Nearly Tri-bimaximal Neutrino Mixing and CP Violation from mu-tau Symmetry Breaking

Assuming the Majorana nature of massive neutrinos, we generalize the Friedberg-Lee neutrino mass model to include CP violation in the neutrino mass matrix $M^{}_\nu$. We show that a favorable neutrino mixing pattern (with $\theta^{}_{12} \approx 35.3^\circ$, $\theta^{}_{23} = 45^\circ$, $\theta^{}_{13} \neq 0^\circ$ and $\delta = 90^\circ$) can naturally be derived from $M^{}_\nu$, if it has an approximate or softly-broken $\mu$-$\tau$ symmetry. We point out a different way to obtain the nearly tri-bimaximal neutrino mixing with $\delta = 0^\circ$ and non-vanishing Majorana phases. The most general case, in which all the free parameters of $M^{}_\nu$ are complex and the resultant neutrino mixing matrix contains both Dirac and Majorana phases of CP violation, is also discussed.Comment: REVTeX 16 pages, 8 PS figures. A numerical error in FIG.1 removed; the parameter space of Majorana phases in Scenario (B) plotted; more discussions added; figures and references update

Dual Modality Prompt Tuning for Vision-Language Pre-Trained Model

With the emergence of large pre-trained vison-language model like CLIP, transferable representations can be adapted to a wide range of downstream tasks via prompt tuning. Prompt tuning tries to probe the beneficial information for downstream tasks from the general knowledge stored in the pre-trained model. A recently proposed method named Context Optimization (CoOp) introduces a set of learnable vectors as text prompt from the language side. However, tuning the text prompt alone can only adjust the synthesized "classifier", while the computed visual features of the image encoder can not be affected , thus leading to sub-optimal solutions. In this paper, we propose a novel Dual-modality Prompt Tuning (DPT) paradigm through learning text and visual prompts simultaneously. To make the final image feature concentrate more on the target visual concept, a Class-Aware Visual Prompt Tuning (CAVPT) scheme is further proposed in our DPT, where the class-aware visual prompt is generated dynamically by performing the cross attention between text prompts features and image patch token embeddings to encode both the downstream task-related information and visual instance information. Extensive experimental results on 11 datasets demonstrate the effectiveness and generalization ability of the proposed method. Our code is available in https://github.com/fanrena/DPT.Comment: 12 pages, 7 figure

Ground-to-Aerial Person Search: Benchmark Dataset and Approach

In this work, we construct a large-scale dataset for Ground-to-Aerial Person Search, named G2APS, which contains 31,770 images of 260,559 annotated bounding boxes for 2,644 identities appearing in both of the UAVs and ground surveillance cameras. To our knowledge, this is the first dataset for cross-platform intelligent surveillance applications, where the UAVs could work as a powerful complement for the ground surveillance cameras. To more realistically simulate the actual cross-platform Ground-to-Aerial surveillance scenarios, the surveillance cameras are fixed about 2 meters above the ground, while the UAVs capture videos of persons at different location, with a variety of view-angles, flight attitudes and flight modes. Therefore, the dataset has the following unique characteristics: 1) drastic view-angle changes between query and gallery person images from cross-platform cameras; 2) diverse resolutions, poses and views of the person images under 9 rich real-world scenarios. On basis of the G2APS benchmark dataset, we demonstrate detailed analysis about current two-step and end-to-end person search methods, and further propose a simple yet effective knowledge distillation scheme on the head of the ReID network, which achieves state-of-the-art performances on both of the G2APS and the previous two public person search datasets, i.e., PRW and CUHK-SYSU. The dataset and source code available on \url{https://github.com/yqc123456/HKD_for_person_search}.Comment: Accepted by ACM MM 202

Prediction of chlorophyll a concentration using HJ-1 satellite imagery for Xiangxi Bay in Three Gorges Reservoir

AbstractSince the impoundment of the Three Gorges Reservoir in 2003, algal blooms have frequently been observed in it. The chlorophyll a concentration is an important parameter for evaluating algal blooms. In this study, the chlorophyll a concentration in Xiangxi Bay, in the Three Gorges Reservoir, was predicted using HJ-1 satellite imagery. Several models were established based on a correlation analysis between in situ measurements of the chlorophyll a concentration and the values obtained from satellite images of the study area from January 2010 to December 2011. Chlorophyll a concentrations in Xiangxi Bay were predicted based on the established models. The results show that the maximum correlation is between the reflectance of the band combination of B4/(B2+B3) and in situ measurements of chlorophyll a concentration. The root mean square errors of the predicted values using the linear and quadratic models are 18.49 mg/m3 and 18.52 mg/m3, respectively, and the average relative errors are 37.79% and 36.79%, respectively. The results provide a reference for water bloom prediction in typical tributaries of the Three Gorges Reservoir and contribute to large-scale remote sensing monitoring and water quality management

Text-based Person Search in Full Images via Semantic-Driven Proposal Generation

Finding target persons in full scene images with a query of text description has important practical applications in intelligent video surveillance.However, different from the real-world scenarios where the bounding boxes are not available, existing text-based person retrieval methods mainly focus on the cross modal matching between the query text descriptions and the gallery of cropped pedestrian images. To close the gap, we study the problem of text-based person search in full images by proposing a new end-to-end learning framework which jointly optimize the pedestrian detection, identification and visual-semantic feature embedding tasks. To take full advantage of the query text, the semantic features are leveraged to instruct the Region Proposal Network to pay more attention to the text-described proposals. Besides, a cross-scale visual-semantic embedding mechanism is utilized to improve the performance. To validate the proposed method, we collect and annotate two large-scale benchmark datasets based on the widely adopted image-based person search datasets CUHK-SYSU and PRW. Comprehensive experiments are conducted on the two datasets and compared with the baseline methods, our method achieves the state-of-the-art performance

Cladding waveguide splitters fabricated by femtosecond laser inscription in Ti:Sapphire crystal

Highly-compact devices capable of beam splitting are intriguing for a broad range of photonic applications. In this work, we report on the fabrication of optical waveguide splitters with rectangular cladding geometry in a Ti:Sapphire crystal by femtosecond laser inscription. Y-splitters are fabricated with 30 μm × 15 μm and 50 μm × 25 μm input ends, corresponding to two 15 μm × 15 μm and 25 μm × 25 μm output ends, respectively. The full branching angle θ between the two output arms are changing from 0.5° to 2°. The performances of the splitters are characterized at 632.8 nm and 1064 nm, showing very good properties including symmetrical output ends, single-mode guidance, equalized splitting ratios, all-angle-polarization light transmission and intact luminescence features in the waveguide cores. The realization of these waveguide splitters with good performances demonstrates the potential of such promising devices in complex monolithic photonic circuits and active optical devices such as miniature tunable lasers.This work is supported by the National Natural Science Foundation of China (No. 11404194). Authors acknowledge support from Junta de Castilla y León (Project SA046U16) and MINECO (FIS2015-71933-REDT). Authors would like to thank Prof. Xiaotao Hao from Shandong University for the help on micro-photoluminescence measurement

Effects of dietary yeast beta-1,3-1,6-glucan on growth performance, intestinal morphology and chosen immunity parameters changes in Haidong chicks

Objective: This study investigated the effects of 1,3-1,6 beta-glucan added to the diet of Haidong chicks reared under hypoxic conditions, to ascertain the growth performances, immunity and intestinal morphology changes. Methods: A total of 750 chicks were divided into five groups and fed diets containing 0.5g/kg, 1.0g/kg and 2.0g/kg 1,3-1,6 beta-glucan from yeast (G1, G2, G3, respectively), 0.2g/kg Taylor rhizomorph (T) and a control feed (C). Results: The body weight and body weight gain was higher in chicks fed 1,3-1,6 beta-glucan and Taylor rhizomorph than in control group. Feed conversion ratio significantly differed for G2 and G3 groups in comparison to control group. The relative weight of bursa was higher in G1, G2 and G3 groups. The white blood cells and lymphocytes were significantly increased in groups fed 1,3-1,6 beta-glucan. The IgG of serum peak appeared in the G3 group. The villous height of the duodenum was higher in 1,3-1,6 beta-glucan feed groups. In the jejunum, the villous height was higher in G2 and G3 groups and crypt depth for all the groups fed β-glucan. At ileum level the villous height and crept depth was higher for groups G1, G2 and G3. Conclusion: The growth performance of Haidong chicks is improved when 10 and 20 g/kg 1,3-1,6 beta-glucan is included in the diet; hence, it is suggested to diet include 1,3-1,6 beta-glucan in poultry diet to reduce and replace the use of antibiotics