What's in a Name? Beyond Class Indices for Image Recognition

Existing machine learning models demonstrate excellent performance in image object recognition after training on a large-scale dataset under full supervision. However, these models only learn to map an image to a predefined class index, without revealing the actual semantic meaning of the object in the image. In contrast, vision-language models like CLIP are able to assign semantic class names to unseen objects in a `zero-shot' manner, although they still rely on a predefined set of candidate names at test time. In this paper, we reconsider the recognition problem and task a vision-language model to assign class names to images given only a large and essentially unconstrained vocabulary of categories as prior information. We use non-parametric methods to establish relationships between images which allow the model to automatically narrow down the set of possible candidate names. Specifically, we propose iteratively clustering the data and voting on class names within them, showing that this enables a roughly 50\% improvement over the baseline on ImageNet. Furthermore, we tackle this problem both in unsupervised and partially supervised settings, as well as with a coarse-grained and fine-grained search space as the unconstrained dictionary

Quasinormal modes and stability of higher dimensional rotating black holes under massive scalar perturbations

We consider the stability of six-dimensional singly rotating Myers-Perry black holes under massive scalar perturbations. Using Leaver's continued fraction method, we compute the quasinormal modes of the massive scalar fields. All modes found are damped under the quasinormal boundary conditions. It is also found that long-living modes called quasiresonances exist for large scalar masses as in the four-dimensional Kerr black hole case. Our numerical results provide a direct and complement evidence for the stability of six-dimensional MP black holes under massive scalar perturbation.Comment: 11 pages,9 figure

Endocytic recycling and vesicular transport systems mediate transcytosis of Leptospira interrogans across cell monolayer.

Many bacterial pathogens can cause septicemia and spread from the bloodstream into internal organs. During leptospirosis, individuals are infected by contact with Leptospira-containing animal urine-contaminated water. The spirochetes invade internal organs after septicemia to cause disease aggravation, but the mechanism of leptospiral excretion and spreading remains unknown. Here, we demonstrated that Leptospira interrogans entered human/mouse endothelial and epithelial cells and fibroblasts by caveolae/integrin-β1-PI3K/FAK-mediated microfilament-dependent endocytosis to form Leptospira (Lep)-vesicles that did not fuse with lysosomes. Lep-vesicles recruited Rab5/Rab11 and Sec/Exo-SNARE proteins in endocytic recycling and vesicular transport systems for intracellular transport and release by SNARE-complex/FAK-mediated microfilament/microtubule-dependent exocytosis. Both intracellular leptospires and infected cells maintained their viability. Leptospiral propagation was only observed in mouse fibroblasts. Our study revealed that L. interrogans utilizes endocytic recycling and vesicular transport systems for transcytosis across endothelial or epithelial barrier in blood vessels or renal tubules, which contributes to spreading in vivo and transmission of leptospirosis

MegDet: A Large Mini-Batch Object Detector

The improvements in recent CNN-based object detection works, from R-CNN [11], Fast/Faster R-CNN [10, 31] to recent Mask R-CNN [14] and RetinaNet [24], mainly come from new network, new framework, or novel loss design. But mini-batch size, a key factor in the training, has not been well studied. In this paper, we propose a Large MiniBatch Object Detector (MegDet) to enable the training with much larger mini-batch size than before (e.g. from 16 to 256), so that we can effectively utilize multiple GPUs (up to 128 in our experiments) to significantly shorten the training time. Technically, we suggest a learning rate policy and Cross-GPU Batch Normalization, which together allow us to successfully train a large mini-batch detector in much less time (e.g., from 33 hours to 4 hours), and achieve even better accuracy. The MegDet is the backbone of our submission (mmAP 52.5%) to COCO 2017 Challenge, where we won the 1st place of Detection task

Microcapsule-enabled self-healing concrete: A bibliometric analysis

With the development of self-healing technology, the overall properties of the microcapsule-enabled self-healing concrete have taken a giant leap. In this research, a detailed assessment of current research on the microcapsule-enabled self-healing concrete is conducted, together with bibliometric analysis. In the bibliometric analysis, various indicators are considered. The current state of progress regarding self-healing concrete is assessed, and an analysis of the temporal distribution of documents, organizations and countries of literature is conducted. Later, a discussion of the citations is analyzed. The research summarizes the improvements of microcapsule-enabled self-healing cementitious composites and provides a concise background overview