An Empirical Evaluation of Current Convolutional Architectures' Ability to Manage Nuisance Location and Scale Variability

We conduct an empirical study to test the ability of Convolutional Neural Networks (CNNs) to reduce the effects of nuisance transformations of the input data, such as location, scale and aspect ratio. We isolate factors by adopting a common convolutional architecture either deployed globally on the image to compute class posterior distributions, or restricted locally to compute class conditional distributions given location, scale and aspect ratios of bounding boxes determined by proposal heuristics. In theory, averaging the latter should yield inferior performance compared to proper marginalization. Yet empirical evidence suggests the converse, leading us to conclude that - at the current level of complexity of convolutional architectures and scale of the data sets used to train them - CNNs are not very effective at marginalizing nuisance variability. We also quantify the effects of context on the overall classification task and its impact on the performance of CNNs, and propose improved sampling techniques for heuristic proposal schemes that improve end-to-end performance to state-of-the-art levels. We test our hypothesis on a classification task using the ImageNet Challenge benchmark and on a wide-baseline matching task using the Oxford and Fischer's datasets.Comment: 10 pages, 5 figures, 3 tables -- CVPR 2016, camera-ready versio

Domain-Size Pooling in Local Descriptors: DSP-SIFT

We introduce a simple modification of local image descriptors, such as SIFT, based on pooling gradient orientations across different domain sizes, in addition to spatial locations. The resulting descriptor, which we call DSP-SIFT, outperforms other methods in wide-baseline matching benchmarks, including those based on convolutional neural networks, despite having the same dimension of SIFT and requiring no training.Comment: Extended version of the CVPR 2015 paper. Technical Report UCLA CSD 14002

On the Design and Analysis of Multiple View Descriptors

We propose an extension of popular descriptors based on gradient orientation histograms (HOG, computed in a single image) to multiple views. It hinges on interpreting HOG as a conditional density in the space of sampled images, where the effects of nuisance factors such as viewpoint and illumination are marginalized. However, such marginalization is performed with respect to a very coarse approximation of the underlying distribution. Our extension leverages on the fact that multiple views of the same scene allow separating intrinsic from nuisance variability, and thus afford better marginalization of the latter. The result is a descriptor that has the same complexity of single-view HOG, and can be compared in the same manner, but exploits multiple views to better trade off insensitivity to nuisance variability with specificity to intrinsic variability. We also introduce a novel multi-view wide-baseline matching dataset, consisting of a mixture of real and synthetic objects with ground truthed camera motion and dense three-dimensional geometry

Study on characteristics of particulate emission of diesel aftertreatment with reciprocating flow

© 2021 The Authors. Energy Science & Engineering published by the Society of Chemical Industry and John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0/In this article, in order to optimize diesel aftertreatment system with periodically reciprocating flow (PRF), an experimental study is conducted to investigate its characteristics such as pollution emissions, regeneration of diesel particulate filter (DPF), concentration and size distribution of particulate matter (PM) escaped as well as temperature distribution under unidirectional flow and PRF operating conditions. The effects of reciprocating flow cycle and exhaust gas flow on the performance of aftertreatment system are investigated in detail. The energy efficiency analysis of the aftertreatment system is also carried out. Experimental results show that (i) as the temperature is lower than the light-off threshold of combustible gas, the aftertreatment system cannot restrain the formation of second particles under the present experiment condition of unidirectional flow; (ii) the aftertreatment system demonstrates excellent performance of trapping particles and filter regeneration as the symmetrical temperature distribution is formed. The PM filter efficiency α_PM is 92% and the specific energy consumption β is 124% for symmetrical temperature distribution; (iii) the increase of reciprocating flow cycle could lead to the shifting of the temperature profiles, this would affect the particle size distribution; (iv) a certain increase of exhaust gas flow from engine would have insignificant change for the temperature distribution; (v) The critical energy efficiency η_c of the system could reach 96.61%.Peer reviewedFinal Published versio

Multi-view feature engineering and learning

We frame the problem of local representation of imaging data as the computation of minimal sufficient statistics that are invariant to nuisance variability induced by viewpoint and illumination. We show that, under very stringent condi-tions, these are related to “feature descriptors ” commonly used in Computer Vision. Such conditions can be relaxed if multiple views of the same scene are available. We pro-pose a sampling-based and a point-estimate based approx-imation of such a representation, compared empirically on image-to-(multiple)image matching, for which we introduce a multi-view wide-baseline matching benchmark, consisting of a mixture of real and synthetic objects with ground truth camera motion and dense three-dimensional geometry. 1

X-Box Binding Protein 1 Is Essential for the Anti-Oxidant Defense and Cell Survival in the Retinal Pigment Epithelium

Damage to the retinal pigment epithelium (RPE) is an early event in the pathogenesis of age-related macular degeneration (AMD). X-box binding protein 1 (XBP1) is a key transcription factor that regulates endoplasmic reticulum (ER) homeostasis and cell survival. This study aimed to delineate the role of endogenous XBP1 in the RPE. Our results show that in a rat model of light-induced retinal degeneration, XBP1 activation was suppressed in the RPE/choroid complex, accompanied by decreased anti-oxidant genes and increased oxidative stress. Knockdown of XBP1 by siRNA resulted in reduced expression of SOD1, SOD2, catalase, and glutathione synthase and sensitized RPE cells to oxidative damage. Using Cre/LoxP system, we generated a mouse line that lacks XBP1 only in RPE cells. Compared to wildtype littermates, RPE-XBP1 KO mice expressed less SOD1, SOD2, and catalase in the RPE, and had increased oxidative stress. At age 3 months and older, these mice exhibited apoptosis of RPE cells, decreased number of cone photoreceptors, shortened photoreceptor outer segment, reduced ONL thickness, and deficit in retinal function. Electron microscopy showed abnormal ultrastructure, Bruch's membrane thickening, and disrupted basal membrane infolding in XBP1-deficient RPE. These results indicate that XBP1 is an important gene involved in regulation of the anti-oxidant defense in the RPE, and that impaired activation of XBP1 may contribute to RPE dysfunction and cell death during retinal degeneration and AMD

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Research on Cavitation Characteristics of Two-Throat Nozzle Submerged Jet

Ship fouling not only increases ship resistance and fuel consumption but is equally a type of biological invasion, which causes severe ecological damage. Submerged cavitation jet cleaning is an environmentally friendly, high-efficiency, and energy-saving cleaning method. The nozzle structure has an essential influence on the cleaning effect. Thus, a two-throat nozzle was designed for application in submerged cavitation jet cleaning. To investigate the cavitation characteristics of the two-throat nozzle, a high-speed photographic visualization experiment and an erosion experiment concerning the submerged cavitation jet were carried out in this study. The frame-difference method (FDM) was used to analyze the dynamic changes in the cavitation cloud in a single period. The dynamic changes in the cavitation cloud and the characteristics of the submerged cavitation jet were investigated under different inlet pressures. The sample mass loss and the macroscopic and microscopic changes in surface morphology were used to evaluate the cavitation intensity of the two-throat nozzle submerged jet. The experimental results demonstrate that the two-throat nozzle has a good cavitation effect, and the cavitation cloud of the submerged jet has obvious periodicity. With the increase in inlet pressure, the length, width, and area of the cavitation cloud continue to increase, and the shedding frequency of the cavitation cloud continues to decrease. The intensity of cavitation erosion is related to target distance and impact time. There is an appropriate target distance by which to achieve the optimal cavitation effect. The collapse of cavitation bubbles near the sample surface is related to the erosion distribution on the sample surface. Moreover, the magnitude of the absolute values of the root-mean-square surface roughness and surface skewness increase with cavitation intensity. The results in this paper are helpful for a better understanding of the cavitation characteristics of the two-throat nozzle submerged jet