The Question Asking Skills of Preschool Teacher Candidates: Turkey and America Example

Question asking is an important skill that teachers should use during class activities. Teachers need to get used to this ability while they are teacher candidates. The aim of this research is to identify the cognitive taxonomy and the structure of the questions asked by the candidate of preschool teachers and to compare the questioning skills of preschool teacher candidates in Michigan, USA and Turkey. The participants were selected from people who are both senior students and have preschool teacher experience. The participant teacher candidates of the present study have stated that they have not taken a questioning skill course, but the topic was taught in some of the courses. In this research, we used document review, a kind of qualitative research technique. Document evaluation was based on question formation forms. The candidate of preschool teachers were asked to write questions on “Question Formation Form”. The questions asked by participant teacher candidates were analyzed and the percent frequency tables of the questions are given. As a result, we observed that teacher candidates have mainly asked questions in the first three levels of the taxonomy. Our results also showed that the teacher candidates from Turkey have rarely asked application questions and candidates from the US have usually asked comprehension questions. When examining structure of the questions, the teacher candidates in Turkey asked more than twice as many close-ended questions than the teacher candidates in the US

ClassCut for Unsupervised Class Segmentation

Abstract. We propose a novel method for unsupervised class segmentation on a set of images. It alternates between segmenting object instances and learning a class model. The method is based on a segmentation energy defined over all images at the same time, which can be optimized efficiently by techniques used before in interactive segmentation. Over iterations, our method progressively learns a class model by integrating observations over all images. In addition to appearance, this model captures the location and shape of the class with respect to an automatically determined coordinate frame common across images. This frame allows us to build stronger shape and location models, similar to those used in object class detection. Our method is inspired by interactive segmentation methods [1], but it is fully automatic and learns models characteristic for the object class rather than specific to one particular object/image. We experimentally demonstrate on the Caltech4, Caltech101, and Weizmann horses datasets that our method (a) transfers class knowledge across images and this improves results compared to segmenting every image independently; (b) outperforms Grabcut [1] for the task of unsupervised segmentation; (c) offers competitive performance compared to the state-of-the-art in unsupervised segmentation and in particular it outperforms the topic model [2].

Target Mass Monitoring and Instrumentation in the Daya Bay Antineutrino Detectors

The Daya Bay experiment measures sin^2 2{\theta}_13 using functionally identical antineutrino detectors located at distances of 300 to 2000 meters from the Daya Bay nuclear power complex. Each detector consists of three nested fluid volumes surrounded by photomultiplier tubes. These volumes are coupled to overflow tanks on top of the detector to allow for thermal expansion of the liquid. Antineutrinos are detected through the inverse beta decay reaction on the proton-rich scintillator target. A precise and continuous measurement of the detector's central target mass is achieved by monitoring the the fluid level in the overflow tanks with cameras and ultrasonic and capacitive sensors. In addition, the monitoring system records detector temperature and levelness at multiple positions. This monitoring information allows the precise determination of the detectors' effective number of target protons during data taking. We present the design, calibration, installation and in-situ tests of the Daya Bay real-time antineutrino detector monitoring sensors and readout electronics.Comment: 22 pages, 20 figures; accepted by JINST. Changes in v2: minor revisions to incorporate editorial feedback from JINS

3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration

In this paper, we propose the 3DFeat-Net which learns both 3D feature detector and descriptor for point cloud matching using weak supervision. Unlike many existing works, we do not require manual annotation of matching point clusters. Instead, we leverage on alignment and attention mechanisms to learn feature correspondences from GPS/INS tagged 3D point clouds without explicitly specifying them. We create training and benchmark outdoor Lidar datasets, and experiments show that 3DFeat-Net obtains state-of-the-art performance on these gravity-aligned datasets.Comment: 17 pages, 6 figures. Accepted in ECCV 201