pwc-archive/papers-with-abstracts huggingface dataset:
The Price of Tolerance in Distribution Testing
Authors: Clément L. Canonne, Ayush Jain, Gautam Kamath, Jerry Li
Date: 2021-06-25 00:00:00

We revisit the problem of tolerant distribution testing. That is, given samples from an unknown distribution $p$ over $\{1, \dots, n\}$, is it $\varepsilon_1$-close to or $\varepsilon_2$-far from a reference distribution $q$ (in total variation distance)? Despite significant interest over the past decade, this problem is well understood only in the extreme cases. In the noiseless setting (i.e., $\varepsilon_1 = 0$) the sample complexity is $\Theta(\sqrt{n})$, strongly sublinear in the domain size. At the other end of the spectrum, when $\varepsilon_1 = \varepsilon_2/2$, the sample complexity jumps to the barely sublinear $\Theta(n/\log n)$. However, very little is known about the intermediate regime. We fully characterize the price of tolerance in distribution testing as a function of $n$, $\varepsilon_1$, $\varepsilon_2$, up to a single $\log n$ factor. Specifically, we show the sample complexity to be \[\tilde \Theta\left(\frac{\sqrt{n}}{\varepsilon_2^{2}} + \frac{n}{\log n} \cdot \max \left\{\frac{\varepsilon_1}{\varepsilon_2^2},\left(\frac{\varepsilon_1}{\varepsilon_2^2}\right)^{\!\!2}\right\}\right),\] providing a smooth tradeoff between the two previously known cases. We also provide a similar characterization for the problem of tolerant equivalence testing, where both $p$ and $q$ are unknown. Surprisingly, in both cases, the main quantity dictating the sample complexity is the ratio $\varepsilon_1/\varepsilon_2^2$, and not the more intuitive $\varepsilon_1/\varepsilon_2$. Of particular technical interest is our lower bound framework, which involves novel approximation-theoretic tools required to handle the asymmetry between $\varepsilon_1$ and $\varepsilon_2$, a challenge absent from previous works.
Feature Grouping and Sparse Principal Component Analysis with Truncated Regularization
Authors: Haiyan Jiang, Shanshan Qin, Oscar Hernan Madrid Padilla
Date: 2021-06-25 00:00:00
Tasks: Dimensionality Reduction, feature selection

In this paper, we consider a new variant for principal component analysis (PCA), aiming to capture the grouping and/or sparse structures of factor loadings simultaneously. To achieve these goals, we employ a non-convex truncated regularization with naturally adjustable sparsity and grouping effects, and propose the Feature Grouping and Sparse Principal Component Analysis (FGSPCA). The proposed FGSPCA method encourages the factor loadings with similar values to collapse into disjoint homogeneous groups for feature grouping or into a special zero-valued group for feature selection, which in turn helps reducing model complexity and increasing model interpretation. Usually, existing structured PCA methods require prior knowledge to construct the regularization term. However, the proposed FGSPCA can simultaneously capture the grouping and/or sparse structures of factor loadings without any prior information. To solve the resulting non-convex optimization problem, we propose an alternating algorithm that incorporates the difference-of-convex programming, augmented Lagrange method and coordinate descent method. Experimental results demonstrate the promising performance and efficiency of the new method on both synthetic and real-world datasets. An R implementation of FGSPCA can be found on github {https://github.com/higeeks/FGSPCA}.
Posterior Covariance Information Criterion for Weighted Inference
Authors: Yukito Iba, Keisuke Yano
Date: 2021-06-25 00:00:00
Tasks: counterfactual

For predictive evaluation based on quasi-posterior distributions, we develop a new information criterion, the posterior covariance information criterion (PCIC. PCIC generalises the widely applicable information criterion WAIC so as to effectively handle predictive scenarios where likelihoods for the estimation and the evaluation of the model may be different. A typical example of such scenarios is the weighted likelihood inference, including prediction under covariate shift and counterfactual prediction. The proposed criterion utilises a posterior covariance form and is computed by using only one Markov chain Monte Carlo run. Through numerical examples, we demonstrate how PCIC can apply in practice. Further, we show that PCIC is asymptotically unbiased to the quasi-Bayesian generalization error under mild conditions in weighted inference with both regular and singular statistical models.
Black Box Probabilistic Numerics
Authors: Onur Teymur, Christopher N. Foley, Philip G. Breen, Toni Karvonen, Chris. J. Oates
Date: 2021-06-15 00:00:00
Proceeding: NeurIPS 2021 12

Probabilistic numerics casts numerical tasks, such the numerical solution of differential equations, as inference problems to be solved. One approach is to model the unknown quantity of interest as a random variable, and to constrain this variable using data generated during the course of a traditional numerical method. However, data may be nonlinearly related to the quantity of interest, rendering the proper conditioning of random variables difficult and limiting the range of numerical tasks that can be addressed. Instead, this paper proposes to construct probabilistic numerical methods based only on the final output from a traditional method. A convergent sequence of approximations to the quantity of interest constitute a dataset, from which the limiting quantity of interest can be extrapolated, in a probabilistic analogue of Richardson's deferred approach to the limit. This black box approach (1) massively expands the range of tasks to which probabilistic numerics can be applied, (2) inherits the features and performance of state-of-the-art numerical methods, and (3) enables provably higher orders of convergence to be achieved. Applications are presented for nonlinear ordinary and partial differential equations, as well as for eigenvalue problems-a setting for which no probabilistic numerical methods have yet been developed.
Combinatorial Learning of Robust Deep Graph Matching: an Embedding based Approach.
Authors: Runzhong Wang, Junchi Yan and Xiaokang Yang.
Date: 2020-06-29 00:00:00
Proceeding: TPAMI 2020 6
Tasks: Graph Matching

Graph matching aims to establish node correspondence between two graphs, which has been a fundamental problem for its NP-complete nature. One practical consideration is the effective modeling of the affinity function in the presence of noise, such that the mathematically optimal matching result is also physically meaningful. This paper resorts to deep neural networks to learn the node and edge feature, as well as the affinity model for graph matching in an end-to-end fashion. The learning is supervised by combinatorial permutation loss over nodes. Specifically, the parameters belong to convolutional neural networks for image feature extraction, graph neural networks for node embedding that convert the structural (beyond second-order) information into node-wise features that leads to a linear assignment problem, as well as the affinity kernel between two graphs. Our approach enjoys flexibility in that the permutation loss is agnostic to the number of nodes, and the embedding model is shared among nodes such that the network can deal with varying numbers of nodes for both training and inference. Moreover, our network is class-agnostic. Experimental results on extensive benchmarks show its state-of-the-art performance. It bears some generalization capability across categories and datasets, and is capable for robust matching against outliers.
Isolated Sign Recognition from RGB Video using Pose Flow and Self-Attention
Authors: Mathieu De Coster, Mieke Van Herreweghe, Joni Dambre
Date: 2021-06-11 00:00:00
Proceeding: Computer Vision and Pattern Recognition Workshops (CVPRW) 2021 6
Tasks: Action Recognition, Sign Language Recognition, Video Understanding

Automatic sign language recognition lies at the intersection of natural language processing (NLP) and computer vision. The highly successful transformer architectures, based on multi-head attention, originate from the field of NLP. The Video Transformer Network (VTN) is an adaptation of this concept for tasks that require video understanding, e.g., action recognition. However, due to the limited amount of labeled data that is commonly available for training automatic sign (language) recognition, the VTN cannot reach its full potential in this domain. In this work, we reduce the impact of this data limitation by automatically pre-extracting useful information from the sign language videos. In our approach, different types of information are offered to a VTN in a multi-modal setup. It includes per-frame human pose keypoints (extracted by OpenPose) to capture the body movement and hand crops to capture the (evolution of) hand shapes. We evaluate our method on the recently released AUTSL dataset for isolated sign recognition and obtain 92.92% accuracy on the test set using only RGB data. For comparison: the VTN architecture without hand crops and pose flow achieved 82% accuracy. A qualitative inspection of our model hints at further potential of multi-modal multi-head attention in a sign language recognition context.
SnakeLines: integrated set of computational pipelines for sequencing reads
Authors: Jaroslav Budis, Werner Krampl, Marcel Kucharik, Rastislav Hekel, Adrian Goga, Michal Lichvar, David Smolak, Miroslav Bohmer, Andrej Balaz, Frantisek Duris, Juraj Gazdarica, Katarina Soltys, Jan Turna, Jan Radvanszky, Tomas Szemes
Date: 2021-06-25 00:00:00

Background: With the rapid growth of massively parallel sequencing technologies, still more laboratories are utilizing sequenced DNA fragments for genomic analyses. Interpretation of sequencing data is, however, strongly dependent on bioinformatics processing, which is often too demanding for clinicians and researchers without a computational background. Another problem represents the reproducibility of computational analyses across separated computational centers with inconsistent versions of installed libraries and bioinformatics tools. Results: We propose an easily extensible set of computational pipelines, called SnakeLines, for processing sequencing reads; including mapping, assembly, variant calling, viral identification, transcriptomics, metagenomics, and methylation analysis. Individual steps of an analysis, along with methods and their parameters can be readily modified in a single configuration file. Provided pipelines are embedded in virtual environments that ensure isolation of required resources from the host operating system, rapid deployment, and reproducibility of analysis across different Unix-based platforms. Conclusion: SnakeLines is a powerful framework for the automation of bioinformatics analyses, with emphasis on a simple set-up, modifications, extensibility, and reproducibility. Keywords: Computational pipeline, framework, massively parallel sequencing, reproducibility, virtual environment
The Effect of Ground Truth Accuracy on the Evaluation of Localization Systems
Authors: Chen Gu, Ahmed Shokry, Moustafa Youssef
Date: 2021-06-25 00:00:00

The ability to accurately evaluate the performance of location determination systems is crucial for many applications. Typically, the performance of such systems is obtained by comparing ground truth locations with estimated locations. However, these ground truth locations are usually obtained by clicking on a map or using other worldwide available technologies like GPS. This introduces ground truth errors that are due to the marking process, map distortions, or inherent GPS inaccuracy. In this paper, we present a theoretical framework for analyzing the effect of ground truth errors on the evaluation of localization systems. Based on that, we design two algorithms for computing the real algorithmic error from the validation error and marking/map ground truth errors, respectively. We further establish bounds on different performance metrics. Validation of our theoretical assumptions and analysis using real data collected in a typical environment shows the ability of our theoretical framework to correct the estimated error of a localization algorithm in the presence of ground truth errors. Specifically, our marking error algorithm matches the real error CDF within 4%, and our map error algorithm provides a more accurate estimate of the median/tail error by 150%/72% when the map is shifted by 6m.
Game theory and scholarly publishing: premises for an agreement around open access
Authors: Abdelghani Maddi
Date: 2021-06-21 00:00:00

Stakeholders in research and scientific publishing are gradually joining the Open-Access (OA) movement, which is gaining momentum to become nowadays at the heart of scientific policies in high-income countries. The rise of OA generates profound changes in the chain of production and dissemination of knowledge. Free access to peer-reviewed research methods and results has contributed to the dynamics of science observed in recent years. The modes of publication and access have also evolved; the classic model, based on journal subscriptions is gradually giving way to new economic models that have appeared with the arrival of OA. The objective of this article is twofold. First, propose a model for the publishing market based on the literature as well as on changes in open science policies. Second, analyze publishing strategies of publishers and institutions. To do so, we relied on game theory in economics. Results show that in the short term, the publisher's equilibrium strategy is to adopt a hybridpublishing model, while the institutions' equilibrium strategy is to publish in OA. This equilibrium is not stable and that in the medium/long term, the two players will converge on an OA publishing strategy. The analysis of the equilibrium in mixed-strategies confirms this result.
Study of Pre-processing Defenses against Adversarial Attacks on State-of-the-art Speaker Recognition Systems
Authors: Sonal Joshi, Jesús Villalba, Piotr Żelasko, Laureano Moro-Velázquez, Najim Dehak
Date: 2021-01-22 00:00:00
Tasks: Speaker Recognition

Adversarial examples to speaker recognition (SR) systems are generated by adding a carefully crafted noise to the speech signal to make the system fail while being imperceptible to humans. Such attacks pose severe security risks, making it vital to deep-dive and understand how much the state-of-the-art SR systems are vulnerable to these attacks. Moreover, it is of greater importance to propose defenses that can protect the systems against these attacks. Addressing these concerns, this paper at first investigates how state-of-the-art x-vector based SR systems are affected by white-box adversarial attacks, i.e., when the adversary has full knowledge of the system. x-Vector based SR systems are evaluated against white-box adversarial attacks common in the literature like fast gradient sign method (FGSM), basic iterative method (BIM)--a.k.a. iterative-FGSM--, projected gradient descent (PGD), and Carlini-Wagner (CW) attack. To mitigate against these attacks, the paper proposes four pre-processing defenses. It evaluates them against powerful adaptive white-box adversarial attacks, i.e., when the adversary has full knowledge of the system, including the defense. The four pre-processing defenses--viz. randomized smoothing, DefenseGAN, variational autoencoder (VAE), and Parallel WaveGAN vocoder (PWG) are compared against the baseline defense of adversarial training. Conclusions indicate that SR systems were extremely vulnerable under BIM, PGD, and CW attacks. Among the proposed pre-processing defenses, PWG combined with randomized smoothing offers the most protection against the attacks, with accuracy averaging 93% compared to 52% in the undefended system and an absolute improvement >90% for BIM attacks with $L_\infty>0.001$ and CW attack.
Optimal Participation of Price-maker Battery Energy Storage Systems in Energy and Ancillary Services Markets Considering Degradation Cost
Authors: Reza Khalilisenobari, Meng Wu
Date: 2020-06-10 00:00:00

This paper proposes a bi-level optimization framework to investigate the optimal market operation strategies of price-maker battery energy storage systems (BESSs) in real-time energy, spinning reserve, and pay as performance regulation markets, with a special focus on understanding BESS's excessive regulation market participation observed by several system operators and the impact of battery degradation cost on BESS market activities. An accurate battery degradation cost function is integrated into the BESS's strategic bidding model. An automatic generation control (AGC) signal dispatch model is proposed to deploy AGC signals in the bi-level framework. This enables thorough studies for BESS's operating characteristics in the frequency regulation market, when both battery degradation and detailed AGC signal following activities are considered. Case studies on a synthetic system with real-world data are performed to study interactions between BESS profit maximization and wholesale market operations, considering system annual load and ancillary service requirements variations. The impacts of BESS capacity and replacement cost on its revenue from energy and ancillary services markets are also investigated.
Cybersecurity Challenges in Distributed Control
Authors: Md Amimul Ehsan
Date: 2021-06-25 00:00:00

Cyber-physical systems are becoming core of the most modern systems consisting control, data sharing and real-time monitoring. While centralized control technique has been implemented in the past, recent innovation in distributed control schemes makes it attractive due to various reasons. One of them is the use of state-of-the-art communication protocols that makes the system more robust toward extreme conditions and ensures observability. Thus, as an application of cyber-physical systems, distributed control architectures are prone to various cyber-vulnerability which makes cybersecurity research critical in this application domain. This paper reviews recent researches of distributed control architectures, their cyber-vulnerabilities, and reported mitigation schemes. Finally, some research needs are addressed.
Effects of current limit for grid forming converters on transient stability: analysis and solution
Authors: Kanakesh Vatta Kkuni, Guangya Yang
Date: 2021-06-25 00:00:00

Grid forming control applied to power converters that interface storage or renewable generation to the power grid, has been identified as a potential solution to facilitate a substantial share of converter-based renewable generation in the power system. Analyzing the response of grid forming converters (GFC) for large frequency, phase, and voltage events, particularly when the GFC enters the current limit operation, is very important for system stability, but studies on this have been limited. This paper presents a quantitative and illustrative analysis of the impact of the current limit in GFC on the transient stability of a system comprising of GFC. Furthermore, a solution based on virtual active power is proposed to improve the transient stability margin of the GFC when the GFC enters the current limit. Finally, the analysis and the proposed method to enhance the transient stability are verified by Power hardware in the loop (PHIL) experimental tests.
A Distributed Methodology for Approximate Uniform Global Minimum Sharing
Authors: Michelangelo Bin, Thomas Parisini
Date: 2020-12-16 00:00:00

The paper deals with the distributed minimum sharing problem: a set of decision-makers compute the minimum of some local quantities of interest in a distributed and decentralized way by exchanging information through a communication network. We propose an adjustable approximate solution which enjoys several properties of crucial importance in applications. In particular, the proposed solution has good decentralization properties and it is scalable in that the number of local variables does not grow with the size or topology of the communication network. Moreover, a global and uniform (both in the initial time and in the initial conditions) asymptotic stability result is provided towards a steady state which can be made arbitrarily close to the sought minimum. Exact asymptotic convergence can be recovered at the price of losing uniformity with respect to the initial time.
Retro-Reflective Beam Communications with Spatially Separated Laser Resonator
Authors: Mingliang Xiong, Mingqing Liu, Qingwei Jiang, Jie zhou, Qingwen Liu, Hao Deng
Date: 2021-01-30 00:00:00

Optical wireless communications (OWC) utilizing infrared or visible light as the carrier attracts great attention in 6G research. Resonant beam communications (RBCom) is an OWC technology which simultaneously satisfies the needs of non-mechanical mobility and high signal-to-noise ratio~(SNR). It has the self-alignment feature and therefore avoids positioning and pointing operations. However, RBCom undergoes echo interference. Here we propose an echo-interference-free RBCom system design based on second harmonic generation. The transmitter and the receiver constitute a spatially separated laser resonator, in which the retro-reflective resonant beam is formed and tracks the receiver automatically. This structure provides the channel with adaptive capability in beamforming and alignment, which is similar to the concept of intelligent reflecting surface (IRS) enhanced communications, but without hardware and software controllers. Besides, we establish an analytical model to evaluate the beam radius, the beam power, and the channel capacity. The results show that our system achieves longer distance and smaller beam diameter for the transmission beyond 10 Gbit/s, compared with the existing OWC technologies.
Detectability Conditions and State Estimation for Linear Time-Varying and Nonlinear Systems
Authors: Markus Tranninger, Richard Seeber, Martin Steinberger, Martin Horn, Christian Pötzsche
Date: 2021-06-25 00:00:00
Tasks: State Estimation

This work proposes a detectability condition for linear time-varying systems based on the exponential dichotomy spectrum. The condition guarantees the existence of an observer, whose gain is determined only by the unstable modes of the system. This allows for an observer design with low computational complexity compared to classical estimation approaches. An extension of this observer design to a class of nonlinear systems is proposed and local convergence of the corresponding estimation error dynamics is proven. Numerical results show the efficacy of the proposed observer design technique.
A Computationally Efficient Hamilton-Jacobi-based Formula for State-Constrained Optimal Control Problems
Authors: Donggun Lee, Claire J. Tomlin
Date: 2021-06-25 00:00:00

This paper investigates a Hamilton-Jacobi (HJ) analysis to solve finite-horizon optimal control problems for high-dimensional systems. Although grid-based methods, such as the level-set method [1], numerically solve a general class of HJ partial differential equations, the computational complexity is exponential in the dimension of the continuous state. To manage this computational complexity, methods based on Lax-Hopf theory have been developed for the state-unconstrained optimal control problem under certain assumptions, such as affine dynamics and state-independent stage cost. Based on the Lax formula [2], this paper proposes an HJ formula for the state-constrained optimal control problem for nonlinear systems. We call this formula \textit{the generalized Lax formula} for the optimal control problem. The HJ formula provides both the optimal cost and an optimal control signal. We also provide an efficient computational method for a class of problems for which the dynamics is affine in the state, and for which the stage and terminal cost, as well as the state constraints, are convex in the state. This class of problems does not require affine dynamics and convex stage cost in the control. This paper also provides three practical examples.