pwc-archive/papers-with-abstracts huggingface dataset:
Tree-based Search Graph for Approximate Nearest Neighbor Search
Authors: Xiaobin Fan, XiaoPing Wang, Kai Lu, Lei Xue, Jinjing Zhao
Date: 2022-01-10 00:00:00

Nearest neighbor search supports important applications in many domains, such as database, machine learning, computer vision. Since the computational cost for accurate search is too high, the community turned to the research of approximate nearest neighbor search (ANNS). Among them, graph-based algorithm is one of the most important branches. Research by Fu et al. shows that the algorithms based on Monotonic Search Network (MSNET), such as NSG and NSSG, have achieved the state-of-the-art search performance in efficiency. The MSNET is dedicated to achieving monotonic search with minimal out-degree of nodes to pursue high efficiency. However, the current MSNET designs did not optimize the probability of the monotonic search, and the lower bound of the probability is only 50%. If they fail in monotonic search stage, they have to suffer tremendous backtracking cost to achieve the required accuracy. This will cause performance problems in search efficiency. To address this problem, we propose (r,p)-MSNET, which achieves guaranteed probability on monotonic search. Due to the high building complexity of a strict (r,p)-MSNET, we propose TBSG, which is an approximation with low complexity. Experiment conducted on four million-scaled datasets show that TBSG outperforms existing state-of-the-art graph-based algorithms in search efficiency. Our code has been released on Github.
Phocus: Picking Valuable Research from a Sea of Citations
Authors: Xinrong Zhang, Zihou Ren, Xi Li, Shuqi Liu, Yunlong Deng, Yadi Xiao, Yuxing Han, Jiangtao Wen
Date: 2022-01-09 00:00:00
Tasks: Sentence

The deluge of new papers has significantly blocked the development of academics, which is mainly caused by author-level and publication-level evaluation metrics that only focus on quantity. Those metrics have resulted in several severe problems that trouble scholars focusing on the important research direction for a long time and even promote an impetuous academic atmosphere. To solve those problems, we propose Phocus, a novel academic evaluation mechanism for authors and papers. Phocus analyzes the sentence containing a citation and its contexts to predict the sentiment towards the corresponding reference. Combining others factors, Phocus classifies citations coarsely, ranks all references within a paper, and utilizes the results of the classifier and the ranking model to get the local influential factor of a reference to the citing paper. The global influential factor of the reference to the citing paper is the product of the local influential factor and the total influential factor of the citing paper. Consequently, an author's academic influential factor is the sum of his contributions to each paper he co-authors.
A Practical Guide to Logical Access Voice Presentation Attack Detection
Authors: Xin Wang, Junichi Yamagishi
Date: 2022-01-10 00:00:00
Tasks: Artifact Detection, Speaker Verification, Speech Synthesis, text-to-speech, Text to Speech, Voice Conversion

Voice-based human-machine interfaces with an automatic speaker verification (ASV) component are commonly used in the market. However, the threat from presentation attacks is also growing since attackers can use recent speech synthesis technology to produce a natural-sounding voice of a victim. Presentation attack detection (PAD) for ASV, or speech anti-spoofing, is therefore indispensable. Research on voice PAD has seen significant progress since the early 2010s, including the advancement in PAD models, benchmark datasets, and evaluation campaigns. This chapter presents a practical guide to the field of voice PAD, with a focus on logical access attacks using text-to-speech and voice conversion algorithms and spoofing countermeasures based on artifact detection. It introduces the basic concept of voice PAD, explains the common techniques, and provides an experimental study using recent methods on a benchmark dataset. Code for the experiments is open-sourced.
Bibliometric analysis of the scientific production found in Scopus and Web of Science about business administration
Authors: Félix Lirio-Loli, William Dextre-Martínez
Date: 2022-01-08 00:00:00
Tasks: Articles, Descriptive, Management

Introduction: This study analyzes the scientific production in business administration in scientific articles based on modeling partial least squares structural equations (Partial Least Squares Structural Equation Modeling PLS-SEM) in the 2011-2020 period. Methodology: The study is exploratory - descriptive and has three phases: a) Selection of keywords and search criteria; (b) Search and refinement of information; c) information analysis. A method of bibliometric review of the specific literature has been used based on the analysis of predefined indicators and completed with a qualitative content synthesis. Results: A total of 167 publications were analyzed, making correlations from the year, search criteria, authors, impact factor by quartile, and by citation variables. More outstanding scientific production comes from Scopus under the search criteria ((pls AND sem) OR "partial least squares") AND (business OR management), being the figure of 4,870 scientific articles, while Web of Science accumulates 3,946 articles Conclusion: There has been a progressive growth in scientific articles with the PLS-SEM technique from 2011 to 2020. Scopus, compared to WoS, presents a more significant number of scientific productions with this statistical approach. The authors who register scientific articles demonstrate a high H index; in addition, there is an important number of scientific articles with a PLS-SEM approach in universities in Malaysia that could be related to the expansion of higher education in that country, as well as in Singapore, Taiwan, and Indonesia. Finally, business administration, accounting, and economics are outstanding scientific production.
High-contrast, speckle-free, true 3D holography via binary CGH optimization
Authors: Byounghyo Lee, Dongyeon Kim, Seungjae Lee, Chun Chen, Byoungho Lee
Date: 2022-01-07 00:00:00
Tasks: 3D Holography, Quantization, Vocal Bursts Intensity Prediction

Holography is a promising approach to implement the three-dimensional (3D) projection beyond the present two-dimensional technology. True 3D holography requires abilities of arbitrary 3D volume projection with high-axial resolution and independent control of all 3D voxels. However, it has been challenging to implement the true 3D holography with high-reconstruction quality due to the speckle. Here, we propose the practical solution to realize speckle-free, high-contrast, true 3D holography by combining random-phase, temporal multiplexing, binary holography, and binary optimization. We adopt the random phase for the true 3D implementation to achieve the maximum axial resolution with fully independent control of the 3D voxels. We develop the high-performance binary hologram optimization framework to minimize the binary quantization noise, which provides accurate and high-contrast reconstructions for 2D as well as 3D cases. Utilizing the fast operation of binary modulation, the full-color high-framerate holographic video projection is realized while the speckle noise of random phase is overcome by temporal multiplexing. Our high-quality true 3D holography is experimentally verified by projecting multiple arbitrary dense images simultaneously. The proposed method can be adopted in various applications of holography, where we show additional demonstration that realistic true 3D hologram in VR and AR near-eye displays. The realization will open a new path towards the next generation of holography.
Two Methods for Iso-Surface Extraction from Volumetric Data and Their Comparison
Authors: Vaclav Skala, Alex Brusi
Date: 2022-01-03 00:00:00

There are various methods for extracting iso-surfaces from volumetric data. Marching cubes or tetrahedra or raytracing methods are mostly used. There are many specific techniques to increase speed of computation and decrease memory requirements. Although a precision of iso-surface extraction is very important, too, it is not mentioned usually. A comparison of the selected methods was made in different aspects: iso-surface extraction process time, number of triangles generated and estimation of radius, area and volume errors based on approximation of a sphere. Surprisingly, experiments proved that there is no direct relation between precision of extracted and human perception of the extracted iso-surface
Decision Trees with Soft Numbers
Authors: Oren Fivel, Moshe Klein, Oded Maimon
Date: 2022-01-10 00:00:00

In the classical probability in continuous random variables there is no distinguishing between the probability involving strict inequality and non strict inequality. Moreover a probability involves equality collapse to zero without distinguishing among the values that we would like that the random variable will have for comparison. This work presents Soft Probability by incorporating of Soft Numbers into probability theory. Soft Numbers are set of new numbers that are linear combinations of multiples of ones and multiples of zeros. In this work, we develop a probability involving equality as a soft zero multiple of a probability density function.
Two-Pass End-to-End ASR Model Compression
Authors: Nauman Dawalatabad, Tushar Vatsal, Ashutosh Gupta, Sungsoo Kim, Shatrughan Singh, Dhananjaya Gowda, Chanwoo Kim
Date: 2022-01-08 00:00:00
Tasks: Decoder, Knowledge Distillation, Model Compression, speech-recognition, Speech Recognition, Vocal Bursts Valence Prediction

Speech recognition on smart devices is challenging owing to the small memory footprint. Hence small size ASR models are desirable. With the use of popular transducer-based models, it has become possible to practically deploy streaming speech recognition models on small devices [1]. Recently, the two-pass model [2] combining RNN-T and LAS modules has shown exceptional performance for streaming on-device speech recognition. In this work, we propose a simple and effective approach to reduce the size of the two-pass model for memory-constrained devices. We employ a popular knowledge distillation approach in three stages using the Teacher-Student training technique. In the first stage, we use a trained RNN-T model as a teacher model and perform knowledge distillation to train the student RNN-T model. The second stage uses the shared encoder and trains a LAS rescorer for student model using the trained RNN-T+LAS teacher model. Finally, we perform deep-finetuning for the student model with a shared RNN-T encoder, RNN-T decoder, and LAS rescorer. Our experimental results on standard LibriSpeech dataset show that our system can achieve a high compression rate of 55% without significant degradation in the WER compared to the two-pass teacher model.
Coordinated Day-ahead Dispatch of Multiple Power Distribution Grids hosting Stochastic Resources: An ADMM-based Framework
Authors: Rahul Gupta, Sherif Fahmy, Mario Paolone
Date: 2022-01-10 00:00:00
Tasks: Distributed Optimization, Stochastic Optimization

This work presents an optimization framework to aggregate the power and energy flexibilities in an interconnected power distribution systems. The aggregation framework is used to compute the day-ahead dispatch plans of multiple and interconnected distribution grids operating at different voltage levels. Specifically, the proposed framework optimizes the dispatch plan of an upstream medium voltage (MV) grid accounting for the flexibility offered by downstream low voltage (LV) grids and the knowledge of the uncertainties of the stochastic resources. The framework considers grid, i.e., operational limits on the nodal voltages, lines, and transformer capacity using a linearized grid model, and controllable resources' constraints. The dispatching problem is formulated as a stochastic-optimization scheme considering uncertainty on stochastic power generation and demands and the voltage imposed by the upstream grid. The problem is solved by a distributed optimization method relying on the Alternating Direction Method of Multipliers (ADMM) that splits the main problem into an aggregator problem (solved at the MV-grid level) and several local problems (solved at the MV-connected-controllable-resources and LV-grid levels). The use of distributed optimization enables a decentralized dispatch computation where the centralized aggregator is agnostic about the parameters/models of the participating resources and downstream grids. The framework is validated for interconnected CIGRE medium- and low-voltage networks hosting heterogeneous stochastic and controllable resources.
Minimax Flow over Acyclic Networks: Distributed Algorithms and Microgrid Application
Authors: Marco Coraggio, Saber Jafarpour, Francesco Bullo, Mario di Bernardo
Date: 2022-01-10 00:00:00

Given a flow network with variable suppliers and fixed consumers, the minimax flow problem consists in minimizing the maximum flow between nodes, subject to flow conservation and capacity constraints. We solve this problem over acyclic graphs in a distributed manner by showing that it can be recast as a consensus problem between the maximum downstream flows, which we define here for the first time. Additionally, we present a distributed algorithm to estimate these quantities. Finally, exploiting our theoretical results, we design an online distributed controller to prevent overcurrent in microgrids consisting of loads and droop-controlled inverters. Our results are validated numerically on the CIGRE benchmark microgrid.
Physics-Guided Neural Networks for Feedforward Control: An Orthogonal Projection-Based Approach
Authors: Johan Kon, Dennis Bruijnen, Jeroen van de Wijdeven, Marcel Heertjes, Tom Oomen
Date: 2022-01-10 00:00:00
Tasks: Friction

Unknown nonlinear dynamics can limit the performance of model-based feedforward control. The aim of this paper is to develop a feedforward control framework for systems with unknown, typically nonlinear, dynamics. To address the unknown dynamics, a physics-based feedforward model is complemented by a neural network. The neural network output in the subspace of the model is penalized through orthogonal projection. This results in uniquely identifiable model coefficients, enabling both increased performance and good generalization. The feedforward control framework is validated on a representative system with performance limiting nonlinear friction characteristics.
A Cooperative Game Approach for Energy Management of Interconnected Microgrids
Authors: Mohadese Movahednia, Hamid Karimi, Shahram Jadid
Date: 2022-01-10 00:00:00
Tasks: energy management, Management

In this study, a cooperative game model is presented to schedule the day-ahead operation of multi-microgrid (MMG) systems. In the proposed model, microgrids are scheduled to achieve a global optimum for the cost of the multi-microgrid system. The minimum cost is achieved by transactions of microgrids with each other. Also, price-based demand response is implemented in the model to build a cost-reducing opportunity for consumers. Applying Shapley value, the optimum cost of the MMG system is fairly allocated between microgrids. To enhance the confidence level of results, data uncertainties are incorporated into the model. The uncertainties of renewable outputs, demand, and prices of trading with the main grid are applied in the model. The presented model is developed as a mixed-integer linear programming problem, and its efficiency is evaluated on a standard test system containing three microgrids. The cost of the MMG system when microgrids form a cooperative game is compared with the isolated status that microgrids do not transact energy with each other. The results indicate that the cost of the MMG system is declined using the proposed cooperative model in comparison with the isolated mode. Also, the cost of microgrid 1, microgrid 2, and microgrid 3 are improved by 2.4, 2.7, and 11.8%, respectively. Therefore, all the microgrids have an incentive to participate in the cooperative game, and both the total cost and each microgrid cost are improved in the cooperative game.
An Explicit Parametrization of Closed Loops for Spatially Distributed Controllers with Sparsity Constraints
Authors: Emily Jensen, Bassam Bamieh
Date: 2020-12-08 00:00:00

In this article, we study the linear time-invariant state-feedback controller design problem for distributed systems. We follow the recently developed system level synthesis (SLS) approach and impose locality structure on the resulting closed-loop mappings; the corresponding controller implementation inherits this prescribed structure. In contrast to existing SLS results, we derive an explicit (rather than implicit) parameterization of all achievable stabilized closed-loops. This admits more efficient IIR representations of the temporal part of the closed-loop dynamics, and it allows for the H2 design problem with closed-loop spatial sparsity constraints to be converted to a standard model matching problem, with the number of transfer function parameters scaling linearly with the closed-loop spatial extent constraint. We illustrate our results with two applications: consensus of first-order subsystems and the vehicular platoons problem. In the case of first-order consensus, we provide analytic solutions and further analyze the architecture of the resulting controller implementation. Results for infinite extent spatially invariant systems are presented to provide insight to the case of a large but finite number of subsystems.
Data-driven Output-feedback Predictive Control: Unknown Plant's Order and Measurement Noise
Authors: Nam H. Jo, Hyungbo Shim
Date: 2022-01-10 00:00:00

The aim of this paper is to propose a new data-driven control scheme for multi-input-multi-output linear time-invariant systems whose system model are completely unknown. Using a non-minimal input-output realization, the proposed method can be applied to the case where the system order is unknown, provided that its upper bound is known. A workaround against measurement noise is proposed and it is shown through simulation study that the proposed method is superior to the conventional methods when dealing with input/output data corrupted by measurement noise.
Performance Analysis of Event-Triggered Consensus Control for Multi-agent Systems under Cyber-Physical Attacks
Authors: Farzaneh Tatari, Aquib Mustafa, Majid Mazouchi, Hamidreza Modares, Christos G. Panayiotou, Marios M. Polycarpou
Date: 2022-01-09 00:00:00

This work presents a rigorous analysis of the adverse effects of cyber-physical attacks on the performance of multi-agent consensus with event-triggered control protocols. It is shown how a strategic malicious attack on sensors and actuators can deceive the triggering condition of both state-based event-triggered mechanism and combinational state-based event-triggered mechanism, which are commonplace and widely used in the literature. More precisely, it is first shown that a deception attack in the case of combinational state-based event-triggered mechanism can result in a non-triggering misbehavior, in the sense that the compromised agent does not trigger any event and consequently results in partial feedback disconnectivity by preventing information from reaching the local neighbors of the compromised agent. This indicates that the combinational state-based event-triggered mechanism can be leveraged by the attacker to harm the network connectivity by rendering the recent data unavailable to agents. It is then shown that the deception attack in the case of state-based event-triggered mechanism can result in a continuous-triggering misbehavior in the sense that the event-triggered mechanism continuously generates triggering events resulting in undesirable phenomenon of Zeno behavior. Finally, numerical simulations are presented to illustrate the theoretical findings.
Prescribed-time Control for Linear Systems in Canonical Form Via Nonlinear Feedback
Authors: Hefu Ye, Yongduan Song
Date: 2022-01-09 00:00:00
Tasks: Form

For systems in canonical form with nonvanishing uncertainties/disturbances, this work presents an approach to full state regulation within prescribed time irrespective of initial conditions. By introducing the smooth hyperbolic-tangent-like function, a nonlinear and time-varying state feedback control scheme is constructed, which is further extended to address output feedback based prescribed-time regulation by invoking the prescribed-time observer, all are applicable over the entire operational time zone. As an alternative to full state regulation within user-assignable time interval, the proposed method analytically bridges the divide between linear and nonlinear feedback based prescribed-time control, and is able to achieve asymptotic stability, exponential stability and prescribed-time stability with a unified control structure.
An Improved Active Disturbance Rejection Control for Bode's Ideal Transfer Function
Authors: Bolin Li, Lijun Zhu
Date: 2022-01-09 00:00:00

This paper presents an active disturbance rejection control (ADRC) scheme with an improved fractional-order extended state observer (IFO-ESO).Based on the new ADRC scheme, the open-loop transfer function of a high-order system can be approximately rendered to a so-called Weighed Bode's ideal transfer function, whose closed-loop performance is less prone to the controller parameter variations. The design of the IFO-ESO helps reduce the number of system states to be estimated and improves the performance of closed-loop system over %the previously proposed the fractional-order active disturbance rejection control (FO-ADRC) in the literature. Compared with the integer-order active disturbance rejection controller (IO-ADRC) and FO-ADRC, the auxiliary tracking controller of IFO-ADRC has a simpler form. Frequency-domain analysis shows that IFO-ESO has better estimation performance than fractional-order ESO, and time-domain simulation demonstrates that the proposed ADRC has better transient performance and is more robust against the parameter variations than FO-ADRC and IO-ADRC. The proposed ADRC is applied to permanent magnet synchronous motor (PMSM) servo control system and demonstrates its capability in a real-world application.
Age-of-information minimization via opportunistic sampling by an energy harvesting source
Authors: Akanksha Jaiswal, Arpan Chattopadhyay, Amokh Varma
Date: 2022-01-08 00:00:00
Tasks: Q-Learning

Herein, minimization of time-averaged age-of-information (AoI) in an energy harvesting (EH) source setting is considered. The EH source opportunistically samples one or multiple processes over discrete time instants and sends the status updates to a sink node over a wireless fading channel. Each time, the EH node decides whether to probe the link quality and then decides whether to sample a process and communicate based on the channel probe outcome. The trade-off is between the freshness of information available at the sink node and the available energy at the source node. We use infinite horizon Markov decision process (MDP) to formulate the AoI minimization problem for two scenarios where energy arrival and channel fading processes are: (i) independent and identically distributed (i.i.d.), (ii) Markovian. In i.i.d. setting, after channel probing, the optimal source sampling policy is shown to be a threshold policy. Also, for unknown channel state and EH characteristics, a variant of the Q-learning algorithm is proposed for the two-stage action model, that seeks to learn the optimal policy. For Markovian system, the problem is again formulated as an MDP, and a learning algorithm is provided for unknown dynamics. Finally, numerical results demonstrate the policy structures and performance trade-offs.