pwc-archive/papers-with-abstracts huggingface dataset:
Distributed Sequential Hypothesis Testing with Dependent Sensor Observations
Date: 2019-11-21 00:00:00

In this paper, we consider the problem of distributed sequential detection
using wireless sensor networks (WSNs) in the presence of imperfect
communication channels between the sensors and the fusion center (FC). We
assume that sensor observations are spatially dependent. We propose a
copula-based distributed sequential detection scheme that characterizes the
spatial dependence. Specifically, each local sensor collects observations
regarding the phenomenon of interest and forwards the information obtained to
the FC over noisy channels. The FC fuses the received messages using a
copula-based sequential test. Moreover, we show the asymptotic optimality of
the proposed copula-based sequential test. Numerical experiments are conducted
to demonstrate the effectiveness of our approach.
Self-Powered Solar Aerial Vehicles: Towards Infinite Endurance UAVs
Date: 2020-07-24 00:00:00
Tasks: Uncertainty Quantification

A self-powered scheme is explored for achieving long-endurance operation,
with the use of solar power and buoyancy lift. The end goal is the capability
of infinite endurance while complying with the Unmanned Aerial Vehicle (UAV)
dynamics and the required control performance, maneuvering, and duty cycles.
Nondimensional power terms related to the UAV power demand and solar energy
input are determined in a framework of Optimal Uncertainty Quantification
(OUQ). OUQ takes uncertainties and incomplete information in the dynamics and
control, available solar energy, and the electric power demand of a solar UAV
model into account, and provides an optimal solution for achieving a
self-sustained system in terms of energy. Self-powered trajectory tracking,
speed and control are discussed. Aerial vehicles of this class can overcome the
flight time limitations of current electric UAVs, thereby meeting the needs of
many applications. This paper serves as a reference in providing a generalized
approach in design of self-powered solar electric multi-rotor UAVs.
Monte Carlo Simulations of DNA Damage and Cellular Response to Hadron Irradiation
Date: 2019-11-18 00:00:00

Numerical simulations are performed on a stochastic model based on Monte
Carlo damage simulation process and Markov Chain Monte Carlo techniques to
investigate the formation and evaluation of isolated and multiple DNA damage
and cellular survival by light ionizing radiation in a colony of tumour cells.
The contribution of the local clustering of the strand breaks and base damage
is taken into account while considering double-strand breaks (DSBs) as primary
lesions in the DNA of the cell nucleus induced by ionizing radiation. The model
incorporates the combined effects of biological processes such as the tumour
oxygenation, cellular multiplication and mutation through various probability
distributions in a full Monte Carlo simulation of fractionated hadrontherapy.
Our results indicate that the linear and quadratic parameters of the model show
a negative correlation, for protons and helium ions, which might suggest an
underlying biological mechanism. Despite using a model with quite different
descriptions of linear and quadratic parameters, the observed results for
linear parameter show largely reasonable agreement while the quadratic
parameter consistent deviations from the results obtained using the LEM model
at low LET. In addition to the LET dependence, RBE values showed a strong
dependence on alpha/beta ratio and a considerable scatter for various particle
types indicate particle specific behaviour of initial its slope. The surviving
curves show a non-linear dose-response suggesting that interaction among DSBs
induced by ionizing radiation contribute significantly to the quadratic term of
the model.
Optimal V2G and Route Scheduling of Mobile Energy Storage Devices Using a Linear Transit Model to Reduce Electricity and Transportation Energy Losses
Date: 2019-11-19 00:00:00
Tasks: Scheduling

Mobile energy storage devices (MESDs) operate as medium- or large-sized
batteries that can be loaded onto electric trucks and connected to charging
stations to provide various ancillary services for distribution grids. This
paper proposes a new strategy for MESD operation, in which their power outputs
and paths are co-optimally scheduled to minimize the total energy loss in both
power and transportation networks. The distances moved by MESDs and time at
different locations are modeled using a set of linear equations, considering
the time-varying traffic flow. The linear transit model is integrated with
linearized constraints to support the reliable operation of the distribution
grid. In particular, an optimal scheduling problem is formulated considering
the maximum limits on incremental variations in bus voltages and line power
flows for active and reactive power outputs of MESDs. A mixed-integer linear
programming solver can be readily applied to the optimization problem, ensuring
the global optimality of the solution. Simulation case studies are carried out
under various power and transportation network conditions. The results of these
case studies confirm that the proposed strategy using MESDs is effective in
reducing total energy losses, compared to conventional methods using stationary
batteries and plug-in electric vehicles.
A Game-Theoretic Approach for Enhancing Security and Data Trustworthiness in IoT Applications
Date: 2019-11-19 00:00:00
Tasks: Management

Wireless sensor networks (WSNs)-based internet of things (IoT) are among the
fast booming technologies that drastically contribute to different systems
management and resilience data accessibility. Designing a robust IoT network
imposes some challenges such as data trustworthiness (DT) and power management.
This paper presents a repeated game model to enhance clustered WSNs-based IoT
security and DT against the selective forwarding (SF) attack. Besides, the
model is capable of detecting the hardware (HW) failure of the cluster members
(CMs) and conserve the power consumption due to packet retransmission. The
model relies on TDMA protocol to facilitate the detection process and to avoid
collision between the delivered packets at the cluster head (CH). The proposed
model aims to keep packets transmitting, isotropic or non-isotropic
transmission, from the CMs to the CH for maximizing the DT and aims to
distinguish between the malicious CM and the one suffering from HW failure.
Accordingly, it can manage the consequently lost power due to the malicious
attack effect or HW malfunction. Simulation results indicate the proposed
mechanism improved performance with TDMA over six different environments
against the SF attack that achieves the Pareto optimal DT as compared to a
non-cooperative defense mechanism.
The structure of N184K amyloidogenic variant of gelsolin highlights the role of the H-bond network for protein stability and aggregation properties
Date: 2019-11-19 00:00:00

Mutations in the gelsolin protein are responsible for a rare conformational
disease known as AGel amyloidosis. Four of these mutations are hosted by the
second domain of the protein (G2): D187N/Y, G167R and N184K. The impact of the
latter has been so far evaluated only by studies on the isolated G2. Here we
report the characterization of full-length gelsolin carrying the N184K mutation
and compare the findings with those obtained on the wild type and the other
variants. The crystallographic structure of the N184K variant in the Ca2+-free
conformation shows remarkable similarities with the wild type protein. Only
minimal local rearrangements can be observed and the mutant is as efficient as
the wild type in severing filamentous actin. However, thermal stability of the
pathological variant is compromised in the Ca2+-free conditions. These data
suggest that the N to K substitution causes a local disruption of the H-bond
network in the core of the G2 domain. Such a subtle rearrangement of the
connections does not lead to significant conformational changes but severely
affects the stability of the protein.
Three-dimensional cell culture model for hepatocytes opens a new avenue of real world research on liver
Authors: Ting Yao, Yi Zhang, Mengjiao Lv, Guoqing Zang, Soon Seng Ng, Xiaohua Chen
Date: 2019-11-19 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Drug Discovery

3-demensional (3D) culture model is a valuable in vitro tool to study liver biology, metabolism, organogenesis, tissue morphology, drug discovery and cell-based assays. Compelling evidence suggests that cells cultured in 3D model exhibit superior liver-specific functions over the conventional 2-dimentional (2D) culture in evaluating hepatobiliary drug disposition and drug-induced hepatotoxicity due to the in vivo-like physiological condition recapitulated by 3D model technologies. We will review the attributes of 3D culture model in acquiring relevant liver phenotypes and functionalities, discuss the critical niche factors found to modulate hepatocytes and highlight recent advancements on 3D cell culture technologies to achieve next-level in vitro tool for preclinical study.
Complexity and Diversity in Sparse Code Priors Improve Receptive Field Characterization of Macaque V1 Neurons
Authors: Ziniu Wu, Harold Rockwell, Yimeng Zhang, Shiming Tang, Tai Sing Lee
Date: 2019-11-19 00:00:00
Tasks: Diversity

System identification techniques -- projection pursuit regression models (PPRs) and convolutional neural networks (CNNs) -- provide state-of-the-art performance in predicting visual cortical neurons' responses to arbitrary input stimuli. However, the constituent kernels recovered by these methods are often noisy and lack coherent structure, making it difficult to understand the underlying component features of a neuron's receptive field. In this paper, we show that using a dictionary of diverse kernels with complex shapes learned from natural scenes based on efficient coding theory, as the front-end for PPRs and CNNs can improve their performance in neuronal response prediction as well as algorithmic data efficiency and convergence speed. Extensive experimental results also indicate that these sparse-code kernels provide important information on the component features of a neuron's receptive field. In addition, we find that models with the complex-shaped sparse code front-end are significantly better than models with a standard orientation-selective Gabor filter front-end for modeling V1 neurons that have been found to exhibit complex pattern selectivity. We show that the relative performance difference due to these two front-ends can be used to produce a sensitive metric for detecting complex selectivity in V1 neurons.
On the conversion from OSA-UCS to CIEXYZ
Date: 2019-11-20 00:00:00

This article revisits Kobayasi's and Yosiki's algorithm for conversion of
OSA-UCS into XYZ cooordinates. It corrects some mistakes on the involved
functions and initial guesses and shows that that hundreds of thousands of
coordinates can be converted in less than a second with full accuracy.
3D Scene Based Beam Selection for mmWave Communications
Authors: Weihua Xu, Feifei Gao, Shi Jin, Ahmed Alkhateeb
Date: 2019-11-19 00:00:00
Tasks: 3D Scene Reconstruction, Position

In this paper, we present a novel framework of 3D scene based beam selection for mmWave communications that relies only on the environmental data and deep learning techniques. Different from other out-of-band side-information aided communication strategies, the proposed one fully utilizes the environmental information, e.g., the shape, the position, and even the materials of the surrounding buildings/cars/trees that are obtained from 3D scene reconstruction. Specifically, we build the neural networks with the input as point cloud of the 3D scene and the output as the beam indices. Compared with the LIDAR aided technique, the reconstructed 3D scene here is achieved from multiple images taken offline from cameras and thus significantly lowers down the cost and makes itself applicable for small mobile terminals. Simulation results show that the proposed 3D scene based beam selection can outperform the LIDAR method in terms of accuracy.
Generative Adversarial Networks For Graph Data Imputation From Signed Observations
Date: 2019-11-20 00:00:00
Tasks: Generative Adversarial Network, Imputation

We study the problem of missing data imputation for graph signals from signed
one-bit quantized observations. More precisely, we consider that the true graph
data is drawn from a distribution of signals that are smooth or bandlimited on
a known graph. However, instead of observing these signals, we observe a signed
version of them and only at a subset of the nodes on the graph. Our goal is to
estimate the true underlying graph signals from our observations. To achieve
this, we propose a generative adversarial network (GAN) where the key is to
incorporate graph-aware losses in the associated minimax optimization problem.
We illustrate the benefits of the proposed method via numerical experiments on
hand-written digits from the MNIST dataset
Artificial intelligence approach to momentum risk-taking
Date: 2020-03-17 00:00:00
Tasks: Decision Making, Management

We propose a mathematical model of momentum risk-taking, which is essentially
real-time risk management focused on short-term volatility of stock markets.
Its implementation, our fully automated momentum equity trading system
presented systematically, proved to be successful in extensive historical and
real-time experiments. Momentum risk-taking is one of the key components of
general decision-making, a challenge for artificial intelligence and machine
learning with deep roots in cognitive science; its variants beyond stock
markets are discussed. We begin with a new algebraic-type theory of news impact
on share-prices, which describes well their power growth, periodicity, and the
market phenomena like price targets and profit-taking. This theory generally
requires Bessel and hypergeometric functions. Its discretization results in
some tables of bids, which are basically expected returns for main investment
horizons, the key in our trading system. The ML procedures we use are similar
to those in neural networking. A preimage of our approach is the new contract
card game provided at the end, a combination of bridge and poker. Relations to
random processes and the fractional Brownian motion are outlined.
Modern Antennas and Microwave Circuits -- A complete master-level course
Authors: A. B. Smolders, H. J. Visser, U. Johannsen
Date: 2019-11-19 00:00:00

Modern antenna systems include passive antenna structures, passive microwave circuits and interconnections to electronics. Therefore, the antenna engineer should have a deep understanding of antenna theory and should be able to apply microwave engineering concepts. This textbook provides all relevant material for Master-level courses in the domain of antenna systems. The book includes comprehensive material on antennas and provides a solid introduction into microwave engineering, ranging from passive components to active circuits. We believe that this is the perfect mixture of know-how for a junior antenna expert. The theoretical material in this textbook can be supplemented by labs in which the students learn how to use state-of-the-art antenna and microwave design tools and test equipment, such as a vector network analyzer or a near-field scanner. Several chapters in this book can be used quite independent from each other. For example, chapter 6 can be used in a dedicated phased-array antenna course without the need to use Maxwell-based antenna theory from chapter 4. Similarly, chapter 3 on transmission lines and microwave circuits does not require deep back-ground knowledge in electromagnetics or antenna theory.
Unsupervised Bayesian Ising Approximation for revealing the neural dictionary in songbirds
Date: 2019-11-19 00:00:00

The problem of deciphering how low-level patterns (action potentials in the
brain, amino acids in a protein, etc.) drive high-level biological features
(sensorimotor behavior, enzymatic function) represents the central challenge of
quantitative biology. The lack of general methods for doing so from the size of
datasets that can be collected experimentally severely limits our understanding
of the biological world. For example, in neuroscience, some sensory and motor
codes have been shown to consist of precisely timed multi-spike patterns.
However, the combinatorial complexity of such pattern codes have precluded
development of methods for their comprehensive analysis. Thus, just as it is
hard to predict a protein's function based on its sequence, we still do not
understand how to accurately predict an organism's behavior based on neural
activity. Here we derive a method for solving this class of problems. We
demonstrate its utility in an application to neural data, detecting precisely
timed spike patterns that code for specific motor behaviors in a songbird vocal
system. Our method detects such codewords with an arbitrary number of spikes,
does so from small data sets, and accounts for dependencies in occurrences of
codewords. Detecting such dictionaries of important spike patterns --- rather
than merely identifying the timescale on which such patterns exist, as in some
prior approaches --- opens the door for understanding fine motor control and
the neural bases of sensorimotor learning in animals. For example, for the
first time, we identify differences in encoding motor exploration versus
typical behavior. Crucially, our method can be used not only for analysis of
neural systems, but also for understanding the structure of correlations in
other biological and nonbiological datasets.
Robust Sub-Meter Level Indoor Localization With a Single WiFi Access Point-Regression Versus Classification
Date: 2019-11-17 00:00:00
Tasks: Indoor Localization, regression

Precise indoor localization is an increasingly demanding requirement for
various emerging applications, like Virtual/Augmented reality and personalized
advertising. Current indoor environments are equipped with pluralities of WiFi
access points (APs), whose deployment is expected to be massive in the future
enabling highly precise localization approaches. Though the conventional
model-based localization schemes have achieved sub-meter level accuracy by
fusing multiple channel state information (CSI) observations, the corresponding
computational overhead is usually significant, especially in the current
multiple-input multiple-output orthogonal frequency division multiplexing
(MIMO-OFDM) systems. In order to address this issue, model-free localization
techniques using deep learning frameworks have been lately proposed, where
mainly classification methods were applied. In this paper, instead of
classification based mechanism, we propose a logistic regression based scheme
with the deep learning framework, combined with Cram\'er-Rao lower bound (CRLB)
assisted robust training, which achieves more robust sub-meter level accuracy
(0.97m median distance error) in the standard laboratory environment and
maintains reasonable online prediction overhead under the single WiFi AP
settings.
Mirror Neuron; A Beautiful Unnecessary Concept
Date: 2019-11-14 00:00:00

The mirror neuron theory that has enjoyed continued validations was developed
with no particular attention to the phenomenon of the vision. Understandably
the perception of vision has always been thought to happen, naturally, as that
for any of the other four senses. However, the reality that underlies this
presumption is by no means obvious; vision perception is based on remote
sensing of the ecology, fundamentally different form that of the other senses,
which have tactile stimulation origin (contact with matter). While its reality,
as explicated here, explains why the above presumption is true, it also bears
heavily on the mirror neuron theory: the revelation of the nature of vision
makes mirror neurons unnecessary. The extensive cognitive neurosciences
investigation of primates and humans, over the past three decades, have
experimentally validated the theory of mirror neurons which had been put
forward early in the period (1980s and 1990s) based on the results of cognitive
research experiments on the macaque monkeys. Based on further experimental
works, phenomena such as learning, empathy, and some aspects of survival, are
ascribed to the operations of this class of additional neurons. Here I reason
that all the results of the efforts of the proponents of the theory can, not
only find explanation in the context of the new theory of vision but also
provide support for it. This new take of the phenomenon of vision is developed
based on the nature of the experimental methods that have succeeded in
developing some measure of vision for the blinds, and the inferences from the
very likely nature of the computational strategy of the brain. I present
evidence that the mental phenomena, which rendered the claim of the mirror
neurons, are in essence the results of subjects beings variably touched by
their ecology, through the coherent tactile operation of all senses.
Robust Adaptive Model Predictive Control with Worst-Case Cost
Date: 2019-11-19 00:00:00
Tasks: Model Predictive Control

A robust adaptive model predictive control (MPC) algorithm is presented for
linear, time invariant systems with unknown dynamics and subject to bounded
measurement noise. The system is characterized by an impulse response model,
which is assumed to lie within a bounded set called the feasible system set.
Online set-membership identification is used to reduce uncertainty in the
impulse response. In the MPC scheme, robust constraints are enforced to ensure
constraint satisfaction for all the models in the feasible set. The performance
objective is formulated as a worst-case cost with respect to the modeling
uncertainties. That is, at each time step an optimization problem is solved in
which the control input is optimized for the worst-case plant in the
uncertainty set. The performance of the proposed algorithm is compared to an
adaptive MPC algorithm from the literature using Monte-Carlo simulations.
Improvements of the REDCRAFT Software Package
Authors: Casey A Cole, Caleb Parks, Julian Rachele, Homayoun Valafar
Date: 2019-11-19 00:00:00

Traditional approaches to elucidation of protein structures by NMR spectroscopy rely on distance restraints also known as nuclear Overhauser effects (NOEs). The use of NOEs as the primary source of structure determination by NMR spectroscopy is time consuming and expensive. Residual Dipolar Couplings (RDCs) have become an alternate approach for structure calculation by NMR spectroscopy. In previous works, the software package REDCRAFT has been presented as a means of harnessing the information containing in RDCs for structure calculation of proteins. In this work, we present significant improvements to the REDCRAFT package including: refinement of the decimation procedure, the inclusion of graphical user interface, adoption of NEF standards, and addition of scripts for enhanced protein modeling options. The improvements to REDCRAFT have resulted in the ability to fold proteins that the previous versions were unable to fold. For instance, we report the results of folding of the protein 1A1Z in the presence of highly erroneous data.