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Histopathology analysis is the gold standard for medical diagnosis. Accurate classification of whole slide images (WSIs) and region-of-interests (ROIs) localization can assist pathologists in diagnosis. The gigapixel resolution of WSI and the absence of fine-grained annotations make direct classification and analysis challenging. In weakly supervised learning, multiple instance learning (MIL) presents a promising approach for WSI classification. The prevailing strategy is to use attention mechanisms to measure instance importance for classification. However, attention mechanisms fail to capture inter-instance information, and self-attention causes quadratic computational complexity. To address these challenges, we propose AMD-MIL, an agent aggregator with a mask denoise mechanism. The agent token acts as an intermediate variable between the query and key for computing instance importance. Mask and denoising matrices, mapped from agents-aggregated value, dynamically mask low-contribution representations and eliminate noise. AMD-MIL achieves better attention allocation by adjusting feature representations, capturing micro-metastases in cancer, and improving interpretability. Extensive experiments on CAMELYON-16, CAMELYON-17, TCGA-KIDNEY, and TCGA-LUNG show AMD-MIL's superiority over state-of-the-art methods.

The rise of dockless bike-sharing systems has led to increased interest in using bike-sharing data for urban transportation and travel behavior research. However, few studies have focused on the individual daily mobility patterns, hindering their alignment with the increasingly refined needs of urban active transportation planning. To bridge this gap, this study presents a two-layer framework, integrating improved flow clustering methods and multiple rule-based decision trees, to mine individual cyclists' daily home-work commuting patterns from vast dockless bike-sharing trip data with users' IDs. The effectiveness and applicability of the framework is demonstrated by over 200 million dockless bike-sharing trip records in Shenzhen. Ultimately, based on the mining results, we obtain two categories of bike-sharing commuters (i.e., 74.38% of Only-biking commuters and 25.62% of Biking-with-transit commuters) and some interesting findings about their daily commuting patterns. For instance, lots of bike-sharing commuters live near urban villages and old communities with lower costs of living, especially in the central city. Only-biking commuters have a higher proportion of overtime than Biking-with-transit commuters, and the Longhua Industrial Park, a manufacturing-oriented area, having the longest average working hours (over 10 hours per day). Massive commuters utilize bike-sharing for commuting to work more frequently than for returning home, which is closely related to the over-demand for bike-sharing around workplaces during commuting peak. Overall, this framework offers a cost-effective way to understand residents' non-motorized mobility patterns. Moreover, it paves the way for subsequent research on fine-scale cycling behaviors that consider demographic disparities in socio-economic attributes.

In the long history of science and technology development, one goal is to diffuse solid alloy elements into the surface of steel materials to form surface alloys with excellent physical and chemical properties. On the basis of plasma nitriding technology, double glow plasma surface metallurgy technology has answered this challenge. This technology, which seems to be a modern-day alchemy, can use any element in the periodic table of chemical elements, including solid metal elements and their combinations, to form many types of surface alloyed layers with high hardness, wear resistance, corrosion resistance and high temperature oxidation resistance on various metal materials. For examples, nickel base alloys, stainless steels and high speed steels are formed on the surfaces of ordinary carbon steels; and high hardness, wear resistance and high temperature oxidation resistance alloy are formed on the surface of titanium alloy.This article briefly introduces the formation and principle of double glow plasma surface metallurgy technology, and summarizes the experimental results and industry application. The significance and development prospect of this technology are discussed., Comment: arXiv admin note: text overlap with arXiv:2003.10250

Early interlaced videos usually contain multiple and interlacing and complex compression artifacts, which significantly reduce the visual quality. Although the high-definition reconstruction technology for early videos has made great progress in recent years, related research on deinterlacing is still lacking. Traditional methods mainly focus on simple interlacing mechanism, and cannot deal with the complex artifacts in real-world early videos. Recent interlaced video reconstruction deep deinterlacing models only focus on single frame, while neglecting important temporal information. Therefore, this paper proposes a multiframe deinterlacing network joint enhancement network for early interlaced videos that consists of three modules, i.e., spatial vertical interpolation module, temporal alignment and fusion module, and final refinement module. The proposed method can effectively remove the complex artifacts in early videos by using temporal redundancy of multi-fields. Experimental results demonstrate that the proposed method can recover high quality results for both synthetic dataset and real-world early interlaced videos., Comment: 12 pages, 14 figures

Global forest cover is critical to the provision of certain ecosystem services. With the advent of the google earth engine cloud platform, fine resolution global land cover mapping task could be accomplished in a matter of days instead of years. The amount of global forest cover (GFC) products has been steadily increasing in the last decades. However, it's hard for users to select suitable one due to great differences between these products, and the accuracy of these GFC products has not been verified on global scale. To provide guidelines for users and producers, it is urgent to produce a validation sample set at the global level. However, this labeling task is time and labor consuming, which has been the main obstacle to the progress of global land cover mapping. In this research, a labor-efficient semi-automatic framework is introduced to build a biggest ever Forest Sample Set (FSS) contained 395280 scattered samples categorized as forest, shrubland, grassland, impervious surface, etc. On the other hand, to provide guidelines for the users, we comprehensively validated the local and global mapping accuracy of all existing 30m GFC products, and analyzed and mapped the agreement of them. Moreover, to provide guidelines for the producers, optimal sampling strategy was proposed to improve the global forest classification. Furthermore, a new global forest cover named GlobeForest2020 has been generated, which proved to improve the previous highest state-of-the-art accuracies (obtained by Gong et al., 2017) by 2.77% in uncertain grids and by 1.11% in certain grids.