pwc-archive/papers-with-abstracts huggingface dataset:
Systemic Greeks: Measuring risk in financial networks
Date: 2018-10-28 00:00:00

Since the latest financial crisis, the idea of systemic risk has received
considerable interest. In particular, contagion effects arising from
cross-holdings between interconnected financial firms have been studied
extensively. Drawing inspiration from the field of complex networks, these
attempts are largely unaware of models and theories for credit risk of
individual firms. Here, we note that recent network valuation models extend the
seminal structural risk model of Merton (1974). Furthermore, we formally
compute sensitivities to various risk factors -- commonly known as Greeks -- in
a network context. In particular, we propose the network $\Delta$ as a
quantitative measure of systemic risk and illustrate our findings on some
numerical examples.
Prediction of severity and treatment outcome for ASD from fMRI
Date: 2018-10-28 00:00:00
Tasks: feature selection, Medical Image Analysis, regression

Autism spectrum disorder (ASD) is a complex neurodevelopmental syndrome.
Early diagnosis and precise treatment are essential for ASD patients. Although
researchers have built many analytical models, there has been limited progress
in accurate predictive models for early diagnosis. In this project, we aim to
build an accurate model to predict treatment outcome and ASD severity from
early stage functional magnetic resonance imaging (fMRI) scans. The difficulty
in building large databases of patients who have received specific treatments
and the high dimensionality of medical image analysis problems are challenges
in this work. We propose a generic and accurate two-level approach for
high-dimensional regression problems in medical image analysis. First, we
perform region-level feature selection using a predefined brain parcellation.
Based on the assumption that voxels within one region in the brain have similar
values, for each region we use the bootstrapped mean of voxels within it as a
feature. In this way, the dimension of data is reduced from number of voxels to
number of regions. Then we detect predictive regions by various feature
selection methods. Second, we extract voxels within selected regions, and
perform voxel-level feature selection. To use this model in both linear and
non-linear cases with limited training examples, we apply two-level elastic net
regression and random forest (RF) models respectively. To validate accuracy and
robustness of this approach, we perform experiments on both task-fMRI and
resting state fMRI datasets. Furthermore, we visualize the influence of each
region, and show that the results match well with other findings.
Dynamic Thresholding Mechanisms for IR-Based Filtering in Efficient Source Code Plagiarism Detection
Authors: Karnalim Oscar, Sulistiani Lisan
Date: 2018-10-28 00:00:00
Tasks: Information Retrieval, Retrieval

To solve time inefficiency issue, only potential pairs are compared in
string-matching-based source code plagiarism detection; wherein potentiality is
defined through a fast-yet-order-insensitive similarity measurement (adapted
from Information Retrieval) and only pairs which similarity degrees are higher
or equal to a particular threshold is selected. Defining such threshold is not
a trivial task considering the threshold should lead to high efficiency
improvement and low effectiveness reduction (if it is unavoidable). This paper
proposes two thresholding mechanisms---namely range-based and pair-count-based
mechanism---that dynamically tune the threshold based on the distribution of
resulted similarity degrees. According to our evaluation, both mechanisms are
more practical to be used than manual threshold assignment since they are more
proportional to efficiency improvement and effectiveness reduction.
Phylodynamics of rapidly adapting pathogens: extinction and speciation of a Red Queen
Date: 2018-10-29 00:00:00

Rapidly evolving pathogens like influenza viruses can persist by accumulating
antigenic novelty fast enough to evade the adaptive immunity of the host
population, yet without continuous accumulation of genetic diversity. This
dynamical state is often compared to the Red Queen evolving as fast as it can
just to maintain its foothold in the host population: Accumulation of antigenic
novelty is balanced by the build-up of host immunity. Such Red Queen States
(RQS) of continuous adaptation in large rapidly mutating populations are well
understood in terms of Traveling Wave (TW) theories of population genetics.
Here we shall make explicit the mapping of the established Multi-strain
Susceptible-Infected-Recovered (SIR) model onto the TW theory and demonstrate
that a pathogen can persist in RQS if cross-immunity is long-ranged and its
population size is large populations allowing for rapid adaptation. We then
investigate the stability of this state focusing on the rate of extinction and
the rate of "speciation" defined as antigenic divergence of viral strains
beyond the range of cross-inhibition. RQS states are transient, but in a
certain range of evolutionary parameters can exist for the time long compared
to the typical time to the most recent common ancestor ($T_{MRCA}$). In this
range the steady TW is unstable and the antigenic advance of the lead strains
relative to the typical co-circulating viruses tends to oscillate. This results
in large fluctuations in prevalence that facilitate extinction. We shall
demonstrate that the rate of TW fission into antigenically uncoupled viral
populations is related to fluctuations of $T_{MRCA}$ and construct a "phase
diagram" identifying different regimes of viral phylodynamics as a function of
evolutionary parameters.
Optimized Signal Distortion for PAPR Reduction of OFDM Signals with IFFT/FFT Complexity via ADMM Approaches
Date: 2018-10-29 00:00:00

In this paper, we propose two low-complexity optimization methods to reduce
peak-to-average power ratio (PAPR) values of orthogonal frequency division
multiplexing (OFDM) signals via alternating direction method of multipliers
(ADMM). First, we formulate a non-convex signal distortion optimization model
based on minimizing data carrier distortion such that the constraints are
placed on PAPR and the power of free carriers. Second, to obtain the model's
approximate optimal solution efficiently, we design two low-complexity ADMM
algorithms, named ADMM-Direct and ADMM-Relax respectively. Third, we show that,
in ADMM-Direct/-Relax, all the optimization subproblems can be solved
semi-analytically and the computational complexity in each iteration is roughly
O(lNlog2(lN)), where l and N are over sampling factor and carrier number
respectively. Moreover, we show that the resulting solution of ADMM-Direct is
guaranteed to be some Karush-Kuhn-Tucker (KKT) point of the non-convex model
when the iteration algorithm is convergent. For ADMM-Relax, we prove that it
has theoretically guaranteed convergence and can approach arbitrarily close to
some KKT point of the model if proper parameters are chosen. Simulation results
demonstrate the effectiveness of the proposed approaches.
Graphene oxide nanosheets disrupt lipid composition, Ca2+ homeostasis and synaptic transmission in primary cortical neurons
Date: 2018-10-29 00:00:00

Graphene has the potential to make a very significant impact on society, with
important applications in the biomedical field. The possibility to engineer
graphene-based medical devices at the neuronal interface is of particular
interest, making it imperative to determine the biocompatibility of graphene
materials with neuronal cells. Here we conducted a comprehensive analysis of
the effects of chronic and acute exposure of rat primary cortical neurons to
few-layers pristine graphene (GR) and monolayer graphene oxide (GO) flakes. By
combining a range of cell biology, microscopy, electrophysiology and omics
approaches we characterized the graphene neuron interaction from the first
steps of membrane contact and internalization to the long-term effects on cell
viability, synaptic transmission and cell metabolism. GR/GO flakes are found in
contact with the neuronal membrane, free in the cytoplasm and internalized
through the endolysosomal pathway, with no significant impact on neuron
viability. However, GO exposure selectively caused the inhibition of excitatory
transmission, paralleled by a reduction in the number of excitatory synaptic
contacts, and a concomitant enhancement of the inhibitory activity. This was
accompanied by induction of autophagy, altered Ca2+ dynamics and by a
downregulation of some of the main players in the regulation of Ca2+
homeostasis in both excitatory and inhibitory neurons. Our results show that,
although graphene exposure does not impact on neuron viability, it does
nevertheless have important effects on neuronal transmission and network
functionality, thus warranting caution when planning to employ this material
for neuro-biological applications.
Asymmetric Network Connectedness of Fears
Authors: Jozef Barunik, Mattia Bevilacqua, Radu Tunaru
Date: 2018-10-29 00:00:00

This paper introduces forward-looking measures of the network connectedness of fears in the financial system, arising due to the good and bad beliefs of market participants about uncertainty that spreads unequally across a network of banks. We argue that this asymmetric network structure extracted from call and put traded option prices of the main U.S. banks contains valuable information for predicting macroeconomic conditions and economic uncertainty, and it can serve as a tool for forward-looking systemic risk monitoring.
1-Bit Massive MIMO Downlink Based on Constructive Interference
Date: 2018-10-29 00:00:00

In this paper, we focus on the multiuser massive multiple-input single-output
(MISO) downlink with low-cost 1-bit digital-to-analog converters (DACs) for PSK
modulation, and propose a low-complexity refinement process that is applicable
to any existing 1-bit precoding approaches based on the constructive
interference (CI) formulation. With the decomposition of the signals along the
detection thresholds, we first formulate a simple symbol-scaling method as the
performance metric. The low-complexity refinement approach is subsequently
introduced, where we aim to improve the introduced symbol-scaling performance
metric by modifying the transmit signal on one antenna at a time. Numerical
results validate the effectiveness of the proposed refinement method on
existing approaches for massive MIMO with 1-bit DACs, and the performance
improvements are most significant for the low-complexity quantized zero-forcing
(ZF) method.
Asymptotic Upper Capacity Bound for Receive Antenna Selection in Massive MIMO Systems
Date: 2018-10-29 00:00:00

This paper studies the receive antenna selection in massive multiple-input
multiple-output (MIMO) system. The receiver, equipped with a large-scale
antenna array whose size is much larger than that of the transmitter, selects a
subset of antennas to receive messages. A low-complexity asymptotic
approximated upper capacity bound is derived in the limit of massive MIMO
systems over independent and identical distributed flat fading Rayleigh
channel, assuming that the channel side information (CSI) is only available at
the receiver. Furthermore, the asymptotic theory is separately applied to two
scenarios which is based on whether the total amount of the selected antennas
exceed that of the transmit antennas. Besides analytical derivations,
simulation results are provided to demonstrate the approximation precision of
the asymptotic results and the tightness of the capacity bound.
Embedding Geographic Locations for Modelling the Natural Environment using Flickr Tags and Structured Data
Authors: Jeawak Shelan S., Jones Christopher B., Schockaert Steven
Date: 2018-10-12 00:00:00

Meta-data from photo-sharing websites such as Flickr can be used to obtain
rich bag-of-words descriptions of geographic locations, which have proven
valuable, among others, for modelling and predicting ecological features. One
important insight from previous work is that the descriptions obtained from
Flickr tend to be complementary to the structured information that is available
from traditional scientific resources. To better integrate these two diverse
sources of information, in this paper we consider a method for learning vector
space embeddings of geographic locations. We show experimentally that this
method improves on existing approaches, especially in cases where structured
information is available.
Ruuh: A Deep Learning Based Conversational Social Agent
Authors: Damani Sonam, Raviprakash Nitya, Gupta Umang, Chatterjee Ankush, Joshi Meghana, Gupta Khyatti, Narahari Kedhar Nath, Agrawal Puneet, Chinnakotla Manoj Kumar, Magapu Sneha, Mathur Abhishek
Date: 2018-10-22 00:00:00
Tasks: Abusive Language, Deep Learning

Dialogue systems and conversational agents are becoming increasingly popular
in the modern society but building an agent capable of holding intelligent
conversation with its users is a challenging problem for artificial
intelligence. In this demo, we demonstrate a deep learning based conversational
social agent called "Ruuh" (facebook.com/Ruuh) designed by a team at Microsoft
India to converse on a wide range of topics. Ruuh needs to think beyond the
utilitarian notion of merely generating "relevant" responses and meet a wider
range of user social needs, like expressing happiness when user's favorite team
wins, sharing a cute comment on showing the pictures of the user's pet and so
on. The agent also needs to detect and respond to abusive language, sensitive
topics and trolling behavior of the users. Many of these problems pose
significant research challenges which will be demonstrated in our demo. Our
agent has interacted with over 2 million real world users till date which has
generated over 150 million user conversations.
Intraday Seasonalities and Nonstationarity of Trading Volume in Financial Markets: Individual and Cross-Sectional Features
Date: 2018-10-22 00:00:00

We study the intraday behaviour of the statistical moments of the trading
volume of the blue chip equities that composed the Dow Jones Industrial Average
index between 2003 and 2014. By splitting that time interval into semesters, we
provide a quantitative account of the non-stationary nature of the intraday
statistical properties as well. Explicitly, we prove the well-known U-shape
exhibited by the average trading volume-as well as the volatility of the price
fluctuations-experienced a significant change from 2008 (the year of the
sub-prime financial crisis) onwards. That has resulted in a faster relaxation
after the market opening and relates to a consistent decrease in the convexity
of the average trading volume intraday profile. Simultaneously, the last part
of the session has become steeper as well, a modification that is likely to
have been triggered by the new short-selling rules that were introduced in 2007
by the Securities and Exchange Commission. The combination of both results
reveals that the has been turning into a t. Additionally, the analysis of
higher-order cumulants namely the skewness and the kurtosis-shows that the
morning and the afternoon parts of the trading session are each clearly
associated with different statistical features and hence dynamical rules.
Concretely, we claim that the large initial trading volume is due to wayward
stocks whereas the large volume during the last part of the session hinges on a
cohesive increase of the trading volume. That dissimilarity between the two
parts of the trading session is stressed in periods of higher uproar in the
market.
Efficient Taxonomic Similarity Joins with Adaptive Overlap Constraint
Authors: Xu Pengfei, Lu Jiaheng
Date: 2018-10-29 00:00:00

A similarity join aims to find all similar pairs between two collections of
records. Established approaches usually deal with synthetic differences like
typos and abbreviations, but neglect the semantic relations between words. Such
relations, however, are helpful for obtaining high-quality joining results. In
this paper, we leverage the taxonomy knowledge (i.e., a set of IS-A
hierarchical relations) to define a similarity measure which finds
semantic-similar records from two datasets. Based on this measure, we develop a
similarity join algorithm with prefix filtering framework to prune away
irrelevant pairs effectively. Our technical contribution here is an algorithm
that judiciously selects critical parameters in a prefix filter to maximise its
filtering power, supported by an estimation technique and Monte Carlo
simulation process. Empirical experiments show that our proposed methods
exhibit high efficiency and scalability, outperforming the state-of-art by a
large margin.
RF photonic delay lines using space-division multiplexing
Date: 2018-10-29 00:00:00

We review our last work on dispersion-engineered heterogeneous multicore
fiber links designed to act as tunable true time delay lines for radiofrequency
signals. This approach allows the realization of fiber distributed signal
processing in the context of fiber-wireless communications, providing both
radiofrequency access distribution and signal processing in the same fiber
medium. We show how to design trench-assisted heterogeneous multicore fibers to
fulfil the requirements for sampled true time delay line operation while
assuring a low level of crosstalk, bend sensitivity and tolerance to possible
fabrication errors. The performance of the designed radiofrequency photonic
delay lines is evaluated in the context of tunable microwave signal filtering
and optical beamforming for phased array antennas.
Multicore fiber delay line performance against bending and twisting effects
Date: 2018-10-29 00:00:00

We report the experimental evaluation of bending and twisting effects on the
propagation and radiofrequency signal processing performance of multicore
fibers. We demonstrate that twisting minimizes to a large extend the group
delay fluctuations over a bent multicore fiber.
Novel Near-Optimal Scalar Quantizers with Exponential Decay Rate and Global Convergence
Date: 2019-02-18 00:00:00
Tasks: Quantization

Many modern distributed real-time signal sensing/monitoring systems require
quantization for efficient signal representation. These distributed sensors
often have inherent computational and energy limitations. Motivated by this
concern, we propose a novel quantization scheme called approximate Lloyd-Max
that is nearly-optimal. Assuming a continuous and finite support probability
distribution of the source, we show that our quantizer converges to the
classical Lloyd-Max quantizer with increasing bitrate. We also show that our
approximate Lloyd-Max quantizer converges exponentially fast with the number of
iterations. The proposed quantizer is modified to account for a relatively new
quantization model which has envelope constraints, termed as the envelope
quantizer. With suitable modifications we show optimality and convergence
properties for the equivalent approximate envelope quantizer. We illustrate our
results using extensive simulations for different finite support source
distributions.
Adapting a plant tissue model to animal development: introducing cell sliding into VirtualLeaf
Date: 2019-03-11 00:00:00

Cell-based, mathematical modeling of collective cell behavior has become a
prominent tool in developmental biology. Cell-based models represent individual
cells as single particles or as sets of interconnected particles, and predict
the collective cell behavior that follows from a set of interaction rules. In
particular, vertex-based models are a popular tool for studying the mechanics
of confluent, epithelial cell layers. They represent the junctions between
three (or sometimes more) cells in confluent tissues as point particles,
connected using structural elements that represent the cell boundaries. A
disadvantage of these models is that cell-cell interfaces are represented as
straight lines. This is a suitable simplification for epithelial tissues, where
the interfaces are typically under tension, but this simplification may not be
appropriate for mesenchymal tissues or tissues that are under compression, such
that the cell-cell boundaries can buckle. In this paper we introduce a variant
of VMs in which this and two other limitations of VMs have been resolved. The
new model can also be seen as on off-the-lattice generalization of the Cellular
Potts Model. It is an extension of the open-source package VirtualLeaf, which
was initially developed to simulate plant tissue morphogenesis where cells do
not move relative to one another. The present extension of VirtualLeaf
introduces a new rule for cell-cell shear or sliding, from which T1 and T2
transitions emerge naturally, allowing application of VirtualLeaf to problems
of animal development. We show that the updated VirtualLeaf yields different
results than the traditional vertex-based models for
differential-adhesion-driven cell sorting and for the neighborhood topology of
soft cellular networks.
MCell-R: A particle-resolution network-free spatial modeling framework
Authors: Jose-Juan Tapia, Ali Sinan Saglam, Jacob Czech, Robert Kuczewski, Thomas M. Bartol, Terrence J. Sejnowski, James R. Faeder
Date: 2018-10-29 00:00:00
Tasks: Decision Making

Spatial heterogeneity can have dramatic effects on the biochemical networks that drive cell regulation and decision-making. For this reason, a number of methods have been developed to model spatial heterogeneity and incorporated into widely used modeling platforms. Unfortunately, the standard approaches for specifying and simulating chemical reaction networks become untenable when dealing with multi-state, multi-component systems that are characterized by combinatorial complexity. To address this issue, we developed MCell-R, a framework that extends the particle-based spatial Monte Carlo simulator, MCell, with the rule-based model specification and simulation capabilities provided by BioNetGen and NFsim. The BioNetGen syntax enables the specification of biomolecules as structured objects whose components can have different internal states that represent such features as covalent modification and conformation and which can bind components of other molecules to form molecular complexes. The network-free simulation algorithm used by NFsim enables efficient simulation of rule-based models even when the size of the network implied by the biochemical rules is too large to enumerate explicitly, which frequently occurs in detailed models of biochemical signaling. The result is a framework that can efficiently simulate systems characterized by combinatorial complexity at the level of spatially-resolved individual molecules over biologically relevant time and length scales.
Toward Creating Subsurface Camera
Date: 2018-10-29 00:00:00
Tasks: Distributed Computing, Seismic Imaging

In this article, the framework and architecture of Subsurface Camera (SAMERA)
is envisioned and described for the first time. A SAMERA is a geophysical
sensor network that senses and processes geophysical sensor signals, and
computes a 3D subsurface image in-situ in real-time. The basic mechanism is:
geophysical waves propagating/reflected/refracted through subsurface enter a
network of geophysical sensors, where a 2D or 3D image is computed and
recorded; a control software may be connected to this network to allow view of
the 2D/3D image and adjustment of settings such as resolution, filter,
regularization and other algorithm parameters. System prototypes based on
seismic imaging have been designed. SAMERA technology is envisioned as a game
changer to transform many subsurface survey and monitoring applications,
including oil/gas exploration and production, subsurface infrastructures and
homeland security, wastewater and CO2 sequestration, earthquake and volcano
hazard monitoring. The system prototypes for seismic imaging have been built.
Creating SAMERA requires an interdisciplinary collaboration and transformation
of sensor networks, signal processing, distributed computing, and geophysical
imaging.