Optimal Mapping Research Articles

Optimizing Decentralized Online Learning for Supervised Regression and Classification Problems

Decentralized learning networks aim to synthesize a single network inference from a set of raw inferences provided by multiple participants. To determine the combined inference, these networks must adopt a mapping from historical participant performance to weights, and to appropriately incentivize contributions they must adopt a mapping from performance to fair rewards. Despite the increased prevalence of decentralized learning networks, there exists no systematic study that performs a calibration of the associated free parameters. Here we present an optimization framework for key parameters governing decentralized online learning in supervised regression and classification problems. These parameters include the slope of the mapping between historical performance and participant weight, the timeframe for performance evaluation, and the slope of the mapping between performance and rewards. These parameters are optimized using a suite of numerical experiments that mimic the design of the Allora Network, but have been extended to handle classification tasks in addition to regression tasks. This setup enables a comparative analysis of parameter tuning and network performance optimization (loss minimization) across both problem types. We demonstrate how the optimal performance-weight mapping, performance timeframe, and performance-reward mapping vary with network composition and problem type. Our findings provide valuable insights for the optimization of decentralized learning protocols, and we discuss how these results can be generalized to optimize any inference synthesis-based, decentralized AI network.

Solving split equality fixed point problem of generalized demimetric mapping and certain optimization problem via dynamic step-size technique

In this article, we study the split equality problem of certain optimization problem in real Hilbert spaces. We propose a new viscosity iterative algorithm for approximating solution for finite families of split equality variational inequality and split equality fixed point problems of generalized demi metric mapping in real Hilbert spaces. Using our iterative method, we establish a strong convergence result for finding a common element for finite families of variational inequality and fixed point problems of generalized demimetric mapping. We present some consequences and application to the convex minimization problem to validate our main result. Our result complements and generalizes some related results in literature.

Back-illuminated Light-spot-mapping Algorithm for Dual Rotation Robotic Fiber Positioners Based on an Artificial Probability Potential Field Method

Abstract Back-illuminated spot mapping is essential for observations with modern spectroscopic survey telescopes. In the fiber position detection system, after the fiber view camera (FVC) detects the positions of the light spots from the robotic fiber positioners (RFPs), these positions must be mapped to the RFPs individually to complete subsequent calculations of their operational data. The back-illuminated images captured by the FVC show thousands of small light spots, each only hundreds of microns in size, randomly distributed on a completely black background, making accurate one-to-one mapping to the actual RFP positions highly challenging due to the lack of additional RFP features. This study constructs an artificial probability potential field to calculate the likelihood of each light spot appearing in the patrol area of an RFP, employing a global probability potential energy optimization method to establish the optimal mapping relationship. The proposed method successfully maps all light spots in the closed-loop fiber positioning task and is ultimately applied to the fiber positioning system of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, achieving accurate mapping of 4000 RFPs on the focal plane. This algorithm effectively addresses challenges such as multitarget interference, uniform background, and RFP failures, providing a reliable approach for mapping the back-illuminated points of tens of thousands of dual-rotation RFPs without requiring special backlight designs in future spectral survey telescopes.

Technology-Dependent Synthesis and Optimization of Circuits for Small S-boxes

Boolean formula minimization is a notoriously hard problem. Circuit minimization, typically studied in the context of a much broader subject known as synthesis and optimization of circuits, introduces another layer of complexity since ultimately those technology-independent representations (e.g., Boolean formulas and truth tables) has to be transformed into a netlist of cells of the target technology library. To manage those complexities, the industrial community typically separates the synthesis process into two steps: technology-independent optimization and technology mapping. In each step, this approach only tries to find the local optimal solution and relies heavily on heuristics rather than a systematic search. However, for small S-boxes, a more systematic exploration of the design space is possible. Aiming at the global optimum, we propose a method which can synthesize a truth table for a small S-box directly into a netlist of the cells of a given technology library. Compared with existing technology-dependent synthesis tools like LIGHTER and PEIGEN, our method produces improved results for many S-boxes with respect to circuit area. In particular, by applying our method to the GF(2^4)-inverter involved in the tower field implementation of the AES S-box, we obtain the currently known lightest implementation of the AES S-box. The search framework can be tweaked to take circuit delay into account. As a result, we find implementations for certain S-boxes with both latency and area improved.

Application of Slam Technology for Autonomous Mobile Robots in Complex Environments

SLAM technology is widely used in many fields, such as whether it is possible to map the environment and determine the direction of progress while placing a robot in an unknown location in an unknown environment. On this basis, this paper studies the use of autonomous movement of robots in complex environments. This article will first introduce vision technology in dynamic environments and camera-based dynamic environmental sensor technology. Additionally, it will discuss parallel research on real time 3D map optimization and algorithm processing based on cameras. Secondly, the map construction and dynamic maintenance of mobile robots and how to improve the application of Road King tracking algorithm in robots are introduced. Finally, the multi-sensor laser mapping technology and algorithm of mobile robot are introduced. This research will significantly contribute to the development and application of mobile robots capable of processing complex information in challenging environments. The integration of these technologies and algorithms paves the way for more robust and adaptable autonomous navigation systems in real-world applications.

Optimization of the phase demodulation algorithms for investigating the optical properties of the polymer fibre during mechanical deformations interferometrically

The mechanical deformations in polymer fibres play an essential role for understanding the mechanism of fracture in this material. These deformations were examined interferometrically. The optical phase map of the deformed fibres has the key information about their structural features. So, the main core of this paper is to find an optimal algorithm for analyzing the interference fringe patterns of deformed fibres and demodulating their phase maps. For performing this task, two beam interference patterns for crazed and fractured polypropylene (PP) fibres were captured using the non-duplicated Pluta interference microscope. The phase map for each deformation demodulated using the spatial carrier frequency, the one- dimensional continuous wavelet transform (1D CWT) and the phase shifting fringe pattern analysis algorithms. The performance of each algorithm for finding the optimal phase map was evaluated using the contour line method. A refined method for calculating the areal craze density of a crazed pattern is presented. Based on the optimal extracted phase values of PP fibre for each type of deformation, the 3D birefringence values were calculated.

Modified moth flame optimization and logistic chaotic map integration for image encryption

Modified moth flame optimization and logistic chaotic map integration for image encryption

Optimization of Branding and Value Chain Mapping Using Artificial Intelligence for the Batik Village Clusters in Indonesia to Achieve Competitive Advantage

This research investigates the role of artificial intelligence (AI) in optimizing branding and mapping value chains to strengthen the competitive advantage of Batik Village Clusters in Indonesia. Employing a quantitative approach, the study analyzes survey data from stakeholders in the batik industry, focusing on their perceptions of AI's impact on branding and value chain processes. The study reveals that AI has a significant positive impact on branding optimization (t-statistic = 29.249, p = 0.000) and value chain mapping (t-statistic = 15.066, p = 0.000). Additionally, both branding optimization (t-statistic = 8.621) and value chain mapping (t-statistic = 16.853) were found to positively affect the competitive advantage of batik clusters. These findings suggest that AI can enhance branding efforts, improve value chain efficiency, and elevate the competitive positioning of Batik Village Clusters. The study provides actionable recommendations for batik entrepreneurs and policymakers, emphasizing the need to incorporate AI technologies to improve global competitiveness and ensure long-term sustainability in the batik industry

A hybrid machine learning modelling for optimization of flood susceptibility mapping in the eastern Mediterranean

A hybrid machine learning modelling for optimization of flood susceptibility mapping in the eastern Mediterranean

Nano-spherical tip-based smoothing with minimal damage for 2D van der Waals heterostructures.

Two-dimensional materials and their heterostructures have significant potential for future developments in materials science and optoelectronics due to their unique properties. However, their fabrication and transfer process often introduce impurities and contaminants that degrade their intrinsic qualities. To address this issue, current atomic force microscopy (AFM) probe contact mode methods provide a solution by allowing in situ cleaning and real-time observation of the nanoscale cleaning process. Nevertheless, existing pyramidal probes may scratch surfaces and damage heterostructures during force application. Therefore, we proposed a method based on the nano-spherical probe contact mode to clean residual and polymer contamination for minimum damage cleaning of MoS2/hBN substrates. Comparative experiments with pyramidal probes in 2DM morphology and photoluminescence (PL) have shown that nano-spherical probes are exceptionally effective in cleaning bubbles of various sizes, compared to uncleaned MoS2, where PL full width at half maximum (FWHM) averages 0.115 eV, nano-spherical probes reduce it by 30% to 0.08 eV. Pyramidal probes, however, only clean smaller bubbles and leave residuals in larger ones, resulting in less optimal PL mapping data with values in both the 0.09 eV and 0.0115 eV regions. We also collected the standard deviation of the FWHM data points for the uncleaned region and the regions cleaned by the pyramidal and nano-spherical probes, which were 0.02773, 0.01895, and 0.00531, respectively. Notably, the standard deviation of the FWHM in the nano-spherical probe-cleaned region is only 28% of that in the pyramidal probe-cleaned region. Then, increasing the applied force leads to damage in the crystal structure, resulting in potential inconsistencies across different areas, as evidenced by KPFM and SEM observations. In contrast, nano-spherical probes demonstrate a uniform potential in KPFM and consistently maintain a smooth surface morphology in SEM throughout the process. This approach highlights the potential of nano-spherical probes to advance minimum-damage cleaning techniques in 2D material research and applications.

Low complexity joint optimization of QAM gray mapping and non-binary LDPC Coefficients

Low complexity joint optimization of QAM gray mapping and non-binary LDPC Coefficients

Improving Accuracy of Gridded Livestock Mapping by Combining Trend Modeling and Residual Assignment

ABSTRACTThe accurate distribution of livestock is a fundamental data set upon which sustainable land development in agricultural and pastoral areas can be promoted. Nevertheless, the current gridded livestock mapping methods exhibit considerable scope for improvement in accuracy, given that the issue of residual assignment has not been addressed effectively. We attempt to propose a combinatorial methodological framework that decomposes gridded livestock mapping into two stages, trend modeling, and residual assignment, through the perspective of uncertainty. Investigating the relative efficacy of different combinations of methods within the framework aims to identify the optimal total mapping solution. Ridge regression (RR), geographically weighted ridge regression (GWRR), and random forest (RF) methods with varying characteristics were used to represent the trend modeling component and combined with area‐to‐point kriging interpolation based on spatial autocorrelation for residual assignment, respectively. The Inner Mongolia Autonomous Region from China provides a multi‐scale case validation of the framework. The experimental results show that GWRR has high robustness and prediction accuracy for predicting livestock distribution trends across scales. By using residual assignment optimization as a post‐processing tool, the total accuracies of the three trend models were improved in different degrees, with the greatest accuracy gains obtained by RF (RMSE reduced by 13.731 and MAE reduced by 12.247, in units/km2). Therefore, the combined two‐stage methodological framework proposed in this study can effectively improve the accuracy of gridded mapping. This provides an effective complement to current gridded livestock mapping, thereby facilitating more sustainable land use, ecological environmental protection, and livestock development.

Regional dynamic early warning model for rainfall-induced landslide in Fujian, China

Recent advances have significantly enhanced dynamic early warning systems for rainfall-induced landslides by integrating rainfall thresholds with susceptibility mapping. However, there is still room for optimizing these models for regional-scale applications. This study introduces a dynamic early warning model in Anxi County, Fujian Province, China—a region prone to rainfall-induced landslides. First, historical landslide data and their controlling factors were analyzed for the study area, and an optimal landslide susceptibility map was produced by integrating an information value model with a logistic regression model. Second, based on the theory of effective rainfall, the regional rainfall threshold model was established according to the indices of daily and hourly rainfall, and the model’s accuracy was evaluated. Third, the more effective E-D (daily index) and EE-D (hourly index) models were coupled with the landslide susceptibility map for landslide dynamic early warning. The model’s validation results show its significant predictive capabilities, with the hourly model proving more accurate for short-duration rainfall events. This study provides valuable insights for local authorities on dynamic early warning for rainfall-induced landslides and offers guidance for refining dynamic early warning systems in similar regions.

Economic optimization scheduling of microgrid group based on chaotic mapping optimization BOA algorithm

Due to the intermittency and volatility of distributed power sources, the microgrid system has poor stability and high operation cost. Therefore, the study proposes an economic optimization scheduling strategy based on the chaotic mapping butterfly optimization algorithm and the mathematical model of microgrid group system. The study creates simulation trials of function poles and microgrid group operation to confirm the strategy’s efficacy. According to the experimental findings, the multimodal function of the enhanced butterfly optimization method had a variance of 0.0000E + 00, and the function’s optimal value was less than 10–30, and the calculation time is 4.5s. The variance on the fixed dimensional function was 0.0000E + 00 and the optimal value of the function was 10 − 3.5,and the calculation time is 4.7s. The algorithmic curve all digging depth was maximum and convergence speed was fastest. The microgrid group system had the lowest economic cost of 4029.32 yuan in the grid-connected mode and 3343.39 yuan in the off-grid mode. The study proves that the energy coordination and economic management of this strategy are greatly optimized, which can effectively protect the energy storage equipment and guarantee the smooth power consumption of the system. This provides an innovative theoretical basis for optimization scheduling of microgrid group.

Stability and statistical inference for semidiscrete optimal transport maps

Stability and statistical inference for semidiscrete optimal transport maps

Bio-inspired optimization technique for optimal beam angle selection in radiotherapy application

A human planner's expertise is currently the most important consideration for determining optimal beam angles for external beam radiotherapy. The necessity of automatically selecting beam angles is especially important in intensity-modulated radiation therapy (IMRT) since fewer modulated beams are utilized in conformal radiotherapy. For an automated beam angle selection (ABAS) approach, the ideal coplanar beam angles correspond to the lowest objective function (OF) value of the dose distributions produced from this collection of candidate beams' intensity-modulated maps. Because of the task's intricacy and the large search space concerned, the ABAS and optimization of intensity maps are addressed independently and repeatedly. The Modified Artificial Bee Colony (MABC) optimization, the integration of Artificial Bee Colony (ABC), and a Firefly algorithm are employed to choose suitable beam angles, and the conjugate gradient (CG) technique is employed to fasten the optimized intensity maps for every selected beam. A 3D full scatter convolution (FSC) approach based on the pencil beam is employed for dose assessment. The effectiveness of MABC is examined using a more difficult instance representing a prostate tumor, and 2 simple cases The simulated MABC output is compared to the ABC optimization method. The results illustrate the reliability and efficiency of the suggested MABC-based ABAS can improve dosage distributions with clinically acceptable computation time.

From virtue to sin: Is the installation of bat boxes an effective conservation measure or a potential pitfall for vulnerable bat species?

Bat boxes are a key element in bat conservation policies being very popular. However, it is a biased action that is only favourable for a few bat species. In this manuscript, we hypothesize that the installation of these elements and an extensive proliferation of them could threaten other bat species more vulnerable due to the niche competition. For this, we calculated the overlapping degree between the bat species that use bat boxes and those that do not, specifically: (1) To determine the favourability area of these bat species through habitat suitability models in Iberian Peninsula; (2) To compare the overlapping degree between both bat groups; (3) To elaborate optimal maps for the installation of bat boxes through optimization algorithm; and (4) To discuss the implications for the conservation of this action. Our results showed that the overlapping between bat boxes users and the other species is high (mean 76.3 %) and there are areas where a bat boxes not user species could compete with at least 9 bat boxes user species. Therefore, the installation of bat boxes could be counterproductive, and we should focus on other conservation actions. We used an optimization algorithm to find out those areas where it is not recommendable to install them and developed a flowchart to evaluate its installation in an area. In conclusion, our study highlights that conservation actions on some occasions could be negative or be a pitfall for themselves, and it is necessary to discuss the effectiveness of certain measures for bat conservation.

Partially unitary learning.

The problem of an optimal mapping between Hilbert spaces IN of |ψ〉 and OUT of |ϕ〉 based on a set of wavefunction measurements (within a phase) ψ_{l}→ϕ_{l},l=1,⋯,M, is formulated as an optimization problem maximizing the total fidelity ∑_{l=1}^{M}ω^{(l)}|〈ϕ_{l}|U|ψ_{l}〉|^{2} subject to probability preservation constraints on U (partial unitarity). The constructed operator U can be considered as an IN to OUT quantum channel; it is a partially unitary rectangular matrix (an isometry) of dimension dim(OUT)×dim(IN) transforming operators as A^{OUT}=UA^{IN}U^{†}. An iterative algorithm for finding the global maximum of this optimization problem is developed, and its application to a number of problems is demonstrated. A software product implementing the algorithm is available from the authors.

Optimization of approximate maps for linear systems arising in discretized PDEs

AbstractGenerally, discretization of partial differential equations (PDEs) creates a sequence of linear systems with well‐known and structured sparsity patterns. Preconditioners are often necessary to achieve fast convergence when solving these linear systems using iterative solvers. We can use preconditioner updates for closely related systems instead of computing a preconditioner for each system from scratch. One such preconditioner update is the sparse approximate map (SAM), which is based on the sparse approximate inverse preconditioner using a least squares approximation. A SAM then acts as a map from one matrix in the sequence to another nearby one for which we have an effective preconditioner. To efficiently compute an effective SAM update (i.e., one that facilitates fast convergence of the iterative solver), we seek to compute an optimal sparsity pattern. In this paper, we examine several sparsity patterns for computing the SAM update to characterize optimal or near‐optimal sparsity patterns for linear systems arising from discretized PDEs.

Mixed data augmentation and osprey search strategy for enhancing YOLO in tomato disease, pest, and weed detection

Diseases, pests, and weeds are the core problem that restricts crop growth. Effective detection of biotic stress information plays an important role in crop growth and management. This paper proposes a mixed data augmentation method and the osprey search strategy for tomato biotic stress detection. Firstly, the channel attention mechanism is used to capture the channel correlation of the convolutional neural network and build an optimized object detection model YOLO_SE. Secondly, in the face of scarce and unbalanced classes, the feature transfer of “diseased leaves” based on CycleGAN and the mixed data augmentation method based on small target oversampling augmentation are used for image processing. Thirdly, a fast response osprey search strategy is proposed to optimize hyperparameters. The algorithm simulates the osprey's predatory behavior. Tent chaotic map, Lévy flight strategy, and data driven evolutionary optimization strategy are respectively used to ensure population position diversity, global optimization ability, and fast response ability. Experiments prove that the optimal validation mAP of the YOLO_SE in this paper is 2.61 % higher than that of the original model YOLOv4, and the optimal validation mAP of the expanded training set is 5.05 % higher than that of the original YOLO_SE. The optimal learning rate obtained using the osprey search strategy is 0.0011426948, and the best verification mAP of YOLO_SE trained by this learning rate is 88.42%, reaching the optimal effect.