pwc-archive/papers-with-abstracts huggingface dataset:
How Safe are European Safe Bonds? An Analysis from the Perspective of Modern Portfolio Credit Risk Models
Authors: Rüdiger Frey, Kevin Kurt, Camilla Damian
Date: 2020-01-30 00:00:00

Several proposals for the reform of the euro area advocate the creation of a market in synthetic securities backed by portfolios of sovereign bonds. Most debated are the so-called European Safe Bonds or ESBies proposed by Brunnermeier, Langfield, Pagano,Reis, Van Nieuwerburgh and Vayanos (2017). The potential benefits of ESBies and other bond-backed securities hinge on the assertion that these products are really safe. In this paper we provide a comprehensive quantitative study of the risks associated with ESBies and related products, using an affine credit risk model with regime switching as vehicle for our analysis. We discuss a recent proposal of Standard and Poors for the rating of ESBies, we analyse the impact of model parameters and attachment points on the size and the volatility of the credit spread of ESBies and we consider several approaches to assess the market risk of ESBies. Moreover, we compare ESBies to synthetic securities created by pooling the senior tranche of national bonds as suggested by Leandro and Zettelmeyer(2019). The paper concludes with a brief discussion of the policy implications from our analysis.
The Impact of Oil and Gold Prices Shock on Tehran Stock Exchange: A Copula Approach
Date: 2020-01-30 00:00:00

There are several researches that deal with the behavior of SEs and their
relationships with different economical factors. These range from papers
dealing with this subject through econometrical procedures to statistical
methods known as copula. This article considers the impact of oil and gold
price on Tehran Stock Exchange market (TSE). Oil and gold are two factors that
are essential for the economy of Iran and their price are determined in the
global market. The model used in this study is ARIMA-Copula. We used data from
January 1998 to January 2011 as training data to find the appropriate model.
The cross validation of model is measured by data from January 2011 to June
2011. We conclude that: (i) there is no significant direct relationship between
gold price and the TSE index, but the TSE is indirectly influenced by gold
price through other factors such as oil; and (ii) the TSE is not independent of
the volatility in oil price and Clayton copula can describe such dependence
structure between TSE and the oil price. Based on the property of Clayton
copula, which has lower tail dependency, as the oil price drops, stock index
falls. This means that decrease in oil price has an adverse effect on Iranian
economy.
A Rule-Based EEG Classification System for Discrimination of Hand Motor Attempts in Stroke Patients
Authors: Xiaotong Gu, Zehong Cao
Date: 2020-01-30 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), General Classification

Stroke patients have symptoms of cerebral functional disturbance that could aggressively impair patient's physical mobility, such as freezing of hand movements. Although rehabilitation training from external devices is beneficial for hand movement recovery, for initiating motor function restoration purposes, there are still valuable research merits for identifying the side of hands in motion. In this preliminary study, we used electroencephalogram (EEG) datasets from 8 stroke patients, with each subject involving 40 EEG trials of left motor attempts and 40 EEG trials of right motor attempts. Then, we proposed a rule-based EEG classification system for identifying the side in motion for stroke patients. In specific, we extracted 1-50 Hz power spectral features as input features of a series of well-known classification models. The predicted labels from these classification models were measured by four types of rules, which determined the finalised predicted label. Our experiment results showed that our proposed rule-based EEG classification system achieved $99.83 \pm 0.42 \% $ accuracy, $ 99.98 \pm 0.13\% $ precision, $ 99.66 \pm 0.84 \% $ recall, and $ 99.83 \pm 0.43\% $ f-score, which outperformed the performance of single well-known classification models. Our findings suggest that the superior performance of our proposed rule-based EEG classification system has the potential for hand rehabilitation in stroke patients.
Effects of Inertia, Load Damping and Dead-Bands on Frequency Histograms and Frequency Control of Power Systems
Authors: Davide del Giudice, Angelo Brambilla, Samuele Grillo, Federico Bizzarri
Date: 2020-01-28 00:00:00

The increasing penetration of renewable energy sources has been leading to the progressive phase-out of synchronous generators, which constitute the main source of frequency stability for electric power systems. In the light of these changes, over the past years, some power systems started to exhibit an odd frequency distribution characterised by a bimodal behavior. This results in increased wear and tear of turbine governors and, in general, in degraded frequency performances. This is a cause of concern for grid operators, which have become increasingly interested in understanding the factors shaping frequency distribution. This paper explores the root causes of unwanted frequency distributions. The influence of some main aggregate system parameters on frequency distribution is detailed. The paper also shows that the implementation of the so-called synthetic inertia can lead to a robust unimodal frequency distribution.
Modeling bursting in neuronal networks using facilitation-depression and afterhyperpolarization
Authors: Lou Zonca, David Holcman
Date: 2020-01-30 00:00:00

In the absence of inhibition, excitatory neuronal networks can alternate between bursts and interburst intervals (IBI), with heterogeneous length distributions. As this dynamic remains unclear, especially the durations of each epoch, we develop here a bursting model based on synaptic depression and facilitation that also accounts for afterhyperpolarization (AHP), which is a key component of IBI. The framework is a novel stochastic three dimensional dynamical system perturbed by noise: numerical simulations can reproduce a succession of bursts and interbursts. Each phase corresponds to an exploration of a fraction of the phase-space, which contains three critical points (one attractor and two saddles) separated by a two-dimensional stable manifold $\Sigma$. We show here that bursting is defined by long deterministic excursions away from the attractor, while IBI corresponds to escape induced by random fluctuations. We show that the variability in the burst durations, depends on the distribution of exit points located on $\Sigma$ that we compute using WKB and the method of characteristics. Finally, to better characterize the role of several parameters such as the network connectivity or the AHP time scale, we compute analytically the mean burst and AHP durations in a linear approximation. To conclude the distribution of bursting and IBI could result from synaptic dynamics modulated by AHP.
Guiding Cascading Failure Search with Interpretable Graph Convolutional Network
Date: 2020-01-30 00:00:00

Power system cascading failures become more time variant and complex because
of the increasing network interconnection and higher renewable energy
penetration. High computational cost is the main obstacle for a more frequent
online cascading failure search, which is essential to improve system security.
In this work, we show that the complex mechanism of cascading failures can be
well captured by training a graph convolutional network (GCN) offline.
Subsequently, the search of cascading failures can be significantly accelerated
with the aid of the trained GCN model. We link the power network topology with
the structure of the GCN, yielding a smaller parameter space to learn the
complex mechanism. We further enable the interpretability of the GCN model by a
layer-wise relevance propagation (LRP) algorithm. The proposed method is tested
on both the IEEE RTS-79 test system and China's Henan Province power system.
The results show that the GCN guided method can not only accelerate the search
of cascading failures, but also reveal the reasons for predicting the potential
cascading failures.
Computational Fluid Dynamic Simulations In a Model of a Carotid Bifurcation Under Steady Flow Conditions
Date: 2020-01-16 00:00:00

Strokes are still one of the leading causes for death after heart diseases
and cancer in all over the world. Most strokes happen because an artery that
carries blood uphill from the heart to the head gets clogged. Most of the time,
as with heart attacks, the problem is atherosclerosis, hardening of the
arteries, calcified buildup of fatty deposits on the vessel wall. The primary
troublemaker is the carotid artery, one on each side of the neck, the main
thoroughfare for blood to the brain. Only within the last 25 years, though,
have researchers been able to put their finger on why the carotid is especially
susceptible to atherosclerosis. In this study, the fluid dynamic simulations
were done in a carotid bifurcation under the steady flow conditions
computationally. In vivo geometry and boundary conditions were obtained from a
diseased who has both sides of stenosis located in his carotid artery patients.
The location of critical flow fields such as low wall shear stress (WSS),
stagnation regions and separation regions were detected. Low WSS was found at
the downstream of the bifurcation.
Safe Trajectory Tracking in Uncertain Environments
Authors: Ivo Batkovic, Mohammad Ali, Paolo Falcone, Mario Zanon
Date: 2020-01-30 00:00:00
Tasks: Model Predictive Control

In Model Predictive Control (MPC) formulations of trajectory tracking problems, infeasible reference trajectories and a-priori unknown constraints can lead to cumbersome designs, aggressive tracking, and loss of recursive feasibility. This is the case, for example, in trajectory tracking applications for mobile systems in the presence of constraints which are not fully known a-priori. In this paper, we propose a new framework called Model Predictive Flexible trajectory Tracking Control (MPFTC), which relaxes the trajectory tracking requirement. Additionally, we accommodate recursive feasibility in the presence of a-priori unknown constraints, which might render the reference trajectory infeasible. In the proposed framework, constraint satisfaction is guaranteed at all times while the reference trajectory is tracked as good as constraint satisfaction allows, thus simplifying the controller design and reducing possibly aggressive tracking behavior. The proposed framework is illustrated with three numerical examples.
Geometry based Stochastic Channel Modeling using Ambit Processes
Date: 2020-01-31 00:00:00

The simulation of vehicular wireless channels using geometry-based radio
channel models is computationally intensive when the number of scatterers is
significantly high. In this paper, we propose a new geometry-based stochastic
channel model to simulate and analyze the aforementioned channels based on a
framework developed from the theory of ambit processes. Under reasonable
assumptions, the underlying mathematical structure of the proposed channel
model enables the characterization of high mobility channels in terms of fading
statistics, spatio-temporal channel correlation, and Doppler spectrum, besides
ensuring tractable analysis. The developed algorithm facilitates fast
simulation of high mobility channels and accounts for key features of vehicular
channels including appearance and disappearance of multi-path components,
spatial consistency, and captures the correlation between time-evolving delay
and Doppler associated with multi-path components. Finally, we carry out
simulations to obtain crucial insights about the characteristics of typical
vehicle-to-infrastructure channels based on the proposed channel model.
Latency Minimization with Optimum Workload Distribution and Power Control for Fog Computing
Date: 2020-01-31 00:00:00
Tasks: Cloud Computing

This paper investigates a three-layer IoT-fog-cloud computing system to
determine the optimum workload and power allocation at each layer. The
objective is to minimize maximum per-layer latency (including both data
processing and transmission delays) with individual power constraints. The
resulting optimum resource allocation problem is a mixed-integer optimization
problem with exponential complexity. Hence, the problem is first relaxed under
appropriate modeling assumptions, and then an efficient iterative method is
proposed to solve the relaxed but still non-convex problem. The proposed
algorithm is based on an alternating optimization approach, which yields
close-to-optimum results with significantly reduced complexity. Numerical
results are provided to illustrate the performance of the proposed algorithm
compared to the exhaustive search method. The latency gain of three-layer
distributed IoT-fog-cloud computing is quantified with respect to fog-only and
cloud-only computing systems.
Model-Based Nonlinear Periodic Event-Triggered Control for Continuous-Time Systems with Sampled-Data Prediction
Date: 2020-01-31 00:00:00
Tasks: Prediction

In this paper, we present a model-based periodic event-triggered control
mechanism for nonlinear continuous-time Networked Control Systems. A
sampled-data prediction of the system behavior is used at the actuator to
reduce the amount of required communication while maintaining a user-defined
performance level. This prediction is based on a possibly inaccurate
discretization of the nonlinear system dynamics and can be implemented on
simple hardware. Nevertheless, guarantees for asymptotic stability and a
user-defined performance level are given for the periodic event-triggered
control (PETC) mechanism, whilst the reduction of the required amount of
transmissions of state information depends on the quality of the prediction. We
discuss furthermore how the prediction can be implemented. The performance of
the proposed PETC mechanism is illustrated with a numerical example. This paper
is the accepted version of [1], containing also the proofs of the main results.
DeepHIPS: A novel Deep Learning based Hippocampus Subfield Segmentation method
Date: 2020-01-31 00:00:00
Tasks: Deep Learning, Hippocampus, Segmentation

The automatic assessment of hippocampus volume is an important tool in the
study of several neurodegenerative diseases such as Alzheimer's disease.
Specifically, the measurement of hippocampus subfields properties is of great
interest since it can show earlier pathological changes in the brain. However,
segmentation of these subfields is very difficult due to their complex
structure and for the need of high-resolution magnetic resonance images
manually labeled. In this work, we present a novel pipeline for automatic
hippocampus subfield segmentation based on a deeply supervised convolutional
neural network. Results of the proposed method are shown for two available
hippocampus subfield delineation protocols. The method has been compared to
other state-of-the-art methods showing improved results in terms of accuracy
and execution time.
Performance Analysis and Optimization of 3D Massive MIMO Multi-Pair Relaying with SWIPT
Date: 2020-02-14 00:00:00

In this paper, we study a massive multiple-input multiple-output (mMIMO)
relay network where multiple source-destination pairs exchange information
through a common relay equipped with a massive antenna array. The source users
perform simultaneous wireless information and power transfer (SWIPT) and the
power-splitting (PS) scheme is used at the relay to first harvest energy from
the received signals, and then, to transmit the decoded signals using the
harvested energy. Relay performs maximum ratio combining/maximum ratio
transmission (MRC/MRT) beam-forming on the received signal. Under the
three-dimensional (3D) directional channel model, we derive a closed-form lower
bound expression for the average signal-to-interference-plus-noise ratio using
results from random matrix theory, which leads to an asymptotic approximation
of the achievable sum-rate. Based on that, we study a joint optimization
problem over the tilt and PS ratio to maximize the achievable sum-rate. Grid
search algorithm is used to solve the non-convex problem. Simulation results
verify our theoretical analysis and the efficiency of our optimized design. In
particular, our optimized system outperforms a conventional system with $\pi/4$
tilt and PS of $0.5$, by at least $61\%$.
Spectrum Sharing for Massive Access in Ultra-Narrowband IoT Systems
Authors: Ghaith Hattab, Petar Popovski, Danijela Cabric
Date: 2020-01-30 00:00:00

Ultra-narrowband (UNB) communications has become a signature feature for many emerging low-power wide-area (LPWA) networks. Specifically, using extremely narrowband signals helps the network connect more Internet-of-things (IoT) devices within a given band. It also improves robustness to interference, extending the coverage of the network. In this paper, we study the coexistence capability of UNB networks and their scalability to enable massive access. To this end, we develop a stochastic geometry framework to analyze and model UNB networks on a large scale. The framework captures the unique characteristics of UNB communications, including the asynchronous time-frequency access, signal repetition, and the absence of base station (BS) association. Closed-form expressions of the transmission success probability and network connection density are presented for several UNB protocols. We further discuss multiband access for UNB networks, proposing a low-complexity protocol. Our analysis reveals several insights on the geographical diversity achieved when devices do not connect to a single BS, the optimal number of signal repetitions, and how to utilize multiple bands without increasing the complexity of BSs. Simulation results are provided to validate the analysis, and they show that UNB communications enables a single BS to connect thousands of devices even when the spectrum is shared with other networks.
Structured climate financing: valuation of CDOs on inhomogeneous asset pools
Authors: N. Packham
Date: 2020-01-31 00:00:00
Tasks: Sensitivity

Recently, a number of structured funds have emerged as public-private partnerships with the intent of promoting investment in renewable energy in emerging markets. These funds seek to attract institutional investors by tranching the asset pool and issuing senior notes with a high credit quality. Financing of renewable energy (RE) projects is achieved via two channels: small RE projects are financed indirectly through local banks that draw loans from the fund's assets, whereas large RE projects are directly financed from the fund. In a bottom-up Gaussian copula framework, we examine the diversification properties and RE exposure of the senior tranche. To this end, we introduce the LH++ model, which combines a homogeneous infinitely granular loan portfolio with a finite number of large loans. Using expected tranche percentage notional (which takes a similar role as the default probability of a loan), tranche prices and tranche sensitivities in RE loans, we analyse the risk profile of the senior tranche. We show how the mix of indirect and direct RE investments in the asset pool affects the sensitivity of the senior tranche to RE investments and how to balance a desired sensitivity with a target credit quality and target tranche size.
Graph Cepstrum: Spatial Feature Extracted from Partially Connected Microphones
Date: 2020-01-30 00:00:00

In this paper, we propose an effective and robust method of spatial feature
extraction for acoustic scene analysis utilizing partially synchronized and/or
closely located distributed microphones. In the proposed method, a new cepstrum
feature utilizing a graph-based basis transformation to extract spatial
information from distributed microphones, while taking into account whether any
pairs of microphones are synchronized and/or closely located, is introduced.
Specifically, in the proposed graph-based cepstrum, the log-amplitude of a
multichannel observation is converted to a feature vector utilizing the inverse
graph Fourier transform, which is a method of basis transformation of a signal
on a graph. Results of experiments using real environmental sounds show that
the proposed graph-based cepstrum robustly extracts spatial information with
consideration of the microphone connections. Moreover, the results indicate
that the proposed method more robustly classifies acoustic scenes than
conventional spatial features when the observed sounds have a large
synchronization mismatch between partially synchronized microphone groups.
Orbital Angular Momentum Multiplexing in Highly Reverberant Environments
Date: 2019-12-15 00:00:00

Previous studies on orbital angular momentum (OAM) communication mainly
considered line-of-sight environments. In this letter, however, it is found
that OAM communication with high-order modulation can be achieved in highly
reverberant environments by combining the OAM multiplexing with a spatial
equalizer. The OAM multiplexing exhibits comparable performance of conventional
multiple-input multiple-output (MIMO) system.
MindReading: An Ultra-Low-Power Photonic Accelerator for EEG-based Human Intention Recognition
Date: 2020-01-30 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), Intent Detection

A scalp-recording electroencephalography (EEG)-based brain-computer interface
(BCI) system can greatly improve the quality of life for people who suffer from
motor disabilities. Deep neural networks consisting of multiple convolutional,
LSTM and fully-connected layers are created to decode EEG signals to maximize
the human intention recognition accuracy. However, prior FPGA, ASIC, ReRAM and
photonic accelerators cannot maintain sufficient battery lifetime when
processing real-time intention recognition. In this paper, we propose an
ultra-low-power photonic accelerator, MindReading, for human intention
recognition by only low bit-width addition and shift operations. Compared to
prior neural network accelerators, to maintain the real-time processing
throughput, MindReading reduces the power consumption by 62.7\% and improves
the throughput per Watt by 168\%.
Deep-AIR: A Hybrid CNN-LSTM Framework forFine-Grained Air Pollution Forecast
Date: 2020-01-29 00:00:00
Tasks: Deep Learning

Poor air quality has become an increasingly critical challenge for many
metropolitan cities, which carries many catastrophicphysical and mental
consequences on human health and quality of life. However, accurately
monitoring and forecasting air qualityremains a highly challenging endeavour.
Limited by geographically sparse data, traditional statistical models and newly
emergingdata-driven methods of air quality forecasting mainly focused on the
temporal correlation between the historical temporal datasets of airpollutants.
However, in reality, both distribution and dispersion of air pollutants are
highly location-dependant. In this paper, we proposea novel hybrid deep
learning model that combines Convolutional Neural Networks (CNN) and Long Short
Term Memory (LSTM)together to forecast air quality at high-resolution. Our
model can utilize the spatial correlation characteristic of our air pollutant
datasetsto achieve higher forecasting accuracy than existing deep learning
models of air pollution forecast.