pwc-archive/papers-with-abstracts huggingface dataset:
Compartmental analysis of dynamic nuclear medicine data: regularization procedure and application to physiology
Authors: Delbary Fabrice, Garbarino Sara
Date: 2016-08-05 00:00:00

Compartmental models based on tracer mass balance are extensively used in
clinical and pre-clinical nuclear medicine in order to obtain quantitative
information on tracer metabolism in the biological tissue. This paper is the
second of a series of two that deal with the problem of tracer coefficient
estimation via compartmental modelling in an inverse problem framework. While
the previous work was devoted to the discussion of identifiability issues for
2, 3 and n-dimension compartmental systems, here we discuss the problem of
numerically determining the tracer coefficients by means of a general
regularized Multivariate Gauss Newton scheme. In this paper, applications
concerning cerebral, hepatic and renal functions are considered, involving
experimental measurements on FDG-PET data on different set of murine models.
Recurrent Dropout without Memory Loss
Authors: Stanislau Semeniuta, Aliaksei Severyn, Erhardt Barth
Date: 2016-03-16 00:00:00
Proceeding: COLING 2016 12

This paper presents a novel approach to recurrent neural network (RNN)
regularization. Differently from the widely adopted dropout method, which is
applied to \textit{forward} connections of feed-forward architectures or RNNs,
we propose to drop neurons directly in \textit{recurrent} connections in a way
that does not cause loss of long-term memory. Our approach is as easy to
implement and apply as the regular feed-forward dropout and we demonstrate its
effectiveness for Long Short-Term Memory network, the most popular type of RNN
cells. Our experiments on NLP benchmarks show consistent improvements even when
combined with conventional feed-forward dropout.
Sparse Subspace Clustering via Diffusion Process
Authors: Qilin Li, Ling Li, Wanquan Liu
Date: 2016-08-05 00:00:00
Tasks: Clustering

Subspace clustering refers to the problem of clustering high-dimensional data
that lie in a union of low-dimensional subspaces. State-of-the-art subspace
clustering methods are based on the idea of expressing each data point as a
linear combination of other data points while regularizing the matrix of
coefficients with L1, L2 or nuclear norms for a sparse solution. L1
regularization is guaranteed to give a subspace-preserving affinity (i.e.,
there are no connections between points from different subspaces) under broad
theoretical conditions, but the clusters may not be fully connected. L2 and
nuclear norm regularization often improve connectivity, but give a
subspace-preserving affinity only for independent subspaces. Mixed L1, L2 and
nuclear norm regularization could offer a balance between the
subspace-preserving and connectedness properties, but this comes at the cost of
increased computational complexity. This paper focuses on using L1 norm and
alleviating the corresponding connectivity problem by a simple yet efficient
diffusion process on subspace affinity graphs. Without adding any tuning
parameter , our method can achieve state-of-the-art clustering performance on
Hopkins 155 and Extended Yale B data sets.
The Evolutionary Process of Image Transition in Conjunction with Box and Strip Mutation
Authors: Aneta Neumann, Bradley Alexander, Frank Neumann
Date: 2016-08-05 00:00:00
Tasks: Evolutionary Algorithms

Evolutionary algorithms have been used in many ways to generate digital art.
We study how evolutionary processes are used for evolutionary art and present a
new approach to the transition of images. Our main idea is to define
evolutionary processes for digital image transition, combining different
variants of mutation and evolutionary mechanisms. We introduce box and strip
mutation operators which are specifically designed for image transition. Our
experimental results show that the process of an evolutionary algorithm in
combination with these mutation operators can be used as a valuable way to
produce unique generative art.
Community Detection in Political Twitter Networks using Nonnegative Matrix Factorization Methods
Authors: Mert Ozer, Nyunsu Kim, Hasan Davulcu
Date: 2016-08-05 00:00:00
Tasks: Clustering, Community Detection, Word Similarity

Community detection is a fundamental task in social network analysis. In this
paper, first we develop an endorsement filtered user connectivity network by
utilizing Heider's structural balance theory and certain Twitter triad
patterns. Next, we develop three Nonnegative Matrix Factorization frameworks to
investigate the contributions of different types of user connectivity and
content information in community detection. We show that user content and
endorsement filtered connectivity information are complementary to each other
in clustering politically motivated users into pure political communities. Word
usage is the strongest indicator of users' political orientation among all
content categories. Incorporating user-word matrix and word similarity
regularizer provides the missing link in connectivity only methods which suffer
from detection of artificially large number of clusters for Twitter networks.
Self-Organising Maps in Computer Security
Authors: Jan Feyereisl, Uwe Aickelin
Date: 2016-08-05 00:00:00
Tasks: Anomaly Detection, Computer Security

Some argue that biologically inspired algorithms are the future of solving
difficult problems in computer science. Others strongly believe that the future
lies in the exploration of mathematical foundations of problems at hand. The
field of computer security tends to accept the latter view as a more
appropriate approach due to its more workable validation and verification
possibilities. The lack of rigorous scientific practices prevalent in
biologically inspired security research does not aid in presenting bio-inspired
security approaches as a viable way of dealing with complex security problems.
This chapter introduces a biologically inspired algorithm, called the Self
Organising Map (SOM), that was developed by Teuvo Kohonen in 1981. Since the
algorithm's inception it has been scrutinised by the scientific community and
analysed in more than 4000 research papers, many of which dealt with various
computer security issues, from anomaly detection, analysis of executables all
the way to wireless network monitoring. In this chapter a review of security
related SOM research undertaken in the past is presented and analysed. The
algorithm's biological analogies are detailed and the author's view on the
future possibilities of this successful bio-inspired approach are given. The
SOM algorithm's close relation to a number of vital functions of the human
brain and the emergence of multi-core computer architectures are the two main
reasons behind our assumption that the future of the SOM algorithm and its
variations is promising, notably in the field of computer security.
A Distance for HMMs based on Aggregated Wasserstein Metric and State Registration
Authors: Yukun Chen, Jianbo Ye, Jia Li
Date: 2016-08-05 00:00:00
Tasks: Retrieval, Time Series Analysis

We propose a framework, named Aggregated Wasserstein, for computing a
dissimilarity measure or distance between two Hidden Markov Models with state
conditional distributions being Gaussian. For such HMMs, the marginal
distribution at any time spot follows a Gaussian mixture distribution, a fact
exploited to softly match, aka register, the states in two HMMs. We refer to
such HMMs as Gaussian mixture model-HMM (GMM-HMM). The registration of states
is inspired by the intrinsic relationship of optimal transport and the
Wasserstein metric between distributions. Specifically, the components of the
marginal GMMs are matched by solving an optimal transport problem where the
cost between components is the Wasserstein metric for Gaussian distributions.
The solution of the optimization problem is a fast approximation to the
Wasserstein metric between two GMMs. The new Aggregated Wasserstein distance is
a semi-metric and can be computed without generating Monte Carlo samples. It is
invariant to relabeling or permutation of the states. This distance quantifies
the dissimilarity of GMM-HMMs by measuring both the difference between the two
marginal GMMs and the difference between the two transition matrices. Our new
distance is tested on the tasks of retrieval and classification of time series.
Experiments on both synthetic data and real data have demonstrated its
advantages in terms of accuracy as well as efficiency in comparison with
existing distances based on the Kullback-Leibler divergence.
A Neural Transducer
Authors: Navdeep Jaitly, David Sussillo, Quoc V. Le, Oriol Vinyals, Ilya Sutskever, Samy Bengio
Date: 2015-11-16 00:00:00

Sequence-to-sequence models have achieved impressive results on various
tasks. However, they are unsuitable for tasks that require incremental
predictions to be made as more data arrives or tasks that have long input
sequences and output sequences. This is because they generate an output
sequence conditioned on an entire input sequence. In this paper, we present a
Neural Transducer that can make incremental predictions as more input arrives,
without redoing the entire computation. Unlike sequence-to-sequence models, the
Neural Transducer computes the next-step distribution conditioned on the
partially observed input sequence and the partially generated sequence. At each
time step, the transducer can decide to emit zero to many output symbols. The
data can be processed using an encoder and presented as input to the
transducer. The discrete decision to emit a symbol at every time step makes it
difficult to learn with conventional backpropagation. It is however possible to
train the transducer by using a dynamic programming algorithm to generate
target discrete decisions. Our experiments show that the Neural Transducer
works well in settings where it is required to produce output predictions as
data come in. We also find that the Neural Transducer performs well for long
sequences even when attention mechanisms are not used.
Sparse Filtered SIRT for Electron Tomography
Authors: Chen Mu, Chiwoo Park
Date: 2016-08-04 00:00:00
Tasks: Denoising, Electron Tomography

Electron tomographic reconstruction is a method for obtaining a
three-dimensional image of a specimen with a series of two dimensional
microscope images taken from different viewing angles. Filtered backprojection,
one of the most popular tomographic reconstruction methods, does not work well
under the existence of image noises and missing wedges. This paper presents a
new approach to largely mitigate the effect of noises and missing wedges. We
propose a novel filtered backprojection that optimizes the filter of the
backprojection operator in terms of a reconstruction error. This data-dependent
filter adaptively chooses the spectral domains of signals and noises,
suppressing the noise frequency bands, so it is very effective in denoising. We
also propose the new filtered backprojection embedded within the simultaneous
iterative reconstruction iteration for mitigating the effect of missing wedges.
Our numerical study is presented to show the performance gain of the proposed
approach over the state-of-the-art.
Identifying Metastases in Sentinel Lymph Nodes with Deep Convolutional Neural Networks
Authors: Richard Chen, Yating Jing, Hunter Jackson
Date: 2016-08-04 00:00:00
Tasks: Cloud Computing, Diagnostic, Specificity, whole slide images

Metastatic presence in lymph nodes is one of the most important prognostic
variables of breast cancer. The current diagnostic procedure for manually
reviewing sentinel lymph nodes, however, is very time-consuming and subjective.
Pathologists have to manually scan an entire digital whole-slide image (WSI)
for regions of metastasis that are sometimes only detectable under high
resolution or entirely hidden from the human visual cortex. From October 2015
to April 2016, the International Symposium on Biomedical Imaging (ISBI) held
the Camelyon Grand Challenge 2016 to crowd-source ideas and algorithms for
automatic detection of lymph node metastasis. Using a generalizable stain
normalization technique and the Proscia Pathology Cloud computing platform, we
trained a deep convolutional neural network on millions of tissue and tumor
image tiles to perform slide-based evaluation on our testing set of whole-slide
images images, with a sensitivity of 0.96, specificity of 0.89, and AUC score
of 0.90. Our results indicate that our platform can automatically scan any WSI
for metastatic regions without institutional calibration to respective stain
profiles.
A Recursive Framework for Expression Recognition: From Web Images to Deep Models to Game Dataset
Authors: Wei Li, Christina Tsangouri, Farnaz Abtahi, Zhigang Zhu
Date: 2016-08-04 00:00:00
Tasks: Dataset Generation, Facial Expression Recognition, Facial Expression Recognition (FER)

In this paper, we propose a recursive framework to recognize facial
expressions from images in real scenes. Unlike traditional approaches that
typically focus on developing and refining algorithms for improving recognition
performance on an existing dataset, we integrate three important components in
a recursive manner: facial dataset generation, facial expression recognition
model building, and interactive interfaces for testing and new data collection.
To start with, we first create a candid-images-for-facial-expression (CIFE)
dataset. We then apply a convolutional neural network (CNN) to CIFE and build a
CNN model for web image expression classification. In order to increase the
expression recognition accuracy, we also fine-tune the CNN model and thus
obtain a better CNN facial expression recognition model. Based on the
fine-tuned CNN model, we design a facial expression game engine and collect a
new and more balanced dataset, GaMo. The images of this dataset are collected
from the different expressions our game users make when playing the game.
Finally, we evaluate the GaMo and CIFE datasets and show that our recursive
framework can help build a better facial expression model for dealing with real
scene facial expression tasks.
Neural Programmer: Inducing Latent Programs with Gradient Descent
Authors: Arvind Neelakantan, Quoc V. Le, Ilya Sutskever
Date: 2015-11-16 00:00:00
Tasks: Question Answering, speech-recognition, Speech Recognition

Deep neural networks have achieved impressive supervised classification
performance in many tasks including image recognition, speech recognition, and
sequence to sequence learning. However, this success has not been translated to
applications like question answering that may involve complex arithmetic and
logic reasoning. A major limitation of these models is in their inability to
learn even simple arithmetic and logic operations. For example, it has been
shown that neural networks fail to learn to add two binary numbers reliably. In
this work, we propose Neural Programmer, an end-to-end differentiable neural
network augmented with a small set of basic arithmetic and logic operations.
Neural Programmer can call these augmented operations over several steps,
thereby inducing compositional programs that are more complex than the built-in
operations. The model learns from a weak supervision signal which is the result
of execution of the correct program, hence it does not require expensive
annotation of the correct program itself. The decisions of what operations to
call, and what data segments to apply to are inferred by Neural Programmer.
Such decisions, during training, are done in a differentiable fashion so that
the entire network can be trained jointly by gradient descent. We find that
training the model is difficult, but it can be greatly improved by adding
random noise to the gradient. On a fairly complex synthetic table-comprehension
dataset, traditional recurrent networks and attentional models perform poorly
while Neural Programmer typically obtains nearly perfect accuracy.
Deploying learning materials to game content for serious education game development: A case study
Authors: Harits Ar Rosyid, Matt Palmerlee, Ke Chen
Date: 2016-08-04 00:00:00

The ultimate goals of serious education games (SEG) are to facilitate
learning and maximizing enjoyment during playing SEGs. In SEG development,
there are normally two spaces to be taken into account: knowledge space
regarding learning materials and content space regarding games to be used to
convey learning materials. How to deploy the learning materials seamlessly and
effectively into game content becomes one of the most challenging problems in
SEG development. Unlike previous work where experts in education have to be
used heavily, we proposed a novel approach that works toward minimizing the
efforts of education experts in mapping learning materials to content space.
For a proof-of-concept, we apply the proposed approach in developing an SEG
game, named \emph{Chem Dungeon}, as a case study in order to demonstrate the
effectiveness of our proposed approach. This SEG game has been tested with a
number of users, and the user survey suggests our method works reasonably well.
Distributed Deep Learning for Question Answering
Authors: Minwei Feng, Bing Xiang, Bo-Wen Zhou
Date: 2015-11-03 00:00:00
Tasks: Answer Selection, Deep Learning, General Classification, Question Answering

This paper is an empirical study of the distributed deep learning for
question answering subtasks: answer selection and question classification.
Comparison studies of SGD, MSGD, ADADELTA, ADAGRAD, ADAM/ADAMAX, RMSPROP,
DOWNPOUR and EASGD/EAMSGD algorithms have been presented. Experimental results
show that the distributed framework based on the message passing interface can
accelerate the convergence speed at a sublinear scale. This paper demonstrates
the importance of distributed training. For example, with 48 workers, a 24x
speedup is achievable for the answer selection task and running time is
decreased from 138.2 hours to 5.81 hours, which will increase the productivity
significantly.
Query Answering in Resource-Based Answer Set Semantics
Authors: Stefania Costantini, Andrea Formisano
Date: 2016-08-04 00:00:00
Tasks: Negation

In recent work we defined resource-based answer set semantics, which is an
extension to answer set semantics stemming from the study of its relationship
with linear logic. In fact, the name of the new semantics comes from the fact
that in the linear-logic formulation every literal (including negative ones)
were considered as a resource. In this paper, we propose a query-answering
procedure reminiscent of Prolog for answer set programs under this extended
semantics as an extension of XSB-resolution for logic programs with negation.
We prove formal properties of the proposed procedure.
  Under consideration for acceptance in TPLP.
Stable Models for Infinitary Formulas with Extensional Atoms
Authors: Amelia Harrison, Vladimir Lifschitz
Date: 2016-08-04 00:00:00

The definition of stable models for propositional formulas with infinite
conjunctions and disjunctions can be used to describe the semantics of answer
set programming languages. In this note, we enhance that definition by
introducing a distinction between intensional and extensional atoms. The
symmetric splitting theorem for first-order formulas is then extended to
infinitary formulas and used to reason about infinitary definitions. This note
is under consideration for publication in Theory and Practice of Logic
Programming.
Learning by tracking: Siamese CNN for robust target association
Authors: Laura Leal-Taixé, Cristian Canton Ferrer, Konrad Schindler
Date: 2016-04-26 00:00:00
Tasks: Multiple People Tracking, Optical Flow Estimation, Position

This paper introduces a novel approach to the task of data association within
the context of pedestrian tracking, by introducing a two-stage learning scheme
to match pairs of detections. First, a Siamese convolutional neural network
(CNN) is trained to learn descriptors encoding local spatio-temporal structures
between the two input image patches, aggregating pixel values and optical flow
information. Second, a set of contextual features derived from the position and
size of the compared input patches are combined with the CNN output by means of
a gradient boosting classifier to generate the final matching probability. This
learning approach is validated by using a linear programming based multi-person
tracker showing that even a simple and efficient tracker may outperform much
more complex models when fed with our learned matching probabilities. Results
on publicly available sequences show that our method meets state-of-the-art
standards in multiple people tracking.
UsingWord Embeddings for Query Translation for Hindi to English Cross Language Information Retrieval
Authors: Paheli Bhattacharya, Pawan Goyal, Sudeshna Sarkar
Date: 2016-08-04 00:00:00
Tasks: Information Retrieval, Retrieval, Translation, Word Embeddings, Word Translation

Cross-Language Information Retrieval (CLIR) has become an important problem
to solve in the recent years due to the growth of content in multiple languages
in the Web. One of the standard methods is to use query translation from source
to target language. In this paper, we propose an approach based on word
embeddings, a method that captures contextual clues for a particular word in
the source language and gives those words as translations that occur in a
similar context in the target language. Once we obtain the word embeddings of
the source and target language pairs, we learn a projection from source to
target word embeddings, making use of a dictionary with word translation
pairs.We then propose various methods of query translation and aggregation. The
advantage of this approach is that it does not require the corpora to be
aligned (which is difficult to obtain for resource-scarce languages), a
dictionary with word translation pairs is enough to train the word vectors for
translation. We experiment with Forum for Information Retrieval and Evaluation
(FIRE) 2008 and 2012 datasets for Hindi to English CLIR. The proposed word
embedding based approach outperforms the basic dictionary based approach by 70%
and when the word embeddings are combined with the dictionary, the hybrid
approach beats the baseline dictionary based method by 77%. It outperforms the
English monolingual baseline by 15%, when combined with the translations
obtained from Google Translate and Dictionary.
Real-Time Intensity-Image Reconstruction for Event Cameras Using Manifold Regularisation
Authors: Christian Reinbacher, Gottfried Graber, Thomas Pock
Date: 2016-07-21 00:00:00
Tasks: Image Reconstruction, Optical Flow Estimation

Event cameras or neuromorphic cameras mimic the human perception system as
they measure the per-pixel intensity change rather than the actual intensity
level. In contrast to traditional cameras, such cameras capture new information
about the scene at MHz frequency in the form of sparse events. The high
temporal resolution comes at the cost of losing the familiar per-pixel
intensity information. In this work we propose a variational model that
accurately models the behaviour of event cameras, enabling reconstruction of
intensity images with arbitrary frame rate in real-time. Our method is
formulated on a per-event-basis, where we explicitly incorporate information
about the asynchronous nature of events via an event manifold induced by the
relative timestamps of events. In our experiments we verify that solving the
variational model on the manifold produces high-quality images without
explicitly estimating optical flow.