pwc-archive/papers-with-abstracts huggingface dataset:
Coding Capacity of Purkinje Cells with Different Schemes of Morphological Reduction
Date: 2019-04-25 00:00:00

The brain as a neuronal system has very complex structure with large
diversity of neuronal types. The most basic complexity is seen from the
structure of neuronal morphology, which usually has a complex tree-like
structure with dendritic spines distributed in branches. For simulating a
large-scale network with spiking neurons, the simple point neuron, such as
integrate-and-fire neuron, is often used. However, recent experimental evidence
suggests that the computational ability of a single neuron is largely enhanced
by its morphological structure, in particular, by various types of dendritic
dynamics. As morphology reduction of detailed biophysical models is one of
classic questions for systems neuroscience, much effort has been taken to
simulate a neuron with a few compartments to include the interaction between
soma and dendritic spines. Yet, novel reduction methods are still needed to
deal with complex dendritic tree. Here by using ten individual Purkinje cells
of the cerebellum from three species of guinea-pig, mouse and rat, we consider
four types of reduction methods and study their effects on the coding capacity
of Purkinje cells in terms of firing rate, timing coding, spiking pattern, and
modulated firing under different stimulation protocols. We find that there is a
variation of reduction performance depending on individual cells and species,
however, all reduction methods can preserve, to some degree, firing activity of
the full model of Purkinje cell. Therefore, when stimulating large-scale
network of neurons, one has to choose a proper type of reduced neuronal model
depending on the questions addressed.
Mathematical Models of Gene Expression
Date: 2019-10-16 00:00:00

In this paper we analyze the equilibrium properties of a large class of
stochastic processes describing the fundamental biological process within
bacterial cells, {\em the production process of proteins}. Stochastic models
classically used in this context to describe the time evolution of the numbers
of mRNAs and proteins are presented and discussed. An extension of these
models, which includes elongation phases of mRNAs and proteins, is introduced.
A convergence result to equilibrium for the process associated to the number of
proteins and mRNAs is proved and a representation of this equilibrium as a
functional of a Poisson process in an extended state space is obtained.
Explicit expressions for the first two moments of the number of mRNAs and
proteins at equilibrium are derived, generalizing some classical formulas.
Approximations used in the biological literature for the equilibrium
distribution of the number of proteins are discussed and investigated in the
light of these results. Several convergence results for the distribution of the
number of proteins at equilibrium are in particular obtained under different
scaling assumptions.
Fusion of Deep Neural Networks for Activity Recognition: A Regular Vine Copula Based Approach
Date: 2019-11-21 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

In this paper, we propose regular vine copula based fusion of multiple deep
neural network classifiers for the problem of multi-sensor based human activity
recognition. We take the cross-modal dependence into account by employing
regular vine copulas that are extremely flexible and powerful graphical models
to characterize complex dependence among multiple modalities. Multiple deep
neural networks are used to extract high-level features from multi-sensing
modalities, with each deep neural network processing the data collected from a
single sensor. The extracted high-level features are then combined using a
regular vine copula model. Numerical experiments are conducted to demonstrate
the effectiveness of our approach.
Spherical Preferences
Date: 2020-02-12 00:00:00

We introduce and study the property of orthogonal independence, a restricted
additivity axiom applying when alternatives are orthogonal. The axiom requires
that the preference for one marginal change over another should be maintained
after each marginal change has been shifted in a direction that is orthogonal
to both.
  We show that continuous preferences satisfy orthogonal independence if and
only if they are spherical: their indifference curves are spheres with the same
center, with preference being "monotone" either away or towards the center.
Spherical preferences include linear preferences as a special (limiting) case.
We discuss different applications to economic and political environments. Our
result delivers Euclidean preferences in models of spatial voting, quadratic
welfare aggregation in social choice, and expected utility in models of choice
under uncertainty.
Finite response time in stripe formation by bacteria with density-suppressed motility
Authors: Xingyu Zhang, Namiko Mitarai
Date: 2019-05-08 00:00:00

Genetically engineered bacteria to increase the tumbling frequency of the run-and-tumble motion for the higher local bacterial density form visible stripe pattern composed of successive high and low density regions on an agar plate. We propose a model that includes a simplified regulatory dynamics of the tumbling frequency in individual cells to clarify the role of finite response time. We show that the time-delay due to the response dynamics results in the instability in a homogeneous steady state allowing a pattern formation. For further understanding, we propose a simplified two-state model that allows us to describe the response time dependence of the instability analytically. We show that the instability occurs at long wave length as long as the response time is comparable with the tumbling timescale and the non-linearity of the response function to the change of the density is high enough. The minimum system size to see the instability grows with the response time $\tau$, proportional to $\sqrt{\tau}$ in the large delay limit.
The mitigating role of regulation on the concentric patterns of broadband diffusion. The case of Finland
Date: 2020-10-23 00:00:00

This article analyzes the role of Finnish regulation in achieving the
broadband penetration goals defined by the National Regulatory Authority. It is
well known that in the absence of regulatory mitigation the population density
has a positive effect on broadband diffusion. Hence, we measure the effect of
the population density on the determinants of broadband diffusion throughout
the postal codes of Finland via Geographically Weighted Regression. We suggest
that the main determinants of broadband diffusion and the population density
follow a spatial pattern that is either concentric with a weak/medium/strong
strength or non-concentric convex/concave. Based on 10 patterns, we argue that
the Finnish spectrum policy encouraged Mobile Network Operators to satisfy
ambitious Universal Service Obligations without the need for a Universal
Service Fund. Spectrum auctions facilitated infrastructure-based competition
via equitable spectrum allocation and coverage obligation delivery via low-fee
licenses. However, state subsidies for fiber deployment did not attract
investment from nationwide operators due to mobile preference. These subsidies
encouraged demand-driven investment, leading to the emergence of fiber consumer
cooperatives. To explain this emergence, we show that when population density
decreases, the level of mobile service quality decreases and community
commitment increases. Hence, we recommend regulators implementing market-driven
strategies for 5G to stimulate local investment. For example, by allocating the
3.5 GHz and higher bands partly through local light licensing.
On age-specific selection and extensive lifespan beyond menopause
Date: 2019-05-18 00:00:00

Extensive post reproductive lifespan (PRLS) is observed only in a few
species, such as humans or resident killer whales, and its origin is under
debate. Hypotheses like mother-care and grandmother-care invoke strategies of
investment--provision to one's descendants to enhance one's overall
reproductive success--to explain PRLS. The contribution of an investment
strategy varies with the age of the caregiver, as the number of care-receiving
descendant changes with age. Here we simulated an agent based model, which is
sensitive to age-specific selection, to examine how the investment strategies
in different hypotheses affect survival and reproduction across different
stages of life. We found that extensive PRLS emerges if we combine multiple
investment strategies, including grandmother-care but not mother-care, which
allow an individual to have an increasing contribution as it ages. We also
found that, if mother-care is further introduced to the PRLS-enabling
strategies, it will let contribution at mid-life to substitute contribution at
late life, which consequently terminates extensive PRLS.
Appointment scheduling model in healthcare using clustering algorithms
Authors: Niloofar Yousefi, Farhad Hasankhani, Mahsa Kiani, Nooshin Yousefi
Date: 2019-05-03 00:00:00
Tasks: Clustering, model, Scheduling

In this study, we provided a scheduling procedure which is a combination of machine learning and mathematical programming that minimizes the waiting time of higher priority outpatients. Outpatients who request for appointment in healthcare facilities have different priorities. Determining the priority of outpatients and allocating the capacity based on the priority classes are important concepts that have to be considered in the scheduling of outpatients. In this study, two stages are defined for scheduling an incoming outpatient. In the initial stage, we employed and evaluated four distinct clustering techniques; K-means clustering, agglomerative hierarchical clustering, DBSCAN, and OPTICS clustering to classify outpatients into priority classes and suggested the best pattern to cluster the outpatients. In the second stage, we modeled the scheduling problem as a Markov Decision Process (MDP) problem since the arrivals are uncertain and the decisions are taken at the end of each day after observing total requests. Due to the curse of dimensionality, we used the fluid approximation method to estimate the optimal solution of the MDP. our methodology is employed in a data set of Shaheed Rajaei Medical and Research Center, and we represented how our model works in prioritizing and scheduling outpatients.
Capacity Bounds and Degrees of Freedom for MIMO Antennas Constrained by Q-Factor
Authors: Casimir Ehrenborg, Mats Gustafsson, Miloslav Capek
Date: 2019-05-06 00:00:00

The optimal spectral efficiency and number of independent channels for MIMO antennas in isotropic multipath channels are investigated when bandwidth requirements are placed on the antenna. By posing the problem as a convex optimization problem restricted by the port Q-factor a semi-analytical expression is formed for its solution. The antennas are simulated by method of moments and the solution is formulated both for structures fed by discrete ports, as well as for design regions characterized by an equivalent current. It is shown that the solution is solely dependent on the eigenvalues of the so-called energy modes of the antenna. The magnitude of these eigenvalues is analyzed for a linear dipole array as well as a plate with embedded antenna regions. The energy modes are also compared to the characteristic modes to validate characteristic modes as a design strategy for MIMO antennas. The antenna performance is illustrated through spectral efficiency over the Q-factor, a quantity that is connected to the capacity. It is proposed that the number of energy modes below a given Q-factor can be used to estimate the degrees of freedom for that Q-factor.
Joint Antenna Selection and Hybrid Beamformer Design using Unquantized and Quantized Deep Learning Networks
Date: 2019-11-23 00:00:00

In millimeter-wave communications, multiple-input-multiple-output (MIMO)
systems use large antenna arrays to achieve high gain and spectral efficiency.
These massive MIMO systems employ hybrid beamformers to reduce power
consumption associated with fully digital beamforming in large arrays. Further
savings in cost and power are possible through the use of subarrays. Unlike
prior works that resort to large latency methods such as optimization and
greedy search for subarray selection, we propose a deep-learning-based approach
in order to overcome the complexity issue without causing significant
performance loss. We formulate antenna selection and hybrid beamformer design
as a classification/prediction problem for convolutional neural networks
(CNNs). For antenna selection, the CNN accepts the channel matrix as input and
outputs a subarray with optimal spectral efficiency. The resultant subarray
channel matrix is then again fed to a CNN to obtain analog and baseband
beamformers. We train the CNNs with several noisy channel matrices that have
different channel statistics in order to achieve a robust performance at the
network output. Numerical experiments show that our CNN framework provides an
order better spectral efficiency and is 10 times faster than the conventional
techniques. Further investigations with quantized-CNNs show that the proposed
network, saved in no more than 5 bits, is also suited for digital mobile
devices.
Privatizaciones, fusiones y adquisiciones: las grandes empresas en M\'exico
Date: 2019-03-19 00:00:00

The present work has as principal objective analyze the evolution of the
process of privatization, mergers and acquisitions of the big companies in the
country in the last decades, to understand the conductive threads that formed
the structural changes of the economy, in order world oligop\'olicas to insert
it to the global market characterized by formations of strategic alliances,
across the mergers and acquisitions that they favour to the transnational
companies.
Initial Crypto-asset Offerings (ICOs), tokenization and corporate governance
Date: 2019-03-27 00:00:00
Tasks: Diversity

This paper discusses the potential impacts of the so-called `initial coin
offerings', and of several developments based on distributed ledger technology
(`DLT'), on corporate governance. While many academic papers focus mainly on
the legal qualification of DLT and crypto-assets, and most notably in relation
to the potential definition of the latter as securities/financial instruments,
the authors analyze some of the use cases based on DLT technology and their
potential for significant changes of the corporate governance analyses. This
article studies the consequences due to the emergence of new kinds of firm
stakeholders, i.e. the crypto-assets holders, on the governance of small and
medium-sized enterprises (`SMEs') as well as of publicly traded companies.
Since early 2016, a new way of raising funds has rapidly emerged as a major
issue for FinTech founders and financial regulators. Frequently referred to as
initial coin offerings, Initial Token Offerings (`ITO'), Token Generation
Events (`TGE') or simply `token sales', we use in our paper the terminology
Initial Crypto-asset Offerings (`ICO'), as it describes more effectively than
`initial coin offerings' the vast diversity of assets that could be created and
which goes far beyond the payment instrument issue.
On the Kolkata index as a measure of income inequality
Date: 2019-10-18 00:00:00

We study the mathematical and economic structure of the Kolkata (k) index of
income inequality. We show that the k-index always exists and is a unique fixed
point of the complementary Lorenz function, where the Lorenz function itself
gives the fraction of cumulative income possessed by the cumulative fraction of
population (when arranged from poorer to richer). We show that the k-index
generalizes Pareto's 80/20 rule. Although the k and Pietra indices both split
the society into two groups, we show that k-index is a more intensive measure
for the poor-rich split. We compare the normalized k-index with the Gini
coefficient and the Pietra index and discuss when they coincide. We establish
that for any income distribution the value of Gini coefficient is no less than
that of the Pietra index and the value of the Pietra index is no less than that
of the normalized k-index. While the Gini coefficient and the Pietra index are
affected by transfers exclusively among the rich or among the poor, the k-index
is only affected by transfers across the two groups.
Efficient Channel AoD/AoA Estimation Using Widebeams for Millimeter Wave MIMO Systems
Date: 2019-05-10 00:00:00

Using millimeter-wave (mmWave) bands is expected to provide high data rates
through large licensed and unlicensed spectrum. Due to large path loss and
sparse scattering propagation properties, proper beam alignment is important in
mmWave systems. Small carrier wavelengths at mmWave bands let wireless
communication systems use large antenna arrays to provide sufficient
beamforming gain with highly directional beams. Hence, high resolution channel
angle-of-departure (AoD) and angle-of-arrival (AoA) estimation is crucial for
beam alignment to get the advantages of large beamforming gain. Using large
antenna arrays, however, can lead to high system complexity and channel
estimation overhead. This paper proposes a channel AoD/AoA estimation technique
using widebeams to lower estimation overhead and auxiliary-beam-pair (ABP) to
get high resolution channel AoD/AoA estimates considering hybrid transceiver
structures. To fully use the hybrid transceiver structures, the linear
combination of discrete Fourier transform (DFT) vectors is considered to
construct widebeams. Numerical results show that the proposed estimator can get
high resolution channel AoD/AoA estimates with lower overhead compared to
previous estimators.
Identifying Present-Bias from the Timing of Choices
Date: 2019-05-10 00:00:00

Timing decisions are common: when to file your taxes, finish a referee
report, or complete a task at work. We ask whether time preferences can be
inferred when \textsl{only} task completion is observed. To answer this
question, we analyze the following model: each period a decision maker faces
the choice whether to complete the task today or to postpone it to later. Cost
and benefits of task completion cannot be directly observed by the analyst, but
the analyst knows that net benefits are drawn independently between periods
from a time-invariant distribution and that the agent has time-separable
utility. Furthermore, we suppose the analyst can observe the agent's exact
stopping probability. We establish that for any agent with quasi-hyperbolic
$\beta,\delta$-preferences and given level of partial naivete $\hat{\beta}$,
the probability of completing the task conditional on not having done it
earlier increases towards the deadline. And conversely, for any given
preference parameters $\beta,\delta$ and (weakly increasing) profile of task
completion probability, there exists a stationary payoff distribution that
rationalizes her behavior as long as the agent is either sophisticated or fully
naive. An immediate corollary being that, without parametric assumptions, it is
impossible to rule out time-consistency even when imposing an a priori
assumption on the permissible long-run discount factor. We also provide an
exact partial identification result when the analyst can, in addition to the
stopping probability, observe the agent's continuation value.
Digital System Reconstruction by Pairwise Transfer Entropy
Date: 2019-05-10 00:00:00

Transfer entropy (TE) is an attractive model-free method to detect causality
and infer structural connectivity of general digital systems. However it relies
on high dimensions used in its definition to clearly remove the memory effect
and distinguish the direct causality from the indirect ones which makes it
almost inoperable in practice. In this work, we try to use a low order and
pairwise TE framework with binary data suitably filtered from the recorded
signals to avoid the high dimensional problem. Under this setting, we find and
explain that the TE values from the connected and unconnected pairs have a
significant difference of magnitude, which can be easily classified by cluster
methods. This phenomenon widely and robustly holds over a wide range of systems
and dynamical regimes. In addition, we find the TE value is quadratically
related to the coupling strength and thus we can establish a quantitative
mapping between the causal and structural connectivity.
Tumor Microenvironment-based Gene Signatures Divides Novel Immune and Stromal Subgroup Classification of Lung Adenocarcinoma
Date: 2019-05-10 00:00:00
Tasks: Prognosis

Tumor microenvironment has complex effects on tumorigenesis and metastasis.
However, there is still a lack of comprehensive understanding of the
relationship among molecular and cellular characteristics in tumor
microenvironment, clinical prognosis and immunotherpy response. In this study,
the immune and stromal (non-immune) signatures of tumor microenvironment were
integrated to identify novel subgroups of lung adenocarcinoma by
eigendecomposition and extraction algorithms of bioinformatics and machine
learning, such as non-negative matrix factorization and multitask learning.
Tumors were classified into 4 groups according to the activation of immunity
and stroma by novel signatures. The 4 groups had different mutation landscape,
molecular, cellular characteristics and prognosis, which have been validation
in 6 independent data sets containing 1551 patients. High-immune and
low-stromal activation group links to high immunocyte infiltration, high
immunocompetence, low fibroblasts, endothelial cells, collagen, laminin, tumor
mutation burden, and better overall survival. We developed a novel model based
on tumor microenvironment by integrating immune and stromal activation, namely
PMBT (prognostic model based on tumor microenvironment). The PMBT showed the
value to predict overall survival and immunotherapy responses.
Non-cooperative Aerial Base Station Placement via Stochastic Optimization
Date: 2019-05-10 00:00:00
Tasks: Stochastic Optimization

Autonomous unmanned aerial vehicles (UAVs) with on-board base station
equipment can potentially provide connectivity in areas where the terrestrial
infrastructure is overloaded, damaged, or absent. Use cases comprise emergency
response, wildfire suppression, surveillance, and cellular communications in
crowded events to name a few. A central problem to enable this technology is to
place such aerial base stations (AirBSs) in locations that approximately
optimize the relevant communication metrics. To alleviate the limitations of
existing algorithms, which require intensive and reliable communications among
AirBSs or between the AirBSs and a central controller, this paper leverages
stochastic optimization and machine learning techniques to put forth an
adaptive and decentralized algorithm for AirBS placement without inter-AirBS
cooperation or communication. The approach relies on a smart design of the
network utility function and on a stochastic gradient ascent iteration that can
be evaluated with information available in practical scenarios. To complement
the theoretical convergence properties, a simulation study corroborates the
effectiveness of the proposed scheme.