pwc-archive/papers-with-abstracts huggingface dataset:
A Production Function with Variable Elasticity of Factor Substitution
Date: 2019-07-29 00:00:00

The main aim of this paper is to prove the existence of a new production
function with variable elasticity of factor substitution. This production
function is a more general form which includes the Cobb-Douglas production
function and the CES production function as particular cases. The econometric
estimates presented in the paper confirm some other results and reinforces the
conclusion that the sigma is well-below the Cobb-Douglas value of one.
Data-driven identification of dissipative linear models for nonlinear systems
Date: 2019-07-29 00:00:00

We consider the problem of identifying a dissipative linear model of an
unknown nonlinear system that is known to be dissipative, from time domain
input-output data. We first learn an approximate linear model of the nonlinear
system using standard system identification techniques and then perturb the
system matrices of the linear model to enforce dissipativity, while closely
approximating the dynamical behavior of the nonlinear system. Further, we
provide an analytical relationship between the size of the perturbation and the
radius in which the dissipativity of the linear model guarantees local
dissipativity of the unknown nonlinear system. We demonstrate the application
of this identification technique to the problem of learning a dissipative model
of a microgrid with high penetration of variable renewable energy sources.
Selection Principles for Gaia
Authors: R. Arthur, A. Nicholson
Date: 2019-07-29 00:00:00
Tasks: Diversity

The Gaia hypothesis considers the life-environment coupled system as a single entity that acts to regulate and maintain habitable conditions on Earth. In this paper we discuss three mechanisms which could potentially lead to Gaia: Selection by Survival, Sequential Selection and Entropic Hierarchy. We use the Tangled Nature Model of co-evolution as a common framework for investigating all three, using an extended version of the standard model to elaborate on Gaia as an example of an entropic hierarchy. This idea, which combines sequential selection together with a reservoir of diversity that acts as a 'memory', implies a tendency towards growth and increasing resilience of the Gaian system over time. We then discuss how Gaian memory could be realised in practice via the microbial seed bank, climate refugia and lateral gene transfer and conclude by discussing testable implications of an entropic hierarchy for the study of Earth history and the search for life in the universe. This paper adds to the existing taxonomy of Gaia hypotheses to suggest an "Entropic Gaia" where we argue that increasing biomass, complexity and enhanced habitability over time is a statistically likely feature of a co-evolving system.
On the Solutions of the Lucas-Uzawa Model
Date: 2019-07-29 00:00:00
Tasks: model

In a recent paper, Naz and Chaudry provided two solutions for the model of
Lucas-Uzawa, via the Partial Hamiltonian Approach. The first one of these
solutions coincides exactly with that determined by Chilarescu. For the second
one, they claim that this is a new solution, fundamentally different than that
obtained by Chilarescu. We will prove in this paper, using the existence and
uniqueness theorem of nonlinear differential equations, that this is not at all
true.
Residual-Guided In-Loop Filter Using Convolution Neural Network
Authors: Wei Jia, Li Li, Zhu Li, Xiang Zhang, Shan Liu
Date: 2019-07-29 00:00:00
Tasks: Blocking

The block-based coding structure in the hybrid video coding framework inevitably introduces compression artifacts such as blocking, ringing, etc. To compensate for those artifacts, extensive filtering techniques were proposed in the loop of video codecs, which are capable of boosting the subjective and objective qualities of reconstructed videos. Recently, neural network based filters were presented with the power of deep learning from a large magnitude of data. Though the coding efficiency has been improved from traditional methods in High-Efficiency Video Coding (HEVC), the rich features and information generated by the compression pipeline has not been fully utilized in the design of neural networks. Therefore, in this paper, we propose the Residual-Reconstruction-based Convolutional Neural Network (RRNet) to further improve the coding efficiency to its full extent, where the compression features induced from bitstream in form of prediction residual is fed into the network as an additional input to the reconstructed frame. In essence, the residual signal can provide valuable information about block partitions and can aid reconstruction of edge and texture regions in a picture. Thus, more adaptive parameters can be trained to handle different texture characteristics. The experimental results show that our proposed RRNet approach presents significant BD-rate savings compared to HEVC and the state-of-the-art CNN-based schemes, indicating that residual signal plays a significant role in enhancing video frame reconstruction.
Spatial Modulation for Ambient Backscatter Communications: Modeling and Analysis
Date: 2019-12-22 00:00:00
Tasks: TAG

Multiple-antenna backscatter is emerging as a promising approach to offer
high communication performance for the data-intensive applications of ambient
backscatter communications (AmBC). Although much has been understood about
multiple-antenna backscatter in conventional backscatter communications (CoBC),
existing analytical models cannot be directly applied to AmBC due to the
structural differences in RF source and tag circuit designs. This paper takes
the first step to fill the gap, by exploring the use of spatial modulation (SM)
in AmBC whenever tags are equipped with multiple antennas. Specifically, we
present a practical multiple-antenna backscatter design for AmBC that exempts
tags from the inter-antenna synchronization and mutual coupling problems while
ensuring high spectral efficiency and ultra-low power consumption. We obtain an
optimal detector for the joint detection of both backscatter signal and source
signal based on the maximum likelihood principle. We also design a two-step
algorithm to derive bounds on the bit error rate (BER) of both signals.
Simulation results validate the analysis and show that the proposed scheme can
significantly improve the throughput compared with traditional systems.
Millimeter-Wave NOMA with User Grouping, Power Allocation and Hybrid Beamforming
Date: 2019-07-30 00:00:00

This paper investigates the application of non-orthogonal multiple access in
millimeter-Wave communications (mmWave-NOMA). Particularly, we consider
downlink transmission with a hybrid beamforming structure. A user grouping
algorithm is first proposed according to the channel correlations of the users.
Whereafter, a joint hybrid beamforming and power allocation problem is
formulated to maximize the achievable sum rate, subject to a minimum rate
constraint for each user. To solve this non-convex problem with
high-dimensional variables, we first obtain the solution of power allocation
under arbitrary fixed hybrid beamforming, which is divided into intra-group
power allocation and inter-group power allocation. Then, given arbitrary fixed
analog beamforming, we utilize the approximate zero-forcing method to design
the digital beamforming to minimize the inter-group interference. Finally, the
analog beamforming problem with the constant-modulus constraint is solved with
a proposed boundary-compressed particle swarm optimization algorithm.
Simulation results show that the proposed joint approach, including user
grouping, hybrid beamforming and power allocation, outperforms the
state-of-the-art schemes and the conventional mmWave orthogonal multiple access
system in terms of achievable sum rate and energy efficiency.
Optimal Dynamic Multi-Resource Management in Earth Observation Oriented Space Information Networks
Date: 2019-07-30 00:00:00
Tasks: Earth Observation, Management, Scheduling, Stochastic Optimization

Space information network (SIN) is an innovative networking architecture to
achieve near-real-time mass data observation, processing and transmission over
the globe. In the SIN environment, it is essential to coordinate
multi-dimensional heterogeneous resources (i.e., observation resource,
computation resource and transmission resource) to improve network performance.
However, the time varying property of both the observation resource and
transmission resource is not fully exploited in existing studies. Dynamic
resource management according to instantaneous channel conditions has a
potential to enhance network performance. To this end, in this paper, we study
the multi-resource dynamic management problem, considering stochastic
observation and transmission channel conditions in SINs. Specifically, we
develop an aggregate optimization framework for observation scheduling,
compression ratio selection and transmission scheduling, and formulate a flow
optimization problem based on extended time expanded graph (ETEG) to maximize
the sum network utility. Then, we equivalently transform the flow optimization
problem on ETEG as a queue stability-related stochastic optimization problem.
An online algorithm is proposed to solve the problem in a slot-by-slot manner
by exploiting the Lyapunov optimization technique. Performance analysis shows
that the proposed algorithm achieves close-to-optimal network utility while
guaranteeing bounded queue occupancy. Extensive simulation results further
validate the efficiency of the proposed algorithm and evaluate the impacts of
various network parameters on the algorithm performance.
Steady-State Simulation for Combined Transmission and Distribution Systems
Date: 2019-07-30 00:00:00

The future electric grid will consist of significant penetration of renewable
and distributed generation that is likely to create a homogenous transmission
and distribution (T&D) system, requiring tools that can model and robustly
simulate the combined T&D networks. Existing tools use disparate models and
formulations for simulation of transmission versus distribution grids and
solving for the steady-state solution of the combined T&D networks often lacks
convergence robustness and scalability to large systems. In this paper, we show
that modeling both the T&D grid elements in terms of currents and voltages
using an equivalent circuit framework enables simulation of combined positive
sequence networks of the transmission grids with three-phase networks of the
distribution grids without loss of generality. We further demonstrate that we
can ensure robust convergence for these resulting large-scale complex T&D
systems when the circuit simulation methods are applied to them. Our results
illustrate robust convergence of combined T&D networks using a direct
Newton-Raphson solver on a single machine for smaller sized systems and using a
parallel Gauss-Seidel-Newton solver on multiple machines for larger sized
systems with greater than million nodes.
Is There Any Recovery Guarantee with Coupled Structured Matrix Factorization for Hyperspectral Super-Resolution?
Date: 2019-07-30 00:00:00
Tasks: Super-Resolution

Coupled structured matrix factorization (CoSMF) for hyperspectral
super-resolution (HSR) has recently drawn significant interest in hyperspectral
imaging for remote sensing. Presently there is very few work that studies the
theoretical recovery guarantees of CoSMF. This paper makes one such endeavor by
considering the CoSMF formulation by Wei et al., which, simply speaking, is
similar to coupled non-negative matrix factorization. Assuming no noise, we
show sufficient conditions under which the globably optimal solution to the
CoSMF problem is guaranteed to deliver certain recovery accuracies. Our
analysis suggests that sparsity and the pure-pixel (or separability) condition
play a hidden role in enabling CoSMF to achieve some good recovery
characteristics.
Two channel compartmentalized microfluidic chip for real time monitoring of the metastatic cascade
Authors: Hilaria Mollica, Roberto Palomba, Rosita Primavera, Paolo Decuzzi
Date: 2019-07-30 00:00:00

Metastases are the primary cause of death in cancer patients. Small animal models are helping in dissecting some of key features in the metastatic cascade. Yet, tools for systematically analyze the contribution of blood flow, vascular permeability, inflammation, tissue architecture, and biochemical stimuli are missing. In this work, a microfluidic chip is designed and tested to replicate in vitro key steps in the metastatic cascade. It comprises two channels, resting on the same plane, connected via an array of rounded pillars to form a permeable micro-membrane. One channel acts as a vascular compartment and is coated by a fully confluent monolayer of endothelial cells, whereas the other channel is filled with a mixture of matrigel and breast cancer cells (MDA-MB-231) and reproduces the malignant tissue. The vascular permeability can be finely modulated by inducing pro-inflammatory conditions in the tissue compartment, which transiently opens up the tight junctions of endothelial cells. Fresh medium flowing continuously in the vascular compartment is sufficient to induce cancer cell intravasation at rates of 8 cells/day with an average velocity of 0.5 um/min. On the other hand, the vascular adhesion and extravasation of circulating cancer cells require TNF-a stimulation. Extravasation occurs at lower rates with 4 cells/day and an average velocity of 0.1 um/min. Finally, the same chip is completely filled with matrigel and the migration of cancer cells from one channel to the other is monitored over a region of about 400 um. Invasion rates of 12 cells/day are documented upon TNF-a stimulation. This work demonstrates that the proposed compartmentalized microfluidic chip can efficiently replicate in vitro, under controlled biophysical and biochemical conditions, the multiple key steps in the cancer metastatic cascade.
Algorithms for Locating and Characterizing Cable Faults via Stepped-Frequency Waveform Reflectometry
Date: 2020-02-18 00:00:00

The paper presents algorithms to realize effectively and accurately the
stepped-frequency waveform reflectometry (SFWR), i.e. the reflectometric
technique based on the use of sinusoidal bursts. This technique is useful for
monitoring the health status of connection cables, but has many other
applications, like other reflectometric techniques. The paper outlines the
theory of SFWR, highlighting the problems associated to the transient
components in the reflected signals; presents a method to minimize the effect
of the transients, estimating the frequency response function (FRF) of interest
with very low systematic error; shows how to use the FRF to locate and
characterize faults in cables; evaluates accurately, using simulated cables
with exactly known characteristics, the errors associated to the proposed
methods. Overall, the paper demonstrates how the SFWR technique can be
effectively used for testing cables, and in general determine, via
reflectometry, parameters of interest of transmission lines.
A Simple Factoring Pricing Model
Authors: Ilaria Nava, Davide Cuccio, Lorenzo Giada, Claudio Nordio
Date: 2019-07-30 00:00:00
Tasks: model

In a simplified setting, we show how to price invoice non-recourse factoring taking into account not only the credit worthiness of the debtor but also the assignor's one, together with the default correlation between the two. Indeed, the possible default of the assignor might impact the payoff by means of the bankruptcy revocatory, especially in case of undisclosed factoring.
A Comparison of First-Difference and Forward Orthogonal Deviations GMM
Date: 2019-07-30 00:00:00

This paper provides a necessary and sufficient instruments condition assuring
two-step generalized method of moments (GMM) based on the forward orthogonal
deviations transformation is numerically equivalent to two-step GMM based on
the first-difference transformation. The condition also tells us when system
GMM, based on differencing, can be computed using forward orthogonal
deviations. Additionally, it tells us when forward orthogonal deviations and
differencing do not lead to the same GMM estimator. When estimators based on
these two transformations differ, Monte Carlo simulations indicate that
estimators based on forward orthogonal deviations have better finite sample
properties than estimators based on differencing.
CVA and vulnerable options in stochastic volatility models
Authors: Elisa Alos, Fabio Antonelli, Alessandro Ramponi, Sergio Scarlatti
Date: 2019-07-30 00:00:00

In this work we want to provide a general principle to evaluate the CVA (Credit Value Adjustment) for a vulnerable option, that is an option subject to some default event, concerning the solvability of the issuer. CVA is needed to evaluate correctly the contract and it is particularly important in presence of WWR (Wrong Way Risk), when a credit deterioration determines an increase of the claim's price. In particular, we are interested in evaluating the CVA in stochastic volatility models for the underlying's price (which often fit quite well the market's prices) when admitting correlation with the default event. By cunningly using Ito's calculus, we provide a general representation formula applicable to some popular models such as SABR, Hull \& White and Heston, which explicitly shows the correction in CVA due to the processes correlation. Later, we specialize this formula and construct its approximation for the three selected models. Lastly, we run a numerical study to test the formula's accuracy, comparing our results with Monte Carlo simulations.
Stratify or Inject: Two Simple Training Strategies to Improve Brain Tumor Segmentation
Date: 2019-07-30 00:00:00
Tasks: Brain Tumor Segmentation, Segmentation, Tumor Segmentation

Deep learning methods for brain tumor segmentation are typically trained in
an ad hoc fashion on all available data. Brain tumors are tremendously
heterogeneous in image appearance and labeled training data is limited. We
argue that incorporation of additional prior information, specifically tumor
grade, associated with tumor imaging phenotypes during model training can
significantly improve segmentation performance. Two strategies for
incorporation of tumor grade during model training are proposed and their
impact on segmentation performance is demonstrated on the BRATS 2018 dataset.
Visual illusions via neural dynamics: Wilson-Cowan-type models and the efficient representation principle
Date: 2019-12-27 00:00:00

In this work we have aimed to reproduce supra-threshold perception phenomena,
specifically visual illusions, with Wilson-Cowan-type models of neuronal
dynamics. We have found that it is indeed possible to do so, but that the
ability to replicate visual illusions is related to how well the neural
activity equations comply with the efficient representation principle. Our
first contribution is to show that the Wilson-Cowan equations can reproduce a
number of brightness and orientation-dependent illusions, and that the latter
type of illusions require that the neuronal dynamics equations consider
explicitly the orientation, as expected. Then, we formally prove that there
can't be an energy functional that the Wilson-Cowan equations are minimizing,
but that a slight modification makes them variational and yields a model that
is consistent with the efficient representation principle. Finally, we show
that this new model provides a better reproduction of visual illusions than the
original Wilson-Cowan formulation.
Visual Information flow in Wilson-Cowan networks
Date: 2019-07-30 00:00:00

In this work we study the communication efficiency of a
psychophysically-tuned cascade of Wilson-Cowan and Divisive Normalization
layers that simulate the retina-V1 pathway. This is the first analysis of
Wilson-Cowan networks in terms of multivariate total correlation. The
parameters of the cortical model have been derived through the relation between
the steady state of the Wilson-Cowan model and the Divisive Normalization
model.
  The communication efficiency has been analyzed in two ways: First, we provide
an analytical expression for the reduction of the total correlation among the
responses of a V1-like population after the application of the Wilson-Cowan
interaction. Second, we empirically study the efficiency with visual stimuli
and statistical tools that were not available before: (1) we use a recent,
radiometrically calibrated, set of natural scenes, and (2) we use a recent
technique to estimate the multivariate total correlation in bits from sets of
visual responses which only involves univariate operations, thus giving better
estimates of the redundancy.
  The theoretical and the empirical results show that although this cascade of
layers was not optimized for statistical independence in any way, the
redundancy between the responses gets substantially reduced along the neural
pathway. Specifically, we show that (1)~the efficiency of a Wilson-Cowan
network is similar to its equivalent Divisive Normalization model, (2) while
initial layers (Von-Kries adaptation and Weber-like brightness) contribute to
univariate equalization, the bigger contributions to the reduction in total
correlation come from the computation of nonlinear local contrast and the
application of local oriented filters, and (3)~psychophysically-tuned models
are more efficient (reduce more total correlation) in the more populated
regions of the luminance-contrast plane.
Detecting Identification Failure in Moment Condition Models
Authors: Jean-Jacques Forneron
Date: 2019-07-30 00:00:00

This paper develops an approach to detect identification failure in moment condition models. This is achieved by introducing a quasi-Jacobian matrix computed as the slope of a linear approximation of the moments on an estimate of the identified set. It is asymptotically singular when local and/or global identification fails, and equivalent to the usual Jacobian matrix which has full rank when the model is point and locally identified. Building on this property, a simple test with chi-squared critical values is introduced to conduct subvector inferences allowing for strong, semi-strong, and weak identification without \textit{a priori} knowledge about the underlying identification structure. Monte-Carlo simulations and an empirical application to the Long-Run Risks model illustrate the results.
Assessment and manipulation of the computational capacity of in vitro neuronal networks through criticality in neuronal avalanches
Date: 2019-07-29 00:00:00

In this work, we report the preliminary analysis of the electrophysiological
behavior of in vitro neuronal networks to identify when the networks are in a
critical state based on the size distribution of network-wide avalanches of
activity. The results presented here demonstrate the importance of selecting
appropriate parameters in the evaluation of the size distribution and indicate
that it is possible to perturb networks showing highly synchronized---or
supercritical---behavior into the critical state by increasing the level of
inhibition in the network. The classification of critical versus non-critical
networks is valuable in identifying networks that can be expected to perform
well on computational tasks, as criticality is widely considered to be the
state in which a system is best suited for computation. This type of analysis
is expected to enable the identification of networks that are well-suited for
computation and the classification of networks as perturbed or healthy. This
study is part of a larger research project, the overarching aim of which is to
develop computational models that are able to reproduce target behaviors
observed in in vitro neuronal networks. These models will ultimately be used to
aid in the realization of these behaviors in nanomagnet arrays to be used in
novel computing hardwares.