pwc-archive/papers-with-abstracts huggingface dataset:
Local power estimation of neuromodulations using point process modeling
Authors: Shailaja Akella, Ali Mohebi, Kiersten Riels, Andreas Keil, Karim Oweiss, Jose C. Principe
Date: 2020-11-16 00:00:00

Extracellular electrical potentials (EEP) recorded from the brain are an active manifestation of all cellular processes that propagate within a volume of brain tissue. A standard approach for their quantification are power spectral analyses methods that reflect the global distribution of signal power over frequency. However, these methods incorporate analysis windows to achieve locality and therefore, are limited by the inherent trade - off between time and frequency resolutions. In this paper, we present a novel approach to estimate local power more precisely at a resolution as high as the sampling frequency. Our methods are well grounded on established neurophysiology of the bio-signals where we model EEPs as comprising of two components: neuromodulations and background activity. A local measure of power, we call Marked Point Process (MPP) spectrogram, is then derived as a power - weighted intensity function of the point process for neuromodulations. We demonstrate our results on two datasets: 1) local field potentials recorded from the prefrontal cortex of 3 rats performing a working memory task and 2) EEPs recorded via electroencephalography from the visual cortex of human subjects performing a conditioned stimulus task. A detailed analysis of the power - specific marked features of neuromodulations confirm high correlation between power spectral density and power in neuromodulations establishing the aptness of MPP spectrogram as a finer measure of power where it is able to track local variations in power while preserving the global structure of signal power distribution.
Strong and weak principles of neural dimension reduction
Authors: Mark D Humphries
Date: 2020-11-16 00:00:00
Tasks: Dimensionality Reduction

If spikes are the medium, what is the message? Answering that question is driving the development of large-scale, single neuron resolution recordings from behaving animals, on the scale of thousands of neurons. But these data are inherently high-dimensional, with as many dimensions as neurons - so how do we make sense of them? For many the answer is to reduce the number of dimensions. Here I argue we can distinguish weak and strong principles of neural dimension reduction. The weak principle is that dimension reduction is a convenient tool for making sense of complex neural data. The strong principle is that dimension reduction shows us how neural circuits actually operate and compute. Elucidating these principles is crucial, for which we subscribe to provides radically different interpretations of the same neural activity data. I show how we could make either the weak or strong principles appear to be true based on innocuous looking decisions about how we use dimension reduction on our data. To counteract these confounds, I outline the experimental evidence for the strong principle that do not come from dimension reduction; but also show there are a number of neural phenomena that the strong principle fails to address. To reconcile these conflicting data, I suggest that the brain has both principles at play.
Switching Device-Cognizant Sequential Distribution System Restoration
Authors: Anmar Arif, Bai Cui, Zhaoyu Wang
Date: 2020-11-16 00:00:00
Tasks: Computational Efficiency

This paper presents an optimization framework for sequential reconfiguration using an assortment of switching devices and repair process in distribution system restoration. Compared to existing studies, this paper considers types, capabilities and operational limits of different switching devices, making it applicable in practice. We develop a novel multi-phase method to find the optimal sequential operation of various switching devices and repair faulted areas. We consider circuit breakers, reclosers, sectionalizers, load breaker switches, and fuses. The switching operation problem is decomposed into two mixed-integer linear programming (MILP) subproblems. The first subproblem determines the optimal network topology and estimates the number of steps to reach that topology, while the second subproblem generates a sequence of switching operations to coordinate the switches. For repairing the faults, we design an MILP model that dispatches repair crews to clear faults and replace melted fuses. After clearing a fault, we update the topology of the network by generating a new sequence of switching operations, and the process continues until all faults are cleared. To improve the computational efficiency, a network reduction algorithm is developed to group line sections, such that only switchable sections are present in the reduced network. The proposed method is validated on the IEEE 123-bus and 8500-bus systems.
Fat tails arise endogenously in asset prices from supply/demand, with or without jump processes
Authors: Gunduz Caginalp
Date: 2020-11-16 00:00:00

We show that the quotient of Levy processes of jump-diffusion type has a fat-tailed distribution. An application is to price theory in economics. We show that fat tails arise endogenously from modeling of price change based on an excess demand analysis resulting in a quotient of arbitrarily correlated demand and supply whether or not jump discontinuities are present. The assumption is that supply and demand are described by drift terms, Brownian (i.e., Gaussian) and compound Poisson jump processes. If $P^{-1}dP/dt$ (the relative price change in an interval $dt$) is given by a suitable function of relative excess demand, $\left( \mathcal{D}% -\mathcal{S}\right) /\mathcal{S}$ (where $\mathcal{D}$ and $\mathcal{S}$ are demand and supply), then the distribution has tail behavior $F\left( x\right) \sim x^{-\zeta}$ for a power $\zeta$ that depends on the function $G$ in $P^{-1}dP/dt=G\left( \mathcal{D}/\mathcal{S}\right) $. For $G\left( x\right) \sim\left\vert x\right\vert ^{1/q}$ one has $\zeta=q.$ The empirical data for assets typically yields a value, $\zeta\tilde{=}3,$ or $\ \zeta \in\left[ 3,5\right] $ for some markets. The discrepancy between the empirical result and theory never arises if one models price dynamics using basic economics methodology, i.e., generalized Walrasian adjustment, rather than the usual starting point for classical finance which assumes a normal distribution of price changes. The function $G$ is deterministic, and can be calibrated with a smaller data set. The results establish a simple link between the decay exponent of the density function and the price adjustment function, a feature that can improve methodology for risk assessment. The mathematical results can be applied to other problems involving the relative difference or quotient of Levy processes of jump-diffusion type.
Option Pricing Incorporating Factor Dynamics in Complete Markets
Authors: Yuan Hu, Abootaleb Shirvani, W. Brent Lindquist, Frank J. Fabozzi, Svetlozar T. Rachev
Date: 2020-11-16 00:00:00

Using the Donsker-Prokhorov invariance principle we extend the Kim-Stoyanov-Rachev-Fabozzi option pricing model to allow for variably-spaced trading instances, an important consideration for short-sellers of options. Applying the Cherny-Shiryaev-Yor invariance principles, we formulate a new binomial path-dependent pricing model for discrete- and continuous-time complete markets where the stock price dynamics depends on the log-return dynamics of a market influencing factor. In the discrete case, we extend the results of this new approach to a financial market with informed traders employing a statistical arbitrage strategy involving trading of forward contracts. Our findings are illustrated with numerical examples employing US financial market data. Our work provides further support for the conclusion that any option pricing model must preserve valuable information on the instantaneous mean log-return, the probability of the stock's upturn movement (per trading interval), and other market microstructure features.
Joint Resource Optimization for IRS-Assisted mmWave MIMO under QoS Constraints
Authors: Qingfeng Ding, Xinpeng Gao, Zexiang Wu
Date: 2020-11-17 00:00:00

This letter focuses on the non-convex joint optimization with a dynamic resource of multi-user for an intelligent reflecting surface-enhanced mmWave system, where all users are concentrated on the unique cluster beam. Firstly, the objective function of the above non-linear problem is converted into a quadratic programming form under the quality of service constraints. Further, a multi-blocks alternating optimization framework with dynamic power allocation is proposed to obtain the maximum sum-rate, where the relaxed ADMM algorithm is adopted to tackle the optimal fulldigital precoder and the corresponding passive reflecting matrix is obtained by the gradient-projection. The numerical results verify that beam optimization should be emphasized in high SINR, but joint dynamic resource allocation can further improve system performance even if the hardware dimensions reaches the limit.
Ultra-Lightweight Speech Separation via Group Communication
Date: 2020-11-18 00:00:00
Tasks: Quantization, Speech Enhancement, Speech Separation

Model size and complexity remain the biggest challenges in the deployment of
speech enhancement and separation systems on low-resource devices such as
earphones and hearing aids. Although methods such as compression, distillation
and quantization can be applied to large models, they often come with a cost on
the model performance. In this paper, we provide a simple model design paradigm
that explicitly designs ultra-lightweight models without sacrificing the
performance. Motivated by the sub-band frequency-LSTM (F-LSTM) architectures,
we introduce the group communication (GroupComm), where a feature vector is
split into smaller groups and a small processing block is used to perform
inter-group communication. Unlike standard F-LSTM models where the sub-band
outputs are concatenated, an ultra-small module is applied on all the groups in
parallel, which allows a significant decrease on the model size. Experiment
results show that comparing with a strong baseline model which is already
lightweight, GroupComm can achieve on par performance with 35.6 times fewer
parameters and 2.3 times fewer operations.
Experimental assessment of polynomial nonlinear state-space and nonlinear-mode models for near-resonant vibrations
Authors: Maren Scheel, Gleb Kleyman, Ali Tatar, Matthew R. W. Brake, Simon Peter, Jean-Philippe Noël, Matthew S. Allen, Malte Krack
Date: 2020-11-17 00:00:00
Tasks: Cantilever Beam, State Space Models

In the present paper, two existing nonlinear system identification methodologies are used to identify data-driven models. The first methodology focuses on identifying the system using steady-state excitations. To accomplish this, a phase-locked loop controller is implemented to acquire periodic oscillations near resonance and construct a nonlinear-mode model. This model is based on amplitude-dependent modal properties, i.e. does not require nonlinear basis functions. The second methodology exploits uncontrolled experiments with broadband random inputs to build polynomial nonlinear state-space models using advanced system identification tools. The methods are applied to two experimental test rigs, a magnetic cantilever beam and a free-free beam with a lap joint. The respective models of both methods and both specimens are then challenged to predict dynamic, near-resonant behavior observed under different sine and sine-sweep excitations. The vibration prediction of the nonlinear-mode and state-space models clearly highlight the capabilities and limitations of the models. The nonlinear-mode model, by design, yields a perfect match at resonance peaks and high accuracy in close vicinity. However, it is limited to well-spaced modes and sinusoidal excitation. The state-space model covers a wider dynamic range, including transient excitations. However, the real-life nonlinearities considered in this study can only be approximated by polynomial basis functions. Consequently, the identified state-space models are found to be highly input-dependent, in particular for sinusoidal excitations where they are found to lead to a low predictive capability.
Generalized Filtrations and Its Application to Binomial Asset Pricing Models
Authors: Takanori Adachi, Katsushi Nakajima, Yoshihiro Ryu
Date: 2020-11-17 00:00:00

We introduce generalized filtration with which we can represent situations such as some agents forget information at some specific time. The filtration is defined as a functor to a category Prob whose objects are all probability spaces and whose arrows correspond to measurable functions satisfying an absolutely continuous requirement [Adachi and Ryu, 2019]. As an application of a generalized filtration, we develop a binomial asset pricing model, and investigate the valuations of financial claims along this type of non-standard filtrations.
Improving magnetic nanothermometry accuracy through mixing-frequency excitation
Date: 2020-11-17 00:00:00

In this study, we proposed a temperature model of magnetic nanoparticle
relaxation and a phase measurement method under a mixing-frequency excitation
field, which can improve the temperature accuracy of magnetic nanothermometry.
According to the Debye-based magnetization model for magnetic nanoparticles,
the phases at the mixing frequencies are used to solve the relaxation phase
delay to the magnetic field with the higher frequency. The method could improve
the signal-to-noise ratio of the magnetic response signal, and also weaken the
phase shift of the detection coils caused by temperature changes. Experimental
results show that the method can achieve static temperature measurement error
less than 0.1K and dynamic temperature measurement error less than 0.2K.
A tunable mixed feedback oscillator
Authors: Weiming Che, Fulvio Forni
Date: 2020-11-17 00:00:00

The interplay of positive and negative feedback loops on different time scales appears to be a fundamental mechanisms for robust and tunable oscillations in both biological systems and electro-mechanical systems. We develop a detailed analysis of a basic three dimensional Lure model to show how controlled oscillations arise from the tuning of positive and negative feedback strengths. Our analysis is based on dominance theory and confirms, from a system-theoretic perspective, that the mixed feedback is a fundamental enabler of robust oscillations. Our results are not limited to three dimensional systems and extend to larger systems via passivity theory, and to uncertain systems via small gain arguments.
Static Hedging of Weather and Price Risks in Electricity Markets
Authors: Javier Pantoja Robayo, Juan C. Vera
Date: 2020-11-17 00:00:00
Tasks: Form

We present the closed-form solution to the problem of hedging price and quantity risks for energy retailers (ER), using financial instruments based on electricity price and weather indexes. Our model considers an ER who is intermediary in a regulated electricity market. ERs buy a fixed quantity of electricity at a variable cost and must serve a variable demand at a fixed cost. Thus ERs are subject to both price and quantity risks. To hedge such risks, an ER could construct a portfolio of financial instruments based on price and weather indexes. We construct the closed form solution for the optimal portfolio for the mean-Var model in the discrete setting. Our model does not make any distributional assumption.
Estimating, Monitoring, and Forecasting the Covid-19 Epidemics: A Spatio-Temporal Approach Applied to NYC Data
Date: 2020-11-17 00:00:00

We propose an SEIR-type meta-population model to simulate and monitor the
Covid-19 epidemic evolution. The basic model consists of seven compartments,
namely susceptible (S), exposed (E), three infective classes, recovered (R),
and deceased (D). We define these compartments for n age and gender groups in m
different spatial locations. So, the resulting model has, for each age group,
gender, and place, all epidemiological classes. The mixing between them is
accomplished by means of time-dependent infection rate matrices. The model is
calibrated with the curve of daily new infections in New York City and its
boroughs, including census data, and the proportions of infections,
hospitalizations, and deaths for each age range. We end up with a model that
matches the reported curves and predicts accurately infection information for
different places and age classes.
Loss Comparison of Electric Vehicle Fuel Cell Integration Methods
Authors: Yuheng Wang, Mehanathan Pathmanathan, Peter W. Lehn
Date: 2020-11-17 00:00:00

This study analyzes and compares the drivetrain losses of two methods of fuel cell integration in electric vehicle drivetrains. The first is a conventional (boosted) two-stage system while the second is a dual inverter based solution. Each source of drivetrain losses is described by mathematical equations and the impact of higher order harmonics are observed through circuit model simulation. The dual inverter system achieves an overall energy efficiency improvement of 5.27% and 10.13% for highway and urban driving compared to the conventional method. The use of lower voltage rated power modules and lower switching frequency in the dual inverter system has significantly reduced the switching losses and improved the driving efficiency.
FPAENet: Pneumonia Detection Network Based on Feature Pyramid Attention Enhancement
Date: 2020-11-16 00:00:00
Tasks: Pneumonia Detection

Automatic pneumonia Detection based on deep learning has increasing clinical
value. Although the existing Feature Pyramid Network (FPN) and its variants
have already achieved some great successes, their detection accuracies for
pneumonia lesions in medical images are still unsatisfactory. In this paper, we
propose a pneumonia detection network based on feature pyramid attention
enhancement, which integrates attended high-level semantic features with
low-level information. We add another information extracting path equipped with
feature enhancement modules, which are conducted with an attention mechanism.
Experimental results show that our proposed method can achieve much better
performances, as a higher value of 4.02% and 3.19%, than the baselines in
detecting pneumonia lesions.
COVID-19 and the stock market: evidence from Twitter
Authors: Rahul Goel, Lucas Javier Ford, Maksym Obrizan, Rajesh Sharma
Date: 2020-11-13 00:00:00

COVID-19 has had a much larger impact on the financial markets compared to previous epidemics because the news information is transferred over the social networks at a speed of light. Using Twitter's API, we compiled a unique dataset with more than 26 million COVID-19 related Tweets collected from February 2nd until May 1st, 2020. We find that more frequent use of the word "stock" in daily Tweets is associated with a substantial decline in log returns of three key US indices - Dow Jones Industrial Average, S&P500, and NASDAQ. The results remain virtually unchanged in multiple robustness checks.
Robust Stability of Suboptimal Moving Horizon Estimation using an Observer-Based Candidate Solution
Authors: Julian D. Schiller, Sven Knüfer, Matthias A. Müller
Date: 2020-11-17 00:00:00
Tasks: Model Predictive Control

In this paper, we propose a suboptimal moving horizon estimator for nonlinear systems. For the stability analysis we transfer the "feasibility-implies-stability/robustness" paradigm from model predictive control to the context of moving horizon estimation in the following sense: Using a suitably defined, feasible candidate solution based on the trajectory of an auxiliary observer, robust stability of the proposed suboptimal estimator is inherited independently of the horizon length and even if no optimization is performed.
A Load Switching Group based Feeder-level Microgrid Energy Management Algorithm for Service Restoration in Power Distribution System
Date: 2020-11-17 00:00:00
Tasks: energy management, Management

This paper presents a load switching group based energy management system
(LSG-EMS) for operating microgrids on a distribution feeder powered by one or
multiple grid-forming distributed energy resources. Loads on a distribution
feeder are divided into load switching groups that can be remotely switched on
and off. The LSG-EMS algorithm, formulated as a mixed-integer linear
programming (MILP) problem, has an objective function of maximizing the served
loads while minimizing the total number of switching actions. A new set of
topology constraints are developed for allowing multiple microgrids to be
formed on the feeder and selecting the optimal supply path. Customer comfort is
accounted for by maximizing the supply duration in the customer preferred
service period and enforcing a minimum service duration. The proposed method is
demonstrated on a modified IEEE 33-bus system using actual customer data.
Simulation results show that the LSG-EMS successfully coordinates multiple
grid-forming sources by selecting an optimal supply topology that maximizes the
supply period of both the critical and noncritical loads while minimizing
customer service interruptions in the service restoration process.
A New Compartmental Epidemiological Model for COVID-19 with a Case Study of Portugal
Date: 2020-11-17 00:00:00

We propose a compartmental mathematical model for the spread of the COVID-19
disease, showing its usefulness with respect to the pandemic in Portugal, from
the first recorded case in the country till the end of the three states of
emergency. New results include the compartmental model, described by a system
of seven ordinary differential equations; proof of positivity and boundedness
of solutions; investigation of equilibrium points and their stability analysis;
computation of the basic reproduction number; and numerical simulations with
official real data from the Portuguese health authorities. Besides completely
new, the proposed model allows to describe quite well the spread of COVID-19 in
Portugal, fitting simultaneously not only the number of active infected
individuals but also the number of hospitalized individuals, respectively with
a $L^2$ error of $9.2152e-04$ and $1.6136e-04$ with respect to the initial
population. Such results are very important, from a practical point of view,
and far from trivial from a mathematical perspective. Moreover, the obtained
value for the basic reproduction number is in agreement with the one given by
the Portuguese authorities at the end of the emergency states.
Learning Sentinel-2 Spectral Dynamics for Long-Run Predictions Using Residual Neural Networks
Authors: Joaquim Estopinan, Guillaume Tochon, Lucas Drumetz
Date: 2020-11-17 00:00:00
Tasks: Time Series, Time Series Analysis

Making the most of multispectral image time-series is a promising but still relatively under-explored research direction because of the complexity of jointly analyzing spatial, spectral and temporal information. Capturing and characterizing temporal dynamics is one of the important and challenging issues. Our new method paves the way to capture real data dynamics and should eventually benefit applications like unmixing or classification. Dealing with time-series dynamics classically requires the knowledge of a dynamical model and an observation model. The former may be incorrect or computationally hard to handle, thus motivating data-driven strategies aiming at learning dynamics directly from data. In this paper, we adapt neural network architectures to learn periodic dynamics of both simulated and real multispectral time-series. We emphasize the necessity of choosing the right state variable to capture periodic dynamics and show that our models can reproduce the average seasonal dynamics of vegetation using only one year of training data.