pwc-archive/papers-with-abstracts huggingface dataset:
Electron transport in DNA bases: An extension of the Geant4-DNA Monte Carlo toolkit
Authors: Sara A. Zein, Marie-Claude Bordage, Ziad Francis, Giovanni Macetti, Alessandro Genoni, Claude Dal Cappello, Wook-Geun Shin, Sebastien Incerti
Date: 2020-10-21 00:00:00

The purpose of this work is to extend the Geant4-DNA Monte Carlo toolkit to include electron interactions with the four DNA bases using a set of cross sections recently implemented in Geant-DNA CPA100 models and available for liquid water. Electron interaction cross sections for elastic scattering, ionisation, and electronic excitation were calculated in the four DNA bases adenine, thymine, guanine and cytosine. The electron energy range is extended to include relativistic electrons. Elastic scattering cross sections were calculated using the independent atom model with amplitude derived from ELSEPA code. Relativistic Binary Encounter Bethe Vriens model was used to calculate ionisation cross sections. The electronic excitation cross sections calculations were based on the water cross sections following the same strategy used in CPA100 code. These were implemented within the Geant4-DNA option6 physics constructor to extend its capability of tracking electrons in DNA material in addition to liquid water. Since DNA nucleobases have different molecular structure than water it is important to perform more accurate simulations especially because DNA is considered the most radiosensitive structure in cells. Differential and integrated cross sections calculations were in good agreement with data from the literature for all DNA bases. Stopping power, range and inelastic mean free path calculations in the four DNA bases using this new extension of Geant4-DNA option6 are in good agreement with calculations done by other studies, especially for high energy electrons. Some deviations are shown at the low electron energy range, which could be attributed to the different interaction models. Comparison with water simulations shows obvious difference which emphasizes the need to include DNA bases cross sections in track structure codes for better estimation of radiation effects on biological material.
OTFS Channel Estimation And Data Detection Designs With Superimposed Pilots
Authors: Himanshu B. Mishra, Prem Singh, Abhishek K. Prasad, Rohit Budhiraja
Date: 2020-10-28 00:00:00

This work proposes a superimposed pilot (SP)-based channel estimation and data detection framework for orthogonal time-frequency space (OTFS) scheme, wherein low-powered pilots are superimposed on to data symbols in the delay-Doppler domain. We propose two channel estimation and data detection designs for SP-OTFS systems which, unlike the existing OTFS designs, do not designate any slots for pilots, which improves their spectral efficiency (SE). The first SP design estimates channel by treating data as interference, which degrades its performance at high signal to noise ratio. The second SP design alleviates this problem by iterating between channel estimation and data detection. Both these designs detect data using message passing algorithm which exploits OTFS channel sparsity, and consequently has low computational complexity. We also derive a lower bound on the signal-to-interference-plus-noise ratio of the proposed designs, and maximize it by optimally allocating power between data and pilot symbols. We numerically validate the derived analytical results, and show that the proposed designs have superior SE than the two state-of-the-art OTFS channel estimation and data detection designs.
Price response functions and spread impact in correlated financial markets
Authors: Juan C. Henao-Londono, Sebastian M. Krause, Thomas Guhr
Date: 2020-10-28 00:00:00

Recent research on the response of stock prices to trading activity revealed long lasting effects, even across stocks of different companies. These results imply non-Markovian effects in price formation and when trading many stocks at the same time, in particular trading costs and price correlations. How the price response is measured depends on data set and research focus. However, it is important to clarify, how the details of the price response definition modify the results. Here, we evaluate different price response implementations for the Trades and Quotes (TAQ) data set from the NASDAQ stock market and find that the results are qualitatively the same for two different definitions of time scale, but the response can vary by up to a factor of two. Further, we show the key importance of the order between trade signs and returns, displaying the changes in the signal strength. Moreover, we confirm the dominating contribution of immediate price response directly after a trade, as we find that delayed responses are suppressed. Finally, we test the impact of the spread in the price response, detecting that large spreads have stronger impact.
Safety-Aware Cascade Controller Tuning Using Constrained Bayesian Optimization
Authors: Christopher König, Mohammad Khosravi, Markus Maier, Roy S. Smith, Alisa Rupenyan, John Lygeros
Date: 2020-10-28 00:00:00
Tasks: Bayesian Optimization, Gaussian Processes

This paper presents an automated, model-free, data-driven method for the safe tuning of PID cascade controller gains based on Bayesian optimization. The optimization objective is composed of data-driven performance metrics and modeled using Gaussian processes. We further introduce a data-driven constraint that captures the stability requirements from system data. Numerical evaluation shows that the proposed approach outperforms relay feedback autotuning and quickly converges to the global optimum, thanks to a tailored stopping criterion. We demonstrate the performance of the method in simulations and experiments. For experimental implementation, in addition to the introduced safety constraint, we integrate a method for automatic detection of the critical gains and extend the optimization objective with a penalty depending on the proximity of the current candidate points to the critical gains. The resulting automated tuning method optimizes system performance while ensuring stability and standardization
Inference of ventricular activation properties from non-invasive electrocardiography
Authors: Julia Camps, Brodie Lawson, Christopher Drovandi, Ana Minchole, Zhinuo Jenny Wang, Vicente Grau, Kevin Burrage, Blanca Rodriguez
Date: 2020-10-28 00:00:00
Tasks: Decision Making, Diagnostic, Dynamic Time Warping, Rhythm

The realisation of precision cardiology requires novel techniques for the non-invasive characterisation of individual patients' cardiac function to inform therapeutic and diagnostic decision-making. The electrocardiogram (ECG) is the most widely used clinical tool for cardiac diagnosis. Its interpretation is, however, confounded by functional and anatomical variability in heart and torso. In this study, we develop new computational techniques to estimate key ventricular activation properties for individual subjects by exploiting the synergy between non-invasive electrocardiography and image-based torso-biventricular modelling and simulation. More precisely, we present an efficient sequential Monte Carlo approximate Bayesian computation-based inference method, integrated with Eikonal simulations and torso-biventricular models constructed based on clinical cardiac magnetic resonance (CMR) imaging. The method also includes a novel strategy to treat combined continuous (conduction speeds) and discrete (earliest activation sites) parameter spaces, and an efficient dynamic time warping-based ECG comparison algorithm. We demonstrate results from our inference method on a cohort of twenty virtual subjects with cardiac volumes ranging from 74 cm3 to 171 cm3 and considering low versus high resolution for the endocardial discretisation (which determines possible locations of the earliest activation sites). Results show that our method can successfully infer the ventricular activation properties from non-invasive data, with higher accuracy for earliest activation sites, endocardial speed, and sheet (transmural) speed in sinus rhythm, rather than the fibre or sheet-normal speeds.
Design Diversity for Improving Efficiency and Reducing Risk in Oil and Gas Well Stimulation under Uncertain Reservoir Conditions
Authors: Cheng Cheng
Date: 2020-10-27 00:00:00
Tasks: Diversity

Hydraulic fracturing stimulates fracture swarm in reservoir formation though pressurized injection fluid. However restricted by the availability of formation data, the variability embraced by reservoir keeps uncertain, driving unstable gas recovery along with low resource efficiency, being responsible for resource scarcity, contaminated water, and injection-induced earthquake. Resource efficiency is qualified though new determined energy efficiency, a scale of recovery and associated environmental footprint. To maximize energy efficiency while minimize its' variation, we issue picked designs at reservoir conditions dependent optimal probabilities, assembling high efficiency portfolios and low risk portfolios for portfolio combination, which balance the variation and efficiency at optimal by adjusting the proportion of each portfolio. Relative to regular design for one well, the optimal portfolio combination applied in multiple wells receive remarkable variation reduction meanwhile substantial energy efficiency increase, in response to the call of more recovery per unit investment and less environment cost per unit nature gas extracted.
Dynamic Default Contagion in Heterogeneous Interbank Systems
Authors: Zachary Feinstein, Andreas Sojmark
Date: 2020-10-28 00:00:00

In this work we provide a simple setting that connects the structural modelling approach of Gai-Kapadia interbank networks with the mean-field approach to default contagion. To accomplish this we make two key contributions. First, we propose a dynamic default contagion model with endogenous early defaults for a finite set of banks, generalising the Gai-Kapadia framework. Second, we reformulate this system as a stochastic particle system leading to a limiting mean-field problem. We study the existence of these clearing systems and, for the mean-field problem, the continuity of the system response.
ACCDOA: Activity-Coupled Cartesian Direction of Arrival Representation for Sound Event Localization and Detection
Authors: Kazuki Shimada, Yuichiro Koyama, Naoya Takahashi, Shusuke Takahashi, Yuki Mitsufuji
Date: 2020-10-29 00:00:00
Tasks: Event Detection, Sound Event Detection, Sound Event Localization and Detection

Neural-network (NN)-based methods show high performance in sound event localization and detection (SELD). Conventional NN-based methods use two branches for a sound event detection (SED) target and a direction-of-arrival (DOA) target. The two-branch representation with a single network has to decide how to balance the two objectives during optimization. Using two networks dedicated to each task increases system complexity and network size. To address these problems, we propose an activity-coupled Cartesian DOA (ACCDOA) representation, which assigns a sound event activity to the length of a corresponding Cartesian DOA vector. The ACCDOA representation enables us to solve a SELD task with a single target and has two advantages: avoiding the necessity of balancing the objectives and model size increase. In experimental evaluations with the DCASE 2020 Task 3 dataset, the ACCDOA representation outperformed the two-branch representation in SELD metrics with a smaller network size. The ACCDOA-based SELD system also performed better than state-of-the-art SELD systems in terms of localization and location-dependent detection.
Short term memory by transient oscillatory dynamics in recurrent neural networks
Authors: Kohei Ichikawa, Kunihiko Kaneko
Date: 2020-10-29 00:00:00

Despite the significance of short-term memory in cognitive function, the process of encoding and sustaining the input information in neural activity dynamics remains elusive. Herein, we unveiled the significance of transient neural dynamics to short-term memory. By training recurrent neural networks to short-term memory tasks and analyzing the dynamics, the characteristics of the short-term memory mechanism were obtained in which the input information was encoded in the amplitude of transient oscillations, rather than the stationary neural activities. This transient trajectory was attracted to a slow manifold, which permitted the discarding of irrelevant information. Additionally, we investigated the process by which the dynamics acquire robustness to noise. In this transient oscillation, the robustness to noise was obtained by a strong contraction of the neural states after perturbation onto the manifold. This mechanism works for several neural network models and tasks, which implies its relevance to neural information processing in general.
DeviceTTS: A Small-Footprint, Fast, Stable Network for On-Device Text-to-Speech
Date: 2020-10-29 00:00:00
Tasks: Decoder, text-to-speech, Text to Speech

With the number of smart devices increasing, the demand for on-device
text-to-speech (TTS) increases rapidly. In recent years, many prominent
End-to-End TTS methods have been proposed, and have greatly improved the
quality of synthesized speech. However, to ensure the qualified speech, most
TTS systems depend on large and complex neural network models, and it's hard to
deploy these TTS systems on-device. In this paper, a small-footprint, fast,
stable network for on-device TTS is proposed, named as DeviceTTS. DeviceTTS
makes use of a duration predictor as a bridge between encoder and decoder so as
to avoid the problem of words skipping and repeating in Tacotron. As we all
know, model size is a key factor for on-device TTS. For DeviceTTS, Deep
Feedforward Sequential Memory Network (DFSMN) is used as the basic component.
Moreover, to speed up inference, mix-resolution decoder is proposed for balance
the inference speed and speech quality. Experiences are done with WORLD and
LPCNet vocoder. Finally, with only 1.4 million model parameters and 0.099
GFLOPS, DeviceTTS achieves comparable performance with Tacotron and FastSpeech.
As far as we know, the DeviceTTS can meet the needs of most of the devices in
practical application.
Bifurcation of the neuronal population dynamics of the modified theta model: transition to macroscopic gamma oscillation
Authors: Kiyoshi Kotani, Akihiko Akao, Hayato Chiba
Date: 2020-10-29 00:00:00

Interactions of inhibitory neurons produce gamma oscillations (30--80 Hz) in the local field potential, which is known to be involved in functions such as cognition and attention. In this study, the modified theta model is considered to investigate the theoretical relationship between the microscopic structure of inhibitory neurons and their gamma oscillations under a wide class of distribution functions of tonic currents on individual neurons. The stability and bifurcation of gamma oscillations for the Vlasov equation of the model is investigated by the generalized spectral theory. It is shown that as a connection probability of neurons increases, a pair of generalized eigenvalues crosses the imaginary axis twice, which implies that a stable gamma oscillation exists only when the connection probability has a value within a suitable range. On the other hand, when the distribution of tonic currents on individual neurons is the Lorentzian distribution, the Vlasov equation is reduced to a finite dimensional dynamical system. The bifurcation analyses of the reduced equation exhibit equivalent results with the generalized spectral theory. It is also demonstrated that the numerical computations of neuronal population follow the analyses of the generalized spectral theory as well as the bifurcation analysis of the reduced equation.
A New Model-Free Method Combined with Neural Networks for MIMO Systems
Date: 2020-10-29 00:00:00

In this brief, a model-free adaptive predictive control (MFAPC) is proposed. It outperforms the current model-free adaptive control (MFAC) for not only solving the time delay problem in multiple-input multiple-output (MIMO) systems but also relaxing the current rigorous assumptions for sake of a wider applicable range. The most attractive merit of the proposed controller is that the controller design, performance analysis and applications are easy for engineers to realize. Furthermore, the problem of how to choose the matrix {\lambda} is finished by analyzing the function of the closed-loop poles rather than the previous contraction mapping method. Additionally, in view of the nonlinear modeling capability and adaptability of neural networks (NNs), we combine these two classes of algorithms together. The feasibility and several interesting results of the proposed method are shown in simulations.
Advanced Biophysical Model to Capture Channel Variability for EQS Capacitive HBC
Authors: Arunashish Datta, Mayukh Nath, David Yang, Shreyas Sen
Date: 2020-10-29 00:00:00

Human Body Communication (HBC) has come up as a promising alternative to traditional radio frequency (RF) Wireless Body Area Network (WBAN) technologies. This is essentially due to HBC providing a broadband communication channel with enhanced signal security in the physical layer due to lower radiation from the human body as compared to its RF counterparts. An in-depth understanding of the mechanism for the channel loss variability and associated biophysical model needs to be developed before EQS-HBC can be used more frequently in WBAN consumer and medical applications. Biophysical models characterizing the human body as a communication channel didn't exist in literature for a long time. Recent developments have shown models that capture the channel response for fixed transmitter and receiver positions on the human body. These biophysical models do not capture the variability in the HBC channel for varying positions of the devices with respect to the human body. In this study, we provide a detailed analysis of the change in path loss in a capacitive-HBC channel in the electroquasistatic (EQS) domain. Causes of channel loss variability namely: inter-device coupling and effects of fringe fields due to body's shadowing effects are investigated. FEM based simulation results are used to analyze the channel response of human body for different positions and sizes of the device which are further verified using measurement results to validate the developed biophysical model. Using the bio-physical model, we develop a closed form equation for the path loss in a capacitive HBC channel which is then analyzed as a function of the geometric properties of the device and the position with respect to the human body which will help pave the path towards future EQSHBC WBAN design.
Distance Invariant Sparse Autoencoder for Wireless Signal Strength Mapping
Authors: Renato Miyagusuku, Koichi Ozaki
Date: 2020-10-29 00:00:00
Tasks: Data Compression

Wireless signal strength based localization can enable robust localization for robots using inexpensive sensors. For this, a location-to-signal-strength map has to be learned for each access point in the environment. Due to the ubiquity of Wireless networks in most environments, this can result in tens or hundreds of maps. To reduce the dimensionality of this problem, we employ autoencoders, which are a popular unsupervised approach for feature extraction and data compression. In particular, we propose the use of sparse autoencoders that learn latent spaces that preserve the relative distance between inputs. Distance invariance between input and latent spaces allows our system to successfully learn compact representations that allow precise data reconstruction but also have a low impact on localization performance when using maps from the latent space rather than the input space. We demonstrate the feasibility of our approach by performing experiments in outdoor environments.
Multiscale characteristics of the emerging global cryptocurrency market
Authors: Marcin Wątorek, Stanisław Drożdż, Jarosław Kwapień, Ludovico Minati, Paweł Oświęcimka, Marek Stanuszek
Date: 2020-10-29 00:00:00
Tasks: Time Series Analysis

The review introduces the history of cryptocurrencies, offering a description of the blockchain technology behind them. Differences between cryptocurrencies and the exchanges on which they are traded have been shown. The central part surveys the analysis of cryptocurrency price changes on various platforms. The statistical properties of the fluctuations in the cryptocurrency market have been compared to the traditional markets. With the help of the latest statistical physics methods the non-linear correlations and multiscale characteristics of the cryptocurrency market are analyzed. In the last part the co-evolution of the correlation structure among the 100 cryptocurrencies having the largest capitalization is retraced. The detailed topology of cryptocurrency network on the Binance platform from bitcoin perspective is also considered. Finally, an interesting observation on the Covid-19 pandemic impact on the cryptocurrency market is presented and discussed: recently we have witnessed a "phase transition" of the cryptocurrencies from being a hedge opportunity for the investors fleeing the traditional markets to become a part of the global market that is substantially coupled to the traditional financial instruments like the currencies, stocks, and commodities. The main contribution is an extensive demonstration that structural self-organization in the cryptocurrency markets has caused the same to attain complexity characteristics that are nearly indistinguishable from the Forex market at the level of individual time-series. However, the cross-correlations between the exchange rates on cryptocurrency platforms differ from it. The cryptocurrency market is less synchronized and the information flows more slowly, which results in more frequent arbitrage opportunities. The methodology used in the review allows the latter to be detected, and lead-lag relationships to be discovered.
Novel Digital Camera with the PCIe Interface
Date: 2020-10-29 00:00:00
Tasks: CPU, Diagnostic

Digital cameras are commonly used for diagnostic purposes in large-scale
physics experiments. A typical image diagnostic system consists of an optical
setup, digital camera, frame grabber, image processing CPU, and data analysis
tool. The standard architecture of the imaging system has a number of
disadvantages. Data transmitted from a camera are buffered multiple times and
must be converted between various protocols before they are finally transmitted
to the host memory. Such an architecture makes the system quite complicated,
limits its performance and, in consequence, increases its price. The
limitations are even more critical for control or protection systems operating
in real-time. Modern megapixel cameras generate large data throughput, easily
exceeding 10 Gb/s, which often requires some additional processing on the host
side. The optimal system architecture should assure low overhead and high
performance of the data transmission and processing. It is particularly
important during the processing of data streams from several imaging devices,
which can be as high as several terabits per second. A novel architecture of
image acquisition and processing system based on the PCI Express interface was
proposed to meet the requirements of real-time imaging systems applied in
large-scale physics experiments. The architecture allows to transfer an image
stream directly from the camera to the data processing unit and therefore
significantly decreases the overhead and improves performance.
An introduction to the mathematical modelling of iPSCs
Authors: Laura E Wadkin, Sirio Orozco-Fuentes, Irina Neganova, Majlinda Lako, Nicholas G Parker, Anvar Shukurov
Date: 2020-10-29 00:00:00

The aim of this chapter is to convey the importance and usefulness of mathematical modelling as a tool to achieve a deeper understanding of stem cell biology. We introduce key mathematical concepts (random walk theory, differential equations and agent-based modelling) which form the basis of current descriptions of induced pluripotent stem cells. We hope to encourage a meaningful dialogue between biologists and mathematicians and highlight the value of such an interdisciplinary approach.