pwc-archive/papers-with-abstracts huggingface dataset:
Combining Generative and Discriminative Neural Networks for Sleep Stages Classification
Authors: Endang Purnama Giri, Mohamad Ivan Fanany, Aniati Murni Arymurthy
Date: 2016-10-06 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), General Classification, Time Series Analysis

Sleep stages pattern provides important clues in diagnosing the presence of
sleep disorder. By analyzing sleep stages pattern and extracting its features
from EEG, EOG, and EMG signals, we can classify sleep stages. This study
presents a novel classification model for predicting sleep stages with a high
accuracy. The main idea is to combine the generative capability of Deep Belief
Network (DBN) with a discriminative ability and sequence pattern recognizing
capability of Long Short-term Memory (LSTM). We use DBN that is treated as an
automatic higher level features generator. The input to DBN is 28 "handcrafted"
features as used in previous sleep stages studies. We compared our method with
other techniques which combined DBN with Hidden Markov Model (HMM).In this
study, we exploit the sequence or time series characteristics of sleep dataset.
To the best of our knowledge, most of the present sleep analysis from
polysomnogram relies only on single instanced label (nonsequence) for
classification. In this study, we used two datasets: an open data set that is
treated as a benchmark; the other dataset is our sleep stages dataset
(available for download) to verify the results further. Our experiments showed
that the combination of DBN with LSTM gives better overall accuracy 98.75\%
(Fscore=0.9875) for benchmark dataset and 98.94\% (Fscore=0.9894) for MKG
dataset. This result is better than the state of the art of sleep stages
classification that was 91.31\%.
Application of Ontologies in Cloud Computing: The State-Of-The-Art
Authors: Fahim T. Imam
Date: 2016-10-06 00:00:00
Tasks: Cloud Computing, Survey

This paper presents a systematic survey on existing literature and seminal
works relevant to the application of ontologies in different aspects of Cloud
computing. Our hypothesis is that ontologies along with their reasoning
capabilities can have significant impact on improving various aspects of the
Cloud computing phenomena. Ontologies can promote intelligent decision support
mechanisms for various Cloud based services. They can also provide effective
interoperability among the Cloud based systems and resources. This survey can
promote a comprehensive understanding on the roles and significance of
ontologies within the overall domain of Cloud Computing. Also, this project can
potentially form the basis of new research area and possibilities for both
ontology and Cloud computing communities.
Reset-Free Guided Policy Search: Efficient Deep Reinforcement Learning with Stochastic Initial States
Authors: William Montgomery, Anurag Ajay, Chelsea Finn, Pieter Abbeel, Sergey Levine
Date: 2016-10-04 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Autonomous learning of robotic skills can allow general-purpose robots to
learn wide behavioral repertoires without requiring extensive manual
engineering. However, robotic skill learning methods typically make one of
several trade-offs to enable practical real-world learning, such as requiring
manually designed policy or value function representations, initialization from
human-provided demonstrations, instrumentation of the training environment, or
extremely long training times. In this paper, we propose a new reinforcement
learning algorithm for learning manipulation skills that can train
general-purpose neural network policies with minimal human engineering, while
still allowing for fast, efficient learning in stochastic environments. Our
approach builds on the guided policy search (GPS) algorithm, which transforms
the reinforcement learning problem into supervised learning from a
computational teacher (without human demonstrations). In contrast to prior GPS
methods, which require a consistent set of initial states to which the system
must be reset after each episode, our approach can handle randomized initial
states, allowing it to be used in environments where deterministic resets are
impossible. We compare our method to existing policy search techniques in
simulation, showing that it can train high-dimensional neural network policies
with the same sample efficiency as prior GPS methods, and present real-world
results on a PR2 robotic manipulator.
Towards Cognitive Exploration through Deep Reinforcement Learning for Mobile Robots
Authors: Lei Tai, Ming Liu
Date: 2016-10-06 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Exploration in an unknown environment is the core functionality for mobile
robots. Learning-based exploration methods, including convolutional neural
networks, provide excellent strategies without human-designed logic for the
feature extraction. But the conventional supervised learning algorithms cost
lots of efforts on the labeling work of datasets inevitably. Scenes not
included in the training set are mostly unrecognized either. We propose a deep
reinforcement learning method for the exploration of mobile robots in an indoor
environment with the depth information from an RGB-D sensor only. Based on the
Deep Q-Network framework, the raw depth image is taken as the only input to
estimate the Q values corresponding to all moving commands. The training of the
network weights is end-to-end. In arbitrarily constructed simulation
environments, we show that the robot can be quickly adapted to unfamiliar
scenes without any man-made labeling. Besides, through analysis of receptive
fields of feature representations, deep reinforcement learning motivates the
convolutional networks to estimate the traversability of the scenes. The test
results are compared with the exploration strategies separately based on deep
learning or reinforcement learning. Even trained only in the simulated
environment, experimental results in real-world environment demonstrate that
the cognitive ability of robot controller is dramatically improved compared
with the supervised method. We believe it is the first time that raw sensor
information is used to build cognitive exploration strategy for mobile robots
through end-to-end deep reinforcement learning.
How much does your data exploration overfit? Controlling bias via information usage
Authors: Daniel Russo, James Zou
Date: 2015-11-16 00:00:00
Tasks: Clustering

Modern data is messy and high-dimensional, and it is often not clear a priori what are the right questions to ask. Instead, the analyst typically needs to use the data to search for interesting analyses to perform and hypotheses to test. This is an adaptive process, where the choice of analysis to be performed next depends on the results of the previous analyses on the same data. Ultimately, which results are reported can be heavily influenced by the data. It is widely recognized that this process, even if well-intentioned, can lead to biases and false discoveries, contributing to the crisis of reproducibility in science. But while %the adaptive nature of exploration any data-exploration renders standard statistical theory invalid, experience suggests that different types of exploratory analysis can lead to disparate levels of bias, and the degree of bias also depends on the particulars of the data set. In this paper, we propose a general information usage framework to quantify and provably bound the bias and other error metrics of an arbitrary exploratory analysis. We prove that our mutual information based bound is tight in natural settings, and then use it to give rigorous insights into when commonly used procedures do or do not lead to substantially biased estimation. Through the lens of information usage, we analyze the bias of specific exploration procedures such as filtering, rank selection and clustering. Our general framework also naturally motivates randomization techniques that provably reduces exploration bias while preserving the utility of the data analysis. We discuss the connections between our approach and related ideas from differential privacy and blinded data analysis, and supplement our results with illustrative simulations.
Effective Classification of MicroRNA Precursors Using Combinatorial Feature Mining and AdaBoost Algorithms
Authors: Ling Zhong, Jason T. L. Wang
Date: 2016-10-06 00:00:00
Tasks: General Classification

MicroRNAs (miRNAs) are non-coding RNAs with approximately 22 nucleotides (nt)
that are derived from precursor molecules. These precursor molecules or
pre-miRNAs often fold into stem-loop hairpin structures. However, a large
number of sequences with pre-miRNA-like hairpins can be found in genomes. It is
a challenge to distinguish the real pre-miRNAs from other hairpin sequences
with similar stem-loops (referred to as pseudo pre-miRNAs). Several
computational methods have been developed to tackle this challenge. In this
paper we propose a new method, called MirID, for identifying and classifying
microRNA precursors. We collect 74 features from the sequences and secondary
structures of pre-miRNAs; some of these features are taken from our previous
studies on non-coding RNA prediction while others were suggested in the
literature. We develop a combinatorial feature mining algorithm to identify
suitable feature sets. These feature sets are then used to train support vector
machines to obtain classification models, based on which classifier ensemble is
constructed. Finally we use an AdaBoost algorithm to further enhance the
accuracy of the classifier ensemble. Experimental results on a variety of
species demonstrate the good performance of the proposed method, and its
superiority over existing tools.
Gamma Belief Networks
Authors: Mingyuan Zhou, Yulai Cong, Bo Chen
Date: 2015-12-09 00:00:00

To infer multilayer deep representations of high-dimensional discrete and
nonnegative real vectors, we propose an augmentable gamma belief network (GBN)
that factorizes each of its hidden layers into the product of a sparse
connection weight matrix and the nonnegative real hidden units of the next
layer. The GBN's hidden layers are jointly trained with an upward-downward
Gibbs sampler that solves each layer with the same subroutine. The
gamma-negative binomial process combined with a layer-wise training strategy
allows inferring the width of each layer given a fixed budget on the width of
the first layer. Example results illustrate interesting relationships between
the width of the first layer and the inferred network structure, and
demonstrate that the GBN can add more layers to improve its performance in both
unsupervisedly extracting features and predicting heldout data. For exploratory
data analysis, we extract trees and subnetworks from the learned deep network
to visualize how the very specific factors discovered at the first hidden layer
and the increasingly more general factors discovered at deeper hidden layers
are related to each other, and we generate synthetic data by propagating random
variables through the deep network from the top hidden layer back to the bottom
data layer.
Automatic Detection of Small Groups of Persons, Influential Members, Relations and Hierarchy in Written Conversations Using Fuzzy Logic
Authors: French Pope III, Rouzbeh A. Shirvani, Mugizi Robert Rwebangira, Mohamed Chouikha, Ayo Taylor, Andres Alarcon Ramirez, Amirsina Torfi
Date: 2016-10-06 00:00:00

Nowadays a lot of data is collected in online forums. One of the key tasks is
to determine the social structure of these online groups, for example the
identification of subgroups within a larger group. We will approach the
grouping of individual as a classification problem. The classifier will be
based on fuzzy logic. The input to the classifier will be linguistic features
and degree of relationships (among individuals). The output of the classifiers
are the groupings of individuals. We also incorporate a method that ranks the
members of the detected subgroup to identify the hierarchies in each subgroup.
Data from the HBO television show The Wire is used to analyze the efficacy and
usefulness of fuzzy logic based methods as alternative methods to classical
statistical methods usually used for these problems. The proposed methodology
could detect automatically the most influential members of each organization
The Wire with 90% accuracy.
Generating Simulations of Motion Events from Verbal Descriptions
Authors: James Pustejovsky, Nikhil Krishnaswamy
Date: 2016-10-06 00:00:00
Proceeding: SEMEVAL 2014 8
Tasks: Informativeness, Unity

In this paper, we describe a computational model for motion events in natural
language that maps from linguistic expressions, through a dynamic event
interpretation, into three-dimensional temporal simulations in a model.
Starting with the model from (Pustejovsky and Moszkowicz, 2011), we analyze
motion events using temporally-traced Labelled Transition Systems. We model the
distinction between path- and manner-motion in an operational semantics, and
further distinguish different types of manner-of-motion verbs in terms of the
mereo-topological relations that hold throughout the process of movement. From
these representations, we generate minimal models, which are realized as
three-dimensional simulations in software developed with the game engine,
Unity. The generated simulations act as a conceptual "debugger" for the
semantics of different motion verbs: that is, by testing for consistency and
informativeness in the model, simulations expose the presuppositions associated
with linguistic expressions and their compositions. Because the model
generation component is still incomplete, this paper focuses on an
implementation which maps directly from linguistic interpretations into the
Unity code snippets that create the simulations.
A Methodology for Customizing Clinical Tests for Esophageal Cancer based on Patient Preferences
Authors: Asis Roy, Sourangshu Bhattacharya, Kalyan Guin
Date: 2016-10-06 00:00:00
Tasks: General Classification, regression

Tests for Esophageal cancer can be expensive, uncomfortable and can have side
effects. For many patients, we can predict non-existence of disease with 100%
certainty, just using demographics, lifestyle, and medical history information.
Our objective is to devise a general methodology for customizing tests using
user preferences so that expensive or uncomfortable tests can be avoided. We
propose to use classifiers trained from electronic health records (EHR) for
selection of tests. The key idea is to design classifiers with 100% false
normal rates, possibly at the cost higher false abnormals. We compare Naive
Bayes classification (NB), Random Forests (RF), Support Vector Machines (SVM)
and Logistic Regression (LR), and find kernel Logistic regression to be most
suitable for the task. We propose an algorithm for finding the best probability
threshold for kernel LR, based on test set accuracy. Using the proposed
algorithm, we describe schemes for selecting tests, which appear as features in
the automatic classification algorithm, using preferences on costs and
discomfort of the users. We test our methodology with EHRs collected for more
than 3000 patients, as a part of project carried out by a reputed hospital in
Mumbai, India. Kernel SVM and kernel LR with a polynomial kernel of degree 3,
yields an accuracy of 99.8% and sensitivity 100%, without the MP features, i.e.
using only clinical tests. We demonstrate our test selection algorithm using
two case studies, one using cost of clinical tests, and other using
"discomfort" values for clinical tests. We compute the test sets corresponding
to the lowest false abnormals for each criterion described above, using
exhaustive enumeration of 15 clinical tests. The sets turn out to different,
substantiating our claim that one can customize test sets based on user
preferences.
A Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency Detection
Authors: Nian Liu, Junwei Han
Date: 2016-10-06 00:00:00
Tasks: Saliency Detection

Traditional saliency models usually adopt hand-crafted image features and
human-designed mechanisms to calculate local or global contrast. In this paper,
we propose a novel computational saliency model, i.e., deep spatial contextual
long-term recurrent convolutional network (DSCLRCN) to predict where people
looks in natural scenes. DSCLRCN first automatically learns saliency related
local features on each image location in parallel. Then, in contrast with most
other deep network based saliency models which infer saliency in local
contexts, DSCLRCN can mimic the cortical lateral inhibition mechanisms in human
visual system to incorporate global contexts to assess the saliency of each
image location by leveraging the deep spatial long short-term memory (DSLSTM)
model. Moreover, we also integrate scene context modulation in DSLSTM for
saliency inference, leading to a novel deep spatial contextual LSTM (DSCLSTM)
model. The whole network can be trained end-to-end and works efficiently when
testing. Experimental results on two benchmark datasets show that DSCLRCN can
achieve state-of-the-art performance on saliency detection. Furthermore, the
proposed DSCLSTM model can significantly boost the saliency detection
performance by incorporating both global spatial interconnections and scene
context modulation, which may uncover novel inspirations for studies on them in
computational saliency models.
Exploiting Depth from Single Monocular Images for Object Detection and Semantic Segmentation
Authors: Yuanzhouhan Cao, Chunhua Shen, Heng Tao Shen
Date: 2016-10-06 00:00:00
Tasks: Depth Estimation, Object, object-detection, Object Detection, Segmentation, Semantic Segmentation

Augmenting RGB data with measured depth has been shown to improve the
performance of a range of tasks in computer vision including object detection
and semantic segmentation. Although depth sensors such as the Microsoft Kinect
have facilitated easy acquisition of such depth information, the vast majority
of images used in vision tasks do not contain depth information. In this paper,
we show that augmenting RGB images with estimated depth can also improve the
accuracy of both object detection and semantic segmentation. Specifically, we
first exploit the recent success of depth estimation from monocular images and
learn a deep depth estimation model. Then we learn deep depth features from the
estimated depth and combine with RGB features for object detection and semantic
segmentation. Additionally, we propose an RGB-D semantic segmentation method
which applies a multi-task training scheme: semantic label prediction and depth
value regression. We test our methods on several datasets and demonstrate that
incorporating information from estimated depth improves the performance of
object detection and semantic segmentation remarkably.
Human Decision-Making under Limited Time
Authors: Pedro A. Ortega, Alan A. Stocker
Date: 2016-10-06 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Decision Making

Subjective expected utility theory assumes that decision-makers possess
unlimited computational resources to reason about their choices; however,
virtually all decisions in everyday life are made under resource constraints -
i.e. decision-makers are bounded in their rationality. Here we experimentally
tested the predictions made by a formalization of bounded rationality based on
ideas from statistical mechanics and information-theory. We systematically
tested human subjects in their ability to solve combinatorial puzzles under
different time limitations. We found that our bounded-rational model accounts
well for the data. The decomposition of the fitted model parameter into the
subjects' expected utility function and resource parameter provide interesting
insight into the subjects' information capacity limits. Our results confirm
that humans gradually fall back on their learned prior choice patterns when
confronted with increasing resource limitations.
Non-Parametric Cluster Significance Testing with Reference to a Unimodal Null Distribution
Authors: Erika S. Helgeson, Eric Bair
Date: 2016-10-05 00:00:00
Tasks: Clustering, Density Estimation

Cluster analysis is an unsupervised learning strategy that can be employed to
identify subgroups of observations in data sets of unknown structure. This
strategy is particularly useful for analyzing high-dimensional data such as
microarray gene expression data. Many clustering methods are available, but it
is challenging to determine if the identified clusters represent distinct
subgroups. We propose a novel strategy to investigate the significance of
identified clusters by comparing the within- cluster sum of squares from the
original data to that produced by clustering an appropriate unimodal null
distribution. The null distribution we present for this problem uses kernel
density estimation and thus does not require that the data follow any
particular distribution. We find that our method can accurately test for the
presence of clustering even when the number of features is high.
Low-tubal-rank Tensor Completion using Alternating Minimization
Authors: Xiao-Yang Liu, Shuchin Aeron, Vaneet Aggarwal, Xiaodong Wang
Date: 2016-10-05 00:00:00
Tasks: 2k, Low-Rank Matrix Completion, Matrix Completion

The low-tubal-rank tensor model has been recently proposed for real-world
multidimensional data. In this paper, we study the low-tubal-rank tensor
completion problem, i.e., to recover a third-order tensor by observing a subset
of its elements selected uniformly at random. We propose a fast iterative
algorithm, called {\em Tubal-Alt-Min}, that is inspired by a similar approach
for low-rank matrix completion. The unknown low-tubal-rank tensor is
represented as the product of two much smaller tensors with the low-tubal-rank
property being automatically incorporated, and Tubal-Alt-Min alternates between
estimating those two tensors using tensor least squares minimization. First, we
note that tensor least squares minimization is different from its matrix
counterpart and nontrivial as the circular convolution operator of the
low-tubal-rank tensor model is intertwined with the sub-sampling operator.
Second, the theoretical performance guarantee is challenging since
Tubal-Alt-Min is iterative and nonconvex in nature. We prove that 1)
Tubal-Alt-Min guarantees exponential convergence to the global optima, and 2)
for an $n \times n \times k$ tensor with tubal-rank $r \ll n$, the required
sampling complexity is $O(nr^2k \log^3 n)$ and the computational complexity is
$O(n^2rk^2 \log^2 n)$. Third, on both synthetic data and real-world video data,
evaluation results show that compared with tensor-nuclear norm minimization
(TNN-ADMM), Tubal-Alt-Min improves the recovery error dramatically (by orders
of magnitude). It is estimated that Tubal-Alt-Min converges at an exponential
rate $10^{-0.4423 \text{Iter}}$ where $\text{Iter}$ denotes the number of
iterations, which is much faster than TNN-ADMM's $10^{-0.0332 \text{Iter}}$,
and the running time can be accelerated by more than $5$ times for a $200
\times 200 \times 20$ tensor.