pwc-archive/papers-with-abstracts huggingface dataset:
Integration of stationary wavelet transform on a dynamic partial reconfiguration: case study separating preictal gamma oscillations from transitory activities for early build up epileptic seizure
Date: 2019-11-16 00:00:00

To define the neural networks responsible of the epileptic seizure, we had to
study the electrophysiological signal in a proper way. The early recognition of
the seizure build up could also be defined through the time space mapping of
the preictal gamma oscillations. The electrophysiological signals present three
types of wave: oscillations, spikes, and a mixture of both. Recent studies
prove that spikes and oscillations should be separated efficiently to define
the accurate neural connectivity for each activity. However retrieving the
transitory activity is a sensitive task due to the frequency interfering
between the gamma oscillatory and the transitory activities. Many filtering
techniques are highlighted to ensure a good separation: reducing false
oscillations in the transitory activity and vis versa. However and for a big
data set, this separation necessitate a big consumption in time execution, this
constraint will be overcome using embedded architecture. The integration of
these filtering techniques would also lead to creating instantaneous monitoring
of the seizure recognition, build up and neurofeedback devices. We propose here
to implement the stationary wavelet transform as a convenient filtering
technique to keep only the preictal gamma oscillations on a partial dynamic
configuration, then we will use the same architecture to integrate the time
space mapping for an early recognition of the build up seizure. We proved a
faster recognition of the build up seizure through the non contaminated
preictal gamma oscillations time space mapping (about 40 times faster),
obtained by the integration of the wavelet transform.
Evaluation of techniques for predicting seizure Build up
Date: 2019-11-16 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

The analysis of electrophysiological signal of scalp: EEG
(electroencephalography), MEG (magnetoencephalography) and depth (intracerebral
EEG) IEEG is a way to delimit epileptogenic zone (EZ). These epileptic signals
present two different activities (oscillations and spikes) which can be
overlapped in the time frequency plane. Automatic recognition of epileptic
seizure occurrence needs several preprocessing steps. In this study, we
evaluated two filtering techniques: the stationary wavelet transforms (SWT) and
the Despikifying in order to extract pre ictal gamma oscillations (bio markers
of seizure build up). Then, we used a temporal basis set of Jmail et al 2017 as
a preprocessing step to evaluate the performance of both technique. Moreover,
we used time-frequency and spatio-temporal mapping of simulated and real data
for both techniques in order to predict seizure build up (in time and space).
We concluded that SWT can detect the oscillations, but Despikyfying is more
robust than SWT in reconstructing pure pre ictal gamma oscillations and hence
in predicting seizure build up.
Causal Inference Under Approximate Neighborhood Interference
Authors: Michael P. Leung
Date: 2019-11-16 00:00:00
Tasks: Causal Inference

This paper studies causal inference in randomized experiments under network interference. Commonly used models of interference posit that treatments assigned to alters beyond a certain network distance from the ego have no effect on the ego's response. However, this assumption is violated in common models of social interactions. We propose a substantially weaker model of "approximate neighborhood interference" (ANI) under which treatments assigned to alters further from the ego have a smaller, but potentially nonzero, effect on the ego's response. We formally verify that ANI holds for well-known models of social interactions. Under ANI, restrictions on the network topology, and asymptotics under which the network size increases, we prove that standard inverse-probability weighting estimators consistently estimate useful exposure effects and are approximately normal. For inference, we consider a network HAC variance estimator. Under a finite population model, we show that the estimator is biased but that the bias can be interpreted as the variance of unit-level exposure effects. This generalizes Neyman's well-known result on conservative variance estimation to settings with interference.
Distributionally Robust Optimal Auction Design under Mean Constraints
Authors: Ethan Che
Date: 2019-11-16 00:00:00

We study a seller who sells a single good to multiple bidders with uncertainty over the joint distribution of bidders' valuations, as well as bidders' higher-order beliefs about their opponents. The seller only knows the (possibly asymmetric) means of the marginal distributions of each bidder's valuation and the range. An adversarial nature chooses the worst-case distribution within this ambiguity set along with the worst-case information structure. We find that a second-price auction with a symmetric, random reserve price obtains the optimal revenue guarantee within a broad class of mechanisms we refer to as competitive mechanisms, which include standard auction formats, including the first-price auction, with or without reserve prices. The optimal mechanism possesses two notable characteristics. First, the mechanism treats all bidders identically even in the presence of ex-ante asymmetries. Second, when bidders are identical and the number of bidders $n$ grows large, the seller's optimal reserve price converges in probability to a non-binding reserve price and the revenue guarantee converges to the best possible revenue guarantee at rate $O(1/n)$.
Inference in Models of Discrete Choice with Social Interactions Using Network Data
Date: 2019-11-16 00:00:00

This paper studies inference in models of discrete choice with social
interactions when the data consists of a single large network. We provide
theoretical justification for the use of spatial and network HAC variance
estimators in applied work, the latter constructed by using network path
distance in place of spatial distance. Toward this end, we prove new central
limit theorems for network moments in a large class of social interactions
models. The results are applicable to discrete games on networks and dynamic
models where social interactions enter through lagged dependent variables. We
illustrate our results in an empirical application and simulation study.
Compressed Channel Estimation and Joint Beamforming for Intelligent Reflecting Surface-Assisted Millimeter Wave Systems
Authors: Peilan Wang, Jun Fang, Huiping Duan, Hongbin Li
Date: 2019-11-17 00:00:00

In this paper, we consider channel estimation for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) systems, where an IRS is deployed to assist the data transmission from the base station (BS) to a user. It is shown that for the purpose of joint active and passive beamforming, the knowledge of a large-size cascade channel matrix needs to be acquired. To reduce the training overhead, the inherent sparsity in mmWave channels is exploited. By utilizing properties of Katri-Rao and Kronecker products, we find a sparse representation of the cascade channel and convert cascade channel estimation into a sparse signal recovery problem. Simulation results show that our proposed method can provide an accurate channel estimate and achieve a substantial training overhead reduction.
Scan-specific, Parameter-free Artifact Reduction in K-space (SPARK)
Date: 2019-11-17 00:00:00

We propose a convolutional neural network (CNN) approach that works
synergistically with physics-based reconstruction methods to reduce artifacts
in accelerated MRI. Given reconstructed coil k-spaces, our network predicts a
k-space correction term for each coil. This is done by matching the difference
between the acquired autocalibration lines and their erroneous reconstructions,
and generalizing this error term over the entire k-space. Application of this
approach on existing reconstruction methods show that SPARK suppresses
reconstruction artifacts at high acceleration, while preserving and improving
on detail in moderate acceleration rates where existing reconstruction
algorithms already perform well; indicating robustness. Introduction Parallel
An Analytical Strategy for Passive Harmonic Filter Placement in Transmission Systems with Stochastic Aggregate Load Models Considering Resonant Conditions
Date: 2019-11-17 00:00:00

High percentage of voltage harmonics has been observed in transmission
networks due to harmonic currents penetrated from the load side amplified by
resonant conditions. This requires the use of suitable harmonic filters in
transmission networks. However, filter placement in a transmission system for
harmonic mitigation is a planning procedure of high complexity. Since filter
placement discounting the overall system response may deteriorate harmonic
conditions at remote buses due to frequency characteristics alteration. The
stochastic behavior of harmonic loads demands adequate models and calculations.
The interlinked topology of a transmission system also necessitates an
extensive system-wide study. This paper presents an analytical methodology for
determining the appropriate number, location, and parameters of C-Type passive
filters in a transmission system to improve the overall harmonic condition. The
proposed strategy targets minimizing the expected value of a system-wide index
given the stochastic behavior of loads, while limiting the 95th percentiles of
voltage distortions to the standard levels prescribed by IEEE Std. 519 and
satisfying the fundamental power flow constraints. A restricted hierarchical
direct search algorithm is utilized to find the near global solution whose
subsets are also effective. The performance of the proposed approach is
evaluated on the IEEE 118-bus test system and its validity is checked by Monte
Carlo simulations and compared with harmonic modal analysis.
Deep Learning for the Digital Pathologic Diagnosis of Cholangiocarcinoma and Hepatocellular Carcinoma: Evaluating the Impact of a Web-based Diagnostic Assistant
Date: 2019-11-18 00:00:00
Tasks: Diagnostic

While artificial intelligence (AI) algorithms continue to rival human
performance on a variety of clinical tasks, the question of how best to
incorporate these algorithms into clinical workflows remains relatively
unexplored. We investigated how AI can affect pathologist performance on the
task of differentiating between two subtypes of primary liver cancer,
hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC). We developed an AI
diagnostic assistant using a deep learning model and evaluated its effect on
the diagnostic performance of eleven pathologists with varying levels of
expertise. Our deep learning model achieved an accuracy of 0.885 on an internal
validation set of 26 slides and an accuracy of 0.842 on an independent test set
of 80 slides. Despite having high accuracy on a hold out test set, the
diagnostic assistant did not significantly improve performance across
pathologists (p-value: 0.184, OR: 1.287 (95% CI 0.886, 1.871)). Model
correctness was observed to significantly bias the pathologist decisions. When
the model was correct, assistance significantly improved accuracy across all
pathologist experience levels and for all case difficulty levels (p-value: <
0.001, OR: 4.289 (95% CI 2.360, 7.794)). When the model was incorrect,
assistance significantly decreased accuracy across all 11 pathologists and for
all case difficulty levels (p-value < 0.001, OR: 0.253 (95% CI 0.126, 0.507)).
Our results highlight the challenges of translating AI models to the clinical
setting, especially for difficult subspecialty tasks such as tumor
classification. In particular, they suggest that incorrect model predictions
could strongly bias an expert's diagnosis, an important factor to consider when
designing medical AI-assistance systems.
FFDNet-Based Channel Estimation for Massive MIMO Visible Light Communication Systems
Date: 2019-11-18 00:00:00
Tasks: Denoising, Image Denoising

Channel estimation is of crucial importance in massive multiple-input
multiple-output (m-MIMO) visible light communication (VLC) systems. In order to
tackle this problem, a fast and flexible denoising convolutional neural network
(FFDNet)-based channel estimation scheme for m-MIMO VLC systems was proposed.
The channel matrix of the m-MIMO VLC channel is identified as a two-dimensional
natural image since the channel has the characteristic of sparsity. A deep
learning-enabled image denoising network FFDNet is exploited to learn from a
large number of training data and to estimate the m-MIMO VLC channel.
Simulation results demonstrate that our proposed channel estimation based on
the FFDNet significantly outperforms the benchmark scheme based on minimum mean
square error.
Ultra-Fast Accurate AoA Estimation via Automotive Massive-MIMO Radar
Authors: Bin Li, Shuseng Wang, Jun Zhang, Xainbin Cao, Chenglin Zhao
Date: 2019-11-18 00:00:00
Tasks: subspace methods

Massive multiple-input multiple-output (MIMO) radar, enabled by millimeter-wave virtual MIMO techniques, provides great promises to the high-resolution automotive sensing and target detection in unmanned ground/aerial vehicles (UGA/UAV). As a long-established problem, however, existing subspace methods suffer from either high complexity or low accuracy. In this work, we propose two efficient methods, to accomplish fast subspace computation and accurate angle of arrival (AoA) acquisition. By leveraging randomized low-rank approximation, our fast multiple signal classification (MUSIC) methods, relying on random sampling and projection techniques, substantially accelerate the subspace estimation by orders of magnitude. Moreover, we establish the theoretical bounds of our proposed methods, which ensure the accuracy of the approximated pseudo-spectrum. As demonstrated, the pseudo-spectrum acquired by our fast-MUSIC would be highly precise; and the estimated AoA is almost as accurate as standard MUSIC. In contrast, our new methods are tremendously faster than standard MUSIC. Thus, our fast-MUSIC enables the high-resolution real-time environmental sensing with massive MIMO radars, which has great potential in the emerging unmanned systems.
Sizing and Allocation of Distributed Energy Resources for Loss Reduction using Heuristic Algorithms
Date: 2020-05-21 00:00:00

Loss minimization in distribution networks (DN) is of great significance
since the trend to the distributed generation (DG) requires the most efficient
operating scenario possible for economic viability variations. Moreover,
voltage instability in DNs is a critical phenomenon and can lead to a major
blackout in the system. The decreasing voltage stability level restricts the
increase of load served by distribution companies. DG can be used to improve DN
capabilities and brings new opportunities to traditional DNs. However,
installation of DG in non-optimal places can result in an increase in system
losses, voltage problems, etc. In this paper, genetic algorithm (GA), harmony
search algorithm (HSA) and improved HSA have been applied to determine the
optimal location of DGs. Simulation results for an IEEE 33 bus network are
compared for different algorithms, and the best algorithm is stated for minimum
losses.
Bayesian Filtering for Multi-period Mean-Variance Portfolio Selection
Authors: Shubhangi Sikaria, Rituparna Sen, Neelesh S. Upadhye
Date: 2019-11-18 00:00:00
Tasks: Portfolio Optimization, Time Series, Time Series Analysis

For a long investment time horizon, it is preferable to rebalance the portfolio weights at intermediate times. This necessitates a multi-period market model in which portfolio optimization is usually done through dynamic programming. However, this assumes a known distribution for the parameters of the financial time series. We consider the situation where this distribution is unknown and needs to be estimated from the data that is arriving dynamically. We applied Bayesian filtering through dynamic linear models to sequentially update the parameters. We considered uncertain investment lifetime to make the model more adaptive to the market conditions. These updated parameters are put into the dynamic mean-variance problem to arrive at optimal efficient portfolios. Extensive simulations are conducted to study the effect of varying underlying parameters and investment horizon on the performance of the method. An implementation of this model to the S&P500 illustrates that the Bayesian updating is strongly favored by the data and that it is practically implementable.
Optimal Control over Multiple Input Lossy Channels
Authors: William Casbolt, Bryn Jones, Iñaki Esnaola
Date: 2019-11-18 00:00:00

The performance of control systems with input packet losses on the controller to plant communication channel is analysed. The main contribution of this work is a proof that linear optimal control systems operating with UDP-like communication protocols have a larger quadratic cost than the same systems operating with TCP-like protocols. The proof is derived for the general case of multidimensional and independent actuation communication channels. In doing so, our results extend previous work to systems with multiple distributed actuators. The difference in cost between two communication protocols is analysed, enabling the maximal difference between the two protocols to be quantified. Numerical examples are presented to highlight the difference in costs induced by the choice of communication protocol.
Characterization of Intact Eukaryotic Cells with Subcellular Spatial Resolution by Photothermal-Induced Resonance Infrared Spectroscopy and Imaging
Date: 2019-11-18 00:00:00

Photothermal-Induced Resonance (PTIR) spectroscopy and imaging with infrared
light has seen increasing application in molecular spectroscopy of biological
samples. The appeal of the technique lies in its capability to provide
information about IR light absorption at a spatial resolution better than
allowed by light diffraction, typically below 100 nm. In the present work we
test the capability of the technique to perform measurements with subcellular
resolution on intact eukaryotic cells, without drying or fixing. We demonstrate
the possibility to obtain PTIR images and spectra from the nucleus and multiple
organelles with high resolution. We obtain particularly strong signal from
bands typically assigned to acyl lipids and proteins. We also show that while a
stronger signal is obtained from some subcellular structures, other large
subcellular components provide a weaker or undetectable PTIR response. The
mechanism that underlies such variability in response is presently unclear. We
propose and discuss different possibilities, addressing thermomechanical,
geometrical and electrical properties of the sample and the presence of
cellular water, from which the difference in response may arise.
One-Bit Sigma-Delta modulation on the circle
Date: 2019-11-14 00:00:00
Tasks: Quantization

Manifold models in data analysis and signal processing have become more
prominent in recent years. In this paper, we will look at one of the main tasks
of modern signal processing, namely, at analog-to-digital (A/D) conversion in
connection with a simple manifold model - the circle. We will focus on
Sigma-Delta modulation which is a popular method for A/D conversion of
bandlimited signals that employs coarse quantization coupled with oversampling.
Classical Sigma-Delta schemes provide mismatches and large errors at the
initialization point if the signal to be converted is defined on the circle. In
this paper, our goal is to get around these problems for Sigma-Delta schemes.
Our results show how to design an update for the first and the second order
schemes based on the reconstruction error analysis such that for the updated
scheme the reconstruction error is improved.
Energy-Efficient MIMO Multiuser Systems: Nash Equilibrium Analysis
Date: 2019-11-18 00:00:00

In this paper, an energy efficiency (EE) game in a MIMO multiple access
channel (MAC) communication system is considered. The existence and the
uniqueness of the Nash Equilibrium (NE) is affirmed. A bisection search
algorithm is designed to find this unique NE. Despite being sub-optimal for
deploying the $\varepsilon$-approximate NE of the game when the number of
antennas in transmitter is unequal to receiver's, the policy found by the
proposed algorithm is shown to be more efficient than the classical allocation
techniques. Moreover, compared to the general algorithm based on fractional
programming technique, our proposed algorithm is easier to implement.
Simulation shows that even the policy found by proposed algorithm is not the NE
of the game, the deviation w.r.t. to the exact NE is small and the resulted
policy actually Pareto-dominates the unique NE of the game at least for 2-user
situation.
Renewal reward perspective on linear switching diffusion systems
Authors: Maria-Veronica Ciocanel, John Fricks, Peter R. Kramer, Scott A. McKinley
Date: 2019-11-18 00:00:00

In many biological systems, the movement of individual agents is commonly characterized as having multiple qualitatively distinct behaviors that arise from various biophysical states. This is true for vesicles in intracellular transport, micro-organisms like bacteria, or animals moving within and responding to their environment. For example, in cells the movement of vesicles, organelles and other cargo are affected by their binding to and unbinding from cytoskeletal filaments such as microtubules through molecular motor proteins. A typical goal of theoretical or numerical analysis of models of such systems is to investigate the effective transport properties and their dependence on model parameters. While the effective velocity of particles undergoing switching diffusion is often easily characterized in terms of the long-time fraction of time that particles spend in each state, the calculation of the effective diffusivity is more complicated because it cannot be expressed simply in terms of a statistical average of the particle transport state at one moment of time. However, it is common that these systems are regenerative, in the sense that they can be decomposed into independent cycles marked by returns to a base state. Using decompositions of this kind, we calculate effective transport properties by computing the moments of the dynamics within each cycle and then applying renewal-reward theory. This method provides a useful alternative large-time analysis to direct homogenization for linear advection-reaction-diffusion partial differential equation models. Moreover, it applies to a general class of semi-Markov processes and certain stochastic differential equations that arise in models of intracellular transport. Applications of the proposed framework are illustrated for case studies such as mRNA transport in developing oocytes and processive cargo movement by teams of motor proteins.
Data-driven Linear Quadratic Regulation via Semidefinite Programming
Date: 2020-08-12 00:00:00

This paper studies the finite-horizon linear quadratic regulation problem
where the dynamics of the system are assumed to be unknown and the state is
accessible. Information on the system is given by a finite set of input-state
data, where the input injected in the system is persistently exciting of a
sufficiently high order. Using data, the optimal control law is then obtained
as the solution of a suitable semidefinite program. The effectiveness of the
approach is illustrated via numerical examples.
Optimal Search and Discovery
Authors: Rafael P. Greminger
Date: 2019-11-18 00:00:00

This paper studies a search problem where a consumer is initially aware of only a few products. At every point in time, the consumer then decides between searching among alternatives he is already aware of and discovering more products. I show that the optimal policy for this search and discovery problem is fully characterized by tractable reservation values. Moreover, I prove that a predetermined index fully specifies the purchase decision of a consumer following the optimal search policy. Finally, a comparison highlights differences to classical random and directed search.