An Analysis on Local Convergence of Inexact Newton-Gauss Method for Solving Singular Systems of Equations

We study the local convergence properties of inexact Newton-Gauss method for singular systems of equations. Unified estimates of radius of convergence balls for one kind of singular systems of equations with constant rank derivatives are obtained. Application to the Smale point estimate theory is provided and some important known results are extended and/or improved

Locality-Aware Hyperspectral Classification

Hyperspectral image classification is gaining popularity for high-precision vision tasks in remote sensing, thanks to their ability to capture visual information available in a wide continuum of spectra. Researchers have been working on automating Hyperspectral image classification, with recent efforts leveraging Vision-Transformers. However, most research models only spectra information and lacks attention to the locality (i.e., neighboring pixels), which may be not sufficiently discriminative, resulting in performance limitations. To address this, we present three contributions: i) We introduce the Hyperspectral Locality-aware Image TransformEr (HyLITE), a vision transformer that models both local and spectral information, ii) A novel regularization function that promotes the integration of local-to-global information, and iii) Our proposed approach outperforms competing baselines by a significant margin, achieving up to 10% gains in accuracy. The trained models and the code are available at HyLITE.Comment: The paper is accepted at BMVC202

Systematic bibliometric and visualized analysis of research hotspots and trends in artificial intelligence in autism spectrum disorder

BackgroundArtificial intelligence (AI) has been the subject of studies in autism spectrum disorder (ASD) and may affect its identification, diagnosis, intervention, and other medical practices in the future. Although previous studies have used bibliometric techniques to analyze and investigate AI, there has been little research on the adoption of AI in ASD. This study aimed to explore the broad applications and research frontiers of AI used in ASD.MethodsCitation data were retrieved from the Web of Science Core Collection (WoSCC) database to assess the extent to which AI is used in ASD. CiteSpace.5.8. R3 and VOSviewer, two online tools for literature metrology analysis, were used to analyze the data.ResultsA total of 776 publications from 291 countries and regions were analyzed; of these, 256 publications were from the United States and 173 publications were from China, and England had the largest centrality of 0.33; Stanford University had the highest H-index of 17; and the largest cluster label of co-cited references was machine learning. In addition, keywords with a high number of occurrences in this field were autism spectrum disorder (295), children (255), classification (156) and diagnosis (77). The burst keywords from 2021 to 2023 were infants and feature selection, and from 2022 to 2023, the burst keyword was corpus callosum.ConclusionThis research provides a systematic analysis of the literature concerning AI used in ASD, presenting an overall demonstration in this field. In this area, the United States and China have the largest number of publications, England has the greatest influence, and Stanford University is the most influential. In addition, the research on AI used in ASD mostly focuses on classification and diagnosis, and “infants, feature selection, and corpus callosum are at the forefront, providing directions for future research. However, the use of AI technologies to identify ASD will require further research

On Local Convergence Analysis of Inexact Newton Method for Singular Systems of Equations under Majorant Condition

We present a local convergence analysis of inexact Newton method for solving singular systems of equations. Under the hypothesis that the derivative of the function associated with the singular systems satisfies a majorant condition, we obtain that the method is well defined and converges. Our analysis provides a clear relationship between the majorant function and the function associated with the singular systems. It also allows us to obtain an estimate of convergence ball for inexact Newton method and some important special cases

An Analysis on Local Convergence of Inexact Newton-Gauss Method for Solving Singular Systems of Equations

We study the local convergence properties of inexact Newton-Gauss method for singular systems of equations. Unified estimates of radius of convergence balls for one kind of singular systems of equations with constant rank derivatives are obtained. Application to the Smale point estimate theory is provided and some important known results are extended and/or improved

Open-Ended Learning Strategies for Learning Complex Locomotion Skills

Teaching robots to learn diverse locomotion skills under complex three-dimensional environmental settings via Reinforcement Learning (RL) is still challenging. It has been shown that training agents in simple settings before moving them on to complex settings improves the training process, but so far only in the context of relatively simple locomotion skills. In this work, we adapt the Enhanced Paired Open-Ended Trailblazer (ePOET) approach to train more complex agents to walk efficiently on complex three-dimensional terrains. First, to generate more rugged and diverse three-dimensional training terrains with increasing complexity, we extend the Compositional Pattern Producing Networks - Neuroevolution of Augmenting Topologies (CPPN-NEAT) approach and include randomized shapes. Second, we combine ePOET with Soft Actor-Critic off-policy optimization, yielding ePOET-SAC, to ensure that the agent could learn more diverse skills to solve more challenging tasks. Our experimental results show that the newly generated three-dimensional terrains have sufficient diversity and complexity to guide learning, that ePOET successfully learns complex locomotion skills on these terrains, and that our proposed ePOET-SAC approach slightly improves upon ePOET

Behaviors and Mechanism of Iron Extraction from Chloride Solutions Using Undiluted Cyphos IL 101

In this study, iron­(III) extraction from acidic chloride solutions using undiluted tri­hexyl­tetra­decyl­phosphonium chloride (Cyphos IL 101) was carried out in a liquid–liquid extraction process. The extraction behaviors under various HCl, chloride, and iron­(III) concentrations; selectivity; and extraction isotherm of iron­(III) were investigated. It was found that iron­(III) was extracted fast and efficiently in a wide chloride concentration range. The highly selective separation of iron­(III) from aluminum­(III), calcium­(II), magnesium­(II), and potassium­(I) in acidic chloride solutions was achieved with a separation factor of Fe­(III) over Al­(III) at 11 000 from a 3 M HCl solution. The maximum loading capacity of iron­(III) reached 83.2 g·L^–1 with a molar ratio of 0.91 for Fe­(III)/Cyphos IL 101. Effective stripping of the loaded iron­(III) was achieved with a 0.5 M H₂SO₄ solution. The iron-chloro complexes in both aqueous phase and Cyphos IL 101 phase were characterized using spectroscopic techniques. Ultraviolet–visible and Raman spectra confirmed that iron­(III) formed a series of iron-chloro complexes in acidic chloride solutions, while present solely in the form of tetrachloroferrate complex ([FeCl₄]⁻) in the Cyphos IL 101 phase. An extraction mechanism was proposed in which both FeCl₃ ion association and [FeCl₄]⁻ anion exchange with the chloride anion of Cyphos IL 101 play the key role during iron­(III) extraction