Exploring Replica-Exchange Wang-Landau sampling in higher-dimensional parameter space

We considered a higher-dimensional extension for the replica-exchange Wang-Landau algorithm to perform a random walk in the energy and magnetization space of the two-dimensional Ising model. This hybrid scheme combines the advantages of Wang-Landau and Replica-Exchange algorithms, and the one-dimensional version of this approach has been shown to be very efficient and to scale well, up to several thousands of computing cores. This approach allows us to split the parameter space of the system to be simulated into several pieces and still perform a random walk over the entire parameter range, ensuring the ergodicity of the simulation. Previous work, in which a similar scheme of parallel simulation was implemented without using replica exchange and with a different way to combine the result from the pieces, led to discontinuities in the final density of states over the entire range of parameters. From our simulations, it appears that the replica-exchange Wang-Landau algorithm is able to overcome this difficulty, allowing exploration of higher parameter phase space by keeping track of the joint density of states.Comment: Proceedings of CCP2014 will appear in Journal of Physics: Conference Series (JPCS), published by the IO

Resolving spin, valley, and moir\'e quasi-angular momentum of interlayer excitons in WSe2/WS2 heterostructures

Moir\'e superlattices provide a powerful way to engineer properties of electrons and excitons in two-dimensional van der Waals heterostructures. The moir\'e effect can be especially strong for interlayer excitons, where electrons and holes reside in different layers and can be addressed separately. In particular, it was recently proposed that the moir\'e superlattice potential not only localizes interlayer exciton states at different superlattice positions, but also hosts an emerging moir\'e quasi-angular momentum (QAM) that periodically switches the optical selection rules for interlayer excitons at different moir\'e sites. Here we report the observation of multiple interlayer exciton states coexisting in a WSe2/WS2 moir\'e superlattice and unambiguously determine their spin, valley, and moir\'e QAM through novel resonant optical pump-probe spectroscopy and photoluminescence excitation spectroscopy. We demonstrate that interlayer excitons localized at different moir\'e sites can exhibit opposite optical selection rules due to the spatially-varying moir\'e QAM. Our observation reveals new opportunities to engineer interlayer exciton states and valley physics with moir\'e superlattices for optoelectronic and valleytronic applications

Superconductivity suppression of Ba0.5K0.5Fe2-2xM2xAs2 single crystals by substitution of transition-metal (M = Mn, Ru, Co, Ni, Cu, and Zn)

We investigated the doping effects of magnetic and nonmagnetic impurities on the single-crystalline p-type Ba0.5K0.5Fe2-2xM2xAs2 (M = Mn, Ru, Co, Ni, Cu and Zn) superconductors. The superconductivity indicates robustly against impurity of Ru, while weakly against the impurities of Mn, Co, Ni, Cu, and Zn. However, the present Tc suppression rate of both magnetic and nonmagnetic impurities remains much lower than what was expected for the s\pm-wave model. The temperature dependence of resistivity data is observed an obvious low-T upturn for the crystals doped with high-level impurity, which is due to the occurrence of localization. Thus, the relatively weak Tc suppression effect from Mn, Co, Ni, Cu, and Zn are considered as a result of localization rather than pair-breaking effect in s\pm-wave model.Comment: 8 pages, 9 figures, to be published in Phys. Rev.

Predictors of functional deterioration in Chinese patients with Psoriatic arthritis: A longitudinal study.

10.1186/1471-2474-15-284BMC Musculoskeletal Disorders15128

Integrated microfluidic systems with sample preparation and nucleic acid amplification

Rapid, efficient and accurate nucleic acid molecule detection is important in the screening of diseases and pathogens, yet remains a limiting factor at point of care (POC) treatment. Microfluidic systems are characterized by fast, integrated, miniaturized features which provide an effective platform for qualitative and quantitative detection of nucleic acid molecules. The nucleic acid detection process mainly includes sample preparation and target molecule amplification. Given the advancements in theoretical research and technological innovations to date, nucleic acid extraction and amplification integrated with microfluidic systems has advanced rapidly. The primary goal of this review is to outline current approaches used for nucleic acid detection in the context of microfluidic systems. The secondary goal is to identify new approaches that will help shape future trends at the intersection of nucleic acid detection and microfluidics, particularly with regard to increasing disease and pathogen detection for improved diagnosis and treatment

Remote generation of entanglement for individual atoms via optical fibers

The generation of atomic entanglement is discussed in a system that atoms are trapped in separate cavities which are connected via optical fibers. Two distant atoms can be projected to Bell-state by synchronized turning off the local laser fields and then performing a single quantum measurement by a distant controller. The distinct advantage of this scheme is that it works in a regime that $\Delta\approx\kappa\gg g$, which makes the scheme insensitive to cavity strong leakage. Moreover, the fidelity is not affected by atomic spontaneous emission.Comment: 4 pages, 3 figure

Genome-wide shRNA screen revealed integrated mitogenic signaling between dopamine receptor D2 (DRD2) and epidermal growth factor receptor (EGFR) in glioblastoma

Glioblastoma remains one of the deadliest of human cancers, with most patients succumbing to the disease within two years of diagnosis. The available data suggest that simultaneous inactivation of critical nodes within the glioblastoma molecular circuitry will be required for meaningful clinical efficacy. We conducted parallel genome-wide shRNA screens to identify such nodes and uncovered a number of G-Protein Coupled Receptor (GPCR) neurotransmitter pathways, including the Dopamine Receptor D2 (DRD2) signaling pathway. Supporting the importance of DRD2 in glioblastoma, DRD2 mRNA and protein expression were elevated in clinical glioblastoma specimens relative to matched non-neoplastic cerebrum. Treatment with independent si-/shRNAs against DRD2 or with DRD2 antagonists suppressed the growth of patient-derived glioblastoma lines both in vitro and in vivo. Importantly, glioblastoma lines derived from independent genetically engineered mouse models (GEMMs) were more sensitive to haloperidol, an FDA approved DRD2 antagonist, than the premalignant astrocyte lines by approximately an order of magnitude. The pro-proliferative effect of DRD2 was, in part, mediated through a GNAI2/Rap1/Ras/ERK signaling axis. Combined inhibition of DRD2 and Epidermal Growth Factor Receptor (EGFR) led to synergistic tumoricidal activity as well as ERK suppression in independent in vivo and in vitro glioblastoma models. Our results suggest combined EGFR and DRD2 inhibition as a promising strategy for glioblastoma treatment

A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction

For e-commerce platforms such as Taobao and Amazon, advertisers play an important role in the entire digital ecosystem: their behaviors explicitly influence users' browsing and shopping experience; more importantly, advertiser's expenditure on advertising constitutes a primary source of platform revenue. Therefore, providing better services for advertisers is essential for the long-term prosperity for e-commerce platforms. To achieve this goal, the ad platform needs to have an in-depth understanding of advertisers in terms of both their marketing intents and satisfaction over the advertising performance, based on which further optimization could be carried out to service the advertisers in the correct direction. In this paper, we propose a novel Deep Satisfaction Prediction Network (DSPN), which models advertiser intent and satisfaction simultaneously. It employs a two-stage network structure where advertiser intent vector and satisfaction are jointly learned by considering the features of advertiser's action information and advertising performance indicators. Experiments on an Alibaba advertisement dataset and online evaluations show that our proposed DSPN outperforms state-of-the-art baselines and has stable performance in terms of AUC in the online environment. Further analyses show that DSPN not only predicts advertisers' satisfaction accurately but also learns an explainable advertiser intent, revealing the opportunities to optimize the advertising performance further