pwc-archive/papers-with-abstracts huggingface dataset:
Modeling Strong Physically Unclonable Functions with Metaheuristics
Authors: Carlos Coello Coello, Marko Djurasevic, Domagoj Jakobovic, Luca Mariot, Stjepan Picek
Date: 2022-02-16 00:00:00
Tasks: Evolutionary Algorithms

Evolutionary algorithms have been successfully applied to attacking Physically Unclonable Functions (PUFs). CMA-ES is recognized as the most powerful option for a type of attack called the reliability attack. While there is no reason to doubt the performance of CMA-ES, the lack of comparison with different metaheuristics and results for the challenge-response pair-based attack leaves open questions if there are better-suited metaheuristics for the problem. In this paper, we take a step back and systematically evaluate several metaheuristics for the challenge-response pair-based attack on strong PUFs. Our results confirm that CMA-ES has the best performance, but we also note several other algorithms with similar performance while having smaller computational costs. More precisely, if we provide a sufficient number of challenge-response pairs to train the algorithm, various configurations show good results. Consequently, we conclude that EAs represent a strong option for challenge-response pair-based attacks on PUFs.
Enhancing Causal Estimation through Unlabeled Offline Data
Authors: Ron Teichner, Ron Meir, Danny Eitan
Date: 2022-02-16 00:00:00

Consider a situation where a new patient arrives in the Intensive Care Unit (ICU) and is monitored by multiple sensors. We wish to assess relevant unmeasured physiological variables (e.g., cardiac contractility and output and vascular resistance) that have a strong effect on the patients diagnosis and treatment. We do not have any information about this specific patient, but, extensive offline information is available about previous patients, that may only be partially related to the present patient (a case of dataset shift). This information constitutes our prior knowledge, and is both partial and approximate. The basic question is how to best use this prior knowledge, combined with online patient data, to assist in diagnosing the current patient most effectively. Our proposed approach consists of three stages: (i) Use the abundant offline data in order to create both a non-causal and a causal estimator for the relevant unmeasured physiological variables. (ii) Based on the non-causal estimator constructed, and a set of measurements from a new group of patients, we construct a causal filter that provides higher accuracy in the prediction of the hidden physiological variables for this new set of patients. (iii) For any new patient arriving in the ICU, we use the constructed filter in order to predict relevant internal variables. Overall, this strategy allows us to make use of the abundantly available offline data in order to enhance causal estimation for newly arriving patients. We demonstrate the effectiveness of this methodology on a (non-medical) real-world task, in situations where the offline data is only partially related to the new observations. We provide a mathematical analysis of the merits of the approach in a linear setting of Kalman filtering and smoothing, demonstrating its utility.
Formulating Beurling LASSO for Source Separation via Proximal Gradient Iteration
Authors: Sören Schulze, Emily J. King
Date: 2022-02-16 00:00:00

Beurling LASSO generalizes the LASSO problem to finite Radon measures regularized via their total variation. Despite its theoretical appeal, this space is hard to parametrize, which poses an algorithmic challenge. We propose a formulation of continuous convolutional source separation with Beurling LASSO that avoids the explicit computation of the measures and instead employs the duality transform of the proximal mapping.
Broken symmetry of recruitment fluctuations in marine fishes: Lévy-stable laws and beyond
Authors: Hiro-Sato Niwa
Date: 2022-02-16 00:00:00

Recruitment is calculated by summing random offspring-numbers entering the population, where the number of summands (i.e. spawning population size) is also a random process. A priori, it is not clear that individual reproductive variability would have a significant impact on aggregate measures for monitoring populations. Usually these variations are averaged out in a large population, and the aggregate output is merely influenced by population-wide environmental disturbances such as climate and fisheries. However, such arguments break down if the distribution of the individual offspring numbers is heavy-tailed. In a world with power-law offspring-number distribution with exponent $1<\alpha<2$, the recruitment distribution has a putative power-law regime in the tail with the same $\alpha$. The question is to what extent individual reproductive variability can have a noticeable impact on the recruitment under environmentally driven population fluctuations. This question is answered by considering the L\'evy-stable fluctuations as embedded in a randomly varying environment. I report fluctuation scaling and asymmetric fluctuations in recruitment of commercially exploited fish stocks throughout the North Atlantic. The linear scaling of recruitment standard deviation with recruitment level implies that the individual reproductive variability is dominated by population fluctuations. The totally asymmetric (skewed to the right) character is a sign of idiosyncratic variation in reproductive success.
Raspberry PI Shield - for measure EEG (PIEEG)
Authors: Ildar Rakhmatulin
Date: 2022-02-16 00:00:00
Proceeding: IEEE conference 2022 2
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG)

Abstract:
The paper presented the easiest way to the neuroscience world with a shield for RaspberryPI - PIEEG. Over the past decade, advances in artificial intelligence have expanded and reshaped many industries. It also affected neuroscience, where machine learning made possible to find practical use for electroencephalogram signals in the brain in various fields of engineering. As a result, popularity of brain-computer interface devices used for recording brain signals at low-cost has increased dramatically. In recent years, several companies started manufacturing devices capable of recording EEG signals using non-invasive electrodes at low cost. Even then, the cost of these devices often exceeds IOOOUSD. In this paper, we aim for yet further cost reduction by proposing a Raspberry PI -based device for reading EEG signal. According to its characteristics, the device is compared to the signal-to-noise level of the ADS1299 device produced by Texas instruments and used for comparison in this paper. The proposed device, on top of satisfying noise level and accuracy of detected artifacts in the signal, was also tested and confirmed to successfully resist the effect of electromagnetic interference, successfully detect the alpha brain wave, and detect the P300 paradigm.
A Short Survey on Business Models of Decentralized Finance (DeFi) Protocols
Authors: Teng Andrea Xu, Jiahua Xu
Date: 2022-02-15 00:00:00

Decentralized Finance (DeFi) services are moving traditional financial operations to the Internet of Value (IOV) by exploiting smart contracts, distributed ledgers, and clever heterogeneous transactions among different protocols. The exponential increase of the Total Value Locked (TVL) in DeFi foreshadows a bright future for automated money transfers in a plethora of services. In this short survey paper, we describe the business model for different DeFi domains - namely, Protocols for Loanable Funds (PLFs), Decentralized Exchanges (DEXs), and Yield Aggregators. We claim that the current state of the literature is still unclear how to value thousands of different competitors (tokens) in DeFi. With this work, we abstract the general business model for different DeFi domains and compare them. Finally, we provide open research challenges that will involve heterogeneous domains such as economics, finance, and computer science.
Natural Language Proof Checking in Introduction to Proof Classes -- First Experiences with Diproche
Authors: Merlin Carl, Hinrich Lorenzen, Michael Schmitz
Date: 2022-02-08 00:00:00

We present and analyze the employment of the Diproche system, a natural language proof checker, within a one-semester mathematics beginners lecture with 228 participants. The system is used to check the students' solution attempts to proving exercises in Boolean set theory and elementary number theory and to give them immediate feedback. The benefits of the employment of the system are assessed via a questionnaire at the end of the semester and via analyzing the solution attempts of a subgroup of the students. Based on our results we develop approaches for future improvements.
Reversible data hiding with dual pixel-value-ordering and1minimum prediction error expansion
Authors: Md. Abdul Wahed, Hussain Nyeem
Date: 2022-02-12 00:00:00

Pixel Value Ordering (PVO) holds an impressive property for high fidelity Reversible Data Hiding (RDH). In this paper, we introduce a dual-PVO (dPVO) for Prediction Error Expansion(PEE), and thereby develop a new RDH scheme to offer a better rate-distortion performance. Particularly, we propose to embed in two phases: forward and backward. In the forward phase, PVO with classic PEE is applied to every non-overlapping image block of size 1x3. In the backward phase,minimum-set and maximum-set of pixels are determined from the pixels predicted in the forward phase. The minimum set only contains the lowest predicted pixels and the maximum set contains the largest predicted pixels of each image block. Proposed dPVO withPEE is then applied to both sets, so that the pixel values of the minimum set are increased and that of the maximum set are decreased by a unit value. Thereby, the pixels predicted in the forward embedding can partially be restored to their original values resulting in both better-embedded image quality and a higher embedding rate. Experimental results have recorded a promising rate-distortion performance of our scheme with a significant improvement of embedded image quality at higher embedding rates compared to the popular and state-of-the-art PVO-based RDHschemes.
The Biosensor based on electrochemical dynamics of fermentation in yeast Saccharomyces Cerevisiae
Authors: Serge Kernbach, Olga Kernbach, Igor Kuksin, Andreas Kernbach, Yury Nepomnyashchiy, Timo Dochow, Andrew Bobrov
Date: 2022-02-07 00:00:00

The zymase activity of the yeast Saccharomyces Cerevisiae is sensitive to environmental parameters and is therefore used as a microbiological sensor for water quality assessment, ecotoxicological characterization or environmental monitoring. Comparing to bacterial bioluminescence approach, this method has no toxicity, excludes usage of genetically modified microorganisms, and enables low-cost express analysis. This work focuses on measuring the yeast fermentation dynamics based on multichannel pressure sensing and electrochemical impedance spectroscopy (EIS). Measurement results are compared with each other in terms of accuracy, reproducibility and ease of use in the field conditions. It has been shown that EIS provides more information about ionic dynamics of metabolic processes and requires less complex measurements. The conducted experiments demonstrated the sensitivity of this approach for assessing biophotonic phenomena, non-chemical water treatments and impact of environmental stressors.
APPLADE: Adjustable Plug-and-play Audio Declipper Combining DNN with Sparse Optimization
Authors: Tomoro Tanaka, Kohei Yatabe, Masahiro Yasuda, Yasuhiro Oikawa
Date: 2022-02-16 00:00:00
Tasks: Audio declipping

In this paper, we propose an audio declipping method that takes advantages of both sparse optimization and deep learning. Since sparsity-based audio declipping methods have been developed upon constrained optimization, they are adjustable and well-studied in theory. However, they always uniformly promote sparsity and ignore the individual properties of a signal. Deep neural network (DNN)-based methods can learn the properties of target signals and use them for audio declipping. Still, they cannot perform well if the training data have mismatches and/or constraints in the time domain are not imposed. In the proposed method, we use a DNN in an optimization algorithm. It is inspired by an idea called plug-and-play (PnP) and enables us to promote sparsity based on the learned information of data, considering constraints in the time domain. Our experiments confirmed that the proposed method is stable and robust to mismatches between training and test data.
An alternative paradigm of fault diagnosis in dynamic systems: orthogonal projection-based methods
Authors: Steven X. Ding, Linlin Li, Tianyu Liu
Date: 2022-02-16 00:00:00
Tasks: Fault Detection, Fault Diagnosis

In this paper, we propose a new paradigm of fault diagnosis in dynamic systems as an alternative to the well-established observer-based framework. The basic idea behind this work is to (i) formulate fault detection and isolation as projection of measurement signals onto (system) subspaces in Hilbert space, and (ii) solve the resulting problems by means of projection methods with orthogonal projection operators and gap metric as major tools. In the new framework, fault diagnosis issues are uniformly addressed both in the model-based and data-driven fashions. Moreover, the design and implementation of the projection-based fault diagnosis systems, from residual generation to threshold setting, can be unifiedly handled. Thanks to the well-defined distance metric for projections in Hilbert subspaces, the projection-based fault diagnosis systems deliver optimal fault detectability. In particular, a new type of residual-driven thresholds is proposed, which significantly increases the fault detectability. In this work, various design schemes are proposed, including a basic projection-based fault detection scheme, fault detection schemes for feedback control systems, fault classification as well as two modified fault detection schemes. As a part of our study, relations to the existing observer-based fault detection systems are investigated, which showcases that, with comparable online computations, the proposed projection-based detection methods offer improved detection performance.
Graph Neural Network and Koopman Models for Learning Networked Dynamics: A Comparative Study on Power Grid Transients Prediction
Authors: Sai Pushpak Nandanoori, Sheng Guan, Soumya Kundu, Seemita Pal, Khushbu Agarwal, Yinghui Wu, Sutanay Choudhury
Date: 2022-02-16 00:00:00
Tasks: Graph Neural Network

Continuous monitoring of the spatio-temporal dynamic behavior of critical infrastructure networks, such as the power systems, is a challenging but important task. In particular, accurate and timely prediction of the (electro-mechanical) transient dynamic trajectories of the power grid is necessary for early detection of any instability and prevention of catastrophic failures. Existing approaches for the prediction of dynamic trajectories either rely on the availability of accurate physical models of the system, use computationally expensive time-domain simulations, or are applicable only at local prediction problems (e.g., a single generator). In this paper, we report the application of two broad classes of data-driven learning models -- along with their algorithmic implementation and performance evaluation -- in predicting transient trajectories in power networks using only streaming measurements and the network topology as input. One class of models is based on the Koopman operator theory which allows for capturing the nonlinear dynamic behavior via an infinite-dimensional linear operator. The other class of models is based on the graph convolutional neural networks which are adept at capturing the inherent spatio-temporal correlations within the power network. Transient dynamic datasets for training and testing the models are synthesized by simulating a wide variety of load change events in the IEEE 68-bus system, categorized by the load change magnitudes, as well as by the degree of connectivity and the distance to nearest generator nodes. The results confirm that the proposed predictive models can successfully predict the post-disturbance transient evolution of the system with a high level of accuracy.
Frequency Response from Aggregated V2G Chargers With Uncertain EV Connections
Authors: Cormac O'Malley, Luis Badesa, Fei Teng, Goran Strbac
Date: 2022-02-16 00:00:00
Tasks: Scheduling

Fast frequency response (FR) is highly effective at securing frequency dynamics after a generator outage in low inertia systems. Electric vehicles (EVs) equipped with vehicle to grid (V2G) chargers could offer an abundant source of FR in future. However, the uncertainty associated with V2G aggregation, driven by the uncertain number of connected EVs at the time of an outage, has not been fully understood and prevents its participation in the existing service provision framework. To tackle this limitation, this paper, for the first time, incorporates such uncertainty into system frequency dynamics, from which probabilistic nadir and steady state frequency requirements are enforced via a derived momeent-based distributionally-robust chance constraint. Field data from over 25,000 chargers is analysed to provide realistic parameters and connection forecasts to examine the value of FR from V2G chargers in annual operation of the the GB 2030 system. The case study demonstrates that uncertainty of EV connections can be effectively managed through the proposed scheduling framework, which results in annual savings of {\pounds}6,300 or 37.4 tCO2 per charger. The sensitivity of this value to renewable capacity and FR delays is explored, with V2G capacity shown to be a third as valuable as the same grid battery capacity.
Separation and Estimation of Periodic/Aperiodic State
Authors: Hisayoshi Muramatsu
Date: 2022-02-16 00:00:00

Periodicity and aperiodicity can exist in a state simultaneously and typically become quasi-periodicity and quasi-aperiodicity in a dynamically changing state. The quasi-periodic and quasi-aperiodic states existing in the periodic/aperiodic state mostly correspond to different phenomena and require different controls. For separation control of these states, this paper defines the periodic/aperiodic, quasi-periodic, and quasi-aperiodic states to construct a periodic/aperiodic separation filter that separates the periodic/aperiodic state into the quasi-periodic and quasi-aperiodic states. Based on these definitions, the linearity of periodic-pass and aperiodic-pass functions and the orthogonality of quasi-periodic and quasi-aperiodic state functions are proved. Subsequently, the periodic/aperiodic separation filter composed of periodic-pass and aperiodic-pass filters that realize the periodic-pass and aperiodic-pass functions is designed and integrated with a Kalman filter for estimation of the quasi-periodic and quasi-aperiodic states.
High-dimensional dynamic factor models: a selective survey and lines of future research
Authors: Marco Lippi, Manfred Deistler, Brian Anderson
Date: 2022-02-15 00:00:00

High-Dimensional Dynamic Factor Models are presented in detail: The main assumptions and their motivation, main results, illustrations by means of elementary examples. In particular, the role of singular ARMA models in the theory and applications of High-Dimensional Dynamic Factor Models is discussed.The emphasis of the paper is on model classes and their structure theory, rather than on estimation in the narrow sense. Our aim is not a comprehensive survey. Rather we try to point out promising lines of research and applications that have not yet been sufficiently developed.
Control Co-design of Actively Controlled Lightweight Structures for High-acceleration Precision Motion Systems
Authors: Jingjie Wu, Lei Zhou
Date: 2022-02-15 00:00:00

Precision motion stages are an essential part of a wide range of manufacturing equipment, and their motion performance are critical to the quality and throughput of the systems. The drastically increasing demand for higher manufacturing throughput in various processes necessities the development of next-generation motion systems with reduced moving weight and high control bandwidth. However, the reduction of moving stage's weight can lower the stage's structural resonance frequencies, making the hardware dynamics and controller design problem strongly coupled. Aiming at this challenge, this paper proposes a new formulation of nested hardware and control co-design framework for precision motion stages. The proposed framework explicitly optimizes the closed-loop control bandwidth with guaranteed robustness, and explicitly considers the limits in the physical system. Two case studies, including a motivating example using lumped-parameter mechanical system and a finite-element-simulated lightweight motion stage, are being used to evaluate the effectiveness of the proposed nested CCD framework. Simulation results show that the proposed nested CCD framework has 42\% of weight reduction and 28\% bandwidth improvement compared with a sequential design baseline, which demonstrates the efficacy of the proposed approach.
Finite- and Fixed-Time Nonovershooting Stabilizers and Safety Filters by Homogeneous Feedback
Authors: Andrey Polyakov, Miroslav Krstic
Date: 2022-02-15 00:00:00

Non-overshooting stabilization is a form of safe control where the setpoint chosen by the user is at the boundary of the safe set. Exponential non-overshooting stabilization, including suitable extensions to systems with deterministic and stochastic disturbances, has been solved by the second author and his coauthors. In this paper we develop homogeneous feedback laws for fixed-time nonovershooting stabilization for nonlinear systems that are input-output linearizable with a full relative degree, i.e., for systems that are diffeomorphically equivalent to the chain of integrators. These homogeneous feedback laws can also assume the secondary role of `fixed-time safety filters' (FxTSf filters) which keep the system within the closed safe set for all time but, in the case where the user's nominal control commands approach to the unsafe set, allow the system to reach the boundary of the safe set no later than a desired time that is independent of nominal control and independent of the value of the state at the time the nominal control begins to be overridden.
Dynamics of stochastic integrate-and-fire networks
Authors: Gabriel Koch Ocker
Date: 2022-02-15 00:00:00

The neural dynamics generating sensory, motor, and cognitive functions are commonly understood through field theories for neural population activity. Classic neural field theories are derived from highly simplified models of individual neurons, while biological neurons are highly complex cells. Integrate-and-fire neuron models balance biophysical detail and analytical tractability. Here, we develop a statistical field theory for networks of integrate-and-fire neurons with stochastic spike emission. This reveals an exact mapping to a self-consistent renewal process and a new mean field theory for the activity in these networks. The mean field theory has a rate-dependent leak, approximating the spike-driven resets of the membrane voltage. This gives rise to bistability between quiescent and active states in homogenous and excitatory-inhibitory pulse-coupled networks. The field-theoretic framework also exposes fluctuation corrections to the mean field theory. We find that due to the spike reset, fluctuations suppress activity. We then examine the roles of spike resets and recurrent inhibition in stabilizing network activity. We calculate the phase diagram for inhibitory stabilization and find that an inhibition-stabilized regime occurs in wide regions of parameter space, consistent with experimental reports of inhibitory stabilization in diverse brain regions. Fluctuations narrow the region of inhibitory stabilization, consistent with their role in suppressing activity through spike resets.