pwc-archive/papers-with-abstracts huggingface dataset:
From Static to Dynamic Prediction: Wildfire Risk Assessment Based on Multiple Environmental Factors
Authors: Tanqiu Jiang, Sidhant K. Bendre, Hanjia Lyu, Jiebo Luo
Date: 2021-03-14 00:00:00
Tasks: counterfactual

Wildfire is one of the biggest disasters that frequently occurs on the west coast of the United States. Many efforts have been made to understand the causes of the increases in wildfire intensity and frequency in recent years. In this work, we propose static and dynamic prediction models to analyze and assess the areas with high wildfire risks in California by utilizing a multitude of environmental data including population density, Normalized Difference Vegetation Index (NDVI), Palmer Drought Severity Index (PDSI), tree mortality area, tree mortality number, and altitude. Moreover, we focus on a better understanding of the impacts of different factors so as to inform preventive actions. To validate our models and findings, we divide the land of California into 4,242 grids of 0.1 degrees $\times$ 0.1 degrees in latitude and longitude, and compute the risk of each grid based on spatial and temporal conditions. To verify the generalizability of our models, we further expand the scope of wildfire risk assessment from California to Washington without any fine tuning. By performing counterfactual analysis, we uncover the effects of several possible methods on reducing the number of high risk wildfires. Taken together, our study has the potential to estimate, monitor, and reduce the risks of wildfires across diverse areas provided that such environment data is available.
Universal Trading for Order Execution with Oracle Policy Distillation
Authors: Yuchen Fang, Kan Ren, Weiqing Liu, Dong Zhou, Weinan Zhang, Jiang Bian, Yong Yu, Tie-Yan Liu
Date: 2021-01-28 00:00:00
Tasks: Algorithmic Trading, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument. Towards effective execution strategy, recent years have witnessed the shift from the analytical view with model-based market assumptions to model-free perspective, i.e., reinforcement learning, due to its nature of sequential decision optimization. However, the noisy and yet imperfect market information that can be leveraged by the policy has made it quite challenging to build up sample efficient reinforcement learning methods to achieve effective order execution. In this paper, we propose a novel universal trading policy optimization framework to bridge the gap between the noisy yet imperfect market states and the optimal action sequences for order execution. Particularly, this framework leverages a policy distillation method that can better guide the learning of the common policy towards practically optimal execution by an oracle teacher with perfect information to approximate the optimal trading strategy. The extensive experiments have shown significant improvements of our method over various strong baselines, with reasonable trading actions.
Prediction of Hydraulic Blockage at Cross Drainage Structures using Regression Analysis
Authors: Umair Iqbal, Johan Barthelemy, Pascal Perez, Wanqing Li
Date: 2021-03-06 00:00:00
Tasks: regression

Hydraulic blockage of cross-drainage structures such as culverts is considered one of main contributor in triggering urban flash floods. However, due to lack of during floods data and highly non-linear nature of debris interaction, conventional modelling for hydraulic blockage is not possible. This paper proposes to use machine learning regression analysis for the prediction of hydraulic blockage. Relevant data has been collected by performing a scaled in-lab study and replicating different blockage scenarios. From the regression analysis, Artificial Neural Network (ANN) was reported best in hydraulic blockage prediction with $R^2$ of 0.89. With deployment of hydraulic sensors in smart cities, and availability of Big Data, regression analysis may prove helpful in addressing the blockage detection problem which is difficult to counter using conventional experimental and hydrological approaches.
Multi-Time-Scale Input Approaches for Hourly-Scale Rainfall-Runoff Modeling based on Recurrent Neural Networks
Authors: Kei Ishida, Masato Kiyama, Ali Ercan, Motoki Amagasaki, Tongbi Tu
Date: 2021-01-30 00:00:00
Tasks: Time Series, Time Series Analysis

This study proposes two straightforward yet effective approaches to reduce the required computational time of the training process for time-series modeling through a recurrent neural network (RNN) using multi-time-scale time-series data as input. One approach provides coarse and fine temporal resolutions of the input time-series to RNN in parallel. The other concatenates the coarse and fine temporal resolutions of the input time-series data over time before considering them as the input to RNN. In both approaches, first, finer temporal resolution data are utilized to learn the fine temporal scale behavior of the target data. Next, coarser temporal resolution data are expected to capture long-duration dependencies between the input and target variables. The proposed approaches were implemented for hourly rainfall-runoff modeling at a snow-dominated watershed by employing a long and short-term memory (LSTM) network, which is a newer type of RNN. Subsequently, the daily and hourly meteorological data were utilized as the input, and hourly flow discharge was considered as the target data. The results confirm that both of the proposed approaches can reduce the computational time for the training of RNN significantly (up to 32.4 times). Furthermore, one of the proposed approaches improves the estimation accuracy.
Data-driven geophysical forecasting: Simple, low-cost, and accurate baselines with kernel methods
Authors: Boumediene Hamzi, Romit Maulik, Houman Owhadi
Date: 2021-02-13 00:00:00
Tasks: Diversity

Modeling geophysical processes as low-dimensional dynamical systems and regressing their vector field from data is a promising approach for learning emulators of such systems. We show that when the kernel of these emulators is also learned from data (using kernel flows, a variant of cross-validation), then the resulting data-driven models are not only faster than equation-based models but are easier to train than neural networks such as the long short-term memory neural network. In addition, they are also more accurate and predictive than the latter. When trained on geophysical observational data, for example, the weekly averaged global sea-surface temperature, considerable gains are also observed by the proposed technique in comparison to classical partial differential equation-based models in terms of forecast computational cost and accuracy. When trained on publicly available re-analysis data for the daily temperature of the North-American continent, we see significant improvements over classical baselines such as climatology and persistence-based forecast techniques. Although our experiments concern specific examples, the proposed approach is general, and our results support the viability of kernel methods (with learned kernels) for interpretable and computationally efficient geophysical forecasting for a large diversity of processes.
Continuous Sign Language Recognition Through Cross-Modal Alignment of Video and Text Embeddings in a Joint-Latent Space
Authors: Ilias Papastratis, Kosmas Dimitropoulos, DIMITRIOS KONSTANTINIDIS, Petros Daras
Date: 2020-05-11 00:00:00
Tasks: cross-modal alignment, Decoder, Descriptive, Sign Language Recognition

Continuous Sign Language Recognition (CSLR) refers to the challenging problem of recognizing sign language glosses and their temporal boundaries from weakly annotated video sequences. Previous methods focus mostly on visual feature extraction neglecting text information and failing to effectively model the intra-gloss dependencies. In this work, a cross-modal learning approach that leverages text information to improve vision-based CSLR is proposed. To this end, two powerful encoding networks are initially used to produce video and text embeddings prior to their mapping and alignment into a joint latent representation. The purpose of the proposed cross-modal alignment is the modelling of intra-gloss dependencies and the creation of more descriptive video-based latent representations for CSLR. The proposed method is trained jointly with video and text latent representations. Finally, the aligned video latent representations are classified using a jointly trained decoder. Extensive experiments on three well-known sign language recognition datasets and comparison with state-of-the-art approaches demonstrate the great potential of the proposed approach.
Video-based Person Re-identiﬁcation with Accumulative Motion Context
Authors: Hao liu, Zequn Jie, Karlekar Jayashree, Meibin Qi, Jianguo Jiang, Shuicheng Yan, Jiashi Feng
Date: 2017-06-13 00:00:00
Tasks: Person Re-Identification, Video-Based Person Re-Identification

Video based person re-identification plays a central role in realistic security and video surveillance. In this paper we propose a novel Accumulative Motion Context (AMOC) network for addressing this important problem, which effectively exploits the long-range motion context for robustly identifying the same person under challenging conditions. Given a video sequence of the same or different persons, the proposed AMOC network jointly learns appearance representation and motion context from a collection of adjacent frames using a two-stream convolutional architecture. Then AMOC accumulates clues from motion context by recurrent aggregation, allowing effective information flow among adjacent frames and capturing dynamic gist of the persons. The architecture of AMOC is end-to-end trainable and thus motion context can be adapted to complement appearance clues under unfavorable conditions (e.g. occlusions). Extensive experiments are conduced on three public benchmark datasets, i.e., the iLIDS-VID, PRID-2011 and MARS datasets, to investigate the performance of AMOC. The experimental results demonstrate that the proposed AMOC network outperforms state-of-the-arts for video-based re-identification significantly and confirm the advantage of exploiting long-range motion context for video based person re-identification, validating our motivation evidently.
MMEA: Entity Alignment for Multi-Modal Knowledge Graphs
Authors: Liyi Chen, Zhi Li, Yijun Wang, Tong Xu, Zhefeng Wang, Enhong Chen
Date: 2020-08-20 00:00:00
Tasks: Entity Alignment, Knowledge Graphs, Multimodal Deep Learning, Multi-modal Entity Alignment

Entity alignment plays an essential role in the knowledge graph (KG) integration. Though large efforts have been made on exploring the association of relational embeddings between different knowledge graphs, they may fail to effectively describe and integrate the multimodal knowledge in the real application scenario. To that end, in this paper, we propose a novel solution called Multi-Modal Entity Alignment (MMEA) to address the problem of entity alignment in a multi-modal view. Specifically, we first design a novel multi-modal knowledge embedding method to generate the entity representations of relational, visual and numerical knowledge, respectively. Along this line, multiple representations of different types of knowledge will be integrated via a multimodal knowledge fusion module. Extensive experiments on two public datasets clearly demonstrate the effectiveness of the MMEA model with a significant margin compared with the state-of-the-art methods.
COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction
Authors: Yijie Lin, Yuanbiao Gou, Zitao Liu, Boyun Li, Jiancheng Lv, Xi Peng
Date: 2021-03-22 00:00:00
Proceeding: CVPR 2021 1
Tasks: Clustering, Contrastive Learning, Incomplete multi-view clustering, MULTI-VIEW LEARNING, Prediction, Representation Learning

In this paper, we study two challenging problems in incomplete multi-view clustering analysis, namely, i) how to learn an informative and consistent representation among different views without the help of labels and ii) how to recover the missing views from data. To this end, we propose a novel objective that incorporates representation learning and data recovery into a unified framework from the view of information theory. To be specific, the informative and consistent representation is learned by maximizing the mutual information across different views through contrastive learning, and the missing views are recovered by minimizing the conditional entropy of different views through dual prediction. To the best of our knowledge, this could be the first work to provide a theoretical framework that unifies the consistent representation learning and cross-view data recovery. Extensive experimental results show the proposed method remarkably outperforms 10 competitive multi-view clustering methods on four challenging datasets. The code is available at https://pengxi.me.
Iterative weak/self-supervised classification framework for abnormal events detection
Authors: Bruno Degardin, Hugo Proença
Date: 2021-01-03 00:00:00
Tasks: Abnormal Event Detection In Video, Anomaly Detection, Anomaly Detection In Surveillance Videos, Classification, Data Augmentation, General Classification, Self-Supervised Anomaly Detection, Self-Supervised Learning, Semi-supervised Anomaly Detection, Semi-Supervised Video Classification, Task 2

The detection of abnormal events in surveillance footage remains a challenge and has been the scope of various research works. Having observed that the state-of-the-art performance is still unsatisfactory, this paper provides a novel solution to the problem, with four-fold contributions: 1) upon the work of Sultani et al., we introduce one iterative learning framework composed of two experts working in the weak and self-supervised paradigms and providing additional amounts of learning data to each other, where the novel instances at each iteration are filtered by a Bayesian framework that supports the iterative data augmentation task; 2) we describe a novel term that is added to the baseline loss to spread the scores in the unit interval, which is crucial for the performance of the iterative framework; 3) we propose a Random Forest ensemble that fuses at the score level the top performing methods and reduces the EER values about 20% over the state-of-the-art; and 4) we announce the availability of the ”UBI-Fights” dataset, fully annotated at the frame level, that can be freely used by the research community. The code, details of the experimental protocols and the dataset are publicly available at http://github.com/DegardinBruno/.
Supervisory Control of Multi-Agent Discrete-Event Systems with Partial Observation
Authors: Yingying Liu, Jan Komenda, Zhiwu Li
Date: 2021-03-19 00:00:00

In this paper we investigate multi-agent discrete-event systems with partial observation. The agents can be divided into several groups in each of which the agents have similar (isomorphic) state transition structures, and thus can be relabeled into the same template. Based on the template a scalable supervisor whose state size and computational cost are independent of the number of agents is designed for the case of partial observation. The scalable supervisor under partial observation does not need to be recomputed regardless of how many agents are added to or removed from the system. We generalize our earlier results to partial observation by proposing sufficient conditions for safety and maximal permissiveness of the scalable least restrictive supervisor on the template level. An example is provided to illustrate the proposed scalable supervisory synthesis.
Leveraging Two-Stage Adaptive Robust Optimization for Power Flexibility Aggregation
Authors: Xin Chen, Na Li
Date: 2020-05-07 00:00:00
Tasks: Vocal Bursts Valence Prediction

Adaptive robust optimization (ARO) is a well-known technique to deal with the parameter uncertainty in optimization problems. While the ARO framework can actually be borrowed to solve some special problems without uncertain parameters, such as the power flexibility aggregation problem studied in this paper. To effectively harness the significant flexibility from massive distributed energy resources (DERs), power flexibility aggregation is performed for a distribution system to compute the feasible region of the exchanged power at the substation over time. Based on two-stage ARO, this paper proposes a novel method to aggregate system-level multi-period power flexibility, considering heterogeneous DER facilities, network operational constraints, and an unbalanced power flow model. This method is applicable to aggregate only the active (or reactive) power, and the joint active-reactive power domain. Accordingly, two power aggregation models with two-stage optimization are developed: one focuses on aggregating active power and computes its optimal feasible intervals over multiple periods, and the other solves the optimal elliptical feasible regions for the aggregate active-reactive power. By leveraging the ARO technique, the disaggregation feasibility of the obtained feasible regions is guaranteed with optimality. Numerical simulations on a real-world distribution feeder with 126 multi-phase nodes demonstrate the effectiveness of the proposed method.
Privacy-Aware Load Ensemble Control: A Linearly-Solvable MDP Approach
Authors: Ali Hassan, Deepjyoti Deka, Yury Dvorkin
Date: 2021-03-19 00:00:00
Tasks: Privacy Preserving

Demand response (DR) programs engage distributed demand-side resources, e.g., controllable residential and commercial loads, in providing ancillary services for electric power systems. Ensembles of these resources can help reducing system load peaks and meeting operational limits by adjusting their electric power consumption. To equip utilities or load aggregators with adequate decision-support tools for ensemble dispatch, we develop a Markov Decision Process (MDP) approach to optimally control load ensembles in a privacy-preserving manner. To this end, the concept of differential privacy is internalized into the MDP routine to protect transition probabilities and, thus, privacy of DR participants. The proposed approach also provides a trade-off between solution optimality and privacy guarantees, and is analyzed using real-world data from DR events in the New York University microgrid in New York, NY.
Joint RFI Mitigation and Radar Echo Recovery for One-Bit UWB Radar
Authors: Tianyi Zhang, Jiaying Ren, Jian Li, Lam H. Nguyen, Petre Stoica
Date: 2021-03-19 00:00:00
Tasks: parameter estimation

Radio frequency interference (RFI) mitigation and radar echo recovery are critically important for the proper functioning of ultra-wideband (UWB) radar systems using one-bit sampling techniques. We recently introduced a technique for one-bit UWB radar, which first uses a majorization-minimization method for RFI parameter estimation followed by a sparse method for radar echo recovery. However, this technique suffers from high computational complexity due to the need to estimate the parameters of each RFI source separately and iteratively. In this paper, we present a computationally efficient joint RFI mitigation and radar echo recovery framework to greatly reduce the computational cost. Specifically, we exploit the sparsity of RFI in the fast-frequency domain and the sparsity of radar echoes in the fast-time domain to design a one-bit weighted SPICE (SParse Iterative Covariance-based Estimation) based framework for the joint RFI mitigation and radar echo recovery of one-bit UWB radar. Both simulated and experimental results are presented to show that the proposed one-bit weighted SPICE framework can not only reduce the computational cost but also outperform the existing approach for decoupled RFI mitigation and radar echo recovery of one-bit UWB radar.
Channel Estimation for RIS-assisted Millimeter-wave MIMO Systems
Authors: Battu Deepak, R. S. Prasobh Sankar, Sundeep Prabhakar Chepuri
Date: 2020-11-02 00:00:00

Channel estimation in reconfigurable intelligent surface (RIS) assisted multiple input multiple output (MIMO) communication systems is challenging as pilots cannot be decoded at or transmitted from a RIS with only passive elements. We assume an angular model for the line-of-sight channel between the user equipment (UE) and the RIS and between the RIS and the base station (BS) with no direct path between the UE and BS. In this letter, assuming that we can compute the RIS-BS channel (up to a complex path gain) from their known locations, we first discuss the ambiguity involved in estimating the UE-RIS channel. We propose a multiple channel sounding procedure in which we observe the channel through different RIS phase shifts and present an algorithm for channel estimation to resolve this ambiguity. Through simulations, we show that the proposed method is comparable to an oracle estimator, which perfectly knows the BS, UE, and RIS locations.
Non-causal regularized least-squares for continuous-time system identification with band-limited input excitations
Authors: Rodrigo A. González, Cristian R. Rojas, Håkan Hjalmarsson
Date: 2021-03-19 00:00:00

In continuous-time system identification, the intersample behavior of the input signal is known to play a crucial role in the performance of estimation methods. One common input behavior assumption is that the spectrum of the input is band-limited. The sinc interpolation property of these input signals yields equivalent discrete-time representations that are non-causal. This observation, often overlooked in the literature, is exploited in this work to study non-parametric frequency response estimators of linear continuous-time systems. We study the properties of non-causal least-square estimators for continuous-time system identification, and propose a kernel-based non-causal regularized least-squares approach for estimating the band-limited equivalent impulse response. The proposed methods are tested via extensive numerical simulations.
2nd edition of webinar on Nanotechnology-Speakers-Yu-Hao Deng
Authors: 2nd edition of webinar on Nanotechnology
Date: 2021-03-09 00:00:00

Title: Analysis of misidentifications in TEM characterization of organic-inorganic hybrid perovskite material 
Yu-Hao Deng, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
Yu-Hao Deng
Academy for Advanced Interdisciplinary Studies, Peking University, Beijing - China
Dr. Yu-Hao Deng obtained his PhD (Condensed matter physics) from Peking University. His research interests are mainly focused on perovskite material growth, optoelectronic devices and characterization techniques (TEM). He has published various papers in Advanced Materials, Physical review letters, Journal of Microscopy and Applied Microscopy et al. 
https://nano.endeavorresearchgroup.com/scientific-program.php