pwc-archive/papers-with-abstracts huggingface dataset:
A framework to construct a longitudinal DW-MRI infant atlas based on mixed effects modeling of dODF coefficients
Date: 2020-03-11 00:00:00

Building of atlases plays a crucial role in the analysis of brain images. In
scenarios where early growth, aging or disease trajectories are of key
importance, longitudinal atlases become necessary as references, most often
created from cross-sectional data. New opportunities will be offered by
creating longitudinal brain atlases from longitudinal subject-specific image
data, where explicit modeling of subject's variability in slope and intercept
leads to a more robust estimation of average trajectories but also to estimates
of confidence bounds. This work focuses on a framework to build a continuous 4D
atlas from longitudinal high angular resolution diffusion images (HARDI) where,
unlike atlases of derived scalar diffusion indices such as FA, statistics on
dODFs is preserved. Multi-scalar images obtained from DW images are used for
geometric alignment, and linear mixed-effects modeling from longitudinal
diffusion orientation distribution functions (dODF) leads to estimation of
continuous dODF changes. The proposed method is applied to a longitudinal
dataset of HARDI images from healthy developing infants in the age range of 3
to 36 months. Verification of mixed-effects modeling is obtained by voxel-wise
goodness of fit calculations. To demonstrate the potential of our method, we
display changes of longitudinal atlas using dODF and derived generalized
fractional anisotropy (GFA) of dODF. We also investigate white matter
maturation patterns in genu, body, and splenium of the corpus callosum. The
framework can be used to build an average dODF atlas from HARDI data and to
derive subject-specific and population-based longitudinal change trajectories.
A price on warming with a supply chain directed market
Authors: John F. Raffensperger
Date: 2020-03-11 00:00:00

Existing emissions trading system (ETS) designs inhibit emissions but do not constrain warming to any fxed level, preventing certainty of the global path of warming. Instead, they have the indirect objective of reducing emissions. They provide poor future price information. And they have high transaction costs for implementation, requiring treaties and laws. To address these shortcomings, this paper proposes a novel double-sided auction mechanism of emissions permits and sequestration contracts tied to temperature. This mechanism constrains warming for many (e.g., 150) years into the future and every auction would provide price information for this time range. In addition, this paper proposes a set of market rules and a bottom-up implementation path. A coalition of businesses begin implementation with jurisdictions joining as they are ready. The combination of the selected market rules and the proposed implementation path appear to incentivize participation. This design appears to be closer to "first best" with a lower cost of mitigation than any in the literature, while increasing the certainty of avoiding catastrophic warming. This design should also have a faster pathway to implementation. A numerical simulation shows surprising results, e.g., that static prices are wrong, prices should evolve over time in a way that contradicts other recent proposals, and "global warming potential" as used in existing ETSs are generally erroneous.
An Improved Square-root Algorithm for V-BLAST Based on Efficient Inverse Cholesky Factorization
Date: 2020-03-11 00:00:00

A fast algorithm for inverse Cholesky factorization is proposed, to compute a
triangular square-root of the estimation error covariance matrix for Vertical
Bell Laboratories Layered Space-Time architecture (V-BLAST). It is then applied
to propose an improved square-root algorithm for V-BLAST, which speedups
several steps in the previous one, and can offer further computational savings
in MIMO Orthogonal Frequency Division Multiplexing (OFDM) systems. Compared to
the conventional inverse Cholesky factorization, the proposed one avoids the
back substitution (of the Cholesky factor), and then requires only half
divisions. The proposed V-BLAST algorithm is faster than the existing efficient
V-BLAST algorithms. The expected speedups of the proposed square-root V-BLAST
algorithm over the previous one and the fastest known recursive V-BLAST
algorithm are 3.9~5.2 and 1.05~1.4, respectively.
On the structure of the world economy: An absorbing Markov chain approach
Date: 2020-03-11 00:00:00

The expansion of global production networks has raised many important
questions about the interdependence among countries and how future changes in
the world economy are likely to affect the countries' positioning in global
value chains. We are approaching the structure and lengths of value chains from
a completely different perspective than has been available so far. By assigning
a random endogenous variable to a network linkage representing the number of
intermediate sales/purchases before absorption (final use or value added), the
discrete-time absorbing Markov chains proposed here shed new light on the world
input/output networks. The variance of this variable can help assess the risk
when shaping the chain length and optimize the level of production. Contrary to
what might be expected simply on the basis of comparative advantage, the
results reveal that both the input and output chains exhibit the same
quasi-stationary product distribution. Put differently, the expected proportion
of time spent in a state before absorption is invariant to changes of the
network type. Finally, the several global metrics proposed here, including the
probability distribution of global value added/final output, provide guidance
for policy makers when estimating the resilience of world trading system and
forecasting the macroeconomic developments.
Compressing Piecewise Smooth Images with the Mumford-Shah Cartoon Model
Date: 2020-06-16 00:00:00
Tasks: Motion Compensation

Compressing piecewise smooth images is important for many data types such as
depth maps in 3D videos or optic flow fields for motion compensation.
Specialised codecs that rely on explicitly stored segmentations excel in this
task since they preserve discontinuities between smooth regions. However,
current approaches rely on ad hoc segmentations that lack a clean
interpretation in terms of energy minimisation. As a remedy, we derive a
generic region merging algorithm from the Mumford-Shah cartoon model. It adapts
the segmentation to arbitrary reconstruction operators for the segment content.
In spite of its conceptual simplicity, our framework can outperform previous
segment-based compression methods as well as BPG by up to 3 dB.
Enabling automated driving by ICT infrastructure: A reference architecture
Date: 2020-03-11 00:00:00

Information and communication technology (ICT) is an enabler for establishing
automated vehicles (AVs) in today's traffic systems. By providing complementary
and/or redundant information via radio communication to the AV's perception by
on-board sensors, higher levels of automated driving become more comfortable,
safer, or even possible without interaction by the driver, especially in
complex scenarios. Additionally, communication between vehicles and/or a
central service can improve the efficiency of traffic flow. This paper presents
a reference architecture for such an infrastructure-based support of AVs. The
architecture combines innovative concepts and technologies from different
technological fields like communication, IT environment and data flows, and
cyber-security and privacy. Being the basis for the EU-funded project ICT4CART,
exemplary implementations of this architecture will show its power for a
variety of use cases on highways and in urban areas in test sites in Austria,
Germany, and Italy, including cross-border interoperability.
An Improved DOA Estimation Method for a Mixture of Circular and Non-Circular Signals Based on Sparse Arrays
Authors: Jingjing Cai, Wei Liu, Ru Zong, Yangyang Dong
Date: 2020-03-11 00:00:00

Sparse arrays have attracted a lot of interests recently for their capability of providing more degrees of freedom than traditional uniform linear arrays. For a mixture of circular and noncircular signals, most of the existing direction of arrival (DOA) estimation methods are based on various uniform arrays. Recently, a class of DOA estimation algorithms based on sparse arrays was developed for a mixture of circular and noncircular signals. To further improve its performance, in this work, a modified algorithm is presented, which can resolve the same number of signals, and simulation results are provided to verified its performance.
Cell cycle heritability and localization phase transition in growing populations
Date: 2020-03-11 00:00:00

The cell cycle duration is a variable cellular phenotype that underlies
long-term population growth and age structures. By analyzing the stationary
solutions of a branching process with heritable cell division times, we
demonstrate existence of a phase transition, which can be continuous or
first-order, by which a non-zero fraction of the population becomes localized
at a minimal division time. Just below the transition, we demonstrate
coexistence of localized and delocalized age-structure phases, and power law
decay of correlation functions. Above it, we observe self-synchronization of
cell cycles, collective divisions, and slow 'aging' of population growth rates.
Coding schemes and Applications for Weather Radars
Date: 2020-02-21 00:00:00

In this paper, we describe the evolution of a pair of polyphase coded
waveform for use in second trip suppression in weather radar. The polyphase
codes were designed and tested on NASA weather radar. The NASA dual-frequency,
dual-polarization Doppler radar (D3R) was developed primarily as a ground
validation tool for the GPM satellite dual-frequency radar. Recently, the D3R
radar was upgraded with new versions of digital receiver hardware and firmware,
which supports larger filter lengths and multiple phase coded waveforms, and
also newer IF sub-systems. This has enhanced the capabilities of radar
manifolds.
An Optimization Problem for Day-Ahead Planning of Electrical Energy Aggregators
Authors: Francesco Conte, Matteo Saviozzi, Samuele Grillo
Date: 2020-03-11 00:00:00

The widespread diffusion of distributed energy resources, especially those based on renewable energy, and energy storage devices has deeply modified power systems. As a consequence, demand response, the ability of customers to respond to regulating signals, has moved from large high-voltage and medium-voltage end-users to small, low-voltage, customers. In order to be effective, the participation to demand response of such small players must be gathered by aggregators. The role and the business models of these new entities have been studied in literature from a variety of viewpoints. Demand response can be clearly applied by sending a dedicated price signal to customers, but this methodology cannot obtain a diverse, punctual, predictable, and reliable response. These characteristics can be achieved by directly controlling the loads units. This approach involves communication problems and technological readiness. This paper proposes a fully decentralized mixed integer linear programming approach for demand response. In this framework, each load unit performs an optimization, subject to technical and user-based constraints, and gives to the aggregator a desired profile along with a reserve, which is guaranteed to comply with the constraints. In this way, the aggregator can trade the reserve coming from several load units, being the only interface to the market. Upon request, then, the aggregator communicates to the load units the modifications to their desired profiles without either knowing or caring how this modification would be accomplished. The effectiveness is simulated on 200 realistic load units.
Geodesic distance on optimally regularized functional connectomes uncovers individual fingerprints
Authors: Kausar Abbas, Mintao Liu, Manasij Venkatesh, Enrico Amico, Alan David Kaplan, Mario Ventresca, Luiz Pessoa, Jaroslaw Harezlak, Joaquín Goñi
Date: 2020-03-11 00:00:00
Tasks: Functional Connectivity

Background: Functional connectomes (FCs), have been shown to provide a reproducible individual fingerprint, which has opened the possibility of personalized medicine for neuro/psychiatric disorders. Thus, developing accurate ways to compare FCs is essential to establish associations with behavior and/or cognition at the individual-level. Methods: Canonically, FCs are compared using Pearson's correlation coefficient of the entire functional connectivity profiles. Recently, it has been proposed that the use of geodesic distance is a more accurate way of comparing functional connectomes, one which reflects the underlying non-Euclidean geometry of the data. Computing geodesic distance requires FCs to be positive-definite and hence invertible matrices. As this requirement depends on the fMRI scanning length and the parcellation used, it is not always attainable and sometimes a regularization procedure is required. Results: In the present work, we show that regularization is not only an algebraic operation for making FCs invertible, but also that an optimal magnitude of regularization leads to systematically higher fingerprints. We also show evidence that optimal regularization is dataset-dependent, and varies as a function of condition, parcellation, scanning length, and the number of frames used to compute the FCs. Discussion: We demonstrate that a universally fixed regularization does not fully uncover the potential of geodesic distance on individual fingerprinting, and indeed could severely diminish it. Thus, an optimal regularization must be estimated on each dataset to uncover the most differentiable across-subject and reproducible within-subject geodesic distances between FCs. The resulting pairwise geodesic distances at the optimal regularization level constitute a very reliable quantification of differences between subjects.
Exact fixation probabilities for the Birth-Death and Death-Birth frequency-dependent Moran processes on the star graph
Date: 2020-03-18 00:00:00

Broom and Rycht\'{a}\v{r} [Proc. R. Soc. A (2008) 464, 2609--2627] found an
exact solution for the fixation probabilities of the Moran process for a
structured population, in which the interaction structure among individuals is
given by the so-called star graph, i.e. one central vertex and $n$ leaves, the
leaves connecting only to the center. We generalize on their solution by
allowing individuals' fitnesses to depend on the population frequency, and also
by allowing a possible change in the order of reproduction and death draws. In
their cited paper, Broom and Rycht\'{a}\v{r} considered the birth-death (BD)
process, in which at each time step an individual is first drawn for
reproduction and then an individual is selected for death. In the death-birth
(DB) process, the order of the draws is reversed. It may be seen that the order
of the draws makes a big difference in the fixation probabilities. Our solution
method applies to both the BD and the DB cases. As expected, the exact formulae
for the fixation probabilities are complicated. We will also illustrate them
with some examples and provide results on the asymptotic behavior of the
fixation probabilities when the number $n$ of leaves in the graph tends to
infinity.
The CRLB and Maximum likelihood in ptychography with Poisson noise
Date: 2020-05-17 00:00:00

We investigate the performance of ptychography with noisy data by analyzing
the Cram\'{e}r Rao Lower Bound. The lower bound of ptychography is derived and
numerically computed for both plane wave and structured illumination. The
influence of Poisson noise on the ptychography reconstruction is discussed. The
computation result shows that, if the estimator is unbiased, the minimum
variance for Poisson noise is mostly determined by the illumination power and
the transmission function of the object. Monte Carlo analysis is conducted to
validate our calculation results for different photon numbers. The performance
of the maximum likelihood method and the approach of amplitude-based cost
function minimization is studied in the Monte Carlo analysis also.
Resilient Distributed Vector Consensus Using Centerpoints
Authors: Waseem Abbas, Mudassir Shabbir, Jiani Li, Xenofon Koutsoukos
Date: 2020-03-11 00:00:00

In this paper, we study the resilient vector consensus problem in networks with adversarial agents and improve resilience guarantees of existing algorithms. A common approach to achieving resilient vector consensus is that every non-adversarial (or normal) agent in the network updates its state by moving towards a point in the convex hull of its \emph{normal} neighbors' states. Since an agent cannot distinguish between its normal and adversarial neighbors, computing such a point, often called as \emph{safe point}, is a challenging task. To compute a safe point, we propose to use the notion of \emph{centerpoint}, which is an extension of the median in higher dimensions, instead of Tverberg partition of points, which is often used for this purpose. We discuss that the notion of centerpoint provides a complete characterization of safe points in $\mathbb{R}^d$. In particular, we show that a safe point is essentially an interior centerpoint if the number of adversaries in the neighborhood of a normal agent $i$ is less than $\frac{N_i}{d+1} $, where $d$ is the dimension of the state vector and $N_i$ is the total number of agents in the neighborhood of $i$. Consequently, we obtain necessary and sufficient conditions on the number of adversarial agents to guarantee resilient vector consensus. Further, by considering the complexity of computing centerpoints, we discuss improvements in the resilience guarantees of vector consensus algorithms and compare with the other existing approaches. Finally, we numerically evaluate the performance of our approach through experiments.
Resource Allocation for Intelligent Reflecting Surface Aided Wireless Powered Mobile Edge Computing in OFDM Systems
Date: 2020-03-11 00:00:00
Tasks: Edge-computing

Wireless powered mobile edge computing (WP-MEC) has been recognized as a
promising technique to provide both enhanced computational capability and
sustainable energy supply to massive low-power wireless devices. However, its
energy consumption becomes substantial, when the transmission link used for
wireless energy transfer (WET) and for computation offloading is hostile. To
mitigate this hindrance, we propose to employ the emerging technique of
intelligent reflecting surface (IRS) in WP-MEC systems, which is capable of
providing an additional link both for WET and for computation offloading.
Specifically, we consider a multi-user scenario where both the WET and the
computation offloading are based on orthogonal frequency-division multiplexing
(OFDM) systems. Built on this model, an innovative framework is developed to
minimize the energy consumption of the IRS-aided WP-MEC network, by optimizing
the power allocation of the WET signals, the local computing frequencies of
wireless devices, both the sub-band-device association and the power allocation
used for computation offloading, as well as the IRS reflection coefficients.
The major challenges of this optimization lie in the strong coupling between
the settings of WET and of computing as well as the unit-modules constraint on
IRS reflection coefficients. To tackle these issues, the technique of
alternative optimization is invoked for decoupling the WET and computing
designs, while two sets of locally optimal IRS reflection coefficients are
provided for WET and for computation offloading separately relying on the
successive convex approximation method. The numerical results demonstrate that
our proposed scheme is capable of monumentally outperforming the conventional
WP-MEC network without IRSs.
Smart Home Energy Management System for Power System Resiliency
Date: 2020-03-12 00:00:00
Tasks: Decision Making, energy management, Management, Model Predictive Control

The need for resiliency of electricity supply is increasing due to increasing
frequency of natural disasters---such as hurricanes---that disrupt supply from
the power grid. Rooftop solar photovoltaic (PV) panels together with batteries
can provide resiliency in many scenarios. Without intelligent and automated
decision making that can trade off conflicting requirements, a large PV system
and a large battery is needed to provide meaningful resiliency. By using
forecast of solar generation and household demand, an intelligent decision
maker can operate the equipment (battery and critical loads) to ensure that the
critical loads are serviced to the maximum duration possible. With the aid of
such an intelligent control system, a smaller (and thus lower cost) system can
service the primary loads for the same duration that a much larger system will
be needed to service otherwise.
  In this paper we propose such an intelligent control system. A model
predictive control (MPC) architecture is used that uses available measurements
and forecasts to make optimal decisions for batteries and critical loads in
real time. The optimization problem is formulated as a MILP (mixed integer
linear program) due to the on/off decisions for the loads. Performance is
compared with a non-intelligent baseline controller, for a PV-battery system
chosen carefully for a single family house in Florida. Simulations are
conducted for a one week period during hurricane Irma in 2017. Simulations show
that the cost of the PV+battery system to provide a certain resiliency
performance, duration the primary load can be serviced successfully, can be
halved by the proposed control system.
50 years since the Marr, Ito, and Albus models of the cerebellum
Date: 2020-06-15 00:00:00
Tasks: Hierarchical Reinforcement Learning

Fifty years have passed since David Marr, Masao Ito, and James Albus proposed
seminal models of cerebellar functions. These models share the essential
concept that parallel-fiber-Purkinje-cell synapses undergo plastic changes,
guided by climbing-fiber activities during sensorimotor learning. However, they
differ in several important respects, including holistic versus complementary
roles of the cerebellum, pattern recognition versus control as computational
objectives, potentiation versus depression of synaptic plasticity, teaching
signals versus error signals transmitted by climbing-fibers, sparse expansion
coding by granule cells, and cerebellar internal models. In this review, we
evaluate the different features of the three models based on recent
computational and experimental studies. While acknowledging that the three
models have greatly advanced our understanding of cerebellar control mechanisms
in eye movements and classical conditioning, we propose a new direction for
computational frameworks of the cerebellum. That is, hierarchical reinforcement
learning with multiple internal models.