pwc-archive/papers-with-abstracts huggingface dataset:
ProtNN: Fast and Accurate Nearest Neighbor Protein Function Prediction based on Graph Embedding in Structural and Topological Space
Authors: Wajdi Dhifli, Abdoulaye Baniré Diallo
Date: 2015-11-02 00:00:00
Tasks: Graph Embedding, Protein Function Prediction

Studying the function of proteins is important for understanding the
molecular mechanisms of life. The number of publicly available protein
structures has increasingly become extremely large. Still, the determination of
the function of a protein structure remains a difficult, costly, and time
consuming task. The difficulties are often due to the essential role of spatial
and topological structures in the determination of protein functions in living
cells. In this paper, we propose ProtNN, a novel approach for protein function
prediction. Given an unannotated protein structure and a set of annotated
proteins, ProtNN finds the nearest neighbor annotated structures based on
protein-graph pairwise similarities. Given a query protein, ProtNN finds the
nearest neighbor reference proteins based on a graph representation model and a
pairwise similarity between vector embedding of both query and reference
protein-graphs in structural and topological spaces. ProtNN assigns to the
query protein the function with the highest number of votes across the set of k
nearest neighbor reference proteins, where k is a user-defined parameter.
Experimental evaluation demonstrates that ProtNN is able to accurately classify
several datasets in an extremely fast runtime compared to state-of-the-art
approaches. We further show that ProtNN is able to scale up to a whole PDB
dataset in a single-process mode with no parallelization, with a gain of
thousands order of magnitude of runtime compared to state-of-the-art
approaches.
On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants
Authors: Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alex Smola
Date: 2015-06-23 00:00:00
Proceeding: NeurIPS 2015 12

We study optimization algorithms based on variance reduction for stochastic
gradient descent (SGD). Remarkable recent progress has been made in this
direction through development of algorithms like SAG, SVRG, SAGA. These
algorithms have been shown to outperform SGD, both theoretically and
empirically. However, asynchronous versions of these algorithms---a crucial
requirement for modern large-scale applications---have not been studied. We
bridge this gap by presenting a unifying framework for many variance reduction
techniques. Subsequently, we propose an asynchronous algorithm grounded in our
framework, and prove its fast convergence. An important consequence of our
general approach is that it yields asynchronous versions of variance reduction
algorithms such as SVRG and SAGA as a byproduct. Our method achieves near
linear speedup in sparse settings common to machine learning. We demonstrate
the empirical performance of our method through a concrete realization of
asynchronous SVRG.
Fashion Apparel Detection: The Role of Deep Convolutional Neural Network and Pose-dependent Priors
Authors: Kota Hara, Vignesh Jagadeesh, Robinson Piramuthu
Date: 2014-11-19 00:00:00
Tasks: object-detection, Object Detection

In this work, we propose and address a new computer vision task, which we
call fashion item detection, where the aim is to detect various fashion items a
person in the image is wearing or carrying. The types of fashion items we
consider in this work include hat, glasses, bag, pants, shoes and so on. The
detection of fashion items can be an important first step of various e-commerce
applications for fashion industry. Our method is based on state-of-the-art
object detection method pipeline which combines object proposal methods with a
Deep Convolutional Neural Network. Since the locations of fashion items are in
strong correlation with the locations of body joints positions, we incorporate
contextual information from body poses in order to improve the detection
performance. Through the experiments, we demonstrate the effectiveness of the
proposed method.
Hyper-Heuristic Algorithm for Finding Efficient Features in Diagnose of Lung Cancer Disease
Authors: Mitra Montazeri, Mahdieh Soleymani Baghshah, Ahmad Enhesari
Date: 2015-12-15 00:00:00
Tasks: feature selection

Background: Lung cancer was known as primary cancers and the survival rate of
cancer is about 15%. Early detection of lung cancer is the leading factor in
survival rate. All symptoms (features) of lung cancer do not appear until the
cancer spreads to other areas. It needs an accurate early detection of lung
cancer, for increasing the survival rate. For accurate detection, it need
characterizes efficient features and delete redundancy features among all
features. Feature selection is the problem of selecting informative features
among all features. Materials and Methods: Lung cancer database consist of 32
patient records with 57 features. This database collected by Hong and Youngand
indexed in the University of California Irvine repository. Experimental
contents include the extracted from the clinical data and X-ray data, etc. The
data described 3 types of pathological lung cancers and all features are taking
an integer value 0-3. In our study, new method is proposed for identify
efficient features of lung cancer. It is based on Hyper-Heuristic. Results: We
obtained an accuracy of 80.63% using reduced 11 feature set. The proposed
method compare to the accuracy of 5 machine learning feature selections. The
accuracy of these 5 methods are 60.94, 57.81, 68.75, 60.94 and 68.75.
Conclusions: The proposed method has better performance with the highest level
of accuracy. Therefore, the proposed model is recommended for identifying an
efficient symptom of Disease. These finding are very important in health
research, particularly in allocation of medical resources for patients who
predicted as high-risks
Eye detection in digital images: challenges and solutions
Authors: Mitra Montazeri, Mahdieh Montazeri, Saeid Saryazdi
Date: 2016-01-19 00:00:00

Eye Detection has an important role in the field of biometric identification
and known as one method of person's identification. In recent years, many
efforts have been done which can detect eye automatically and with different
image conditions. However, each method has its own drawbacks which can control
some of these conditions. In this paper, different methods of eye detection
will be categorized and explained. In each category, the advantages and
disadvantages of each method will be presented.
Morpho-syntactic Lexicon Generation Using Graph-based Semi-supervised Learning
Authors: Manaal Faruqui, Ryan Mcdonald, Radu Soricut
Date: 2015-12-16 00:00:00
Proceeding: TACL 2016 1
Tasks: Dependency Parsing, Morphological Tagging

Morpho-syntactic lexicons provide information about the morphological and
syntactic roles of words in a language. Such lexicons are not available for all
languages and even when available, their coverage can be limited. We present a
graph-based semi-supervised learning method that uses the morphological,
syntactic and semantic relations between words to automatically construct wide
coverage lexicons from small seed sets. Our method is language-independent, and
we show that we can expand a 1000 word seed lexicon to more than 100 times its
size with high quality for 11 languages. In addition, the automatically created
lexicons provide features that improve performance in two downstream tasks:
morphological tagging and dependency parsing.
Fast Binary Embedding via Circulant Downsampled Matrix -- A Data-Independent Approach
Authors: Sung-Hsien Hsieh, Chun-Shien Lu, Soo-Chang Pei
Date: 2016-01-24 00:00:00

Binary embedding of high-dimensional data aims to produce low-dimensional
binary codes while preserving discriminative power. State-of-the-art methods
often suffer from high computation and storage costs. We present a simple and
fast embedding scheme by first downsampling N-dimensional data into
M-dimensional data and then multiplying the data with an MxM circulant matrix.
Our method requires O(N +M log M) computation and O(N) storage costs. We prove
if data have sparsity, our scheme can achieve similarity-preserving well.
Experiments further demonstrate that though our method is cost-effective and
fast, it still achieves comparable performance in image applications.
My Reflections on the First Man vs. Machine No-Limit Texas Hold 'em Competition
Authors: Sam Ganzfried
Date: 2015-10-29 00:00:00

The first ever human vs. computer no-limit Texas hold 'em competition took
place from April 24-May 8, 2015 at River's Casino in Pittsburgh, PA. In this
article I present my thoughts on the competition design, agent architecture,
and lessons learned.
Very Deep Multilingual Convolutional Neural Networks for LVCSR
Authors: Tom Sercu, Christian Puhrsch, Brian Kingsbury, Yann Lecun
Date: 2015-09-29 00:00:00
Tasks: speech-recognition, Speech Recognition

Convolutional neural networks (CNNs) are a standard component of many current
state-of-the-art Large Vocabulary Continuous Speech Recognition (LVCSR)
systems. However, CNNs in LVCSR have not kept pace with recent advances in
other domains where deeper neural networks provide superior performance. In
this paper we propose a number of architectural advances in CNNs for LVCSR.
First, we introduce a very deep convolutional network architecture with up to
14 weight layers. There are multiple convolutional layers before each pooling
layer, with small 3x3 kernels, inspired by the VGG Imagenet 2014 architecture.
Then, we introduce multilingual CNNs with multiple untied layers. Finally, we
introduce multi-scale input features aimed at exploiting more context at
negligible computational cost. We evaluate the improvements first on a Babel
task for low resource speech recognition, obtaining an absolute 5.77% WER
improvement over the baseline PLP DNN by training our CNN on the combined data
of six different languages. We then evaluate the very deep CNNs on the Hub5'00
benchmark (using the 262 hours of SWB-1 training data) achieving a word error
rate of 11.8% after cross-entropy training, a 1.4% WER improvement (10.6%
relative) over the best published CNN result so far.
Solving Dense Image Matching in Real-Time using Discrete-Continuous Optimization
Authors: Alexander Shekhovtsov, Christian Reinbacher, Gottfried Graber, Thomas Pock
Date: 2016-01-23 00:00:00
Tasks: Optical Flow Estimation, Stereo Matching, Stereo Matching Hand

Dense image matching is a fundamental low-level problem in Computer Vision,
which has received tremendous attention from both discrete and continuous
optimization communities. The goal of this paper is to combine the advantages
of discrete and continuous optimization in a coherent framework. We devise a
model based on energy minimization, to be optimized by both discrete and
continuous algorithms in a consistent way. In the discrete setting, we propose
a novel optimization algorithm that can be massively parallelized. In the
continuous setting we tackle the problem of non-convex regularizers by a
formulation based on differences of convex functions. The resulting hybrid
discrete-continuous algorithm can be efficiently accelerated by modern GPUs and
we demonstrate its real-time performance for the applications of dense stereo
matching and optical flow.
Person Re-Identification by Discriminative Selection in Video Ranking
Authors: Taiqing Wang, Shaogang Gong, Xiatian Zhu, Shengjin Wang
Date: 2016-01-23 00:00:00
Tasks: Gait Recognition, Person Re-Identification

Current person re-identification (ReID) methods typically rely on
single-frame imagery features, whilst ignoring space-time information from
image sequences often available in the practical surveillance scenarios.
Single-frame (single-shot) based visual appearance matching is inherently
limited for person ReID in public spaces due to the challenging visual
ambiguity and uncertainty arising from non-overlapping camera views where
viewing condition changes can cause significant people appearance variations.
In this work, we present a novel model to automatically select the most
discriminative video fragments from noisy/incomplete image sequences of people
from which reliable space-time and appearance features can be computed, whilst
simultaneously learning a video ranking function for person ReID. Using the
PRID$2011$, iLIDS-VID, and HDA+ image sequence datasets, we extensively
conducted comparative evaluations to demonstrate the advantages of the proposed
model over contemporary gait recognition, holistic image sequence matching and
state-of-the-art single-/multi-shot ReID methods.
Minimax Lower Bounds for Linear Independence Testing
Authors: Aaditya Ramdas, David Isenberg, Aarti Singh, Larry Wasserman
Date: 2016-01-23 00:00:00
Tasks: Two-sample testing

Linear independence testing is a fundamental information-theoretic and
statistical problem that can be posed as follows: given $n$ points
$\{(X_i,Y_i)\}^n_{i=1}$ from a $p+q$ dimensional multivariate distribution
where $X_i \in \mathbb{R}^p$ and $Y_i \in\mathbb{R}^q$, determine whether $a^T
X$ and $b^T Y$ are uncorrelated for every $a \in \mathbb{R}^p, b\in
\mathbb{R}^q$ or not. We give minimax lower bound for this problem (when $p+q,n
\to \infty$, $(p+q)/n \leq \kappa < \infty$, without sparsity assumptions). In
summary, our results imply that $n$ must be at least as large as $\sqrt
{pq}/\|\Sigma_{XY}\|_F^2$ for any procedure (test) to have non-trivial power,
where $\Sigma_{XY}$ is the cross-covariance matrix of $X,Y$. We also provide
some evidence that the lower bound is tight, by connections to two-sample
testing and regression in specific settings.
Using compatible shape descriptor for lexicon reduction of printed Farsi subwords
Authors: Homa Davoudi, Ehsanollah Kabir
Date: 2016-01-23 00:00:00

This Paper presents a method for lexicon reduction of Printed Farsi subwords
based on their holistic shape features. Because of the large number of Persian
subwords variously shaped from a simple letter to a complex combination of
several connected characters, it is not easy to find a fixed shape descriptor
suitable for all subwords. In this paper, we propose to select the descriptor
according to the input shape characteristics. To do this, a neural network is
trained to predict the appropriate descriptor of the input image. This network
is implemented in the proposed lexicon reduction system to decide on the
descriptor used for comparison of the query image with the lexicon entries.
Evaluating the proposed method on a dataset of Persian subwords allows one to
attest the effectiveness of the proposed idea of dealing differently with
various query shapes.
Automatic recognition of element classes and boundaries in the birdsong with variable sequences
Authors: Takuya Koumura, Kazuo Okanoya
Date: 2016-01-23 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Boundary Detection, General Classification, speech-recognition, Speech Recognition

Researches on sequential vocalization often require analysis of vocalizations
in long continuous sounds. In such studies as developmental ones or studies
across generations in which days or months of vocalizations must be analyzed,
methods for automatic recognition would be strongly desired. Although methods
for automatic speech recognition for application purposes have been intensively
studied, blindly applying them for biological purposes may not be an optimal
solution. This is because, unlike human speech recognition, analysis of
sequential vocalizations often requires accurate extraction of timing
information. In the present study we propose automated systems suitable for
recognizing birdsong, one of the most intensively investigated sequential
vocalizations, focusing on the three properties of the birdsong. First, a song
is a sequence of vocal elements, called notes, which can be grouped into
categories. Second, temporal structure of birdsong is precisely controlled,
meaning that temporal information is important in song analysis. Finally, notes
are produced according to certain probabilistic rules, which may facilitate the
accurate song recognition. We divided the procedure of song recognition into
three sub-steps: local classification, boundary detection, and global
sequencing, each of which corresponds to each of the three properties of
birdsong. We compared the performances of several different ways to arrange
these three steps. As results, we demonstrated a hybrid model of a deep neural
network and a hidden Markov model is effective in recognizing birdsong with
variable note sequences. We propose suitable arrangements of methods according
to whether accurate boundary detection is needed. Also we designed the new
measure to jointly evaluate the accuracy of note classification and boundary
detection. Our methods should be applicable, with small modification and
tuning, to the songs in other species that hold the three properties of the
sequential vocalization.
Artificial Persuasion in Pedagogical Games
Authors: Zhiwei Zeng
Date: 2016-01-23 00:00:00

A Persuasive Teachable Agent (PTA) is a special type of Teachable Agent which
incorporates a persuasion theory in order to provide persuasive and more
personalized feedback to the student. By employing the persuasion techniques,
the PTA seeks to maintain the student in a high motivation and high ability
state in which he or she has higher cognitive ability and his or her changes in
attitudes are more persistent. However, the existing model of the PTA still has
a few limitations. Firstly, the existing PTA model focuses on modelling the
PTA's ability to persuade, while does not model its ability to be taught by the
student and to practice the knowledge it has learnt. Secondly, the quantitative
model for computational processes in the PTA has low reusability. Thirdly,
there is still a gap between theoretical models and practical implementation of
the PTA.
  To address these three limitations, this book proposes an improved agent
model which follows a goal-oriented approach and models the PTA in its totality
by integrating the Persuasion Reasoning of the PTA with the Teachability
Reasoning and the Practicability Reasoning. The project also proposes a more
abstract and generalized quantitative model for the computations in the PTA.
With higher level of abstraction, the reusability of the quantitative model is
also improved. New system architecture is introduced to bridge the gap between
theoretical models and implementation of the PTA.
Super-resolution reconstruction of hyperspectral images via low rank tensor modeling and total variation regularization
Authors: Shiying He, Haiwei Zhou, Yao Wang, Wenfei Cao, Zhi Han
Date: 2016-01-23 00:00:00
Tasks: Hyperspectral Image Super-Resolution, Image Super-Resolution, Super-Resolution

In this paper, we propose a novel approach to hyperspectral image
super-resolution by modeling the global spatial-and-spectral correlation and
local smoothness properties over hyperspectral images. Specifically, we utilize
the tensor nuclear norm and tensor folded-concave penalty functions to describe
the global spatial-and-spectral correlation hidden in hyperspectral images, and
3D total variation (TV) to characterize the local spatial-and-spectral
smoothness across all hyperspectral bands. Then, we develop an efficient
algorithm for solving the resulting optimization problem by combing the local
linear approximation (LLA) strategy and alternative direction method of
multipliers (ADMM). Experimental results on one hyperspectral image dataset
illustrate the merits of the proposed approach.
Unsupervised Network Pretraining via Encoding Human Design
Authors: Ming-Yu Liu, Arun Mallya, Oncel C. Tuzel, Xi Chen
Date: 2015-02-19 00:00:00
Tasks: Object Recognition

Over the years, computer vision researchers have spent an immense amount of
effort on designing image features for the visual object recognition task. We
propose to incorporate this valuable experience to guide the task of training
deep neural networks. Our idea is to pretrain the network through the task of
replicating the process of hand-designed feature extraction. By learning to
replicate the process, the neural network integrates previous research
knowledge and learns to model visual objects in a way similar to the
hand-designed features. In the succeeding finetuning step, it further learns
object-specific representations from labeled data and this boosts its
classification power. We pretrain two convolutional neural networks where one
replicates the process of histogram of oriented gradients feature extraction,
and the other replicates the process of region covariance feature extraction.
After finetuning, we achieve substantially better performance than the baseline
methods.
Decision Aids for Adversarial Planning in Military Operations: Algorithms, Tools, and Turing-test-like Experimental Validation
Authors: Alexander Kott, Ray Budd, Larry Ground, Lakshmi Rebbapragada, John Langston
Date: 2016-01-22 00:00:00
Tasks: Scheduling

Use of intelligent decision aids can help alleviate the challenges of
planning complex operations. We describe integrated algorithms, and a tool
capable of translating a high-level concept for a tactical military operation
into a fully detailed, actionable plan, producing automatically (or with human
guidance) plans with realistic degree of detail and of human-like quality.
Tight interleaving of several algorithms -- planning, adversary estimates,
scheduling, routing, attrition and consumption estimates -- comprise the
computational approach of this tool. Although originally developed for Army
large-unit operations, the technology is generic and also applies to a number
of other domains, particularly in critical situations requiring detailed
planning within a constrained period of time. In this paper, we focus
particularly on the engineering tradeoffs in the design of the tool. In an
experimental evaluation, reminiscent of the Turing test, the tool's performance
compared favorably with human planners.
Learning Minimum Volume Sets and Anomaly Detectors from KNN Graphs
Authors: Jonathan Root, Venkatesh Saligrama, Jing Qian
Date: 2016-01-22 00:00:00
Tasks: Anomaly Detection, Computational Efficiency, Learning-To-Rank

We propose a non-parametric anomaly detection algorithm for high dimensional
data. We first rank scores derived from nearest neighbor graphs on $n$-point
nominal training data. We then train limited complexity models to imitate these
scores based on the max-margin learning-to-rank framework. A test-point is
declared as an anomaly at $\alpha$-false alarm level if the predicted score is
in the $\alpha$-percentile. The resulting anomaly detector is shown to be
asymptotically optimal in that for any false alarm rate $\alpha$, its decision
region converges to the $\alpha$-percentile minimum volume level set of the
unknown underlying density. In addition, we test both the statistical
performance and computational efficiency of our algorithm on a number of
synthetic and real-data experiments. Our results demonstrate the superiority of
our algorithm over existing $K$-NN based anomaly detection algorithms, with
significant computational savings.
Unsupervised convolutional neural networks for motion estimation
Authors: Aria Ahmadi, Ioannis Patras
Date: 2016-01-22 00:00:00
Tasks: Motion Estimation, Optical Flow Estimation

Traditional methods for motion estimation estimate the motion field F between
a pair of images as the one that minimizes a predesigned cost function. In this
paper, we propose a direct method and train a Convolutional Neural Network
(CNN) that when, at test time, is given a pair of images as input it produces a
dense motion field F at its output layer. In the absence of large datasets with
ground truth motion that would allow classical supervised training, we propose
to train the network in an unsupervised manner. The proposed cost function that
is optimized during training, is based on the classical optical flow
constraint. The latter is differentiable with respect to the motion field and,
therefore, allows backpropagation of the error to previous layers of the
network. Our method is tested on both synthetic and real image sequences and
performs similarly to the state-of-the-art methods.