pwc-archive/papers-with-abstracts huggingface dataset:
SurfelWarp: Efficient Non-Volumetric Single View Dynamic Reconstruction
Authors: Wei Gao, Russ Tedrake
Date: 2019-04-30 00:00:00
Tasks: Dynamic Reconstruction, GPU

We contribute a dense SLAM system that takes a live stream of depth images as
input and reconstructs non-rigid deforming scenes in real time, without
templates or prior models. In contrast to existing approaches, we do not
maintain any volumetric data structures, such as truncated signed distance
function (TSDF) fields or deformation fields, which are performance and memory
intensive. Our system works with a flat point (surfel) based representation of
geometry, which can be directly acquired from commodity depth sensors. Standard
graphics pipelines and general purpose GPU (GPGPU) computing are leveraged for
all central operations: i.e., nearest neighbor maintenance, non-rigid
deformation field estimation and fusion of depth measurements. Our pipeline
inherently avoids expensive volumetric operations such as marching cubes,
volumetric fusion and dense deformation field update, leading to significantly
improved performance. Furthermore, the explicit and flexible surfel based
geometry representation enables efficient tackling of topology changes and
tracking failures, which makes our reconstructions consistent with updated
depth observations. Our system allows robots to maintain a scene description
with non-rigidly deformed objects that potentially enables interactions with
dynamic working environments.
Cross-Modal Message Passing for Two-stream Fusion
Authors: Dong Wang, Yuan Yuan, Qi. Wang
Date: 2019-04-30 00:00:00
Tasks: Action Recognition, General Classification, Optical Flow Estimation, Temporal Action Localization, Vocal Bursts Valence Prediction

Processing and fusing information among multi-modal is a very useful
technique for achieving high performance in many computer vision problems. In
order to tackle multi-modal information more effectively, we introduce a novel
framework for multi-modal fusion: Cross-modal Message Passing (CMMP).
Specifically, we propose a cross-modal message passing mechanism to fuse
two-stream network for action recognition, which composes of an appearance
modal network (RGB image) and a motion modal (optical flow image) network. The
objectives of individual networks in this framework are two-fold: a standard
classification objective and a competing objective. The classification object
ensures that each modal network predicts the true action category while the
competing objective encourages each modal network to outperform the other one.
We quantitatively show that the proposed CMMP fuses the traditional two-stream
network more effectively, and outperforms all existing two-stream fusion method
on UCF-101 and HMDB-51 datasets.
Minimal model of permutation symmetry in unsupervised learning
Authors: Tianqi Hou, K. Y. Michael Wong, Haiping Huang
Date: 2019-04-30 00:00:00
Tasks: model

Permutation of any two hidden units yields invariant properties in typical deep generative neural networks. This permutation symmetry plays an important role in understanding the computation performance of a broad class of neural networks with two or more hidden units. However, a theoretical study of the permutation symmetry is still lacking. Here, we propose a minimal model with only two hidden units in a restricted Boltzmann machine, which aims to address how the permutation symmetry affects the critical learning data size at which the concept-formation (or spontaneous symmetry breaking in physics language) starts, and moreover semi-rigorously prove a conjecture that the critical data size is independent of the number of hidden units once this number is finite. Remarkably, we find that the embedded correlation between two receptive fields of hidden units reduces the critical data size. In particular, the weakly-correlated receptive fields have the benefit of significantly reducing the minimal data size that triggers the transition, given less noisy data. Inspired by the theory, we also propose an efficient fully-distributed algorithm to infer the receptive fields of hidden units. Furthermore, our minimal model reveals that the permutation symmetry can also be spontaneously broken following the spontaneous symmetry breaking. Overall, our results demonstrate that the unsupervised learning is a progressive combination of spontaneous symmetry breaking and permutation symmetry breaking which are both spontaneous processes driven by data streams (observations). All these effects can be analytically probed based on the minimal model, providing theoretical insights towards understanding unsupervised learning in a more general context.
Synthesis of Biologically Realistic Human Motion Using Joint Torque Actuation
Authors: Yifeng Jiang, Tom Van Wouwe, Friedl De Groote, C. Karen Liu
Date: 2019-04-30 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning

Using joint actuators to drive the skeletal movements is a common practice in character animation, but the resultant torque patterns are often unnatural or infeasible for real humans to achieve. On the other hand, physiologically-based models explicitly simulate muscles and tendons and thus produce more human-like movements and torque patterns. This paper introduces a technique to transform an optimal control problem formulated in the muscle-actuation space to an equivalent problem in the joint-actuation space, such that the solutions to both problems have the same optimal value. By solving the equivalent problem in the joint-actuation space, we can generate human-like motions comparable to those generated by musculotendon models, while retaining the benefit of simple modeling and fast computation offered by joint-actuation models. Our method transforms constant bounds on muscle activations to nonlinear, state-dependent torque limits in the joint-actuation space. In addition, the metabolic energy function on muscle activations is transformed to a nonlinear function of joint torques, joint configuration and joint velocity. Our technique can also benefit policy optimization using deep reinforcement learning approach, by providing a more anatomically realistic action space for the agent to explore during the learning process. We take the advantage of the physiologically-based simulator, OpenSim, to provide training data for learning the torque limits and the metabolic energy function. Once trained, the same torque limits and the energy function can be applied to drastically different motor tasks formulated as either trajectory optimization or policy learning. Codebase: https://github.com/jyf588/lrle and https://github.com/jyf588/lrle-rl-examples
Wearable Travel Aid for Environment Perception and Navigation of Visually Impaired People
Authors: Jinqiang Bai, Zhaoxiang Liu, Yimin Lin, Ye Li, Shiguo Lian, Dijun Liu
Date: 2019-04-30 00:00:00
Tasks: Navigate, Object, object-detection, Object Detection

This paper presents a wearable assistive device with the shape of a pair of
eyeglasses that allows visually impaired people to navigate safely and quickly
in unfamiliar environment, as well as perceive the complicated environment to
automatically make decisions on the direction to move. The device uses a
consumer Red, Green, Blue and Depth (RGB-D) camera and an Inertial Measurement
Unit (IMU) to detect obstacles. As the device leverages the ground height
continuity among adjacent image frames, it is able to segment the ground from
obstacles accurately and rapidly. Based on the detected ground, the optimal
walkable direction is computed and the user is then informed via converted beep
sound. Moreover, by utilizing deep learning techniques, the device can
semantically categorize the detected obstacles to improve the users' perception
of surroundings. It combines a Convolutional Neural Network (CNN) deployed on a
smartphone with a depth-image-based object detection to decide what the object
type is and where the object is located, and then notifies the user of such
information via speech. We evaluated the device's performance with different
experiments in which 20 visually impaired people were asked to wear the device
and move in an office, and found that they were able to avoid obstacle
collisions and find the way in complicated scenarios.
Optimal Clustering Framework for Hyperspectral Band Selection
Authors: Qi. Wang, Fahong Zhang, Xuelong. Li
Date: 2019-04-30 00:00:00
Tasks: Clustering

Band selection, by choosing a set of representative bands in hyperspectral
image (HSI), is an effective method to reduce the redundant information without
compromising the original contents. Recently, various unsupervised band
selection methods have been proposed, but most of them are based on
approximation algorithms which can only obtain suboptimal solutions toward a
specific objective function. This paper focuses on clustering-based band
selection, and proposes a new framework to solve the above dilemma, claiming
the following contributions: 1) An optimal clustering framework (OCF), which
can obtain the optimal clustering result for a particular form of objective
function under a reasonable constraint. 2) A rank on clusters strategy (RCS),
which provides an effective criterion to select bands on existing clustering
structure. 3) An automatic method to determine the number of the required
bands, which can better evaluate the distinctive information produced by
certain number of bands. In experiments, the proposed algorithm is compared to
some state-of-the-art competitors. According to the experimental results, the
proposed algorithm is robust and significantly outperform the other methods on
various data sets.
Deep Learning Based Robot for Automatically Picking up Garbage on the Grass
Authors: Jinqiang Bai, Shiguo Lian, Zhaoxiang Liu, Kai Wang, Dijun Liu
Date: 2019-04-30 00:00:00

This paper presents a novel garbage pickup robot which operates on the grass.
The robot is able to detect the garbage accurately and autonomously by using a
deep neural network for garbage recognition. In addition, with the ground
segmentation using a deep neural network, a novel navigation strategy is
proposed to guide the robot to move around. With the garbage recognition and
automatic navigation functions, the robot can clean garbage on the ground in
places like parks or schools efficiently and autonomously. Experimental results
show that the garbage recognition accuracy can reach as high as 95%, and even
without path planning, the navigation strategy can reach almost the same
cleaning efficiency with traditional methods. Thus, the proposed robot can
serve as a good assistance to relieve dustman's physical labor on garbage
cleaning tasks.
Collaborative Filtering via High-Dimensional Regression
Authors: Harald Steck
Date: 2019-04-30 00:00:00
Tasks: Collaborative Filtering, regression, Vocal Bursts Intensity Prediction

While the SLIM approach obtained high ranking-accuracy in many experiments in
the literature, it is also known for its high computational cost of learning
its parameters from data. For this reason, we focus in this paper on variants
of high-dimensional regression problems that have closed-form solutions.
Moreover, we motivate a re-scaling rather than a re-weighting approach for
dealing with biases regarding item-popularities in the data. We also discuss
properties of the sparse solution, and outline a computationally efficient
approximation. In experiments on three publicly available data sets, we
observed not only extremely reduced training times, but also significantly
improved ranking accuracy compared to SLIM. Surprisingly, various
state-of-the-art models, including deep non-linear autoencoders, were also
outperformed on two of the three data sets in our experiments, in particular
for recommendations with highly personalized relevance.
A Deep Q-Learning Method for Downlink Power Allocation in Multi-Cell Networks
Authors: Kazi Ishfaq Ahmed, Ekram Hossain
Date: 2019-04-30 00:00:00
Tasks: Benchmarking, Deep Reinforcement Learning, Q-Learning, Reinforcement Learning

Optimal resource allocation is a fundamental challenge for dense and
heterogeneous wireless networks with massive wireless connections. Because of
the non-convex nature of the optimization problem, it is computationally
demanding to obtain the optimal resource allocation. Recently, deep
reinforcement learning (DRL) has emerged as a promising technique in solving
non-convex optimization problems. Unlike deep learning (DL), DRL does not
require any optimal/ near-optimal training dataset which is either unavailable
or computationally expensive in generating synthetic data. In this paper, we
propose a novel centralized DRL based downlink power allocation scheme for a
multi-cell system intending to maximize the total network throughput.
Specifically, we apply a deep Q-learning (DQL) approach to achieve near-optimal
power allocation policy. For benchmarking the proposed approach, we use a
Genetic Algorithm (GA) to obtain near-optimal power allocation solution.
Simulation results show that the proposed DRL-based power allocation scheme
performs better compared to the conventional power allocation schemes in a
multi-cell scenario.
Virtual-Blind-Road Following Based Wearable Navigation Device for Blind People
Authors: Jinqiang Bai, Shiguo Lian, Zhaoxiang Liu, Kai Wang, Dijun Liu
Date: 2019-04-30 00:00:00

To help the blind people walk to the destination efficiently and safely in
indoor environment, a novel wearable navigation device is presented in this
paper. The locating, way-finding, route following and obstacle avoiding modules
are the essential components in a navigation system, while it remains a
challenging task to consider obstacle avoiding during route following, as the
indoor environment is complex, changeable and possibly with dynamic objects. To
address this issue, we propose a novel scheme which utilizes a dynamic sub-goal
selecting strategy to guide the users to the destination and help them bypass
obstacles at the same time. This scheme serves as the key component of a
complete navigation system deployed on a pair of wearable optical see-through
glasses for the ease of use of blind people's daily walks. The proposed
navigation device has been tested on a collection of individuals and proved to
be effective on indoor navigation tasks. The sensors embedded are of low cost,
small volume and easy integration, making it possible for the glasses to be
widely used as a wearable consumer device.
A self-attention based deep learning method for lesion attribute detection from CT reports
Authors: Yifan Peng, Ke Yan, Veit Sandfort, Ronald M. Summers, Zhiyong Lu
Date: 2019-04-30 00:00:00
Tasks: Attribute, Sentence

In radiology, radiologists not only detect lesions from the medical image,
but also describe them with various attributes such as their type, location,
size, shape, and intensity. While these lesion attributes are rich and useful
in many downstream clinical applications, how to extract them from the
radiology reports is less studied. This paper outlines a novel deep learning
method to automatically extract attributes of lesions of interest from the
clinical text. Different from classical CNN models, we integrated the
multi-head self-attention mechanism to handle the long-distance information in
the sentence, and to jointly correlate different portions of sentence
representation subspaces in parallel. Evaluation on an in-house corpus
demonstrates that our method can achieve high performance with 0.848 in
precision, 0.788 in recall, and 0.815 in F-score. The new method and
constructed corpus will enable us to build automatic systems with a
higher-level understanding of the radiological world.
On Stationary-Point Hitting Time and Ergodicity of Stochastic Gradient Langevin Dynamics
Authors: Xi Chen, Simon S. Du, Xin T. Tong
Date: 2019-04-30 00:00:00
Tasks: Stochastic Optimization

Stochastic gradient Langevin dynamics (SGLD) is a fundamental algorithm in stochastic optimization. Recent work by Zhang et al. [2017] presents an analysis for the hitting time of SGLD for the first and second order stationary points. The proof in Zhang et al. [2017] is a two-stage procedure through bounding the Cheeger's constant, which is rather complicated and leads to loose bounds. In this paper, using intuitions from stochastic differential equations, we provide a direct analysis for the hitting times of SGLD to the first and second order stationary points. Our analysis is straightforward. It only relies on basic linear algebra and probability theory tools. Our direct analysis also leads to tighter bounds comparing to Zhang et al. [2017] and shows the explicit dependence of the hitting time on different factors, including dimensionality, smoothness, noise strength, and step size effects. Under suitable conditions, we show that the hitting time of SGLD to first-order stationary points can be dimension-independent. Moreover, we apply our analysis to study several important online estimation problems in machine learning, including linear regression, matrix factorization, and online PCA.
Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluators
Authors: Sanghyun Yi, Rahul Goel, Chandra Khatri, Alessandra Cervone, Tagyoung Chung, Behnam Hedayatnia, Anu Venkatesh, Raefer Gabriel, Dilek Hakkani-Tur
Date: 2019-04-30 00:00:00
Proceeding: WS 2019 10
Tasks: Chatbot, Decoder, Open-Domain Dialog, Reranking, Response Generation

Encoder-decoder based neural architectures serve as the basis of state-of-the-art approaches in end-to-end open domain dialog systems. Since most of such systems are trained with a maximum likelihood~(MLE) objective they suffer from issues such as lack of generalizability and the generic response problem, i.e., a system response that can be an answer to a large number of user utterances, e.g., "Maybe, I don't know." Having explicit feedback on the relevance and interestingness of a system response at each turn can be a useful signal for mitigating such issues and improving system quality by selecting responses from different approaches. Towards this goal, we present a system that evaluates chatbot responses at each dialog turn for coherence and engagement. Our system provides explicit turn-level dialog quality feedback, which we show to be highly correlated with human evaluation. To show that incorporating this feedback in the neural response generation models improves dialog quality, we present two different and complementary mechanisms to incorporate explicit feedback into a neural response generation model: reranking and direct modification of the loss function during training. Our studies show that a response generation model that incorporates these combined feedback mechanisms produce more engaging and coherent responses in an open-domain spoken dialog setting, significantly improving the response quality using both automatic and human evaluation.
Reconstruction of Natural Visual Scenes from Neural Spikes with Deep Neural Networks
Authors: Yichen Zhang, Shanshan Jia, Yajing Zheng, Zhaofei Yu, Yonghong Tian, Siwei Ma, Tiejun Huang, Jian. K. Liu
Date: 2019-04-30 00:00:00
Tasks: Decoder

Neural coding is one of the central questions in systems neuroscience for understanding how the brain processes stimulus from the environment, moreover, it is also a cornerstone for designing algorithms of brain-machine interface, where decoding incoming stimulus is highly demanded for better performance of physical devices. Traditionally researchers have focused on functional magnetic resonance imaging (fMRI) data as the neural signals of interest for decoding visual scenes. However, our visual perception operates in a fast time scale of millisecond in terms of an event termed neural spike. There are few studies of decoding by using spikes. Here we fulfill this aim by developing a novel decoding framework based on deep neural networks, named spike-image decoder (SID), for reconstructing natural visual scenes, including static images and dynamic videos, from experimentally recorded spikes of a population of retinal ganglion cells. The SID is an end-to-end decoder with one end as neural spikes and the other end as images, which can be trained directly such that visual scenes are reconstructed from spikes in a highly accurate fashion. Our SID also outperforms on the reconstruction of visual stimulus compared to existing fMRI decoding models. In addition, with the aid of a spike encoder, we show that SID can be generalized to arbitrary visual scenes by using the image datasets of MNIST, CIFAR10, and CIFAR100. Furthermore, with a pre-trained SID, one can decode any dynamic videos to achieve real-time encoding and decoding of visual scenes by spikes. Altogether, our results shed new light on neuromorphic computing for artificial visual systems, such as event-based visual cameras and visual neuroprostheses.
Anytime Integrated Task and Motion Policies for Stochastic Environments
Authors: Naman Shah, Deepak Kala Vasudevan, Kislay Kumar, Pranav Kamojjhala, Siddharth Srivastava
Date: 2019-04-30 00:00:00
Tasks: Motion Planning, Task and Motion Planning

In order to solve complex, long-horizon tasks, intelligent robots need to carry out high-level, abstract planning and reasoning in conjunction with motion planning. However, abstract models are typically lossy and plans or policies computed using them can be unexecutable. These problems are exacerbated in stochastic situations where the robot needs to reason about, and plan for multiple contingencies. We present a new approach for integrated task and motion planning in stochastic settings. In contrast to prior work in this direction, we show that our approach can effectively compute integrated task and motion policies whose branching structures encoding agent behaviors handling multiple execution-time contingencies. We prove that our algorithm is probabilistically complete and can compute feasible solution policies in an anytime fashion so that the probability of encountering an unresolved contingency decreases over time. Empirical results on a set of challenging problems show the utility and scope of our methods.
Curvature: A signature for Action Recognition in Video Sequences
Authors: He Chen, Gregory S. Chirikjian
Date: 2019-04-30 00:00:00
Tasks: Action Recognition, Few-Shot Learning, Machine Translation, speech-recognition, Speech Recognition, Temporal Action Localization, Text Generation, Translation

In this paper, a novel signature of human action recognition, namely the curvature of a video sequence, is introduced. In this way, the distribution of sequential data is modeled, which enables few-shot learning. Instead of depending on recognizing features within images, our algorithm views actions as sequences on the universal time scale across a whole sequence of images. The video sequence, viewed as a curve in pixel space, is aligned by reparameterization using the arclength of the curve in pixel space. Once such curvatures are obtained, statistical indexes are extracted and fed into a learning-based classifier. Overall, our method is simple but powerful. Preliminary experimental results show that our method is effective and achieves state-of-the-art performance in video-based human action recognition. Moreover, we see latent capacity in transferring this idea into other sequence-based recognition applications such as speech recognition, machine translation, and text generation.