pwc-archive/papers-with-abstracts huggingface dataset:
Heter-LP: A heterogeneous label propagation algorithm and its application in drug repositioning
Authors: Maryam Lotfi Shahreza, Nasser Ghadiri, Seyed Rasul Mossavi, Jaleh Varshosaz, James Green
Date: 2016-11-08 00:00:00
Tasks: Data Integration, Drug Discovery

Drug repositioning offers an effective solution to drug discovery, saving
both time and resources by finding new indications for existing drugs.
Typically, a drug takes effect via its protein targets in the cell. As a
result, it is necessary for drug development studies to conduct an
investigation into the interrelationships of drugs, protein targets, and
diseases. Although previous studies have made a strong case for the
effectiveness of integrative network-based methods for predicting these
interrelationships, little progress has been achieved in this regard within
drug repositioning research. Moreover, the interactions of new drugs and
targets (lacking any known targets and drugs, respectively) cannot be
accurately predicted by most established methods. In this paper, we propose a
novel semi-supervised heterogeneous label propagation algorithm named Heter-LP,
which applies both local as well as global network features for data
integration. To predict drug-target, disease-target, and drug-disease
associations, we use information about drugs, diseases, and targets as
collected from multiple sources at different levels. Our algorithm integrates
these various types of data into a heterogeneous network and implements a label
propagation algorithm to find new interactions. Statistical analyses of 10-fold
cross-validation results and experimental analysis support the effectiveness of
the proposed algorithm.
Understanding and Exploiting Object Interaction Landscapes
Authors: Sören Pirk, Vojtech Krs, Kaimo Hu, Suren Deepak Rajasekaran, Hao Kang, Bedrich Benes, Yusuke Yoshiyasu, Leonidas J. Guibas
Date: 2016-09-27 00:00:00
Tasks: Object

Interactions play a key role in understanding objects and scenes, for both
virtual and real world agents. We introduce a new general representation for
proximal interactions among physical objects that is agnostic to the type of
objects or interaction involved. The representation is based on tracking
particles on one of the participating objects and then observing them with
sensors appropriately placed in the interaction volume or on the interaction
surfaces. We show how to factorize these interaction descriptors and project
them into a particular participating object so as to obtain a new functional
descriptor for that object, its interaction landscape, capturing its observed
use in a spatio-temporal framework. Interaction landscapes are independent of
the particular interaction and capture subtle dynamic effects in how objects
move and behave when in functional use. Our method relates objects based on
their function, establishes correspondences between shapes based on functional
key points and regions, and retrieves peer and partner objects with respect to
an interaction.
Multispectral Deep Neural Networks for Pedestrian Detection
Authors: Jingjing Liu, Shaoting Zhang, Shu Wang, Dimitris N. Metaxas
Date: 2016-11-08 00:00:00
Tasks: 2D Object Detection, 3D Object Detection, Multispectral Object Detection, Object Detection, Pedestrian Detection

Multispectral pedestrian detection is essential for around-the-clock
applications, e.g., surveillance and autonomous driving. We deeply analyze
Faster R-CNN for multispectral pedestrian detection task and then model it into
a convolutional network (ConvNet) fusion problem. Further, we discover that
ConvNet-based pedestrian detectors trained by color or thermal images
separately provide complementary information in discriminating human instances.
Thus there is a large potential to improve pedestrian detection by using color
and thermal images in DNNs simultaneously. We carefully design four ConvNet
fusion architectures that integrate two-branch ConvNets on different DNNs
stages, all of which yield better performance compared with the baseline
detector. Our experimental results on KAIST pedestrian benchmark show that the
Halfway Fusion model that performs fusion on the middle-level convolutional
features outperforms the baseline method by 11% and yields a missing rate 3.5%
lower than the other proposed architectures.
Estimating motion with principal component regression strategies
Authors: Felipe P. do Carmo, Vania Vieira Estrela, Joaquim Teixeira de Assis
Date: 2016-11-08 00:00:00
Tasks: Optical Flow Estimation, regression

In this paper, two simple principal component regression methods for
estimating the optical flow between frames of video sequences according to a
pel-recursive manner are introduced. These are easy alternatives to dealing
with mixtures of motion vectors in addition to the lack of prior information on
spatial-temporal statistics (although they are supposed to be normal in a local
sense). The 2D motion vector estimation approaches take into consideration
simple image properties and are used to harmonize regularized least square
estimates. Their main advantage is that no knowledge of the noise distribution
is necessary, although there is an underlying assumption of localized
smoothness. Preliminary experiments indicate that this approach provides robust
estimates of the optical flow.
Self-Wiring Question Answering Systems
Authors: Ricardo Usbeck, Jonathan Huthmann, Nico Duldhardt, Axel-Cyrille Ngonga Ngomo
Date: 2016-11-06 00:00:00
Tasks: Question Answering

Question answering (QA) has been the subject of a resurgence over the past
years. The said resurgence has led to a multitude of question answering (QA)
systems being developed both by companies and research facilities. While a few
components of QA systems get reused across implementations, most systems do not
leverage the full potential of component reuse. Hence, the development of QA
systems is currently still a tedious and time-consuming process. We address the
challenge of accelerating the creation of novel or tailored QA systems by
presenting a concept for a self-wiring approach to composing QA systems. Our
approach will allow the reuse of existing, web-based QA systems or modules
while developing new QA platforms. To this end, it will rely on QA modules
being described using the Web Ontology Language. Based on these descriptions,
our approach will be able to automatically compose QA systems using a
data-driven approach automatically.
Spectral Graph Cut from a Filtering Point of View
Authors: Chengxi Ye, Yuxu Lin, Mingli Song, Chun Chen, David W. Jacobs
Date: 2012-05-20 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Spectral graph theory is well known and widely used in computer vision. In
this paper, we analyze image segmentation algorithms that are based on spectral
graph theory, e.g., normalized cut, and show that there is a natural connection
between spectural graph theory based image segmentationand and edge preserving
filtering. Based on this connection we show that the normalized cut algorithm
is equivalent to repeated iterations of bilateral filtering. Then, using this
equivalence we present and implement a fast normalized cut algorithm for image
segmentation. Experiments show that our implementation can solve the original
optimization problem in the normalized cut algorithm 10 to 100 times faster.
Furthermore, we present a new algorithm called conditioned normalized cut for
image segmentation that can easily incorporate color image patches and
demonstrate how this segmentation problem can be solved with edge preserving
filtering.
A Fuzzy Logic System to Analyze a Student's Lifestyle
Authors: Sourish Ghosh, Aaditya Sanjay Boob, Nishant Nikhil, Nayan Raju Vysyaraju, Ankit Kumar
Date: 2016-10-13 00:00:00
Tasks: Management

A college student's life can be primarily categorized into domains such as
education, health, social and other activities which may include daily chores
and travelling time. Time management is crucial for every student. A self
realisation of one's daily time expenditure in various domains is therefore
essential to maximize one's effective output. This paper presents how a mobile
application using Fuzzy Logic and Global Positioning System (GPS) analyzes a
student's lifestyle and provides recommendations and suggestions based on the
results.
An Automated System for Essay Scoring of Online Exams in Arabic based on Stemming Techniques and Levenshtein Edit Operations
Authors: Emad Fawzi Al-Shalabi
Date: 2016-11-08 00:00:00

In this article, an automated system is proposed for essay scoring in Arabic
language for online exams based on stemming techniques and Levenshtein edit
operations. An online exam has been developed on the proposed mechanisms,
exploiting the capabilities of light and heavy stemming. The implemented online
grading system has shown to be an efficient tool for automated scoring of essay
questions.
Discriminative Acoustic Word Embeddings: Recurrent Neural Network-Based Approaches
Authors: Shane Settle, Karen Livescu
Date: 2016-11-08 00:00:00
Tasks: Dynamic Time Warping, General Classification, speech-recognition, Speech Recognition, Word Embeddings

Acoustic word embeddings --- fixed-dimensional vector representations of
variable-length spoken word segments --- have begun to be considered for tasks
such as speech recognition and query-by-example search. Such embeddings can be
learned discriminatively so that they are similar for speech segments
corresponding to the same word, while being dissimilar for segments
corresponding to different words. Recent work has found that acoustic word
embeddings can outperform dynamic time warping on query-by-example search and
related word discrimination tasks. However, the space of embedding models and
training approaches is still relatively unexplored. In this paper we present
new discriminative embedding models based on recurrent neural networks (RNNs).
We consider training losses that have been successful in prior work, in
particular a cross entropy loss for word classification and a contrastive loss
that explicitly aims to separate same-word and different-word pairs in a
"Siamese network" training setting. We find that both classifier-based and
Siamese RNN embeddings improve over previously reported results on a word
discrimination task, with Siamese RNNs outperforming classification models. In
addition, we present analyses of the learned embeddings and the effects of
variables such as dimensionality and network structure.
Getting Started with Neural Models for Semantic Matching in Web Search
Authors: Kezban Dilek Onal, Ismail Sengor Altingovde, Pinar Karagoz, Maarten de Rijke
Date: 2016-11-08 00:00:00
Tasks: Information Retrieval, Retrieval, Survey

The vocabulary mismatch problem is a long-standing problem in information
retrieval. Semantic matching holds the promise of solving the problem. Recent
advances in language technology have given rise to unsupervised neural models
for learning representations of words as well as bigger textual units. Such
representations enable powerful semantic matching methods. This survey is meant
as an introduction to the use of neural models for semantic matching. To remain
focused we limit ourselves to web search. We detail the required background and
terminology, a taxonomy grouping the rapidly growing body of work in the area,
and then survey work on neural models for semantic matching in the context of
three tasks: query suggestion, ad retrieval, and document retrieval. We include
a section on resources and best practices that we believe will help readers who
are new to the area. We conclude with an assessment of the state-of-the-art and
suggestions for future work.
The Loss Surface of Residual Networks: Ensembles and the Role of Batch Normalization
Authors: Etai Littwin, Lior Wolf
Date: 2016-11-08 00:00:00

Deep Residual Networks present a premium in performance in comparison to
conventional networks of the same depth and are trainable at extreme depths. It
has recently been shown that Residual Networks behave like ensembles of
relatively shallow networks. We show that these ensembles are dynamic: while
initially the virtual ensemble is mostly at depths lower than half the
network's depth, as training progresses, it becomes deeper and deeper. The main
mechanism that controls the dynamic ensemble behavior is the scaling
introduced, e.g., by the Batch Normalization technique. We explain this
behavior and demonstrate the driving force behind it. As a main tool in our
analysis, we employ generalized spin glass models, which we also use in order
to study the number of critical points in the optimization of Residual
Networks.
Cognitive Discriminative Mappings for Rapid Learning
Authors: Wen-Chieh Fang, Yi-ting Chiang
Date: 2016-11-08 00:00:00

Humans can learn concepts or recognize items from just a handful of examples,
while machines require many more samples to perform the same task. In this
paper, we build a computational model to investigate the possibility of this
kind of rapid learning. The proposed method aims to improve the learning task
of input from sensory memory by leveraging the information retrieved from
long-term memory. We present a simple and intuitive technique called cognitive
discriminative mappings (CDM) to explore the cognitive problem. First, CDM
separates and clusters the data instances retrieved from long-term memory into
distinct classes with a discrimination method in working memory when a sensory
input triggers the algorithm. CDM then maps each sensory data instance to be as
close as possible to the median point of the data group with the same class.
The experimental results demonstrate that the CDM approach is effective for
learning the discriminative features of supervised classifications with few
training sensory input instances.
On the prediction loss of the lasso in the partially labeled setting
Authors: Pierre C. Bellec, Arnak S. Dalalyan, Edwin Grappin, Quentin Paris
Date: 2016-06-20 00:00:00

In this paper we revisit the risk bounds of the lasso estimator in the
context of transductive and semi-supervised learning. In other terms, the
setting under consideration is that of regression with random design under
partial labeling. The main goal is to obtain user-friendly bounds on the
off-sample prediction risk. To this end, the simple setting of bounded response
variable and bounded (high-dimensional) covariates is considered. We propose
some new adaptations of the lasso to these settings and establish oracle
inequalities both in expectation and in deviation. These results provide
non-asymptotic upper bounds on the risk that highlight the interplay between
the bias due to the mis-specification of the linear model, the bias due to the
approximate sparsity and the variance. They also demonstrate that the presence
of a large number of unlabeled features may have significant positive impact in
the situations where the restricted eigenvalue of the design matrix vanishes or
is very small.
On the Prediction Performance of the Lasso
Authors: Arnak S. Dalalyan, Mohamed Hebiri, Johannes Lederer
Date: 2014-02-07 00:00:00
Tasks: Prediction, regression

Although the Lasso has been extensively studied, the relationship between its
prediction performance and the correlations of the covariates is not fully
understood. In this paper, we give new insights into this relationship in the
context of multiple linear regression. We show, in particular, that the
incorporation of a simple correlation measure into the tuning parameter can
lead to a nearly optimal prediction performance of the Lasso even for highly
correlated covariates. However, we also reveal that for moderately correlated
covariates, the prediction performance of the Lasso can be mediocre
irrespective of the choice of the tuning parameter. We finally show that our
results also lead to near-optimal rates for the least-squares estimator with
total variation penalty.
Moral Lineage Tracing
Authors: Florian Jug, Evgeny Levinkov, Corinna Blasse, Eugene W. Myers, Bjoern Andres
Date: 2015-11-17 00:00:00
Proceeding: CVPR 2016 6
Tasks: Superpixels

Lineage tracing, the tracking of living cells as they move and divide, is a
central problem in biological image analysis. Solutions, called lineage
forests, are key to understanding how the structure of multicellular organisms
emerges. We propose an integer linear program (ILP) whose feasible solutions
define a decomposition of each image in a sequence into cells (segmentation),
and a lineage forest of cells across images (tracing). Unlike previous
formulations, we do not constrain the set of decompositions, except by
contracting pixels to superpixels. The main challenge, as we show, is to
enforce the morality of lineages, i.e., the constraint that cells do not merge.
To enforce morality, we introduce path-cut inequalities. To find feasible
solutions of the NP-hard ILP, with certified bounds to the global optimum, we
define efficient separation procedures and apply these as part of a
branch-and-cut algorithm. We show the effectiveness of this approach by
analyzing feasible solutions for real microscopy data in terms of bounds and
run-time, and by their weighted edit distance to ground truth lineage forests
traced by humans.
Automatic recognition of child speech for robotic applications in noisy environments
Authors: Samuel Fernando, Roger K. Moore, David Cameron, Emily C. Collins, Abigail Millings, Amanda J. Sharkey, Tony J. Prescott
Date: 2016-11-08 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Augmentation, speech-recognition, Speech Recognition

Automatic speech recognition (ASR) allows a natural and intuitive interface
for robotic educational applications for children. However there are a number
of challenges to overcome to allow such an interface to operate robustly in
realistic settings, including the intrinsic difficulties of recognising child
speech and high levels of background noise often present in classrooms. As part
of the EU EASEL project we have provided several contributions to address these
challenges, implementing our own ASR module for use in robotics applications.
We used the latest deep neural network algorithms which provide a leap in
performance over the traditional GMM approach, and apply data augmentation
methods to improve robustness to noise and speaker variation. We provide a
close integration between the ASR module and the rest of the dialogue system,
allowing the ASR to receive in real-time the language models relevant to the
current section of the dialogue, greatly improving the accuracy. We integrated
our ASR module into an interactive, multimodal system using a small humanoid
robot to help children learn about exercise and energy. The system was
installed at a public museum event as part of a research study where 320
children (aged 3 to 14) interacted with the robot, with our ASR achieving 90%
accuracy for fluent and near-fluent speech.
SGD with Variance Reduction beyond Empirical Risk Minimization
Authors: Massil Achab, Agathe Guilloux, Stéphane Gaïffas, Emmanuel Bacry
Date: 2015-10-16 00:00:00
Tasks: Survival Analysis

We introduce a doubly stochastic proximal gradient algorithm for optimizing a
finite average of smooth convex functions, whose gradients depend on
numerically expensive expectations. Our main motivation is the acceleration of
the optimization of the regularized Cox partial-likelihood (the core model used
in survival analysis), but our algorithm can be used in different settings as
well. The proposed algorithm is doubly stochastic in the sense that gradient
steps are done using stochastic gradient descent (SGD) with variance reduction,
where the inner expectations are approximated by a Monte-Carlo Markov-Chain
(MCMC) algorithm. We derive conditions on the MCMC number of iterations
guaranteeing convergence, and obtain a linear rate of convergence under strong
convexity and a sublinear rate without this assumption. We illustrate the fact
that our algorithm improves the state-of-the-art solver for regularized Cox
partial-likelihood on several datasets from survival analysis.
Proceedings of the First International Workshop on Argumentation in Logic Programming and Non-Monotonic Reasoning (Arg-LPNMR 2016)
Authors: Sarah Alice Gaggl, Juan Carlos Nieves, Hannes Strass
Date: 2016-11-08 00:00:00

This volume contains the papers presented at Arg-LPNMR 2016: First
International Workshop on Argumentation in Logic Programming and Nonmonotonic
Reasoning held on July 8-10, 2016 in New York City, NY.
Building a comprehensive syntactic and semantic corpus of Chinese clinical texts
Authors: Bin He, Bin Dong, Yi Guan, Jinfeng Yang, Zhipeng Jiang, Qiubin Yu, Jianyi Cheng, Chunyan Qu
Date: 2016-11-07 00:00:00
Tasks: Active Learning, POS

Objective: To build a comprehensive corpus covering syntactic and semantic
annotations of Chinese clinical texts with corresponding annotation guidelines
and methods as well as to develop tools trained on the annotated corpus, which
supplies baselines for research on Chinese texts in the clinical domain.
  Materials and methods: An iterative annotation method was proposed to train
annotators and to develop annotation guidelines. Then, by using annotation
quality assurance measures, a comprehensive corpus was built, containing
annotations of part-of-speech (POS) tags, syntactic tags, entities, assertions,
and relations. Inter-annotator agreement (IAA) was calculated to evaluate the
annotation quality and a Chinese clinical text processing and information
extraction system (CCTPIES) was developed based on our annotated corpus.
  Results: The syntactic corpus consists of 138 Chinese clinical documents with
47,424 tokens and 2553 full parsing trees, while the semantic corpus includes
992 documents that annotated 39,511 entities with their assertions and 7695
relations. IAA evaluation shows that this comprehensive corpus is of good
quality, and the system modules are effective.
  Discussion: The annotated corpus makes a considerable contribution to natural
language processing (NLP) research into Chinese texts in the clinical domain.
However, this corpus has a number of limitations. Some additional types of
clinical text should be introduced to improve corpus coverage and active
learning methods should be utilized to promote annotation efficiency.
  Conclusions: In this study, several annotation guidelines and an annotation
method for Chinese clinical texts were proposed, and a comprehensive corpus
with its NLP modules were constructed, providing a foundation for further study
of applying NLP techniques to Chinese texts in the clinical domain.
An Improved Algorithm for Eye Corner Detection
Authors: Anirban Dasgupta, Anshit Mandloi, Anjith George, Aurobinda Routray
Date: 2015-09-16 00:00:00

In this paper, a modified algorithm for the detection of nasal and temporal
eye corners is presented. The algorithm is a modification of the Santos and
Proenka Method. In the first step, we detect the face and the eyes using
classifiers based on Haar-like features. We then segment out the sclera, from
the detected eye region. From the segmented sclera, we segment out an
approximate eyelid contour. Eye corner candidates are obtained using Harris and
Stephens corner detector. We introduce a post-pruning of the Eye corner
candidates to locate the eye corners, finally. The algorithm has been tested on
Yale, JAFFE databases as well as our created database.