pwc-archive/papers-with-abstracts huggingface dataset:
Deep learning the high variability and randomness inside multimode fibres
Authors: Fan Pengfei, Zhao Tianrui, Su Lei
Date: 2018-07-18 00:00:00
Tasks: Deep Learning, Vocal Bursts Intensity Prediction

Multimode fibres (MMF) are remarkable high-capacity information channels
owing to the large number of transmitting fibre modes, and have recently
attracted significant renewed interest in applications such as optical
communication, imaging, and optical trapping. At the same time, the optical
transmitting modes inside MMFs are highly sensitive to external perturbations
and environmental changes, resulting in MMF transmission channels being highly
variable and random. This largely limits the practical application of MMFs and
hinders the full exploitation of their information capacity. Despite great
research efforts made to overcome the high variability and randomness inside
MMFs, any geometric change to the MMF leads to completely different
transmission matrices, which unavoidably fails at the information recovery.
Here, we show the successful binary image transmission using deep learning
through a single MMF, which is stationary or subject to dynamic shape
variations. We found that a single convolutional neural network has excellent
generalisation capability with various MMF transmission states. This deep
neural network can be trained by multiple MMF transmission states to accurately
predict unknown information at the other end of the MMF at any of these states,
without knowing which state is present. Our results demonstrate that deep
learning is a promising solution to address the variability and randomness
challenge of MMF based information channels. This deep-learning approach is the
starting point of developing future high-capacity MMF optical systems and
devices, and is applicable to optical systems concerning other diffusing media.
Multi-Agent Coverage Control with Energy Depletion and Repletion
Date: 2018-07-24 00:00:00

We develop a hybrid system model to describe the behavior of multiple agents
cooperatively solving an optimal coverage problem under energy depletion and
repletion constraints. The model captures the controlled switching of agents
between coverage (when energy is depleted) and battery charging (when energy is
replenished) modes. It guarantees the feasibility of the coverage problem by
defining a guard function on each agent's battery level to prevent it from
dying on its way to a charging station. The charging station plays the role of
a centralized scheduler to solve the contention problem of agents competing for
the only charging resource in the mission space. The optimal coverage problem
is transformed into a parametric optimization problem to determine an optimal
recharging policy. This problem is solved through the use of Infinitesimal
Perturbation Analysis (IPA), with simulation results showing that a full
recharging policy is optimal.
Extreme value analysis of gut microbial alterations in colorectal cancer
Date: 2019-02-13 00:00:00

Gut microbes play a key role in colorectal carcinogenesis, yet reaching a
consensus on microbial signatures remains a challenge. This is in part due to a
reliance on mean value estimates. We present an extreme value analysis for
overcoming these limitations. By characterizing a power law fit to the relative
abundances of microbes, we capture the same microbial signatures as more
complex meta-analyses. Importantly, we show that our method is robust to the
variations inherent in microbial community profiling and point to future
directions for developing sensitive, reliable analytical methods.
Entropy Analysis of Financial Time Series
Date: 2018-07-25 00:00:00
Tasks: State Space Models, Time Series, Time Series Analysis

This thesis applies entropy as a model independent measure to address three
research questions concerning financial time series. In the first study we
apply transfer entropy to drawdowns and drawups in foreign exchange rates, to
study their correlation and cross correlation. When applied to daily and hourly
EUR/USD and GBP/USD exchange rates, we find evidence of dependence among the
largest draws (i.e. 5% and 95% quantiles), but not as strong as the correlation
between the daily returns of the same pair of FX rates. In the second study we
use state space models (Hidden Markov Models) of volatility to investigate
volatility spill overs between exchange rates. Among the currency pairs, the
co-movement of EUR/USD and CHF/USD volatility states show the strongest
observed relationship. With the use of transfer entropy, we find evidence for
information flows between the volatility state series of AUD, CAD and BRL. The
third study uses the entropy of S&P realised volatility in detecting changes of
volatility regime in order to re-examine the theme of market volatility timing
of hedge funds. A one-factor model is used, conditioned on information about
the entropy of market volatility, to measure the dynamic of hedge funds equity
exposure. On a cross section of around 2500 hedge funds with a focus on the US
equity markets we find that, over the period from 2000 to 2014, hedge funds
adjust their exposure dynamically in response to changes in volatility regime.
This adds to the literature on the volatility timing behaviour of hedge fund
manager, but using entropy as a model independent measure of volatility regime.
Channel Dependent Mutual Information in Index Modulations
Date: 2018-07-25 00:00:00

Mutual Information is the metric that is used to perform link adaptation,
which allows to achieve rates near capacity. The computation of adaptive
transmission modes is achieved by employing the mapping between the Signal to
Noise Ratio and the Mutual Information. Due to the high complexity of the
computation of the Mutual Information, this process is performed off-line via
Monte Carlo simulations, whose results are stored in look-up tables. However,
in Index Modulations, such as Spatial Modulation or Polarized Modulation, this
is not feasible since the constellation and the Mutual Information are channel
dependent and it would require to compute this metric at each time instant if
the channel is time varying. In this paper, we propose different approximations
in order to obtain a simple closed-form expression that allows to compute the
Mutual Information at each time instant and thus, making feasible the link
adaptation.
Neural measures of the causal role of observers' facial mimicry on visual working memory for facial expressions
Date: 2018-08-11 00:00:00
Tasks: Change Detection

Simulation models of facial expressions propose that sensorimotor regions may
increase the clarity of facial expressions representations in extrastriate
areas. We monitored the event-related potential marker of visual working memory
(VWM) representations, namely the sustained posterior contralateral negativity
(SPCN), also termed contralateral delay activity (CDA), while participants
performed a change detection task including to-be-memorized faces with
different intensities of anger. In one condition participants could freely use
their facial mimicry during the encoding/VWM maintenance of the faces, while in
a different condition, participants had their facial mimicry blocked by a gel.
Notably, SPCN amplitude was reduced for faces in the blocked mimicry condition
when compared to the free mimicry condition. This modulation interacted with
the empathy levels of participants such that only participants with medium-high
empathy scores showed such reduction of the SPCN amplitude when their mimicry
was blocked. The SPCN amplitude was larger for full expressions when compared
to neutral and subtle expressions, while subtle expressions elicited lower SPCN
amplitudes than neutral faces. These findings provide evidence of a functional
link between mimicry and VWM for faces, and further shed light on how this
memory system may receive feedbacks from sensorimotor regions during the
processing of facial expressions.
A Generalized Spectral Shaping Method for OFDM Signals
Date: 2018-07-25 00:00:00

Orthogonal frequency division multiplexing (OFDM) signals with rectangularly
windowed pulses exhibit low spectral confinement. Two approaches usually
referred to as pulse-shaping and active interference cancellation (AIC) are
classically employed to reduce the out-of-band emission (OOBE) without
affecting the receiver. This paper proposes a spectral shaping method that
generalizes and unifies these two strategies. To this end, the OFDM carriers
are shaped with novel pulses, referred to as generalized pulses, that consist
of the ones used in conventional OFDM systems plus a series of cancellation
terms aimed at reducing the OOBE of the former. Hence, each generalized pulse
embeds all the terms required to reduce its spectrum in the desired bands. This
leads to a data-independent optimization problem that notably simplifies the
implementation complexity and allows the analytical calculation of the
resulting power spectral density (PSD), which in most methods found in the
literature can only be estimated by means of simulations. As an example of its
performance, the proposed technique allows complying with the stringent PSD
mask imposed by the EN 50561-1 with a data carrier loss lower than 4%. By
contrasts, 28% of the data carriers have to be nulled when pulse-shaping is
employed in this scenario.
Epigenetic Ratchet: Spontaneous Adaptation via Stochastic Gene Expression
Date: 2019-01-17 00:00:00

Adaptation mechanism of cells on the basis of stochastic gene expression and
epigenetic modification is proposed. From marginally stable states generated by
epigenetic modification, a gene expression pattern that achieves greater cell
growth is selected, as confirmed by simulations and analysis of several models.
The mechanism does not require any design of gene regulation networks and is
shown to be generic in a stochastic system with marginal stability. General
relevance of the mechanism to cell biology is also discussed.
Deep Learning Detection Networks in MIMO Decode-Forward Relay Channels
Authors: Jin Xianglan, Kim Hyoung-Nam
Date: 2018-07-12 00:00:00
Tasks: Deep Learning

In this paper, we consider signal detection algorithms in a multiple-input
multiple-output (MIMO) decode-forward (DF) relay channel with one source, one
relay, and one destination. The existing suboptimal near maximum likelihood
(NML) detector and the NML with two-level pair-wise error probability
(NMLw2PEP) detector achieve excellent performance with instantaneous channel
state information (CSI) of the source-relay (SR) link and with statistical CSI
of the SR link, respectively. However, the NML detectors require an
exponentially increasing complexity as the number of transmit antennas
increases. Using deep learning algorithms, NML-based detection networks
(NMLDNs) are proposed with and without the CSI of the SR link at the
destination. The NMLDNs detect signals in changing channels after a single
training using a large number of randomly distributed channels. The detection
networks require much lower detection complexity than the exhaustive search NML
detectors while exhibiting good performance. To evaluate the performance, we
introduce semidefinite relaxation detectors with polynomial complexity based on
the NML detectors. Additionally, new linear detectors based on the zero
gradient of the NML metrics are proposed. Applying various detection algorithms
at the relay (DetR) and detection algorithms at the destination (DetD), we
present some DetR-DetD methods in MIMO DF relay channels. An appropriate
DetR-DetD method can be employed according to the required error probability
and detection complexity. The complexity analysis and simulation results
validate the arguments of this paper.
Apologia Pro Vita Sua: The Vanishing of the White Whale in the Mists
Date: 2018-07-25 00:00:00

There are many analogies among fortune hunting in business, politics, and
science. The prime task of the gold digger was to go to the Klondikes, find the
right mine and mine the richest veins. This task requires motivation, sense of
purpose and ability. Techniques and equipment must be developed. Fortune
hunting in New England was provided at one time by hunting for whales. One went
to a great whalers' station such as New Bedford and joined the whale hunters.
The hunt in academic research is similar. A single-minded passion is called
for. These notes here are the wrap-up comments containing some terminal
observations of mine on a hunt for a theory money and financial institutions.
SME investment best strategies. Outliers for assessing how to optimize performance
Date: 2018-06-13 00:00:00

Any research on strategies for reaching business excellence aims at revealing
the appropriate course of actions any executive should consider. Thus,
discussions take place on how effective a performance measurement system can be
estimated, or/and validated. Can one find an adequate measure (i) on the
performance result due to whatever level of investment, and (ii) on the timing
of such investments? We argue that extreme value statistics provide the answer.
We demonstrate that the level and timing of investments allow to be forecasting
small and medium size enterprises (SME) performance, - at financial crisis
times. The "investment level" is taken as the yearly total tangible asset
(TTA). The financial/economic performance indicators defining growth are the
sales or total assets variations; profitability is defined from returns on
investments or returns on sales. Companies on the Italian Stock Exchange STAR
Market serve as example. It is found from the distributions extreme values that
outlier companies (with positive performance) are those with the lowest but
growing TTA. In contrast, the SME with low TTA, but which did not increase its
TTA, before the crisis, became a negative outlier. The outcome of these
statistical findings should suggest strategies to SME board members.
Towards equation of state for a market: A thermodynamical paradigm of economics
Date: 2018-07-26 00:00:00

Foundations of equilibrium thermodynamics are the equation of state (EoS) and
four postulated laws of thermodynamics. We use equilibrium thermodynamics
paradigms in constructing the EoS for microeconomics system that is a market.
This speculation is hoped to be first step towards whole pictures of
thermodynamical paradigm of economics.
Hospitality Students' Perceptions towards Working in Hotels: a case study of the faculty of tourism and hotels in Alexandria University
Date: 2018-07-22 00:00:00

The tourism and hospitality industry worldwide has been confronted with the
problem of attracting and retaining quality employees. If today's students are
to become the effective practitioners of tomorrow, it is fundamental to
understand their perceptions of tourism employment. Therefore, this research
aims at investigating the perceptions of hospitality students at the Faculty of
Tourism in Alexandria University towards the industry as a career choice. A
self-administrated questionnaire was developed to rate the importance of 20
factors in influencing career choice, and the extent to which hospitality as a
career offers these factors. From the results, it is clear that students
generally do not believe that the hospitality career will offer them the
factors they found important. However, most of respondents (70.6%) indicated
that they would work in the industry after graduation. Finally, a set of
specific remedial actions that hospitality stakeholders could initiate to
improve the perceptions of hospitality career are discussed.
An Adaptive CM Array Preconditioner for Blind Multi-User Separation
Date: 2018-07-25 00:00:00
Tasks: Direction of Arrival Estimation

The family of constant-modulus algorithms is widely used in wireless
communication systems and in radar. The classical constant-modulus adaptive
(CMA) algorithm, however, fails to lock onto a single mode when used in
conjunction with an antenna array. Instead, it equalizes the entire spatial
spectrum. In this paper, we describe in full detail our recently proposed
approach for the separation of multiple users in a radio system with frequency
reuse, such as a cellular network, making use of the CMA algorithm. Based on
the observation that the differential filter weights resemble a superposition
of the array steering vectors, we cast the original task to a
direction-of-arrival estimation problem. With rigorous theoretical analysis of
the array response based on the discrete-space Fourier transform we elaborate a
solution that solves the problem by finding the roots of a polynomial equation.
We provide a numerical example to demonstrate the validity of the approach
under high-SNR conditions. In addition, we propose a more general preprocessor
for the CMA array which allows the modulated signals to differ in amplitude. As
a byproduct, the preprocessor yields a low-cost estimate of the number of
concurrent users, i.e. the model order, by simply counting the roots with the
strongest response.
Multi-Perspective Neural Architecture for Recommendation System
Authors: Xiao Han, Chen Yidong, Shi Xiaodong
Date: 2018-07-12 00:00:00
Tasks: Recommendation Systems

Currently, there starts a research trend to leverage neural architecture for
recommendation systems. Though several deep recommender models are proposed,
most methods are too simple to characterize users' complex preference. In this
paper, for a fine-grain analysis, users' ratings are explained from multiple
perspectives, based on which, we propose our neural architecture. Specifically,
our model employs several sequential stages to encode the user and item into
hidden representations. In one stage, the user and item are represented from
multiple perspectives and in each perspective, the representations of user and
item put attentions to each other. Last, we metric the output representations
of final stage to approach the users' rating. Extensive experiments demonstrate
that our method achieves substantial improvements against baselines.
Data Infrastructure and Approaches for Ontology-Based Drug Repurposing
Authors: Boyer Stephen, Griffin Thomas, Swaminathan Sarath, Clarkson Kenneth L., Zubarev Dmitry
Date: 2018-07-12 00:00:00
Tasks: Information Retrieval, Recommendation Systems, Retrieval

We report development of a data infrastructure for drug repurposing that
takes advantage of two currently available chemical ontologies. The data
infrastructure includes a database of compound- target associations augmented
with molecular ontological labels. It also contains two computational tools for
prediction of new associations. We describe two drug-repurposing systems: one,
Nascent Ontological Information Retrieval for Drug Repurposing (NOIR-DR), based
on an information retrieval strategy, and another, based on non-negative matrix
factorization together with compound similarity, that was inspired by
recommender systems. We report the performance of both tools on a
drug-repurposing task.
Interval Simulation of Narmax Models Based on Computer Arithmetic
Date: 2018-07-25 00:00:00

System identification is an important area of science, which aims to describe
the characteristics of the system, representing them by mathematical models.
Since many of these models can be seen as recursive functions, it is extremely
important to control the errors in these functions, because small errors
introduced in each computational step can grow exponentially due to the
sensitivity to initial conditions present in this type of functions. One of the
ways to control rounding and truncation errors is through interval arithmetic,
since it is not possible to represent all numbers in the computer because of
the finite representation in them. Thus, in arithmetic interval a number is
represented by an interval in which the true number is within that interval. In
this manuscript we developed an algorithm that performs the operations of
interval arithmetic using basic functions. We have compared compared our
results with the Matlab-toolbox Intlab. Numerical experiments have shown that
our method is superior producing narrower intervals.
An Affective Robot Companion for Assisting the Elderly in a Cognitive Game Scenario
Authors: Churamani Nikhil, Sutherland Alexander, Barros Pablo
Date: 2018-07-12 00:00:00
Tasks: Decision Making, Dialogue Management, Management, Playing the Game of 2048, Position, reinforcement-learning, Reinforcement Learning (RL)

Being able to recognize emotions in human users is considered a highly
desirable trait in Human-Robot Interaction (HRI) scenarios. However, most
contemporary approaches rarely attempt to apply recognized emotional features
in an active manner to modulate robot decision-making and dialogue for the
benefit of the user. In this position paper, we propose a method of
incorporating recognized emotions into a Reinforcement Learning (RL) based
dialogue management module that adapts its dialogue responses in order to
attempt to make cognitive training tasks, like the 2048 Puzzle Game, more
enjoyable for the users.
Iterative evaluation of LSTM cells
Authors: Palma Leandro, Argerich Luis
Date: 2018-07-11 00:00:00
Tasks: Language Modeling, Language Modelling

In this work we present a modification in the conventional flow of
information through a LSTM network, which we consider well suited for RNNs in
general. The modification leads to a iterative scheme where the computations
performed by the LSTM cell are repeated over a constant input and cell state
values, while updating the hidden state a finite number of times. We provide
theoretical and empirical evidence to support the augmented capabilities of the
iterative scheme and show examples related to language modeling. The
modification yields an enhancement in the model performance comparable with the
original model augmented more than 3 times in terms of the total amount of
parameters.
Incremental Sharpe and other performance ratios
Date: 2018-12-17 00:00:00

We present a new methodology of computing incremental contribution for
performance ratios for portfolio like Sharpe, Treynor, Calmar or Sterling
ratios. Using Euler's homogeneous function theorem, we are able to decompose
these performance ratios as a linear combination of individual modified
performance ratios. This allows understanding the drivers of these performance
ratios as well as deriving a condition for a new asset to provide incremental
performance for the portfolio. We provide various numerical examples of this
performance ratio decomposition.
Wavelet analysis in problems of classification of ECG signals
Date: 2018-07-26 00:00:00
Tasks: Classification

In this paper, the wavelet analysis is used to study the ECG signal. We show
that the high-frequency wavelet components of the ECG signal contain
information on the functioning of the heart and can be used in diagnosis. We
describe the automated classification system that separates the ECG of sick and
healthy persons using only a high-frequency ECG component.
General Context-Aware Data Matching and Merging Framework
Authors: Žitnik Slavko, Šubelj Lovro, Lavbič Dejan, Vasilecas Olegas, Bajec Marko
Date: 2018-07-26 00:00:00
Tasks: Attribute, Entity Resolution, Management

Due to numerous public information sources and services, many methods to
combine heterogeneous data were proposed recently. However, general end-to-end
solutions are still rare, especially systems taking into account different
context dimensions. Therefore, the techniques often prove insufficient or are
limited to a certain domain. In this paper we briefly review and rigorously
evaluate a general framework for data matching and merging. The framework
employs collective entity resolution and redundancy elimination using three
dimensions of context types. In order to achieve domain independent results,
data is enriched with semantics and trust. However, the main contribution of
the paper is evaluation on five public domain-incompatible datasets.
Furthermore, we introduce additional attribute, relationship, semantic and
trust metrics, which allow complete framework management. Besides overall
results improvement within the framework, metrics could be of independent
interest.