Aqua­bis­(benzoato-κO)(1,10-phenanthroline-κ2 N,N′)zinc(II)

The Zn atom in the title compound, [Zn(C7H5O2)2(C12H8N2)(H2O)], is five-coordinate in a distorted trigonal–bipyramidal coordination environment involving two O atoms of two monodentate benzoates, two N atoms of a 1,10-phenanthroline mol­ecule and one O atom of a water mol­ecule. The axial positions are occupied by a carboxyl­ate O atom from the benzoate ligand and an N atom from the 1,10-phenanthroline ligand [N—Zn—O = 146.90 (7)°]. The water mol­ecule forms an intra­molecular O—H⋯O hydrogen bond; an inter­molecular O—H⋯O hydrogen bond gives rise to a dimer

Capture on High Curvature Region: Aggregation of Colloidal Particle Bound to Giant Phospholipid Vesicles

A very recent observation on the membrane mediated attraction and ordered aggregation of colloidal particles bound to giant phospholipid vesicles (I. Koltover, J. O. R\"{a}dler, C. R. Safinya, Phys. Rev. Lett. {\bf 82}, 1991(1999)) is investigated theoretically within the frame of Helfrich curvature elasticity theory of lipid bilayer fluid membrane. Since the concave or waist regions of the vesicle possess the highest local bending energy density, the aggregation of colloidal beads on these places can reduce the elastic energy in maximum. Our calculation shows that a bead in the concave region lowers its energy $\sim 20 k_B T$. For an axisymmetrical dumbbell vesicle, the local curvature energy density along the waist is equally of maximum, the beads can thus be distributed freely with varying separation distance.Comment: 12 pages, 2 figures. REVte

Privacy-Preserving Federated Deep Clustering based on GAN

Federated clustering (FC) is an essential extension of centralized clustering designed for the federated setting, wherein the challenge lies in constructing a global similarity measure without the need to share private data. Conventional approaches to FC typically adopt extensions of centralized methods, like K-means and fuzzy c-means. However, these methods are susceptible to non-independent-and-identically-distributed (non-IID) data among clients, leading to suboptimal performance, particularly with high-dimensional data. In this paper, we present a novel approach to address these limitations by proposing a Privacy-Preserving Federated Deep Clustering based on Generative Adversarial Networks (GANs). Each client trains a local generative adversarial network (GAN) locally and uploads the synthetic data to the server. The server applies a deep clustering network on the synthetic data to establish $k$ cluster centroids, which are then downloaded to the clients for cluster assignment. Theoretical analysis demonstrates that the GAN-generated samples, shared among clients, inherently uphold certain privacy guarantees, safeguarding the confidentiality of individual data. Furthermore, extensive experimental evaluations showcase the effectiveness and utility of our proposed method in achieving accurate and privacy-preserving federated clustering

Federated clustering with GAN-based data synthesis

Federated clustering (FC) is an extension of centralized clustering in federated settings. The key here is how to construct a global similarity measure without sharing private data, since the local similarity may be insufficient to group local data correctly and the similarity of samples across clients cannot be directly measured due to privacy constraints. Obviously, the most straightforward way to analyze FC is to employ the methods extended from centralized ones, such as K-means (KM) and fuzzy c-means (FCM). However, they are vulnerable to non independent-and-identically-distributed (non-IID) data among clients. To handle this, we propose a new federated clustering framework, named synthetic data aided federated clustering (SDA-FC). It trains generative adversarial network locally in each client and uploads the generated synthetic data to the server, where KM or FCM is performed on the synthetic data. The synthetic data can make the model immune to the non-IID problem and enable us to capture the global similarity characteristics more effectively without sharing private data. Comprehensive experiments reveals the advantages of SDA-FC, including superior performance in addressing the non-IID problem and the device failures