pwc-archive/papers-with-abstracts huggingface dataset:
Incremental Deep Learning for Robust Object Detection in Unknown Cluttered Environments
Authors: Shin Dong Kyun, Ahmed Minhaz Uddin, Rhee Phill Kyu
Date: 2018-10-13 00:00:00
Tasks: Action Recognition, Active Learning, Deep Learning, Object, object-detection, Object Detection, Object Tracking, Robot Navigation, Robust Object Detection

Object detection in streaming images is a major step in different
detection-based applications, such as object tracking, action recognition,
robot navigation, and visual surveillance applications. In mostcases, image
quality is noisy and biased, and as a result, the data distributions are
disturbed and imbalanced. Most object detection approaches, such as the faster
region-based convolutional neural network (Faster RCNN), Single Shot Multibox
Detector with 300x300 inputs (SSD300), and You Only Look Once version 2
(YOLOv2), rely on simple sampling without considering distortions and noise
under real-world changing environments, despite poor object labeling. In this
paper, we propose an Incremental active semi-supervised learning (IASSL)
technology for unseen object detection. It combines batch-based active learning
(AL) and bin-based semi-supervised learning (SSL) to leverage the strong points
of AL's exploration and SSL's exploitation capabilities. A collaborative
sampling method is also adopted to measure the uncertainty and diversity of AL
and the confidence in SSL. Batch-based AL allows us to select more informative,
confident, and representative samples with low cost. Bin-based SSL divides
streaming image samples into several bins, and each bin repeatedly transfers
the discriminative knowledge of convolutional neural network (CNN) deep
learning to the next bin until the performance criterion is reached. IASSL can
overcome noisy and biased labels in unknown, cluttered data distributions. We
obtain superior performance, compared to state-of-the-art technologies such as
Faster RCNN, SSD300, and YOLOv2.
Coherence Constraints in Facial Expression Recognition
Authors: Graziani Lisa, Melacci Stefano, Gori Marco
Date: 2018-10-17 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER)

Recognizing facial expressions from static images or video sequences is a
widely studied but still challenging problem. The recent progresses obtained by
deep neural architectures, or by ensembles of heterogeneous models, have shown
that integrating multiple input representations leads to state-of-the-art
results. In particular, the appearance and the shape of the input face, or the
representations of some face parts, are commonly used to boost the quality of
the recognizer. This paper investigates the application of Convolutional Neural
Networks (CNNs) with the aim of building a versatile recognizer of expressions
in static images that can be further applied to video sequences. We first study
the importance of different face parts in the recognition task, focussing on
appearance and shape-related features. Then we cast the learning problem in the
Semi-Supervised setting, exploiting video data, where only a few frames are
supervised. The unsupervised portion of the training data is used to enforce
three types of coherence, namely temporal coherence, coherence among the
predictions on the face parts and coherence between appearance and shape-based
representation. Our experimental analysis shows that coherence constraints can
improve the quality of the expression recognizer, thus offering a suitable
basis to profitably exploit unsupervised video sequences. Finally we present
some examples with occlusions where the shape-based predictor performs better
than the appearance one.
Vehicle classification using ResNets, localisation and spatially-weighted pooling
Authors: Watkins Rohan, Pears Nick, Manandhar Suresh
Date: 2018-10-15 00:00:00
Tasks: Classification, Fine-Grained Vehicle Classification, General Classification

We investigate whether ResNet architectures can outperform more traditional
Convolutional Neural Networks on the task of fine-grained vehicle
classification. We train and test ResNet-18, ResNet-34 and ResNet-50 on the
Comprehensive Cars dataset without pre-training on other datasets. We then
modify the networks to use Spatially Weighted Pooling. Finally, we add a
localisation step before the classification process, using a network based on
ResNet-50. We find that using Spatially Weighted Pooling and localisation both
improve classification accuracy of ResNet50. Spatially Weighted Pooling
increases accuracy by 1.5 percent points and localisation increases accuracy by
3.4 percent points. Using both increases accuracy by 3.7 percent points giving
a top-1 accuracy of 96.351\% on the Comprehensive Cars dataset. Our method
achieves higher accuracy than a range of methods including those that use
traditional CNNs. However, our method does not perform quite as well as
pre-trained networks that use Spatially Weighted Pooling.
Projecting Trouble: Light Based Adversarial Attacks on Deep Learning Classifiers
Authors: Nichols Nicole, Jasper Robert
Date: 2018-10-16 00:00:00
Tasks: Deep Learning, General Classification, image-classification, Image Classification

This work demonstrates a physical attack on a deep learning image
classification system using projected light onto a physical scene. Prior work
is dominated by techniques for creating adversarial examples which directly
manipulate the digital input of the classifier. Such an attack is limited to
scenarios where the adversary can directly update the inputs to the classifier.
This could happen by intercepting and modifying the inputs to an online API
such as Clarifai or Cloud Vision. Such limitations have led to a vein of
research around physical attacks where objects are constructed to be inherently
adversarial or adversarial modifications are added to cause misclassification.
Our work differs from other physical attacks in that we can cause
misclassification dynamically without altering physical objects in a permanent
way.
  We construct an experimental setup which includes a light projection source,
an object for classification, and a camera to capture the scene. Experiments
are conducted against 2D and 3D objects from CIFAR-10. Initial tests show
projected light patterns selected via differential evolution could degrade
classification from 98% to 22% and 89% to 43% probability for 2D and 3D targets
respectively. Subsequent experiments explore sensitivity to physical setup and
compare two additional baseline conditions for all 10 CIFAR classes. Some
physical targets are more susceptible to perturbation. Simple attacks show near
equivalent success, and 6 of the 10 classes were disrupted by light.
Visions of a generalized probability theory
Authors: Cuzzolin Fabio
Date: 2018-10-18 00:00:00
Tasks: Object Tracking, Pose Estimation

In this Book we argue that the fruitful interaction of computer vision and
belief calculus is capable of stimulating significant advances in both fields.
From a methodological point of view, novel theoretical results concerning the
geometric and algebraic properties of belief functions as mathematical objects
are illustrated and discussed in Part II, with a focus on both a perspective
'geometric approach' to uncertainty and an algebraic solution to the issue of
conflicting evidence. In Part III we show how these theoretical developments
arise from important computer vision problems (such as articulated object
tracking, data association and object pose estimation) to which, in turn, the
evidential formalism is able to provide interesting new solutions. Finally,
some initial steps towards a generalization of the notion of total probability
to belief functions are taken, in the perspective of endowing the theory of
evidence with a complete battery of estimation and inference tools to the
benefit of all scientists and practitioners.
On closedness of law-invariant convex sets in rearrangement invariant spaces
Date: 2019-12-19 00:00:00

This paper presents relations between several types of closedness of a
law-invariant convex set in a rearrangement invariant space $\mathcal{X}$. In
particular, we show that order closedness,
$\sigma(\mathcal{X},\mathcal{X}_n^\sim)$-closedness and
$\sigma(\mathcal{X},L^\infty)$-closedness of a law-invariant convex set in
$\mathcal{X}$ are equivalent, where $\mathcal{X}_n^\sim$ is the order
continuous dual of $\mathcal{X}$. We also provide some application to proper
quasiconvex law-invariant functionals with the Fatou property.
Wanted Dead or Alive Extraterrestrial Life Forms (Thermodynamic criterion for life is a growing open system that performs self-assembly processes)
Authors: Marko Popovic
Date: 2018-10-23 00:00:00

For more than 100 years, humanity (both specialists and enthusiastic laics) has been searching for extraterrestrial life hoping we are not alone. The first step in the quest for extraterrestrial life is to define what and where exactly to look for. Thus, the basic definition of living matter is a conditio sine qua non for the quest. The diversity of species on Earth is so large that our quest for extraterrestrial life cannot be limited to forms and shapes present and known to us from our environment. However, there are two formal conditions that must be fulfilled in order for something to be assumed as living matter. First, it should represent a growing open thermodynamic system (in biological terms - a cell), and thus be a system out of equilibrium. Second, it must perform synthesis, self-assembly and accumulation processes (in biological terms to grow, maintain homeostasis, respond to environment, reproduce, exchange matter and energy, evolve). Populated planets are consisted of two components: biosphere and its environment (geosphere, hydrosphere and atmosphere). Living matter and its environment are out of equilibrium. Thus, the candidate planets are dynamic inhomogeneous systems for two reasons. First, a planet receives energy from its star, which leads to disequilibrium for external reasons. Animate matter contributes to disequilibrium for internal reasons: accumulation of matter and self-assembly. In practice, for a screening, an astrobiologist should search for increase in inhomogeneity on a candidate planet.
Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networks
Date: 2018-10-24 00:00:00
Tasks: Multi-agent Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Unmanned aerial vehicles (UAVs) are capable of serving as aerial base
stations (BSs) for providing both cost-effective and on-demand wireless
communications. This article investigates dynamic resource allocation of
multiple UAVs enabled communication networks with the goal of maximizing
long-term rewards. More particularly, each UAV communicates with a ground user
by automatically selecting its communicating users, power levels and
subchannels without any information exchange among UAVs. To model the
uncertainty of environments, we formulate the long-term resource allocation
problem as a stochastic game for maximizing the expected rewards, where each
UAV becomes a learning agent and each resource allocation solution corresponds
to an action taken by the UAVs. Afterwards, we develop a multi-agent
reinforcement learning (MARL) framework that each agent discovers its best
strategy according to its local observations using learning. More specifically,
we propose an agent-independent method, for which all agents conduct a decision
algorithm independently but share a common structure based on Q-learning.
Finally, simulation results reveal that: 1) appropriate parameters for
exploitation and exploration are capable of enhancing the performance of the
proposed MARL based resource allocation algorithm; 2) the proposed MARL
algorithm provides acceptable performance compared to the case with complete
information exchanges among UAVs. By doing so, it strikes a good tradeoff
between performance gains and information exchange overheads.
Graphene oxide upregulates the homeostatic functions of primary astrocytes and modulates astrocyte-to-neuron communication
Date: 2018-10-24 00:00:00

Graphene-based materials are the focus of intense research efforts to devise
novel theranostic strategies for targeting the central nervous system. In this
work, we have investigated the consequences of long-term exposure of primary
rat astrocytes to pristine graphene (GR) and graphene oxide (GO) flakes. We
demonstrate that GR/GO interfere with a variety of intracellular processes as a
result of their internalization through the endo-lysosomal pathway.
Graphene-exposed astrocytes acquire a more differentiated morphological
phenotype associated with extensive cytoskeletal rearrangements. Profound
functional alterations are induced by GO internalization, including the
upregulation of inward-rectifying K+ channels and of Na+-dependent glutamate
uptake, which are linked to the astrocyte capacity to control the extracellular
homeostasis. Interestingly, GO-pretreated astrocytes promote the functional
maturation of co-cultured primary neurons by inducing an increase in intrinsic
excitability and in the density of GABAergic synapses. The results indicate
that graphene nanomaterials profoundly affect astrocyte physiology in vitro,
with consequences for neuronal network activity. This work supports the view
that GO-based materials could be of great interest to address pathologies of
the central nervous system associated to astrocyte dysfunctions.
Building and Querying Semantic Layers for Web Archives (Extended Version)
Authors: Fafalios Pavlos, Holzmann Helge, Kasturia Vaibhav, Nejdl Wolfgang
Date: 2018-10-24 00:00:00

Web archiving is the process of collecting portions of the Web to ensure that
the information is preserved for future exploitation. However, despite the
increasing number of web archives worldwide, the absence of efficient and
meaningful exploration methods still remains a major hurdle in the way of
turning them into a usable and useful information source. In this paper, we
focus on this problem and propose an RDF/S model and a distributed framework
for building semantic profiles ("layers") that describe semantic information
about the contents of web archives. A semantic layer allows describing metadata
information about the archived documents, annotating them with useful semantic
information (like entities, concepts and events), and publishing all this data
on the Web as Linked Data. Such structured repositories offer advanced query
and integration capabilities, and make web archives directly exploitable by
other systems and tools. To demonstrate their query capabilities, we build and
query semantic layers for three different types of web archives. An
experimental evaluation showed that a semantic layer can answer information
needs that existing keyword-based systems are not able to sufficiently satisfy.
Deep Neural Networks for Choice Analysis: A Statistical Learning Theory Perspective
Date: 2019-09-16 00:00:00
Tasks: Demand Forecasting, Learning Theory

While researchers increasingly use deep neural networks (DNN) to analyze
individual choices, overfitting and interpretability issues remain as obstacles
in theory and practice. By using statistical learning theory, this study
presents a framework to examine the tradeoff between estimation and
approximation errors, and between prediction and interpretation losses. It
operationalizes the DNN interpretability in the choice analysis by formulating
the metrics of interpretation loss as the difference between true and estimated
choice probability functions. This study also uses the statistical learning
theory to upper bound the estimation error of both prediction and
interpretation losses in DNN, shedding light on why DNN does not have the
overfitting issue. Three scenarios are then simulated to compare DNN to binary
logit model (BNL). We found that DNN outperforms BNL in terms of both
prediction and interpretation for most of the scenarios, and larger sample size
unleashes the predictive power of DNN but not BNL. DNN is also used to analyze
the choice of trip purposes and travel modes based on the National Household
Travel Survey 2017 (NHTS2017) dataset. These experiments indicate that DNN can
be used for choice analysis beyond the current practice of demand forecasting
because it has the inherent utility interpretation, the flexibility of
accommodating various information formats, and the power of automatically
learning utility specification. DNN is both more predictive and interpretable
than BNL unless the modelers have complete knowledge about the choice task, and
the sample size is small. Overall, statistical learning theory can be a
foundation for future studies in the non-asymptotic data regime or using
high-dimensional statistical models in choice analysis, and the experiments
show the feasibility and effectiveness of DNN for its wide applications to
policy and behavioral analysis.
Patch-based Interferometric Phase Estimation via Mixture of Gaussian Density Modelling & Non-local Averaging in the Complex Domain
Date: 2018-10-24 00:00:00
Tasks: Denoising, Image Denoising

This paper addresses interferometric phase (InPhase) image denoising, i.e.,
the denoising of phase modulo-2p images from sinusoidal 2p-periodic and noisy
observations. The wrapping discontinuities present in the InPhase images, which
are to be preserved carefully, make InPhase denoising a challenging inverse
problem. We propose a novel two-step algorithm to tackle this problem by
exploiting the non-local self-similarity of the InPhase images. In the first
step, the patches of the phase images are modelled using Mixture of Gaussian
(MoG) densities in the complex domain. An Expectation Maximization(EM)
algorithm is formulated to learn the parameters of the MoG from the noisy data.
The learned MoG is used as a prior for estimating the InPhase images from the
noisy images using Minimum Mean Square Error (MMSE) estimation. In the second
step, an additional exploitation of non-local self-similarity is done by
performing a type of non-local mean filtering. Experiments conducted on
simulated and real (MRI and InSAR) datasets show results which are competitive
with the state-of-the-art techniques.
Visual Rendering of Shapes on 2D Display Devices Guided by Hand Gestures
Authors: Singla Abhik, Roy Partha Pratim, Dogra Debi Prosad
Date: 2018-10-22 00:00:00

Designing of touchless user interface is gaining popularity in various
contexts. Using such interfaces, users can interact with electronic devices
even when the hands are dirty or non-conductive. Also, user with partial
physical disability can interact with electronic devices using such systems.
Research in this direction has got major boost because of the emergence of
low-cost sensors such as Leap Motion, Kinect or RealSense devices. In this
paper, we propose a Leap Motion controller-based methodology to facilitate
rendering of 2D and 3D shapes on display devices. The proposed method tracks
finger movements while users perform natural gestures within the field of view
of the sensor. In the next phase, trajectories are analyzed to extract extended
Npen++ features in 3D. These features represent finger movements during the
gestures and they are fed to unidirectional left-to-right Hidden Markov Model
(HMM) for training. A one-to-one mapping between gestures and shapes is
proposed. Finally, shapes corresponding to these gestures are rendered over the
display using MuPad interface. We have created a dataset of 5400 samples
recorded by 10 volunteers. Our dataset contains 18 geometric and 18
non-geometric shapes such as "circle", "rectangle", "flower", "cone", "sphere"
etc. The proposed methodology achieves an accuracy of 92.87% when evaluated
using 5-fold cross validation method. Our experiments revel that the extended
3D features perform better than existing 3D features in the context of shape
representation and classification. The method can be used for developing useful
HCI applications for smart display devices.
Revealed Stochastic Preference: A One-Paragraph Proof and Generalization
Date: 2019-02-20 00:00:00

McFadden and Richter (1991) and later McFadden (2005) show that the Axiom of
Revealed Stochastic Preference characterizes rationalizability of choice
probabilities through random utility models on finite universal choice spaces.
This note proves the result in one short, elementary paragraph and extends it
to set valued choice. The latter requires a different axiom than is reported in
McFadden (2005).
The Case for Formation of ISP-Content Providers Consortiums by Nash Bargaining for Internet Content Delivery
Date: 2018-10-25 00:00:00

The formation of consortiums of a broadband access Internet Service Provider
(ISP) and multiple Content Providers (CP) is considered for large-scale content
caching. The consortium members share costs from operations and investments in
the supporting infrastructure. Correspondingly, the model's cost function
includes marginal and fixed costs; the latter has been important in determining
industry structure. Also, if Net Neutrality regulations permit, additional
network capacity on the ISP's last mile may be contracted by the CPs. The
number of subscribers is determined by a combination of users' price elasticity
of demand and Quality of Experience. The profit generated by a coalition after
pricing and design optimization determines the game's characteristic function.
Coalition formation is by a bargaining procedure due to Okada (1996) based on
random proposers in a non-cooperative, multi-player game-theoretic framework. A
necessary and sufficient condition is obtained for the Grand Coalition to form,
which bounds subsidies from large to small contributors. Caching is generally
supported even under Net Neutrality regulations. The Grand Coalition's profit
matches upper bounds. Numerical results illustrate the analytic results.
Generalized Beamspace Modulation Using Multiplexing: A Breakthrough in mmWave MIMO
Date: 2018-10-25 00:00:00

Spatial multiplexing (SMX) multiple-input multiple-output (MIMO) over the
best beamspace was considered as the best solution for millimeter wave (mmWave)
communications regarding spectral efficiency (SE), referred as the best
beamspace selection (BBS) solution. The equivalent MIMO water-filling (WF-MIMO)
channel capacity was treated as an unsurpassed SE upper bound. Recently,
researchers have proposed various schemes trying to approach the benchmark and
the performance bound. But, are they the real limit of mmWave MIMO systems with
reduced radio-frequency (RF) chains? In this paper, we challenge the benchmark
and the corresponding bound by proposing a better transmission scheme that
achieves higher SE, namely the Generalized Beamspace Modulation using
Multiplexing (GBMM). Inspired by the concept of spatial modulation, besides the
selected beamspace, the selection operation is used to carry information. We
prove that GBMM is superior to BBS in terms of SE and can break through the
well known `upper bound'. That is, GBMM renews the upper bound of the SE. We
investigate SE-oriented precoder activation probability optimization,
fully-digital precoder design, optimal power allocation and hybrid precoder
design for GBMM. A gradient ascent algorithm is developed to find the optimal
solution, which is applicable in all signal-to-noise-ratio (SNR) regimes. The
best solution is derived in the high SNR regime. Additionally, we investigate
the hybrid receiver design and deduce the minimum number of receive RF chains
configured to gain from GBMM in achievable SE. We propose a coding approach to
realize the optimized precoder activation. An extension to mmWave broadband
communications is also discussed. Comparisons with the benchmark (i.e., WF-MIMO
channel capacity) are made under different system configurations to show the
superiority of GBMM.
How Not To Do Mean-Variance Analysis
Date: 2018-10-25 00:00:00

We use the 2014 market history of two high-returning biotechnology
exchange-traded funds to illustrate how ex post mean-variance analysis should
not be done. Unfortunately, the way it should not be done is the way it
generally is done -- to our knowledge.