Development and Control of Soybean Aphid, Aphis glycines, in Heilongjiang Province

Originating text in Chinese.Citation: Wang, Chunrong, Chen, Jiguang, Guo, Yuren, Gong, Xiangyu, Xu, Zhaofei, Lin, Chao. (1998). Development and Control of Soybean Aphid, Aphis glycines, in Heilongjiang Province. Soybean Bulletin, 6, 15

Decays of 1−+1^{-+} Charmoniumlike Hybrid

By extracting the transition amplitudes, we give the first lattice QCD prediction of the two-body decay partial widths of the $1^{-+}$ charmoniumlike hybrid $\eta_{c1}$. Given the calculated mass value $m_{\eta_{c1}}=4.329(36)$ GeV, the $\eta_{c1}$ decay is dominated by the open charm modes $D_1\bar{D}$, $D^*\bar{D}$ and $D^*\bar{D}^*$ with partial widths of $258(133)$ MeV, $88(18)$ MeV and $150(118)$ MeV, respectively. The coupling of $\eta_{c1}$ to $\chi_{c1}$ plus a flavor singlet pseudoscalar is not small, but $\chi_{c1}\eta$ decay is suppressed by the small $\eta-\eta'$ mixing angle. The partial width of $\eta_{c1}\to \eta_c\eta'$ is estimated to be around 1 MeV. We suggest experiments to search for $\eta_{c1}$ in the $P$-wave $D^*\bar{D}$ and $D^*\bar{D}^*$ systems. Especially, the polarization of $D^*\bar{D}^*$ can be used to distinguish the $1^{-+}$ product (total spin $S=1$) from $1^{--}$ products ($S=0$).Comment: 16 pages, 8 figure

PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation

The need for fine-grained perception in autonomous driving systems has resulted in recently increased research on online semantic segmentation of single-scan LiDAR. Despite the emerging datasets and technological advancements, it remains challenging due to three reasons: (1) the need for near-real-time latency with limited hardware; (2) uneven or even long-tailed distribution of LiDAR points across space; and (3) an increasing number of extremely fine-grained semantic classes. In an attempt to jointly tackle all the aforementioned challenges, we propose a new LiDAR-specific, nearest-neighbor-free segmentation algorithm - PolarNet. Instead of using common spherical or bird's-eye-view projection, our polar bird's-eye-view representation balances the points across grid cells in a polar coordinate system, indirectly aligning a segmentation network's attention with the long-tailed distribution of the points along the radial axis. We find that our encoding scheme greatly increases the mIoU in three drastically different segmentation datasets of real urban LiDAR single scans while retaining near real-time throughput.Comment: Accepted by CVPR 2020; Code at https://github.com/edwardzhou130/PolarSe

Meta-Transformer: A Unified Framework for Multimodal Learning

Multimodal learning aims to build models that can process and relate information from multiple modalities. Despite years of development in this field, it still remains challenging to design a unified network for processing various modalities ($\textit{e.g.}$ natural language, 2D images, 3D point clouds, audio, video, time series, tabular data) due to the inherent gaps among them. In this work, we propose a framework, named Meta-Transformer, that leverages a $\textbf{frozen}$ encoder to perform multimodal perception without any paired multimodal training data. In Meta-Transformer, the raw input data from various modalities are mapped into a shared token space, allowing a subsequent encoder with frozen parameters to extract high-level semantic features of the input data. Composed of three main components: a unified data tokenizer, a modality-shared encoder, and task-specific heads for downstream tasks, Meta-Transformer is the first framework to perform unified learning across 12 modalities with unpaired data. Experiments on different benchmarks reveal that Meta-Transformer can handle a wide range of tasks including fundamental perception (text, image, point cloud, audio, video), practical application (X-Ray, infrared, hyperspectral, and IMU), and data mining (graph, tabular, and time-series). Meta-Transformer indicates a promising future for developing unified multimodal intelligence with transformers. Code will be available at https://github.com/invictus717/MetaTransformerComment: Project website: https://kxgong.github.io/meta_transformer

1−+1^{-+} Hybrid in J/ψJ/\psi Radiative Decays from Lattice QCD

We present the first theoretical prediction of the production rate of $1^{-+}$ light hybrid meson $\eta_1$ in $J/\psi$ radiative decays. In the $N_f=2$ lattice QCD formalism with the pion mass $m_\pi\approx 350$ MeV, the related electromagnetic multipole form factors are extracted from the three-point functions that involve necessarily quark annihilation diagrams, which are calculated through the distillation method. The partial width of $J/\psi\to \gamma \eta_1$ is determined to be $2.29(77)~\mathrm{eV}$ at the $\eta_1$ mass $m_{\eta_1}=2.23(4)$ GeV. If $\eta_1$ corresponds to the recently observed $\eta_1(1855)$ in the process $J/\psi\to \gamma\eta_1(1855)\to \gamma \eta\eta'$ by BESIII, then the branching fraction $\mathrm{Br}(J/\psi\to \gamma\eta_1(1855))$ is estimated to be $6.2(2.2)\times 10^{-5}$, which implies $\mathrm{Br}(\eta_1(1855)\to \eta\eta')\sim 4.3\%$

The dilemma of public information disclosures

In this paper, we document a novel fact that disclosures of public information reshape social dynamics in China. Using the staggered roll-out of a quasi-natural experiment of air pollution information disclosure and a novel high-frequency data set of social and public events, we find socioeconomic cooperation and protests both significantly decrease after disclosure. The negative effects are larger when the disclosed pollution level is higher and when residents have higher environmental awareness and lower trust in local governments. Our results are rationalized in a theoretical model and suggest that information disclosure involves a tradeoff between economic efficiency and political stability and leads to a dilemma for policymakers

Extreme high temperatures and adaptation by social dynamics: Theory and Evidence from China

Using a novel city-level high-frequency panel data set of social and public events in Chinese cities, we document that extreme high temperatures significantly reshape social dynamics. Extreme high temperatures lead to an increase in social cooperation, and the effects are more salient when productivity is lower and labor is more intensively used. This implies extreme high temperatures boost the relative returns of cooperation given lowered productivity. Our estimates and quantitative model suggest that the human race adapts to global warming by reshaping its social dynamics: adaptation via social dynamics offsets about one-third of the negative impacts of extreme high temperatures on the economy