N-Benzoyl-N′-(2-chloro-3-pyrid­yl)thio­urea

The title compound, C13H10ClN3OS, was prepared by the reaction of 3-amino-2-chloropyridine with benzoyl isothio­cyanate at room temperature. The thio­urea group makes dihedral angles of 47.17 (5) and 51.88 (4)°, respectively, with the benzene and pyridyl rings, while the angle between the benzene and pyridine rings is 8.91 (3)°. Inter­molecular hydrogen-bond inter­actions link neighbouring mol­ecules into an infinite supra­molecular structure

Bis{(E)-2-[(2-chloro-3-pyrid­yl)imino­meth­yl]-6-meth­oxy­phenolato-κ2 N,O 1}copper(II)

In the title mononuclear copper(II) complex, [Cu(C13H10ClN2O2)2], the CuII ion, lying on an inversion center, is four-coordinated in a trans-CuN2O2 square-planar geometry by two phenolate O and two imino N atoms from two symmetry-related N,O-bidentate Schiff base ligands. The shortest Cu⋯Cu distance is 7.5743 (9) Å. However, there are weak intra­molecular electrostatic inter­actions between the Cu atom and the Cl atom of the ligand, with a Cu⋯Cl distance of 3.3845 (9) Å

DeepCluE: Enhanced Image Clustering via Multi-layer Ensembles in Deep Neural Networks

Deep clustering has recently emerged as a promising technique for complex data clustering. Despite the considerable progress, previous deep clustering works mostly build or learn the final clustering by only utilizing a single layer of representation, e.g., by performing the K-means clustering on the last fully-connected layer or by associating some clustering loss to a specific layer, which neglect the possibilities of jointly leveraging multi-layer representations for enhancing the deep clustering performance. In view of this, this paper presents a Deep Clustering via Ensembles (DeepCluE) approach, which bridges the gap between deep clustering and ensemble clustering by harnessing the power of multiple layers in deep neural networks. In particular, we utilize a weight-sharing convolutional neural network as the backbone, which is trained with both the instance-level contrastive learning (via an instance projector) and the cluster-level contrastive learning (via a cluster projector) in an unsupervised manner. Thereafter, multiple layers of feature representations are extracted from the trained network, upon which the ensemble clustering process is further conducted. Specifically, a set of diversified base clusterings are generated from the multi-layer representations via a highly efficient clusterer. Then the reliability of clusters in multiple base clusterings is automatically estimated by exploiting an entropy-based criterion, based on which the set of base clusterings are re-formulated into a weighted-cluster bipartite graph. By partitioning this bipartite graph via transfer cut, the final consensus clustering can be obtained. Experimental results on six image datasets confirm the advantages of DeepCluE over the state-of-the-art deep clustering approaches.Comment: To appear in IEEE Transactions on Emerging Topics in Computational Intelligenc

4,4′-Dimethyl-1,1′-[ethyl­enedioxy­bis(nitrilo­methyl­idyne)]dibenzene

The Schiff base, C18H20N2O2, which lies about an inversion centre, adopts a linear conformation. The mol­ecules are packed by C—H⋯π inter­actions, forming a two-dimensional supra­molecular network

Bis{(E)-4-chloro-2-[(2-chloro-3-pyrid­yl)imino­methyl-κN]phenolato-κO}copper(II)

In the title complex, [Cu(C12H7Cl2N2O)2], the CuII center is tetra­coordinated by two phenolic O and two azomethine N atoms from two bidentate 4-chloro-2-[(2-chloro-3-pyrid­yl)imino­meth­yl]phenolate (L) ligands. In the crystal structure, the CuII atom has a distorted square-planar coordination environment. The dihedral angles between the benzene and pyridyl rings are 54.39 (3) and 80.14 (4)°, indicating that the pyridine ring has a considerably weaker steric hindrance. The packing of the mol­ecule is controlled by C—H⋯π(Ph) inter­actions and short O⋯Cl inter­actions [3.196 (4) Å], linking the mol­ecules into a chain-like structure along the c axis