pwc-archive/papers-with-abstracts huggingface dataset:
Covert Surveillance via Proactive Eavesdropping Under Channel Uncertainty
Date: 2020-02-15 00:00:00

Surveillance performance is studied for a wireless eavesdropping system,
where a full-duplex legitimate monitor eavesdrops a suspicious link efficiently
with the artificial noise (AN) assistance. Different from the existing work in
the literature, the suspicious receiver in this paper is assumed to be capable
of detecting the presence of AN. Once such receiver detects the AN, the
suspicious user will stop transmission, which is harmful for the surveillance
performance. Hence, to improve the surveillance performance, AN should be
transmitted covertly with a low detection probability by the suspicious
receiver. Under these assumptions, an optimization problem is formulated to
maximize the eavesdropping non-outage probability under a covert constraint.
Based on the detection ability at the suspicious receiver, a novel scheme is
proposed to solve the optimization problem by iterative search. Moreover, we
investigate the impact of both the suspicious link uncertainty and the jamming
link uncertainty on the covert surveillance performance. Simulations are
performed to verify the analyses. We show that the suspicious link uncertainty
benefits the surveillance performance, while the jamming link uncertainty can
degrade the surveillance performance.
Two-Step Codeword Design for Millimeter Wave Massive MIMO Systems with Quantized Phase Shifters
Date: 2020-02-15 00:00:00

In this paper, a two-step codeword design approach for millimeter wave
(mmWave) massive MIMO systems is presented. Ideal codewords are first designed,
which ignores the hardware constraints in terms of phase shifter resolution and
the number of RF chains. Based on the ideal codewords, practical codewords are
then obtained taking the hardware constraints into consideration. For the ideal
codeword design in the first step, additional phase is introduced to the beam
gain to provide extra degree of freedom. We develop a phase-shifted ideal
codeword design (PS-ICD) method, which is based on alternative minimization
with each iteration having a closed-form solution and can be extended to design
more general beamforming vectors with different beam patterns. Once the ideal
codewords are obtained in the first step, the practical codeword design problem
in the second step is to approach the ideal codewords by considering the
hardware constraints of the hybrid precoding structure in terms of phase
shifter resolution and the number of RF chains. We propose a fast search based
alternative minimization (FS-AltMin) algorithm that alternatively designs the
analog precoder and digital precoder. Simulation results verify the
effectiveness of the proposed methods and show that the codewords designed
based on the two-step approach outperform those designed by the existing
approaches.
Single-cell entropy to quantify the cellular transcription from single-cell RNA-seq data
Date: 2020-02-15 00:00:00
Tasks: Benchmarking, Classification, Clustering

We present the use of single-cell entropy (scEntropy) to measure the order of
the cellular transcriptome profile from single-cell RNA-seq data, which leads
to a method of unsupervised cell type classification through scEntropy followed
by the Gaussian mixture model (scEGMM). scEntropy is straightforward in
defining an intrinsic transcriptional state of a cell. scEGMM is a coherent
method of cell type classification that includes no parameters and no
clustering; however, it is comparable to existing machine learning-based
methods in benchmarking studies and facilitates biological interpretation.
DNA methylation heterogeneity induced by collaborations between enhancers
Authors: Yusong Ye, Zhuoqin Yang, Jinzhi Lei
Date: 2020-02-15 00:00:00

During mammalian embryo development, reprogramming of DNA methylation plays important roles in the erasure of parental epigenetic memory and the establishment of na\"{i}ve pluripogent cells. Multiple enzymes that regulate the processes of methylation and demethylation work together to shape the pattern of genome-scale DNA methylation and guid the process of cell differentiation. Recent availability of methylome information from single-cell whole genome bisulfite sequencing (scBS-seq) provides an opportunity to study DNA methylation dynamics in the whole genome in individual cells, which reveal the heterogeneous methylation distributions of enhancers in embryo stem cells (ESCs). In this study, we developed a computational model of enhancer methylation inheritance to study the dynamics of genome-scale DNA methylation reprogramming during exit from pluripotency. The model enables us to track genome-scale DNA methylation reprogramming at single-cell level during the embryo development process, and reproduce the DNA methylation heterogeneity reported by scBS-seq. Model simulations show that DNA methylation heterogeneity is an intrinsic property driven by cell division along the development process, and the collaboration between neighboring enhancers is required for heterogeneous methylation. Our study suggest that the mechanism of genome-scale oscillation proposed by Rulands et al. (2018) might not necessary to the DNA methylation during exit from pluripotency.
Performance Analysis of RSU-based Multihomed Multilane Vehicular Networks
Date: 2020-02-16 00:00:00
Tasks: Autonomous Vehicles

Motivated by the potentially high downlink traffic demands of commuters in
future autonomous vehicles, we study a network architecture where vehicles use
Vehicle-to-Vehicle (V2V) links to form relay network clusters, which in turn
use Vehicle-to-Infrastructure (V2I) links to connect to one or more Road Side
Units (RSUs). Such cluster-based multihoming offers improved performance, e.g.,
in coverage and per user shared rate, but depends on the penetration of V2V-V2I
capable vehicles and possible blockage, by legacy vehicles, of line of sight
based V2V links, such as those based on millimeter-wave and visible light
technologies. This paper provides a performance analysis of a typical vehicle's
connectivity and throughput on a highway in the free-flow regime, exploring its
dependence on vehicle density, sensitivity to blockages, number of lanes and
heterogeneity across lanes. The results show that, even with moderate vehicle
densities and penetration of V2V-V2I capable vehicles, such architectures can
achieve substantial improvements in connectivity and reduction in per-user rate
variability as compared to V2I based networks. The typical vehicle's
performance is also shown to improve considerably in the multilane highway
setting as compared to a single lane road. This paper also sheds light on how
the network performance is affected when vehicles can control their relative
positions, by characterizing the connectivity-throughput tradeoff faced by the
clusters of vehicles.
Coordinate-free Circumnavigation of a Moving Target via a PD-like Controller
Authors: Fei Dong, Keyou You, Lihua Xie, Qinglei Hu
Date: 2020-02-16 00:00:00

This paper proposes a coordinate-free controller for a nonholonomic vehicle to circumnavigate a fully-actuated moving target by using range-only measurements. If the range rate is available, our Proportional Derivative (PD)-like controller has a simple structure as the standard PD controller, except the design of an additive constant bias and a saturation function in the error feedback. We show that if the target is stationary, the vehicle asymptotically encloses the target with a predefined radius at an exponential convergence rate, i.e., an exact circumnavigation pattern can be completed. For a moving target, the circumnavigation error converges to a small region whose size is shown proportional to the maneuverability of the target, e.g., the maximum linear speed and acceleration. Moreover, we design a second-order sliding mode (SOSM) filter to estimate the range rate and show that the SOSM filter can recover the range rate in a finite time. Finally, the effectiveness and advantages of our controller are validated via both numerical simulations and real experiments.
Path-dependent connectivity, not modularity, consistently predicts controllability of structural brain networks
Authors: Shubhankar P. Patankar, Jason Z. Kim, Fabio Pasqualetti, Danielle S. Bassett
Date: 2020-02-16 00:00:00

The human brain displays rich communication dynamics that are thought to be particularly well-reflected in its marked community structure. Yet, the precise relationship between community structure in structural brain networks and the communication dynamics that can emerge therefrom is not well-understood. In addition to offering insight into the structure-function relationship of networked systems, such an understanding is a critical step towards the ability to manipulate the brain's large-scale dynamical activity in a targeted manner. We investigate the role of community structure in the controllability of structural brain networks. At the region level, we find that certain network measures of community structure are sometimes statistically correlated with measures of linear controllability. However, we then demonstrate that this relationship depends on the distribution of network edge weights. We highlight the complexity of the relationship between community structure and controllability by performing numerical simulations using canonical graph models with varying mesoscale architectures and edge weight distributions. Finally, we demonstrate that weighted subgraph centrality, a measure rooted in the graph spectrum, and which captures higher-order graph architecture, is a stronger and more consistent predictor of controllability. Our study contributes to an understanding of how the brain's diverse mesoscale structure supports transient communication dynamics.
An optimal mechanism charging for priority in a queue
Date: 2020-02-16 00:00:00

We derive a revenue-maximizing scheme that charges customers who are
homogeneous with respect to their waiting cost parameter for a random fee in
order to become premium customers. This scheme incentivizes all customers to
purchase priority, each at his/her drawn price. We also design a
revenue-maximizing scheme for the case where customers are heterogeneous with
respect to their waiting cost parameter. Now lower cost parameter customers are
encouraged to join the premium class at a low price: Given that, those with
high cost parameter would be willing to pay even more for this privilege.
Flexible Subcarrier Allocation for Interleaved Frequency Division Multiple Access
Date: 2020-02-16 00:00:00
Tasks: Blocking

Interleaved Frequency Division Multiple Access (IFDMA) and Orthogonal FDMA
(OFDMA) belong to a class of signal modulation and multiple-access techniques
in which information of multiple users are multiplexed and carried on
subcarriers within a shared spectrum. Compared with OFDMA, IFDMA has lower
Peak-to-Average Power Ratio (PAPR). However, IFDMA poses two rigid constraints
on subcarrier allocation: 1) the subcarriers occupied by a user must be
evenly-spaced among the available subcarriers. 2) the number of subcarriers
used by a user must be a divisor of the total number of subcarriers. This paper
investigates how to overcome these constraints to allow flexible and
fine-grained subcarrier allocation in IFDMA. Specifically, we put forth 1) a
bit-reversal subcarrier allocation scheme whereby the problem of allocating
evenly-spaced subcarriers is transformed to a more intuitive problem of filling
contiguous bins; 2) a multi-stream IFDMA scheme whereby a user can have an
arbitrary number of subcarriers. For the synchronous scenario in which user
requests arrive in a synchronous manner, we show that IFDMA can achieve the
same level of flexibility and granularity as OFDMA in subcarrier allocation.
For the asynchronous scenario in which user requests arrive and depart
asynchronously, we show that the blocking probability of IFDMA is only slightly
worse than that of OFDMA: specifically, the gap between the blocking
probabilities of IFDMA and OFDMA is only 2:56% at a moderate offered load.
Synchronization of endogenous business cycles
Authors: Marco Pangallo
Date: 2020-02-16 00:00:00
Tasks: Attribute

Business cycles tend to comove across countries. However, standard models that attribute comovement to propagation of exogenous shocks struggle to generate a level of comovement that is as high as in the data. In this paper, we consider models that produce business cycles endogenously, through some form of non-linear dynamics -- limit cycles or chaos. These models generate stronger comovement, because they combine shock propagation with synchronization of endogenous dynamics. In particular, we study a demand-driven reduced-form model in which business cycles emerge from strategic complementarities within countries, synchronizing their oscillations through international trade linkages. We develop an eigendecomposition that explores the interplay between non-linear dynamics, shock propagation and network structure, and use this theory to understand the mechanisms of synchronization. Next, we calibrate the model to data on 24 countries and show that the empirical level of comovement can only be matched by combining endogenous business cycles with exogenous shocks. Despite the limitations of using a stylized model, our results support the hypothesis that business cycles are at least in part caused by underlying non-linear dynamics.
Regulated State Synchronization for Discrete-Time Homogeneous Networks of Non-Introspective Agents in Presence of Unknown Non-Uniform Input Delays: A Scale-Free Protocol Design (with a general MATLAB implementation)
Date: 2020-02-23 00:00:00

This paper studies regulated state synchronization of discrete-time
homogeneous networks of non-introspective agents in presence of unknown
non-uniform input delays. A scale free protocol is designed based on additional
information exchange, which does not need any knowledge of the directed network
topology and the spectrum of the associated Laplacian matrix. The proposed
protocol is scalable and achieves state synchronization for any arbitrary
number of agents. Meanwhile, an upper bound for the input delay tolerance is
obtained, which explicitly depends on the agent dynamics.
Fully Distributed Resilient State Estimation based on Distributed Median Solver
Authors: Jin Gyu Lee, Junsoo Kim, Hyungbo Shim
Date: 2020-02-16 00:00:00
Tasks: State Estimation

In this paper, we present a scheme of fully distributed resilient state estimation for linear dynamical systems under sensor attacks. The proposed state observer consists of a network of local observers, where each of them utilizes local measurements and information transmitted from the neighbors. As a fully distributed scheme, it does not necessarily collect a majority of sensing data for the sake of attack identification, while the compromised sensors are eventually identified by the distributed network and excluded from the observers. For this, the overall network (not the individual local observer) is assumed to have redundant sensors and assumed to be connected. The proposed scheme is based on a novel design of a distributed median solver, which approximately recovers the median value of local estimates.
A Novel Numerical Index for Assessing Results of Frequency Response Analysis (FRA): an Experimental Study on Electrical Machines
Date: 2020-08-31 00:00:00

This paper is an effort to evaluate the numerical indices for the assessment
of the frequency response analysis (FRA) to provide a deeper understanding of
their characteristics. The study introduces the indices in the literature and
categorizes them into two groups. The results of an actual FRA experimental
setup on an electrical machine are employed to examine the indices from the
various aspects. The main features of the indices are extracted, and the better
ones in various aspects are marked. Moreover, based on the observations from
the experimental studies, a new numerical index for comparing FRA results is
proposed in this paper to enhance FRA results assessment and interpretation in
both electrical machines and transformers. The advantages of the proposed index
are also shown by comparing it with the existing ones.
Performance Limits of Differential Power Processing
Date: 2020-03-23 00:00:00

This paper investigates the performance limits of differential power
processing (DPP) and presents quantitative and systematic design guidelines for
the selection and comparison of DPP topologies. A stochastic model is developed
to evaluate the expected power losses of a variety of DPP topologies with
probabilistic load distribution. The expected losses of several DPP topologies
are derived and compared against traditional dc-dc converters to reveal their
performance limits. The impacts of the load distribution and load scale on the
expected losses are investigated. The theoretical models are verified with
SPICE simulations and experimental results.
Visualization of fully connected layer weights in deep learning CT reconstruction
Date: 2020-02-17 00:00:00
Tasks: Computed Tomography (CT), CT Reconstruction

Recently, the use of deep learning techniques to reconstruct computed
tomography (CT) images has become a hot research topic, including sinogram
domain methods, image domain methods and sinogram domain to image domain
methods. All these methods have achieved favorable results. In this article, we
have studied the important functions of fully connected layers used in the
sinogram domain to image domain approach. First, we present a simple domain
mapping neural networks. Then, we analyze the role of the fully connected
layers of these networks and visually analyze the weights of the fully
connected layers. Finally, by visualizing the weights of the fully connected
layer, we found that the main role of the fully connected layer is to implement
the back projection function in CT reconstruction. This finding has important
implications for the use of deep learning techniques to reconstruct computed
tomography (CT) images. For example, since fully connected layer weights need
to consume huge memory resources, the back-projection function can be
implemented by using analytical algorithms to avoid resource occupation, which
can be embedded in the entire network.
On the Volatility of Optimal Control Policies and the Capacity of a Class of Linear Quadratic Regulators
Date: 2020-09-21 00:00:00

It is well known that highly volatile control laws, while theoretically
optimal for certain systems, are undesirable from an engineering perspective,
being generally deleterious to the controlled system. In this article we are
concerned with the temporal volatility of the control process of the regulator
in discrete time Linear Quadratic Regulators (LQRs). Our investigation in this
paper unearths a surprising connection between the cost functional which an LQR
is tasked with minimizing and the temporal variations of its control laws.
  We first show that optimally controlling the system always implies high
levels of control volatility, i.e., it is impossible to reduce volatility in
the optimal control process without sacrificing cost. We also show that, akin
to communication systems, every LQR has a $Capacity~Region$ associated with it,
that dictates and quantifies how much cost is achievable at a given level of
control volatility. This additionally establishes the fact that no admissible
control policy can simultaneously achieve low volatility and low cost. We then
employ this analysis to explain the phenomenon of temporal price volatility
frequently observed in deregulated electricity markets.
Robust Initial Alignment for SINS/DVL Based on Reconstructed Observation Vectors
Date: 2020-02-17 00:00:00

Misalignment angle will result in a considerable error for the integration of
Doppler Velocity Log (DVL) and of Strapdown Inertial Navigation System (SINS).
In this paper, a robust initial alignment method for SINS/DVL is proposed to
solve a practical applicable issue, which is that the outputs of DVL are often
corrupted by the outliers. Firstly, the alignment principle for SINS/DVL is
summarized. Secondly, based on the principle of this alignment method, the
apparent velocity model is investigated, and the parameters expression of the
apparent velocity model are derived detailed. Using the apparent velocity
model, the unknown parameters of the apparent velocity model are estimated by
the developed Robust Kalman Filter (RKF), then the reconstructed observation
vector, where the outliers are detected and isolated, is reconstructed by the
estimated parameters. Based on the reconstructed observation vectors, the
initial attitude is determined. Finally, the simulation and field tests are
carried out to verify the performance of the proposed method. The test results
are shown that the proposed method can detect and isolate the outliers
effectively and get better performance than the previous work.
Trimming the Sail: A Second-order Learning Paradigm for Stock Prediction
Date: 2020-02-17 00:00:00
Tasks: BIG-bench Machine Learning, Prediction, Stock Prediction

Nowadays, machine learning methods have been widely used in stock prediction.
Traditional approaches assume an identical data distribution, under which a
learned model on the training data is fixed and applied directly in the test
data. Although such assumption has made traditional machine learning techniques
succeed in many real-world tasks, the highly dynamic nature of the stock market
invalidates the strict assumption in stock prediction. To address this
challenge, we propose the second-order identical distribution assumption, where
the data distribution is assumed to be fluctuating over time with certain
patterns. Based on such assumption, we develop a second-order learning paradigm
with multi-scale patterns. Extensive experiments on real-world Chinese stock
data demonstrate the effectiveness of our second-order learning paradigm in
stock prediction.
Cross-sectional Stock Price Prediction using Deep Learning for Actual Investment Management
Authors: Masaya Abe, Kei Nakagawa
Date: 2020-02-17 00:00:00
Tasks: Management, Prediction, Stock Price Prediction

Stock price prediction has been an important research theme both academically and practically. Various methods to predict stock prices have been studied until now. The feature that explains the stock price by a cross-section analysis is called a "factor" in the field of finance. Many empirical studies in finance have identified which stocks having features in the cross-section relatively increase and which decrease in terms of price. Recently, stock price prediction methods using machine learning, especially deep learning, have been proposed since the relationship between these factors and stock prices is complex and non-linear. However, there are no practical examples for actual investment management. In this paper, therefore, we present a cross-sectional daily stock price prediction framework using deep learning for actual investment management. For example, we build a portfolio with information available at the time of market closing and invest at the time of market opening the next day. We perform empirical analysis in the Japanese stock market and confirm the profitability of our framework.