pwc-archive/papers-with-abstracts huggingface dataset:
Small protein number effects in stochastic models of autoregulated bursty gene expression
Authors: Chen Jia, Ramon Grima
Date: 2020-01-08 00:00:00

A stochastic model of autoregulated bursty gene expression by Kumar et al. [Phys. Rev. Lett. 113, 268105 (2014)] has been exactly solved in steady-state conditions under the implicit assumption that protein numbers are sufficiently large such that fluctuations in protein numbers due to reversible protein-promoter binding can be ignored. Here we derive an alternative model that takes into account these fluctuations and hence can be used to study low protein number effects. The exact steady-state protein number distributions is derived as a sum of Gaussian hypergeometric functions. We use the theory to study how promoter switching rates and the type of feedback influence the size of protein noise and noise-induced bistability. Furthermore we show that our model predictions for the protein number distribution are significantly different from those of Kumar et al. when the protein mean is small, gene switching is fast, and protein binding is faster than unbinding.
Decentralized Automotive Radar Spectrum Allocation to Avoid Mutual Interference Using Reinforcement Learning
Authors: PengFei Liu, Yimin Liu, Tianyao Huang, Yuxiang Lu, Xiqin Wang
Date: 2020-01-07 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning (RL)

Nowadays, mutual interference among automotive radars has become a problem of wide concern. In this paper, a decentralized spectrum allocation approach is presented to avoid mutual interference among automotive radars. Although decentralized spectrum allocation has been extensively studied in cognitive radio sensor networks, two challenges are observed for automotive sensors using radar. First, the allocation approach should be dynamic as all radars are mounted on moving vehicles. Second, each radar does not communicate with the others so it has quite limited information. A machine learning technique, reinforcement learning, is utilized because it can learn a decision making policy in an unknown dynamic environment. As a single radar observation is incomplete, a long short-term memory recurrent network is used to aggregate radar observations through time so that each radar can learn to choose a frequency subband by combining both the present and past observations. Simulation experiments are conducted to compare the proposed approach with other common spectrum allocation methods such as the random and myopic policy, indicating that our approach outperforms the others.
Wind profiling in the lower atmosphere from wind-induced perturbations to multirotor UAS
Date: 2020-01-10 00:00:00
Tasks: State Estimation

We present a model-based approach to wind velocity profiling using motion
perturbations of a multirotor unmanned aircraft system (UAS) in both hovering
and steady ascending flight. A state estimation framework was adapted to a set
of closed-loop rigid body models identified for an off-the-shelf quadrotor. The
quadrotor models used for wind estimation were characterized for hovering and
steady ascending flight conditions ranging between 0 and 2 m/s. The closed-loop
models were obtained using system identification algorithms to determine model
structures and estimate model parameters. The wind measurement method was
validated experimentally above the Virginia Tech Kentland Experimental Aircraft
Systems Laboratory by comparing quadrotor and independent sensor measurements
from a sonic anemometer and two SoDARs. Comparison results demonstrated
quadrotor wind estimation in close agreement with the independent wind velocity
measurements. Wind velocity profiles were difficult to validate using
time-synchronized SoDAR measurements, however. Analysis of the noise intensity
and signal-to-noise ratio of the SoDARs proved that close-proximity quadrotor
operations can corrupt wind measurement from SoDARs.
Optimal quantizer structure for binary discrete input continuous output channels under an arbitrary quantized-output constraint
Date: 2020-01-10 00:00:00

Given a channel having binary input X = (x_1, x_2) having the probability
distribution p_X = (p_{x_1}, p_{x_2}) that is corrupted by a continuous noise
to produce a continuous output y \in Y = R. For a given conditional
distribution p(y|x_1) = \phi_1(y) and p(y|x_2) = \phi_2(y), one wants to
quantize the continuous output y back to the final discrete output Z = (z_1,
z_2, ..., z_N) with N \leq 2 such that the mutual information between input and
quantized-output I(X; Z) is maximized while the probability of the
quantized-output p_Z = (p_{z_1}, p_{z_2}, ..., p_{z_N}) has to satisfy a
certain constraint. Consider a new variable r_y=p_{x_1}\phi_1(y)/
(p_{x_1}\phi_1(y)+p_{x_2}\phi_2(y)), we show that the optimal quantizer has a
structure of convex cells in the new variable r_y. Based on the convex cells
property of the optimal quantizers, a fast algorithm is proposed to find the
global optimal quantizer in a polynomial time complexity.
Beyond Fourier transform: super-resolving optical coherence tomography
Date: 2020-05-19 00:00:00

Optical coherence tomography (OCT) is a volumetric imaging modality that
empowers clinicians and scientists to noninvasively visualize the
cross-sections of biological samples. As the latest generation of its kind,
Fourier-domain OCT (FD-OCT) offers a micrometer-scale axial resolution by
taking advantage of coherence gating. Based on the current theory, it is
believed the only way to obtain a higher-axial-resolution OCT image is to
physically extend the system's spectral bandwidth given a certain central
wavelength. Here, we showed the belief is wrong. We proposed a novel
reconstruction framework, which integrates prior knowledge and exploits the
\emph{shift-variance}, to retrospectively super-resolve OCT images without
altering the system configurations. Both numerical and experimental results
confirmed the processed image manifested an axial resolution beyond the
previous theoretical prediction. We believe this result not only opens new
horizons for future research directions in OCT reconstruction but also promises
an immediate upgrade to tens of thousands of legacy OCT units currently
deployed.
Semimartingale price systems in models with transaction costs beyond efficient friction
Authors: Christoph Kühn, Alexander Molitor
Date: 2020-01-09 00:00:00
Tasks: Friction

A standing assumption in the literature on proportional transaction costs is efficient friction. Together with robust no free lunch with vanishing risk, it rules out strategies of infinite variation, as they usually appear in frictionless markets. In this paper, we show how the models with and without transaction costs can be unified. The bid and the ask price of a risky asset are given by c\'adl\'ag processes which are locally bounded from below and may coincide at some points. In a first step, we show that if the bid-ask model satisfies "no unbounded profit with bounded risk" for simple strategies, then there exists a semimartingale lying between the bid and the ask price process. In a second step, under the additional assumption that the zeros of the bid-ask spread are either starting points of an excursion away from zero or inner points from the right, we show that for every bounded predictable strategy specifying the amount of risky assets, the semimartingale can be used to construct the corresponding self-financing risk-free position in a consistent way. Finally, the set of most general strategies is introduced, which also provides a new view on the frictionless case.
A General Initialization Scheme for Electromagnetic Transient Simulation: Towards Large-Scale Hybrid AC-DC Grids
Date: 2020-01-10 00:00:00

With the large-scale hybrid AC-DC grids coming into being, electromagnetic
transient (EMT) simulation is required to accurately describe the dynamics of
systems. However, the EMT steady-state initialization for hybrid AC-DC system
is difficult and time-consuming when the system scale is huge. In order to
provide a stable snapshot for EMT simulation with nonlinear components and
black-box components, this paper proposes a general initialization scheme for
EMT simulation (EMT-GIS) which can be implemented in the electromagnetic
transient program (EMTP)-type simulators. First, an integrated power flow (IPF)
algorithm is introduced to provide the steady-state results. Then, an
initialized snapshot calculation-and-splicing mechanism is designed for
EMT-GIS. The proposed EMT-GIS is tested using a hybrid AC-DC system in China on
the CloudPSS simulation platform. Test results verify the effectiveness of the
proposed EMT-GIS.
Projection assisted Dynamic Mode Decomposition of large scale data
Date: 2020-01-10 00:00:00
Tasks: Time Series Analysis

We have deluge of data in time series format for numerous phenomena. The
number of snapshots, resolution and many other factors come into play as we
look to identify the dynamics in a given problem. The pre-processing and
post-processing steps while working with the data may be related to the
resources in terms of the hardware used to collect data and computations that
we perform on the data to create a model for the problem. Dynamic Mode
Decomposition (DMD) is a data based modeling tool that identifies a matrix to
map a quantity at some time instant to the same quantity in future. It is
possible to generate a model by projecting the high dimensional spatiotemporal
data to a lower dimensional subspace in a probabilistic framework. Sampling and
gaussian projection have been used in the past to increase efficiency in the
computation. Here, we design an optimized version of DMD that utilizes time
delay coordinates and a projection matrix. In our proposal, we discussed about
two projection matrices -- one is inspired by the Krylov subspace and the other
promotes and leverages sparsity to bring computational benefits in producing a
model. Satisfactory results are obtained as they are tested on data related to
Double gyre (present in ocean mixing) and on a 2D compressible signal. The
motivation behind this scheme of DMD comes from the fact that data from many
phenomena are 'big' and 'highly oscillatory.'
Sum Rate Maximization for Reconfigurable Intelligent Surface Assisted Device-to-Device Communications
Authors: Yashuai Cao, Tiejun Lv
Date: 2020-01-10 00:00:00

In this letter, we propose to employ reconfigurable intelligent surfaces (RISs) for enhancing the D2D underlaying system performance. We study the joint power control, receive beamforming, and passive beamforming for RIS assisted D2D underlaying cellular communication systems, which is formulated as a sum rate maximization problem. To address this issue, we develop a block coordinate descent method where uplink power, receive beamformer and refection phase shifts are alternatively optimized. Then, we provide the closed-form solutions for both uplink power and receive beamformer. We further propose a quadratic transform based semi-definite relaxation algorithm to optimize the RIS phase shifts, where the original passive beamforming problem is translated into a separable quadratically constrained quadratic problem. Numerical results demonstrate that the proposed RIS assisted design significantly improves the sum-rate performance.
External noise removed from magnetoencephalographic signal using Independent Component Analyses of reference channels
Date: 2020-01-10 00:00:00

Background: Many magnetoencephalographs (MEG) contain, in addition to data
channels, a set of reference channels positioned relatively far from the head
that provide information on magnetic fields not originating from the brain.
This information is used to subtract sources of non-neural origin, with either
geometrical or least mean squares (LMS) methods. LMS methods in particular tend
to be biased toward more constant noise sources and are often unable to remove
intermittent noise.
  New Method: To better identify and eliminate external magnetic noise, we
propose performing ICA directly on the MEG reference channels. This in most
cases produces several components which are clear summaries of external noise
sources with distinct spatio-temporal patterns. We present two algorithms for
identifying and removing such noise components from the data which can in many
cases significantly improve data quality.
  Results: We performed simulations using forward models that contained both
brain sources and external noise sources. First, traditional LMS-based methods
were applied. While this removed a large amount of noise, a significant portion
still remained. In many cases, this portion could be removed using the proposed
technique, with little to no false positives.
  Comparison with existing method(s): The proposed method removes significant
amounts of noise to which existing LMS-based methods tend to be insensitive.
  Conclusions: The proposed method complements and extends traditional
reference based noise correction with little extra computational cost and low
chances of false positives. Any MEG system with reference channels could profit
from its use, particularly in labs with intermittent noise sources.
Text as Data: Real-time Measurement of Economic Welfare
Date: 2020-01-10 00:00:00
Tasks: Survey

Economists are showing increasing interest in the use of text as an input to
economic research. Here, we analyse online text to construct a real time metric
of welfare. For purposes of description, we call it the Feel Good Factor (FGF).
The particular example used to illustrate the concept is confined to data from
the London area, but the methodology is readily generalisable to other
geographical areas. The FGF illustrates the use of online data to create a
measure of welfare which is not based, as GDP is, on value added in a
market-oriented economy. There is already a large literature which measures
wellbeing/happiness. But this relies on conventional survey approaches, and
hence on the stated preferences of respondents. In unstructured online media
text, users reveal their emotions in ways analogous to the principle of
revealed preference in consumer demand theory. The analysis of online media
offers further advantages over conventional survey-based measures of sentiment
or well-being. It can be carried out in real time rather than with the lags
which are involved in survey approaches. In addition, it is very much cheaper.
Transmission System Planning for Integration of Renewable Electricity Generation Units
Date: 2020-01-10 00:00:00

This paper summarizes the operational challenges imposed by integration of
renewable electricity generation units in transmission level where the most
common renewable generation units are solar and wind farms at the scale of 100s
to 1000s MW. Such units, because of their stochastic nature, introduce new
complexity and uncertainty to the grid. Throughout this paper, some results
from the recent planning study of integration of 1,000 MW offshore wind farm
into the U.S. Eastern Interconnection transmission system are shown.
Impact of Sampler Offset on Jitter Transfer in Clock and Data Recovery Circuits
Date: 2020-01-10 00:00:00

This paper shows how the input offset of sampling flip-flops in the Alexander
phase detector affects the jitter transfer from data to the recovered clock in
a clock data recovery circuit. The Alexander phase detector samples the data at
both the edges of the clock in order to recover the data, as well as the clock
timing information. The timing information is used in a clock recovery circuit,
which is basically a PLL or a DLL. Once the PLL (or DLL) is locked, the phase
detector samples the data at the center of the eye as well as at the data
transitions. It is shown how the offset of the sampling flip-flop that samples
the data at its transitions influences the jitter transfer from data to the
recovered clock. Importantly, it is shown that zero offset is not always the
best case. The effect is studied for different levels of data dependent jitter.
The mechanism of this phenomenon is explained and the predictions are supported
with simulations. The paper also discusses a tracking circuit that keeps the
offset at the minimum jitter point.
The use of biophysical approaches to understand ciliary beating
Date: 2020-01-10 00:00:00

Motile cilia are a striking example of functional cellular organelle,
conserved across all the eukaryotic species. Motile cilia allow swimming of
cells and small organisms and transport of liquids across epithelial tissues.
Whilst the molecular structure is now very well understood, the dynamics of
cilia is not well established either at the single cilium level nor at the
level of collective beating. Indeed, a full understanding of this requires
connecting together behaviour across various lengthscales, from the molecular
to the organelle, then at cellular level and up to the tissue scale. Aside from
the fundamental interest in this system, understanding beating is important to
elucidate aspects of embryonic development and a variety of health conditions
from fertility to genetic and infectious diseases of the airways.
Identification and nonlinearity compensation of hysteresis using NARX models
Date: 2020-01-10 00:00:00

This paper deals with two problems: the identification and compensation of
hysteresis nonlinearity in dynamical systems using nonlinear polynomial
autoregressive models with exogenous inputs (NARX). First, based on gray-box
identification techniques, some constraints on the structure and parameters of
NARX models are proposed to ensure that the identified models display a
key-feature of hysteresis. In addition, a more general framework is developed
to explain how hysteresis occurs in such models. Second, two strategies to
design hysteresis compensators are presented. In one strategy the compensation
law is obtained through simple algebraic manipulations performed on the
identified models. It has been found that the compensators based on gray-box
models outperform the cases with models identified using black-box techniques.
In the second strategy, the compensation law is directly identified from the
data. Both numerical and experimental results are presented to illustrate the
efficiency of the proposed procedures.
Paradoxical Results of Long-Term Potentiation explained by Voltage-based Plasticity Rule
Date: 2020-11-18 00:00:00
Tasks: Hippocampus

Experiments have shown that the same stimulation pattern that causes
Long-Term Potentiation in proximal synapses, will induce Long-Term Depression
in distal ones. In order to understand these, and other, surprising
observations we use a phenomenological model of Hebbian plasticity at the
location of the synapse. Our computational model describes the Hebbian
condition of joint activity of pre- and post-synaptic neuron in a compact form
as the interaction of the glutamate trace left by a presynaptic spike with the
time course of the postsynaptic voltage. We test the model using experimentally
recorded dendritic voltage traces in hippocampus and neocortex. We find that
the time course of the voltage in the neighborhood of a stimulated synapse is a
reliable predictor of whether a stimulated synapse undergoes potentiation,
depression, or no change. Our model can explain the existence of different --
at first glance seemingly paradoxical -- outcomes of synaptic potentiation and
depression experiments depending on the dendritic location of the synapse and
the frequency or timing of the stimulation.
L2-Stability Analysis of the SM-NLMS Algorithm
Date: 2020-01-11 00:00:00

In this letter, we analyze, two properties, the local and the global
robustness of the set-membership normalized least mean square (SM-NLMS)
algorithm. We will show that the SM-NLMS algorithm has l2-stability. Indeed,
the SM-NLMS algorithm never diverges; no matter how the parameters of the
SM-NLMS algorithm has been selected. Ultimately, the numerical simulations
corroborate the validity of our analysis.
Extreme Software Defined Radio -- GHz in Real Time
Date: 2020-01-10 00:00:00
Tasks: GPU

Software defined radio is a widely accepted paradigm for design of
reconfigurable modems. The continuing march of Moore's law makes real-time
signal processing on general purpose processors feasible for a large set of
waveforms. Data rates in the low Mbps can be processed on low-power ARM
processors, and much higher data rates can be supported on large x86
processors. The advantages of all-software development (vs. FPGA/DSP/GPU) are
compelling: much wider pool of talent, lower development time and cost, and
easier maintenance and porting. However, very high-rate systems (above 100
Mbps) are still firmly in the domain of custom and semi-custom hardware (mostly
FPGAs). In this paper we describe an architecture and testbed for an SDR that
can be easily scaled to support over 3 GHz of bandwidth and data rate up to 10
Gbps. The paper covers a novel technique to parallelize typically serial
algorithms for phase and symbol tracking, followed by a discussion of data
distribution for a massively parallel architecture. We provide a brief
description of a mixed-signal front end and conclude with measurement results.
To the best of the author's knowledge, the system described in this paper is an
order of magnitude faster than any prior published result.
Commuting Service Platform: Concept and Analysis
Date: 2020-01-10 00:00:00
Tasks: Management

We propose and investigate the concept of commuting service platforms (CSP)
that leverage emerging mobility services to provide commuting services and
connect directly commuters (employees) and their worksites (employers). By
applying the two-sided market analysis framework, we show under what conditions
a CSP may present the two-sidedness. Both the monopoly and duopoly CSPs are
then analyzed. We showhowthe price allocation, i.e., the prices charged to
commuters and worksites, can impact the participation and profit of the CSPs.
We also add demand constraints to the duopoly model so that the participation
rates ofworksites and employees are (almost) the same. With demand constraints,
the competition between the two CSPs becomes less intense in general.
Discussions are presented on how the results and findings in this paper may
help build CSP in practice and how to develop new, CSP-based travel demand
management strategies.
Closed-Form Bit Error Probabilities for FBMC Systems
Date: 2020-01-11 00:00:00
Tasks: Form

This paper analyses the data reconstruction effects emerged from the
deployment of non-perfect prototype filters in Filter Bank MultiCarrier (FBMC)
systems operating over Additive White Gaussian Noise (AWGN) and frequency-flat
Rayleigh channels considering frequency-flatness for each subcarrier. This goal
is attained by studying the Bit Error Rate (BER) effects of the prototype
filters, increasing the scenario complexity progressively. Despite the
complexity, both exact and approximate Bit Error Probability (BEP) expressions
portray the BER degradation analytically for any FBMC prototype filter.
Numerical results demonstrate that the proposed BEP expressions match perfectly
with the simulated BER performance for FBMC systems, regardless of the
prototype filter choice.