pwc-archive/papers-with-abstracts huggingface dataset:
Multiple Patients Behavior Detection in Real-time using mmWave Radar and Deep CNNs
Authors: Feng Jin, Renyuan Zhang, Arindam Sengupta, Siyang Cao, Salim Hariri, Nimit K. Agarwal, Sumit K. Agarwal
Date: 2019-11-14 00:00:00
Tasks: GPU

To address potential gaps noted in patient monitoring in the hospital, a novel patient behavior detection system using mmWave radar and deep convolution neural network (CNN), which supports the simultaneous recognition of multiple patients' behaviors in real-time, is proposed. In this study, we use an mmWave radar to track multiple patients and detect the scattering point cloud of each one. For each patient, the Doppler pattern of the point cloud over a time period is collected as the behavior signature. A three-layer CNN model is created to classify the behavior for each patient. The tracking and point clouds detection algorithm was also implemented on an mmWave radar hardware platform with an embedded graphics processing unit (GPU) board to collect Doppler pattern and run the CNN model. A training dataset of six types of behavior were collected, over a long duration, to train the model using Adam optimizer with an objective to minimize cross-entropy loss function. Lastly, the system was tested for real-time operation and obtained a very good inference accuracy when predicting each patient's behavior in a two-patient scenario.
MmWave Radar Point Cloud Segmentation using GMM in Multimodal Traffic Monitoring
Authors: Feng Jin, Arindam Sengupta, Siyang Cao, Yao-Jan Wu
Date: 2019-11-14 00:00:00
Tasks: General Classification, Point Cloud Segmentation

In multimodal traffic monitoring, we gather traffic statistics for distinct transportation modes, such as pedestrians, cars and bicycles, in order to analyze and improve people's daily mobility in terms of safety and convenience. On account of its robustness to bad light and adverse weather conditions, and inherent speed measurement ability, the radar sensor is a suitable option for this application. However, the sparse radar data from conventional commercial radars make it extremely challenging for transportation mode classification. Thus, we propose to use a high-resolution millimeter-wave(mmWave) radar sensor to obtain a relatively richer radar point cloud representation for a traffic monitoring scenario. Based on a new feature vector, we use the multivariate Gaussian mixture model (GMM) to do the radar point cloud segmentation, i.e. `point-wise' classification, in an unsupervised learning environment. In our experiment, we collected radar point clouds for pedestrians and cars, which also contained the inevitable clutter from the surroundings. The experimental results using GMM on the new feature vector demonstrated a good segmentation performance in terms of the intersection-over-union (IoU) metrics. The detailed methodology and validation metrics are presented and discussed.
Dimensionality Reduction of Movement Primitives in Parameter Space
Authors: Samuele Tosatto, Jonas Stadtmueller, Jan Peters
Date: 2020-02-26 00:00:00
Tasks: Dimensionality Reduction

Movement primitives are an important policy class for real-world robotics. However, the high dimensionality of their parametrization makes the policy optimization expensive both in terms of samples and computation. Enabling an efficient representation of movement primitives facilitates the application of machine learning techniques such as reinforcement on robotics. Motions, especially in highly redundant kinematic structures, exhibit high correlation in the configuration space. For these reasons, prior work has mainly focused on the application of dimensionality reduction techniques in the configuration space. In this paper, we investigate the application of dimensionality reduction in the parameter space, identifying principal movements. The resulting approach is enriched with a probabilistic treatment of the parameters, inheriting all the properties of the Probabilistic Movement Primitives. We test the proposed technique both on a real robotic task and on a database of complex human movements. The empirical analysis shows that the dimensionality reduction in parameter space is more effective than in configuration space, as it enables the representation of the movements with a significant reduction of parameters.
Scalable Multi-Task Imitation Learning with Autonomous Improvement
Authors: Avi Singh, Eric Jang, Alexander Irpan, Daniel Kappler, Murtaza Dalal, Sergey Levine, Mohi Khansari, Chelsea Finn
Date: 2020-02-25 00:00:00
Tasks: Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

While robot learning has demonstrated promising results for enabling robots to automatically acquire new skills, a critical challenge in deploying learning-based systems is scale: acquiring enough data for the robot to effectively generalize broadly. Imitation learning, in particular, has remained a stable and powerful approach for robot learning, but critically relies on expert operators for data collection. In this work, we target this challenge, aiming to build an imitation learning system that can continuously improve through autonomous data collection, while simultaneously avoiding the explicit use of reinforcement learning, to maintain the stability, simplicity, and scalability of supervised imitation. To accomplish this, we cast the problem of imitation with autonomous improvement into a multi-task setting. We utilize the insight that, in a multi-task setting, a failed attempt at one task might represent a successful attempt at another task. This allows us to leverage the robot's own trials as demonstrations for tasks other than the one that the robot actually attempted. Using an initial dataset of multi-task demonstration data, the robot autonomously collects trials which are only sparsely labeled with a binary indication of whether the trial accomplished any useful task or not. We then embed the trials into a learned latent space of tasks, trained using only the initial demonstration dataset, to draw similarities between various trials, enabling the robot to achieve one-shot generalization to new tasks. In contrast to prior imitation learning approaches, our method can autonomously collect data with sparse supervision for continuous improvement, and in contrast to reinforcement learning algorithms, our method can effectively improve from sparse, task-agnostic reward signals.
Metric-Based Imitation Learning Between Two Dissimilar Anthropomorphic Robotic Arms
Authors: Marcus Ebner von Eschenbach, Binyamin Manela, Jan Peters, Armin Biess
Date: 2020-02-25 00:00:00
Tasks: Deep Reinforcement Learning, Imitation Learning, Reinforcement Learning, Vocal Bursts Valence Prediction

The development of autonomous robotic systems that can learn from human demonstrations to imitate a desired behavior - rather than being manually programmed - has huge technological potential. One major challenge in imitation learning is the correspondence problem: how to establish corresponding states and actions between expert and learner, when the embodiments of the agents are different (morphology, dynamics, degrees of freedom, etc.). Many existing approaches in imitation learning circumvent the correspondence problem, for example, kinesthetic teaching or teleoperation, which are performed on the robot. In this work we explicitly address the correspondence problem by introducing a distance measure between dissimilar embodiments. This measure is then used as a loss function for static pose imitation and as a feedback signal within a model-free deep reinforcement learning framework for dynamic movement imitation between two anthropomorphic robotic arms in simulation. We find that the measure is well suited for describing the similarity between embodiments and for learning imitation policies by distance minimization.
Dimensionality Reduction and Motion Clustering during Activities of Daily Living: 3, 4, and 7 Degree-of-Freedom Arm Movements
Authors: Yuri Gloumakov, Adam J. Spiers, Aaron M. Dollar
Date: 2020-02-17 00:00:00
Tasks: Clustering, Dimensionality Reduction, Dynamic Time Warping

The wide variety of motions performed by the human arm during daily tasks makes it desirable to find representative subsets to reduce the dimensionality of these movements for a variety of applications, including the design and control of robotic and prosthetic devices. This paper presents a novel method and the results of an extensive human subjects study to obtain representative arm joint angle trajectories that span naturalistic motions during Activities of Daily Living (ADLs). In particular, we seek to identify sets of useful motion trajectories of the upper limb that are functions of a single variable, allowing, for instance, an entire prosthetic or robotic arm to be controlled with a single input from a user, along with a means to select between motions for different tasks. Data driven approaches are used to obtain clusters as well as representative motion averages for the full-arm 7 degree of freedom (DOF), elbow-wrist 4 DOF, and wrist-only 3 DOF motions. The proposed method makes use of well-known techniques such as dynamic time warping (DTW) to obtain a divergence measure between motion segments, DTW barycenter averaging (DBA) to obtain averages, Ward's distance criterion to build hierarchical trees, batch-DTW to simultaneously align multiple motion data, and functional principal component analysis (fPCA) to evaluate cluster variability. The clusters that emerge associate various recorded motions into primarily hand start and end location for the full-arm system, motion direction for the wrist-only system, and an intermediate between the two qualities for the elbow-wrist system. The proposed clustering methodology is justified by comparing results against alternative approaches.
A deep learning framework for solution and discovery in solid mechanics
Authors: Ehsan Haghighat, Maziar Raissi, Adrian Moure, Hector Gomez, Ruben Juanes
Date: 2020-02-14 00:00:00
Tasks: Transfer Learning

We present the application of a class of deep learning, known as Physics Informed Neural Networks (PINN), to learning and discovery in solid mechanics. We explain how to incorporate the momentum balance and constitutive relations into PINN, and explore in detail the application to linear elasticity, and illustrate its extension to nonlinear problems through an example that showcases von~Mises elastoplasticity. While common PINN algorithms are based on training one deep neural network (DNN), we propose a multi-network model that results in more accurate representation of the field variables. To validate the model, we test the framework on synthetic data generated from analytical and numerical reference solutions. We study convergence of the PINN model, and show that Isogeometric Analysis (IGA) results in superior accuracy and convergence characteristics compared with classic low-order Finite Element Method (FEM). We also show the applicability of the framework for transfer learning, and find vastly accelerated convergence during network re-training. Finally, we find that honoring the physics leads to improved robustness: when trained only on a few parameters, we find that the PINN model can accurately predict the solution for a wide range of parameters new to the network---thus pointing to an important application of this framework to sensitivity analysis and surrogate modeling.
Whole-Body Control of a Mobile Manipulator using End-to-End Reinforcement Learning
Authors: Julien Kindle, Fadri Furrer, Tonci Novkovic, Jen Jen Chung, Roland Siegwart, Juan Nieto
Date: 2020-02-25 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Mobile manipulation is usually achieved by sequentially executing base and manipulator movements. This simplification, however, leads to a loss in efficiency and in some cases a reduction of workspace size. Even though different methods have been proposed to solve Whole-Body Control (WBC) online, they are either limited by a kinematic model or do not allow for reactive, online obstacle avoidance. In order to overcome these drawbacks, in this work, we propose an end-to-end Reinforcement Learning (RL) approach to WBC. We compared our learned controller against a state-of-the-art sampling-based method in simulation and achieved faster overall mission times. In addition, we validated the learned policy on our mobile manipulator RoyalPanda in challenging narrow corridor environments.
Learning spatiotemporal representations for human fall detection in surveillance video
Authors: Yongqiang Kong, Jianhui Huang, Shanshan Huang, Zhengang Wei, Shengke Wang
Date: 2019-01-12 00:00:00
Proceeding: Journal of Visual Communication and Image Representation 2019 1

In this paper, a computer vision based framework is proposed that detects falls from surveillance videos. Firstly, we employ background subtraction and rank pooling to model spatial and temporal representations in videos, respectively. We then introduce a novel three-stream Convolutional Neural Networks as an event classifier. Silhouettes and their motion history images serve as input to the first two streams, while dynamic images whose temporal duration is equal to motion history images, are used as input to the third stream. Finally, we apply voting on the results of event classification to perform multicamera fall detection. The main novelty of our method against the conventional ones is that highquality spatiotemporal representations in different levels are learned to take full advantage of the appearance and motion information. Extensive experiments have been conducted on two widely used fall datasets. The results have shown to demonstrate the effectiveness of the proposed method.
A Quantitative History of A.I. Research in the United States and China
Authors: Daniel Ish, Andrew Lohn, Christian Curriden
Date: 2020-03-05 00:00:00

Motivated by recent interest in the status and consequences of competition between the U.S. and China in A.I. research, we analyze 60 years of abstract data scraped from Scopus to explore and quantify trends in publications on A.I. topics from institutions affiliated with each country. We find the total volume of publications produced in both countries grows with a remarkable regularity over tens of years. While China initially experienced faster growth in publication volume than the U.S., growth slowed in China when it reached parity with the U.S. and the growth rates of both countries are now similar. We also see both countries undergo a seismic shift in topic choice around 1990, and connect this to an explosion of interest in neural network methods. Finally, we see evidence that between 2000 and 2010, China's topic choice tended to lag that of the U.S. but that in recent decades the topic portfolios have come into closer alignment.
ViCE: Visual Counterfactual Explanations for Machine Learning Models
Authors: Oscar Gomez, Steffen Holter, Jun Yuan, Enrico Bertini
Date: 2020-03-05 00:00:00
Tasks: BIG-bench Machine Learning, counterfactual

The continued improvements in the predictive accuracy of machine learning models have allowed for their widespread practical application. Yet, many decisions made with seemingly accurate models still require verification by domain experts. In addition, end-users of a model also want to understand the reasons behind specific decisions. Thus, the need for interpretability is increasingly paramount. In this paper we present an interactive visual analytics tool, ViCE, that generates counterfactual explanations to contextualize and evaluate model decisions. Each sample is assessed to identify the minimal set of changes needed to flip the model's output. These explanations aim to provide end-users with personalized actionable insights with which to understand, and possibly contest or improve, automated decisions. The results are effectively displayed in a visual interface where counterfactual explanations are highlighted and interactive methods are provided for users to explore the data and model. The functionality of the tool is demonstrated by its application to a home equity line of credit dataset.
Adaptive Neural Connections for Sparsity Learning
Authors: Prakhar Kaushik, Alex Gain, Hava Siegelmann
Date: 2020-03-05 00:00:00
Proceeding: The IEEE Winter Conference on Applications of Computer Vision (WACV), 2020 2020 3
Tasks: Model Compression, Network Pruning, Sparse Learning

Sparsity learning aims to decrease the computational and memory costs of large deep neural networks (DNNs) via pruning neural connections while simultaneously retaining high accuracy. A large body of work has developed sparsity learning approaches, with recent large-scale experiments showing that two main methods, magnitude pruning and Variational Dropout (VD), achieve similar state-of-the-art results for classification tasks. We propose Adaptive Neural Connections (ANC), a method for explicitly parameterizing fine-grained neuron-to-neuron connections via adjacency matrices at each layer that are learned through backpropagation. Explicitly parameterizing neuron-to-neuron connections confers two primary advantages: 1. Sparsity can be explicitly optimized for via norm-based regularization on the adjacency matrices; and 2. When combined with VD (which we term, ANC-VD), the adjacencies can be interpreted as learned weight importance parameters, which we hypothesize leads to improved convergence for VD. Experiments with ResNet18 show that architectures augmented with ANC outperform their vanilla counterparts.
Discovering Volatile Events in Your Neighborhood: Local-Area Topic Extraction from Blog Entries
Authors: Komukai Toshiba-cho, Saiwai-ku, Kawasaki
Date: 2009-10-21 00:00:00
Proceeding: Information Retrieval Technology 2009 10
Tasks: Clustering, Time Series, Time Series Analysis

This paper presents a method for the detection of occasional or volatile
local events using topic extraction technologies. This is a new application of
topic extraction technologies that has not been addressed in general locationbased
services. A two-level hierarchical clustering method was applied to topics
and their transitions using time-series blog entries collected with search queries
including place names. According to experiments using 764 events from 37 locations
in Tokyo and its vicinity, our method achieved 77.0% event findability.
It was found that the number of blog entries in urban areas was sufficient for the
extraction of topics, and the proposed method could extract typical volatile
events, such as performances of music groups, and places of interest, such as
popular restaurants.
Extracting Key Entities and Significant Events from Online Daily News
Authors: Mingrong Liu，Yicen Liu，Liang Xiang.
Date: 2008-11-02 00:00:00
Proceeding: Intelligent Data Engineering and Automated Learning 2008 11
Tasks: Articles, Clustering

Summary: To help people obtain the most important information daily in the shortest time, a novel framework is presented for simultaneous key entities extraction and significant events mining from daily web news. The technique is mainly based on modeling entities and news documents as weighted undirected bipartite graph, which consists of three steps. First, key entities are extracted by scoring all candidate entities on a specific day and tracking their trends within a specific time window. Second, a weighted undirected bipartite graph is built based on entities and related news documents, then mutual reinforcement is imposed on the bipartite graph to rank both of them. Third, clustering on news articles generates daily significant events. Experimental study shows effectiveness of this approach.
metapath2vec: Scalable Representation Learning for Heterogeneous Networks
Authors: Yuxiao Dong, Nitesh Vijay Chawla, Ananthram Swami
Date: 2017-08-01 00:00:00
Proceeding: KDD 17 2017 8
Tasks: Clustering, Link Prediction, Network Embedding, Node Classification, Node Property Prediction, Representation Learning

We study the problem of representation learning in heterogeneous networks. Its unique challenges come from the existence of multiple types of nodes and links, which limit the feasibility of the conventional network embedding techniques. We develop two scalable representation learning models, namely metapath2vec and metapath2vec++. The metapath2vec model formalizes meta-path-based random walks to construct the heterogeneous neighborhood of a node and then leverages a heterogeneous skip-gram model to perform node embeddings. The metapath2vec++ model further enables the simultaneous modeling of structural and semantic correlations in heterogeneous networks. Extensive experiments show that metapath2vec and metapath2vec++ are able to not only outperform state-of-the-art embedding models in various heterogeneous network mining tasks, such as node classification, clustering, and similarity search, but also discern the structural and semantic correlations between diverse network objects.
Non-local Attention Learning on Large Heterogeneous Information Networks
Authors: Yuxin Xiao, Zecheng Zhang, Carl Yang, ChengXiang Zhai
Date: 2019-12-12 00:00:00
Proceeding: 2019 IEEE International Conference on Big Data (Big Data) 2019 12
Tasks: Heterogeneous Node Classification, Representation Learning

Heterogeneous information network (HIN) summarizes rich structural information in real-world datasets and plays an important role in many big data applications. Recently, graph neural networks have been extended to the representation learning of HIN. One very recent advancement is the hierarchical attention mechanism which incorporates both nodewise and semantic-wise attention. However, since HIN is more likely to be densely connected given its diverse types of edges, repeatedly applying graph convolutional layers can make the node embeddings indistinguishable very quickly. In order to avoid oversmoothness, existing graph neural networks targeting HIN generally suffer from a shallow structure. Consequently, those approaches ignore information beyond the local neighborhood. This design flaw violates the concept of non-local learning, which emphasizes the importance of capturing long-range dependencies. To properly address this limitation, we propose a novel framework of non-local attention in heterogeneous information networks (NLAH). Our framework utilizes a non-local attention structure to complement the hierarchical attention mechanism. In this way, it leverages both local and non-local information simultaneously. Moreover, a weighted sampling schema is designed for NLAH to reduce the computation cost for largescale datasets. Extensive experiments on three different realworld heterogeneous information networks illustrate that our framework exhibits extraordinary scalability and outperforms state-of-the-art baselines with significant margins.
Tighter Bound Estimation of Sensitivity Analysis for Incremental and Decremental Data Modification
Authors: Kaichen Zhou, Shiji Song, Gao Huang, Wu Cheng, Quan Zhou
Date: 2020-03-06 00:00:00
Tasks: Incremental Learning, L2 Regularization, Sensitivity

In large-scale classification problems, the data set always be faced with frequent updates when a part of the data is added to or removed from the original data set. In this case, conventional incremental learning, which updates an existing classifier by explicitly modeling the data modification, is more efficient than retraining a new classifier from scratch. However, sometimes, we are more interested in determining whether we should update the classifier or performing some sensitivity analysis tasks. To deal with these such tasks, we propose an algorithm to make rational inferences about the updated linear classifier without exactly updating the classifier. Specifically, the proposed algorithm can be used to estimate the upper and lower bounds of the updated classifier's coefficient matrix with a low computational complexity related to the size of the updated dataset. Both theoretical analysis and experiment results show that the proposed approach is superior to existing methods in terms of tightness of coefficients' bounds and computational complexity.