pwc-archive/papers-with-abstracts huggingface dataset:
Parameterized and GPU-Parallelized Real-Time Model Predictive Control for High Degree of Freedom Robots
Date: 2020-01-14 00:00:00
Tasks: GPU, Model Predictive Control

This work presents and evaluates a novel input parameterization method which
improves the tractability of model predictive control (MPC) for high degree of
freedom (DoF) robots. Experimental results demonstrate that by parameterizing
the input trajectory more than three quarters of the optimization variables
used in traditional MPC can be eliminated with practically no effect on system
performance. This parameterization also leads to trajectories which are more
conservative, producing less overshoot in underdamped systems with modeling
error. In this paper we present two MPC solution methods that make use of this
parameterization. The first uses a convex solver, and the second makes use of
parallel computing on a graphics processing unit (GPU). We show that both
approaches drastically reduce solve times for large DoF, long horizon MPC
problems allowing solutions at real-time rates. Through simulation and hardware
experiments, we show that the parameterized convex solver MPC has faster solve
times than traditional MPC for high DoF cases while still achieving similar
performance. For the GPU-based MPC solution method, we use an evolutionary
algorithm and that we call Evolutionary MPC (EMPC). EMPC is shown to have even
faster solve times for high DoF systems. Solve times for EMPC are shown to
decrease even further through the use of a more powerful GPU. This suggests
that parallelized MPC methods will become even more advantageous with the
improvement and prevalence of GPU technology.
Matrix recovery from bilinear and quadratic measurements
Date: 2020-02-13 00:00:00

Matrix (or operator) recovery from linear measurements is a well-studied
problem. However, there are situations where only bilinear or quadratic
measurements are available. A bilinear or quadratic problem can easily be
transformed into a linear one, but it raises questions when the linearized
problem is solvable and what is the cost of linearization. In this work, we
study a few specific cases of this general problem and show when the bilinear
problem is solvable. Using this result and certain properties of polynomial
rings, we present a scenario when the quadratic problem can be linearized at
the cost of just a linear number of additional measurements. Finally, we link
our results back to two applications that inspired it: Time Encoding Machines
and Continuous Localisation.
Beam-space Multiplexing: Practice, Theory, and Trends-From 4G TD-LTE, 5G, to 6G and Beyond
Date: 2020-01-14 00:00:00

In this article, the new term, namely beam-space multiplexing, is proposed
for the former multi-layer beamforming for 4G TD-LTE in 3GPP releases. We
provide a systematic overview of beam-space multiplexing from engineering and
theoretical perspectives. Firstly, we clarify the fundamental theory of
beam-space multiplexing. Specifically, we provide a comprehensive comparison
with the antenna-space multiplexing in terms of theoretical analysis, channel
state information acquisition, and engineering implementation constraints.
Then, we summarize the key technologies and 3GPP standardization of beam-space
multiplexing in 4G TD-LTE and 5G new radio (NR) in terms of multi-layer
beamforming and massive beamforming, respectively. We also provide system-level
performance evaluation of beam-space multiplexing schemes and field results
from current commercial TD-LTE networks and field trial of 5G. The practical
deployments of 4G TD-LTE and 5G cellular networks demonstrate the superiority
of beam-space multiplexing within the limitations of implementation complexity
and practical deployment scenarios. Finally, the future trends of beam-space
multiplexing in 6G and beyond are discussed, including massive beamforming for
extremely large-scale MIMO (XL-MIMO), low earth orbit (LEO) satellites
communication, data-driven intelligent massive beamforming, and multi-target
spatial signal processing, i.e., joint communication and sensing, positioning,
etc.
Derivation of Fitts' law from the Task Dynamics model of speech production
Date: 2020-03-17 00:00:00

Fitts' law is a linear equation relating movement time to an index of
movement difficulty. The recent finding that Fitts' law applies to voluntary
movement of the vocal tract raises the question of whether the theory of speech
production implies Fitts' law. The present letter establishes a theoretical
connection between Fitts' law and the Task Dynamics model of speech production.
We derive a variant of Fitts' law where the intercept and slope are functions
of the parameters of the Task Dynamics model and the index of difficulty is a
product logarithm, or Lambert W function, rather than a logarithm.
Spike Train Cumulants for Linear-Nonlinear Poisson Cascade Models
Date: 2020-01-14 00:00:00

Spiking activity in cortical networks is nonlinear in nature. The
linear-nonlinear cascade model, some versions of which are also known as
point-process generalized linear model, can efficiently capture the nonlinear
dynamics exhibited by such networks. Of particular interest in such models are
theoretical predictions of spike train statistics. However, due to the
moment-closure problem, approximations are inevitable. We suggest here a series
expansion that explains how higher-order moments couple to lower-order ones.
Our approach makes predictions in terms of certain integrals, the so-called
loop integrals. In previous studies these integrals have been evaluated
numerically, but numerical instabilities are sometimes encountered rendering
the results unreliable. Analytic solutions are presented here to overcome this
problem, and to arrive at more robust evaluations. We were able to deduce these
analytic solutions by switching to Fourier space and making use of complex
analysis, specifically Cauchy's residue theorem. We formalized the loop
integrals and explicitly solved them for specific response functions. To
quantify the importance of these corrections for spike train cumulants, we
numerically simulated spiking networks and compared their sample statistics to
our theoretical predictions. Our results demonstrate that the magnitude of the
nonlinear corrections depends on the working point of the nonlinear network
dynamics, and that it is related to the eigenvalues of the mean-field stability
matrix. For our example, the corrections for the firing rates are in the range
between 4 % and 21 % on average. Precise and robust predictions of spike train
statistics accounting for nonlinear effects are, for example, highly relevant
for theories involving spike-timing dependent plasticity (STDP).
Efficient Coding in the Economics of Human Brain Connectomics
Authors: Dale Zhou, Christopher W. Lynn, Zaixu Cui, Rastko Ciric, Graham L. Baum, Tyler M. Moore, David R. Roalf, John A. Detre, Ruben C. Gur, Raquel E. Gur, Theodore D. Satterthwaite, Danielle S. Bassett
Date: 2020-01-14 00:00:00

In systems neuroscience, most models posit that brain regions communicate information under constraints of efficiency. Yet, evidence for efficient communication in structural brain networks characterized by hierarchical organization and highly connected hubs remains sparse. The principle of efficient coding proposes that the brain transmits maximal information in a metabolically economical or compressed form to improve future behavior. To determine how structural connectivity supports efficient coding, we develop a theory specifying minimum rates of message transmission between brain regions to achieve an expected fidelity, and we test five predictions from the theory based on random walk communication dynamics. In doing so, we introduce the metric of compression efficiency, which quantifies the trade-off between lossy compression and transmission fidelity in structural networks. In a large sample of youth (n = 1,042; age 8-23 years), we analyze structural networks derived from diffusion weighted imaging and metabolic expenditure operationalized using cerebral blood flow. We show that structural networks strike compression efficiency trade-offs consistent with theoretical predictions. We find that compression efficiency prioritizes fidelity with development, heightens when metabolic resources and myelination guide communication, explains advantages of hierarchical organization, links higher input fidelity to disproportionate areal expansion, and shows that hubs integrate information by lossy compression. Lastly, compression efficiency is predictive of behavior--beyond a conventional metric--for cognitive domains including executive function, memory, complex reasoning, and social cognition. Our findings elucidate how macroscale connectivity supports efficient coding, and serve to foreground communication processes that utilize random walk dynamics constrained by network connectivity.
Production externalities and dispersion process in a multi-region economy
Authors: Minoru Osawa, José M. Gaspar
Date: 2020-01-15 00:00:00

We consider an economic geography model with two inter-regional proximity structures: one governing goods trade and the other governing production externalities across regions. We investigate how the introduction of the latter affects the timing of endogenous agglomeration and the spatial distribution of workers across regions. As transportation costs decline, the economy undergoes a progressive dispersion process. Mono-centric agglomeration emerges when inter-regional trade and/or production externalities incur high transportation costs, while uniform dispersion occurs when these costs become negligibly small (i.e., when distance dies). In multi-regional geography, the network structure of production externalities can determine the geographical distribution of workers as economic integration increases. If production externalities are governed solely by geographical distance, a mono-centric spatial distribution emerges in the form of suburbanization. However, if geographically distant pairs of regions are connected through tight production linkages, multi-centric spatial distribution can be sustainable.
Constraining Counterexamples in Hybrid System Falsification: Penalty-Based Approaches
Date: 2020-04-13 00:00:00

Falsification of hybrid systems is attracting ever-growing attention in
quality assurance of Cyber-Physical Systems (CPS) as a practical alternative to
exhaustive formal verification. In falsification, one searches for a falsifying
input that drives a given black-box model to output an undesired signal. In
this paper, we identify input constraints---such as the constraint "the
throttle and brake pedals should not pressed simultaneously" for an automotive
powertrain model---as a key factor for the practical value of falsification
methods. We propose three approaches for systematically addressing input
constraints in optimization-based falsification, two among which come from the
lexicographic method studied in the context of constrained multi-objective
optimization. Our experiments show the approaches' effectiveness.
The Nonequilibrium Mechanism of Noise Enhancer synergizing with Activator in HIV Latency Reactivation
Authors: Xiaolu Guo, Tao Tang, Minxuan Duan, Lei Zhang, Hao Ge
Date: 2020-01-15 00:00:00
Tasks: Translation

Noise-modulating chemicals can synergize with transcriptional activators in reactivating latent HIV to eliminate latent HIV reservoirs. To understand the underlying biomolecular mechanism, we investigate a previous two-gene-state model and identify two necessary conditions for the synergy: an assumption of inhibition effect of transcription activators on noise enhancers; and frequent transitions to the gene non-transcription-permissive state. We then develop a loop-four-gene-state model with Tat transcription/translation and find that drug synergy is mainly determined by the magnitude and direction of energy input into the genetic regulatory kinetics of the HIV promoter. The inhibition effect of transcription activators is actually a phenomenon of energy dissipation in the nonequilibrium gene transition system. Overall, the loop-four-state model demonstrates that energy dissipation plays a crucial role in HIV latency reactivation, which might be useful for improving drug effects and identifying other synergies on lentivirus latency reactivation.
Rational Kernel on Pricing Models of Inflation Derivatives
Date: 2020-01-27 00:00:00

The aim of this thesis is to analyze and renovate few main-stream models on
inflation derivatives. In the first chapter of the thesis, concepts of
financial instruments and fundamental terms are introduced, such as coupon
bond, inflation-indexed bond, swap.
  In the second chapter of the thesis, classic models along the history of
developing quantified interest rate models are introduced and analyzed.
Moreover, the classification of interest rate models is introduced to help
audiences understand the intrinsic ideology behind each type of models.
  In the third chapter of the thesis, the related mathematical knowledge is
introduced. This part has the contribution on understanding the terms and
relation among terms in each model introduced previously.
  In the fourth part of the thesis, the renovation of HJM frame work is
introduced and analysis has been initiated.
Recommendations to enhance rigor and reproducibility in biomedical research
Authors: Jaqueline J. Brito, Jun Li, Jason H. Moore, Casey S. Greene, Nicole A. Nogoy, Lana X. Garmire, Serghei Mangul
Date: 2020-01-15 00:00:00

Computational methods have reshaped the landscape of modern biology. While the biomedical community is increasingly dependent on computational tools, the mechanisms ensuring open data, open software, and reproducibility are variably enforced by academic institutions, funders, and publishers. Publications may present academic software for which essential materials are or become unavailable, such as source code and documentation. Publications that lack such information compromise the role of peer review in evaluating technical strength and scientific contribution. Incomplete ancillary information for an academic software package may bias or limit any subsequent work produced with the tool. We provide eight recommendations across four different domains to improve reproducibility, transparency, and rigor in computational biology - precisely on the main values which should be emphasized in life science curricula. Our recommendations for improving software availability, usability, and archival stability aim to foster a sustainable data science ecosystem in biomedicine and life science research.
Terrain estimation via vehicle vibration measurement and cubature Kalman filtering
Date: 2020-01-15 00:00:00
Tasks: parameter estimation, Terrain Estimation

The extent of vibrations experienced by a vehicle driving over natural
terrain defines its ride quality. Generally, surface irregularities, ranging
from single discontinuities to random variations of the elevation profile, act
as a major source of excitation that induces vibrations in the vehicle body
through the tire-soil interaction and suspension system. Therefore, the ride
response of off-road vehicles is tightly connected with the ground properties.
The objective of this research is to develop a model-based observer that
estimates automatically terrain parameters using available onboard sensors. Two
acceleration signals, one coming from the vehicle body and one from the wheel
suspension, are fed into a dynamic vehicle model that takes into account
tire/terrain interaction to estimate ground properties. To solve the resulting
nonlinear simultaneous state and parameter estimation problem, the cubature
Kalman filter is used, which is shown to outperform the standard extended
Kalman filter in terms of accuracy and stability. An extensive set of
simulation tests is presented to assess the performance of the proposed
estimator under various surface roughness and deformability conditions. Results
show the potential of the proposed observer to estimate automatically terrain
properties during operations that could be implemented onboard of a general
family of intelligent vehicles, ranging from off-road high-speed passenger cars
to lightweight and low-speed planetary rovers.
A theoretical analysis of Guyon's toy volatility model
Authors: Ofelia Bonesini, Antoine Jacquier, Chloe Lacombe
Date: 2020-01-15 00:00:00
Tasks: model

We provide a thorough analysis of the path-dependent volatility model introduced by Guyon \cite{G17}, proving existence and uniqueness of a strong solution, characterising its behaviour at boundary points, providing asymptotic closed-form option prices as well as deriving small-time behaviour estimates.
Estimating the extent of glioblastoma invasion
Authors: Christian Engwer, Michael Wenske
Date: 2020-01-15 00:00:00
Tasks: Prognosis

Glioblastoma Multiforme is a malignant brain tumor with poor prognosis. There have been numerous attempts to model the invasion of tumorous glioma cells via partial differential equations in the form of advection-diffusion-reaction equations. The patient-wise parametrisation of these models, and their validation via experimental data has been found to be difficult, as time sequence measurements are generally missing. Also the clinical interest lies in the actual (invisible) tumor extent for a particular MRI/DTI scan and not in a predictive estimate. Therefore we propose a stationalised approach to estimate the extent of glioblastoma (GBM) invasion at the time of a given MRI/DTI scan. The underlying dynamics can be derived from an instationary GBM model, falling into the wide class of advection-diffusion-reaction equations. The stationalisation is introduced via an analytical solution of the Fisher-KPP equation, the simplest model in the considered model class. We investigate the applicability in 1D and 2D, in the presence of inhomogeneous diffusion coefficients and on a real 3D DTI-dataset.
Intensity-Modulated Fiber-Optic Voltage Sensors for Power Distribution Systems
Date: 2020-01-15 00:00:00
Tasks: Retrieval

We design, test, and analyze fiber-optic voltage sensors based on optical
reflection from a piezoelectric transducer. By controlling the physical
dimensions of the device, we can tune the frequency of its natural resonance to
achieve a desired sensitivity and bandwidth combination. In this work, we fully
characterize sensors designed with a 2 kHz characteristic resonance,
experimentally verifying a readily usable frequency range from approximately 10
Hz to 3 kHz. Spectral noise measurements indicate detectable voltage levels
down to 300 mV rms at 60 Hz, along with a full-scale dynamic range of 60 dB,
limited currently by the readout electronics, not the inherent performance of
the transducer in the sensor. Additionally, we demonstrate a digital signal
processing approach to equalize the measured frequency response, enabling
accurate retrieval of short-pulse inputs. Our results suggest the value and
applicability of intensity-modulated fiber-optic voltage sensors for measuring
both steady-state waveforms and broadband transients which, coupled with the
straightforward and compact design of the sensors, should make them effective
tools in electric grid monitoring.
Supercritical angle microscopy and spectroscopy
Date: 2020-01-15 00:00:00

Fluorescence detection, either involving propagating or near-field emission,
is widely being used in spectroscopy, sensing and microscopy. Total internal
reflection fluorescence (TIRF) confines fluorescence excitation by an
evanescent (near-) field and it is a popular contrast generator for
surface-selective fluorescence assays. Its emission equivalent, supercritical
angle fluorescence (SAF) is comparably less established although it achieves a
similar optical sectioning as does TIRF. SAF emerges when a fluorescing
molecule is located very close to an interface and its near-field emission
couples to the higher-refractive index medium (n2 > n1) and becomes
propagative. Then, most fluorescence is detectable on the side of the
higher-index substrate and a large fraction of this fluorescence is emitted
into angles forbidden by Snell's law. SAF as well as the undercritical angle
fluorescence (UAF) (far-field emission) components can be collected with
microscope objectives having a high-enough detection aperture NA > n2 and be
separated in the back-focal plane (BFP) by Fourier filtering. The BFP image
encodes information about the fluorophore radiation pattern, and it can be
analysed to yield precise information about the refractive index in which the
emitters are embedded, their nanometric distance from the interface and their
orientation. A SAF microscope can retrieve this near-field information through
wide-field optics in a spatially resolved manner, and this functionality can be
added to any existing inverted microscope.
System Energy-Efficient Hybrid Beamforming for mmWave Multi-user Systems
Date: 2020-01-15 00:00:00
Tasks: compressed sensing, Form

This paper develops energy-efficient hybrid beamforming designs for mmWave
multi-user systems where analog precoding is realized by switches and phase
shifters such that radio frequency (RF) chain to transmit antenna connections
can be switched off for energy saving. By explicitly considering the effect of
each connection on the required power for baseband and RF signal processing, we
describe the total power consumption in a sparsity form of the analog precoding
matrix. However, these sparsity terms and sparsity-modulus constraints of the
analog precoding make the system energy-efficiency maximization problem
non-convex and challenging to solve. To tackle this problem, we first transform
it into a subtractive-form weighted sum rate and power problem. A compressed
sensing-based re-weighted quadratic-form relaxation method is employed to deal
with the sparsity parts and the sparsity-modulus constraints. We then exploit
alternating minimization of the mean-squared error to solve the equivalent
problem where the digital precoding vectors and the analog precoding matrix are
updated sequentially. The energy efficiency upper bound and a heuristic
algorithm are also examined for comparison purposes. Numerical results confirm
the superior performances of the proposed algorithm over benchmark
energy-efficiency hybrid precoding algorithms and heuristic ones.
An Attack-Resilient Pulse-Based Synchronization Strategy for General Connected Topologies
Date: 2020-01-15 00:00:00

Synchronization of pulse-coupled oscillators (PCOs) has gained significant
attention recently due to increased applications in sensor networks and
wireless communications. Given the distributed and unattended nature of
wireless sensor networks, it is imperative to enhance the resilience of
pulse-based synchronization against malicious attacks. However, most existing
results on resilient PCO synchronization are obtained for all-to-all networks.
We propose a new pulse-based synchronization mechanism to improve the
resilience of PCO synchronization that is applicable under general connected
topologies. Under the proposed synchronization mechanism, we rigorously
characterize the condition for stealthy Byzantine attacks and prove that
perfect synchronization of legitimate oscillators can be guaranteed in the
presence of multiple stealthy Byzantine attackers, irrespective of whether the
attackers collude with each other or not. The new mechanism can guarantee
resilient synchronization even when the initial phases of legitimate
oscillators are widely distributed in a half circle, which is in distinct
difference from most existing attack-resilient synchronization algorithms
(including the seminal paper from Lamport and Melliar-Smith [1]) that require a
priori (almost) synchronization among legitimate oscillators. Numerical
simulation results are given to confirm the theoretical results.