pwc-archive/papers-with-abstracts huggingface dataset:
Data-driven modeling of power networks
Authors: Bita Safaee, Serkan Gugercin
Date: 2021-04-13 00:00:00

We develop a non-intrusive data-driven modeling framework for power network dynamics using the Lift and Learn approach of \cite{QianWillcox2020}. A lifting map is applied to the snapshot data obtained from the original nonlinear swing equations describing the underlying power network such that the lifted-data corresponds to quadratic nonlinearity. The lifted data is then projected onto a lower dimensional basis and the reduced quadratic matrices are fit to this reduced lifted data using a least-squares measure. The effectiveness of the proposed approach is investigated by two power network models.
Topology Estimation Following Islanding and its Impact on Preventive Control of Cascading Failure
Authors: Sai Gopal Vennelaganti, Nilanjan Ray Chaudhuri, Ting He, Thomas La Porta
Date: 2021-04-13 00:00:00

Knowledge of power grid's topology during cascading failure is an essential element of centralized blackout prevention control, given that multiple islands are typically formed, as a cascade progresses. Moreover, academic research on interdependency between cyber and physical layers of the grid indicate that power failure during a cascade may lead to outages in communication networks, which progressively reduce the observable areas. These challenge the current literature on line outage detection, which assumes that the grid remains as a single connected component. We propose a new approach to eliminate that assumption. Following an islanding event, first the buses forming that connected components are identified and then further line outages within the individual islands are detected. In addition to the power system measurements, observable breaker statuses are integrated as constraints in our topology identification algorithm. The impact of error propagation on the estimation process as reliance on previous estimates keeps growing during cascade is also studied. Finally, the estimated admittance matrix is used in preventive control of cascading failure, creating a closed-loop system. The impact of such an interlinked estimation and control on that total load served is studied for the first time. Simulations in IEEE-118 bus system and 2,383-bus Polish network demonstrate the effectiveness of our approach.
Joint Matrix Completion and Compressed Sensing for State Estimation in Low-observable Distribution System
Authors: Shweta Dahale, Balasubramaniam Natarajan
Date: 2021-04-13 00:00:00
Tasks: compressed sensing, Compressive Sensing, Matrix Completion, State Estimation

Limited measurement availability at the distribution grid presents challenges for state estimation and situational awareness. This paper combines the advantages of two sparsity-based state estimation approaches (matrix completion and compressive sensing) that have been proposed recently to address the challenge of unobservability. The proposed approach exploits both the low rank structure and a suitable transform domain representation to leverage the correlation structure of the spatio-temporal data matrix while incorporating the power-flow constraints of the distribution grid. Simulations are carried out on three phase unbalanced IEEE 37 test system to verify the effectiveness of the proposed approach. The performance results reveal - (1) the superiority over traditional matrix completion and (2) very low state estimation errors for high compression ratios representing very low observability.
Multiscale modelling of de novo anaerobic granulation
Authors: A. Tenore, F. Russo, M. R. Mattei, B. D'Acunto, G. Collins, L. Frunzo
Date: 2021-04-13 00:00:00

A multiscale mathematical model is presented to describe the de novo granulation and the evolution of multispecies granular biofilms within a continuous reactor. The granule is modelled as a spherical free boundary domain with radial symmetry. The equation which governs the free boundary is derived from global mass balance considerations and takes into account the growth of sessile biomass and the exchange fluxes with the bulk liquid. Starting from a vanishing initial value, the expansion of the free boundary is initiated by the attachment process, which depends on the microbial species concentrations within the bulk liquid and their specific attachment velocity. Nonlinear hyperbolic PDEs model the growth of the sessile microbial species, while quasi-linear parabolic PDEs govern the dynamics of substrates and invading species within the granular biofilm. Nonlinear ODEs govern the evolution of soluble substrates and planktonic biomass within the bulk liquid. The model is applied to an anaerobic granular-based system and solved numerically to test its qualitative behaviour and explore the main aspects of de novo anaerobic granulation: ecology, biomass distribution, relative abundance, dimensional evolution of the granules and soluble substrates and planktonic biomass dynamics within the reactor. The numerical results confirm that the model accurately describes the ecology and the concentrically-layered structure of anaerobic granules observed experimentally, and is able to predict the effects of some significant factors, such as influent wastewater composition, granulation properties of planktonic biomass, biomass density and hydrodynamic and shear stress conditions, on the process performance.
Underwater dual-magnification imaging for automated lake plankton monitoring
Authors: E. Merz, T. Kozakiewicz, M. Reyes, C. Ebi, P. Isles, M. Baity-Jesi, P. Roberts, J. S. Jaffe, S. Dennis, T. Hardeman, N. Stevens, T. Lorimer, F. Pomati
Date: 2021-04-14 00:00:00
Tasks: Time Series Analysis

We present an approach for automated in-situ monitoring of phytoplankton and zooplankton communities based on a dual magnification dark-field imaging microscope/camera. We describe the Dual Scripps Plankton Camera (DSPC) system and associated image processing, and assess its capabilities in detecting and characterizing plankton species of different size and taxonomic categories, and in measuring their abundances in both laboratory and field applications. In the laboratory, body size and abundance estimates by the DSPC significantly and robustly scale with the same measurements derived by traditional microscopy. In the field, a DSPC installed permanently at 3 m depth in Lake Greifensee (Switzerland), delivered images of plankton individuals, colonies, and heterospecific aggregates without disrupting natural arrangements of interacting organisms, their microenvironment or their behavior at hourly timescales. The DSPC was able to track the dynamics of taxa in the size range between ~10 $\mu$m to ~ 1 cm, covering virtually all the components of the planktonic food web (including parasites and potentially toxic cyanobacteria). Comparing data from the field-deployed DSPC to traditional sampling and microscopy revealed a general overall agreement in estimates of plankton diversity and abundances, despite imaging limitations in detecting small phytoplankton species and rare and large zooplankton taxa (e.g. carnivorous zooplankton). The most significant disagreements between traditional methods and the DSPC resided in the measurements of community properties of zooplankton, organisms that are heterogeneously distributed spatially and temporally, and whose demography appeared to be better captured by automated imaging. Time series collected by the DSPC depicted ecological succession patterns, algal bloom dynamics and circadian fluctuations with a temporal frequency and morphological [continues...]
A fast and Accurate Similarity-constrained Subspace Clustering Framework for Unsupervised Hyperspectral Image Classification
Authors: Carlos Hinojosa, Esteban Vera, Henry Arguello
Date: 2021-04-14 00:00:00
Tasks: Clustering, Hyperspectral Image Classification, image-classification, Image Classification, Segmentation

Accurate land cover segmentation of spectral images is challenging and has drawn widespread attention in remote sensing due to its inherent complexity. Although significant efforts have been made for developing a variety of methods, most of them rely on supervised strategies. Subspace clustering methods, such as Sparse Subspace Clustering (SSC), have become a popular tool for unsupervised learning due to their high performance. However, the computational complexity of SSC methods prevents their use on large spectral remotely sensed datasets. Furthermore, since SSC ignores the spatial information in the spectral images, its discrimination capability is limited, hampering the clustering results' spatial homogeneity. To address these two relevant issues, in this paper, we propose a fast algorithm that obtains a sparse representation coefficient matrix by first selecting a small set of pixels that best represent their neighborhood. Then, it performs spatial filtering to enforce the connectivity of neighboring pixels and uses fast spectral clustering to get the final segmentation. Extensive simulations with our method demonstrate its effectiveness in land cover segmentation, obtaining remarkable high clustering performance compared with state-of-the-art SSC-based algorithms and even novel unsupervised-deep-learning-based methods. Besides, the proposed method is up to three orders of magnitude faster than SSC when clustering more than $2 \times 10^4$ spectral pixels.
Classifying herbal medicine origins by temporal and spectral data mining of electronic nose
Authors: Li Liu, Xianghao Zhan, Ziheng Duan, Yi Wu, Rumeng Wu, Xiaoqing Guan, Zhan Wang, You Wang, Guang Li
Date: 2021-04-14 00:00:00
Tasks: Dimensionality Reduction

The origins of herbal medicines are important for their treatment effect, which could be potentially distinguished by electronic nose system. As the odor fingerprint of herbal medicines from different origins can be tiny, the discrimination of origins can be much harder than that of different categories. Better feature extraction methods are significant for this task to be more accurately done, but there lacks systematic studies on different feature extraction methods. In this study, we classified different origins of three categories of herbal medicines with different feature extraction methods: manual feature extraction, mathematical transformation, deep learning algorithms. With 50 repetitive experiments with bootstrapping, we compared the effectiveness of the extractions with a two-layer neural network w/o dimensionality reduction methods (principal component analysis, linear discriminant analysis) as the three base classifiers. Compared with the conventional aggregated features, the Fast Fourier Transform method and our novel approach (longitudinal-information-in-a-line) showed an significant accuracy improvement(p < 0.05) on all 3 base classifiers and all three herbal medicine categories. Two of the deep learning algorithm we applied also showed partially significant improvement: one-dimensional convolution neural network(1D-CNN) and a novel graph pooling based framework - multivariate time pooling(MTPool).
SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning
Authors: Lokesh Das, Myounggyu Won
Date: 2021-03-06 00:00:00
Tasks: Autonomous Vehicles, Deep Reinforcement Learning, Reinforcement Learning (RL)

We present a novel adaptive cruise control (ACC) system namely SAINT-ACC: {S}afety-{A}ware {Int}elligent {ACC} system (SAINT-ACC) that is designed to achieve simultaneous optimization of traffic efficiency, driving safety, and driving comfort through dynamic adaptation of the inter-vehicle gap based on deep reinforcement learning (RL). A novel dual RL agent-based approach is developed to seek and adapt the optimal balance between traffic efficiency and driving safety/comfort by effectively controlling the driving safety model parameters and inter-vehicle gap based on macroscopic and microscopic traffic information collected from dynamically changing and complex traffic environments. Results obtained through over 12,000 simulation runs with varying traffic scenarios and penetration rates demonstrate that SAINT-ACC significantly enhances traffic flow, driving safety and comfort compared with a state-of-the-art approach.
ML-CB: Machine Learning Canvas Block
Authors: Nathan Reitinger, Michelle L. Mazurek
Date: 2021-07-12 00:00:00
Tasks: BIG-bench Machine Learning, Blocking

With the aim of increasing online privacy, we present a novel, machine-learning based approach to blocking one of the three main ways website visitors are tracked online—canvas fingerprinting. Because the act of canvas fingerprinting uses, at its core, a JavaScript program, and because many of these programs are reused across the web, we are able to fit several machine learning models around a semantic representation of a potentially offending program, achieving accurate and robust classifiers. Our supervised learning approach is trained on a dataset we created by scraping roughly half a million websites using a custom Google Chrome extension storing information related to the canvas. Classification leverages our key insight that the images drawn by canvas fingerprinting programs have a facially distinct appearance, allowing us to manually classify files based on the images drawn; we take this approach one step further and train our classifiers not on the malleable images themselves, but on the more-difficult-to-change, underlying source code generating the images. As a result, ML-CB allows for more accurate tracker blocking.
The i3DPost multi-view and 3D human action/interaction,
Authors: N. Gkalelis, H. Kim, A. Hilton, N. Nikolaidis and I. Pitas
Date: 2009-11-13 00:00:00

In this paper a new multi-view/3D human action/interaction database is presented. The database has been created using a convergent eight camera setup to produce high definition multi-view videos, where each video depicts one of eight persons performing one of twelve different human motions. Various types of motions have been recorded, i.e., scenes where one person performs a specific movement, scenes where a person executes different movements in a succession and scenes where two persons interact with each other. Moreover, the subjects have different body sizes, clothing and are of different sex, nationalities, etc.. The multi-view videos have been further processed to produce a 3D mesh at each frame describing the respective 3D human body surface. To increase the applicability of the database, for each person a multi-view video depicting the person performing sequentially the six basic facial expressions separated by the neutral expression has also been recorded. The database is freely available for research purposes.
Prescribed-time control with linear decay for nonlinear systems
Authors: Amir Shakouri, Nima Assadian
Date: 2021-04-14 00:00:00

In this letter, a new notion of stability is introduced, which is called triangular stability. A system is called triangularly stable if the norm of its state vector is bounded by a decreasing linear function of time such that its intersection point with the time axis can be arbitrarily commanded by the user. Triangular stability implies prescribed-time stability, which means that the nonlinear system is converged to zero equilibrium at an arbitrary finite time. A prescribed-time controller with guaranteed triangular stability is developed for normal form nonlinear systems with uncertain input gain, which is able to reject the disturbances and unmodeled dynamics. Numerical simulations are carried out to visualize the results for second and fourth-order systems.
Discover the Hidden Attack Path in Multi-domain Cyberspace Based on Reinforcement Learning
Authors: Lei Zhang, Wei Bai, Wei Li, Shiming Xia, Qibin Zheng
Date: 2021-04-15 00:00:00
Tasks: Reinforcement Learning (RL)

In this work, we present a learning-based approach to analysis cyberspace security configuration. Unlike prior methods, our approach has the ability to learn from past experience and improve over time. In particular, as we train over a greater number of agents as attackers, our method becomes better at discovering hidden attack paths for previously methods, especially in multi-domain cyberspace. To achieve these results, we pose discovering attack paths as a Reinforcement Learning (RL) problem and train an agent to discover multi-domain cyberspace attack paths. To enable our RL policy to discover more hidden attack paths and shorter attack paths, we ground representation introduction an multi-domain action select module in RL. Our objective is to discover more hidden attack paths and shorter attack paths by our proposed method, to analysis the weakness of cyberspace security configuration. At last, we designed a simulated cyberspace experimental environment to verify our proposed method, the experimental results show that our method can discover more hidden multi-domain attack paths and shorter attack paths than existing baseline methods.
Orthogonalizing Convolutional Layers with the Cayley Transform
Authors: Asher Trockman, J. Zico Kolter
Date: 2021-04-14 00:00:00
Proceeding: ICLR 2021 1
Tasks: Adversarial Robustness

Recent work has highlighted several advantages of enforcing orthogonality in the weight layers of deep networks, such as maintaining the stability of activations, preserving gradient norms, and enhancing adversarial robustness by enforcing low Lipschitz constants. Although numerous methods exist for enforcing the orthogonality of fully-connected layers, those for convolutional layers are more heuristic in nature, often focusing on penalty methods or limited classes of convolutions. In this work, we propose and evaluate an alternative approach to directly parameterize convolutional layers that are constrained to be orthogonal. Specifically, we propose to apply the Cayley transform to a skew-symmetric convolution in the Fourier domain, so that the inverse convolution needed by the Cayley transform can be computed efficiently. We compare our method to previous Lipschitz-constrained and orthogonal convolutional layers and show that it indeed preserves orthogonality to a high degree even for large convolutions. Applied to the problem of certified adversarial robustness, we show that networks incorporating the layer outperform existing deterministic methods for certified defense against $\ell_2$-norm-bounded adversaries, while scaling to larger architectures than previously investigated. Code is available at https://github.com/locuslab/orthogonal-convolutions.
Auto-Tuned Sim-to-Real Transfer
Authors: Yuqing Du, Olivia Watkins, Trevor Darrell, Pieter Abbeel, Deepak Pathak
Date: 2021-04-15 00:00:00

Policies trained in simulation often fail when transferred to the real world due to the `reality gap' where the simulator is unable to accurately capture the dynamics and visual properties of the real world. Current approaches to tackle this problem, such as domain randomization, require prior knowledge and engineering to determine how much to randomize system parameters in order to learn a policy that is robust to sim-to-real transfer while also not being too conservative. We propose a method for automatically tuning simulator system parameters to match the real world using only raw RGB images of the real world without the need to define rewards or estimate state. Our key insight is to reframe the auto-tuning of parameters as a search problem where we iteratively shift the simulation system parameters to approach the real-world system parameters. We propose a Search Param Model (SPM) that, given a sequence of observations and actions and a set of system parameters, predicts whether the given parameters are higher or lower than the true parameters used to generate the observations. We evaluate our method on multiple robotic control tasks in both sim-to-sim and sim-to-real transfer, demonstrating significant improvement over naive domain randomization. Project videos and code at https://yuqingd.github.io/autotuned-sim2real/
Zooming SlowMo: An Efficient One-Stage Framework for Space-Time Video Super-Resolution
Authors: Xiaoyu Xiang, Yapeng Tian, Yulun Zhang, Yun Fu, Jan P. Allebach, Chenliang Xu
Date: 2021-04-15 00:00:00
Tasks: Space-time Video Super-resolution, Super-Resolution, Video Frame Interpolation, Video Super-Resolution

In this paper, we address the space-time video super-resolution, which aims at generating a high-resolution (HR) slow-motion video from a low-resolution (LR) and low frame rate (LFR) video sequence. A na\"ive method is to decompose it into two sub-tasks: video frame interpolation (VFI) and video super-resolution (VSR). Nevertheless, temporal interpolation and spatial upscaling are intra-related in this problem. Two-stage approaches cannot fully make use of this natural property. Besides, state-of-the-art VFI or VSR deep networks usually have a large frame reconstruction module in order to obtain high-quality photo-realistic video frames, which makes the two-stage approaches have large models and thus be relatively time-consuming. To overcome the issues, we present a one-stage space-time video super-resolution framework, which can directly reconstruct an HR slow-motion video sequence from an input LR and LFR video. Instead of reconstructing missing LR intermediate frames as VFI models do, we temporally interpolate LR frame features of the missing LR frames capturing local temporal contexts by a feature temporal interpolation module. Extensive experiments on widely used benchmarks demonstrate that the proposed framework not only achieves better qualitative and quantitative performance on both clean and noisy LR frames but also is several times faster than recent state-of-the-art two-stage networks. The source code is released in https://github.com/Mukosame/Zooming-Slow-Mo-CVPR-2020 .
KnowPrompt: Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction
Authors: Xiang Chen, Ningyu Zhang, Xin Xie, Shumin Deng, Yunzhi Yao, Chuanqi Tan, Fei Huang, Luo Si, Huajun Chen
Date: 2021-04-15 00:00:00
Tasks: Dialog Relation Extraction, Language Modeling, Language Modelling, Masked Language Modeling, Relation, Relation Extraction, Representation Learning

Recently, prompt-tuning has achieved promising results for specific few-shot classification tasks. The core idea of prompt-tuning is to insert text pieces (i.e., templates) into the input and transform a classification task into a masked language modeling problem. However, for relation extraction, determining an appropriate prompt template requires domain expertise, and it is cumbersome and time-consuming to obtain a suitable label word. Furthermore, there exists abundant semantic and prior knowledge among the relation labels that cannot be ignored. To this end, we focus on incorporating knowledge among relation labels into prompt-tuning for relation extraction and propose a Knowledge-aware Prompt-tuning approach with synergistic optimization (KnowPrompt). Specifically, we inject latent knowledge contained in relation labels into prompt construction with learnable virtual type words and answer words. Then, we synergistically optimize their representation with structured constraints. Extensive experimental results on five datasets with standard and low-resource settings demonstrate the effectiveness of our approach. Our code and datasets are available in https://github.com/zjunlp/KnowPrompt for reproducibility.