Cooling mechanical resonators to quantum ground state from room temperature

Ground-state cooling of mesoscopic mechanical resonators is a fundamental requirement for test of quantum theory and for implementation of quantum information. We analyze the cavity optomechanical cooling limits in the intermediate coupling regime, where the light-enhanced optomechanical coupling strength is comparable with the cavity decay rate. It is found that in this regime the cooling breaks through the limits in both the strong and weak coupling regimes. The lowest cooling limit is derived analytically at the optimal conditions of cavity decay rate and coupling strength. In essence, cooling to the quantum ground state requires $Q_{\mathrm{m}}>2.4n_{\mathrm{th}}$, with $Q_{\mathrm{m}}$ being the mechanical quality factor and $n_{\mathrm{th}}$ being the thermal phonon number. Remarkably, ground-state cooling is achievable starting from room temperature, when mechanical $Q$-frequency product $Q_{\mathrm{m}}{\nu>1.5}\times10^{13}$, and both of the cavity decay rate and the coupling strength exceed the thermal decoherence rate. Our study provides a general framework for optimizing the backaction cooling of mesoscopic mechanical resonators

GPA-Net:No-Reference Point Cloud Quality Assessment with Multi-task Graph Convolutional Network

With the rapid development of 3D vision, point cloud has become an increasingly popular 3D visual media content. Due to the irregular structure, point cloud has posed novel challenges to the related research, such as compression, transmission, rendering and quality assessment. In these latest researches, point cloud quality assessment (PCQA) has attracted wide attention due to its significant role in guiding practical applications, especially in many cases where the reference point cloud is unavailable. However, current no-reference metrics which based on prevalent deep neural network have apparent disadvantages. For example, to adapt to the irregular structure of point cloud, they require preprocessing such as voxelization and projection that introduce extra distortions, and the applied grid-kernel networks, such as Convolutional Neural Networks, fail to extract effective distortion-related features. Besides, they rarely consider the various distortion patterns and the philosophy that PCQA should exhibit shifting, scaling, and rotational invariance. In this paper, we propose a novel no-reference PCQA metric named the Graph convolutional PCQA network (GPA-Net). To extract effective features for PCQA, we propose a new graph convolution kernel, i.e., GPAConv, which attentively captures the perturbation of structure and texture. Then, we propose the multi-task framework consisting of one main task (quality regression) and two auxiliary tasks (distortion type and degree predictions). Finally, we propose a coordinate normalization module to stabilize the results of GPAConv under shift, scale and rotation transformations. Experimental results on two independent databases show that GPA-Net achieves the best performance compared to the state-of-the-art no-reference PCQA metrics, even better than some full-reference metrics in some cases

Changes in Climate Extremes and Catastrophic Events in the Mongolian Plateau from 1951 to 2012

AbstractThe spatiotemporal changes in 21 indices of extreme temperature and precipitation for the Mongolian Plateau from 1951 to 2012 were investigated on the basis of daily temperature and precipitation data from 70 meteorological stations. Changes in catastrophic events, such as droughts, floods, and snowstorms, were also investigated for the same period. The correlations between catastrophic events and the extreme indices were examined. The results show that the Mongolian Plateau experienced an asymmetric warming trend. Both the cold extremes and warm extremes showed greater warming at night than in the daytime. The spatial changes in significant trends showed a good homogeneity and consistency in Inner Mongolia. Changes in the precipitation extremes were not as obvious as those in the temperature extremes. The spatial distributions in changes of precipitation extremes were complex. A decreasing trend was shown for total precipitation from west to east as based on the spatial distribution of decadal trends. Drought was the most serious extreme disaster, and prolonged drought for longer than 3 yr occurred about every 7–11 yr. An increasing trend in the disaster area was apparent for flood events from 1951 to 2012. A decreasing trend was observed for the maximum depth of snowfall from 1951 to 2012, with a decreased average maximum depth of 10 mm from the 1990s.</jats:p

Multi-Domain Learning From Insufficient Annotations

Multi-domain learning (MDL) refers to simultaneously constructing a model or a set of models on datasets collected from different domains. Conventional approaches emphasize domain-shared information extraction and domain-private information preservation, following the shared-private framework (SP models), which offers significant advantages over single-domain learning. However, the limited availability of annotated data in each domain considerably hinders the effectiveness of conventional supervised MDL approaches in real-world applications. In this paper, we introduce a novel method called multi-domain contrastive learning (MDCL) to alleviate the impact of insufficient annotations by capturing both semantic and structural information from both labeled and unlabeled data.Specifically, MDCL comprises two modules: inter-domain semantic alignment and intra-domain contrast. The former aims to align annotated instances of the same semantic category from distinct domains within a shared hidden space, while the latter focuses on learning a cluster structure of unlabeled instances in a private hidden space for each domain. MDCL is readily compatible with many SP models, requiring no additional model parameters and allowing for end-to-end training. Experimental results across five textual and image multi-domain datasets demonstrate that MDCL brings noticeable improvement over various SP models.Furthermore, MDCL can further be employed in multi-domain active learning (MDAL) to achieve a superior initialization, eventually leading to better overall performance.Comment: This paper has been accepted to ECAI-2

Dydrogesterone has no effect on uterine fibroids when used to prevent miscarriage in pregnant women with uterine fibroids

Objectives: To analyse the effect of dydrogesterone use during pregnancy on uterine fibroids, pregnancy complications, and pregnancy outcome. Material and methods: In all, 372 pregnant women with uterine fibroids who were treated at the Affiliated Provincial Hospital of Shandong University were included in this study. Thirty-three of these women received dydrogesterone and constituted the treatment group, and the 27 women who were found to have uterine fibroids during the first trimester but did not receive intervention to prevent miscarriage composed the control group. The changes in uterine fibroids before and after pregnancy and the pregnancy complications were recorded; immunohistochemistry was used to detect the expression of progesterone receptor (PR) and proliferation- and apoptosis-related proteins in the uterine fibroid tissue. Results: No significant difference was observed in the change in uterine fibroid volume during pregnancy between the treatment group and the control group (p > 0.05). The percentage of uterine fibroids with red degeneration was lower in the treatment group than in the control group, but the difference was not statistically significant. No significant difference was observed in newborn weight, height, Apgar score, threatened miscarriage, or premature birth, among other characteristics, between the two groups (p > 0.05). Immunohistochemistry showed no significant difference in the expression of PR, cyclinD1, insulin-like growth factor (IGF1), or B-cell lymphoma 2 (Bcl2) between the two groups. Conclusions: The use of dydrogesterone during pregnancy has no significant effect on uterine fibroids, pregnancy progression, or pregnancy outcomes in pregnant patients with uterine fibroids

5-Bromo-2-iodo-1,3-dimethyl­benzene

The asymmetric unit of the title compound, C8H8BrI, contains three independent mol­ecules. In each molecule, the Br, I and C atoms of the methyl groups lie in the benzene ring plane. Intra­molecular C—H⋯I hydrogen bonds result in the formation of three planar five-membered rings, which are nearly coplanar with the adjacent rings

4-Bromo-2,6-dimethyl­aniline

The asymmetric unit of the title compound, C8H10BrN, contains two independent mol­ecules. The Br, N and methyl group C atoms lie in the benzene ring planes. In the crystal structure, N—H⋯N hydrogen bonds link the mol­ecules

1-Diphenyl­phosphino-1′-(diphenyl­phosphinoyl)cobaltocenium hexa­fluorido­phosphate

The title compound, [Co(C17H14OP)(C17H14P)]PF6, was obtained unintentionally as the product of an attempted synthesis of [1,1′-bis­(oxodiphenyl­phospho­ranyl)cobaltocenium] hexa­fluorido­phosphate. The O atom of the oxo group is disordered over two positions with site occupancies of 0.65:0.35. The crystal structure contains weak inter­molecular C—H⋯F hydrogen bonds, connecting the components of the structure into chains parallel to [010]