The CKM matrix from anti-SU(7) unification of GUT families

We estimate the CKM matrix elements in the recently proposed minimal model, anti-SU(7) GUT for the family unification, $[\,3\,]+2\,[\,2\,]+8\,[\,\bar{1}\,]$+\,(singlets). It is shown that the real angles of the right-handed unitary matrix diagonalizing the mass matrix can be determined to fit the Particle Data Group data. However, the phase in the right-handed unitary matrix is not constrained very much. We also includes an argument about allocating the Jarlskog phase in the CKM matrix. Phenomenologically, there are three classes of possible parametrizations, \delq=\alpha,\beta, or $\gamma$ of the unitarity triangle. For the choice of \delq=\alpha, the phase is close to a maximal one.Comment: 11 pages of LaTex file with 2 figure

Prevalence and spatial concordance of visual field deterioration in fellow eyes of glaucoma patients.

PurposeTo examine the prevalence of visual field deterioration in contralateral eyes of patients with worsening open-angle glaucoma and to evaluate the spatial concordance of visual field deterioration between both eyes.MethodsOne hundred sixteen open-angle glaucoma patients who underwent 8 or more visual field examinations over ≥ 6 years of follow-up were included. The rates of the fast and slow components of visual field decay for each of 52 visual field test locations were calculated with point-wise exponential regression analysis. The spatial concordance of visual field deterioration in contralateral eyes was evaluated with a concordance ratio (calculated as the number of overlapping locations divided by the total number of deteriorating locations) and by comparing the rate of decay in corresponding modified glaucoma hemifield test clusters.ResultsThe average visual field mean deviation (± standard deviation [SD]) was -8.5 (± 6.4) dB and the mean (± SD) follow-up time was 9.0 (± 1.6) years. Sixty-three patients had mild damage, 23 had moderate damage, and 30 had severe damage. The mean concordance ratio (± SD) was 0.46 (± 0.32) for the mild group, 0.33 (± 0.27) for the moderate group, and 0.35 (± 0.21) for the severe group. Thirty-one patients (27%) had deterioration in concordant locations (p < 0.05). Visual field deterioration was greater in the superior hemifield than the inferior hemifield (p < 0.05) when evaluated with both the concordance ratio and modified glaucoma hemifield test cluster analysis methods.ConclusionsThere is only fair spatial concordance with regard to visual field deterioration between the both eyes of an individual. We conclude that testing algorithms taking advantage of inter-eye spatial concordance would not be particularly advantageous in the early detection of glaucomatous deterioration

Optimal Economic Ordering Policy with Trade Credit and Discount Cash-Flow Approach

In this paper, an inventory model for deteriorating items under two levels of trade credit will be established. The trade credit policy depends on the retailer’s order quantity. When the retailer’s order quantity is greater than or equal to a predetermined quantity, both of the supplier and the retailer are taking trade credit policy; otherwise, the delay in payments is not permitted. Since the same cash amount has different values at different points of time, the discount cash-flow (DCF) is used to analysis the inventory model. The purpose of this paper is to find an optimal ordering policy to minimizing the present value of all future cash-flows cost by using DCF approach. The method to determine the optimal ordering policy efficiently is presented. Some numerical examples are provided to demonstrate the model and sensitivity of some important parameters are illustrated the optimal solutions

A Generalized Multi-Modal Fusion Detection Framework

LiDAR point clouds have become the most common data source in autonomous driving. However, due to the sparsity of point clouds, accurate and reliable detection cannot be achieved in specific scenarios. Because of their complementarity with point clouds, images are getting increasing attention. Although with some success, existing fusion methods either perform hard fusion or do not fuse in a direct manner. In this paper, we propose a generic 3D detection framework called MMFusion, using multi-modal features. The framework aims to achieve accurate fusion between LiDAR and images to improve 3D detection in complex scenes. Our framework consists of two separate streams: the LiDAR stream and the camera stream, which can be compatible with any single-modal feature extraction network. The Voxel Local Perception Module in the LiDAR stream enhances local feature representation, and then the Multi-modal Feature Fusion Module selectively combines feature output from different streams to achieve better fusion. Extensive experiments have shown that our framework not only outperforms existing benchmarks but also improves their detection, especially for detecting cyclists and pedestrians on KITTI benchmarks, with strong robustness and generalization capabilities. Hopefully, our work will stimulate more research into multi-modal fusion for autonomous driving tasks

A Lite Fireworks Algorithm with Fractal Dimension Constraint for Feature Selection

As the use of robotics becomes more widespread, the huge amount of vision data leads to a dramatic increase in data dimensionality. Although deep learning methods can effectively process these high-dimensional vision data. Due to the limitation of computational resources, some special scenarios still rely on traditional machine learning methods. However, these high-dimensional visual data lead to great challenges for traditional machine learning methods. Therefore, we propose a Lite Fireworks Algorithm with Fractal Dimension constraint for feature selection (LFWA+FD) and use it to solve the feature selection problem driven by robot vision. The "LFWA+FD" focuses on searching the ideal feature subset by simplifying the fireworks algorithm and constraining the dimensionality of selected features by fractal dimensionality, which in turn reduces the approximate features and reduces the noise in the original data to improve the accuracy of the model. The comparative experimental results of two publicly available datasets from UCI show that the proposed method can effectively select a subset of features useful for model inference and remove a large amount of noise noise present in the original data to improve the performance.Comment: International Conference on Pharmaceutical Sciences 202

The Formation History of Subhalos and the Evolution of Satellite Galaxies

Satellites constitute an important fraction of the overall galaxy population and are believed to form in dark matter subhalos. Here we use the cosmological hydrodynamic simulation TNG100 to investigate how the formation histories of subhalos affect the properties and evolution of their host galaxies. We use a scaled formation time ($a_{\rm nf}$) to characterize the mass assembly histories of the subhalos before they are accreted by massive host halos. We find that satellite galaxies in young subhalos (low $a_{\rm nf}$) are less massive and more gas rich, and have stronger star formation and a higher fraction of ex situ stellar mass than satellites in old subhalos (high $a_{\rm nf}$). Furthermore, these low $a_{\rm nf}$ satellites require longer timescales to be quenched as a population than the high $a_{\rm nf}$ counterparts. We find very different merger histories between satellites in fast accretion (FA, $a_{\rm nf}1.3$) subhalos. For FA satellites, the galaxy merger frequency dramatically increases just after accretion, which enhances the star formation at accretion. While, for SA satellites, the mergers occur smoothly and continuously across the accretion time. Moreover, mergers with FA satellites happen mainly after accretion, while a contrary trend is found for SA satellites. Our results provide insight into the evolution and star formation quenching of the satellite population.Comment: 21 pages, 10 figures, accepted for publication in Ap