Difference of optical conductivity between one- and two-dimensional doped nickelates

We study the optical conductivity in doped nickelates, and find the dramatic difference of the spectrum in the gap ($\omega$\alt4 eV) between one- (1D) and two-dimensional (2D) nickelates. The difference is shown to be caused by the dependence of hopping integral on dimensionality. The theoretical results explain consistently the experimental data in 1D and 2D nickelates, Y$_{2-x}$Ca$_x$BaNiO$_5$ and La$_{2-x}$Sr$_x$NiO$_4$, respectively. The relation between the spectrum in the X-ray aborption experiments and the optical conductivity in La$_{2-x}$Sr$_x$NiO$_4$ is discussed.Comment: RevTeX, 4 pages, 4 figure

Stellar Parameters of Main Sequence Turn-off Star Candidates Observed with the LAMOST and Kepler

Main sequence turn-off (MSTO) stars have advantages as indicators of Galactic evolution since their ages could be robustly estimated from atmospheric parameters. Hundreds of thousands of MSTO stars have been selected from the LAMOST Galactic sur- vey to study the evolution of the Galaxy, and it is vital to derive accurate stellar parameters. In this work, we select 150 MSTO star candidates from the MSTO stars sample of Xiang that have asteroseismic parameters and determine accurate stellar parameters for these stars combing the asteroseismic parameters deduced from the Kepler photometry and atmospheric parameters deduced from the LAMOST spectra.With this sample, we examine the age deter- mination as well as the contamination rate of the MSTO stars sample. A comparison of age between this work and Xiang shows a mean difference of 0.53 Gyr (7%) and a dispersion of 2.71 Gyr (28%). The results show that 79 of the candidates are MSTO stars, while the others are contaminations from either main sequence or sub-giant stars. The contamination rate for the oldest stars is much higher than that for the younger stars. The main cause for the high contamination rate is found to be the relatively large systematic bias in the LAMOST surface gravity estimates.Comment: accepted by RA

Experimental and three-dimensional finite element method studies on pounding responses of bridge structures subjected to spatially varying ground motions

Pounding and unseating damages to bridge superstructures have been commonly observed in many previous major earthquakes. These damages can essentially attribute to the large closing or opening relative displacement between adjacent structures. This article carries out an experimental study on the pounding responses of adjacent bridge structures considering spatially varying ground motions using a shaking table array system. Two sets of large-scale (1:6) bridge models involving two bridge frames were constructed. The bridge models were subjected to the stochastically simulated ground motions in bi-direction based on the response spectra of Chinese Guideline for Seismic Design of Highway Bridge for three different site conditions, considering three coherency levels. Two types of boundary conditions, that is, the fixed foundation and rocking foundation, were applied to investigate the influence of the foundation type. In addition, a detailed three-dimensional finite element model was constructed to simulate an experimental case. The nonlinear material behavior including strain rate effects of concrete and steel reinforcement is included. The applicability and accuracy of the finite element model in simulating bridge pounding responses subjected to spatially varying ground motions are discussed. The experimental and numerical results demonstrate that non-uniform excitations and foundation rocking can affect the relative displacements and pounding responses significantly

Disentangled Contrastive Learning for Learning Robust Textual Representations

Although the self-supervised pre-training of transformer models has resulted in the revolutionizing of natural language processing (NLP) applications and the achievement of state-of-the-art results with regard to various benchmarks, this process is still vulnerable to small and imperceptible permutations originating from legitimate inputs. Intuitively, the representations should be similar in the feature space with subtle input permutations, while large variations occur with different meanings. This motivates us to investigate the learning of robust textual representation in a contrastive manner. However, it is non-trivial to obtain opposing semantic instances for textual samples. In this study, we propose a disentangled contrastive learning method that separately optimizes the uniformity and alignment of representations without negative sampling. Specifically, we introduce the concept of momentum representation consistency to align features and leverage power normalization while conforming the uniformity. Our experimental results for the NLP benchmarks demonstrate that our approach can obtain better results compared with the baselines, as well as achieve promising improvements with invariance tests and adversarial attacks. The code is available in https://github.com/zjunlp/DCL.Comment: Work in progres

Normal vs. Adversarial: Salience-based Analysis of Adversarial Samples for Relation Extraction

Recent neural-based relation extraction approaches, though achieving promising improvement on benchmark datasets, have reported their vulnerability towards adversarial attacks. Thus far, efforts mostly focused on generating adversarial samples or defending adversarial attacks, but little is known about the difference between normal and adversarial samples. In this work, we take the first step to leverage the salience-based method to analyze those adversarial samples. We observe that salience tokens have a direct correlation with adversarial perturbations. We further find the adversarial perturbations are either those tokens not existing in the training set or superficial cues associated with relation labels. To some extent, our approach unveils the characters against adversarial samples. We release an open-source testbed, "DiagnoseAdv" in https://github.com/zjunlp/DiagnoseAdv.Comment: IJCKG 202

Effects of Acute Cold Stress After Long-Term Cold Stimulation on Antioxidant Status, Heat Shock Proteins, Inflammation and Immune Cytokines in Broiler Heart

To investigate the effects of acute cold stress (ACS) on chicken heart after cold stimulation, female broilers were raised in either normal (C) or gradually decreasing temperatures (CS I and CS II) for 34 days followed by a 24 h ACS at 7°C. Cardiac tissues were collected from the pre-ACS and ACS time points to analyze the histopathological changes, antioxidant status and the expression of heat shock proteins, inflammatory factors and immune-related cytokines. The CS II heart tissues showed shrunken cell membranes and nuclei, disordered or ruptured myocardial fibers, higher MDA content and upregulation in HSP27, HSP40, HSP70, NF-κB, COX-2, PTGEs, iNOS, TNF-α and IL-4 mRNAs, and in protein levels of HSP40, NF-κB and iNOS and reduction in CAT, GSH-px and SOD activity, as well as HSP90 and IFN-γ levels compared to the control tissues before ACS. In contrast, the HSPs were significantly increased, and the inflammatory and immune related factors were unaltered prior to the ACS in the CS I compared to the C group. Following ACS, MDA content was significantly increased and antioxidant activity was significantly decreased in the CS I and CS II groups compared to the C group. The levels of HSP27, HSP70, HSP90, inflammatory factors and IL-4 were significantly reduced and that of IFN-γ was significantly increased in CS I broiler hearts; the reverse trends were seen in CS II relative to CS I. Compared to the pre-ACS levels, that of HSP27, HSP40, HSP60, inflammatory factors and IL-4 were increased and IFN-γ was decreased in the C and CS II groups after ACS. Therefore, cold stimulation at drastically lower temperatures induced cardiac damage, which was further aggravated by ACS. In contrast, cold stimulation at only 3°C lower than normal temperature improved the adaptability of the broilers to ACS

Distributed quantum computing over 7.0 km

Distributed quantum computing provides a viable approach towards scalable quantum computation, which relies on nonlocal quantum gates to connect distant quantum nodes, to overcome the limitation of a single device. However, such an approach has only been realized within single nodes or between nodes separated by a few tens of meters, preventing the target of harnessing computing resources in large-scale quantum networks. Here, we demonstrate distributed quantum computing between two nodes spatially separated by 7.0 km, using stationary qubits based on multiplexed quantum memories, flying qubits at telecom wavelengths, and active feedforward control based on field-deployed fiber. Specifically, we illustrate quantum parallelism by implementing Deutsch-Jozsa algorithm and quantum phase estimation algorithm between the two remote nodes. These results represent the first demonstration of distributed quantum computing over metropolitan-scale distances and lay the foundation for the construction of large-scale quantum computing networks relying on existing fiber channels.Comment: 6 pages, 3 figure

Molecular subtypes predict the preferential site of distant metastasis in advanced breast cancer: a nationwide retrospective study

ObjectiveThis study aimed to explore possible associations between molecular subtypes and site of distant metastasis in advanced breast cancer (ABC).Methods3577 ABC patients were selected from 21 hospitals of seven geographic regions in China from 2012-2014. A questionnaire was designed to collect medical information regarding demographic characteristics, risk factors, molecular subtype, recurrence/metastasis information, and disease-free survival (DFS). The cancers were classified into Luminal A, Luminal B, HER2-enriched and Triple Negative subtypes. Chi-square test and multivariate Cox proportional hazard models were performed to explore the associations between molecular subtypes and distant metastasis sites.ResultsA total of 2393 cases with molecular subtypes information were finally examined. Patients with Luminal A (51.1%) and Luminal B (44.7%) were most prone to bone metastasis, whereas liver metastasis was more frequently observed in HER2-enriched ABC patients (29.1%).The cumulative recurrence and metastasis rates of ABC patients at 36 months of DFS were the most significant within molecular types, of which Triple Negative was the highest (82.7%), while that of Luminal A was the lowest (58.4%). In the adjusted Cox regression analysis, Luminal B, HER2-enriched and Triple Negative subtypes increased the risk of visceral metastasis by 23%, 46% and 87% respectively. In addition, Triple Negative patients had a higher probability of brain metastasis (HR 3.07, 95% CI: 1.04-9.07).ConclusionMolecular subtypes can predict the preferential sites of distant metastasis, emphasizing that these associations were of great help in choices for surveillance, developing appropriate screening and cancer management strategies for follow-up and personalized therapy in ABC patients