pwc-archive/papers-with-abstracts huggingface dataset:
Simplified Stability Assessment of Power Systems with Variable-Delay Wide-Area Damping Control
Authors: K. K. Gajjar, Kaustav Dey, A. M. Kulkarni
Date: 2022-03-02 00:00:00

Power electronic devices such as HVDC and FACTS can be used to improve the damping of poorly damped inter-area modes in large power systems. This involves the use of wide-area feedback signals, which are transmitted via communication networks. The performance of the closed-loop system is strongly influenced by the delay associated with wide-area signals. The random nature of this delay introduces a switched linear system model. The stability assessment of such a system requires linear matrix inequality based approaches. This makes the stability analysis more complicated as the system size increases. To address this challenge, this paper proposes a delay-processing strategy that simplifies the modelling and analysis in discrete-domain. In contrast to the existing stability assessment techniques, the proposed approach is advantageous because the stability, as well as damping performance, can be accurately predicted by a simplified analysis. The proposed methodology is verified with a case study on the 2-area 4-machine power system with a series compensated tie-line. The results are found to be in accordance with the predictions of the proposed simplified analysis.
Design and comparison of two linear controllers with precompensation gain for the Quadruple inverted pendulum
Authors: Franklin Josue Ticona Coaquira
Date: 2022-02-28 00:00:00
Tasks: Position

In this work we present a workflow for designing two linear control techniques applied to the dynamic system quadruple inverted pendulum mounted on a cart (QIP) where the steady state error on cart position is eliminated through a precompensation gain. The first control law designed was based on LQR, technique that stabilizes the system states based on a minimization of the total energy of the system states and the second control technique was pole placement by 2nd order system approximation, this method allowed to define a settling time and overshoot of the desired response of the plant, for this control the system was considered SISO, being the only output of the plant the horizontal position of the cart. To eliminate the steady-state error generated by both linear controllers, an analysis of the stationary response of the system under control in the Laplace Domain was performed. The simulation environment used to validate the results presented in this paper was Simscape of Matlab, it is a simulation environment that allowed to simulate the dynamic of nonlinear mechanical systems. The results obtained indicate that both controllers are able to stabilize the plant and move the cart position to the desired setpoint. Finally, we discuss about the performances of both linear control techniques.
Multi-system intervention optimization for interdependent infrastructure
Authors: Omar Kammouh, Maria Nogal, Ruud Binnekamp, Rogier Wolfert
Date: 2022-02-23 00:00:00

The wellbeing of modern societies is dependent upon the functioning of their infrastructure networks. This paper introduces the 3C concept, an integrative multi-system and multi-stakeholder optimization approach for managing infrastructure interventions (e.g., maintenance, renovation, etc.). The proposed approach takes advantage of the benefits achieved by grouping (i.e., optimizing) intervention activities. Intervention optimization leads to substantial savings on both direct intervention costs (operator) and indirect unavailability costs (society) by reducing the number of system interruptions. The proposed optimization approach is formalized into a structured mathematical model that can account for the interactions between multiple infrastructure networks and the impact on multiple stakeholders (e.g., society and infrastructure operators), and it can accommodate different types of intervention, such as maintenance, removal, and upgrading. The different types of interdependencies, within and across infrastructures, are modeled using a proposed interaction matrix (IM). The IM allows integrating the interventions of different infrastructure networks whose interventions are normally planned independently. Moreover, the introduced 3C concept accounts for central interventions, which are those that must occur at a pre-established time moment, where neither delay nor advance is permitted. To demonstrate the applicability of the proposed approach, an illustrative example of a multi-system and multi-actor intervention planning is introduced. Results show a substantial reduction in the operator and societal costs. In addition, the optimal intervention program obtained in the analysis shows no predictable patterns, which indicates it is a useful managerial decision support tool.
The Cost of Influence: How Gifts to Physicians Shape Prescriptions and Drug Costs
Authors: Melissa Newham, Marica Valente
Date: 2022-03-03 00:00:00
Tasks: Causal Inference

This paper studies how gifts - monetary or in-kind payments - from drug firms to physicians in the US affect prescriptions and drug costs. We estimate heterogeneous treatment effects by combining physician-level data on antidiabetic prescriptions and payments with causal inference and machine learning methods. We find that payments cause physicians to prescribe more brand drugs, resulting in a cost increase of $30 per dollar received. Responses differ widely across physicians, and are primarily explained by variation in patients' out-of-pocket costs. A gift ban is estimated to decrease drug costs by 3-4%. Taken together, these novel findings reveal how payments shape prescription choices and drive up costs.
Constitutional Implementation of Affirmative Action Policies in India
Authors: Tayfun Sonmez, M. Bumin Yenmez
Date: 2022-03-03 00:00:00

India is home to a comprehensive affirmative action program that reserves a fraction of positions at governmental institutions for various disadvantaged groups. While there is a Supreme Court-endorsed mechanism to implement these reservation policies when all positions are identical, courts have refrained from endorsing explicit mechanisms when positions are heterogeneous. This lacunae has resulted in widespread adoption of unconstitutional mechanisms, countless lawsuits, and inconsistent court rulings. Formulating mandates in the landmark Supreme Court judgment Saurav Yadav (2020) as technical axioms, we show that the 2SMH-DA mechanism is uniquely suited to overcome these challenges.
ADPCM with nonlinear prediction
Authors: Marcos Faundez-Zanuy, Oscar Oliva-Suarez
Date: 2022-02-24 00:00:00
Tasks: Prediction

Many speech coders are based on linear prediction coding (LPC), nevertheless with LPC is not possible to model the nonlinearities present in the speech signal. Because of this there is a growing interest for nonlinear techniques. In this paper we discuss ADPCM schemes with a nonlinear predictor based on neural nets, which yields an increase of 1-2.5dB in the SEGSNR over classical methods. This paper will discuss the block-adaptive and sample-adaptive predictions.
Multi-Objective Design Space Exploration for the Optimization of the HEVC Mode Decision Process
Authors: Christian Herglotz, Rafael Rosales, Michael Glass, Jürgen Teich, André Kaup
Date: 2022-03-03 00:00:00

Finding the best possible encoding decisions for compressing a video sequence is a highly complex problem. In this work, we propose a multi-objective Design Space Exploration (DSE) method to automatically find HEVC encoder implementations that are optimized for several different criteria. The DSE shall optimize the coding mode evaluation order of the mode decision process and jointly explore early skip conditions to minimize the four objectives a) bitrate, b) distortion, c) encoding time, and d) decoding energy. In this context, we use a SystemC-based actor model of the HM test model encoder for the evaluation of each explored solution. The evaluation that is based on real measurements shows that our framework can automatically generate encoder solutions that save more than 60% of encoding time or 3% of decoding energy when accepting bitrate increases of around 3%.
Estimation of Non-Functional Properties for Embedded Hardware with Application to Image Processing
Authors: Christian Herglotz, Jürgen Seiler, André Kaup, Arne Hendricks, Marc Reichenbach, Dietmar Fey
Date: 2022-03-03 00:00:00
Tasks: CPU

In recent years, due to a higher demand for portable devices, which provide restricted amounts of processing capacity and battery power, the need for energy and time efficient hard- and software solutions has increased. Preliminary estimations of time and energy consumption can thus be valuable to improve implementations and design decisions. To this end, this paper presents a method to estimate the time and energy consumption of a given software solution, without having to rely on the use of a traditional Cycle Accurate Simulator (CAS). Instead, we propose to utilize a combination of high-level functional simulation with a mechanistic extension to include non-functional properties: Instruction counts from virtual execution are multiplied with corresponding specific energies and times. By evaluating two common image processing algorithms on an FPGA-based CPU, where a mean relative estimation error of 3% is achieved for cacheless systems, we show that this estimation tool can be a valuable aid in the development of embedded processor architectures. The tool allows the developer to reach well-suited design decisions regarding the optimal processor hardware configuration for a given algorithm at an early stage in the design process.
Estimating the HEVC Decoding Energy Using the Decoder Processing Time
Authors: Christian Herglotz, Elisabeth Walencik, André Kaup
Date: 2022-03-03 00:00:00
Tasks: Decoder, valid, Video Compression

This paper presents a method to accurately estimate the required decoding energy for a given HEVC software decoding solution. We show that the decoder's processing time as returned by common C++ and UNIX functions is a highly suitable parameter to obtain valid estimations for the actual decoding energy. We verify this hypothesis by performing an exhaustive measurement series using different decoder setups and video bit streams. Our findings can be used by developers and researchers in the search for new energy saving video compression algorithms.
Joint Optimization of Rate, Distortion, and Decoding Energy for HEVC Intraframe Coding
Authors: Christian Herglotz, André Kaup
Date: 2022-03-03 00:00:00
Tasks: Decoder, Quantization

This paper presents a novel algorithm that aims at minimizing the required decoding energy by exploiting a general energy model for HEVC-decoder solutions. We incorporate the energy model into the HEVC encoder such that it is capable of constructing a bit stream whose decoding process consumes less energy than the decoding process of a conventional bit stream. To achieve this, we propose to extend the traditional Rate-Distortion-Optimization scheme to a Decoding-Energy-Rate-Distortion approach. To obtain fast encoding decisions in the optimization process, we derive a fixed relation between the quantization parameter and the Lagrange multiplier for energy optimization. Our experiments show that this concept is applicable for intraframe-coded videos and that for local playback as well as online streaming scenarios, up to 15% of the decoding energy can be saved at the expense of a bitrate increase of approximately the same magnitude.
Modeling the Energy Consumption of HEVC Intra Decoding
Authors: Christian Herglotz, Dominic Springer, Andrea Eichenseer, André Kaup
Date: 2022-03-03 00:00:00
Tasks: CPU, Decoder

Battery life is one of the major limitations to mobile device use, which makes research on energy efficient soft- and hardware an important task. This paper investigates the energy required by a CPU when decoding compressed bitstream videos on mobile platforms. A model is derived that describes the energy consumption of the new HEVC decoder for intra coded videos. We show that the relative estimation error of the model is smaller than 3.2% and that the model can be used to build encoders aiming at minimizing decoding energy.
Gaussian Process-based Spatial Reconstruction of Electromagnetic fields
Authors: Angesom Ataklity Tesfay, Laurent Clavier
Date: 2022-03-03 00:00:00
Tasks: Bayesian Inference, Model Selection

These days we live in a world with a permanent electromagnetic field. This raises many questions about our health and the deployment of new equipment. The problem is that these fields remain difficult to visualize easily, which only some experts can understand. To tackle this problem, we propose to spatially estimate the level of the field based on a few observations at all positions of the considered space. This work presents an algorithm for spatial reconstruction of electromagnetic fields using the Gaussian Process. We consider a spatial, physical phenomenon observed by a sensor network. A Gaussian Process regression model with selected mean and covariance function is implemented to develop a 9 sensors-based estimation algorithm. A Bayesian inference approach is used to perform the model selection of the covariance function and to learn the hyperparameters from our data set. We present the prediction performance of the proposed model and compare it with the case where the mean is zero. The results show that the proposed Gaussian Process-based prediction model reconstructs the EM fields in all positions only using 9 sensors.
Calculation of Sub-bands {1,2,5,6} for 64-Point Complex FFT and Its extension to N (=2^N) Point FFT
Authors: Bhaskar Sen
Date: 2022-03-03 00:00:00
Tasks: Time Series, Time Series Analysis

FFT algorithm is one of the most applied algorithmsin digital signal processing. Digital signal processing hasgradually become important in biomedical application. Herehardware implementation of FFTs have found useful appli-cations for bio-wearable devices. However, for these devices, low-power and low-area are of utmost importance.In this report, we investigate a sub-structure of decimation-in-frequency (DIF) FFT where a number of sub-bands areof interest to us. Specifically, we divide the range of frequencies into 8 sub-bands (0-7) and calculate 4 of them( 1,2,5,6). We show that using concepts likepushingandradix22, the number of complex multiplications can be dras-tically reduced for 16-point, 32-point and 64-point FFTswhile computing those specific bands. Later, we also extendit toN= 2n-point FFT based on optimized 64-point FFTstructure. The number of complex multiplications is furtherreduced usingmerge-FFT. Our results show that the numberof multiplications (and hence power) can be reduced greatlyusing our optimized structure compared to an unoptimizedstructure. This can find application in biomedical signal processing specifically while computingp ower spectral density of a physiological time series where reducing computational power is of utmost importance
A mathematical study of the role of tBregs in breast cancer
Authors: Vasiliki Bitsouni, Nikolaos Gialelis, Vasilis Tsilidis
Date: 2022-03-02 00:00:00

A model for the mathematical study of immune response to breast cancer is proposed and studied, both analytically and numerically. It is a simplification of a complex one, recently introduced by two of the present authors. It serves for a compact study of the dynamical role in cancer promotion of a relatively recently described subgroup of regulatory B cells, which are evoked by the tumour.
Optimal Exploration of an Exhaustible Resource with Stochastic Discoveries
Authors: Ivar Ekeland, Wolfram Schlenker, Peter Tankov, Brian Wright
Date: 2022-03-03 00:00:00

The standard Hotelling model assumes that the stock of an exhaustible resource is known. We expand on the model by Arrow and Chang that introduced stochastic discoveries and for the first time completely solve such a model using impulse control. The model has two state variables: the "proven" reserves as well as a finite unexplored area available for exploration with constant marginal cost, resulting in a Poisson process of new discoveries. We prove that a frontier of critical levels of "proven" reserves exists, above which exploration is stopped, and below which it happens at infinite speed. This frontier is increasing in the explored area, and higher "proven" reserve levels along this critical threshold are indicative of more scarcity, not less. In this stochastic generalization of Hotelling's rule, the expected shadow price of reserves rises at the rate of interest across exploratory episodes. However, the actual trajectories of prices realized prior to exhaustion of the exploratory area may jump up or down upon exploration. Conditional on non-exhaustion, expected price arises at a rate bounded above by the rate of interest, consistent with most empirical tests based on observed price histories.
Tail-GAN: Learning to Simulate Tail Risk Scenarios
Authors: Rama Cont, Mihai Cucuringu, Renyuan Xu, Chao Zhang
Date: 2022-03-03 00:00:00
Tasks: Generative Adversarial Network

The estimation of loss distributions for dynamic portfolios requires the simulation of scenarios representing realistic joint dynamics of their components. We propose a novel data-driven approach for simulating realistic, high-dimensional multi-asset scenarios, focusing on accurately representing tail risk for a class of static and dynamic trading strategies. We exploit the joint elicitability property of Value-at-Risk (VaR) and Expected Shortfall (ES) to design a Generative Adversarial Network (GAN) that learns to simulate price scenarios preserving these tail risk features. We demonstrate the performance of our algorithm on synthetic and market data sets through detailed numerical experiments. In contrast to previously proposed data-driven scenario generators, our proposed method correctly captures tail risk for a broad class of trading strategies and demonstrates strong generalization capabilities. In addition, combining our method with principal component analysis of the input data enhances its scalability to large-dimensional multi-asset time series, setting our framework apart from the univariate settings commonly considered in the literature.
Short and Long Range Population Dynamics of the Monarch
Authors: Komi Messan, Kyle Smith, Shawn Tsosie, Shuchen Zhu, Sergei Suslov
Date: 2011-12-16 00:00:00

The monarch butterfly annually migrates from central Mexico to southern Canada. During recent decades, its population has been reduced due to human interaction with their habitat. We examine the effect of herbicide usage on the monarch butterfly's population by creating a system of linear and non-linear ordinary differential equations that describe the interaction between the monarch's population and its environment at various stages of migration: spring migration, summer loitering, and fall migration. The model has various stages that are used to describe the dynamics of the monarch butterfly population over multiple generations. In Stage 1, we propose a system of coupled ordinary differential equations that model the populations of the monarch butterflies and larvae during spring migration. In Stage 2, we propose a predator-prey model with age structure to model the population dynamics at the summer breeding site. In Stages 3 and 4, we propose exponential decay functions to model the monarch butterfly's fall migration to central Mexico and their time at the overwintering site. The model is used to analyze the long-term behavior of the monarch butterflies through numerical analysis, given data available in the research literature.
Adversarial Instance Augmentation for Building Change Detection in Remote Sensing Images
Authors: Hao Chen, Wenyuan Li, Zhenwei Shi
Date: 2021-03-25 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2021 3
Tasks: Building change detection for remote sensing images, Change Detection, Image Augmentation

Training deep learning-based change detection (CD) models heavily relies on large labeled data sets. However, it is time-consuming and labor-intensive to collect large-scale bitemporal images that contain building change, due to both its rarity and sparsity. Contemporary methods to tackle the data insufficiency mainly focus on transformation-based global image augmentation and cost-sensitive algorithms. In this article, we propose a novel data-level solution, namely, Instance-level change Augmentation (IAug), to generate bitemporal images that contain changes involving plenty and diverse buildings by leveraging generative adversarial training. The key of IAug is to blend synthesized building instances onto appropriate positions of one of the bitemporal images. To achieve this, a building generator is employed to produce realistic building images that are consistent with the given layouts. Diverse styles are later transferred onto the generated images. We further propose context-aware blending for a realistic composite of the building and the background. We augment the existing CD data sets and also design a simple yet effective CD model—CD network (CDNet). Our method (CDNet + IAug) has achieved state-of-the-art results in two building CD data sets (LEVIR-CD and WHU-CD). Interestingly, we achieve comparable results with only 20% of the training data as the current state-of-the-art methods using 100% data. Extensive experiments have validated the effectiveness of the proposed IAug. Our augmented data set has a lower risk of class imbalance than the original one. Conventional learning on the synthesized data set outperforms several popular cost-sensitive algorithms on the original data set. Our code and data are available at https://github.com/justchenhao/IAug_CDNet .
Multi-branch Neural Networks for Video Anomaly Detection in Adverse Lighting and Weather Conditions
Authors: Sam Leroux, Bo Li, Pieter Simoens
Date: 2021-10-10 00:00:00
Proceeding: WACV 2021 10
Tasks: Anomaly Detection, Anomaly Detection In Surveillance Videos, Video Anomaly Detection

Automated anomaly detection in surveillance videos has attracted much interest as it provides a scalable alternative to manual monitoring. Most existing approaches achieve good performance on clean benchmark datasets recorded in well-controlled environments. However, detecting anomalies is much more challenging in the real world. Adverse weather conditions like rain or changing brightness levels cause a significant shift in the input data distribution, which in turn can lead to the detector model incorrectly reporting high anomaly scores. Additionally, surveillance cameras are usually deployed in evolving environments such as a city street of which the appearance changes over time because of seasonal changes or roadworks. The anomaly detection model will need to be updated periodically to deal with these issues. In this paper, we introduce a multi-branch model that is equipped with a trainable preprocessing step and multiple identical branches for detecting anomalies during day and night as well as in sunny and rainy conditions. We experimentally validate our approach on a distorted version of the Avenue dataset and provide qualitative results on real-world surveillance camera data. Experimental results show that our method outperforms the existing methods in terms of detection accuracy while being faster and more robust on scenes with varying visibility.