pwc-archive/papers-with-abstracts huggingface dataset:
Nonlinear Full Information and Moving Horizon Estimation: Robust Global Asymptotic Stability
Authors: Sven Knuefer, Matthias A. Mueller
Date: 2021-05-06 00:00:00

In this paper, we propose time-discounted schemes for full information estimation (FIE) and moving horizon estimation (MHE) that are robustly globally asymptotically stable (RGAS). We consider general nonlinear system dynamics with nonlinear process and output disturbances that are a priori unknown. For FIE being RGAS, our only assumptions are that the system is time-discounted incrementally input-output-to-state-stable (i-IOSS) and that the time-discounted FIE cost function is compatible with the i-IOSS estimate. Since for i-IOSS systems such a compatible cost function can always be designed, we show that i-IOSS is sufficient for the existence of RGAS observers. Based on the stability result for FIE, we provide sufficient conditions such that the induced MHE scheme is RGAS as well for sufficiently large horizons. For both schemes, we can guarantee convergence of the estimation error in case the disturbances converge to zero without incorporating a priori knowledge. Finally, we present explicit converge rates and show how to verify that the MHE results approach the FIE results for increasing horizons.
A Reinforcement Learning-based Economic Model Predictive Control Framework for Autonomous Operation of Chemical Reactors
Authors: Khalid Alhazmi, Fahad Albalawi, S. Mani Sarathy
Date: 2021-05-06 00:00:00
Tasks: Model Predictive Control, parameter estimation, Reinforcement Learning (RL)

Economic model predictive control (EMPC) is a promising methodology for optimal operation of dynamical processes that has been shown to improve process economics considerably. However, EMPC performance relies heavily on the accuracy of the process model used. As an alternative to model-based control strategies, reinforcement learning (RL) has been investigated as a model-free control methodology, but issues regarding its safety and stability remain an open research challenge. This work presents a novel framework for integrating EMPC and RL for online model parameter estimation of a class of nonlinear systems. In this framework, EMPC optimally operates the closed loop system while maintaining closed loop stability and recursive feasibility. At the same time, to optimize the process, the RL agent continuously compares the measured state of the process with the model's predictions (nominal states), and modifies model parameters accordingly. The major advantage of this framework is its simplicity; state-of-the-art RL algorithms and EMPC schemes can be employed with minimal modifications. The performance of the proposed framework is illustrated on a network of reactions with challenging dynamics and practical significance. This framework allows control, optimization, and model correction to be performed online and continuously, making autonomous reactor operation more attainable.
Security Protection in Cooperative Control of Multi-agent Systems
Authors: Shuai Sun, Yilin Mo
Date: 2021-05-06 00:00:00
Tasks: Privacy Preserving

Due to the wide application of average consensus algorithm, its security and privacy problems have attracted great attention. In this paper, we consider the system threatened by a set of unknown agents that are both "malicious" and "curious", who add additional input signals to the system in order to perturb the final consensus value or prevent consensus, and try to infer the initial state of other agents. At the same time, we design a privacy-preserving average consensus algorithm equipped with an attack detector with a time-varying exponentially decreasing threshold for every benign agent, which can guarantee the initial state privacy of every benign agent, under mild conditions. The attack detector will trigger an alarm if it detects the presence of malicious attackers. An upper bound of false alarm rate in the absence of malicious attackers and the necessary and sufficient condition for there is no undetectable input by the attack detector in the system are given. Specifically, we show that under this condition, the system can achieve asymptotic consensus almost surely when no alarm is triggered from beginning to end, and an upper bound of convergence rate and some quantitative estimates about the error of final consensus value are given. Finally, numerical case is used to illustrate the effectiveness of some theoretical results.
Sensor-less Angle and Stiffness Control of Antagonistic PAM Actuator Using Reference Set
Authors: Takaya Shin, Kiminao Kogiso
Date: 2021-05-06 00:00:00

This paper proposes a simultaneous control method for the angle and stiffness of the joint in an antagonistic pneumatic artificial muscle (PAM) actuator system using only pressure measurements, and clarifies the allowable references for the PAM actuator system. To achieve a sensor-less control, the proposed method estimates the joint angle and contraction forces using an unscented Kalman filter that employs a detailed model of the actuator system. Unlike previous control methods, the proposed method does not require any encoder and force sensor to achieve angle and stiffness control of the PAM actuator system. Experimental validations using three control scenarios confirm that the proposed method can control the joint angle and stiffness simultaneously and independently. Moreover, it is shown that a reference admissible set can be used as an indicator to establish reference values by demonstrating that the reference set covers the experimentally obtained trajectories of the angle and stiffness.
Resilient Time-Varying Output Formation Tracking of Linear Multi-Agent Systems Against Unbounded FDI Sensor Attacks and Unreliable Digraphs
Authors: Zhi Feng, Guoqiang Hu
Date: 2021-05-06 00:00:00

One salient feature of cooperative formation tracking is its distributed nature that relies on localized control and information sharing over a sparse communication network. That is, a distributed control manner could be prone to malicious attacks and unreliable communication that deteriorate the formation tracking performance or even destabilize the whole multi-agent system. This paper studies a safe and reliable time-varying output formation tracking problem of linear multi-agent systems, where an attacker adversely injects any unbounded time-varying signals (false data injection (FDI) attacks), while an interruption of communication channels between the agents is caused by an unreliable network. Both characteristics improve the practical relevance of the problem to be addressed, which poses some technical challenges to the distributed algorithm design and stability analysis. To mitigate the adverse effect, a novel resilient distributed control architecture is established to guarantee time-varying output formation tracking exponentially. The key features of the proposed framework are threefold: 1) an observer-based identifier is integrated to compensate for adverse effects; 2) a reliable distributed algorithm is proposed to deal with time-varying topologies caused by unreliable communication; and 3) in contrast to the existing remedies that deal with attacks as bounded disturbances/faults with known knowledge, we propose resilience strategies to handle unknown and unbounded attacks for exponential convergence of dynamic formation tracking errors, whereas most of existing results achieve uniformly ultimately boundedness (UUB) results. Numerical simulations are given to show the effectiveness of the proposed design.
A Small-Gain Theorem for Discrete-Time Convergent Systems and Its Applications
Authors: Jiayen Chen, Hendra I. Nurdin
Date: 2021-05-06 00:00:00

Convergent, contractive or incremental stability properties of nonlinear systems have attracted interest for control tasks such as observer design, output regulation and synchronization. The convergence property plays a central role in the neuromorphic (brain-inspired) computing of reservoir computing, which seeks to harness the information processing capability of nonlinear systems. This paper presents a small-gain theorem for discrete-time output-feedback interconnected systems to be uniformly input-to-output convergent (UIOC) with outputs converging to a bounded reference output uniquely determined by the input. A small-gain theorem for interconnected time-varying discrete-time uniform input-to-output stable systems that could be of separate interest is also presented as an intermediate result. Applications of the UIOC small-gain theorem are illustrated in the design of observer-based controllers and interconnected nonlinear classical and quantum dynamical systems (as reservoir computers) for black-box system identification.
A High-Performance, Reconfigurable, Fully Integrated Time-Domain Reflectometry Architecture Using Digital I/Os
Authors: Zhenyu Xu, Thomas Mauldin, Zheyi Yao, Gerald Hefferman, Tao Wei
Date: 2021-05-01 00:00:00

Time-domain reflectometry (TDR) is an established means of measuring impedance inhomogeneity of a variety of waveguides, providing critical data necessary to characterize and optimize the performance of high-bandwidth computational and communication systems. However, TDR systems with both the high spatial resolution (sub-cm) and voltage resolution (sub-$\muV$) required to evaluate high-performance waveguides are physically large and often cost-prohibitive, severely limiting their utility as testing platforms and greatly limiting their use in characterizing and trouble-shooting fielded hardware. Consequently, there exists a growing technical need for an electronically simple, portable, and low-cost TDR technology. The receiver of a TDR system plays a key role in recording reflection waveforms; thus, such a receiver must have high analog bandwidth, high sampling rate, and high-voltage resolution. However, these requirements are difficult to meet using low-cost analog-to-digital converters (ADCs). This article describes a new TDR architecture, namely, jitter-based APC (JAPC), which obviates the need for external components based on an alternative concept, analog-to-probability conversion (APC) that was recently proposed. These results demonstrate that a fully reconfigurable and highly integrated TDR (iTDR) can be implemented on a field-programmable gate array (FPGA) chip without using any external circuit components. Empirical evaluation of the system was conducted using an HDMI cable as the device under test (DUT), and the resulting impedance inhomogeneity pattern (IIP) of the DUT was extracted with spatial and voltage resolutions of 5 cm and 80 $\muV$, respectively. These results demonstrate the feasibility of using the prototypical JAPC-based iTDR for real-world waveguide characterization applications
Evaluating Sensor Data Quality in Internet ofThings Smart Agriculture Applications
Authors: Kaneez Fizza, Prem Prakash Jayaraman, Abhik Banerjee, Dimitrios Georgakopoulos, Rajiv Ranjan
Date: 2021-04-28 00:00:00

The unprecedented growth of Internet of Things (IoT) and its applications in areas such as Smart Agriculture compels the need to devise newer ways for evaluating the quality of such applications. While existing models for application quality focus on the quality experienced by the end-user (captured using likert scale), IoT applications have minimal human involvement and rely on machine to machine communication and analytics to drive decision via actuations. In this paper, we first present a conceptual framework for the evaluation of IoT application quality. Subsequently, we propose, develop and validate via empirical evaluations a novel model for evaluating sensor data quality that is a key component in assessing IoT application quality. We present an implementation of the sensor data quality model and demonstrate how the IoT sensor data quality can be integrated with a Smart Agriculture application. Results of experimental evaluations conducted using data from a real-world testbed concludes the paper.
Real-Time Real Economic Activity: Entering and Exiting the Pandemic Recession of 2020
Authors: Francis X. Diebold
Date: 2020-06-26 00:00:00

Entering and exiting the Pandemic Recession, I study the high-frequency real-activity signals provided by a leading nowcast, the ADS Index of Business Conditions produced and released in real time by the Federal Reserve Bank of Philadelphia. I track the evolution of real-time vintage beliefs and compare them to a later-vintage chronology. Real-time ADS plunges and then swings as its underlying economic indicators swing, but the ADS paths quickly converge to indicate a return to brisk positive growth by mid-May. I show, moreover, that the daily real activity path was highly correlated with the daily COVID-19 cases. Finally, I provide a comparative assessment of the real-time ADS signals provided when exiting the Great Recession.
Stock Price Forecasting in Presence of Covid-19 Pandemic and Evaluating Performances of Machine Learning Models for Time-Series Forecasting
Authors: Navid Mottaghi, Sara Farhangdoost
Date: 2021-05-04 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting

With the heightened volatility in stock prices during the Covid-19 pandemic, the need for price forecasting has become more critical. We investigated the forecast performance of four models including Long-Short Term Memory, XGBoost, Autoregression, and Last Value on stock prices of Facebook, Amazon, Tesla, Google, and Apple in COVID-19 pandemic time to understand the accuracy and predictability of the models in this highly volatile time region. To train the models, the data of all stocks are split into train and test datasets. The test dataset starts from January 2020 to April 2021 which covers the COVID-19 pandemic period. The results show that the Autoregression and Last value models have higher accuracy in predicting the stock prices because of the strong correlation between the previous day and the next day's price value. Additionally, the results suggest that the machine learning models (Long-Short Term Memory and XGBoost) are not performing as well as Autoregression models when the market experiences high volatility.
Epidemics in modern economies
Authors: Torsten Heinrich
Date: 2021-05-06 00:00:00

How are economies in a modern age impacted by epidemics? In what ways is economic life disrupted? How can pandemics be modeled? What can be done to mitigate and manage the danger? Does the threat of pandemics increase or decrease in the modern world? The Covid-19 pandemic has demonstrated the importance of these questions and the potential of complex systems science to provide answers. This article offers a broad overview of the history of pandemics, of established facts, and of models of infection diffusion, mitigation strategies, and economic impact. The example of the Covid-19 pandemic is used to illustrate the theoretical aspects, but the article also includes considerations concerning other historic epidemics and the danger of more infectious and less controllable outbreaks in the future.
Robustly Optimal Mechanisms for Selling Multiple Goods
Authors: Yeon-Koo Che, Weijie Zhong
Date: 2021-05-06 00:00:00

We study robustly optimal mechanisms for selling multiple items. The seller maximizes revenue against a worst-case distribution of a buyer's valuations within a set of distributions, called an "ambiguity" set. We identify the exact forms of robustly optimal selling mechanisms and the worst-case distributions when the ambiguity set satisfies various moment conditions on the values of subsets of goods. The analysis reveals general properties of the ambiguity set that justifies categorical bundling, which includes separate sales and pure bundling as special cases.
Designing Heaven's Will: The job assignment in the Chinese imperial civil service
Authors: Inácio Bó, Li Chen
Date: 2021-05-06 00:00:00

We provide an original analysis of historical documents to describe the assignment procedures used to allocate entry-level civil service jobs in China from the tenth to the early twentieth century. The procedures tried to take different objectives into account through trial and error. By constructing a formal model that combines these procedures into a common framework, we compare their effectiveness in minimizing unfilled jobs and prioritizing high-level posts. We show that the problem was inherently complex such that changes made to improve the outcome could have the opposite effect. Based on a small modification of the last procedure used, we provide a new mechanism for producing maximum matchings under constraints in a transparent and public way.
ER-IQA: Boosting Perceptual Quality Assessment Using External Reference Images
Authors: Jingyu Guo, Wei Wang, Wenming Yang, Qingmin Liao, Jie zhou
Date: 2021-05-06 00:00:00
Tasks: Image Quality Assessment, NR-IQA

Recently, image quality assessment (IQA) has achieved remarkable progress with the success of deep learning. However, the strict pre-condition of full-reference (FR) methods has limited its application in real scenarios. And the no-reference (NR) scheme is also inconvenient due to its unsatisfying performance as a result of ignoring the essence of image quality. In this paper, we introduce a brand new scheme, namely external-reference image quality assessment (ER-IQA), by introducing external reference images to bridge the gap between FR and NR-IQA. As the first implementation and a new baseline of ER-IQA, we propose a new Unpaired-IQA network to process images in a content-unpaired manner. A Mutual Attention-based Feature Enhancement (MAFE) module is well-designed for the unpaired features in ER-IQA. The MAFE module allows the network to extract quality-discriminative features from distorted images and content variability-robust features from external reference ones. Extensive experiments demonstrate that the proposed model outperforms the state-of-the-art NR-IQA methods, verifying the effectiveness of ER-IQA and the possibility of narrowing the gap of the two existing categories.
Primary and Secondary Social Media Source Identification
Authors: Brian C Hosler, Matthew C Stamm
Date: 2021-05-05 00:00:00
Tasks: Misinformation

Social networks like Facebook and WhatsApp have enabled users to share images with other users around the world. Along with this has come the rapid spread of misinformation. One step towards verifying the authenticity of an image is understanding its origin, including it distribution history through social media. In this paper, we present a method for tracing the posting history of an image across different social networks. To do this, we propose a two-stage deep-learning-based approach, which takes advantage of cascaded fingerprints in images left by social networks during uploading. Our proposed system is not reliant upon metadata or similar easily falsifiable information. Through a series of experiments, we show that we are able to outperform existing social media source identification algorithms. and identify chains of social networks up to length two with over over 84% accuracy.
Non-Semantic Evaluation of Image Forensics Tools: Methodology and Database
Authors: Quentin Bammey, Tina Nikoukhah, Marina Gardella, Rafael Grompone, Miguel Colom, Jean-Michel Morel
Date: 2021-05-04 00:00:00
Tasks: Image Forensics

With the aim of evaluating image forensics tools, we propose a methodology to create forgeries traces, leaving intact the semantics of the image. Thus, the only forgery cues left are the specific alterations of one or several aspects of the image formation pipeline. This methodology creates automatically forged images that are challenging to detect for forensic tools and overcomes the problem of creating convincing semantic forgeries. Based on this methodology, we create the Trace database and conduct an evaluation of the main state-of-the-art image forensics tools.
Design of Non-Coherent and Coherent Receivers for Chirp Spread Spectrum Systems
Authors: Tung T. Nguyen, Ha H. Nguyen
Date: 2021-05-06 00:00:00

LoRaWAN is a prominent communication standard to enable reliable low-power, long-range communications for the Internet of Things (IoT). The modulation technique used in LoRaWAN, commonly known as LoRa modulation, is based on the principle of chirp spread spectrum (CSS). While extensive research has been conducted on improving various aspects of LoRa transmitter, the design of LoRa receivers that can operate under practical conditions of timing and frequency offsets is missing. To fill this gap, this paper develops and presents detailed designs of timing, frequency and phase synchronization circuits for both non-coherent and coherent detection of CSS signals. More importantly, the proposed receiver can be used to detect the recently proposed CSS-based modulation that embeds extra information bits in the starting phases of conventional CSS symbols. Such a transmission scheme, referred to as phase-shift keying CSS (PSK-CSS) helps to improve the transmission rates of the conventional CSS system. In particular, it is shown that the bit error rate performance of the PSK-CSS scheme achieved with the proposed practical coherent receiver has only 0.25 dB gaps as compared to the ideal co-coherent receiver.
A Binarizing NUV Prior and its Use for M-Level Control and Digital-to-Analog Conversion
Authors: Raphael Keusch, Hans-Andrea Loeliger
Date: 2021-05-06 00:00:00

Priors with a NUV representation (normal with unknown variance) have mostly been used for sparsity. In this paper, a novel NUV prior is proposed that effectively binarizes. While such a prior may have many uses, in this paper, we explore its use for discrete-level control (with M $\geq$ 2 levels) including, in particular, a practical scheme for digital-to-analog conversion. The resulting computations, for each planning period, amount to iterating forward-backward Gaussian message passing recursions (similar to Kalman smoothing), with a complexity (per iteration) that is linear in the planning horizon. In consequence, the proposed method is not limited to a short planning horizon and can therefore outperform "optimal" methods. A preference for sparse level switches can easily be incorporated.
Differential Transmission Schemes for Generalized Spatial Modulation
Authors: Deepak Jose, Sameer S. M
Date: 2021-05-06 00:00:00

Differential modulation schemes are very relevant in receivers having power and processing limitations, as these schemes dispense with the need for knowledge of channel coefficients for symbol detection. Spatial modulation (SM) is a scheme used in multi-antenna transmission scenarios where the data is transmitted in the amplitude, phase and spatial domains through selected antennas. In the coherent domain, generalized SM (GSM) employs multiple antennas in combination during every time slot to enhance the spectral efficiency (SE). In this paper, we propose two differential schemes which activate two or more antennas at a time to transmit the modulated symbol. These schemes achieve higher SE using a lesser number of antennas and lower order modulation schemes instead of the higher number of antennas required for conventional SM schemes based on differential modulation. Simulation studies reveal that the proposed schemes have better bit error rate performance than traditional differential SM schemes. We also derive the analytical union bound for the proposed schemes and is satisfied from medium to high signal-to-noise ratio (SNR) ranges.