Bertrand Ngansop, Stefan Götz, Martin Eckl
Date: 2022-04-25 00:00:00

Particle swarm optimization (PSO) is attracting an ever-growing attention and more than ever it has found many application areas for many challenging optimization problems. It is, however, a known fact that PSO has a severe drawback in the update of its global best (gbest) particle, which has a crucial role of guiding the rest of the swarm. In this paper, we propose three efficient solutions to remedy this problem using the SPSA Algorithm. In the first approach, gbest is updated with respect to a global estimation of the gradient and can avoid getting trapped into a local optimum. The second approach is based on the formation of an alternative or artificial global best particle, the so-called aGB, which can replace the native gbest particle for a better guidance, the decision of which is held by a fair competition between the two. The third approach is based on the update of the swarm particle. For this purpose we use simultaneous perturbation stochastic approximation (SPSA) for its low cost. Since SPSA is applied only to the gbest (not to the entire swarm) or to the entire swarm, both approaches result thus in a negligible overhead cost for the entire PSO process. Both approaches are shown to significantly improve the performance of PSO over a wide range of non-linear functions, especially if SPSA and PSO parameters are well selected to fit the problem at hand. As in the basic PSO application, experimental results show that the proposed approaches significantly improved the quality of the Optimization process as measured by a statistic analysis.
Network location and clustering of genetic mutations determine chronicity in a stylized model of genetic diseases
Authors: Piotr Nyczka, Johannes Falk, Marc-Thorsten Hütt
Date: 2022-04-26 00:00:00

In a highly simplified view, a disease can be seen as the phenotype emerging from the interplay of genetic predisposition and fluctuating environmental stimuli. We formalize this situation in a minimal model, where a network (representing cellular regulation) serves as an interface between an input layer (representing environment) and an output layer (representing functional phenotype). Genetic predisposition for a disease is represented as a loss of function of some network nodes. Reduced, but non-zero, output indicates disease. The simplicity of this genetic disease model and its deep relationship to percolation theory allows us to understand the interplay between disease, network topology and the location and clusters of affected network nodes. We find that our model generates two different characteristics of diseases, which can be interpreted as chronic and acute diseases. In its stylized form, our model provides a new view on the relationship between genetic mutations and the type and severity of a disease.
Influence of invasion on natural selection in dispersal-structured populations
Authors: D. Navidad Maeso, M. Patriarca, E. Heinsalu
Date: 2022-04-25 00:00:00

We investigate how initial and boundary conditions influence the competition dynamics and outcome in dispersal-structured populations. The study is carried out through numerical modeling of the heterogeneous Brownian bugs model, in which individuals are characterized by diversified diffusion coefficients and compete for resources within a finite interaction range. We observed that if instead of being distributed randomly across the domain the population is initially localized within a small region, the dynamics and the stationary state of the system differ significantly. The outcome of the natural selection is determined by different competition processes, fluctuations, and spreading of the organisms.
Rapid, high-sensitivity detection of biomolecules using dual-comb biosensing: application to the SARS-CoV-2 nucleocapsid protein
Authors: Shogo Miyamura, Ryo Oe, Takuya Nakahara, Shota Okada, Shuji Taue, Yu Tokizane, Takeo Minamikawa, Taka-aki Yano, Kunihiro Otsuka, Ayuko Sakane, Takuya Sasaki, Koji Yasutomo, Taira Kajisa, Takeshi Yasui
Date: 2022-04-25 00:00:00
Tasks: Sensitivity

Rapid, sensitive detection of biomolecules is important for improved testing methods for viruses as well as biomarkers and environmental hormones. For example, testing for SARS-CoV-2 is essential in the fight against the COVID-19 pandemic. Reverse-transcription polymerase chain reaction (RT-PCR) is the current standard for COVID-19 testing; however, it is hampered by the long testing process. Shortening the testing process while achieving high sensitivity would facilitate sooner quarantine and thus presumably prevention of the spread of SARS-CoV-2. Here, we aim to achieve rapid, sensitive detection of the SARS-CoV-2 nucleocapsid protein by enhancing the performance of optical biosensing with a dual-comb configuration of optical frequency combs. The virus-concentration-dependent optical spectrum shift is transformed into a photonic RF shift by frequency conversion between the optical and RF regions, facilitating mature electrical frequency measurements. Furthermore, active-dummy temperature-drift compensation enables very small changes in the virus-concentration-dependent signal to be extracted from the large, variable background signal. This dual-comb biosensing technique has the potential to reduce the COVID-19 testing time to 10 min while maintaining sensitivity close to that of RT-PCR. Furthermore, this system can be applied for sensing of not only viruses but also various biomolecules for clinical diagnosis, health care, and environmental monitoring.
GMM is Inadmissible Under Weak Identification
Authors: Isaiah Andrews, Anna Mikusheva
Date: 2022-04-26 00:00:00

We consider estimation in moment condition models and show that under any bound on identification strength, asymptotically admissible (i.e. undominated) estimators in a wide class of estimation problems must be uniformly continuous in the sample moment function. GMM estimators are in general discontinuous in the sample moments, and are thus inadmissible. We show, by contrast, that bagged, or bootstrap aggregated, GMM estimators as well as quasi-Bayes posterior means have superior continuity properties, while results in the literature imply that they are equivalent to GMM when identification is strong. In simulations calibrated to published instrumental variables specifications, we find that these alternatives often outperform GMM.
A One-Covariate-at-a-Time Method for Nonparametric Additive Models
Authors: Liangjun Su, Thomas Tao Yang, Yonghui Zhang, Qiankun Zhou
Date: 2022-04-26 00:00:00
Tasks: Additive models

This paper proposes a one-covariate-at-a-time multiple testing (OCMT) approach to choose significant variables in high-dimensional nonparametric additive regression models. Similarly to Chudik, Kapetanios and Pesaran (2018), we consider the statistical significance of individual nonparametric additive components one at a time and take into account the multiple testing nature of the problem. One-stage and multiple-stage procedures are both considered. The former works well in terms of the true positive rate only if the marginal effects of all signals are strong enough; the latter helps to pick up hidden signals that have weak marginal effects. Simulations demonstrate the good finite sample performance of the proposed procedures. As an empirical application, we use the OCMT procedure on a dataset we extracted from the Longitudinal Survey on Rural Urban Migration in China. We find that our procedure works well in terms of the out-of-sample forecast root mean square errors, compared with competing methods.
An empirical equilibrium model of formal and informal credit markets in developing countries
Authors: Fan Wang
Date: 2022-04-26 00:00:00

I develop and estimate a dynamic equilibrium model of risky entrepreneurs' borrowing and savings decisions incorporating both formal and local-informal credit markets. Households have access to an exogenous formal credit market and to an informal credit market in which the interest rate is endogenously determined by the local demand and supply of credit. I estimate the model via Simulated Maximum Likelihood using Thai village data during an episode of formal credit market expansion. My estimates suggest that a 49 percent reduction in fixed costs increased the proportion of households borrowing formally by 36 percent, and that a doubling of the collateralized borrowing limits lowered informal interest rates by 24 percent. I find that more productive households benefited from the policies that expanded borrowing access, but less productive households lost in terms of welfare due to diminished savings opportunities. Gains are overall smaller than would be predicted by models that do not consider the informal credit market.
Changes in Retirement Savings During the COVID Pandemic
Authors: Elena Derby, Lucas Goodman, Kathleen Mackie, Jacob Mortenson
Date: 2022-04-26 00:00:00

This paper documents changes in retirement saving patterns at the onset of the COVID-19 pandemic. We construct a large panel of U.S. tax data, including tens of millions of person-year observations, and measure retirement savings contributions and withdrawals. We use these data to document several important changes in retirement savings patterns during the pandemic relative to the years preceding the pandemic or the Great Recession. First, unlike during the Great Recession, contributions to retirement savings vehicles did not meaningfully decline. Second, driven by the suspension of required minimum distribution rules, IRA withdrawals substantially declined in 2020 for those older than age 72. Third, potentially driven by partial suspension of the early withdrawal penalty, employer-plan withdrawals increased for those under age 60.
UAS Imagery and Computer Vision for Site-Specific Weed Control in Corn
Authors: Ranjan Sapkota, Paulo Flores
Date: 2022-04-26 00:00:00

Currently, weed control in a corn field is performed by a blanket application of herbicides which do not consider spatial distribution information of weeds and also uses an extensive amount of chemical herbicides. In order to reduce the amount of chemicals, we used drone based high-resolution imagery and computer-vision techniwue to perform site-specific weed control in corn.
A Novel Framework for Characterization of Tumor-Immune Spatial Relationships in Tumor Microenvironment
Authors: Mahmudul Hasan, Jakub R. Kaczmarzyk, David Paredes, Lyanne Oblein, Jaymie Oentoro, Shahira Abousamra, Michael Horowitz, Dimitris Samaras, Chao Chen, Tahsin Kurc, Kenneth R. Shroyer, Joel Saltz
Date: 2022-04-23 00:00:00

Understanding the impact of tumor biology on the composition of nearby cells often requires characterizing the impact of biologically distinct tumor regions. Biomarkers have been developed to label biologically distinct tumor regions, but challenges arise because of differences in the spatial extent and distribution of differentially labeled regions. In this work, we present a framework for systematically investigating the impact of distinct tumor regions on cells near the tumor borders, accounting their cross spatial distributions. We apply the framework to multiplex immunohistochemistry (mIHC) studies of pancreatic cancer and show its efficacy in demonstrating how biologically different tumor regions impact the immune response in the tumor microenvironment. Furthermore, we show that the proposed framework can be extended to largescale whole slide image analysis.
Design and Experimental Evaluation of a Bluetooth 5.1 Antenna Array for Angle-of-Arrival Estimation
Authors: Nuno Paulino, Luís M. Pessoa, André Branquinho, Edgar Gonçalves
Date: 2022-04-26 00:00:00

On the of the applications in the realm of the Internet-of-Things (IoT) is real-time localization of assets in specific application environments where satellite based global positioning is unviable. Numerous approaches for localization relying on wireless sensor mesh systems have been evaluated, but the recent Bluetooth Low Energy (BLE) 5.1 direction finding features based on Angle-of-Arrival (AoA) promise a low-cost solution for this application. In this paper, we present an implementation of a BLE 5.1 based circular antenna array, and perform two experimental evaluations over the quality of the retrieved data sampled from the array. Specifically, we retrieve samples of the phase value of the Constant Tone Extension which enables the direction finding functionalities through calculation of phase differences between antenna pairs. We evaluate the quality of the sampled phase data in an anechoic chamber, and in a real-world environment using a setup composed of four BLE beacons.