Random Output Research Articles

Resilience evaluation of memristor based PUF against machine learning attacks

Physical unclonable functions (PUFs) have emerged as a favorable hardware security primitive, they exploit the process variations to provide unique signatures or secret keys amidst other critical cryptographic applications. CMOS-based PUFs are the most popular type, they generate unique bit strings using process variations in semiconductor fabrication. However, most existing CMOS PUFs are found to be vulnerable to modeling attacks based on machine learning (ML) algorithms. Memristors leveraging nanotechnology fabrication processes and highly nonlinear behavior became an interesting alternative to the existing CMOS-based PUF technology, introducing cryptographic and resilient random outputs. Memristor-based PUFs are emerging due to the inherent randomness at both the memristor level due to the cycle-to-cycle (C2C) programming variation of the device and the fabrication process level such as the cross-sectional area and variations. Our study focuses on building a machine learning analysis and attack framework of tools on Cu/HfO2-x/p++Si\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Cu/HfO_{2-x}/p^{++}Si$$\\end{document} memristor-based PUF (MR-PUF). Our objective is to test the resiliency of the security margins of the presented PUF using machine learning analysis tools, on-top of holistic NIST cryptographic randomness testing initially provided, to provide a high level of certainty in predicting the randomness output of the verified Memrister-based PUF. Our main contribution is a holistic study that focuses on attacking the randomness output resiliency based on building randomness predictors using Logistic Regression (LR), Support Vector Machine (SVM), Gaussian Mixture Models (GMM), K-means, K-means++\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$++$$\\end{document}, Random Forest, XGBoost and LSTM, within efficient time, and data complexity. Our results yield low accuracy and ROC results of within 0.49-0.52\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.49-0.52$$\\end{document} and 0.49-0.52\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.49-0.52$$\\end{document} respectively, indicating failure in predicting random data demonstrates efficient randomness prediction resiliency of the MR-PUF. The efficient time and data complexities of these attacks illustrated in this study are yielded to be linear and quadratic resulting in attack execution time in seconds and 5032 training samples combined with 2157 testing samples to verify the randomness of PUF.

Exploring statistical complexity and enabling speckle-free imaging with Dye-Kaolinite colloidal random lasers

This article extends our previous investigation into random lasers (RLs), focusing on the incorporation of Rhodamine 6G (R6G) dye in methanol with Kaolinite nanoclay scatterers as a disordered active medium for colloidal RLs. Building upon the prior exploration, which specifically focused on understanding the performance dependence related to the concentration of gain medium and scatterers, this report takes a closer look. It conducts a thorough statistical analysis of the incoherent dye-Kaolinite random lasing spectra, particularly in the context of replica symmetry breaking (RSB) and Lévy statistics. This study highlights that RSB is observed around the threshold energy and can serve as an indicator of the threshold. Gaussian distribution prevailed below, near, and far above the lasing threshold estimated from the levy (α) exponent, suggesting the absence of Lévy flight behavior. Additionally, Pearson correlation coefficients (PCC) were used to quantify the correlation between intensity fluctuations at different wavelengths, with the results visually represented by heatmaps. Furthermore, the random lasing output is utilized to achieve speckle-free imaging.

Subsampling Suffices for Adaptive Data Analysis

Ensuring that analyses performed on a dataset are representative of the entire population is one of the central problems in statistics. Most classical techniques assume that the dataset is independent of the analyst’s query and break down in the common setting where a dataset is reused for multiple, adaptively chosen, queries. This problem of adaptive data analysis was formalized in the seminal works of Dwork et al. (STOC, 2015) and Hardt and Ullman (FOCS, 2014). We identify a remarkably simple set of assumptions under which the queries will continue to be representative even when chosen adaptively: The only requirements are that each query takes as input a random subsample and outputs few bits. This result shows that the noise inherent in subsampling is sufficient to guarantee that query responses generalize. The simplicity of this subsampling-based framework allows it to model a variety of real-world scenarios not covered by prior work. In addition to its simplicity, we demonstrate the utility of this framework by designing mechanisms for two foundational tasks, statistical queries and median finding. In particular, our mechanism for answering the broadly applicable class of statistical queries is both extremely simple and state of the art in many parameter regimes.

Evaluation of power system instability probability considering output randomness of grid-connected converter

Abstract In recent years, there has been a notable increase in the installed capacity of new energy sources. Nevertheless, the random nature of the output power of G-CCs may potentially result in power system instability. This paper presents a closed-loop interconnection model comprising a G-CC as a feedback subsystem and a VSC-HVDC system as a feedforward subsystem. The instability condition of the closed-loop system is derived from the open-loop mode resonance theory. Subsequently, a mathematical model is developed to represent the random output of the G-CC. This approach allows the probability of open-loop mode resonant instability of the power system to be evaluated, as well as the risk of sub-synchronous resonant instability caused by the output randomness of the G-CC. Finally, validation is performed by building a simulation example system and a time-domain simulation system in MATLAB.

Dynamic event‐triggered model‐free adaptive control for networked control systems with random packet loss

AbstractA novel dual‐channel dynamic event‐triggered model‐free adaptive control method with compensation is proposed for nonlinear networked control systems (NCSs) subjected to random packet loss. In order to tackle the issue of redundant data transmission, a dual‐channel triggering control framework is designed, where data transmission only occurs upon meeting the triggering conditions. Compared with the traditional static event‐triggered schemes with fixed triggering thresholds, the proposed method allows for dynamically adjustable triggering thresholds. This means that the number of data transmissions in the forward and feedback channels can be further reduced. In addition, considering the detrimental impact of random packet loss and output event‐triggered on the system, compensation is applied to the system's output data using the linear data model of the nonlinear system, which is directly derived from I/O data without incorporating any other mechanistic model information, thereby ensuring optimal control performance. In contrast to existing results, the proposed control strategy can enhance system performance while conserving network communication resources. The effectiveness and superiority of the proposed scheme have been validated through two simulations.

Natural gradient hybrid variational inference with application to deep mixed models

Stochastic models with global parameters and latent variables are common, and for which variational inference (VI) is popular. However, existing methods are often either slow or inaccurate in high dimensions. We suggest a fast and accurate VI method for this case that employs a well-defined natural gradient variational optimization that targets the joint posterior of the global parameters and latent variables. It is a hybrid method, where at each step the global parameters are updated using the natural gradient and the latent variables are generated from their conditional posterior. A fast to compute expression for the Tikhonov damped Fisher information matrix is used, along with the re-parameterization trick, to provide a stable natural gradient. We apply the approach to deep mixed models, which are an emerging class of Bayesian neural networks with random output layer coefficients to allow for heterogeneity. A range of simulations show that using the natural gradient is substantially more efficient than using the ordinary gradient, and that the approach is faster and more accurate than two cutting-edge natural gradient VI methods. In a financial application we show that accounting for industry level heterogeneity using the deep mixed model improves the accuracy of asset pricing models. MATLAB code to implement the method and replicate the results can be found at https://github.com/WeibenZhang07/NG-HVI

Fault diagnosis of photovoltaic array with multi-module fusion under hyperparameter optimization

Photovoltaic (PV) arrays’ random and intermittent output characteristics impact power system safety. To improve the performance of the PV array fault diagnosis model, a novel online fault monitoring technique is introduced. (1) Fault diagnostic model construction: Significant differences in PV arrays’ I-V and P-V curves under various fault conditions led to constructing a 3D channel feature map based on I, V, and P features. (2) Multi-source information fusion network (MSIFN): this multi-module fusion model includes a time–frequency domain fusion module (TDFM), a multi-feature shuffle expansion convolution module (MSECM), a parameter-free parallel hybrid attention enhancement module, and a multi-scale mixed pooling fusion classification module (MMPCM). (3) Multi-strategy fusion whale optimization algorithm (MSFWOA): addressing the original WOA’s deficiencies, we designed time control, parameter modification, and greedy control strategies based on lens imaging to optimize MSIFN’s hyper-parameters. Experimental results show that the MSFWOA-MSIFN model excels in PV array fault diagnosis (Paccuracy=Pprecision=Precall = 99.92 %). In three types of noise experiments with 15 dB, 25 dB, and 30 dB, the average performance index remained above 99 %. In practical experiments, the average performance indices werePaccuracy = 97.53 %, Pprecision = 97.32 %, andPrecall = 97.41 %, further demonstrating its excellent diagnostic performance. This model effectively diagnoses various faults in PV arrays, providing scientific and theoretical support for PV system operations.

Convex relaxation of two-stage network-constrained stochastic programming for CHP microgrid optimal scheduling

Different from most studies only incorporating economic aspect into operation scheduling, this paper develops a novel network-constrained framework that can address both economic and secure issues for combined heating and power (CHP) microgrids integrated with wind power considering uncertainties. According to quadratically constrained programs, a two-stage network-constrained stochastic programming (TSNCSP) model is formulated to improve economic benefits and meanwhile capture active/reactive power and off-nominal bus voltage constraints in the full decision variable domain. In the first stage, namely day-ahead, the schedule of transacted electricity with the upstream power grid and the generation cost of heat is determined according to the forecast information. In the second stage, namely real-time, a recourse function of adjustable resources is defined to reduce the expected cost incurred by the perturbation of random wind power outputs. Moreover, the original non-convex problem is innovatively transformed into a semidefinite programming through incorporation of demand response program (DRP), duality, complementary slackness, and relaxation techniques to improve the solving efficiency. Finally, a proposition is presented and proved that provides a sufficient condition for the exactness of the proposed convex relaxation. Numerical simulations on the 33-bus test system verify the effectiveness of the proposed framework in multi-period scenario-based scheduling problems.

Prediction of water–main failures and management of the associated risks using integrated predictive analytics approach

Water distribution networks are vital to delivering potable water. They commonly include interconnected water mains (WMs), pumps and other hydraulic controls. Numerous WM failures have occurred due to ageing and harsh climate. This paper aims to predict the probability of future failures and integrate the predictions into risk-management strategies. The novelty lies in emphasising on the relevance to networks in cold regions like Canada. This study applied clustering and principal component analysis to the WM data from the Canadian City of Kitchener network. Clustering is shown to improve the failure prediction outcomes from the random forest algorithm and risk analysis output. Compared to without implementing clustering, the improvement reached 67–80% for WMs with high-rating risk. This paper successfully produced risk maps for Kitchener’s network, showing that only a small percentage (0.07–1.02%) of the existing WMs needs immediate action (prioritised rehabilitation or replacement). In addition to WM length and diameter, freeze index is shown to be an influence factor for failure predictions. The integrated, proactive approach discussed in this article can be applied to other cold-region WDNs. The results help reduce water losses and develop cost-effective, practical risk-management strategies.

Learning Quantum Processes Without Input Control

We introduce a general statistical learning theory for processes that take as input a classical random variable and output a quantum state. Our setting is motivated by the practical situation in which one desires to learn a quantum process governed by classical parameters that are out of one’s control. This framework is applicable, for example, to the study of astronomical phenomena, disordered systems and biological processes not controlled by the observer. We provide an algorithm for learning with high probability in this setting with a finite amount of samples, even if the concept class is infinite. To do this, we review and adapt existing algorithms for shadow tomography and hypothesis selection, and combine their guarantees with the uniform convergence on the data of the loss functions of interest. As a byproduct, we obtain sufficient conditions for performing shadow tomography of classical-quantum states with a number of copies, which depends on the dimension of the quantum register, but not on the dimension of the classical one. We give concrete examples of processes that can be learned in this manner, based on quantum circuits or physically motivated classes, such as systems governed by Hamiltonians with random perturbations or data-dependent phase shifts. Published by the American Physical Society 2024

Two-photon pumped random lasing in MAPbBr3 with directional output for far-field applications

We investigate the random lasing properties of polycrystalline MAPbBr3 grown in a hollow fiber. Coupling of the random lasing on the inner wall of the hollow fiber into the annular cylinder and guided by the inner surface of hollow cavity enable directional output of the random lasing emission. The polycrystalline MAPbBr3 was grown by the capillary effect, when one end of the hollow fiber was dipped into the precursor solution. A two-photon pumping scheme was employed, where femtosecond pulses centered at about 800 nm was focused onto the MAPbBr3 polycrystals through the side wall of the hollow fiber. Random lasing was achieved due to the strong optical scattering by the interfaces within the polycrystals on the inner wall of the hollow fiber. The random lasing output was re-collimated into a roughly parallel beam with a circular transverse mode, which favors far-field applications of random lasers. Two-photon pumping enables larger penetration depth and larger excitation volume than single-photon pumping, so that strong lasing spot can also be observed in the center area of the output laser beam.

Binary quantum random number generator based on value indefinite observables

All quantum random number generators based on measuring value indefinite observables are at least three-dimensional because the Kochen–Specker Theorem and the Located Kochen–Specker Theorem are false in dimension two. In this article, we construct quantum random number generators based on measuring a three-dimensional value indefinite observable that generates binary quantum random outputs with the same randomness qualities as the ternary ones: the outputs are maximally unpredictable.

Boundary Feedback Fiber Random Microcavity Laser Based on Disordered Cladding Structures

The cavity form of complex microcavity lasers predominantly relies on disordered structures, whether found in nature or artificially prepared. These structures, characterized by disorder, facilitate random lasing through the feedback effect of the cavity boundary and the internal scattering medium via various mechanisms. In this paper, we report on a random fiber laser employing a disordered scattering cladding medium affixed to the inner cladding of a hollow-core fiber. The internal flowing liquid gain establishes a stable liquid-core waveguide environment, enabling long-term directional coupling output for random laser emission. Through theoretical analysis and experimental validation, we demonstrate that controlling the disorder at the cavity boundary allows liquid-core fiber random microcavities to exhibit random lasing output with different mechanisms. This provides a broad platform for in-depth research into the generation and control of complex microcavity lasers, as well as the detection of scattered matter within micro- and nanostructures.

Implement quantum random number generation on the IBM quantum computer platform

Random numbers are a crucial component of any encryption activity in modern cryptography. Quantum Random Number Generators (QRNGs) produce truly random output strings to replace pseudo-random ones. The principle of QRNG relies on measuring qubit states, which excel in quantum computing applications, particularly on IBM's quantum computing platform. To construct a random number generator, the authors utilized IBM Q Experience's Qiskit quantum development toolkit. We developed QRNG applications on IBM quantum computers (7-qubit, 16-qubit, and 127-qubit) and tested the program's functionality on these quantum computing platforms. The quality assessment of the random strings was conducted according to NIST and AIS-31 standards. For NIST standards, to achieve good quality, the output string must reach a minimum of 1,593,088 bits to pass 16 tests per SP800-22 standard. According to AIS-31 standards, to achieve good quality, the output string must reach a minimum of 8,000,000 bits to pass 8 tests of the standard

Output uncertainty mitigation in competitive markets

Output uncertainty is a major concern for industries prone to exogenous, persistent and large fluctuations in output, such as agriculture, wind and solar power generation, while technology adoption aimed at mitigating output uncertainty can improve social welfare. This paper constructs a competitive market model with random output fluctuations to examine the scale of technology adoption at the long-term equilibrium and its comparison with the social optimum. We show that the First Welfare Theorem no longer holds in general, and depending on the characteristics of the demand function, the scale of technology adoption in the competitive market may be greater or less than the socially optimal scale.

Exploiting holographically encoded variance to transmit labelled images through a multimode optical fiber.

Artificial intelligence has emerged as promising tool to decode an image transmitted through a multimode fiber (MMF) by applying deep learning techniques. By transmitting thousands of images through the MMF, deep neural networks (DNNs) are able to decipher the seemingly random output speckle patterns and unveil the intrinsic input-output relationship. High fidelity reconstruction is obtained for datasets with a large degree of homogeneity, which underutilizes the capacity of the combined MMF-DNN system. Here, we show that holographic modulation can encode an additional layer of variance on the output speckle pattern, improving the overall transmissive capabilities of the system. Operatively, we have implemented this by adding a holographic label to the original dataset and injecting the resulting phase image into the fiber facet through a Fourier transform lens. The resulting speckle pattern dataset can be clustered primarily by holographic label, and can be reconstructed without loss of fidelity. As an application, we describe how color images may be segmented into RGB components and each color component may then be labelled by distinct hologram. A ResUNet architecture was then used to decode each class of speckle patterns and reconstruct the color image without the need for temporal synchronization between sender and receiver.

Iterative learning based convergence analysis for nonlinear impulsive differential inclusion systems with randomly varying trial lengths

SummaryThis paper studies the finite‐time tracking problem for nonlinear impulsive differential inclusion systems with randomly varying trial lengths. First, we convert the set‐valued mapping in the differential inclusion systems to single‐valued mapping by a Steiner‐type selector. For the tracking problem of random discontinuous output trajectories, this paper defines a piecewise continuous variable by zero‐order holder to correct the tracking error of segmented continuity. Then, we introduce the average operator with forgetting factor to design three novel learning schemes, and establish convergence results by using the mathematical analysis tools such as impulsive Gronwall inequality and ‐norm. Finally, a numerical example verifies the validity of the theoretical results, and we compare the tracking performance of the output trajectories for different forgetting factors.

Optimal modeling of nonlinear systems Method of variable injections

Our work addresses a development and justification of the new approach to the modeling of nonlinear systems. Let $\f$ be an unknown input-output map of the system with a random input and output $\y$ and $\x$, respectively. It is assumed that $\y$ and $\x$ are available and covariance matrices formed from $\y$ and $\x$ are known. We determine a model of $\f$ so that an associated error is minimized. To this end, the model $\ttt_p$ is constructed as a sum of $p+1$ particular parts, in the form $\ttt_p (\y) = \sum_{j=0}^{p}G_j H_j Q_j(\vv_j)$ where $G_j$ and $ H_j$, for $j=0,\ldots, p$, are matrices to be determined, and $\vv_j$, for $j=1,\ldots,p$, is a special random vector called the injection. We denote $\vv_0=\y$. Injections $\vv_1,\ldots, \vv_p$ are aimed to diminish the error associated with the proposed model $\ttt_p$. Further, $Q_j$ is a special transform aimed to facilitate the numerical realization of model $\ttt_p$. It is determined in the way allowing us to optimally determine $G_j$ and $ H_j$ as a solution of $p+1$ separate error minimization problems which are simpler than the original minimization problem. The empirical determination of injections $\vv_1,\ldots, \vv_p$ is considered. The proposed method has several degrees of freedom to diminish the associated error. They are `degree' $p$ of $\ttt_p$, choice of matrices $G_0, H_0, \ldots,$ $G_p, H_p$, dimensions of matrices $G_0, H_0, \ldots,$ $G_p, H_p$ and injections $\vv_1,\ldots, \vv_p$, respectively. Four numerical examples are provided. At the end, the open problem is formulated.

Stochastic simulation of magnetically controlled convection in porous media with random porosity via Karhunen-Loève expansion and intrusive polynomial chaos expansion

Understanding and quantifying uncertainty factors are crucial for accurately predicting thermomagnetic convection phenomena. This study presented a mathematical model and algorithm framework for uncertainty analysis of thermomagnetic convection in porous media with random porosity. The proposed method combined Karhunen-Loève expansion and intrusive polynomial chaos expansion to represent the input random parameters and output response, respectively. The Galerkin projection method was employed to decouple the stochastic governing equations of thermomagnetic convection into deterministic governing equations that can be efficiently solved using the finite volume method. By solving the polynomial chaos expansion of the output response and employing the stochastic projection method to solve the associated decoupled governing equations, the temporal evolution and statistical characteristics of the output response were obtained. The study revealed that porosity uncertainty in the porous medium affects the thermomagnetic convection of paramagnetic fluid, exhibiting significant chaos effects. Monte Carlo simulations were performed to validate the accuracy of the proposed approach and demonstrate its computational efficiency compared to traditional Monte Carlo methods. This research provides valuable insights into the uncertainty analysis of thermomagnetic convection and offers a promising methodology for analyzing heat transfer in various porous media applications.

MACHINE LEARNING CLASSIFICATION OF THE EPIDEMIOLOGIC STAGES OF INFLAMMATORY BOWEL DISEASE ACROSS GEOGRAPHY AND TIME

Abstract BACKGROUND Epidemiologic stages of inflammatory bowel disease (IBD) have been proposed: 1. Emergence (low incidence and prevalence); 2. Acceleration in Incidence (rapidly rising incidence, low prevalence); and 3. Compounding Prevalence (stabilizing incidence, rapidly rising prevalence). To date, these stages have been theoretical without quantified definitions of incidence and prevalence. AIM To use machine learning to determine incidence and prevalence ranges corresponding to the epidemiologic stages and provide stage classifications across time for global regions. METHODS We built a supervised random forest classifier in R to determine epidemiologic stages of IBD from population-based studies (n=340), a subset derived from a systematic review on the incidence and prevalence of IBD. A labelled training data set comprising rates of incidence and prevalence of Crohn’s disease (CD) and ulcerative colitis (UC) extracted from the systematic review was used to predict classifications of stage 1, stage 2, or stage 3 for each region, stratified by decade (1960–2019). Model accuracy was measured using a blind validation data set. The validated model was then used to predict stage classifications for regions in the data set. Interquartile ranges for incidence and prevalence of CD and UC were calculated on the random forest output, and the distributions were compared using negative binomial regression. RESULTS The random forest’s classification accuracy on the blinded validation data was 93.7% (95%CI: 90.6, 96.1) indicating an appropriate model fit and performance. Significant differences between all stages for the incidence and prevalence of CD and UC (p<0.001) were found. The clear distinction across stages defines the incidence and prevalence ranges (25th–75th, per 100,000) for IBD as: CD incidence 0.0–0.3, UC incidence 0.2–0.7, CD prevalence 0.3–2.2, and UC prevalence 1.7–8.1 for stage 1; CD incidence 1.0–4.4, UC incidence 2.3–6.3, CD prevalence 9.0–33.9, and UC prevalence 22.8–73.3 for stage 2; and CD incidence 6.6–14.0, UC incidence 10.1–18.1, CD prevalence 163.2–274.7, and UC prevalence 189.1–323.2 for stage 3 (Figure 1). A decade-by-decade analysis shows global regions transitioning across the epidemiologic stages (Figure 2). By the 2010s, North America, Scandinavia, Western Europe, Australia, and New Zealand were in stage 3. Most regions in Asia and Latin America were in stage 1 in the last half of the 20th century, with many transitioning to stage 2 in the 2010s. DISCUSSION Temporal incidence and prevalence data show that regions transition across epidemiologic stages. Numerical definitions of the epidemiologic stages can be used to establish the anticipated burden growth of IBD by providing estimated rates of the number of incident and prevalent IBD cases a region can expect as it transitions between IBD epidemiologic stages in the future. Figure 1 Coalescing ranges for incidence (panel A) and prevalence (panel B) by Crohn’s disease and ulcerative colitis at epidemiologic stage 1, stage 2, and stage 3. Data were categorized by data type (incidence or prevalence), disease type (Crohn’s disease or ulcerative colitis), and epidemiologic stage, as per results from the random forest classifier. The 25th and 75th percentiles were calculated using the rates across all regions included in the analysis for all available time points for each box group. Figure 2 Global maps depicting epidemiologic stages of IBD evolution from 1960 to 2019 broken down by decade, as predicted by the random forest model. Panel A contains stage classifications from 1960 to 1969; panel B contains stage classifications from 1970 to 1979; panel C contains stage classifications from 1980 to 1989; panel D contains stage classifications from 1990 to 1999; panel E contains stage classifications from 2000 to 2009; and panel F contains stage classifications from 2010 to 2019.