pwc-archive/papers-with-abstracts huggingface dataset:
Towards Finding Non-obvious Papers: An Analysis of Citation Recommender Systems
Authors: Jia Haofeng, Saule Erik
Date: 2018-12-29 00:00:00
Tasks: Citation Recommendation, Recommendation Systems

As science advances, the academic community has published millions of
research papers. Researchers devote time and effort to search relevant
manuscripts when writing a paper or simply to keep up with current research. In
this paper, we consider the problem of citation recommendation by extending a
set of known-to-be-relevant references. Our analysis shows the degrees of cited
papers in the subgraph induced by the citations of a paper, called projection
graph, follow a power law distribution. Existing popular methods are only good
at finding the long tail papers, the ones that are highly connected to others.
In other words, the majority of cited papers are loosely connected in the
projection graph but they are not going to be found by existing methods. To
address this problem, we propose to combine author, venue and keyword
information to interpret the citation behavior behind those loosely connected
papers. Results show that different methods are finding cited papers with
widely different properties. We suggest multiple recommended lists by different
algorithms could satisfy various users for a real citation recommendation
system. Moreover, we also explore the fast local approximation for combined
methods in order to improve the efficiency.
Online Low Frequency Oscillation Detection and Analysis System with an Ensemble Filter
Date: 2020-01-14 00:00:00

The widespread deployment of phasor measurement unit (PMU) overpower systems
makes it possible to monitor and analyze grid dynamics in real-time.
Low-frequency oscillation is harmful to power system equipment and operation,
and in the worst-case scenario may lead to cascading failures. Therefore, it is
critical to detect and identify them as soon as they appear. This paper
presents an online low-frequency oscillation detection and analysis (LFODA)
system, which has the merit of significantly reducing the chance of false alarm
via a voting schema and a time-serial filter. A novel algorithm based on
density-based spatial clustering of applications with noise (DBSCAN) is
proposed to classify oscillation modes as well as to group their corresponding
buses/monitoring sites. Performance of the LFODA system is evaluated through
experiments using both simulated and real-world PMU data.
The gruesome murder of Jamal Khashoggi : Saudi Arabia's new economy dream at risk ?
Date: 2018-12-29 00:00:00

With the horrific Jamal Khashoggi killing, Mohammed Bin Salman's image in the
international community has been damaged. This study seeks to test whether
Khashoggi murder discourage businesses from investing in Saudi Arabia. We use
an event-study methodology and asset pricing model to assess, at sectoral
level, the dynamics of stock prices surrounding the killing of the Saudi
journalist on 2 October at the kingdom's consulate in Istanbul. A series of
robustness tests, including the Corrado ranking test and the non-parametric
conditional distribution approach, have been conducted. We consistently show
that the khashoggi killing had the most adverse impact on banks and financial
services, materials, and technology. Oil and gas companies, however, were
moderately or insignificantly affected. Overall, our results suggest that the
crown prince's ambitious project for a Saudi Arabian economy moving beyond oil
wealth are threatened as this recent event dampened foreign interest in
investing in the kingdom.
Adaptive Synchrosqueezing Transform with a Time-Varying Parameter for Non-stationary Signal Separation
Date: 2019-09-26 00:00:00

The continuous wavelet transform (CWT) is a linear time-frequency
representation and a powerful tool for analyzing non-stationary signals. The
synchrosqueezing transform (SST) is a special type of the reassignment method
which not only enhances the energy concentration of CWT in the time-frequency
plane, but also separates the components of multicomponent signals. The "bump
wavelet" and Morlet's wavelet are commonly used continuous wavelets for the
wavelet-based SST. There is a parameter in these wavelets which controls the
widths of the time-frequency localization window. In most literature on SST,
this parameter is a fixed positive constant. In this paper, we consider the CWT
with a time-varying parameter (called the adaptive CWT) and the corresponding
SST (called the adaptive SST) for instantaneous frequency estimation and
multicomponent signal separation. We also introduce the 2nd-order adaptive SST.
We analyze the separation conditions for non-stationary multicomponent signals
with the local approximation of linear frequency modulation mode. We derive
well-separated conditions of a multicomponent signal based on the adaptive CWT.
We propose methods to select the time-varying parameter so that the
corresponding adaptive SSTs of the components of a multicomponent signal have
sharp representations and are well-separated, and hence the components can be
recovered more accurately. We provide comparison experimental results to
demonstrate the efficiency and robustness of the proposed adaptive CWT and
adaptive SST in separating components of multicomponent signals with fast
varying frequencies.
Precise spatial scaling in the early fly embryo
Date: 2018-12-29 00:00:00

The early fly embryo offers a relatively pure version of the problem of
spatial scaling in biological pattern formation. Within three hours, a
"blueprint" for the final segmented body plan of the animal is visible in
striped patterns of gene expression. We measure the positions of these stripes
in an ensemble of 100+ embryos from a laboratory strain of Drosophila
melanogaster, under controlled conditions. These embryos vary in length by only
4% (rms), yet stripes are positioned with 1% accuracy; precision and scaling of
the pattern are intertwined. We can see directly the variation of absolute
stripe positions with length, and the precision is so high as to exclude
alternatives, such as combinations of unscaled signals from the two ends of the
embryo.
Tropical Abstraction of Biochemical Reaction Networks with Guarantees
Date: 2019-03-21 00:00:00

Biochemical molecules interact through modification and binding reactions,
giving raise to a combinatorial number of possible biochemical species. The
time-dependent evolution of concentrations of the species is commonly described
by a system of coupled ordinary differential equations (ODEs). However, the
analysis of such high-dimensional, non-linear system of equations is often
computationally expensive and even prohibitive in practice. The major challenge
towards reducing such models is providing the guarantees as to how the solution
of the reduced model relates to that of the original model, while avoiding to
solve the original model. In this paper, we have designed and tested an
approximation method for ODE models of biochemical reaction systems, in which
the guarantees are our major requirement. Borrowing from tropical analysis
techniques, dominance relations among terms of each species' ODE are exploited
to simplify the original model, by neglecting the dominated terms. As the
dominant subsystems can change during the system's dynamics, depending on which
species dominate the others, several possible modes exist. Thus, simpler models
consisting of only the dominant subsystems can be assembled into hybrid,
piecewise smooth models, which approximate the behavior of the initial system.
By combining the detection of dominated terms with symbolic bounds propagation,
we show how to approximate the original model by an assembly of simpler models,
consisting in ordinary differential equations that provide time-dependent lower
and upper bounds for the concentrations of the initial models species. Our
method provides sound interval bounds for the concentrations of the chemical
species, and hence can serve to evaluate the faithfulness of
tropicalization-based reduction heuristics for ODE models of biochemical
reduction systems. The method is tested on several case studies.
Thought Viruses and Asset Prices
Date: 2018-12-29 00:00:00
Tasks: Epidemiology

We use insights from epidemiology, namely the SIR model, to study how agents
infect each other with "investment ideas." Once an investment idea "goes
viral," equilibrium prices exhibit the typical "fever peak," which is
characteristic for speculative excesses. Using our model, we identify a time
line of symptoms that indicate whether a boom is in its early or later stages.
Regarding the market's top, we find that prices start to decline while the
number of infected agents, who buy the asset, is still rising. Moreover, the
presence of fully rational agents (i) accelerates booms (ii) lowers peak prices
and (iii) produces broad, drawn-out, market tops.
High-performance Decoder for Convolutional Code with Deep Neural Network
Date: 2018-12-30 00:00:00
Tasks: Decoder, Vocal Bursts Intensity Prediction

The use of deep neural network for decoding error control code will encounter
two problems, namely, the high-precision requirements of the error control code
and the complexity of the neural network due to the long code. In this paper, a
deep neural network decoder is proposed to solve the decoding problem of long
code by using the nature of convolutional code window decoding. A deep neural
network decoder is utilized as a weak classifier, and an integrated decoder is
proposed to improve the decoding performance greatly. The Viterbi decoder is
improved by approximately 2 db at a bit error rate of level 5. Both decoder
methods proposed in this paper can be decoded in parallel and are suitable for
high-bit-rate applications. This study reveals that the accuracy of neural
networks can reach level 8 more.
E-commerce in Hungary: A Market Analysis
Date: 2018-12-30 00:00:00

E-commerce is on the rise in Hungary, with significantly growing numbers of
customers shopping online. This paper aims to identify the direct and indirect
drivers of the double-digit growth rate, including the related macroeconomic
indicators and the Digital Economy and Society Index (DESI). Moreover, this
study provides a deep insight into industry trends and outlooks, including high
industry concentration and top industrial players. It also draws the profile of
the typical online shopper and the dominant characteristics of online
purchases. Development of e-commerce is robust, but there is still plenty of
potential for growth and progress in Hungary.
Extraction of electrokinetically separated analytes with on-demand encapsulation
Date: 2018-12-30 00:00:00

Microchip electrokinetic methods are capable of increasing the sensitivity of
molecular assays by enriching and purifying target analytes. However, their use
is currently limited to assays that can be performed under a high external
electric field, as spatial separation and focusing is lost when the electric
field is removed. We present a novel method that uses two-phase encapsulation
to overcome this limitation. The method uses passive filling and pinning of an
oil phase in hydrophobic channels to encapsulate electrokinetically separated
and focused analytes with a brief pressure pulse. The resulting encapsulated
sample droplet maintains its concentration over long periods of time without
requiring an electric field and can be manipulated for further analysis, either
on- or off- chip. We demonstrate the method by encapsulating DNA
oligonucleotides in a 240 pL aqueous segment after isotachophoresis (ITP)
focusing, and show that the concentration remains at 60% of the initial value
for tens of minutes, a 22-fold increase over free diffusion after 20 minutes.
Furthermore, we demonstrate manipulation of a single droplet by selectively
encapsulating amplicon after ITP purification from a polymerase chain reaction
(PCR) mix, and performing parallel off-chip detection reactions using the
droplet. We provide geometrical design guidelines for devices implementing the
encapsulation method, and show how the method can be scaled to multiple analyte
zones.
Optical Wireless Communication Systems, A Survey
Date: 2018-12-30 00:00:00
Tasks: Survey

In the past few years, the demand for high data rate services has increased
dramatically. The congestion in the radio frequency (RF) spectrum (3 kHz ~ 300
GHz) is expected to limit the growth of future wireless systems unless new
parts of the spectrum are opened. Even with the use of advanced engineering,
such as signal processing and advanced modulation schemes, it will be very
challenging to meet the demands of the users in the next decades using the
existing carrier frequencies. On the other hand, there is a potential band of
the spectrum available that can provide tens of Gbps to Tbps for users in the
near future. Optical wireless communication (OWC) systems are among the
promising solutions to the bandwidth limitation problem faced by radio systems.
In this paper, we give a tutorial survey of the most significant issues in OWC
systems that operate at short ranges such as indoor systems. We consider the
challenging issues facing these systems such as (i) link design and system
requirements, (ii) transmitter structures, (iii) receiver structures, (iv)
challenges and possible techniques to mitigate the impairments in these
systems, (v) the main applications and (vi) open research issues. In indoor OWC
systems we describe channel modelling, mobility and dispersion mitigation
techniques. Infrared communication (IRC) and visible light communication (VLC)
are presented as potential implementation approaches for OWC systems and are
comprehensively discussed. Moreover, open research issues in OWC systems are
discussed.
Figure 1 Theory Meets Figure 2 Experiments in the Study of Gene Expression
Date: 2018-12-30 00:00:00

It is tempting to believe that we now own the genome. The ability to read and
re-write it at will has ushered in a stunning period in the history of science.
Nonetheless, there is an Achilles heel exposed by all of the genomic data that
has accrued: we still don't know how to interpret it. Many genes are subject to
sophisticated programs of transcriptional regulation, mediated by DNA sequences
that harbor binding sites for transcription factors which can up- or
down-regulate gene expression depending upon environmental conditions. This
gives rise to an input-output function describing how the level of expression
depends upon the parameters of the regulated gene { for instance, on the number
and type of binding sites in its regulatory sequence. In recent years, the
ability to make precision measurements of expression, coupled with the ability
to make increasingly sophisticated theoretical predictions, have enabled an
explicit dialogue between theory and experiment that holds the promise of
covering this genomic Achilles heel. The goal is to reach a predictive
understanding of transcriptional regulation that makes it possible to calculate
gene expression levels from DNA regulatory sequence. This review focuses on the
canonical simple repression motif to ask how well the models that have been
used to characterize it actually work. We consider a hierarchy of increasingly
sophisticated experiments in which the minimal parameter set learned at one
level is applied to make quantitative predictions at the next. We show that
these careful quantitative dissections provide a template for a predictive
understanding of the many more complex regulatory arrangements found across all
domains of life.
Optimal Insurance with Limited Commitment in a Finite Horizon
Date: 2019-01-11 00:00:00

We study a finite horizon optimal contracting problem of a risk-neutral
principal and a risk-averse agent who receives a stochastic income stream when
the agent is unable to make commitments. The problem involves an infinite
number of constraints at each time and each state of the world. Miao and Zhang
(2015) have developed a dual approach to the problem by considering a
Lagrangian and derived a Hamilton-Jacobi-Bellman equation in an infinite
horizon. We consider a similar Lagrangian in a finite horizon, but transform
the dual problem into an infinite series of optimal stopping problems. For each
optimal stopping problem we provide an analytic solution by providing an
integral equation representation for the free boundary. We provide a
verification theorem that the value function of the original principal's
problem is the Legender-Fenchel transform of the integral of the value
functions of the optimal stopping problems. We also provide some numerical
simulation results of optimal contracting strategies
A Neural Network Based Explainable Recommender System
Authors: Lin Jionghao, Liu Yiren
Date: 2018-12-31 00:00:00
Tasks: Collaborative Filtering, Feature Engineering, Prediction, Recommendation Systems

Recommendation system could help the companies to persuade users to visit or
consume at a particular place, which was based on many traditional methods such
as the set of collaborative filtering algorithms. Most research discusses the
model design or feature engineering methods to minimize the root mean square
error (RMSE) of rating prediction, but lacks exploring the ways to generate the
reasons for recommendations. This paper proposed an integrated neural network
based model which integrates rating scores prediction and explainable words
generation. Based on the experimental results, this model presented lower RMSE
compared with traditional methods, and generate the explanation of
recommendation to convince customers to visit the recommended place.
Fundamental bounds on learning performance in neural circuits
Date: 2018-12-31 00:00:00

How does the size of a neural circuit influence its learning performance?
Intuitively, we expect the learning capacity of a neural circuit to grow with
the number of neurons and synapses. Larger brains tend to be found in species
with higher cognitive function and learning ability. Similarly, adding
connections and units to artificial neural networks can allow them to solve
more complex tasks. However, we show that in a biologically relevant setting
where synapses introduce an unavoidable amount of noise, there is an optimal
size of network for a given task. Beneath this optimal size, our analysis shows
how adding apparently redundant neurons and connections can make tasks more
learnable. Therefore large neural circuits can either devote connectivity to
generating complex behaviors, or exploit this connectivity to achieve faster
and more precise learning of simpler behaviors. Above the optimal network size,
the addition of neurons and synaptic connections starts to impede learning
performance. This suggests that overall brain size may be constrained by the
need to learn efficiently with unreliable synapses, and may explain why some
neurological learning deficits are associated with hyperconnectivity. Our
analysis is independent of specific learning rules and uncovers fundamental
relationships between learning rate, task performance, network size and
intrinsic noise in neural circuits.
Learning and Selfconfirming Equilibria in Network Games
Authors: Pierpaolo Battigalli, Fabrizio Panebianco, Paolo Pin
Date: 2018-12-31 00:00:00

Consider a set of agents who play a network game repeatedly. Agents may not know the network. They may even be unaware that they are interacting with other agents in a network. Possibly, they just understand that their payoffs depend on an unknown state that is, actually, an aggregate of the actions of their neighbors. Each time, every agent chooses an action that maximizes her instantaneous subjective expected payoff and then updates her beliefs according to what she observes. In particular, we assume that each agent only observes her realized payoff. A steady state of the resulting dynamic is a selfconfirming equilibrium given the assumed feedback. We characterize the structure of the set of selfconfirming equilibria in the given class of network games, we relate selfconfirming and Nash equilibria, and we analyze simple conjectural best-reply paths whose limit points are selfconfirming equilibria.
Mathematics Content Understanding for Cyberlearning via Formula Evolution Map
Authors: Jiang Zhuoren, Gao Liangcai, Yuan Ke, Gao Zheng, Tang Zhi, Liu Xiaozhong
Date: 2018-12-31 00:00:00
Tasks: Graph Mining, Math

Although the scientific digital library is growing at a rapid pace,
scholars/students often find reading Science, Technology, Engineering, and
Mathematics (STEM) literature daunting, especially for the
math-content/formula. In this paper, we propose a novel problem, ``mathematics
content understanding'', for cyberlearning and cyberreading. To address this
problem, we create a Formula Evolution Map (FEM) offline and implement a novel
online learning/reading environment, PDF Reader with Math-Assistant (PRMA),
which incorporates innovative math-scaffolding methods. The proposed
algorithm/system can auto-characterize student emerging math-information need
while reading a paper and enable students to readily explore the formula
evolution trajectory in FEM. Based on a math-information need, PRMA utilizes
innovative joint embedding, formula evolution mining, and heterogeneous graph
mining algorithms to recommend high quality Open Educational Resources (OERs),
e.g., video, Wikipedia page, or slides, to help students better understand the
math-content in the paper. Evaluation and exit surveys show that the PRMA
system and the proposed formula understanding algorithm can effectively assist
master and PhD students better understand the complex math-content in the class
readings.
Nanorobots-assisted Detection of Multifocal Cancer: A Multimodal Optimization Perspective
Date: 2018-12-25 00:00:00

We propose a new framework of computing-inspired multifocal cancer detection
procedure (MCDP). Under the rubric of MCDP, the tumor foci to be detected are
regarded as solutions of the objective function, the tissue region around the
cancer areas represents the parameter space, and the nanorobots loaded with
contrast medium molecules for cancer detection correspond to the optimization
agents. The process that the nanorobots detect tumors by swimming in the
high-risk tissue region can be regarded as the process that the agents search
for the solutions of an objective function in the parameter space with some
constraints. For multimodal optimization (MMO) aiming to locate multiple
optimal solutions in a single simulation run, niche technology has been widely
used. Specifically, the Niche Genetic Algorithm (NGA) has been shown to be
particularly effective in solving MMO. It can be used to identify the global
optima of multiple hump functions in a running, keep effectively the diversity
of the population, and avoid premature of the genetic algorithm. Learning from
the optimization procedure of NGA, we propose the NGA-inspired MCDP in order to
locate the tumor targets efficiently while taking into account realistic in
vivo propagation and controlling of nanorobots, which is different from the use
scenario of the standard NGA. To improve the performance of the MCDP, we also
modify the crossover operator of the original NGA from crossing within a
population to crossing between two populations. Finally, we present
comprehensive numerical examples to demonstrate the effectiveness of the
NGA-inspired MCDP when the biological objective function is associated with the
blood flow velocity profile caused by tumor-induced angiogenesis.