pwc-archive/papers-with-abstracts huggingface dataset:
Go with the FLOW: Visualizing spatiotemporal dynamics in optical widefield calcium imaging
Authors: Nathaniel J Linden, Dennis R Tabuena, Nicholas A Steinmetz, William J Moody, Steven L Brunton, Bingni W Brunton
Date: 2020-09-29 00:00:00
Tasks: Time Series, Time Series Analysis

Widefield calcium imaging has recently emerged as a powerful experimental technique to record coordinated large-scale brain activity. These measurements present a unique opportunity to characterize spatiotemporal coherent structures that underlie neural activity across many regions of the brain. In this work, we leverage analytic techniques from fluid dynamics to develop a visualization framework that highlights features of flow across the cortex, mapping wave fronts that may be correlated with behavioral events. First, we transform the time series of widefield calcium images into time-varying vector fields using optic flow. Next, we extract concise diagrams summarizing the dynamics, which we refer to as FLOW (flow lines in optical widefield imaging) portraits. These FLOW portraits provide an intuitive map of dynamic calcium activity, including regions of initiation and termination, as well as the direction and extent of activity spread. To extract these structures, we use the finite-time Lyapunov exponent (FTLE) technique developed to analyze time-varying manifolds in unsteady fluids. Importantly, our approach captures coherent structures that are poorly represented by traditional modal decomposition techniques. We demonstrate the application of FLOW portraits on three simple synthetic datasets and two widefield calcium imaging datasets, including cortical waves in the developing mouse and spontaneous cortical activity in an adult mouse.
Evidence of state-dependence in the effectiveness of responsive neurostimulation for seizure modulation
Authors: Sharon Chiang, Ankit N. Khambhati, Emily T. Wang, Marina Vannucci, Edward F. Chang, Vikram R. Rao
Date: 2020-09-29 00:00:00

An implanted device for brain-responsive neurostimulation (RNS System) is approved as an effective treatment to reduce seizures in adults with medically-refractory focal epilepsy. Clinical trials of the RNS System demonstrate population-level reduction in average seizure frequency, but therapeutic response is highly variable. Recent evidence links seizures to cyclical fluctuations in underlying risk. We tested the hypothesis that effectiveness of responsive neurostimulation varies based on current state within cyclical risk fluctuations. We analyzed retrospective data from 25 adults with medically-refractory focal epilepsy implanted with the RNS System. Chronic electrocorticography was used to record electrographic seizures, and hidden Markov models decoded seizures into fluctuations in underlying risk. State-dependent associations of RNS System stimulation parameters with changes in risk were estimated. Higher charge density was associated with improved outcomes, both for remaining in a low seizure risk state and for transitioning from a high to a low seizure risk state. The effect of stimulation frequency depended on initial seizure risk state: when starting in a low risk state, higher stimulation frequencies were associated with remaining in a low risk state, but when starting in a high risk state, lower stimulation frequencies were associated with transition to a low risk state. Findings were consistent across bipolar and monopolar stimulation configurations. The impact of RNS on seizure frequency exhibits state-dependence, such that stimulation parameters which are effective in one seizure risk state may not be effective in another. These findings represent conceptual advances in understanding the therapeutic mechanism of RNS, and directly inform current practices of RNS tuning and the development of next-generation neurostimulation systems.
Co-Located vs Distributed vs Semi-Distributed MIMO: Measurement-Based Evaluation
Authors: Thomas Choi, Peng Luo, Akshay Ramesh, Andreas F. Molisch
Date: 2020-09-29 00:00:00

With the growing interest in cell-free massive multiple-input multiple-output (MIMO) systems, the benefits of single-antenna access points (APs) versus multi-antenna APs must be analyzed in order to optimize deployment. In this paper, we compare various antenna system topologies based on achievable downlink spectral efficiency, using both measured and synthetic channel data in an indoor environment. We assume multi-user scenarios, analyzing both conjugate beamforming (or maximum-ratio transmission (MRT)) and zero-forcing (ZF) precoding methods. The results show that the semi-distributed multi-antenna APs can reduce the number of APs, and still achieve the comparable achievable rates as the fully-distributed single-antenna APs with the same total number of antennas.
Local Regression Distribution Estimators
Date: 2020-09-30 00:00:00
Tasks: counterfactual, regression, valid

This paper investigates the large sample properties of local regression distribution estimators, which include a class of boundary adaptive density estimators as a prime example. First, we establish a pointwise Gaussian large sample distributional approximation in a unified way, allowing for both boundary and interior evaluation points simultaneously. Using this result, we study the asymptotic efficiency of the estimators, and show that a carefully crafted minimum distance implementation based on "redundant" regressors can lead to efficiency gains. Second, we establish uniform linearizations and strong approximations for the estimators, and employ these results to construct valid confidence bands. Third, we develop extensions to weighted distributions with estimated weights and to local $L^{2}$ least squares estimation. Finally, we illustrate our methods with two applications in program evaluation: counterfactual density testing, and IV specification and heterogeneity density analysis. Companion software packages in Stata and R are available.
Pareto's 80/20 Rule and the Gaussian Distribution
Authors: Katsuaki Tanabe
Date: 2020-09-30 00:00:00

The statistical state for the empirical Pareto's 80/20 rule has been found to correspond to a normal or Gaussian distribution with a standard deviation that is twice the mean. This finding represents large characteristic variations in our society and nature. In this distribution, the rule can be also referred to as, for example, the 25/5, 45/10, 60/15, or 90/25 rule. In addition, our result suggests the existence of implicit negative contributors.
Learning to Reflect and to Beamform for Intelligent Reflecting Surface with Implicit Channel Estimation
Authors: Tao Jiang, Hei Victor Cheng, Wei Yu
Date: 2020-09-30 00:00:00
Tasks: Graph Neural Network

Intelligent reflecting surface (IRS), which consists of a large number of tunable reflective elements, is capable of enhancing the wireless propagation environment in a cellular network by intelligently reflecting the electromagnetic waves from the base-station (BS) toward the users. The optimal tuning of the phase shifters at the IRS is, however, a challenging problem, because due to the passive nature of reflective elements, it is difficult to directly measure the channels between the IRS, the BS, and the users. Instead of following the traditional paradigm of first estimating the channels then optimizing the system parameters, this paper advocates a machine learning approach capable of directly optimizing both the beamformers at the BS and the reflective coefficients at the IRS based on a system objective. This is achieved by using a deep neural network to parameterize the mapping from the received pilots (plus any additional information, such as the user locations) to an optimized system configuration, and by adopting a permutation invariant/equivariant graph neural network (GNN) architecture to capture the interactions among the different users in the cellular network. Simulation results show that the proposed implicit channel estimation based approach is generalizable, can be interpreted, and can efficiently learn to maximize a sum-rate or minimum-rate objective from a much fewer number of pilots than the traditional explicit channel estimation based approaches.
On The Quest For Economic Prosperity: A Higher Education Strategic Perspective For The Mena Region
Date: 2020-09-30 00:00:00

In a fast-changing technology-driven era, drafting an implementable strategic
roadmap to achieve economic prosperity becomes a real challenge. Although the
national and international strategic development plans may vary, they usually
target the improvement of the quality of living standards through boosting the
national GDP per capita and the creation of decent jobs. There is no doubt that
human capacity building, through higher education, is vital to the availability
of highly qualified workforce supporting the implementation of the
aforementioned strategies. In other words, fulfillment of most strategic
development plan goals becomes dependent on the drafting and implementation of
successful higher education strategies. For MENA region countries, this is
particularly crucial due to many specific challenges, some of which are
different from those facing developed nations. More details on the MENA region
higher education strategic planning challenges as well as the proposed higher
education strategic requirements to support national economic prosperity and
fulfill the 2030 UN SDGs are given in the paper.
FMCW SAR with New Synthesis Method Based on A-SPC Technique
Authors: Junhyeong Park, Dae-Hwan Jung, Seong-Ook Park
Date: 2020-09-30 00:00:00

Frequency modulated continuous wave (FMCW) radar is emerging as a trendy radar system for synthetic aperture radar (SAR). This letter proposes a novel method for the extraction of the SAR image with the FMCW radar. The proposed method can improve the quality of the SAR image. For the verification, we built an automobile SAR (AutoSAR) system and conducted experiments to extract the SAR map by using the AutoSAR system. Then, we synthesized SAR images through both the conventional method and the proposed method to demonstrate the performance of the proposed method. The experimental results show that the SAR image has been successfully improved by the proposed method.
Small Drone Classification with Light CNN and New Micro-Doppler Signature Extraction Method Based on A-SPC Technique
Authors: Junhyeong Park, Jun-Sung Park, Seong-Ook Park
Date: 2020-09-30 00:00:00
Tasks: Classification, General Classification

As the threats of small drones increase, not only the detection but also the classification of small drones has become important. Many recent studies have applied an approach to utilize the micro-Doppler signature (MDS) for the small drone classification by using frequency modulated continuous wave (FMCW) radars. In this letter, we propose a novel method to extract the MDS images of the small drones with the FMCW radar. Moreover, we propose a light convolutional neural network (CNN) whose structure is straightforward, and the number of parameters is quite small for fast classification. The proposed method contributes to increasing the classification accuracy by improving the quality of MDS images. We classified the small drones with the MDS images extracted by the conventional method and the proposed method through the proposed CNN. The experimental results showed that the total classification accuracy was increased by 10.00 % due to the proposed method. The total classification accuracy was recorded at 97.14 % with the proposed MDS extraction method and the proposed light CNN.
Efficient, Decentralized, and Collaborative Multi-Robot Exploration using Optimal Transport Theory
Date: 2020-09-29 00:00:00
Tasks: Efficient Exploration

An Optimal Transport (OT)-based decentralized collaborative multi-robot
exploration strategy is proposed in this paper. This method is to achieve an
efficient exploration with a predefined priority in the given domain. In this
context, the efficiency indicates how a team of robots (agents) cover the
domain reflecting the corresponding priority map (or degrees of importance) in
the domain. The decentralized exploration implies that each agent carries out
their exploration task independently in the absence of any supervisory
agent/computer. When an agent encounters another agent within a communication
range, each agent receives the information about which areas are already
covered by other agents, yielding a collaborative exploration. The OT theory is
employed to quantify the difference between the distribution formed by the
robot trajectories and the given reference spatial distribution indicating the
priority. A computationally feasible way is developed to measure the
performance of the proposed exploration scheme. Further, the formal algorithm
is provided for the efficient, decentralized, and collaborative exploration
plan. Simulation results are presented to validate the proposed methods.
Numerical Predictive Control for Delay Compensation
Date: 2020-09-30 00:00:00
Tasks: Numerical Integration

We present a delay-compensating control method that transforms exponentially
stabilizing controllers for an undelayed system into a sample-based predictive
controller with numerical integration. Our method handles both first-order and
transport delays in actuators and trades-off numerical accuracy with
computation delay to guaranteed stability under hardware limitations. Through
hybrid stability analysis and numerical simulation, we demonstrate the efficacy
of our method from both theoretical and simulation perspectives.
Forced variational integrator for distance-based shape control with flocking behavior of multi-agent systems
Date: 2020-09-30 00:00:00

A multi-agent system designed to achieve distance-based shape control with
flocking behavior can be seen as a mechanical system described by a Lagrangian
function and subject to additional external forces. Forced variational
integrators are given by the discretization of Lagrange-d'Alembert principle
for systems subject to external forces, and have proved useful for numerical
simulation studies of complex dynamical systems. We derive forced variational
integrators that can be employed in the context of control algorithms for
distance-based shape with velocity consensus. In particular, we provide an
accurate numerical integrator with a lower computational cost than traditional
solutions, while preserving the configuration space and symmetries. We also
provide an explicit expression for the integration scheme in the case of an
arbitrary number of agents with double integrator dynamics. For a numerical
comparison of the performances, we use a planar formation consisting of three
autonomous agents.
Discussions on Inverse Kinematics based on Levenberg-Marquardt Method and Model-Free Adaptive (Predictive) Control
Authors: Feilong Zhang
Date: 2020-09-30 00:00:00

In this brief, the current robust numerical solution to the inverse kinematics based on Levenberg-Marquardt (LM) method is reanalyzed through control theory instead of numerical method. Compared to current works, the robustness of computation and convergence performance of computational error are analyzed much more clearly by analyzing the control performance of the corrected model free adaptive control (MFAC). Then mainly motivated by minimizing the predictive tracking error, this study suggests a new method of model free adaptive predictive control (MFAPC) to solve the inverse kinematics problem. At last, we apply the MFAPC as a controller for the robotic kinematic control problem in simulation. It not only shows an excellent control performance but also efficiently acquires the solution to inverse kinematic.
Modelling the effect of ribosome mobility on the rate of protein synthesis
Authors: Olivier Dauloudet, Izaak Neri, Jean-Charles Walter, Jérôme Dorignac, Frédéric Geniet, Andrea Parmeggiani
Date: 2020-09-30 00:00:00
Tasks: Translation

Translation is one of the main steps in the synthesis of proteins. It consists of ribosomes that translate sequences of nucleotides encoded on mRNA into polypeptide sequences of amino acids. Ribosomes bound to mRNA move unidirectionally, while unbound ribosomes diffuse in the cytoplasm. It has been hypothesized that finite diffusion of ribosomes plays an important role in ribosome recycling and that mRNA circularization enhances the efficiency of translation. In order to estimate the effect of cytoplasmic diffusion on the rate of translation, we consider a Totally Asymmetric Simple Exclusion Process (TASEP) coupled to a finite diffusive reservoir, which we call the Ribosome Transport model with Diffusion (RTD). In this model, we derive an analytical expression for the rate of protein synthesis as a function of the diffusion constant of ribosomes, which is corroborated with results from continuous-time Monte Carlo simulations. Using a wide range of biological relevant parameters, we conclude that diffusion in biological cells is fast enough so that it does not play a role in controlling the rate of translation initiation.
Robust Utility Maximization in a Multivariate Financial Market with Stochastic Drift
Authors: Jörn Sass, Dorothee Westphal
Date: 2020-09-30 00:00:00
Tasks: Portfolio Optimization

We study a utility maximization problem in a financial market with a stochastic drift process, combining a worst-case approach with filtering techniques. Drift processes are difficult to estimate from asset prices, and at the same time optimal strategies in portfolio optimization problems depend crucially on the drift. We approach this problem by setting up a worst-case optimization problem with a time-dependent uncertainty set for the drift. Investors assume that the worst possible drift process with values in the uncertainty set will occur. This leads to local optimization problems, and the resulting optimal strategy needs to be updated continuously in time. We prove a minimax theorem for the local optimization problems and derive the optimal strategy. Further, we show how an ellipsoidal uncertainty set can be defined based on filtering techniques and demonstrate that investors need to choose a robust strategy to be able to profit from additional information.
Are cryptocurrencies becoming more interconnected?
Authors: Nektarios Aslanidis, Aurelio F. Bariviera, Alejandro Perez-Laborda
Date: 2020-09-30 00:00:00

This paper studies the dynamic market linkages among cryptocurrencies during August 2015 - July 2020 and finds a substantial increase in market linkages for both returns and volatilities. We use different methodologies to check the different aspects of market linkages. Financial and regulatory implications are discussed.
Machine Learning and Computational Mathematics
Authors: E Weinan
Date: 2020-09-23 00:00:00
Tasks: BIG-bench Machine Learning

Neural network-based machine learning is capable of approximating functions
in very high dimension with unprecedented efficiency and accuracy. This has
opened up many exciting new possibilities, not just in traditional areas of
artificial intelligence, but also in scientific computing and computational
science. At the same time, machine learning has also acquired the reputation of
being a set of "black box" type of tricks, without fundamental principles. This
has been a real obstacle for making further progress in machine learning. In
this article, we try to address the following two very important questions: (1)
How machine learning has already impacted and will further impact computational
mathematics, scientific computing and computational science? (2) How
computational mathematics, particularly numerical analysis, {can} impact
machine learning? We describe some of the most important progress that has been
made on these issues. Our hope is to put things into a perspective that will
help to integrate machine learning with computational mathematics.
The Cubli: Modeling and Nonlinear Control Utilizing Unit Complex Numbers
Date: 2020-09-28 00:00:00

This paper covers the modeling and nonlinear control of the Cubli, a cube
with three reaction wheels mounted on orthogonal faces that becomes a reaction
wheel-based 1D/3D inverted pendulum when positioned in one of its edges (1D) or
vertices (3D). Instead of angles, unit complex numbers are used as control
states for the 1D configuration. This approach is useful not only to get rid of
trigonometric functions, but mainly because it is a specific case of the 3D
configuration, that utilizes unit ultra-complex numbers (quaternions) as system
states, and therefore facilitates its understanding. The derived nonlinear
control law is equivalent to a linear one and is characterized by only three
straightforward tuning parameters. Experiment results are presented to validate
modeling and control.
The Cubli: Modeling Utilizing Quaternions
Date: 2020-09-28 00:00:00

This paper performs the modeling of a Cubli, a cube with three reaction
wheels mounted on orthogonal faces that becomes a reaction wheel based 3D
inverted pendulum when positioned in one of its vertices. The approach novelty
is that quaternions are used instead of Euler angles. One nice advantage of
quaternions, besides the usual arguments to avoid singularities and
trigonometric functions, is that it allows working out quite complex dynamic
equations completely by hand utilizing vector notation. Modeling is performed
utilizing Lagrange equations and it is validated through computer simulations
and Poinsot trajectories analysis.
CAD Applications and Emerging Research Potential in Medical Imaging
Date: 2020-09-30 00:00:00

Computer Aided Detection (CAD) is a valuable technique for precisely
interpreting medical images and it has a global business opportunity of about
USD 1.8 billion. The current aspects with reference to the four sub stages such
as image pre-processing, segmentation, feature extraction and classification
and the future scope of CAD in medical imaging has been discussed in this
paper. Many reviewers have emphasized the need for synergy between engineers
and medical professionals for successful development of CAD systems and the
current work is a move in that direction. The engineering aspects of the above
four stages in four imaging modalities viz. computed tomography, magnetic
resonance imaging, mammography and bone scintigraphy used in the diagnosis of
five critical diseases have been discussed with a clinical background.
Automatic classification of image can play an important role in preliminary
screening of very critical ailments bringing down the cost of health care.
Another recent advancement is using artificial intelligence and machine
learning techniques. This paper reviews these engineering aspects with a view
to explore the opportunities to researchers as well as the medical industry to
offer affordable medical services with accessibility in even remote locations.