Input Distribution Research Articles

Secrecy-Capacity Bounds for Visible Light Communications With Signal-Dependent Noise

In physical-layer security, secrecy capacity is an important performance metric. This work aims to determine the secrecy capacity for an indoor visible light communication system consisting of a transmitter, a legitimate receiver and an eavesdropping receiver. In such a system, both signal-independent noise and signal-dependent noise are considered. Under nonnegativity and average optical intensity constraints, lower and upper bounds on secrecy capacity are derived by the variational method, the dual expression of the secrecy capacity, and the concept of “the optimal input distribution that escapes to infinity”. By an asymptotic analysis at large optical intensity, there is a small gap between the asymptotic upper and lower bounds. Then, by adding a peak optical intensity constraint, we further analyze the exact and asymptotic secrecy-capacity bounds. For practical considerations, the effects of imperfect channel state information, multi-photodiode eavesdropper, and artificial noise on secrecy performance are also discussed. Finally, the derived secrecy-capacity bounds are verified by numerical results.

RestorNet: An efficient network for multiple degradation image restoration

Even though Single Degradation Image Restoration (SDIR) has made significant progress and achieved remarkable performance, Multiple Degradation Image Restoration (MDIR) remains a long-term and arduous challenge to achieve the similar levels of success. To further improve the performance and efficiency of MDIR, we propose a novel MDIR method named RestorNet, which comprises an unsupervised degradation encoder for the learning of multi-scale degradation representations and a Multi-scale Degradation-assisted Restoration Module (MDRM) for image reconstruction. Our RestorNet aims to remove noise, rain, and haze in a unified network from the following three aspects. Firstly, to better distinguish among different degradations and learn the corruption information more accurately, we introduce a degradation-specific contrastive loss based on contrastive learning. Next, we develop a multi-scale degradation representation learning method to improve preservation of the spatial structure and distribution of inputs, and to extract multi-scale information to satisfy the diverse requirements of restoring different degraded images. Finally, to make a more reasonable use of degradation representation, we present a novel semi-guided strategy for effective feature transformation, where the multi-scale degradation representations are only incorporated into the MDRM encoder. For image denoising, deraining, and dehazing, by integrating the approaches above, RestorNet not only outperforms the recent state-of-the-art MDIR algorithms with lower computational complexity, but also achieves impressive performance in SDIR. Extensive experiments demonstrate the effectiveness and superiority of our proposed method.

Large Eddy Simulation of a Low-Pressure Turbine Cascade with Turbulent End Wall Boundary Layers

We present results of implicit large eddy simulation (LES) and different Reynolds-averaged Navier–Stokes (RANS) models of the MTU 161 low pressure turbine at an exit Reynolds number of 90000\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$90\\,000$$\\end{document} and exit Mach number of 0.6. The LES results are based on a high-order discontinuous Galerkin method and the RANS is computed using a classical finite-volume approach. The paper discusses the steps taken to create realistic inflow boundary conditions in terms of end wall boundary layer thickness and freestream turbulence intensity. This is achieved by tailoring the input distribution of total pressure and temperature, Reynolds stresses and turbulence length scale to a Fourier series based synthetic turbulence generator. With this procedure, excellent agreement with the experiment can be achieved in terms of blade loading at midspan and wake total pressure losses at midspan and over the channel height. Based on the validated setup, we focus on the discussion of secondary flow structures emerging due to the interaction of the incoming boundary layer and the turbine blade and compare the LES to two commonly used RANS models. Since we are able to create consistent setups for both LES and RANS, all discrepancies can be directly attributed to physical modelling problems. We show that both a linear eddy viscosity model and a differential Reynolds stress model coupled with a state-of-the-art correlation-based transition model fail, in this case, to predict the separation induced transition process around midspan. Moreover, their prediction of secondary flow losses leaves room for improvement as shown by a detailed discussion of turbulence kinetic energy and anisotropy fields.

Information transfer rate in BCIs: Towards tightly integrated symbiosis

Objective:The information transmission rate (ITR), or effective bit rate, is a popular and widely used information measurement metric, particularly popularized for SSVEP-based Brain–Computer (BCI) interfaces. By combining speed and accuracy into a single-valued parameter, this metric aids in the evaluation and comparison of various target identification algorithms across different BCI communities. In order to calculate ITR, it is customary to assume a uniform input distribution and an oversimplified channel model that is memoryless, stationary, and symmetrical in nature with discrete alphabet sizes. To accurately depict performance and inspire an end-to-end design for futuristic BCI designs, a more thorough examination and definition of ITR is therefore required. Approach:We model the symbiotic communication medium, hosted by the retinogeniculate visual pathway, as a discrete memoryless channel and use the modified capacity expressions to redefine the ITR. We leverage a result for directed graphs to characterize the relationship between the asymmetry of the transition statistics and the ITR gain due to the new definition, leading to potential bounds on data rate performance. Main Results:On two well-known SSVEP datasets, we compared two cutting-edge target identification methods. Results indicate that the induced DM channel asymmetry has a greater impact on the actual perceived ITR than the change in input distribution. Moreover, it is demonstrated that the ITR gain under the new definition is inversely correlated with the asymmetry in the channel transition statistics. Individual input customizations are further shown to yield perceived ITR performance improvements. Finally, an algorithm is proposed to find the capacity of binary classification and further discussions are given to extend such results to multi-class case through ensemble techniques. Significance:We anticipate that the results of our study will contribute to the characterization of the highly dynamic BCI channel capacities, performance thresholds, and improved BCI stimulus designs for a tighter symbiosis between the human brain and computer systems while ensuring the efficient utilization of the underlying communication resources.

A Spatial Learning-Based Fault Tolerant Lateral Tracking Control for Autonomous Driving

In this paper, a spatial learning-based fault-tolerant control strategy is proposed for precise lateral tracking of autonomous vehicles subject to dynamical uncertainties, external disturbances as well as actuator failures. In order to facilitate the controller design, the uncertain vehicle dynamics are firstly transformed into a parametric form in the space domain, where the system uncertainties are reorganized and combined into the parametric and input distribution uncertainties. Furthermore, considering the under-actuated property of the vehicle dynamics, a novel technique in dealing with the non-square input distribution matrix is employed, in which a pseudo-like inverse matrix and a robust term are introduced into the controller to compensate the mismatch between the number of inputs and outputs. Then the proposed spatial learning-based fault tolerant control algorithm is developed, which is equipped with two adaptive parametric updating laws to estimate the parametric uncertainties and the multiplicative actuator faults correspondingly. Consequently, the convergence of the control algorithm is analyzed rigorously under the framework of composite energy function. Case studies verify the feasibility and effectiveness of the proposed control scheme.

Multimodal Batch-Wise Change Detection.

We address the problem of detecting distribution changes in a novel batch-wise and multimodal setup. This setup is characterized by a stationary condition where batches are drawn from potentially different modalities among a set of distributions in [Formula: see text] represented in the training set. Existing change detection (CD) algorithms assume that there is a unique-possibly multipeaked-distribution characterizing stationary conditions, and in batch-wise multimodal context exhibit either low detection power or poor control of false positives. We present MultiModal QuantTree (MMQT), a novel CD algorithm that uses a single histogram to model the batch-wise multimodal stationary conditions. During testing, MMQT automatically identifies which modality has generated the incoming batch and detects changes by means of a modality-specific statistic. We leverage the theoretical properties of QuantTree to: 1) automatically estimate the number of modalities in a training set and 2) derive a principled calibration procedure that guarantees false-positive control. Our experiments show that MMQT achieves high detection power and accurate control over false positives in synthetic and real-world multimodal CD problems. Moreover, we show the potential of MMQT in Stream Learning applications, where it proves effective at detecting concept drifts and the emergence of novel classes by solely monitoring the input distribution.

Meta variance reduction for Monte Carlo estimation of energetic particle confinement during stellarator optimization

This work introduces meta estimators that combine multiple multifidelity techniques based on control variates, importance sampling, and information reuse to yield a quasi-multiplicative amount of variance reduction. The proposed meta estimators are particularly efficient within outer-loop applications when the input distribution of the uncertainties changes during the outer loop, which is often the case in reliability-based design and shape optimization. We derive asymptotic bounds of the variance reduction of the meta estimators in the limit of convergence of the outer-loop results. We demonstrate the meta estimators, using data-driven surrogate models and biasing densities, on a design problem under uncertainty motivated by magnetic confinement fusion, namely the optimization of stellarator coil designs to maximize the estimated confinement of energetic particles. The meta estimators outperform all of their constituent variance reduction techniques alone, ultimately yielding two orders of magnitude speedup compared to standard Monte Carlo estimation at the same computational budget.

Thermo-mechanical analysis of functionally graded nickel/titanium alloys under different process parameters of directed energy deposition

The build-up of residual stress and porosity plays an important role in the mechanical properties of as-built parts during directed energy deposition (DED). However, the thermo-mechanical behavior of functionally graded nickel/titanium alloys (FGNTAs) in the DED process remains unclear. In this study, a computationally efficient thermo-mechanical coupled model of DED-printed FGNTAs with different porosities is developed. The temperature-dependent effective material properties of FGNTAs are first determined based on the rule of mixture. Subsequently, the temperature, residual stress, and deformation fields within as-built FGNTAs are predicted using the differential quadrature method. The predicted results show excellent agreement with the predictions and experimental data in the literature. Furthermore, the effect of key parameters on the thermo-mechanical responses of DED-printed FGNTAs is investigated, including energy density, porosity distributions, part dimensions, temperature settings, and structural modes. The results show that the residual stress and vertical deflection increase with the higher energy input, length-to-width ratio, and evener porosity distributions, while decreasing with larger porosity, preheating as well environment temperatures, external loads, and power index. These results can provide a useful guideline for the design and manufacturing of high-quality FGNTAs during DED.

Reflecting Parameter Uncertainty in Addition to Variability in Constrained Healthcare Resource Discrete Event Simulations: Worth Going the Extra Mile or a Road to Nowhere?

ObjectivesProbabilistic sensitivity analysis (PSA) has been shown to reduce bias in outcomes of health economic models. However, only 1 existing study has been identified that incorporates PSA within a resource-constrained discrete event simulation (DES) model. This article aims to assess whether it is feasible and appropriate to use PSA to characterize parameter uncertainty in DES models that are primarily constructed to explore the impact of constrained resources. MethodsPSA is incorporated into a new case study of an Emergency Department DES. Structured expert elicitation is used to derive the variability and uncertainty input distributions associated with length of time taken to complete key activities within the Emergency Department. Potential challenges of implementation and analysis are explored. ResultsThe results of a trial of the model, which used the best estimates of the elicited means and variability around the time taken to complete activities, provided a reasonable fit to the data for length of time within the Emergency Department. However, there was substantial and skewed uncertainty around the activity times estimated from the elicitation exercise. This led to patients taking almost 3 weeks to leave the Emergency Department in some PSA runs, which would not occur in practice. ConclusionsStructured expert elicitation can be used to derive plausible estimates of activity times and their variability, but experts’ uncertainty can be substantial. For parameters that have an impact on interactions within a resource-constrained simulation model, PSA can lead to implausible model outputs; hence, other methods may be needed.

SIMULATION MODEL OF A QS WITH HYPEREXPONENTIAL DISTRIBUTION IN THE GPSS WORLD ENVIRONMENT

This article presents results obtained on the development of a pseudo-random sequence generator for simulation modeling of queueing system (QS) with second-order hyperexponential input distribution. In the scientific literature on GPSS WORLD, including web resources, the authors did not find any data in this subject area. The GPSS WORLD library includes many generators of various distribution laws, but there are no generators for such important composite distributions according to the queuing theory. It is known that the hyperexponential distribution concept provides a wide range of changes in the random variable variation coefficient from unity to an arbitrarily large value. Due to the fact that the variation coefficients of arrival and service intervals play an important role in estimating the delay of queue claims in queuing systems, thus this distribution concept plays a special role in modeling of QS. For a hyperexponential distribution of the second order, the authors previously obtained numerical-analytical results based on the Lindley integral equation spectral solution method. The article describes a developed algorythm and a program on GPSS WORLD to simulate the functioning of QS with hyperexponential input distribution incl. description of operation of logical switches. The difference between analytical and simulation modeling of the two-phase representation of a given distribution concept is explained on the example of the developed model. The adequacy of the obtained results was confirmed by comparing the simulation results with the results of numerical simulation in the Mathcad environment.

Distribution and Trend of Wind Power Input to Near‐Inertial Motions in the Southern Ocean

AbstractWind power input to near‐inertial motions is an important energy source for generating diapycnal mixing in the ocean. However, the distribution and long‐term trend of this input over the Southern Ocean have yet to be quantified. In this study, we investigate the near‐inertial wind power input (WPIi) to the Southern Ocean using a global eddy‐permitting coupled ocean‐sea ice model forced by a high‐resolution atmospheric reanalysis product. Our results reveal a zonally asymmetric distribution of WPIi in the Southern Ocean, with the strongest input in the South Indian Ocean and the weakest in the South Pacific. The integrated WPIi between 30°S and 60°S exhibits a significant positive trend over the past four decades due to the intensification of mesoscale weather systems. The surface mixed‐layer depth is found to modulate the spatial pattern and trend of WPIi by altering the surface near‐inertial currents.

Data-driven sparse polynomial chaos expansion for models with dependent inputs

Polynomial chaos expansions (PCEs) have been used in many real-world engineering applications to quantify how the uncertainty of an output is propagated from inputs by decomposing the output in terms of polynomials of the inputs. PCEs for models with independent inputs have been extensively explored in the literature. Recently, different approaches have been proposed for models with dependent inputs to expand the use of PCEs to more real-world applications. Typical approaches include building PCEs based on the Gram–Schmidt algorithm or transforming the dependent inputs into independent inputs. However, the two approaches have their limitations regarding computational efficiency and additional assumptions about the input distributions, respectively. In this paper, we propose a data-driven approach to build sparse PCEs for models with dependent inputs without any distributional assumptions. The proposed algorithm recursively constructs orthonormal polynomials using a set of monomials based on their correlations with the output. The proposed algorithm on building sparse PCEs not only reduces the number of minimally required observations but also improves the numerical stability and computational efficiency. Four numerical examples are implemented to validate the proposed algorithm. The source code is made publicly available for reproducibility.

Energy Efficiency of Kenaf Cultivation in Kelantan, Malaysia by Using Data Envelopment Analysis

Agricultural production has become aggressive energy concentrated in a determination to supply more food to the increasing population and provide sufficient and adequate essentials. The limited natural resources and the effect of the use of different energy sources on the environment and human health such as global warming, it is necessary to investigate energy consumption patterns in agriculture. In this study data envelopment analysis method approach has been used to determine the efficiencies of kenaf farmers concerning energy use in kenaf cultivation production activities in Kelantan, Malaysia. The study’s empirical result will help to segregate the 15 most efficient farmers and inefficient ones. Other than that, the data envelopment analysis method also will help to identify wasteful uses of energy from different sources by inefficient farmers and to suggest reasonable savings in energy uses from different sources of inputs. The rank method and distribution of virtual inputs are used to get insights into the individual Kenaf farmer performance, rank efficient farmers and identify the improved operating practices followed by a group of truly efficient Kenaf farmers. The result of the analysis indicates the average value of technical efficiency, Pure technical efficiency and Scale efficiency scores of kenaf cultivation production were 0.854, 0.962 and 0.887 respectively. These results imply that all inefficient farmers operate at optimum scale size and 11.3% energy saving is possible without affecting Kenaf yield level. Result also reveals higher potential contribution to total energy saving is from fertilizer (86.5%), Diesel (13%), Pesticides (0.3%) and human labor (0.2%).

Spatial Heterogeneity of Total and Labile Soil Organic Carbon Pools in Poplar Agroforestry Systems

Agroforestry systems are considered effective methods of carbon sequestration. In these systems, most of the carbon is stored in the soil, and the pattern of tree planting can influence the spatial distribution of organic matter input into the soil. However, limited information is available about the extent of this influence. In this study, the horizontal and vertical distributions of soil organic carbon (SOC) and labile fractions were investigated in four planting systems: a pure poplar (Populus deltoides cv. “35”) planting system, a wide-row (14 m spacing) poplar and wheat (Triticum aestivum L.) agroforestry system, a narrow-row (7 m spacing) poplar and wheat agroforestry system, and a pure wheat field. The results showed that although the poplar system had the highest vegetation biomass (147.50 t ha−1), the agroforestry systems overall had higher SOC contents than the pure poplar system and wheat fields. Especially in the wide-row agroforestry system, the SOC, readily oxidizable carbon, and dissolved organic carbon contents were, respectively, 25.3%, 42.4%, and 99.3% higher than those of the pure poplar system and 60.3%, 148.7%, and 6.3% higher than those of the wheat field in a 1 m soil profile, and it also had the highest fine root biomass. However, the microbial biomass carbon content was highest in the pure poplar system. The SOC of the three poplar planting systems was spatially heterogeneous, with the highest values occurring at 1.5 m in the narrow-row systems and within the tree rows in the wide-row system, similar to the distribution of fine root biomass. Additionally, we found that the larger the diameter at the breast height of the trees, the greater their positive effect on SOC at greater distances.

Future Response of Antarctic Continental Shelf Temperatures to Ice Shelf Basal Melting and Calving

AbstractWe investigate feedbacks between subsurface continental shelf ocean temperatures and Antarctic glacial melt using a coupled climate model. The model was forced with SSP5‐8.5 and an uncoupled projection of basal melt and calving fluxes. SSP5‐8.5 forcing with fixed pre‐industrial glacial melt warms all continental shelves, such that historically “cool” and “fresh” shelves transition to “warm.” Additional glacial melt, added at depth, cools the Eastern Ross, Amundsen, and Bellingshausen seas, suggesting a negative feedback on basal melt—a novel result for a coarse resolution coupled model. From the Weddell Sea, along East Antarctica, and into the western Ross Sea—where continental shelves transition to a “warm” state—additional glacial melt increases temperatures at the continental shelf sea floor, suggesting a positive feedback. The sign of the glacial melt–subsurface temperature feedback is critically dependent on continental shelf properties, climate state, and the vertical distribution of glacial melt inputs.

Adaptation in L2 sentence processing: An EEG study

According to rational adaptation approaches of language processing, readers adjust their expectations of upcoming information depending on the distributional properties of the preceding language input. However, adaptation to sentence structures has not been systematically attested, especially not in second-language (L2) processing. To further our understanding of adaptive processes, we recorded electroencephalogram (EEG) from L1-Mandarin–L2-English speakers while they read English sentences containing a coordination ambiguity. This ambiguity was always resolved toward a less-preferred clausal coordination in the first half of the study, and towards a noun-phrase coordination in the second half. Group-level results suggest that L2 readers adapted but at a slow rate and a coarse level. Individuals differed in that some changed their processing strategies, and some did not. These findings suggest that adaptation is not a direct function of fine-grained input distributions, and are problematic for the idea that adaptation is important for language learning.

Algorithmic Computability and Approximability of Capacity-Achieving Input Distributions

The capacity of a channel can usually be characterized as a maximization of certain entropic quantities. From a practical point of view it is of primary interest to not only compute the capacity value, but also to find the corresponding optimizer, i.e., the capacity-achieving input distribution. This paper addresses the general question of whether or not it is possible to find algorithms that can compute the optimal input distribution depending on the channel. For this purpose, the concept of Turing machines is used which provides the fundamental performance limits of digital computers and therewith fully specifies which tasks are algorithmically feasible in principle. It is shown for discrete memoryless channels that it is impossible to algorithmically compute the capacity-achieving input distribution, where the channel is given as an input to the algorithm (or Turing machine). Finally, it is further shown that it is even impossible to algorithmically approximate these input distributions.

Poster Session: Oculomotor influences on retinal input signals in myopia.

Studies of emmetropization have traditionally focused on the spatial characteristics of visual input signals. Yet the input to the retina is not a two-dimensional pattern but a temporally-varying luminance flow. The temporal structure of this flow is predominately determined by eye movements, as the human eyes move incessantly. Even when fixating on a single point, a persistent motion known as ocular drift reformats the luminance flow in a way that counterbalances the spectra of natural scenes. It is established that emmetropes are highly sensitive to these luminance modulations. However, their visual consequences in myopia and hyperopia are unknown. Here, we first review how the temporal-frequency distribution of retinal input signals varies with the amount of ocular drift. We then use a detailed optical/geometrical model of the eye to study how the eye movements jointly shape retinal input as a function of refraction. We show that, within the temporal range of sensitivity of the retina, the spatial frequency distribution of the input signals conveys signed information about defocus. Specifically, for a given degree of defocus, myopic retinas experience more power from low spatial frequency stimuli than hyperopic retinas. These redistribution of input power may have a consequence during eye growth supporting the proposal that eye movements should be taken into consideration in the process of emmetropization.

Regenerative braking energy recovery control strategy for electric vehicles with battery temperature measurement

Aiming at the problems of short mileage and overheating and easy damage of the battery in electric vehicles, the fuzzy control regenerative braking strategy with vehicle speed, braking intensity, battery state of charge (SOC), and the introduction of battery temperature as fuzzy control input and regenerative braking distribution percentage as output are proposed; secondly, the ideal braking force distribution curve and ECE regulations are used as the boundary to determine the braking force distribution range of pure electric vehicles. Finally, the control strategy designed in this paper is simulated under selected working conditions by ADVISOR software, and the effectiveness and superiority of this control strategy are verified. The strategy can not only reduce the damage to the battery caused by high temperatures but also obtain more braking energy.

Comments on “Channel Coding Rate in the Finite Blocklength Regime”: On the Quadratic Decaying Property of the Information Rate Function

The quadratic decaying property of the information rate function states that given a fixed conditional distribution $p_{\mathsf{Y}|\mathsf{X}}$, the mutual information between the (finite) discrete random variables $\mathsf{X}$ and $\mathsf{Y}$ decreases at least quadratically in the Euclidean distance as $p_\mathsf{X}$ moves away from the capacity-achieving input distributions. It is a property of the information rate function that is particularly useful in the study of higher order asymptotics and finite blocklength information theory, where it was already implicitly used by Strassen [1] and later, more explicitly, by Polyanskiy-Poor-Verd\'u [2]. However, the proofs outlined in both works contain gaps that are nontrivial to close. This comment provides an alternative, complete proof of this property.