A novel Quaternion-based 2D-3D Registration Algorithm with Line Correspondence

Image’s registration includes 2D-2D, 3D-3D and 3D-2D registration. This paper only concentrates on the 2D-3D registration, the image’s attitude is represented by a rotation matrix R, while the position is a translation vector T. Traditional approaches mainly focus on points correspondences, and state-of-the-art approaches concentrate on high-order structures, i.e. lines, rectangle, parallelepiped etc. Mathematically, Most existing solutions adapt either linear optimization or iteration methods.  However, they need the position, attitude initialization, which are not always available in real scene, and they do not guarantee to find global solutions.In this paper, instead of solving these polynomials directly, we introduce a novel approach ( say qLR ), which treat these multivariate polynomial equations as “monomials” and express R in a quaternion vision, resulting in dramatic decrement of the number of equations. DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.446

Solid Solution Strengthened Fe Alloys

Iron (Fe)-based alloys (such as steel) are widely used structural materials in industry. Numerous methods have been applied to improve their mechanical properties. In this study, we used a technique know as magnetron sputtering to deposit various Fe-based binary alloy coatings to investigate the influence of solutes on solid solution hardening. Several factors contribute to the solid solution hardening of the alloys, such as composition, atomic radius, modulus, and lattice parameter. After preliminary calculations and analysis, we selected several solutes, including molybdenum (Mo), niobium (Nb), and zirconium (Zr). The compositions of solutes were varied to be 2.5, 5, 8 atomic %. Our nanoindentation hardness measurements show that among the three solid solution alloys, Fe-Zr has the highest hardness. The influences of solutes on microstructural and hardness evolution in these solid solution alloys are discussed

Strengthening mechanisms of highly textured Cu/Co and Ag/Al nanolayers with high density twins and stacking faults

Metallic nanolayers have attracted increasing attention as they provide unique opportunity to investigate the influence of layer interfaces on mechanical properties of metallic nanocomposites. High strength is often achieved at small (several nm) individual layer thickness (h). Recently, we discovered high-density stacking faults in FCC Co in highly (100) textured Cu/Co multilayers. In contrast in (111) textured Cu/Co nanolayers, Co remained its stable HCP structure at large h. The two Cu/Co systems have very different size dependent strengthening behavior. HCP Cu/Co has much greater peak strength than FCC Cu/Co. The large discrepancy in their strengthening mechanisms is discussed and compared to those of highly textured Cu/Ni multilayer systems. In another highly textured nanolayers system, Ag/Al, epitaxial interfaces were observed across various h (1‑200 nm). High-density nanotwins and stacking faults appear in both Ag and Al layers, and stacking fault density in Al increases sharply with decreasing h. At smaller h, hardness of Ag/Al nanolayers increases monotonically and no softening was observed. These studies allow us to investigate the influence of layer interfaces, stacking faults and nanotwins on strengthening mechanisms of metallic nanolayers. This research is funded by DOE–OBES

Production of dileptons in ultra-peripheral heavy ion collisions with two-photon processes

We study the photoproduction process of dileptons in heavy ion collision at Relativistic Heavy Ion Collider (RHIC) and Large Hadron Collider (LHC) energys. The equivalent photon approximation, which equates the electromagnetic field of high-energy charged particles to the virtual photon flux, is used to calculate the processes of dileptons production. The numerical results demonstrate that the experimental study of dileptons in ultra-peripheral collisions is feasible at RHIC and LHC energies.Comment: 11 pages, 6 figure

End-to-end Phase Field Model Discovery Combining Experimentation, Crowdsourcing, Simulation and Learning

The availability of tera-byte scale experiment data calls for AI driven approaches which automatically discover scientific models from data. Nonetheless, significant challenges present in AI-driven scientific discovery: (i) The annotation of large scale datasets requires fundamental re-thinking in developing scalable crowdsourcing tools. (ii) The learning of scientific models from data calls for innovations beyond black-box neural nets. (iii) Novel visualization and diagnosis tools are needed for the collaboration of experimental and theoretical physicists, and computer scientists. We present Phase-Field-Lab platform for end-to-end phase field model discovery, which automatically discovers phase field physics models from experiment data, integrating experimentation, crowdsourcing, simulation and learning. Phase-Field-Lab combines (i) a streamlined annotation tool which reduces the annotation time (by ~50-75%), while increasing annotation accuracy compared to baseline; (ii) an end-to-end neural model which automatically learns phase field models from data by embedding phase field simulation and existing domain knowledge into learning; and (iii) novel interfaces and visualizations to integrate our platform into the scientific discovery cycle of domain scientists. Our platform is deployed in the analysis of nano-structure evolution in materials under extreme conditions (high temperature and irradiation). Our approach reveals new properties of nano-void defects, which otherwise cannot be detected via manual analysis

PREPARATION OF HIGH-STRENGTH NANOMETER SCALE TWINNED COATING AND FOIL

Very high strength single phase stainless steel coating has been prepared by magnetron sputtering onto a substrate. The coating has a unique microstructure of nanometer spaced twins that are parallel to each other and to the substrate surface. For cases where the coating and substrate do not bind strongly, the coating can be peeled off to provide foil

SInViG: A Self-Evolving Interactive Visual Agent for Human-Robot Interaction

Linguistic ambiguity is ubiquitous in our daily lives. Previous works adopted interaction between robots and humans for language disambiguation. Nevertheless, when interactive robots are deployed in daily environments, there are significant challenges for natural human-robot interaction, stemming from complex and unpredictable visual inputs, open-ended interaction, and diverse user demands. In this paper, we present SInViG, which is a self-evolving interactive visual agent for human-robot interaction based on natural languages, aiming to resolve language ambiguity, if any, through multi-turn visual-language dialogues. It continuously and automatically learns from unlabeled images and large language models, without human intervention, to be more robust against visual and linguistic complexity. Benefiting from self-evolving, it sets new state-of-the-art on several interactive visual grounding benchmarks. Moreover, our human-robot interaction experiments show that the evolved models consistently acquire more and more preferences from human users. Besides, we also deployed our model on a Franka robot for interactive manipulation tasks. Results demonstrate that our model can follow diverse user instructions and interact naturally with humans in natural language, despite the complexity and disturbance of the environment

Sparse Spectral Training and Inference on Euclidean and Hyperbolic Neural Networks

The growing computational demands posed by increasingly number of neural network's parameters necessitate low-memory-consumption training approaches. Previous memory reduction techniques, such as Low-Rank Adaptation (LoRA) and ReLoRA, suffer from the limitation of low rank and saddle point issues, particularly during intensive tasks like pre-training. In this paper, we propose Sparse Spectral Training (SST), an advanced training methodology that updates all singular values and selectively updates singular vectors of network weights, thereby optimizing resource usage while closely approximating full-rank training. SST refines the training process by employing a targeted updating strategy for singular vectors, which is determined by a multinomial sampling method weighted by the significance of the singular values, ensuring both high performance and memory reduction. Through comprehensive testing on both Euclidean and hyperbolic neural networks across various tasks, including natural language generation, machine translation, node classification and link prediction, SST demonstrates its capability to outperform existing memory reduction training methods and is comparable with full-rank training in some cases. On OPT-125M, with rank equating to 8.3% of embedding dimension, SST reduces the perplexity gap to full-rank training by 67.6%, demonstrating a significant reduction of the performance loss with prevalent low-rank methods. This approach offers a strong alternative to traditional training techniques, paving the way for more efficient and scalable neural network training solutions

Interface Effects on He Ion Irradiation in Nanostructured Materials

In advanced fission and fusion reactors, structural materials suffer from high dose irradiation by energetic particles and are subject to severe microstructure damage. He atoms, as a byproduct of the (n) transmutation reaction, could accumulate to form deleterious cavities, which accelerate radiation-induced embrittlement, swelling and surface deterioration, ultimately degrade the service lifetime of reactor materials. Extensive studies have been performed to explore the strategies that can mitigate He ion irradiation damage. Recently, nanostructured materials have received broad attention because they contain abundant interfaces that are efficient sinks for radiation-induced defects. In this review, we summarize and analyze the current understandings on interface effects on He ion irradiation in nanostructured materials. Some key challenges and research directions are highlighted for studying the interface effects on radiation damage in nanostructured materials

High strength, deformable nanotwinned Al–Co alloys

Aluminum (Al) alloys have been widely used in the transportation industry. However, most highstrength Al alloys to date have limited mechanical strength, on the order of a few hundred MPa, which is much lower than the flow stress of high-strength steels. In this study, we show the fabrication of nanocrystalline Al alloys with high-density growth twins enabled by a few atomic percent of Co solute. In situ uniaxial compression tests show that the flow stress of Al–Co solid solution alloys exceeds 1.5 GPa, while good work hardening capability is maintained. This study provides a new perspective on the design of high-strength Al alloys for various applications