Subtropical middle atmosphere dynamics observed by the Chung Li radar

The Chung Li Radar (24.91 N; 121.24 E) has been operating since 1986. A five beam observational configuration was used on a regular basis to study the various dynamics processes in the atmosphere-lower stratosphere height region. Due to its geographical location, the annual Typhoon and Mei-Yu seasons provide good opportunities to study the various interesting dynamic processes such as instabilities, generation of gravity waves, wave mean field interaction, etc. Three dimensional air motions due to these fronts are presented. Special cases of gravity wave generation, propagation and their effects on the turbulent layers are discussed

On the use of colour reflectivity plots to monitor the structure of the troposphere and stratosphere

The radar reflectivity, defined as the range squared corrected power of VHF radar echoes, can be used to monitor and study the temporal development of inversion layer, frontal boundaries and convective turbulence. From typical featurs of upward or downward motion of reflectivity structures, the advection/convection of cold and warm air can be predicted. High resolution color plots appear to be useful to trace and to study the life history of these structures, particularly their persistency, descent and ascent. These displays allow an immediate determination of the tropopause height as well as the determination of the tropopause structure. The life history of warm fronts, cold fronts, and occlusions can be traced, and these reflectivity plots allow detection of even very weak events which cannot be seen in the traditional meteorological data sets. The life history of convective turbulence, particular evolving from the planetary boundary layer, can be tracked quite easily. Its development into strong convection reaching the middle troposphere can be followed and predicted

The first operation and results of the Chung-Li VHF radar

The Chung-Li Very High Frequency (VHF) radar is used in the dual-mode operations, applying Doppler beam-swinging as well as the spaced-antenna-drift method. The design of the VHF radar is examined. Results of performance tests are discussed

WSOD^2: Learning Bottom-up and Top-down Objectness Distillation for Weakly-supervised Object Detection

We study on weakly-supervised object detection (WSOD) which plays a vital role in relieving human involvement from object-level annotations. Predominant works integrate region proposal mechanisms with convolutional neural networks (CNN). Although CNN is proficient in extracting discriminative local features, grand challenges still exist to measure the likelihood of a bounding box containing a complete object (i.e., "objectness"). In this paper, we propose a novel WSOD framework with Objectness Distillation (i.e., WSOD^2) by designing a tailored training mechanism for weakly-supervised object detection. Multiple regression targets are specifically determined by jointly considering bottom-up (BU) and top-down (TD) objectness from low-level measurement and CNN confidences with an adaptive linear combination. As bounding box regression can facilitate a region proposal learning to approach its regression target with high objectness during training, deep objectness representation learned from bottom-up evidences can be gradually distilled into CNN by optimization. We explore different adaptive training curves for BU/TD objectness, and show that the proposed WSOD^2 can achieve state-of-the-art results.Comment: Accepted as a ICCV 2019 poster pape