Luttinger-volume violating Fermi liquid in the pseudogap phase of the cuprate superconductors

Based on the NMR measurements on Bi$_2$Sr$_{2-x}$La$_x$CuO$_{6+\delta}$ (La-Bi2201) in strong magnetic fields, we identify the non-superconducting pseudogap phase in the cuprates as a Luttinger-volume violating Fermi liquid (LvvFL). This state is a zero temperature quantum liquid that does not break translational symmetry, and yet, the Fermi surface encloses a volume smaller than the large one given by the Luttinger theorem. The particle number enclosed by the small Fermi surface in the LvvFL equals the doping level $p$, not the total electron number $n_e=1-p$. Both the phase string theory and the dopon theory are introduced to describe the LvvFL. For the dopon theory, we can obtain a semi-quantitative agreement with the NMR experiments.Comment: The final version in PR

Efficient single-photon-assisted entanglement concentration for partially entangled photon pairs

We present two realistic entanglement concentration protocols (ECPs) for pure partially entangled photons. A partially entangled photon pair can be concentrated to a maximally entangled pair with only an ancillary single photon in a certain probability, while the conventional ones require two copies of partially entangled pairs at least. Our first protocol is implemented with linear optics and the second one is implemented with cross-Kerr nonlinearities. Compared with other ECPs, they do not need to know the accurate coefficients of the initial state. With linear optics, it is feasible with current experiment. With cross-Kerr nonlinearities, it does not require the sophisticated single-photon detectors and can be repeated to get a higher success probability. Moreover, the second protocol can get the higher entanglement transformation efficiency and it maybe the most economical one by far. Meanwhile, both of protocols are more suitable for multi-photon system concentration, because they need less operations and classical communications. All these advantages make two protocols be useful in current long-distance quantum communications

Building international partnerships

Health is a global concern. Although nursing is a global profession, most schools of nursing concentrate on teaching health exclusively within the context of their own nation. Sister-school partnerships that cross national boundaries are one way of extending the learning opportunities of faculties and students. An example of a 5-year partnership is described and analysed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74777/1/j.1466-7657.2001.00058.x.pd

Adaptive Semantic-Visual Tree for Hierarchical Embeddings

Merchandise categories inherently form a semantic hierarchy with different levels of concept abstraction, especially for fine-grained categories. This hierarchy encodes rich correlations among various categories across different levels, which can effectively regularize the semantic space and thus make predictions less ambiguous. However, previous studies of fine-grained image retrieval primarily focus on semantic similarities or visual similarities. In a real application, merely using visual similarity may not satisfy the need of consumers to search merchandise with real-life images, e.g., given a red coat as a query image, we might get a red suit in recall results only based on visual similarity since they are visually similar. But the users actually want a coat rather than suit even the coat is with different color or texture attributes. We introduce this new problem based on photoshopping in real practice. That's why semantic information are integrated to regularize the margins to make "semantic" prior to "visual". To solve this new problem, we propose a hierarchical adaptive semantic-visual tree (ASVT) to depict the architecture of merchandise categories, which evaluates semantic similarities between different semantic levels and visual similarities within the same semantic class simultaneously. The semantic information satisfies the demand of consumers for similar merchandise with the query while the visual information optimizes the correlations within the semantic class. At each level, we set different margins based on the semantic hierarchy and incorporate them as prior information to learn a fine-grained feature embedding. To evaluate our framework, we propose a new dataset named JDProduct, with hierarchical labels collected from actual image queries and official merchandise images on an online shopping application. Extensive experimental results on the public CARS196 and CUB

The psychometric properties of the quick inventory of depressive symptomatology-self-report (QIDS-SR) in patients with HBV-related liver disease

Background: Comorbid depression in Hepatitis B virus (HBV) is common. Developing accurate and time efficient tools to measure depressive symptoms in HBV is important for research and clinical practice in China. Aims: This study tested the psychometric properties of the Chinese version of the 16-item Quick Inventory of Depressive Symptomatology (QIDS-SR) in HBV patients. Methods: The study recruited 245 depressed patients with HBV and related liver disease. The severity of depressive symptoms was assessed with the Montgomery-Asberg Depression Rating Scale (MADRS) and the QIDS-SR. Results: Internal consistency (Cronbach’s alpha) was 0.796 for QIDS-SR. The QIDS-SR total score was significantly correlated with the MADRS total score (r=0.698, p. Conclusions: The QIDS-SR (Chinese version) has good psychometric properties in HBV patients and appears to be useful in assessing depression in clinical settings

Comparison of Diagnostic Effects of T2-Weighted Imaging, DWI, SWI, and DTI in Acute Cerebral Infarction

Objective: To achieve precision medicine, the use of imaging methods to help the clinical detection of cerebral infarction is conducive to the clinical development of a treatment plan and increase of the cure rate and improvement of the prognosis of patients. Methods: In this work, T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), susceptibility-weighted imaging (SWI), and diffusion tensor imaging (DTI) examinations were performed on 34 patients with clinically diagnosed cerebral infarction to measure the difference in signal intensity between the lesion and its mirror area and make a comparative analysis by means of the Student-Newman-Keuls method. Results: The detection rate of T2WI was 79% (27/34), the detection rate of DWI was 97% (33/34), the detection rate of SWI was 88% (30/34), and the detection rate of DTI was 94% (32/34). Conclusion: The imaging performance was in the order DWI &gt; DTI &gt; SWI &gt; T2WI for the diagnosis of cerebral infarction, and combined imaging is better than single imaging. </p