Few-Shot Image Recognition by Predicting Parameters from Activations

In this paper, we are interested in the few-shot learning problem. In particular, we focus on a challenging scenario where the number of categories is large and the number of examples per novel category is very limited, e.g. 1, 2, or 3. Motivated by the close relationship between the parameters and the activations in a neural network associated with the same category, we propose a novel method that can adapt a pre-trained neural network to novel categories by directly predicting the parameters from the activations. Zero training is required in adaptation to novel categories, and fast inference is realized by a single forward pass. We evaluate our method by doing few-shot image recognition on the ImageNet dataset, which achieves the state-of-the-art classification accuracy on novel categories by a significant margin while keeping comparable performance on the large-scale categories. We also test our method on the MiniImageNet dataset and it strongly outperforms the previous state-of-the-art methods

Research and simulation of fast, strong exothermic reaction in gas-solid fluidized bed about temperature distribution and hot spot problem

Gas-solid fluidized bed is widely used in petro-chemical and coal-chemical industry and other fields because of its superior heat transfer and mass transfer performances. In consideration of these performances, it is generally believed that there is a uniform temperature distribution and no hot spot in gas-solid fluidized bed compared with fixed bed. But in real industrial processes of fast, strong exothermic reactions, there are great axial and radial temperature differences and even hot spots in gas-solid fluidized bed. In this study, two-dimensional diffusion model based upon the momentum and energy conservation equations was successfully used to compute the temperature distribution of aniline reaction in fluidized bed. The result is in good agreement with real industrial measurement. In addition, this study discussed the influence of velocity and fluidized bed diameter on the temperature distribution. The result showed that in contrast to the fixed bed, increasing gas velocity during turbulent region in fluidized bed would help eliminate hot spot and reduce temperature difference. Finally, based on the comprehensive consideration of velocity and diameter, this study showed a stability region for scaling up of gas-solid fluidized bed with fast, strong exothermic reactions which helps to guide the practical operation. Please click Additional Files below to see the full abstract

The Expression Levels of XLF and Mutant P53 Are Inversely Correlated in Head and Neck Cancer Cells.

XRCC4-like factor (XLF), also known as Cernunnos, is a protein encoded by the human NHEJ1 gene and an important repair factor for DNA double-strand breaks. In this study, we have found that XLF is over-expressed in HPV(+) versus HPV(-) head and neck squamous cell carcinoma (HNSCC) and significantly down-regulated in the HNSCC cell lines expressing high level of mutant p53 protein versus those cell lines harboring wild-type TP53 gene with low p53 protein expression. We have also demonstrated that Werner syndrome protein (WRN), a member of the NHEJ repair pathway, binds to both mutant p53 protein and NHEJ1 gene promoter, and siRNA knockdown of WRN leads to the inhibition of XLF expression in the HNSCC cells. Collectively, these findings suggest that WRN and p53 are involved in the regulation of XLF expression and the activity of WRN might be affected by mutant p53 protein in the HNSCC cells with aberrant TP53 gene mutations, due to the interaction of mutant p53 with WRN. As a result, the expression of XLF in these cancer cells is significantly suppressed. Our study also suggests that XLF is over-expressed in HPV(+) HNSCC with low expression of wild type p53, and might serve as a potential biomarker for HPV(+) HNSCC. Further studies are warranted to investigate the mechanisms underlying the interactive role of WRN and XLF in NHEJ repair pathway

Stability analysis of gas solids separation in scaling-up fluidized bed reactors

In large industrial fluidized bed reactors with high gas solids flow rates, small cyclones working in parallel are often preferred to achieve higher efficiency in the case of uniform distribution of gas-solid two-phase flow across each inlet. However, there is mounting evidence1-5 that gas-solid suspensions pass through identical paths in parallel can be significantly non-uniform, resulting in a dramatically drop in overall efficiency. In this study we used the direct Liapunov method by considering the interaction between gas and solids to detect the instability of uniformity. Owing to the special symmetry in this system, the criterion can be simplified into identifying the concavity (concave or convex) of pressure drop across a single cyclone with respect to operational parameter CT. Then, based on the stability analysis of uniformity, a novel design principle is provided to prevent non-uniform distribution at high dust loading. The effect of geometrical factor, i.e. dimensionless vortex finder diameter dr, on the stability of uniformity has been further investigated. The phase diagram of stability is calculated to give a clue of designing robust parallel cyclones system. Please click Additional Files below to see the full abstract

High spatial-resolution imaging of label-free in vivo protein aggregates by VISTA

Amyloid aggregation, formed by aberrant proteins, is a pathological hallmark for neurodegenerative diseases, including Alzheimer's disease and Huntington's disease. High-resolution holistic mapping of the fine structures from these aggregates should facilitate our understanding of their pathological roles. Here, we achieved label-free high-resolution imaging of the polyQ and the amyloid-beta (Aβ) aggregates in cells and tissues utilizing a sample-expansion stimulated Raman strategy. We further focused on characterizing the Aβ plaques in 5XFAD mouse brain tissues. 3D volumetric imaging enabled visualization of the whole plaques, resolving both the fine protein filaments and the surrounding components. Coupling our expanded label-free Raman imaging with machine learning, we obtained specific segmentation of aggregate cores, peripheral filaments together with cell nuclei and blood vessels by pre-trained convolutional neural network models. Combining with 2-channel fluorescence imaging, we achieved a 6-color holistic view of the same sample. This ability for precise and multiplex high-resolution imaging of the protein aggregates and their micro-environment without the requirement of labeling would open new biomedical applications