Design of a large dynamic range readout unit for the PSD detector of DAMPE

A large dynamic range is required by the Plastic Scintillator Detector (PSD) of DArk Matter Paricle Explorer (DAMPE), and a double-dynode readout has been developed. To verify this design, a prototype detector module has been constructed and tested with cosmic rays and heavy ion beams. The results match with the estimation and the readout unit could easily cover the required dynamic range

SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection

Vision-based vehicle detection approaches achieve incredible success in recent years with the development of deep convolutional neural network (CNN). However, existing CNN based algorithms suffer from the problem that the convolutional features are scale-sensitive in object detection task but it is common that traffic images and videos contain vehicles with a large variance of scales. In this paper, we delve into the source of scale sensitivity, and reveal two key issues: 1) existing RoI pooling destroys the structure of small scale objects, 2) the large intra-class distance for a large variance of scales exceeds the representation capability of a single network. Based on these findings, we present a scale-insensitive convolutional neural network (SINet) for fast detecting vehicles with a large variance of scales. First, we present a context-aware RoI pooling to maintain the contextual information and original structure of small scale objects. Second, we present a multi-branch decision network to minimize the intra-class distance of features. These lightweight techniques bring zero extra time complexity but prominent detection accuracy improvement. The proposed techniques can be equipped with any deep network architectures and keep them trained end-to-end. Our SINet achieves state-of-the-art performance in terms of accuracy and speed (up to 37 FPS) on the KITTI benchmark and a new highway dataset, which contains a large variance of scales and extremely small objects.Comment: Accepted by IEEE Transactions on Intelligent Transportation Systems (T-ITS

Spectral mapping of thermal conductivity through nanoscale ballistic transport

Controlling thermal properties is central to many applications, such as thermoelectric energy conversion and the thermal management of integrated circuits. Progress has been made over the past decade by structuring materials at different length scales, but a clear relationship between structure size and thermal properties remains to be established. The main challenge comes from the unknown intrinsic spectral distribution of energy among heat carriers. Here, we experimentally measure this spectral distribution by probing quasi-ballistic transport near nanostructured heaters down to 30 nm using ultrafast optical spectroscopy. Our approach allows us to quantify up to 95% of the total spectral contribution to thermal conductivity from all phonon modes. The measurement agrees well with multiscale and first-principles-based simulations. We further demonstrate the direct construction of mean free path distributions. Our results provide a new fundamental understanding of thermal transport and will enable materials design in a rational way to achieve high performance

Oral peripheral ameloblastoma : a retrospective series study of 25 cases

Peripheral ameloblastoma (PA) is a rare and unusual variant of odontogenic tumor, which was described only in isolated case reports in literature. The objective of this study was to investigate the clinical profile, treatment and outcome of PA in a consecutive case series. A total of 25 patients with histologically confirmed PA from 2001 to 2015 were retrospectively reviewed in our institution. Of the 25 patients, 22 males and 3 females were identified (male: female = 7.3:1). The average age was 48.3 years (range 11-81 years) with lingual or palate gingival region being the most common site (76%). The course of disease was less than 6 months in 92.0% (23/25) of all patients (mean, 3.3 months; range, 1-12 months). All patients underwent complete surgical removal of the lesions, and one lesion recurrence occurred during the follow-up period. The clinical profile and outcome of PA from Eastern China were elucidated in this retrospective analysis based on a case series. Our experience may provide some insights into the differential diagnosis and clinical management of PA. The first choice of treatment is surgical excision, which can result in a good prognosis

A Bimodel Algorithm with Data-Divider to Predict Stock Index

There is not yet reliable software for stock prediction, because most experts of this area have been trying to predict an exact stock index. Considering that the fluctuation of a stock index usually is no more than 1% in a day, the error between the forecasted and the actual values should be no more than 0.5%. It is too difficult to realize. However, forecasting whether a stock index will rise or fall does not need to be so exact a numerical value. A few scholars noted the fact, but their systems do not yet work very well because different periods of a stock have different inherent laws. So, we should not depend on a single model or a set of parameters to solve the problem. In this paper, we developed a data-divider to divide a set of historical stock data into two parts according to rising period and falling period, training, respectively, two neural networks optimized by a GA. Above all, the data-divider enables us to avoid the most difficult problem, the effect of unexpected news, which could hardly be predicted. Experiments show that the accuracy of our method increases 20% compared to those of traditional methods