pwc-archive/papers-with-abstracts huggingface dataset:
Surface reaction-diffusion kinetics on lattice at the microscopic scale
Authors: Wei-Xiang Chew, Kazunari Kaizu, Masaki Watabe, Sithi V. Muniandy, Koichi Takahashi, Satya N. V. Arjunan
Date: 2018-11-17 00:00:00

Microscopic models of reaction-diffusion processes on the cell membrane can link local spatiotemporal effects to macroscopic self-organized patterns often observed on the membrane. Simulation schemes based on the microscopic lattice method (MLM) can model these processes at the microscopic scale by tracking individual molecules, represented as hard-spheres, on fine lattice voxels. Although MLM is simple to implement and is generally less computationally demanding than off-lattice approaches, its accuracy and consistency in modeling surface reactions have not been fully verifed. Using the Spatiocyte scheme, we study the accuracy of MLM in diffusion-influenced surface reactions. We derive the lattice-based bimolecular association rates for two-dimensional surface-surface reaction and one-dimensional volume-surface adsorption according to the Smoluchowski-Collins-Kimball model and random walk theory. We match the time-dependent rates on lattice with off-lattice counterparts to obtain the correct expressions for MLM parameters in terms of physical constants. The expressions indicate that the voxel size needs to be at least 0.6% larger than the molecule to accurately simulate surface reactions on triangular lattice. On square lattice, the minimum voxel size should be even larger, at 5%. We also demonstrate the ability of MLM-based schemes such as Spatiocyte to simulate a reaction-diffusion model that involves all dimensions: three-dimensional diffusion in the cytoplasm, two-dimensional diffusion on the cell membrane and one-dimensional cytoplasm-membrane adsorption. With the model, we examine the contribution of the 2D reaction pathway to the overall reaction rate at different reactant diffusivity, reactivity and concentrations.
A Big data analytical framework for portfolio optimization
Date: 2018-11-24 00:00:00
Tasks: Asset Management, Management, Portfolio Optimization

With the advent of Web 2.0, various types of data are being produced every
day. This has led to the revolution of big data. Huge amount of structured and
unstructured data are produced in financial markets. Processing these data
could help an investor to make an informed investment decision. In this paper,
a framework has been developed to incorporate both structured and unstructured
data for portfolio optimization. Portfolio optimization consists of three
processes: Asset selection, Asset weighting and Asset management. This
framework proposes to achieve the first two processes using a 5-stage
methodology. The stages include shortlisting stocks using Data Envelopment
Analysis (DEA), incorporation of the qualitative factors using text mining,
stock clustering, stock ranking and optimizing the portfolio using heuristics.
This framework would help the investors to select appropriate assets to make
portfolio, invest in them to minimize the risk and maximize the return and
monitor their performance.
Determination of the L\'evy Exponent in Asset Pricing Models
Date: 2019-02-14 00:00:00

We consider the problem of determining the L\'evy exponent in a L\'evy model
for asset prices given the price data of derivatives. The model, formulated
under the real-world measure $\mathbb P$, consists of a pricing kernel
$\{\pi_t\}_{t\geq0}$ together with one or more non-dividend-paying risky assets
driven by the same L\'evy process. If $\{S_t\}_{t\geq0}$ denotes the price
process of such an asset then $\{\pi_t S_t\}_{t\geq0}$ is a $\mathbb
P$-martingale. The L\'evy process $\{ \xi_t \}_{t\geq0}$ is assumed to have
exponential moments, implying the existence of a L\'evy exponent $\psi(\alpha)
= t^{-1}\log \mathbb E(\rm e^{\alpha \xi_t})$ for $\alpha$ in an interval $A
\subset \mathbb R$ containing the origin as a proper subset. We show that if
the initial prices of power-payoff derivatives, for which the payoff is $H_T =
(\zeta_T)^q$ for some time $T>0$, are given for a range of values of $q$, where
$\{\zeta_t\}_{t\geq0}$ is the so-called benchmark portfolio defined by $\zeta_t
= 1/\pi_t$, then the L\'evy exponent is determined up to an irrelevant linear
term. In such a setting, derivative prices embody complete information about
price jumps: in particular, the spectrum of the price jumps can be worked out
from current market prices of derivatives. More generally, if $H_T = (S_T)^q$
for a general non-dividend-paying risky asset driven by a L\'evy process, and
if we know that the pricing kernel is driven by the same L\'evy process, up to
a factor of proportionality, then from the current prices of power-payoff
derivatives we can infer the structure of the L\'evy exponent up to a
transformation $\psi(\alpha) \rightarrow \psi(\alpha + \mu) - \psi(\mu) + c
\alpha$, where $c$ and $\mu$ are constants.
Portfolio Theory, Information Theory and Tsallis Statistics
Date: 2019-10-23 00:00:00

We developed a strategic of optimal portfolio based on information theory and
Tsallis statistics. The growth rate of a stock market is defined by using
$q$-deformed functions and we find that the wealth after n days with the
optimal portfolio is given by a $q$-exponential function. In this context, the
asymptotic optimality is investigated on causal portfolios, showing advantages
of the optimal portfolio over an arbitrary choice of causal portfolios.
Finally, we apply the formulation in a small number of stocks in brazilian
stock market $[B]^{3}$ and analyzed the results.
Effects of sampling interaction partners and competitors in evolutionary games
Date: 2018-11-18 00:00:00

The sampling of interaction partners depends on often implicit modelling
assumptions, yet has marked effects on the dynamics in evolutionary games. One
particularly important aspect is whether or not competitors also interact.
Population structures naturally affect sampling such that in a microscopic
interpretation of the replicator dynamics in well-mixed populations competing
individuals do not interact but do interact in structured populations. In
social dilemmas interactions with competitors invariably inhibit cooperation
while limited local interactions in structured populations support cooperation
by reducing exploitation through cluster formation. These antagonistic effects
of population structures on cooperation affect interpretations and the
conclusions depend on the details of the comparison. For example, in the
Snowdrift game, spatial structure may inhibit cooperation when compared to the
replicator dynamics. However, modifying the replicator dynamics to include
interactions between competitors lowers the equilibrium frequency of
cooperators, which changes the conclusions and space is invariably beneficial,
just as in the Prisoner's Dilemma. These conclusions are confirmed by
comparisons with random matching models, which mimic population structures but
randomly reshuffle individuals to inhibit spatial correlations. Finally, the
differences in the dynamics with and without interactions among competing
individuals underlies the differences between death-birth and birth-death
updating in the spatial Moran process: death-birth supports cooperation because
competitors tend not to interact whereas they tend to do for birth-death
updating and hence cooperators provide direct support to competitors to their
own detriment.
Prediction of Signal Sequences in Abiotic Stress Inducible Genes from Main Crops by Association Rule Mining
Date: 2018-11-18 00:00:00

It is important to study on genes affecting to growing environment of main
crops. Especially the recognition problem of promoter region, which is the
problem to predict whether DNA sequences contain promoter regions or not, is
prior to find abiotic stress-inducible genes. Studies on predicting promoter
sequences in DNA sequences have been studied by traditional pattern matching
methods and machine learning methods in biology and computer science.
Convolutional-Sparse-Coded Dynamic Mode Decomposition and Its Application to River State Estimation
Date: 2019-02-20 00:00:00
Tasks: State Estimation, Time Series, Time Series Analysis

This work proposes convolutional-sparse-coded dynamic mode decomposition
(CSC-DMD) by unifying extended dynamic mode decomposition (EDMD) and
convolutional sparse coding. EDMD is a data driven analysis method for
describing a nonlinear dynamical system with a linear time-evolution equation.
Compared with existing EDMD methods, CSC-DMD has an advantage of reflecting
spatial structure of the target. As an example, the proposed method is applied
to river bed shape estimation from the water surface observation. The
estimation problem is reduced to sparsity-aware restoration with a hard
constraint, which is given by the CSC-DMD prediction, where the algorithm is
derived by the primal-dual splitting method. A time series set of water surface
and bed shape measured through an experimental river setup is used to train and
test the system. From the result, the significance of the proposed method is
verified.
CVA and vulnerable options pricing by correlation expansions
Date: 2018-11-18 00:00:00

We consider the problem of computing the Credit Value Adjustment ({CVA}) of a
European option in presence of the Wrong Way Risk ({WWR}) in a default
intensity setting. Namely we model the asset price evolution as solution to a
linear equation that might depend on different stochastic factors and we
provide an approximate evaluation of the option's price, by exploiting a
correlation expansion approach, introduced in \cite{AS}. We compare the
numerical performance of such a method with that recently proposed by Brigo et
al. (\cite{BR18}, \cite{BRH18}) in the case of a call option driven by a GBM
correlated with the CIR default intensity. We additionally report some
numerical evaluations obtained by other methods.
Proximity Full-Text Search with a Response Time Guarantee by Means of Additional Indexes
Authors: Veretennikov Alexander B.
Date: 2018-11-18 00:00:00
Tasks: Information Retrieval, Retrieval

Full-text search engines are important tools for information retrieval. Term
proximity is an important factor in relevance score measurement. In a proximity
full-text search, we assume that a relevant document contains query terms near
each other, especially if the query terms are frequently occurring words. A
methodology for high-performance full-text query execution is discussed. We
build additional indexes to achieve better efficiency. For a word that occurs
in the text, we include in the indexes some information about nearby words.
What types of additional indexes do we use? How do we use them? These questions
are discussed in this work. We present the results of experiments showing that
the average time of search query execution is 44-45 times less than that
required when using ordinary inverted indexes.
  This is a pre-print of a contribution "Veretennikov A.B. Proximity Full-Text
Search with a Response Time Guarantee by Means of Additional Indexes" published
in "Arai K., Kapoor S., Bhatia R. (eds) Intelligent Systems and Applications.
IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 868"
published by Springer, Cham. The final authenticated version is available
online at: https://doi.org/10.1007/978-3-030-01054-6_66. The work was supported
by Act 211 Government of the Russian Federation, contract no 02.A03.21.0006.
Effect of Passive Reflectors for Enhancing Coverage of 28 GHz mmWave Systems in an Outdoor Setting
Date: 2018-11-18 00:00:00

The availability of large unused spectrum at millimeter wave (mmWave)
frequency bands has steered the future 5G research towards these bands.
However, mmWave signals are attenuated severely in the non-lineof-sight (NLOS)
scenarios, thereby leaving the strong link quality by a large margin to
line-of-sight (LOS) links. In this paper, a passive metallic reflector is used
to enhance the coverage for mmWave signals in an outdoor, NLOS propagation
scenarios. The received power from different azimuth and elevation angles are
measured at 28 GHz in a parking lot setting. Our results show that using a 33
inch by 33 inch metallic reflector, the received power can be enhanced by 19 dB
compared to no reflector case.
MALTS: Matching After Learning to Stretch
Authors: Harsh Parikh, Cynthia Rudin, Alexander Volfovsky
Date: 2018-11-18 00:00:00
Tasks: Causal Inference

We introduce a flexible framework that produces high-quality almost-exact matches for causal inference. Most prior work in matching uses ad-hoc distance metrics, often leading to poor quality matches, particularly when there are irrelevant covariates. In this work, we learn an interpretable distance metric for matching, which leads to substantially higher quality matches. The learned distance metric stretches the covariate space according to each covariate's contribution to outcome prediction: this stretching means that mismatches on important covariates carry a larger penalty than mismatches on irrelevant covariates. Our ability to learn flexible distance metrics leads to matches that are interpretable and useful for the estimation of conditional average treatment effects.
The Frequent Network Neighborhood Mapping of the Human Hippocampus Shows Much More Frequent Neighbor Sets in Males Than in Females
Date: 2018-11-23 00:00:00
Tasks: Hippocampus

In the study of the human connectome, the vertices and the edges of the
network of the human brain are analyzed: the vertices of the graphs are the
anatomically identified gray matter areas of the subjects; this set is exactly
the same for all the subjects. The edges of the graphs correspond to the axonal
fibers, connecting these areas. In the biological applications of graph theory,
it happens very rarely that scientists examine numerous large graphs on the
very same, labeled vertex set. Exactly this is the case in the study of the
connectomes. Because of the particularity of these sets of graphs, novel,
robust methods need to be developed for their analysis. Here we introduce the
new method of the Frequent Network Neighborhood Mapping for the connectome,
which serves as a robust identification of the neighborhoods of given vertices
of special interest in the graph. We apply the novel method for mapping the
neighborhoods of the human hippocampus and discover strong statistical
asymmetries between the connectomes of the sexes, computed from the Human
Connectome Project. We analyze 413 braingraphs, each with 463 nodes. We show
that the hippocampi of men have much more significantly frequent neighbor sets
than women; therefore, in a sense, the connections of the hippocampi are more
regularly distributed in men and more varied in women. Our results are in
contrast to the volumetric studies of the human hippocampus, where it was shown
that the relative volume of the hippocampus is the same in men and women.
Low Complexity Iterative Detection for a Large-scale Distributed MIMO Prototyping System
Date: 2018-11-19 00:00:00
Tasks: Decoder

In this paper, we study the low-complexity iterative soft-input soft-output
(SISO) detection algorithm in a large-scale distributed multiple-input
multiple-output (MIMO) system. The uplink interference suppression matrix is
designed to decompose the received multi-user signal into independent
single-user receptions. An improved minimum-mean-square-error iterative soft
decision interference cancellation (MMSE-ISDIC) based on eigenvalue
decomposition (EVD-MMSE-ISDIC) is proposed to perform low-complexity detection
of the decomposed signals. Furthermore, two iteration schemes are given to
improve receiving performance, which are iterative detection and decoding (IDD)
scheme and iterative detection (ID) scheme. While IDD utilizes the external
information generated by the decoder for iterative detection, the output
information of the detector is directly exploited with ID. In particular, a
large-scale distributed MIMO prototyping system is introduced and a 32$ \times
$32 (4 user equipments (UEs) and 4 remote antenna units (RAUs), each equipped
with 8 antennas) experimental tests at 3.5 GHz was performed. The experimental
results show that the proposed iterative receiver greatly outperforms the
linear MMSE receiver, since it reduces the average number of error blocks of
the system significantly.
Reconstructing the visual perception of honey bees in complex 3-D worlds
Date: 2019-06-21 00:00:00

Over the last decades, honeybees have been a fascinating model to study
insect navigation. While there is some controversy about the complexity of
underlying neural correlates, the research of honeybee navigation makes
progress through both the analysis of flight behavior and the synthesis of
agent models. Since visual cues are believed to play a crucial role for the
behavioral output of a navigating bee we have developed a realistic
3-dimensional virtual world, in which simulated agents can be tested, or in
which the visual input of experimentally traced animals can be reconstructed.
In this paper we present implementation details on how we reconstructed a large
3-dimensional world from aerial imagery of one of our field sites, how the
distribution of ommatidia and their view geometry was modeled, and how the
system samples from the scene to obtain realistic bee views. This system is
made available as an open-source project to the community on
\url{http://github.com/bioroboticslab/bee_view}.
How neutral and niche forces contribute to speciation processes?
Date: 2018-11-19 00:00:00

The evolutionary and ecological processes behind the origin of species are
among the most fundamental problems in biology. In fact, many theoretical
hypothesis on different type of speciation have been proposed. In particular,
models of sympatric speciation leading to the formation of new species without
geographical isolation, are based on the niche hypothesis: the diversification
of the population is induced by the competition for a limited set of the
available resources. On the other hand, neutral models of evolution have shown
that stochastic forces are sufficient to generate coexistence of different
species. In this work, we bring this dichotomy to the context of species
formation, and we study how neutral and niche forces contribute to sympatric
speciation in a model ecosystem. In particular, we study the evolution of a
population of individuals with asexual reproduction whose inherited characters
or phenotypes are specified by both niche-based and neutral traits. We analyse
the stationary state of the dynamics, and study the distribution of individuals
in the whole space of possible phenotypes. We show, both by numerical
simulations and analytics, that there is a non-trivial coupling between neutral
and niche forces induced by stochastic effects in the evolution of the
population that allows the formation of clusters (i.e., species) in the
phenotypic space. Our framework can be generalised also to sexual reproduction
or other type of population dynamics.
Analysis of DNN Speech Signal Enhancement for Robust Speaker Recognition
Date: 2018-11-19 00:00:00
Tasks: Data Augmentation, Denoising, Speaker Recognition, Speaker Verification, Speech Enhancement

In this work, we present an analysis of a DNN-based autoencoder for speech
enhancement, dereverberation and denoising. The target application is a robust
speaker verification (SV) system. We start our approach by carefully designing
a data augmentation process to cover wide range of acoustic conditions and
obtain rich training data for various components of our SV system. We augment
several well-known databases used in SV with artificially noised and
reverberated data and we use them to train a denoising autoencoder (mapping
noisy and reverberated speech to its clean version) as well as an x-vector
extractor which is currently considered as state-of-the-art in SV. Later, we
use the autoencoder as a preprocessing step for text-independent SV system. We
compare results achieved with autoencoder enhancement, multi-condition PLDA
training and their simultaneous use. We present a detailed analysis with
various conditions of NIST SRE 2010, 2016, PRISM and with re-transmitted data.
We conclude that the proposed preprocessing can significantly improve both
i-vector and x-vector baselines and that this technique can be used to build a
robust SV system for various target domains.
Mismatch error correction for time interleaved analog-to-digital converter over a wide frequency range
Date: 2018-11-19 00:00:00

High-speed high-resolution Analog-to-Digital Conversion is the key part for
waveform digitization in physics experiments and many other domains. This paper
presents a new fully digital correction of mismatch errors among the channels
in Time Interleaved Analog-to-Digital Converter (TIADC) systems. We focus on
correction with wide-band input signal, which means that we can correct the
mismatch errors for any frequency point in a broad band with only one set of
filter coefficients. Studies were also made to show how to apply the correction
algorithm beyond the base band, i.e. other Nyquist zones in the under-sampling
situation. Structure of the correction algorithm is presented in this paper, as
well as simulation results. To evaluate the correction performance, we actually
conducted a series of tests with two TIADC systems. The results indicate that
the performance of both two TIADC systems can be greatly improved by
correction, and the Effective Number Of Bits (ENOB) is successfully improved to
be better than 9.5 bits and 5.5 bits for an input signal up to the bandwidth
(-3dB) range in the 1.6-Gsps 14-bit and the 10-Gsps 8-bit TIADC systems,
respectively. Tests were also conducted for input signal frequencies in the
second Nyquist zone, which shows that the correction algorithms also work well
as expected.
Handwriting Recognition of Historical Documents with few labeled data
Authors: Chammas Edgard, Mokbel Chafic, Likforman-Sulem Laurence
Date: 2018-11-10 00:00:00
Tasks: Handwriting Recognition, HTR

Historical documents present many challenges for offline handwriting
recognition systems, among them, the segmentation and labeling steps. Carefully
annotated textlines are needed to train an HTR system. In some scenarios,
transcripts are only available at the paragraph level with no text-line
information. In this work, we demonstrate how to train an HTR system with few
labeled data. Specifically, we train a deep convolutional recurrent neural
network (CRNN) system on only 10% of manually labeled text-line data from a
dataset and propose an incremental training procedure that covers the rest of
the data. Performance is further increased by augmenting the training set with
specially crafted multiscale data. We also propose a model-based normalization
scheme which considers the variability in the writing scale at the recognition
phase. We apply this approach to the publicly available READ dataset. Our
system achieved the second best result during the ICDAR2017 competition.
Addressing the Invisible: Street Address Generation for Developing Countries with Deep Learning
Authors: Demir Ilke, Raskar Ramesh
Date: 2018-11-10 00:00:00

More than half of the world's roads lack adequate street addressing systems.
Lack of addresses is even more visible in daily lives of people in developing
countries. We would like to object to the assumption that having an address is
a luxury, by proposing a generative address design that maps the world in
accordance with streets. The addressing scheme is designed considering several
traditional street addressing methodologies employed in the urban development
scenarios around the world. Our algorithm applies deep learning to extract
roads from satellite images, converts the road pixel confidences into a road
network, partitions the road network to find neighborhoods, and labels the
regions, roads, and address units using graph- and proximity-based algorithms.
We present our results on a sample US city, and several developing cities,
compare travel times of users using current ad hoc and new complete addresses,
and contrast our addressing solution to current industrial and open geocoding
alternatives.