pwc-archive/papers-with-abstracts huggingface dataset:
Faster Bounding Box Annotation for Object Detection in Indoor Scenes
Authors: Bishwo Adhikari, Jukka Peltomäki, Jussi Puura, Heikki Huttunen
Date: 2018-07-03 00:00:00
Tasks: Object, object-detection, Object Detection, Object Detection In Indoor Scenes

This paper proposes an approach for rapid bounding box annotation for object
detection datasets. The procedure consists of two stages: The first step is to
annotate a part of the dataset manually, and the second step proposes
annotations for the remaining samples using a model trained with the first
stage annotations. We experimentally study which first/second stage split
minimizes to total workload. In addition, we introduce a new fully labeled
object detection dataset collected from indoor scenes. Compared to other indoor
datasets, our collection has more class categories, different backgrounds,
lighting conditions, occlusion and high intra-class differences. We train deep
learning based object detectors with a number of state-of-the-art models and
compare them in terms of speed and accuracy. The fully annotated dataset is
released freely available for the research community.
PRED18: Dataset and Further Experiments with DAVIS Event Camera in Predator-Prey Robot Chasing
Authors: Diederik Paul Moeys, Daniel Neil, Federico Corradi, Emmett Kerr, Philip Vance, Gautham Das, Sonya A. Coleman, Thomas M. McGinnity, Dermot Kerr, Tobi Delbruck
Date: 2018-07-02 00:00:00
Tasks: Position

Machine vision systems using convolutional neural networks (CNNs) for robotic
applications are increasingly being developed. Conventional vision CNNs are
driven by camera frames at constant sample rate, thus achieving a fixed latency
and power consumption tradeoff. This paper describes further work on the first
experiments of a closed-loop robotic system integrating a CNN together with a
Dynamic and Active Pixel Vision Sensor (DAVIS) in a predator/prey scenario. The
DAVIS, mounted on the predator Summit XL robot, produces frames at a fixed 15
Hz frame-rate and Dynamic Vision Sensor (DVS) histograms containing 5k ON and
OFF events at a variable frame-rate ranging from 15-500 Hz depending on the
robot speeds. In contrast to conventional frame-based systems, the latency and
processing cost depends on the rate of change of the image. The CNN is trained
offline on the 1.25h labeled dataset to recognize the position and size of the
prey robot, in the field of view of the predator. During inference, combining
the ten output classes of the CNN allows extracting the analog position vector
of the prey relative to the predator with a mean 8.7% error in angular
estimation. The system is compatible with conventional deep learning
technology, but achieves a variable latency-power tradeoff that adapts
automatically to the dynamics. Finally, investigations on the robustness of the
algorithm, a human performance comparison and a deconvolution analysis are also
explored.
Synthetic contrast enhancement in cardiac CT with Deep Learning
Authors: Gianmarco Santini, Lorena M. Zumbo, Nicola Martini, Gabriele Valvano, Andrea Leo, Andrea Ripoli, Francesco Avogliero, Dante Chiappino, Daniele Della Latta
Date: 2018-07-02 00:00:00
Tasks: Deep Learning

In Europe the 20% of the CT scans cover the thoracic region. The acquired
images contain information about the cardiovascular system that often remains
latent due to the lack of contrast in the cardiac area. On the other hand, the
contrast enhanced computed tomography (CECT) represents an imaging technique
that allows to easily assess the cardiac chambers volumes and the contrast
dynamics. With this work we aim to face the problem of extraction and
presentation of these latent information, using a deep learning approach with
convolutional neural networks. Starting from the extraction of relevant
features from the image without contrast medium, we try to re-map them on
features typical of CECT, to synthesize an image characterized by an
attenuation in the cardiac chambers as if a virtually iodine contrast medium
was injected. The purposes are to guarantee an estimation of the left cardiac
chambers volume and to perform an evaluation of the contrast dynamics. Our
approach is based on a deconvolutional network trained on a set of 120 patients
who underwent both CT acquisitions in the same contrastographic arterial phase
and the same cardiac phase. To ensure a reliable predicted CECT image, in terms
of values and morphology, a custom loss function is defined by combining an
error function to find a pixel-wise correspondence, which takes into account
the similarity in term of Hounsfield units between the input and output images
and by a cross-entropy computed on the binarized versions of the synthesized
and of the real CECT image. The proposed method is finally tested on 20
subjects.
Point-Set Registration: Coherent Point Drift
Authors: Andriy Myronenko, Xubo Song
Date: 2009-05-15 00:00:00
Tasks: Density Estimation

Point set registration is a key component in many computer vision tasks. The
goal of point set registration is to assign correspondences between two sets of
points and to recover the transformation that maps one point set to the other.
Multiple factors, including an unknown non-rigid spatial transformation, large
dimensionality of point set, noise and outliers, make the point set
registration a challenging problem. We introduce a probabilistic method, called
the Coherent Point Drift (CPD) algorithm, for both rigid and non-rigid point
set registration. We consider the alignment of two point sets as a probability
density estimation problem. We fit the GMM centroids (representing the first
point set) to the data (the second point set) by maximizing the likelihood. We
force the GMM centroids to move coherently as a group to preserve the
topological structure of the point sets. In the rigid case, we impose the
coherence constraint by re-parametrization of GMM centroid locations with rigid
parameters and derive a closed form solution of the maximization step of the EM
algorithm in arbitrary dimensions. In the non-rigid case, we impose the
coherence constraint by regularizing the displacement field and using the
variational calculus to derive the optimal transformation. We also introduce a
fast algorithm that reduces the method computation complexity to linear. We
test the CPD algorithm for both rigid and non-rigid transformations in the
presence of noise, outliers and missing points, where CPD shows accurate
results and outperforms current state-of-the-art methods.
The Qatar Genome: A Population-Specific Tool for Precision Medicine in the Middle East
Authors: Khalid A. Fakhro, Michelle R. Staudt, Monica Denise Ramstetter, Amal Robay, Joel A. Malek, Ramin Badii, Ajayeb Al-Nabet Al-Marri, Charbel Abi Khalil, Alya Al-Shakaki, Omar Chidiac, Dora Stadler, Mahmoud Zirie, Amin Jayyousi, Jacqueline Salit, Jason G. Mezey, Ronald G. Crystal, Juan L. Rodriguez-Flores
Date: 2018-05-08 00:00:00

Reaching the full potential of precision medicine depends on the quality of
personalized genome interpretation. In order to facilitate precision medicine
in regions of the Middle East and North Africa (MENA), a population-specific
reference genome for the indigenous Arab popula-tion of Qatar (QTRG) was
constructed by incorporating allele frequency data from sequencing of 1,161
Qataris, representing 0.4% of the population. A total of 20.9 million SNP and
3.1 million indels were observed in Qatar, including an average of 1.79% novel
variants per individual ge-nome. Replacement of the GRCh37 standard reference
with QTRG in a best practices genome analysis workflow resulted in an average
of 7* deeper coverage depth (an improvement of 23%), and 756,671 fewer variants
on average, a reduction of 16% that is attributed to common Qatari alleles
being present in the QTRG reference. The benefit for using QTRG varies across
ances-tries, a factor that should be taken into consideration when selecting an
appropriate reference for analysis.
Cosmic Reionization and the 21-cm signal: Comparison between an analytical model and a simulation
Authors: Mario G. Santos, Alexandre Amblard, Jonathan Pritchard, Hy Trac, Renyue Cen, Asantha Cooray
Date: 2007-08-20 00:00:00

We measure several properties of the reionization process and the
corresponding low-frequency 21-cm signal associated with the neutral hydrogen
distribution, using a large volume, high resolution simulation of cosmic
reionization. The brightness temperature of the 21-cm signal is derived by
post-processing this numerical simulation with a semi-analytical prescription.
Our study extends to high redshifts (z ~ 25) where, in addition to collisional
coupling, our post-processed simulations take into account the inhomogeneities
in the heating of the neutral gas by X-rays and the effect of an inhomogeneous
Lya radiation field. Unlike the well-studied case where spin temperature is
assumed to be significantly greater than the temperature of the cosmic
microwave background due to uniform heating of the gas by X-rays, spatial
fluctuations in both the Lya radiation field and X-ray intensity impact
predictions related to the brightness temperature at z > 10, during the early
stages of reionization and gas heating. The statistics of the 21-cm signal from
our simulation are then compared to existing analytical models in the
literature and we find that these analytical models provide a reasonably
accurate description of the 21-cm power spectrum at z < 10. Such an agreement
is useful since analytical models are better suited to quickly explore the full
astrophysical and cosmological parameter space relevant for future 21-cm
surveys. We find, nevertheless, non-negligible differences that can be
attributed to differences in the inhomogeneous X-ray heating and Lya coupling
at z > 10 and, with upcoming interferometric data, these differences in return
can provide a way to better understand the astrophysical processes during
reionization.
CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving
Authors: Xiaodan Liang, Tairui Wang, Luona Yang, Eric Xing
Date: 2018-07-10 00:00:00
Proceeding: ECCV 2018 9
Tasks: Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Autonomous urban driving navigation with complex multi-agent dynamics is
under-explored due to the difficulty of learning an optimal driving policy. The
traditional modular pipeline heavily relies on hand-designed rules and the
pre-processing perception system while the supervised learning-based models are
limited by the accessibility of extensive human experience. We present a
general and principled Controllable Imitative Reinforcement Learning (CIRL)
approach which successfully makes the driving agent achieve higher success
rates based on only vision inputs in a high-fidelity car simulator. To
alleviate the low exploration efficiency for large continuous action space that
often prohibits the use of classical RL on challenging real tasks, our CIRL
explores over a reasonably constrained action space guided by encoded
experiences that imitate human demonstrations, building upon Deep Deterministic
Policy Gradient (DDPG). Moreover, we propose to specialize adaptive policies
and steering-angle reward designs for different control signals (i.e. follow,
straight, turn right, turn left) based on the shared representations to improve
the model capability in tackling with diverse cases. Extensive experiments on
CARLA driving benchmark demonstrate that CIRL substantially outperforms all
previous methods in terms of the percentage of successfully completed episodes
on a variety of goal-directed driving tasks. We also show its superior
generalization capability in unseen environments. To our knowledge, this is the
first successful case of the learned driving policy through reinforcement
learning in the high-fidelity simulator, which performs better-than supervised
imitation learning.
Efficient identification, localization and quantification of grapevine inflorescences in unprepared field images using Fully Convolutional Networks
Authors: Robert Rudolph, Katja Herzog, Reinhard Töpfer, Volker Steinhage
Date: 2018-07-10 00:00:00
Tasks: Image Segmentation, Management, Semantic Segmentation

Yield and its prediction is one of the most important tasks in grapevine
breeding purposes and vineyard management. Commonly, this trait is estimated
manually right before harvest by extrapolation, which mostly is
labor-intensive, destructive and inaccurate. In the present study an automated
image-based workflow was developed quantifying inflorescences and single
flowers in unprepared field images of grapevines, i.e. no artificial background
or light was applied. It is a novel approach for non-invasive, inexpensive and
objective phenotyping with high-throughput.
  First, image regions depicting inflorescences were identified and localized.
This was done by segmenting the images into the classes "inflorescence" and
"non-inflorescence" using a Fully Convolutional Network (FCN). Efficient image
segmentation hereby is the most challenging step regarding the small geometry
and dense distribution of flowers (several hundred flowers per inflorescence),
similar color of all plant organs in the fore- and background as well as the
circumstance that only approximately 5% of an image show inflorescences. The
trained FCN achieved a mean Intersection Over Union (IOU) of 87.6% on the test
data set. Finally, individual flowers were extracted from the
"inflorescence"-areas using Circular Hough Transform. The flower extraction
achieved a recall of 80.3% and a precision of 70.7% using the segmentation
derived by the trained FCN model.
  Summarized, the presented approach is a promising strategy in order to
predict yield potential automatically in the earliest stage of grapevine
development which is applicable for objective monitoring and evaluations of
breeding material, genetic repositories or commercial vineyards.
Kernel-Based Learning for Smart Inverter Control
Authors: Aditie Garg, Mana Jalali, Vassilis Kekatos, Nikolaos Gatsis
Date: 2018-07-10 00:00:00
Tasks: Multi-Task Learning

Distribution grids are currently challenged by frequent voltage excursions
induced by intermittent solar generation. Smart inverters have been advocated
as a fast-responding means to regulate voltage and minimize ohmic losses. Since
optimal inverter coordination may be computationally challenging and preset
local control rules are subpar, the approach of customized control rules
designed in a quasi-static fashion features as a golden middle. Departing from
affine control rules, this work puts forth non-linear inverter control
policies. Drawing analogies to multi-task learning, reactive control is posed
as a kernel-based regression task. Leveraging a linearized grid model and given
anticipated data scenarios, inverter rules are jointly designed at the feeder
level to minimize a convex combination of voltage deviations and ohmic losses
via a linearly-constrained quadratic program. Numerical tests using real-world
data on a benchmark feeder demonstrate that nonlinear control rules driven also
by a few non-local readings can attain near-optimal performance.
Is Q-learning Provably Efficient?
Authors: Chi Jin, Zeyuan Allen-Zhu, Sebastien Bubeck, Michael. I. Jordan
Date: 2018-07-10 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Q-Learning, Reinforcement Learning, Reinforcement Learning (RL)

Model-free reinforcement learning (RL) algorithms, such as Q-learning,
directly parameterize and update value functions or policies without explicitly
modeling the environment. They are typically simpler, more flexible to use, and
thus more prevalent in modern deep RL than model-based approaches. However,
empirical work has suggested that model-free algorithms may require more
samples to learn [Deisenroth and Rasmussen 2011, Schulman et al. 2015]. The
theoretical question of "whether model-free algorithms can be made sample
efficient" is one of the most fundamental questions in RL, and remains unsolved
even in the basic scenario with finitely many states and actions.
  We prove that, in an episodic MDP setting, Q-learning with UCB exploration
achieves regret $\tilde{O}(\sqrt{H^3 SAT})$, where $S$ and $A$ are the numbers
of states and actions, $H$ is the number of steps per episode, and $T$ is the
total number of steps. This sample efficiency matches the optimal regret that
can be achieved by any model-based approach, up to a single $\sqrt{H}$ factor.
To the best of our knowledge, this is the first analysis in the model-free
setting that establishes $\sqrt{T}$ regret without requiring access to a
"simulator."
SimArch: A Multi-agent System For Human Path Simulation In Architecture Design
Authors: Yen-Chia Hsu
Date: 2018-07-10 00:00:00

Human moving path is an important feature in architecture design. By studying
the path, architects know where to arrange the basic elements (e.g. structures,
glasses, furniture, etc.) in the space. This paper presents SimArch, a
multi-agent system for human moving path simulation. It involves a behavior
model built by using a Markov Decision Process. The model simulates human
mental states, target range detection, and collision prediction when agents are
on the floor, in a particular small gallery, looking at an exhibit, or leaving
the floor. It also models different kinds of human characteristics by assigning
different transition probabilities. A modified weighted A* search algorithm
quickly plans the sub-optimal path of the agents. In an experiment, SimArch
takes a series of preprocessed floorplans as inputs, simulates the moving path,
and outputs a density map for evaluation. The density map provides the
prediction that how likely a person will occur in a location. A following
discussion illustrates how architects can use the density map to improve their
floorplan design.
Latent Alignment and Variational Attention
Authors: Yuntian Deng, Yoon Kim, Justin Chiu, Demi Guo, Alexander M. Rush
Date: 2018-07-10 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Hard Attention, Machine Translation, Question Answering, Translation, Variational Inference, Visual Question Answering, Visual Question Answering (VQA)

Neural attention has become central to many state-of-the-art models in
natural language processing and related domains. Attention networks are an
easy-to-train and effective method for softly simulating alignment; however,
the approach does not marginalize over latent alignments in a probabilistic
sense. This property makes it difficult to compare attention to other alignment
approaches, to compose it with probabilistic models, and to perform posterior
inference conditioned on observed data. A related latent approach, hard
attention, fixes these issues, but is generally harder to train and less
accurate. This work considers variational attention networks, alternatives to
soft and hard attention for learning latent variable alignment models, with
tighter approximation bounds based on amortized variational inference. We
further propose methods for reducing the variance of gradients to make these
approaches computationally feasible. Experiments show that for machine
translation and visual question answering, inefficient exact latent variable
models outperform standard neural attention, but these gains go away when using
hard attention based training. On the other hand, variational attention retains
most of the performance gain but with training speed comparable to neural
attention.
Representation Learning with Contrastive Predictive Coding
Authors: Aaron van den Oord, Yazhe Li, Oriol Vinyals
Date: 2018-07-10 00:00:00
Tasks: Reinforcement Learning, Representation Learning, Self-Supervised Image Classification, Semi-Supervised Image Classification

While supervised learning has enabled great progress in many applications,
unsupervised learning has not seen such widespread adoption, and remains an
important and challenging endeavor for artificial intelligence. In this work,
we propose a universal unsupervised learning approach to extract useful
representations from high-dimensional data, which we call Contrastive
Predictive Coding. The key insight of our model is to learn such
representations by predicting the future in latent space by using powerful
autoregressive models. We use a probabilistic contrastive loss which induces
the latent space to capture information that is maximally useful to predict
future samples. It also makes the model tractable by using negative sampling.
While most prior work has focused on evaluating representations for a
particular modality, we demonstrate that our approach is able to learn useful
representations achieving strong performance on four distinct domains: speech,
images, text and reinforcement learning in 3D environments.
Scalable Sparse Subspace Clustering via Ordered Weighted $\ell_1$ Regression
Authors: Urvashi Oswal, Robert Nowak
Date: 2018-07-10 00:00:00
Tasks: Clustering, regression

The main contribution of the paper is a new approach to subspace clustering
that is significantly more computationally efficient and scalable than existing
state-of-the-art methods. The central idea is to modify the regression
technique in sparse subspace clustering (SSC) by replacing the $\ell_1$
minimization with a generalization called Ordered Weighted $\ell_1$ (OWL)
minimization which performs simultaneous regression and clustering of
correlated variables. Using random geometric graph theory, we prove that OWL
regression selects more points within each subspace, resulting in better
clustering results. This allows for accurate subspace clustering based on
regression solutions for only a small subset of the total dataset,
significantly reducing the computational complexity compared to SSC. In
experiments, we find that our OWL approach can achieve a speedup of 20$\times$
to 30$\times$ for synthetic problems and 4$\times$ to 8$\times$ on real data
problems.
Network Classification in Temporal Networks Using Motifs
Authors: Kun Tu, Jian Li, Don Towsley, Dave Braines, Liam D. Turner
Date: 2018-07-10 00:00:00
Tasks: Classification, General Classification

Network classification has a variety of applications, such as detecting
communities within networks and finding similarities between those representing
different aspects of the real world. However, most existing work in this area
focus on examining static undirected networks without considering directed
edges or temporality. In this paper, we propose a new methodology that utilizes
feature representation for network classification based on the temporal motif
distribution of the network and a null model for comparing against random
graphs. Experimental results show that our method improves accuracy by up
$10\%$ compared to the state-of-the-art embedding method in network
classification, for tasks such as classifying network type, identifying
communities in email exchange network, and identifying users given their
app-switching behaviors.