Multi-Dimensional Ability Diagnosis for Machine Learning Algorithms

Machine learning algorithms have become ubiquitous in a number of applications (e.g. image classification). However, due to the insufficient measurement of traditional metrics (e.g. the coarse-grained Accuracy of each classifier), substantial gaps are usually observed between the real-world performance of these algorithms and their scores in standardized evaluations. In this paper, inspired by the psychometric theories from human measurement, we propose a task-agnostic evaluation framework Camilla, where a multi-dimensional diagnostic metric Ability is defined for collaboratively measuring the multifaceted strength of each machine learning algorithm. Specifically, given the response logs from different algorithms to data samples, we leverage cognitive diagnosis assumptions and neural networks to learn the complex interactions among algorithms, samples and the skills (explicitly or implicitly pre-defined) of each sample. In this way, both the abilities of each algorithm on multiple skills and some of the sample factors (e.g. sample difficulty) can be simultaneously quantified. We conduct extensive experiments with hundreds of machine learning algorithms on four public datasets, and our experimental results demonstrate that Camilla not only can capture the pros and cons of each algorithm more precisely, but also outperforms state-of-the-art baselines on the metric reliability, rank consistency and rank stability

Perivascular adipose tissue (PVAT) in atherosclerosis: a double-edged sword

Abstract Perivascular adipose tissue (PVAT), the adipose tissue that surrounds most of the vasculature, has emerged as an active component of the blood vessel wall regulating vascular homeostasis and affecting the pathogenesis of atherosclerosis. Although PVAT characteristics resemble both brown and white adipose tissues, recent evidence suggests that PVAT develops from its own distinct precursors implying a closer link between PVAT and vascular system. Under physiological conditions, PVAT has potent anti-atherogenic properties mediated by its ability to secrete various biologically active factors that induce non-shivering thermogenesis and metabolize fatty acids. In contrast, under pathological conditions (mainly obesity), PVAT becomes dysfunctional, loses its thermogenic capacity and secretes pro-inflammatory adipokines that induce endothelial dysfunction and infiltration of inflammatory cells, promoting atherosclerosis development. Since PVAT plays crucial roles in regulating key steps of atherosclerosis development, it may constitute a novel therapeutic target for the prevention and treatment of atherosclerosis. Here, we review the current literature regarding the roles of PVAT in the pathogenesis of atherosclerosis.https://deepblue.lib.umich.edu/bitstream/2027.42/145729/1/12933_2018_Article_777.pd

Generating Person Images with Appearance-aware Pose Stylizer

Generation of high-quality person images is challenging, due to the sophisticated entanglements among image factors, e.g., appearance, pose, foreground, background, local details, global structures, etc. In this paper, we present a novel end-to-end framework to generate realistic person images based on given person poses and appearances. The core of our framework is a novel generator called Appearance-aware Pose Stylizer (APS) which generates human images by coupling the target pose with the conditioned person appearance progressively. The framework is highly flexible and controllable by effectively decoupling various complex person image factors in the encoding phase, followed by re-coupling them in the decoding phase. In addition, we present a new normalization method named adaptive patch normalization, which enables region-specific normalization and shows a good performance when adopted in person image generation model. Experiments on two benchmark datasets show that our method is capable of generating visually appealing and realistic-looking results using arbitrary image and pose inputs.Comment: Appearing at IJCAI 2020. The code is available at https://github.com/siyuhuang/PoseStylize

CCL21/CCR7 enhances the proliferation, migration, and invasion of human bladder cancer T24 cells

Objective To investigate the effects of CCL21/CCR7 on the proliferation, migration, and invasion of T24 cells and the possible associated mechanisms: expression of MMP-2 and MMP-9, and regulation of BCL-2 and BAX proteins. Methods T24 cells received corresponding treatments including vehicle control, antibody (20ng/mL CCR7 antibody and 50 ng/ml CCL21), and 50, 100. and 200 ng/ml CCL21. Proliferation was evaluated by MTT assay; cell migration and invasion were assayed using a transwell chamber. Cell apoptosis was induced by Adriamycin (ADM). The rate of cell apoptosis was examined by flow cytometry using annexin V-FITC/PI staining. Western-blot was used to analyze MMP-2 and MMP-9 and BCL-2 and BAX proteins. Results CCL21 promoted T24 cell proliferation in concentration-dependent manner with that 200 ng/mL induced the largest amount of proliferation. Significant differences of cell migration were found between CCL21treatment groups and the control group in both the migration and invasion studies (P \u3c 0.001 for all). The expressions of MMP-2 and MMP-9 proteins were significantly increased after CCL21 treatment (p \u3c 0.05 for all). Protein expression of Bcl-21 follows an ascending trend while the expression of Bax follows a descending trend as the concentration of CCL21 increases. No difference was found between the control group and antibody group for all assessments. Conclusion CCL21/CCR7 promoted T24 cell proliferation and enhanced its migration and invasion via the increased expression of MMP-2 and MMP-9. CCL21/CCR7 had antiapoptotic activities on T24 cells via regulation of Bcl-2 and Bax proteins. CCL21/CCR7 may promote bladder cancer development and metastasis

Average and instantaneous velocities of energy of evanescent modes

Many theoretical and experimental investigations have presented a conclusion that evanescent electromagnetic modes can superluminally propagate. However, in this paper, we show that the average energy velocity of evanescent modes inside a cut-off waveguide is always less than or equal to the velocity of light in vacuum, while the instantaneous energy velocity can be superluminal, which does not violate causality according to quantum field theory: the fact that a particle can propagate over a space-like interval does preserve causality provided that here a measurement performed at one point cannot affect another measurement at a point separated from the first with a space-like interval.Comment: 12 pages, 1 figure, to be published in Physical Review