pwc-archive/papers-with-abstracts huggingface dataset:
Psychological model of the investor and manager behavior in risk
Date: 2019-01-25 00:00:00
Tasks: Decision Making, Management

All people have to make risky decisions in everyday life. And we do not know
how true they are. But is it possible to mathematically assess the correctness
of our choice? This article discusses the model of decision making under risk
on the example of project management. This is a game with two players, one of
which is Investor, and the other is the Project Manager. Each player makes a
risky decision for himself, based on his past experience. With the help of a
mathematical model, the players form a level of confidence, depending on who
the player accepts the strategy or does not accept. The project manager
assesses the costs and compares them with the level of confidence. An investor
evaluates past results. Also visit the case where the strategy of the player
accepts the part.
Accuracy Limits of Polarization-Independent Optical Depolarizers Based on Rotating Waveplates
Date: 2019-01-25 00:00:00

Optical depolarizers for monochromatic waves which work independent of input
polarization can be built from cascaded electrooptic rotating waveplates. If
the waveplate retardations deviate from their desired values then the
worst-case residual degree-of-polarization DOPmax is larger than its desired
value 0. In a depolarizer consisting of one rotating halfwave and one rotating
quarterwave plate, DOPmax roughly equals the retardation error, which is <<1.
However, with just one rotating quarterwave plate more, DOPmax roughly equals
the square of the retardation error which is a much smaller value. Thereby
depolarizer accuracy is substantially improved. Waveplate sequence and rotation
frequency combinations suitable for fast depolarization are discussed.
Expanding Click and Buy rates: Exploration of evaluation metrics that measure the impact of personalized recommendation engines on e-commerce platforms
Authors: Chaudhary Namrata, Chowdhury Drimik Roy
Date: 2019-01-20 00:00:00

To identify the most appropriate recommendation model for an e-commerce
business, a live evaluation should be performed on the shopping website to
measure the influence of personalization in real-time. The aim of this paper is
to introduce and justify two new metrics -- CTR NoRepeat and Click & Buy rate
-- which stem from the standard metrics, Click-through(CTR) and Buy-through
rate(BTR), respectively. The former variation tackles the issue of
overestimation of clicks in the original CTR while the latter accounts for
noting purchases of products that have been previously clicked, in order to
validate that the buy included in the metric is a result of customer
interactions. A significance test for independence of two means is conducted
for multiple datasets, between each of the new metrics and its respective
parent to determine the novelty and necessity of the variants. The
Pearson-correlation coefficient is calculated to assess the strength of the
linear relationships and conclude on the predictability factor amongst the
aforementioned factors to investigate unknown connections between customer
clicks and buys. Additionally, other metrics such as hits per customer, buyers
per customer, clicks per customer etc. are introduced that help explain
indicators of customer behavior on the e-commerce website in reference.
Transient Stability Assessment of Power Systems Based on Local Learning Machine and Bacterial Colony Chemotaxis Algorithm
Date: 2019-01-23 00:00:00

In order to improve the classification accuracy of transient stability
assessment of power systems, a novel method based on local learning machine and
an improved bacterial colony chemotaxis (BCC) algorithm is proposed, where
local learning machine(LLM) is used to build a TSA model. Considering the
possible real-time information provided by PMU, a group of system-level
classification features extracted from the power system operation parameters
are employed as inputs, and the stability result is used as output of the LLM
model. The relation ship between input and output is trained and the ideal
model is obtained by applying the improved BCC combined with chaotic search
strategy to determine the optimal parameters of LLM automatically. The
effectiveness of the proposed method is shown by the simulation results on the
New England 10-unit-39-bus power system.
Theories and Practice of Agent based Modeling: Some practical Implications for Economic Planners
Date: 2019-01-23 00:00:00

Nowadays, we are surrounded by a large number of complex phenomena ranging
from rumor spreading, social norms formation to rise of new economic trends and
disruption of traditional businesses. To deal with such phenomena,Complex
Adaptive System (CAS) framework has been found very influential among social
scientists,especially economists. As the most powerful methodology of CAS
modeling, Agent-based modeling (ABM) has gained a growing application among
academicians and practitioners. ABMs show how simple behavioral rules of agents
and local interactions among them at micro-scale can generate surprisingly
complex patterns at macro-scale. Despite a growing number of ABM publications,
those researchers unfamiliar with this methodology have to study a number of
works to understand (1) the why and what of ABMs and (2) the ways they are
rigorously developed. Therefore, the major focus of this paper is to help
social sciences researchers,especially economists get a big picture of ABMs and
know how to develop them both systematically and rigorously.
Queue-reactive Hawkes models for the order flow
Date: 2019-01-25 00:00:00

In this work we introduce two variants of multivariate Hawkes models with an
explicit dependency on various queue sizes aimed at modeling the stochastic
time evolution of a limit order book. The models we propose thus integrate the
influence of both the current book state and the past order flow. The first
variant considers the flow of order arrivals at a specific price level as
independent from the other one and describes this flow by adding a Hawkes
component to the arrival rates provided by the continuous time Markov "Queue
Reactive" model of Huang et al. Empirical calibration using Level-I order book
data from Eurex future assets (Bund and DAX) show that the Hawkes term
dramatically improves the pure "Queue-Reactive" model not only for the
description of the order flow properties (as e.g. the statistics of inter-event
times) but also with respect to the shape of the queue distributions. The
second variant we introduce describes the joint dynamics of all events
occurring at best bid and ask sides of some order book during a trading day.
This model can be considered as a queue dependent extension of the multivariate
Hawkes order-book model of Bacry et al. We provide an explicit way to calibrate
this model either with a Maximum-Likelihood method or with a Least-Square
approach. Empirical estimation from Bund and DAX level-I order book data allow
us to recover the main features of Hawkes interactions uncovered in Bacry et
al. but also to unveil their joint dependence on bid and ask queue sizes. We
notably find that while the market order or mid-price changes rates can mainly
be functions on the volume imbalance this is not the case for the arrival rate
of limit or cancel orders. Our findings also allows us to clearly bring to
light various features that distinguish small and large tick assets.
Topological and Semantic Graph-based Author Disambiguation on DBLP Data in Neo4j
Authors: Franzoni Valentina, Lepri Michele, Milani Alfredo
Date: 2019-01-25 00:00:00
Tasks: Entity Resolution, Link Prediction, Specificity

In this work, we introduce a novel method for entity resolution author
disambiguation in bibliographic networks. Such a method is based on a 2-steps
network traversal using topological similarity measures for rating candidate
nodes. Topological similarity is widely used in the Link Prediction application
domain to assess the likelihood of an unknown link. A similarity function can
be a good approximation for equality, therefore can be used to disambiguate,
basing on the hypothesis that authors with many common co-authors are similar.
Our method has experimented on a graph-based representation of the public DBLP
Computer Science database. The results obtained are extremely encouraging
regarding Precision, Accuracy, and Specificity. Further good aspects are the
locality of the method for disambiguation assessment which avoids the need to
know the global network, and the exploitation of only a few data, e.g. author
name and paper title (i.e., co-authorship data).
How the investor's risk preferences influence the optimal allocation in a credibilistic portfolio problem
Date: 2019-01-25 00:00:00

A classical portfolio theory deals with finding the optimal proportion in
which an agent invests a wealth in a risk-free asset and a probabilistic risky
asset. Formulating and solving the problem depend on how the risk is
represented and how, combined with the utility function defines a notion of
expected utility. In this paper the risk is a fuzzy variable and the notion of
expected utility is defined in the setting of Liu's credibility theory. Thus
the portfolio choice problem is formulated as an optimization problem in which
the objective function is a credibilistic expected utility. Different
approximation calculation formulas for the optimal allocation of the
credibilistic risky asset are proved. These formulas contain two types of
parameters: various credibilistic moments associated with fuzzy variables
(expected value, variance, skewness and kurtosis) and the risk aversion,
prudence and temperance indicators of the utility function.
Robust Vector Perturbation Precoding Design for MIMO Broadcast Channel
Date: 2019-01-26 00:00:00

We consider the vector perturbation (VP) precoder design for multiuser
multiple-input single output (MU-MISO) broadcast channel systems which is
robust to power scaling factor errors. VP precoding has so far been developed
and analyzed under the assumption that receivers could have known the power
scaling factor in advance of tranmission perfectly, which is hard to obtain due
to the large dynamic range and limited feedforward. However, as demonstrated in
our results the performance of VP precoding is quite sensitive to the accuracy
of power scaling factor and always encounter an error floor at mid to high
signal-to-noise ratio (SNR) regimes. Motivated by such observations, we propose
a robust VP precoder based on the minimum mean square error (MMSE) criterion.
Simulation results show that, the robust VP precoder outperforms conventional
VP precoding designs, as less sensitive to power scaling factor errors.
Modelling and simulation of nabla fractional dynamic systems with nonzero initial conditions
Date: 2019-08-23 00:00:00

The paper focuses on the numerical approximation of nabla fractional order
systems with the conditions of nonzero initial instant and nonzero initial
state. First, the inverse nabla Laplace transform is developed and the
equivalent infinite dimensional frequency distributed models of discrete
fractional order system are introduced. Then, resorting the nabla Laplace
transform, the rationality of the finite dimensional frequency distributed
model approaching the infinite one is illuminated. Based on this, an original
algorithm to estimate the parameters of the approximate model is proposed with
the help of vector fitting method. Additionally, the applicable object is
extended from a sum operator to a general system. Three numerical examples are
performed to illustrate the applicability and flexibility of the introduced
methodology.
HetMEC: Latency-optimal Task Assignment and Resource Allocation for Heterogeneous Mobile Edge Computing
Date: 2019-01-27 00:00:00
Tasks: Edge-computing

Driven by great demands on low-latency services of the edge devices (EDs),
mobile edge computing (MEC) has been proposed to enable the computing
capacities at the edge of the radio access network. However, conventional MEC
servers suffer disadvantages such as limited computing capacity, preventing the
computation-intensive tasks to be processed in time. To relief this issue, we
propose the heterogeneous MEC (HetMEC) where the data that cannot be timely
processed at the edge are allowed be offloaded to the upper-layer MEC servers,
and finally to the cloud center (CC) with more powerful computing capacity. We
design the latency minimization algorithm by jointly coordinating the task
assignment, computing and transmission resources among the EDs, multi-layer MEC
servers, and the CC. Simulation results indicate that our proposed algorithm
can achieve a lower latency and higher processing rate than the conventional
MEC scheme.
High-dimensional statistical arbitrage with factor models and stochastic control
Authors: Jorge Guijarro-Ordonez
Date: 2019-01-27 00:00:00
Tasks: Form, Vocal Bursts Intensity Prediction

The present paper provides a study of high-dimensional statistical arbitrage that combines factor models with the tools from stochastic control, obtaining closed-form optimal strategies which are both interpretable and computationally implementable in a high-dimensional setting. Our setup is based on a general statistically-constructed factor model with mean-reverting residuals, in which we show how to construct analytically market-neutral portfolios and we analyze the problem of investing optimally in continuous time and finite horizon under exponential and mean-variance utilities. We also extend our model to incorporate constraints on the investor's portfolio like dollar-neutrality and market frictions in the form of temporary quadratic transaction costs, provide extensive Monte Carlo simulations of the previous strategies with 100 assets, and describe further possible extensions of our work.
Signal Shaping for Generalized Spatial Modulation and Generalized Quadrature Spatial Modulation
Date: 2019-01-27 00:00:00

This paper investigates generic signal shaping methods for
multiple-data-stream generalized spatial modulation (GenSM) and generalized
quadrature spatial modulation (GenQSM) based on the maximizing the minimum
Euclidean distance (MMED) criterion. Three cases with different channel state
information at the transmitter (CSIT) are considered, including no CSIT,
statistical CSIT and perfect CSIT. A unified optimization problem is formulated
to find the optimal transmit vector set under size, power and sparsity
constraints. We propose an optimization-based signal shaping (OBSS) approach by
solving the formulated problem directly and a codebook-based signal shaping
(CBSS) approach by finding sub-optimal solutions in discrete space. In the OBSS
approach, we reformulate the original problem to optimize the signal
constellations used for each transmit antenna combination (TAC). Both the size
and entry of all signal constellations are optimized. Specifically, we suggest
the use of a recursive design for size optimization. The entry optimization is
formulated as a non-convex large-scale quadratically constrained quadratic
programming (QCQP) problem and can be solved by existing optimization
techniques with rather high complexity. To reduce the complexity, we propose
the CBSS approach using a codebook generated by quadrature amplitude modulation
(QAM) symbols and a low-complexity selection algorithm to choose the optimal
transmit vector set. Simulation results show that the OBSS approach exhibits
the optimal performance in comparison with existing benchmarks. However, the
OBSS approach is impractical for large-size signal shaping and adaptive signal
shaping with instantaneous CSIT due to the demand of high computational
complexity. As a low-complexity approach, CBSS shows comparable performance and
can be easily implemented in large-size systems.
The Minimum Environmental Perturbation Principle: A New Perspective on Niche Theory
Date: 2019-12-18 00:00:00

Fifty years ago, Robert MacArthur showed that stable equilibria optimize
quadratic functions of the population sizes in several important ecological
models. Here, we generalize this finding to a broader class of systems within
the framework of contemporary niche theory, and precisely state the conditions
under which an optimization principle (not necessarily quadratic) can be
obtained. We show that conducting the optimization in the space of
environmental states instead of population sizes leads to a universal and
transparent physical interpretation of the objective function. Specifically,
the equilibrium state minimizes the perturbation of the environment induced by
the presence of the competing species, subject to the constraint that no
species has a positive net growth rate. We use this "minimum environmental
perturbation principle" to make new predictions for eco-evolution and community
assembly, and describe a simple experimental setting where its conditions of
validity have been empirically tested.
Tumor Connectomics: Mapping the intra-tumoral complex interaction network
Date: 2019-01-28 00:00:00

Tumors are extremely heterogeneous and comprise of a number of intratumor
microenvironments or sub-regions. These tumor microenvironments may interact
with eac based on complex high-level relationships, which could provide
important insight into the organizational structure of the tumor network. To
that end, we developed a tumor connectomics framework (TCF) to understand and
model the complex functional and morphological interactions within the tumor.
Then, we demonstrate the TCF's potential in predicting treatment response in
breast cancer patients being treated with neoadjuvant chemotherapy. The TCF was
implemented on a breast cancer patient cohort of thirty-four patients with
dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) undergoing
neodjuvant chemotherapy treatment. The intra-tumor network connections (tumor
connectome) before and after treatment were modeled using advanced graph
theoretic centrality, path length and clustering metrics from the DCE-MRI. The
percentage change of the graph metrics between two time-points (Baseline and
1st cycle) was computed to predict the patient's final response to treatment.
The TCF visualized the inter-voxel network connections across multiple
time-points and was able to evaluate specific changes in the tumor connectome
with treatment. Degree centrality was identified as the most significant
predictor of treatment response with an AUC of 0.83 for classifying responders
from non-responders. In conclusion, the TCF graph metrics produced excellent
biomarkers for prediction of breast cancer treatment response with improved
visualization and interpretability of changes both locally and globally in the
tumor.
Orthogonal Chirp Division Multiplexing for Power Line Sensing via Time-Domain Reflectometry
Date: 2019-01-28 00:00:00

In this study, a time-domain reflectometry (TDR) system based on a baseband
version of the orthogonal chirp-division multiplexing (OCDM) scheme, which is
relies on a modified discrete Fresnel transform (DFnT), is proposed for power
line sensing. After a detailed description of the system model, a multiple
access scheme that exploits the contolution theorem of the modified DFnT for
enabling distributed reflectometric and transferometric sensing of the
monitored power line. Considering typical European underground low-voltage and
US overhead medium-voltage (MV) power distribution networks, range resolution
and maximum unambiguous range for the measurements are assessed for the
proposed scheme. Next, a comparison with multiple access schemes based on the
Hermitian symmetric orthogonal frequency-division multiplexing (HS-OFDM)
discussed in a previous work is performed considering a Brazilian MV overhead
scenario, being the number of measurements obtained over time, as well as
signal-to-interference-plus-noise ratio (SINR) used as performance metrics. The
attained results show that the proposed multiple access scheme results in the
same range resolution as the others. Also, the highest number of measurements
over time is obtained by the proposed scheme, which produces orthogonality
among signals transmitted by different power line modems (PLMs) neither and
time nor frequency domains, but rather in the Fresnel domain. Meanwhile,
although its yielded SINR values are fair among the PLMs consisting the
distributed sensing system, the proposed scheme is slightly outperformed by the
HS-OFDM based on code-division multiple access and by the HS-OFDM based on
frequency-division multiple access at some PLMs.
Steroid Receptors and Vertebrate Evolution
Date: 2019-04-19 00:00:00

Considering that life on earth evolved about 3.7 billion years ago,
vertebrates are young, appearing in the fossil record during the Cambrian
explosion about 542 to 515 million years ago. Results from sequence analyses of
genomes from bacteria, yeast, plants, invertebrates and vertebrates indicate
that receptors for adrenal steroids (aldosterone, cortisol), and sex steroids
(estrogen, progesterone, testosterone) also are young, with receptors for
estrogens and 3-ketosteroids first appearing in basal chordates
(cephalochordates: amphioxus), which are close ancestors of vertebrates. An
ancestral progesterone receptor and an ancestral corticoid receptor, the common
ancestor of the glucocorticoid and mineralocorticoid receptors, evolved in
jawless vertebrates (cyclostomes: lampreys, hagfish). This was followed by
evolution of an androgen receptor and distinct glucocorticoid and
mineralocorticoid receptors in cartilaginous fishes (gnathostomes: sharks).
Adrenal and sex steroid receptors are not found in echinoderms: and
hemichordates, which are ancestors in the lineage of cephalochordates and
vertebrates. The presence of steroid receptors in vertebrates, in which these
steroid receptors act as master switches to regulate differentiation,
development, reproduction, immune responses, electrolyte homeostasis and stress
responses, argues for an important role for steroid receptors in the
evolutionary success of vertebrates, considering that the human genome contains
about 22,000 genes, which is not much larger than genomes of invertebrates,
such as Caenorhabditis elegans (~18,000 genes) and Drosophila (~14,000 genes).
Unveiling new disease, pathway, and gene associations via multi-scale neural networks
Date: 2020-04-10 00:00:00

Diseases involve complex processes and modifications to the cellular
machinery. The gene expression profile of the affected cells contains
characteristic patterns linked to a disease. Hence, biological knowledge
pertaining to a disease can be derived from a patient cell's profile, improving
our diagnosis ability, as well as our grasp of disease risks. This knowledge
can be used for drug re-purposing, or by physicians to evaluate a patient's
condition and co-morbidity risk. Here, we look at differential gene expression
obtained from microarray technology for patients diagnosed with various
diseases. Based on this data and cellular multi-scale organization, we aim to
uncover disease--disease links, as well as disease-gene and disease--pathways
associations. We propose neural networks with structures inspired by the
multi-scale organization of a cell. We show that these models are able to
correctly predict the diagnosis for the majority of the patients. Through the
analysis of the trained models, we predict and validate disease-disease,
disease-pathway, and disease-gene associations with comparisons to known
interactions and literature search, proposing putative explanations for the
novel predictions that come from our study.