Lepton-portal Dark Matter in Hidden Valley model and the DAMPE recent results

We study the recent $e^\pm$ cosmic ray excess reported by DAMPE in a Hidden Valley Model with lepton-portal dark matter. We find the electron-portal can account for the excess well and satisfy the DM relic density and direct detection bounds, while electron+muon/electron+muon+tau-portal suffers from strong constraints from lepton flavor violating observables, such as $\mu \to 3 e$. We also discuss possible collider signatures of our model, both at the LHC and a future 100 TeV hadron collider.Comment: invited by Science China, accepted versio

Improving Person Re-identification by Attribute and Identity Learning

Person re-identification (re-ID) and attribute recognition share a common target at learning pedestrian descriptions. Their difference consists in the granularity. Most existing re-ID methods only take identity labels of pedestrians into consideration. However, we find the attributes, containing detailed local descriptions, are beneficial in allowing the re-ID model to learn more discriminative feature representations. In this paper, based on the complementarity of attribute labels and ID labels, we propose an attribute-person recognition (APR) network, a multi-task network which learns a re-ID embedding and at the same time predicts pedestrian attributes. We manually annotate attribute labels for two large-scale re-ID datasets, and systematically investigate how person re-ID and attribute recognition benefit from each other. In addition, we re-weight the attribute predictions considering the dependencies and correlations among the attributes. The experimental results on two large-scale re-ID benchmarks demonstrate that by learning a more discriminative representation, APR achieves competitive re-ID performance compared with the state-of-the-art methods. We use APR to speed up the retrieval process by ten times with a minor accuracy drop of 2.92% on Market-1501. Besides, we also apply APR on the attribute recognition task and demonstrate improvement over the baselines.Comment: Accepted to Pattern Recognition (PR

Portable GPU-Based Artificial Neural Networks For Data-Driven Modeling

Artificial neural network (ANN) is widely applied as data-driven modeling tool in hydroinformatics due to its broad applicability of handing implicit and nonlinear relationships between the input and output data. To obtain a reliable ANN model, training ANN using the data is essential, but the training is usually taking many hours for large data set and/or for large systems with many variants. This may not be a concern when ANN is trained for offline applications, but it is of great importance when ANN is trained or retrained for real-time and near real-time applications, which are becoming an increasingly interested research theme while the hydroinformatics tools will be an integral part of smart city operation system. Based on author’s previous research projects, which proved that GPU-based ANN is more than 10X efficient than CPU-based ANN for constructing the meta-model (fast simulation), applied as a surrogate of the physics-based model (slow simulation). This paper presents the latest development of GPU-based ANN computing kernels that is implemented with OpenCL an Open Compute Language. The generalized ANN can be used an efficient machine learning library for data-driven modeling. The performance of the implemented library has been tested with the benchmark example and compared with the previous results

Heterogeneous Computing Paradigm For Parallel Water Distribution System Analysis

Hydraulic and water quality models have been developed and used over decades for water distribution system (WDS) analysis. The models prove to be powerful tools for engineers to gain systematic understanding on the WDS conditions and conduct technically-sound WDS management. However, it takes significantly amount of computation time to perform one simulation run for large system with tens or hundreds of thousands of pipes over an adequately long period of duration in order to gain good results for system hydraulic and water quality dynamics. In the meantime, WDS analysis solvers are not developed to take the advantages of available computing units, which are no longer homogeneous, but heterogeneous, including many cores of Central Processing Unit (CPU) and Graphics Processing Unit (GPU). Both CPUs and GPUs commonly co-exist in the personal computers, tablets and smart phones. WDS analysis models must be able to take the full computing powers offered by this heterogeneous computing paradigm. In this paper, we report a parallel computation architecture that combines task parallelization and data parallelization on both CPU and GPU for efficient WDS hydraulic and water quality analysis. The task parallelization on CPUs is implemented with multi-threading computing technique while the GPU parallelization is developed using OpenCL to ensure the portability of the parallelized solvers on various hardware vendors’ devices. With the parallelized WDS models, hydraulic and water quality simulations can be executed in parallel on CPUs, and in the same time the water quality analysis can be speeded up on GPU with massively parallel computing threads. This paper also presents the performance analysis of the parallelized solvers using the heterogeneous and portable parallel computing paradigm with CPU and GPU

Volatile components of fruits of Ligustrum lucidum Ait. stimulate proliferation and differentiation of rat calvarial osteoblasts

The fruits of Ligustrum lucidum Ait., (FLL), which contain rich volatile components, are commonly used as tonic for kidney and liver in the  traditional Chinese medicine prescriptions. This study aimed to investigate the effects of volatile components of FLL on the proliferation and   differentiation of rat calvarial osteoblasts by the MTT method and  measuring the activity of alkaline phosphatase (ALP). Results showed that volatile components (1 to 100 μg/mL) of FLL significantly (p<0.01) stimulated the proliferation and increased the ALP activity of rat calvarial osteoblasts which indicated that volatile components of FLL played an important role in osteoblastic bone formation just as non-volatile  components in FLL. Such finding accredited the FLL as a potential  candidate that might be useful in bone engineering and in treating bone defects including osteoporosis. The volatile components were analyzed by GC-MS. A total of 67 compounds were identified and the main components included (Z,Z)- 9,12-octadecadienoic acid (33.47%), n-hexadecanoic acid (15.02%), (E)-9-octadecenoic acid (9.03%), α-cadinol (6.51%), 4-hexyl-2,5-dihydro-2,5-dioxo-3-furanacetic acid (4.93%) and (E)-8-octadecenoic acid methyl ester (2.69%).Key words: Ligustrum lucidum, volatile components, rat calvarial osteoblasts

Many-body non-Hermitian skin effect under dynamic gauge coupling

We study an atom-cavity hybrid system where fermionic atoms in a one-dimensional lattice are subject to a cavity-induced dynamic gauge potential. The gauge coupling leads to highly-degenerate steady states in which the fermions accumulate to one edge of the lattice under an open boundary condition. Such a phenomenon originates from the many-body Liouvillian superoperator of the system, which, being intrinsically non-Hermitian, is unstable against boundary perturbations and manifests the non-Hermitian skin effect. Contrary to the single-body case, the steady state of a multi-atom system is approached much slower under the open boundary condition, as the long-time damping of the cavity mode exhibits distinct rates at different times. This stage-wise slowdown is attributed to the competition between light-assisted hopping and the dynamic gauge coupling, which significantly reduces the steady-state degeneracy under the open boundary condition, as distinct hosts of quasi-steady states dominate the dynamics at different time scales.Comment: 13 pages, 7 figure