pwc-archive/papers-with-abstracts huggingface dataset:
An analysis of voltage source inverter switches fault classification using short time Fourier transform
Authors: Mustafa Manap, Srete Nikolovski, Aleksandr Skamyin, Rony Karim, Tole Sutikno, Mohd Hatta Jopri
Date: 2021-11-12 00:00:00
Tasks: Classification

The dependability of power electronics systems, such as three-phase inverters, is critical in a variety of applications. Different types of failures that occur in an inverter circuit might affect system operation and raise the entire cost of the manufacturing process. As a result, detecting and identifying inverter problems for such devices is critical in industry. This study presents the short-time Fourier transform (STFT) for fault classification and identification in three-phase type, voltage source inverter (VSI) switches. Time-frequency representation (TFR) represents the signal analysis of STFT, which includes total harmonic distortion, instantaneous RMS current, RMS fundamental current, total non harmonic distortion, total waveform distortion and average current. The features of the faults are used with a rule-based classifier based on the signal parameters to categorise and detect the switch faults. The suggested method's performance is evaluated using 60 signals containing short and open circuit faults with varying characteristics for each switch in VSI. The classification results demonstrate the proposed technique is good to be implemented for VSI switches faults classification, with an accuracy classification rate of 98.3%.
Phase Response Curves and the Role of Coordinates
Authors: Simon Wilshin, Matthew D. Kvalheim, Shai Revzen
Date: 2021-11-12 00:00:00

The "Phase Response Curve" (PRC) is a common tool used to analyze phase resetting in the natural sciences. We make the observation that the PRC with respect to a coordinate $y\in\mathbb{R}$ actually depends on the full choice of coordinates $(x,y)$, $x\in\mathbb{R}^d$. We give a coordinate-free definition of the PRC making this observation obvious. We show how by controlling $y$, using delay coordinates of $y$, and postulating the dynamics of $x$ as a function of $x$ and $y$, we can sometimes reconstruct the PRC with respect to the $(x,y)$ coordinates. This suggests a means for obtaining the PRC of, e.g., a neuron via a voltage clamp.
Dynamic treatment effects: high-dimensional inference under model misspecification
Authors: Yuqian Zhang, Weijie Ji, Jelena Bradic
Date: 2021-11-12 00:00:00

Estimating dynamic treatment effects is crucial across various disciplines, providing insights into the time-dependent causal impact of interventions. However, this estimation poses challenges due to time-varying confounding, leading to potentially biased estimates. Furthermore, accurately specifying the growing number of treatment assignments and outcome models with multiple exposures appears increasingly challenging to accomplish. Double robustness, which permits model misspecification, holds great value in addressing these challenges. This paper introduces a novel "sequential model doubly robust" estimator. We develop novel moment-targeting estimates to account for confounding effects and establish that root-$N$ inference can be achieved as long as at least one nuisance model is correctly specified at each exposure time, despite the presence of high-dimensional covariates. Although the nuisance estimates themselves do not achieve root-$N$ rates, the carefully designed loss functions in our framework ensure final root-$N$ inference for the causal parameter of interest. Unlike off-the-shelf high-dimensional methods, which fail to deliver robust inference under model misspecification even within the doubly robust framework, our newly developed loss functions address this limitation effectively.
Global and local epidemiology of Group A Streptococcus indicates that naturally-acquired immunity is enduring and strain-specific
Authors: Rebecca H. Chisholm, Jake A. Lacey, Jan Kokko, Patricia T. Campbell, Malcolm I. McDonald, Jukka Corander, Mark R. Davies, Steven Y. C. Tong, Jodie McVernon, Nicholas Geard
Date: 2021-11-11 00:00:00
Tasks: Diversity, Epidemiology

The bacterium Group A Streptococcus (Streptococcus pyogenes, GAS) is a human-specific pathogen and a major cause of global morbidity and mortality. Despite decades of research our knowledge of GAS infection and immunity is incomplete, hampering vaccine design and other efforts to reduce disease prevalence. Epidemiological studies indicate positive associations between the prevalence of GAS-related disease, the diversity of circulating strains and the degree of poverty in host populations. However, the infection and immune mechanisms underlying these associations are not clear. In this work, we use an agent-based model to demonstrate that observed diversity and prevalence are best accounted for by the hypothesis that GAS infection confers enduring strain-specific immunity, with reduced or absent cross-protection against infection by other strains. Our results suggest that the success of GAS vaccines will depend on their ability to elicit long-lasting cross-protective immunity over multiple strain types.
Profit warnings and stock returns: Evidence from moroccan stock exchange
Authors: Ilyas El Ghordaf, Abdelbari El Khamlichi
Date: 2021-11-12 00:00:00

There is an important literature focused on profit warnings and its impact on stock returns. We provide evidence from Moroccan stock market which aims to become an African financial hub. Despite this practical improvement, academic researches that focused on this market are scarce and our study is a first investigation in this context. Using the event study methodology and a sample of companies listed in Casablanca Stock Exchange for the period of 2009 to 2016, we examined whether the effect of qualitative warning is more negative compared to quantitative warnings in a short event window. Our empirical findings show that the average abnormal return on the date of announcement is negative and statistically significant. The magnitude of this negative abnormal return is greater for qualitative warnings than quantitative ones.
The cavity method for minority games between arbitrageurs on financial markets
Authors: Tim Ritmeester, Hildegard Meyer-Ortmanns
Date: 2021-11-12 00:00:00

We use the cavity method from statistical physics for analyzing the transient and stationary dynamics of a minority game that is played by agents performing market arbitrage. On the level of linear response the method allows to include the reaction of the market to individual actions of the agents as well as the reaction of the agents to individual information items of the market. This way we derive a self-consistent solution to the minority game. In particular we analyze the impact of general nonlinear price functions on the amount of arbitrage if noise from external fluctuations is present. We identify the conditions under which arbitrage gets reduced due to the presence of noise. When the cavity method is extended to time dependent response of the market price to previous actions of the agents, the individual contributions of noise can be pursued over different time scales in the transient dynamics until a stationary state is reached and when the stationary state is reached. The contributions are from external fluctuations in price and information and from noise due to the choice of strategies. The dynamics explains the time evolution of scores of the agents' strategies: it changes from initially a random walk to non-Markovian dynamics and bounded excursions on an intermediate time scale to effectively random switching in the choice between strategies on long time scales. In contrast to the Curie-Weiss level of a mean-field approach, the market response included by the cavity method captures the realistic feature that the agents can have a preference for a certain choice of strategies without getting stuck to a single choice. The breakdown of the method in the phase transition region indicates possible market mechanisms leading to critical volatility and a possible regime shift.
Fine-tuning giant neural networks on commodity hardware with automatic pipeline model parallelism
Authors: Saar Eliad, Ido Hakimi, Alon De Jager, Mark Silberstein, Assaf Schuster
Date: 2021-07-14 00:00:00
Proceeding: USENIX Annual Technical Conference 2021 7
Tasks: GPU, Transfer Learning

Fine-tuning is an increasingly common technique that leverages transfer learning to dramatically expedite the training of huge, high-quality models.
Critically, fine-tuning holds the potential to make giant state-of-the-art models pre-trained on high-end super-computing-grade systems readily available for users that lack access to such costly resources. 
Unfortunately, this potential is still difficult to realize because the models often do not fit in the memory of a single commodity GPU, making fine-tuning a challenging problem.

We present \projname, a system that explores a new dimension of pipeline model parallelism, making multi-GPU execution of fine-tuning tasks for giant neural networks readily accessible on commodity hardware.
A key idea is a novel approach to model partitioning and task allocation, called \mpipe.  \mpipe
partitions the model into arbitrary computational blocks rather than layers, and relaxes the model topology constraints when assigning blocks to GPUs, allowing non-adjacent blocks to be executed on the same GPU. More flexible partitioning affords a much better balance of the compute- and memory-load on the GPUs compared to prior works, yet does not increase the communication overheads. Moreover, and perhaps surprisingly, when applied to asynchronous training, \mpipe has negligible or no effect on the end-to-end accuracy of fine-tuning tasks despite the addition of pipeline stages. 

Our extensive experiments on giant state-of-the-art NLP models (BERT-340M, GPT2-1.5B, and T5-3B) show that \projname achieves up to 3$\times$ speedup and state-of-the-art accuracy when fine-tuning giant transformers with billions of parameters. These models require from 12GB to 59GB of GPU memory, and \projname executes them on 8 commodity RTX2080-Ti GPUs, each with 11GB memory and standard PCIe.
ClaSP - Time Series Segmentation
Authors: Patrick Schäfer, Arik Ermshaus, Ulf Leser
Date: 2021-10-26 00:00:00
Proceeding: International Conference on Information & Knowledge Management 2021 10
Tasks: Change Point Detection, Segmentation, Semantic Segmentation, Time Series, Time Series Analysis

The study of biological or physical processes often results in long sequences of temporally-ordered values, aka time series (TS). Changes in the observed processes, e.g. as a cause of natural events or internal state changes, result in changes of the measured values. Time series segmentation (TSS) tries to find such changes in TS to deduce changes in the underlying process. TSS is typically approached as an unsupervised learning problem aiming at the identification of segments distinguishable by some statistical property. We present ClaSP, a novel and highly accurate method for TSS. ClaSP hierarchically splits a TS into two parts, where each split point is determined by training a binary TS classifier for each possible split point and selecting the one with highest accuracy, i.e., the one that is best at identifying subsequences to be from either of the partitions. In our experimental evaluation using a benchmark of 98 datasets, we show that ClaSP outperforms the state-of-the-art in terms of accuracy and is also faster than the second best method. We highlight properties of ClaSP using several real-life time series.
Through-Foliage Tracking with Airborne Optical Sectioning
Authors: Rakesh John Amala Arokia Nathan, Indrajit Kurmi, David C. Schedl, Oliver Bimber
Date: 2021-11-12 00:00:00
Tasks: Anomaly Detection

Detecting and tracking moving targets through foliage is difficult, and for many cases even impossible in regular aerial images and videos. We present an initial light-weight and drone-operated 1D camera array that supports parallel synthetic aperture aerial imaging. Our main finding is that color anomaly detection benefits significantly from image integration when compared to conventional raw images or video frames (on average 97% vs. 42% in precision in our field experiments). We demonstrate, that these two contributions can lead to the detection and tracking of moving people through densely occluding forest.
Simultaneously Achieving Sublinear Regret and Constraint Violations for Online Convex Optimization with Time-varying Constraints
Authors: Qingsong Liu, Wenfei Wu, Longbo Huang, Zhixuan Fang
Date: 2021-11-15 00:00:00

In this paper, we develop a novel virtual-queue-based online algorithm for online convex optimization (OCO) problems with long-term and time-varying constraints and conduct a performance analysis with respect to the dynamic regret and constraint violations. We design a new update rule of dual variables and a new way of incorporating time-varying constraint functions into the dual variables. To the best of our knowledge, our algorithm is the first parameter-free algorithm to simultaneously achieve sublinear dynamic regret and constraint violations. Our proposed algorithm also outperforms the state-of-the-art results in many aspects, e.g., our algorithm does not require the Slater condition. Meanwhile, for a group of practical and widely-studied constrained OCO problems in which the variation of consecutive constraints is smooth enough across time, our algorithm achieves $O(1)$ constraint violations. Furthermore, we extend our algorithm and analysis to the case when the time horizon $T$ is unknown. Finally, numerical experiments are conducted to validate the theoretical guarantees of our algorithm, and some applications of our proposed framework will be outlined.
Fault Diagnosis of Nonlinear Systems Using a Hybrid-Degree Dual Cubature-based Estimation Scheme
Authors: Yanyan Shen, Khashayar Khorasani
Date: 2021-11-13 00:00:00
Tasks: Diagnostic, Fault Detection, Fault Diagnosis, parameter estimation

In this paper, a novel hybrid-degree dual estimation approach based on cubature rules and cubature-based nonlinear filters is proposed for fault diagnosis of nonlinear systems through simultaneous state and time-varying parameter estimation. Our proposed dual nonlinear filtering scheme is developed based on case-dependent cubature rules that are motivated by the following observations and facts, namely (i) dynamic characteristics of nonlinear system states and parameters generally are distinct and posses different degrees of complexities, and (ii) performance of cubature rules depend on the system dynamics and vary due to handling of high-dimensional integrations approximations. For improving the robustness capability of our proposed methodologies modified cubature point propagation method is incorporated. The performance of our proposed dual estimation strategy is demonstrated and evaluated by application to a nonlinear gas turbine engine for addressing the component fault diagnosis problem within an integrated fault detection, isolation and identification framework. Robustness analysis is implemented to verify the capability of our proposed approaches to deal with parametric uncertainties and unmodeled dynamics. Extensive simulation case studies and discussions with respect to component fouling, erosion or abrupt faults are provided to substantiate and justify the superiority of our proposed fault diagnosis methodology when compared to other well-known alternative diagnostic techniques such as the Unscented Kalman Filters (UKF) and Particle Filters (PF) that are commonly available in the literature.
Generate plane quad mesh with neural networks and tree search
Authors: Hua Tong
Date: 2021-11-15 00:00:00

The quality of mesh generation has long been considered a vital aspect in providing engineers with reliable simulation results throughout the history of the Finite Element Method (FEM). The element extraction method, which is currently the most robust method, is used in business software. However, in order to speed up extraction, the approach is done by finding the next element that optimizes a target function, which can result in local mesh of bad quality after many time steps. We provide TreeMesh, a method that uses this method in conjunction with reinforcement learning (also possible with supervised learning) and a novel Monte-Carlo tree search (MCTS) (Coulom(2006), Kocsis and Szepesv\'ari(2006), Browne et~al.(2012)). The algorithm is based on a previously proposed approach (Pan et~al.(2021)). After making many improvements on DRL (algorithm, state-action-reward setting) and adding a MCTS, it outperforms the former work on the same boundary. Furthermore, using tree search, our program reveals much preponderance on seed-density-changing boundaries, which is common on thin-film materials.
Property Inference Attacks Against GANs
Authors: Junhao Zhou, Yufei Chen, Chao Shen, Yang Zhang
Date: 2021-11-15 00:00:00
Tasks: Attribute, Fairness, Inference Attack

While machine learning (ML) has made tremendous progress during the past decade, recent research has shown that ML models are vulnerable to various security and privacy attacks. So far, most of the attacks in this field focus on discriminative models, represented by classifiers. Meanwhile, little attention has been paid to the security and privacy risks of generative models, such as generative adversarial networks (GANs). In this paper, we propose the first set of training dataset property inference attacks against GANs. Concretely, the adversary aims to infer the macro-level training dataset property, i.e., the proportion of samples used to train a target GAN with respect to a certain attribute. A successful property inference attack can allow the adversary to gain extra knowledge of the target GAN's training dataset, thereby directly violating the intellectual property of the target model owner. Also, it can be used as a fairness auditor to check whether the target GAN is trained with a biased dataset. Besides, property inference can serve as a building block for other advanced attacks, such as membership inference. We propose a general attack pipeline that can be tailored to two attack scenarios, including the full black-box setting and partial black-box setting. For the latter, we introduce a novel optimization framework to increase the attack efficacy. Extensive experiments over four representative GAN models on five property inference tasks show that our attacks achieve strong performance. In addition, we show that our attacks can be used to enhance the performance of membership inference against GANs.
Measuring the Contribution of Multiple Model Representations in Detecting Adversarial Instances
Authors: Daniel Steinberg, Paul Munro
Date: 2021-11-13 00:00:00
Proceeding: AAAI Workshop AdvML 2022 2
Tasks: speech-recognition, Speech Recognition

Deep learning models have been used for a wide variety of tasks. They are prevalent in computer vision, natural language processing, speech recognition, and other areas. While these models have worked well under many scenarios, it has been shown that they are vulnerable to adversarial attacks. This has led to a proliferation of research into ways that such attacks could be identified and/or defended against. Our goal is to explore the contribution that can be attributed to using multiple underlying models for the purpose of adversarial instance detection. Our paper describes two approaches that incorporate representations from multiple models for detecting adversarial examples. We devise controlled experiments for measuring the detection impact of incrementally utilizing additional models. For many of the scenarios we consider, the results show that performance increases with the number of underlying models used for extracting representations.
Adversarial Skill Chaining for Long-Horizon Robot Manipulation via Terminal State Regularization
Authors: Youngwoon Lee, Joseph J. Lim, Anima Anandkumar, Yuke Zhu
Date: 2021-11-15 00:00:00
Tasks: Reinforcement Learning (RL), Robot Manipulation

Skill chaining is a promising approach for synthesizing complex behaviors by sequentially combining previously learned skills. Yet, a naive composition of skills fails when a policy encounters a starting state never seen during its training. For successful skill chaining, prior approaches attempt to widen the policy's starting state distribution. However, these approaches require larger state distributions to be covered as more policies are sequenced, and thus are limited to short skill sequences. In this paper, we propose to chain multiple policies without excessively large initial state distributions by regularizing the terminal state distributions in an adversarial learning framework. We evaluate our approach on two complex long-horizon manipulation tasks of furniture assembly. Our results have shown that our method establishes the first model-free reinforcement learning algorithm to solve these tasks; whereas prior skill chaining approaches fail. The code and videos are available at https://clvrai.com/skill-chaining
Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking
Authors: Octavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi Jaakkola, Andreas Krause
Date: 2021-11-15 00:00:00
Proceeding: ICLR 2022 4
Tasks: Drug Design, Graph Matching, Translation

Protein complex formation is a central problem in biology, being involved in most of the cell's processes, and essential for applications, e.g. drug design or protein engineering. We tackle rigid body protein-protein docking, i.e., computationally predicting the 3D structure of a protein-protein complex from the individual unbound structures, assuming no conformational change within the proteins happens during binding. We design a novel pairwise-independent SE(3)-equivariant graph matching network to predict the rotation and translation to place one of the proteins at the right docked position relative to the second protein. We mathematically guarantee a basic principle: the predicted complex is always identical regardless of the initial locations and orientations of the two structures. Our model, named EquiDock, approximates the binding pockets and predicts the docking poses using keypoint matching and alignment, achieved through optimal transport and a differentiable Kabsch algorithm. Empirically, we achieve significant running time improvements and often outperform existing docking software despite not relying on heavy candidate sampling, structure refinement, or templates.