In this paper, we propose an extremely efficient algorithm for visual re-ranking. By considering the original pairwise distance in the contextual space, we develop a feature vector called sparse ...contextual activation (SCA) that encodes the local distribution of an image. Hence, re-ranking task can be simply accomplished by vector comparison under the generalized Jaccard metric, which has its theoretical meaning in the fuzzy set theory. In order to improve the time efficiency of re-ranking procedure, inverted index is successfully introduced to speed up the computation of generalized Jaccard metric. As a result, the average time cost of re-ranking for a certain query can be controlled within 1 ms. Furthermore, inspired by query expansion, we also develop an additional method called local consistency enhancement on the proposed SCA to improve the retrieval performance in an unsupervised manner. On the other hand, the retrieval performance using a single feature may not be satisfactory enough, which inspires us to fuse multiple complementary features for accurate retrieval. Based on SCA, a robust feature fusion algorithm is exploited that also preserves the characteristic of high time efficiency. We assess our proposed method in various visual re-ranking tasks. Experimental results on Princeton shape benchmark (3D object), WM-SRHEC07 (3D competition), YAEL data set B (face), MPEG-7 data set (shape), and Ukbench data set (image) manifest the effectiveness and efficiency of SCA.
This letter introduces a robust representation of 3-D shapes, named DeepPano, learned with deep convolutional neural networks (CNN). Firstly, each 3-D shape is converted into a panoramic view, namely ...a cylinder projection around its principle axis. Then, a variant of CNN is specifically designed for learning the deep representations directly from such views. Different from typical CNN, a row-wise max-pooling layer is inserted between the convolution and fully-connected layers, making the learned representations invariant to the rotation around a principle axis. Our approach achieves state-of-the-art retrieval/classification results on two large-scale 3-D model datasets (ModelNet-10 and ModelNet-40), outperforming typical methods by a large margin.
From 2019, countries worldwide have been negatively affected by the corona virus disease 2019 (COVID-19) in all aspects of social life. The high-tech digital industry represented by emerging digital ...technologies is still vigorous, and correspondingly, the digital economy has become an important force to promote the stable recovery and re-prosperity of the national economy. The digital economy plays a memorable role in preventing and controlling COVID-19, the resumption of work and production, and the creation of new business formats and models. Urban big data (UBD) involves a wide range of dynamic and static data with high dimensions, but there are no mature and clear data classification and grading standards. Currently, it is urgent to strengthen the security protection of high-value datasets. Therefore, a UBD classification and grading method is proposed based on the lightweight (LWT) deep learning (DL) clustering algorithm. It uses a semi-intelligent path based on partial artificial to form data classification (DC) and hierarchical thesaurus, corpus, rule base, and model base. Subsequently, a big data analysis system is built for unstructured and structured data association analysis based on deep learning, spatiotemporal correlation, and big data technology to improve data value and adapt to multiscenario applications. Meanwhile, with the help of data and graphics processing tool Tableau, the present work analyzes the development status and existing problems of digital resources in China. The results show that although China’s digital infrastructure is the top in the world, the trading infrastructure is still only 41.65 percentage points. This shows that China’s digital economy still has a lot of room for growth in distribution and trading. The analysis of the ownership of data resources indicates that the scores of China’s digital economy in accounting, privacy, and security are very low, only 2.4 points, 5.1 points, and 11 points, respectively. This study has solved the problems of distribution and trade in China’s digital economy through research and put forward corresponding suggestions for the current development of China’s digital economy market. Hence, a preliminary summary and suggestions are made on the development of China’s data resources, to promote the open sharing of data, strengthen the management of data quality, activate the data resource market, strengthen data security, and enhance the vitality of the market economy.
Most existing person re-identification algorithms either extract robust visual features or learn discriminative metrics for person images. However, the underlying manifold which those images reside ...on is rarely investigated. That arises a problem that the learned metric is not smooth with respect to the local geometry structure of the data manifold. In this paper, we study person re-identification with manifold-based affinity learning, which did not receive enough attention from this area. An unconventional manifold-preserving algorithm is proposed, which can 1) make best use of supervision from training data, whose label information is given as pairwise constraints, 2) scale up to large repositories with low on-line time complexity, and 3) be plunged into most existing algorithms, serving as a generic postprocessing procedure to further boost the identification accuracies. Extensive experimental results on five popular person re-identification benchmarks consistently demonstrate the effectiveness of our method. Especially, on the largest CUHK03 and Market-1501, our method outperforms the state-of-the-art alternatives by a large margin with high efficiency, which is more appropriate for practical applications.
The non-local module works as a particularly useful technique for semantic segmentation while criticized for its prohibitive computation and GPU memory occupation. In this paper, we present ...Asymmetric Non-local Neural Network to semantic segmentation, which has two prominent components: Asymmetric Pyramid Non-local Block (APNB) and Asymmetric Fusion Non-local Block (AFNB). APNB leverages a pyramid sampling module into the non-local block to largely reduce the computation and memory consumption without sacrificing the performance. AFNB is adapted from APNB to fuse the features of different levels under a sufficient consideration of long range dependencies and thus considerably improves the performance. Extensive experiments on semantic segmentation benchmarks demonstrate the effectiveness and efficiency of our work. In particular, we report the state-of-the-art performance of 81.3 mIoU on the Cityscapes test set. For a 256x128 input, APNB is around 6 times faster than a non-local block on GPU while 28 times smaller in GPU running memory occupation. Code is available at: https://github.com/MendelXu/ANN.git.
Surface charge state plays an important role in tuning the catalytic performance of nanocrystals in various reactions. Herein, we report a synthetic approach to unique Pt–Pd–graphene stack structures ...with controllable Pt shell thickness. These unique hybrid structures allow us to correlate the Pt thickness with performance in the hydrogen‐evolution reaction (HER). The HER activity increases with a decrease in the Pt thickness, which is well explained by surface polarization mechanism as suggested by first‐principles simulations. In this hybrid system, the difference in work functions of Pt and Pd results in surface polarization on the Pt surface, tuning its charge state for hydrogen reduction. Meanwhile, the supporting graphene provides two‐dimensional channels for efficient charge transport, improving the HER activities. This work opens up possibilities of reducing Pt usage while achieving high HER performance.
Less is more: Pt–Pd–graphene stack structures (see picture) are prepared by a new method that allows control of the thickness of the Pt shell. This thickness correlates with performance in the hydrogen evolution reaction (HER). As a result of surface polarization, the HER activity actually increases with decreasing Pt thickness, opening possibilities of using less Pt.
Due to their unique properties, polymers - typically thermal insulators - can open up opportunities for advanced thermal management when they are transformed into thermal conductors. Recent studies ...have shown polymers can achieve high thermal conductivity, but the transport mechanisms have yet to be elucidated. Here we report polyethylene films with a high thermal conductivity of 62 Wm
K
, over two orders-of-magnitude greater than that of typical polymers (~0.1 Wm
K
) and exceeding that of many metals and ceramics. Structural studies and thermal modeling reveal that the film consists of nanofibers with crystalline and amorphous regions, and the amorphous region has a remarkably high thermal conductivity, over ~16 Wm
K
. This work lays the foundation for rational design and synthesis of thermally conductive polymers for thermal management, particularly when flexible, lightweight, chemically inert, and electrically insulating thermal conductors are required.
Ammonia (NH
3
) is considered a promising alternative fuel, capable of producing energy with zero CO
2
emissions. Its combustion, however, poses a series of challenges due to the low reactivity of NH
...3
and the formation of very high quantities of NO
x
. This work numerically investigates the combustion and emission characteristics of ammonia in three modern stationary gas turbine concepts, namely (a) lean-burn dry-low emissions (DLE); (b) rich-burn, quick-quench and lean-burn (RQL); and (c) moderate or intense low oxygen dilution (MILD), under operating conditions typical of commercial gas turbines (inlet temperatures of 500 K and pressure of 20 bar). Numerical simulations employing detailed chemical kinetic mechanisms are carried out to study the propagation speed of ammonia, the combustor temperatures, and the emissions of NO
x
and NH
3
. The simulations are first validated against literature NO
x
data and then the most accurate mechanism is selected. The performance of the different gas turbine engine concepts is subsequently compared based on the results from the selected mechanism. The results show that the lowest emissions are achieved with the RQL and MILD combustion concepts, while the DLE combustion concept only presents acceptable emission values under conditions deemed unstable, where the laminar flame speeds are below 3 cm/s.
•Robust topological design under bounded loads and material uncertainty is studied.•A new ellipsoid convex model for bounded random loads is proposed.•Loads are assumed uniformly distributed ...according to the maximum entropy theorem.•Spatially correlated material uncertainty is modeled as a random field by EOLE.•Polynomial chaos expansion with sparse grid rule is used in uncertainty analysis.
Design optimization considering uncertainties arising from different sources has been one of the hotspots in the area of structural optimization. This paper presents a robust topology optimization method for structures with bounded loads and spatially correlated material uncertainties. We first develop a new model for random structural loads with bounded nature, in which the uncertain load components are assumed to be uniformly distributed within a convex set represented by an ellipsoid model. This model is then combined with the random field discretized by means of the expansion optimized linear estimation to provide uncertainty modelling of structures exhibiting both load and material uncertainties. The robust topological design problem is formulated as a bi-criteria optimization problem for minimizing the mean value and standard deviation of the structural compliance under these uncertainties. The polynomial chaos expansion, in conjunction with the sparse grid quadrature rule, is employed for calculating the statistical moments of the structural responses. The adjoint sensitivity analysis scheme is derived, and a gradient based optimizer is employed for updating the design variables. Numerical examples show that the structural designs obtained by the proposed robust topology optimization method exhibit higher robustness against uncertainties as compared with their deterministic counterpart.
Structural changes at the active site of an enzyme induced by binding to a substrate molecule can result in enhanced activity in biological systems. Herein, we report that the new hybrid ...ultramicroporous material sql‐SIFSIX‐bpe‐Zn exhibits an induced fit binding mechanism when exposed to acetylene, C2H2. The resulting phase change affords exceptionally strong C2H2 binding that in turn enables highly selective C2H2/C2H4 and C2H2/CO2 separation demonstrated by dynamic breakthrough experiments. sql‐SIFSIX‐bpe‐Zn was observed to exhibit at least four phases: as‐synthesised (α); activated (β); and C2H2 induced phases (β′ and γ). sql‐SIFSIX‐bpe‐Zn‐β exhibited strong affinity for C2H2 at ambient conditions as demonstrated by benchmark isosteric heat of adsorption (Qst) of 67.5 kJ mol−1 validated through in situ pressure gradient differential scanning calorimetry (PG‐DSC). Further, in situ characterisation and DFT calculations provide insight into the mechanism of the C2H2 induced fit transformation, binding positions and the nature of host‐guest and guest‐guest interactions.
We introduce the new flexible physisorbent sql‐SIFSIX‐bpe‐Zn and reveal that it exhibits an induced fit structural change that results in extraordinary affinity for C2H2 that in turn enables high selectivity for separation of C2H2 over C2H4 and CO2.
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