N′-[(E)-2-Chloro-5-nitro­benzyl­idene]-2-nitro­benzohydrazide

In the title compound, C14H9ClN4O5, the mol­ecule exists in a trans geometry with respect to the methyl­idene unit. The dihedral angle between the two substituted benzene rings is 62.7 (2)°. In the crystal, inversion dimers linked by pairs of N—H⋯O hydrogen bonds generate R 2 2(8) loops

Learning Discriminative Representations for Skeleton Based Action Recognition

Human action recognition aims at classifying the category of human action from a segment of a video. Recently, people have dived into designing GCN-based models to extract features from skeletons for performing this task, because skeleton representations are much more efficient and robust than other modalities such as RGB frames. However, when employing the skeleton data, some important clues like related items are also discarded. It results in some ambiguous actions that are hard to be distinguished and tend to be misclassified. To alleviate this problem, we propose an auxiliary feature refinement head (FR Head), which consists of spatial-temporal decoupling and contrastive feature refinement, to obtain discriminative representations of skeletons. Ambiguous samples are dynamically discovered and calibrated in the feature space. Furthermore, FR Head could be imposed on different stages of GCNs to build a multi-level refinement for stronger supervision. Extensive experiments are conducted on NTU RGB+D, NTU RGB+D 120, and NW-UCLA datasets. Our proposed models obtain competitive results from state-of-the-art methods and can help to discriminate those ambiguous samples. Codes are available at https://github.com/zhysora/FR-Head.Comment: Accepted by CVPR2023. 10 pages, 5 figures, 5 table

Improved Rates for Differentially Private Stochastic Convex Optimization with Heavy-Tailed Data

We study stochastic convex optimization with heavy-tailed data under the constraint of differential privacy (DP). Most prior work on this problem is restricted to the case where the loss function is Lipschitz. Instead, as introduced by Wang, Xiao, Devadas, and Xu \cite{WangXDX20}, we study general convex loss functions with the assumption that the distribution of gradients has bounded $k$-th moments. We provide improved upper bounds on the excess population risk under concentrated DP for convex and strongly convex loss functions. Along the way, we derive new algorithms for private mean estimation of heavy-tailed distributions, under both pure and concentrated DP. Finally, we prove nearly-matching lower bounds for private stochastic convex optimization with strongly convex losses and mean estimation, showing new separations between pure and concentrated DP

(E)-N′-[4-(Dimethyl­amino)­benzyl­idene]-4-hy­droxy­benzohydrazide hemihydrate

In the title compound, C16H17N3O2·0.5H2O, the two hydrazide mol­ecules are approximately planar: the dihedral angles between the two substituted benzene rings are 7.7 (2) and 4.2 (2)°. Both hydrazone mol­ecules exist in a trans geometry with respect to their methyl­idene units. In the crystal, the water mol­ecule lies between the two organic mol­ecules and makes bifurcated O—H⋯(N,O) hydrogen bonds to both of them. The hydrazide mol­ecules form N—H⋯O and O—H⋯O hydrogen bonds, resulting in a three-dimensional network

(E)-N′-[4-(Dimethyl­amino)­benzyl­idene]-4-methyl­benzohydrazide methanol monosolvate

In the title compound, C17H19N3O·CH3OH, the hydrazone mol­ecule exists in a trans geometry with respect to the methyl­idene unit and the dihedral angle between the two substituted benzene rings is 42.6 (2)°. In the crystal, the components are linked through N—H⋯O and O—H⋯O hydrogen bonds, forming [100] chains of alternating hydrazone and methanol mol­ecules

Reliable Collaborative Filtering on Spatio-Temporal Privacy Data

Lots of multilayer information, such as the spatio-temporal privacy check-in data, is accumulated in the location-based social network (LBSN). When using the collaborative filtering algorithm for LBSN location recommendation, one of the core issues is how to improve recommendation performance by combining the traditional algorithm with the multilayer information. The existing approaches of collaborative filtering use only the sparse user-item rating matrix. It entails high computational complexity and inaccurate results. A novel collaborative filtering-based location recommendation algorithm called LGP-CF, which takes spatio-temporal privacy information into account, is proposed in this paper. By mining the users check-in behavior pattern, the dataset is segmented semantically to reduce the data size that needs to be computed. Then the clustering algorithm is used to obtain and narrow the set of similar users. User-location bipartite graph is modeled using the filtered similar user set. Then LGP-CF can quickly locate the location and trajectory of users through message propagation and aggregation over the graph. Through calculating users similarity by spatio-temporal privacy data on the graph, we can finally calculate the rating of recommendable locations. Experiments results on the physical clusters indicate that compared with the existing algorithms, the proposed LGP-CF algorithm can make recommendations more accurately

Dichlorido{[2-(diphenyl­phosphino)phenyl­imino­meth­yl]ferrocene-κ2 N,P}palladium(II) dichloro­methane hemi­solvate

There are two independent PdII complex mol­ecules in the asymmetric unit of the title compound, [PdCl2{Fe(C5H5)(C24H19NP)}]·0.5CH2Cl2. One ferrocenyl ring of one complex mol­ecule is disordered over two sites with half-occupancy for each component. Both PdII cations adopt a distorted square-planar coordination geometry with a bidentate [2-(diphenyl­phosphino)phenyl­imino­meth­yl]ferrocene ligand and two chloride anions