pwc-archive/papers-with-abstracts huggingface dataset:
Learning Personalized Thermal Preferences via Bayesian Active Learning with Unimodality Constraints
Authors: Nimish Awalgaonkar, Ilias Bilionis, Xiaoqi Liu, Panagiota Karava, Athanasios Tzempelikos
Date: 2019-03-21 00:00:00
Tasks: Active Learning, Bayesian Inference

Thermal preferences vary from person to person and may change over time. The
main objective of this paper is to sequentially pose intelligent queries to
occupants in order to optimally learn the indoor air temperature values which
maximize their satisfaction. Our central hypothesis is that an occupant's
preference relation over indoor air temperature can be described using a scalar
function of these temperatures, which we call the "occupant's thermal utility
function". Information about an occupant's preference over these temperatures
is available to us through their response to thermal preference queries :
"prefer warmer," "prefer cooler" and "satisfied" which we interpret as
statements about the derivative of their utility function, i.e. the utility
function is "increasing", "decreasing" and "constant" respectively. We model
this hidden utility function using a Gaussian process prior with built-in
unimodality constraint, i.e., the utility function has a unique maximum, and we
train this model using Bayesian inference. This permits an expected improvement
based selection of next preference query to pose to the occupant, which takes
into account both exploration (sampling from areas of high uncertainty) and
exploitation (sampling from areas which are likely to offer an improvement over
current best observation). We use this framework to sequentially design
experiments and illustrate its benefits by showing that it requires drastically
fewer observations to learn the maximally preferred temperature values as
compared to other methods. This framework is an important step towards the
development of intelligent HVAC systems which would be able to respond to
occupants' personalized thermal comfort needs. In order to encourage the use of
our PE framework and ensure reproducibility in results, we publish an
implementation of our work named GPPrefElicit as an open-source package in
Python.
Quotienting Impertinent Camera Kinematics for 3D Video Stabilization
Authors: Thomas W. Mitchel, Christian Wuelker, Jin Seob Kim, Sipu Ruan, Gregory S. Chirikjian
Date: 2019-03-21 00:00:00
Tasks: Motion Estimation, Video Stabilization

With the recent advent of methods that allow for real-time computation, dense 3D flows have become a viable basis for fast camera motion estimation. Most importantly, dense flows are more robust than the sparse feature matching techniques used by existing 3D stabilization methods, able to better handle large camera displacements and occlusions similar to those often found in consumer videos. Here we introduce a framework for 3D video stabilization that relies on dense scene flow alone. The foundation of this approach is a novel camera motion model that allows for real-world camera poses to be recovered directly from 3D motion fields. Moreover, this model can be extended to describe certain types of non-rigid artifacts that are commonly found in videos, such as those resulting from zooms. This framework gives rise to several robust regimes that produce high-quality stabilization of the kind achieved by prior full 3D methods while avoiding the fragility typically present in feature-based approaches. As an added benefit, our framework is fast: the simplicity of our motion model and efficient flow calculations combine to enable stabilization at a high frame rate.
An Efficient Solution to Non-Minimal Case Essential Matrix Estimation
Authors: Ji Zhao
Date: 2019-03-21 00:00:00

Finding relative pose between two calibrated images is a fundamental task in computer vision. Given five point correspondences, the classical five-point methods can be used to calculate the essential matrix efficiently. For the case of $N$ ($N > 5$) inlier point correspondences, which is called $N$-point problem, existing methods are either inefficient or prone to local minima. In this paper, we propose a certifiably globally optimal and efficient solver for the $N$-point problem. First we formulate the problem as a quadratically constrained quadratic program (QCQP). Then a certifiably globally optimal solution to this problem is obtained by semidefinite relaxation. This allows us to obtain certifiably globally optimal solutions to the original non-convex QCQPs in polynomial time. The theoretical guarantees of the semidefinite relaxation are also provided, including tightness and local stability. To deal with outliers, we propose a robust $N$-point method using M-estimators. Though global optimality cannot be guaranteed for the overall robust framework, the proposed robust $N$-point method can achieve good performance when the outlier ratio is not high. Extensive experiments on synthetic and real-world datasets demonstrated that our $N$-point method is $2\sim3$ orders of magnitude faster than state-of-the-art methods. Moreover, our robust $N$-point method outperforms state-of-the-art methods in terms of robustness and accuracy.
Prescriptive Cluster-Dependent Support Vector Machines with an Application to Reducing Hospital Readmissions
Authors: Taiyao Wang, Ioannis Ch. Paschalidis
Date: 2019-03-21 00:00:00

We augment linear Support Vector Machine (SVM) classifiers by adding three
important features: (i) we introduce a regularization constraint to induce a
sparse classifier; (ii) we devise a method that partitions the positive class
into clusters and selects a sparse SVM classifier for each cluster; and (iii)
we develop a method to optimize the values of controllable variables in order
to reduce the number of data points which are predicted to have an undesirable
outcome, which, in our setting, coincides with being in the positive class. The
latter feature leads to personalized prescriptions/recommendations. We apply
our methods to the problem of predicting and preventing hospital readmissions
within 30-days from discharge for patients that underwent a general surgical
procedure. To that end, we leverage a large dataset containing over 2.28
million patients who had surgeries in the period 2011--2014 in the U.S. The
dataset has been collected as part of the American College of Surgeons National
Surgical Quality Improvement Program (NSQIP).
Megapixel Photon-Counting Color Imaging using Quanta Image Sensor
Authors: Abhiram Gnanasambandam, Omar Elgendy, Jiaju Ma, and Stanley H. Chan
Date: 2019-03-21 00:00:00
Tasks: Demosaicking, Denoising, Image Reconstruction

Quanta Image Sensor (QIS) is a single-photon detector designed for extremely low light imaging conditions. Majority of the existing QIS prototypes are monochrome based on single-photon avalanche diodes (SPAD). Passive color imaging has not been demonstrated with single-photon detectors due to the intrinsic difficulty of shrinking the pixel size and increasing the spatial resolution while maintaining acceptable intra-pixel cross-talk. In this paper, we present image reconstruction of the first color QIS with a resolution of $1024 \times 1024$ pixels, supporting both single-bit and multi-bit photon counting capability. Our color image reconstruction is enabled by a customized joint demosaicing-denoising algorithm, leveraging truncated Poisson statistics and variance stabilizing transforms. Experimental results of the new sensor and algorithm demonstrate superior color imaging performance for very low-light conditions with a mean exposure of as low as a few photons per pixel in both real and simulated images.
Exploiting Promising Sub-Sequences of Jobs to solve the No-Wait Flowshop Scheduling Problem
Authors: Lucien Mousin, Marie-Eléonore Kessaci, Clarisse Dhaenens
Date: 2019-03-21 00:00:00
Tasks: Combinatorial Optimization, Scheduling, Specificity

The no-wait flowshop scheduling problem is a variant of the classical
permutation flowshop problem, with the additional constraint that jobs have to
be processed by the successive machines without waiting time. To efficiently
address this NP-hard combinatorial optimization problem we conduct an analysis
of the structure of good quality solutions. This analysis shows that the
No-Wait specificity gives them a common structure: they share identical
sub-sequences of jobs, we call super-jobs. After a discussion on the way to
identify these super-jobs, we propose IG-SJ, an algorithm that exploits
super-jobs within the state-of-the-art algorithm for the classical permutation
flowshop, the well-known Iterated Greedy (IG) algorithm. An iterative approach
of IG-SJ is also proposed. Experiments are conducted on Taillard's instances.
The experimental results show that exploiting super-jobs is successful since
IG-SJ is able to find 64 new best solutions.
Equivariant Entity-Relationship Networks
Authors: Devon Graham, Junhao Wang, Siamak Ravanbakhsh
Date: 2019-03-21 00:00:00
Tasks: Knowledge Graphs

The relational model is a ubiquitous representation of big-data, in part due to its extensive use in databases. In this paper, we propose the Equivariant Entity-Relationship Network (EERN), which is a Multilayer Perceptron equivariant to the symmetry transformations of the Entity-Relationship model. To this end, we identify the most expressive family of linear maps that are exactly equivariant to entity relationship symmetries, and further show that they subsume recently introduced equivariant maps for sets, exchangeable tensors, and graphs. The proposed feed-forward layer has linear complexity in the data and can be used for both inductive and transductive reasoning about relational databases, including database embedding, and the prediction of missing records. This provides a principled theoretical foundation for the application of deep learning to one of the most abundant forms of data. Empirically, EERN outperforms different variants of coupled matrix tensor factorization in both synthetic and real-data experiments.
Generative Models For Deep Learning with Very Scarce Data
Authors: Juan Maroñas, Roberto Paredes, Daniel Ramos
Date: 2019-03-21 00:00:00
Tasks: Deep Learning, General Classification

The goal of this paper is to deal with a data scarcity scenario where deep
learning techniques use to fail. We compare the use of two well established
techniques, Restricted Boltzmann Machines and Variational Auto-encoders, as
generative models in order to increase the training set in a classification
framework. Essentially, we rely on Markov Chain Monte Carlo (MCMC) algorithms
for generating new samples. We show that generalization can be improved
comparing this methodology to other state-of-the-art techniques, e.g.
semi-supervised learning with ladder networks. Furthermore, we show that RBM is
better than VAE generating new samples for training a classifier with good
generalization capabilities.
Latent Simplex Position Model: High Dimensional Multi-view Clustering with Uncertainty Quantification
Authors: Leo L. Duan
Date: 2019-03-21 00:00:00
Tasks: Clustering, Dimensionality Reduction, Position, Uncertainty Quantification, Variable Selection

High dimensional data often contain multiple facets, and several clustering patterns can co-exist under different variable subspaces, also known as the views. While multi-view clustering algorithms were proposed, the uncertainty quantification remains difficult --- a particular challenge is in the high complexity of estimating the cluster assignment probability under each view, and sharing information among views. In this article, we propose an approximate Bayes approach --- treating the similarity matrices generated over the views as rough first-stage estimates for the co-assignment probabilities; in its Kullback-Leibler neighborhood, we obtain a refined low-rank matrix, formed by the pairwise product of simplex coordinates. Interestingly, each simplex coordinate directly encodes the cluster assignment uncertainty. For multi-view clustering, we let each view draw a parameterization from a few candidates, leading to dimension reduction. With high model flexibility, the estimation can be efficiently carried out as a continuous optimization problem, hence enjoys gradient-based computation. The theory establishes the connection of this model to a random partition distribution under multiple views. Compared to single-view clustering approaches, substantially more interpretable results are obtained when clustering brains from a human traumatic brain injury study, using high-dimensional gene expression data. KEY WORDS: Co-regularized Clustering, Consensus, PAC-Bayes, Random Cluster Graph, Variable Selection
Recent advances in conversational NLP : Towards the standardization of Chatbot building
Authors: Maali Mnasri
Date: 2019-03-21 00:00:00
Tasks: Chatbot

Dialogue systems have become recently essential in our life. Their use is
getting more and more fluid and easy throughout the time. This boils down to
the improvements made in NLP and AI fields. In this paper, we try to provide an
overview to the current state of the art of dialogue systems, their categories
and the different approaches to build them. We end up with a discussion that
compares all the techniques and analyzes the strengths and weaknesses of each.
Finally, we present an opinion piece suggesting to orientate the research
towards the standardization of dialogue systems building.
Subgraph Networks with Application to Structural Feature Space Expansion
Authors: Qi Xuan, Jinhuan Wang, Minghao Zhao, Junkun Yuan, Chenbo Fu, Zhongyuan Ruan, Guanrong Chen
Date: 2019-03-21 00:00:00
Tasks: General Classification, Graph Classification, Network Embedding

Real-world networks exhibit prominent hierarchical and modular structures, with various subgraphs as building blocks. Most existing studies simply consider distinct subgraphs as motifs and use only their numbers to characterize the underlying network. Although such statistics can be used to describe a network model, or even to design some network algorithms, the role of subgraphs in such applications can be further explored so as to improve the results. In this paper, the concept of subgraph network (SGN) is introduced and then applied to network models, with algorithms designed for constructing the 1st-order and 2nd-order SGNs, which can be easily extended to build higher-order ones. Furthermore, these SGNs are used to expand the structural feature space of the underlying network, beneficial for network classification. Numerical experiments demonstrate that the network classification model based on the structural features of the original network together with the 1st-order and 2nd-order SGNs always performs the best as compared to the models based only on one or two of such networks. In other words, the structural features of SGNs can complement that of the original network for better network classification, regardless of the feature extraction method used, such as the handcrafted, network embedding and kernel-based methods.
Localization of Unmanned Aerial Vehicles in Corridor Environments using Deep Learning
Authors: Ram Prasad Padhy, Shahzad Ahmad, Sachin Verma, Pankaj Kumar Sa, Sambit Bakshi
Date: 2019-03-21 00:00:00
Tasks: Navigate, Pose Estimation

Vision-based pose estimation of Unmanned Aerial Vehicles (UAV) in unknown
environments is a rapidly growing research area in the field of robot vision.
The task becomes more complex when the only available sensor is a static single
camera (monocular vision). In this regard, we propose a monocular vision
assisted localization algorithm, that will help a UAV to navigate safely in
indoor corridor environments. Always, the aim is to navigate the UAV through a
corridor in the forward direction by keeping it at the center with no
orientation either to the left or right side. The algorithm makes use of the
RGB image, captured from the UAV front camera, and passes it through a trained
deep neural network (DNN) to predict the position of the UAV as either on the
left or center or right side of the corridor. Depending upon the divergence of
the UAV with respect to the central bisector line (CBL) of the corridor, a
suitable command is generated to bring the UAV to the center. When the UAV is
at the center of the corridor, a new image is passed through another trained
DNN to predict the orientation of the UAV with respect to the CBL of the
corridor. If the UAV is either left or right tilted, an appropriate command is
generated to rectify the orientation. We also propose a new corridor dataset,
named NITRCorrV1, which contains images as captured by the UAV front camera
when the UAV is at all possible locations of a variety of corridors. An
exhaustive set of experiments in different corridors reveal the efficacy of the
proposed algorithm.
Towards a Forensic Event Ontology to Assist Video Surveillance-based Vandalism Detection
Authors: Faranak Sobhani, Umberto Straccia
Date: 2019-03-21 00:00:00
Tasks: General Classification

The detection and representation of events is a critical element in automated
surveillance systems. We present here an ontology for representing complex
semantic events to assist video surveillance-based vandalism detection. The
ontology contains the definition of a rich and articulated event vocabulary
that is aimed at aiding forensic analysis to objectively identify and represent
complex events. Our ontology has then been applied in the context of London
Riots, which took place in 2011. We report also on the experiments conducted to
support the classification of complex criminal events from video data.
Cocoercivity, Smoothness and Bias in Variance-Reduced Stochastic Gradient Methods
Authors: Martin Morin, Pontus Giselsson
Date: 2019-03-21 00:00:00

With the purpose of examining biased updates in variance-reduced stochastic gradient methods, we introduce SVAG, a SAG/SAGA-like method with adjustable bias. SVAG is analyzed in a cocoercive root-finding setting, a setting which yields the same results as in the usual smooth convex optimization setting for the ordinary proximal-gradient method. We show that the same is not true for SVAG when biased updates are used. The step-size requirements for when the operators are gradients are significantly less restrictive compared to when they are not. This highlights the need to not rely solely on cocoercivity when analyzing variance-reduced methods meant for optimization. Our analysis either match or improve on previously known convergence conditions for SAG and SAGA. However, in the biased cases they still do not correspond well with practical experiences and we therefore examine the effect of bias numerically on a set of classification problems. The choice of bias seem to primarily affect the early stages of convergence and in most cases the differences vanish in the later stages of convergence. However, the effect of the bias choice is still significant in a couple of cases.
Variational Bayesian modelling of mixed-effects
Authors: Jean Daunizeau
Date: 2019-03-21 00:00:00

This note is concerned with an accurate and computationally efficient
variational bayesian treatment of mixed-effects modelling. We focus on group
studies, i.e. empirical studies that report multiple measurements acquired in
multiple subjects. When approached from a bayesian perspective, such
mixed-effects models typically rely upon a hierarchical generative model of the
data, whereby both within- and between-subject effects contribute to the
overall observed variance. The ensuing VB scheme can be used to assess
statistical significance at the group level and/or to capture inter-individual
differences. Alternatively, it can be seen as an adaptive regularization
procedure, which iteratively learns the corresponding within-subject priors
from estimates of the group distribution of effects of interest (cf. so-called
"empirical bayes" approaches). We outline the mathematical derivation of the
ensuing VB scheme, whose open-source implementation is available as part the
VBA toolbox.
A unified spectra analysis workflow for the assessment of microbial contamination of ready to eat green salads: Comparative study and application of non-invasive sensors
Authors: Panagiotis Tsakanikas, Lemonia Christina Fengou, Evanthia Manthou, Alexandra Lianou, Efstathios Z. Panagou, George John E. Nychas
Date: 2019-03-21 00:00:00
Tasks: feature selection, regression

The present study provides a comparative assessment of non-invasive sensors
as means of estimating the microbial contamination and time-on-shelf (i.e.
storage time) of leafy green vegetables, using a novel unified spectra analysis
workflow. Two fresh ready-to-eat green salads were used in the context of this
study for the purpose of evaluating the efficiency and practical application of
the presented workflow: rocket and baby spinach salads. The employed analysis
workflow consisted of robust data normalization, powerful feature selection
based on random forests regression, and selection of the number of partial
least squares regression coefficients in the training process by estimating the
knee-point on the explained variance plot. Training processes were based on
microbiological and spectral data derived during storage of green salad samples
at isothermal conditions (4, 8 and 12C), whereas testing was performed on data
during storage under dynamic temperature conditions (simulating real-life
temperature fluctuations in the food supply chain). Since an increasing
interest in the use of non-invasive sensors in food quality assessment has been
made evident in recent years, the unified spectra analysis workflow described
herein, by being based on the creation/usage of limited sized featured sets,
could be very useful in food-specific low-cost sensor development.
Hydra: A method for strain-minimizing hyperbolic embedding of network- and distance-based data
Authors: Martin Keller-Ressel, Stephanie Nargang
Date: 2019-03-21 00:00:00

We introduce hydra (hyperbolic distance recovery and approximation), a new method for embedding network- or distance-based data into hyperbolic space. We show mathematically that hydra satisfies a certain optimality guarantee: It minimizes the `hyperbolic strain' between original and embedded data points. Moreover, it recovers points exactly, when they are located on a hyperbolic submanifold of the feature space. Testing on real network data we show that the embedding quality of hydra is competitive with existing hyperbolic embedding methods, but achieved at substantially shorter computation time. An extended method, termed hydra+, outperforms existing methods in both computation time and embedding quality.
Multi-Task Time Series Analysis applied to Drug Response Modelling
Authors: Alex Bird, Christopher K. I. Williams, Christopher Hawthorne
Date: 2019-03-21 00:00:00
Tasks: Multi-Task Learning, Time Series, Time Series Analysis

Time series models such as dynamical systems are frequently fitted to a
cohort of data, ignoring variation between individual entities such as
patients. In this paper we show how these models can be personalised to an
individual level while retaining statistical power, via use of multi-task
learning (MTL). To our knowledge this is a novel development of MTL which
applies to time series both with and without control inputs. The modelling
framework is demonstrated on a physiological drug response problem which
results in improved predictive accuracy and uncertainty estimation over
existing state-of-the-art models.