Nano-additive manufacturing of multilevel strengthened aluminum matrix composites

Nanostructured materials are being actively developed, while it remains an open question how to rapidly scale them up to bulk engineering materials for broad industrial applications. This study propose an industrial approach to rapidly fabricate high-strength large-size nanostructured metal matrix composites and attempts to investigate and optimize the deposition process and strengthening mechanism. Here, advanced nanocrystalline aluminum matrix composites (nanoAMCs) were assembled for the first time by a novel nano-additive manufacturing method that was guided by numerical simulations (i.e. the in-flight particle model and the porefree deposition model). The present nanoAMC with a mean grain size <50 nm in matrix exhibited hardness eight times higher than the bulk aluminum and shows the highest hardness among all Al–Al2O3 composites reported to date in the literature, which are the outcome of controlling multiscale strengthening mechanisms from tailoring solution atoms, dislocations, grain boundaries, precipitates, and externally introduced reinforcing particles. The present high-throughput strategy and method can be extended to design and architect advanced coatings or bulk materials in a highly efficient (synthesizing a nanostructured bulk with dimensions of 50 × 20 × 4 mm3 in 9 min) and highly flexible (regulating the gradient microstructures in bulk) way, which is conducive to industrial production and application

FISEdit: Accelerating Text-to-image Editing via Cache-enabled Sparse Diffusion Inference

Due to the recent success of diffusion models, text-to-image generation is becoming increasingly popular and achieves a wide range of applications. Among them, text-to-image editing, or continuous text-to-image generation, attracts lots of attention and can potentially improve the quality of generated images. It's common to see that users may want to slightly edit the generated image by making minor modifications to their input textual descriptions for several rounds of diffusion inference. However, such an image editing process suffers from the low inference efficiency of many existing diffusion models even using GPU accelerators. To solve this problem, we introduce Fast Image Semantically Edit (FISEdit), a cached-enabled sparse diffusion model inference engine for efficient text-to-image editing. The key intuition behind our approach is to utilize the semantic mapping between the minor modifications on the input text and the affected regions on the output image. For each text editing step, FISEdit can automatically identify the affected image regions and utilize the cached unchanged regions' feature map to accelerate the inference process. Extensive empirical results show that FISEdit can be $3.4\times$ and $4.4\times$ faster than existing methods on NVIDIA TITAN RTX and A100 GPUs respectively, and even generates more satisfactory images.Comment: 12 pages, 7 figure

Construction of Nahm data and BPS monopoles with continuous symmetries

We study solutions to Nahm's equations with continuous symmetries and, under certain (mild) hypotheses, we classify the corresponding Ans\"atze. Using our classification, we construct novel Nahm data, and prescribe methods for generating further solutions. Finally, we use these results to construct new BPS monopoles with spherical symmetry.Comment: 36 pages, 1 table, 2 figures. Submitted version. Comments are welcome

Multitask Learning for Citation Purpose Classification

We present our entry into the 2021 3C Shared Task Citation Context Classification based on Purpose competition. The goal of the competition is to classify a citation in a scientific article based on its purpose. This task is important because it could potentially lead to more comprehensive ways of summarizing the purpose and uses of scientific articles, but it is also difficult, mainly due to the limited amount of available training data in which the purposes of each citation have been hand-labeled, along with the subjectivity of these labels. Our entry in the competition is a multi-task model that combines multiple modules designed to handle the problem from different perspectives, including hand-generated linguistic features, TF-IDF features, and an LSTM-with-attention model. We also provide an ablation study and feature analysis whose insights could lead to future work.Comment: Second Workshop on Scholarly Document Processin

Spectral-Spatial Hyperspectral Image Classification Using Subspace-Based Support Vector Machines and Adaptive Markov Random Fields

This paper introduces a new supervised classification method for hyperspectral images that combines spectral and spatial information. A support vector machine (SVM) classifier, integrated with a subspace projection method to address the problems of mixed pixels and noise, is first used to model the posterior distributions of the classes based on the spectral information. Then, the spatial information of the image pixels is modeled using an adaptive Markov random field (MRF) method. Finally, the maximum posterior probability classification is computed via the simulated annealing (SA) optimization algorithm. The combination of subspace-based SVMs and adaptive MRFs is the main contribution of this paper. The resulting methods, called SVMsub-eMRF and SVMsub-aMRF, were experimentally validated using two typical real hyperspectral data sets. The obtained results indicate that the proposed methods demonstrate superior performance compared with other classical hyperspectral image classification methods.Ritrýnt tímaritPeer Reviewe