pwc-archive/papers-with-abstracts huggingface dataset:
Online Target Speaker Voice Activity Detection for Speaker Diarization
Authors: Weiqing Wang, Qingjian Lin, Ming Li
Date: 2022-07-13 00:00:00
Tasks: Action Detection, Activity Detection, Clustering, speaker-diarization, Speaker Diarization

This paper proposes an online target speaker voice activity detection system for speaker diarization tasks, which does not require a priori knowledge from the clustering-based diarization system to obtain the target speaker embeddings. First, we employ a ResNet-based front-end model to extract the frame-level speaker embeddings for each coming block of a signal. Next, we predict the detection state of each speaker based on these frame-level speaker embeddings and the previously estimated target speaker embedding. Then, the target speaker embeddings are updated by aggregating these frame-level speaker embeddings according to the predictions in the current block. We iteratively extract the results for each block and update the target speaker embedding until reaching the end of the signal. Experimental results show that the proposed method is better than the offline clustering-based diarization system on the AliMeeting dataset.
A Cyclical Approach to Synthetic and Natural Speech Mismatch Refinement of Neural Post-filter for Low-cost Text-to-speech System
Authors: Yi-Chiao Wu, Patrick Lumban Tobing, Kazuki Yasuhara, Noriyuki Matsunaga, Yamato Ohtani, Tomoki Toda
Date: 2022-07-13 00:00:00
Tasks: text-to-speech, Text to Speech

Neural-based text-to-speech (TTS) systems achieve very high-fidelity speech generation because of the rapid neural network developments. However, the huge labeled corpus and high computation cost requirements limit the possibility of developing a high-fidelity TTS system by small companies or individuals. On the other hand, a neural vocoder, which has been widely adopted for the speech generation in neural-based TTS systems, can be trained with a relatively small unlabeled corpus. Therefore, in this paper, we explore a general framework to develop a neural post-filter (NPF) for low-cost TTS systems using neural vocoders. A cyclical approach is proposed to tackle the acoustic and temporal mismatches (AM and TM) of developing an NPF. Both objective and subjective evaluations have been conducted to demonstrate the AM and TM problems and the effectiveness of the proposed framework.
Distilled Non-Semantic Speech Embeddings with Binary Neural Networks for Low-Resource Devices
Authors: Harlin Lee, Aaqib Saeed
Date: 2022-07-12 00:00:00
Tasks: Emotion Recognition, Keyword Spotting, Knowledge Distillation, Language Identification, Representation Learning, Spoken language identification

This work introduces BRILLsson, a novel binary neural network-based representation learning model for a broad range of non-semantic speech tasks. We train the model with knowledge distillation from a large and real-valued TRILLsson model with only a fraction of the dataset used to train TRILLsson. The resulting BRILLsson models are only 2MB in size with a latency less than 8ms, making them suitable for deployment in low-resource devices such as wearables. We evaluate BRILLsson on eight benchmark tasks (including but not limited to spoken language identification, emotion recognition, health condition diagnosis, and keyword spotting), and demonstrate that our proposed ultra-light and low-latency models perform as well as large-scale models.
Social Network Mining (SNM): A Definition of Relation between the Resources and SNA
Authors: Mahyuddin K. M. Nasution
Date: 2022-06-27 00:00:00
Tasks: Relation

Social Network Mining (SNM) has become one of the main themes in big data agenda. As a resultant network, we can extract social network from different sources of information, but the information sources were growing dynamically require a flexible approach. To determine the appropriate approach needs the data engineering in order to get the behavior associated with the data. Each social network has the resources and the information source, but the relationship between resources and information sources requires explanation. This paper aimed to address the behavior of the resource as a part of social network analysis (SNA) in the growth of social networks by using the statistical calculations to explain the evolutionary mechanisms. To represent the analysis unit of the SNA, this paper only considers the degree of a vertex, where it is the core of all the analysis in the SNA and it is basic for defining the relation between resources and SNA in SNM. There is a strong effect on the growth of the resources of social networks. In total, the behavior of resources has positive effects. Thus, different information sources behave similarly and have relations with SNA.
Event-triggered Control of Port-Hamiltonian Systems under Time-delay Communication
Authors: Ernesto Aranda-Escolástico, Leonardo J. Colombo, María Guinaldo, Antonio Visioli
Date: 2022-07-13 00:00:00

We study the problem of periodic event-triggered control of interconnected port-Hamiltonian systems subject to time-varying delays in their communication. In particular, we design a threshold parameter for the event-triggering condition, a sampling period, and a maximum allowable delay such that interconnected port-Hamiltonian control systems with periodic event-triggering mechanism under a time-delayed communication are able to achieve asymptotically stable behaviour. Simulation results are presented to validate the theory.
SnapperGPS: Open Hardware for Energy-Efficient, Low-Cost Wildlife Location Tracking with Snapshot GNSS
Authors: Jonas Beuchert, Amanda Matthes, Alex Rogers
Date: 2022-07-13 00:00:00

Location tracking with global navigation satellite systems (GNSS), such as the GPS, is used in many applications, including the tracking of wild animals for research. Snapshot GNSS is a technique that only requires milliseconds of satellite signals to infer the position of a receiver. This is ideal for low-power applications such as animal tracking. However, there are few existing snapshot systems, none of which is open source. To address this, we developed SnapperGPS, a fully open-source, low-cost, and low-power location tracking system designed for wildlife tracking. SnapperGPS comprises three parts, all of which are open-source: (i) a small, low-cost, and low-power receiver; (ii) a web application to configure the receiver via USB; and (iii) a cloud-based platform for processing recorded data. This paper presents the hardware side of this project. The total component cost of the receiver is under $30, making it feasible for field work with restricted budgets and low recovery rates. The receiver records very short and low-resolution samples resulting in particularly low power consumption, outperforming existing systems. It can run for more than a year on a 40 mAh battery. We evaluated SnapperGPS in controlled static and dynamic tests in a semi-urban environment where it achieved median errors of 12 m. Additionally, SnapperGPS has already been deployed for two wildlife tracking studies on sea turtles and sea birds.
Data-Driven Identification of Dynamic Quality Models in Drinking Water Networks
Authors: Shen Wang, Ankush Chakrabarty, Ahmad F. Taha
Date: 2022-07-13 00:00:00

Traditional control and monitoring of water quality in drinking water distribution networks (WDN) rely on mostly model- or toolbox-driven approaches, where the network topology and parameters are assumed to be known. In contrast, system identification (SysID) algorithms for generic dynamic system models seek to approximate such models using only input-output data without relying on network parameters. The objective of this paper is to investigate SysID algorithms for water quality model approximation. This research problem is challenging due to (i) complex water quality and reaction dynamics and (ii) the mismatch between the requirements of SysID algorithms and the properties of water quality dynamics. In this paper, we present the first attempt to identify water quality models in WDNs using only input-output experimental data and classical SysID methods without knowing any WDN parameters. Properties of water quality models are introduced, the ensuing challenges caused by these properties when identifying water quality models are discussed, and remedial solutions are given. Through case studies, we demonstrate the applicability of SysID algorithms, show the corresponding performance in terms of accuracy and computational time, and explore the possible factors impacting water quality model identification.
A Simple Novel Global Optimization Algorithm and Its Performance on Some Benchmark Functions
Authors: Yuanyuan Liu
Date: 2022-07-13 00:00:00
Tasks: global-optimization

This paper propose a new frame work for finding global minima which we call optimization by cut. In each iteration, it takes some samples from the feasible region and evaluates the objective function at these points. Based on the observations it cuts off from the feasible region a subregion that is unlikely to contain a global minimum. The procedure is then repeated with the feasible region replaced by the remaining region until the remaining region is ``small'' enough. If a global minimum is kept in the remaining region of each iteration, then it can be located with an arbitrary precision. The frame work is surprisingly efficient in view of its simple form and can be applied to black-box functions since neither special structure nor derivative information is required. The performance of the proposed frame work is evaluated on some benchmark functions and the results show that it can find a global minimum rather quickly.
Analytical stochastic macroscopic fundamental diagram driven by Wiener process
Authors: HongSheng Qi
Date: 2022-07-13 00:00:00

The macroscopic fundamental diagram (MFD) is a powerful and popular tool that describes a network scale traffic operational state and serve as the plant model of perimeter control. As both the supply and the demand suffer from random disturbances, the traffic flow dynamics cannot be said to be deterministic. A stochastic MFD model can generate a stochastic evolution of the system state with desired distribution of aggregated variables is still lacking. A stochastic formulation of MFD, that considers the accumulation-dependent variations, is proposed to fill this gap. The model is based on the stochastic differential equation (SDE) theory. First, the exit flow variation is formulated as a Wiener-driven process, which admits the accumulation-of dependent variations. The stochastic MFD model is then constructed by combining the exit flow variations model. The solution of the system state is derived by the forward Fokker-Planck equation. The stability of the model is analyzed, and the parameters of a calibration method are provided. Several cases of the model are then tested. The results show that the model can be applied to different functional MFD forms, and the hysteresis and gridlock phenomenon is reproduced. The proposed MFD model can be used in the network analysis and control that considers the system's stochastic evolution.
Compactly Restrictable Metric Policy Optimization Problems
Authors: Victor D. Dorobantu, Kamyar Azizzadenesheli, Yisong Yue
Date: 2022-07-12 00:00:00
Tasks: continuous-control, Continuous Control

We study policy optimization problems for deterministic Markov decision processes (MDPs) with metric state and action spaces, which we refer to as Metric Policy Optimization Problems (MPOPs). Our goal is to establish theoretical results on the well-posedness of MPOPs that can characterize practically relevant continuous control systems. To do so, we define a special class of MPOPs called Compactly Restrictable MPOPs (CR-MPOPs), which are flexible enough to capture the complex behavior of robotic systems but specific enough to admit solutions using dynamic programming methods such as value iteration. We show how to arrive at CR-MPOPs using forward-invariance. We further show that our theoretical results on CR-MPOPs can be used to characterize feedback linearizable control affine systems.
Solving Bilevel Optimal Bidding Problems Using Deep Convolutional Neural Networks
Authors: Domagoj Vlah, Karlo Šepetanc, Hrvoje Pandžić
Date: 2022-07-12 00:00:00
Tasks: Bilevel Optimization

Current state-of-the-art solution techniques for solving bilevel optimization problems either assume strong problem regularity criteria or are computationally intractable. In this paper we address power system problems of bilevel structure, commonly arising after the deregulation of the power industry. Such problems are predominantly solved by converting the lower-level problem into a set of equivalent constraints using the Karush-Kuhn-Tucker optimality conditions at an expense of binary variables. Furthermore, in case the lower-level problem is nonconvex, the strong duality does not hold rendering the single-level reduction techniques inapplicable. To overcome this, we propose an effective numerical scheme based on bypassing the lower level completely using an approximation function that replicates the relevant lower level effect on the upper level. The approximation function is constructed by training a deep convolutional neural network. The numerical procedure is run iteratively to enhance the accuracy. As a case study, the proposed method is applied to a price-maker energy storage optimal bidding problem that considers an AC power flow-based market clearing in the lower level. The results indicate that greater actual profits are achieved as compared to the less accurate DC market representation.
Bridging the information and dynamics attributes of neural activities
Authors: Yang Tian, Guoqi Li, Pei Sun
Date: 2022-07-13 00:00:00

The brain works as a dynamic system to process information. Various challenges remain in understanding the connection between information and dynamics attributes in the brain. The present research pursues exploring how the characteristics of neural information functions are linked to neural dynamics. We attempt to bridge dynamics (e.g., Kolmogorov-Sinai entropy) and information (e.g., mutual information and Fisher information) metrics on the stimulus-triggered stochastic dynamics in neural populations. On the one hand, our unified analysis identifies various essential features of the information-processing-related neural dynamics. We discover spatiotemporal differences in the dynamic randomness and chaotic degrees of neural dynamics during neural information processing. On the other hand, our framework reveals the fundamental role of neural dynamics in shaping neural information processing. The neural dynamics creates an oppositely directed variation of encoding and decoding properties under specific conditions, and it determines the neural representation of stimulus distribution. Overall, our findings demonstrate a potential direction to explain the emergence of neural information processing from neural dynamics and help understand the intrinsic connections between the informational and the physical brain.
Optical Field Recovery in Jones Space
Authors: Qi Wu, Yixiao Zhu, Hexun Jiang, Qunbi Zhuge, Weisheng Hu
Date: 2022-06-22 00:00:00
Tasks: Diversity

Optical full-field recovery makes it possible to compensate for fiber impairments such as chromatic dispersion and polarization mode dispersion (PMD) in the digital signal processing. For cost-sensitive short-reach optical networks, some advanced single-polarization (SP) optical field recovery schemes are recently proposed to avoid chromatic dispersion-induced power fading effect, and improve the spectral efficiency for larger potential capacity. Polarization division multiplexing (PDM) can further double both the spectral efficiency and the system capacity of these SP carrier-assisted direct detection (DD) schemes. However, the so-called polarization fading phenomenon induced by random polarization rotation is a fundamental obstacle which prevents SP carrier-assisted DD systems from polarization diversity. In this paper, we propose a receiver of Jones-space field recovery (JSFR) to realize polarization diversity with SP carrier-assisted DD schemes in Jones space. Different receiver structures and simplified recovery procedures for JSFR are explored theoretically. The proposed JSFR pushes the SP DD schemes towards PDM without extra optical signal-to-noise ratio (OSNR) penalty. In addition, the JSFR shows good tolerance to PMD since the optical field recovery is conducted before polarization recovery. In the concept-of-proof experiment, we demonstrate 448-Gb/s reception over 80-km single-mode fiber using the proposed JSFR based on 22 couplers. Furthermore, we qualitatively compare the optical field recovery in Jones space and Stokes space from the perspective of the modulation dimension. Qualitatively, we compare the optical field recovery in the Jones space and Stokes space from the perspective of the modulation dimension.
Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machines
Authors: Jonas Jäger, Roman V. Krems
Date: 2022-07-12 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning is considered to be one of the most promising applications of quantum computing. Therefore, the search for quantum advantage of the quantum analogues of machine learning models is a key research goal. Here, we show that variational quantum classifiers and support vector machines with quantum kernels can solve a classification problem based on the $k$-Forrelation problem, which is known to be PromiseBQP-complete. Because the PromiseBQP complexity class includes all Bounded-Error Quantum Polynomial-Time (BQP) decision problems, our results imply that there exists a feature map and a quantum kernel that make variational quantum classifiers and quantum kernel support vector machines efficient solvers for any BQP problem. Hence, this work implies that their feature map and quantum kernel, respectively, can be designed to have a quantum advantage for any classification problem that cannot be classically solved in polynomial time but contrariwise by a quantum computer.
Two-stage differences in differences
Authors: John Gardner
Date: 2022-07-13 00:00:00
Tasks: regression, Vocal Bursts Valence Prediction

A recent literature has shown that when adoption of a treatment is staggered and average treatment effects vary across groups and over time, difference-in-differences regression does not identify an easily interpretable measure of the typical effect of the treatment. In this paper, I extend this literature in two ways. First, I provide some simple underlying intuition for why difference-in-differences regression does not identify a group$\times$period average treatment effect. Second, I propose an alternative two-stage estimation framework, motivated by this intuition. In this framework, group and period effects are identified in a first stage from the sample of untreated observations, and average treatment effects are identified in a second stage by comparing treated and untreated outcomes, after removing these group and period effects. The two-stage approach is robust to treatment-effect heterogeneity under staggered adoption, and can be used to identify a host of different average treatment effect measures. It is also simple, intuitive, and easy to implement. I establish the theoretical properties of the two-stage approach and demonstrate its effectiveness and applicability using Monte-Carlo evidence and an example from the literature.
On the value of distribution tail in the valuation of travel time variability
Authors: Zhaoqi Zang, Richard Batley, Xiangdong Xu, David Z. W. Wang
Date: 2022-07-13 00:00:00

Extensive empirical studies show that the long distribution tail of travel time and the corresponding unexpected delay can have much more serious consequences than expected or moderate delay. However, the unexpected delay due to the distribution tail of travel time has received limited attention in recent studies of the valuation of travel time variability. As a complement to current valuation research, this paper proposes the concept of the value of travel time distribution tail, which quantifies the value that travelers place on reducing the unexpected delay for hedging against travel time variability. Methodologically, we define the summation of all unexpected delays as the unreliability area to quantify travel time distribution tail and show that it is a key element of two well-defined measures accounting for unreliable aspects of travel time. We then formally derive the value of distribution tail, show that it is distinct from the more established value of reliability (VOR), and combine it and the VOR in an overall value of travel time variability (VOV). We prove theoretically that the VOV exhibits diminishing marginal benefit in terms of the traveler's punctuality requirements under a validity condition. This implies that it may be economically inefficient for travelers to blindly pursue a higher probability of not being late. We then proceed to develop the concept of the travel time variability ratio, which gives the implicit cost of the punctuality requirement imposed on any given trip. Numerical examples reveal that the cost of travel time distribution tail can account for more than 10% of the trip cost, such that its omission could introduce non-trivial bias into route choice models and transportation appraisal more generally.
Does DeFi remove the need for trust? Evidence from a natural experiment in stablecoin lending
Authors: Kanis Saengchote, Talis Putniņš, Krislert Samphantharak
Date: 2022-07-13 00:00:00

Decentralized Finance (DeFi) is built on a fundamentally different paradigm: rather than having to trust individuals and institutions, participants in DeFi potentially only have to trust computer code that is enforced by a decentralized network of computers. We examine a natural experiment that exogenously stress tests this alternative paradigm by revealing the identities of individuals associated with a DeFi protocol, including a convicted criminal. We find that, in practice, DeFi does not (yet) fully remove the need for trust in individuals. Our findings suggest that that because smart contracts are incomplete, they are subject to run risk (Allen and Gale, 2004) and personal character and trust of individuals are still relevant in this alternative financial system.
Journal of Economic Literature codes classification system (JEL)
Authors: Jussi T. S. Heikkila
Date: 2022-07-13 00:00:00
Tasks: Articles, Classification

The Journal of Economic Literature codes classification system (JEL) published by the American Economic Association (AEA) is the de facto standard classification system for research literature in economics. The JEL classification system is used to classify articles, dissertations, books, book reviews, and working papers in EconLit, a database maintained by the AEA. Over time, it has evolved and extended to a system with over 850 subclasses. This paper reviews the history and development of the JEL classification system, describes the current version, and provides a selective overview of its uses and applications in research. The JEL codes classification system has been adopted by several publishers, and their instructions are reviewed. There are interesting avenues for future research as the JEL classification system has been surprisingly little used in existing bibliometric and scientometric research as well as in library classification systems.