pwc-archive/papers-with-abstracts huggingface dataset:
A Survey on CSI-Based Human Behavior Recognition in Through-the-Wall Scenario
Authors: Zhengjie Wang, Kangkang Jiang, Yushan Hou, Zehua Huang, Wenwen Dou, Chengming Zhang, Yinjing Guo
Date: 2019-06-12 00:00:00
Proceeding: IEEE Access ( Volume: 7 ) 2019 6
Tasks: RF-based Action Recognition, RF-based Pose Estimation

Recent years have witnessed increasing research interest in human behavior recognition as it provides attractive applications in various sensing scenarios. Among these encouraging implementations, device-free behavior recognition based on WiFi channel state information (CSI) has attracted significant attention due to the popularity of WiFi devices and abundant channel characteristics from CSI. Meanwhile, the CSI signal provides us with additional benefits because it can propagate through a wall. This through-the-wall and device-free scheme not only enables us to identify specific human actions but also to infer person activities by collecting the data from different rooms. This paper presents a survey on the state-of-art progresses in device-free through-the-wall human behavior recognition based on CSI. Specifically, this paper first introduces the basic concept of CSI and describes the signal variation caused by human behavior. Then, it illustrates that different human behaviors can cause signal transformation. Therefore, the unique map relationship between action and signal variation can be leveraged to recognize human behavior. Next, it provides the general architecture of through-the-wall behavior recognition and highlights its core characteristic. It investigates the state-of-art applications in various scenarios and analyzes specific design schemes and implementations. Afterward, it discusses the various across wall applications and makes a detailed comparison between non-through-the-wall and through-the-wall applications. Meanwhile, it analyzes many factors that affect recognition accuracy and emphasizes performance differences under across wall scenarios. Finally, this paper concludes by summarizing the issues and challenges faced and providing insights into the possible solution and future research trend.
mID: Tracking and Identifying People with Millimeter Wave Radar
Authors: Peijun Zhao, Chris Xiaoxuan Lu, Jianan Wang, Changhao Chen, Wei Wang, Niki Trigoni, and Andrew Markham
Date: 2019-05-29 00:00:00
Proceeding: 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS) 2019 5
Tasks: RF-based Visual Tracking, Visual Tracking

The key to offering personalised services in smart spaces is knowing where a particular person is with a high degree of accuracy. Visual tracking is one such solution, but concerns arise around the potential leakage of raw video information and many people are not comfortable accepting cameras in their homes or workplaces. We propose a human tracking and identification system (mID) based on millimeter wave radar which has a high tracking accuracy, without being visually compromising. Unlike competing techniques based on WiFi Channel State Information (CSI), it is capable of tracking and identifying multiple people simultaneously. Using a lowcost, commercial, off-the-shelf radar, we first obtain sparse point clouds and form temporally associated trajectories. With the aid of a deep recurrent network, we identify individual users. We evaluate and demonstrate our system across a variety of scenarios, showing median position errors of 0.16 m and identification accuracy of 89% for 12 people.
Through-Wall Object Recognition and Pose Estimation
Authors: Wang, Ruoyu; Xiang, Siyuan; Feng, Chen; Wang, Pu; Ergan, Semiha; Yi Fang
Date: 2019-05-21 00:00:00
Proceeding: ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction 2019 5
Tasks: Material Recognition, Object, Object Recognition, Pose Estimation, RF-based Pose Estimation

Robots need to perceive beyond lines of sight, e.g., to avoid cutting water pipes or electric wires when drilling holes on a wall. Recent off-the-shelf radio frequency (RF) imaging sensors ease the process of 3D sensing inside or through walls. Yet unlike optical images, RF images are difficult to understand by a human. Meanwhile, in practice, RF components are often subject to hardware imperfections, resulting in distorted RF images, whose quality could be far from the claimed specifications. Thus, we introduce several challenging geometric and semantic perception tasks on such signals, including object and material recognition, fine-grained property classification and pose estimation. Since detailed forward modeling of such sensors is sometimes difficult, due to hidden or inaccessible system parameters, onboard processing procedures and limited access to raw RF waveform, we tackled the above tasks by supervised machine learning. We collected a large dataset of RF images of utility objects through a mock wall as the input of our algorithm, and the corresponding optical images were taken from the other side of the wall simultaneously as the ground truth. We designed three learning algorithms based on nearest neighbors or neural networks, and 
report their performances on the dataset. Our experiments showed reasonable results for semantic perception tasks yet unsatisfactory results for geometric ones, calling for more efforts in this research direction.
AI-Enhanced 3D RF Representation Using Low-Cost mmWave Radar
Authors: Shiwei Fang, Shahriar Nirjon
Date: 2018-11-04 00:00:00
Proceeding: SenSys '18 Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems 2018 11
Tasks: RF-based Pose Estimation, Robot Navigation

This paper introduces a system that takes radio frequency (RF) signals from an off-the-shelf, low-cost, 77 GHz mm Wave radar and produces an enhanced 3D RF representation of a scene. Such a system can be used in scenarios where camera and other types of sensors do not work, or their performance is impacted due to bad lighting conditions and occlusions, or an alternate RF sensing system like synthetic aperture radar (SAR) is too large, inconvenient, and costly. The enhanced RF representation can be used in numerous applications such as robot navigation, human-computer interaction, and patient monitoring. We use off-the-shelf parts to capture RF signals and collect our own data set for training and testing of the approach. The novelty of the system lies in its use of AI to generate a fine-grained 3D representation of an RF scene from its sparse RF representation which a mm Wave radar of the same class cannot achieve.
Real Time 3D Indoor Human Image Capturing Based on FMCW Radar
Authors: Hangqing Guo, Nan Zhang, Wenjun Shi, Saeed ALI-AlQarni, Shaoen Wu, Honggang Wang
Date: 2019-08-05 00:00:00
Proceeding: 2019 IEEE International Conference on Multimedia and Expo (ICME) 2019 8
Tasks: RF-based Pose Estimation

Compared to traditional camera-based computer vision and imaging, radio imaging based on wireless sensing does not require lighting and is friendly to privacy. This work proposes a deep learning radio imaging solution to visualize real-time user indoor activities. The proposed solution uses a low-power, MIMO Frequency Modulated Continuous Wave (FMCW) radar array to capture the reflected signals from human objects, and then constructs 3D human visualization through a serials of data analytics including: 1) a data preprocessing mechanism to remove background static reflection, 2) a signal processing mechanism to transfer received complex radar signals to a matrix containing spatial information, and 3) a deep learning scheme to filter abnormal frames resulted from rough surface of human body. This solution has been extensively evaluated in an indoor research lab. The constructed real-time human images are compared to the camera images captured at the same time. The results show that the proposed radio imaging solution can result in significantly high accuracy.

IEEE publication: "Real-Time Indoor 3D Human Imaging Based on MIMO Radar Sensing"
https://doi.org/10.1109/ICME.2019.00244
Segmented convolutional gated recurrent neural networks for human activity recognition in ultra-wideband radar
Authors: Hao Du, Tian Jin, Yuan He, Yongping Song, Yongpeng Dai
Date: 2019-04-27 00:00:00
Proceeding: Neurocomputing 2019 4
Tasks: Activity Recognition, Human Activity Recognition, RF-based Action Recognition, RF-based Pose Estimation

The automatic detection and recognition of human activities are valuable for physical security, gaming, and intelligent interface. Compared to an optical recognition system, radar is more robust to variations in lighting conditions and occlusions. The centimeter-wave ultra-wideband radar can even track human motion when the target is fully occluded from it. In this work, we propose a neural network architecture, namely segmented convolutional gated recurrent neural network (SCGRNN), to recognize human activities based on micro-Doppler spectrograms measured by the ultra-wideband radar. Unlike most existing approaches which treat the micro-Doppler spectrograms the same way as natural images, we extract segmented features of spectrograms via convolution operation and encode the feature maps along the time axis with gated recurrent units. Taking advantage of regularities in both the time and Doppler frequency domains in this way, our model can detect activities with arbitrary lengths. The experiments show that our method outperforms existing models in fine temporal resolution, noise robustness, and generalization performance. The radar system can thus recognize human behavior when visible light is blocked by opaque objects.

Keywords:
Micro-Doppler spectrograms, Human activity recognition, Deep learning, Convolutional neural network, Recurrent neural network
RF-based 3D skeletons
Authors: Ming-Min Zhao, Yonglong Tian, Hang Zhao, Mohammad Abu Alsheikh, Tianhong Li, Rumen Hristov, Zachary Kabelac, Dina Katabi, Antonio Torralba
Date: 2018-08-20 00:00:00
Proceeding: SIGCOMM '18 Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication 2018 8
Tasks: RF-based Pose Estimation

This paper introduces RF-Pose3D, the first system that infers 3D human skeletons from RF signals. It requires no sensors on the body, and works with multiple people and across walls and occlusions. Further, it generates dynamic skeletons that follow the people as they move, walk or sit. As such, RF-Pose3D provides a significant leap in RF-based sensing and enables new applications in gaming, healthcare, and smart homes.

RF-Pose3D is based on a novel convolutional neural network (CNN) architecture that performs high-dimensional convolutions by decomposing them into low-dimensional operations. This property allows the network to efficiently condense the spatio-temporal information in RF signals. The network first zooms in on the individuals in the scene, and crops the RF signals reflected off each person. For each individual, it localizes and tracks their body parts - head, shoulders, arms, wrists, hip, knees, and feet. Our evaluation results show that RF-Pose3D tracks each keypoint on the human body with an average error of 4.2 cm, 4.0 cm, and 4.9 cm along the X, Y, and Z axes respectively. It maintains this accuracy even in the presence of multiple people, and in new environments that it has not seen in the training set. Demo videos are available at our website: http://rfpose3d.csail.mit.edu.
Handling missing values: A study of popular imputation packages in R
Authors: Madan Lal Yadav, Basav Roychoudhury
Date: 2018-06-28 00:00:00
Proceeding: journal 2018 6
Tasks: Imputation, Missing Values

In real world data are often plagued by missing values which adversely affects the final outcome of the analysis
based on such data. The missing values can be handled using various techniques like deletion or imputation. Of
late, R has become one of the most preferred platform for carrying out data analysis, and its popularity is
growing further. R provides various packages for handling missing values through imputation. The presence of
multiple packages however, calls for an analysis of their comparative performance and examine their suitability
for handling a given set of data. The performance of different R packages may differ for different datasets and
may depend on the size of the dataset and richness of the missing values in the datasets. In this paper, the authors
perform comparative study of the performance of the common R packages, namely VIM, MICE, MissForest, and
HMISC, used for missing value imputation. The authors measured the performances of the said packages in terms
of their imputation time, imputation efficiency and the effect on the variance. The imputation efficiency was
measured in terms of the difference in predictive performance of a model built using original dataset vis-à-vis a
dataset with imputed values. Similarly, the variance of the variables in the original dataset was compared that of
corresponding variables in the imputed dataset. A missing value imputation package can be considered to be
better if it consumes less imputation time and provides high imputation accuracy. Also in terms of variance, one
would like to have the imputation package maintain the original variance of the variables. On analysing the four
imputation packages on two datasets over three predictive algorithms–Logistic Regression, Support Vector
Machines, and Artificial Neural Networks–it was observed that the performances varies depending on the size of
the dataset, and the missing values present in them. The study highlights that certain missing value package used
in conjunction with a given predictive algorithm provides better performance, which is again a function of the
dataset characteristics
Inferring species interactions using Granger causality and convergent cross mapping
Authors: Frederic Barraquand, Coralie Picoche, Matteo Detto, Florian Hartig
Date: 2019-09-02 00:00:00
Tasks: Time Series, Time Series Analysis, valid

Identifying directed interactions between species from time series of their population densities has many uses in ecology. This key statistical task is equivalent to causal time series inference, which connects to the Granger causality (GC) concept: $x$ causes $y$ if $x$ improves the prediction of $y$ in a dynamic model. However, the entangled nature of nonlinear ecological systems has led to question the appropriateness of Granger causality, especially in its classical linear Multivariate AutoRegressive (MAR) model form. Convergent-cross mapping (CCM), developed for deterministic dynamical systems, has been suggested as an alternative, although less grounded in statistical theory. Here, we show that linear GC and CCM are able to uncover interactions with surprisingly similar performance, for predator-prey cycles, 2-species deterministic (chaotic) or stochastic competition, as well 10- and 20-species interaction networks. There is no correspondence between the degree of nonlinearity of the dynamics and which method performs best. Our results therefore imply that Granger causality, even in its linear MAR($p$) formulation, is a valid method for inferring interactions in nonlinear ecological networks; using GC or CCM (or both) can instead be decided based on the aims and specifics of the analysis.
The Differentiable Cross-Entropy Method
Authors: Brandon Amos, Denis Yarats
Date: 2019-09-27 00:00:00
Proceeding: ICML 2020 1
Tasks: BIG-bench Machine Learning, continuous-control, Continuous Control, Structured Prediction

We study the cross-entropy method (CEM) for the non-convex optimization of a continuous and parameterized objective function and introduce a differentiable variant that enables us to differentiate the output of CEM with respect to the objective function's parameters. In the machine learning setting this brings CEM inside of the end-to-end learning pipeline where this has otherwise been impossible. We show applications in a synthetic energy-based structured prediction task and in non-convex continuous control. In the control setting we show how to embed optimal action sequences into a lower-dimensional space. DCEM enables us to fine-tune CEM-based controllers with policy optimization.
Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop
Authors: Nikos Kolotouros, Georgios Pavlakos, Michael J. Black, Kostas Daniilidis
Date: 2019-09-27 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D Human Pose Estimation, 3D Human Shape Estimation, 3D Multi-Person Pose Estimation, Pose Estimation

Model-based human pose estimation is currently approached through two different paradigms. Optimization-based methods fit a parametric body model to 2D observations in an iterative manner, leading to accurate image-model alignments, but are often slow and sensitive to the initialization. In contrast, regression-based methods, that use a deep network to directly estimate the model parameters from pixels, tend to provide reasonable, but not pixel accurate, results while requiring huge amounts of supervision. In this work, instead of investigating which approach is better, our key insight is that the two paradigms can form a strong collaboration. A reasonable, directly regressed estimate from the network can initialize the iterative optimization making the fitting faster and more accurate. Similarly, a pixel accurate fit from iterative optimization can act as strong supervision for the network. This is the core of our proposed approach SPIN (SMPL oPtimization IN the loop). The deep network initializes an iterative optimization routine that fits the body model to 2D joints within the training loop, and the fitted estimate is subsequently used to supervise the network. Our approach is self-improving by nature, since better network estimates can lead the optimization to better solutions, while more accurate optimization fits provide better supervision for the network. We demonstrate the effectiveness of our approach in different settings, where 3D ground truth is scarce, or not available, and we consistently outperform the state-of-the-art model-based pose estimation approaches by significant margins. The project website with videos, results, and code can be found at https://seas.upenn.edu/~nkolot/projects/spin.
A Generalized Training Approach for Multiagent Learning
Authors: Paul Muller, Shayegan Omidshafiei, Mark Rowland, Karl Tuyls, Julien Perolat, Si-Qi Liu, Daniel Hennes, Luke Marris, Marc Lanctot, Edward Hughes, Zhe Wang, Guy Lever, Nicolas Heess, Thore Graepel, Remi Munos
Date: 2019-09-27 00:00:00
Proceeding: ICLR 2020 1
Tasks: MuJoCo

This paper investigates a population-based training regime based on game-theoretic principles called Policy-Spaced Response Oracles (PSRO). PSRO is general in the sense that it (1) encompasses well-known algorithms such as fictitious play and double oracle as special cases, and (2) in principle applies to general-sum, many-player games. Despite this, prior studies of PSRO have been focused on two-player zero-sum games, a regime wherein Nash equilibria are tractably computable. In moving from two-player zero-sum games to more general settings, computation of Nash equilibria quickly becomes infeasible. Here, we extend the theoretical underpinnings of PSRO by considering an alternative solution concept, $\alpha$-Rank, which is unique (thus faces no equilibrium selection issues, unlike Nash) and applies readily to general-sum, many-player settings. We establish convergence guarantees in several games classes, and identify links between Nash equilibria and $\alpha$-Rank. We demonstrate the competitive performance of $\alpha$-Rank-based PSRO against an exact Nash solver-based PSRO in 2-player Kuhn and Leduc Poker. We then go beyond the reach of prior PSRO applications by considering 3- to 5-player poker games, yielding instances where $\alpha$-Rank achieves faster convergence than approximate Nash solvers, thus establishing it as a favorable general games solver. We also carry out an initial empirical validation in MuJoCo soccer, illustrating the feasibility of the proposed approach in another complex domain.
Hamiltonian Graph Networks with ODE Integrators
Authors: Alvaro Sanchez-Gonzalez, Victor Bapst, Kyle Cranmer, Peter Battaglia
Date: 2019-09-27 00:00:00
Tasks: Zero-shot Generalization

We introduce an approach for imposing physically informed inductive biases in learned simulation models. We combine graph networks with a differentiable ordinary differential equation integrator as a mechanism for predicting future states, and a Hamiltonian as an internal representation. We find that our approach outperforms baselines without these biases in terms of predictive accuracy, energy accuracy, and zero-shot generalization to time-step sizes and integrator orders not experienced during training. This advances the state-of-the-art of learned simulation, and in principle is applicable beyond physical domains.
Stock Market Forecasting Based on Text Mining Technology: A Support Vector Machine Method
Authors: Yancong Xie, Hongxun Jiang
Date: 2019-09-27 00:00:00
Tasks: Sentiment Analysis

News items have a significant impact on stock markets but the ways are obscure. Many previous works have aimed at finding accurate stock market forecasting models. In this paper, we use text mining and sentiment analysis on Chinese online financial news, to predict Chinese stock tendency and stock prices based on support vector machine (SVM). Firstly, we collect 2,302,692 news items, which date from 1/1/2008 to 1/1/2015. Secondly, based on this dataset, a specific domain stop-word dictionary and a precise sentiment dictionary are formed. Thirdly, we propose a forecasting model using SVM. On the algorithm of SVM implementation, we also propose two-parameter optimization algorithms to search for the best initial parameter setting. The result shows that parameter G has the main effect, while parameter C's effect is not obvious. Furthermore, support vector regression (SVR) models for different Chinese stocks are similar whereas in support vector classification (SVC) models best parameters are quite differential. Series of contrast experiments show that: a) News has significant influence on stock market; b) Expansion input vector for additional situations when that day has no news data is better than normal input in SVR, yet is worse in SVC; c) SVR shows a fantastic degree of fitting in predicting stock fluctuation while such result has some time lag; d) News effect time lag for stock market is less than two days; e) In SVC, historic stock data has a most efficient time lag which is about 10 days, whereas in SVR this effect is not obvious. Besides, based on the special structure of the input vector, we also design a method to calculate the financial source impact factor. Result suggests that the news quality and audience number both have a significant effect on the source impact factor. Besides, for Chinese investors, traditional media has more influence than digital media.