pwc-archive/papers-with-abstracts huggingface dataset:
On-Orbit Smart Camera System to Observe Illuminated and Unilluminated Space Objects
Authors: Steve Morad, Ravi teja Nallapu, Himangshu Kalita, Byon Kwon, Vishnu Reddy, Roberto Furfaro, Erik Asphaug, Jekan Thangavelautham
Date: 2018-09-06 00:00:00
Tasks: Navigate, Object, object-detection, Object Detection, Optical Flow Estimation

The wide availability of Commercial Off-The-Shelf (COTS) electronics that can
withstand Low Earth Orbit conditions has opened avenue for wide deployment of
CubeSats and small-satellites. CubeSats thanks to their low developmental and
launch costs offer new opportunities for rapidly demonstrating on-orbit
surveillance capabilities. In our earlier work, we proposed development of
SWIMSat (Space based Wide-angle Imaging of Meteors) a 3U CubeSat demonstrator
that is designed to observe illuminated objects entering the Earth's
atmosphere. The spacecraft would operate autonomously using a smart camera with
vision algorithms to detect, track and report of objects. Several CubeSats can
track an object in a coordinated fashion to pinpoint an object's trajectory. An
extension of this smart camera capability is to track unilluminated objects
utilizing capabilities we have been developing to track and navigate to Near
Earth Objects (NEOs). This extension enables detecting and tracking objects
that can't readily be detected by humans. The system maintains a dense star map
of the night sky and performs round the clock observations. Standard optical
flow algorithms are used to obtain trajectories of all moving objects in the
camera field of view. Through a process of elimination, certain stars maybe
occluded by a transiting unilluminated object which is then used to first
detect and obtain a trajectory of the object. Using multiple cameras observing
the event from different points of view, it may be possible then to triangulate
the position of the object in space and obtain its orbital trajectory. In this
work, the performance of our space object detection algorithm coupled with a
spacecraft guidance, navigation, and control system is demonstrated.
Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks
Authors: Linfeng Song, Zhiguo Wang, Mo Yu, Yue Zhang, Radu Florian, Daniel Gildea
Date: 2018-09-06 00:00:00
Tasks: Multi-Hop Reading Comprehension, Question Answering, Reading Comprehension

Multi-hop reading comprehension focuses on one type of factoid question,
where a system needs to properly integrate multiple pieces of evidence to
correctly answer a question. Previous work approximates global evidence with
local coreference information, encoding coreference chains with DAG-styled GRU
layers within a gated-attention reader. However, coreference is limited in
providing information for rich inference. We introduce a new method for better
connecting global evidence, which forms more complex graphs compared to DAGs.
To perform evidence integration on our graphs, we investigate two recent graph
neural networks, namely graph convolutional network (GCN) and graph recurrent
network (GRN). Experiments on two standard datasets show that richer global
information leads to better answers. Our method performs better than all
published results on these datasets.
Evaluating Syntactic Properties of Seq2seq Output with a Broad Coverage HPSG: A Case Study on Machine Translation
Authors: Johnny Tian-Zheng Wei, Khiem Pham, Brian Dillon, Brendan O'Connor
Date: 2018-09-06 00:00:00
Tasks: Machine Translation, Translation

Sequence to sequence (seq2seq) models are often employed in settings where
the target output is natural language. However, the syntactic properties of the
language generated from these models are not well understood. We explore
whether such output belongs to a formal and realistic grammar, by employing the
English Resource Grammar (ERG), a broad coverage, linguistically precise
HPSG-based grammar of English. From a French to English parallel corpus, we
analyze the parseability and grammatical constructions occurring in output from
a seq2seq translation model. Over 93\% of the model translations are parseable,
suggesting that it learns to generate conforming to a grammar. The model has
trouble learning the distribution of rarer syntactic rules, and we pinpoint
several constructions that differentiate translations between the references
and our model.
Planning with Arithmetic and Geometric Attributes
Authors: David Folqué, Sainbayar Sukhbaatar, Arthur Szlam, Joan Bruna
Date: 2018-09-06 00:00:00

A desirable property of an intelligent agent is its ability to understand its
environment to quickly generalize to novel tasks and compose simpler tasks into
more complex ones. If the environment has geometric or arithmetic structure,
the agent should exploit these for faster generalization. Building on recent
work that augments the environment with user-specified attributes, we show that
further equipping these attributes with the appropriate geometric and
arithmetic structure brings substantial gains in sample complexity.
Gaussian Process Regression for Binned Data
Authors: Michael Thomas Smith, Mauricio A. Alvarez, Neil D. Lawrence
Date: 2018-09-06 00:00:00
Tasks: regression

Many datasets are in the form of tables of binned data. Performing regression on these data usually involves either reading off bin heights, ignoring data from neighbouring bins or interpolating between bins thus over or underestimating the true bin integrals. In this paper we propose an elegant method for performing Gaussian Process (GP) regression given such binned data, allowing one to make probabilistic predictions of the latent function which produced the binned data. We look at several applications. First, for differentially private regression; second, to make predictions over other integrals; and third when the input regions are irregularly shaped collections of polytopes. In summary, our method provides an effective way of analysing binned data such that one can use more information from the histogram representation, and thus reconstruct a more useful and precise density for making predictions.
3D Surface Reconstruction by Pointillism
Authors: Olivia Wiles, Andrew Zisserman
Date: 2018-09-06 00:00:00
Tasks: Surface Reconstruction

The objective of this work is to infer the 3D shape of an object from a
single image. We use sculptures as our training and test bed, as these have
great variety in shape and appearance.
  To achieve this we build on the success of multiple view geometry (MVG) which
is able to accurately provide correspondences between images of 3D objects
under varying viewpoint and illumination conditions, and make the following
contributions: first, we introduce a new loss function that can harness
image-to-image correspondences to provide a supervisory signal to train a deep
network to infer a depth map. The network is trained end-to-end by
differentiating through the camera. Second, we develop a processing pipeline to
automatically generate a large scale multi-view set of correspondences for
training the network. Finally, we demonstrate that we can indeed obtain a depth
map of a novel object from a single image for a variety of sculptures with
varying shape/texture, and that the network generalises at test time to new
domains (e.g. synthetic images).
Dual Ask-Answer Network for Machine Reading Comprehension
Authors: Han Xiao, Feng Wang, Jian-Feng Yan, Jingyao Zheng
Date: 2018-09-06 00:00:00
Tasks: Machine Reading Comprehension, Question Answering, Question Generation, Question-Generation, Reading Comprehension

There are three modalities in the reading comprehension setting: question,
answer and context. The task of question answering or question generation aims
to infer an answer or a question when given the counterpart based on context.
We present a novel two-way neural sequence transduction model that connects
three modalities, allowing it to learn two tasks simultaneously and mutually
benefit one another. During training, the model receives
question-context-answer triplets as input and captures the cross-modal
interaction via a hierarchical attention process. Unlike previous joint
learning paradigms that leverage the duality of question generation and
question answering at data level, we solve such dual tasks at the architecture
level by mirroring the network structure and partially sharing components at
different layers. This enables the knowledge to be transferred from one task to
another, helping the model to find a general representation for each modality.
The evaluation on four public datasets shows that our dual-learning model
outperforms the mono-learning counterpart as well as the state-of-the-art joint
models on both question answering and question generation tasks.
Dense Pose Transfer
Authors: Natalia Neverova, Riza Alp Guler, Iasonas Kokkinos
Date: 2018-09-06 00:00:00
Proceeding: ECCV 2018 9
Tasks: Pose Estimation, Pose Transfer

In this work we integrate ideas from surface-based modeling with neural
synthesis: we propose a combination of surface-based pose estimation and deep
generative models that allows us to perform accurate pose transfer, i.e.
synthesize a new image of a person based on a single image of that person and
the image of a pose donor. We use a dense pose estimation system that maps
pixels from both images to a common surface-based coordinate system, allowing
the two images to be brought in correspondence with each other. We inpaint and
refine the source image intensities in the surface coordinate system, prior to
warping them onto the target pose. These predictions are fused with those of a
convolutional predictive module through a neural synthesis module allowing for
training the whole pipeline jointly end-to-end, optimizing a combination of
adversarial and perceptual losses. We show that dense pose estimation is a
substantially more powerful conditioning input than landmark-, or mask-based
alternatives, and report systematic improvements over state of the art
generators on DeepFashion and MVC datasets.
Evaluation Measures for Quantification: An Axiomatic Approach
Authors: Fabrizio Sebastiani
Date: 2018-09-06 00:00:00

Quantification is the task of estimating, given a set $\sigma$ of unlabelled
items and a set of classes $\mathcal{C}=\{c_{1}, \ldots, c_{|\mathcal{C}|}\}$,
the prevalence (or `relative frequency') in $\sigma$ of each class $c_{i}\in
\mathcal{C}$. While quantification may in principle be solved by classifying
each item in $\sigma$ and counting how many such items have been labelled with
$c_{i}$, it has long been shown that this `classify and count' (CC) method
yields suboptimal quantification accuracy. As a result, quantification is no
longer considered a mere byproduct of classification, and has evolved as a task
of its own. While the scientific community has devoted a lot of attention to
devising more accurate quantification methods, it has not devoted much to
discussing what properties an \emph{evaluation measure for quantification}
(EMQ) should enjoy, and which EMQs should be adopted as a result. This paper
lies down a number of interesting properties that an EMQ may or may not enjoy,
discusses if (and when) each of these properties is desirable, surveys the EMQs
that have been used so far, and discusses whether they enjoy or not the above
properties. As a result of this investigation, some of the EMQs that have been
used in the literature turn out to be severely unfit, while others emerge as
closer to what the quantification community actually needs. However, a
significant result is that no existing EMQ satisfies all the properties
identified as desirable, thus indicating that more research is needed in order
to identify (or synthesize) a truly adequate EMQ.
Multi-Expert Gender Classification on Age Group by Integrating Deep Neural Networks
Authors: Jun Beom Kho
Date: 2018-09-06 00:00:00
Tasks: Age And Gender Classification, Age Estimation, Classification, Gender Classification, Gender Prediction, General Classification, Multi-Task Learning

Generally, facial age variations affect gender classification accuracy
significantly, because facial shape and skin texture change as they grow old.
This requires re-examination on the gender classification system to consider
facial age information. In this paper, we propose Multi-expert Gender
Classification on Age Group (MGA), an end-to-end multi-task learning schemes of
age estimation and gender classification. First, two types of deep neural
networks are utilized; Convolutional Appearance Network (CAN) for facial
appearance feature and Deep Geometry Network (DGN) for facial geometric
feature. Then, CAN and DGN are integrated by the proposed model integration
strategy and fine-tuned in order to improve age and gender classification
accuracy. The facial images are categorized into one of three age groups
(young, adult and elder group) based on their estimated age, and the system
makes a gender prediction according to average fusion strategy of three gender
classification experts, which are trained to fit gender characteristics of each
age group. Rigorous experimental results conducted on the challenging databases
suggest that the proposed MGA outperforms several state-of-art researches with
smaller computational cost.
Pore detection in high-resolution fingerprint images using Deep Residual Network
Authors: Vijay Anand, Vivek kanhangad
Date: 2018-09-06 00:00:00
Tasks: Vocal Bursts Intensity Prediction

This letter presents a residual learning-based convolutional neural network,
referred to as DeepResPore, for detection of pores in high-resolution
fingerprint images. Specifically, the proposed DeepResPore model generates a
pore intensity map from the input fingerprint image. Subsequently, the local
maxima filter is operated on the pore intensity map to identify the pore
coordinates. The results of our experiments indicate that the proposed approach
is effective in extracting pores with a true detection rate of 94:49% on Test
set I and 93:78% on Test set II of the publicly available PolyU HRF dataset.
Most importantly, the proposed approach achieves state-of-the-art performance
on both test sets.
Training Millions of Personalized Dialogue Agents
Authors: Pierre-Emmanuel Mazaré, Samuel Humeau, Martin Raison, Antoine Bordes
Date: 2018-09-06 00:00:00
Proceeding: EMNLP 2018 10

Current dialogue systems are not very engaging for users, especially when
trained end-to-end without relying on proactive reengaging scripted strategies.
Zhang et al. (2018) showed that the engagement level of end-to-end dialogue
models increases when conditioning them on text personas providing some
personalized back-story to the model. However, the dataset used in Zhang et al.
(2018) is synthetic and of limited size as it contains around 1k different
personas. In this paper we introduce a new dataset providing 5 million personas
and 700 million persona-based dialogues. Our experiments show that, at this
scale, training using personas still improves the performance of end-to-end
systems. In addition, we show that other tasks benefit from the wide coverage
of our dataset by fine-tuning our model on the data from Zhang et al. (2018)
and achieving state-of-the-art results.
Code-switched Language Models Using Dual RNNs and Same-Source Pretraining
Authors: Saurabh Garg, Tanmay Parekh, Preethi Jyothi
Date: 2018-09-06 00:00:00
Proceeding: EMNLP 2018 10

This work focuses on building language models (LMs) for code-switched text.
We propose two techniques that significantly improve these LMs: 1) A novel
recurrent neural network unit with dual components that focus on each language
in the code-switched text separately 2) Pretraining the LM using synthetic text
from a generative model estimated using the training data. We demonstrate the
effectiveness of our proposed techniques by reporting perplexities on a
Mandarin-English task and derive significant reductions in perplexity.
Cascaded Mutual Modulation for Visual Reasoning
Authors: Yiqun Yao, Jiaming Xu, Feng Wang, Bo Xu
Date: 2018-09-06 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA), Visual Reasoning

Visual reasoning is a special visual question answering problem that is
multi-step and compositional by nature, and also requires intensive text-vision
interactions. We propose CMM: Cascaded Mutual Modulation as a novel end-to-end
visual reasoning model. CMM includes a multi-step comprehension process for
both question and image. In each step, we use a Feature-wise Linear Modulation
(FiLM) technique to enable textual/visual pipeline to mutually control each
other. Experiments show that CMM significantly outperforms most related models,
and reach state-of-the-arts on two visual reasoning benchmarks: CLEVR and NLVR,
collected from both synthetic and natural languages. Ablation studies confirm
that both our multistep framework and our visual-guided language modulation are
critical to the task. Our code is available at
https://github.com/FlamingHorizon/CMM-VR.
A tutorial on Particle Swarm Optimization Clustering
Authors: Augusto Luis Ballardini
Date: 2018-09-06 00:00:00
Tasks: Clustering

This paper proposes a tutorial on the Data Clustering technique using the
Particle Swarm Optimization approach. Following the work proposed by Merwe et
al. here we present an in-deep analysis of the algorithm together with a Matlab
implementation and a short tutorial that explains how to modify the proposed
implementation and the effect of the parameters of the original algorithm.
Moreover, we provide a comparison against the results obtained using the well
known K-Means approach. All the source code presented in this paper is publicly
available under the GPL-v2 license.
Why are Sequence-to-Sequence Models So Dull? Understanding the Low-Diversity Problem of Chatbots
Authors: Shaojie Jiang, Maarten de Rijke
Date: 2018-09-06 00:00:00
Proceeding: WS 2018 10
Tasks: Diversity, Information Retrieval, Response Generation, Retrieval

Diversity is a long-studied topic in information retrieval that usually
refers to the requirement that retrieved results should be non-repetitive and
cover different aspects. In a conversational setting, an additional dimension
of diversity matters: an engaging response generation system should be able to
output responses that are diverse and interesting. Sequence-to-sequence
(Seq2Seq) models have been shown to be very effective for response generation.
However, dialogue responses generated by Seq2Seq models tend to have low
diversity. In this paper, we review known sources and existing approaches to
this low-diversity problem. We also identify a source of low diversity that has
been little studied so far, namely model over-confidence. We sketch several
directions for tackling model over-confidence and, hence, the low-diversity
problem, including confidence penalties and label smoothing.
Disentangled Variational Representation for Heterogeneous Face Recognition
Authors: Xiang Wu, Huaibo Huang, Vishal M. Patel, Ran He, Zhenan Sun
Date: 2018-09-06 00:00:00
Tasks: Face Recognition, Heterogeneous Face Recognition

Visible (VIS) to near infrared (NIR) face matching is a challenging problem
due to the significant domain discrepancy between the domains and a lack of
sufficient data for training cross-modal matching algorithms. Existing
approaches attempt to tackle this problem by either synthesizing visible faces
from NIR faces, extracting domain-invariant features from these modalities, or
projecting heterogeneous data onto a common latent space for cross-modal
matching. In this paper, we take a different approach in which we make use of
the Disentangled Variational Representation (DVR) for cross-modal matching.
First, we model a face representation with an intrinsic identity information
and its within-person variations. By exploring the disentangled latent variable
space, a variational lower bound is employed to optimize the approximate
posterior for NIR and VIS representations. Second, aiming at obtaining more
compact and discriminative disentangled latent space, we impose a minimization
of the identity information for the same subject and a relaxed correlation
alignment constraint between the NIR and VIS modality variations. An
alternative optimization scheme is proposed for the disentangled variational
representation part and the heterogeneous face recognition network part. The
mutual promotion between these two parts effectively reduces the NIR and VIS
domain discrepancy and alleviates over-fitting. Extensive experiments on three
challenging NIR-VIS heterogeneous face recognition databases demonstrate that
the proposed method achieves significant improvements over the state-of-the-art
methods.
One-shot Learning for iEEG Seizure Detection Using End-to-end Binary Operations: Local Binary Patterns with Hyperdimensional Computing
Authors: Alessio Burrello, Kaspar Schindler, Luca Benini, Abbas Rahimi
Date: 2018-09-06 00:00:00
Tasks: Few-Shot Learning, One-Shot Learning, Seizure Detection, Specificity, Time Series, Time Series Analysis

This paper presents an efficient binarized algorithm for both learning and
classification of human epileptic seizures from intracranial
electroencephalography (iEEG). The algorithm combines local binary patterns
with brain-inspired hyperdimensional computing to enable end-to-end learning
and inference with binary operations. The algorithm first transforms iEEG time
series from each electrode into local binary pattern codes. Then atomic
high-dimensional binary vectors are used to construct composite representations
of seizures across all electrodes. For the majority of our patients (10 out of
16), the algorithm quickly learns from one or two seizures (i.e., one-/few-shot
learning) and perfectly generalizes on 27 further seizures. For other patients,
the algorithm requires three to six seizures for learning. Overall, our
algorithm surpasses the state-of-the-art methods for detecting 65 novel
seizures with higher specificity and sensitivity, and lower memory footprint.
Dynamic Block Matching to assess the longitudinal component of the dense motion field of the carotid artery wall in B-mode ultrasound sequences -- Association with coronary artery disease
Authors: Guillaume Zahnd, Kozue Saito, Kazuyuki Nagatsuka, Yoshito Otake, Yoshinobu Sato
Date: 2018-09-06 00:00:00

Purpose: The motion of the common carotid artery tissue layers along the vessel axis during the cardiac cycle, observed in ultrasound imaging, is associated with the presence of established cardiovascular risk factors. However, the vast majority of the methods are based on the tracking of a single point, thus failing to capture the overall motion of the entire arterial wall. The aim of this work is to introduce a motion tracking framework able to simultaneously extract the trajectory of a large collection of points spanning the entire exploitable width of the image. Method: The longitudinal motion, which is the main focus of the present work, is determined in two steps. First, a series of independent block matching operations are carried out for all the tracked points. Then, an original dynamic-programming approach is exploited to regularize the collection of similarity maps and estimate the globally optimal motion over the entire vessel wall. Sixty-two atherosclerotic participants at high cardiovascular risk were involved in this study. Results: A dense displacement field, describing the longitudinal motion of the carotid far wall over time, was extracted. For each cine-loop, the method was evaluated against manual reference tracings performed on three local points, with an average absolute error of 150+/-163 um. A strong correlation was found between motion inhomogeneity and the presence of coronary artery disease (beta-coefficient=0.586, p=0.003). Conclusions: To the best of our knowledge, this is the first time that a method is specifically proposed to assess the dense motion field of the carotid far wall. This approach has potential to evaluate the (in)homogeneity of the wall dynamics. The proposed method has promising performances to improve the analysis of arterial longitudinal motion and the understanding of the underlying patho-physiological parameters.