pwc-archive/papers-with-abstracts huggingface dataset:
A Scalable and Robust Framework for Intelligent Real-time Video Surveillance
Authors: Shreenath Dutt, Ankita Kalra
Date: 2016-10-30 00:00:00

In this paper, we present an intelligent, reliable and storage-efficient
video surveillance system using Apache Storm and OpenCV. As a Storm topology,
we have added multiple information extraction modules that only write important
content to the disk. Our topology is extensible, capable of adding novel
algorithms as per the use case without affecting the existing ones, since all
the processing is independent of each other. This framework is also highly
scalable and fault tolerant, which makes it a best option for organisations
that need to monitor a large network of surveillance cameras.
Diversity Promoting Online Sampling for Streaming Video Summarization
Authors: Rushil Anirudh, Ahnaf Masroor, Pavan Turaga
Date: 2016-10-29 00:00:00
Tasks: Clustering, Diversity, Video Summarization

Many applications benefit from sampling algorithms where a small number of
well chosen samples are used to generalize different properties of a large
dataset. In this paper, we use diverse sampling for streaming video
summarization. Several emerging applications support streaming video, but
existing summarization algorithms need access to the entire video which
requires a lot of memory and computational power. We propose a memory efficient
and computationally fast, online algorithm that uses competitive learning for
diverse sampling. Our algorithm is a generalization of online K-means such that
the cost function reduces clustering error, while also ensuring a diverse set
of samples. The diversity is measured as the volume of a convex hull around the
samples. Finally, the performance of the proposed algorithm is measured against
human users for 50 videos in the VSUMM dataset. The algorithm performs better
than batch mode summarization, while requiring significantly lower memory and
computational requirements.
Compact Compositional Models
Authors: Marc Goessling, Yali Amit
Date: 2014-12-11 00:00:00

Learning compact and interpretable representations is a very natural task,
which has not been solved satisfactorily even for simple binary datasets. In
this paper, we review various ways of composing experts for binary data and
argue that competitive forms of interaction are best suited to learn
low-dimensional representations. We propose a new composition rule that
discourages experts from focusing on similar structures and that penalizes
opposing votes strongly so that abstaining from voting becomes more attractive.
We also introduce a novel sequential initialization procedure, which is based
on a process of oversimplification and correction. Experiments show that with
our approach very intuitive models can be learned.
Finding Low-Rank Solutions via Non-Convex Matrix Factorization, Efficiently and Provably
Authors: Dohyung Park, Anastasios Kyrillidis, Constantine Caramanis, Sujay Sanghavi
Date: 2016-06-10 00:00:00

A rank-$r$ matrix $X \in \mathbb{R}^{m \times n}$ can be written as a product
$U V^\top$, where $U \in \mathbb{R}^{m \times r}$ and $V \in \mathbb{R}^{n
\times r}$. One could exploit this observation in optimization: e.g., consider
the minimization of a convex function $f(X)$ over rank-$r$ matrices, where the
set of rank-$r$ matrices is modeled via the factorization $UV^\top$. Though
such parameterization reduces the number of variables, and is more
computationally efficient (of particular interest is the case $r \ll \min\{m,
n\}$), it comes at a cost: $f(UV^\top)$ becomes a non-convex function w.r.t.
$U$ and $V$.
  We study such parameterization for optimization of generic convex objectives
$f$, and focus on first-order, gradient descent algorithmic solutions. We
propose the Bi-Factored Gradient Descent (BFGD) algorithm, an efficient
first-order method that operates on the $U, V$ factors. We show that when $f$
is (restricted) smooth, BFGD has local sublinear convergence, and linear
convergence when $f$ is both (restricted) smooth and (restricted) strongly
convex. For several key applications, we provide simple and efficient
initialization schemes that provide approximate solutions good enough for the
above convergence results to hold.
Sequence-to-sequence neural network models for transliteration
Authors: Mihaela Rosca, Thomas Breuel
Date: 2016-10-29 00:00:00
Tasks: Machine Translation, Translation, Transliteration

Transliteration is a key component of machine translation systems and
software internationalization. This paper demonstrates that neural
sequence-to-sequence models obtain state of the art or close to state of the
art results on existing datasets. In an effort to make machine transliteration
accessible, we open source a new Arabic to English transliteration dataset and
our trained models.
Property-driven State-Space Coarsening for Continuous Time Markov Chains
Authors: Michalis Michaelides, Dimitrios Milios, Jane Hillston, Guido Sanguinetti
Date: 2016-06-03 00:00:00
Tasks: Computational Efficiency, Dimensionality Reduction

Dynamical systems with large state-spaces are often expensive to thoroughly
explore experimentally. Coarse-graining methods aim to define simpler systems
which are more amenable to analysis and exploration; most current methods,
however, focus on a priori state aggregation based on similarities in
transition rates, which is not necessarily reflected in similar behaviours at
the level of trajectories. We propose a way to coarsen the state-space of a
system which optimally preserves the satisfaction of a set of logical
specifications about the system's trajectories. Our approach is based on
Gaussian Process emulation and Multi-Dimensional Scaling, a dimensionality
reduction technique which optimally preserves distances in non-Euclidean
spaces. We show how to obtain low-dimensional visualisations of the system's
state-space from the perspective of properties' satisfaction, and how to define
macro-states which behave coherently with respect to the specifications. Our
approach is illustrated on a non-trivial running example, showing promising
performance and high computational efficiency.
FEAST: An Automated Feature Selection Framework for Compilation Tasks
Authors: Pai-Shun Ting, Chun-Chen Tu, Pin-Yu Chen, Ya-Yun Lo, Shin-Ming Cheng
Date: 2016-10-29 00:00:00
Tasks: BIG-bench Machine Learning, feature selection

The success of the application of machine-learning techniques to compilation
tasks can be largely attributed to the recent development and advancement of
program characterization, a process that numerically or structurally quantifies
a target program. While great achievements have been made in identifying key
features to characterize programs, choosing a correct set of features for a
specific compiler task remains an ad hoc procedure. In order to guarantee a
comprehensive coverage of features, compiler engineers usually need to select
excessive number of features. This, unfortunately, would potentially lead to a
selection of multiple similar features, which in turn could create a new
problem of bias that emphasizes certain aspects of a program's characteristics,
hence reducing the accuracy and performance of the target compiler task. In
this paper, we propose FEAture Selection for compilation Tasks (FEAST), an
efficient and automated framework for determining the most relevant and
representative features from a feature pool. Specifically, FEAST utilizes
widely used statistics and machine-learning tools, including LASSO, sequential
forward and backward selection, for automatic feature selection, and can in
general be applied to any numerical feature set. This paper further proposes an
automated approach to compiler parameter assignment for assessing the
performance of FEAST. Intensive experimental results demonstrate that, under
the compiler parameter assignment task, FEAST can achieve comparable results
with about 18% of features that are automatically selected from the entire
feature pool. We also inspect these selected features and discuss their roles
in program execution.
Solving Large-scale Systems of Random Quadratic Equations via Stochastic Truncated Amplitude Flow
Authors: Gang Wang, Georgios B. Giannakis, Jie Chen
Date: 2016-10-29 00:00:00
Tasks: Retrieval

A novel approach termed \emph{stochastic truncated amplitude flow} (STAF) is
developed to reconstruct an unknown $n$-dimensional real-/complex-valued signal
$\bm{x}$ from $m$ `phaseless' quadratic equations of the form
$\psi_i=|\langle\bm{a}_i,\bm{x}\rangle|$. This problem, also known as phase
retrieval from magnitude-only information, is \emph{NP-hard} in general.
Adopting an amplitude-based nonconvex formulation, STAF leads to an iterative
solver comprising two stages: s1) Orthogonality-promoting initialization
through a stochastic variance reduced gradient algorithm; and, s2) A series of
iterative refinements of the initialization using stochastic truncated gradient
iterations. Both stages involve a single equation per iteration, thus rendering
STAF a simple, scalable, and fast approach amenable to large-scale
implementations that is useful when $n$ is large. When $\{\bm{a}_i\}_{i=1}^m$
are independent Gaussian, STAF provably recovers exactly any
$\bm{x}\in\mathbb{R}^n$ exponentially fast based on order of $n$ quadratic
equations. STAF is also robust in the presence of additive noise of bounded
support. Simulated tests involving real Gaussian $\{\bm{a}_i\}$ vectors
demonstrate that STAF empirically reconstructs any $\bm{x}\in\mathbb{R}^n$
exactly from about $2.3n$ magnitude-only measurements, outperforming
state-of-the-art approaches and narrowing the gap from the
information-theoretic number of equations $m=2n-1$. Extensive experiments using
synthetic data and real images corroborate markedly improved performance of
STAF over existing alternatives.
Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities
Authors: Mohsen Ghafoorian, Nico Karssemeijer, Tom Heskes, Inge van Uden, Clara Sanchez, Geert Litjens, Frank-Erik de Leeuw, Bram van Ginneken, Elena Marchiori, Bram Platel
Date: 2016-10-16 00:00:00
Tasks: Decision Making, Medical Image Analysis

The anatomical location of imaging features is of crucial importance for
accurate diagnosis in many medical tasks. Convolutional neural networks (CNN)
have had huge successes in computer vision, but they lack the natural ability
to incorporate the anatomical location in their decision making process,
hindering success in some medical image analysis tasks.
  In this paper, to integrate the anatomical location information into the
network, we propose several deep CNN architectures that consider multi-scale
patches or take explicit location features while training. We apply and compare
the proposed architectures for segmentation of white matter hyperintensities in
brain MR images on a large dataset. As a result, we observe that the CNNs that
incorporate location information substantially outperform a conventional
segmentation method with hand-crafted features as well as CNNs that do not
integrate location information. On a test set of 46 scans, the best
configuration of our networks obtained a Dice score of 0.791, compared to 0.797
for an independent human observer. Performance levels of the machine and the
independent human observer were not statistically significantly different
(p-value=0.17).
Multi-Camera Occlusion and Sudden-Appearance-Change Detection Using Hidden Markovian Chains
Authors: Xudong Ma
Date: 2016-10-29 00:00:00
Tasks: Change Detection, Object Tracking

This paper was originally submitted to Xinova as a response to a Request for
Invention (RFI) on new event monitoring methods. In this paper, a new object
tracking algorithm using multiple cameras for surveillance applications is
proposed. The proposed system can detect sudden-appearance-changes and
occlusions using a hidden Markovian statistical model. The experimental results
confirm that our system detect the sudden-appearance changes and occlusions
reliably.
One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities
Authors: Michalis K. Titsias
Date: 2016-09-23 00:00:00
Proceeding: NeurIPS 2016
Tasks: General Classification, Language Modeling, Language Modelling, Recommendation Systems, Stochastic Optimization

The softmax representation of probabilities for categorical variables plays a
prominent role in modern machine learning with numerous applications in areas
such as large scale classification, neural language modeling and recommendation
systems. However, softmax estimation is very expensive for large scale
inference because of the high cost associated with computing the normalizing
constant. Here, we introduce an efficient approximation to softmax
probabilities which takes the form of a rigorous lower bound on the exact
probability. This bound is expressed as a product over pairwise probabilities
and it leads to scalable estimation based on stochastic optimization. It allows
us to perform doubly stochastic estimation by subsampling both training
instances and class labels. We show that the new bound has interesting
theoretical properties and we demonstrate its use in classification problems.
Finding Street Gang Members on Twitter
Authors: Lakshika Balasuriya, Sanjaya Wijeratne, Derek Doran, Amit Sheth
Date: 2016-10-29 00:00:00

Most street gang members use Twitter to intimidate others, to present
outrageous images and statements to the world, and to share recent illegal
activities. Their tweets may thus be useful to law enforcement agencies to
discover clues about recent crimes or to anticipate ones that may occur.
Finding these posts, however, requires a method to discover gang member Twitter
profiles. This is a challenging task since gang members represent a very small
population of the 320 million Twitter users. This paper studies the problem of
automatically finding gang members on Twitter. It outlines a process to curate
one of the largest sets of verifiable gang member profiles that have ever been
studied. A review of these profiles establishes differences in the language,
images, YouTube links, and emojis gang members use compared to the rest of the
Twitter population. Features from this review are used to train a series of
supervised classifiers. Our classifier achieves a promising F1 score with a low
false positive rate.
Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences
Authors: Daniel Neil, Michael Pfeiffer, Shih-Chii Liu
Date: 2016-10-29 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Temporal Sequences

Recurrent Neural Networks (RNNs) have become the state-of-the-art choice for
extracting patterns from temporal sequences. However, current RNN models are
ill-suited to process irregularly sampled data triggered by events generated in
continuous time by sensors or other neurons. Such data can occur, for example,
when the input comes from novel event-driven artificial sensors that generate
sparse, asynchronous streams of events or from multiple conventional sensors
with different update intervals. In this work, we introduce the Phased LSTM
model, which extends the LSTM unit by adding a new time gate. This gate is
controlled by a parametrized oscillation with a frequency range that produces
updates of the memory cell only during a small percentage of the cycle. Even
with the sparse updates imposed by the oscillation, the Phased LSTM network
achieves faster convergence than regular LSTMs on tasks which require learning
of long sequences. The model naturally integrates inputs from sensors of
arbitrary sampling rates, thereby opening new areas of investigation for
processing asynchronous sensory events that carry timing information. It also
greatly improves the performance of LSTMs in standard RNN applications, and
does so with an order-of-magnitude fewer computes at runtime.
Deep Multi-scale Location-aware 3D Convolutional Neural Networks for Automated Detection of Lacunes of Presumed Vascular Origin
Authors: Mohsen Ghafoorian, Nico Karssemeijer, Tom Heskes, Mayra Bergkamp, Joost Wissink, Jiri Obels, Karlijn Keizer, Frank-Erik de Leeuw, Bram van Ginneken, Elena Marchiori, Bram Platel
Date: 2016-10-24 00:00:00

Lacunes of presumed vascular origin (lacunes) are associated with an
increased risk of stroke, gait impairment, and dementia and are a primary
imaging feature of the small vessel disease. Quantification of lacunes may be
of great importance to elucidate the mechanisms behind neuro-degenerative
disorders and is recommended as part of study standards for small vessel
disease research. However, due to the different appearance of lacunes in
various brain regions and the existence of other similar-looking structures,
such as perivascular spaces, manual annotation is a difficult, elaborative and
subjective task, which can potentially be greatly improved by reliable and
consistent computer-aided detection (CAD) routines.
  In this paper, we propose an automated two-stage method using deep
convolutional neural networks (CNN). We show that this method has good
performance and can considerably benefit readers. We first use a fully
convolutional neural network to detect initial candidates. In the second step,
we employ a 3D CNN as a false positive reduction tool. As the location
information is important to the analysis of candidate structures, we further
equip the network with contextual information using multi-scale analysis and
integration of explicit location features. We trained, validated and tested our
networks on a large dataset of 1075 cases obtained from two different studies.
Subsequently, we conducted an observer study with four trained observers and
compared our method with them using a free-response operating characteristic
analysis. Shown on a test set of 111 cases, the resulting CAD system exhibits
performance similar to the trained human observers and achieves a sensitivity
of 0.974 with 0.13 false positives per slice. A feasibility study also showed
that a trained human observer would considerably benefit once aided by the CAD
system.
A MAP-MRF filter for phase-sensitive coil combination in autocalibrating partially parallel susceptibility weighted MRI
Authors: Sreekanth Madhusoodhanan, Joseph Suresh Paul
Date: 2016-10-29 00:00:00

A statistical approach for combination of channel phases is developed for
optimizing the Contrast-to-Noise Ratio (CNR) in Susceptibility Weighted Images
(SWI) acquired using autocalibrating partially parallel techniques. The
unwrapped phase images of each coil are filtered using local random field based
probabilistic weights, derived using energy functions representative of noisy
sensitivity and tissue information pertaining to venous structure in the
individual channel phase images. The channel energy functions are obtained as
functions of local image intensities, first or second order clique phase
difference and a threshold scaling parameter dependent on the input noise
level. Whereas the expectation of the individual energy functions with respect
to the noise distribution in clique phase differences is to be maximized for
optimal filtering, the expectation of tissue energy function decreases and
noise energy function increases with increase in threshold scale parameter. The
optimum scaling parameter is shown to occur at the point where expectations of
both energy functions contribute to the largest possible extent. It is shown
that implementation of the filter in the same lines as that of Iterated
Conditional Modes (ICM) algorithm provides structural enhancement in the coil
combined phase, with reduced noise amplification. Application to simulated and
in vivo multi-channel SWI shows that CNR of combined phase obtained using
MAP-MRF filter is higher as compared to that of coil combination using weighted
average.
Machine learning methods for accurate delineation of tumors in PET images
Authors: Jakub Czakon, Filip Drapejkowski, Grzegorz Zurek, Piotr Giedziun, Jacek Zebrowski, Witold Dyrka
Date: 2016-10-29 00:00:00
Tasks: BIG-bench Machine Learning

In oncology, Positron Emission Tomography imaging is widely used in
diagnostics of cancer metastases, in monitoring of progress in course of the
cancer treatment, and in planning radiotherapeutic interventions. Accurate and
reproducible delineation of the tumor in the Positron Emission Tomography scans
remains a difficult task, despite being crucial for delivering appropriate
radiation dose, minimizing adverse side-effects of the therapy, and reliable
evaluation of treatment. In this piece of research we attempt to solve the
problem of automated delineation of the tumor using 3d implementations of the
spatial distance weighted fuzzy c-means, the deep convolutional neural network
and a dictionary model. The methods, in diverse ways, combine intensity and
spatial information.
SDP Relaxation with Randomized Rounding for Energy Disaggregation
Authors: Kiarash Shaloudegi, András György, Csaba Szepesvári, Wilsun Xu
Date: 2016-10-29 00:00:00
Proceeding: NeurIPS 2016 12

We develop a scalable, computationally efficient method for the task of
energy disaggregation for home appliance monitoring. In this problem the goal
is to estimate the energy consumption of each appliance over time based on the
total energy-consumption signal of a household. The current state of the art is
to model the problem as inference in factorial HMMs, and use quadratic
programming to find an approximate solution to the resulting quadratic integer
program. Here we take a more principled approach, better suited to integer
programming problems, and find an approximate optimum by combining convex
semidefinite relaxations randomized rounding, as well as a scalable ADMM method
that exploits the special structure of the resulting semidefinite program.
Simulation results both in synthetic and real-world datasets demonstrate the
superiority of our method.
A general multiblock method for structured variable selection
Authors: Tommy Löfstedt, Fouad Hadj-Selem, Vincent Guillemot, Cathy Philippe, Nicolas Raymond, Edouard Duchesney, Vincent Frouin, Arthur Tenenhaus
Date: 2016-10-29 00:00:00
Tasks: Variable Selection

Regularised canonical correlation analysis was recently extended to more than
two sets of variables by the multiblock method Regularised generalised
canonical correlation analysis (RGCCA). Further, Sparse GCCA (SGCCA) was
proposed to address the issue of variable selection. However, for technical
reasons, the variable selection offered by SGCCA was restricted to a covariance
link between the blocks (i.e., with $\tau=1$). One of the main contributions of
this paper is to go beyond the covariance link and to propose an extension of
SGCCA for the full RGCCA model (i.e., with $\tau\in[0, 1]$). In addition, we
propose an extension of SGCCA that exploits structural relationships between
variables within blocks. Specifically, we propose an algorithm that allows
structured and sparsity-inducing penalties to be included in the RGCCA
optimisation problem. The proposed multiblock method is illustrated on a real
three-block high-grade glioma data set, where the aim is to predict the
location of the brain tumours, and on a simulated data set, where the aim is to
illustrate the method's ability to reconstruct the true underlying weight
vectors.
A reliable order-statistics-based approximate nearest neighbor search algorithm
Authors: Luisa Verdoliva, Davide Cozzolino, Giovanni Poggi
Date: 2015-09-11 00:00:00

We propose a new algorithm for fast approximate nearest neighbor search based
on the properties of ordered vectors. Data vectors are classified based on the
index and sign of their largest components, thereby partitioning the space in a
number of cones centered in the origin. The query is itself classified, and the
search starts from the selected cone and proceeds to neighboring ones. Overall,
the proposed algorithm corresponds to locality sensitive hashing in the space
of directions, with hashing based on the order of components. Thanks to the
statistical features emerging through ordering, it deals very well with the
challenging case of unstructured data, and is a valuable building block for
more complex techniques dealing with structured data. Experiments on both
simulated and real-world data prove the proposed algorithm to provide a
state-of-the-art performance.