pwc-archive/papers-with-abstracts huggingface dataset:
Stokes Inversion based on Convolutional Neural Networks
Authors: A. Asensio Ramos, C. Diaz Baso
Date: 2019-04-07 00:00:00

Spectropolarimetric inversions are routinely used in the field of Solar Physics for the extraction of physical information from observations. The application to two-dimensional fields of view often requires the use of supercomputers with parallelized inversion codes. Even in this case, the computing time spent on the process is still very large. Our aim is to develop a new inversion code based on the application of convolutional neural networks that can quickly provide a three-dimensional cube of thermodynamical and magnetic properties from the interpretation of two-dimensional maps of Stokes profiles. We train two different architectures of fully convolutional neural networks. To this end, we use the synthetic Stokes profiles obtained from two snapshots of three-dimensional magneto-hydrodynamic numerical simulations of different structures of the solar atmosphere. We provide an extensive analysis of the new inversion technique, showing that it infers the thermodynamical and magnetic properties with a precision comparable to that of standard inversion techniques. However, it provides several key improvements: our method is around one million times faster, it returns a three-dimensional view of the physical properties of the region of interest in geometrical height, it provides quantities that cannot be obtained otherwise (pressure and Wilson depression) and the inferred properties are decontaminated from the blurring effect of instrumental point spread functions for free. The code is provided for free on a specific repository, with options for training and evaluation.
AI Meets Austen: Towards Human-Robot Discussions of Literary Metaphor
Authors: Natalie Parde, Rodney D. Nielsen
Date: 2019-04-07 00:00:00
Tasks: Lifelong learning

Artificial intelligence is revolutionizing formal education, fueled by
innovations in learning assessment, content generation, and instructional
delivery. Informal, lifelong learning settings have been the subject of less
attention. We provide a proof-of-concept for an embodied book discussion
companion, designed to stimulate conversations with readers about particularly
creative metaphors in fiction literature. We collect ratings from 26
participants, each of whom discuss Jane Austen's "Pride and Prejudice" with the
robot across one or more sessions, and find that participants rate their
interactions highly. This suggests that companion robots could be an
interesting entryway for the promotion of lifelong learning and cognitive
exercise in future applications.
Planar Geometry and Image Recovery from Motion-Blur
Authors: Kuldeep Purohit, Subeesh Vasu, M. Purnachandra Rao, A. N. Rajagopalan
Date: 2019-04-07 00:00:00
Tasks: Deblurring

Existing works on motion deblurring either ignore the effects of depth-dependent blur or work with the assumption of a multi-layered scene wherein each layer is modeled in the form of fronto-parallel plane. In this work, we consider the case of 3D scenes with piecewise planar structure i.e., a scene that can be modeled as a combination of multiple planes with arbitrary orientations. We first propose an approach for estimation of normal of a planar scene from a single motion blurred observation. We then develop an algorithm for automatic recovery of number of planes, the parameters corresponding to each plane, and camera motion from a single motion blurred image of a multiplanar 3D scene. Finally, we propose a first-of-its-kind approach to recover the planar geometry and latent image of the scene by adopting an alternating minimization framework built on our findings. Experiments on synthetic and real data reveal that our proposed method achieves state-of-the-art results.
A Novel Apex-Time Network for Cross-Dataset Micro-Expression Recognition
Authors: Min Peng, Chongyang Wang, Tao Bi, Tong Chen, Xiangdong Zhou, Yu Shi
Date: 2019-04-07 00:00:00
Tasks: Deep Learning, Micro Expression Recognition, Micro-Expression Recognition

The automatic recognition of micro-expression has been boosted ever since the successful introduction of deep learning approaches. As researchers working on such topics are moving to learn from the nature of micro-expression, the practice of using deep learning techniques has evolved from processing the entire video clip of micro-expression to the recognition on apex frame. Using the apex frame is able to get rid of redundant video frames, but the relevant temporal evidence of micro-expression would be thereby left out. This paper proposes a novel Apex-Time Network (ATNet) to recognize micro-expression based on spatial information from the apex frame as well as on temporal information from the respective-adjacent frames. Through extensive experiments on three benchmarks, we demonstrate the improvement achieved by learning such temporal information. Specially, the model with such temporal information is more robust in cross-dataset validations.
On-line and on-board planning and perception for quadrupedal locomotion
Authors: Carlos Mastalli, Ioannis Havoutis, Alexander W. Winkler, Darwin G. Caldwell, Claudio Semini
Date: 2019-04-07 00:00:00
Tasks: Motion Planning

We present a legged motion planning approach for quadrupedal locomotion over
challenging terrain. We decompose the problem into body action planning and
footstep planning. We use a lattice representation together with a set of
defined body movement primitives for computing a body action plan. The lattice
representation allows us to plan versatile movements that ensure feasibility
for every possible plan. To this end, we propose a set of rules that define the
footstep search regions and footstep sequence given a body action. We use
Anytime Repairing A* (ARA*) search that guarantees bounded suboptimal plans.
Our main contribution is a planning approach that generates on-line versatile
movements. Experimental trials demonstrate the performance of our planning
approach in a set of challenging terrain conditions. The terrain information
and plans are computed on-line and on-board.
Unsupervised Domain Adaptation for Multispectral Pedestrian Detection
Authors: Dayan Guan, Xing Luo, Yanpeng Cao, Jiangxin Yang, Yanlong Cao, George Vosselman, Michael Ying Yang
Date: 2019-04-07 00:00:00
Tasks: Autonomous Driving, Domain Adaptation, Pedestrian Detection, Unsupervised Domain Adaptation

Multimodal information (e.g., visible and thermal) can generate robust
pedestrian detections to facilitate around-the-clock computer vision
applications, such as autonomous driving and video surveillance. However, it
still remains a crucial challenge to train a reliable detector working well in
different multispectral pedestrian datasets without manual annotations. In this
paper, we propose a novel unsupervised domain adaptation framework for
multispectral pedestrian detection, by iteratively generating pseudo
annotations and updating the parameters of our designed multispectral
pedestrian detector on target domain. Pseudo annotations are generated using
the detector trained on source domain, and then updated by fixing the
parameters of detector and minimizing the cross entropy loss without
back-propagation. Training labels are generated using the pseudo annotations by
considering the characteristics of similarity and complementarity between
well-aligned visible and infrared image pairs. The parameters of detector are
updated using the generated labels by minimizing our defined multi-detection
loss function with back-propagation. The optimal parameters of detector can be
obtained after iteratively updating the pseudo annotations and parameters.
Experimental results show that our proposed unsupervised multimodal domain
adaptation method achieves significantly higher detection performance than the
approach without domain adaptation, and is competitive with the supervised
multispectral pedestrian detectors.
Proposing a Localized Relevance Vector Machine for Pattern Classification
Authors: Farhood Rismanchian, Karim Rahimian
Date: 2019-04-07 00:00:00
Tasks: Classification, General Classification

Relevance vector machine (RVM) can be seen as a probabilistic version of
support vector machines which is able to produce sparse solutions by linearly
weighting a small number of basis functions instead using all of them.
Regardless of a few merits of RVM such as giving probabilistic predictions and
relax of parameter tuning, it has poor prediction for test instances that are
far away from the relevance vectors. As a solution, we propose a new
combination of RVM and k-nearest neighbor (k-NN) rule which resolves this issue
with regionally dealing with every test instance. In our settings, we obtain
the relevance vectors for each test instance in the local area given by k-NN
rule. In this way, relevance vectors are closer and more relevant to the test
instance which results in a more accurate model. This can be seen as a
piece-wise learner which locally classifies test instances. The model is hence
called localized relevance vector machine (LRVM). The LRVM is examined on
several datasets of the University of California, Irvine (UCI) repository.
Results supported by statistical tests indicate that the performance of LRVM is
competitive as compared with a few state-of-the-art classifiers.
Parametrization of stochastic inputs using generative adversarial networks with application in geology
Authors: Shing Chan, Ahmed H. Elsheikh
Date: 2019-04-07 00:00:00
Tasks: Dimensionality Reduction, parameter estimation, Uncertainty Quantification

We investigate artificial neural networks as a parametrization tool for
stochastic inputs in numerical simulations. We address parametrization from the
point of view of emulating the data generating process, instead of explicitly
constructing a parametric form to preserve predefined statistics of the data.
This is done by training a neural network to generate samples from the data
distribution using a recent deep learning technique called generative
adversarial networks. By emulating the data generating process, the relevant
statistics of the data are replicated. The method is assessed in subsurface
flow problems, where effective parametrization of underground properties such
as permeability is important due to the high dimensionality and presence of
high spatial correlations. We experiment with realizations of binary
channelized subsurface permeability and perform uncertainty quantification and
parameter estimation. Results show that the parametrization using generative
adversarial networks is very effective in preserving visual realism as well as
high order statistics of the flow responses, while achieving a dimensionality
reduction of two orders of magnitude.
Every Local Minimum Value is the Global Minimum Value of Induced Model in Non-convex Machine Learning
Authors: Kenji Kawaguchi, Jiaoyang Huang, Leslie Pack Kaelbling
Date: 2019-04-07 00:00:00
Tasks: BIG-bench Machine Learning, Representation Learning

For nonconvex optimization in machine learning, this article proves that every local minimum achieves the globally optimal value of the perturbable gradient basis model at any differentiable point. As a result, nonconvex machine learning is theoretically as supported as convex machine learning with a handcrafted basis in terms of the loss at differentiable local minima, except in the case when a preference is given to the handcrafted basis over the perturbable gradient basis. The proofs of these results are derived under mild assumptions. Accordingly, the proven results are directly applicable to many machine learning models, including practical deep neural networks, without any modification of practical methods. Furthermore, as special cases of our general results, this article improves or complements several state-of-the-art theoretical results on deep neural networks, deep residual networks, and overparameterized deep neural networks with a unified proof technique and novel geometric insights. A special case of our results also contributes to the theoretical foundation of representation learning.
Robust Building-based Registration of Airborne LiDAR Data and Optical Imagery on Urban Scenes
Authors: Thanh Huy Nguyen, Sylvie Daniel, Didier Gueriot, Christophe Sintes, Jean-Marc Le Caillec
Date: 2019-04-07 00:00:00

The motivation of this paper is to address the problem of registering
airborne LiDAR data and optical aerial or satellite imagery acquired from
different platforms, at different times, with different points of view and
levels of detail. In this paper, we present a robust registration method based
on building regions, which are extracted from optical images using mean shift
segmentation, and from LiDAR data using a 3D point cloud filtering process. The
matching of the extracted building segments is then carried out using Graph
Transformation Matching (GTM) which allows to determine a common pattern of
relative positions of segment centers. Thanks to this registration, the
relative shifts between the data sets are significantly reduced, which enables
a subsequent fine registration and a resulting high-quality data fusion.
Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots
Authors: Dieter Büchler, Roberto Calandra, Jan Peters
Date: 2019-04-07 00:00:00
Tasks: Bayesian Optimization, Safe Exploration

High-speed and high-acceleration movements are inherently hard to control.
Applying learning to the control of such motions on anthropomorphic robot arms
can improve the accuracy of the control but might damage the system. The
inherent exploration of learning approaches can lead to instabilities and the
robot reaching joint limits at high speeds. Having hardware that enables safe
exploration of high-speed and high-acceleration movements is therefore
desirable. To address this issue, we propose to use robots actuated by
Pneumatic Artificial Muscles (PAMs). In this paper, we present a four degrees
of freedom (DoFs) robot arm that reaches high joint angle accelerations of up
to 28000 deg/s^2 while avoiding dangerous joint limits thanks to the
antagonistic actuation and limits on the air pressure ranges. With this robot
arm, we are able to tune control parameters using Bayesian optimization
directly on the hardware without additional safety considerations. The achieved
tracking performance on a fast trajectory exceeds previous results on
comparable PAM-driven robots. We also show that our system can be controlled
well on slow trajectories with PID controllers due to careful construction
considerations such as minimal bending of cables, lightweight kinematics and
minimal contact between PAMs and PAMs with the links. Finally, we propose a
novel technique to control the the co-contraction of antagonistic muscle pairs.
Experimental results illustrate that choosing the optimal co-contraction level
is vital to reach better tracking performance. Through the use of PAM-driven
robots and learning, we do a small step towards the future development of
robots capable of more human-like motions.
SEQ^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression
Authors: Christos Baziotis, Ion Androutsopoulos, Ioannis Konstas, Alexandros Potamianos
Date: 2019-04-07 00:00:00
Tasks: Decoder, Language Modeling, Language Modelling, Reinforcement Learning, Sentence, Sentence Compression, Unsupervised Sentence Compression

Neural sequence-to-sequence models are currently the dominant approach in several natural language processing tasks, but require large parallel corpora. We present a sequence-to-sequence-to-sequence autoencoder (SEQ^3), consisting of two chained encoder-decoder pairs, with words used as a sequence of discrete latent variables. We apply the proposed model to unsupervised abstractive sentence compression, where the first and last sequences are the input and reconstructed sentences, respectively, while the middle sequence is the compressed sentence. Constraining the length of the latent word sequences forces the model to distill important information from the input. A pretrained language model, acting as a prior over the latent sequences, encourages the compressed sentences to be human-readable. Continuous relaxations enable us to sample from categorical distributions, allowing gradient-based optimization, unlike alternatives that rely on reinforcement learning. The proposed model does not require parallel text-summary pairs, achieving promising results in unsupervised sentence compression on benchmark datasets.
Bayesian Estimation of Mixed Multinomial Logit Models: Advances and Simulation-Based Evaluations
Authors: Prateek Bansal, Rico Krueger, Michel Bierlaire, Ricardo A. Daziano, Taha H. Rashidi
Date: 2019-04-07 00:00:00

Variational Bayes (VB) methods have emerged as a fast and computationally-efficient alternative to Markov chain Monte Carlo (MCMC) methods for scalable Bayesian estimation of mixed multinomial logit (MMNL) models. It has been established that VB is substantially faster than MCMC at practically no compromises in predictive accuracy. In this paper, we address two critical gaps concerning the usage and understanding of VB for MMNL. First, extant VB methods are limited to utility specifications involving only individual-specific taste parameters. Second, the finite-sample properties of VB estimators and the relative performance of VB, MCMC and maximum simulated likelihood estimation (MSLE) are not known. To address the former, this study extends several VB methods for MMNL to admit utility specifications including both fixed and random utility parameters. To address the latter, we conduct an extensive simulation-based evaluation to benchmark the extended VB methods against MCMC and MSLE in terms of estimation times, parameter recovery and predictive accuracy. The results suggest that all VB variants with the exception of the ones relying on an alternative variational lower bound constructed with the help of the modified Jensen's inequality perform as well as MCMC and MSLE at prediction and parameter recovery. In particular, VB with nonconjugate variational message passing and the delta-method (VB-NCVMP-Delta) is up to 16 times faster than MCMC and MSLE. Thus, VB-NCVMP-Delta can be an attractive alternative to MCMC and MSLE for fast, scalable and accurate estimation of MMNL models.
Policy Gradient Search: Online Planning and Expert Iteration without Search Trees
Authors: Thomas Anthony, Robert Nishihara, Philipp Moritz, Tim Salimans, John Schulman
Date: 2019-04-07 00:00:00
Tasks: Neural Network simulation

Monte Carlo Tree Search (MCTS) algorithms perform simulation-based search to
improve policies online. During search, the simulation policy is adapted to
explore the most promising lines of play. MCTS has been used by
state-of-the-art programs for many problems, however a disadvantage to MCTS is
that it estimates the values of states with Monte Carlo averages, stored in a
search tree; this does not scale to games with very high branching factors. We
propose an alternative simulation-based search method, Policy Gradient Search
(PGS), which adapts a neural network simulation policy online via policy
gradient updates, avoiding the need for a search tree. In Hex, PGS achieves
comparable performance to MCTS, and an agent trained using Expert Iteration
with PGS was able defeat MoHex 2.0, the strongest open-source Hex agent, in 9x9
Hex.
Real-Time Quality Assessment of Pediatric MRI via Semi-Supervised Deep Nonlocal Residual Neural Networks
Authors: Siyuan Liu, Kim-Han Thung, Weili Lin, Pew-Thian Yap, Dinggang Shen
Date: 2019-04-07 00:00:00
Tasks: Image Quality Assessment

In this paper, we introduce an image quality assessment (IQA) method for pediatric T1- and T2-weighted MR images. IQA is first performed slice-wise using a nonlocal residual neural network (NR-Net) and then volume-wise by agglomerating the slice QA results using random forest. Our method requires only a small amount of quality-annotated images for training and is designed to be robust to annotation noise that might occur due to rater errors and the inevitable mix of good and bad slices in an image volume. Using a small set of quality-assessed images, we pre-train NR-Net to annotate each image slice with an initial quality rating (i.e., pass, questionable, fail), which we then refine by semi-supervised learning and iterative self-training. Experimental results demonstrate that our method, trained using only samples of modest size, exhibit great generalizability, capable of real-time (milliseconds per volume) large-scale IQA with near-perfect accuracy.
Learning to Learn Relation for Important People Detection in Still Images
Authors: Wei-Hong Li, Fa-Ting Hong, Wei-Shi Zheng
Date: 2019-04-07 00:00:00
Proceeding: CVPR 2019 6
Tasks: Relation, Relation Network

Humans can easily recognize the importance of people in social event images,
and they always focus on the most important individuals. However, learning to
learn the relation between people in an image, and inferring the most important
person based on this relation, remains undeveloped. In this work, we propose a
deep imPOrtance relatIon NeTwork (POINT) that combines both relation modeling
and feature learning. In particular, we infer two types of interaction modules:
the person-person interaction module that learns the interaction between people
and the event-person interaction module that learns to describe how a person is
involved in the event occurring in an image. We then estimate the importance
relations among people from both interactions and encode the relation feature
from the importance relations. In this way, POINT automatically learns several
types of relation features in parallel, and we aggregate these relation
features and the person's feature to form the importance feature for important
people classification. Extensive experimental results show that our method is
effective for important people detection and verify the efficacy of learning to
learn relations for important people detection.
Adaptive NMS: Refining Pedestrian Detection in a Crowd
Authors: Songtao Liu, Di Huang, Yunhong Wang
Date: 2019-04-07 00:00:00
Proceeding: CVPR 2019 6
Tasks: Object Detection, Pedestrian Detection

Pedestrian detection in a crowd is a very challenging issue. This paper
addresses this problem by a novel Non-Maximum Suppression (NMS) algorithm to
better refine the bounding boxes given by detectors. The contributions are
threefold: (1) we propose adaptive-NMS, which applies a dynamic suppression
threshold to an instance, according to the target density; (2) we design an
efficient subnetwork to learn density scores, which can be conveniently
embedded into both the single-stage and two-stage detectors; and (3) we achieve
state of the art results on the CityPersons and CrowdHuman benchmarks.