An ATC Simulation Platform based Compass Satellite Navigation System

AbstractBased real-time, high precision characteristics of satellite navigation system, the aircraft in flight can be continuous and accurate positioned its location. Therefore the interval width of en-route and the interval separation could be reduced. And the reduced flight time, the increased flights, and the high utilization of airspace could meet the development of airline transportation. Thus the free flight could be achieved in near future. The compass satellite navigation system is established by the Chinese regional navigation and positioning system. The system could provide users around the clock, the clock real-time location services, short message service and precision timing services. According to the compass satellite navigation system, an air traffic control simulation platform based compass satellite navigation system has been proposed in this paper. The structure, functions and the future research fields have been introduced in detail. The research of this paper plays an important role to impel applications of air traffic management based compass navigation satellite system

Optical Dipole Structure and Orientation of GaN Defect Single-Photon Emitters

GaN has recently been shown to host bright, photostable, defect single photon emitters in the 600-700 nm wavelength range that are promising for quantum applications. The nature and origin of these defect emitters remain elusive. In this work, we study the optical dipole structures and orientations of these defect emitters using the defocused imaging technique. In this technique, the far-field radiation pattern of an emitter in the Fourier plane is imaged to obtain information about the structure of the optical dipole moment and its orientation in 3D. Our experimental results, backed by numerical simulations, show that these defect emitters in GaN exhibit a single dipole moment that is oriented almost perpendicular to the wurtzite crystal c-axis. Data collected from many different emitters shows that the angular orientation of the dipole moment in the plane perpendicular to the c-axis exhibits a distribution that shows peaks centered at the angles corresponding to the nearest Ga-N bonds and also at the angles corresponding to the nearest Ga-Ga (or N-N) directions. Moreover, the in-plane angular distribution shows little difference among defect emitters with different emission wavelengths in the 600-700 nm range. Our work sheds light on the nature and origin of these GaN defect emitters.Comment: 15 pages, 4 figure

Flows: Building Blocks of Reasoning and Collaborating AI

Recent advances in artificial intelligence (AI) have produced highly capable and controllable systems. This creates unprecedented opportunities for structured reasoning as well as collaboration among multiple AI systems and humans. To fully realize this potential, it is essential to develop a principled way of designing and studying such structured interactions. For this purpose, we introduce the conceptual framework of Flows: a systematic approach to modeling complex interactions. Flows are self-contained building blocks of computation, with an isolated state, communicating through a standardized message-based interface. This modular design allows Flows to be recursively composed into arbitrarily nested interactions, with a substantial reduction of complexity. Crucially, any interaction can be implemented using this framework, including prior work on AI--AI and human--AI interactions, prompt engineering schemes, and tool augmentation. We demonstrate the potential of Flows on the task of competitive coding, a challenging task on which even GPT-4 struggles. Our results suggest that structured reasoning and collaboration substantially improve generalization, with AI-only Flows adding +$21$ and human--AI Flows adding +$54$ absolute points in terms of solve rate. To support rapid and rigorous research, we introduce the aiFlows library. The library comes with a repository of Flows that can be easily used, extended, and composed into novel, more complex Flows. The aiFlows library is available at https://github.com/epfl-dlab/aiflows. Data and Flows for reproducing our experiments are available at https://github.com/epfl-dlab/cc_flows

YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications

For years, the YOLO series has been the de facto industry-level standard for efficient object detection. The YOLO community has prospered overwhelmingly to enrich its use in a multitude of hardware platforms and abundant scenarios. In this technical report, we strive to push its limits to the next level, stepping forward with an unwavering mindset for industry application. Considering the diverse requirements for speed and accuracy in the real environment, we extensively examine the up-to-date object detection advancements either from industry or academia. Specifically, we heavily assimilate ideas from recent network design, training strategies, testing techniques, quantization, and optimization methods. On top of this, we integrate our thoughts and practice to build a suite of deployment-ready networks at various scales to accommodate diversified use cases. With the generous permission of YOLO authors, we name it YOLOv6. We also express our warm welcome to users and contributors for further enhancement. For a glimpse of performance, our YOLOv6-N hits 35.9% AP on the COCO dataset at a throughput of 1234 FPS on an NVIDIA Tesla T4 GPU. YOLOv6-S strikes 43.5% AP at 495 FPS, outperforming other mainstream detectors at the same scale~(YOLOv5-S, YOLOX-S, and PPYOLOE-S). Our quantized version of YOLOv6-S even brings a new state-of-the-art 43.3% AP at 869 FPS. Furthermore, YOLOv6-M/L also achieves better accuracy performance (i.e., 49.5%/52.3%) than other detectors with a similar inference speed. We carefully conducted experiments to validate the effectiveness of each component. Our code is made available at https://github.com/meituan/YOLOv6.Comment: technical repor

CRL4 antagonizes SCFFbxo7-mediated turnover of cereblon and BK channel to regulate learning and memory

Intellectual disability (ID), one of the most common human developmental disorders, can be caused by genetic mutations in Cullin 4B (Cul4B) and cereblon (CRBN). CRBN is a substrate receptor for the Cul4A/B-DDB1 ubiquitin ligase (CRL4) and can target voltage- and calcium-activated BK channel for ER retention. Here we report that ID-associated CRL4CRBNmutations abolish the interaction of the BK channel with CRL4, and redirect the BK channel to the SCFFbxo7ubiquitin ligase for proteasomal degradation. Glioma cell lines harbouring CRBN mutations record density-dependent decrease of BK currents, which can be restored by blocking Cullin ubiquitin ligase activity. Importantly, mice with neuron-specific deletion of DDB1 or CRBN express reduced BK protein levels in the brain, and exhibit similar impairment in learning and memory, a deficit that can be partially rescued by activating the BK channel. Our results reveal a competitive targeting of the BK channel by two ubiquitin ligases to achieve exquisite control of its stability, and support changes in neuronal excitability as a common pathogenic mechanism underlying CRL4CRBN–associated ID