True Output Research Articles

Efficient Algorithm for Secure Outsourcing of Modular Exponentiation with Single Server

Outsourcing computation allows an outsourcer with limited resource to delegate the computation load to a powerful server without the exposure of true inputs and outputs. It is well known that modular exponentiation is one of the most expensive operations in public key cryptosystems. Currently, most of outsourcing algorithms for modular exponentiation are based on two untrusted servers or have small checkability with single server. In this paper, we first propose an efficient outsourcing algorithm of modular exponentiation based on two untrusted servers, where the outsourcer can detect the error based on Euler theorem with a probability of 1 if one of the servers misbehaves. We then present an outsourcing algorithm of modular exponentiation with single server, and the outsourcer can also check the failure with a probability of 1. Therefore, the proposed algorithm with single server improves efficiency and checkability simultaneously compare with the previous ones. Finally, we provide the experimental evaluations to demonstrate that the proposed two algorithms are the most efficient ones in all of the outsourcing algorithms for an outsourcer.

Comparison of laser power output from the fiber tip during endovenous laser ablation against displayed power and the “first treatment” effect

ObjectiveInternational guidelines recommend endovenous laser ablation as one of the first-line treatments for truncal venous reflux associated with varicose veins. Clinicians use linear endovenous energy density to measure the energy used during treatment. The aim of this study was to investigate the power output from the fiber tip and to see if this changed with use. MethodsWe placed 15 mL of water in a thermally insulated 25-mL beaker. Two thermocouples were placed equidistantly adjacent to the laser fiber tip. A 1470-nm laser was fired at 5W for 2 minutes and the temperature change was measured. Three fibers were used on different days to allow the laser to cool for 24 hours between fibers. Each fiber was tested three times in a row. We also tested the “first treatment” effect by comparing the power output when the fiber was fired immediately after the laser was switched on, compared with treatments when the laser had been switched on for 1 hour. To assess whether this was due to the console being “cold” on the first firing of the day, we repeated the experiment having switched on the laser console 1 hour before firing to “prewarm” the console. However, the diode was not fired during this hour. To measure fiber degradation, three runs of the experiment were performed successively before firing the laser continuously for 20 minutes, then three more runs were conducted, resulting in delivery of approximately 10,000 J. ResultsThe actual power output seemed to be lower than the console suggested. The power output from the first fiber used in a succession of three with the same laser had a significantly lower power output than the following two runs (P = .0004 and P < .0001, respectively). When the laser was prewarmed for 1 hour without firing, no change in this output pattern was noted (P = .293). Fiber degradation was not found in any of the fibers that were tested within the maximum recommended for the fiber (10,000 J). ConclusionsThe first use of a fiber in a treatment session has a significantly lower power output from the treatment tip than subsequent uses, even if the machine is prewarmed and the console displays the same power for each. The authors believe that this phenomenon is due to the diode being less efficient when first used after switching it on. Clinicians need to be aware that the true power output at the fiber tip may not be as indicated by the console display and may be variable during a treatment session.

Antithetical stratified sampling estimator for filtering signals with discontinuities

A novel approach to signal filtering using digital alias-free signal processing (DASP) is presented in this paper. We propose an unbiased, fast-converging estimator of the output of a finite impulse response (FIR) continuous-time filter. The estimator processes 2N signal samples collected with the use of random antithetical stratified (AnSt) sampling technique. To assess the estimator convergence rate as a function of N, we consider various forms of smoothness of the input signal, filter impulse response and windowing function. The cases are piecewise-continuous second-order derivative (SOD), piecewise-continuous first-order derivative (FOD) and piecewise-continuous zero-order derivative (ZOD). In each case we assume that the respective derivative has a finite number of bounded discontinuities. We prove that the proposed estimator converges to the true filter output at the rate of N−5 in the first case. But for the other two the rate drops to N−4 and N−2 respectively.

Orthogonal Components Forming of the Microprocessor-Based Protection Input Signals

The use of orthogonal components (OS) is the main direction of determining information parameters in microprocessor relay protection and automation of electric power systems. Most of the measuring devices used in modern protection and automation devices can be implemented using known operating systems. Digital non-recursive frequency filters based on discrete Fourier transform are used for OS selection. The main disadvantage of these filters is their low performance that exceeds the period of industrial frequency. For the construction of high-speed measuring devices, this time of establishing the true output signal is often unacceptable. The article proposes to form the equivalent signal OS in microprocessor defenses based on the values of the cosine and sine axes of the main harmonic formed using a discrete Fourier transform, by multiplying them by a correction factor, which is a function of the values of the input signal amplitude and its main harmonic. The proposed algorithm for generating OS input signals in microprocessor defenses is characterized by high performance in transient modes and has wide functionality. A block diagram of an OS equivalent signal generator has been developed, all blocks of which can be implemented according to known schemes on a microelectronic and microprocessor element base. The OS shaper model is implemented in the MatLab-Simulink dynamic modeling environment. The model functioning was checked using two types of test actions, viz. a sinusoidal signal with a frequency of 50 Hz (idealized action) and a signal close to the real secondary current of a short-circuit current transformer. As a result of the performed calculations, a significant (up to two times) in the speed of the proposed method of OS formation in comparison with the formers based on the discrete Fourier transform, frequency properties of both formers being identical.

Assisting the Visually Impaired in Multi-object Scene Description Using OWA-Based Fusion of CNN Models

Advances in technology can provide a lot of support for visually impaired (VI) persons. In particular, computer vision and machine learning can provide solutions for object detection and recognition. In this work, we propose a multi-label image classification solution for assisting a VI person in recognizing the presence of multiple objects in a scene. The solution is based on the fusion of two deep CNN models using the induced ordered weighted averaging (OWA) approach. Namely, in this work, we fuse the outputs of two pre-trained CNN models, VGG16 and SqueezeNet. To use the induced OWA approach, we need to estimate a confidence measure in the outputs of the two CNN base models. To this end, we propose the residual error between the predicted output and the true output as a measure of confidence. We estimate this residual error using another dedicated CNN model that is trained on the residual errors computed from the main CNN models. Then, the OAW technique uses these estimated residual errors as confidence measures and fuses the decisions of the two main CNN models. When tested on four image datasets of indoor environments from two separate locations, the proposed novel method improves the detection accuracy compared to both base CNN models. The results are also significantly better than state-of-the-art methods reported in the literature.

A Quantum-Inspired Self-Supervised Network model for automatic segmentation of brain MR images

The classical self-supervised neural network architectures suffer from slow convergence problem and incorporation of quantum computing in classical self-supervised networks is a potential solution towards it. In this article, a fully self-supervised novel quantum-inspired neural network model referred to as Quantum-Inspired Self-Supervised Network (QIS-Net) is proposed and tailored for fully automatic segmentation of brain MR images to obviate the challenges faced by deeply supervised Convolutional Neural Network (CNN) architectures. The proposed QIS-Net architecture is composed of three layers of quantum neuron (input, intermediate and output) expressed as qbits. The intermediate and output layers of the QIS-Net architecture are inter-linked through bi-directional propagation of quantum states, wherein the image pixel intensities (quantum bits) are self-organized in between these two layers without any external supervision or training. Quantum observation allows to obtain the true output once the superimposed quantum states interact with the external environment. The proposed self-supervised quantum-inspired network model has been tailored for and tested on Dynamic Susceptibility Contrast (DSC) brain MR images from Nature data sets for detecting complete tumor and reported promising accuracy and reasonable dice similarity scores in comparison with the unsupervised Fuzzy C-Means clustering, self-trained QIBDS Net, Opti-QIBDS Net, deeply supervised U-Net and Fully Convolutional Neural Networks (FCNNs).

Uncertainty quantification and statistical model validation for an offshore jacket structure panel given limited test data and simulation model

Due to inherent variability (i.e., aleatory uncertainty) in material properties, loading conditions, manufacturing processes, etc., simulation output responses should follow certain distributions, which are the outcome of input distribution models and simulation models. Thus, uncertainty quantification (UQ) using simulation-based methods is accurate only if we have (1) accurate input distribution models and (2) an accurate simulation model. However, in practical engineering applications, only limited numbers of input test data are available for modeling input distributions. Moreover, the simulation model could be biased due to assumptions and idealizations in the modeling process. Thus, the simulation model needs to be validated to correctly predict the output response of the physical system. The statistical validation of the simulation model would require large numbers of physical test data, which is extremely expensive. This study presents a computational method to obtain a target output distribution, which is a good approximation of the true output distribution given limited test data and a biased model. Using Bayesian analysis, possible candidates of output distribution are obtained, and a target output distribution is selected at the posterior median. The target output distribution is used to measure the bias of the simulation models and the surrogate models for UQ and statistical model validation. Furthermore, the cumulative distribution function of the simulation model prediction error is presented to provide (1) the median of model prediction error and (2) model confidence at a user-specified error level. To demonstrate the proposed statistical model validation method, a simulation model of an offshore jacket structure panel is generated using ANSYS. This example has eight uncertain input parameters (six of them are geometrical parameters and two of them are material properties). For input distributions of geometrical parameters, the standard deviation of manufacturing tolerances is used, and three material tests are carried out to obtain input distributions of two material properties. The output response of interest is a reaction force. The simulation model is validated using three sets of material test data and experimental test data to determine model confidence on output prediction and the validity of the simulation model. The result of statistical model validation shows how much bias the simulation model has and how much confidence engineers can have in the model prediction at a user-specified error level.

Estimating total maximum isometric force output of trunk and hip muscles after spinal cord injury.

Functional neuromuscular stimulation (FNS) can be used to restore seated trunk function in individuals paralyzed due to spinal cord injury (SCI). Musculoskeletal models allow for the design and tuning of controllers for use with FNS; however, these models often use aggregated estimates for parameters of the musculotendon elements, the most significant of which is maximum isometric force (MIF). Stimulated MIF for individuals with SCI is typically assumed to be approximately 50% of the values exhibited by able-bodied muscles, which itself varies between studies and individuals. A method for estimating subject-specific MIF during dynamic motions in individuals with SCI produced by electrical stimulation has been developed to test this assumption and obtained more accurate estimates for biomechanical analysis and controller design. A simple on-off controller was applied to individuals with SCI seated in the workspace of a motion capture system to record joint angles of three types of trunk motions: forward flexion, left and right lateral bending followed by returning, un-aided, to upright posture via neural stimulation delivered to activate the muscles of the hips and trunk. System identification was used with a musculoskeletal model to find the optimal MIF values that reproduced the experimentally observed motions. Experiments with five volunteers with SCI indicate that an MIF of the 50% able-bodied values commonly used is significantly lower than the identified estimates in 33 of 44 muscle groups tested. This suggests that the strengths of paralyzed muscles when stimulated with FNS have been underestimated in many situations and their true force outputs may be higher than the values suggested for use in simulation studies with musculoskeletal models. These findings indicate that subject-specific musculoskeletal models can more closely mimic the motions of subjects by using individualized estimates of MIF, which may allow the design and tuning of controllers while reducing the time spent with subjects in the loop.

Global convergence of the EM algorithm for ARX models with uncertain communication channels

An expectation maximization (EM) algorithm is presented for ARX modeling with uncertain communication channels. The considered model consists of two parts: a dynamic model which is expressed by an ARX model, and an output model, both subject to white Gaussian noises. Since the true outputs of the ARX model are assumed to be unknown, a modified Kalman filter is derived to estimate the output, and then the parameters are estimated by the EM algorithm using the estimated outputs. The Kullback–Leibler divergence and the submartingale are used to prove that the parameter estimates can converge to the true values with the EM algorithm. Furthermore, a simulation example is presented to verify the theoretical results.

Riemannian Optimal Identification Method for Linear Systems With Symmetric Positive-Definite Matrix

This article develops identification methods for linear continuous-time symmetric systems, such as electrical network systems, multiagent network systems, and temperature dynamics in buildings. To this end, we formulate three system identification problems for the corresponding discrete-time systems. The first is a least-squares problem in which we wish to minimize the sum of squared errors between the true and model outputs on the product manifold of the manifold of symmetric positive-definite matrices and two Euclidean spaces. In the second problem, to reduce the search dimensions, the product manifold is replaced with the quotient set under a specified group action by the orthogonal group. In the third problem, the manifold of symmetric positive-definite matrices in the first problem is replaced by the manifold of matrices with only positive diagonal elements. In particular, we examine the quotient geometry in the second problem. We propose Riemannian conjugate gradient methods for the three problems, and select initial points using a popular subspace method. The effectiveness of our proposed methods is demonstrated through numerical simulations and comparisons with the Gauss–Newton method, which is one of the most popular approach for solving least-squares problems.

A Generation Expansion Planning model for integrating high shares of renewable energy: A Meta-Model Assisted Evolutionary Algorithm approach

This study presents a complementary model for Generation Expansion Planning (GEP). A GEP problem commonly determines optimal investment decisions in new power generation plants by minimizing total cost over a mid towards long planning horizon subjected by a set of constraints. The model aims to capture operational challenges arising when a transition towards higher shares of intermittent renewable generation is considered. It embeds a computationally expensive Operational Cost Simulation Model (OCSM), which may exhibit a high level of temporal and technical representation of the short-term operation of a power system to model the unit commitment. The emerging computationally expensive integer non-linear programming constrained optimization model is solved by a problem-customized Meta-model Assisted Evolutionary Algorithm (MAEA). The MAEA employs, off-line trained and on-line refined, approximation models to estimate the output of an OCSM to attain a near-optimal solution by utilizing a limited number of computationally expensive OCSM simulations. The approach is applied on an illustrative test case for a 15 year planning period considering the short-term operation of thermal, hydroelectric and storage units and generation from renewable energy sources. Moreover, the impact of technical resolution is examined through a simple comparative study. The results reveal the efficiency of the proposed problem-customized MAEA. Moreover, the trained approximation models exhibit a low relative error indicating that they may adequately approximate the true output of the OCSM. It is demonstrated that neglecting technical limitations of thermal units may underestimate the utilization of flexible units, i.e. thermal and non-thermal units, affecting the attained investment decisions.

Influence of Human Activities on Broad-Scale Estuarine-Marine Habitats Using Omics-Based Approaches Applied to Marine Sediments.

Rapid urban expansion and increased human activities have led to the progressive deterioration of many marine ecosystems. The diverse microbial communities that inhabit these ecosystems are believed to influence large-scale geochemical processes and, as such, analyzing their composition and functional metabolism can be a means to assessing an ecosystem’s resilience to physical and chemical perturbations, or at the very least provide baseline information and insight into future research needs. Here we show the utilization of organic and inorganic contaminant screening coupled with metabolomics and bacterial 16S rRNA gene sequencing to assess the microbial community structure of marine sediments and their functional metabolic output. The sediments collected from Moreton Bay (Queensland, Australia) contained low levels of organic and inorganic contaminants, typically below guideline levels. The sequencing dataset suggest that sulfur and nitrite reduction, dehalogenation, ammonia oxidation, and xylan degradation were the major metabolic functions. The community metabolites suggest a level of functional homogeneity down the 40-cm core depth sampled, with sediment habitat identified as a significant driver for metabolic differences. The communities present in river and sandy channel samples were found to be the most active, with the river habitats likely to be dominated by photoheterotrophs that utilized carbohydrates, fatty acids and alcohols as well as reduce nitrates to release atmospheric nitrogen and oxidize sulfur. Bioturbated mud habitats showed overlapping faunal activity between riverine and sandy ecosystems. Nitrogen-fixing bacteria and lignin-degrading bacteria were most abundant in the sandy channel and bioturbated mud, respectively. The use of omics-based approaches provide greater insight into the functional metabolism of these impacted habitats, extending beyond discrete monitoring to encompassing whole community profiling that represents true phenotypical outputs. Ongoing omics-based monitoring that focuses on more targeted pathway analyses is recommended in order to quantify the flux changes within these systems and establish variations from these baseline measurements.

Study on the Output Low-Level Property of the SN74LS00N Chip

Generally, the load capacity of the integrated gate circuit depends on its output low-level property. This paper gives a theoretical and experimental study on the output low-level volt-ampere property of the current popular TTL NAND-gate SN74LS00N, and the true output property model and reasonable parameters that are considered to be inconsistent with those in relevant classical textbooks. In addition, with the actual measurement of the change of the power supply current of each gate under different logic states, the method and specific values of the key circuit parameters of the internal part of the integrated circuit—the input stage resistor R1 and the inverting stage pull-up resistor R2—are given. This will play an active role in understanding the TTL 74LS series of electronic device circuits and their experimental teaching, related electronic engineering design, as well as the writing, publishing and teaching of the textbooks.

Target output distribution and distribution of bias for statistical model validation given a limited number of test data

Simulation model must be validated with experimental data to correctly predict the outputs of engineered systems before they can be used with confidence. While doing so, pointwise comparison between predicted output by simulation model and experimental data for model verification and validation (VV (2) the reallocated test dataset by a quality checking process and balance sampling leads to better matching to the true output distribution; and (3) the distribution of bias is effectively used to understand the model’s accuracy and model confidence for comparison study.

Particle filtering based parameter estimation for systems with output-error type model structures

The output-error model structure is often used in practice and its identification is important for analysis of output-error type systems. This paper considers the parameter identification of linear and nonlinear output-error models. A particle filter which approximates the posterior probability density function with a weighted set of discrete random sampling points is utilized to estimate the unmeasurable true process outputs. To improve the convergence rate of the proposed algorithm, the scalar innovations are grouped into an innovation vector, thus more past information can be utilized. The convergence analysis shows that the parameter estimates can converge to their true values. Finally, both linear and nonlinear results are verified by numerical simulation and engineering.

Phenotypical and functional heterogeneity of neural stem cells in the aged hippocampus.

Adult neurogenesis persists in the hippocampus of most mammal species during postnatal and adult life, including humans, although it declines markedly with age. The mechanisms driving the age‐dependent decline of hippocampal neurogenesis are yet not fully understood. The progressive loss of neural stem cells (NSCs) is a main factor, but the true neurogenic output depends initially on the actual number of activated NSCs in each given time point. Because the fraction of activated NSCs remains constant relative to the total population, the real number of activated NSCs declines in parallel to the total NSC pool. We investigated aging‐associated changes in NSCs and found that there are at least two distinct populations of NSCs. An alpha type, which maintains the classic type‐1 radial morphology and accounts for most of the overall NSC mitotic activity; and an omega type characterized by increased reactive‐like morphological complexity and much lower probability of division even under a pro‐activation challenge. Finally, our results suggest that alpha‐type NSCs are able to transform into omega‐type cells overtime and that this phenotypic and functional change might be facilitated by the chronic inflammation associated with aging.

Glutamate Signaling in the Fly Visual System

SummaryFor a proper understanding of neural circuit function, it is important to know which signals neurons relay to their downstream partners. Calcium imaging with genetically encoded calcium sensors like GCaMP has become the default approach for mapping these responses. How well such measurements represent the true neurotransmitter output of any given cell, however, remains unclear. Here, we demonstrate the viability of the glutamate sensor iGluSnFR for 2-photon in vivo imaging in Drosophila melanogaster and prove its usefulness for estimating spatiotemporal receptive fields in the visual system. We compare the results obtained with iGluSnFR with the ones obtained with GCaMP6f and find that the spatial aspects of the receptive fields are preserved between indicators. In the temporal domain, however, measurements obtained with iGluSnFR reveal the underlying response properties to be much faster than those acquired with GCaMP6f. Our approach thus offers a more accurate description of glutamatergic neurons in the fruit fly.

The Google Scholar h-index: useful but burdensome metric

A recent paper in Scientometrics highlighted how the h-index of an academic can be represented differently by different platforms, for example by Web of Science, Scopus, Google Scholar or ResearchGate. Although users (academics) create content on these platforms, usually in the form of professional profiles that describe their publication records, and in the case of Google Scholar, content is also added externally. If a user is not cautious or does not frequently revise their publication record to cleanse their accounts of falsely introduced publications, or duplicates, they risk having professional profiles that are erroneous, due to no error of their own. Academics are constantly pressed for time, so any deviation from their ability to manage their schedule effectively represents additional weight and responsibility. This note indicates how, in just a four-month period (16 February to 25 June 2018), the Google Scholar account of the author has become polluted with literature that was not published by the author. A total of 54 false (i.e., not the author’s) papers had been added to the account, attributing falsely a total of 325 citations to the author that in fact did not belong to the author. Of the 54 falsely added papers, 31 did not have any citations. Those false entries alone inflated the total i10-index from 282 to 290. In an age where the element of “fake” is causing considerable consternation for academics and publishers alike, undermining the integrity of science overall, academics do not need the introduction of false academic variables into professional academic social media accounts. Google Scholar is a useful platform, but the introduction of false elements into academics’ accounts misrepresent their true output. Not only is this a burdensome irritant, it reduces the academic value of Google Scholar. Reliability can only be regained when false entries stop being introduced by Google into academics’ accounts.

Mechanical power output in rowing should not be determined from oar forces and oar motion alone

ABSTRACTMechanical power output is a key performance-determining variable in many cyclic sports. In rowing, instantaneous power output is commonly determined as the dot product of handle force moment and oar angular velocity. The aim of this study was to show that this commonly used proxy is theoretically flawed and to provide an indication of the magnitude of the error. To obtain a consistent dataset, simulations were performed using a previously proposed forward dynamical model. Inputs were previously recorded rower kinematics and horizontal oar angle, at 20 and 32 strokes∙min−1. From simulation outputs, true power output and power output according to the common proxy were calculated. The error when using the common proxy was quantified as the difference between the average power output according to the proxy and the true average power output (P̅residual), and as the ratio of this difference to the true average power output (ratiores./rower). At stroke rate 20, P̅residual was 27.4 W and ratiores./rower was 0.143; at stroke rate 32, P̅residual was 44.3 W and ratiores./rower was 0.142. Power output in rowing appears to be underestimated when calculated according to the common proxy. Simulations suggest this error to be at least 10% of the true power output.

Improved determination of mechanical power output in rowing: Experimental results

ABSTRACTIn rowing, mechanical power output is a key parameter for biophysical analyses and performance monitoring and should therefore be measured accurately. It is common practice to estimate on-water power output as the time average of the dot product of the moment of the handle force relative to the oar pin and the oar angular velocity. In a theoretical analysis we have recently shown that this measure differs from the true power output by an amount that equals the mean of the rower’s mass multiplied by the rower’s center of mass acceleration and the velocity of the boat. In this study we investigated the difference between a rower’s power output calculated using the common proxy and the true power output under different rowing conditions. Nine rowers participated in an on-water experiment consisting of 7 trials in a single scull. Stroke rate, technique and forces applied to the oar were varied. On average, rowers’ power output was underestimated with 12.3% when determined using the common proxy. Variations between rowers and rowing conditions were small (SD = 1.1%) and mostly due to differences in stroke rate. To analyze and monitor rowing performance accurately, a correction of the determination of rowers’ on-water power output is therefore required.