pwc-archive/papers-with-abstracts huggingface dataset:
Quantification of Alveolar Recruitment for the Optimization of Mechanical Ventilation using Quasi-static Pressure Volume Curve
Date: 2018-08-11 00:00:00

Quasi-static, pulmonary pressure-volume (P-V) curves over an
inflation-deflation cycle are analyzed using a respiratory system model (RSM),
which had been developed for quantitative characterization of the mechanical
behavior of the total respiratory system. Optimum mechanical ventilation
setting of Positive End Expiratory Pressure (PEEP) for total alveolar
recruitment is quantified based on the existing P-V curves of healthy and
injured animal models. Our analytical predictions may contribute to the
optimization of mechanical ventilation settings for the Acute Respiratory
Distress Syndrome (ARDS) patients.
Robust XVA
Authors: Maxim Bichuch, Agostino Capponi, Stephan Sturm
Date: 2018-08-14 00:00:00

We introduce an arbitrage-free framework for robust valuation adjustments. An investor trades a credit default swap portfolio with a risky counterparty, and hedges credit risk by taking a position in defaultable bonds. The investor does not know the return rate of her counterparty's bond, but is confident that it lies within an uncertainty interval. We derive both upper and lower bounds for the XVA process of the portfolio, and show that these bounds may be recovered as solutions of nonlinear ordinary differential equations. The presence of collateralization and closeout payoffs leads to important differences with respect to classical credit risk valuation. The value of the super-replicating portfolio cannot be directly obtained by plugging one of the extremes of the uncertainty interval in the valuation equation, but rather depends on the relation between the XVA replicating portfolio and the close-out value throughout the life of the transaction. Our comparative statics analysis indicates that credit contagion has a nonlinear effect on the replication strategies and on the XVA.
A Unified Framework for Efficient Estimation of General Treatment Models
Date: 2018-08-17 00:00:00

This paper presents a weighted optimization framework that unifies the
binary,multi-valued, continuous, as well as mixture of discrete and continuous
treatment, under the unconfounded treatment assignment. With a general loss
function, the framework includes the average, quantile and asymmetric least
squares causal effect of treatment as special cases. For this general
framework, we first derive the semiparametric efficiency bound for the causal
effect of treatment, extending the existing bound results to a wider class of
models. We then propose a generalized optimization estimation for the causal
effect with weights estimated by solving an expanding set of equations. Under
some sufficient conditions, we establish consistency and asymptotic normality
of the proposed estimator of the causal effect and show that the estimator
attains our semiparametric efficiency bound, thereby extending the existing
literature on efficient estimation of causal effect to a wider class of
applications. Finally, we discuss etimation of some causal effect functionals
such as the treatment effect curve and the average outcome. To evaluate the
finite sample performance of the proposed procedure, we conduct a small scale
simulation study and find that the proposed estimation has practical value. To
illustrate the applicability of the procedure, we revisit the literature on
campaign advertise and campaign contributions. Unlike the existing procedures
which produce mixed results, we find no evidence of campaign advertise on
campaign contribution.
Sparse Transmit Array Design for Dual-Function Radar Communications by Antenna Selection
Date: 2018-08-15 00:00:00

Dual-function radar communications (DFRC) systems have recently been proposed
to enable the coexistence of radar and wireless communications, which in turn
alleviates the increased spectrum congestion crisis. In this paper, we consider
the problem of sparse transmit array design for DFRC systems by antenna
selection where same or different antennas are assigned to different functions.
We consider three different types of DFRC systems which implement different
simultaneous beamformers associated with single and different sparse arrays
with shared aperture. We utilize the array configuration as an additional
spatial degree of freedom (DoF) to suppress the cross-interference and
facilitate the cohabitation of the two system functions. It is shown that the
use of sparse arrays adds to improved angular resolution with well-controlled
sidelobes on DFRC system paradigm. The utilization of sparse arrays in DFRC
systems is validated using simulation examples.
Neural Collaborative Ranking
Authors: Song Bo, Yang Xin, Cao Yi, Xu Congfu
Date: 2018-08-15 00:00:00
Tasks: Collaborative Filtering, Collaborative Ranking, Recommendation Systems, speech-recognition, Speech Recognition

Recommender systems are aimed at generating a personalized ranked list of
items that an end user might be interested in. With the unprecedented success
of deep learning in computer vision and speech recognition, recently it has
been a hot topic to bridge the gap between recommender systems and deep neural
network. And deep learning methods have been shown to achieve state-of-the-art
on many recommendation tasks. For example, a recent model, NeuMF, first
projects users and items into some shared low-dimensional latent feature space,
and then employs neural nets to model the interaction between the user and item
latent features to obtain state-of-the-art performance on the recommendation
tasks. NeuMF assumes that the non-interacted items are inherent negative and
uses negative sampling to relax this assumption. In this paper, we examine an
alternative approach which does not assume that the non-interacted items are
necessarily negative, just that they are less preferred than interacted items.
Specifically, we develop a new classification strategy based on the widely used
pairwise ranking assumption. We combine our classification strategy with the
recently proposed neural collaborative filtering framework, and propose a
general collaborative ranking framework called Neural Network based
Collaborative Ranking (NCR). We resort to a neural network architecture to
model a user's pairwise preference between items, with the belief that neural
network will effectively capture the latent structure of latent factors. The
experimental results on two real-world datasets show the superior performance
of our models in comparison with several state-of-the-art approaches.
Complexity of human response delay in intermittent control: The case of virtual stick balancing
Date: 2018-08-21 00:00:00

Response delay is an inherent and essential part of human actions. In the
context of human balance control, the response delay is traditionally modeled
using the formalism of delay-differential equations, which adopts the
approximation of fixed delay. However, experimental studies revealing
substantial variability, adaptive anticipation, and non-stationary dynamics of
response delay provide evidence against this approximation. In this paper, we
call for development of principally new mathematical formalism describing human
response delay. To support this, we present the experimental data from a simple
virtual stick balancing task. Our results demonstrate that human response delay
is a widely distributed random variable with complex properties, which can
exhibit oscillatory and adaptive dynamics characterized by long-range
correlations. Given this, we argue that the fixed-delay approximation ignores
essential properties of human response, and conclude with possible directions
for future developments of new mathematical notions describing human control.
On Shadowing the $\kappa$-$\mu$ Fading Model
Date: 2019-07-18 00:00:00
Tasks: model, Vocal Bursts Type Prediction

In this paper, we extensively investigate the way in which $\kappa$-$\mu$
fading channels can be impacted by shadowing. A family of shadowed
$\kappa$-$\mu$ fading models are introduced and classified according to whether
the underlying $\kappa$-$\mu$ fading undergoes single or double shadowing. We
discuss three types of single shadowed $\kappa$-$\mu$ model (denoted Type I to
Type III) and three types of double shadowed $\kappa$-$\mu$ model (denoted Type
I to Type III). The taxonomy of the single shadowed Type I - III models is
dependent upon whether the fading model assumes that the dominant component,
the scattered waves, or both experience shadowing. The categorization of the
double shadowed Type I - III models is dependent upon whether a) the envelope
experiences shadowing of the dominant component, which is preceded (or
succeeded) by a secondary round of shadowing (multiplicative), or b) the
dominant and scattered contributions are fluctuated by two independent
shadowing processes, or c) the scattered waves of the envelope are subject to
shadowing, which is also preceded (or succeeded) by a secondary round of
multiplicative shadowing. Although the physical definition of the examined
models make no predetermination of the statistics of the shadowing process, for
illustrative purposes, two example cases are provided for each type of single
and double shadowed model by assuming that the shadowing is shaped by a
Nakagami-$m$ random variable (RV), an inverse Nakagami-$m$ RV or their mixture.
The double shadowed $\kappa$-$\mu$ models offer remarkable flexibility as they
include the $\kappa$-$\mu$, $\eta$-$\mu$, and the various types of single
shadowed $\kappa$-$\mu$ distribution as special cases. Moreover, we demonstrate
a practical application of the double shadowed $\kappa$-$\mu$ Type I model by
applying it to channel measurements obtained for body area networks operating
at 2.45 GHz.
Game-theoretic dynamic investment model with incomplete information: futures contracts
Date: 2018-08-15 00:00:00

Over the past few years, the futures market has been successfully developing
in the North-West region. Futures markets are one of the most effective and
liquid-visible trading mechanisms. A large number of buyers are forced to
compete with each other and raise their prices. A large number of sellers make
them reduce prices. Thus, the gap between the prices of offers of buyers and
sellers is reduced due to high competition, and this is a good criterion for
the liquidity of the market. This high degree of liquidity contributed to the
fact that futures trading took such an important role in commerce and finance.
A multi-step, non-cooperative n persons game is formalized and studied
A multiple timescales approach to bridging spiking- and population-level dynamics
Date: 2018-08-15 00:00:00

A rigorous bridge between spiking-level and macroscopic quantities is an
on-going and well-developed story for asynchronously firing neurons, but focus
has shifted to include neural populations exhibiting varying synchronous
dynamics. Recent literature has used the Ott--Antonsen ansatz (2008) to great
effect, allowing a rigorous derivation of an order parameter for large
oscillator populations. The ansatz has been successfully applied using several
models including networks of Kuramoto oscillators, theta models, and
integrate-and-fire neurons, along with many types of network topologies. In the
present study, we take a converse approach: given the mean field dynamics of
slow synapses, predict the synchronization properties of finite neural
populations. The slow synapse assumption is amenable to averaging theory and
the method of multiple timescales. Our proposed theory applies to two
heterogeneous populations of N excitatory n-dimensional and N inhibitory
m-dimensional oscillators with homogeneous synaptic weights. We then
demonstrate our theory using two examples. In the first example we take a
network of excitatory and inhibitory theta neurons and consider the case with
and without heterogeneous inputs. In the second example we use Traub models
with calcium for the excitatory neurons and Wang-Buzs{\'a}ki models for the
inhibitory neurons. We accurately predict phase drift and phase locking in each
example even when the slow synapses exhibit non-trivial mean-field dynamics.
Robustness of functional networks at criticality against structural defects
Date: 2018-08-15 00:00:00

The robustness of dynamical properties of neuronal networks against
structural damages is a central problem in computational and experimental
neuroscience. Research has shown that the cortical network of a healthy brain
works near a critical state, and moreover, that functional neuronal networks
often have scale-free and small-world properties. In this work, we study how
the robustness of simple functional networks at criticality is affected by
structural defects. In particular, we consider a 2D Ising model at the critical
temperature and investigate how its functional network changes with the
increasing degree of structural defects. We show that the scale-free and
small-world properties of the functional network at criticality are robust
against large degrees of structural lesions while the system remains below the
percolation limit. Although the Ising model is only a conceptual description of
a two-state neuron, our research reveals fundamental robustness properties of
functional networks derived from classical statistical mechanics models.
A New Nonparametric Estimate of the Risk-Neutral Density with Applications to Variance Swaps
Authors: Liyuan Jiang, Shuang Zhou, Keren Li, Fangfang Wang, Jie Yang
Date: 2018-08-15 00:00:00

We develop a new nonparametric approach for estimating the risk-neutral density of asset prices and reformulate its estimation into a double-constrained optimization problem. We evaluate our approach using the S\&P 500 market option prices from 1996 to 2015. A comprehensive cross-validation study shows that our approach outperforms the existing nonparametric quartic B-spline and cubic spline methods, as well as the parametric method based on the Normal Inverse Gaussian distribution. As an application, we use the proposed density estimator to price long-term variance swaps, and the model-implied prices match reasonably well with those of the variance future downloaded from the CBOE website.
When Do Households Invest in Solar Photovoltaics? An Application of Prospect Theory
Date: 2018-08-16 00:00:00

While investments in renewable energy sources (RES) are incentivized around
the world, the policy tools that do so are still poorly understood, leading to
costly misadjustments in many cases. As a case study, the deployment dynamics
of residential solar photovoltaics (PV) invoked by the German feed-in tariff
legislation are investigated. Here we report a model showing that the question
of when people invest in residential PV systems is found to be not only
determined by profitability, but also by profitability's change compared to the
status quo. This finding is interpreted in the light of loss aversion, a
concept developed in Kahneman and Tversky's Prospect Theory. The model is able
to reproduce most of the dynamics of the uptake with only a few financial and
behavioral assumptions
Information-theoretic analysis of multivariate single - cell signaling responses using SLEMI
Date: 2018-08-16 00:00:00

Mathematical methods of information theory constitute essential tools to
describe how stimuli are encoded in activities of signaling effectors.
Exploring the information-theoretic perspective, however, remains conceptually,
experimentally and computationally challenging. Specifically, existing
computational tools enable efficient analysis of relatively simple systems,
usually with one input and output only. Moreover, their robust and readily
applicable implementations are missing. Here, we propose a novel algorithm to
analyze signaling data within the framework of information theory. Our approach
enables robust as well as statistically and computationally efficient analysis
of signaling systems with high-dimensional outputs and a large number of input
values. Analysis of the NF-kB single - cell signaling responses to TNF-a
uniquely reveals that the NF-kB signaling dynamics improves discrimination of
high concentrations of TNF-a with a modest impact on discrimination of low
concentrations. Our readily applicable R-package, SLEMI - statistical learning
based estimation of mutual information, allows the approach to be used by
computational biologists with only elementary knowledge of information theory.
IceBreaker: Solving Cold Start Problem for Video Recommendation Engines
Authors: Kumar Yaman, Sharma Agniv, Khaund Abhigyan, Kumar Akash, Kumaraguru Ponnurangam, Shah Rajiv Ratn
Date: 2018-08-16 00:00:00
Tasks: Recommendation Systems

Internet has brought about a tremendous increase in content of all forms and,
in that, video content constitutes the major backbone of the total content
being published as well as watched. Thus it becomes imperative for video
recommendation engines such as Hulu to look for novel and innovative ways to
recommend the newly added videos to their users. However, the problem with new
videos is that they lack any sort of metadata and user interaction so as to be
able to rate the videos for the consumers. To this effect, this paper
introduces the several techniques we develop for the Content Based Video
Relevance Prediction (CBVRP) Challenge being hosted by Hulu for the ACM
Multimedia Conference 2018. We employ different architectures on the CBVRP
dataset to make use of the provided frame and video level features and generate
predictions of videos that are similar to the other videos. We also implement
several ensemble strategies to explore complementarity between both the types
of provided features. The obtained results are encouraging and will impel the
boundaries of research for multimedia based video recommendation systems.
Not even wrong: The spurious link between biodiversity and ecosystem functioning
Date: 2018-08-16 00:00:00

Resolving the relationship between biodiversity and ecosystem functioning has
been one of the central goals of modern ecology. Early debates about the
relationship were finally resolved with the advent of a statistical
partitioning scheme that decomposed the biodiversity effect into a "selection"
effect and a "complementarity" effect. We prove that both the biodiversity
effect and its statistical decomposition into selection and complementarity are
fundamentally flawed because these methods use a na\"ive null expectation based
on neutrality, likely leading to an overestimate of the net biodiversity
effect, and they fail to account for the nonlinear abundance-ecosystem
functioning relationships observed in nature. Furthermore, under such
nonlinearity no statistical scheme can be devised to partition the biodiversity
effects. We also present an alternative metric providing a more reasonable
estimate of biodiversity effect. Our results suggest that all studies conducted
since the early 1990s likely overestimated the positive effects of biodiversity
on ecosystem functioning.
Optimal Scheduling of an Isolated Microgrid with Battery Storage Considering Load and Renewable Generation Uncertainties
Date: 2018-08-16 00:00:00
Tasks: Scheduling

By modeling the uncertainty of spinning reserves provided by energy storage
with probabilistic constraints, a new optimal scheduling mode is proposed for
minimizing the operating costs of an isolated microgrid (MG) by using
chance-constrained programming. The model is transformed into a readily
solvable mixed integer linear programming (MILP) formulation in GAMS via a
proposed discretized step transformation (DST) approach and finally solved by
applying the CPLEX solver. By properly setting the confidence levels of the
spinning reserve probability constraints, the MG operation can be achieved a
trade-off between reliability and economy. The test results on the modified
ORNL DECC lab MG test system reveal that the proposal significantly exceeds the
commonly used hybrid intelligent algorithm with much better and more stable
optimization results and significantly reduced calculation times.
The triennial International Pigment Cell Conference (IPCC)
Authors: Neil F. Box, Lionel Larue, Prashiela Manga, Lluis Montoliu, Richard A. Spritz, Fabian V. Filipp
Date: 2018-08-15 00:00:00
Tasks: Epidemiology, Philosophy

The International Federation of Pigment Cell Societies (IFPCS) held its XXIII triennial International Pigment Cell Conference (IPCC) in Denver, Colorado in August 2017. The goal of the summit was to provide a venue promoting a vibrant interchange among leading basic and clinical researchers working on leading-edge aspects of melanocyte biology and disease. The philosophy of the meeting, entitled Breakthroughs in Pigment Cell and Melanoma Research, was to deliver a comprehensive program in an inclusive environment fostering scientific exchange and building new academic bridges. This document provides an outlook on the history, accomplishments, and sustainability of the pigment cell and melanoma research community. Shared progress in the understanding of cellular homeostasis of pigment cells but also clinical successes and hurdles in the treatment of melanoma and dermatological disorders continue to drive future research activities. A sustainable direction of the societies creates an international forum identifying key areas of imminent needs in laboratory research and clinical care and ensures the future of this vibrant, diverse and unique research community at the same time. Important advances showcase wealth and breadth of the field in melanocyte and melanoma research and include emerging frontiers in melanoma immunotherapy, medical and surgical oncology, dermatology, vitiligo, albinism, genomics and systems biology, precision bench-to-bedside approaches, epidemiology, pigment biophysics and chemistry, and evolution. This report recapitulates highlights of the federate meeting agenda designed to advance clinical and basic research frontiers from melanoma and dermatological sciences followed by a historical perspective of the associated societies and conferences.
Unsupervised Feature Learning for Online Voltage Stability Evaluation and Monitoring Based on Variational Autoencoder
Date: 2020-03-31 00:00:00

With the increase of uncertain elements in power systems and extensive
deployment of online monitoring devices, it is necessary to search a more
real-time and robust voltage stability assessment method. This study, using PMU
monitoring data, explores a novel data-driven approach for long-term voltage
stability assessment based on variational autoencoder (VAE). Our method is
capable of extracting the most representative features by an unsupervised data
mining method in a probabilistic learning way. Different from most of familiar
feature extraction methods, it regularizes latent features in an expected
stochastic distribution. Furthermore, a statistical indicator by sampling
latent features after variance reduction is proposed to assess long-term
voltage stability. Our approach is tested in various simulated power systems
with different load increment models. Other cases show the accuracy and speed
of our approach for estimating voltage collapse point. These testing cases
successfully demonstrate the accuracy and effectiveness of our approach.