pwc-archive/papers-with-abstracts huggingface dataset:
Collective learning from individual experiences and information transfer during group foraging
Date: 2019-01-22 00:00:00

Living in groups brings benefits to many animals, such as a protection
against predators and an improved capacity for sensing and making decisions
while searching for resources in uncertain environments. A body of studies has
shown how collective behaviors within animal groups on the move can be useful
for pooling information about the current state of the environment. The effects
of interactions on collective motion have been mostly studied in models of
agents with no memory. Thus, whether coordinated behaviors can emerge from
individuals with memory and different foraging experiences is still poorly
understood. By means of an agent based model, we quantify how individual memory
and information fluxes can contribute to improving the foraging success of a
group in complex environments. In this context, we define collective learning
as a coordinated change of behavior within a group resulting from individual
experiences and information transfer. We show that an initially scattered
population of foragers visiting dispersed resources can gradually achieve
cohesion and become selectively localized in space around the most salient
resource sites. Coordination is lost when memory or information transfer among
individuals is suppressed. The present modelling framework provides predictions
for empirical studies of collective learning and could also find applications
in swarm robotics and motivate new search algorithms based on reinforcement.
CREATE: Cohort Retrieval Enhanced by Analysis of Text from Electronic Health Records using OMOP Common Data Model
Authors: Liu Sijia, Wang Yanshan, Wen Andrew, Wang Liwei, Hong Na, Shen Feichen, Bedrick Steven, Hersh William, Liu Hongfang
Date: 2019-01-22 00:00:00
Tasks: Information Retrieval, Retrieval

Background: Widespread adoption of electronic health records (EHRs) has
enabled secondary use of EHR data for clinical research and healthcare
delivery. Natural language processing (NLP) techniques have shown promise in
their capability to extract the embedded information in unstructured clinical
data, and information retrieval (IR) techniques provide flexible and scalable
solutions that can augment the NLP systems for retrieving and ranking relevant
records. Methods: In this paper, we present the implementation of Cohort
Retrieval Enhanced by Analysis of Text from EHRs (CREATE), a cohort retrieval
system that can execute textual cohort selection queries on both structured and
unstructured EHR data. CREATE is a proof-of-concept system that leverages a
combination of structured queries and IR techniques on NLP results to improve
cohort retrieval performance while adopting the Observational Medical Outcomes
Partnership (OMOP) Common Data Model (CDM) to enhance model portability. The
NLP component empowered by cTAKES is used to extract CDM concepts from textual
queries. We design a hierarchical index in Elasticsearch to support CDM concept
search utilizing IR techniques and frameworks. Results: Our case study on 5
cohort identification queries evaluated using the IR metric, P@5 (Precision at
5) at both the patient-level and document-level, demonstrates that CREATE
achieves an average P@5 of 0.90, which outperforms systems using only
structured data or only unstructured data with average P@5s of 0.54 and 0.74,
respectively.
A Noncooperative Model of Contest Network Formation
Date: 2020-05-19 00:00:00
Tasks: model

In this paper we study a model of weighted network formation. The bilateral
interaction is modeled as a Tullock contest game with the possibility of a
draw. We describe stable networks under different concepts of stability. We
show that a Nash stable network is either the empty network or the complete
network. The complete network is not immune to bilateral deviations. When we
allow for limited farsightedness, stable networks immune to bilateral
deviations must be complete $M$-partite networks, with partitions of different
sizes. The empty network is the efficient network. We provide several
comparative statics results illustrating the importance of network structure in
mediating the effects of shocks and interventions. In particular, we show that
an increase in the likelihood of a draw has a non-monotonic effect on the level
of wasteful contest spending in the society. To the best of our knowledge, this
paper is the first attempt to model weighted network formation when the actions
of individuals are neither strategic complements nor strategic substitutes.
MIMO-UFMC Transceiver Schemes for Millimeter Wave Wireless Communications
Date: 2019-01-22 00:00:00

The UFMC modulation is among the most considered solutions for the
realization of beyond-OFDM air interfaces for future wireless networks. This
paper focuses on the design and analysis of an UFMC transceiver equipped with
multiple antennas and operating at millimeter wave carrier frequencies. The
paper provides the full mathematical model of a MIMO-UFMC transceiver, taking
into account the presence of hybrid analog/digital beamformers at both ends of
the communication links. Then, several detection structures are proposed, both
for the case of single-packet isolated transmission, and for the case of
multiple-packet continuous transmission. In the latter situation, the paper
also considers the case in which no guard time among adjacent packets is
inserted, trading off an increased level of interference with higher values of
spectral efficiency. At the analysis stage, the several considered detection
structures and transmission schemes are compared in terms of bit-error-rate,
root-mean-square-error, and system throughput. The numerical results show that
the proposed transceiver algorithms are effective and that the linear MMSE data
detector is capable of well managing the increased interference brought by the
removal of guard times among consecutive packets, thus yielding throughput
gains of about 10 - 13 $\%$. The effect of phase noise at the receiver is also
numerically assessed, and it is shown that the recursive implementation of the
linear MMSE exhibits some degree of robustness against this disturbance.
Academic Engagement and Commercialization in an Institutional Transition Environment: Evidence from Shanghai Maritime University
Date: 2019-01-23 00:00:00

Does academic engagement accelerate or crowd out the commercialization of
university knowledge? Research on this topic seldom considers the impact of the
institutional environment, especially when a formal institution for encouraging
the commercial activities of scholars has not yet been established. This study
investigates this question in the context of China, which is in the
institutional transition stage. Based on a survey of scholars from Shanghai
Maritime University, we demonstrate that academic engagement has a positive
impact on commercialization and that this impact is greater for risk-averse
scholars than for other risk-seeking scholars. Our results suggest that in an
institutional transition environment, the government should consider
encouraging academic engagement to stimulate the commercialization activities
of conservative scholars.
Boosting Frequent Itemset Mining via Early Stopping Intersections
Authors: Nguyen Huu Hiep
Date: 2019-01-23 00:00:00

Mining frequent itemsets from a transaction database has emerged as a
fundamental problem in data mining and committed itself as a building block for
many pattern mining tasks. In this paper, we present a general technique to
reduce support checking time in existing depth-first search generate-and-test
schemes such as Eclat/dEclat and PrePost+. Our technique allows infrequent
candidate itemsets to be detected early. The technique is based on an
early-stopping criterion and is general enough to be applicable in many
frequent itemset mining algorithms. We have applied the technique to two
TID-list based schemes (Eclat/dEclat) and one N-list based scheme (PrePost+).
Our technique has been tested over a variety of datasets and confirmed its
effectiveness in runtime reduction.
On the Uplink Achievable Rate of Massive MIMO System With Low-Resolution ADC and RF Impairments
Date: 2019-01-22 00:00:00
Tasks: Quantization

This paper considers channel estimation and uplink achievable rate of the
coarsely quantized massive multiple-input multiple-output (MIMO) system with
radio frequency (RF) impairments. We utilize additive quantization noise model
(AQNM) and extended error vector magnitude (EEVM) model to analyze the impacts
of low-resolution analog-to-digital converters (ADCs) and RF impairments
respectively. We show that hardware impairments cause a nonzero floor on the
channel estimation error, which contraries to the conventional case with ideal
hardware. The maximal-ratio combining (MRC) technique is then used at the
receiver, and an approximate tractable expression for the uplink achievable
rate is derived. The simulation results illustrate the appreciable
compensations between ADCs' resolution and RF impairments. The proposed studies
support the feasibility of equipping economical coarse ADCs and economical
imperfect RF components in practical massive MIMO systems.
Uncertainty Principle in Distributed MIMO Radars
Date: 2019-01-23 00:00:00
Tasks: Position

Radar uncertainty principle indicates that there is an inherent invariance in
the product of the time-delay and Doppler-shift measurement accuracy and
resolution which can be tuned by the waveform at transmitter. In this paper,
based on the radar uncertainty principle, a conceptual waveform design is
proposed for a distributed multiple-input multiple-output (MIMO) radar system
in order to improve the Cramer-Rao lower bound (CRLB) of the target position
and velocity. To this end, a non-convex band constrained optimization problem
is formulated, and a local and the global solution to the problem are obtained
by sequential quadratic programming (SQP) and particle swarm algorithms,
respectively. Numerical results are also included to illustrate the
effectiveness of the proposed mechanism on the CRLB of the target position and
velocity. By numerical results, it is also concluded that the global solution
to the optimization problem is obtained at a vertex of the bounding box.
DarwinML: A Graph-based Evolutionary Algorithm for Automated Machine Learning
Authors: Qi Fei, Xia Zhaohui, Tang Gaoyang, Yang Hang, Song Yu, Qian Guangrui, An Xiong, Lin Chunhuan, Shi Guangming
Date: 2018-11-20 00:00:00
Tasks: AutoML, BIG-bench Machine Learning

As an emerging field, Automated Machine Learning (AutoML) aims to reduce or
eliminate manual operations that require expertise in machine learning. In this
paper, a graph-based architecture is employed to represent flexible
combinations of ML models, which provides a large searching space compared to
tree-based and stacking-based architectures. Based on this, an evolutionary
algorithm is proposed to search for the best architecture, where the mutation
and heredity operators are the key for architecture evolution. With Bayesian
hyper-parameter optimization, the proposed approach can automate the workflow
of machine learning. On the PMLB dataset, the proposed approach shows the
state-of-the-art performance compared with TPOT, Autostacker, and auto-sklearn.
Some of the optimized models are with complex structures which are difficult to
obtain in manual design.
Accelerometer Based Method for Tire Load and Slip Angle Estimation
Date: 2019-01-23 00:00:00

Tire mounted sensors are emerging as a promising technology, capable of
providing information about important tire states. This paper presents a survey
of the state-of-the-art in the field of smart tire technology, with a special
focus on the different signal processing techniques proposed by researchers to
estimate the tire load and slip angle using tire mounted accelerometers. Next,
details about the research activities undertaken as part of this study to
develop a smart tire are presented. Finally, novel algorithms for estimating
the tire load and slip angle are presented. Experimental results demonstrate
the effectiveness of the proposed algorithms.
Decoding multimodal behavior using time differences of MEG events
Date: 2019-01-23 00:00:00
Tasks: feature selection

Multimodal behavior involves multiple processing stations distributed across
distant brain regions, but our understanding of how such distributed processing
is coordinated in the brain is limited. Here we take a decoding approach to
this problem, aiming to quantify how temporal aspects of brain-wide neural
activity may be used to infer specific multimodal behaviors. Using high
temporal resolution measurements by MEG, we detect bursts of activity from
hundreds of locations across the surface of the brain at millisecond
resolution. We then compare decoding using three characteristics of neural
activity bursts, decoding with event counts, with latencies and with time
differences between pairs of events. Training decoders in this regime is
particularly challenging because the number of samples is smaller by orders of
magnitude than the input dimensionality. We develop a new decoding approach for
this regime that combines non-parametric modelling with aggressive feature
selection. Surprisingly, we find that decoding using time-differences, based on
thousands of region pairs, is significantly more accurate than using other
activity characteristics, reaching 90% accuracy consistently across subjects.
These results suggest that relevant information about multimodal brain function
is provided by subtle time differences across remote brain areas.
Exploring the threshold of epidemic spreading for a stochastic SIR model with local and global contacts
Date: 2019-10-16 00:00:00

The spread of an epidemic process is considered in the context of a spatial
SIR stochastic model that includes a parameter $0\le p\le 1$ that assigns
weights $p$ and $1- p$ to global and local infective contacts respectively. The
model was previously studied by other authors in different contexts. In this
work we characterized the behavior of the system around the threshold for
epidemic spreading. We first used a deterministic approximation of the
stochastic model and checked the existence of a threshold value of $p$ for
exponential epidemic spread. An analytical expression, which defines a function
of the quotient $\alpha$ between the transmission and recovery rates, is
obtained to approximate this threshold. We then performed different analyses
based on intensive stochastic simulations and found that this expression is
also a good estimate for a similar threshold value of $p$ obtained in the
stochastic model. The dynamics of the average number of infected individuals
and the average size of outbreaks show a behavior across the threshold that is
well described by the deterministic approximation. The distributions of the
outbreak sizes at the threshold present common features for all the cases
considered corresponding to different values of $\alpha>1$. These features are
otherwise already known to hold for the standard stochastic SIR model at its
threshold, $\alpha=1$: (i) the probability of having an outbreak of size $n$
goes asymptotically as $n^{-3/2}$ for an infinite system, (ii) the maximal size
of an outbreak scales as $N^{2/3}$ for a finite system of size $N$.
The Economic Complexity of US Metropolitan Areas
Date: 2019-01-23 00:00:00

We calculate measures of economic complexity for US metropolitan areas for
the years 2007-2015 based on industry employment data. We show that the concept
of economic complexity translates well from the cross-country to the regional
setting, and is able to incorporate local as well as traded industries. The
largest cities and the Northeast of the US have the highest average complexity,
while traded industries are more complex than local-serving ones on average,
but with some exceptions. On average, regions with higher complexity have a
higher income per capita, but those regions also were more affected by the
financial crisis. Finally, economic complexity is a significant predictor of
within-decreases in income per capita and population. Our findings highlight
the importance of subnational regions, and particularly metropolitan areas, as
units of economic geography.
Imaging-free object recognition enabled by optical coherence
Date: 2019-01-23 00:00:00
Tasks: Object, Object Recognition

Visual object recognition is one of the most important perception functions
for a wide range of intelligent machines. A conventional recognition process
begins with forming a clear optical image of the object, followed by its
computer analysis. In contrast, it is possible to carry out recognition without
imaging by using coherent illumination and directly analyzing the optical
interference pattern of the scattered light as captured by an image sensor.
Here we show that such direct visual recognition can overcome traditional
limitations of imaging optics to realize excellent recognition without
focusing, beyond diffraction limit, or in the absence of direct line-of-sight.
Fixation in Fluctuating Populations
Date: 2019-05-23 00:00:00

We investigate the dynamics of the voter model in which the population itself
changes endogenously via the birth-death process. There are two species of
voters, labeled A and B, and the population of each species can grow or shrink
by the birth-death process at equal rates $b$. Individuals of opposite species
also undergo voter model dynamics in which an AB pair can equiprobably become
AA or BB with rate $v$---neutral evolution. In the limit $b/v\to\infty$, the
distribution of consensus times varies as $t^{-3}$ and the probability that the
population size equals $n$ at the moment of consensus varies as $n^{-3}$. As
the birth/death rate $b$ is increased, fixation occurs more more quickly; that
is, population fluctuations promote consensus.
The unbalanced reorganization of weaker functional connections induces the altered brain network topology in schizophrenia
Authors: Rossana Mastrandrea, Fabrizio Piras, Andrea Gabrielli, Nerisa Banaj, Guido Caldarelli, Gianfranco Spalletta, Tommaso Gili
Date: 2019-01-24 00:00:00

Network neuroscience shed some light on the functional and structural modifications occurring to the brain associated with the phenomenology of schizophrenia. In particular, resting-state functional networks have helped our understanding of the illness by highlighting the global and local alterations within the cerebral organization. We investigated the robustness of the brain functional architecture in forty-four medicated schizophrenic patients and forty healthy comparators through an advanced network analysis of resting-state functional magnetic resonance imaging data. The networks in patients showed more resistance to disconnection than in healthy controls, with an evident discrepancy between the two groups in the node degree distribution computed along a percolation process. Despite a substantial similarity of the basal functional organization between the two groups, the expected hierarchy of healthy brains modular organization is crumbled in schizophrenia, showing a peculiar arrangement of the functional connections, characterized by several topologically equivalent backbones.
Readership Data and Research Impact
Authors: Mohammadi Ehsan, Thelwall Mike
Date: 2019-01-24 00:00:00

Reading academic publications is a key scholarly activity. Scholars accessing
and recording academic publications online are producing new types of
readership data. These include publisher, repository, and academic social
network download statistics as well as online reference manager records. This
chapter discusses the use of download and reference manager data for research
evaluation and library collection development. The focus is on the validity and
application of readership data as an impact indicator for academic publications
across different disciplines. Mendeley is particularly promising in this
regard, although all data sources are not subjected to rigorous quality control
and can be manipulated.
Stochastic modeling of phenotypic switching and chemoresistance in cancer cell populations
Date: 2019-01-24 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Sensitivity

Phenotypic heterogeneity in cancer cells is widely observed and is often
linked to drug resistance. In several cases, such heterogeneity in drug
sensitivity of tumors is driven by stochastic and reversible acquisition of a
drug tolerant phenotype by individual cells even in an isogenic population.
Accumulating evidence further suggests that cell-fate transitions such as the
epithelial to mesenchymal transition (EMT) are associated with drug resistance.
In this study, we analyze stochastic models of phenotypic switching to provide
a framework for analyzing cell-fate transitions such as EMT as a source of
phenotypic variability in drug sensitivity. Motivated by our cell-culture based
experimental observations connecting phenotypic switching in EMT and drug
resistance, we analyze a coarse-grained model of phenotypic switching between
two states in the presence of cytotoxic stress from chemotherapy. We derive
analytical results for time-dependent probability distributions that provide
insights into the rates of phenotypic switching and characterize initial
phenotypic heterogeneity of cancer cells. The results obtained can also shed
light on fundamental questions relating to adaptation and selection scenarios
in tumor response to cytotoxic therapy.
Performance Analysis of Joint Transmission Schemes in Ultra-Dense Networks - An Unified Approach
Date: 2019-01-24 00:00:00

Ultra-dense network (UDN) is one of the enabling technologies to achieve
1000-fold capacity increase in 5G communication systems, and the application of
joint transmission (JT) is an effective method to deal with severe inter-cell
interferences in UDNs. However, most works done for performance analysis on JT
schemes in the literature were based largely on simulation results due to the
difficulties in quantitatively identifying the numbers of desired and
interfering transmitters. In this work, we are motivated to propose an
analytical approach to investigate the performance of JT schemes with a unified
approach based on stochastic geometry, which is in particular useful for
studying different JT methods and conventional transmission schemes without JT.
Using the proposed approach, we can unveil the statistic characteristics (i.e.,
expectation, moment generation function, variance) of desired signal and
interference powers of a given user equipment (UE), and thus system
performances, such as average signal-to-interference-plus-noise ratio (SINR),
and area spectral efficiency, can be evaluated analytically. The simulation
results are used to verify the effectiveness of the proposed unified approach.
Lattice investment projects support process model with corruption
Date: 2019-01-25 00:00:00
Tasks: Prehistory

Lattice investment projects support process model with corruption is
formulated and analyzed. The model is based on the Ising lattice model of
ferromagnetic but takes deal with the social phenomenon. Set of corruption
agents is considered. It is supposed that agents are placed in sites of the
lattice. Agents take decision about participation in corruption activity at
discrete moments of time. The decision may lead to profit or to loss. It
depends on prehistory of the system. Profit and its dynamics are defined by
stochastic Markov process. Stochastic nature of the process models influence of
external and individual factors on agents profits. The model is formulated
algorithmically and is studied by means of computer simulation. Numerical
results are given which demonstrate different asymptotic state of a corruption
network for various conditions of simulation.