Random Source Research Articles

Improvement of multiscale decomposition for space-based gravitational wave signal processing technology.

During the process of detecting gravitational waves in space, addressing noise issues caused by terrestrial vibrations, natural environmental changes, and the factors intrinsic to the detectors, this paper proposes a multiscale variational mode adaptive denoising algorithm based on momentum gradient descent. This algorithm integrates momentum factors and multiscale concepts into the variational mode algorithm to resolve the issue of multiple local optima encountered during operation, reduce oscillations in regions with large or unstable gradient changes, and improve convergence speed. Additionally, the algorithm combines the least mean squares algorithm to automatically adjust weights, thereby mitigating the impact of noise, addressing the issue of noise from multiple and random sources, effectively suppressing noise in the gravitational wave signal, and enhancing the quality and reliability of the gravitational wave signal. Experimental results demonstrate that this algorithm performs better than other algorithms in noise suppression, effectively reducing noise in gravitational wave signals and meeting the noise suppression requirements for space-based gravitational wave detection.

Generalized measurements on qubits in quantum randomness certification and expansion

Quantum mechanics has greatly impacted our understanding of microscopic nature. One of the key concepts of this theory is generalized measurements, which have proven useful in various quantum information processing tasks. However, despite their significance, they have not yet been shown empirically to provide an advantage in quantum randomness certification and expansion protocols. This investigation explores scenarios where generalized measurements can yield more than 1 bit of certified randomness with a single-qubit system measurement on untrusted devices and against a quantum adversary. We compare the robustness of several protocols to exhibit the advantage of exploiting generalized measurements. In our analysis of experimental data, we were able to obtain 1.21 bits of min-entropy from a measurement taken on one qubit of an entangled state. We also obtained 1.07 bits of min-entropy from an experiment with quantum state preparation and generalized measurement on a single qubit. We also provide finite data analysis for a protocol using generalized measurements and the Entropy Accumulation Theorem. Our exploration demonstrates the potential of generalized measurements to improve the certification of quantum sources of randomness and enhance the security of quantum cryptographic protocols and other areas of quantum information. Published by the American Physical Society 2024

Inertial amplification as a performance enhancement method for snap-through vibration energy harvester

In recent years, there has been a lot of interest in exploiting the bistable behavior of snap-through systems to harvest energy from vibration sources. The efficient operation of any bistable VEH depends on its ability to exhibit large-amplitude interwell motion. Under weak ambient excitation, bistable VEH performs marginally because of the confinement of motion to a single well. Frequency up-conversion, multi-stability, and adaptive techniques are some of the performance enhancement strategies suggested for bistable-VEH. Considering the VEH's space constraints, the above designs are hard to implement in practical cases. This study introduces an inertial amplification mechanism (IAM) as a simple passive strategy to enhance the performance of a snap-through VEH, a concept not explored in previous studies. The addition of IAM increases the effective mass without increasing the physical mass and thereby enhances energy harvesting, especially from weak ambient excitation sources. The dynamics and performance of the enhanced snap-through VEH are investigated analytically and numerically under harmonic and random excitations. The harmonic balance method (HBM) derives the frequency-amplitude relationship, which shows a hardening behavior and an increase in bandwidth. The effective potential method provides a closed-form expression for the joint probability density function (Joint PDF), which is governed by the Fokker-Planck equation. The joint PDF shows a transition from bimodal to unimodal with an increase in the value of the geometrical parameter. The stochastic averaging method is employed to obtain the stationary probability density function, which defines the long-term dynamics of the VEH. The effects of noise intensity, mass ratio, and inertial amplifier angle on the dynamics are investigated. Finally, the performance of the proposed VEH is compared with a conventional snap-through VEH, an equivalent linear VEH, and a multistable VEH under harmonic and random excitation conditions. The findings suggest that the snap-through VEH with the IAM has advantages over the linear and multistable nonlinear VEH in terms of extracting energy from low-intensity harmonic and random excitation sources. This simple augmentation strategy preserves the original system's bistability, eliminating the need for the complex design of a multistable VEH.

Revealing the True Cost of Locally Differentially Private Protocols: An Auditing Perspective

While the existing literature on Differential Privacy (DP) auditing predominantly focuses on the centralized model (e.g., in auditing the DP-SGD algorithm), we advocate for extending this approach to audit Local DP (LDP). To achieve this, we introduce the LDP-Auditor framework for empirically estimating the privacy loss of locally differentially private mechanisms. This approach leverages recent advances in designing privacy attacks against LDP frequency estimation protocols. More precisely, through the analysis of numerous state-of-the-art LDP protocols, we extensively explore the factors influencing the privacy audit, such as the impact of different encoding and perturbation functions. Additionally, we investigate the influence of the domain size and the theoretical privacy loss parameters ϵ and δ on local privacy estimation. In-depth case studies are also conducted to explore specific aspects of LDP auditing, including distinguishability attacks on LDP protocols for longitudinal studies and multidimensional data. Finally, we present a notable achievement of our LDP-Auditor framework, which is the discovery of a bug in a state-of-the-art LDP Python package. Overall, our LDP-Auditor framework as well as our study offer valuable insights into the sources of randomness and information loss in LDP protocols. These contributions collectively provide a realistic understanding of the local privacy loss, which can help practitioners in selecting the LDP mechanism and privacy parameters that best align with their specific requirements. We open-sourced LDP-Auditor in [4].

Carleman estimates for space semi-discrete approximations of one-dimensional stochastic parabolic equation and its applications

Abstract In this paper, we study discrete Carleman estimates for space semi-discrete approximations of one-dimensional stochastic parabolic equation. We then apply these Carleman estimates to investigate two inverse problems for the space semi-discrete stochastic parabolic equations, including a discrete inverse random source problem and a discrete Cauchy problem. We firstly establish two Carleman estimates for a one-dimensional semi-discrete stochastic parabolic equation, one for homogeneous boundary and the other for non-homogeneous boundary. Then we apply these two estimates separately to derive two stability results. The first one is the Lipschitz stability for the discrete inverse random source problem. The second one is the H"{o}lder stability for the discrete Cauchy problem.

Photonic probabilistic machine learning using quantum vacuum noise

Probabilistic machine learning utilizes controllable sources of randomness to encode uncertainty and enable statistical modeling. Harnessing the pure randomness of quantum vacuum noise, which stems from fluctuating electromagnetic fields, has shown promise for high speed and energy-efficient stochastic photonic elements. Nevertheless, photonic computing hardware which can control these stochastic elements to program probabilistic machine learning algorithms has been limited. Here, we implement a photonic probabilistic computer consisting of a controllable stochastic photonic element – a photonic probabilistic neuron (PPN). Our PPN is implemented in a bistable optical parametric oscillator (OPO) with vacuum-level injected bias fields. We then program a measurement-and-feedback loop for time-multiplexed PPNs with electronic processors (FPGA or GPU) to solve certain probabilistic machine learning tasks. We showcase probabilistic inference and image generation of MNIST-handwritten digits, which are representative examples of discriminative and generative models. In both implementations, quantum vacuum noise is used as a random seed to encode classification uncertainty or probabilistic generation of samples. In addition, we propose a path towards an all-optical probabilistic computing platform, with an estimated sampling rate of ~1 Gbps and energy consumption of ~5 fJ/MAC. Our work paves the way for scalable, ultrafast, and energy-efficient probabilistic machine learning hardware.

Matched cross‐spectrum phase processing for source depth estimation in deep water

AbstractZhou et al. (2022) proposed a method for instantaneous source depth estimation in deep water using matched beam intensity processing (MBIP) that leverages depth‐related oscillatory features arising from Lloyd's mirror interference in the frequency domain (Appl. Acoust., 2022, 186, 108493). However, the efficacy of MBIP diminishes with a random source spectrum. To counteract the effect of a random source spectrum, a vertical line array (VLA) positioned near the sea bottom is employed and a cross‐spectrum is generated from the conventional beamforming output, which is achieved by dividing the VLA into upper‐half and lower‐half arrays. The authors find that the cross‐spectrum phase interference pattern is determined by the source depth and is unaffected by the source spectrum. Consequently, a matched cross‐spectrum phase processing method is proposed, which demonstrates appreciable depth estimation accuracy compared to that of the existing techniques, as evidenced by the results from both simulated and experimental data.

Iterative processing of the noise coherence matrix to determine the acoustic properties of the water area

Abstract An expression is given for the coherence matrix of the acoustic field produced by random sources in an inhomogeneous medium in the Born approximation. An iterative algorithm is proposed to determine the wave number inside the inhomogeneity based on this expression. The reconstruction results are analyzed for the cases of precise input data, data with interference, and incomplete data.

Metalens array for quantum random number

Quantum random number generation (QRNG) leveraging intrinsic quantum uncertainty has attracted significant interest in the field of integrated photonic architecture, with applications in quantum cryptography, tests of quantum nonlocality, and beyond. The demand for compact, low-energy consumption, robust, fast, and cost-effective QRNGs integrated into photonic chips is highlighted, whereas most previous works focused on bulk optics. Here, based on the metalens array entangled source, we experimentally realized a miniaturized, high-dimensional quantum random number generator via a meta-device without post-randomness extraction. Specifically, the device has a high-density output with 100 channels per square millimeter. This chip-scale quantum randomness source can obtain random number arrays without post-randomness extraction and enable compact integration for quantum applications needing secure keys or randomness. Our approach demonstrates potential in secure key generation and randomness for quantum applications.

Dynamic group fuzzy extractor

The group fuzzy extractor allows group users to extract and reproduce group cryptographic keys from their individual non-uniform random sources. It can be easily used in group-oriented cryptographic applications. However, current group fuzzy extractors are not dynamic, i.e. they spend a large cost when dealing with user revocation. In this work, we propose the formal definition and construction of dynamic group fuzzy extractor (DGFE) to address this issue. For the revocation, DGFE allows unrevoked group users to reproduce updated group keys from the existing group help data. Meanwhile, it prevents any revoked group user from generating new group keys using the previously authorized individual help data. We propose a DGFE construction based on the revocable group signature. Furthermore, we give formal proofs of reusability, anonymity and traceability of our construction.

Multi-source ensemble method with random source selection for virtual metrology

Multi-source ensemble method with random source selection for virtual metrology

A new methodology for experimental analysis of single-cavity bubble’s nucleation, growth and detachment in saturated HFE-7100

The study of single bubble nucleation, growth and detachment processes has being carried out with remarkable relevance in the last fifty years. The complexity of the associated phenomena are still a challenge for the researchers in the field, yielding a lot of works trying to explain the enrolled mechanisms. These studies include experimental, numerical and even theoretical approaches. In particular but not exclusively for numerical approaches, it is of essential importance having a solid and detailed experimental description of the full event. It is in this context that this work emerges, proposing a methodology for characterizing the single bubble nucleation, growth and detachment process. This methodology is based in the determination of a “typical bubble” that represents the whole phenomena, providing the reconstruction of a main bubble life-cycle with its uncertainty (both in space and time). This reconstruction allows the determination of several important characteristics, such as bubble volume, apparent contact angle, dry radius, and the forces acting on the bubble. This work analyzes a set of bubbles growing from a cavity of 91μm diameter, under saturated conditions at 0,583 bar and superheating of 25,8 °C. All the outputs were obtained considering a carefully uncertainty determination and propagation from both systematic and random sources, which are not negligible as it is demonstrated in this work. The importance of considering a sufficient number of bubbles to characterize the phenomenon was also addressed, considering an analysis of uncertainties for different set of bubbles. The methodology was also confronted to previous approaches reported elsewhere, that ensured its performance, robustness, and validation.

One Noise to Rule Them All: Learning a Unified Model of Spatially-Varying Noise Patterns

Procedural noise is a fundamental component of computer graphics pipelines, offering a flexible way to generate textures that exhibit "natural" random variation. Many different types of noise exist, each produced by a separate algorithm. In this paper, we present a single generative model which can learn to generate multiple types of noise as well as blend between them. In addition, it is capable of producing spatially-varying noise blends despite not having access to such data for training. These features are enabled by training a denoising diffusion model using a novel combination of data augmentation and network conditioning techniques. Like procedural noise generators, the model's behavior is controllable via interpretable parameters plus a source of randomness. We use our model to produce a variety of visually compelling noise textures. We also present an application of our model to improving inverse procedural material design; using our model in place of fixed-type noise nodes in a procedural material graph results in higher-fidelity material reconstructions without needing to know the type of noise in advance. Open-sourced materials can be found at https://armanmaesumi.github.io/onenoise/

Comparative Analysis Multistage Algorithms for the Localization of Random Pulsed-Point Sources by Efficiency

Comparative Analysis Multistage Algorithms for the Localization of Random Pulsed-Point Sources by Efficiency

Software and hardware realizations for different designs of chaos-based secret image sharing systems

Secret image sharing (SIS) conveys a secret image to mutually suspicious receivers by sending meaningless shares to the participants, and all shares must be present to recover the secret. This paper proposes and compares three systems for secret sharing, where a visual cryptography system is designed with a fast recovery scheme as the backbone for all systems. Then, an SIS system is introduced for sharing any type of image, where it improves security using the Lorenz chaotic system as the source of randomness and the generalized Arnold transform as a permutation module. The second SIS system further enhances security and robustness by utilizing SHA-256 and RSA cryptosystem. The presented architectures are implemented on a field programmable gate array (FPGA) to enhance computational efficiency and facilitate real-time processing. Detailed experimental results and comparisons between the software and hardware realizations are presented. Security analysis and comparisons with related literature are also introduced with good results, including statistical tests, differential attack measures, robustness tests against noise and crop attacks, key sensitivity tests, and performance analysis.

Effects of source space resolution, randomization and averaging in focal EEG source localization: Estimation of forward and inverse errors

In this article, we study randomized multiresolution forward modeling and source localization in electroencephalography (EEG). Our goal is to determine numerically the effects of variable source space resolution on both forward modeling and source localization perspectives and to examine how it affects the detection of the sources at different depths inside the brain. Our concentration is in the main principles of multiresolution methods: random source space generation and the reconstruction obtained via averaging over multiple maximum a posteriori (MAP) estimations obtained at a finite set of different sub source spaces, and their suggested enhancement in distinguishability of weak components. The motivation of averaging is to reduce the discretization and optimization error and marginalize other variable factors. Due to the unguided randomized source space generation, multiple samples of the same size source spaces are needed. For this very reason, a large number of source spaces of multiple source counts are used to obtain statistically feasible results in this paper. This study verifies the described postulates of the multiresolution technique by showing averaging to be crucial in order to obtain a robust projection to a lower resolution, where the lead field matrix is overdetermined. From the viewpoint of source localization, the multiresolution approach was found to improve the reconstruction accuracy obtained with each resolution level below the highest and observed to be necessary to achieve an accuracy comparable to that of forward modeling.

Saponin Extracts Utilization as Dietary Additive in Ruminant Nutrition: A Meta-Analysis of In Vivo Studies.

The present meta-analysis aimed to determine the underlying effects of different saponins extracted from different sources on the production performance, milk yield, digestibility, rumen fermentation, blood metabolites, and nitrogen utilization of ruminants. A total of 26 papers comprising 66 in vivo studies (148 data points of dietary treatments) were evaluated in the present study. The databases were statistically analyzed using the mixed model procedure of SAS, where experiments considered random effects and tannin-related factors were treated as fixed effects. Statistical procedures were then continued in comparing different sources of saponin extract through Mixed Model analysis, where experiments were also random factors and sources of saponin extract were fixed factors. The evidence revealed in the present meta-analysis that saponin supplementation of up to 40 g/kg DM appears to have no detrimental impact on feed intake across ruminant types, suggesting that it does not significantly affect diet palatability. However, the results indicated that there are species-specific responses to saponin supplementation, particularly in relation to palatability and nutrient absorption efficiency, with larger ruminants being better able to tolerate the bitterness induced by saponin extracts. Furthermore, the study found that saponin extracts can influence nutrient digestibility and rumen fermentation dynamics, with different effects observed in large and small ruminants. While some saponin extracts can enhance average daily weight gain and milk yield, others can have adverse effects, highlighting the importance of considering both saponin sources and animal physiological condition when developing nutritional strategies. Additionally, optimization of ruminant production by utilizing saponin extracts is necessary to avoid negative health implications, such as increased blood creatinine levels. Different saponin extracts utilization in ruminant nutrition and environmental management, have a distinct understanding associated to their various bioactive properties. However, among the saponin sources, saponin extracted from Quilaja saponaria is more likely to improve large ruminant production performance while maintaining ruminant health and metabolism, but negatively affect small ruminants. Further research is needed to unravel the intricate effects of different saponin sources on ruminant health and productivity, emphasizing the importance of tailored dietary strategies that consider the unique physiological and metabolic characteristics of the target livestock.

ON WHICH SOCIOECONOMIC GROUPS DO REVERSE MORTGAGES HAVE THE GREATEST IMPACT? EVIDENCE FROM SPAIN

Reverse mortgage is one of the products (perhaps the main one) that is good to obtain additional income by using the habitual residence as collateral. The main objective of this paper is to analyse the effects that reverse mortgage contracting has on household finances over the lifetime of a family according to the socioeconomic group to which it belongs in Spain. Four indicators are employed to measure the immediate and long-term effects. We use a stochastic model with a double source of randomness, survival and entry into dependency, and apply it to the three socioeconomic groups obtained with cluster methodology from the 2017 Spanish Household Financial Survey data. We conclude that the effects are very different depending on the group: regarding only the effects of hiring a reverse mortgage on the income of the family, widowed women aged between 81 and 85 years, with low income and expenses as well as little net wealth, and a habitual residence that represents half of her net wealth (Cluster 1) are the most benefited; considering that the highest impact indicators are on the probability of illiquidity and on the value of lack of liquidity, the use of reverse mortgages benefits more the families in Cluster 3 (high income and expenses and really high net wealth, head of household aged between 76 and 80 years) and less the families in Cluster 2 (medium income, net wealth and expenses, head of household aged between 65 and 75 years).

Blood-inspired random bit generation using microfluidics system

The development of random number generators (RNGs) using speckle patterns is pivotal for secure encryption key generation, drawing from the recent statistical properties identified in speckle-based imaging. Speckle-based RNG systems generate a sequence of random numbers through the unpredictable and reproducible nature of speckle patterns, ensuring a source of randomness that is independent of algorithms. However, to guarantee their effectiveness and reliability, these systems demand a meticulous and rigorous approach. In this study, we present a blood-inspired RNG system with a microfluidics device, designed to generate random numbers at a rate of 5.5 MHz and a high-speed of 1250 fps. This process is achieved by directing a laser beam through a volumetric scattering medium to procure speckle patterns. Additionally, designed microfluidic device requires only a minimal blood sample of 5 µl to capture these speckle patterns effectively. After implementing the two-pass tuple-output von Neumann debiasing algorithm to counteract statistical biases, we utilized the randomness statistical test suite from the National Institute of Standards and Technology for validation. The generated numbers successfully passed these tests, ensuring their randomness and unpredictability. Our blood-inspired RNG, utilizing whole blood, offers a pathway for affordable, high-output applications in fields like encryption, computer security, and data protection.

Percolation on dense random graphs with given degrees

In this paper, we study the order of the largest connected component of a random graph having two sources of randomness: first, the graph is chosen randomly from all graphs with a given degree sequence, and then bond percolation is applied. Far from being able to classify all such degree sequences, we exhibit several new threshold phenomena for the order of the largest component in terms of both sources of randomness. We also provide an example of a degree sequence for which the order of the largest component undergoes an unbounded number of jumps in terms of the percolation parameter, giving rise to a behavior that cannot be observed without percolation.