pwc-archive/papers-with-abstracts huggingface dataset:
Feature Weighting and Regularization of Common Spatial Patterns in EEG-Based Motor Imagery BCI
Authors: Vasilisa Mishuhina, Xudong Jiang
Date: 2018-04-05 00:00:00
Proceeding: IEEE Signal Processing Letters 2018 4
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), feature selection, Motor Imagery

Electroencephalography signals have very low spatial resolution and electrodes capture signals that are overlapping each other. To extract the discriminative features and alleviate overfitting problem for motor imagery brain-computer interface (BCI), spatial filtering is widely applied but often only very few common spatial patterns (CSP) are selected as features while ignoring all others. However, using only few CSP features, though alleviates overfitting problem, loses the discriminating information, which limits the BCI performance. This letter proposes a novel feature weighting and regularization (FWR) method that utilizes all CSP features to avoid information loss. The proposed method can be applied in all CSP-based approaches. Experiments of this letter show the effect of the proposed method applied in the standard CSP and its two extensions, common spatio-spectral patterns and regularized CSP. Results on BCI Competition III Dataset IIIa and IV Dataset IIa demonstrate that the proposed FWR method enhances the classification accuracy comparing to the conventional feature selection approaches.
Adaptiope: A Modern Benchmark for Unsupervised Domain Adaptation
Authors: Tobias Ringwald, Rainer Stiefelhagen
Date: 2021-01-05 00:00:00
Proceeding: WACV 2021 1
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) deals with the adaptation process of a given source domain with labeled training data to a target domain for which only unannotated data is available. This is a challenging task as the domain shift leads to degraded performance on the target domain data if not addressed. In this paper, we analyze commonly used UDA classification datasets and discover systematic problems with regard to dataset setup, ground truth ambiguity and annotation quality. We manually clean the most popular UDA dataset in the research area (Office-31) and quantify the negative effects of inaccurate annotations through thorough experiments. Based on these insights, we collect the Adaptiope dataset - a large scale, diverse UDA dataset with synthetic, product and real world data - and show that its transfer tasks provide a challenge even when considering recent UDA algorithms. Our datasets are available at https://gitlab.com/tringwald/adaptiope.
Maximizing Convergence Time in Network Averaging Dynamics Subject to Edge Removal
Authors: S. Rasoul Etesami
Date: 2020-09-11 00:00:00

We consider the consensus interdiction problem (CIP), in which the goal is to maximize the convergence time of consensus averaging dynamics subject to removing a limited number of network edges. We first show that CIP can be cast as an effective resistance interdiction problem (ERIP), in which the goal is to remove a limited number of network edges to maximize the effective resistance between a source node and a sink node. We show that ERIP is strongly NP-hard, even for bipartite graphs of diameter three with fixed source/sink edges, and establish the same hardness result for the CIP. We then show that both ERIP and CIP cannot be approximated up to a (nearly) polynomial factor assuming exponential time hypothesis. Subsequently, we devise a polynomial-time $mn$-approximation algorithm for the ERIP that only depends on the number of nodes $n$ and the number of edges $m$, but is independent of the size of edge resistances. Finally, using a quadratic program formulation for the CIP, we devise an iterative approximation algorithm to find a first-order stationary solution for the CIP and evaluate its good performance through numerical results.
Information Spillover in Multiple Zero-sum Games
Authors: Lucas Pahl
Date: 2021-11-02 00:00:00

This paper considers an infinitely repeated three-player Bayesian game with lack of information on two sides, in which an informed player plays two zero-sum games simultaneously at each stage against two uninformed players. This is a generalization of the Aumann et al. [1] two-player zero-sum one-sided incomplete information model. Under a correlated prior, the informed player faces the problem of how to optimally disclose information among two uninformed players in order to maximize his long-term average payoffs. Our objective is to understand the adverse effects of \information spillover" from one game to the other in the equilibrium payoff set of the informed player. We provide conditions under which the informed player can fully overcome such adverse effects and characterize equilibrium payoffs. In a second result, we show how the effects of information spillover on the equilibrium payoff set of the informed player might be severe.
A Finite Characterization of Perfect Equilibria
Authors: Ivonne Callejas, Srihari Govindan, Lucas Pahl
Date: 2021-11-02 00:00:00

Govindan and Klumpp [7] provided a characterization of perfect equilibria using Lexicographic Probability Systems (LPSs). Their characterization was essentially finite in that they showed that there exists a finite bound on the number of levels in the LPS, but they did not compute it explicitly. In this note, we draw on two recent developments in Real Algebraic Geometry to obtain a formula for this bound.
Design of Tight Minimum-Sidelobe Windows by Riemannian Newton's Method
Authors: Daichi Kitahara, Kohei Yatabe
Date: 2021-11-02 00:00:00

The short-time Fourier transform (STFT), or the discrete Gabor transform (DGT), has been extensively used in signal analysis and processing. Their properties are characterized by a window function. For signal processing, designing a special window called tight window is important because it is known to make DGT-domain processing robust to error. In this paper, we propose a method of designing tight windows that minimize the sidelobe energy. It is formulated as a constrained spectral concentration problem, and a Newton's method on an oblique manifold is derived to efficiently obtain a solution. Our numerical example showed that the proposed algorithm requires only several iterations to reach a stationary point.
Minimizing Energy Consumption Leads to the Emergence of Gaits in Legged Robots
Authors: Zipeng Fu, Ashish Kumar, Jitendra Malik, Deepak Pathak
Date: 2021-10-25 00:00:00

Legged locomotion is commonly studied and expressed as a discrete set of gait patterns, like walk, trot, gallop, which are usually treated as given and pre-programmed in legged robots for efficient locomotion at different speeds. However, fixing a set of pre-programmed gaits limits the generality of locomotion. Recent animal motor studies show that these conventional gaits are only prevalent in ideal flat terrain conditions while real-world locomotion is unstructured and more like bouts of intermittent steps. What principles could lead to both structured and unstructured patterns across mammals and how to synthesize them in robots? In this work, we take an analysis-by-synthesis approach and learn to move by minimizing mechanical energy. We demonstrate that learning to minimize energy consumption plays a key role in the emergence of natural locomotion gaits at different speeds in real quadruped robots. The emergent gaits are structured in ideal terrains and look similar to that of horses and sheep. The same approach leads to unstructured gaits in rough terrains which is consistent with the findings in animal motor control. We validate our hypothesis in both simulation and real hardware across natural terrains. Videos at https://energy-locomotion.github.io
Multi-input Architecture and Disentangled Representation Learning for Multi-dimensional Modeling of Music Similarity
Authors: Sebastian Ribecky, Jakob Abeßer, Hanna Lukashevich
Date: 2021-11-02 00:00:00
Tasks: Disentanglement, Information Retrieval, Music Information Retrieval, Representation Learning, Retrieval, Triplet

In the context of music information retrieval, similarity-based approaches are useful for a variety of tasks that benefit from a query-by-example scenario. Music however, naturally decomposes into a set of semantically meaningful factors of variation. Current representation learning strategies pursue the disentanglement of such factors from deep representations, resulting in highly interpretable models. This allows the modeling of music similarity perception, which is highly subjective and multi-dimensional. While the focus of prior work is on metadata driven notions of similarity, we suggest to directly model the human notion of multi-dimensional music similarity. To achieve this, we propose a multi-input deep neural network architecture, which simultaneously processes mel-spectrogram, CENS-chromagram and tempogram in order to extract informative features for the different disentangled musical dimensions: genre, mood, instrument, era, tempo, and key. We evaluated the proposed music similarity approach using a triplet prediction task and found that the proposed multi-input architecture outperforms a state of the art method. Furthermore, we present a novel multi-dimensional analysis in order to evaluate the influence of each disentangled dimension on the perception of music similarity.
Design and Evaluation of Active Noise Control on Machinery Noise
Authors: Shulin Wen, Duy Hai Nguyen, Miqing Wang, Woon-Seng Gan
Date: 2021-11-02 00:00:00

Construction workers and residents live near around construction sites are exposed to noises that might cause hearing loss, high blood pressure, heart disease, sleep disturbance and stress. Regulations has been carried out by national governments to limit the maximum permissible noise levels for construction works. A four-channel active noise control system mounted on the opening of an enclosure is designed to prevent the machinery noise from spreading around and retaining the heat diffusion path. Multi-channel FxLMS algorithm in time domain is implemented on the main controller. A Genelec speaker is placed inside the box as the primary noise source to play back different types of noises. Analyses and experiments are carried out to investigate the controllable frequency range of this ANC system in detail. Considerable noise reduction performance is achieved for different recorded practical construction noises.
AVASpeech-SMAD: A Strongly Labelled Speech and Music Activity Detection Dataset with Label Co-Occurrence
Authors: Yun-Ning Hung, Karn N. Watcharasupat, Chih-Wei Wu, Iroro Orife, Kelian Li, Pavan Seshadri, Junyoung Lee
Date: 2021-11-02 00:00:00
Tasks: Action Detection, Activity Detection

We propose a dataset, AVASpeech-SMAD, to assist speech and music activity detection research. With frame-level music labels, the proposed dataset extends the existing AVASpeech dataset, which originally consists of 45 hours of audio and speech activity labels. To the best of our knowledge, the proposed AVASpeech-SMAD is the first open-source dataset that features strong polyphonic labels for both music and speech. The dataset was manually annotated and verified via an iterative cross-checking process. A simple automatic examination was also implemented to further improve the quality of the labels. Evaluation results from two state-of-the-art SMAD systems are also provided as a benchmark for future reference.
A modified gravity model based on network efficiency for vital nodes identification in complex networks
Authors: Hanwen Li, Qiuyan Shang, Yong Deng
Date: 2021-10-12 00:00:00

Vital nodes identification is an essential problem in network science. Various methods have been proposed to solve this problem. In particular, based on the gravity model, a series of improved gravity models are proposed to find vital nodes better in complex networks. However, they still have the room to be improved. In this paper, a novel and improved gravity model, which is named network efficiency gravity centrality model (NEG), integrates gravity model and network efficiency is proposed. Compared to other methods based on different gravity models, the proposed method considers the effect of the nodes on structure robustness of the network better. To solidate the superiority of the proposed method, experiments on varieties of real-world networks are carried out.
Small-Signal Stability Techniques for Power System Modal Analysis, Control, and Numerical Integration
Authors: Georgios Tzounas
Date: 2021-11-02 00:00:00
Tasks: Numerical Integration

This thesis proposes novel Small-Signal Stability Analysis (SSSA)-based techniques that contribute to electric power system modal analysis, automatic control, and numerical integration. Modal analysis is a fundamental tool for power system stability analysis and control. The thesis proposes a SSSA approach to determine the Participation Factors (PFs) of algebraic variables in power system dynamic modes. The thesis also explores SSSA techniques for the design of power system controllers. The contributions on this topic are twofold: i) Investigate a promising control approach, that is to synthesize automatic regulators for power systems based on the theory of fractional calculus. ii) Propose a novel perspective on the potential impact of time delays on power system stability. Through SSSA, the thesis systematically identifies the control parameter settings for which delays in PSSs improve the damping of a power system. Both analytical and simulation-based results are presented. Finally, SSSA is utilized in the thesis to systematically propose a delay-based method to reduce the coupling of the equations of power system models for transient stability analysis. The method consists in identifying the variables that, when subjected to a delay equal to the time step of the numerical integration, leave practically unchanged the system trajectories. Such an one-step-delay approximation increases the sparsity of the system Jacobian matrices and can be used in conjunction with state-of-the-art techniques for the integration of DAEs. The proposed approach is evaluated in terms of accuracy, convergence and computational burden. Throughout the thesis, the proposed techniques are duly validated through numerical tests based on real-world network models.
Energy and Resource Efficiency by User Traffic Prediction and Classification in Cellular Networks
Authors: Amin Azari, Fateme Salehi, Panagiotis Papapetrou, Cicek Cavdar
Date: 2021-11-02 00:00:00
Tasks: feature selection, Management, Time Series Analysis, Traffic Prediction

There is a lack of research on the analysis of per-user traffic in cellular networks, for deriving and following traffic-aware network management. \textcolor{black}{In fact, the legacy design approach, in which resource provisioning and operation control are performed based on the cell-aggregated traffic scenarios, are not so energy- and cost-efficient and need to be substituted with user-centric predictive analysis of mobile network traffic and proactive network resource management.} Here, we shed light on this problem by designing traffic prediction tools that utilize standard machine learning (ML) tools, including long short-term memory (LSTM) and autoregressive integrated moving average (ARIMA) on top of per-user data. We present an expansive empirical evaluation of the designed solutions over a real network traffic dataset. Within this analysis, the impact of different parameters, such as the time granularity, the length of future predictions, and feature selection are investigated. As a potential application of these solutions, we present an ML-powered Discontinuous reception (DRX) scheme for energy saving. Towards this end, we leverage the derived ML models for dynamic DRX parameter adaptation to user traffic. The performance evaluation results demonstrate the superiority of LSTM over ARIMA in general, especially when the length of the training time series is high enough, and it is augmented by a \textit{wisely}-selected set of features. Furthermore, the results show that adaptation of DRX parameters by online prediction of future traffic provides much more energy-saving at low latency cost in comparison with the legacy cell-wide DRX parameter adaptation.
The $k$-Compound of a Difference-Algebraic System
Authors: Ron Ofir, Michael Margaliot
Date: 2021-11-02 00:00:00

The multiplicative and additive compounds of a matrix have important applications in geometry, linear algebra, and the analysis of dynamical systems. In particular, the $k$-compounds allow to build a $k$-compound dynamical system that tracks the evolution of $k$-dimensional parallelotopes along the original dynamics. This has recently found many applications in the analysis of non-linear systems described by ODEs and difference equations. Here, we introduce the $k$-compound system corresponding to a differential-algebraic system, and describe several applications to the analysis of discrete-time dynamical systems described by difference-algebraic equations.
Cooperative Learning for P2P Energy Trading via Inverse Optimization and Interval Analysis
Authors: Dinh Hoa Nguyen
Date: 2020-11-05 00:00:00
Tasks: energy management, energy trading, Management

Peer-to-peer (P2P) energy systems have recently emerged as a promising approach for integrating renewable and distributed energy resources into energy grids to reduce carbon emissions. However, market-clearing energy price and amounts, resulted from solving optimal P2P energy management problems, might not be satisfactory for peers/agents. This is because peers/agents in practice do not know how to set their cost function parameters when participating into P2P energy markets. To resolve such drawback, this paper proposes a novel approach, in which an inverse optimization problem is formulated for peers/agents to cooperatively learn to choose their objective function parameters, given their intervals of desired energy prices and amounts. The result is that peers/agents can set their objective function parameters in the intervals computed analytically from the lower and upper bounds of their energy price and amounts, if the ratio of their maximum total buying and selling energy amounts lies in a certain interval subject to be learned by them. A case study is then carried out, which validates the effectiveness of the proposed approach.
DaDRA: A Python Library for Data-Driven Reachability Analysis
Authors: Jared Mejia, Alex Devonport, Murat Arcak
Date: 2021-11-02 00:00:00

Reachability analysis is used to determine all possible states that a system acting under uncertainty may reach. It is a critical component to obtain guarantees of various safety-critical systems both for safety verification and controller synthesis. Though traditional approaches to reachability analysis provide formal guarantees of the reachable set, they involve complex algorithms that require full system information, which is impractical for use in real world settings. We present DaDRA, a Python library that allows for data-driven reachability analysis with arbitrarily robust probabilistic guarantees. We demonstrate the practical functionality of DaDRA on various systems including: an analytically intractable chaotic system, benchmarks for systems with nonlinear dynamics, and a realistic system acting under complex disturbance signals and controlled with an intricate controller across multiple dimensions.
Verifying Contracts for Perturbed Control Systems using Linear Programming
Authors: Miel Sharf, Bart Besselink, Karl Henrik Johansson
Date: 2021-11-01 00:00:00
Tasks: Autonomous Driving

Verifying specifications for large-scale control systems is of utmost importance, but can be hard in practice as most formal verification methods can not handle high-dimensional dynamics. Contract theory has been proposed as a modular alternative to formal verification in which specifications are defined by assumptions on the inputs to a component and guarantees on its outputs. In this paper, we present linear-programming-based tools for verifying contracts for control systems. We first consider the problem of verifying contracts defined by time-invariant inequalities for unperturbed systems. We use $k$-induction to show that contract verification can be achieved by considering a collection of implications between inequalities, which are then recast as linear programs. We then move our attention to perturbed systems. We present a comparison-based framework, verifying that a perturbed system satisfies a contract by checking that the corresponding unperturbed system satisfies a robustified (and $\epsilon$-approximated) contract. In both cases, we present explicit algorithms for contract verification, proving their correctness and analyzing their complexity. We also demonstrate the verification process for two case studies, one considering a two-vehicle autonomous driving scenario, and one considering formation control of a multi-agent system.
Complex common spatial patterns on time-frequency decomposed EEG for brain-computer interface
Authors: Vasilisa Mishuhina, Xudong Jiang
Date: 2021-07-01 00:00:00
Proceeding: Pattern Recognition 2021 7
Tasks: Brain Computer Interface, Classification, EEG, EEG Signal Classification, Electroencephalogram (EEG), Motor Imagery

Motor imagery brain-computer interface (MI-BCI) has many promising applications but there are problems such as poor classification accuracy and robustness which need to be addressed. We propose a novel approach called time-frequency common spatial patterns (TFCSP) to enhance the robustness and accuracy of the electroencephalogram (EEG) signal classification. The proposed approach decomposes the EEG signal into time stages and frequency components to find the most robust and discriminative features. Common spatial patterns (CSP) are extracted from every decomposed time-frequency cell and unreliable features are removed while remaining features are weighted and regularized for the classification. Comparison on three publicly available datasets from BCI competition III and IV shows that the proposed TFCSP outperforms state-of-the-art methods. This demonstrates that adopting subject reaction time paradigm is useful to enhance the classification performance. It also shows that the complex CSP in the frequency domain significantly effective than the commonly used bandpass-filters in time domain. Finally, this work proves that weighting and regularizing CSP features are better techniques than selecting the leading CSP features because the former alleviates information loss.