pwc-archive/papers-with-abstracts huggingface dataset:
Artificial Neural Network and Its Application Research Progress in Chemical Process
Authors: Li Sun, Fei Liang, Wutai Cui
Date: 2021-10-18 00:00:00
Tasks: Chemical Process, Fault Diagnosis

Most chemical processes, such as distillation, absorption, extraction, and catalytic reactions, are extremely complex processes that are affected by multiple factors. The relationships between their input variables and output variables are non-linear, and it is difficult to optimize or control them using traditional methods. Artificial neural network (ANN) is a systematic structure composed of multiple neuron models. Its main function is to simulate multiple basic functions of the nervous system of living organisms. ANN can achieve nonlinear control without relying on mathematical models, and is especially suitable for more complex control objects. This article will introduce the basic principles and development history of artificial neural networks, and review its application research progress in chemical process control, fault diagnosis, and process optimization.
Navigation of a UAV Equipped with a Reconfigurable Intelligent Surface for LoS Wireless Communication with a Ground Vehicle
Authors: Mohsen Eskandari, Hailong Huang, Andrey V. Savkin, Wei Ni
Date: 2021-10-18 00:00:00

Unmanned aerial vehicles (UAVs) have been successfully adopted to enhance the flexibility and robustness of wireless communication networks. And recently, the reconfigurable intelligent surface (RIS) technology has been paid increasing attention to improve the throughput of the fifth-generation (5G) millimeter-wave (mmWave) wireless communication. In this work, we propose an RIS-outfitted UAV (RISoUAV) to secure an uninterrupted line-of-sight (LoS) link with a ground moving target (MT). The MT can be an emergency ambulance and need a secure wireless communication link for continuous monitoring and diagnosing the health condition of a patient, which is vital for delivering critical patient care. In this light, real-time communication is required for sending various clinical multimedia data including videos, medical images, and vital signs. This significant target is achievable thanks to the 5G wireless communication assisted with RISoUAV. A two-stage optimization method is proposed to optimize the RISoUAV trajectory limited to UAV motion and LoS constraints. At the first stage, the optimal tube path of the RISoUAV is determined by taking into account the energy consumption, instant LoS link, and UAV speed/acceleration constraints. At the second stage, an accurate RISoUAV trajectory is obtained by considering the communication channel performance and passive beamforming. Simulation results show the accuracy and effectiveness of the method.
Joint SCSP-LROM: A novel approach to detect Cerebrovascular Anomalies from EEG signals
Authors: Debojyoti Seth
Date: 2021-10-17 00:00:00
Tasks: compressed sensing, EEG, Electroencephalogram (EEG)

It has always been a big challenge to identify subtle changes in Electroencephalogram (EEG) signals. Minor differences often lead to vital decisions, for example, which grade a certain tumour belong to or whether a haemorrhage can result in benign blood clots or cancerous ones. In recent studies on brain computer interfaces (BCIs), one of the biggest challenges is recovering maximum information for realistic predictions. In order to choose EEG channels with highest accuracy, a novel notion of including sparsity in a modified common spatial pattern (CSP) algorithm is introduced here. Being influenced by the existing concept of compressed sensing, an optimization model is also developed alongside to recover the cosparse signal and retain maximum information. The state-of-the-art Joint Sparsity Induced Modified Common Spatial Pattern Algorithm and Low Rank Optimization Model (SCSP-LROM) developed here is capable of identifying and describing tumours and lesions in great detail at an overall accuracy of 96.3%.
Sensor Scheduling for Linear Systems: A Covariance Tracking Approach
Authors: Dipankar Maity, David Hartman, John S. Baras
Date: 2021-10-17 00:00:00
Tasks: Scheduling

We consider the classical sensor scheduling problem for linear systems where only one sensor is activated at each time. We show that the sensor scheduling problem has a close relation to the sensor design problem and the solution of a sensor schedule problem can be extracted from an equivalent sensor design problem. We propose a convex relaxation to the sensor design problem and a reference covariance trajectory is obtained from solving the relaxed sensor design problem. Afterwards, a covariance tracking algorithm is designed to obtain an approximate solution to the sensor scheduling problem using the reference covariance trajectory obtained from the sensor design problem. While the sensor scheduling problem is NP-hard, the proposed framework circumvents this computational complexity by decomposing this problem into a convex sensor design problem and a covariance tracking problem. We provide theoretical justification and a sub-optimality bound for the proposed method using dynamic programming. The proposed method is validated over several experiments portraying the efficacy of the framework.
Neural-adaptive Stochastic Attitude Filter on SO(3)
Authors: Hashim A. Hashim, Mohammed Abouheaf, Kyriakos G. Vamvoudakis
Date: 2021-10-17 00:00:00

Successful control of a rigid-body rotating in three dimensional space requires accurate estimation of its attitude. The attitude dynamics are highly nonlinear and are posed on the Special Orthogonal Group $SO(3)$. In addition, measurements supplied by low-cost sensing units pose a challenge for the estimation process. This paper proposes a novel stochastic nonlinear neural-adaptive-based filter on $SO(3)$ for the attitude estimation problem. The proposed filter produces good results given measurements extracted from low-cost sensing units (e.g., IMU or MARG sensor modules). The filter is guaranteed to be almost semi-globally uniformly ultimately bounded in the mean square. In addition to Lie Group formulation, quaternion representation of the proposed filter is provided. The effectiveness of the proposed neural-adaptive filter is tested and evaluated in its discrete form under the conditions of large initialization error and high measurement uncertainties. keywords / index-terms: Neuro-adaptive, stochastic differential equations (SDEs), Brownian motion process, attitude estimator, Special Orthogonal Group, Unit-quaternion, SO(3), IMU, MARG.
Stable Marriage Matching for Traffic-Aware Space-Air-Ground Integrated Networks: A Gale-Shapley Algorithmic Approach
Authors: Hyunsoo Lee, Haemin Lee, Soyi Jung, Joongheon Kim
Date: 2021-10-17 00:00:00

In keeping with the rapid development of communication technology, a new communication structure is required in a next-generation communication system. In particular, research using High Altitude Platform (HAP) or Unmanned Aerial Vehicle(UAV) in existing terrestrial networks is active. In this paper, we propose matching HAP and UAV using the Gale-Shapley algorithm in a relay communication situation. The numerical simulation results demonstrate that applying the Gale-Shapley algorithm shows superior performance compared to random matching.
On Estimating the Probabilistic Region of Attraction for Partially Unknown Nonlinear Systems: An Sum-of-Squares Approach
Authors: Hejun Huang, Dongkun Han
Date: 2021-10-17 00:00:00
Tasks: Gaussian Processes

Estimating the region of attraction for partially unknown nonlinear systems is a challenging issue. In this paper, we propose a tractable method to generate an estimated region of attraction with probability bounds, by searching an optimal polynomial barrier function. Chebyshev interpolants, Gaussian processes and sum-of-squares programmings are used in this paper. To approximate the unknown non-polynomial dynamics, a polynomial mean function of Gaussian processes model is computed to represent the exact dynamics based on the Chebyshev interpolants. Furthermore, probabilistic conditions are given such that all the estimates are located in certain probability bounds. Numerical examples are provided to demonstrate the effectiveness of the proposed method.
Can CAV Reduce Non-Recurrent Urban Road Congestion?
Authors: Yunkai Li, Haotian Li, Beatriz Martinez-Pastor, Shen Wang
Date: 2021-10-16 00:00:00
Tasks: Autonomous Vehicles

A well-designed resilient and sustainable urban transportation system can recover quickly from the non-recurrent road congestion (NRC), which is often caused by en-route events (e.g., road closure due to car collisions). Existing solutions, such as on-board navigation systems and temporary rerouting road signs, are not effective due to delayed responses. Connected Autonomous Vehicles (CAV) can be helpful in improving recurrent traffic as they can autonomously adjust their speed according to their real-time surrounding traffic, sensed by vehicular communications. Preliminary simulation results in this short paper show that CAV can also improve traffic when non-recurrent congestion occurs. Other results in fuel consumption, CO2 emission, and traditional traffic safety indicators are open for future discussions.
Feedforward Control of DGs for a Self-healing Microgrid
Authors: Young-Jin Kim
Date: 2021-10-16 00:00:00

Network reconfiguration (NR) has recently received significant attention due to its potential to improve grid resilience by realizing self-healing microgrids (MGs). This paper proposes a new strategy for the real-time frequency regulation of a reconfigurable MG, wherein the feedforward control of synchronous and inverter-interfaced distributed generators (DGs) is achieved in coordination with the operations of sectionalizing and tie switches (SWs). This enables DGs to compensate more quickly, and preemptively, for a forthcoming variation in load demand due to NR-aided restoration. An analytical dynamic model of a reconfigurable MG is developed to analyze the MG frequency response to NR and hence determine the desired dynamics of the feedforward controllers, with the integration of feedback loops for inertial response emulation and primary and secondary frequency control. A small-signal analysis is conducted to analyze the contribution of the supplementary feedforward control to the MG frequency regulation. Simulation case studies of NR-aided load restoration are also performed. The results of the small-signal analysis and case studies confirm that the proposed strategy is effective for improving the MG frequency regulation under various conditions of load demand, model parameter errors, and communication time delays.
Non-Isolated Single-Switch Zeta Based High-Step up DC-DC Converter with Coupled Inductor
Authors: Armin Abadifard, Pedram Ghavidel, Seyed Hossein Hosseini, Masoud Farhadi
Date: 2021-10-15 00:00:00

In this paper, a non-isolated high step-up DC-DC converter has been proposed for renewable energy applications. The proposed structure converter has been derived from the fundamental Zeta converter, in both of which only a single switch is employed. The voltage gain ratio has considerably enhanced in this converter with the absence of using switched capacity. To magnify voltage gain of the converter, a coupled-inductor has adopted. Increase and decrease of gain by changing the ratio of coupled-inductor assist the duty cycle. The number of components is low in this structure. The operating principle and evaluation of the proposed converter, considering designing approaches for elements, are discussed in detail. To verify the feasibility of the proposed converter, simulation results have been provided and evaluated.
Coherent oscillations in balanced neural networks driven by endogenous fluctuations
Authors: Matteo di Volo, Marco Segneri, Denis Goldobin, Antonio Politi, Alessandro Torcini
Date: 2021-10-18 00:00:00

We present a detailed analysis of the dynamical regimes observed in a balanced network of identical Quadratic Integrate-and-Fire (QIF) neurons with a sparse connectivity for homogeneous and heterogeneous in-degree distribution. Depending on the parameter values, either an asynchronous regime or periodic oscillations spontaneously emerge. Numerical simulations are compared with a mean field model based on a self-consistent Fokker-Planck equation (FPE). The FPE reproduces quite well the asynchronous dynamics in the homogeneous case by either assuming a Poissonian or renewal distribution for the incoming spike trains. An exact self consistent solution for the mean firing rate obtained in the limit of infinite in-degree allows identifying balanced regimes that can be either mean- or fluctuation-driven. A low-dimensional reduction of the FPE in terms of circular cumulants is also considered. Two cumulants suffice to reproduce the transition scenario observed in the network. The emergence of periodic collective oscillations is well captured both in the homogeneous and heterogeneous setups by the mean field models upon tuning either the connectivity, or the input DC current. In the heterogeneous situation we analyze also the role of structural heterogeneity.
Path-integral solution of MacArthur's resource-competition model for large ecosystems with random species-resources couplings
Authors: A. R. Batista-Tomas, Andrea De Martino, Roberto Mulet
Date: 2021-10-18 00:00:00

We solve MacArthur's resource-competition model with random species-resource couplings in the `thermodynamic' limit of infinitely many species and resources using dynamical path-integrals a la De Domincis. We analyze how the steady state picture changes upon modifying several parameters, including the degree of heterogeneity of metabolic strategies (encoding the preferences of species) and of maximal resource levels (carrying capacities), and discuss its stability. Ultimately, the scenario obtained by other approaches is recovered by analyzing an effective one-species-one-resource ecosystem that is fully equivalent to the original multi-species one. The technique used here can be applied for the analysis of other model ecosystems related to the version of MacArthur's model considered here.
Prediction of Occurrence of Extreme Events using Machine Learning
Authors: J. Meiyazhagan, S. Sudharsan, A. Venkatasen, M. Senthilvelan
Date: 2021-10-11 00:00:00
Tasks: BIG-bench Machine Learning, Prediction

Machine learning models play a vital role in the prediction task in several fields of study. In this work, we utilize the ability of machine learning algorithms to predict the occurrence of extreme events in a nonlinear mechanical system. Extreme events are rare events that occur ubiquitously in nature. We consider four machine learning models, namely Logistic Regression, Support Vector Machine, Random Forest and Multi-Layer Perceptron in our prediction task. We train these four machine learning models using training set data and compute the performance of each model using the test set data. We show that the Multi-Layer Perceptron model performs better among the four models in the prediction of extreme events in the considered system. The persistent behaviour of the considered machine learning models is cross-checked with randomly shuffled training set and test set data.
On the dynamics of the contagious rate under isolation measures
Authors: Alejandro Cabo Montes de Oca, Nana Geraldine Cabo Bizet
Date: 2021-10-17 00:00:00

The infection dynamics of a population under stationary isolation conditions is modeled. It is underlined that the stationary character of the isolation measures can be expected to imply that an effective SIR model with constant parameters should describe the infection process. Then, a derivation of this property is presented, assuming that the statistical fluctuations in the number of infection and recovered cases are disregarded. This effective SIR model shows a reduced population number and a constant $\beta$ parameter. The effects of also including the retardation between recovery and infection process is also considered. Next, it is shown that any solution of the effective SIR also solves the linear problem to which the SIR equations reduce when the total population is much larger than the number of the infected cases. Then, it is also argued that this equivalence follows for a specific contagious parameter $\beta(t)$ which time dependence is analytically derived here. Then, two equivalent predictive calculational methods for the infection dynamics under stationary isolation measures are proposed. The results represent a solutions for the known and challenging problem of defining the time dependence of the contagion parameter, when the SIR parameter $N$ is assumed to be the whole population number. Finally, the model is applied to describe the known infection curves for countries that already had passed the epidemic process under strict stationary isolation measures. The cases of Iceland, New Zealand, Korea and Cuba were considered. Although, non subject to stationary isolation measures the cases of U.S.A. and Mexico are also examined due to their interest. The results support the argued validity of SIR model including retardation.
Cellular resource allocation strategies for cell size and shape control in bacteria
Authors: Diana Serbanescu, Nikola Ojkic, Shiladitya Banerjee
Date: 2021-10-16 00:00:00

Bacteria are highly adaptive microorganisms that thrive in a wide range of growth conditions via changes in cell morphologies and macromolecular composition. How bacterial morphologies are regulated in diverse environmental conditions is a longstanding question. Regulation of cell size and shape implies control mechanisms that couple the growth and division of bacteria to their cellular environment and macromolecular composition. In the past decade, simple quantitative laws have emerged that connect cell growth to proteomic composition and the nutrient availability. However, the relationships between cell size, shape and growth physiology remain challenging to disentangle and unifying models are lacking. In this review, we focus on regulatory models of cell size control that reveal the connections between bacterial cell morphology and growth physiology. In particular, we discuss how changes in nutrient conditions and translational perturbations regulate the cell size, growth rate and proteome composition. Integrating quantitative models with experimental data, we identify the physiological principles of bacterial size regulation, and discuss the optimization strategies of cellular resource allocation for size control.
Comparing One-step and Two-step Scatter Correction and Density Reconstruction in X-ray CT
Authors: Alexander N. Sietsema, Michael T. McCann, Marc L. Klasky, Saiprasad Ravishankar
Date: 2021-10-15 00:00:00
Tasks: Computed Tomography (CT)

In this work, we compare one-step and two-step approaches for X-ray computed tomography (CT) scatter correction and density reconstruction. X-ray CT is an important imaging technique in medical and industrial applications. In many cases, the presence of scattered X-rays leads to loss of contrast and undesirable artifacts in reconstructed images. Many approaches to computationally removing scatter treat scatter correction as a preprocessing step that is followed by a reconstruction step. Treating scatter correction and reconstruction jointly as a single, more complicated optimization problem is less studied. It is not clear from the existing literature how these two approaches compare in terms of reconstruction accuracy. In this paper, we compare idealized versions of these two approaches with synthetic experiments. Our results show that the one-step approach can offer improved reconstructions over the two-step approach, although the gap between them is highly object-dependent.
Prosecutor Politics: The Impact of Election Cycles on Criminal Sentencing in the Era of Rising Incarceration
Authors: Chika O. Okafor
Date: 2021-10-18 00:00:00

I investigate how political incentives affect the behavior of district attorneys (DAs). I develop a theoretical model that predicts DAs will increase sentencing intensity in an election period compared to the period prior. To empirically test this prediction, I compile one of the most comprehensive datasets to date on the political careers of all district attorneys in office during the steepest rise in incarceration in U.S. history (roughly 1986-2006). Using quasi-experimental methods, I find causal evidence that being in a DA election year increases total admissions per capita and total months sentenced per capita. I estimate that the election year effects on admissions are akin to moving 0.85 standard deviations along the distribution of DA behavior within state (e.g., going from the 50th to 80th percentile in sentencing intensity). I find evidence that election effects are larger (1) when DA elections are contested, (2) in Republican counties, and (3) in the southern United States--all these factors are consistent with the perspective that election effects arise from political incentives influencing DAs. Further, I find that district attorney election effects decline over the period 1986-2006, in tandem with U.S. public opinion softening regarding criminal punishment. These findings suggest DA behavior may respond to voter preferences--in particular to public sentiment regarding the harshness of the court system.
Estimating returns to special education: combining machine learning and text analysis to address confounding
Authors: Aurélien Sallin
Date: 2021-10-17 00:00:00

Leveraging unique insights into the special education placement process through written individual psychological records, I present results from the first ever study to examine short- and long-term returns to special education programs with causal machine learning and computational text analysis methods. I find that special education programs in inclusive settings have positive returns in terms of academic performance as well as labor-market integration. Moreover, I uncover a positive effect of inclusive special education programs in comparison to segregated programs. This effect is heterogenous: segregation has least negative effects for students with emotional or behavioral problems, and for nonnative students with special needs. Finally, I deliver optimal program placement rules that would maximize aggregated school performance and labor market integration for students with special needs at lower program costs. These placement rules would reallocate most students with special needs from segregation to inclusion.
Gender identity and relative income within household: Evidence from China
Authors: Han Dongcheng, Kong Fanbo, Wang Zixun
Date: 2021-10-17 00:00:00

How does women's obedience to traditional gender roles affect their labour outcomes? To investigate on this question, we employ discontinuity tests and fixed effect regressions with time lag to measure how married women in China diminish their labour outcomes so as to maintain the bread-winning status of their husbands. In the first half of this research, our discontinuity test exhibits a missing mass of married women who just out-earn their husbands, which is interpreted as an evidence showing that these females diminish their earnings under the influence of gender norms. In the second half, we use fixed effect regressions with time lag to assess the change of a female's future labour outcomes if she currently earns more than her husband. Our results suggest that women's future labour participation decisions (whether they still join the workforce) are unaffected, but their yearly incomes and weekly working hours will be reduced in the future. Lastly, heterogeneous studies are conducted, showing that low-income and less educated married women are more susceptible to the influence of gender norms.