pwc-archive/papers-with-abstracts huggingface dataset:
Sine Cosine Crow Search Algorithm: A powerful hybrid meta heuristic for global optimization
Authors: Seyed Hamid Reza Pasandideh, Soheyl Khalilpourazari
Date: 2018-01-25 00:00:00
Tasks: global-optimization

This paper presents a novel hybrid algorithm named Since Cosine Crow Search
Algorithm. To propose the SCCSA, two novel algorithms are considered including
Crow Search Algorithm (CSA) and Since Cosine Algorithm (SCA). The advantages of
the two algorithms are considered and utilize to design an efficient hybrid
algorithm which can perform significantly better in various benchmark
functions. The combination of concept and operators of the two algorithms
enable the SCCSA to make an appropriate trade-off between exploration and
exploitation abilities of the algorithm. To evaluate the performance of the
proposed SCCSA, seven well-known benchmark functions are utilized. The results
indicated that the proposed hybrid algorithm is able to provide very
competitive solution comparing to other state-of-the-art meta heuristics.
An Integrated Soft Computing Approach to a Multi-biometric Security Model
Authors: Prem Sewak Sudhish
Date: 2018-01-25 00:00:00
Tasks: Unity

The abstract of the thesis consists of three sections, videlicet,
  Motivation
  Chapter Organization
  Salient Contributions.
  The complete abstract is included with the thesis. The final section on
Salient Contributions is reproduced below.
  Salient Contributions
  The research presents the following salient contributions:
  i. A novel technique has been developed for comparing biographical
information, by combining the average impact of Levenshtein,
Damerau-Levenshtein, and editor distances. The impact is calculated as the
ratio of the edit distance to the maximum possible edit distance between two
strings of the same lengths as the given pair of strings. This impact lies in
the range [0, 1] and can easily be converted to a similarity (matching) score
by subtracting the impact from unity.
  ii. A universal soft computing framework is proposed for adaptively fusing
biometric and biographical information by making real-time decisions to
determine after consideration of each individual identifier whether computation
of matching scores and subsequent fusion of additional identifiers, including
biographical information is required. This proposed framework not only improves
the accuracy of the system by fusing less reliable information (e.g.
biographical information) only for instances where such a fusion is required,
but also improves the efficiency of the system by computing matching scores for
various available identifiers only when this computation is considered
necessary.
  iii. A scientific method for comparing efficiency of fusion strategies
through a predicted effort to error trade-off curve.
Auxiliary gradient-based sampling algorithms
Authors: Michalis K. Titsias, Omiros Papaspiliopoulos
Date: 2016-10-30 00:00:00
Tasks: Binary Classification

We introduce a new family of MCMC samplers that combine auxiliary variables,
Gibbs sampling and Taylor expansions of the target density. Our approach
permits the marginalisation over the auxiliary variables yielding marginal
samplers, or the augmentation of the auxiliary variables, yielding auxiliary
samplers. The well-known Metropolis-adjusted Langevin algorithm (MALA) and
preconditioned Crank-Nicolson Langevin (pCNL) algorithm are shown to be special
cases. We prove that marginal samplers are superior in terms of asymptotic
variance and demonstrate cases where they are slower in computing time compared
to auxiliary samplers. In the context of latent Gaussian models we propose new
auxiliary and marginal samplers whose implementation requires a single tuning
parameter, which can be found automatically during the transient phase.
Extensive experimentation shows that the increase in efficiency (measured as
effective sample size per unit of computing time) relative to (optimised
implementations of) pCNL, elliptical slice sampling and MALA ranges from
10-fold in binary classification problems to 25-fold in log-Gaussian Cox
processes to 100-fold in Gaussian process regression, and it is on par with
Riemann manifold Hamiltonian Monte Carlo in an example where the latter has the
same complexity as the aforementioned algorithms. We explain this remarkable
improvement in terms of the way alternative samplers try to approximate the
eigenvalues of the target. We introduce a novel MCMC sampling scheme for
hyperparameter learning that builds upon the auxiliary samplers. The MATLAB
code for reproducing the experiments in the article is publicly available and a
Supplement to this article contains additional experiments and implementation
details.
Convolutional Invasion and Expansion Networks for Tumor Growth Prediction
Authors: Ling Zhang, Le Lu, Ronald M. Summers, Electron Kebebew, Jianhua Yao
Date: 2018-01-25 00:00:00

Tumor growth is associated with cell invasion and mass-effect, which are
traditionally formulated by mathematical models, namely reaction-diffusion
equations and biomechanics. Such models can be personalized based on clinical
measurements to build the predictive models for tumor growth. In this paper, we
investigate the possibility of using deep convolutional neural networks
(ConvNets) to directly represent and learn the cell invasion and mass-effect,
and to predict the subsequent involvement regions of a tumor. The invasion
network learns the cell invasion from information related to metabolic rate,
cell density and tumor boundary derived from multimodal imaging data. The
expansion network models the mass-effect from the growing motion of tumor mass.
We also study different architectures that fuse the invasion and expansion
networks, in order to exploit the inherent correlations among them. Our network
can easily be trained on population data and personalized to a target patient,
unlike most previous mathematical modeling methods that fail to incorporate
population data. Quantitative experiments on a pancreatic tumor data set show
that the proposed method substantially outperforms a state-of-the-art
mathematical model-based approach in both accuracy and efficiency, and that the
information captured by each of the two subnetworks are complementary.
Identifying Corresponding Patches in SAR and Optical Images with a Pseudo-Siamese CNN
Authors: Lloyd H. Hughes, Michael Schmitt, Lichao Mou, Yuanyuan Wang, Xiao Xiang Zhu
Date: 2018-01-25 00:00:00
Tasks: Key Point Matching

In this letter, we propose a pseudo-siamese convolutional neural network
(CNN) architecture that enables to solve the task of identifying corresponding
patches in very-high-resolution (VHR) optical and synthetic aperture radar
(SAR) remote sensing imagery. Using eight convolutional layers each in two
parallel network streams, a fully connected layer for the fusion of the
features learned in each stream, and a loss function based on binary
cross-entropy, we achieve a one-hot indication if two patches correspond or
not. The network is trained and tested on an automatically generated dataset
that is based on a deterministic alignment of SAR and optical imagery via
previously reconstructed and subsequently co-registered 3D point clouds. The
satellite images, from which the patches comprising our dataset are extracted,
show a complex urban scene containing many elevated objects (i.e. buildings),
thus providing one of the most difficult experimental environments. The
achieved results show that the network is able to predict corresponding patches
with high accuracy, thus indicating great potential for further development
towards a generalized multi-sensor key-point matching procedure. Index
Terms-synthetic aperture radar (SAR), optical imagery, data fusion, deep
learning, convolutional neural networks (CNN), image matching, deep matching
A Distributed Framework for the Construction of Transport Maps
Authors: Diego A. Mesa, Justin Tantiongloc, Marcela Mendoza, Todd P. Coleman
Date: 2018-01-25 00:00:00
Tasks: Bayesian Inference

The need to reason about uncertainty in large, complex, and multi-modal
datasets has become increasingly common across modern scientific environments.
The ability to transform samples from one distribution $P$ to another
distribution $Q$ enables the solution to many problems in machine learning
(e.g. Bayesian inference, generative modeling) and has been actively pursued
from theoretical, computational, and application perspectives across the fields
of information theory, computer science, and biology. Performing such
transformations, in general, still leads to computational difficulties,
especially in high dimensions. Here, we consider the problem of computing such
"measure transport maps" with efficient and parallelizable methods. Under the
mild assumptions that $P$ need not be known but can be sampled from, and that
the density of $Q$ is known up to a proportionality constant, and that $Q$ is
log-concave, we provide in this work a convex optimization problem pertaining
to relative entropy minimization. We show how an empirical minimization
formulation and polynomial chaos map parameterization can allow for learning a
transport map between $P$ and $Q$ with distributed and scalable methods. We
also leverage findings from nonequilibrium thermodynamics to represent the
transport map as a composition of simpler maps, each of which is learned
sequentially with a transport cost regularized version of the aforementioned
problem formulation. We provide examples of our framework within the context of
Bayesian inference for the Boston housing dataset and generative modeling for
handwritten digit images from the MNIST dataset.
Soft Computing Techniques for Dependable Cyber-Physical Systems
Authors: Muhammad Atif, Siddique Latif, Rizwan Ahmad, Adnan Khalid Kiani, Junaid Qadir, Adeel Baig, Hisao Ishibuchi, Waseem Abbas
Date: 2018-01-25 00:00:00

Cyber-Physical Systems (CPS) allow us to manipulate objects in the physical world by providing a communication bridge between computation and actuation elements. In the current scheme of things, this sought-after control is marred by limitations inherent in the underlying communication network(s) as well as by the uncertainty found in the physical world. These limitations hamper fine-grained control of elements that may be separated by large-scale distances. In this regard, soft computing is an emerging paradigm that can help to overcome the vulnerabilities, and unreliability of CPS by using techniques including fuzzy systems, neural network, evolutionary computation, probabilistic reasoning and rough sets. In this paper, we present a comprehensive contemporary review of soft computing techniques for CPS dependability modeling, analysis, and improvement. This paper provides an overview of CPS applications, explores the foundations of dependability engineering, and highlights the potential role of soft computing techniques for CPS dependability with various case studies, while identifying common pitfalls and future directions. In addition, this paper provides a comprehensive survey on the use of various soft computing techniques for making CPS dependable.
Nearest Descent, In-Tree, and Clustering
Authors: Teng Qiu, Kai-Fu Yang, Chao-Yi Li, Yong-Jie Li
Date: 2014-12-07 00:00:00
Tasks: Clustering

In this paper, we propose a physically inspired graph-theoretical clustering
method, which first makes the data points organized into an attractive graph,
called In-Tree, via a physically inspired rule, called Nearest Descent (ND). In
particular, the rule of ND works to select the nearest node in the descending
direction of potential as the parent node of each node, which is in essence
different from the classical Gradient Descent or Steepest Descent. The
constructed In-Tree proves a very good candidate for clustering due to its
particular features and properties. In the In-Tree, the original clustering
problem is reduced to a problem of removing a very few of undesired edges from
this graph. Pleasingly, the undesired edges in In-Tree are so distinguishable
that they can be easily determined in either automatic or interactive way,
which is in stark contrast to the cases in the widely used Minimal Spanning
Tree and k-nearest-neighbor graph. The cluster number in the proposed method
can be easily determined based on some intermediate plots, and the cluster
assignment for each node is easily made by quickly searching its root node in
each sub-graph (also an In-Tree). The proposed method is extensively evaluated
on both synthetic and real-world datasets. Overall, the proposed clustering
method is a density-based one, but shows significant differences and advantages
in comparison to the traditional ones. The proposed method is simple yet
efficient and reliable, and is applicable to various datasets with diverse
shapes, attributes and any high dimensionality
C2MSNet: A Novel approach for single image haze removal
Authors: Akshay Dudhane, Subrahmanyam Murala
Date: 2018-01-25 00:00:00
Tasks: Image Dehazing, Single Image Dehazing, Single Image Haze Removal, SSIM

Degradation of image quality due to the presence of haze is a very common
phenomenon. Existing DehazeNet [3], MSCNN [11] tackled the drawbacks of hand
crafted haze relevant features. However, these methods have the problem of
color distortion in gloomy (poor illumination) environment. In this paper, a
cardinal (red, green and blue) color fusion network for single image haze
removal is proposed. In first stage, network fusses color information present
in hazy images and generates multi-channel depth maps. The second stage
estimates the scene transmission map from generated dark channels using multi
channel multi scale convolutional neural network (McMs-CNN) to recover the
original scene. To train the proposed network, we have used two standard
datasets namely: ImageNet [5] and D-HAZY [1]. Performance evaluation of the
proposed approach has been carried out using structural similarity index
(SSIM), mean square error (MSE) and peak signal to noise ratio (PSNR).
Performance analysis shows that the proposed approach outperforms the existing
state-of-the-art methods for single image dehazing.
Understanding Human Behaviors in Crowds by Imitating the Decision-Making Process
Authors: Haosheng Zou, Hang Su, Shihong Song, Jun Zhu
Date: 2018-01-25 00:00:00
Tasks: Collision Avoidance, Decision Making, Imitation Learning, Sequential Decision Making

Crowd behavior understanding is crucial yet challenging across a wide range
of applications, since crowd behavior is inherently determined by a sequential
decision-making process based on various factors, such as the pedestrians' own
destinations, interaction with nearby pedestrians and anticipation of upcoming
events. In this paper, we propose a novel framework of Social-Aware Generative
Adversarial Imitation Learning (SA-GAIL) to mimic the underlying
decision-making process of pedestrians in crowds. Specifically, we infer the
latent factors of human decision-making process in an unsupervised manner by
extending the Generative Adversarial Imitation Learning framework to anticipate
future paths of pedestrians. Different factors of human decision making are
disentangled with mutual information maximization, with the process modeled by
collision avoidance regularization and Social-Aware LSTMs. Experimental results
demonstrate the potential of our framework in disentangling the latent
decision-making factors of pedestrians and stronger abilities in predicting
future trajectories.
Global and Local Consistent Age Generative Adversarial Networks
Authors: Pei-Pei Li, Yibo Hu, Qi Li, Ran He, Zhenan Sun
Date: 2018-01-25 00:00:00
Tasks: Attribute, Generative Adversarial Network, Human Aging, MORPH

Age progression/regression is a challenging task due to the complicated and
non-linear transformation in human aging process. Many researches have shown
that both global and local facial features are essential for face
representation, but previous GAN based methods mainly focused on the global
feature in age synthesis. To utilize both global and local facial information,
we propose a Global and Local Consistent Age Generative Adversarial Network
(GLCA-GAN). In our generator, a global network learns the whole facial
structure and simulates the aging trend of the whole face, while three crucial
facial patches are progressed or regressed by three local networks aiming at
imitating subtle changes of crucial facial subregions. To preserve most of the
details in age-attribute-irrelevant areas, our generator learns the residual
face. Moreover, we employ an identity preserving loss to better preserve the
identity information, as well as age preserving loss to enhance the accuracy of
age synthesis. A pixel loss is also adopted to preserve detailed facial
information of the input face. Our proposed method is evaluated on three face
aging datasets, i.e., CACD dataset, Morph dataset and FG-NET dataset.
Experimental results show appealing performance of the proposed method by
comparing with the state-of-the-art.
Data-Driven Impulse Response Regularization via Deep Learning
Authors: Carl Andersson, Niklas Wahlström, Thomas B. Schön
Date: 2018-01-25 00:00:00
Tasks: Deep Learning

We consider the problem of impulse response estimation of stable linear
single-input single-output systems. It is a well-studied problem where flexible
non-parametric models recently offered a leap in performance compared to the
classical finite-dimensional model structures. Inspired by this development and
the success of deep learning we propose a new flexible data-driven model. Our
experiments indicate that the new model is capable of exploiting even more of
the hidden patterns that are present in the input-output data as compared to
the non-parametric models.
DeepWriting: Making Digital Ink Editable via Deep Generative Modeling
Authors: Emre Aksan, Fabrizio Pece, Otmar Hilliges
Date: 2018-01-25 00:00:00
Tasks: Handwriting generation, Handwritten Word Generation, Style Transfer

Digital ink promises to combine the flexibility and aesthetics of handwriting
and the ability to process, search and edit digital text. Character recognition
converts handwritten text into a digital representation, albeit at the cost of
losing personalized appearance due to the technical difficulties of separating
the interwoven components of content and style. In this paper, we propose a
novel generative neural network architecture that is capable of disentangling
style from content and thus making digital ink editable. Our model can
synthesize arbitrary text, while giving users control over the visual
appearance (style). For example, allowing for style transfer without changing
the content, editing of digital ink at the word level and other application
scenarios such as spell-checking and correction of handwritten text. We
furthermore contribute a new dataset of handwritten text with fine-grained
annotations at the character level and report results from an initial user
evaluation.
Probabilistic Planning by Probabilistic Programming
Authors: Vaishak Belle
Date: 2018-01-25 00:00:00
Tasks: Probabilistic Programming

Automated planning is a major topic of research in artificial intelligence,
and enjoys a long and distinguished history. The classical paradigm assumes a
distinguished initial state, comprised of a set of facts, and is defined over a
set of actions which change that state in one way or another. Planning in many
real-world settings, however, is much more involved: an agent's knowledge is
almost never simply a set of facts that are true, and actions that the agent
intends to execute never operate the way they are supposed to. Thus,
probabilistic planning attempts to incorporate stochastic models directly into
the planning process. In this article, we briefly report on probabilistic
planning through the lens of probabilistic programming: a programming paradigm
that aims to ease the specification of structured probability distributions. In
particular, we provide an overview of the features of two systems, HYPE and
ALLEGRO, which emphasise different strengths of probabilistic programming that
are particularly useful for complex modelling issues raised in probabilistic
planning. Among other things, with these systems, one can instantiate planning
problems with growing and shrinking state spaces, discrete and continuous
probability distributions, and non-unique prior distributions in a first-order
setting.
Collaborative Large-Scale Dense 3D Reconstruction with Online Inter-Agent Pose Optimisation
Authors: Stuart Golodetz, Tommaso Cavallari, Nicholas A. Lord, Victor A. Prisacariu, David W. Murray, Philip H. S. Torr
Date: 2018-01-25 00:00:00
Tasks: 3D Reconstruction

Reconstructing dense, volumetric models of real-world 3D scenes is important for many tasks, but capturing large scenes can take significant time, and the risk of transient changes to the scene goes up as the capture time increases. These are good reasons to want instead to capture several smaller sub-scenes that can be joined to make the whole scene. Achieving this has traditionally been difficult: joining sub-scenes that may never have been viewed from the same angle requires a high-quality camera relocaliser that can cope with novel poses, and tracking drift in each sub-scene can prevent them from being joined to make a consistent overall scene. Recent advances, however, have significantly improved our ability to capture medium-sized sub-scenes with little to no tracking drift: real-time globally consistent reconstruction systems can close loops and re-integrate the scene surface on the fly, whilst new visual-inertial odometry approaches can significantly reduce tracking drift during live reconstruction. Moreover, high-quality regression forest-based relocalisers have recently been made more practical by the introduction of a method to allow them to be trained and used online. In this paper, we leverage these advances to present what to our knowledge is the first system to allow multiple users to collaborate interactively to reconstruct dense, voxel-based models of whole buildings using only consumer-grade hardware, a task that has traditionally been both time-consuming and dependent on the availability of specialised hardware. Using our system, an entire house or lab can be reconstructed in under half an hour and at a far lower cost than was previously possible.
Dual Asymmetric Deep Hashing Learning
Authors: Jinxing Li, Bob Zhang, Guangming Lu, David Zhang
Date: 2018-01-25 00:00:00
Tasks: Deep Hashing, Retrieval

Due to the impressive learning power, deep learning has achieved a remarkable
performance in supervised hash function learning. In this paper, we propose a
novel asymmetric supervised deep hashing method to preserve the semantic
structure among different categories and generate the binary codes
simultaneously. Specifically, two asymmetric deep networks are constructed to
reveal the similarity between each pair of images according to their semantic
labels. The deep hash functions are then learned through two networks by
minimizing the gap between the learned features and discrete codes.
Furthermore, since the binary codes in the Hamming space also should keep the
semantic affinity existing in the original space, another asymmetric pairwise
loss is introduced to capture the similarity between the binary codes and
real-value features. This asymmetric loss not only improves the retrieval
performance, but also contributes to a quick convergence at the training phase.
By taking advantage of the two-stream deep structures and two types of
asymmetric pairwise functions, an alternating algorithm is designed to optimize
the deep features and high-quality binary codes efficiently. Experimental
results on three real-world datasets substantiate the effectiveness and
superiority of our approach as compared with state-of-the-art.