pwc-archive/papers-with-abstracts huggingface dataset:
Random Utility and Limited Consideration
Authors: Victor H. Aguiar, Maria Jose Boccardi, Nail Kashaev, Jeongbin Kim
Date: 2018-12-22 00:00:00

The random utility model (RUM, McFadden and Richter, 1990) has been the standard tool to describe the behavior of a population of decision makers. RUM assumes that decision makers behave as if they maximize a rational preference over a choice set. This assumption may fail when consideration of all alternatives is costly. We provide a theoretical and statistical framework that unifies well-known models of random (limited) consideration and generalizes them to allow for preference heterogeneity. We apply this methodology in a novel stochastic choice dataset that we collected in a large-scale online experiment. Our dataset is unique since it exhibits both choice set and (attention) frame variation. We run a statistical survival race between competing models of random consideration and RUM. We find that RUM cannot explain the population behavior. In contrast, we cannot reject the hypothesis that decision makers behave according to the logit attention model (Brade and Rehbeck, 2016).
Geometric constraints in protein folding
Date: 2018-12-23 00:00:00
Tasks: Protein Folding

The intricate three-dimensional geometries of protein tertiary structures
underlie protein function and emerge through a folding process from
one-dimensional chains of amino acids. The exact spatial sequence and
configuration of amino acids, the biochemical environment and the temporal
sequence of distinct interactions yield a complex folding process that cannot
yet be easily tracked for all proteins. To gain qualitative insights into the
fundamental mechanisms behind the folding dynamics and generic features of the
folded structure, we propose a simple model of structure formation that takes
into account only fundamental geometric constraints and otherwise assumes
randomly paired connections. We find that despite its simplicity, the model
results in a network ensemble consistent with key overall features of the
ensemble of Protein Residue Networks we obtained from more than 1000 biological
protein geometries as available through the Protein Data Base. Specifically,
the distribution of the number of interaction neighbors a unit (amino acid)
has, the scaling of the structure's spatial extent with chain length, the
eigenvalue spectrum and the scaling of the smallest relaxation time with chain
length are all consistent between model and real proteins. These results
indicate that geometric constraints alone may already account for a number of
generic features of protein tertiary structures.
Functional importance of noise in neuronal information processing
Date: 2018-12-28 00:00:00

Noise is an inherent part of neuronal dynamics, and thus of the brain. It can
be observed in neuronal activity at different spatiotemporal scales, including
in neuronal membrane potentials, local field potentials,
electroencephalography, and magnetoencephalography. A central research topic in
contemporary neuroscience is to elucidate the functional role of noise in
neuronal information processing. Experimental studies have shown that a
suitable level of noise may enhance the detection of weak neuronal signals by
means of stochastic resonance. In response, theoretical research, based on the
theory of stochastic processes, nonlinear dynamics, and statistical physics,
has made great strides in elucidating the mechanism and the many benefits of
stochastic resonance in neuronal systems. In this perspective, we review recent
research dedicated to neuronal stochastic resonance in biophysical mathematical
models. We also explore the regulation of neuronal stochastic resonance, and we
outline important open questions and directions for future research. A deeper
understanding of neuronal stochastic resonance may afford us new insights into
the highly impressive information processing in the brain.
A new model for the emergence of blood capillary networks
Date: 2018-12-24 00:00:00

We propose a new model for the emergence of blood capillary networks. We
assimilate the tissue and extra cellular matrix as a porous medium, using
Darcy's law for describing both blood and intersticial fluid flows. Oxygen
obeys a convection-diffusion-reaction equation describing advection by the
blood, diffusion and consumption by the tissue. Discrete agents named capillary
elements and modelling groups of endothelial cells are created or deleted
according to different rules involving the oxygen concentration gradient, the
blood velocity, the sheer stress or the capillary element density. Once
created, a capillary element locally enhances the hydraulic conductivity
matrix, contributing to a local increase of the blood velocity and oxygen flow.
No connectivity between the capillary elements is imposed. The coupling between
blood, oxygen flow and capillary elements provides a positive feedback
mechanism which triggers the emergence of a network of channels of high
hydraulic conductivity which we identify as new blood capillaries. We provide
two different, biologically relevant geometrical settings and numerically
analyze the influence of each of the capillary creation mechanism in detail.
All mechanisms seem to concur towards a harmonious network but the most
important ones are those involving oxygen gradient and sheer stress. A detailed
discussion of this model with respect to the literature and its potential
future developments concludes the paper.
TransNFCM: Translation-Based Neural Fashion Compatibility Modeling
Authors: Yang Xun, Ma Yunshan, Liao Lizi, Wang Meng, Chua Tat-Seng
Date: 2018-12-25 00:00:00
Tasks: Recommendation Systems, Representation Learning, Translation

Identifying mix-and-match relationships between fashion items is an urgent
task in a fashion e-commerce recommender system. It will significantly enhance
user experience and satisfaction. However, due to the challenges of inferring
the rich yet complicated set of compatibility patterns in a large e-commerce
corpus of fashion items, this task is still underexplored. Inspired by the
recent advances in multi-relational knowledge representation learning and deep
neural networks, this paper proposes a novel Translation-based Neural Fashion
Compatibility Modeling (TransNFCM) framework, which jointly optimizes fashion
item embeddings and category-specific complementary relations in a unified
space via an end-to-end learning manner. TransNFCM places items in a unified
embedding space where a category-specific relation (category-comp-category) is
modeled as a vector translation operating on the embeddings of compatible items
from the corresponding categories. By this way, we not only capture the
specific notion of compatibility conditioned on a specific pair of
complementary categories, but also preserve the global notion of compatibility.
We also design a deep fashion item encoder which exploits the complementary
characteristic of visual and textual features to represent the fashion
products. To the best of our knowledge, this is the first work that uses
category-specific complementary relations to model the category-aware
compatibility between items in a translation-based embedding space. Extensive
experiments demonstrate the effectiveness of TransNFCM over the
state-of-the-arts on two real-world datasets.
Duesenberry's Theory of Consumption: Habit, Learning, and Ratcheting
Date: 2018-12-25 00:00:00

This paper investigates the consumption and risk taking decision of an
economic agent with partial irreversibility of consumption decision by
formalizing the theory proposed by Duesenberry (1949). The optimal policies
exhibit a type of the (s, S) policy: there are two wealth thresholds within
which consumption stays constant. Consumption increases or decreases at the
thresholds and after the adjustment new thresholds are set. The share of risky
investment in the agent's total investment is inversely U-shaped within the (s,
S) band, which generates time-varying risk aversion that can fluctuate widely
over time. This property can explain puzzles and questions on asset pricing and
households' portfolio choices, e.g., why aggregate consumption is so smooth
whereas the high equity premium is high and the equity return has high
volatility, why the risky share is so low whereas the estimated risk aversion
by the micro-level data is small, and whether and when an increase in wealth
has an impact on the risky share. Also, the partial irreversibility model can
explain both the excess sensitivity and the excess smoothness of consumption.
Turbulence-Resistant All Optical Relaying Based on Few-Mode EDFA in Free-Space Optical Systems
Date: 2018-12-25 00:00:00
Tasks: All

We investigate the communication performance of a few-mode EDFA based
all-optical relaying system for atmospheric channels in this paper. A dual-hop
free space optical communication model based on the relay with two-mode EDFA is
derived. The BER performance is numerically calculated. Compared with
all-optical relaying system with single-mode EDFA, the power budget is
increased by 4 dB, 7.5 dB and 11.5 dB at BER = 1E-4 under the refractive index
structure constant Cn2 = 2E-14, 5E-14 and 1E-13 respectively when a few mode
fiber supporting 4 modes is utilized as the receiving fiber at the destination.
The optimal relay location is slightly backward from the middle of the link.
The BER performance is the best when mode-dependent gain of FM-EDFA is zero.
Revisiting Transformation and Directional Technology Distance Functions
Date: 2018-12-25 00:00:00
Tasks: Translation

In the first part of the paper, we prove the equivalence of the unsymmetric
transformation function and an efficient joint production function (JPF) under
strong monotonicity conditions imposed on input and output correspondences.
Monotonicity, continuity, and convexity properties sufficient for a symmetric
transformation function to be an efficient JPF are also stated. In the second
part, we show that the most frequently used functional form for the directional
technology distance function (DTDF), the quadratic, does not satisfy
homogeneity of degree $-1$ in the direction vector. This implies that the
quadratic function is not the directional technology distance function. We
provide derivation of the DTDF from a symmetric transformation function and
show how this approach can be used to obtain functional forms that satisfy both
translation property and homogeneity of degree $-1$ in the direction vector if
the optimal solution of an underlying optimization problem can be expressed in
closed form.
Hierarchical feature fusion framework for frequency recognition in SSVEP-based BCIs
Date: 2019-03-21 00:00:00
Tasks: SSVEP

Effective frequency recognition algorithms are critical in steady-state
visual evoked potential (SSVEP) based brain-computer interfaces (BCIs). In this
study, we present a hierarchical feature fusion framework which can be used to
design high-performance frequency recognition methods. The proposed framework
includes two primary technique for fusing features: spatial dimension fusion
(SD) and frequency dimension fusion (FD). Both SD and FD fusions are obtained
using a weighted strategy with a nonlinear function. To assess our novel
methods, we used the correlated component analysis (CORRCA) method to
investigate the efficiency and effectiveness of the proposed framework.
Experimental results were obtained from a benchmark dataset of thirty-five
subjects and indicate that the extended CORRCA method used within the framework
significantly outperforms the original CORCCA method. Accordingly, the proposed
framework holds promise to enhance the performance of frequency recognition
methods in SSVEP-based BCIs.
Cartel Stability under Quality Differentiation
Date: 2018-12-26 00:00:00

This note considers cartel stability when the cartelized products are
vertically differentiated. If market shares are maintained at pre-collusive
levels, then the firm with the lowest competitive price-cost margin has the
strongest incentive to deviate from the collusive agreement. The lowest-quality
supplier has the tightest incentive constraint when the difference in unit
production costs is sufficiently small.
Hybrid Codeword Position Index Modulation for Sparse Code Multiple Access System
Date: 2019-04-29 00:00:00
Tasks: Position

In this paper, a novel variation of codeword position index based sparse code
multiple access (CPI-SCMA) system, which is termed as hybrid codeword position
index modulated sparse code multiple access (HCPI-SCMA), is proposed to further
improve the transmission efficiency (TE). In this scheme, unlike the
conventional CPI-SCMA that uses only one kind of bits-toindices (BTI) mapper,
the codeword positions which are padded with zeros in CPI-SCMA are also
utilized to transmit additional information. Since multiple index selectors are
used in a HCPISCMA codeword, the original message passing algorithm (MPA) no
longer works in HCPI-SCMA; hence, a modified MPA is proposed to detect the
received signals. It is shown in the simulations and analysis that the proposed
scheme can achieve both higher TE and better error rate performance in the
region of high signal-to-noise ratio (SNR) compare to the conventional SCMA
(C-SCMA). Moreover, compared with CPI-SCMA, HCPISCMA can achieve higher TE with
approximately the same error rate performance compared to CPI-SCMA at high
SNRs.
Machine Learning-Based Antenna Selection in Untrusted Relay Networks
Date: 2018-12-26 00:00:00
Tasks: BIG-bench Machine Learning

This paper studies the transmit antenna selection based on machine learning
(ML) schemes in untrusted relay networks. First, we state the conventional
antenna selection scheme. Then, we implement three ML schemes, namely, the
support vector machine-based scheme, the naive-Bayes-based scheme, and the
k-nearest neighbors-based scheme, which are applied to select the best antenna
with the highest secrecy rate. The simulation results are presented in terms of
system secrecy rate and secrecy outage probability. From the simulation, we can
conclude that the proposed ML-based antenna selection schemes can achieve the
same performance without amplification at the relay, or small performance
degradation with transmitted power constraint at the relay, comparing with
conventional schemes. However, when the training is completed, the proposed
schemes can perform the antenna selection with a small computational
complexity.
Equivalent Choice Functions and Stable Mechanisms
Authors: Jan Christoph Schlegel
Date: 2018-12-26 00:00:00

We study conditions for the existence of stable and group-strategy-proof mechanisms in a many-to-one matching model with contracts if students' preferences are monotone in contract terms. We show that "equivalence", properly defined, to a choice profile under which contracts are substitutes and the law of aggregate holds is a necessary and sufficient condition for the existence of a stable and group-strategy-proof mechanism. Our result can be interpreted as a (weak) embedding result for choice functions under which contracts are observable substitutes and the observable law of aggregate demand holds.
Chaotic dynamics of movements stochastic instability and the hypothesis of N.A. Bernstein about "repetition without repetition"
Authors: V. V. Eskov, V. T. Volov, V. M. Eskov, L. K. Ilyashenko
Date: 2018-12-21 00:00:00

The registration of tremor was performed in two groups of subjects (15 people in each group) with different physical fitness at rest and at a static loads of 3N. Each subject has been tested 15 series (number of series N=15) in both states (with and without physical loads) and each series contained 15 samples (n=15) of tremorogramm measurements (500 elements in each sample, registered coordinates x1(t) of the finger position relative to eddy current sensor) of the finger. Using non-parametric Wilcoxon test of each series of experiment a pairwise comparison was made forming 15 tables in which the results of calculation of pairwise comparison was presented as a matrix (15x15) for tremorogramms are presented. The average number of hits random pairs of samples (<k>) and standard deviation {\sigma} were calculated for all 15 matrices without load and under the impact of physical load (3N), which showed an increase almost in twice in the number k of pairs of matching samples of tremorogramms at conditions of a static load. For all these samples it was calculated special quasi-attractor (this square was presented the distinguishes between physical load and without it. All samples present the stochastic unstable state.
3-D Localization with Multiple LEDs Lamps in OFDM-VLC system
Date: 2018-12-22 00:00:00
Tasks: Indoor Localization

Visible light communication (VLC) based localization is a potential candidate
for wide range indoor localization applications. In this paper, we propose a
VLC architecture based on orthogonal frequency division multiplexing (OFDM)
with multiple functionalities integrated in the same system, i.e., the 3- D
receiver location, the control of the room illumination intensity, as well as
the data transmission capability. Herein we propose an original methodology for
LED power discrimination applying spatial optical OFDM (SO-OFDM) structure for
position estimation. The hybrid locator initially makes a first estimate using
a weighted angle-of-arrival (WAoA)-based locator which is then used as the
starting point of the recursive estimator based on the strength of the received
signal (RSS). Hence, the first stage is deployed to increase convergence
probability, reducing the root-mean-square error (RMSE) and the number of
iterations of the second stage. Also, a performance vs computational complexity
comparative analysis is carried out with parameter variations of these
estimators. The numerical results indicate a decade improvement in the RMSE for
each two decades of decrement of power noise on the receiver photodiode. The
best clipping factor is obtained through the analysis of locator accuracy and
transmission capacity for each simulated system. Finally, the numerical results
also demonstrate effectiveness, robustness, and efficiency of the proposed
architecture.
Deep Item-based Collaborative Filtering for Sparse Implicit Feedback
Authors: Galron Daniel A., Brovman Yuri M., Chung Jin, Wieja Michal, Wang Paul
Date: 2018-12-26 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Recommender systems are ubiquitous in the domain of e-commerce, used to
improve the user experience and to market inventory, thereby increasing revenue
for the site. Techniques such as item-based collaborative filtering are used to
model users' behavioral interactions with items and make recommendations from
items that have similar behavioral patterns. However, there are challenges when
applying these techniques on extremely sparse and volatile datasets. On some
e-commerce sites, such as eBay, the volatile inventory and minimal structured
information about items make it very difficult to aggregate user interactions
with an item. In this work, we describe a novel deep learning-based method to
address the challenges. We propose an objective function that optimizes a
similarity measure between binary implicit feedback vectors between two items.
We demonstrate formally and empirically that a model trained to optimize this
function estimates the log of the cosine similarity between the feedback
vectors. We also propose a neural network architecture optimized on this
objective. We present the results of experiments comparing the output of the
neural network with traditional item-based collaborative filtering models on an
implicit-feedback dataset, as well as results of experiments comparing
different neural network architectures on user purchase behavior on eBay.
Finally, we discuss the results of an A/B test that show marked improvement of
the proposed technique over eBay's existing collaborative filtering recommender
system.
Structural insights into characterizing binding sites in EGFR kinase mutants
Date: 2018-12-27 00:00:00
Tasks: Drug Design

Over the last two decades epidermal growth factor receptor (EGFR) kinase has
become an important target to treat non-small cell lung cancer (NSCLC).
Currently, three generations of EGFR kinase-targeted small molecule drugs have
been FDA approved. They nominally produce a response at the start of treatment
and lead to a substantial survival benefit for patients. However, long-term
treatment results in acquired drug resistance and further vulnerability to
NSCLC. Therefore, novel EGFR kinase inhibitors that specially overcome acquired
mutations are urgently needed. To this end, we carried out a comprehensive
study of different EGFR kinase mutants using a structural systems pharmacology
strategy. Our analysis shows that both wild-type and mutated structures exhibit
multiple conformational states that have not been observed in solved crystal
structures. We show that this conformational flexibility accommodates diverse
types of ligands with multiple types of binding modes. These results provide
insights for designing a new-generation of EGFR kinase inhibitor that combats
acquired drug-resistant mutations through a multi-conformation-based drug
design strategy.
Predicting the Stock Price of Frontier Markets Using Modified Black-Scholes Option Pricing Model and Machine Learning
Date: 2018-12-27 00:00:00
Tasks: BIG-bench Machine Learning, Ensemble Learning

The Black-Scholes Option pricing model (BSOPM) has long been in use for
valuation of equity options to find the prices of stocks. In this work, using
BSOPM, we have come up with a comparative analytical approach and numerical
technique to find the price of call option and put option and considered these
two prices as buying price and selling price of stocks of frontier markets so
that we can predict the stock price (close price). Changes have been made to
the model to find the parameters strike price and the time of expiration for
calculating stock price of frontier markets. To verify the result obtained
using modified BSOPM we have used machine learning approach using the software
Rapidminer, where we have adopted different algorithms like the decision tree,
ensemble learning method and neural network. It has been observed that, the
prediction of close price using machine learning is very similar to the one
obtained using BSOPM. Machine learning approach stands out to be a better
predictor over BSOPM, because Black-Scholes-Merton equation includes risk and
dividend parameter, which changes continuously. We have also numerically
calculated volatility. As the prices of the stocks goes high due to
overpricing, volatility increases at a tremendous rate and when volatility
becomes very high market tends to fall, which can be observed and determined
using our modified BSOPM. The proposed modified BSOPM has also been explained
based on the analogy of Schrodinger equation (and heat equation) of quantum
physics.
Effect of cryopreservation on the structural and functional integrity of cell membranes of sugarcane (Saccharum sp.) embryogenic calluses
Date: 2018-12-27 00:00:00

In this paper, we investigated if the differences consistently noted in
survival and plantlet production between cryopreserved and non-cryopreserved,
control sugarcane embryogenic calluses were related to modifications induced
during cryopreservation in the structural and functional integrity of cell
membranes. For this, the evolution of electrolyte leakage, lipid peroxidation
products and cell membrane protein contents was measured during 5 d after
cryopreservation. Differences between control and frozen calluses were observed
only during the first 2 (electrolyte leakage) or 3 d (lipid peroxidation
products and membrane protein content) after freezing. It was not possible to
link these differences with the differences noted in survival and plant
production between control and cryopreserved calluses. Additional studies are
thus needed to elucidate which biochemical factors, linked to survival and
plantlet regeneration, are affected by cryopreservation.