Gapped spin liquid with Z2\mathbb{Z}_2-topological order for kagome Heisenberg model

We apply symmetric tensor network state (TNS) to study the nearest neighbor spin-1/2 antiferromagnetic Heisenberg model on Kagome lattice. Our method keeps track of the global and gauge symmetries in TNS update procedure and in tensor renormalization group (TRG) calculation. We also introduce a very sensitive probe for the gap of the ground state -- the modular matrices, which can also determine the topological order if the ground state is gapped. We find that the ground state of Heisenberg model on Kagome lattice is a gapped spin liquid with the $\mathbb{Z}_2$-topological order (or toric code type), which has a long correlation length $\xi\sim 10$ unit cell length. We justify that the TRG method can handle very large systems with over thousands of spins. Such a long $\xi$ explains the gapless behaviors observed in simulations on smaller systems with less than 300 spins or shorter than 10 unit cell length. We also discuss experimental implications of the topological excitations encoded in our symmetric tensors.Comment: 10 pages, 7 figure

Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN

Discriminative localization is essential for fine-grained image classification task, which devotes to recognizing hundreds of subcategories in the same basic-level category. Reflecting on discriminative regions of objects, key differences among different subcategories are subtle and local. Existing methods generally adopt a two-stage learning framework: The first stage is to localize the discriminative regions of objects, and the second is to encode the discriminative features for training classifiers. However, these methods generally have two limitations: (1) Separation of the two-stage learning is time-consuming. (2) Dependence on object and parts annotations for discriminative localization learning leads to heavily labor-consuming labeling. It is highly challenging to address these two important limitations simultaneously. Existing methods only focus on one of them. Therefore, this paper proposes the discriminative localization approach via saliency-guided Faster R-CNN to address the above two limitations at the same time, and our main novelties and advantages are: (1) End-to-end network based on Faster R-CNN is designed to simultaneously localize discriminative regions and encode discriminative features, which accelerates classification speed. (2) Saliency-guided localization learning is proposed to localize the discriminative region automatically, avoiding labor-consuming labeling. Both are jointly employed to simultaneously accelerate classification speed and eliminate dependence on object and parts annotations. Comparing with the state-of-the-art methods on the widely-used CUB-200-2011 dataset, our approach achieves both the best classification accuracy and efficiency.Comment: 9 pages, to appear in ACM MM 201

A Model of Two-Way Selection System for Human Behavior

We propose a model of two-way selection system. It appears in the processes like choosing a mate between men and women, making contracts between job hunters and recruiters, and trading between buyers and sellers. In this paper, we propose a model of two-way selection system, and present its analytic solution for the expectation of successful matching total and the regular pattern that the matching rate trends toward an inverse proportion to either the ratio between the two sides or the ratio of the state total to the smaller people number. The proposed model is verified by empirical data of the matchmaking fairs. Results indicate that the model well predicts this typical real-world two- way selection behavior to the bounded error extent, thus it is helpful for understanding the dynamics mechanism of the real-world two-way selection system.Comment: 8 pages, 4 figure

Macroautophagy in T Lymphocyte Development and Function

Macroautophagy (referred to as autophagy) is a fundamental intracellular process characterized by the sequestration of cytoplasmic compartments through double-membrane vesicles, termed autophagosomes. Recent studies have established important roles of autophagy in regulating T lymphocyte development and function. Resting T lymphocytes have basal levels of autophagy that is upregulated by T cell receptor stimulation. Several specific knockout or transgenic models have been developed during the past few years, and it has been revealed that autophagy plays an essential role in regulating thymocyte selection, peripheral T cell survival, and proliferation. The regulation of T cell development and function by autophagy is mediated through its role in regulating self-antigen presentation, intracellular organelle homeostasis, and energy production. Here we will review the current findings concerning how autophagy regulates T cell function, as well as compare different models in studying autophagy in T lymphocytes

One-Shot Fine-Grained Instance Retrieval

Fine-Grained Visual Categorization (FGVC) has achieved significant progress recently. However, the number of fine-grained species could be huge and dynamically increasing in real scenarios, making it difficult to recognize unseen objects under the current FGVC framework. This raises an open issue to perform large-scale fine-grained identification without a complete training set. Aiming to conquer this issue, we propose a retrieval task named One-Shot Fine-Grained Instance Retrieval (OSFGIR). "One-Shot" denotes the ability of identifying unseen objects through a fine-grained retrieval task assisted with an incomplete auxiliary training set. This paper first presents the detailed description to OSFGIR task and our collected OSFGIR-378K dataset. Next, we propose the Convolutional and Normalization Networks (CN-Nets) learned on the auxiliary dataset to generate a concise and discriminative representation. Finally, we present a coarse-to-fine retrieval framework consisting of three components, i.e., coarse retrieval, fine-grained retrieval, and query expansion, respectively. The framework progressively retrieves images with similar semantics, and performs fine-grained identification. Experiments show our OSFGIR framework achieves significantly better accuracy and efficiency than existing FGVC and image retrieval methods, thus could be a better solution for large-scale fine-grained object identification.Comment: Accepted by MM2017, 9 pages, 7 figure

Protective Effect of FTY720 on Several Markers of Liver Injury Induced by Concanavalin A in Mice

AbstractBackground2-Amino-2-[2-(4-octylphenyl)ethyl] propane-1,3-diol hydrochloride (FTY720) is a novel agent with protective effect on several markers of liver injury. It is a chemical substance derived by modifying myriocin from the ascomycete Isaria sinclairii. It has been reported that FTY720 is able to treat autoimmune encephalomyelitis, renal cancer, asthma, and multiple sclerosis. More potent clinical applications of FTY720 need to be investigated.ObjectiveThe aim of this study was to evaluate the protective effect of FTY720 on several markers of experimental liver injury and to investigate the possible mechanism of action.MethodsConcanavalin A (Con A) at a dose of 15 mg/kg was intravenously. injected in mice, and 10 days before the Con A challenge, 1 mg/kg, 3 mg/kg, and 6 mg/kg of FTY720 were administered to mice. The liver injury was monitored biochemically by measuring serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) and tumor necrosis factor-α (TNF-α) levels. TNF-α and nuclear factor-κB (NF-κB) in liver tissue were detected by Western blot analysis.ResultsFTY720, when administered intragastrically for 10 days in mice with Con A–induced liver injury, dose-dependently reduced serum ALT and AST and TNF-α levels. The differences were statistically significant (P ≤ 0.05). It was also found that FTY720 decreases TNF-α and NF-κB protein expression in liver tissue.ConclusionsFTY720 is able to improve several markers of Con A–induced liver injury in mice, including serum ALT, serum AST, TNF-α, and NF-κB, which might be at least in part related to its ability to reduce TNF-α/NF-κB cascade activity

Geometry and optics calibration of WFCTA prototype telescopes using star light

The Large High Altitude Air Shower Observatory project is proposed to study high energy gamma ray astronomy ( 40 GeV-1 PeV ) and cosmic ray physics ( 20 TeV-1 EeV ). The wide field of view Cherenkov telescope array, as a component of the LHAASO project, will be used to study energy spectrum and compositions of cosmic ray by measuring the total Cherenkov light generated by air showers and shower maximum depth. Two prototype telescopes have been in operation since 2008. The pointing accuracy of each telescope is crucial to the direction reconstruction of the primary particles. On the other hand the primary energy reconstruction relies on the shape of the Cherenkov image on the camera and the unrecorded photons due to the imperfect connections between photomultiplier tubes. UV bright stars are used as point-like objects to calibrate the pointing and to study the optical properties of the camera, the spot size and the fractions of unrecorded photons in the insensitive areas of the camera.Comment: 5 pages, 6 figures, submitted to Chinese Physics