Observation of In-Gap States in a Two-Dimensional CrI2/NbSe2 Heterostructure.

Low-dimensional magnetic structures coupled with superconductors are promising platforms for realizing Majorana zero modes, which have potential applications in topological quantum computing. Here, we report a two-dimensional (2D) magnetic-superconducting heterostructure consisting of single-layer chromium diiodide (CrI2) on a niobium diselenide (NbSe2) superconductor. Single-layer CrI2 nanosheets, which hold antiferromagnetic (AFM) ground states by our first-principles calculations, were epitaxially grown on the layered NbSe2 substrate. Using scanning tunneling microscopy/spectroscopy, we observed robust in-gap states spatially located at the edge of the nanosheets and defect-induced zero-energy peaks inside the CrI2 nanosheets. Magnetic-flux vortices induced by an external field exhibit broken 3-fold rotational symmetry of the pristine NbSe2 superconductor, implying the efficient modulation of the interfacial superconducting states by the epitaxial CrI2 layer. A phenomenological model suggests the existence of chiral edge states in a 2D AFM-superconducting hybrid system with an even Chern number, providing a qualitatively plausible understanding for our experimental observation.

Orbital-dependent electron correlation in double-layer nickelate La3Ni2O7.

The latest discovery of high temperature superconductivity near 80 K in La3Ni2O7 under high pressure has attracted much attention. Many proposals are put forth to understand the origin of superconductivity. The determination of electronic structures is a prerequisite to establish theories to understand superconductivity in nickelates but is still lacking. Here we report our direct measurement of the electronic structures of La3Ni2O7 by high-resolution angle-resolved photoemission spectroscopy. The Fermi surface and band structures of La3Ni2O7 are observed and compared with the band structure calculations. Strong electron correlations are revealed which are orbital- and momentum-dependent. A flat band is formed from the Ni-3d z 2 orbitals around the zone corner which is ~ 50 meV below the Fermi level and exhibits the strongest electron correlation. In many theoretical proposals, this band is expected to play the dominant role in generating superconductivity in La3Ni2O7. Our observations provide key experimental information to understand the electronic structure and origin of high temperature superconductivity in La3Ni2O7.

Channel Augmentation for Visible-Infrared Re-Identification.

This paper introduces a simple yet powerful channel augmentation for visible-infrared re-identification. Most existing augmentation operations designed for single-modality visible images do not fully consider the imagery properties in visible to infrared matching. Our basic idea is to homogeneously generate color-irrelevant images by randomly exchanging the color channels. It can be seamlessly integrated into existing augmentation operations, consistently improving the robustness against color variations. For cross-modality metric learning, we design an enhanced channel-mixed learning strategy to simultaneously handle the intra- and cross-modality variations with squared difference for stronger discriminability. Besides, a weak-and-strong augmentation joint learning strategy is further developed to explicitly optimize the outputs of augmented images, which mutually integrates the channel augmented images (strong) and the general augmentation operations (weak) with consistency regularization. Furthermore, by conducting the label association between the channel augmented images and infrared modalities with modality-specific clustering, a simple yet effective unsupervised learning baseline is designed, which significantly outperforms existing unsupervised single-modality solutions. Extensive experiments with insightful analysis on two visible-infrared recognition tasks show that the proposed strategies consistently improve the accuracy. Without auxiliary information, the Rank-1/mAP achieves 71.48%/68.15% on the large-scale SYSU-MM01 dataset.

SketchTrans: Disentangled Prototype Learning With Transformer for Sketch-Photo Recognition.

Matching hand-drawn sketches with photos (a.k.a sketch-photo recognition or re-identification) faces the information asymmetry challenge due to the abstract nature of the sketch modality. Existing works tend to learn shared embedding spaces with CNN models by discarding the appearance cues for photo images or introducing GAN for sketch-photo synthesis. The former unavoidably loses discriminability, while the latter contains ineffaceable generation noise. In this paper, we start the first attempt to design an information-aligned sketch transformer (SketchTrans +) via cross-modal disentangled prototype learning, while the transformer has shown great promise for discriminative visual modelling. Specifically, we design an asymmetric disentanglement scheme with a dynamic updatable auxiliary sketch (A-sketch) to align the modality representations without sacrificing information. The asymmetric disentanglement decomposes the photo representations into sketch-relevant and sketch-irrelevant cues, transferring sketch-irrelevant knowledge into the sketch modality to compensate for the missing information. Moreover, considering the feature discrepancy between the two modalities, we present a modality-aware prototype contrastive learning method that mines representative modality-sharing information using the modality-aware prototypes rather than the original feature representations. Extensive experiments on category- and instance-level sketch-based datasets validate the superiority of our proposed method under various metrics.

Structure-Aware Positional Transformer for Visible-Infrared Person Re-Identification.

Visible-infrared person re-identification (VI-ReID) is a cross-modality retrieval problem, which aims at matching the same pedestrian between the visible and infrared cameras. Due to the existence of pose variation, occlusion, and huge visual differences between the two modalities, previous studies mainly focus on learning image-level shared features. Since they usually learn a global representation or extract uniformly divided part features, these methods are sensitive to misalignments. In this paper, we propose a structure-aware positional transformer (SPOT) network to learn semantic-aware sharable modality features by utilizing the structural and positional information. It consists of two main components: attended structure representation (ASR) and transformer-based part interaction (TPI). Specifically, ASR models the modality-invariant structure feature for each modality and dynamically selects the discriminative appearance regions under the guidance of the structure information. TPI mines the part-level appearance and position relations with a transformer to learn discriminative part-level modality features. With a weighted combination of ASR and TPI, the proposed SPOT explores the rich contextual and structural information, effectively reducing cross-modality difference and enhancing the robustness against misalignments. Extensive experiments indicate that SPOT is superior to the state-of-the-art methods on two cross-modal datasets. Notably, the Rank-1/mAP value on the SYSU-MM01 dataset has improved by 8.43%/6.80%.

Essential factors of an integrated moving bed biofilm reactor-membrane bioreactor: Adhesion characteristics and microbial community of the biofilm.

This work aims at revealing the adhesion characteristics and microbial community of the biofilm in an integrated moving bed biofilm reactor-membrane bioreactor, and further evaluating their variations over time. With multiple methods, the adhesion characteristics and microbial community of the biofilm on the carriers were comprehensively illuminated, which showed their dynamic variation along with the operational time. Results indicated that: (1) the roughness of biofilm on the carriers increased very quickly to a maximum value at the start-up stage, then, decreased to become a flat curve, which indicated a layer of smooth biofilm formed on the surface; (2) the tightly-bound protein and polysaccharide was the most important factor influencing the stability of biofilm; (3) the development of biofilm could be divided into three stages, and Gammaproteobacteria were the most dominant microbial species in class level at the last stage, which occupied the largest ratio (51.48%) among all microbes.

A new insight into resource recovery of excess sewage sludge: feasibility of extracting mixed amino acids as an environment-friendly corrosion inhibitor for industrial pickling.

The work mainly presented a laboratory-scale investigation on an effective process to extract a value-added product from municipal excess sludge. The functional groups in the hydrolysate were characterized with Fourier transform infrared spectrum, and the contained amino acids were measured by means of an automatic amino acid analyzer. The corrosion-inhibition characteristics of the hydrolysate were determined with weight-loss measurement, electrochemical polarization and scanning electron microscopy. Results indicated that the hydrolysate contained 15 kinds of amino acid, and their adsorption on the surface could effectively inhibit the corrosion reaction of the steel from the acid medium. Polarization curves indicated that the obtained hydrolysate was a mixed-type inhibitor, but mainly restricted metal dissolution on the anode. The adsorption accorded well with the Langmuir adsorption isotherm, involved an increase in entropy, and was a spontaneous, exothermic process.

Biodiversity and succession of microbial community in a multi-habitat membrane bioreactor.

The present study focused on establishing a multi-habitat membrane bioreactor, as well as exploring its biodiversity and succession of microbial communities. In a long-term operational period (100 days), the dissolved oxygen level of a local zone within the bioreactor decreased consistently from the original oxic state to the final anaerobic state, which led to a continuous succession of the microbial community in the bioreactor. The results revealed that the biodiversity of the microbial community in different zones simultaneously increased, with a similar microbial composition in their final successional stage. The results also indicated that the dominant species during the whole operation were distributed among 6 major phyla. At the initial operational stages, the dominant species in the anoxic-anaerobic and the oxic zones exhibited distinguished difference, whereas at the final operational stage, both zones presented nearly the same dominant microbial species and a rather similar structure in their microbial communities.