pwc-archive/papers-with-abstracts huggingface dataset:
Part-Aligned Bilinear Representations for Person Re-identification
Authors: Yumin Suh, Jingdong Wang, Siyu Tang, Tao Mei, Kyoung Mu Lee
Date: 2018-04-19 00:00:00
Proceeding: ECCV 2018 9
Tasks: 2D Human Pose Estimation, Person Re-Identification, Pose Estimation

We propose a novel network that learns a part-aligned representation for
person re-identification. It handles the body part misalignment problem, that
is, body parts are misaligned across human detections due to pose/viewpoint
change and unreliable detection. Our model consists of a two-stream network
(one stream for appearance map extraction and the other one for body part map
extraction) and a bilinear-pooling layer that generates and spatially pools a
part-aligned map. Each local feature of the part-aligned map is obtained by a
bilinear mapping of the corresponding local appearance and body part
descriptors. Our new representation leads to a robust image matching
similarity, which is equivalent to an aggregation of the local similarities of
the corresponding body parts combined with the weighted appearance similarity.
This part-aligned representation reduces the part misalignment problem
significantly. Our approach is also advantageous over other pose-guided
representations (e.g., extracting representations over the bounding box of each
body part) by learning part descriptors optimal for person re-identification.
For training the network, our approach does not require any part annotation on
the person re-identification dataset. Instead, we simply initialize the part
sub-stream using a pre-trained sub-network of an existing pose estimation
network, and train the whole network to minimize the re-identification loss. We
validate the effectiveness of our approach by demonstrating its superiority
over the state-of-the-art methods on the standard benchmark datasets, including
Market-1501, CUHK03, CUHK01 and DukeMTMC, and standard video dataset MARS.
Introducing two Vietnamese Datasets for Evaluating Semantic Models of (Dis-)Similarity and Relatedness
Authors: Kim Anh Nguyen, Sabine Schulte im Walde, Ngoc Thang Vu
Date: 2018-04-15 00:00:00
Proceeding: NAACL 2018 6
Tasks: Semantic Similarity, Semantic Textual Similarity, Vietnamese Datasets

We present two novel datasets for the low-resource language Vietnamese to
assess models of semantic similarity: ViCon comprises pairs of synonyms and
antonyms across word classes, thus offering data to distinguish between
similarity and dissimilarity. ViSim-400 provides degrees of similarity across
five semantic relations, as rated by human judges. The two datasets are
verified through standard co-occurrence and neural network models, showing
results comparable to the respective English datasets.
A New Decidable Class of Tuple Generating Dependencies: The Triangularly-Guarded Class
Authors: Vernon Asuncion, Yan Zhang
Date: 2018-04-17 00:00:00

In this paper we introduce a new class of tuple-generating dependencies
(TGDs) called triangularly-guarded TGDs, which are TGDs with certain
restrictions on the atomic derivation track embedded in the underlying rule
set. We show that conjunctive query answering under this new class of TGDs is
decidable. We further show that this new class strictly contains some other
decidable classes such as weak-acyclic, guarded, sticky and shy, which, to the
best of our knowledge, provides a unified representation of all these
aforementioned classes.
Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection
Authors: Björn Barz, Erik Rodner, Yanira Guanche Garcia, Joachim Denzler
Date: 2018-04-19 00:00:00
Tasks: Anomaly Detection, Fraud Detection, Time Series, Time Series Analysis

Automatic detection of anomalies in space- and time-varying measurements is an important tool in several fields, e.g., fraud detection, climate analysis, or healthcare monitoring. We present an algorithm for detecting anomalous regions in multivariate spatio-temporal time-series, which allows for spotting the interesting parts in large amounts of data, including video and text data. In opposition to existing techniques for detecting isolated anomalous data points, we propose the "Maximally Divergent Intervals" (MDI) framework for unsupervised detection of coherent spatial regions and time intervals characterized by a high Kullback-Leibler divergence compared with all other data given. In this regard, we define an unbiased Kullback-Leibler divergence that allows for ranking regions of different size and show how to enable the algorithm to run on large-scale data sets in reasonable time using an interval proposal technique. Experiments on both synthetic and real data from various domains, such as climate analysis, video surveillance, and text forensics, demonstrate that our method is widely applicable and a valuable tool for finding interesting events in different types of data.
A Trajectory Calculus for Qualitative Spatial Reasoning Using Answer Set Programming
Authors: George Baryannis, Ilias Tachmazidis, Sotiris Batsakis, Grigoris Antoniou, Mario Alviano, Timos Sellis, Pei-Wei Tsai
Date: 2018-04-19 00:00:00
Tasks: Spatial Reasoning

Spatial information is often expressed using qualitative terms such as
natural language expressions instead of coordinates; reasoning over such terms
has several practical applications, such as bus routes planning. Representing
and reasoning on trajectories is a specific case of qualitative spatial
reasoning that focuses on moving objects and their paths. In this work, we
propose two versions of a trajectory calculus based on the allowed properties
over trajectories, where trajectories are defined as a sequence of
non-overlapping regions of a partitioned map. More specifically, if a given
trajectory is allowed to start and finish at the same region, 6 base relations
are defined (TC-6). If a given trajectory should have different start and
finish regions but cycles are allowed within, 10 base relations are defined
(TC-10). Both versions of the calculus are implemented as ASP programs; we
propose several different encodings, including a generalised program capable of
encoding any qualitative calculus in ASP. All proposed encodings are
experimentally evaluated using a real-world dataset. Experiment results show
that the best performing implementation can scale up to an input of 250
trajectories for TC-6 and 150 trajectories for TC-10 for the problem of
discovering a consistent configuration, a significant improvement compared to
previous ASP implementations for similar qualitative spatial and temporal
calculi. This manuscript is under consideration for acceptance in TPLP.
Consistent CCG Parsing over Multiple Sentences for Improved Logical Reasoning
Authors: Masashi Yoshikawa, Koji Mineshima, Hiroshi Noji, Daisuke Bekki
Date: 2018-04-19 00:00:00
Proceeding: NAACL 2018 6
Tasks: Automated Theorem Proving, Formal Logic, Logical Reasoning, Natural Language Inference, RTE, Sentence

In formal logic-based approaches to Recognizing Textual Entailment (RTE), a
Combinatory Categorial Grammar (CCG) parser is used to parse input premises and
hypotheses to obtain their logical formulas. Here, it is important that the
parser processes the sentences consistently; failing to recognize a similar
syntactic structure results in inconsistent predicate argument structures among
them, in which case the succeeding theorem proving is doomed to failure. In
this work, we present a simple method to extend an existing CCG parser to parse
a set of sentences consistently, which is achieved with an inter-sentence
modeling with Markov Random Fields (MRF). When combined with existing
logic-based systems, our method always shows improvement in the RTE experiments
on English and Japanese languages.
Attacking Convolutional Neural Network using Differential Evolution
Authors: Jiawei Su, Danilo Vasconcellos Vargas, Kouichi Sakurai
Date: 2018-04-19 00:00:00

The output of Convolutional Neural Networks (CNN) has been shown to be
discontinuous which can make the CNN image classifier vulnerable to small
well-tuned artificial perturbations. That is, images modified by adding such
perturbations(i.e. adversarial perturbations) that make little difference to
human eyes, can completely alter the CNN classification results. In this paper,
we propose a practical attack using differential evolution(DE) for generating
effective adversarial perturbations. We comprehensively evaluate the
effectiveness of different types of DEs for conducting the attack on different
network structures. The proposed method is a black-box attack which only
requires the miracle feedback of the target CNN systems. The results show that
under strict constraints which simultaneously control the number of pixels
changed and overall perturbation strength, attacking can achieve 72.29%, 78.24%
and 61.28% non-targeted attack success rates, with 88.68%, 99.85% and 73.07%
confidence on average, on three common types of CNNs. The attack only requires
modifying 5 pixels with 20.44, 14.76 and 22.98 pixel values distortion. Thus,
the result shows that the current DNNs are also vulnerable to such simpler
black-box attacks even under very limited attack conditions.
Exploring Partially Observed Networks with Nonparametric Bandits
Authors: Kaushalya Madhawa, Tsuyoshi Murata
Date: 2018-04-19 00:00:00

Real-world networks such as social and communication networks are too large
to be observed entirely. Such networks are often partially observed such that
network size, network topology, and nodes of the original network are unknown.
In this paper we formalize the Adaptive Graph Exploring problem. We assume that
we are given an incomplete snapshot of a large network and additional nodes can
be discovered by querying nodes in the currently observed network. The goal of
this problem is to maximize the number of observed nodes within a given query
budget. Querying which set of nodes maximizes the size of the observed network?
We formulate this problem as an exploration-exploitation problem and propose a
novel nonparametric multi-arm bandit (MAB) algorithm for identifying which
nodes to be queried. Our contributions include: (1) $i$KNN-UCB, a novel
nonparametric MAB algorithm, applies $k$-nearest neighbor UCB to the setting
when the arms are presented in a vector space, (2) provide theoretical
guarantee that $i$KNN-UCB algorithm has sublinear regret, and (3) applying
$i$KNN-UCB algorithm on synthetic networks and real-world networks from
different domains, we show that our method discovers up to 40% more nodes
compared to existing baselines.
LightRel SemEval-2018 Task 7: Lightweight and Fast Relation Classification
Authors: Tyler Renslow, Günter Neumann
Date: 2018-04-19 00:00:00
Tasks: Classification, General Classification, Relation, Relation Classification, Relation Extraction, Word Embeddings

We present LightRel, a lightweight and fast relation classifier. Our goal is
to develop a high baseline for different relation extraction tasks. By defining
only very few data-internal, word-level features and external knowledge sources
in the form of word clusters and word embeddings, we train a fast and simple
linear classifier.
Now you see me: evaluating performance in long-term visual tracking
Authors: Alan Lukežič, Luka Čehovin Zajc, Tomáš Vojíř, Jiří Matas, Matej Kristan
Date: 2018-04-19 00:00:00
Tasks: Benchmarking, Visual Tracking

We propose a new long-term tracking performance evaluation methodology and
present a new challenging dataset of carefully selected sequences with many
target disappearances. We perform an extensive evaluation of six long-term and
nine short-term state-of-the-art trackers, using new performance measures,
suitable for evaluating long-term tracking - tracking precision, recall and
F-score. The evaluation shows that a good model update strategy and the
capability of image-wide re-detection are critical for long-term tracking
performance. We integrated the methodology in the VOT toolkit to automate
experimental analysis and benchmarking and to facilitate the development of
long-term trackers.
SegDenseNet: Iris Segmentation for Pre and Post Cataract Surgery
Authors: Aditya Lakra, Pavani Tripathi, Rohit Keshari, Mayank Vatsa, Richa Singh
Date: 2018-01-30 00:00:00
Tasks: Iris Recognition, Iris Segmentation, Segmentation

Cataract is caused due to various factors such as age, trauma, genetics,
smoking and substance consumption, and radiation. It is one of the major common
ophthalmic diseases worldwide which can potentially affect iris-based biometric
systems. India, which hosts the largest biometrics project in the world, has
about 8 million people undergoing cataract surgery annually. While existing
research shows that cataract does not have a major impact on iris recognition,
our observations suggest that the iris segmentation approaches are not well
equipped to handle cataract or post cataract surgery cases. Therefore, failure
in iris segmentation affects the overall recognition performance. This paper
presents an efficient iris segmentation algorithm with variations due to
cataract and post cataract surgery. The proposed algorithm, termed as
SegDenseNet, is a deep learning algorithm based on DenseNets. The experiments
on the IIITD Cataract database show that improving the segmentation enhances
the identification by up to 25% across different sensors and matchers.
A Robot to Shape your Natural Plant: The Machine Learning Approach to Model and Control Bio-Hybrid Systems
Authors: Mostafa Wahby, Mary Katherine Heinrich, Daniel Nicolas Hofstadler, Payam Zahadat, Sebastian Risi, Phil Ayres, Thomas Schmickl, Heiko Hamann
Date: 2018-04-18 00:00:00
Tasks: Decision Making

Bio-hybrid systems---close couplings of natural organisms with
technology---are high potential and still underexplored. In existing work,
robots have mostly influenced group behaviors of animals. We explore the
possibilities of mixing robots with natural plants, merging useful attributes.
Significant synergies arise by combining the plants' ability to efficiently
produce shaped material and the robots' ability to extend sensing and
decision-making behaviors. However, programming robots to control plant motion
and shape requires good knowledge of complex plant behaviors. Therefore, we use
machine learning to create a holistic plant model and evolve robot controllers.
As a benchmark task we choose obstacle avoidance. We use computer vision to
construct a model of plant stem stiffening and motion dynamics by training an
LSTM network. The LSTM network acts as a forward model predicting change in the
plant, driving the evolution of neural network robot controllers. The evolved
controllers augment the plants' natural light-finding and tissue-stiffening
behaviors to avoid obstacles and grow desired shapes. We successfully verify
the robot controllers and bio-hybrid behavior in reality, with a physical setup
and actual plants.
Predicting resonant properties of plasmonic structures by deep learning
Authors: Iman Sajedian, Jeonghyun Kim, Junsuk Rho
Date: 2018-04-19 00:00:00
Tasks: Deep Learning

Deep learning can be used to extract meaningful results from images. In this
paper, we used convolutional neural networks combined with recurrent neural
networks on images of plasmonic structures and extract absorption data form
them. To provide the required data for the model we did 100,000 simulations
with similar setups and random structures. By designing a deep network we could
find a model that could predict the absorption of any structure with similar
setup. We used convolutional neural networks to get the spatial information
from the images and we used recurrent neural networks to help the model find
the relationship between the spatial information obtained from convolutional
neural network model. With this design we could reach a very low loss in
predicting the absorption compared to the results obtained from numerical
simulation in a very short time.
Online Non-Additive Path Learning under Full and Partial Information
Authors: Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Holakou Rahmanian, Manfred K. Warmuth
Date: 2018-04-18 00:00:00
Tasks: Structured Prediction

We study the problem of online path learning with non-additive gains, which
is a central problem appearing in several applications, including ensemble
structured prediction. We present new online algorithms for path learning with
non-additive count-based gains for the three settings of full information,
semi-bandit and full bandit with very favorable regret guarantees. A key
component of our algorithms is the definition and computation of an
intermediate context-dependent automaton that enables us to use existing
algorithms designed for additive gains. We further apply our methods to the
important application of ensemble structured prediction. Finally, beyond
count-based gains, we give an efficient implementation of the EXP3 algorithm
for the full bandit setting with an arbitrary (non-additive) gain.
Analyzing Solar Irradiance Variation From GPS and Cameras
Authors: Shilpa Manandhar, Soumyabrata Dev, Yee Hui Lee, Yu Song Meng
Date: 2018-04-19 00:00:00

The total amount of solar irradiance falling on the earth's surface is an
important area of study amongst the photo-voltaic (PV) engineers and remote
sensing analysts. The received solar irradiance impacts the total amount of
generated solar energy. However, this generation is often hindered by the high
degree of solar irradiance variability. In this paper, we study the main
factors behind such variability with the assistance of Global Positioning
System (GPS) and ground-based, high-resolution sky cameras. This analysis will
also be helpful for understanding cloud phenomenon and other events in the
earth's atmosphere.
Interdependent Gibbs Samplers
Authors: Mark Kozdoba, Shie Mannor
Date: 2018-04-11 00:00:00

Gibbs sampling, as a model learning method, is known to produce the most
accurate results available in a variety of domains, and is a de facto standard
in these domains. Yet, it is also well known that Gibbs random walks usually
have bottlenecks, sometimes termed "local maxima", and thus samplers often
return suboptimal solutions. In this paper we introduce a variation of the
Gibbs sampler which yields high likelihood solutions significantly more often
than the regular Gibbs sampler.
  Specifically, we show that combining multiple samplers, with certain
dependence (coupling) between them, results in higher likelihood solutions.
This side-steps the well known issue of identifiability, which has been the
obstacle to combining samplers in previous work. We evaluate the approach on a
Latent Dirichlet Allocation model, and also on HMM's, where precise computation
of likelihoods and comparisons to the standard EM algorithm are possible.
Learning to Extract Coherent Summary via Deep Reinforcement Learning
Authors: Yuxiang Wu, Baotian Hu
Date: 2018-04-19 00:00:00
Tasks: Deep Reinforcement Learning, Extractive Summarization, Feature Engineering, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sentence, Text Summarization

Coherence plays a critical role in producing a high-quality summary from a
document. In recent years, neural extractive summarization is becoming
increasingly attractive. However, most of them ignore the coherence of
summaries when extracting sentences. As an effort towards extracting coherent
summaries, we propose a neural coherence model to capture the cross-sentence
semantic and syntactic coherence patterns. The proposed neural coherence model
obviates the need for feature engineering and can be trained in an end-to-end
fashion using unlabeled data. Empirical results show that the proposed neural
coherence model can efficiently capture the cross-sentence coherence patterns.
Using the combined output of the neural coherence model and ROUGE package as
the reward, we design a reinforcement learning method to train a proposed
neural extractive summarizer which is named Reinforced Neural Extractive
Summarization (RNES) model. The RNES model learns to optimize coherence and
informative importance of the summary simultaneously. Experimental results show
that the proposed RNES outperforms existing baselines and achieves
state-of-the-art performance in term of ROUGE on CNN/Daily Mail dataset. The
qualitative evaluation indicates that summaries produced by RNES are more
coherent and readable.
Reconstruction of Simulation-Based Physical Field by Reconstruction Neural Network Method
Authors: Yu Li, Hu Wang, Kangjia Mo, Tao Zeng
Date: 2018-04-19 00:00:00
Tasks: Generative Adversarial Network

A variety of modeling techniques have been developed in the past decade to
reduce the computational expense and improve the accuracy of modeling. In this
study, a new framework of modeling is suggested. Compared with other popular
methods, a distinctive characteristic is "from image based model to analysis
based model (e.g. stress, strain, and deformation)". In such a framework, a
reconstruction neural network (ReConNN) model designed for simulation-based
physical field's reconstruction is proposed. The ReConNN contains two submodels
that are convolutional neural network (CNN) and generative adversarial net-work
(GAN). The CNN is employed to construct the mapping between contour images of
physical field and objective function. Subsequently, the GAN is utilized to
generate more images which are similar to the existing contour images. Finally,
Lagrange polynomial is applied to complete the reconstruction. However, the
existing CNN models are commonly applied to the classification tasks, which
seem to be difficult to handle with regression tasks of images. Meanwhile, the
existing GAN architectures are insufficient to generate high-accuracy "pseudo
contour images". Therefore, a ReConNN model based on a Convolution in
Convolution (CIC) and a Convolutional AutoEncoder based on Wasserstein
Generative Adversarial Network (WGAN-CAE) is suggested. To evaluate the
performance of the proposed model representatively, a classical topology
optimization procedure is considered. Then the ReConNN is utilized to the
reconstruction of heat transfer process of a pin fin heat sink. It demonstrates
that the proposed ReConNN model is proved to be a potential capability to
reconstruct physical field for multidisciplinary, such as structural
optimization.
Algorithms and Conditional Lower Bounds for Planning Problems
Authors: Krishnendu Chatterjee, Wolfgang Dvořák, Monika Henzinger, Alexander Svozil
Date: 2018-04-19 00:00:00

We consider planning problems for graphs, Markov decision processes (MDPs),
and games on graphs. While graphs represent the most basic planning model, MDPs
represent interaction with nature and games on graphs represent interaction
with an adversarial environment. We consider two planning problems where there
are k different target sets, and the problems are as follows: (a) the coverage
problem asks whether there is a plan for each individual target set, and (b)
the sequential target reachability problem asks whether the targets can be
reached in sequence. For the coverage problem, we present a linear-time
algorithm for graphs and quadratic conditional lower bound for MDPs and games
on graphs. For the sequential target problem, we present a linear-time
algorithm for graphs, a sub-quadratic algorithm for MDPs, and a quadratic
conditional lower bound for games on graphs. Our results with conditional lower
bounds establish (i) model-separation results showing that for the coverage
problem MDPs and games on graphs are harder than graphs and for the sequential
reachability problem games on graphs are harder than MDPs and graphs; (ii)
objective-separation results showing that for MDPs the coverage problem is
harder than the sequential target problem.