pwc-archive/papers-with-abstracts huggingface dataset:
Prediction of the Optimal Threshold Value in DF Relay Selection Schemes Based on Artificial Neural Networks
Authors: Ferdi Kara, Hakan Kaya, Okan Erkaymaz, Ertan Ozturk
Date: 2018-01-18 00:00:00
Tasks: Prediction

In wireless communications, the cooperative communication (CC) technology
promises performance gains compared to traditional Single-Input Single Output
(SISO) techniques. Therefore, the CC technique is one of the nominees for 5G
networks. In the Decode-and-Forward (DF) relaying scheme which is one of the CC
techniques, determination of the threshold value at the relay has a key role
for the system performance and power usage. In this paper, we propose
prediction of the optimal threshold values for the best relay selection scheme
in cooperative communications, based on Artificial Neural Networks (ANNs) for
the first time in literature. The average link qualities and number of relays
have been used as inputs in the prediction of optimal threshold values using
Artificial Neural Networks (ANNs): Multi-Layer Perceptron (MLP) and Radial
Basis Function (RBF) networks. The MLP network has better performance from the
RBF network on the prediction of optimal threshold value when the same number
of neurons is used at the hidden layer for both networks. Besides, the optimal
threshold values obtained using ANNs are verified by the optimal threshold
values obtained numerically using the closed form expression derived for the
system. The results show that the optimal threshold values obtained by ANNs on
the best relay selection scheme provide a minimum Bit-Error-Rate (BER) because
of the reduction of the probability that error propagation may occur. Also, for
the same BER performance goal, prediction of optimal threshold values provides
2dB less power usage, which is great gain in terms of green communicationBER
performance goal, prediction of optimal threshold values provides 2dB less
power usage, which is great gain in terms of green communication.
3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies
Authors: Alexander Khvostikov, Karim Aderghal, Jenny Benois-Pineau, Andrey Krylov, Gwenaelle Catheline
Date: 2018-01-18 00:00:00
Tasks: Data Augmentation, General Classification, Medical Image Analysis

Computer-aided early diagnosis of Alzheimers Disease (AD) and its prodromal
form, Mild Cognitive Impairment (MCI), has been the subject of extensive
research in recent years. Some recent studies have shown promising results in
the AD and MCI determination using structural and functional Magnetic Resonance
Imaging (sMRI, fMRI), Positron Emission Tomography (PET) and Diffusion Tensor
Imaging (DTI) modalities. Furthermore, fusion of imaging modalities in a
supervised machine learning framework has shown promising direction of
research.
  In this paper we first review major trends in automatic classification
methods such as feature extraction based methods as well as deep learning
approaches in medical image analysis applied to the field of Alzheimer's
Disease diagnostics. Then we propose our own algorithm for Alzheimer's Disease
diagnostics based on a convolutional neural network and sMRI and DTI modalities
fusion on hippocampal ROI using data from the Alzheimers Disease Neuroimaging
Initiative (ADNI) database (http://adni.loni.usc.edu). Comparison with a single
modality approach shows promising results. We also propose our own method of
data augmentation for balancing classes of different size and analyze the
impact of the ROI size on the classification results as well.
PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark
Authors: Qiao Liu, Zhenyu He, Xin Li, Yuan Zheng
Date: 2018-01-18 00:00:00
Tasks: Attribute, Thermal Infrared Object Tracking

Thermal infrared (TIR) pedestrian tracking is one of the important components among numerous applications of computer vision, which has a major advantage: it can track pedestrians in total darkness. The ability to evaluate the TIR pedestrian tracker fairly, on a benchmark dataset, is significant for the development of this field. However, there is not a benchmark dataset. In this paper, we develop a TIR pedestrian tracking dataset for the TIR pedestrian tracker evaluation. The dataset includes 60 thermal sequences with manual annotations. Each sequence has nine attribute labels for the attribute based evaluation. In addition to the dataset, we carry out the large-scale evaluation experiments on our benchmark dataset using nine publicly available trackers. The experimental results help us understand the strengths and weaknesses of these trackers.In addition, in order to gain more insight into the TIR pedestrian tracker, we divide its functions into three components: feature extractor, motion model, and observation model. Then, we conduct three comparison experiments on our benchmark dataset to validate how each component affects the tracker's performance. The findings of these experiments provide some guidelines for future research. The dataset and evaluation toolkit can be downloaded at {https://github.com/QiaoLiuHit/PTB-TIR_Evaluation_toolkit}.
Computation of the Maximum Likelihood estimator in low-rank Factor Analysis
Authors: Koulik Khamaru, Rahul Mazumder
Date: 2018-01-18 00:00:00
Tasks: Dimensionality Reduction, Econometrics

Factor analysis, a classical multivariate statistical technique is popularly
used as a fundamental tool for dimensionality reduction in statistics,
econometrics and data science. Estimation is often carried out via the Maximum
Likelihood (ML) principle, which seeks to maximize the likelihood under the
assumption that the positive definite covariance matrix can be decomposed as
the sum of a low rank positive semidefinite matrix and a diagonal matrix with
nonnegative entries. This leads to a challenging rank constrained nonconvex
optimization problem. We reformulate the low rank ML Factor Analysis problem as
a nonlinear nonsmooth semidefinite optimization problem, study various
structural properties of this reformulation and propose fast and scalable
algorithms based on difference of convex (DC) optimization. Our approach has
computational guarantees, gracefully scales to large problems, is applicable to
situations where the sample covariance matrix is rank deficient and adapts to
variants of the ML problem with additional constraints on the problem
parameters. Our numerical experiments demonstrate the significant usefulness of
our approach over existing state-of-the-art approaches.
An Overview of Machine Teaching
Authors: Xiaojin Zhu, Adish Singla, Sandra Zilles, Anna N. Rafferty
Date: 2018-01-18 00:00:00

In this paper we try to organize machine teaching as a coherent set of ideas.
Each idea is presented as varying along a dimension. The collection of
dimensions then form the problem space of machine teaching, such that existing
teaching problems can be characterized in this space. We hope this organization
allows us to gain deeper understanding of individual teaching problems,
discover connections among them, and identify gaps in the field.
Extend the shallow part of Single Shot MultiBox Detector via Convolutional Neural Network
Authors: Liwen Zheng, Canmiao Fu, Yong Zhao
Date: 2018-01-18 00:00:00
Tasks: GPU, object-detection, Object Detection

Single Shot MultiBox Detector (SSD) is one of the fastest algorithms in the
current object detection field, which uses fully convolutional neural network
to detect all scaled objects in an image. Deconvolutional Single Shot Detector
(DSSD) is an approach which introduces more context information by adding the
deconvolution module to SSD. And the mean Average Precision (mAP) of DSSD on
PASCAL VOC2007 is improved from SSD's 77.5% to 78.6%. Although DSSD obtains
higher mAP than SSD by 1.1%, the frames per second (FPS) decreases from 46 to
11.8. In this paper, we propose a single stage end-to-end image detection model
called ESSD to overcome this dilemma. Our solution to this problem is to
cleverly extend better context information for the shallow layers of the best
single stage (e.g. SSD) detectors. Experimental results show that our model can
reach 79.4% mAP, which is higher than DSSD and SSD by 0.8 and 1.9 points
respectively. Meanwhile, our testing speed is 25 FPS in Titan X GPU which is
more than double the original DSSD.
On the influence of Dice loss function in multi-class organ segmentation of abdominal CT using 3D fully convolutional networks
Authors: Chen Shen, Holger R. Roth, Hirohisa ODA, Masahiro Oda, Yuichiro Hayashi, Kazunari Misawa, Kensaku MORI
Date: 2018-01-18 00:00:00
Tasks: Computed Tomography (CT), Organ Segmentation, Segmentation

Deep learning-based methods achieved impressive results for the segmentation
of medical images. With the development of 3D fully convolutional networks
(FCNs), it has become feasible to produce improved results for multi-organ
segmentation of 3D computed tomography (CT) images. The results of multi-organ
segmentation using deep learning-based methods not only depend on the choice of
networks architecture, but also strongly rely on the choice of loss function.
In this paper, we present a discussion on the influence of Dice-based loss
functions for multi-class organ segmentation using a dataset of abdominal CT
volumes. We investigated three different types of weighting the Dice loss
functions based on class label frequencies (uniform, simple and square) and
evaluate their influence on segmentation accuracies. Furthermore, we compared
the influence of different initial learning rates. We achieved average Dice
scores of 81.3%, 59.5% and 31.7% for uniform, simple and square types of
weighting when the learning rate is 0.001, and 78.2%, 81.0% and 58.5% for each
weighting when the learning rate is 0.01. Our experiments indicated a strong
relationship between class balancing weights and initial learning rate in
training.
A state of the art of urban reconstruction: street, street network, vegetation, urban feature
Authors: Remi Cura, Julien Perret, Nicolas Paparoditis
Date: 2018-01-18 00:00:00

World population is raising, especially the part of people living in cities.
With increased population and complex roles regarding their inhabitants and
their surroundings, cities concentrate difficulties for design, planning and
analysis. These tasks require a way to reconstruct/model a city. Traditionally,
much attention has been given to buildings reconstruction, yet an essential
part of city were neglected: streets. Streets reconstruction has been seldom
researched. Streets are also complex compositions of urban features, and have a
unique role for transportation (as they comprise roads). We aim at completing
the recent state of the art for building reconstruction (Musialski2012) by
considering all other aspect of urban reconstruction. We introduce the need for
city models. Because reconstruction always necessitates data, we first analyse
which data are available. We then expose a state of the art of street
reconstruction, street network reconstruction, urban features
reconstruction/modelling, vegetation , and urban objects
reconstruction/modelling.
  Although reconstruction strategies vary widely, we can order them by the role
the model plays, from data driven approach, to model-based approach, to inverse
procedural modelling and model catalogue matching. The main challenges seems to
come from the complex nature of urban environment and from the limitations of
the available data. Urban features have strong relationships, between them, and
to their surrounding, as well as in hierarchical relations. Procedural
modelling has the power to express these relations, and could be applied to the
reconstruction of urban features via the Inverse Procedural Modelling paradigm.
Bootstrapped synthetic likelihood
Authors: Richard G. Everitt
Date: 2017-11-15 00:00:00
Tasks: Bayesian Inference

Approximate Bayesian computation (ABC) and synthetic likelihood (SL)
techniques have enabled the use of Bayesian inference for models that may be
simulated, but for which the likelihood cannot be evaluated pointwise at values
of an unknown parameter $\theta$. The main idea in ABC and SL is to, for
different values of $\theta$ (usually chosen using a Monte Carlo algorithm),
build estimates of the likelihood based on simulations from the model
conditional on $\theta$. The quality of these estimates determines the
efficiency of an ABC/SL algorithm. In standard ABC/SL, the only means to
improve an estimated likelihood at $\theta$ is to simulate more times from the
model conditional on $\theta$, which is infeasible in cases where the simulator
is computationally expensive. In this paper we describe how to use
bootstrapping as a means for improving SL estimates whilst using fewer
simulations from the model, and also investigate its use in ABC. Further, we
investigate the use of the bag of little bootstraps as a means for applying
this approach to large datasets, yielding Monte Carlo algorithms that
accurately approximate posterior distributions whilst only simulating
subsamples of the full data. Examples of the approach applied to i.i.d.,
temporal and spatial data are given.
Structured Nonconvex and Nonsmooth Optimization: Algorithms and Iteration Complexity Analysis
Authors: Bo Jiang, Tianyi Lin, Shiqian Ma, Shuzhong Zhang
Date: 2016-05-09 00:00:00

Nonconvex and nonsmooth optimization problems are frequently encountered in
much of statistics, business, science and engineering, but they are not yet
widely recognized as a technology in the sense of scalability. A reason for
this relatively low degree of popularity is the lack of a well developed system
of theory and algorithms to support the applications, as is the case for its
convex counterpart. This paper aims to take one step in the direction of
disciplined nonconvex and nonsmooth optimization. In particular, we consider in
this paper some constrained nonconvex optimization models in block decision
variables, with or without coupled affine constraints. In the case of without
coupled constraints, we show a sublinear rate of convergence to an
$\epsilon$-stationary solution in the form of variational inequality for a
generalized conditional gradient method, where the convergence rate is shown to
be dependent on the H\"olderian continuity of the gradient of the smooth part
of the objective. For the model with coupled affine constraints, we introduce
corresponding $\epsilon$-stationarity conditions, and apply two proximal-type
variants of the ADMM to solve such a model, assuming the proximal ADMM updates
can be implemented for all the block variables except for the last block, for
which either a gradient step or a majorization-minimization step is
implemented. We show an iteration complexity bound of $O(1/\epsilon^2)$ to
reach an $\epsilon$-stationary solution for both algorithms. Moreover, we show
that the same iteration complexity of a proximal BCD method follows
immediately. Numerical results are provided to illustrate the efficacy of the
proposed algorithms for tensor robust PCA.
Quantile Markov Decision Process
Authors: Xiaocheng Li, Huaiyang Zhong, Margaret L. Brandeau
Date: 2017-11-15 00:00:00

The goal of a traditional Markov decision process (MDP) is to maximize expected cumulativereward over a defined horizon (possibly infinite). In many applications, however, a decision maker may beinterested in optimizing a specific quantile of the cumulative reward instead of its expectation. In this paperwe consider the problem of optimizing the quantiles of the cumulative rewards of a Markov decision process(MDP), which we refer to as a quantile Markov decision process (QMDP). We provide analytical resultscharacterizing the optimal QMDP value function and present a dynamic programming-based algorithm tosolve for the optimal policy. The algorithm also extends to the MDP problem with a conditional value-at-risk(CVaR) objective. We illustrate the practical relevance of our model by evaluating it on an HIV treatmentinitiation problem, where patients aim to balance the potential benefits and risks of the treatment.
Global Convergence of Unmodified 3-Block ADMM for a Class of Convex Minimization Problems
Authors: Tianyi Lin, Shiqian Ma, Shuzhong Zhang
Date: 2015-05-16 00:00:00

The alternating direction method of multipliers (ADMM) has been successfully
applied to solve structured convex optimization problems due to its superior
practical performance. The convergence properties of the 2-block ADMM have been
studied extensively in the literature. Specifically, it has been proven that
the 2-block ADMM globally converges for any penalty parameter $\gamma>0$. In
this sense, the 2-block ADMM allows the parameter to be free, i.e., there is no
need to restrict the value for the parameter when implementing this algorithm
in order to ensure convergence. However, for the 3-block ADMM, Chen \etal
\cite{Chen-admm-failure-2013} recently constructed a counter-example showing
that it can diverge if no further condition is imposed. The existing results on
studying further sufficient conditions on guaranteeing the convergence of the
3-block ADMM usually require $\gamma$ to be smaller than a certain bound, which
is usually either difficult to compute or too small to make it a practical
algorithm. In this paper, we show that the 3-block ADMM still globally
converges with any penalty parameter $\gamma>0$ if the third function $f_3$ in
the objective is smooth and strongly convex, and its condition number is in
$[1,1.0798)$, besides some other mild conditions. This requirement covers an
important class of problems to be called regularized least squares
decomposition (RLSD) in this paper.
Smile detection in the wild based on transfer learning
Authors: Xin Guo, Luisa F. Polanía, Kenneth E. Barner
Date: 2018-01-17 00:00:00
Tasks: 4k, Face Recognition, Transfer Learning

Smile detection from unconstrained facial images is a specialized and
challenging problem. As one of the most informative expressions, smiles convey
basic underlying emotions, such as happiness and satisfaction, which lead to
multiple applications, e.g., human behavior analysis and interactive
controlling. Compared to the size of databases for face recognition, far less
labeled data is available for training smile detection systems. To leverage the
large amount of labeled data from face recognition datasets and to alleviate
overfitting on smile detection, an efficient transfer learning-based smile
detection approach is proposed in this paper. Unlike previous works which use
either hand-engineered features or train deep convolutional networks from
scratch, a well-trained deep face recognition model is explored and fine-tuned
for smile detection in the wild. Three different models are built as a result
of fine-tuning the face recognition model with different inputs, including
aligned, unaligned and grayscale images generated from the GENKI-4K dataset.
Experiments show that the proposed approach achieves improved state-of-the-art
performance. Robustness of the model to noise and blur artifacts is also
evaluated in this paper.
Efficient Online Bandit Multiclass Learning with $\tilde{O}(\sqrt{T})$ Regret
Authors: Alina Beygelzimer, Francesco Orabona, Chicheng Zhang
Date: 2017-02-25 00:00:00

We present an efficient second-order algorithm with
$\tilde{O}(\frac{1}{\eta}\sqrt{T})$ regret for the bandit online multiclass
problem. The regret bound holds simultaneously with respect to a family of loss
functions parameterized by $\eta$, for a range of $\eta$ restricted by the norm
of the competitor. The family of loss functions ranges from hinge loss
($\eta=0$) to squared hinge loss ($\eta=1$). This provides a solution to the
open problem of (J. Abernethy and A. Rakhlin. An efficient bandit algorithm for
$\sqrt{T}$-regret in online multiclass prediction? In COLT, 2009). We test our
algorithm experimentally, showing that it also performs favorably against
earlier algorithms.
McDiarmid Drift Detection Methods for Evolving Data Streams
Authors: Ali Pesaranghader, Herna Viktor, Eric Paquet
Date: 2017-10-05 00:00:00
Tasks: Drift Detection, General Classification

Increasingly, Internet of Things (IoT) domains, such as sensor networks,
smart cities, and social networks, generate vast amounts of data. Such data are
not only unbounded and rapidly evolving. Rather, the content thereof
dynamically evolves over time, often in unforeseen ways. These variations are
due to so-called concept drifts, caused by changes in the underlying data
generation mechanisms. In a classification setting, concept drift causes the
previously learned models to become inaccurate, unsafe and even unusable.
Accordingly, concept drifts need to be detected, and handled, as soon as
possible. In medical applications and emergency response settings, for example,
change in behaviours should be detected in near real-time, to avoid potential
loss of life. To this end, we introduce the McDiarmid Drift Detection Method
(MDDM), which utilizes McDiarmid's inequality in order to detect concept drift.
The MDDM approach proceeds by sliding a window over prediction results, and
associate window entries with weights. Higher weights are assigned to the most
recent entries, in order to emphasize their importance. As instances are
processed, the detection algorithm compares a weighted mean of elements inside
the sliding window with the maximum weighted mean observed so far. A
significant difference between the two weighted means, upper-bounded by the
McDiarmid inequality, implies a concept drift. Our extensive experimentation
against synthetic and real-world data streams show that our novel method
outperforms the state-of-the-art. Specifically, MDDM yields shorter detection
delays as well as lower false negative rates, while maintaining high
classification accuracies.
Interactive in-base street model edit: how common GIS software and a database can serve as a custom Graphical User Interface
Authors: Remi Cura, Julien Perret, Nicolas Paparoditis
Date: 2018-01-17 00:00:00

Our modern world produces an increasing quantity of data, and especially
geospatial data, with advance of sensing technologies, and growing complexity
and organisation of vector data. Tools are needed to efficiently create and
edit those vector geospatial data. Procedural generation has been a tool of
choice to generate strongly organised data, yet it may be hard to control.
Because those data may be involved to take consequence-full real life
decisions, user interactions are required to check data and edit it. The
classical process to do so would be to build an adhoc Graphical User Interface
(GUI) tool adapted for the model and method being used. This task is difficult,
takes a large amount of resources, and is very specific to one model, making it
hard to share and re-use.
  Besides, many common generic GUI already exists to edit vector data, each
having its specialities. We propose a change of paradigm; instead of building a
specific tool for one task, we use common GIS software as GUIs, and deport the
specific interactions from the software to within the database. In this
paradigm, GIS software simply modify geometry and attributes of database
layers, and those changes are used by the database to perform automated task.
  This new paradigm has many advantages. The first one is genericity. With
in-base interaction, any GIS software can be used to perform edition, whatever
the software is a Desktop sofware or a web application. The second is
concurrency and coherency. Because interaction is in-base, use of database
features allows seamless multi-user work, and can guarantee that the data is in
a coherent state. Last we propose tools to facilitate multi-user edits, both
during the edit phase (each user knows what areas are edited by other users),
and before and after edit (planning of edit, analyse of edited areas).