Cross-modal hashing has sparked much attention in large-scale information retrieval for its storage and query efficiency. Despite the great success achieved by supervised approaches, existing ...unsupervised hashing methods still suffer from the lack of reliable learning guidance and cross-modal discrepancy. In this paper, we propose Aggregation-based Graph Convolutional Hashing (AGCH) to tackle these obstacles. First, considering that a single similarity metric can hardly represent data relationships comprehensively, we develop an efficient aggregation strategy that utilises multiple metrics to construct a more precise affinity matrix for learning. Specifically, we apply various similarity measures to exploit the structural information of multiple modalities from different perspectives and then aggregate the obtained information to produce a joint similarity matrix. Furthermore, a novel deep model is designed to learn unified binary codes across different modalities, where the key components include modality-specific encoders, Graph Convolutional Networks (GCNs) and a fusion module. The modality-specific encoders are tasked to learn feature embeddings for each individual modality. On this basis, we leverage GCNs to further excavate the semantic structure of data, along with a fusion module to correlate different modalities. Extensive experiments on three real-world datasets demonstrate that the proposed method significantly outperforms the state-of-the-art competitors.
Covalent organic frameworks (COFs) are crystalline porous materials bearing microporous or mesoporous pores. The type and size of pores play crucial roles in regulating the properties of COFs. In ...this work, a novel COF, which bears two different kinds of ordered pores with controllable sizes: one within microporous range (7.1 Å) and the other in mesoporous range (26.9 Å), has been constructed via one-step synthesis. The structure of the dual-pore COF was confirmed by PXRD investigation, nitrogen adsorption–desorption study, and theoretical calculations.
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We herein report the construction of a new heteropore COF which consists of two different kinds of micropores with unprecedented shapes. It exists as hollow microspheres and exhibits an extremely ...high volatile iodine uptake (up to 481 wt%) by encapsulating iodine in the inner cavities and porous shells of the microspheres.
In this paper, we present a novel supervised cross-modal hashing framework, namely Scalable disCRete mATrix faCtorization Hashing (SCRATCH). First, it utilizes collective matrix factorization on ...original features together with label semantic embedding, to learn the latent representations in a shared latent space. Thereafter, it generates binary hash codes based on the latent representations. During optimization, it avoids using a large <inline-formula> <tex-math notation="LaTeX">n\times n </tex-math></inline-formula> similarity matrix and generates hash codes discretely. Besides, based on different objective functions, learning strategy, and features, we further present three models in this framework, i.e., SCRATCH-o, SCRATCH-t, and SCRATCH-d. The first one is a one-step method, learning the hash functions and the binary codes in the same optimization problem. The second is a two-step method, which first generates the binary codes and then learns the hash functions based on the learned hash codes. The third one is a deep version of SCRATCH-t, which utilizes deep neural networks as hash functions. The extensive experiments on two widely used benchmark datasets demonstrate that SCRATCH-o and SCRATCH-t outperform some state-of-the-art shallow hashing methods for cross-modal retrieval. The SCRATCH-d also outperforms some state-of-the-art deep hashing models.
It is very important to create novel topologies and improve structural complexity for covalent organic frameworks (COFs) that might lead to unprecedented properties and applications. Despite the ...progress achieved over the past decade, the structural diversity and complexity of COFs are quite limited. In this Communication, we report the construction of COFs bearing three different kinds of pores through the heterostructural mixed linker strategy involving the condensation of a D 2h -symmetric tetraamine and two C 2-symmetric dialdehydes of different lengths. The complicated structures of the triple-pore COFs have been confirmed by powder X-ray diffraction and pore size distribution analyses.
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Constructing two-dimensional (2D) polymers with complex tessellation patterns via synthetic chemistry makes a significant contribution not only to the understanding of the emergence of complex ...hierarchical systems in living organisms, but also to the fabrication of advanced hierarchical materials. However, to achieve such tasks is a great challenge. In this communication we report a facile and general approach to tessellate 2D covalent organic frameworks (COFs) by three or four geometric shapes/sizes, which affords 2D COFs bearing three or four different kinds of pores and increases structural complexity in tessellations of 2D polymers to a much higher level. The complex tessellation patterns of the COFs are elucidated by powder X-ray diffraction studies, theoretical simulations and high-resolution TEM.
Two fluorescent covalent organic frameworks (COFs) which bear two kinds of pores with different sizes and shapes have been synthesized. The heteropore COFs exhibit spectroscopic and color changes to ...2,4,6-trinitrophenol (TNP) with extremely high selectivity and sensitivity, which makes them excellent macroscopic chemosensors for the selective detection of TNP.
Covalent organic frameworks (COFs) are an emerging class of crystalline porous organic polymers with potential for innovative applications. Here we report the use of COFs as precursors for the ...fabrication of well-defined tubular nanomaterials. A proof-of-concept study is presented for the controllable fabrication of organic nanotubes through selective disassembly of two-dimensional heteropore COFs. Two dual-pore COFs are constructed based on orthogonal reactions. Each COF possesses two different kinds of pores, which are formed by linking all-hydrzaone-bonded nanopores with boroxines. Selectively hydrolyzing boroxine rings in the COFs while keeping hydrazone linkages untouched gives rise to organic nanotubes with diameters and shapes corresponding to the nanochannels of the COFs.
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In modern society, clothing matching plays a pivotal role in people's daily life, as suitable outfits can beautify their appearance directly. Nevertheless, how to make a suitable outfit has become a ...daily headache for many people, especially those who do not have much sense of aesthetics. In the light of this, many research efforts have been dedicated to the task of complementary clothing matching and have achieved great success relying on the advanced data-driven neural networks. However, most existing methods overlook the rich valuable knowledge accumulated by our human beings in the fashion domain, especially the rules regarding clothing matching, like "coats go with dresses" and "silk tops cannot go with chiffon bottoms". Towards this end, in this work, we propose a knowledge-guided neural compatibility modeling scheme, which is able to incorporate the rich fashion domain knowledge to enhance the performance of the compatibility modeling in the context of clothing matching. To better integrate the huge and implicit fashion domain knowledge into the data-driven neural networks, we present a probabilistic knowledge distillation (PKD) method, which is able to encode vast knowledge rules in a probabilistic manner. Extensive experiments on two real-world datasets have verified the guidance of rules from different sources and demonstrated the effectiveness and portability of our model. As a byproduct, we released the codes and involved parameters to benefit the research community.
Diabetic nephropathy (DN) is the leading cause of end-stage renal disease (ESRD), and renal tubular cell dysfunction contributes to the pathogenesis of DN. Soluble epoxide hydrolase (sEH) is an ...enzyme that can hydrolyze epoxyeicosatrienoic acids (EETs) and other epoxy fatty acids (EpFAs) into the less biologically active metabolites. Inhibition of sEH has multiple beneficial effects on renal function, however, the exact role of sEH in hyperglycemia-induced dysfunction of tubular cells is still not fully elucidated. In the present study, we showed that human proximal tubular epithelial (HK-2) cells revealed an upregulation of sEH expression accompanied by the impairment of autophagic flux, mitochondrial dysfunction, ubiquitinated protein accumulation and enhanced endoplasmic reticulum (ER) stress after high glucose (HG) treatment. Furthermore, dysfunctional mitochondria accumulated in the cytoplasm, which resulted in excessive reactive oxygen species (ROS) generation, Bax translocation, cytochrome c release, and apoptosis. However, t-AUCB, an inhibitor of sEH, partially reversed these negative outcomes. Moreover, we also observed increased sEH expression, impaired autophagy flux, mitochondrial dysfunction and enhanced ER stress in the renal proximal tubular cells of db/db diabetic mice. Notably, inhibition of sEH by treatment with t-AUCB attenuated renal injury and partially restored autophagic flux, improved mitochondrial function, and reduced ROS generation and ER stress in the kidneys of db/db mice. Taken together, these results suggest that inhibition of sEH by t-AUCB plays a protective role in hyperglycemia-induced proximal tubular injury and that the potential mechanism of t-AUCB-mediated protective autophagy is involved in modulating mitochondrial function and ER stress. Thus, we provide new evidence linking sEH to the autophagic response during proximal tubular injury in the pathogenesis of DN and suggest that inhibition of sEH can be considered a potential therapeutic strategy for the amelioration of DN.
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