pwc-archive/papers-with-abstracts huggingface dataset:
Effective Connectivity-Based Neural Decoding: A Causal Interaction-Driven Approach
Authors: Saba Emrani, Hamid Krim
Date: 2016-07-24 00:00:00
Tasks: Time Series, Time Series Analysis

We propose a geometric model-free causality measurebased on multivariate
delay embedding that can efficiently detect linear and nonlinear causal
interactions between time series with no prior information. We then exploit the
proposed causal interaction measure in real MEG data analysis. The results are
used to construct effective connectivity maps of brain activity to decode
different categories of visual stimuli. Moreover, we discovered that the
MEG-based effective connectivity maps as a response to structured images
exhibit more geometric patterns, as disclosed by analyzing the evolution of
toplogical structures of the underlying networks using persistent homology.
Extensive simulation and experimental result have been carried out to
substantiate the capabilities of the proposed approach.
Distilling Word Embeddings: An Encoding Approach
Authors: Lili Mou, Ran Jia, Yan Xu, Ge Li, Lu Zhang, Zhi Jin
Date: 2015-06-15 00:00:00
Tasks: Word Embeddings

Distilling knowledge from a well-trained cumbersome network to a small one
has recently become a new research topic, as lightweight neural networks with
high performance are particularly in need in various resource-restricted
systems. This paper addresses the problem of distilling word embeddings for NLP
tasks. We propose an encoding approach to distill task-specific knowledge from
a set of high-dimensional embeddings, which can reduce model complexity by a
large margin as well as retain high accuracy, showing a good compromise between
efficiency and performance. Experiments in two tasks reveal the phenomenon that
distilling knowledge from cumbersome embeddings is better than directly
training neural networks with small embeddings.
Online Trajectory Segmentation and Summary With Applications to Visualization and Retrieval
Authors: Yehezkel S. Resheff
Date: 2016-07-24 00:00:00
Tasks: Retrieval, Segmentation

Trajectory segmentation is the process of subdividing a trajectory into parts
either by grouping points similar with respect to some measure of interest, or
by minimizing a global objective function. Here we present a novel online
algorithm for segmentation and summary, based on point density along the
trajectory, and based on the nature of the naturally occurring structure of
intermittent bouts of locomotive and local activity. We show an application to
visualization of trajectory datasets, and discuss the use of the summary as an
index allowing efficient queries which are otherwise impossible or
computationally expensive, over very large datasets.
Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition
Authors: Jun Liu, Amir Shahroudy, Dong Xu, Gang Wang
Date: 2016-07-24 00:00:00
Tasks: 3D Action Recognition, Action Analysis, Action Recognition, Skeleton Based Action Recognition, Temporal Action Localization

3D action recognition - analysis of human actions based on 3D skeleton data -
becomes popular recently due to its succinctness, robustness, and
view-invariant representation. Recent attempts on this problem suggested to
develop RNN-based learning methods to model the contextual dependency in the
temporal domain. In this paper, we extend this idea to spatio-temporal domains
to analyze the hidden sources of action-related information within the input
data over both domains concurrently. Inspired by the graphical structure of the
human skeleton, we further propose a more powerful tree-structure based
traversal method. To handle the noise and occlusion in 3D skeleton data, we
introduce new gating mechanism within LSTM to learn the reliability of the
sequential input data and accordingly adjust its effect on updating the
long-term context information stored in the memory cell. Our method achieves
state-of-the-art performance on 4 challenging benchmark datasets for 3D human
action analysis.
On Reward Function for Survival
Authors: Naoto Yoshida
Date: 2016-06-18 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Obtaining a survival strategy (policy) is one of the fundamental problems of
biological agents. In this paper, we generalize the formulation of previous
research related to the survival of an agent and we formulate the survival
problem as a maximization of the multi-step survival probability in future time
steps. We introduce a method for converting the maximization of multi-step
survival probability into a classical reinforcement learning problem. Using
this conversion, the reward function (negative temporal cost function) is
expressed as the log of the temporal survival probability. And we show that the
objective function of the reinforcement learning in this sense is proportional
to the variational lower bound of the original problem. Finally, We empirically
demonstrate that the agent learns survival behavior by using the reward
function introduced in this paper.
Impact of Physical Activity on Sleep:A Deep Learning Based Exploration
Authors: Aarti Sathyanarayana, Shafiq Joty, Luis Fernandez-Luque, Ferda Ofli, Jaideep Srivastava, Ahmed Elmagarmid, Shahrad Taheri, Teresa Arora
Date: 2016-07-24 00:00:00
Tasks: Activity Recognition, Deep Learning, Human Activity Recognition, Sleep Quality, Sleep Quality Prediction

The importance of sleep is paramount for maintaining physical, emotional and
mental wellbeing. Though the relationship between sleep and physical activity
is known to be important, it is not yet fully understood. The explosion in
popularity of actigraphy and wearable devices, provides a unique opportunity to
understand this relationship. Leveraging this information source requires new
tools to be developed to facilitate data-driven research for sleep and activity
patient-recommendations.
  In this paper we explore the use of deep learning to build sleep quality
prediction models based on actigraphy data. We first use deep learning as a
pure model building device by performing human activity recognition (HAR) on
raw sensor data, and using deep learning to build sleep prediction models. We
compare the deep learning models with those build using classical approaches,
i.e. logistic regression, support vector machines, random forest and adaboost.
Secondly, we employ the advantage of deep learning with its ability to handle
high dimensional datasets. We explore several deep learning models on the raw
wearable sensor output without performing HAR or any other feature extraction.
  Our results show that using a convolutional neural network on the raw
wearables output improves the predictive value of sleep quality from physical
activity, by an additional 8% compared to state-of-the-art non-deep learning
approaches, which itself shows a 15% improvement over current practice.
Moreover, utilizing deep learning on raw data eliminates the need for data
pre-processing and simplifies the overall workflow to analyze actigraphy data
for sleep and physical activity research.
On the Number of Many-to-Many Alignments of Multiple Sequences
Authors: Steffen Eger
Date: 2015-11-02 00:00:00

We count the number of alignments of $N \ge 1$ sequences when match-up types
are from a specified set $S\subseteq \mathbb{N}^N$. Equivalently, we count the
number of nonnegative integer matrices whose rows sum to a given fixed vector
and each of whose columns lie in $S$. We provide a new asymptotic formula for
the case $S=\{(s_1,\ldots,s_N) \:|\: 1\le s_i\le 2\}$.
Redundancy-free Verbalization of Individuals for Ontology Validation
Authors: E. V. Vinu, P. Sreenivasa Kumar
Date: 2016-07-24 00:00:00

We investigate the problem of verbalizing Web Ontology Language (OWL) axioms
of domain ontologies in this paper. The existing approaches address the problem
of fidelity of verbalized OWL texts to OWL semantics by exploring different
ways of expressing the same OWL axiom in various linguistic forms. They also
perform grouping and aggregating of the natural language (NL) sentences that
are generated corresponding to each OWL statement into a comprehensible
structure. However, no efforts have been taken to try out a semantic reduction
at logical level to remove redundancies and repetitions, so that the reduced
set of axioms can be used for generating a more meaningful and
human-understandable (what we call redundancy-free) text. Our experiments show
that, formal semantic reduction at logical level is very helpful to generate
redundancy-free descriptions of ontology entities. In this paper, we
particularly focus on generating descriptions of individuals of SHIQ based
ontologies. The details of a case study are provided to support the usefulness
of the redundancy-free NL descriptions of individuals, in knowledge validation
application.
Forward Table-Based Presynaptic Event-Triggered Spike-Timing-Dependent Plasticity
Authors: Bruno U. Pedroni, Sadique Sheik, Siddharth Joshi, Georgios Detorakis, Somnath Paul, Charles Augustine, Emre Neftci, Gert Cauwenberghs
Date: 2016-07-11 00:00:00

Spike-timing-dependent plasticity (STDP) incurs both causal and acausal
synaptic weight updates, for negative and positive time differences between
pre-synaptic and post-synaptic spike events. For realizing such updates in
neuromorphic hardware, current implementations either require forward and
reverse lookup access to the synaptic connectivity table, or rely on
memory-intensive architectures such as crossbar arrays. We present a novel
method for realizing both causal and acausal weight updates using only forward
lookup access of the synaptic connectivity table, permitting memory-efficient
implementation. A simplified implementation in FPGA, using a single timer
variable for each neuron, closely approximates exact STDP cumulative weight
updates for neuron refractory periods greater than 10 ms, and reduces to exact
STDP for refractory periods greater than the STDP time window. Compared to
conventional crossbar implementation, the forward table-based implementation
leads to substantial memory savings for sparsely connected networks supporting
scalable neuromorphic systems with fully reconfigurable synaptic connectivity
and plasticity.
LIME: A Method for Low-light IMage Enhancement
Authors: Xiaojie Guo
Date: 2016-05-17 00:00:00
Tasks: Image Enhancement, Low-Light Image Enhancement

When one captures images in low-light conditions, the images often suffer
from low visibility. This poor quality may significantly degrade the
performance of many computer vision and multimedia algorithms that are
primarily designed for high-quality inputs. In this paper, we propose a very
simple and effective method, named as LIME, to enhance low-light images. More
concretely, the illumination of each pixel is first estimated individually by
finding the maximum value in R, G and B channels. Further, we refine the
initial illumination map by imposing a structure prior on it, as the final
illumination map. Having the well-constructed illumination map, the enhancement
can be achieved accordingly. Experiments on a number of challenging real-world
low-light images are present to reveal the efficacy of our LIME and show its
superiority over several state-of-the-arts.
DOC: Deep OCclusion Estimation From a Single Image
Authors: Peng Wang, Alan Yuille
Date: 2015-11-20 00:00:00
Tasks: Occlusion Estimation

Recovering the occlusion relationships between objects is a fundamental human
visual ability which yields important information about the 3D world. In this
paper we propose a deep network architecture, called DOC, which acts on a
single image, detects object boundaries and estimates the border ownership
(i.e. which side of the boundary is foreground and which is background). We
represent occlusion relations by a binary edge map, to indicate the object
boundary, and an occlusion orientation variable which is tangential to the
boundary and whose direction specifies border ownership by a left-hand rule. We
train two related deep convolutional neural networks, called DOC, which exploit
local and non-local image cues to estimate this representation and hence
recover occlusion relations. In order to train and test DOC we construct a
large-scale instance occlusion boundary dataset using PASCAL VOC images, which
we call the PASCAL instance occlusion dataset (PIOD). This contains 10,000
images and hence is two orders of magnitude larger than existing occlusion
datasets for outdoor images. We test two variants of DOC on PIOD and on the
BSDS occlusion dataset and show they outperform state-of-the-art methods.
Finally, we perform numerous experiments investigating multiple settings of DOC
and transfer between BSDS and PIOD, which provides more insights for further
study of occlusion estimation.
Deep Deformation Network for Object Landmark Localization
Authors: Xiang Yu, Feng Zhou, Manmohan Chandraker
Date: 2016-05-03 00:00:00
Tasks: Face Alignment, Object, Pose Estimation

We propose a novel cascaded framework, namely deep deformation network (DDN),
for localizing landmarks in non-rigid objects. The hallmarks of DDN are its
incorporation of geometric constraints within a convolutional neural network
(CNN) framework, ease and efficiency of training, as well as generality of
application. A novel shape basis network (SBN) forms the first stage of the
cascade, whereby landmarks are initialized by combining the benefits of CNN
features and a learned shape basis to reduce the complexity of the highly
nonlinear pose manifold. In the second stage, a point transformer network (PTN)
estimates local deformation parameterized as thin-plate spline transformation
for a finer refinement. Our framework does not incorporate either handcrafted
features or part connectivity, which enables an end-to-end shape prediction
pipeline during both training and testing. In contrast to prior cascaded
networks for landmark localization that learn a mapping from feature space to
landmark locations, we demonstrate that the regularization induced through
geometric priors in the DDN makes it easier to train, yet produces superior
results. The efficacy and generality of the architecture is demonstrated
through state-of-the-art performances on several benchmarks for multiple tasks
such as facial landmark localization, human body pose estimation and bird part
localization.
Autonomous Ingress of a UAV through a window using Monocular Vision
Authors: Abhinav Pachauri, Vikrant More, Pradeep Gaidhani, Nitin Gupta
Date: 2016-07-24 00:00:00

The use of autonomous UAVs for surveillance purposes and other reconnaissance
tasks is increasingly becoming popular and convenient.These tasks requires the
ability to successfully ingress through the rectangular openings or windows of
the target structure.In this paper, a method to robustly detect the window in
the surrounding using basic image processing techniques and efficient distance
measure, is proposed.Furthermore, a navigation scheme which incorporates this
detection method for performing navigation task has also been proposed.The
whole navigation task is performed and tested in the simulation environment
GAZEBO.
Recurrent Regression for Face Recognition
Authors: Yang Li, Wenming Zheng, Zhen Cui
Date: 2016-07-24 00:00:00
Tasks: Face Recognition, regression

To address the sequential changes of images including poses, in this paper we
propose a recurrent regression neural network(RRNN) framework to unify two
classic tasks of cross-pose face recognition on still images and video-based
face recognition. To imitate the changes of images, we explicitly construct the
potential dependencies of sequential images so as to regularize the final
learning model. By performing progressive transforms for sequentially adjacent
images, RRNN can adaptively memorize and forget the information that benefits
for the final classification. For face recognition of still images, given any
one image with any one pose, we recurrently predict the images with its
sequential poses to expect to capture some useful information of others poses.
For video-based face recognition, the recurrent regression takes one entire
sequence rather than one image as its input. We verify RRNN in static face
dataset MultiPIE and face video dataset YouTube Celebrities(YTC). The
comprehensive experimental results demonstrate the effectiveness of the
proposed RRNN method.
Community Detection in Degree-Corrected Block Models
Authors: Chao Gao, Zongming Ma, Anderson Y. Zhang, Harrison H. Zhou
Date: 2016-07-24 00:00:00
Tasks: Community Detection

Community detection is a central problem of network data analysis. Given a
network, the goal of community detection is to partition the network nodes into
a small number of clusters, which could often help reveal interesting
structures. The present paper studies community detection in Degree-Corrected
Block Models (DCBMs). We first derive asymptotic minimax risks of the problem
for a misclassification proportion loss under appropriate conditions. The
minimax risks are shown to depend on degree-correction parameters, community
sizes, and average within and between community connectivities in an intuitive
and interpretable way. In addition, we propose a polynomial time algorithm to
adaptively perform consistent and even asymptotically optimal community
detection in DCBMs.
Interactive Learning from Multiple Noisy Labels
Authors: Shankar Vembu, Sandra Zilles
Date: 2016-07-24 00:00:00
Tasks: BIG-bench Machine Learning, General Classification

Interactive learning is a process in which a machine learning algorithm is
provided with meaningful, well-chosen examples as opposed to randomly chosen
examples typical in standard supervised learning. In this paper, we propose a
new method for interactive learning from multiple noisy labels where we exploit
the disagreement among annotators to quantify the easiness (or meaningfulness)
of an example. We demonstrate the usefulness of this method in estimating the
parameters of a latent variable classification model, and conduct experimental
analyses on a range of synthetic and benchmark datasets. Furthermore, we
theoretically analyze the performance of perceptron in this interactive
learning framework.
Scalable Link Prediction in Dynamic Networks via Non-Negative Matrix Factorization
Authors: Linhong Zhu, Dong Guo, Junming Yin, Greg Ver Steeg, Aram Galstyan
Date: 2014-11-13 00:00:00
Tasks: global-optimization, Link Prediction, Prediction

We propose a scalable temporal latent space model for link prediction in
dynamic social networks, where the goal is to predict links over time based on
a sequence of previous graph snapshots. The model assumes that each user lies
in an unobserved latent space and interactions are more likely to form between
similar users in the latent space representation. In addition, the model allows
each user to gradually move its position in the latent space as the network
structure evolves over time. We present a global optimization algorithm to
effectively infer the temporal latent space, with a quadratic convergence rate.
Two alternative optimization algorithms with local and incremental updates are
also proposed, allowing the model to scale to larger networks without
compromising prediction accuracy. Empirically, we demonstrate that our model,
when evaluated on a number of real-world dynamic networks, significantly
outperforms existing approaches for temporal link prediction in terms of both
scalability and predictive power.
Combined Classifiers for Invariant Face Recognition
Authors: Ahmad H. A. Eid
Date: 2016-07-23 00:00:00
Tasks: Face Recognition

No single classifier can alone solve the complex problem of face recognition.
Researchers found that combining some base classifiers usually enhances their
recognition rate. The weaknesses of the base classifiers are reflected on the
resulting combined system. In this work, a system for combining unstable, low
performance classifiers is proposed. The system is applied to face images of
392 persons. The system shows remarkable stability and high recognition rate
using a reduced number of parameters. The system illustrates the possibility of
designing a combined system that benefits from the strengths of its base
classifiers while avoiding many of their weaknesses.
Authorship attribution via network motifs identification
Authors: Vanessa Queiroz Marinho, Graeme Hirst, Diego Raphael Amancio
Date: 2016-07-23 00:00:00
Tasks: Authorship Attribution, Extractive Summarization

Concepts and methods of complex networks can be used to analyse texts at
their different complexity levels. Examples of natural language processing
(NLP) tasks studied via topological analysis of networks are keyword
identification, automatic extractive summarization and authorship attribution.
Even though a myriad of network measurements have been applied to study the
authorship attribution problem, the use of motifs for text analysis has been
restricted to a few works. The goal of this paper is to apply the concept of
motifs, recurrent interconnection patterns, in the authorship attribution task.
The absolute frequencies of all thirteen directed motifs with three nodes were
extracted from the co-occurrence networks and used as classification features.
The effectiveness of these features was verified with four machine learning
methods. The results show that motifs are able to distinguish the writing style
of different authors. In our best scenario, 57.5% of the books were correctly
classified. The chance baseline for this problem is 12.5%. In addition, we have
found that function words play an important role in these recurrent patterns.
Taken together, our findings suggest that motifs should be further explored in
other related linguistic tasks.
What's the Point: Semantic Segmentation with Point Supervision
Authors: Amy Bearman, Olga Russakovsky, Vittorio Ferrari, Li Fei-Fei
Date: 2015-06-06 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

The semantic image segmentation task presents a trade-off between test time
accuracy and training-time annotation cost. Detailed per-pixel annotations
enable training accurate models but are very time-consuming to obtain,
image-level class labels are an order of magnitude cheaper but result in less
accurate models. We take a natural step from image-level annotation towards
stronger supervision: we ask annotators to point to an object if one exists. We
incorporate this point supervision along with a novel objectness potential in
the training loss function of a CNN model. Experimental results on the PASCAL
VOC 2012 benchmark reveal that the combined effect of point-level supervision
and objectness potential yields an improvement of 12.9% mIOU over image-level
supervision. Further, we demonstrate that models trained with point-level
supervision are more accurate than models trained with image-level,
squiggle-level or full supervision given a fixed annotation budget.
Neural Sentence Ordering
Authors: Xinchi Chen, Xipeng Qiu, Xuanjing Huang
Date: 2016-07-23 00:00:00
Tasks: Document Summarization, Multi-Document Summarization, Sentence, Sentence Ordering, Text Generation

Sentence ordering is a general and critical task for natural language
generation applications. Previous works have focused on improving its
performance in an external, downstream task, such as multi-document
summarization. Given its importance, we propose to study it as an isolated
task. We collect a large corpus of academic texts, and derive a data driven
approach to learn pairwise ordering of sentences, and validate the efficacy
with extensive experiments. Source codes and dataset of this paper will be made
publicly available.