pwc-archive/papers-with-abstracts huggingface dataset:
Sparse Travel Time Estimation from Streaming Data
Authors: Saif Eddin Jabari, Nikolaos M. Freris, Deepthi Mary Dilip
Date: 2018-04-22 00:00:00
Tasks: Travel Time Estimation

We address two shortcomings in online travel time estimation methods for congested urban traffic. The first shortcoming is related to the determination of the number of mixture modes, which can change dynamically, within day and from day to day. The second shortcoming is the wide-spread use of Gaussian probability densities as mixture components. Gaussian densities fail to capture the positive skew in travel time distributions and, consequently, large numbers of mixture components are needed for reasonable fitting accuracy when applied as mixture components. They also assign positive probabilities to negative travel times. To address these issues, this paper derives a mixture distribution with Gamma component densities, which are asymmetric and supported on the positive numbers. We use sparse estimation techniques to ensure parsimonious models and propose a generalization of Gamma mixture densities using Mittag-Leffler functions, which provides enhanced fitting flexibility and improved parsimony. In order to accommodate within-day variability and allow for online implementation of the proposed methodology (i.e., fast computations on streaming travel time data), we introduce a recursive algorithm which efficiently updates the fitted distribution whenever new data become available. Experimental results using real-world travel time data illustrate the efficacy of the proposed methods.
Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering
Authors: Bryon Aragam, Chen Dan, Eric P. Xing, Pradeep Ravikumar
Date: 2018-02-12 00:00:00
Tasks: Clustering, Nonparametric Clustering

Motivated by problems in data clustering, we establish general conditions under which families of nonparametric mixture models are identifiable, by introducing a novel framework involving clustering overfitted \emph{parametric} (i.e. misspecified) mixture models. These identifiability conditions generalize existing conditions in the literature, and are flexible enough to include for example mixtures of Gaussian mixtures. In contrast to the recent literature on estimating nonparametric mixtures, we allow for general nonparametric mixture components, and instead impose regularity assumptions on the underlying mixing measure. As our primary application, we apply these results to partition-based clustering, generalizing the notion of a Bayes optimal partition from classical parametric model-based clustering to nonparametric settings. Furthermore, this framework is constructive so that it yields a practical algorithm for learning identified mixtures, which is illustrated through several examples on real data. The key conceptual device in the analysis is the convex, metric geometry of probability measures on metric spaces and its connection to the Wasserstein convergence of mixing measures. The result is a flexible framework for nonparametric clustering with formal consistency guarantees.
Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstration
Authors: Rouhollah Rahmatizadeh, Pooya Abolghasemi, Ladislau Bölöni, Sergey Levine
Date: 2017-07-10 00:00:00
Tasks: Multi-Task Learning, Position

We propose a technique for multi-task learning from demonstration that trains
the controller of a low-cost robotic arm to accomplish several complex picking
and placing tasks, as well as non-prehensile manipulation. The controller is a
recurrent neural network using raw images as input and generating robot arm
trajectories, with the parameters shared across the tasks. The controller also
combines VAE-GAN-based reconstruction with autoregressive multimodal action
prediction. Our results demonstrate that it is possible to learn complex
manipulation tasks, such as picking up a towel, wiping an object, and
depositing the towel to its previous position, entirely from raw images with
direct behavior cloning. We show that weight sharing and reconstruction-based
regularization substantially improve generalization and robustness, and
training on multiple tasks simultaneously increases the success rate on all
tasks.
A Perceptual Measure for Deep Single Image Camera Calibration
Authors: Yannick Hold-Geoffroy, Kalyan Sunkavalli, Jonathan Eisenmann, Matt Fisher, Emiliano Gambaretto, Sunil Hadap, Jean-François Lalonde
Date: 2017-12-02 00:00:00
Proceeding: CVPR 2018 6
Tasks: Camera Calibration, Image Retrieval, Retrieval

Most current single image camera calibration methods rely on specific image
features or user input, and cannot be applied to natural images captured in
uncontrolled settings. We propose directly inferring camera calibration
parameters from a single image using a deep convolutional neural network. This
network is trained using automatically generated samples from a large-scale
panorama dataset, and considerably outperforms other methods, including recent
deep learning-based approaches, in terms of standard L2 error. However, we
argue that in many cases it is more important to consider how humans perceive
errors in camera estimation. To this end, we conduct a large-scale human
perception study where we ask users to judge the realism of 3D objects
composited with and without ground truth camera calibration. Based on this
study, we develop a new perceptual measure for camera calibration, and
demonstrate that our deep calibration network outperforms other methods on this
measure. Finally, we demonstrate the use of our calibration network for a
number of applications including virtual object insertion, image retrieval and
compositing.
Reduce, Reuse, Recycle: New uses for old QA resources
Authors: Jeff Mitchell, Sebastian Riedel
Date: 2018-04-22 00:00:00
Tasks: Relation, Relation Extraction, slot-filling, Slot Filling

We investigate applying repurposed generic QA data and models to a recently
proposed relation extraction task. We find that training on SQuAD produces
better zero-shot performance and more robust generalisation compared to the
task specific training set. We also show that standard QA architectures (e.g.
FastQA or BiDAF) can be applied to the slot filling queries without the need
for model modification.
Matching Fingerphotos to Slap Fingerprint Images
Authors: Debayan Deb, Tarang Chugh, Joshua Engelsma, Kai Cao, Neeta Nain, Jake Kendall, Anil K. Jain
Date: 2018-04-22 00:00:00
Tasks: TAR

We address the problem of comparing fingerphotos, fingerprint images from a
commodity smartphone camera, with the corresponding legacy slap contact-based
fingerprint images. Development of robust versions of these technologies would
enable the use of the billions of standard Android phones as biometric readers
through a simple software download, dramatically lowering the cost and
complexity of deployment relative to using a separate fingerprint reader. Two
fingerphoto apps running on Android phones and an optical slap reader were
utilized for fingerprint collection of 309 subjects who primarily work as
construction workers, farmers, and domestic helpers. Experimental results show
that a True Accept Rate (TAR) of 95.79 at a False Accept Rate (FAR) of 0.1% can
be achieved in matching fingerphotos to slaps (two thumbs and two index
fingers) using a COTS fingerprint matcher. By comparison, a baseline TAR of
98.55% at 0.1% FAR is achieved when matching fingerprint images from two
different contact-based optical readers. We also report the usability of the
two smartphone apps, in terms of failure to acquire rate and fingerprint
acquisition time. Our results show that fingerphotos are promising to
authenticate individuals (against a national ID database) for banking, welfare
distribution, and healthcare applications in developing countries.
Feature-Based Aggregation and Deep Reinforcement Learning: A Survey and Some New Implementations
Authors: Dimitri P. Bertsekas
Date: 2018-04-12 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper we discuss policy iteration methods for approximate solution of
a finite-state discounted Markov decision problem, with a focus on
feature-based aggregation methods and their connection with deep reinforcement
learning schemes. We introduce features of the states of the original problem,
and we formulate a smaller "aggregate" Markov decision problem, whose states
relate to the features. We discuss properties and possible implementations of
this type of aggregation, including a new approach to approximate policy
iteration. In this approach the policy improvement operation combines
feature-based aggregation with feature construction using deep neural networks
or other calculations. We argue that the cost function of a policy may be
approximated much more accurately by the nonlinear function of the features
provided by aggregation, than by the linear function of the features provided
by neural network-based reinforcement learning, thereby potentially leading to
more effective policy improvement.
Performance Impact Caused by Hidden Bias of Training Data for Recognizing Textual Entailment
Authors: Masatoshi Tsuchiya
Date: 2018-04-22 00:00:00
Proceeding: LREC 2018 5
Tasks: Natural Language Inference, RTE, Sentence

The quality of training data is one of the crucial problems when a
learning-centered approach is employed. This paper proposes a new method to
investigate the quality of a large corpus designed for the recognizing textual
entailment (RTE) task. The proposed method, which is inspired by a statistical
hypothesis test, consists of two phases: the first phase is to introduce the
predictability of textual entailment labels as a null hypothesis which is
extremely unacceptable if a target corpus has no hidden bias, and the second
phase is to test the null hypothesis using a Naive Bayes model. The
experimental result of the Stanford Natural Language Inference (SNLI) corpus
does not reject the null hypothesis. Therefore, it indicates that the SNLI
corpus has a hidden bias which allows prediction of textual entailment labels
from hypothesis sentences even if no context information is given by a premise
sentence. This paper also presents the performance impact of NN models for RTE
caused by this hidden bias.
Vortex Pooling: Improving Context Representation in Semantic Segmentation
Authors: Chen-Wei Xie, Hong-Yu Zhou, Jianxin Wu
Date: 2018-04-17 00:00:00
Tasks: Semantic Segmentation

Semantic segmentation is a fundamental task in computer vision, which can be
considered as a per-pixel classification problem. Recently, although fully
convolutional neural network (FCN) based approaches have made remarkable
progress in such task, aggregating local and contextual information in
convolutional feature maps is still a challenging problem. In this paper, we
argue that, when predicting the category of a given pixel, the regions close to
the target are more important than those far from it. To tackle this problem,
we then propose an effective yet efficient approach named Vortex Pooling to
effectively utilize contextual information. Empirical studies are also provided
to validate the effectiveness of the proposed method. To be specific, our
approach outperforms the previous state-of-the-art model named DeepLab v3 by
1.5% on the PASCAL VOC 2012 val set and 0.6% on the test set by replacing the
Atrous Spatial Pyramid Pooling (ASPP) module in DeepLab v3 with the proposed
Vortex Pooling. Moreover, our model (10.13FPS) shares similar computation cost
with DeepLab v3 (10.37 FPS).
A Novel Low-cost FPGA-based Real-time Object Tracking System
Authors: Peng Gao, Ruyue Yuan, Zhicong Lin, Linsheng Zhang, Yan Zhang
Date: 2018-04-16 00:00:00
Tasks: CPU, GPU, Object, Object Tracking, Visual Object Tracking

In current visual object tracking system, the CPU or GPU-based visual object
tracking systems have high computational cost and consume a prohibitive amount
of power. Therefore, in this paper, to reduce the computational burden of the
Camshift algorithm, we propose a novel visual object tracking algorithm by
exploiting the properties of the binary classifier and Kalman predictor.
Moreover, we present a low-cost FPGA-based real-time object tracking hardware
architecture. Extensive evaluations on OTB benchmark demonstrate that the
proposed system has extremely compelling real-time, stability and robustness.
The evaluation results show that the accuracy of our algorithm is about 48%,
and the average speed is about 309 frames per second.
Social Algorithms
Authors: Xin-She Yang
Date: 2018-04-22 00:00:00

This article concerns the review of a special class of swarm intelligence
based algorithms for solving optimization problems and these algorithms can be
referred to as social algorithms. Social algorithms use multiple agents and the
social interactions to design rules for algorithms so as to mimic certain
successful characteristics of the social/biological systems such as ants, bees,
bats, birds and animals.
A Novel Parallel Ray-Casting Algorithm
Authors: Yan Zhang, Peng Gao, Xiao-Qing Li
Date: 2018-04-16 00:00:00

The Ray-Casting algorithm is an important method for fast real-time surface
display from 3D medical images. Based on the Ray-Casting algorithm, a novel
parallel Ray-Casting algorithm is proposed in this paper. A novel operation is
introduced and defined as a star operation, and star operations can be computed
in parallel in the proposed algorithm compared with the serial chain of star
operations in the Ray-Casting algorithm. The computation complexity of the
proposed algorithm is reduced from $O(n)$ to $O(\log^n_2)$.
Why the Firefly Algorithm Works?
Authors: Xin-She Yang, Xingshi He
Date: 2018-04-22 00:00:00
Tasks: Nature-Inspired Optimization Algorithm

Firefly algorithm is a nature-inspired optimization algorithm and there have
been significant developments since its appearance about ten years ago. This
chapter summarizes the latest developments about the firefly algorithm and its
variants as well as their diverse applications. Future research directions are
also highlighted.
Integration of Absolute Orientation Measurements in the KinectFusion Reconstruction pipeline
Authors: Silvio Giancola, Jens Schneider, Peter Wonka, Bernard S. Ghanem
Date: 2018-02-12 00:00:00
Tasks: 3D Reconstruction, GPU

In this paper, we show how absolute orientation measurements provided by
low-cost but high-fidelity IMU sensors can be integrated into the KinectFusion
pipeline. We show that integration improves both runtime, robustness and
quality of the 3D reconstruction. In particular, we use this orientation data
to seed and regularize the ICP registration technique. We also present a
technique to filter the pairs of 3D matched points based on the distribution of
their distances. This filter is implemented efficiently on the GPU. Estimating
the distribution of the distances helps control the number of iterations
necessary for the convergence of the ICP algorithm. Finally, we show
experimental results that highlight improvements in robustness, a speed-up of
almost 12%, and a gain in tracking quality of 53% for the ATE metric on the
Freiburg benchmark.
SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos
Authors: Silvio Giancola, Mohieddine Amine, Tarek Dghaily, Bernard Ghanem
Date: 2018-04-12 00:00:00
Tasks: Action Classification, Action Detection, Action Recognition, Action Spotting

In this paper, we introduce SoccerNet, a benchmark for action spotting in
soccer videos. The dataset is composed of 500 complete soccer games from six
main European leagues, covering three seasons from 2014 to 2017 and a total
duration of 764 hours. A total of 6,637 temporal annotations are automatically
parsed from online match reports at a one minute resolution for three main
classes of events (Goal, Yellow/Red Card, and Substitution). As such, the
dataset is easily scalable. These annotations are manually refined to a one
second resolution by anchoring them at a single timestamp following
well-defined soccer rules. With an average of one event every 6.9 minutes, this
dataset focuses on the problem of localizing very sparse events within long
videos. We define the task of spotting as finding the anchors of soccer events
in a video. Making use of recent developments in the realm of generic action
recognition and detection in video, we provide strong baselines for detecting
soccer events. We show that our best model for classifying temporal segments of
length one minute reaches a mean Average Precision (mAP) of 67.8%. For the
spotting task, our baseline reaches an Average-mAP of 49.7% for tolerances
$\delta$ ranging from 5 to 60 seconds. Our dataset and models are available at
https://silviogiancola.github.io/SoccerNet.
Continuation of Nesterov's Smoothing for Regression with Structured Sparsity in High-Dimensional Neuroimaging
Authors: Fouad Hadj-Selem, Tommy Lofstedt, Elvis Dohmatob, Vincent Frouin, Mathieu Dubois, Vincent Guillemot, Edouard Duchesnay
Date: 2016-05-31 00:00:00
Tasks: regression

Predictive models can be used on high-dimensional brain images for diagnosis
of a clinical condition. Spatial regularization through structured sparsity
offers new perspectives in this context and reduces the risk of overfitting the
model while providing interpretable neuroimaging signatures by forcing the
solution to adhere to domain-specific constraints. Total Variation (TV)
enforces spatial smoothness of the solution while segmenting predictive regions
from the background. We consider the problem of minimizing the sum of a smooth
convex loss, a non-smooth convex penalty (whose proximal operator is known) and
a wide range of possible complex, non-smooth convex structured penalties such
as TV or overlapping group Lasso. Existing solvers are either limited in the
functions they can minimize or in their practical capacity to scale to
high-dimensional imaging data. Nesterov's smoothing technique can be used to
minimize a large number of non-smooth convex structured penalties but
reasonable precision requires a small smoothing parameter, which slows down the
convergence speed. To benefit from the versatility of Nesterov's smoothing
technique, we propose a first order continuation algorithm, CONESTA, which
automatically generates a sequence of decreasing smoothing parameters. The
generated sequence maintains the optimal convergence speed towards any globally
desired precision. Our main contributions are: To propose an expression of the
duality gap to probe the current distance to the global optimum in order to
adapt the smoothing parameter and the convergence speed. We provide a
convergence rate, which is an improvement over classical proximal gradient
smoothing methods. We demonstrate on both simulated and high-dimensional
structural neuroimaging data that CONESTA significantly outperforms many
state-of-the-art solvers in regard to convergence speed and precision.
Segmented and Non-Segmented Stacked Denoising Autoencoder for Hyperspectral Band Reduction
Authors: Muhammad Ahmad, Asad Khan, Adil Mehmood Khan, Rasheed Hussain
Date: 2017-05-19 00:00:00
Tasks: Clustering, Denoising, Dimensionality Reduction, Hyperspectral image analysis

Hyperspectral image analysis often requires selecting the most informative
bands instead of processing the whole data without losing the key information.
Existing band reduction (BR) methods have the capability to reveal the
nonlinear properties exhibited in the data but at the expense of loosing its
original representation. To cope with the said issue, an unsupervised
non-linear segmented and non-segmented stacked denoising autoencoder (UDAE)
based BR method is proposed. Our aim is to find an optimal mapping and
construct a lower-dimensional space that has a similar structure to the
original data with least reconstruction error. The proposed method first
confronts the original hyperspectral data into smaller regions in a spatial
domain and then each region is processed by UDAE individually. This results in
reduced complexity and improved efficiency of BR for both semi-supervised and
unsupervised tasks, i.e. classification and clustering. Our experiments on
publicly available hyperspectral datasets with various types of classifiers
demonstrate the effectiveness of UDAE method which equates favorably with other
state-of-the-art dimensionality reduction and BR methods.
Swarm robotics in wireless distributed protocol design for coordinating robots involved in cooperative tasks
Authors: F. De Rango, N. Palmieri, X. S. Yang, S. Marano
Date: 2018-04-22 00:00:00

The mine detection in an unexplored area is an optimization problem where
multiple mines, randomly distributed throughout an area, need to be discovered
and disarmed in a minimum amount of time. We propose a strategy to explore an
unknown area, using a stigmergy approach based on ants behavior, and a novel
swarm based protocol to recruit and coordinate robots for disarming the mines
cooperatively. Simulation tests are presented to show the effectiveness of our
proposed Ant-based Task Robot Coordination (ATRC) with only the exploration
task and with both exploration and recruiting strategies. Multiple minimization
objectives have been considered: the robots' recruiting time and the overall
area exploration time. We discuss, through simulation, different cases under
different network and field conditions, performed by the robots. The results
have shown that the proposed decentralized approaches enable the swarm of
robots to perform cooperative tasks intelligently without any central control.
IIIDYT at SemEval-2018 Task 3: Irony detection in English tweets
Authors: Edison Marrese-Taylor, Suzana Ilic, Jorge A. Balazs, Yutaka Matsuo, Helmut Prendinger
Date: 2018-04-22 00:00:00
Proceeding: SEMEVAL 2018 6
Tasks: Representation Learning

In this paper we introduce our system for the task of Irony detection in
English tweets, a part of SemEval 2018. We propose representation learning
approach that relies on a multi-layered bidirectional LSTM, without using
external features that provide additional semantic information. Although our
model is able to outperform the baseline in the validation set, our results
show limited generalization power over the test set. Given the limited size of
the dataset, we think the usage of more pre-training schemes would greatly
improve the obtained results.
New directional bat algorithm for continuous optimization problems
Authors: Asma Chakri, Rabia Khelif, Mohamed Benouaret, Xin-She Yang
Date: 2018-04-22 00:00:00

Bat algorithm (BA) is a recent optimization algorithm based on swarm
intelligence and inspiration from the echolocation behavior of bats. One of the
issues in the standard bat algorithm is the premature convergence that can
occur due to the low exploration ability of the algorithm under some
conditions. To overcome this deficiency, directional echolocation is introduced
to the standard bat algorithm to enhance its exploration and exploitation
capabilities. In addition to such directional echolocation, three other
improvements have been embedded into the standard bat algorithm to enhance its
performance. The new proposed approach, namely the directional Bat Algorithm
(dBA), has been then tested using several standard and non-standard benchmarks
from the CEC2005 benchmark suite. The performance of dBA has been compared with
ten other algorithms and BA variants using non-parametric statistical tests.
The statistical test results show the superiority of the directional bat
algorithm.