pwc-archive/papers-with-abstracts huggingface dataset:
FNS: an event-driven spiking neural network simulator based on the LIFL neuron model
Date: 2020-07-16 00:00:00

Limitations in processing capabilities and memory of today's computers make
spiking neuron-based (human) whole-brain simulations inevitably characterized
by a compromise between bio-plausibility and computational cost. It translates
into brain models composed of a reduced number of neurons and a simplified
neuron's mathematical model, leading to the search for new simulation
strategies. Taking advantage of the sparse character of brain-like computation,
the event-driven technique could represent a way to carry out efficient
simulation of large-scale Spiking Neural Networks (SNN). The recent Leaky
Integrate-and-Fire with Latency (LIFL) spiking neuron model is event-driven
compatible and exhibits some realistic neuronal features, opening new avenues
for brain modelling. In this paper we introduce FNS, the first LIFL-based
spiking neural network framework, which combines spiking/synaptic neural
modelling with the event-driven approach, allowing us to define heterogeneous
neuron modules and multi-scale connectivity with delayed connections and
plastic synapses. In order to allow multi-thread implementations a novel
parallelization strategy is also introduced. This paper presents mathematical
models, software implementation and simulation routines on which FNS is based.
Finally, a brain subnetwork is modeled on the basis of real brain structural
data, and the resulting simulated activity is compared with associated brain
functional (source-space MEG) data, demonstrating a good matching between the
activity of the model and that of the experimetal data. This work aims to lay
the groundwork for future event-driven based personalised brain models.
Simple Explicit Formula for Near-Optimal Stochastic Lifestyling
Authors: Aleš Černý, Igor Melicherčík
Date: 2018-01-03 00:00:00

In life-cycle economics the Samuelson paradigm (Samuelson, 1969) states that the optimal investment is in constant proportions out of lifetime wealth composed of current savings and the present value of future income. It is well known that in the presence of credit constraints this paradigm no longer applies. Instead, optimal lifecycle investment gives rise to so-called stochastic lifestyling (Cairns et al., 2006), whereby for low levels of accumulated capital it is optimal to invest fully in stocks and then gradually switch to safer assets as the level of savings increases. In stochastic lifestyling not only does the ratio between risky and safe assets change but also the mix of risky assets varies over time. While the existing literature relies on complex numerical algorithms to quantify optimal lifestyling the present paper provides a simple formula that captures the main essence of the lifestyling effect with remarkable accuracy.
Comparing the Forecasting Performances of Linear Models for Electricity Prices with High RES Penetration
Date: 2019-11-12 00:00:00

This paper compares alternative univariate versus multivariate models,
frequentist versus Bayesian autoregressive and vector autoregressive
specifications, for hourly day-ahead electricity prices, both with and without
renewable energy sources. The accuracy of point and density forecasts are
inspected in four main European markets (Germany, Denmark, Italy and Spain)
characterized by different levels of renewable energy power generation. Our
results show that the Bayesian VAR specifications with exogenous variables
dominate other multivariate and univariate specifications, in terms of both
point and density forecasting.
Multiplication with Fourier Optics Simulating 16-bit Modular Multiplication
Date: 2018-10-19 00:00:00

This paper will describe a simulator developed by the authors to explore the
design of Fourier transform based multiplication using optics. Then it will
demonstrate an application to the problem of constructing an all-optical
modular multiplication circuit. That circuit implements a novel approximate
version of the Montgomery multiplication algorithm that enables the calculation
to be performed entirely in the analog domain. The results will be used to
corroborate the feasibility of scaling the design up to 16-bits without the
need for analog to digital conversions at intermediate steps.
Effect of Inhibitory Spike-Timing-Dependent Plasticity on Fast Sparsely Synchronized Rhythms in A Small-World Neuronal Network
Date: 2018-05-14 00:00:00

We consider the Watts-Strogatz small-world network (SWN) consisting of
inhibitory fast spiking Izhikevich interneurons. This inhibitory neuronal
population has adaptive dynamic synaptic strengths governed by the inhibitory
spike-timing-dependent plasticity (iSTDP). In previous works without iSTDP,
fast sparsely synchronized rhythms, associated with diverse cognitive
functions, were found to appear in a range of large noise intensities for fixed
strong synaptic inhibition strengths. Here, we investigate the effect of iSTDP
on fast sparse synchronization (FSS) by varying the noise intensity $D$. We
employ an asymmetric anti-Hebbian time window for the iSTDP update rule [which
is in contrast to the Hebbian time window for the excitatory STDP (eSTDP)].
Depending on values of $D$, population-averaged values of saturated synaptic
inhibition strengths are potentiated [long-term potentiation (LTP)] or
depressed [long-term depression (LTD)] in comparison with the initial mean
value, and dispersions from the mean values of LTP/LTD are much increased when
compared with the initial dispersion, independently of $D$. In most cases of
LTD where the effect of mean LTD is dominant in comparison with the effect of
dispersion, good FSS (with higher spiking measure) is found to get better via
LTD, while bad FSS (with lower spiking measure) is found to get worse via LTP.
This kind of Matthew effect in inhibitory synaptic plasticity is in contrast to
that in excitatory synaptic plasticity where good (bad) synchronization gets
better (worse) via LTP (LTD). Emergences of LTD and LTP of synaptic inhibition
strengths are intensively investigated via a microscopic method based on the
distributions of time delays between the pre- and the post-synaptic spike
times. Furthermore, we also investigate the effects of network architecture on
FSS by changing the rewiring probability $p$ of the SWN in the presence of
iSTDP.
Computational Analysis for the Rational Design of Anti-Amyloid Beta (ABeta) Antibodies
Date: 2018-02-22 00:00:00
Tasks: Drug Design

Alzheimer's Disease (AD) is a neurodegenerative disorder that lacks effective
treatment options. Anti-amyloid beta (ABeta) antibodies are the leading drug
candidates to treat AD, but the results of clinical trials have been
disappointing. Introducing rational mutations into anti-ABeta antibodies to
increase their effectiveness is a way forward, but the path to take is unclear.
In this study, we demonstrate the use of computational fragment-based docking
and MMPBSA binding free energy calculations in the analysis of anti-ABeta
antibodies for rational drug design efforts. Our fragment-based docking method
successfully predicted the emergence of the common EFRH epitope, MD simulations
coupled with MMPBSA binding free energy calculations were used to analyze
scenarios described in prior studies, and we introduced rational mutations into
PFA1 to improve its calculated binding affinity towards the pE3-ABeta3-8 form
of ABeta. Two out of four proposed mutations stabilized binding. Our study
demonstrates that a computational approach may lead to an improved drug
candidate for AD in the future.
Diffusion Leaky Zero Attracting Least Mean Square Algorithm and Its Performance Analysis
Date: 2018-10-29 00:00:00

Recently, the leaky diffusion least-mean-square (DLMS) algorithm has obtained
much attention because of its good performance for high input eigenvalue spread
and low signal-to-noise ratio (SNR). However, the leaky DLMS algorithm may
suffer from performance deterioration in the sparse system. To overcome this
drawback, the leaky zero attracting DLMS (LZA-DLMS) algorithm is developed in
this paper, which adds an l1-norm penalty to the cost function to exploit the
property of sparse system. The leaky reweighted zero attracting DLMS
(LRZA-DLMS) algorithm is also put forward, which can improve the estimation
performance in the presence of time-varying sparsity. Instead of using the
l1-norm penalty, in the reweighted version, a log-sum function is employed as
the substitution. Based on the weight error variance relation and several
common assumptions, we analyze the transient behavior of our findings and
determine the stability bound of the step-size. Moreover, we implement the
steady state theoretical analysis for the proposed algorithms. Simulations in
the context of distributed network system identification illustrate that the
proposed schemes outperform various existing algorithms and validate the
accuracy of the theoretical results.
Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons
Date: 2018-02-22 00:00:00

Correlations in sensory neural networks have both extrinsic and intrinsic
origins. Extrinsic or stimulus correlations arise from shared inputs to the
network, and thus depend strongly on the stimulus ensemble. Intrinsic or noise
correlations reflect biophysical mechanisms of interactions between neurons,
which are expected to be robust to changes of the stimulus ensemble. Despite
the importance of this distinction for understanding how sensory networks
encode information collectively, no method exists to reliably separate
intrinsic interactions from extrinsic correlations in neural activity data,
limiting our ability to build predictive models of the network response. In
this paper we introduce a general strategy to infer {population models of
interacting neurons that collectively encode stimulus information}. The key to
disentangling intrinsic from extrinsic correlations is to infer the {couplings
between neurons} separately from the encoding model, and to combine the two
using corrections calculated in a mean-field approximation. We demonstrate the
effectiveness of this approach on retinal recordings. The same coupling network
is inferred from responses to radically different stimulus ensembles, showing
that these couplings indeed reflect stimulus-independent interactions between
neurons. The inferred model predicts accurately the collective response of
retinal ganglion cell populations as a function of the stimulus.
Maximum entropy models reveal the excitatory and inhibitory correlation structures in cortical neuronal activity
Date: 2018-07-10 00:00:00

Maximum Entropy models can be inferred from large data-sets to uncover how
collective dynamics emerge from local interactions. Here, such models are
employed to investigate neurons recorded by multielectrode arrays in the human
and monkey cortex. Taking advantage of the separation of excitatory and
inhibitory neuron types, we construct a model including this distinction. This
approach allows to shed light upon differences between excitatory and
inhibitory activity across different brain states such as wakefulness and deep
sleep, in agreement with previous findings. Additionally, Maximum Entropy
models can also unveil novel features of neuronal interactions, which are found
to be dominated by pairwise interactions during wakefulness, but are
population-wide during deep sleep. In particular, inhibitory neurons are
observed to be strongly tuned to the inhibitory population. Overall, we
demonstrate Maximum Entropy models can be useful to analyze data-sets with
classified neuron types, and to reveal the respective roles of excitatory and
inhibitory neurons in organizing coherent dynamics in the cerebral cortex.
Root Mean Square Minimum Distance as a Quality Metric for Localization Nanoscopy Images
Date: 2018-11-22 00:00:00

A localization algorithm in stochastic optical localization nanoscopy plays
an important role in obtaining a high-quality image. A universal and objective
metric is crucial and necessary to evaluate qualities of nanoscopy images and
performances of localization algorithms. In this paper, we propose root mean
square minimum distance (RMSMD) as a quality metric for localization nanoscopy
images. RMSMD measures an average, local, and mutual fitness between two sets
of points. Its properties common to a distance metric as well as unique to
itself are presented. The ambiguity, discontinuity, and inappropriateness of
the metrics of accuracy, precision, recall, and Jaccard index, which are
currently used in the literature, are analyzed. A numerical example
demonstrates the advantages of RMSMD over the four existing metrics that fail
to distinguish qualities of different nanoscopy images in certain conditions.
The unbiased Gaussian estimator that achieves the Fisher information and
Cramer-Rao lower bound (CRLB) of a single data frame is proposed to benchmark
the quality of localization nanoscopy images and the performance of
localization algorithms. The information-achieving estimator is simulated in an
example and the result demonstrates the superior sensitivity of RMSMD over the
other four metrics. As a universal and objective metric, RMSMD can be broadly
employed in various applications to measure the mutual fitness of two sets of
points.
Multiplex core-periphery organization of the human connectome
Date: 2017-12-23 00:00:00
Tasks: Functional Connectivity

The behavior of many complex systems is determined by a core of densely
interconnected units. While many methods are available to identify the core of
a network when connections between nodes are all of the same type, a principled
approach to define the core when multiple types of connectivity are allowed is
still lacking. Here we introduce a general framework to define and extract the
core-periphery structure of multi-layer networks by explicitly taking into
account the connectivity of the nodes at each layer. We show how our method
works on synthetic networks with different size, density, and overlap between
the cores at the different layers. We then apply the method to multiplex brain
networks whose layers encode information both on the anatomical and the
functional connectivity among regions of the human cortex. Results confirm the
presence of the main known hubs, but also suggest the existence of novel brain
core regions that have been discarded by previous analysis which focused
exclusively on the structural layer. Our work is a step forward in the
identification of the core of the human connectome, and contributes to shed
light to a fundamental question in modern neuroscience.
Learning from Neighbors about a Changing State
Authors: Krishna Dasaratha, Benjamin Golub, Nir Hak
Date: 2018-01-06 00:00:00
Tasks: Diversity

Agents learn about a changing state using private signals and their neighbors' past estimates of the state. We present a model in which Bayesian agents in equilibrium use neighbors' estimates simply by taking weighted sums with time-invariant weights. The dynamics thus parallel those of the tractable DeGroot model of learning in networks, but arise as an equilibrium outcome rather than a behavioral assumption. We examine whether information aggregation is nearly optimal as neighborhoods grow large. A key condition for this is signal diversity: each individual's neighbors have private signals that not only contain independent information, but also have sufficiently different distributions. Without signal diversity $\unicode{x2013}$ e.g., if private signals are i.i.d. $\unicode{x2013}$ learning is suboptimal in all networks and highly inefficient in some. Turning to social influence, we find it is much more sensitive to one's signal quality than to one's number of neighbors, in contrast to standard models with exogenous updating rules.
Mean squared displacement and sinuosity of three-dimensional random search movements
Authors: Simon Benhamou
Date: 2018-01-08 00:00:00

Correlated random walks (CRW) have been used for a long time as a null model for animal's random search movement in two dimensions (2D). An increasing number of studies focus on animals' movement in three dimensions (3D), but the key properties of CRW, such as the way the mean squared displacement is related to the path length, are well known only in 1D and 2D. In this paper I derive such properties for 3D CRW, in a consistent way with the expression of these properties in 2D. This should allow 3D CRW to act as a null model when analyzing actual 3D movements similarly to what is done in 2D
The Gaussian Noise Model in the Presence of Inter-channel Stimulated Raman Scattering
Date: 2017-12-31 00:00:00

A Gaussian noise (GN) model is presented that properly accounts for an
arbitrary frequency dependent signal power profile along the link. This enables
the evaluation of the impact of inter-channel stimulated Raman scattering
(ISRS) on the optical Kerr nonlinearity. Additionally, the frequency dependent
fiber attenuation can be taken into account and transmission systems that use
hybrid amplification schemes can be modeled, where distributed Raman
amplification is partly applied over the optical spectrum. To include the
latter two cases, a set of coupled ordinary differential equations must be
numerically solved in order to obtain the signal power profile yielding a
semi-analytical model. However for lumped amplification and negligible
variation in fiber attenuation, a less complex and fully analytical model is
presented which is referred to as the ISRS GN model. The derived model is exact
to first-order for Gaussian modulated signals and extensively validated by
numerical split-step simulations. A maximum deviation of $0.1$~dB in nonlinear
interference power between simulations and the ISRS GN model is found. The
model is applied to a transmission system that occupies an optical bandwidth of
$10$~THz, representing the entire C+L band. At optimum launch power, changes of
up to $2$~dB in nonlinear interference power due to ISRS are reported.
Furthermore, comparable models published in the literature are benchmarked
against the ISRS GN model.
From fixation probabilities to d-player games: an inverse problem in evolutionary dynamics
Date: 2018-11-20 00:00:00
Tasks: Math, Unity

The probability that the frequency of a particular trait will eventually
become unity, the so-called fixation probability, is a central issue in the
study of population evolution. Its computation, once we are given a stochastic
finite population model without mutations and a (possibly frequency dependent)
fitness function, is straightforward and it can be done in several ways.
Nevertheless, despite the fact that the fixation probability is an important
macroscopic property of the population, its precise knowledge does not give any
clear information about the interaction patterns among individuals in the
population. Here we address the inverse problem: From a given fixation pattern
and population size, we want to infer what is the game being played by the
population. This is done by first exploiting the framework developed in FACC
Chalub and MO Souza, J. Math. Biol. 75: 1735, 2017., which yields a fitness
function that realises this fixation pattern in the Wright-Fisher model. This
fitness function always exists, but it is not necessarily unique. Subsequently,
we show that any such fitness function can be approximated, with arbitrary
precision, using $d$-player game theory, provided $d$ is large enough. The
pay-off matrix that emerges naturally from the approximating game will provide
useful information about the individual interaction structure that is not
itself apparent in the fixation pattern. We present extensive numerical support
for our conclusions.
Web2Text: Deep Structured Boilerplate Removal
Authors: Vogels Thijs, Ganea Octavian-Eugen, Eickhoff Carsten
Date: 2018-03-27 00:00:00
Tasks: Information Retrieval, Retrieval

Web pages are a valuable source of information for many natural language
processing and information retrieval tasks. Extracting the main content from
those documents is essential for the performance of derived applications. To
address this issue, we introduce a novel model that performs sequence labeling
to collectively classify all text blocks in an HTML page as either boilerplate
or main content. Our method uses a hidden Markov model on top of potentials
derived from DOM tree features using convolutional neural networks. The
proposed method sets a new state-of-the-art performance for boilerplate removal
on the CleanEval benchmark. As a component of information retrieval pipelines,
it improves retrieval performance on the ClueWeb12 collection.
Symbolic relative entropy in quantifying nonlinear dynamics of equalities-involved heartbeats
Date: 2019-01-28 00:00:00

Symbolic relative entropy, an efficient nonlinear complexity parameter
measuring probabilistic divergences of symbolic sequences, is proposed in our
nonlinear dynamics analysis of heart rates considering equal states. Equalities
are not rare in discrete heartbeats because of the limits of resolution of
signals collection, and more importantly equal states contain underlying
important cardiac regulation information which is neglected by some chaotic
deterministic parameters and temporal asymmetric measurements. The relative
entropy of symbolization associated with equal states has satisfied nonlinear
dynamics complexity detections in heartbeats and shows advantages to some
nonlinear dynamics parameters without considering equalities. Researches on
cardiac activities suggest the highest probabilistic divergence of the healthy
young heart rates and highlight the facts that heart diseases and aging reduce
the nonlinear dynamical complexity of heart rates.
Diversification, economies of scope, and exports growth of Chinese firms
Date: 2018-01-23 00:00:00

In the 1990s, China started a process of structural reforms and of trade
liberalization, which was followed by the accession to the World Trade
Organization (WTO) in 2001. In this paper, we analyze trade patterns of Chinese
firms for the period 2000-2006, characterized by a notable increase in exports
volumes. Theoretically, in a more open economy, firms are expected to move from
the production of a set of less-competitive products towards more
internationally competitive ones, which implies specialization. We study
several stylized facts on the distribution of Chinese firms trade and growth
rates, and we analyze whether firms have diversified or specialized their trade
patterns between 2000 and 2006. We show that Chinese export patterns are very
heterogeneous, that the volatility of growth rates depends on the level of
exports, and that volatility is stronger after trade liberalization. Both,
diversification in products and destinations have a positive impact on trade
growth, but diversification of destinations has a stronger effect. We conclude
that the success of Chinese exports is not only due to an increase in the
intensive margin, related to the existence of economies of scale, but also due
to an increase in the extensive margin, related to the existence of economies
of scope.
Revealed Price Preference: Theory and Empirical Analysis
Authors: Rahul Deb, Yuichi Kitamura, John K. -H. Quah, Jörg Stoye
Date: 2018-01-08 00:00:00
Tasks: counterfactual, Relation

To determine the welfare implications of price changes in demand data, we introduce a revealed preference relation over prices. We show that the absence of cycles in this relation characterizes a consumer who trades off the utility of consumption against the disutility of expenditure. Our model can be applied whenever a consumer's demand over a strict subset of all available goods is being analyzed; it can also be extended to settings with discrete goods and nonlinear prices. To illustrate its use, we apply our model to a single-agent data set and to a data set with repeated cross-sections. We develop a novel test of linear hypotheses on partially identified parameters to estimate the proportion of the population who are revealed better off due to a price change in the latter application. This new technique can be used for nonparametric counterfactual analysis more broadly.