pwc-archive/papers-with-abstracts huggingface dataset:
Simulation Based Evaluation and Optimization for PEV Charging Stations Deployment in Transportation Networks
Date: 2018-10-24 00:00:00

Plug-in electric vehicles are receiving global attention. However,
large-scale plug-in electric vehicles bring challenge to charging station
deployment. This paper provides a deployment evaluation method and a planning
method for plug-in electric vehicles charging stations based on simulation and
genetic algorithm. In this research, reasonable user behaviors changes i.e.,
detouring for recharging, is included in the method for more practical
application. A detailed logic for describing the plug-in electric vehicles
users' detour behaviors is designed in this paper for the charging station
deployment evaluation and further influence the later charging station planning
strategy formulation. Intuitively, by taking the behaviors change of plug-in
electric vehicles users after a charging station deployment is given into
account, our evaluation method is the more reasonable. Actually a series of
control experiments should be carried out to illustrate the different insights
from our method. However, in this paper, due to the limited space, we just
provide the result of our method in a relatively simple case with some
analyses. Different insights from our method may work as useful suggestion for
charging station plan in a city area.
A Concept-Centered Hypertext Approach to Case-Based Retrieval
Authors: Marchesin Stefano
Date: 2018-11-27 00:00:00
Tasks: Decision Making, Relational Reasoning, Retrieval

The goal of case-based retrieval is to assist physicians in the clinical
decision making process, by finding relevant medical literature in large
archives. We propose a research that aims at improving the effectiveness of
case-based retrieval systems through the use of automatically created
document-level semantic networks. The proposed research tackles different
aspects of information systems and leverages the recent advancements in
information extraction and relational learning to revisit and advance the core
ideas of concept-centered hypertext models. We propose a two-step methodology
that in the first step addresses the automatic creation of document-level
semantic networks, then in the second step it designs methods that exploit such
document representations to retrieve relevant cases from medical literature.
For the automatic creation of documents' semantic networks, we design a
combination of information extraction techniques and relational learning
models. Mining concepts and relations from text, information extraction
techniques represent the core of the document-level semantic networks' building
process. On the other hand, relational learning models have the task of
enriching the graph with additional connections that have not been detected by
information extraction algorithms and strengthening the confidence score of
extracted relations. For the retrieval of relevant medical literature, we
investigate methods that are capable of comparing the documents' semantic
networks in terms of structure and semantics. The automatic extraction of
semantic relations from documents, and their centrality in the creation of the
documents' semantic networks, represent our attempt to go one step further than
previous graph-based approaches.
The Making of a Creative Worldview
Date: 2018-11-27 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction

Research at the interface between cognitive psychology, neuroscience, and the
science of complex, dynamical systems, is piecing together an understanding of
the creative process, including how it works, how it can be fostered, and the
developmental antecedents and personality traits of particularly creative
people. This chapter examines the workings of creative minds, those with the
potential to significantly impact the evolution of human culture.
A Deep Learning Strategy for Vehicular Floating Content Management
Authors: Manzo Gaetano, Otalora Juan Sebastian, Marsan Marco Ajmone, Rizzo Gianluca
Date: 2018-10-24 00:00:00
Tasks: Deep Learning, Management

Floating Content (FC) is a communication paradigm for the local dissemination
of contextualized information through D2D connectivity, in a way which
minimizes the use of resources while achieving some specified performance
target. Existing approaches to FC dimensioning are based on unrealistic system
assumptions that make them, highly inaccurate and overly conservative when
applied in realistic settings. In this paper, we present a first step towards
the development of a cognitive approach to efficient dynamic management of FC.
We propose a deep learning strategy for FC dimensioning, which exploits a
Convolutional Neural Network(CNN) to efficiently modulate over time the
resources employed by FC in a QoS-aware manner. Numerical evaluations show that
our approach achieves a maximum rejection rate of3%, and resource savings of
37.5% with respect to the benchmark strategy
Calculating CVaR and bPOE for Common Probability Distributions With Application to Portfolio Optimization and Density Estimation
Date: 2019-02-17 00:00:00
Tasks: Density Estimation, Portfolio Optimization

Conditional Value-at-Risk (CVaR) and Value-at-Risk (VaR), also called the
superquantile and quantile, are frequently used to characterize the tails of
probability distribution's and are popular measures of risk. Buffered
Probability of Exceedance (bPOE) is a recently introduced characterization of
the tail which is the inverse of CVaR, much like the CDF is the inverse of the
quantile. These quantities can prove very useful as the basis for a variety of
risk-averse parametric engineering approaches. Their use, however, is often
made difficult by the lack of well-known closed-form equations for calculating
these quantities for commonly used probability distribution's. In this paper,
we derive formulas for the superquantile and bPOE for a variety of common
univariate probability distribution's. Besides providing a useful collection
within a single reference, we use these formulas to incorporate the
superquantile and bPOE into parametric procedures. In particular, we consider
two: portfolio optimization and density estimation. First, when portfolio
returns are assumed to follow particular distribution families, we show that
finding the optimal portfolio via minimization of bPOE has advantages over
superquantile minimization. We show that, given a fixed threshold, a single
portfolio is the minimal bPOE portfolio for an entire class of distribution's
simultaneously. Second, we apply our formulas to parametric density estimation
and propose the method of superquantile's (MOS), a simple variation of the
method of moment's (MM) where moment's are replaced by superquantile's at
different confidence levels. With the freedom to select various combinations of
confidence levels, MOS allows the user to focus the fitting procedure on
different portions of the distribution, such as the tail when fitting
heavy-tailed asymmetric data.
Swimming with Wealthy Sharks: Longevity, Volatility and the Value of Risk Pooling
Date: 2018-11-28 00:00:00

Who {\em values} life annuities more? Is it the healthy retiree who expects
to live long and might become a centenarian, or is the unhealthy retiree with a
short life expectancy more likely to appreciate the pooling of longevity risk?
What if the unhealthy retiree is pooled with someone who is much healthier and
thus forced to pay an implicit loading? To answer these and related questions
this paper examines the empirical conditions under which retirees benefit (or
may not) from longevity risk pooling by linking the {\em economics} of annuity
equivalent wealth (AEW) to {\em actuarially} models of aging. I focus attention
on the {\em Compensation Law of Mortality} which implies that individuals with
higher relative mortality (e.g. lower income) age more slowly and experience
greater longevity uncertainty. Ergo, they place higher utility value on the
annuity. The impetus for this research today is the increasing evidence on the
growing disparity in longevity expectations between rich and poor.
Option Pricing in a Regime Switching Jump Diffusion Model
Authors: Anindya Goswami, Omkar Manjarekar, Anjana R
Date: 2018-11-28 00:00:00

This paper presents the solution to a European option pricing problem by considering a regime-switching jump diffusion model of the underlying financial asset price dynamics. The regimes are assumed to be the results of an observed pure jump process, driving the values of interest rate and volatility coefficient. The pure jump process is assumed to be a semi-Markov process on finite state space. This consideration helps to incorporate a specific type of memory influence in the asset price. Under this model assumption, the locally risk minimizing price of the European type path-independent options is found. The F\"{o}llmer-Schweizer decomposition is adopted to show that the option price satisfies an evolution problem, as a function of time, stock price, market regime, and the stagnancy period. To be more precise, the evolution problem involves a linear, parabolic, degenerate and non-local system of integro-partial differential equations. We have established existence and uniqueness of classical solution to the evolution problem in an appropriate class.
Modeling the respiratory Central Pattern Generator with resonate-and-fire Izhikevich-Neurons
Date: 2018-11-28 00:00:00

Computational models of the respiratory central pattern generator (rCPG) are
usually based on biologically-plausible Hodgkin Huxley neuron models. Such
models require numerous parameters and thus are prone to overfitting. The HH
approach is motivated by the assumption that the biophysical properties of
neurons determine the network dynamics. Here, we implement the rCPG using
simpler Izhikevich resonate-and-fire neurons. Our rCPG model generates a
3-phase respiratory motor pattern based on established connectivities and can
reproduce previous experimental and theoretical observations. Further, we
demonstrate the flexibility of the model by testing whether intrinsic bursting
properties are necessary for rhythmogenesis. Our simulations demonstrate that
replacing predicted mandatory bursting properties of pre-inspiratory neurons
with spike adapting properties yields a model that generates comparable
respiratory activity patterns. The latter supports our view that the importance
of the exact modeling parameters of specific respiratory neurons is
overestimated.
Speed-detachment tradeoff and its effect on track bound transport of single motor protein
Authors: Mohd Suhail Rizvi
Date: 2018-11-28 00:00:00

The transportation of the cargoes in biological cells is primarily driven by the motor proteins on filamentous protein tracks. The stochastic nature of the motion of motor protein often leads to its spontaneous detachment from the track. Using the available experimental data, we demonstrate a tradeoff between the speed of the motor and its rate of spontaneous detachment from the track. Further, it is also shown that this speed-detachment relation follows a power law where its exponent dictates the nature of the motor protein processivity. We utilize this information to study the motion of motor protein on track using a random-walk model. We obtain the average distance travelled in fixed duration and average time required for covering a given distance by the motor protein. These analyses reveal non-monotonic dependence of the motor protein speed on its transport and, therefore, optimal motor speeds can be identified for the time and distance controlled conditions.
Exploiting "Quantum-like Interference" in Decision Fusion for Ranking Multimodal Documents
Authors: Gkoumas Dimitris, Sogn Dawei
Date: 2018-11-28 00:00:00
Tasks: Retrieval

Fusing and ranking multimodal information remains always a challenging task.
A robust decision-level fusion method should not only be dynamically adaptive
for assigning weights to each representation but also incorporate
inter-relationships among different modalities. In this paper, we propose a
quantum-inspired model for fusing and ranking visual and textual information
accounting for the dependency between the aforementioned modalities. At first,
we calculate the text-based and image-based similarity individually. Two
different approaches have been applied for computing each unimodal similarity.
The first one makes use of the bag-of-words model. For the second one, a
pre-trained VGG19 model on ImageNet has been used for calculating the image
similarity, while a query expansion approach has been applied to the text-based
query for improving the retrieval performance. Afterward, the local similarity
scores fit the proposed quantum-inspired model. The inter-dependency between
the two modalities is captured implicitly through "quantum interference".
Finally, the documents are ranked based on the proposed similarity measurement.
We test our approach on ImageCLEF2007photo data collection and show the
effectiveness of the proposed approach. A series of interesting findings are
discussed, which would provide theoretical and empirical foundations for future
development of this direction.
Resting-state ASL : Toward an optimal sequence duration
Date: 2018-11-28 00:00:00

Resting-state functional Arterial Spin Labeling (rs-fASL) in clinical daily
practice and academic research stay discreet compared to resting-state BOLD.
However, by giving direct access to cerebral blood flow maps, rs-fASL leads to
significant clinical subject scaled application as CBF can be considered as a
biomarker in common neuropathology. Our work here focuses on the link between
overall quality of rs-fASL and duration of acquisition. To this end, we
consider subject self-Default Mode Network (DMN), and assess DMN quality
depletion compared to a gold standard DMN depending on the duration of
acquisition.
Modelling Social Evolutionary Processes and Peer Effects in Agricultural Trade Networks: the Rubber Value Chain in Indonesia
Date: 2018-11-28 00:00:00

Understanding market participants' channel choices is important to policy
makers because it yields information on which channels are effective in
transmitting information. These channel choices are the result of a recursive
process of social interactions and determine the observable trading networks.
They are characterized by feedback mechanisms due to peer interaction and
therefore need to be understood as complex adaptive systems (CAS). When
modelling CAS, conventional approaches like regression analyses face severe
drawbacks since endogeneity is omnipresent. As an alternative, process-based
analyses allow researchers to capture these endogenous processes and multiple
feedback loops. This paper applies an agent-based modelling approach (ABM) to
the empirical example of the Indonesian rubber trade. The feedback mechanisms
are modelled via an innovative approach of a social matrix, which allows
decisions made in a specific period to feed back into the decision processes in
subsequent periods, and allows agents to systematically assign different
weights to the decision parameters based on their individual characteristics.
In the validation against the observed network, uncertainty in the found
estimates, as well as under determination of the model, are dealt with via an
approach of evolutionary calibration: a genetic algorithm finds the combination
of parameters that maximizes the similarity between the simulated and the
observed network. Results indicate that the sellers' channel choice decisions
are mostly driven by physical distance and debt obligations, as well as
peer-interaction. Within the social matrix, the most influential individuals
are sellers that live close by to other traders, are active in social groups
and belong to the ethnic majority in their village.
Acoustics-guided evaluation (AGE): a new measure for estimating performance of speech enhancement algorithms for robust ASR
Date: 2018-11-28 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, Speech Enhancement, speech-recognition, Speech Recognition

One challenging problem of robust automatic speech recognition (ASR) is how
to measure the goodness of a speech enhancement algorithm (SEA) without
calculating the word error rate (WER) due to the high costs of manual
transcriptions, language modeling and decoding process. Traditional measures
like PESQ and STOI for evaluating the speech quality and intelligibility were
verified to have relatively low correlations with WER. In this study, a novel
acoustics-guided evaluation (AGE) measure is proposed for estimating
performance of SEAs for robust ASR. AGE consists of three consecutive steps,
namely the low-level representations via the feature extraction, high-level
representations via the nonlinear mapping with the acoustic model (AM), and the
final AGE calculation between the representations of clean speech and degraded
speech. Specifically, state posterior probabilities from neural network based
AM are adopted for the high-level representations and the cross-entropy
criterion is used to calculate AGE. Experiments demonstrate AGE could yield
consistently highest correlations with WER and give the most accurate
estimation of ASR performance compared with PESQ, STOI, and acoustic confidence
measure using Entropy. Potentially, AGE could be adopted to guide the parameter
optimization of deep learning based SEAs to further improve the recognition
performance.
A Residual Bootstrap for Conditional Expected Shortfall
Date: 2018-11-26 00:00:00
Tasks: valid

This paper studies a fixed-design residual bootstrap method for the two-step
estimator of Francq and Zako\"ian (2015) associated with the conditional
Expected Shortfall. For a general class of volatility models the bootstrap is
shown to be asymptotically valid under the conditions imposed by Beutner et al.
(2018). A simulation study is conducted revealing that the average coverage
rates are satisfactory for most settings considered. There is no clear evidence
to have a preference for any of the three proposed bootstrap intervals. This
contrasts results in Beutner et al. (2018) for the VaR, for which the
reversed-tails interval has a superior performance.
Quantity over Quality: Dithered Quantization for Compressive Radar Systems
Date: 2018-11-28 00:00:00
Tasks: Compressive Sensing, Quantization

In this paper, we investigate a trade-off between the number of radar
observations (or measurements) and their resolution in the context of radar
range estimation. To this end, we introduce a novel estimation scheme that can
deal with strongly quantized received signals, going as low as 1-bit per signal
sample. We leverage for this a dithered quantized compressive sensing framework
that can be applied to classic radar processing and hardware. This allows us to
remove ambiguous scenarios prohibiting correct range estimation from
(undithered) quantized base-band radar signal. Two range estimation algorithms
are studied: Projected Back Projection (PBP) and Quantized Iterative Hard
Thresholding (QIHT). The effectiveness of the reconstruction methods combined
with the dithering strategy is shown through Monte Carlo simulations.
Furthermore we show that: (i), in dithered quantization, the accuracy of target
range estimation improves when the bit-rate (i.e., the total number of measured
bits) increases, whereas the accuracy of other undithered schemes saturate in
this case; and (ii), for fixed, low bit-rate scenarios, severely quantized
dithered schemes exhibit better performances than their full resolution
counterparts. These observations are confirmed using real measurements obtained
in a controlled environment, demonstrating the feasibility of the method in
real ranging applications.
Millimeter-Wave Downlink Positioning with a Single-Antenna Receiver
Date: 2018-11-28 00:00:00

The paper addresses the problem of determining the unknown position of a
mobile station for a mmWave MISO system. This setup is motivated by the fact
that massive arrays will be initially implemented only on 5G base stations,
likely leaving mobile stations with one antenna. The maximum likelihood
solution to this problem is devised based on the time of flight and angle of
departure of received downlink signals. While positioning in the uplink would
rely on angle of arrival, it presents scalability limitations that are avoided
in the downlink. To circumvent the multidimensional optimization of the optimal
joint estimator, we propose two novel approaches amenable to practical
implementation thanks to their reduced complexity. A thorough analysis, which
includes the derivation of relevant Cram\'er-Rao lower bounds, shows that it is
possible to achieve quasi-optimal performance even in presence of few
transmissions, low SNRs, and multipath propagation effects.
Towards a full quantitative description of single-molecule reaction kinetics in biological cells
Authors: Denis S. Grebenkov, Ralf Metzler, Gleb Oshanin
Date: 2018-11-27 00:00:00

The first-passage time (FPT), i.e., the moment when a stochastic process reaches a given threshold value for the first time, is a fundamental mathematical concept with immediate applications. In particular, it quantifies the statistics of instances when biomolecules in a biological cell reach their specific binding sites and trigger cellular regulation. Typically, the first-passage properties are given in terms of mean first-passage times. However, modern experiments now monitor single-molecular binding-processes in living cells and thus provide access to the full statistics of the underlying first-passage events, in particular, inherent cell-to-cell fluctuations. We here present a robust explicit approach for obtaining the distribution of FPTs to a small partially-reactive target in cylindrical-annulus domains, which represent typical bacterial and neuronal cell shapes. We investigate various asymptotic behaviours of this FPT distribution and show that it typically is very broad in many biological situations: thus, the mean FPT can differ from the most probable FPT by orders of magnitude. The most probable FPT is shown to strongly depend only on the starting position within the geometry and to be almost independent of the target size and reactivity. These findings demonstrate the dramatic relevance of knowing the full distribution of FPTs and thus open new perspectives for a more reliable description of many intracellular processes initiated by the arrival of one or few biomolecules to a small, spatially localised region inside the cell.
Prospective strict no-arbitrage and the fundamental theorem of asset pricing under transaction costs
Date: 2019-04-30 00:00:00

In discrete time markets with proportional transaction costs, Schachermayer
(2004) shows that robust no-arbitrage is equivalent to the existence of a
strictly consistent price system.
  In this paper, we introduce the concept of prospective strict no-arbitrage
that is a variant of the strict no-arbitrage property from Kabanov, R\'asonyi,
and Stricker (2002). The prospective strict no-arbitrage condition is slightly
weaker than robust no-arbitrage, and it implies that the set of portfolios
attainable from zero initial endowment is closed in probability. A weak version
of prospective strict no-arbitrage turns out to be equivalent to the existence
of a consistent price system. In contrast to the fundamental theorem of asset
pricing of Schachermayer (2004), the consistent frictionless prices may lie on
the boundary of the bid-ask spread.
  On the technical level, a crucial difference to Schachermayer (2004) and
Kabanov-R\'asonyi-Stricker (2003) is that we prove closedness without having at
hand that the null-strategies form a linear space.