pwc-archive/papers-with-abstracts huggingface dataset:
Understanding and Modelling the Complexity of the Immune System: Systems Biology for Integration and Dynamical Reconstruction of Lymphocyte Multi-Scale Dynamics
Date: 2020-08-26 00:00:00
Tasks: Diversity

Understanding and modelling the complexity of the immune system is a
challenge that is shared by the ImmunoComplexiT$^1$ thematic network from the
RNSC. The immune system is a complex biological, adaptive, highly diversified,
self-organized and degenerative cognitive network of entities, allowing for a
robust and resilient system with emergent properties such as anamnestic
responses and regulation. The adaptive immune system has evolved into a complex
system of billions of highly diversified lymphocytes all interacting as a
connective dynamic, multi-scale organised and distributed system, in order to
collectively insure body and species preservation. The immune system is
characterized by complexity at different levels: network organisation through
fluid cell populations with inter-and intra-cell signalling, lymphocyte
receptor diversity, cell clonotype selection and competition at cell level,
migration and interaction inside the immunological tissues and fluid
dissemination through the organism, homeostatic regulation while rapid
adaptation to a changing environment.
Beyond 5G Wireless Localization with Reconfigurable Intelligent Surfaces
Authors: Henk Wymeersch, Benoît Denis
Date: 2020-08-26 00:00:00

5G radio positioning exploits information in both angle and delay, by virtue of increased bandwidth and large antenna arrays. When large arrays are embedded in surfaces, they can passively steer electromagnetic waves in preferred directions of space. Reconfigurable intelligent surfaces (RIS), which are seen as a transformative "beyond 5G" technology, can thus control the physical propagation environment. Whereas such RIS have been mainly intended for communication purposes so far, we herein state and analyze a RIS-aided downlink positioning problem from the Fisher Information perspective. Then, based on this analysis, we propose a two-step optimization scheme that selects the best RIS combination to be activated and controls the phases of their constituting elements so as to improve positioning performance. Preliminary simulation results show coverage and accuracy gains in comparison with natural scattering, while pointing out limitations in terms of low signal to noise ratio (SNR) and inter-path interference.
Relative limitations of increasing the number of modulation levels in computer generated holography
Date: 2020-08-24 00:00:00

Phase and amplitude spatial light modulators (SLMs) capable of both binary
and multi-level modulation are widely available and offer a wide range of
technologies to choose from for holographic applications. While the replay
fields generated with multi-level phase-only SLMs are of a significantly higher
quality than those generated by equivalent binary phase-only SLMs, evidence is
presented in this letter that this improvement is not as marked for amplitude
SLMs, where multi-level devices offer only a small benefit over their binary
counterparts. Heuristic and numerical justifications for this are discussed and
conclusions drawn.
Parameterization of All Output-Rectifying Retrofit Controllers
Authors: Hampei Sasahara, Takayuki Ishizaki, Jun-ichi Imura
Date: 2020-08-26 00:00:00
Tasks: All

This study investigates a parameterization of all output-rectifying retrofit controllers for distributed design of a structured controller. It has been discovered that all retrofit controllers can be characterized as a constrained Youla parameterization, which is difficult to solve analytically. For synthesis, a tractable and insightful class of retrofit controllers, referred to as output-rectifying retrofit controllers, has been introduced. An unconstrained parameterization of all output-rectifying retrofit controllers can be derived under a technical assumption on measurability of particular signals. The aim of this note is to reveal the structure of all output-rectifying retrofit controllers in the general output-feedback case. It is found out that the existing developments can be generalized based on the notions of state projection and an inverse system. The result leads to the conclusion that output-rectifying retrofit controllers can readily be designed even in the general case.
Investigation of Flash Crash via Topological Data Analysis
Authors: Wonse Kim, Younng-Jin Kim, Gihyun Lee, Woong Kook
Date: 2020-08-26 00:00:00
Tasks: Time Series, Time Series Analysis, Topological Data Analysis

Topological data analysis has been acknowledged as one of the most successful mathematical data analytic methodologies in various fields including medicine, genetics, and image analysis. In this paper, we explore the potential of this methodology in finance by applying persistence landscape and dynamic time series analysis to analyze an extreme event in the stock market, known as Flash Crash. We will provide results of our empirical investigation to confirm the effectiveness of our new method not only for the characterization of this extreme event but also for its prediction purposes.
Coupling BM3D with directional wavelet packets for image denoising
Authors: Amir Averbuch, Pekka Neittaanmaki, Valery Zheludev, Moshe Salhov, Jonathan Hauser
Date: 2020-08-25 00:00:00
Tasks: Denoising, Image Denoising

The paper presents an image denoising algorithm by combining a method that is based on directional quasi-analytic wavelet packets (qWPs) with the popular BM3D algorithm. The qWPs and its corresponding transforms are designed in [1]. The denoising algorithm qWP (qWPdn) applies an adaptive localized soft thresholding to the transform coefficients using the Bivariate Shrinkage methodology. The combined method consists of several iterations of qWPdn and BM3D algorithms, where the output from one algorithm updates the input to the other (cross-boosting).The qWPdn and BM3D methods complement each other. The qWPdn capabilities to capture edges and fine texture patterns are coupled with utilizing the sparsity in real images and self-similarity of patches in the image that is inherent in the BM3D. The obtained results are quite competitive with the best state-of-the-art algorithms. We compare the performance of the combined methodology with the performances of cptTP-CTF6, DAS-2 algorithms, which use directional frames, and the BM3D algorithm. In the overwhelming majority of the experiments, the combined algorithm outperformed the above methods.
High-resolution Multi-spectral Imaging with Diffractive Lenses and Learned Reconstruction
Authors: Figen S. Oktem, Oğuzhan Fatih Kar, Can Deniz Bezek, Farzad Kamalabadi
Date: 2020-08-26 00:00:00
Tasks: Diagnostic, Rolling Shutter Correction, Vocal Bursts Intensity Prediction

Spectral imaging is a fundamental diagnostic technique with widespread application. Conventional spectral imaging approaches have intrinsic limitations on spatial and spectral resolutions due to the physical components they rely on. To overcome these physical limitations, in this paper, we develop a novel multi-spectral imaging modality that enables higher spatial and spectral resolutions. In the developed computational imaging modality, we exploit a diffractive lens, such as a photon sieve, for both dispersing and focusing the optical field, and achieve measurement diversity by changing the focusing behavior of this lens. Because the focal length of a diffractive lens is wavelength-dependent, each measurement is a superposition of differently blurred spectral components. To reconstruct the individual spectral images from these superimposed and blurred measurements, model-based fast reconstruction algorithms are developed with deep and analytical priors using alternating minimization and unrolling. Finally, the effectiveness and performance of the developed technique is illustrated for an application in astrophysical imaging under various observation scenarios in the extreme ultraviolet (EUV) regime. The results demonstrate that the technique provides not only diffraction-limited high spatial resolution, as enabled by diffractive lenses, but also the capability of resolving close-by spectral sources that would not otherwise be possible with the existing techniques. This work enables high resolution multi-spectral imaging with low cost designs for a variety of applications and spectral regimes.
Output Feedback Control of Coupled Linear Parabolic ODE-PDE-ODE Systems
Date: 2020-08-26 00:00:00

This paper deals with the backstepping design of observer-based compensators
for parabolic ODE-PDE-ODE systems. The latter consist of n coupled parabolic
PDEs with distinct diffusion coefficients and spatially-varying coefficients,
that are bidirectionally coupled to ODEs at both boundaries. The actuation and
sensing appears through these ODEs resulting in a challenging control problem.
For this setup a systematic backstepping approach is proposed, in order to
determine a state feedback controller and an observer. In particular, the state
feedback loop and the observer error dynamics are mapped into stable
ODE-PDE-ODE cascades by making use of a sequence of transformations. With this,
the design can be traced back to the solution of kernel equations already found
in the literature as well as initial and boundary value problems, that can be
solved numerically. Exponential stability of the closed-loop system is
verified, wherein the decay rate can be directly specified in the design. The
results of the paper are illustrated by the output feedback control of an
unstable ODE-PDE-ODE system with two coupled parabolic PDEs.
The Time Function of Stock Price
Authors: Shengfeng Mei, Hong Gao
Date: 2020-08-11 00:00:00
Tasks: Management

This paper tends to define the quantitative relationship between the stock price and time as a time function. Based on the empirical evidence that the log-return of a stock is the series of white noise, a mathematical model of the integral white noise is established to describe the phenomenon of stock price movement. A deductive approach is used to derive the auto-correlation function, displacement formula and power spectral density of the stock price movement, which reveals not only the characteristics and rules of the movement but also the predictability of the stock price. The deductive fundamental is provided for the price analysis, prediction and risk management of portfolio investment.
A hydrophobic-interaction-based mechanism trigger docking between the SARS CoV 2 spike and angiotensin-converting enzyme 2
Authors: Jiacheng Li, Xiaoliang Ma, Shuai Guo, Chengyu Hou, Liping Shi, Hongchi Zhang, Bing Zheng, Chencheng Liao, Lin Yang, Lin Ye, Xiaodong He
Date: 2020-08-27 00:00:00

A recent experimental study found that the binding affinity between the cellular receptor human angiotensin converting enzyme 2 (ACE2) and receptor-binding domain (RBD) in spike (S) protein of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is more than 10-fold higher than that of the original severe acute respiratory syndrome coronavirus (SARS-CoV). However, main-chain structures of the SARS-CoV-2 RBD are almost the same with that of the SARS-CoV RBD. Understanding physical mechanism responsible for the outstanding affinity between the SARS-CoV-2 S and ACE2 is the "urgent challenge" for developing blockers, vaccines and therapeutic antibodies against the coronavirus disease 2019 (COVID-19) pandemic. Considering the mechanisms of hydrophobic interaction, hydration shell, surface tension, and the shielding effect of water molecules, this study reveals a hydrophobic-interaction-based mechanism by means of which SARS-CoV-2 S and ACE2 bind together in an aqueous environment. The hydrophobic interaction between the SARS-CoV-2 S and ACE2 protein is found to be significantly greater than that between SARS-CoV S and ACE2. At the docking site, the hydrophobic portions of the hydrophilic side chains of SARS-CoV-2 S are found to be involved in the hydrophobic interaction between SARS-CoV-2 S and ACE2. We propose a method to design live attenuated viruses by mutating several key amino acid residues of the spike protein to decrease the hydrophobic surface areas at the docking site. Mutation of a small amount of residues can greatly reduce the hydrophobic binding of the coronavirus to the receptor, which may be significant reduce infectivity and transmissibility of the virus.
Spatiotemporal Modelling of Multi-Gateway LoRa Networks with Imperfect SF Orthogonality
Authors: Yathreb Bouazizi, Fatma Benkhelifa, Julie McCann
Date: 2020-08-27 00:00:00

Meticulous modelling and performance analysis of Low-Power Wide-Area (LPWA) networks are essential for large scale dense Internet-of-Things (IoT) deployments. As Long Range (LoRa) is currently one of the most prominent LPWA technologies, we propose in this paper a stochastic-geometry-based framework to analyse the uplink transmission performance of a multi-gateway LoRa network modelled by a Matern Cluster Process (MCP). The proposed model is first to consider all together the multi-cell topology, imperfect spreading factor (SF) orthogonality, random start times, and geometric data arrival rates. Accounting for all of these factors, we initially develop the SF-dependent collision overlap time function for any start time distribution. Then, we analyse the Laplace transforms of intra-cluster and inter-cluster interference, and formulate the uplink transmission success probability. Through simulation results, we highlight the vulnerability of each SF to interference, illustrate the impact of parameters such as the network density, and the power allocation scheme on the network performance. Uniquely, our results shed light on when it is better to activate adaptive power mechanisms, as we show that an SF-based power allocation that approximates LoRa ADR, negatively impacts nodes near the cluster head. Moreover, we show that the interfering SFs degrading the performance the most depend on the decoding threshold range and the power allocation scheme.
Estimating Uniqueness of I-Vector Representation of Human Voice
Authors: Erkam Sinan Tandogan, Husrev Taha Sencar
Date: 2020-08-27 00:00:00
Tasks: Speaker Recognition, Speaker Verification

We study the individuality of the human voice with respect to a widely used feature representation of speech utterances, namely, the i-vector model. As a first step toward this goal, we compare and contrast uniqueness measures proposed for different biometric modalities. Then, we introduce a new uniqueness measure that evaluates the entropy of i-vectors while taking into account speaker level variations. Our measure operates in the discrete feature space and relies on accurate estimation of the distribution of i-vectors. Therefore, i-vectors are quantized while ensuring that both the quantized and original representations yield similar speaker verification performance. Uniqueness estimates are obtained from two newly generated datasets and the public VoxCeleb dataset. The first custom dataset contains more than one and a half million speech samples of 20,741 speakers obtained from TEDx Talks videos. The second one includes over twenty one thousand speech samples from 1,595 actors that are extracted from movie dialogues. Using this data, we analyzed how several factors, such as the number of speakers, number of samples per speaker, sample durations, and diversity of utterances affect uniqueness estimates. Most notably, we determine that the discretization of i-vectors does not cause a reduction in speaker recognition performance. Our results show that the degree of distinctiveness offered by i-vector-based representation may reach 43-70 bits considering 5-second long speech samples; however, under less constrained variations in speech, uniqueness estimates are found to reduce by around 30 bits. We also find that doubling the sample duration increases the distinctiveness of the i-vector representation by around 20 bits.
Graph-convolution neural network-based flexible docking utilizing coarse-grained distance matrix
Authors: Amr H. Mahmoud, Jonas F. Lill, Markus A. Lill
Date: 2020-08-27 00:00:00
Tasks: Drug Design, Prediction, Re-Ranking

Prediction of protein-ligand complexes for flexible proteins remains still a challenging problem in computational structural biology and drug design. Here we present two novel deep neural network approaches with significant improvement in efficiency and accuracy of binding mode prediction on a large and diverse set of protein systems compared to standard docking. Whereas the first graph convolutional network is used for re-ranking poses the second approach aims to generate and rank poses independent of standard docking approaches. This novel approach relies on the prediction of distance matrices between ligand atoms and protein C_alpha atoms thus incorporating side-chain flexibility implicitly.
DrumGAN: Synthesis of Drum Sounds With Timbral Feature Conditioning Using Generative Adversarial Networks
Authors: J. Nistal, S. Lattner, G. Richard
Date: 2020-08-27 00:00:00
Tasks: Audio Synthesis, Generative Adversarial Network

Synthetic creation of drum sounds (e.g., in drum machines) is commonly performed using analog or digital synthesis, allowing a musician to sculpt the desired timbre modifying various parameters. Typically, such parameters control low-level features of the sound and often have no musical meaning or perceptual correspondence. With the rise of Deep Learning, data-driven processing of audio emerges as an alternative to traditional signal processing. This new paradigm allows controlling the synthesis process through learned high-level features or by conditioning a model on musically relevant information. In this paper, we apply a Generative Adversarial Network to the task of audio synthesis of drum sounds. By conditioning the model on perceptual features computed with a publicly available feature-extractor, intuitive control is gained over the generation process. The experiments are carried out on a large collection of kick, snare, and cymbal sounds. We show that, compared to a specific prior work based on a U-Net architecture, our approach considerably improves the quality of the generated drum samples, and that the conditional input indeed shapes the perceptual characteristics of the sounds. Also, we provide audio examples and release the code used in our experiments.
Macroscopic limit of a kinetic model describing the switch in T cell migration modes via binary interactions
Authors: Gissell Estrada-Rodriguez, Tommaso Lorenzi
Date: 2020-07-27 00:00:00

Experimental results on the immune response to cancer indicate that activation of cytotoxic T lymphocytes (CTLs) through interactions with dendritic cells (DCs) can trigger a change in CTL migration patterns. In particular, while CTLs in the pre-activation state move in a non-local search pattern, the search pattern of activated CTLs is more localised. In this paper, we develop a kinetic model for such a switch in CTL migration modes. The model is formulated as a coupled system of balance equations for the one-particle distribution functions of CTLs in the pre-activation state, activated CTLs and DCs. CTL activation is modelled via binary interactions between CTLs in the pre-activation state and DCs. Moreover, cell motion is represented as a velocity-jump process, with the running time of CTLs in the pre-activation state following a long-tailed distribution, which is consistent with a L\'evy walk, and the running time of activated CTLs following a Poisson distribution, which corresponds to Brownian motion. We formally show that the macroscopic limit of the model comprises a coupled system of balance equations for the cell densities whereby activated CTL movement is described via a classical diffusion term, whilst a fractional diffusion term describes the movement of CTLs in the pre-activation state. The modelling approach presented here and its possible generalisations are expected to find applications in the study of the immune response to cancer and in other biological contexts in which switch from non-local to localised migration patterns occurs.
Marchuk's models of infection diseases: new developments
Authors: Irina Volinsky, Alexander Domoshnitsky, Marina Bershadsky, Roman Shklyar
Date: 2020-07-23 00:00:00

We consider mathematical models of infection diseases built by G.I. Marchuk in his well known book on immunology. These models are in the form of systems of ordinary delay differential equations. We add a distributed control in one of the equations describing the dynamics of the antibody concentration rate. Distributed control looks here naturally since the change of this concentration rather depends on the corresponding average value of the difference of the current and normal antibody concentrations on the time interval than on their difference at the point t only.
Coexisting Hidden and self-excited attractors in an economic system of integer or fractional order
Authors: Marius-F. Danca
Date: 2020-07-21 00:00:00

In this paper the dynamics of an economic system with foreign financing, of integer or fractional order, are analyzed. The symmetry of the system determines the existence of two pairs of coexisting attractors. The integer-order version of the system proves to have several combinations of coexisting hidden attractors with self-excited attractors. Because one of the system variables represents the foreign capital in ow, the presence of hidden attractors could be of a real interest in economic models. The fractional-order variant presents another interesting coexistence of attractors in the fractional order space.
Defect Prediction of Railway Wheel Flats based on Hilbert Transform and Wavelet Packet Decomposition
Authors: Euiyoul Kim, Nithya Jayaprakasam, Yong Cui, Ullrich Martin
Date: 2020-08-27 00:00:00
Tasks: Data Augmentation

For efficient railway operation and maintenance, the demand for onboard monitoring systems is increasing with technological advances in high-speed trains. Wheel flats, one of the common defects, can be monitored in real-time through accelerometers mounted on each axle box so that the criteria of relevant standards are not exceeded. This study aims to identify the location and height of a single wheel flat based on non-stationary axle box acceleration (ABA) signals, which are generated through a train dynamics model with flexible wheelsets. The proposed feature extraction method is applied to extract the root mean square distribution of decomposed ABA signals on a balanced binary tree as orthogonal energy features using the Hilbert transform and wavelet packet decomposition. The neural network-based defect prediction model is created to define the relationship between input features and output labels. For insufficient input features, data augmentation is performed by the linear interpolation of existing features. The performance of defect prediction is evaluated in terms of the accuracy of detection and localization and improved by augmented input features and highly decomposed ABA signals. The results show that the trained neural network can predict the height and location of a single wheel flat from orthogonal energy features with high accuracy.