pwc-archive/papers-with-abstracts huggingface dataset:
A possible alternative evaluation method for the non-use and nonmarket values of ecosystem services
Date: 2018-11-20 00:00:00
Tasks: Management

Monetization of the non-use and nonmarket values of ecosystem services is
important especially in the areas of environmental cost-benefit analysis,
management and environmental impact assessment. However, the reliability of
valuation estimations has been criticized due to the biases that associated
with methods like the popular contingent valuation method (CVM). In order to
provide alternative valuation results for comparison purpose, we proposed the
possibility of using a method that incorporates fact-based costs and contingent
preferences for evaluating non-use and nonmarket values, which we referred to
as value allotment method (VAM). In this paper, we discussed the economic
principles of VAM, introduced the performing procedure, analyzed assumptions
and potential biases that associated with the method and compared VAM with CVM
through a case study in Guangzhou, China. The case study showed that the VAM
gave more conservative estimates than the CVM, which could be a merit since CVM
often generates overestimated values. We believe that this method can be used
at least as a referential alternative to CVM and might be particularly useful
in assessing the non-use and nonmarket values of ecosystem services from
human-invested ecosystems, such as restored ecosystems, man-made parks and
croplands.
Comprehensive study on influence of low dose chronic consumption of silver nanoparticles on cognitive functions and identification of the effect reasons
Date: 2018-11-20 00:00:00
Tasks: Hippocampus

The influence of the daily prolonged administration of silver nanoparticles
on cognitive functions of model mammals was studied. The accumulation of silver
in whole brain, hippocampus, cerebellum, cortex and residual part of mice brain
was investigated by highly precise and representative Neutron Activation
Analysis and the histological studies were conducted in the present research.
Some of the main results of the comprehensive study are the description of the
biokinetics of silver in the hippocampus, cerebellum and cortex, the finding of
the jump increase of silver in the hippocampus and cerebellum after the period
of administration of 120 days. Accumulation of silver in the mice brain and
hippocampus itself as well as the integrity violation of the CA2 subregion
caused long-term contextual memory impairment at 180 days of silver
nanoparticle exposure. However, destruction of CA2 had started 60 days before
the visible changes in behavior appeared. Complex of the three types of the
approach let us find the cognitive impairment and understand the reasons of it.
Proceedings of the LOCATA Challenge Workshop -- a satellite event of IWAENC 2018
Date: 2019-08-20 00:00:00
Tasks: Sound Source Localization

Algorithms for acoustic source localization and tracking provide estimates of
the positional information about active sound sources in acoustic environments
and are essential for a wide range of applications such as personal assistants,
smart homes, tele-conferencing systems, hearing aids, or autonomous systems.
The aim of the IEEE-AASP Challenge on sound source localization and tracking
(LOCATA) was to objectively benchmark state-of-the-art localization and
tracking algorithms using an open-access data corpus of recordings for
scenarios typically encountered in audio and acoustic signal processing
applications. The challenge tasks ranged from the localization of a single
source with a static microphone array to the tracking of multiple moving
sources with a moving microphone array.
Direct Single Molecule Imaging of Enhanced Enzyme Diffusion
Authors: Mengqi Xu, Lyanne Valdez, Aysuman Sen, Jennifer L. Ross
Date: 2018-11-20 00:00:00

Recent experimental results have shown that active enzymes can diffuse faster when they are in the presence of their substrates. Fluorescence correlation spectroscopy (FCS), which relies on analyzing the fluctuations in fluorescence intensity signal to measure the diffusion coefficient of particles, has typically been employed in most of the prior studies. However, flaws in the FCS method, due to its high sensitivity to the environment, have recently been evaluated, calling the prior diffusion results into question. It behooves us to adopt complimentary and direct methods to measure the mobility of enzymes in solution. Herein, we use a novel technique of direct single-molecule imaging to observe the diffusion of single enzymes. This technique is less sensitive to intensity fluctuations and gives the diffusion coefficient directly based on the trajectory of the enzymes. Our measurements recapitulate that enzyme diffusion is enhanced in the presence of its substrate and find that the relative increase in diffusion of a single enzyme is even higher than those previously reported using FCS. We also use this complementary method to test if the total enzyme concentration affects the relative increase in diffusion and if enzyme oligomerization state changes during catalytic turnover. We find that the diffusion increase is independent of the total background concentration of enzyme and the catalysis of substrate does not change the oligomerization state of enzymes.
Edge-adaptive l2 regularization image reconstruction from non-uniform Fourier data
Date: 2018-11-20 00:00:00
Tasks: Edge Detection, Image Reconstruction, L2 Regularization

Total variation regularization based on the l1 norm is ubiquitous in image
reconstruction. However, the resulting reconstructions are not always as sparse
in the edge domain as desired. Iteratively reweighted methods provide some
improvement in accuracy, but at the cost of extended runtime. In this paper we
examine these methods for the case of data acquired as non-uniform Fourier
samples. We then develop a non-iterative weighted regularization method that
uses a pre-processing edge detection to find exactly where the sparsity should
be in the edge domain. We show that its performance in terms of both accuracy
and speed has the potential to outperform reweighted TV regularization methods.
Global Sensitivity Analysis of High Dimensional Neuroscience Models: An Example of Neurovascular Coupling
Date: 2018-11-20 00:00:00
Tasks: Dimensionality Reduction, Sensitivity

The complexity and size of state-of-the-art cell models have significantly
increased in part due to the requirement that these models possess complex
cellular functions which are thought--but not necessarily proven--to be
important. Modern cell models often involve hundreds of parameters; the values
of these parameters come, more often than not, from animal experiments whose
relationship to the human physiology is weak with very little information on
the errors in these measurements. The concomitant uncertainties in parameter
values result in uncertainties in the model outputs or Quantities of Interest
(QoIs). Global Sensitivity Analysis (GSA) aims at apportioning to individual
parameters (or sets of parameters) their relative contribution to output
uncertainty thereby introducing a measure of influence or importance of said
parameters. New GSA approaches are required to deal with increased model size
and complexity; a three stage methodology consisting of screening (dimension
reduction), surrogate modeling, and computing Sobol' indices, is presented. The
methodology is used to analyze a physiologically validated numerical model of
neurovascular coupling which possess 160 uncertain parameters. The sensitivity
analysis investigates three quantities of interest (QoIs), the average value of
$K^+$ in the extracellular space, the average volumetric flow rate through the
perfusing vessel, and the minimum value of the actin/myosin complex in the
smooth muscle cell. GSA provides a measure of the influence of each parameter,
for each of the three QoIs, giving insight into areas of possible physiological
dysfunction and areas of further investigation.
An Aspect of Optimal Regression Design for LSMC
Date: 2019-05-29 00:00:00
Tasks: regression

Practitioners sometimes suggest to use a combination of Sobol sequences and
orthonormal polynomials when applying an LSMC algorithm for evaluation of
option prices or in the context of risk capital calculation under the Solvency
II regime. In this paper, we give a theoretical justification why good
implementations of an LSMC algorithm should indeed combine these two features
in order to assure numerical stability. Moreover, an explicit bound for the
number of outer scenarios necessary to guarantee a prescribed degree of
numerical stability is derived. We embed our observations into a coherent
presentation of the theoretical background of LSMC in the insurance setting.
CM Modeling of Trajectory
Authors: Reza Rezaie, X. Rong Li
Date: 2018-11-21 00:00:00
Tasks: Prediction, Trajectory Modeling, Trajectory Prediction

Information about the waypoints of a moving object, e.g., an airliner in an air traffic control (ATC) problem, should be considered in trajectory modeling and prediction. Due to the ATC regulations, trajectory design criteria, and restricted motion capability of airliners there are long-range dependencies in trajectories of airliners. Waypoint information can be used for modeling such dependencies in trajectories. This paper proposes a conditionally Markov (CM) sequence for modeling trajectories passing by waypoints. A dynamic model governing the proposed sequence is obtained. Filtering and trajectory prediction formulations are presented. The use of the proposed sequence for modeling trajectories with waypoints is justified.
Motor Learning Without Moving: Hand Localization after Passive Training
Date: 2018-11-21 00:00:00
Tasks: Position

An accurate estimate of limb position is necessary for movement. Where we
localize our unseen hand after a reach depends on felt hand position, or
proprioception, but often only predicted sensory consequences based on
efference copies of motor commands are considered. Both signals should
contribute, so here we use passive training with rotated visual feedback of
hand position to prevent updates of predicted sensory consequences, but still
recalibrate proprioception. After this training we measure participants' hand
location estimates based on both efference-based predictions and afferent
proprioceptive signals with self-generated hand movements as well as based on
proprioception only with robot-generated movements. The changes in hand
localization are equally large after training with robot- and self-generated
hand movements. Both motor and proprioceptive changes are only slightly smaller
as those after training with self-generated movements, confirming that
recalibrated proprioception contributes to motor learning.
A sparse grid approach to balance sheet risk measurement
Date: 2018-11-21 00:00:00
Tasks: Management

In this work, we present a numerical method based on a sparse grid
approximation to compute the loss distribution of the balance sheet of a
financial or an insurance company. We first describe, in a stylised way, the
assets and liabilities dynamics that are used for the numerical estimation of
the balance sheet distribution. For the pricing and hedging model, we chose a
classical Black & Scholes model with a stochastic interest rate following a
Hull & White model. The risk management model describing the evolution of the
parameters of the pricing and hedging model is a Gaussian model. The new
numerical method is compared with the traditional nested simulation approach.
We review the convergence of both methods to estimate the risk indicators under
consideration. Finally, we provide numerical results showing that the sparse
grid approach is extremely competitive for models with moderate dimension.
Close spatial arrangement of mutants favors and disfavors fixation
Date: 2019-08-21 00:00:00
Tasks: Clustering

Cooperation is ubiquitous across all levels of biological systems ranging
from microbial communities to human societies. It, however, seemingly
contradicts the evolutionary theory, since cooperators are exploited by
free-riders and thus are disfavored by natural selection. Many studies based on
evolutionary game theory have tried to solve the puzzle and figure out the
reason why cooperation exists and how it emerges. Network reciprocity is one of
the mechanisms to promote cooperation, where nodes refer to individuals and
links refer to social relationships. The spatial arrangement of mutant
individuals, which refers to the clustering of mutants, plays a key role in
network reciprocity. Besides, many other mechanisms supporting cooperation
suggest that the clustering of mutants plays an important role in the expansion
of mutants. However, the clustering of mutants and the game dynamics are
typically coupled. It is still unclear how the clustering of mutants alone
alters the evolutionary dynamics. To this end, we employ a minimal model with
frequency independent fitness on a circle. It disentangles the clustering of
mutants from game dynamics. The distance between two mutants on the circle is
adopted as a natural indicator for the clustering of mutants or assortment. We
find that the assortment is an amplifier of the selection for the connected
mutants compared with the separated ones. Nevertheless, as mutants are
separated, the more dispersed mutants are, the greater the chance of invasion
is. It gives rise to the non-monotonic effect of clustering, which is
counterintuitive. On the other hand, we find that less assortative mutants
speed up fixation. Our model shows that the clustering of mutants plays a
non-trivial role in fixation, which has emerged even if the game interaction is
absent.
Neural Network for CVA: Learning Future Values
Date: 2018-11-21 00:00:00

A new challenge to quantitative finance after the recent financial crisis is
the study of credit valuation adjustment (CVA), which requires modeling of the
future values of a portfolio. In this paper, following recent work in [Weinan
E(2017), Han(2017)], we apply deep learning to attack this problem. The future
values are parameterized by neural networks, and the parameters are then
determined through optimization. Two concrete products are studied: Bermudan
swaption and Mark-to-Market cross-currency swap. We obtain their expected
positive/negative exposures, and further study the resulting functional form of
future values. Such an approach represents a new framework for modeling XVA,
and it also sheds new lights on other methods like American Monte Carlo.
Overcoming low-utility facets for complex answer retrieval
Authors: MacAvaney Sean, Yates Andrew, Cohan Arman, Soldaini Luca, Hui Kai, Goharian Nazli, Frieder Ophir
Date: 2018-11-21 00:00:00
Tasks: Knowledge Graph Embeddings, Retrieval

Many questions cannot be answered simply; their answers must include numerous
nuanced details and additional context. Complex Answer Retrieval (CAR) is the
retrieval of answers to such questions. In their simplest form, these questions
are constructed from a topic entity (e.g., `cheese') and a facet (e.g., `health
effects'). While topic matching has been thoroughly explored, we observe that
some facets use general language that is unlikely to appear verbatim in
answers. We call these low-utility facets. In this work, we present an approach
to CAR that identifies and addresses low-utility facets. We propose two
estimators of facet utility. These include exploiting the hierarchical
structure of CAR queries and using facet frequency information from training
data. To improve the retrieval performance on low-utility headings, we also
include entity similarity scores using knowledge graph embeddings. We apply our
approaches to a leading neural ranking technique, and evaluate using the TREC
CAR dataset. We find that our approach perform significantly better than the
unmodified neural ranker and other leading CAR techniques. We also provide a
detailed analysis of our results, and verify that low-utility facets are indeed
more difficult to match, and that our approach improves the performance for
these difficult queries.
Entropy and Transfer Entropy: The Dow Jones and the build up to the 1997 Asian Crisis
Date: 2018-11-20 00:00:00
Tasks: Time Series, Time Series Analysis

Entropy measures in their various incarnations play an important role in the
study of stochastic time series providing important insights into both the
correlative and the causative structure of the stochastic relationships between
the individual components of a system. Recent applications of entropic
techniques and their linear progenitors such as Pearson correlations and
Granger causality have have included both normal as well as critical periods in
a system's dynamical evolution. Here I measure the entropy, Pearson correlation
and transfer entropy of the intra-day price changes of the Dow Jones Industrial
Average in the period immediately leading up to and including the Asian
financial crisis and subsequent mini-crash of the DJIA on the 27th October
1997. I use a novel variation of transfer entropy that dynamically adjusts to
the arrival rate of individual prices and does not require the binning of data
to show that quite different relationships emerge from those given by the
conventional Pearson correlations between equities. These preliminary results
illustrate how this modified form of the TE compares to results using Pearson
correlation.
Effectiveness of 3VQM in Capturing Depth Inconsistencies
Date: 2018-11-21 00:00:00
Tasks: SSIM

The 3D video quality metric (3VQM) was proposed to evaluate the temporal and
spatial variation of the depth errors for the depth values that would lead to
inconsistencies between left and right views, fast changing disparities, and
geometric distortions. Previously, we evaluated 3VQM against subjective scores.
In this paper, we show the effectiveness of 3VQM in capturing errors and
inconsistencies that exist in the rendered depth-based 3D videos. We further
investigate how 3VQM could measure excessive disparities, fast changing
disparities, geometric distortions, temporal flickering and/or spatial noise in
the form of depth cues inconsistency. Results show that 3VQM best captures the
depth inconsistencies based on errors in the reference views. However, the
metric is not sensitive to depth map mild errors such as those resulting from
blur. We also performed a subjective quality test and showed that 3VQM performs
better than PSNR, weighted PSNR and SSIM in terms of accuracy, coherency and
consistency.
Decoding Staircase Codes with Marked Bits
Date: 2018-11-21 00:00:00
Tasks: Decoder

Staircase codes (SCCs) are typically decoded using iterative bounded-distance
decoding (BDD) and hard decisions. In this paper, a novel decoding algorithm is
proposed, which partially uses soft information from the channel. The proposed
algorithm is based on marking certain number of highly reliable and highly
unreliable bits. These marked bits are used to improve the
miscorrection-detection capability of the SCC decoder and the error-correcting
capability of BDD. For SCCs with $2$-error-correcting BCH component codes, our
algorithm improves upon standard SCC decoding by up to $0.30$~dB at a bit-error
rate of $10^{-7}$. The proposed algorithm is shown to achieve almost half of
the gain achievable by an idealized decoder with this structure.
Guessing the upper bound free-energy difference between native-like structures
Date: 2018-11-21 00:00:00

Use of a combination of statistical thermodynamics and the Gershgorin theorem
enable us to guess, in the thermodynamic limit, a plausible value for the upper
bound free-energy difference between native-like structures of monomeric
globular proteins. Support to our result in light of both the observed
free-energy change between the native and denatured states and the
microstability free-energy values obtained from the observed micro-unfolding
tendency of nine globular proteins, will be here discussed.
A Study of Language and Classifier-independent Feature Analysis for Vocal Emotion Recognition
Authors: Noroozi Fatemeh, Marjanovic Marina, Njegus Angelina, Escalera Sergio, Anbarjafari Gholamreza
Date: 2018-11-14 00:00:00
Tasks: Emotion Recognition

Every speech signal carries implicit information about the emotions, which
can be extracted by speech processing methods. In this paper, we propose an
algorithm for extracting features that are independent from the spoken language
and the classification method to have comparatively good recognition
performance on different languages independent from the employed classification
methods. The proposed algorithm is composed of three stages. In the first
stage, we propose a feature ranking method analyzing the state-of-the-art voice
quality features. In the second stage, we propose a method for finding the
subset of the common features for each language and classifier. In the third
stage, we compare our approach with the recognition rate of the
state-of-the-art filter methods. We use three databases with different
languages, namely, Polish, Serbian and English. Also three different
classifiers, namely, nearest neighbour, support vector machine and gradient
descent neural network, are employed. It is shown that our method for selecting
the most significant language-independent and method-independent features in
many cases outperforms state-of-the-art filter methods.
Performance Analysis of Analog Intermittently Nonlinear Filter in the Presence of Impulsive Noise
Date: 2018-11-21 00:00:00

An Adaptive Nonlinear Differential Limiter (ANDL) is proposed in this paper
to efficiently alleviate the impact of impulsive noise (IN) in a communication
system. Unlike existing nonlinear methods, the ANDL is implemented in the
analog domain where the broader acquisition bandwidth makes outliers more
detectable and consequently it is easier to remove them. While the proposed
ANDL behaves like a linear filter when there is no outlier, it exhibits
intermittent nonlinearity in response to IN. Therefore, the structure of the
matched filter in the receiver is modified to compensate the filtering effect
of the ANDL in the linear regime. In this paper, we quantify the performance of
the ANDL by deriving a closed-form analytical bound for the average
signal-to-noise ratio (SNR) at the output of the filter. The calculation is
based on the idea that the ANDL can be perceived as a time-variant linear
filter whose bandwidth is modified based on the intensity of the IN. In
addition, by linearizing the filter time parameter variations, we treat the
ANDL as a set of linear filters where the exact operating filter at a given
time depends upon the magnitude of the outliers. The theoretical average bit
error rate (BER) is validated through simulations and the performance gains
relative to classical methods such as blanking and clipping are quantified.
The transmission of liquidity shocks via China's segmented money market: evidence from recent market events
Date: 2018-11-21 00:00:00

This is the first study to explore the transmission paths for liquidity
shocks in China's segmented money market. We examine how money market
transactions create such pathways between China's closely-guarded banking
sector and the rest of its financial system, and empirically capture the
transmission of liquidity shocks through these pathways during two recent
market events. We find strong indications that money market transactions allow
liquidity shocks to circumvent certain regulatory restrictions and financial
market segmentation in China. Our findings suggest that a widespread
illiquidity contagion facilitated by money market transactions can happen in
China and new policy measures are needed to prevent such contagion.