pwc-archive/papers-with-abstracts huggingface dataset:
Discovering Users Topic of Interest from Tweet
Authors: Hossen Muhammad Kamal, Faiad Md. Ali, Chowdhury Md. Shahnur Azad, Islam Md. Sajjatul
Date: 2018-03-10 00:00:00

Nowadays social media has become one of the largest gatherings of people in
online. There are many ways for the industries to promote their products to the
public through advertising. The variety of advertisement is increasing
dramatically. Businessmen are so much dependent on the advertisement that
significantly it really brought out success in the market and hence practiced
by major industries. Thus, companies are trying hard to draw the attention of
customers on social networks through online advertisement. One of the most
popular social media is Twitter which is popular for short text sharing named
Tweet. People here create their profile with basic information. To ensure the
advertisements are shown to relative people, Twitter targets people based on
language, gender, interest, follower, device, behavior, tailored audiences,
keyword, and geography targeting. Twitter generates interest sets based on
their activities on Twitter. What our framework does is that it determines the
topic of interest from a given list of Tweets if it has any. This process is
called Entity Intersect Categorizing Value (EICV). Each category topic
generates a set of words or phrases related to that topic. An entity set is
created from processing tweets by keyword generation and Twitters data using
Twitter API. Value of entities is matched with the set of categories. If they
cross a threshold value, it results in the category which matched the desired
interest category. For smaller amounts of data sizes, the results show that our
framework performs with higher accuracy rate.
Ridge Regression Estimated Linear Probability Model Predictions of N-glycosylation in Proteins with Structural and Sequence Data
Date: 2018-03-15 00:00:00
Tasks: regression

Absent experimental evidence, a robust methodology to predict the likelihood
of N-glycosylation in human proteins is essential for guiding experimental
work. Based on the distribution of amino acids in the neighborhood of the NxS/T
sequon (N-site); the structural attributes of the N-site that include
Accessible Surface Area, secondary structural elements, main-chain phi-psi,
turn types; the relative location of the N-site in the primary sequence; and
the nature of the glycan bound, the ridge regression estimated linear
probability model is used to predict this likelihood. This model yields a
Kolmogorov-Smirnov (Gini coefficient) statistic value of about 74% (89%), which
is reasonable.
Large Matrix Asymptotic Analysis of ZF and MMSE Crosstalk Cancelers for Wireline Channels
Date: 2018-03-15 00:00:00

We present asymptotic expressions for user throughput in a multi-user
wireline system with a linear decoder, in increasingly large system sizes. This
analysis can be seen as a generalization of results obtained for wireless
communication. The features of the diagonal elements of the wireline channel
matrices make wireless asymptotic analyses inapplicable for wireline systems.
Further, direct application of results from random matrix theory (RMT) yields a
trivial lower bound. This paper presents a novel approach to asymptotic
analysis, where an alternative sequence of systems is constructed that includes
the system of interest in order to approximate the spectral efficiency of the
linear zero-forcing (ZF) and minimum mean squared error (MMSE) crosstalk
cancelers. Using works in the field of large dimensional random matrices, we
show that the user rate in this sequence converges to a non-zero rate. The
approximation of the user rate for both the ZF and MMSE cancelers are very
simple to evaluate and does not need to take specific channel realizations into
account. The analysis reveals the intricate behavior of the throughput as a
function of the transmission power and the channel crosstalk. This unique
behavior has not been observed for linear decoders in other systems. The
approximation presented here is much more useful for the next generation G.fast
wireline system than earlier digital subscriber line (DSL) systems as
previously computed performance bounds, which are strictly larger than zero
only at low frequencies. We also provide a numerical performance analysis over
measured and simulated DSL channels which show that the approximation is
accurate even for relatively low dimensional systems and is useful for many
scenarios in practical DSL systems.
Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks
Date: 2018-03-16 00:00:00
Tasks: Anomaly Detection, Intrusion Detection, Video Prediction

Intrusion detection has become one of the most critical tasks in a wireless
network to prevent service outages that can take long to fix. The sheer variety
of anomalous events necessitates adopting cognitive anomaly detection methods
instead of the traditional signature-based detection techniques. This paper
proposes an anomaly detection methodology for wireless systems that is based on
monitoring and analyzing radio frequency (RF) spectrum activities. Our
detection technique leverages an existing solution for the video prediction
problem, and uses it on image sequences generated from monitoring the wireless
spectrum. The deep predictive coding network is trained with images
corresponding to the normal behavior of the system, and whenever there is an
anomaly, its detection is triggered by the deviation between the actual and
predicted behavior. For our analysis, we use the images generated from the
time-frequency spectrograms and spectral correlation functions of the received
RF signal. We test our technique on a dataset which contains anomalies such as
jamming, chirping of transmitters, spectrum hijacking, and node failure, and
evaluate its performance using standard classifier metrics: detection ratio,
and false alarm rate. Simulation results demonstrate that the proposed
methodology effectively detects many unforeseen anomalous events in real time.
We discuss the applications, which encompass industrial IoT, autonomous vehicle
control and mission-critical communications services.
Business Cycles in Economics
Date: 2018-03-16 00:00:00

The business cycles are generated by the oscillating macro-/micro-/nano-
economic output variables in the economy of the scale and the scope in the
amplitude/frequency/phase/time domains in the economics. The accurate forward
looking assumptions on the business cycles oscillation dynamics can optimize
the financial capital investing and/or borrowing by the economic agents in the
capital markets. The book's main objective is to study the business cycles in
the economy of the scale and the scope, formulating the Ledenyov unified
business cycles theory in the Ledenyov classic and quantum econodynamics.
Selection for synchronized cell division in simple multicellular organisms
Date: 2018-03-16 00:00:00

The evolution of multicellularity was a major transition in the history of
life on earth. Conditions under which multicellularity is favored have been
studied theoretically and experimentally. But since the construction of a
multicellular organism requires multiple rounds of cell division, a natural
question is whether these cell divisions should be synchronous or not. We study
a simple population model in which there compete simple multicellular organisms
that grow either by synchronous or asynchronous cell divisions. We demonstrate
that natural selection can act differently on synchronous and asynchronous cell
division, and we offer intuition for why these phenotypes are generally not
neutral variants of each other.
Efficient construction of threshold networks of stock markets
Date: 2018-08-24 00:00:00

Although the threshold network is one of the most used tools to characterize
the underlying structure of a stock market, the identification of the optimal
threshold to construct a reliable stock network remains challenging. In this
paper, the concept of dynamic consistence between the threshold network and the
stock market is proposed. The optimal threshold is estimated by maximizing the
consistence function. The application of this procedure to stocks belonging to
Standard \& Pool's 500 Index from January 2006 to December 2011 yields the
threshold value 0.28. In analyzing topological characteristics of the generated
network, three globally financial crises can be distinguished well from the
evolutionary perspective.
Power Distribution System Synchrophasors with Non-Gaussian Errors: Real-World Measurements and Analysis
Date: 2018-03-13 00:00:00

This letter studies the synchrophasor measurement error of electric power
distribution systems with on-line and off-line measurements using graphical and
numerical tests. It demonstrates that the synchrophasor measurement error
follows a non-Gaussian distribution instead of the traditionally-assumed
Gaussian distribution. It suggests the need to use non-Gaussian or Gaussian
mixture models to represent the synchrophasor measurement error. These models
are more realistic to accurately represent the error than the traditional
Gaussian model. The measurements and underlying analysis will be helpful for
the understanding of distribution system measurement characteristics, and also
for the modeling and simulation of distribution system applications.
A Programmable CMOS Transceiver for Structural Health Monitoring
Date: 2018-03-14 00:00:00
Tasks: Structural Health Monitoring

We describe a highly-integrated CMOS transceiver for active structural health
monitoring (SHM). The chip actuates piezoelectric transducers and also senses
ultrasound waves received by the same or another transducer. The transmitter
uses an integer-N frequency synthesizer and pulse-width modulation (PWM) to
generate low-distortion, band-limited waveforms up to 12.7 Vpp with center
frequency from 0.1-2.75 MHz. The integrated offset-canceling fully-differential
receiver has programmable gain and bandwidth, and uses quadrature demodulation
to extract both amplitude and phase of the received waveforms for further
signal processing. The transceiver was fabricated in a 0.5 um CMOS process and
has been validated using (2D) damage localization on an SHM test bed.
3D Video Quality Metric for Mobile Applications
Date: 2018-03-13 00:00:00

In this paper, we propose a new full-reference quality metric for mobile 3D
content. Our method is modeled around the Human Visual System, fusing the
information of both left and right channels, considering color components, the
cyclopean views of the two videos and disparity. Our method is assessing the
quality of 3D videos displayed on a mobile 3DTV, taking into account the effect
of resolution, distance from the viewers eyes, and dimensions of the mobile
display. Performance evaluations showed that our mobile 3D quality metric
monitors the degradation of quality caused by several representative types of
distortion with 82 percent correlation with results of subjective tests, an
accuracy much better than that of the state of the art mobile 3D quality
metric.
Accurate evaluation of size and refractive index for spherical objects in quantitative phase imaging
Date: 2018-04-26 00:00:00

Measuring the average refractive index (RI) of spherical objects, such as
suspended cells, in quantitative phase imaging (QPI) requires a decoupling of
RI and size from the QPI data. This has been commonly achieved by determining
the object's radius with geometrical approaches, neglecting light-scattering.
Here, we present a novel QPI fitting algorithm that reliably uncouples the RI
using Mie theory and a semi-analytical, corrected Rytov approach. We assess the
range of validity of this algorithm in silico and experimentally investigate
various objects (oil and protein droplets, microgel beads, cells) and noise
conditions. In addition, we provide important practical cues for future studies
in cell biology.
Example-based super-resolution for point-cloud video
Date: 2018-03-17 00:00:00
Tasks: Denoising, Super-Resolution

We propose a mixed-resolution point-cloud representation and an example-based
super-resolution framework, from which several processing tools can be derived,
such as compression, denoising and error concealment. By inferring the
high-frequency content of low-resolution frames based on the similarities
between adjacent full-resolution frames, the proposed framework achieves an
average 1.18 dB gain over low-pass versions of the point-cloud, for a
projection-based distortion metric[1-2].
Modeling stock markets through the reconstruction of market processes
Date: 2018-03-18 00:00:00

There are two possible ways of interpreting the seemingly stochastic nature
of financial markets: the Efficient Market Hypothesis (EMH) and a set of
stylized facts that drive the behavior of the markets. We show evidence for
some of the stylized facts such as memory-like phenomena in price volatility in
the short term, a power-law behavior and non-linear dependencies on the
returns.
  Given this, we construct a model of the market using Markov chains. Then, we
develop an algorithm that can be generalized for any N-symbol alphabet and
K-length Markov chain. Using this tool, we are able to show that it's, at
least, always better than a completely random model such as a Random Walk. The
code is written in MATLAB and maintained in GitHub.
Non-reciprocal Components Based on Switched Transmission Lines
Date: 2018-03-18 00:00:00

Non-reciprocal components, such as isolators and circulators, are critical to
wireless communication and radar applications. Traditionally, non-reciprocal
components have been implemented using ferrite materials, which exhibit
non-reciprocity under the influence of an external magnetic field. However,
ferrite materials cannot be integrated into IC fabrication processes, and
consequently are bulky and expensive. In the recent past, there has been strong
interest in achieving non-reciprocity in a non-magnetic IC-compatible fashion
using spatio-temporal modulation. In this paper, we present a general approach
to non-reciprocity based on switched transmission lines. Switched transmission
lines enable broadband, lossless and compact non-reciprocity, and a wide range
of non-reciprocal functionalities, including non-reciprocal phase shifters,
ultra-broadband gyrators and isolators, frequency-conversion isolators, and
high-linearity/high-frequency/ultra-broadband circulators. We present a
detailed theoretical analysis of the various non-idealities that impact
insertion loss and provide design guidelines. The theory is validated by
experimental results from discrete-component-based gyrators and isolators, and
a 25GHz circulator fabricated in 45nm SOI CMOS technology.
Eshel Ben-Jacob: A unique individual in the science of collective phenomena
Authors: Herbert Levine
Date: 2018-03-18 00:00:00

Eshel Ben-Jacob, one of the co-organizers of this meeting on collective behavior and one of the pioneers in the field of collective behavior in biology, passed away suddenly just before we convened. This article presents a brief glimpse of Eshel's life-long path through science, seen from the perspective of a decades long collaboration on many disparate yet ultimately connected topics. The article attempts to convey how the concept of self-organization of complex interacting objects into higher order functional units, as evidenced so wonderfully by Eshel's experiments on bacterial colony formation, provides a unifying theme for the study of collective behavior. Our entire field will miss his unique ability to "let the complex become simple".
Directional emphasis in ambisonics
Date: 2018-05-24 00:00:00

We describe an ambisonics enhancement method that increases the signal
strength in specified directions at low computational cost. The method can be
used in a static setup to emphasize the signal arriving from a particular
direction or set of directions. It can also be used in an adaptive arrangement
where it sharpens directionality and reduces the distortion in timbre
associated with low-degree ambisonics representations. The emphasis operator
has very low computational complexity and can be applied to time-domain as well
as time-frequency ambisonics representations. The operator upscales a
low-degree ambisonics representation to a higher degree representation.
The Strategic LQG System: A Dynamic Stochastic VCG Framework for Optimal Coordination
Date: 2019-06-11 00:00:00

The classic Vickrey-Clarke-Groves (VCG) mechanism ensures incentive
compatibility, i.e., that truth-telling of all agents is a dominant strategy,
for a static one-shot game. However, in a dynamic environment that unfolds over
time, the agents' intertemporal payoffs depend on the expected future controls
and payments, and a direct extension of the VCG mechanism is not sufficient to
guarantee incentive compatibility. In fact, it does not appear to be feasible
to construct mechanisms that ensure the dominance of dynamic truth-telling for
agents comprised of general stochastic dynamic systems. The contribution of
this paper is to show that such a dynamic stochastic extension does exist for
the special case of Linear-Quadratic-Gaussian (LQG) agents with a careful
construction of a sequence of layered payments over time. For a set of LQG
agents, we propose a modified layered version of the VCG mechanism for payments
that decouples the intertemporal effect of current bids on future payoffs, and
prove that truth-telling of dynamic states forms a dominant strategy if system
parameters are known and agents are rational.
  An important example of a problem needing such optimal dynamic coordination
of stochastic agents arises in power systems where an Independent System
Operator (ISO) has to ensure balance of generation and consumption at all time
instants, while ensuring social optimality. The challenge is to determine a
bidding scheme between all agents and the ISO that maximizes social welfare,
while taking into account the stochastic dynamic models of agents, since
renewable energy resources such as solar/wind are stochastic and dynamic in
nature, as are consumptions by loads which are influenced by factors such as
local temperatures and thermal inertias of facilities.
Metabolomic signature of type 1 diabetes-induced sensory loss and nerve damage in diabetic neuropathy
Date: 2018-03-18 00:00:00

Diabetic-induced peripheral neuropathy (DPN) is a diabetic late complication.
The molecular mechanisms underlying the pathophysiology of nerve damage &
sensory loss remain largely unclear. Recently, alterations in metabolic flux
have gained attention a basis for organ damage in diabetes; however, peripheral
sensory neurons have not been adequately analyzed. In the present study, we
attempted to delineate the role of alteration of metabolic pathways in relation
to nerve damage & sensory loss. We employed STZ-injected mouse model of type1
diabetes. To investigate the progression of DPN by behavioral measurements of
sensitivity to thermal & mechanical stimuli and quantitative assessment of
intraepidermal nerve fiber density. We employed a MS-based screen to address
alterations in levels of metabolites in peripheral sciatic nerve (SN) & amino
acids (AA) in serum over several months post-STZ administration. Although
hyperglycemia & body weight changes occurred early, sensory loss & reduced
intraepithelial branching of nociceptive nerves was only evident at 22 wks
post-STZ. The longitudinal metabolites screen in SN demonstrated that mice at
12 and 22 wks post-STZ showed an early impairment the tricarboxylic acid. We
found that levels of citric acid, ketoglutaric acid, succinic acid, fumaric
acid & malic acid were observed to be significantly reduced in SN at 22 wks
post-STZ. In addition, we also found the increase in levels of sorbitol &
L-Lactate in SN from 12 wks post-STZ injection. AA screen in serum showed that
the amino acids Val, Ile and Leu, increased more than 2-fold from 12 wks
post-STZ. Similarly, the levels of Tyr, Asn, Ser, His, Ala, & Pro showed
progressive increase. Our results indicate that the impaired TCA cycle
metabolites in peripheral nerve is the primary cause of shunting metabolic
substrate to compensatory pathways which leads to mitochondrial dysfunction &
nerve damage.
Learning to Coordinate in a Decentralized Cognitive Radio Network in Presence of Jammers
Date: 2018-11-16 00:00:00

Efficient utilization of licensed spectrum in the cognitive radio network is
challenging due to lack of coordination among the Secondary Users (SUs).
Distributed algorithms proposed in the literature aim to maximize the network
throughput by ensuring orthogonal channel allocation for the SUs. However,
these algorithms work under the assumption that all the SUs faithfully follow
the algorithms which may not always hold due to the decentralized nature of the
network. In this paper, we study distributed algorithms that are robust against
malicious behavior (jamming attack). We consider both the cases of jammers
launching coordinated and uncoordinated attacks. In the coordinated attack, the
jammers select non-overlapping channels to attack in each time slot and can
significantly increase the number of collisions for SUs. We setup the problem
in each scenario as a multi-player bandit and develop algorithms. The analysis
shows that when the SUs faithfully implement proposed algorithms, the regret is
constant with high probability. We validate our claims through exhaustive
synthetic experiments and also through a realistic USRP based experiments.
NLOS Mitigation Using Sparsity Feature And Iterative Methods
Date: 2018-03-19 00:00:00

Well-known methods are employed to localize mobile station (MS) using line of
sight (LOS) measurements. These methods may result in large error if they are
fed with non LOS (NLOS) measurements. Our proposed algorithm, referred to as
Sparse Recovery of NLOS using IMAT (SRNI), considers NLOS as unknown variables
and solves the resultant underdetermined system emphasizing on its sparsity
feature based on IMAT methods. Simulations are conducted to investigate the
performance of SRNI in comparison of other conventional algorithms. Results
demonstrate that SRNI is fast enough to deal with large combination of BSs and
also accurate in lower number of BSs
The eBDIMS path-sampling server: generation, classification and interactive visualization of protein ensembles and transition pathways in 2D-motion space
Date: 2018-03-19 00:00:00

The recent rise of cryo-EM and X-ray high-throughput techniques is providing
a wealth of new structures trapped in different conformations. Understanding
how proteins transition between different conformers, and how they relate to
each other in terms of function is not straightforward, and highly depends on
the choice of the right set of degrees of freedom. Here we present eBDIMS
server, an online tool and software for automatic classification of structural
ensembles and reconstruction of transition pathways using coarse-grained (CG)
simulations. The server generates CG-pathways between two protein conformations
along with a representation in a simplified 2D-motion landscape based on the
Principal Components (PCs) from experimental structures. For a conformationally
rich ensemble, the PCs provide powerful reaction coordinates for automatic
structure classification, detection of on-pathway intermediates and validation
of in silico pathways. When the number of available structures is low or
sampling is limited, Normal Modes (NMs) provide alternative motion axes for
trajectory analysis. The path-generation eBDIMS method is available at a
user-friendly website: https://login.biophysics.kth.se/eBDIMS/ or as standalone
software. The server incorporates a powerful interactive graphical interface
for simultaneous visualization of transition pathways in 2D-motion space and
3D-molecular graphics, which greatly facilitates the exploration of the
relationships between different conformations.