pwc-archive/papers-with-abstracts huggingface dataset:
Microwave Tube Fault-Current Model for Design of Crowbar Protection
Date: 2018-02-20 00:00:00

Many applications that use high energy plasma are realized using Microwave
tubes (MWT) that operate at peak power in the range of hundreds of MW and
frequency in GHz. One failure mode of the MWT is due to the excess energy in
the tube during internal arcing events. Crowbar is used to protect the MWT by
diverting the energy during fault. To compute the energy released into the MWT,
the dc fault current model and the MWT model are essential. An equivalent fuse
wire model is utilized for the MWT for the crowbar applications. The paper
proposes a model for the dc fault current, the analysis for which is based on
Joules Integral energy concept. The model provides flexibility to choose a
range of practically observed reactance to resistance ratio (X/R) of
transformer and also allows the use of a range of dc current limiting
resistances that are utilized in the High Voltage (HV) power supply circuits in
Microwave applications. The non-linearity of the system due to the multipulse
diode rectifier is also considered by introducing a correction factor in the
model. This paper shows that the same correction factor can be applied for both
dc side parallel and series connected rectifier circuits. Both dc fault current
and MWT models are verified experimentally. Using the model a 10kV , 1kA
crowbar is built to limit the energy in MWT below 10J.
Low Intensity LiDAR using Compressed Sensing and a Photon Number Resolving Detector
Date: 2018-02-22 00:00:00
Tasks: compressed sensing, Sensitivity

LiDAR (laser based radar) systems are a major part of many new real-world
interactive systems, one of the most notable being autonomous cars. The current
market LiDAR systems are limited by detector sensitivity: when output power is
at eye-safe levels, the range is limited. Long range operation also slows image
acquisition as flight-time increases. We present an approach that combines a
high sensitivity photon number resolving diode with machine learning and a
micro-mechanical digital mirror device to achieve safe and fast long range 3D
scanning.
Plant species accumulation curves are determined by evenness and spatial aggregation in drylands worldwide
Date: 2018-02-26 00:00:00
Tasks: Form

Species accumulation curves (SAC), i.e. the relationship between species
richness and the number of sampling units in a given community, can be used to
describe diversity patterns while accounting for the well-known
scale-dependence of species richness. Despite their value, the functional form
and the parameters of SAC, as well as their determinants, have barely been
investigated in plant communities, particularly in drylands. We characterized
the SAC of perennial plant communities from 233 dryland ecosystems from six
continents by comparing the fit of major functions (power-law, logarithmic and
Michaelis-Menten). We tested the theoretical prediction that the effects of
aridity and soil pH on SAC are mediated by vegetation attributes such as
evenness, cover, and spatial aggregation. We found that the logarithmic
relationship was the most common functional form, followed by Michaelis-Menten
and power-law. Functional form was mainly determined by evenness while the SAC
parameters (intercept and slope) were largely determined by spatial
aggregation. In addition, aridity decreased small scale richness (intercept of
SAC) but did not affect accumulation rate (slope of the SAC). Our results
highlight the role that attributes such as spatial aggregation and evenness
play as main mediators of the SAC of vegetation in drylands, the Earth's
largest biome.
DTER: Schedule Optimal RF Energy Request and Harvest for Internet of Things
Date: 2018-02-20 00:00:00

We propose a new energy harvesting strategy that uses a dedicated energy
source (ES) to optimally replenish energy for radio frequency (RF) energy
harvesting powered Internet of Things. Specifically, we develop a two-step dual
tunnel energy requesting (DTER) strategy that minimizes the energy consumption
on both the energy harvesting device and the ES. Besides the causality and
capacity constraints that are investigated in the existing approaches, DTER
also takes into account the overhead issue and the nonlinear charge
characteristics of an energy storage component to make the proposed strategy
practical. Both offline and online scenarios are considered in the second step
of DTER. To solve the nonlinear optimization problem of the offline scenario,
we convert the design of offline optimal energy requesting problem into a
classic shortest path problem and thus a global optimal solution can be
obtained through dynamic programming (DP) algorithms. The online suboptimal
transmission strategy is developed as well. Simulation study verifies that the
online strategy can achieve almost the same energy efficiency as the global
optimal solution in the long term.
Lossy Compression of Decimated Gaussian Random Walks
Date: 2018-02-26 00:00:00
Tasks: Decoder

We consider the problem of estimating a Gaussian random walk from a lossy
compression of its decimated version. Hence, the encoder operates on the
decimated random walk, and the decoder estimates the original random walk from
its encoded version under a mean squared error (MSE) criterion. It is
well-known that the minimal distortion in this problem is attained by an
estimate-and-compress (EC) source coding strategy, in which the encoder first
estimates the original random walk and then compresses this estimate subject to
the bit constraint. In this work, we derive a closed-form expression for this
minimal distortion as a function of the bitrate and the decimation factor.
Next, we consider a compress-and-estimate (CE) source coding scheme, in which
the encoder first compresses the decimated sequence subject to an MSE criterion
(with respect to the decimated sequence), and the original random walk is
estimated only at the decoder. We evaluate the distortion under CE in a closed
form and show that there exists a nonzero gap between the distortion under the
two schemes. This difference in performance illustrates the importance of
having the decimation factor at the encoder.
Optimal Control of Autonomous Vehicles Approaching A Traffic Light
Date: 2018-02-26 00:00:00
Tasks: Autonomous Vehicles

This paper devotes to the development of an optimal acceleration/speed
profile for autonomous vehicles approaching a traffic light. The design
objective is to achieve both short travel time and low energy consumption as
well as avoid idling at a red light. This is achieved by taking full advantage
of the traffic light information based on vehicle-to-infrastructure
communication. The problem is modeled as a mixed integer programming, which is
equivalently transformed into optimal control problems by relaxing the integer
constraint. Then the direct adjoining approach is used to solve both free and
fixed terminal time optimal control problems subject to state constraints. By
an elaborate analysis, we are able to produce a real-time online analytical
solution, distinguishing our method from most existing approaches based on
numerical calculations. Extensive simulations are executed to compare the
performance of autonomous vehicles under the proposed speed profile and human
driving vehicles. The results show quantitatively the advantages of the
proposed algorithm in terms of energy consumption and travel time.
Artificial Noise Aided Secure Cognitive Beamforming for Cooperative MISO-NOMA Using SWIPT
Date: 2018-02-20 00:00:00

Cognitive radio (CR) and non-orthogonal multiple access (NOMA) have been
deemed two promising technologies due to their potential to achieve high
spectral efficiency and massive connectivity. This paper studies a
multiple-input single-output NOMA CR network relying on simultaneous wireless
information and power transfer (SWIPT) conceived for supporting a massive
population of power limited battery-driven devices. In contrast to most of the
existing works, which use an ideally linear energy harvesting model, this study
applies a more practical non-linear energy harvesting model. In order to
improve the security of the primary network, an artificial-noise-aided
cooperative jamming scheme is proposed. The artificial-noise-aided beamforming
design problems are investigated subject to the practical secrecy rate and
energy harvesting constraints. Specifically, the transmission power
minimization problems are formulated under both perfect channel state
information (CSI) and the bounded CSI error model. The problems formulated are
non-convex, hence they are challenging to solve. A pair of algorithms either
using semidefinite relaxation (SDR) or a cost function are proposed for solving
these problems. Our simulation results show that the proposed cooperative
jamming scheme succeeds in establishing secure communications and NOMA is
capable of outperforming the conventional orthogonal multiple access in terms
of its power efficiency. Finally, we demonstrate that the cost function
algorithm outperforms the SDR-based algorithm.
An Expanded Local Variance Gamma model
Authors: Peter Carr, Andrey Itkin
Date: 2018-02-26 00:00:00
Tasks: model

The paper proposes an expanded version of the Local Variance Gamma model of Carr and Nadtochiy by adding drift to the governing underlying process. Still in this new model it is possible to derive an ordinary differential equation for the option price which plays a role of Dupire's equation for the standard local volatility model. It is shown how calibration of multiple smiles (the whole local volatility surface) can be done in such a case. Further, assuming the local variance to be a piecewise linear function of strike and piecewise constant function of time this ODE is solved in closed form in terms of Confluent hypergeometric functions. Calibration of the model to market smiles does not require solving any optimization problem and, in contrast, can be done term-by-term by solving a system of non-linear algebraic equations for each maturity, which is fast.
Cognition and Reality
Date: 2017-12-27 00:00:00
Tasks: Retrieval

We discuss the two moments of human cognition, namely, apprehension (A),
whereby a coherent perception emerges from the recruitment of neuronal groups,
and judgment(B),that entails the comparison of two apprehensions acquired at
different times, coded in a suitable language and retrieved by memory. (B)
entails self-consciousness, in so far as the agent who expresses the judgment
must be aware that the two apprehensions are submitted to his/her own scrutiny
and that it is his/her task to extract a mutual relation. Since (B) lasts
around 3 seconds, the semantic value of the pieces under comparison must be
decided within that time. This implies a fast search of the memory contents. As
a fact, exploring human subjects with sequences of simple words, we find
evidence of a limited time window , corresponding to the memory retrieval of a
linguistic item in order to match it with the next one in a text flow (be it
literary, or musical,or figurative). While apprehension is globally explained
as a Bayes inference, judgment tresults from an inverse Bayes inference. As a
consequence, two hermeneutics emerge (called respectively circle and coil). The
first one acts in a pre-assigned space of features. The second one provides the
discovery of novel features, thus unveiling previously unknown aspects and
hence representing the road to reality.
User Association for Offloading in Heterogeneous Network Based on Matern Cluster Process
Date: 2018-02-27 00:00:00

Future mobile networks are converging toward heterogeneous multi-tier
networks, where various classes of base stations (BS) are deployed based on
user demand. So it is quite necessary to utilize the BSs resources rationally
when BSs are sufficient. In this paper, we develop a more realistic model that
fully considering the inter-tier dependence and the dependence between users
and BSs, where the macro base stations (MBSs) are distributed according to a
homogeneous Poisson point process (PPP) and the small base stations (SBSs)
follows a Matern cluster process (MCP) whose parent points are located in the
positions of the MBSs in order to offload the users from the over-loaded MBSs.
We also assume the users are just randomly located in the circles centered at
the MBSs. Under this model, we derive the association probability and the
average ergodic rate by stochastic geometry. An interesting result that the
density of MBS and the radius of the clusters jointly affect the association
probabilities in a joint form is obtained. We also observe that using the
clustered SBSs results in aggressive offloading compared with previous cellular
networks.
Information-theoretic signatures of biodiversity in the barcoding gene
Date: 2018-02-27 00:00:00

The COI mitochondrial gene is present in all animal phyla and in a few
others, and is the leading candidate for species identification through DNA
barcoding. Calculating a generalized form of total correlation on publicly
available data on the gene yields distinctive information-theoretic descriptors
of the phyla represented in the data. Moreover, performing principal component
analysis on standardized versions of these descriptors reveals a strong
correlation between the first principal component and the natural logarithm of
the number of known living species. The descriptors thus constitute clear
information-theoretic signatures of the processes whereby evolution has given
rise to current biodiversity.
Option Pricing Models Driven by the Space-Time Fractional Diffusion: Series Representation and Applications
Date: 2018-02-27 00:00:00

In this paper, we focus on option pricing models based on space-time
fractional diffusion. We briefly revise recent results which show that the
option price can be represented in the terms of rapidly converging
double-series and apply these results to the data from real markets. We focus
on estimation of model parameters from the market data and estimation of
implied volatility within the space-time fractional option pricing models.
On the solution of the variational optimisation in the rational inattention framework
Date: 2018-07-18 00:00:00

I analyse the solution method for the variational optimisation problem in the
rational inattention framework proposed by Christopher A. Sims. The solution,
in general, does not exist, although it may exist in exceptional cases. I show
that the solution does not exist for the quadratic and the logarithmic
objective functions analysed by Sims (2003, 2006). For a linear-quadratic
objective function a solution can be constructed under restrictions on all but
one of its parameters. This approach is, therefore, unlikely to be applicable
to a wider set of economic models.
Price Impact Under Heterogeneous Beliefs and Restricted Participation
Authors: Michail Anthropelos, Constantinos Kardaras
Date: 2018-02-27 00:00:00

We consider a financial market in which traders potentially face restrictions in trading some of the available securities. Traders are heterogeneous with respect to their beliefs and risk profiles, and the market is assumed thin: traders strategically trade against their price impacts. We prove existence and uniqueness of a corresponding equilibrium, and provide an efficient algorithm to numerically obtain the equilibrium prices and allocations given market's inputs. We find that restrictions may increase the market's welfare if traders have different views regarding the covariance matrix of securities returns. The latter heterogeneity regarding covariance matrix disagreement is essential in modelling; for instance, when traders agree on the covariance matrix, restricting participation in some securities for some traders leaves equilibrium prices unaltered in the unrestricted securities, a certainly undesirable model effect.
Use of Two-Mode Circuitry and Optimal Energy-Efficient Power Control Under Target Delay-Outage Constraints
Date: 2018-02-27 00:00:00

An accurate energy efficiency analytical model based on a two-mode circuitry
was recently proposed; and the model showed that the use of this circuitry can
significantly improve a system's energy efficiency. In this paper, we use this
analytical model to develop a new power control scheme, a scheme that is
capable of allocating a minimum transmission power precisely within the
delay-outage probability constraint. Precision brings substantial benefits as
numerical results show that the energy efficiency using our scheme is much
higher than other schemes. Results further suggest that data rate values affect
energy efficiency non-uniformly, i.e., there exists a specific data rate value
that achieves maximum energy efficiency.
Valuation, Liquidity Price, and Stability of Cryptocurrencies
Date: 2018-02-27 00:00:00

Cryptocurrencies are examined through the asset flow equations and
experimental asset markets. Since tangible value of a typical cryptocurrency is
non-existent, the theory suggests that price will gravitate toward liquidity
value, i.e., the total amount of cash available for purchase of the asset
divided by the number of units. Thus it is unlikely that cryptocurrencies in
their current form will be stable in the absence of a mechanism of a link to
value.
Nonlinear Energy Harvesting Models in Wireless Information and Power Transfer
Date: 2018-02-24 00:00:00
Tasks: Sensitivity

This work compares different linear and nonlinear RF energy harvesting
models, including limited or unlimited sensitivity, for simultaneous wireless
information and power transfer (SWIPT). The probability of successful SWIPT
reception under a family of RF harvesting models is rigorously quantified,
using state-of-the-art rectifiers in the context of commercial RFIDs. A
significant portion of SWIPT literature uses oversimplified models that do not
account for limited sensitivity or nonlinearity of the underlying harvesting
circuitry. This work demonstrates that communications signals are not always
appropriate for simultaneous energy transfer and concludes that for practical
SWIPT studies, the inherent non-ideal characteristics of the harvester should
be carefully taken into account; specific harvester's modeling methodology is
also offered.
Private Information, Credit Risk and Graph Structure in P2P Lending Networks
Date: 2018-02-11 00:00:00

This research investigated the potential for improving Peer-to-Peer (P2P)
credit scoring by using "private information" about communications and travels
of borrowers. We found that P2P borrowers' ego networks exhibit scale-free
behavior driven by underlying preferential attachment mechanisms that connect
borrowers in a fashion that can be used to predict loan profitability. The
projection of these private networks onto networks of mobile phone
communication and geographical locations from mobile phone GPS potentially give
loan providers access to private information through graph and location metrics
which we used to predict loan profitability. Graph topology was found to be an
important predictor of loan profitability, explaining over 5.5% of variability.
Networks of borrower location information explain an additional 19% of the
profitability. Machine learning algorithms were applied to the data set
previously analyzed to develop the predictive model and resulted in a 4%
reduction in mean squared error.
The Information Content of Sarbanes-Oxley in Predicting Security Breaches
Date: 2018-02-11 00:00:00
Tasks: Management

We investigated publicly reported security breaches of internal controls in
corporate systems to determine whether SOX assessments are information bearing
with respect to breaches which can lead to materially significant losses and
misstatements. SOX Section 404 adverse decisions on effectiveness of controls
occurred in 100% of credit card data breaches and around 33% of insider
breaches. SOX 404 audits provided a contrarian "effective" control decisions on
88% of situations where there was a control breach concerning a portable
device. We found that management and SOX 404 auditors do not general agree on
the underlying internal control situation at any time; instead the SOX 404 team
was likely to discover material weaknesses and "educate" management and
internal audit teams about the importance of these control weaknesses. SOX
attestations were poor at identifying control weaknesses from unintended
disclosures, physical losses, hacking and malware. Hazard and occupancy models
showed that both SOX 302 and 404 section audits provided information on the
frequency of breaches, with SOX 404 being three times as informative as section
302 reports. The hazard model found an expected 2.88% reduction in breaches
when SOX 302 controls are effective; management "material weakness'
attestations provided no information in this structural model, whereas there
would be around a 1% increase in breach occurrence when there are significant
deficiencies. SOX 404 attestations were the most informative, and a negative
SOX 404 attestation is projected to increase the frequency of breaches by
around 8.5%.
Broadly heterogeneous network topology begets order-based representation by privileged neurons
Date: 2018-02-27 00:00:00

How spiking activity reverberates through neuronal networks, how evoked and
spontaneous activity interact and blend, and how the combined activities
represent external stimulation are pivotal questions in neuroscience. We
simulated minimal models of unstructured spiking networks in silico, asking
whether and how gentle external stimulation might be subsequently reflected in
spontaneous activity fluctuations. Consistent with earlier findings in silico
and in vitro, we observe a privileged sub-population of 'pioneer neurons' that,
by their firing order, reliably encode previous external stimulation. We show
that the distinctive role of pioneer neurons is owed to a combination of
exceptional sensitivity to, and pronounced influence on, network activity. We
further show that broadly heterogeneous connection topology - a broad "middle
class" in degree of connectedness - not only increases the number of 'pioneer
neurons' in unstructured networks, but also renders the emergence of 'pioneer
neurons' more robust to changes in the excitatory-inhibitory balance. In
conclusion, we offer a minimal model for the emergence and representational
role of 'pioneer neurons', as observed experimentally in vitro. In addition, we
show how broadly heterogeneous connectivity can enhance the representational
capacity of unstructured networks.
Graph Signal Processing: Filter Design and Spectral Statistics
Date: 2018-02-27 00:00:00

Graph signal processing analyzes signals supported on the nodes of a graph by
defining the shift operator in terms of a matrix, such as the graph adjacency
matrix or Laplacian matrix, related to the structure of the graph. With respect
to the graph shift operator, polynomial functions of the shift matrix perform
filtering. An application considered in this paper, convergence acceleration
filters for distributed average consensus may be viewed as lowpass graph
filters periodically applied to the states. Design of graph filters depends on
the shift matrix eigendecomposition. Consequently, random graphs present a
challenge as this information is often difficult to obtain. Nevertheless, the
asymptotic behavior of the shift matrix empirical spectral distribution
provides a substitute for suitable random matrix models. This paper employs
deterministic approximations for empirical spectral statistics from other works
to propose optimization criteria for consensus acceleration filters, evaluating
the results through simulation.