Extended main-sequence turnoffs in the double cluster hh and χ\chi Persei: The complex role of stellar rotation

Using {\sl Gaia} Data Release 2 photometry, we report the detection of extended main-sequence turnoff (eMSTO) regions in the color--magnitude diagrams (CMDs) of the $\sim 14$ Myr-old double clusters $h$ and $\chi$ Persei (NGC 869 and NGC 884). We find that stars with masses below $\sim$1.3 $M_{\odot}$ in both $h$ and $\chi$ Persei populate narrow main sequences (MSs), while more massive stars define the eMSTO, closely mimicking observations of young Galactic and Magellanic Cloud clusters (with ages older than $\sim$30 Myr). Previous studies based on clusters older than $\sim$30 Myr find that rapidly rotating MS stars are redder than slow rotators of similar luminosity, suggesting that stellar rotation may be the main driver of the eMSTO. By combining photometry and projected rotational velocities from the literature of stars in $h$ and $\chi$ Persei, we find no obvious relation between the rotational velocities and colors of non-emission-line eMSTO stars, in contrast with what is observed in older clusters. Similarly to what is observed in Magellanic Cloud clusters, most of the extremely rapidly rotating stars, identified by their strong H$\alpha$ emission lines, are located in the red part of the eMSTOs. This indicates that stellar rotation plays a role in the color and magnitude distribution of MSTO stars. By comparing the observations with simulated CMDs, we find that a simple population composed of coeval stars that span a wide range of rotation rates is unable to reproduce the color spread of the clusters' MSs. We suggest that variable stars, binary interactions, and stellar rotation affect the eMSTO morphology of these very young clusters.Comment: 14 pages, 12 figures, ApJ accepte

MAE-DFER: Efficient Masked Autoencoder for Self-supervised Dynamic Facial Expression Recognition

Dynamic facial expression recognition (DFER) is essential to the development of intelligent and empathetic machines. Prior efforts in this field mainly fall into supervised learning paradigm, which is severely restricted by the limited labeled data in existing datasets. Inspired by recent unprecedented success of masked autoencoders (e.g., VideoMAE), this paper proposes MAE-DFER, a novel self-supervised method which leverages large-scale self-supervised pre-training on abundant unlabeled data to largely advance the development of DFER. Since the vanilla Vision Transformer (ViT) employed in VideoMAE requires substantial computation during fine-tuning, MAE-DFER develops an efficient local-global interaction Transformer (LGI-Former) as the encoder. Moreover, in addition to the standalone appearance content reconstruction in VideoMAE, MAE-DFER also introduces explicit temporal facial motion modeling to encourage LGI-Former to excavate both static appearance and dynamic motion information. Extensive experiments on six datasets show that MAE-DFER consistently outperforms state-of-the-art supervised methods by significant margins (e.g., +6.30\% UAR on DFEW and +8.34\% UAR on MAFW), verifying that it can learn powerful dynamic facial representations via large-scale self-supervised pre-training. Besides, it has comparable or even better performance than VideoMAE, while largely reducing the computational cost (about 38\% FLOPs). We believe MAE-DFER has paved a new way for the advancement of DFER and can inspire more relevant research in this field and even other related tasks. Codes and models are publicly available at https://github.com/sunlicai/MAE-DFER.Comment: ACM MM 2023 (camera ready). Codes and models are publicly available at https://github.com/sunlicai/MAE-DFE

EBVCR: A Energy Balanced Virtual Coordinate Routing in Wireless Sensor Networks

AbstractGeographic routing can provide efficient routing at a fixed overhead. However, the performance of geographic routing is impacted by physical voids, and localization errors. Accordingly, virtual coordinate systems (VCS) were proposed as an alternative approach that is resilient to localization errors and that naturally routes around physical voids. However, since VCS faces virtual anomalies,existing geographic routing can’t work to banlance energy efficiently. Moreover, there are no effective complementary routing algorithm that can be used to address energy balance.In this paper we present An Energy Balanced virtual coordinate Routing in Wireless Sensor Networks(EBVCR),which combines both distance- and direction-based strategies in a flexible manner, is Proposed to resolve energy balance of Geographic routing in VCS .Our simulation results show that the proposed algorithm outperforms the best existing solution, over a variety of network densities and scenarios

Poly[[diaqua­nickel(II)]-μ2-4,4′-bipyridine-κ2 N:N′-μ-p-phenyl­enedioxy­diacetato-κ2 O:O′]

The title coordination polymer, [Ni(C10H8O6)(C10H8N2)(H2O)2]n, was obtained by the hydro­thermal reaction of nickel(II) sulfate, benzene-1,4-dioxy­diacetic acid (p-phenyl­enedioxy­diacetic acid) and 4,4′-bipyridine (4,4′-bpy) in alkaline aqueous solution. Each NiII atom is coordinated by two O atoms from two benzene-1,4-dioxy­diacetate ligands, two N atoms from two 4,4′-bpy ligands and two water mol­ecules, and displays a distorted octa­hedral geometry. The NiII atom and benzene-1,4-dioxy­diacetate and 4,4′-bpy moieties lie on inversion centres. The benzene-1,4-dioxy­diacetate ligands bridge the NiII atoms to form infinite zigzag chains, which are further inter­connected by 4,4′-bpy ligands to form a grid-like layer parallel to the (01) plane. Moreover, there are O—H⋯O hydrogen-bonding inter­actions within the grid-like layer between the coordinated water mol­ecules and the carboxyl­ate O atoms