pwc-archive/papers-with-abstracts huggingface dataset:
A Survey on Theoretical Advances of Community Detection in Networks
Authors: Zhao Yunpeng
Date: 2018-08-26 00:00:00
Tasks: Clustering, Community Detection, Model Selection, Survey

Real-world networks usually have community structure, that is, nodes are
grouped into densely connected communities. Community detection is one of the
most popular and best-studied research topics in network science and has
attracted attention in many different fields, including computer science,
statistics, social sciences, among others. Numerous approaches for community
detection have been proposed in literature, from ad-hoc algorithms to
systematic model-based approaches. The large number of available methods leads
to a fundamental question: whether a certain method can provide consistent
estimates of community labels. The stochastic blockmodel (SBM) and its variants
provide a convenient framework for the study of such problems. This article is
a survey on the recent theoretical advances of community detection. The authors
review a number of community detection methods and their theoretical
properties, including graph cut methods, profile likelihoods, the
pseudo-likelihood method, the variational method, belief propagation, spectral
clustering, and semidefinite relaxations of the SBM. The authors also briefly
discuss other research topics in community detection such as robust community
detection, community detection with nodal covariates and model selection, as
well as suggest a few possible directions for future research.
Geometric Local Variance Gamma model
Authors: Peter Carr, Andrey Itkin
Date: 2018-09-19 00:00:00
Tasks: model

This paper describes another extension of the Local Variance Gamma model originally proposed by P. Carr in 2008, and then further elaborated on by Carr and Nadtochiy, 2017 (CN2017), and Carr and Itkin, 2018 (CI2018). As compared with the latest version of the model developed in CI2018 and called the ELVG (the Expanded Local Variance Gamma model), here we provide two innovations. First, in all previous papers the model was constructed based on a Gamma time-changed {\it arithmetic} Brownian motion: with no drift in CI2017, and with drift in CI2018, and the local variance to be a function of the spot level only. In contrast, here we develop a {\it geometric} version of this model with drift. Second, in CN2017 the model was calibrated to option smiles assuming the local variance is a piecewise constant function of strike, while in CI2018 the local variance is a piecewise linear} function of strike. In this paper we consider 3 piecewise linear models: the local variance as a function of strike, the local variance as function of log-strike, and the local volatility as a function of strike (so, the local variance is a piecewise quadratic function of strike). We show that for all these new constructions it is still possible to derive an ordinary differential equation for the option price, which plays a role of Dupire's equation for the standard local volatility model, and, moreover, it can be solved in closed form. Finally, similar to CI2018, we show that given multiple smiles the whole local variance/volatility surface can be recovered which does not require solving any optimization problem. Instead, it can be done term-by-term by solving a system of non-linear algebraic equations for each maturity which is fast.
Harnessing Avidity: Quantifying Entropic and Energetic Effects of Linker Length and Rigidity Required for Multivalent Binding of Antibodies to HIV-1 Spikes
Date: 2019-05-22 00:00:00

Due to the low density of envelope (Env) spikes on the surface of HIV-1,
neutralizing IgG antibodies rarely bind bivalently using both antigen-binding
arms (Fabs) to crosslink between spikes (inter-spike crosslinking), instead
resorting to weaker monovalent binding that is more sensitive to Env mutations.
Synthetic antibodies designed to bivalently bind a single Env trimer
(intra-spike crosslinking) were previously shown to exhibit increased
neutralization potencies. In initial work, diFabs joined by varying lengths of
rigid double-stranded DNA (dsDNA) were considered. Anticipating future
experiments to improve synthetic antibodies, we investigate whether linkers
with different rigidities could enhance diFab potency by modeling DNA-Fabs
containing different combinations of rigid dsDNA and flexible single-stranded
DNA (ssDNA) and characterizing their neutralization potential. Model
predictions suggest that while a long flexible polymer may be capable of
bivalent binding, it exhibits weak neutralization due to the large loss in
entropic degrees of freedom when both Fabs are bound. In contrast, the
strongest neutralization potencies are predicted to require a rigid linker that
optimally spans the distance between two Fab binding sites on an Env trimer,
and avidity can be further boosted by incorporating more Fabs into these
constructs. These results inform the design of multivalent anti-HIV-1
therapeutics that utilize avidity effects to remain potent against HIV-1 in the
face of the rapid mutation of Env spikes.
Shapley-like values without symmetry
Date: 2019-05-10 00:00:00

Following the work of Lloyd Shapley on the Shapley value, and tangentially
the work of Guillermo Owen, we offer an alternative non-probabilistic
formulation of part of the work of Robert J. Weber in his 1978 paper
"Probabilistic values for games." Specifically, we focus upon efficient but not
symmetric allocations of value for cooperative games. We retain standard
efficiency and linearity, and offer an alternative condition, "reasonableness,"
to replace the other usual axioms. In the pursuit of the result, we discover
properties of the linear maps that describe the allocations. This culminates in
a special class of games for which any other map that is "reasonable,
efficient" can be written as a convex combination of members of this special
class of allocations, via an application of the Krein-Milman theorem.
Stationary frequencies and mixing times for neutral drift processes with spatial structure
Date: 2018-09-20 00:00:00

We study a general setting of neutral evolution in which the population is of
finite, constant size and can have spatial structure. Mutation leads to
different genetic types ("traits"), which can be discrete or continuous. Under
minimal assumptions, we show that the marginal trait distributions of the
evolutionary process, which specify the probability that any given individual
has a certain trait, all converge to the stationary distribution of the
mutation process. In particular, the stationary frequencies of traits in the
population are independent of its size, spatial structure, and evolutionary
update rule, and these frequencies can be calculated by evaluating a simple
stochastic process describing a population of size one (i.e. the mutation
process itself). We conclude by analyzing mixing times, which characterize
rates of convergence of the mutation process along the lineages, in terms of
demographic variables of the evolutionary process.
A Millimeter Wave MIMO Testbed for 5G Communications
Date: 2018-09-19 00:00:00

This paper presents a 2 x 2 millimeter wave (mm-wave)
multiple-input-multiple-output (MIMO) testbed that operates at around 30 GHz.
The link assessment of the system operating at 26.25 GHz was carried out on a
test bench, with a short communication distance between the transmitting and
receiving antennas. A user-programmable, reconfigurable and real-time signal
processing field-programmable gate arrays (FPGAs)-based software defined radio
(SDR) system was employed as part of the testbed to validate the system-level
performance for a downlink time division long-term evolution (TD-LTE) duplex
scheme. Constellation diagram for quadrature phase shift keying (QPSK) digital
modulation were acquired while the testbed was operating at 30 GHz. The testbed
could be employed for the development of signal test, communication algorithm
and measurement metrology for 5G communications.
Prediction of SINR using BER and EVM for Massive MIMO Applications
Date: 2018-09-19 00:00:00

Future communication systems employing massive multiple input multiple output
will not have the ability to use channel state information at the mobile user
terminals. Instead, it will be necessary for such devices to evaluate the
downlink signal to interference and noise ratio (SINR) with interference both
from the base station serving other users within the same cell and other base
stations from adjacent cells. The SINR will act as an indicator of how well the
precoders have been applied at the base station. The results presented in this
paper from a 32 x 3 massive MIMO channel sounder measurement campaign at 2.4
GHz show how the received bit error rate and error vector magnitudes can be
used to obtain a prediction of both the average and dynamically changing SINR.
Efficient Uncertainty Evaluation of Vector Network Analyser Measurements Using Two-Tier Bayesian Analysis and Monte Carlo Method
Date: 2018-09-19 00:00:00

Antennas are a key element in any communication system and vector network
analyser (VNA) is popular tool for charactering antenna impedance bandwidth. In
this paper, an efficient uncertainty evaluation method is proposed for VNA
measurement based on its uncertainty propagation mechanism using Bayesian
analysis and Monte Carlo method. The proposed method is generic and can be
applied to VNA with arbitrary number of ports. In order to obtain the complete
information of measurement uncertainty distribution, a two-tier Bayesian
analytic process is carried out. The proposed method contains three steps. In
the first step, the posterior distribution of each uncertainty source of VNA
calibrations is deduced by the use of prior and current sample information
through the first-tier Bayesian analysis. In the second step, the obtained
posterior distributions of uncertainty sources are taken into the Monte Carlo
simulation of one-port VNA measurement uncertainties. In the last step, the
results obtained in the second step are used as the prior distribution of the
secondary Bayesian evaluation, then the evaluation results of the measurement
uncertainty can be obtained with the means, variances and skewness of the
probabilistic distribution. The numerical analysis using an antenna measurement
results demonstrate the high-efficiency and reliability of this proposed
method.
Empty Substrate Integrated Waveguide-Fed MMW Aperture-Coupled Patch Antenna for 5G Applications
Date: 2018-09-19 00:00:00

This paper introduces a microstrip square patch antenna with a novel low-cost
and efficient feeding mechanism for millimetre-wave frequencies targeting at 28
GHz. The radiating patch is excited by the technique of empty substrate
integrated waveguide, and two different slot configurations have been examined
in this regard. It has been observed that the antenna gain profile and
radiation efficiency is significantly improved in both configurations as
compared to the microstrip square patch antenna provided with conventional
feeding.
Single Layer PCB Broadband Circular Polarisation Millimetre Wave Massive MIMO Array
Date: 2018-09-19 00:00:00

A single layer broadband millimetre wave (mm-wave) array with circular
polarisation is proposed for the fifth generation (5G) mm-wave communication
applications. The antenna element is designed to integrate the matching circuit
into a single layer. Hence no additional matching and feeding networks are
required which makes the antenna element very easy to synthesise a massive
array. The array has a 10 dB return loss bandwidth between 26 GHz and 32 GHz.
The polar angle axial ratio is less than 3 within the beamwidth of -60 degree
to 60 degree. The proposed antenna structure is simple, low cost and robust,
which we envisage suitable for the massive multiple-input and multiple-output
(MIMO) applications.
An Evaluation of Distortion and Interference Sources originating Within a Millimeter-wave MIMO Testbed for 5G Communications
Date: 2018-09-19 00:00:00

This paper presents an evaluation of distortion and interference sources,
namely, the harmonic distortion and antenna crosstalk, originating within a 2 x
2 millimeter-wave (mm-wave) multiple-input-multiple-output (MIMO) testbed. The
experience gained through the insight into the built testbed could be fed into
the design of future mm-wave massive MIMO testbeds.
A rigorous link performance and measurement uncertainty assessment for MIMO OTA characterisation
Date: 2018-09-19 00:00:00

In this paper, a rigorous link performance test for MIMO OTA
(multiple-input-multiple-output over-the-air) characterisation with traceable
signal-to-interference-plus-noise ratio (SINR) is presented. Measurements were
made with three different testbeds which represent the 4G (fourth-generation)
LTE (long term evolution) SISO (single-input-single-output), 4G LTE MIMO and 5G
(fifth-generation) millimetre-wave (mm-wave) MIMO communication systems,
respectively, in the small antenna radiated test (SMART) screened fully
anechoic chamber, screened control room and reverberation chamber at the UK
National Physical Laboratory (NPL). The measurement campaign comprised of
automated data acquisition of channel power, downlink (DL) & uplink (UL) error
vector magnitude (EVM) and throughput. The measurement repeatability has been
assessed with standard deviation plotted as error bars and the uncertainty
sources in the OTA test are analysed.
A comparison of MIMO antenna efficiency measurements performed in Anechoic Chamber and Reverberation Chamber
Date: 2018-09-19 00:00:00

Multiple-input-multiple-output (MIMO) antenna will play a key role in the
development of fifth generation (5G) wireless mobile communication systems due
to their performance-enhancement capability in multipath environment. Antenna
radiation efficiency is an important parameter for MIMO antenna system. In this
paper, we present a comparison of MIMO antenna efficiency measurements
performed in Anechoic Chamber (AC) and Reverberation Chamber (RC) at the UK
National Physical Laboratory. Two commercial available directional dual
polarized full LTE band MIMO antennas were measured both in AC and RC between 1
GHz and 3 GHz.
Decentralized dynamic task allocation for UAVs with limited communication range
Authors: Pujol-Gonzalez Marc, Cerquides Jesus, Meseguer Pedro, Rodriguez-Aguilar Juan A., Tambe Milind
Date: 2018-08-31 00:00:00

We present the Limited-range Online Routing Problem (LORP), which involves a
team of Unmanned Aerial Vehicles (UAVs) with limited communication range that
must autonomously coordinate to service task requests. We first show a general
approach to cast this dynamic problem as a sequence of decentralized task
allocation problems. Then we present two solutions both based on modeling the
allocation task as a Markov Random Field to subsequently assess decisions by
means of the decentralized Max-Sum algorithm. Our first solution assumes
independence between requests, whereas our second solution also considers the
UAVs' workloads. A thorough empirical evaluation shows that our workload-based
solution consistently outperforms current state-of-the-art methods in a wide
range of scenarios, lowering the average service time up to 16%. In the
best-case scenario there is no gap between our decentralized solution and
centralized techniques. In the worst-case scenario we manage to reduce by 25%
the gap between current decentralized and centralized techniques. Thus, our
solution becomes the method of choice for our problem.
State-dependent Hawkes processes and their application to limit order book modelling
Authors: Maxime Morariu-Patrichi, Mikko S. Pakkanen
Date: 2018-09-21 00:00:00

We study statistical aspects of state-dependent Hawkes processes, which are an extension of Hawkes processes where a self- and cross-exciting counting process and a state process are fully coupled, interacting with each other. The excitation kernel of the counting process depends on the state process that, reciprocally, switches state when there is an event in the counting process. We first establish the existence and uniqueness of state-dependent Hawkes processes and explain how they can be simulated. Then we develop maximum likelihood estimation methodology for parametric specifications of the process. We apply state-dependent Hawkes processes to high-frequency limit order book data, allowing us to build a novel model that captures the feedback loop between the order flow and the shape of the limit order book. We estimate two specifications of the model, using the bid-ask spread and the queue imbalance as state variables, and find that excitation effects in the order flow are strongly state-dependent. Additionally, we find that the endogeneity of the order flow, measured by the magnitude of excitation, is also state-dependent, being more pronounced in disequilibrium states of the limit order book.
Short-term Cognitive Networks, Flexible Reasoning and Nonsynaptic Learning
Authors: Nápoles Gonzalo, Vanhoenshoven Frank, Vanhoof Koen
Date: 2018-09-16 00:00:00

While the machine learning literature dedicated to fully automated reasoning
algorithms is abundant, the number of methods enabling the inference process on
the basis of previously defined knowledge structures is scanter. Fuzzy
Cognitive Maps (FCMs) are neural networks that can be exploited towards this
goal because of their flexibility to handle external knowledge. However, FCMs
suffer from a number of issues that range from the limited prediction horizon
to the absence of theoretically sound learning algorithms able to produce
accurate predictions. In this paper, we propose a neural network system named
Short-term Cognitive Networks that tackle some of these limitations. In our
model weights are not constricted and may have a causal nature or not. As a
second contribution, we present a nonsynaptic learning algorithm to improve the
network performance without modifying the previously defined weights. Moreover,
we derive a stop condition to prevent the learning algorithm from iterating
without decreasing the simulation error.
PMU based Monitoring and Mitigation of Delayed Voltage Recovery using Admittances
Date: 2018-09-17 00:00:00

This paper analyzes the delayed voltage recovery phenomenon by simplifying
the Western Electricity Coordinating Council (WECC) composite load model using
the load admittance and the thermal relay dynamics. From this analysis, a
closed form expression approximating the recovery time is derived and the key
load parameters impacting the behavior of voltage recovery are identified. A
monitoring scheme based on the measured load admittance is then proposed to
detect the onset of stalling, even in the presence of voltage oscillations, and
estimate the duration of the delayed voltage recovery. A mitigation scheme
utilizing smart thermostats and offline learning is also derived to ensure that
the voltage recovers to the pre-contingency voltage within a specified time.
Both the monitoring and mitigation schemes only need local measurements at a
substation making them promising for online applications. Results for the
monitoring and mitigation schemes are described in detail for the IEEE 162 bus
system validating the various assumptions used for the analysis and
establishing the connection between the delayed voltage recovery phenomenon and
load admittance.
A Game of Tax Evasion: evidences from an agent-based model
Date: 2018-09-21 00:00:00

This paper presents a simple agent-based model of an economic system,
populated by agents playing different games according to their different view
about social cohesion and tax payment. After a first set of simulations,
correctly replicating results of existing literature, a wider analysis is
presented in order to study the effects of a dynamic-adaptation rule, in which
citizens may possibly decide to modify their individual tax compliance
according to individual criteria, such as, the strength of their ethical
commitment, the satisfaction gained by consumption of the public good and the
perceived opinion of neighbors. Results show the presence of thresholds levels
in the composition of society - between taxpayers and evaders - which explain
the extent of damages deriving from tax evasion.
Sampler Design for Bayesian Personalized Ranking by Leveraging View Data
Authors: Ding Jingtao, Yu Guanghui, He Xiangnan, Li Yong, Jin Depeng
Date: 2018-09-21 00:00:00

Bayesian Personalized Ranking (BPR) is a representative pairwise learning
method for optimizing recommendation models. It is widely known that the
performance of BPR depends largely on the quality of negative sampler. In this
paper, we make two contributions with respect to BPR. First, we find that
sampling negative items from the whole space is unnecessary and may even
degrade the performance. Second, focusing on the purchase feedback of
E-commerce, we propose an effective sampler for BPR by leveraging the
additional view data. In our proposed sampler, users' viewed interactions are
considered as an intermediate feedback between those purchased and unobserved
interactions. The pairwise rankings of user preference among these three types
of interactions are jointly learned, and a user-oriented weighting strategy is
considered during learning process, which is more effective and flexible.
Compared to the vanilla BPR that applies a uniform sampler on all candidates,
our view-enhanced sampler enhances BPR with a relative improvement over 37.03%
and 16.40% on two real-world datasets. Our study demonstrates the importance of
considering users' additional feedback when modeling their preference on
different items, which avoids sampling negative items indiscriminately and
inefficiently.
A model of cortical cognitive function using hierarchical interactions of gating matrices in internal agents coding relational representations
Date: 2018-10-15 00:00:00

Flexible cognition requires the ability to rapidly detect systematic
functions of variables and guide future behavior based on predictions. The
model described here proposes a potential framework for patterns of neural
activity to detect systematic functions and relations between components of
sensory input and apply them in a predictive manner. This model includes
multiple internal gating agents that operate within the state space of neural
activity, in analogy to external agents behaving in the external environment.
The multiple internal gating agents represent patterns of neural activity that
detect and gate patterns of matrix connectivity representing the relations
between different neural populations. The patterns of gating matrix
connectivity represent functions that can be used to predict future components
of a series of sensory inputs or the relationship between different features of
a static sensory stimulus. The model is applied to the prediction of dynamical
trajectories, the internal relationship between features of different sensory
stimuli and to the prediction of affine transformations that could be useful
for solving cognitive tasks such as the Ravens progressive matrices task.