pwc-archive/papers-with-abstracts huggingface dataset:
Model-Driven DNN Decoder for Turbo Codes: Design, Simulation and Experimental Results
Authors: Yunfeng He, Jing Zhang, Shi Jin, Chao-Kai Wen, Geoffrey Ye Li
Date: 2020-06-16 00:00:00
Tasks: Decoder

This paper presents a novel model-driven deep learning (DL) architecture, called TurboNet, for turbo decoding that integrates DL into the traditional max-log-maximum a posteriori (MAP) algorithm. The TurboNet inherits the superiority of the max-log-MAP algorithm and DL tools and thus presents excellent error-correction capability with low training cost. To design the TurboNet, the original iterative structure is unfolded as deep neural network (DNN) decoding units, where trainable weights are introduced to the max-log-MAP algorithm and optimized through supervised learning. To efficiently train the TurboNet, a loss function is carefully designed to prevent tricky gradient vanishing issue. To further reduce the computational complexity and training cost of the TurboNet, we can prune it into TurboNet+. Compared with the existing black-box DL approaches, the TurboNet+ has considerable advantage in computational complexity and is conducive to significantly reducing the decoding overhead. Furthermore, we also present a simple training strategy to address the overfitting issue, which enable efficient training of the proposed TurboNet+. Simulation results demonstrate TurboNet+'s superiority in error-correction ability, signal-to-noise ratio generalization, and computational overhead. In addition, an experimental system is established for an over-the-air (OTA) test with the help of a 5G rapid prototyping system and demonstrates TurboNet's strong learning ability and great robustness to various scenarios.
Analysing the resilience of the European commodity production system with PyResPro, the Python Production Resilience package
Authors: Matteo Zampieri, Andrea Toreti, Andrej Ceglar, Pierluca De Palma, Thomas Chatzopoulos
Date: 2020-06-16 00:00:00
Tasks: Time Series, Time Series Analysis

This paper presents a Python object-oriented software and code to compute the annual production resilience indicator. The annual production resilience indicator can be applied to different anthropic and natural systems such as agricultural production, natural vegetation and water resources. Here, we show an example of resilience analysis of the economic values of the agricultural production in Europe. The analysis is conducted for individual time-series in order to estimate the resilience of a single commodity and to groups of time-series in order to estimate the overall resilience of diversified production systems composed of different crops and/or different countries. The proposed software is powerful and easy to use with publicly available datasets such as the one used in this study.
Stick-slip and convergence of feedback-controlled systems with Coulomb friction
Authors: Michael Ruderman
Date: 2020-06-16 00:00:00
Tasks: Friction

An analysis of stick-slip behavior and convergence of trajectories in the feedback-controlled motion systems with discontinuous Coulomb friction is provided. A closed-form parameter-dependent stiction region, around an invariant equilibrium set, is proved to be always reachable and globally attractive. It is shown that only asymptotic convergence can be achieved, with at least one but mostly an infinite number of consecutive stick-slip cycles, independent of the initial conditions. Theoretical developments are supported by a number of numerical results with dedicated convergence examples.
Quantization of Acoustic Model Parameters in Automatic Speech Recognition Framework
Date: 2020-06-16 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Quantization, speech-recognition, Speech Recognition

Robust automatic speech recognition (ASR) system exploits state-of-the-art
deep neural networks (DNN) based acoustic model (AM) trained with Lattice
Free-Maximum Mutual Information (LF-MMI) criterion and n-gram language models.
These systems are quite large and require significant parameter reduction to
operate on embedded devices. Impact of the parameter quantization on the
overall word recognition performance is studied in this paper. Following three
approaches are presented: (i) AM trained in Kaldi framework with conventional
factorized TDNN (TDNN-F) architecture. (ii) the TDNN built in Kaldi is loaded
into the Pytorch toolkit using a C++ wrapper. The weights and activation
parameters are then quantized and the inference is performed in Pytorch. (iii)
post quantization training for fine-tuning. Results obtained on standard
Librispeech setup provide an interesting overview of recognition accuracy
w.r.t. applied quantization scheme.
A specifically designed machine learning algorithm for GNSS position time series prediction and its applications in outlier and anomaly detection and earthquake prediction
Authors: M. Kiani
Date: 2020-06-16 00:00:00
Tasks: Anomaly Detection, BIG-bench Machine Learning, Earthquake prediction, Outlier Detection, Position, Time Series, Time Series Analysis, Time Series Prediction

We present a simple yet efficient supervised machine learning algorithm that is designed for the GNSS position time series prediction. This algorithm has four steps. First, the mean value of the time series is subtracted from it. Second, the trends in the time series are removed. Third, wavelets are used to separate the high and low frequencies. And fourth, a number of frequencies are derived and used for finding the weights between the hidden and the output layers, using the product of the identity and sine and cosine functions. The role of the observation precision is taken into account in this algorithm. A large-scale study of three thousand position times series of GNSS stations across the globe is presented. Seventeen different machine learning algorithms are examined. The accuracy levels of these algorithms are checked against the rigorous statistical method of Theta. It is shown that the most accurate machine learning algorithm is the method we present, in addition to being faster. Two applications of the algorithm are presented. In the first application, it is shown that the outliers and anomalies in a time series can be detected and removed by the proposed algorithm. In a large scale study, ten other methods of time series outlier detection are compared with the proposed algorithm. The study reveals that the proposed algorithm is approximately 3.22 percent more accurate in detecting outliers. In the second application, the suitability of the algorithm for earthquake prediction is investigated. A case study is presented for the Tohoku 2011 earthquake. It is shown that this earthquake could have been predicted approximately 2 hours before its happening, solely based on each of the 845 GEONET station time series. Comparison with four different studies show the improvement in prediction of the time of the earthquake.
Characterization of Dielectric Materials by Sparse Signal Processing with Iterative Dictionary Updates
Authors: Udaya S. K. P. Miriya Thanthrige, Jan Barowski, Ilona Rolfes, Daniel Erni, Thomas Kaiser, Aydin Sezgin
Date: 2020-06-16 00:00:00

Estimating parameters and properties of various materials without causing damage to the material under test (MUT) is important in many applications. Thus, in this letter, we address this by wireless sensing. Here, the accuracy of the estimation depends on the accurate estimation of the properties of the reflected signal from the MUT (e.g., number of reflections, their amplitudes and time delays). For a layered MUT, there are multiple reflections and, due to the limited bandwidth at the receiver, these reflections superimpose each other. Since the number of reflections coming from the MUT is limited, we propose sparse signal processing (SSP) to decompose the reflected signal. In SSP, a so called dictionary is required to obtain a sparse representation of the signal. Here, instead of a fixed dictionary, a dictionary update technique is proposed to improve the estimation of the reflected signal. To validate the proposed method, a vector network analyzer (VNA) based measurement setup is used. It turns out that the estimated dielectric constants are in close agreement with the dielectric constants of the MUTs reported in literature. Further, the proposed approach outperforms the state-of-the-art model-based curve-fitting approach in thickness estimation.
A small molecule drug candidate targeting SARS-CoV-2 main protease
Date: 2020-06-17 00:00:00
Tasks: Blocking, Drug Discovery, Translation

A new coronavirus identified as SARS-CoV-2 virus has brought the world to a
state of crisis, causing a major pandemic, claiming more than 433,000 lives and
instigating major financial damage to the global economy. Despite current
efforts, developing safe and effective treatments remains a major challenge.
Moreover, new strains of the virus are likely to emerge in the future. To
prevent future pandemics, several drugs with various mechanisms of action are
required. Drug discovery efforts against the virus fall into two main
categories: (a) monoclonal antibodies targeting the spike protein of the virus
and blocking it from entry; (b) small molecule inhibitors targeting key
proteins of the virus, interfering with replication and translation of the
virus. In this study, we are presenting a computational investigation of a
potential drug candidate that targets SARS-CoV-2 protease, a viral protein
critical for replication and translation of the virus.
Active manipulation of Helmholtz scalar fields: Near field synthesis with directional far field control
Date: 2020-06-24 00:00:00

In this article, we propose a strategy for the active manipulation of scalar
Helmholtz fields in bounded near field regions of an active source while
maintaining desired radiation patterns in prescribed far field directions. This
control problem is considered in two environments: free space and respectively,
homogeneous ocean of constant depth. In both media, we proved the existence of
and characterized the surface input, modeled as Neumann data (normal velocity)
or Dirichlet data (surface pressure) such that the radiated field satisfies the
control constraints. We also provide a numerical strategy to construct this
predicted surface input by using a method of moments-approach with a Morozov
discrepancy principle-based Tikhonov regularization. Several numerical
simulations are presented to demonstrate the proposed scheme in scenarios
relevant to practical applications.
Policy Gradient-based Algorithms for Continuous-time Linear Quadratic Control
Date: 2020-06-12 00:00:00

We consider the continuous-time Linear-Quadratic-Regulator (LQR) problem in
terms of optimizing a real-valued matrix function over the set of feedback
gains. The results developed are in parallel to those in Bu et al. [1] for
discrete-time LTI systems. In this direction, we characterize several
analytical properties (smoothness, coerciveness, quadratic growth) that are
crucial in the analysis of gradient-based algorithms. We also point out
similarities and distinctive features of the continuous time setup in
comparison with its discrete time analogue. First, we examine three types of
well-posed flows direct policy update for LQR: gradient flow, natural gradient
flow and the quasi-Newton flow. The coercive property of the corresponding cost
function suggests that these flows admit unique solutions while the gradient
dominated property indicates that the underling Lyapunov functionals decay at
an exponential rate; quadratic growth on the other hand guarantees that the
trajectories of these flows are exponentially stable in the sense of Lyapunov.
We then discuss the forward Euler discretization of these flows, realized as
gradient descent, natural gradient descent and quasi-Newton iteration. We
present stepsize criteria for gradient descent and natural gradient descent,
guaranteeing that both algorithms converge linearly to the global optima. An
optimal stepsize for the quasi-Newton iteration is also proposed, guaranteeing
a $Q$-quadratic convergence rate--and in the meantime--recovering the
Kleinman-Newton iteration. Lastly, we examine LQR state feedback synthesis with
a sparsity pattern. In this case, we develop the necessary formalism and
insights for projected gradient descent, allowing us to guarantee a sublinear
rate of convergence to a first-order stationary point.
Trackability for Discrete-Time LTI Systems: A Brief Review and New Insights
Date: 2020-06-16 00:00:00

`Trackability', the ability of systems to follow arbitrary reference
commands, is investigated in this work. Controllability is not useful in
explaining the tracking behavior of system outputs, a gap that is often
overlooked. Trackability addresses this gap by characterizing whether system
dynamics permits tracking of certain reference commands, irrespective of the
controller/control action used. While earlier literature has discussed
closely-related ideas, lack of consistency in terminology and focus in the
literature has necessitated defining trackability in a simple but rigorous
manner. We present definitions for trackability based on elementary linear
algebra and propose a rank test for determining trackability of systems, while
posing tracking control as an existence question. We discuss in some detail the
relationship of this rank test with other results in the literature. These
results are then used to generate insights about trackable systems and more
importantly to gain insights in to tracking behavior of systems that are not
trackable. We also define three indices that indicate the expected tracking
behavior of the system. Furthermore, we also present a Venn diagram explaining
in detail the connections between trackability and other fundamental system
properties like controllability, observability and output controllability,
while discussing several facts that elaborate these connections. Specifically,
we show that trackability is fundamentally different from controllability and
output controllability. The presented work is expected to serve as a foundation
for a deeper investigation into the topic of tracking control and provide a
framework for receiving new theoretical insights on trackability.
Primary User Emulation and Jamming Attack Detection in Cognitive Radio via Sparse Coding
Authors: H. M. Furqan, M. A. Aygul, M. Nazzal, H. Arslan
Date: 2020-06-16 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making

Cognitive radio is an intelligent and adaptive radio that improves the utilization of the spectrum by its opportunistic sharing. However, it is inherently vulnerable to primary user emulation and jamming attacks that degrade the spectrum utilization. In this paper, an algorithm for the detection of primary user emulation and jamming attacks in cognitive radio is proposed. The proposed algorithm is based on the sparse coding of the compressed received signal over a channel-dependent dictionary. More specifically, the convergence patterns in sparse coding according to such a dictionary are used to distinguish between a spectrum hole, a legitimate primary user, and an emulator or a jammer. The process of decision-making is carried out as a machine learning-based classification operation. Extensive numerical experiments show the effectiveness of the proposed algorithm in detecting the aforementioned attacks with high success rates. This is validated in terms of the confusion matrix quality metric. Besides, the proposed algorithm is shown to be superior to energy detection-based machine learning techniques in terms of receiver operating characteristics curves and the areas under these curves
Effective immunity and second waves: a dynamic causal modelling study
Date: 2020-06-16 00:00:00

This technical report addresses a pressing issue in the trajectory of the
coronavirus outbreak; namely, the rate at which effective immunity is lost
following the first wave of the pandemic. This is a crucial epidemiological
parameter that speaks to both the consequences of relaxing lockdown and the
propensity for a second wave of infections. Using a dynamic causal model of
reported cases and deaths from multiple countries, we evaluated the evidence
models of progressively longer periods of immunity. The results speak to an
effective population immunity of about three months that, under the model,
defers any second wave for approximately six months in most countries. This may
have implications for the window of opportunity for tracking and tracing, as
well as for developing vaccination programmes, and other therapeutic
interventions.
A demographic microsimulation model with an integrated household alignment method
Date: 2020-06-15 00:00:00

Many dynamic microsimulation models have shown their ability to reasonably
project detailed population and households using non-data based household
formation and dissolution rules. Although, those rules allow modellers to
simplify changes in the household construction, they typically fall short in
replicating household projections or if applied retrospectively the observed
household numbers. Consequently, such models with biased estimation for
household size and other household related attributes lose their usefulness in
applications that are sensitive to household size, such as in travel demand and
housing demand modelling. Nonetheless, these demographic microsimulation models
with their associated shortcomings have been commonly used to assess various
planning policies which can result in misleading judgements. In this paper, we
contribute to the literature of population microsimulation by introducing a
fully integrated system of models for different life event where a household
alignment method adjusts household size distribution to closely align with any
given target distribution. Furthermore, some demographic events that are
generally difficult to model, such as incorporating immigrant families into a
population, can be included. We illustrated an example of the household
alignment method and put it to test in a dynamic microsimulation model that we
developed using dymiumCore, a general-purpose microsimulation toolkit in R, to
show potential improvements and weaknesses of the method. The implementation of
this model has been made publicly available on GitHub.
Symmetry Abstractions for Hybrid Systems and their Applications
Date: 2020-06-16 00:00:00

A symmetry of a dynamical system is a map that transforms one trajectory to
another trajectory. We introduce a new type of abstraction for hybrid automata
based on symmetries. The abstraction combines different modes in a concrete
automaton A, whose trajectories are related by symmetries, into a single mode
in the abstract automaton B. The abstraction sets the guard and reset of an
abstract edge to be the union of the symmetry-transformed guards and resets of
the concrete edges. We establish the soundness of the abstraction using a
forward simulation relation (FSR) and present several examples. Our abstraction
results in simpler automata, that are more amenable for formal analysis and
design. We illustrate an application of this abstraction in making reachability
analysis faster and enabling unbounded time safety verification. We show how a
fixed point of the reachable set computation of B can be used to answer
reachability queries for A, even if the latter visits an infinite and unbounded
sequences of modes. We present our implementation of the abstraction
construction, the fixed point check, and the map that transforms abstract
reachable sets to concrete ones in a software tool. Finally, we show the
advantage of our method over existing ones, and the different aspects of our
abstraction, in a sequence of experiments including scenarios with linear and
nonlinear agents following waypoints.
Optimal Transport and Risk Aversion in Kyle's Model of Informed Trading
Authors: Kerry Back, Francois Cocquemas, Ibrahim Ekren, Abraham Lioui
Date: 2020-06-16 00:00:00

We establish connections between optimal transport theory and the dynamic version of the Kyle model, including new characterizations of informed trading profits via conjugate duality and Monge-Kantorovich duality. We use these connections to extend the model to multiple assets, general distributions, and risk-averse market makers. With risk-averse market makers, liquidity is lower, assets exhibit short-term reversals, and risk premia depend on market maker inventories, which are mean reverting. We illustrate the model by showing that implied volatilities predict stock returns when there is informed trading in stocks and options and market makers are risk averse.
Nonlinear dynamic analysis of an epidemiological model for COVID-19 including public behavior and government action
Date: 2020-10-14 00:00:00

This paper is concerned with nonlinear modeling and analysis of the COVID-19
pandemic currently ravaging the planet. There are two objectives: to arrive at
an appropriate model that captures the collected data faithfully, and to use
that as a basis to explore the nonlinear behavior. We use a nonlinear SEIR
(Susceptible, Exposed, Infectious & Removed) transmission model with added
behavioral and government policy dynamics. We develop a genetic algorithm
technique to identify key model parameters employing COVID19 data from South
Korea. Stability, bifurcations and dynamic behavior are analyzed. Parametric
analysis reveals conditions for sustained epidemic equilibria to occur. This
work points to the value of nonlinear dynamic analysis in pandemic modeling and
demonstrates the dramatic influence of social and government behavior on
disease dynamics.
iConViz: Interactive Visual Exploration of the Default Contagion Risk of Networked-Guarantee Loans
Authors: Zhibin Niu, Runlin Li, Junqi Wu, Dawei Cheng, Jiawan Zhang
Date: 2020-06-16 00:00:00

Groups of enterprises can serve as guarantees for one another and form complex networks when obtaining loans from commercial banks. During economic slowdowns, corporate default may spread like a virus and lead to large-scale defaults or even systemic financial crises. To help financial regulatory authorities and banks manage the risk associated with networked loans, we identified the default contagion risk, a pivotal issue in developing preventive measures, and established iConVis, an interactive visual analysis tool that facilitates the closed-loop analysis process. A novel financial metric, the contagion effect, was formulated to quantify the infectious consequences of guarantee chains in this type of network. Based on this metric, we designed and implement a series of novel and coordinated views that address the analysis of financial problems. Experts evaluated the system using real-world financial data. The proposed approach grants practitioners the ability to avoid previous ad hoc analysis methodologies and extend coverage of the conventional Capital Accord to the banking industry.
Multi-cue kinetic model with non-local sensing for cell migration on a fibers network with chemotaxis
Authors: N. Loy, M. Conte
Date: 2020-06-17 00:00:00

Cells perform directed motion in response to external stimuli that they detect by sensing the environment with their membrane protrusions. In particular, several biochemical and biophysical cues give rise to tactic migration in the direction of their specific targets. This defines a multi-cue environment in which cells have to sort and combine different, and potentially competitive, stimuli. We propose a non-local kinetic model for cell migration in presence of two external factors both influencing cell polarization: contact guidance and chemotaxis. We propose two different sensing strategies and we analyze the two resulting models by recovering the appropriate macroscopic limit in different regimes, in order to see how the size of the cell, with respect to the variation of both external fields, influences the overall behavior. Moreover, we integrate numerically the kinetic transport equation in a two-dimensional setting in order to investigate qualitatively various scenarios.