Sun Yat-sen University

About: Sun Yat-sen University is a education organization based out in Guangzhou, Guangdong, China. It is known for research contribution in the topics: Population & Cancer. The organization has 115149 authors who have published 113763 publications receiving 2286465 citations. The organization is also known as: Zhongshan University & SYSU.

Single-crystal X-ray diffraction studies on structural transformations of porous coordination polymers

Abstract: X-Ray single-crystal diffraction has been the most straightforward and important technique in structural determination of crystalline materials for understanding their structure–property relationships. This powerful tool can be used to directly visualize the precise and detailed structural information of porous coordination polymers or metal–organic frameworks at different states, which are unique for their flexible host frameworks compared with conventional adsorbents. With a series of selected recent examples, this review gives a brief overview of single-crystal X-ray diffraction studies and single-crystal to single-crystal transformations of porous coordination polymers under various chemical and physical stimuli such as solvent and gas sorption/desorption/exchange, chemical reaction and temperature change.

Iridium single-atom catalyst on nitrogen-doped carbon for formic acid oxidation synthesized using a general host-guest strategy.

Abstract: Single-atom catalysts not only maximize metal atom efficiency, they also display properties that are considerably different to their more conventional nanoparticle equivalents, making them a promising family of materials to investigate. Herein we developed a general host–guest strategy to fabricate various metal single-atom catalysts on nitrogen-doped carbon (M1/CN, M = Pt, Ir, Pd, Ru, Mo, Ga, Cu, Ni, Mn). The iridium variant Ir1/CN electrocatalyses the formic acid oxidation reaction with a mass activity of 12.9 $${{{\rm{A}}\,{\rm{mg}}^{-1}_{{\rm{Ir}}}}}$$ whereas an Ir/C nanoparticle catalyst is almost inert (~4.8 × 10−3 $${{{\rm{A}}\,{\rm{mg}}^{-1}_{{\rm{Ir}}}}}$$). The activity of Ir1/CN is also 16 and 19 times greater than those of Pd/C and Pt/C, respectively. Furthermore, Ir1/CN displays high tolerance to CO poisoning. First-principle density functional theory reveals that the properties of Ir1/CN stem from the spatial isolation of iridium sites and from the modified electronic structure of iridium with respect to a conventional nanoparticle catalyst. Single-atom catalysts maximize metal atom efficiency and exhibit properties that can be considerably different to their nanoparticle equivalent. Now a general host–guest strategy to make various single-atom catalysts on nitrogen-doped carbon has been developed; the iridium variant electrocatalyses the formic acid oxidation reaction with high mass activity and displays high tolerance to CO poisoning.

RGB-Infrared Cross-Modality Person Re-identification

Abstract: Person re-identification (Re-ID) is an important problem in video surveillance, aiming to match pedestrian images across camera views. Currently, most works focus on RGB-based Re-ID. However, in some applications, RGB images are not suitable, e.g. in a dark environment or at night. Infrared (IR) imaging becomes necessary in many visual systems. To that end, matching RGB images with infrared images is required, which are heterogeneous with very different visual characteristics. For person Re-ID, this is a very challenging cross-modality problem that has not been studied so far. In this work, we address the RGB-IR cross-modality Re-ID problem and contribute a new multiple modality Re-ID dataset named SYSU-MM01, including RGB and IR images of 491 identities from 6 cameras, giving in total 287,628 RGB images and 15,792 IR images. To explore the RGB-IR Re-ID problem, we evaluate existing popular cross-domain models, including three commonly used neural network structures (one-stream, two-stream and asymmetric FC layer) and analyse the relation between them. We further propose deep zero-padding for training one-stream network towards automatically evolving domain-specific nodes in the network for cross-modality matching. Our experiments show that RGB-IR cross-modality matching is very challenging but still feasible using the proposed model with deep zero-padding, giving the best performance. Our dataset is available at http:// isee.sysu.edu.cn/project/RGBIRReID.htm.

Anomaly Detection in Hyperspectral Images Based on Low-Rank and Sparse Representation

Abstract: A novel method for anomaly detection in hyperspectral images (HSIs) is proposed based on low-rank and sparse representation. The proposed method is based on the separation of the background and the anomalies in the observed data. Since each pixel in the background can be approximately represented by a background dictionary and the representation coefficients of all pixels form a low-rank matrix, a low-rank representation is used to model the background part. To better characterize each pixel's local representation, a sparsity-inducing regularization term is added to the representation coefficients. Moreover, a dictionary construction strategy is adopted to make the dictionary more stable and discriminative. Then, the anomalies are determined by the response of the residual matrix. An important advantage of the proposed algorithm is that it combines the global and local structure in the HSI. Experimental results have been conducted using both simulated and real data sets. These experiments indicate that our algorithm achieves very promising anomaly detection performance.