pwc-archive/papers-with-abstracts huggingface dataset:
Quantifying and attenuating pathologic tremor in virtual reality
Date: 2018-09-16 00:00:00

We present a virtual reality (VR) experience that creates a research-grade
benchmark in assessing patients with active upper-limb tremor, while
simultaneously offering the opportunity for patients to engage with VR
experiences without their pathologic tremor. Accurate and precise use of
handheld motion controllers in VR gaming applications may be limited for
patients with upper limb tremor. In parallel, objective tools measuring tremor
are not in widespread, routine clinical use. We used a commercially available
VR system and designed a challenging virtual-balloon-popping test mimicking a
common nose-to-target pointing task used by medical practitioners to
subjectively evaluate tremor in the exam room. Within our VR experience, we
offer a software mode which uses a low-pass filter to adjust hand position and
pointing orientation over a series of past data points. This digital filter
creates a smoothing function for hand movement which effectively removes the
patient's tremor in the VR representation. While the patient completes trials
of the reaching task, quantitative data on the pathologic tremor is digitally
recorded. With speed, accuracy, and the tremor components computed across three
axes of movement, patients can be evaluated for their tremor amplitudes in a
quantitative, replicable, and enjoyable manner. Removal of tremor in digital
space may allow patients having significant upper limb tremor to have both an
objective clinical measurement of symptoms while providing patients positive
feedback and interaction.
Fast Maximum Power Point Tracking for PV Arrays under Partial shaded Conditions
Date: 2018-09-17 00:00:00
Tasks: Point Tracking

P-V characteristic of photovoltaic (PV) arrays under partially shaded
conditions (PSC) has multiple peaks, and conventional maximum power point
tracking (MPPT) algorithms may fail to track the global maximum power point
(GMPP) because of their insufficient intelligence in discriminating the local
and global peaks. This paper proposes a novel fast MPPT method to achieve GMPP
of PV array under all PSCs. The proposed method reduces the number of required
samples and increases the speed of GMPP tracking based on comprehensive study
of I-V and P-V characteristics of PV array. Performance of the proposed method
has been evaluated in simulations of different PSCs. Also, its performance has
been compared with two selected methods in the literature through simulations.
Comparisons highlight the superiority of the proposed method.
New Reconfigurable L-Band Digital Aeronautical Communication System
Date: 2018-12-13 00:00:00

To meet an ever-increasing demand of the spectrum for communication between
aircraft and ground terminals, orthogonal frequency division multiplexing
(OFDM) based L-band (960-1164MHz) Digital Aeronautical Communication System
(LDACS) has been recently proposed as an alternative to existing narrowband
systems. However, OFDM based LDACS needs additional control signaling for time
and frequency alignment and the use of cyclic prefix and high out-of-band
emission limits the spectrum utilization efficiency to less than 50%. In this
paper, a new waveform has been proposed which offers better spectrum
utilization than OFDM without compromising on computational complexity and
interference to legacy users in L-band. It also allows transceivers to
dynamically adapt the transmission bandwidth to meet the desired quality of
service. Since the proposed waveform employs a reconfigurable multiband linear
phase filter, it is referred to as reconfigurable filtered OFDM (Ref-OFDM).
Simulation results and extensive analysis show that the proposed Ref-OFDM
offers around 40 dB better out-of-band emission than OFDM which in turn leads
to significant increase in the transmission bandwidth for a given BER and
interference constraints. The computational complexity of Ref-OFDM is slightly
higher than that of OFDM but it is significantly less than other waveforms
making Ref-OFDM an attractive waveform for next generation air-to-ground
communications. An end to end hardware prototyping of LDACS-DME coexistence is
also presented in this report.
Matrix Completion with Weighted Constraint for Haplotype Estimation
Date: 2018-09-17 00:00:00
Tasks: Matrix Completion

A new optimization design is proposed for matrix completion by weighting the
measurements and deriving the corresponding error bound. Accordingly, the
Haplotype reconstruction using nuclear norm minimization with Weighted
Constraint (HapWeC) is devised for haplotype estimation. Computer simulations
show the outperformance of the HapWeC compared to some recent algorithms in
terms of the normalized reconstruction error and reconstruction rate.
cf2vec: Collaborative Filtering algorithm selection using graph distributed representations
Authors: Cunha Tiago, Soares Carlos, de Carvalho André C. P. L. F.
Date: 2018-09-17 00:00:00
Tasks: Collaborative Filtering, Representation Learning

Algorithm selection using Metalearning aims to find mappings between problem
characteristics (i.e. metafeatures) with relative algorithm performance to
predict the best algorithm(s) for new datasets. Therefore, it is of the utmost
importance that the metafeatures used are informative. In Collaborative
Filtering, recent research has created an extensive collection of such
metafeatures. However, since these are created based on the practitioner's
understanding of the problem, they may not capture the most relevant aspects
necessary to properly characterize the problem. We propose to overcome this
problem by taking advantage of Representation Learning, which is able to create
an alternative problem characterizations by having the data guide the design of
the representation instead of the practitioner's opinion. Our hypothesis states
that such alternative representations can be used to replace standard
metafeatures, hence hence leading to a more robust approach to Metalearning. We
propose a novel procedure specially designed for Collaborative Filtering
algorithm selection. The procedure models Collaborative Filtering as graphs and
extracts distributed representations using graph2vec. Experimental results show
that the proposed procedure creates representations that are competitive with
state-of-the-art metafeatures, while requiring significantly less data and
without virtually any human input.
Noninvasive Blockade of Action Potential by Electromagnetic Induction
Date: 2018-08-29 00:00:00

Conventional anesthesia methods such as injective anesthetic agents may cause
various side effects such as injuries, allergies, and infections. We aim to
investigate a noninvasive scheme of an electromagnetic radiator system to block
action potential (AP) in neuron fibers. We achieved a high-gradient and
unipolar tangential electric field by designing circular geometric coils on an
electric rectifier filter layer. An asymmetric sawtooth pulse shape supplied
the coils in order to create an effective blockage. The entire setup was placed
5 cm above 50 motor and sensory neurons of the spinal cord. A validated
time-domain full-wave analysis code Based on cable model of the neurons and the
electric and magnetic potentials is used to simulate and investigate the
proposed scheme. We observed action potential blockage on both motor and
sensory neurons. In addition, the introduced approach shows promising potential
for AP manipulation in the spinal cord.
A Dataset and Preliminary Results for Umpire Pose Detection Using SVM Classification of Deep Features
Authors: Ravi Aravind, Venugopal Harshwin, Paul Sruthy, Tizhoosh Hamid R.
Date: 2018-09-11 00:00:00
Tasks: Game of Cricket, Video Summarization

In recent years, there has been increased interest in video summarization and
automatic sports highlights generation. In this work, we introduce a new
dataset, called SNOW, for umpire pose detection in the game of cricket. The
proposed dataset is evaluated as a preliminary aid for developing systems to
automatically generate cricket highlights. In cricket, the umpire has the
authority to make important decisions about events on the field. The umpire
signals important events using unique hand signals and gestures. We identify
four such events for classification namely SIX, NO BALL, OUT and WIDE based on
detecting the pose of the umpire from the frames of a cricket video.
Pre-trained convolutional neural networks such as Inception V3 and VGG19
net-works are selected as primary candidates for feature extraction. The
results are obtained using a linear SVM classifier. The highest classification
performance was achieved for the SVM trained on features extracted from the
VGG19 network. The preliminary results suggest that the proposed system is an
effective solution for the application of cricket highlights generation.
Facial Recognition with Encoded Local Projections
Authors: Sharma Dhruv, Zafar Sarim, Babaie Morteza, Tizhoosh H. R.
Date: 2018-09-11 00:00:00

Encoded Local Projections (ELP) is a recently introduced dense sampling image
descriptor which uses projections in small neighbourhoods to construct a
histogram/descriptor for the entire image. ELP has shown to be as accurate as
other state-of-the-art features in searching medical images while being time
and resource efficient. This paper attempts for the first time to utilize ELP
descriptor as primary features for facial recognition and compare the results
with LBP histogram on the Labeled Faces in the Wild dataset. We have evaluated
descriptors by comparing the chi-squared distance of each image descriptor
versus all others as well as training Support Vector Machines (SVM) with each
feature vector. In both cases, the results of ELP were better than LBP in the
same sub-image configuration.
LSTM knowledge transfer for HRV-based sleep staging
Date: 2018-09-12 00:00:00
Tasks: Heart Rate Variability, Photoplethysmography (PPG), Sleep Staging, Transfer Learning

Automated sleep stage classification using heart-rate variability is an
active field of research. In this work limitations of the current
state-of-the-art are addressed through the use of deep learning techniques and
their efficacy is demonstrated. First, a temporal model is proposed for the
inference of sleep stages from electrocardiography using a deep long- and
short-term (LSTM) classifier and it is shown that this model outperforms
previous approaches which were often limited to non-temporal or Markovian
classifiers on a comprehensive benchmark data set (292 participants, 541214
samples) comprising a wide range of ages and pathological profiles, achieving a
Cohen's $\kappa$ of $0.61\pm0.16$ and accuracy of $76.30\pm10.17$ annotated
according to the Rechtschaffen & Kales annotation standard.
  Subsequently, it is demonstrated how knowledge learned on this large
benchmark data set can be re-used through transfer learning for the
classification of photoplethysmography (PPG) data. This is done using a smaller
data set (60 participants, 91479 samples) that is annotated with the more
recent American Association of Sleep Medicine annotation standard, achieving a
Cohen's $\kappa$ of $0.63\pm0.13$ and accuracy of $74.65\pm8.63$ for
wrist-mounted PPG-based sleep stage classification, higher than any previously
reported performance using this sensor modality. This demonstrates the
feasibility of knowledge transfer in sleep staging to adapt models for new
sensor modalities as well as different annotation strategies.
Solving a non-linear model of HIV infection for CD4+T cells by combining Laplace transformation and Homotopy analysis method
Authors: Samad Noeiaghdam, Emran Khoshrouye Ghiasi
Date: 2018-09-13 00:00:00

The aim of this paper is to find the approximate solution of HIV infection model of CD4+T cells. For this reason, the homotopy analysis transform method (HATM) is applied. The presented method is combination of traditional homotopy analysis method (HAM) and the Laplace transformation. The convergence of presented method is discussed by preparing a theorem which shows the capabilities of method. The numerical results are shown for different values of iterations. Also, the regions of convergence are demonstrated by plotting several h-curves. Furthermore in order to show the efficiency and accuracy of method, the residual error for different iterations are presented.
Phase retrieval with complexity guidance
Date: 2018-09-17 00:00:00
Tasks: Retrieval

Iterative phase retrieval methods based on the Gerchberg-Saxton (GS) or
Fienup algorithm require a large number of iterations to converge to a
meaningful solution. For complex-valued or phase objects, these approaches also
suffer from stagnation problems where the solution does not change much from
iteration to iteration but the resultant solution shows artifacts such as
presence of a twin. We introduce a complexity parameter $\zeta$ that can be
computed directly from the Fourier magnitude data and provides a measure of
fluctuations in the desired phase retrieval solution. It is observed that when
initiated with a uniformly random phase map, the complexity of the Fienup
solution containing stagnation artifacts stabilizes at a numerical value that
is much higher than $\zeta$. We propose a modified Fienup algorithm that uses a
controlled sparsity enhancing step such that in every iteration the complexity
of the resulting solution is explicitly made close to $\zeta$. This approach
which we refer to as complexity guided phase retrieval (CGPR) is seen to
significantly reduce the number of phase retrieval iterations required for
convergence to a meaningful solution and automatically addresses the stagnation
problems. The CGPR methodology can enable new applications of iterative phase
retrieval that are considered practically difficult due to large number of
iterations required for a reliable phase recovery.
In Vitro Vascularized Tumor Platform for Modeling Tumor-Vasculature Interactions of Inflammatory Breast Cancer
Authors: Manasa Gadde, Caleb Phillips, Neda Ghousifam, Anna G. Sorace, Enoch Wong, Savitri Krishnamurthy, Anum Syed, Omar Rahal, Thomas E. Yankeelov, Wendy A. Woodward, Marissa Nichole Rylander
Date: 2018-09-17 00:00:00

Inflammatory breast cancer (IBC), a rare form of breast cancer associated with increased angiogenesis and metastasis, is largely driven by tumor-stromal interactions with the vasculature and the extracellular matrix (ECM). However, there is currently a lack of understanding of the role these interactions play in initiation and progression of the disease. In this study, we developed the first three-dimensional, in vitro, vascularized, breast tumor platform to quantify the spatial and temporal dynamics of tumor-vasculature and tumor-ECM interactions specific to IBC. Platforms consisting of collagen type 1 ECM with an endothelialized blood vessel were cultured with IBC cells, MDA-IBC3 (HER2+) or SUM149 (triple negative), and for comparison to non-IBC cells, MDA-MB-231 (triple negative). An acellular collagen platform with an endothelial blood vessel served as control. SUM149 and MDA-MB-231 platforms exhibited a significantly (p<0.05) higher vessel permeability and decreased endothelial coverage of the vessel lumen compared to the control. Both IBC platforms, MDA-IBC3 and SUM149, expressed higher levels of VEGF (p<0.05) and increased collagen ECM porosity compared to non-IBC MDA-MB-231 (p<0.05) and control (p<0.01) platforms. Additionally, unique to the MDA-IBC3 platform, we observed progressive sprouting of the endothelium over time resulting in viable vessels with lumen. The newly sprouted vessels encircled clusters of MDA-IBC3 cells replicating a feature of in vivo IBC. The IBC in vitro vascularized platforms introduced in this study model well-described in vivo and clinical IBC phenotypes and provide an adaptable, high throughout tool for systematically and quantitatively investigating tumor-stromal mechanisms and dynamics of tumor progression.
"FabSearch" : A 3D CAD Model Based Search Engine for Sourcing Manufacturing Services
Authors: Angrish Atin, Craver Benjamin, Starly Binil
Date: 2018-09-17 00:00:00

In this paper, we present "FabSearch", a prototype search engine for sourcing
manufacturer service providers, by making use of the product manufacturing
information contained within a 3D digital file of a product. FabSearch is
designed to take in a query 3D model, such as the .STEP file of a part model
which then produces a ranked list of job shop service providers who are best
suited to fabricate the part. Service providers may have potentially built
hundreds to thousands of parts with associated part 3D models over time.
FabSearch assumes that these service providers have shared shape signatures of
the part models built previously to enable the algorithm to most effectively
rank the service providers who have the most experience to build the query part
model. FabSearch has two important features that helps it produce relevant
results. First, it makes use of the shape characteristics of the 3D part by
calculating the Spherical Harmonics signature of the part to calculate the most
similar shapes built previously be job shop service providers. Second,
FabSearch utilizes meta-data about each part, such as material specification,
tolerance requirements to help improve the search results based on the specific
query model requirements. The algorithm is tested against a repository
containing more than 2000 models distributed across various job shop service
providers. For the first time, we show the potential for utilizing the rich
information contained within a 3D part model to automate the sourcing and
eventual selection of manufacturing service providers.
A Flexible Design for Funding Public Goods
Date: 2020-08-16 00:00:00
Tasks: Philosophy

We propose a design for philanthropic or publicly-funded seeding to allow
(near) optimal provision of a decentralized, self-organizing ecosystem of
public goods. The concept extends ideas from Quadratic Voting to a funding
mechanism for endogenous community formation. Individuals make public goods
contributions to projects of value to them. The amount received by the project
is (proportional to) the square of the sum of the square roots of contributions
received. Under the "standard model" this yields first best public goods
provision. Variations can limit the cost, help protect against collusion and
aid coordination. We discuss applications to campaign finance, open source
software ecosystems, news media finance and urban public projects. More
broadly, we offer a resolution to the classic liberal-communitarian debate in
political philosophy by providing neutral and non-authoritarian rules that
nonetheless support collective organization.
A General Framework for Temporal Fair User Scheduling in NOMA Systems
Date: 2018-09-17 00:00:00
Tasks: Fairness, Scheduling

Non-orthogonal multiple access (NOMA) is one of the promising radio access
techniques for next generation wireless networks. Opportunistic multi-user
scheduling is necessary to fully exploit multiplexing gains in NOMA systems,
but compared with traditional scheduling, interrelations between users'
throughputs induced by multi-user interference poses new challenges in the
design of NOMA schedulers. A successful NOMA scheduler has to carefully balance
the following three objectives: maximizing average system utility, satisfying
desired fairness constraints among the users and enabling real-time, and low
computational cost implementations. In this paper, scheduling for NOMA systems
under temporal fairness constraints is considered. Temporal fair scheduling
leads to communication systems with predictable latency as opposed to
utilitarian fair schedulers for which latency can be highly variable. It is
shown that optimal system utility is achieved using a class of opportunistic
scheduling schemes called threshold based strategies (TBS). One of the
challenges in temporal fair scheduling for heterogeneous NOMA scenarios - where
only specific users may be activated simultaneously - is to determine the set
of feasible temporal shares. In this work, a variable elimination algorithm is
proposed to accomplish this task. Furthermore, an (online) iterative algorithm
based on the Robbins-Monro method is proposed to construct a TBS by finding the
optimal thresholds for a given system utility metric. Various numerical
simulations of practical scenarios are provided to illustrate the effectiveness
of the proposed NOMA scheduling in static and mobile scenarios.
A Language for Large-Scale Collaboration in Economics: A Streamlined Computational Representation of Financial Models
Date: 2018-09-17 00:00:00

This paper introduces Sigma, a domain-specific computational representation
for collaboration in large-scale for the field of economics. A computational
representation is not a programming language or a software platform. A
computational representation is a domain-specific representation system based
on three specific elements: facets, contributions, and constraints of data.
Facets are definable aspects that make up a subject or an object. Contributions
are shareable and formal evidence, carrying specific properties, and produced
as a result of a crowd-based scientific investigation. Constraints of data are
restrictions defining domain-specific rules of association between entities and
relationships. A computational representation serves as a layer of abstraction
that is required in order to define domain-specific concepts in computers, in a
way these concepts can be shared in a crowd for the purposes of a controlled
scientific investigation in large-scale by crowds. Facets, contributions, and
constraints of data are defined for any domain of knowledge by the application
of a generic set of inputs, procedural steps, and products called a
representational process. The application of this generic process to our domain
of knowledge, the field of economics, produces Sigma. Sigma is described in
this paper in terms of its three elements: facets (streaming, reactives,
distribution, and simulation), contributions (financial models, processors, and
endpoints), and constraints of data (configuration, execution, and simulation
meta-model). Each element of the generic representational process and the Sigma
computational representation is described and formalized in details.
Estimating grouped data models with a binary dependent variable and fixed effects: What are the issues
Date: 2018-09-18 00:00:00

This article deals with asimple issue: if we have grouped data with a binary
dependent variable and want to include fixed effects (group specific
intercepts) in the specification, is Ordinary Least Squares (OLS) in any way
superior to a (conditional) logit form? In particular, what are the
consequences of using OLS instead of a fixed effects logit model with respect
to the latter dropping all units which show no variability in the dependent
variable while the former allows for estimation using all units. First, we show
that the discussion of fthe incidental parameters problem is based on an
assumption about the kinds of data being studied; for what appears to be the
common use of fixed effect models in political science the incidental
parameters issue is illusory. Turning to linear models, we see that OLS yields
a linear combination of the estimates for the units with and without variation
in the dependent variable, and so the coefficient estimates must be carefully
interpreted. The article then compares two methods of estimating logit models
with fixed effects, and shows that the Chamberlain conditional logit is as good
as or better than a logit analysis which simply includes group specific
intercepts (even though the conditional logit technique was designed to deal
with the incidental parameters problem!). Related to this, the article
discusses the estimation of marginal effects using both OLS and logit. While it
appears that a form of logit with fixed effects can be used to estimate
marginal effects, this method can be improved by starting with conditional
logit and then using the those parameter estimates to constrain the logit with
fixed effects model. This method produces estimates of sample average marginal
effects that are at least as good as OLS, and much better when group size is
small or the number of groups is large. .