pwc-archive/papers-with-abstracts huggingface dataset:
A multilayer interstitial fluid flow along vascular adventitia
Authors: Hongyi Li, You Lv, Xiaoliang Chen, Bei Li, Qi Hua, Fusui Ji, Yajun Yin, Hua Li
Date: 2020-09-23 00:00:00

Objective: Interstitial fluid flow through vascular adventitia has been disclosed recently. However, its kinetic pattern was unclear. Methods and Results: We used histological and topographical identifications to observe ISF flow along venous vessels in rabbits. By MRI in alive subjects, the inherent ISF flow pathways in legs, abdomen and thorax were enhanced by paramagnetic contrast from ankle dermis. By fluorescence stereomicroscopy and layer-by-layer dissection after the rabbits were sacrificed, the perivascular and adventitial connective tissues (PACT) along the saphenous veins and inferior vena cava were found to be stained by sodium fluorescein from ankle dermis, which coincided with the findings by MRI. By confocal microscopy and histological analysis, the stained PACT pathways were verified to be the fibrous connective tissues and consisted of longitudinally assembled fibers. By usages of nanoparticles and surfactants, a PACT pathway was found to be accessible for a nanoparticle under 100nm and contain two parts: a tunica channel part and an absorptive part. In real-time observations, the calculated velocity of a continuous ISF flow along fibers of a PACT pathway was 3.6-15.6 mm/sec. Conclusion: These data further revealed more kinetic features of a continuous ISF flow along vascular vessel. A multiscale, multilayer, and multiform interstitial/interfacial fluid flow throughout perivascular and adventitial connective tissues was suggested as one of kinetic and dynamic mechanisms for ISF flow, which might be another principal fluid dynamic pattern besides convective/vascular and diffusive transport in biological system.
Modular Multilevel Converter with Sensorless Diode-Clamped Balancing through Level-Adjusted Phase-Shifted Modulation
Date: 2020-09-23 00:00:00

Cascaded H-bridge and modular multilevel converters (MMC) are on the rise
with emerging applications in renewable energy generation, energy storage, and
electric motor drives. However, their well-known advantages come at the price
of complicated balancing, high-bandwidth isolated monitoring, and numerous
sensors that can prevent MMCs from expanding into highly cost driven markets.
Therefore, an obvious trend in research is developing control and topologies
that depend less on measurements and benefit from simpler control.
Diode-clamped topologies are considered among the more applicable solutions.
The main problem with a diode-clamped topology is that it can only balance the
module voltages of a string in one direction; therefore, it cannot provide a
completely balanced operation. This paper proposes an effective balancing
technique for the diode-clamped topology. The proposed solution exploits the dc
component of the arm current by introducing a symmetrically level-adjusted
phase-shifted modulation scheme, and ensures the balancing current flow is
always in the correct direction. The main advantages of this method are
sensorless operation, no added computation and control effort, and low overall
cost. Analysis and detailed simulations provide insight into the operation of
the system as well as the new balancing technique and the experimental results
confirm the provided discussions.
Solutions for 400 Gbit/s Inter Data Center WDM Transmission
Authors: Annika Dochhan, Nicklas Eiselt, Helmut Grießer, Michael Eiselt, Juan José Vegas Olmos, Idelfonso Tafur Monroy, Jörg-Peter Elbers
Date: 2020-09-23 00:00:00

We review some currently discussed solutions for 400 Gbit/s inter-data center WDM transmission for up to 100 km. We focus on direct detected solutions, namely PAM4 and DMT, and present two WDM systems based on these formats.
56 Gb/s DMT Transmission with VCSELs in 1.5 um Wavelength Range over up to 12 km for DWDM Intra-Data Center Connects
Authors: Annika Dochhan, Nicklas Eiselt, Robert Hohenleitner, Helmut Griesser, Michael Eiselt, Markus Ortsiefer, Christian Neumeyr, Juan José Vegas Olmos, Idelfonso Tafur Monroy, Jörg-Peter Elbers
Date: 2020-09-23 00:00:00

We demonstrate up to 12 km, 56 Gb/s DMT transmission using high-speed VCSELs in the 1.5 um wavelength range for future 400Gb/s intra-data center connects, enabled by vestigial sideband filtering of the transmit signal.
Perceptual Video Quality Prediction Emphasizing Chroma Distortions
Date: 2020-09-24 00:00:00
Tasks: Prediction, Quantization

Measuring the quality of digital videos viewed by human observers has become
a common practice in numerous multimedia applications, such as adaptive video
streaming, quality monitoring, and other digital TV applications. Here we
explore a significant, yet relatively unexplored problem: measuring perceptual
quality on videos arising from both luma and chroma distortions from
compression. Toward investigating this problem, it is important to understand
the kinds of chroma distortions that arise, how they relate to luma compression
distortions, and how they can affect perceived quality. We designed and carried
out a subjective experiment to measure subjective video quality on both luma
and chroma distortions, introduced both in isolation as well as together.
Specifically, the new subjective dataset comprises a total of $210$ videos
afflicted by distortions caused by varying levels of luma quantization
commingled with different amounts of chroma quantization. The subjective scores
were evaluated by $34$ subjects in a controlled environmental setting. Using
the newly collected subjective data, we were able to demonstrate important
shortcomings of existing video quality models, especially in regards to chroma
distortions. Further, we designed an objective video quality model which builds
on existing video quality algorithms, by considering the fidelity of chroma
channels in a principled way. We also found that this quality analysis implies
that there is room for reducing bitrate consumption in modern video codecs by
creatively increasing the compression factor on chroma channels. We believe
that this work will both encourage further research in this direction, as well
as advance progress on the ultimate goal of jointly optimizing luma and chroma
compression in modern video encoders.
Disconnection-aware Attack Detection and Isolation with Separation-based Detector Reconfiguration
Authors: Hampei Sasahara, Takayuki Ishizaki, Jun-ichi Imura, Henrik Sandberg
Date: 2020-09-23 00:00:00

This study addresses incident handling during an adverse event for dynamical networked control systems. Incident handling can be divided into five steps: detection, analysis, containment, eradication, and recovery. For networked control systems, the containment step can be conducted through physical disconnection of an attacked subsystem. In accordance with the disconnection, the equipped attack detection unit should be reconfigured to maintain its detection capability. In particular, separating the detection subunit associated with the disconnected subsystem is considered as a specific reconfiguration scheme in this study. This paper poses the problem of disconnection-aware attack detection and isolation with the separation-based detector reconfiguration. The objective is to find an attack detection unit that preserves its detection and isolation capability even under any possible disconnection and separation. The difficulty arises from network topology variation caused by disconnection that can possibly lead to stability loss of the distributed observer inside the attack detection unit. A solution is proposed based on an existing controller design technique referred to as retrofit control. Furthermore, an application to low-voltage power distribution networks with distributed generation is exhibited. Numerical examples evidence the practical use of the proposed method through a benchmark distribution network.
Edge Intelligence Empowered UAVs for Automated Wind Farm Monitoring in Smart Grids
Date: 2020-09-23 00:00:00

With the exploitation of wind power, more turbines will be deployed at remote
areas possibly with harsh working conditions (e.g., offshore wind farm). The
adverse working environment may lead to massive operating and maintenance costs
of turbines. Deploying unmanned aerial vehicles (UAVs) for turbine inspection
is considered as a viable alternative to manual inspections. An important
objective of automated UAV inspection is to minimize the flight time of the
UAVs to inspect all the turbines. A first contribution of this paper is thus
formulating an optimization problem to compute the optimal routes for turbine
inspection satisfying the above goal. On the other hand, the limited
computational capability on UAVs can be used to increase the power generation
of wind turbine. Power generation from the turbines can be optimized by
controlling the yaw angle of the turbines. Forecasting wind conditions such as
wind speed and wind direction is crucial for solving both optimization
problems. Therefore, UAVs can utilize their limited computational capability to
perform wind forecasting. In this way, UAVs form edge intelligence in offshore
wind farm. With the forecasted wind conditions, we design two algorithms to
solve the formulated problems, and then evaluate the proposed methods with
realworld data. The results reveal that the proposed methods offer an
improvement of 44% of the power generation from the turbine compared to
hour-ahead forecasting and 25% reduction of the flight time of the UAVs
compared to the chosen baseline method.
Portfolio Optimization on Multivariate Regime Switching GARCH Model with Normal Tempered Stable Innovation
Authors: Cheng Peng, Young Shin Kim, Stefan Mittnik
Date: 2020-09-23 00:00:00
Tasks: Portfolio Optimization

This paper uses simulation-based portfolio optimization to mitigate the left tail risk of the portfolio. The contribution is twofold. (i) We propose the Markov regime-switching GARCH model with multivariate normal tempered stable innovation (MRS-MNTS-GARCH) to accommodate fat tails, volatility clustering and regime switch. The volatility of each asset independently follows the regime-switch GARCH model, while the correlation of joint innovation of the GARCH models follows the Hidden Markov Model. (ii) We use tail risk measures, namely conditional value-at-risk (CVaR) and conditional drawdown-at-risk (CDaR), in the portfolio optimization. The optimization is performed with the sample paths simulated by the MRS-MNTS-GARCH model. We conduct an empirical study on the performance of optimal portfolios. Out-of-sample tests show that the optimal portfolios with tail measures outperform the optimal portfolio with standard deviation measure and the equally weighted portfolio in various performance measures. The out-of-sample performance of the optimal portfolios is also more robust to suboptimality on the efficient frontier.
Fast Adaptation Nonlinear Observer for SLAM
Authors: Trevor P. Drayton, Abdul A. Jaiyeola, Nazmul Hoque, Mikhayla Maurer, Hashim A. Hashim
Date: 2020-09-23 00:00:00
Tasks: Position, Simultaneous Localization and Mapping

The process of simultaneously mapping the environment in three dimensional (3D) space and localizing a moving vehicle's pose (orientation and position) is termed Simultaneous Localization and Mapping (SLAM). SLAM is a core task in robotics applications. In the SLAM problem, each of the vehicle's pose and the environment are assumed to be completely unknown. This paper takes the conventional SLAM design as a basis and proposes a novel approach that ensures fast adaptation of the nonlinear observer for SLAM. Due to the fact that the true SLAM problem is nonlinear and is modeled on the Lie group of $\mathbb{SLAM}_{n}\left(3\right)$, the proposed observer for SLAM is nonlinear and modeled on $\mathbb{SLAM}_{n}\left(3\right)$. The proposed observer compensates for unknown bias attached to velocity measurements. The results of the simulation illustrate the robustness of the proposed approach.
Optimal Minimax Mobile Sensor Scheduling Over a Network
Date: 2020-09-23 00:00:00
Tasks: Scheduling

We investigate the problem of monitoring multiple targets using a single
mobile sensor, with the goal of minimizing the maximum estimation error among
all the targets over long time horizons. The sensor can move in a
network-constrained structure, where it has to plan which targets to visit and
for how long to dwell at each node. We prove that in an optimal observation
time allocation, the peak uncertainty is the same among all the targets. By
further restricting the agent policy to only visit each target once every
cycle, we develop a scheme to optimize the agent's behavior that is
significantly simpler computationally when compared to previous approaches for
similar problems.
Experimental Demonstration of 4,294,967,296-QAM Based Y-00 Quantum Stream Cipher Carrying 160-Gb/s 16-QAM Signals
Authors: Xi Chen, Ken Tanizawa, Peter Winzer, Po Dong, Junho Cho, Fumio Futami, Kentaro Kato, Argishti Melikyan, Kw Kim
Date: 2020-09-23 00:00:00

We demonstrate a 4,294,967,296-ary quadrature amplitude modulation (QAM) based Y-00 quantum stream cipher system carrying 160-Gb/s 16-QAM signal transmitted over 320-km SSMF. The ultra-dense QAM cipher template is realized by an integrated two-segment silicon photonics I/Q modulator.
A Research Review on Detection and Classification of Power Quality Disturbances caused by Integration of Renewable Energy Sources
Authors: Utkarsh Singh
Date: 2020-09-24 00:00:00
Tasks: Classification, General Classification

With the increased interest in integrating renewable energy sources (RES) such as wind power and solar into the power systems owing to their zero greenhouse gas emissions and the involvement of power converters for integration in grid, the detection, classification and mitigation of power quality events has become indispensable. For employing an appropriate mitigation technique, it is a pre-requisite to correctly classify the various types of disturbances in power quality. This paper, therefore, presents a detailed research reviews on detection and classification of power quality disturbances caused by the integration of renewable energy sources and associated works present in literature till date. Attempts are also made to highlight the current and future issues involved in the detection, classification and mitigation of PQ disturbances. Best efforts have been made to make this paper serve as a full-fledged reference for the future work in this field. A list of 230 research publications on the subject is also appended for quick reference.
Control Policies for Recovery of Interdependent Systems After Disruptions
Date: 2020-09-24 00:00:00

We examine a control problem where the states of the components of a system
deteriorate after a disruption, if they are not being repaired by an entity.
There exist a set of dependencies in the form of precedence constraints between
the components, captured by a directed acyclic graph (DAG). The objective of
the entity is to maximize the number of components whose states are brought
back to the fully repaired state within a given time. We prove that the general
problem is NP-hard, and therefore we characterize near-optimal control policies
for special instances of the problem. We show that when the deterioration rates
are larger than or equal to the repair rates and the precedence constraints are
given by a DAG, it is optimal to continue repairing a component until its state
reaches the fully recovered state before switching to repair any other
component. Under the aforementioned assumptions and when the deterioration and
the repair rates are homogeneous across all the components, we prove that the
control policy that targets the healthiest component at each time-step while
respecting the precedence and time constraints fully repairs at least half the
number of components that would be fully repaired by an optimal policy.
Finally, we prove that when the repair rates are sufficiently larger than the
deterioration rates, the precedence constraints are given by a set of disjoint
trees that each contain at most k nodes, and there is no time constraint, the
policy that targets the component with the least value of health minus the
deterioration rate at each time-step while respecting the precedence
constraints fully repairs at least 1/k times the number of components that
would be fully repaired by an optimal policy.
Deep Bayesian U-Nets for Efficient, Robust and Reliable Post-Disaster Damage Localization
Authors: Xiao Liang, Seyed Omid Sajedi
Date: 2020-09-24 00:00:00
Tasks: Image Segmentation, Management, Semantic Segmentation

Post-disaster inspections are critical to emergency management after earthquakes. The availability of data on the condition of civil infrastructure immediately after an earthquake is of great importance for emergency management. Stakeholders require this information to take effective actions and to better recover from the disaster. The data-driven SHM has shown great promises to achieve this goal in near real-time. There have been several proposals to automate the inspection process from different sources of input using deep learning. The existing models in the literature only provide a final prediction output, while the risks of utilizing such models for safety-critical assessments should not be ignored. This paper is dedicated to developing deep Bayesian U-Nets where the uncertainty of predictions is a second output of the model, which is made possible through Monte Carlo dropout sampling in test time. Based on a grid-like data structure, the concept of semantic damage segmentation (SDS) is revisited. Compared to image segmentation, it is shown that a much higher level of precision is necessary for damage diagnosis. To validate and test the proposed framework, a benchmark dataset, 10,800 nonlinear response history analyses on a 10-story-10-bay 2D reinforced concrete moment frame, is utilized. Compared to the benchmark SDS model, Bayesian models exhibit superior robustness with enhanced global and mean class accuracies. Finally, the model's uncertainty output is studied by monitoring the softmax class variance of different predictions. It is shown that class variance correlates well with locations where the model makes mistakes. This output can be used in combination with the prediction results to increase the reliability of this data-driven framework in structural inspections.
Intelligent Reflecting Surface Enhanced Indoor Robot Path Planning Using Radio Maps
Authors: Xidong Mu, Yuanwei Liu, Li Guo, Jiaru Lin, Robert Schober
Date: 2020-09-24 00:00:00
Tasks: Robot Navigation

An indoor robot navigation system is investigated, where an intelligent reflecting surface (IRS) is employed to enhance the connectivity between the access point (AP) and a mobile robotic user. The considered system is optimized for minimization of the travelling time/distance of the mobile robotic user from a given starting point to a predefined final location, while satisfying constraints on the communication quality. To tackle this problem, a radio map based approach is proposed to exploit location-dependent channel propagation knowledge. Specifically, a channel power gain map is constructed, which characterizes the spatial distribution of the maximum expected effective channel power gain of the mobile robotic user for the optimal IRS phase shifts. Based on the obtained channel power gain map, the communication-aware robot path planing problem is solved as a shortest path problem by exploiting graph theory. Numerical results show that: 1) Deploying an IRS can significantly extend the coverage of the AP and reduce the travelling distance of the mobile robotic user; 2) 2- or 3-bit IRS phase shifters can achieve nearly the same performance as continuous IRS phase shifters.
Complex Convolutional Neural Networks for Ultrafast Ultrasound Image Reconstruction from In-Phase/Quadrature Signal
Authors: Jingfeng Lu, Fabien Millioz, Damien Garcia, Sebastien Salles, Dong Ye, Denis Friboulet
Date: 2020-09-24 00:00:00
Tasks: Image Reconstruction

Ultrafast ultrasound imaging remains an active area of interest in the ultrasound community due to its ultra-high frame rates. Recently, a wide variety of studies based on deep learning have sought to improve ultrafast ultrasound imaging. Most of these approaches have been performed on radio frequency (RF) signals. However, inphase/quadrature (I/Q) digital beamformers are now widely used as low-cost strategies. In this work, we used complex convolutional neural networks for reconstruction of ultrasound images from I/Q signals. We recently described a convolutional neural network architecture called ID-Net, which exploited an inception layer designed for reconstruction of RF diverging-wave ultrasound images. In the present study, we derive the complex equivalent of this network; i.e., the Complex-valued Inception for Diverging-wave Network (CID-Net) that operates on I/Q data. We provide experimental evidence that CID-Net provides the same image quality as that obtained from RF-trained convolutional neural networks; i.e., using only three I/Q images, the CID-Net produces high-quality images that can compete with those obtained by coherently compounding 31 RF images. Moreover, we show that CID-Net outperforms the straightforward architecture that consists of processing the real and imaginary parts of the I/Q signal separately, which thereby indicates the importance of consistently processing the I/Q signals using a network that exploits the complex nature of such signals.
Genome-wide association and transcriptome analysis reveals serum ghrelin to be linked with GFRAL
Authors: Dirk Alexander Wittekind, Markus Scholz, Jürgen Kratzsch, Markus Löffler, Katrin Horn, Holger Kirsten, Veronica Witte, Arno Villringer, Michael Kluge
Date: 2020-09-24 00:00:00

Objective: Ghrelin is an orexigenic peptide hormone involved in the regulation of energy homeostasis, food intake and glucose metabolism. Serum levels increase anticipating a meal and fall afterwards. Underlying genetic mechanisms of the ghrelin secretion are unknown. Methods: Total serum ghrelin was measured in 1501 subjects selected from the population-based LIFE-ADULT-sample after an overnight fast. A genome-wide association study (GWAS) was performed. Gene-based expression association analyses (transcriptome-wide association study (TWAS)) were done using MetaXcan. Results: In the GWAS, three loci reached genome-wide significance: the WW-domain containing the oxidoreductase-gene (WWOX; p=1.80E-10) on chromosome 16q23.3-24.1 (SNP: rs76823993); the Contactin-Associated Protein-Like 2 gene (CNTNAP2; p=9.0E-9) on chromosome 7q35-q36 (SNP: rs192092592) and the Ghrelin And Obestatin Prepropeptide gene (GHRL; p=2.72E-8) on chromosome 3p25.3 (SNP: rs143729751). In the TWAS, serum ghrelin was negatively associated with RNA expression of the GDNF Family Receptor Alpha Like (GFRAL), receptor of the anorexigenic Growth Differentiation Factor-15 (GDF15), (z-score=-4.288, p=1.81E-05). Furthermore, ghrelin was positively associated with Ribosomal Protein L36 (RPL36; z-score=4.848, p=1.25E-06). Conclusions: Our findings provide evidence of a functional link between two major players of weight regulation, the ghrelin system and the GDF15/GFRAL-pathway.
A note on the impact of news on US household inflation expectations
Authors: Ben Zhe Wang, Jeffrey Sheen, Stefan Trück, Shih-Kang Chao, Wolfgang Karl Härdle
Date: 2020-09-24 00:00:00

Monthly disaggregated US data from 1978 to 2016 reveals that exposure to news on inflation and monetary policy helps to explain inflation expectations. This remains true when controlling for household personal characteristics, perceptions of government policy effectiveness, future interest rates and unemployment expectations, and sentiment. We find an asymmetric impact of news on inflation and monetary policy after 1983, with news on rising inflation and easier monetary policy having a stronger effect in comparison to news on lowering inflation and tightening monetary policy. Our results indicate the impact on inflation expectations of monetary policy news manifested through consumer sentiment during the lower bound period.