The exploitation of a low-cost catalyst is desirable for hydrogen generation from electrolysis or photoelectrolysis. In this study we have demonstrated that nickel phosphide (Ni12P5) nanoparticles have efficient and stable catalytic activity for the hydrogen evolution reaction. The catalytic performance of Ni12P5 nanoparticles is favorably comparable to those of recently reported efficient nonprecious catalysts. The optimal overpotential required for 20 mA/cm(2) current density is 143 ± 3 mV in acidic solution (H2SO4, 0.5 M). The catalytic activity of Ni12P5 is likely to be correlated with the charged natures of Ni and P. Ni12P5 nanoparticles were introduced to silicon nanowires, and the power conversion efficiency of the resulting composite is larger than that of silicon nanowires decorated with platinum particles. This result demonstrates the promising application potential of metal phosphide in photoelectrochemical hydrogen generation.

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Cobalt phosphide (Co 2 P) nanorods are found to exhibit efficient catalytic activity in hydrogen evolution reaction (HER), with the overpotential required for the current density of 20 mA/cm 2 as small as 167 mV in acidic solution and 171 mV in basic solution. In addition, the Co 2 P nanorods can work stably in both acidic and basic solution during hydrogen production. This performance can be favorably comparable to typical high efficiently non-precious catalysts, and suggest the promising application potential of the Co 2 P nanorods in the field of hydrogen production. The HER process follows a Volmer-Heyrovsky mechanism, and the rates of the discharge step and desorption step appear to be comparable during the HER process. The similarity of charged natures of Co and P in the Co 2 P nanorods to those of the hydride-acceptor and proton-acceptor in high efficient Ni 2 P catalyst, [NiFe] hydrogenase, and its analogues implies that the HER catalytic activity of Co 2 P nanorods might be correlated with the charged natures of Co and P.

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We propose a densely semantically aligned person reidentification framework. It fundamentally addresses the body misalignment problem caused by pose/viewpoint variations, imperfect person detection, occlusion, etc. By leveraging the estimation of the dense semantics of a person image, we construct a set of densely semantically aligned part images (DSAP-images), where the same spatial positions have the same semantics across different images. We design a two-stream network that consists of a main full image stream (MF-Stream) and a densely semantically-aligned guiding stream (DSAG-Stream). The DSAG-Stream, with the DSAP-images as input, acts as a regulator to guide the MF-Stream to learn densely semantically aligned features from the original image. In the inference, the DSAG-Stream is discarded and only the MF-Stream is needed, which makes the inference system computationally efficient and robust. To the best of our knowledge, we are the first to make use of fine grained semantics to address the misalignment problems for re-ID. Our method achieves rank-1 accuracy of 78.9% (new protocol) on the CUHK03 dataset, 90.4% on the CUHK01 dataset, and 95.7% on the Mar-ket1501 dataset, outperforming state-of-the-art methods.

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Existing fully-supervised person re-identification (ReID) methods usually suffer from poor generalization capability caused by domain gaps. The key to solving this problem lies in filtering out identity-irrelevant interference and learning domain-invariant person representations. In this paper, we aim to design a generalizable person ReID framework which trains a model on source domains yet is able to generalize/perform well on target domains. To achieve this goal, we propose a simple yet effective Style Normalization and Restitution (SNR) module. Specifically, we filter out style variations (e.g., illumination, color contrast) by Instance Normalization (IN). However, such a process inevitably removes discriminative information. We propose to distill identity-relevant feature from the removed information and restitute it to the network to ensure high discrimination. For better disentanglement, we enforce a dual causality loss constraint in SNR to encourage the separation of identity-relevant features and identity-irrelevant features. Extensive experiments demonstrate the strong generalization capability of our framework. Our models empowered by the SNR modules significantly outperform the state-of-the-art domain generalization approaches on multiple widely-used person ReID benchmarks, and also show superiority on unsupervised domain adaptation.

Image-to-image translation tasks have been widely investigated with Generative Adversarial Networks (GANs) and dual learning. However, existing models lack the ability to control the translated results in the target domain and their results usually lack of diversity in the sense that a fixed image usually leads to (almost) deterministic translation result. In this paper, we study a new problem, conditional image-to-image translation, which is to translate an image from the source domain to the target domain conditioned on a given image in the target domain. It requires that the generated image should inherit some domain-specific features of the conditional image from the target domain. Therefore, changing the conditional image in the target domain will lead to diverse translation results for a fixed input image from the source domain, and therefore the conditional input image helps to control the translation results. We tackle this problem with unpaired data based on GANs and dual learning. We twist two conditional translation models (one translation from A domain to B domain, and the other one from B domain to A domain) together for inputs combination and reconstruction while preserving domain independent features. We carry out experiments on men's faces from-to women's faces translation and edges to shoes&bags translations. The results demonstrate the effectiveness of our proposed method.