pwc-archive/papers-with-abstracts huggingface dataset:
Juxtaposition of System Dynamics and Agent-based Simulation for a Case Study in Immunosenescence
Authors: Grazziela P. Figueredo, Peer-Olaf Siebers, Uwe Aickelin, Amanda Whitbrook, Jonathan M. Garibaldi
Date: 2016-07-20 00:00:00

Advances in healthcare and in the quality of life significantly increase
human life expectancy. With the ageing of populations, new un-faced challenges
are brought to science. The human body is naturally selected to be
well-functioning until the age of reproduction to keep the species alive.
However, as the lifespan extends, unseen problems due to the body deterioration
emerge. There are several age-related diseases with no appropriate treatment;
therefore, the complex ageing phenomena needs further understanding.
Immunosenescence, the ageing of the immune system, is highly correlated to the
negative effects of ageing, such as the increase of auto-inflammatory diseases
and decrease in responsiveness to new diseases. Besides clinical and
mathematical tools, we believe there is opportunity to further exploit
simulation tools to understand immunosenescence. Compared to real-world
experimentation, benefits include time and cost effectiveness due to the
laborious, resource-intensiveness of the biological environment and the
possibility of conducting experiments without ethic restrictions. Contrasted
with mathematical models, simulation modelling is more suitable for
representing complex systems and emergence. In addition, there is the belief
that simulation models are easier to communicate in interdisciplinary contexts.
Our work investigates the usefulness of simulations to understand
immunosenescence by employing two different simulation methods, agent-based and
system dynamics simulation, to a case study of immune cells depletion with age.
Large Scale Deep Convolutional Neural Network Features Search with Lucene
Authors: Claudio Gennaro
Date: 2016-03-31 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

In this work, we propose an approach to index Deep Convolutional Neural
Network Features to support efficient content-based retrieval on large image
databases. To this aim, we have converted the these features into a textual
form, to index them into an inverted index by means of Lucene. In this way, we
were able to set up a robust retrieval system that combines full-text search
with content-based image retrieval capabilities. We evaluated different
strategies of textual representation in order to optimize the index occupation
and the query response time. In order to show that our approach is able to
handle large datasets, we have developed a web-based prototype that provides an
interface for combined textual and visual searching into a dataset of about 100
million of images.
Indebted households profiling: a knowledge discovery from database approach
Authors: Rodrigo Scarpel, Alexandros Ladas, Uwe Aickelin
Date: 2016-07-20 00:00:00
Tasks: Management

A major challenge in consumer credit risk portfolio management is to classify
households according to their risk profile. In order to build such risk
profiles it is necessary to employ an approach that analyses data
systematically in order to detect important relationships, interactions,
dependencies and associations amongst the available continuous and categorical
variables altogether and accurately generate profiles of most interesting
household segments according to their credit risk. The objective of this work
is to employ a knowledge discovery from database process to identify groups of
indebted households and describe their profiles using a database collected by
the Consumer Credit Counselling Service (CCCS) in the UK. Employing a framework
that allows the usage of both categorical and continuous data altogether to
find hidden structures in unlabelled data it was established the ideal number
of clusters and such clusters were described in order to identify the
households who exhibit a high propensity of excessive debt levels.
Identifying Candidate Risk Factors for Prescription Drug Side Effects using Causal Contrast Set Mining
Authors: Jenna Reps, Zhaoyang Guo, Haoyue Zhu, Uwe Aickelin
Date: 2016-07-20 00:00:00
Tasks: Causal Inference

Big longitudinal observational databases present the opportunity to extract
new knowledge in a cost effective manner. Unfortunately, the ability of these
databases to be used for causal inference is limited due to the passive way in
which the data are collected resulting in various forms of bias. In this paper
we investigate a method that can overcome these limitations and determine
causal contrast set rules efficiently from big data. In particular, we present
a new methodology for the purpose of identifying risk factors that increase a
patients likelihood of experiencing the known rare side effect of renal failure
after ingesting aminosalicylates. The results show that the methodology was
able to identify previously researched risk factors such as being prescribed
diuretics and highlighted that patients with a higher than average risk of
renal failure may be even more susceptible to experiencing it as a side effect
after ingesting aminosalicylates.
Towards Storytelling from Visual Lifelogging: An Overview
Authors: Marc Bolaños, Mariella Dimiccoli, Petia Radeva
Date: 2015-07-22 00:00:00

Visual lifelogging consists of acquiring images that capture the daily
experiences of the user by wearing a camera over a long period of time. The
pictures taken offer considerable potential for knowledge mining concerning how
people live their lives, hence, they open up new opportunities for many
potential applications in fields including healthcare, security, leisure and
the quantified self. However, automatically building a story from a huge
collection of unstructured egocentric data presents major challenges. This
paper provides a thorough review of advances made so far in egocentric data
analysis, and in view of the current state of the art, indicates new lines of
research to move us towards storytelling from visual lifelogging.
Modelling Office Energy Consumption: An Agent Based Approach
Authors: Tao Zhang, Peer-Olaf Siebers, Uwe Aickelin
Date: 2016-07-20 00:00:00
Tasks: energy management, Management

In this paper, we develop an agent-based model which integrates four
important elements, i.e. organisational energy management policies/regulations,
energy management technologies, electric appliances and equipment, and human
behaviour, based on a case study, to simulate the energy consumption in office
buildings. With the model, we test the effectiveness of different energy
management strategies, and solve practical office energy consumption problems.
This paper theoretically contributes to an integration of four elements
involved in the complex organisational issue of office energy consumption, and
practically contributes to an application of agent-based approach for office
building energy consumption study.
Superpixel-based Two-view Deterministic Fitting for Multiple-structure Data
Authors: Guobao Xiao, Hanzi Wang, Yan Yan, David Suter
Date: 2016-07-20 00:00:00
Tasks: Model Selection, Superpixels, Vocal Bursts Valence Prediction

This paper proposes a two-view deterministic geometric model fitting method,
termed Superpixel-based Deterministic Fitting (SDF), for multiple-structure
data. SDF starts from superpixel segmentation, which effectively captures prior
information of feature appearances. The feature appearances are beneficial to
reduce the computational complexity for deterministic fitting methods. SDF also
includes two original elements, i.e., a deterministic sampling algorithm and a
novel model selection algorithm. The two algorithms are tightly coupled to
boost the performance of SDF in both speed and accuracy. Specifically, the
proposed sampling algorithm leverages the grouping cues of superpixels to
generate reliable and consistent hypotheses. The proposed model selection
algorithm further makes use of desirable properties of the generated
hypotheses, to improve the conventional fit-and-remove framework for more
efficient and effective performance. The key characteristic of SDF is that it
can efficiently and deterministically estimate the parameters of model
instances in multi-structure data. Experimental results demonstrate that the
proposed SDF shows superiority over several state-of-the-art fitting methods
for real images with single-structure and multiple-structure data.
Personalization Effect on Emotion Recognition from Physiological Data: An Investigation of Performance on Different Setups and Classifiers
Authors: Varvara Kollia
Date: 2016-07-20 00:00:00
Tasks: Emotion Recognition, General Classification

This paper addresses the problem of emotion recognition from physiological
signals. Features are extracted and ranked based on their effect on
classification accuracy. Different classifiers are compared. The inter-subject
variability and the personalization effect are thoroughly investigated, through
trial-based and subject-based cross-validation. Finally, a personalized model
is introduced, that would allow for enhanced emotional state prediction, based
on the physiological data of subjects that exhibit a certain degree of
similarity, without the requirement of further feedback.
An Adaptation of Topic Modeling to Sentences
Authors: Ruey-Cheng Chen, Reid Swanson, Andrew S. Gordon
Date: 2016-07-20 00:00:00
Tasks: Sentence

Advances in topic modeling have yielded effective methods for characterizing
the latent semantics of textual data. However, applying standard topic modeling
approaches to sentence-level tasks introduces a number of challenges. In this
paper, we adapt the approach of latent-Dirichlet allocation to include an
additional layer for incorporating information about the sentence boundaries in
documents. We show that the addition of this minimal information of document
structure improves the perplexity results of a trained model.
You want to survive the data deluge: Be careful, Computational Intelligence will not serve you as a rescue boat
Authors: Emanuel Diamant
Date: 2016-07-20 00:00:00

We are at the dawn of a new era, where advances in computer power, broadband
communication and digital sensor technologies have led to an unprecedented
flood of data inundating our surrounding. It is generally believed that means
such as Computational Intelligence will help to outlive these tough times.
However, these hopes are improperly high. Computational Intelligence is a
surprising composition of two mutually exclusive and contradicting constituents
that could be coupled only if you disregard and neglect their controversies:
"Computational" implies reliance on data processing and "Intelligence" implies
reliance on information processing. Only those who are indifferent to
data-information discrepancy can believe that such a combination can be viable.
We do not believe in miracles, so we will try to share with you our
reservations.
Neural Contextual Conversation Learning with Labeled Question-Answering Pairs
Authors: Kun Xiong, Anqi Cui, Zefeng Zhang, Ming Li
Date: 2016-07-20 00:00:00
Tasks: Question Answering

Neural conversational models tend to produce generic or safe responses in
different contexts, e.g., reply \textit{"Of course"} to narrative statements or
\textit{"I don't know"} to questions. In this paper, we propose an end-to-end
approach to avoid such problem in neural generative models. Additional memory
mechanisms have been introduced to standard sequence-to-sequence (seq2seq)
models, so that context can be considered while generating sentences. Three
seq2seq models, which memorize a fix-sized contextual vector from hidden input,
hidden input/output and a gated contextual attention structure respectively,
have been trained and tested on a dataset of labeled question-answering pairs
in Chinese. The model with contextual attention outperforms others including
the state-of-the-art seq2seq models on perplexity test. The novel contextual
model generates diverse and robust responses, and is able to carry out
conversations on a wide range of topics appropriately.
Deep Cross Residual Learning for Multitask Visual Recognition
Authors: Brendan Jou, Shih-Fu Chang
Date: 2016-04-05 00:00:00
Tasks: Object Recognition

Residual learning has recently surfaced as an effective means of constructing
very deep neural networks for object recognition. However, current incarnations
of residual networks do not allow for the modeling and integration of complex
relations between closely coupled recognition tasks or across domains. Such
problems are often encountered in multimedia applications involving large-scale
content recognition. We propose a novel extension of residual learning for deep
networks that enables intuitive learning across multiple related tasks using
cross-connections called cross-residuals. These cross-residuals connections can
be viewed as a form of in-network regularization and enables greater network
generalization. We show how cross-residual learning (CRL) can be integrated in
multitask networks to jointly train and detect visual concepts across several
tasks. We present a single multitask cross-residual network with >40% less
parameters that is able to achieve competitive, or even better, detection
performance on a visual sentiment concept detection problem normally requiring
multiple specialized single-task networks. The resulting multitask
cross-residual network also achieves better detection performance by about
10.4% over a standard multitask residual network without cross-residuals with
even a small amount of cross-task weighting.
Finding a sparse vector in a subspace: Linear sparsity using alternating directions
Authors: Qing Qu, Ju Sun, John Wright
Date: 2014-12-15 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Dictionary Learning

Is it possible to find the sparsest vector (direction) in a generic subspace
$\mathcal{S} \subseteq \mathbb{R}^p$ with $\mathrm{dim}(\mathcal{S})= n < p$?
This problem can be considered a homogeneous variant of the sparse recovery
problem, and finds connections to sparse dictionary learning, sparse PCA, and
many other problems in signal processing and machine learning. In this paper,
we focus on a **planted sparse model** for the subspace: the target sparse
vector is embedded in an otherwise random subspace. Simple convex heuristics
for this planted recovery problem provably break down when the fraction of
nonzero entries in the target sparse vector substantially exceeds
$O(1/\sqrt{n})$. In contrast, we exhibit a relatively simple nonconvex approach
based on alternating directions, which provably succeeds even when the fraction
of nonzero entries is $\Omega(1)$. To the best of our knowledge, this is the
first practical algorithm to achieve linear scaling under the planted sparse
model. Empirically, our proposed algorithm also succeeds in more challenging
data models, e.g., sparse dictionary learning.
Hierarchical Manifold Clustering on Diffusion Maps for Connectomics (MIT 18.S096 final project)
Authors: Gergely Odor
Date: 2016-07-20 00:00:00
Tasks: Clustering, Segmentation

In this paper, we introduce a novel algorithm for segmentation of imperfect
boundary probability maps (BPM) in connectomics. Our algorithm can be a
considered as an extension of spectral clustering. Instead of clustering the
diffusion maps with traditional clustering algorithms, we learn the manifold
and compute an estimate of the minimum normalized cut. We proceed by divide and
conquer. We also introduce a novel criterion for determining if further splits
are necessary in a component based on it's topological properties. Our
algorithm complements the currently popular agglomeration approaches in
connectomics, which overlook the geometrical aspects of this segmentation
problem.
Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking
Authors: Guanghan Ning, Zhi Zhang, Chen Huang, Zhihai He, Xiaobo Ren, Haohong Wang
Date: 2016-07-19 00:00:00
Tasks: Binary Classification, object-detection, Object Detection, Object Tracking, regression, Visual Object Tracking

In this paper, we develop a new approach of spatially supervised recurrent
convolutional neural networks for visual object tracking. Our recurrent
convolutional network exploits the history of locations as well as the
distinctive visual features learned by the deep neural networks. Inspired by
recent bounding box regression methods for object detection, we study the
regression capability of Long Short-Term Memory (LSTM) in the temporal domain,
and propose to concatenate high-level visual features produced by convolutional
networks with region information. In contrast to existing deep learning based
trackers that use binary classification for region candidates, we use
regression for direct prediction of the tracking locations both at the
convolutional layer and at the recurrent unit. Our extensive experimental
results and performance comparison with state-of-the-art tracking methods on
challenging benchmark video tracking datasets shows that our tracker is more
accurate and robust while maintaining low computational cost. For most test
video sequences, our method achieves the best tracking performance, often
outperforms the second best by a large margin.
TensorLog: A Differentiable Deductive Database
Authors: William W. Cohen
Date: 2016-05-20 00:00:00

Large knowledge bases (KBs) are useful in many tasks, but it is unclear how
to integrate this sort of knowledge into "deep" gradient-based learning
systems. To address this problem, we describe a probabilistic deductive
database, called TensorLog, in which reasoning uses a differentiable process.
In TensorLog, each clause in a logical theory is first converted into certain
type of factor graph. Then, for each type of query to the factor graph, the
message-passing steps required to perform belief propagation (BP) are
"unrolled" into a function, which is differentiable. We show that these
functions can be composed recursively to perform inference in non-trivial
logical theories containing multiple interrelated clauses and predicates. Both
compilation and inference in TensorLog are efficient: compilation is linear in
theory size and proof depth, and inference is linear in database size and the
number of message-passing steps used in BP. We also present experimental
results with TensorLog and discuss its relationship to other first-order
probabilistic logics.
PRIIME: A Generic Framework for Interactive Personalized Interesting Pattern Discovery
Authors: Mansurul Bhuiyan, Mohammad Al Hasan
Date: 2016-07-19 00:00:00

The traditional frequent pattern mining algorithms generate an exponentially
large number of patterns of which a substantial proportion are not much
significant for many data analysis endeavors. Discovery of a small number of
personalized interesting patterns from the large output set according to a
particular user's interest is an important as well as challenging task.
Existing works on pattern summarization do not solve this problem from the
personalization viewpoint. In this work, we propose an interactive pattern
discovery framework named PRIIME which identifies a set of interesting patterns
for a specific user without requiring any prior input on the interestingness
measure of patterns from the user. The proposed framework is generic to support
discovery of the interesting set, sequence and graph type patterns. We develop
a softmax classification based iterative learning algorithm that uses a limited
number of interactive feedback from the user to learn her interestingness
profile, and use this profile for pattern recommendation. To handle sequence
and graph type patterns PRIIME adopts a neural net (NN) based unsupervised
feature construction approach. We also develop a strategy that combines
exploration and exploitation to select patterns for feedback. We show
experimental results on several real-life datasets to validate the performance
of the proposed method. We also compare with the existing methods of
interactive pattern discovery to show that our method is substantially superior
in performance. To portray the applicability of the framework, we present a
case study from the real-estate domain.
Multi-category Angle-based Classifier Refit
Authors: Guo Xian Yau, Chong Zhang
Date: 2016-07-19 00:00:00
Tasks: General Classification

Classification is an important statistical learning tool. In real application, besides high prediction accuracy, it is often desirable to estimate class conditional probabilities for new observations. For traditional problems where the number of observations is large, there exist many well developed approaches. Recently, high dimensional low sample size problems are becoming increasingly popular. Margin-based classifiers, such as logistic regression, are well established methods in the literature. On the other hand, in terms of probability estimation, it is known that for binary classifiers, the commonly used methods tend to under-estimate the norm of the classification function. This can lead to biased probability estimation. Remedy approaches have been proposed in the literature. However, for the simultaneous multicategory classification framework, much less work has been done. We fill the gap in this paper. In particular, we give theoretical insights on why heavy regularization terms are often needed in high dimensional applications, and how this can lead to bias in probability estimation. To overcome this difficulty, we propose a new refit strategy for multicategory angle-based classifiers. Our new method only adds a small computation cost to the problem, and is able to attain prediction accuracy that is as good as the regular margin-based classifiers. On the other hand, the improvement of probability estimation can be very significant. Numerical results suggest that the new refit approach is highly competitive.
Information-theoretical label embeddings for large-scale image classification
Authors: François Chollet
Date: 2016-07-19 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, regression

We present a method for training multi-label, massively multi-class image
classification models, that is faster and more accurate than supervision via a
sigmoid cross-entropy loss (logistic regression). Our method consists in
embedding high-dimensional sparse labels onto a lower-dimensional dense sphere
of unit-normed vectors, and treating the classification problem as a cosine
proximity regression problem on this sphere. We test our method on a dataset of
300 million high-resolution images with 17,000 labels, where it yields
considerably faster convergence, as well as a 7% higher mean average precision
compared to logistic regression.
Stochastic Backpropagation through Mixture Density Distributions
Authors: Alex Graves
Date: 2016-07-19 00:00:00
Tasks: Variational Inference

The ability to backpropagate stochastic gradients through continuous latent
distributions has been crucial to the emergence of variational autoencoders and
stochastic gradient variational Bayes. The key ingredient is an unbiased and
low-variance way of estimating gradients with respect to distribution
parameters from gradients evaluated at distribution samples. The
"reparameterization trick" provides a class of transforms yielding such
estimators for many continuous distributions, including the Gaussian and other
members of the location-scale family. However the trick does not readily extend
to mixture density models, due to the difficulty of reparameterizing the
discrete distribution over mixture weights. This report describes an
alternative transform, applicable to any continuous multivariate distribution
with a differentiable density function from which samples can be drawn, and
uses it to derive an unbiased estimator for mixture density weight derivatives.
Combined with the reparameterization trick applied to the individual mixture
components, this estimator makes it straightforward to train variational
autoencoders with mixture-distributed latent variables, or to perform
stochastic variational inference with a mixture density variational posterior.