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This study proposes a measure of opinion divergence based on the trading process and investigates the effect of noise trading and institutional trading on the influence of opinion divergence on stock returns using intraday data. The results show that, although for 5-min intervals, the divergence of opinion is not negatively related to future stock returns, for 10-min intervals and beyond, divergence of opinion becomes negatively related to future stock returns, as proposed by Miller (1977). By differentiating the measure of divergence of opinion from the measure of noise trading and institutional trading, we provide solid evidence that institutional trading affects the relation between divergence of opinion and future stock returns, on which noise trading does not seem to have any effect. However, the effect of institutional trading is mainly due to the fact that stocks with high institutional trading have low divergence of opinion, rather than being a result of sophistication and good stock-picking skills among institutional traders.

We investigate whether low price synchronicity means a better information environment and more price informativeness in terms of the integration speed for firm-specific information and market-wide ...

Photons in kilovolt (kV) X-ray dual-energy computed tomography (DECT) interact with matter mainly by photoelectric effect and Compton scatter. Any material’s mass attenuation coefficient (MAC) function is composed of these two effects. Thus, from a function space perspective, any MAC function can be written as a linear combination of the MAC functions of two basis materials. The projection-domain method based on this decomposition model is fit for two data sets of kV DECT. However, photons in megavolt (MV) X-ray DECT interact with matter through another way, pair production. Accordingly, three basis functions are needed to delineate this 3-D function space. Direct implementation of the projection-domain method with the decomposition model of two basis materials is feasible in spite of the unsatisfactory results. However, the projection-domain method based on the decomposition model of three basis materials becomes unsolvable, because the number of unknowns exceeds the number of data sets. Several methods add mass or volume conservation constraint to make the problem solvable, but they are not appropriate for MV DECT. We propose a new method in which the influence of photoelectric effect is numerically eliminated to regain the consistency among the decomposition model, physics model, and data sets of MV DECT. An iteration framework based on the projection-domain method with the decomposition model of two basis materials is designed to achieve this goal. The results from the numerical experiments show that the proposed method provides better decomposition coefficients and material information. Although our method is theoretically designed for element objects, water as a common compound can also be accurately decomposed. Moreover, beside the projection-domain method, the proposed one here can also be applied with other processing methods.

The investor recognition hypothesis and the bonding hypothesis, which help us understand the market quality of stocks that are cross-listed on different stock markets, imply improved market efficie...

Dual-energy mega-electron-volt (MeV) X-ray container radiography has become a well-established technique in customs security application, because of its material discrimination capability. The main difficulty of X-ray radiography is dealing with the materials overlapping problem. When two or more materials exist along the X-ray beam path, its material discrimination performance will be obviously affected. Computed tomography (CT) collects many X-ray measurements taken from different angles surrounding an object to produce cross-sectional (tomographic) images of the scanned object. Therefore, CT can provide real 3-D images inside the object. However, due to the bulky container volume and complex types of cargos, it is very hard to develop such a huge CT system for container inspection. To the best of our knowledge, there has no such commercial X-ray CT system for container inspection yet. This paper presents the design of a dual MeV energy X-ray CT system for cargo container inspection which uses an accelerator with fast 6/9 MeV switching spectra, an arc detector array and rotating mechanism. A dual MeV energy X-ray CT image reconstruction and material decomposition algorithm are developed. An experimental system was built with the same accelerator and detector array as used in the designed container CT system. Experimental results that prove the validity and effectiveness of the algorithm and CT system are presented.

Dual-energy X-ray radiography has become a well-established technique in medical, industrial, and security applications, because of its material or tissue discrimination capability. The main difficulty of this technique is dealing with the materials overlapping problem. When there are two or more materials along the X-ray beam path, its material discrimination performance will be affected. In order to solve this problem, a new dynamic material discrimination algorithm is proposed for dual-energy X-ray digital radiography, which can also be extended to multi-energy X-ray situations. The algorithm has three steps: α-curve-based pre-classification, decomposition of overlapped materials, and the final material recognition. The key of the algorithm is to establish a dual-energy radiograph database of both pure basis materials and pair combinations of them. After the pre-classification results, original dual-energy projections of overlapped materials can be dynamically decomposed into two sets of dual-energy radiographs of each pure material by the algorithm. Thus, more accurate discrimination results can be provided even with the existence of the overlapping problem. Both numerical and experimental results that prove the validity and effectiveness of the algorithm are presented.

A decomposition model with an iterative solving framework is proposed for use in MeV dual energy computed tomography (DECT). The decomposition model can deal with a broad range of materials and better matches the physics of MeV photons. The proposed iterative solving framework is based on the conventional projection-domain decomposition method, and verified by numerical experiments and experiments. It is shown that images reconstructed with the proposed method have less artifacts and provide more accurate estimates of the atomic number of materials than the conventional projection-domain decomposition method.

Dual energy computed tomography(DECT) can give the variation of the attenuation coefficient with energy. Furthermore, the material information of the scanned object can be deduced. In medical application of DECT, the scanned body can be seen as a combination of several materials, such as bone, soft tissue, fat and blood. A two-steps multi-materials decomposition method was proposed to attain the composition information. Firstly, a projection domain decomposition is performed to estimate the attenuation coefficient function of the scanned object. Then the estimated attenuation coefficient function is decomposed with the attenuation coefficient functions of basis materials. There is a problem that in the first step, the spectrum of the x-ray source or additional calibration data is necessary but they may not be available in practice. We proposed an image domain multi-materials decomposition method. The basic framework is the same with the two-steps multi-materials decomposition method. The difference occurs in the first step that a direct reconstruction to dual energy datasets not the projection domain decomposition is conducted. Accordingly, the attenuation coefficients of basis materials for decomposition in the second step should be carefully selected or constructed because the direct reconstructed value is the effective attenuation coefficient. The attenuation coefficients of basis materials are chosen from a density-based clustering to the dual energy reconstruction data. We did some experiments based on clinical DECT data. Without knowing exact spectrum, the material information acquired by the proposed method is relatively accurate.

In medical dual-energy CT, projection-domain decomposition is typically used to process the acquired two projection datasets. However, when a high atomic-number contrast-agent is introduced, its K-edge discontinuity challenges this method. In this work, we take advantage of the fact that the contrast agent and its solvent are known in advance and propose to first decompose the attenuation coefficient functions of the contrast agent and its solvent to estimate the attenuation contribution of the K-shell photoelectric. Then, the contribution of the K-edge attenuation to the projection data is eliminated and the problem is decomposed to determine the attenuation coefficient functions of the other biological materials. Numerical experiments are shown to verify that the proposed method can give better results with less artifacts than the result of the direct projection-domain decomposition. Moreover, the variances of the decomposition coefficients, which is the index of the noise level of the decomposition coefficient image on the contrast regions, are smaller.

In keV dual-energy computed tomography (DECT), photons interact with matter mainly in two ways, photoelectric effect and Compton scatter. When the contrast agent exists, the dimension of the attenuation coefficient function (ACF) space increases. The decomposition model of ACF needs three basis functions, but the problem of the projection domain method based on this model cannot be solved by two datasets of DECT. Although it is still feasible to perform the projection domain method based on the decomposition model with two basis functions, the decomposition result would have artifacts. We propose to eliminate the K-shell photoelectric effect from the ACF and projection. After this elimination, the decomposition model and datasets become consistent and the projection domain method would give better results with less artifacts. The preliminary numerical results shows that our method gives better decomposition image quality with less metal artifacts.

High energy dual-energy X-ray Digital Radiography(DR) imaging is mainly used in material recognition of the cargo inspection. We introduce the development history and the principle of the technology and describe the data process flow of our system. The system corrects original data to get the dual-energy transparence image. Material categories of all points in the image are identified by the classification curve which is related to the X-ray energy spectrum. For the calibration of classification curve, our strategy involves a basic curve calibration and a real-time correction devoted to enhance the classification accuracy. Image segmentation and denoising methods are applied to smooth the image. The image contains more information after colorization. Some results show that our methods achieve the desired effect., 7 pages, 6figures