Displacement and equilibrium mesh-free formulation based on integrated radial basis functions for dual yield design

This paper presents displacement and equilibrium mesh-free formulation based on integrated radial basis functions(iRBF) for upper and lower bound yield design problems. In these approaches, displacement and stress fields are approximated by the integrated radial basis functions, and the equilibrium equations and boundary conditions are imposed directly at the collocation points. In this paper it has been shown that direct nodal integration of the iRBF approximation can prevent volumetric locking in the kinematic formulation, and instability problems can also be avoided. Moreover, with the use of the collocation method in the static problem, equilibrium equations and yield conditions only need to be enforced at the nodes, leading to the reduction in computational cost. The mean value of the approximated upper and lower bound is found to be in excellent agreement with the available analytical solution, and can be considered as the actual collapse load multiplier for most practical engineering problems, for which exact solution is unknown

MaskDiff: Modeling Mask Distribution with Diffusion Probabilistic Model for Few-Shot Instance Segmentation

Few-shot instance segmentation extends the few-shot learning paradigm to the instance segmentation task, which tries to segment instance objects from a query image with a few annotated examples of novel categories. Conventional approaches have attempted to address the task via prototype learning, known as point estimation. However, this mechanism depends on prototypes (\eg mean of $K-$shot) for prediction, leading to performance instability. To overcome the disadvantage of the point estimation mechanism, we propose a novel approach, dubbed MaskDiff, which models the underlying conditional distribution of a binary mask, which is conditioned on an object region and $K-$shot information. Inspired by augmentation approaches that perturb data with Gaussian noise for populating low data density regions, we model the mask distribution with a diffusion probabilistic model. We also propose to utilize classifier-free guided mask sampling to integrate category information into the binary mask generation process. Without bells and whistles, our proposed method consistently outperforms state-of-the-art methods on both base and novel classes of the COCO dataset while simultaneously being more stable than existing methods. The source code is available at: https://github.com/minhquanlecs/MaskDiff.Comment: Accepted at AAAI 2024 (oral presentation

Phase Shift Design for RIS-Aided Cell-Free Massive MIMO with Improved Differential Evolution

This paper proposes a novel phase shift design for cell-free massive multiple-input and multiple-output (MIMO) systems assisted by reconfigurable intelligent surface (RIS), which only utilizes channel statistics to achieve the uplink sum ergodic throughput maximization under spatial channel correlations. Due to the non-convexity and the scale of the derived optimization problem, we develop an improved version of the differential evolution (DE) algorithm. The proposed scheme is capable of providing high-quality solutions within reasonable computing time. Numerical results demonstrate superior improvements of the proposed phase shift designs over the other benchmarks, particularly in scenarios where direct links are highly probable.Comment: 5 pages, 2 figures. Accepted by IEEE WC

MirrorNet: Bio-Inspired Camouflaged Object Segmentation

Camouflaged objects are generally difficult to be detected in their natural environment even for human beings. In this paper, we propose a novel bio-inspired network, named the MirrorNet, that leverages both instance segmentation and mirror stream for the camouflaged object segmentation. Differently from existing networks for segmentation, our proposed network possesses two segmentation streams: the main stream and the mirror stream corresponding with the original image and its flipped image, respectively. The output from the mirror stream is then fused into the main stream's result for the final camouflage map to boost up the segmentation accuracy. Extensive experiments conducted on the public CAMO dataset demonstrate the effectiveness of our proposed network. Our proposed method achieves 89% in accuracy, outperforming the state-of-the-arts. Project Page: https://sites.google.com/view/ltnghia/research/camoComment: Under Revie

Magnetic anisotropy in epitaxial Mn5Ge3 films

High crystalline quality Mn 5 Ge 3 films with thicknesses ranging 4–200 nm have been grown on Ge(111) substrates by solid phase epitaxy. The basal hexagonal plane of Mn 5 Ge 3 is in epitaxy with the Ge(111) plane. Magnetic properties of the films have been investigated as a function of the film thickness and the magnetization curves have been analyzed using a theory that includes a description of magnetic domains in uniaxial thin films. The results clearly indicate the existence of a critical thickness below which the magnetic stripe phase disappears. We have determined the value of this thickness to lie between 10 and 25 nm from the analysis of experimental magnetization curves and the theoretical fit of the in-plane remanent magnetization. Although analogies can be drawn between the behavior observed in our system and that of hcp Co, we have shown that the critical thickness is considerably smaller in Mn 5 Ge 3 ; this has the potential to open new fields of applications for Mn 5 Ge 3 thin films in magnetic recording and spintronics

Screening for resistance to Phytophthora

Identifying and evaluating disease resistance depends on rapid, reliable and robust bioassays that can rapidly screen large numbers of genotypes and breeding progenies. We developed seedling, leaf and stem bioassays to screen durian germplasm from Thailand, Vietnam and Australia for resistance to Phytophthora palmivora. Detached leaf assays segregated durian cultivars into classes consistent with field observations, and are recommended as an early screen in breeding programs. Durian cultivar Chanee emerged as the least susceptible cultivar in Thai and Vietnamese tests

Few-Shot Object Detection via Synthetic Features with Optimal Transport

Few-shot object detection aims to simultaneously localize and classify the objects in an image with limited training samples. However, most existing few-shot object detection methods focus on extracting the features of a few samples of novel classes that lack diversity. Hence, they may not be sufficient to capture the data distribution. To address that limitation, in this paper, we propose a novel approach in which we train a generator to generate synthetic data for novel classes. Still, directly training a generator on the novel class is not effective due to the lack of novel data. To overcome that issue, we leverage the large-scale dataset of base classes. Our overarching goal is to train a generator that captures the data variations of the base dataset. We then transform the captured variations into novel classes by generating synthetic data with the trained generator. To encourage the generator to capture data variations on base classes, we propose to train the generator with an optimal transport loss that minimizes the optimal transport distance between the distributions of real and synthetic data. Extensive experiments on two benchmark datasets demonstrate that the proposed method outperforms the state of the art. Source code will be available

Unveiling the atomic position of C in Mn5Ge3 Cx thin films

Heavily carbon-doped Mn5Ge3 is a unique compound for spintronics applications as it meets all the requirements for spin injection and detection in group-IV semiconductors. Despite the great improvement of the magnetic properties induced by C incorporation into Mn5Ge3 compounds, very little information is available on its structural properties and the genuine role played by C atoms. In this paper, we have used a combination of advanced techniques to extensively characterize the structural and magnetic properties of Mn5Ge3Cx films grown on Ge(111) by solid phase epitaxy as a function of C concentration. The increase of the Curie temperature induced by C doping up to 435 K is accompanied by a decrease of the out-of-plane c-lattice parameter. The Mn and C chemical environments and positions in the Mn5Ge3 lattice have been thoroughly investigated using x-ray absorption spectroscopy techniques (x-ray absorption near-edge structures and extended x-ray absorption fine structures) and scanning transmission electronic microscopy (STEM) combined to electron energy loss spectroscopy for the chemical analysis. The results have been systematically compared to a variety of structures that were identified as favorable in terms of formation energy by ab initio calculations. For x≤0.5, the C atoms are mainly located in the octahedral voids formed by Mn atoms, which is confirmed by simulations and seen for the first time in real space by STEM. However, the latter reveals an inhomogeneous C incorporation, which is qualitatively correlated to the broad magnetic transition temperature. A higher C concentration leads to the formation of manganese carbide clusters that we identified as Mn23C6. Interestingly, other types of defects, such as interstitial Ge atoms, vacancies of Mn, and their association into line defects have been detected. They take part in the strain relaxation process and are likely to be intimately related to the growth process. This paper provides a complete picture of the structure of Mn5Ge3Cx in thin films grown by solid phase epitaxy, which is essential for optimizing their magnetic properties