pwc-archive/papers-with-abstracts huggingface dataset:
Simultaneous Speech Extraction for Multiple Target Speakers under the Meeting Scenarios
Authors: Bang Zeng, Hongbing Suo, Yulong Wan, Ming Li
Date: 2022-06-17 00:00:00
Tasks: Action Detection, Activity Detection, speaker-diarization, Speaker Diarization, Speech Extraction, Speech Separation

The common target speech separation directly estimate the target source, ignoring the interrelationship between different speakers at each frame. We propose a multiple-target speech separation model (MTSS) to simultaneously extract each speaker's voice from the mixed speech rather than just optimally estimating the target source. Moreover, we propose a speaker diarization (SD) aware MTSS system (SD-MTSS), which consists of a SD module and MTSS module. By exploiting the TSVAD decision and the estimated mask, our SD-MTSS model can extract the speech signal of each speaker concurrently in a conversational recording without additional enrollment audio in advance. Experimental results show that our MTSS model achieves 1.38dB SDR, 1.34dB SI-SDR, and 0.13 PESQ improvements over the baseline on the WSJ0-2mix-extr dataset, respectively. The SD-MTSS system makes 19.2% relative speaker dependent character error rate (CER) reduction on the Alimeeting dataset.
Asymptotically Stable Optimal Multi-rate Rigid Body Attitude Estimation based on Lagrange-d'Alembert Principle
Authors: Maulik Bhatt, Amit K. Sanyal, Srikant Sukumar
Date: 2020-08-07 00:00:00
Tasks: Relation

The rigid body attitude estimation problem is treated using the discrete-time Lagrange-d'Alembert principle. Three different possibilities are considered for the multi-rate relation between angular velocity measurements and direction vector measurements for attitude: 1) integer relation between sampling rates, 2) time-varying sampling rates, 3) non-integer relation between sampling rates. In all cases, it is assumed that angular velocity measurements are sampled at a higher rate compared to the inertial vectors. The attitude determination problem from two or more vector measurements in the body-fixed frame is formulated as Wahba's problem. At instants when direction vector measurements are absent, a discrete-time model for attitude kinematics is used to propagate past measurements. A discrete-time Lagrangian is constructed as the difference between a kinetic energy-like term that is quadratic in the angular velocity estimation error and an artificial potential energy-like term obtained from Wahba's cost function. An additional dissipation term is introduced and the discrete-time Lagrange-d'Alembert principle is applied to the Lagrangian with this dissipation to obtain an optimal filtering scheme. A discrete-time Lyapunov analysis is carried out to show that the optimal filtering scheme is asymptotically stable in the absence of measurement noise and the domain of convergence is almost global. For a realistic evaluation of the scheme, numerical experiments are conducted with inputs corrupted by bounded measurement noise. These numerical simulations exhibit convergence of the estimated states to a bounded neighborhood of the actual states.
Analysis of Time- versus Event-Triggered Consensus for a Single-Integrator Multi-Agent System
Authors: David Meister, Frank Aurzada, Mikhail A. Lifshits, Frank Allgöwer
Date: 2022-06-17 00:00:00

It is well known that the employed triggering scheme has great impact on the control performance when control loops operate under scarce communication resources. Various practical and simulative works have demonstrated the potential of event-triggered control to reduce communication while providing a similar performance level when compared to time-triggered control. For non-cooperative networked control systems, analytical performance comparisons of time- and event-triggered control support this finding under certain assumptions. While being well-studied in the non-cooperative setting, it remains unclear if and how the performance relationship of the triggering schemes is altered in a multi-agent system setup. To close this gap, in this paper, we consider a homogeneous single-integrator multi-agent consensus problem for which we compare the performance of time- and event-triggered control schemes analytically. Under the assumption of equal average triggering rates, we use the long-term average of the quadratic deviation from consensus as a performance measure to contrast the triggering schemes. Contrary to the non-cooperative setting, we prove that event-triggered control performs worse than time-triggered control beyond a certain number of agents in this setup. In addition, we derive the asymptotic order of the performance measure as a function of the number of agents under both triggering schemes.
Convex reformulations for a special class of nonlinear MPC problems
Authors: Manuel Klädtke, Moritz Schulze Darup
Date: 2022-06-17 00:00:00

We show how the solution to NMPC problems for a special type of input-affine discrete-time systems can be obtained by reformulating the underlying non-convex optimal control problem in terms of a finite number of convex subproblems. The reformulation is facilitated by exact (input-state) linearization, which is shown to provide beneficial properties for the treated class of systems. We characterize possible types of the resulting convex subproblems and illustrate our approach with three numerical examples.
Air Gap Control of the Novel Cross (+) Type 4-Pole MAGLEV Carrier System
Authors: Enes Mahmut Göker, Ahmet Fevzi Bozkurt, Bora Baykal, Kadir Erkan
Date: 2022-06-17 00:00:00

Mechanical non-contact carrier systems based on magnetic levitation (MAGLEV) are used in special transportation areas (clean rooms, chemical areas, etc.). Among these types of carriers, 4-pole hybrid electromagnetic systems (containing permanent magnets and electromagnets) stand out with their low energy consumption. The main problem of maglev carrier systems is their non-linear characteristics and unstable open-loop response. In this study, PID and I-PD controllers are designed for the air gap control of the new cross-type 4-pole mechanical contactless carrier system. Thus, the instability problem was overcome and the desired reference tracking for each degree of freedom was successfully carried out in simulation environments, and the results were compared.
Combined Plant and Controller Optimization of an Underwater Energy Harvesting Kite System
Authors: Kartik Naik, Sumedh Beknalkar, James Reed, Andre Mazzoleni, Hosam Fathy, Chris Vermillion
Date: 2022-06-16 00:00:00

This paper presents the formulation and results for a control-aware optimization of the combined geometric and structural design of an energy-harvesting underwater kite. Because kite-based energy-harvesting systems, both airborne and underwater, possess strong coupling between closed-loop flight control, geometric design, and structural design, consideration of all three facets of the design within a single co-design framework is highly desirable. However, while prior literature has addressed one or two attributes of the design at a time, the present work constitutes the first comprehensive effort aimed at addressing all three. In particular, focusing on the goals of power maximization and mass minimization, we present a co-design formulation that fuses a geometric optimization tool, structural optimization tool, and closed-loop flight efficiency map. The resulting integrated co-design tool is used to address two mathematical optimization formulations that exhibit subtle differences: a Pareto optimal formulation and a dual-objective formulation that focuses on a weighted power-to-mass ratio as the techno-economic metric of merit. Based on the resulting geometric and structural designs, using a medium-fidelity closed-loop simulation tool, the proposed formulation is shown to achieve more than three times the power-to-mass ratio of a previously published, un-optimized benchmark design.
Wireless Picosecond Time Synchronization for Distributed Antenna Arrays
Authors: Jason M. Merlo, Serge R. Mghabghab, Jeffrey A. Nanzer
Date: 2022-06-16 00:00:00

Distributed antenna arrays have been proposed for many applications ranging from space-based observatories to automated vehicles. Achieving good performance in distributed antenna systems requires stringent synchronization at the wavelength and information level to ensure that the transmitted signals arrive coherently at the target, or that scattered and received signals can be appropriately processed via distributed algorithms. In this paper we address the challenge of high precision time synchronization to align the operations of elements in a distributed antenna array and to overcome time-varying bias between platforms due to oscillator drift. We use a spectrally sparse two-tone waveform, which obtains approximately optimal time estimation accuracy, in a two-way time transfer process. We also describe a technique for determining the true time delay using the ambiguous two-tone matched filter output, and we compare the time synchronization precision of the two-tone waveform with the more common linear frequency modulation (LFM) waveform. We experimentally demonstrate wireless time synchronization using a single pulse 40$\,$MHz two-tone waveform over a 90$\,$cm 5.8$\,$GHz wireless link in a laboratory setting, obtaining a timing precision of 2.26$\,$ps.
Phase Identification of Distribution System Users Through a MILP Extension of State Estimation
Authors: Marta Vanin, Tom Van Acker, Reinhilde D'hulst, Dirk Van Hertem
Date: 2022-06-16 00:00:00
Tasks: Management, State Estimation

To address the challenges and exploit the opportunities that the decarbonization of the energy sector is bringing about, advanced distribution network management and operation strategies are being developed. Many of these require accurate network models to work effectively, including user phase connectivity. However, such information is often not available. This paper proposes a novel method to identify the phase connectivity of single- and three-phase distribution consumers using smart meter measurements. The method is based on state estimation and mixed-integer linear programming, and requires shorter measurement collection times compared to statistical and machine learning-based techniques, for the same level of accuracy. Consequently, the duration of measurement efforts/campaigns aimed at obtaining this type of information reduces, accelerating the system knowledge acquisition process and potentially reducing the campaigns' costs. A technique is illustrated that allows to solve large networks in acceptable time despite the considerable number of integer variables. Extensive computational results are presented for publicly available low voltage feeders.
Effects of Neural Heterogeneity on Spiking Neural Network Dynamics
Authors: Richard Gast, Sara A. Solla, Ann Kennedy
Date: 2022-06-17 00:00:00

The brain is composed of complex networks of interacting neurons that express considerable heterogeneity in their physiology and spiking characteristics. How does neural heterogeneity affect macroscopic neural dynamics and how does it contribute to neurodynamic functions? In this letter, we address these questions by studying the macroscopic dynamics of networks of heterogeneous Izhikevich neurons. We derive mean-field equations for these networks and examine how heterogeneity in the spiking thresholds of Izhikevich neurons affects the emergent macroscopic dynamics. Our results suggest that the level of heterogeneity of inhibitory populations controls resonance and hysteresis properties of systems of coupled excitatory and inhibitory neurons. Neural heterogeneity may thus serve as a means to control the dynamic repertoire of mesoscopic brain circuits.
Semiparametric Single-Index Estimation for Average Treatment Effects
Authors: Difang Huang, Jiti Gao, Tatsushi Oka
Date: 2022-06-17 00:00:00
Tasks: valid

We propose a semiparametric method to estimate the average treatment effect under the assumption of unconfoundedness given observational data. Our estimation method alleviates misspecification issues of the propensity score function by estimating the single-index link function involved through Hermite polynomials. Our approach is computationally tractable and allows for moderately large dimension covariates. We provide the large sample properties of the estimator and show its validity. Also, the average treatment effect estimator achieves the parametric rate and asymptotic normality. Our extensive Monte Carlo study shows that the proposed estimator is valid in finite samples. Applying our method to maternal smoking and infant health, we find that conventional estimates of smoking's impact on birth weight may be biased due to propensity score misspecification, and our analysis of job training programs reveals earnings effects that are more precisely estimated than in prior work. These applications demonstrate how addressing model misspecification can substantively affect our understanding of key policy-relevant treatment effects.
Fast and Accurate Variational Inference for Large Bayesian VARs with Stochastic Volatility
Authors: Joshua C. C. Chan, Xuewen Yu
Date: 2022-06-16 00:00:00
Tasks: Variational Inference

We propose a new variational approximation of the joint posterior distribution of the log-volatility in the context of large Bayesian VARs. In contrast to existing approaches that are based on local approximations, the new proposal provides a global approximation that takes into account the entire support of the joint distribution. In a Monte Carlo study we show that the new global approximation is over an order of magnitude more accurate than existing alternatives. We illustrate the proposed methodology with an application of a 96-variable VAR with stochastic volatility to measure global bank network connectedness.
Environmentally extended input-output analysis in complex networks: a multilayer approach
Authors: Alessandra Cornaro, Giorgio Rizzini
Date: 2022-06-17 00:00:00

In this paper we propose a methodology suitable for a comprehensive analysis of the global embodied energy flow trough a complex network approach. To this end, we extend the existing literature, providing a multilayer framework based on the environmentally extended input-output analysis. The multilayer structure, with respect to the traditional approach, allows us to unveil the different role of sectors and economies in the system. In order to identify key sectors and economies, we make use of hub and authority scores, by adapting to our framework an extension of the Kleinberg algorithm, called Multi-Dimensional HITS (MD-HITS). A numerical analysis based on multi-region input-output tables shows how the proposed approach provides meaningful insights.
How to Solve Big Problems: Bespoke Versus Platform Strategies
Authors: Atif Ansar, Bent Flyvbjerg
Date: 2022-06-09 00:00:00

How should government and business solve big problems? In bold leaps or in many smaller moves? We show that bespoke, one-off projects are prone to poorer outcomes than projects built on a repeatable platform. Repeatable projects are cheaper, faster, and scale at lower risk of failure. We compare evidence from 203 space missions at NASA and SpaceX, on cost, speed-to-market, schedule, and scalability. We find that SpaceX's platform strategy was 10X cheaper and 2X faster than NASA's bespoke strategy. Moreover, SpaceX's platform strategy was financially less risky, virtually eliminating cost overruns. Finally, we show that achieving platform repeatability is a strategically diligent process involving experimental learning sequences. Sectors of the economy where governments find it difficult to control spending or timeframes or to realize planned benefits - e.g., health, education, climate, defence - are ripe for a platform rethink.
On Existence of Berk-Nash Equilibria in Misspecified Markov Decision Processes with Infinite Spaces
Authors: Robert M. Anderson, Haosui Duanmu, Aniruddha Ghosh, M. Ali Khan
Date: 2022-06-16 00:00:00

Model misspecification is a critical issue in many areas of theoretical and empirical economics. In the specific context of misspecified Markov Decision Processes, Esponda and Pouzo (2021) defined the notion of Berk-Nash equilibrium and established its existence in the setting of finite state and action spaces. However, many substantive applications (including two of the three motivating examples presented by Esponda and Pouzo, as well as Gaussian and log-normal distributions, and CARA, CRRA and mean-variance preferences) involve continuous state or action spaces, and are thus not covered by the Esponda-Pouzo existence theorem. We extend the existence of Berk-Nash equilibrium to compact action spaces and sigma-compact state spaces, with possibly unbounded payoff functions. A complication arises because the Berk-Nash equilibrium notion depends critically on Radon-Nikodym derivatives, which are necessarily bounded in the finite case but typically unbounded in misspecified continuous models. The proofs rely on nonstandard analysis and, relative to previous applications of nonstandard analysis in economic theory, draw on novel argumentation traceable to work of the second author on nonstandard representations of Markov processes.
Ring deconvolution microscopy: exploiting symmetry for efficient spatially varying aberration correction
Authors: Amit Kohli, Anastasios N. Angelopoulos, David McAllister, Esther Whang, Sixian You, Kyrollos Yanny, Federico M. Gasparoli, Bo-Jui Chang, Reto Fiolka, Laura Waller
Date: 2022-06-17 00:00:00
Tasks: Deblurring

The most ubiquitous form of computational aberration correction for microscopy is deconvolution. However, deconvolution relies on the assumption that the point spread function is the same across the entire field-of-view. This assumption is often inadequate, but space-variant deblurring techniques generally require impractical amounts of calibration and computation. We present a new imaging pipeline that leverages symmetry to provide simple and fast spatially-varying aberration correction. Our ring deconvolution microscopy (RDM) method leverages the rotational symmetry of most microscopes and cameras, and naturally extends to sheet deconvolution in the case of lateral symmetry. We formally derive theory and algorithms for image recovery and additionally propose a neural network based on Seidel coefficients as a fast alternative, as well as extension of RDM to blind deconvolution. We demonstrate significant improvements in speed and image quality as compared to standard deconvolution and existing spatially-varying deconvolution across a diverse range of microscope modalities, including miniature microscopy, multicolor fluorescence microscopy, point-scanning multimode fiber micro-endoscopy, and light-sheet fluorescence microscopy. Our approach enables near-isotropic, subcellular resolution in each of these applications.
An incomplete taxonomy of self-assigned color specialties
Authors: Jan Morovic
Date: 2022-06-16 00:00:00

Every discipline in science or professional practice can be sub-divided into specialties and subspecialties. E.g., in medicine a doctor could indicate that their specialty is ophthalmology and their sub-specialty the retina, or in chemistry a specialty could be analytical chemistry and a sub-specialty spectroscopy. But, what are the specialties and sub-specialties of the color discipline? The present report shares the anonymized results of a 152-participant, online survey conducted between 23rd May and 8th June 2022 that suggests 11 top-level color specialties.
Characteristics of Interference in Licensed and Unlicensed Bands for Intelligent Spectrum Management
Authors: Zhuoran Su, Kaveh Pahlavan
Date: 2022-06-17 00:00:00
Tasks: Management

The exponential growth of IoT devices and the demand of smart devices for higher data rates has heightened the need for sharing and managing spectrum resources in cellular 5G/6G operating in licensed bands and Wi-Fi technologies operating in unlicensed bands. Intelligent spectrum management has emerged as a key concept in dynamic spectrum allocation. To understand the interference existing in the spectrum, researchers usually monitor the interference in a fixed location and either focus on the cellular band or Wi-Fi band. In this study, we conduct experiments for collecting real-time spectrum data in indoor and outdoor environments with a mobile receiver, the spectrum analyzer. For outdoor, we mount the spectrum analyzer on a car seat and drive on the selected route in an urban area. We put the analyzer on a cart and moved it around in the laboratory for indoor. The frequency of interest in this study is 1.9 - 2.5 GHz, including both licensed and unlicensed bands. Temporal and frequency domain behavior is compared between licensed and unlicensed bands. We first normalize and binarize the data with a threshold. Then we calculate the spectrum occupancy by counting how many consecutive ones. Based on our observation, the spectrum occupancy of the outdoor environment is more remarkable than the indoor environment. The interference in the licensed band shows more variations in the frequency domain than that in the unlicensed band. This study provides a better understanding of the interference behavior for different environments and frequency bands.
Robust Information Criterion for Model Selection in Sparse High-Dimensional Linear Regression Models
Authors: Prakash B. Gohain, Magnus Jansson
Date: 2022-06-17 00:00:00
Tasks: Model Selection, regression

Model selection in linear regression models is a major challenge when dealing with high-dimensional data where the number of available measurements (sample size) is much smaller than the dimension of the parameter space. Traditional methods for model selection such as Akaike information criterion, Bayesian information criterion (BIC) and minimum description length are heavily prone to overfitting in the high-dimensional setting. In this regard, extended BIC (EBIC), which is an extended version of the original BIC and extended Fisher information criterion (EFIC), which is a combination of EBIC and Fisher information criterion, are consistent estimators of the true model as the number of measurements grows very large. However, EBIC is not consistent in high signal-to-noise-ratio (SNR) scenarios where the sample size is fixed and EFIC is not invariant to data scaling resulting in unstable behaviour. In this paper, we propose a new form of the EBIC criterion called EBIC-Robust, which is invariant to data scaling and consistent in both large sample size and high-SNR scenarios. Analytical proofs are presented to guarantee its consistency. Simulation results indicate that the performance of EBIC-Robust is quite superior to that of both EBIC and EFIC.
Integrated Sensing and Communication with Reconfigurable Intelligent Surfaces: Opportunities, Applications, and Future Directions
Authors: Rang Liu, Ming Li, Honghao Luo, Qian Liu, A. Lee Swindlehurst
Date: 2022-06-17 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated sensing and communication (ISAC) is emerging as a key enabler to address the growing spectrum congestion problem and satisfy increasing demands for ubiquitous sensing and communication. By sharing various resources and information, ISAC achieves much higher spectral, energy, hardware, and economic efficiencies. Concurrently, reconfigurable intelligent surface (RIS) technology has been deemed as a promising approach due to its capability of intelligently manipulating the wireless propagation environment in an energy and hardware efficient manner. In this article, we analyze the potential of deploying RIS to improve communication and sensing performance in ISAC systems. We first describe the fundamentals of RIS and its applications in traditional communication and sensing systems, then introduce the principles of ISAC and overview existing explorations on RIS-assisted ISAC, followed by one case study to verify the advantages of deploying RIS in ISAC systems. Finally, open challenges and research directions are discussed to stimulate this line of research and pave the way for practical applications.