pwc-archive/papers-with-abstracts huggingface dataset:
Energy-Efficient Task Offloading for Vehicular Edge Computing: Joint Optimization of Offloading and Bit Allocation
Date: 2019-10-15 00:00:00
Tasks: Edge-computing

With the rapid development of vehicular networks, various applications that
require high computation resources have emerged. To efficiently execute these
applications, vehicular edge computing (VEC) can be employed. VEC offloads the
computation tasks to the VEC node, i.e., the road side unit (RSU), which
improves vehicular service and reduces energy consumption of the vehicle.
However, communication environment is time-varying due to the movement of the
vehicle, so that finding the optimal offloading parameters is still an open
problem. Therefore, it is necessary to investigate an optimal offloading
strategy for effective energy savings in energy-limited vehicles. In this
paper, we consider the changes of communication environment due to various
speeds of vehicles, which are not considered in previous studies. Then, we
jointly optimize the offloading proportion and uplink/computation/downlink bit
allocation of multiple vehicles, for the purpose of minimizing the total energy
consumption of the vehicles under the delay constraint. Numerical results
demonstrate that the proposed energy-efficient offloading strategy
significantly reduces the total energy consumption.
Ballistocardiogram artifact reduction in simultaneous EEG-fMRI using deep learning
Date: 2019-10-15 00:00:00
Proceeding: arXiv:1910.06659 2019 10
Tasks: Deep Learning, EEG, Electroencephalogram (EEG)

Objective: The concurrent recording of electroencephalography (EEG) and
functional magnetic resonance imaging (fMRI) is a technique that has received
much attention due to its potential for combined high temporal and spatial
resolution. However, the ballistocardiogram (BCG), a large-amplitude artifact
caused by cardiac induced movement contaminates the EEG during EEG-fMRI
recordings. Removal of BCG in software has generally made use of linear
decompositions of the corrupted EEG. This is not ideal as the BCG signal is
non-stationary and propagates in a manner which is non-linearly dependent on
the electrocardiogram (ECG). In this paper, we present a novel method for BCG
artifact suppression using recurrent neural networks (RNNs). Methods: EEG
signals were recovered by training RNNs on the nonlinear mappings between ECG
and the BCG corrupted EEG. We evaluated our model's performance against the
commonly used Optimal Basis Set (OBS) method at the level of individual
subjects, and investigated generalization across subjects. Results: We show
that our algorithm can generate larger average power reduction of the BCG at
critical frequencies, while simultaneously improving task relevant EEG based
classification. Conclusion: The presented deep learning architecture can be
used to reduce BCG related artifacts in EEG-fMRI recordings. Significance: We
present a deep learning approach that can be used to suppress the BCG artifact
in EEG-fMRI without the use of additional hardware. This method may have scope
to be combined with current hardware methods, operate in real-time and be used
for direct modeling of the BCG.
The Cobb-Douglas production function revisited
Date: 2019-10-20 00:00:00

Charles Cobb and Paul Douglas in 1928 used data from the US manufacturing
sector for 1899-1922 to introduce what is known today as the Cobb-Douglas
production function that has been widely used in economic theory for decades.
We employ the R programming language to fit the formulas for the parameters of
the Cobb-Douglas production function generated by the authors recently via the
bi-Hamiltonian approach to the same data set utilized by Cobb and Douglas. We
conclude that the formulas for the output elasticities and total factor
productivity are compatible with the original 1928 data.
Optimal principal component Analysis of STEM XEDS spectrum images
Date: 2019-10-14 00:00:00

STEM XEDS spectrum images can be drastically denoised by application of the
principal component analysis (PCA). This paper looks inside the PCA workflow
step by step on an example of a complex semiconductor structure consisting of a
number of different phases. Typical problems distorting the principal
components decomposition are highlighted and solutions for the successful PCA
are described. Particular attention is paid to the optimal truncation of
principal components in the course of reconstructing denoised data. A novel
accurate and robust method, which overperforms the existing truncation methods
is suggested for the first time and described in details.
Robust portfolio optimization with multi-factor stochastic volatility
Date: 2020-06-15 00:00:00
Tasks: Portfolio Optimization

This paper studies a robust portfolio optimization problem under the
multi-factor volatility model introduced by Christoffersen et al. (2009). The
optimal strategy is derived analytically under the worst-case scenario with or
without derivative trading. To illustrate the effects of ambiguity, we compare
our optimal robust strategy with some strategies that ignore the information of
uncertainty, and provide the corresponding welfare analysis. The effects of
derivative trading to the optimal portfolio selection are also discussed by
considering alternative strategies. Our study is further extended to the cases
with jump risks in asset price and correlated volatility factors, respectively.
Numerical experiments are provided to demonstrate the behavior of the optimal
portfolio and utility loss.
Towards systems tissue engineering: elucidating the dynamics, spatial coordination, and individual cells driving emergent behaviors
Authors: Matthew S Hall, Joseph T Decker, Lonnie D Shea
Date: 2019-10-15 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Translation

Biomaterial systems have allowed for the in vitro production of complex, emergent tissue behaviors that were not possible with conventional 2D culture systems allowing for analysis of the normal development as well as disease processes. We propose that the path towards developing the design parameters for biomaterial systems lies with identifying the molecular drivers of emergent behavior through leveraging technological advances in systems biology, including single cell omics, genetic engineering, and high content imaging. This research focus, which we term systems tissue engineering, can uniquely interrogate the mechanisms by which complex tissue behaviors emerge with the potential to capture the contribution of i) dynamic regulation of tissue development and dysregulation, ii) single cell heterogeneity and the function of rare cell types, and iii) the spatial distribution and structure of individual cells and cell types within a tissue. Collectively, systems tissue engineering can facilitate the identification of biomaterial design parameters that will accelerate basic science discovery and translation.
Optimal ratcheting of dividends in insurance
Authors: Hansjoerg Albrecher, Pablo Azcue, Nora Muler
Date: 2019-10-10 00:00:00

We address a long-standing open problem in risk theory, namely the optimal strategy to pay out dividends from an insurance surplus process, if the dividend rate can never be decreased. The optimality criterion here is to maximize the expected value of the aggregate discounted dividend payments up to the time of ruin. In the framework of the classical Cram\'{e}r-Lundberg risk model, we solve the corresponding two-dimensional optimal control problem and show that the value function is the unique viscosity solution of the corresponding Hamilton-Jacobi-Bellman equation. We also show that the value function can be approximated arbitrarily closely by ratcheting strategies with only a finite number of possible dividend rates and identify the free boundary and the optimal strategies in several concrete examples. These implementations illustrate that the restriction of ratcheting does not lead to a large efficiency loss when compared to the classical un-constrained optimal dividend strategy.
AFDP: An Automated Function Description Prediction Approach to Improve Accuracy of Protein Function Predictions
Date: 2019-10-15 00:00:00
Tasks: Protein Function Prediction

With the rapid growth in high-throughput biological sequencing technologies
and subsequently the amount of produced omics data, it is essential to develop
automated methods to annotate the functionality of unknown genes and proteins.
There are developed tools such as AHRD applying known proteins characterization
to annotate unknown ones. Some other algorithms such as eggNOG apply
orthologous groups of proteins to detect the most probable function. However,
while the available tools focus on the detection of the most similar
characterization, they are not able to generalize and integrate information
from multiple homologs while maintaining accuracy. Here, we devise AFDP, an
integrated approach for protein function prediction which benefits from the
combination of two available tools, AHRD and eggNOG, to predict the
functionality of novel proteins and produce more precise human readable
descriptions by applying our stCFExt algorithm. StCFExt creates function
descriptions applying available manually curated descriptions in swiss-prot.
Using a benchmark dataset we show that the annotations predicted by our
approach are more accurate than eggNOG and AHRD annotations.
Dynamically Aggregating Diverse Information
Authors: Annie Liang, Xiaosheng Mu, Vasilis Syrgkanis
Date: 2019-10-15 00:00:00
Tasks: Attribute

An agent has access to multiple information sources, each of which provides information about a different attribute of an unknown state. Information is acquired continuously -- where the agent chooses both which sources to sample from, and also how to allocate attention across them -- until an endogenously chosen time, at which point a decision is taken. We provide an exact characterization of the optimal information acquisition strategy under weak conditions on the agent's prior belief about the different attributes. We then apply this characterization to derive new results regarding: (1) endogenous information acquisition for binary choice, (2) strategic information provision by biased news sources, and (3) the dynamic consequences of attention manipulation.
On Harmonic Approximations of Inharmonic Signals
Date: 2019-10-28 00:00:00

In this work, we present the misspecified Gaussian Cram\'er-Rao lower bound
for the parameters of a harmonic signal, or pitch, when signal measurements are
collected from an almost, but not quite, harmonic model. For the asymptotic
case of large sample sizes, we present a closed-form expression for the bound
corresponding to the pseduo-true fundamental frequency. Using simulation
studies, it is shown that the bound is sharp and is attained by maximum
likelihood estimators derived under the misspecified harmonic assumption. It is
shown that misspecified harmonic models achieve a lower mean squared error than
correctly specified unstructured models for moderately inharmonic signals.
Examining voices from a speech database, we conclude that human speech belongs
to this class of signals, verifying that the use of a harmonic model for voiced
speech is preferable.
Statistic-Based Security Analysis of Ring Oscillator PUFs
Date: 2019-10-15 00:00:00

Ring oscillators (ROs) are a robust way to implement a physical unclonable
function (PUF) into ASICs or FPGAs, but claims of predictability arose
recently. We describe why this likely results from not using adjacent ROs for
pairwise comparison because of spatial patterns in mean frequency and
correlation coefficients found by principal component analysis. We show that
the covariance is too small for our approach to estimate bits if adjacent ROs
are compared. Our assumption of normality for the inter-device distribution
passes an Anderson-Darling test and we outline that devices with proximate
serial numbers are not more similar than other devices.
Automatic In-line Quantitative Myocardial Perfusion Mapping: processing algorithm and implementation
Date: 2019-10-16 00:00:00

Quantitative myocardial perfusion mapping has advantages over qualitative
assessment, including the ability to detect global flow reduction. However, it
is not clinically available and remains as a research tool. Building upon the
previously described imaging sequence, this paper presents algorithm and
implementation of an automated solution for inline perfusion flow mapping with
step by step performance characterization.
  An inline perfusion flow mapping workflow is proposed and demonstrated on
normal volunteers. Initial evaluation demonstrates the fully automated proposed
solution for the respiratory motion correction, AIF LV mask detection and
pixel-wise mapping, from free-breathing myocardial perfusion imaging.
Joint Transmit and Reflective Beamforming Design for IRS-Assisted Multiuser MISO SWIPT Systems
Date: 2019-10-16 00:00:00

This paper studies an intelligent reflecting surface (IRS)-assisted multiuser
multiple-input single-output (MISO) simultaneous wireless information and power
transfer (SWIPT) system. In this system, a multi-antenna access point (AP) uses
transmit beamforming to send both information and energy signals to a set of
receivers each for information decoding (ID) or energy harvesting (EH), and a
dedicatedly deployed IRS properly controls its reflecting phase shifts to form
passive reflection beams for facilitating both ID and EH at receivers. Under
this setup, we jointly optimize the (active) information and energy transmit
beamforming at the AP together with the (passive) reflective beamforming at the
IRS, to maximize the minimum power received at all EH receivers, subject to
individual signal-to-interference-plus-noise ratio (SINR) constraints at ID
receivers, and the maximum transmit power constraint at the AP. Although the
formulated SINR-constrained min-energy maximization problem is highly
non-convex, we present an efficient algorithm to obtain a high-quality solution
by using the techniques of alternating optimization and semi-definite
relaxation (SDR). Numerical results show that the proposed IRS-assisted SWIPT
system with both information and energy signals achieves significant
performance gains over benchmark schemes without IRS deployed and/or without
dedicated energy signals used.
Machine learning based identification of buried objects using sparse whitened NMF
Date: 2019-10-16 00:00:00
Tasks: BIG-bench Machine Learning

In this paper, a whitening-based algorithm has been applied to sparse
non-negative matrix factorization (NMF) as a preprocessing practice enhancing
the identification of buried object, significantly. In fact, without utilizing
a suitable whitening algorithm, the input signals are very similar to one
another. Therefore, conventional approaches are not able to identify and
properly discriminate desired buried objects. A suitable whitening algorithm
makes the covariance matrix diagonal, such that the processed signals highly
resembles the unprocessed versions. Such a similarity would be helpful to
identify new received signal. In this study, we have employed the zero-phase
component analysis (ZCA) as our whitening method. Hence, although signals of
different targets are very similar in many cases, the mentioned advantages,
achieved by whitening method, make identification of targets much easier.
Sensor-Movement-Robust Angle Estimation for 3-DoF Lower Limb Joints Without Calibration
Date: 2019-10-16 00:00:00

Inertial measurement unit (IMU)-based 3-DoF angle estimation methods for
lower limb joints have been studied for decades, however the calibration
motions and/or careful sensor placement are still necessary due to challenges
of real-time application. This study proposes a novel sensormovement-robust
3-DoF method for lower-limb joint angle estimation without calibration. A
realtime optimization process, which is based on a feedback iteration progress
to identify three joint axes of a 3-DoF joint, has been presented with a
reference frame calibration algorithm, and a safe-guarded strategy is proposed
to detect and compensate for the errors caused by sensor movements. The
experimental results obtained from a 3-DoF gimbal and ten healthy subjects
demonstrate a promising performance on 3-DoF angle estimation. Specially, the
experiments on ten subjects are performed with three gait modes and a 2-min
level walking. The root mean square error is below 2 deg for level walking and
5 deg for other two gait modes. The result of 2-min level walking shows our
algorithms stability under long run. The robustness against sensor movement are
demonstrated through data from multiple sets of IMUs. In addition, results from
the 3-DoF gimbal indicate that the accuracy of 3-DoF angle estimation could be
improved by 84.9% with our reference frame calibration algorithm. In
conclusion, our study proposes and validates a sensor-movement-robust 3-DoF
angle estimation for lowerlimb joints based on IMU. To the best of our
knowledge, our approach is the first experimental implementation of IMUbased
3-DoF angle estimation for lower-limb joints without calibration.
Weighted Monte Carlo with least squares and randomized extended Kaczmarz for option pricing
Date: 2019-10-16 00:00:00

We propose a methodology for computing single and multi-asset European option
prices, and more generally expectations of scalar functions of (multivariate)
random variables. This new approach combines the ability of Monte Carlo
simulation to handle high-dimensional problems with the efficiency of function
approximation. Specifically, we first generalize the recently developed method
for multivariate integration in [arXiv:1806.05492] to integration with respect
to probability measures. The method is based on the principle "approximate and
integrate" in three steps i) sample the integrand at points in the integration
domain, ii) approximate the integrand by solving a least-squares problem, iii)
integrate the approximate function. In high-dimensional applications we face
memory limitations due to large storage requirements in step ii). Combining
weighted sampling and the randomized extended Kaczmarz algorithm we obtain a
new efficient approach to solve large-scale least-squares problems. Our
convergence and cost analysis along with numerical experiments show the
effectiveness of the method in both low and high dimensions, and under the
assumption of a limited number of available simulations.
Bimodular continuous attractor neural networks with static and moving stimuli
Authors: Min Yan, Wen-Hao Zhang, He Wang, K. Y. Michael Wong
Date: 2019-10-16 00:00:00
Tasks: Causal Inference

We investigated the dynamical behaviors of bimodular continuous attractor neural networks, each processing a modality of sensory input and interacting with each other. We found that when bumps coexist in both modules, the position of each bump is shifted towards the other input when the intermodular couplings are excitatory and is shifted away when inhibitory. When one intermodular coupling is excitatory while another is moderately inhibitory, temporally modulated population spikes can be generated. On further increase of the inhibitory coupling, momentary spikes will emerge. In the regime of bump coexistence, bump heights are primarily strengthened by excitatory intermodular couplings, but there is a lesser weakening effect due to a bump being displaced from the direct input. When bimodular networks serve as decoders of multisensory integration, we extend the Bayesian framework to show that excitatory and inhibitory couplings encode attractive and repulsive priors, respectively. At low disparity, the bump positions decode the posterior means in the Bayesian framework, whereas at high disparity, multiple steady states exist. In the regime of multiple steady states, the less stable state can be accessed if the input causing the more stable state arrives after a sufficiently long delay. When one input is moving, the bump in the corresponding module is pinned when the moving stimulus is weak, unpinned at intermediate stimulus strength, and tracks the input at strong stimulus strength, and the stimulus strengths for these transitions increase with the velocity of the moving stimulus. These results are important to understanding multisensory integration of static and dynamic stimuli.
What Did Your Adversary Believe? Optimal Filtering and Smoothing in Counter-Adversarial Autonomous Systems
Date: 2019-10-16 00:00:00

We consider fixed-interval smoothing problems for counter-adversarial
autonomous systems. An adversary deploys an autonomous filtering and control
system that i) measures our current state via a noisy sensor, ii) computes a
posterior estimate (belief) and iii) takes an action that we can observe. Based
on such observed actions and our knowledge of our state sequence, we aim to
estimate the adversary's past and current beliefs -- this forms a foundation
for predicting, and counteracting against, future actions. We derive the
optimal smoother for the adversary's beliefs (we treat the problem in a
Bayesian framework). Moreover, we demonstrate how the smoother can be computed
for discrete systems even though the corresponding backward variables do not
admit a finite-dimensional characterization. Finally, we illustrate our results
in numerical simulations.
Variance State Propagation for Structured Sparse Bayesian Learning
Date: 2019-10-16 00:00:00
Tasks: compressed sensing

We propose a compressed sensing algorithm termed variance state propagation
(VSP) for block-sparse signals, i.e., sparse signals that have nonzero
coefficients occurring in clusters. The VSP algorithm is developed under the
Bayesian framework. A hierarchical Gaussian prior is introduced to depict the
clustered patterns in the sparse signal. Markov random field (MRF) is
introduced to characterize the state of the variances of the Gaussian priors.
Such a hierarchical prior has the potential to encourage clustered patterns and
suppress isolated coefficients whose patterns are different from their
respective neighbors. The core idea of our algorithm is to iteratively update
the variances in the prior Gaussian distribution. The message passing technique
is employed in the design of the algorithm. For messages that are difficult to
calculate, we correspondingly design reasonable methods to achieve approximate
calculations. The hyperparameters can be updated within the iteration process.
Simulation results demonstrate that the VSP algorithm is able to handle a
variety of block-sparse signal recovery tasks and presents a significant
advantage over the existing methods.