Spin-dependent Rotating Wigner Molecules in Quantum dots

The spin-dependent trial wave functions with rotational symmetry are introduced to describe rotating Wigner molecular states with spin degree of freedom in four- and five-electron quantum dots under magnetic fields. The functions are constructed with unrestricted Hartree-Fock orbits and projection technique in long-range interaction limit. They highly overlap with the exact-diagonalized ones and give the accurate energies in strong fields. The zero points, i.e. vortices of the functions have straightforward relations to the angular momenta of the states. The functions with different total spins automatically satisfy the angular momentum transition rules with the increase of magnetic fields and explicitly show magnetic couplings and characteristic oscillations with respect to the angular momenta. Based on the functions, it is demonstrated that the entanglement entropies of electrons depend on the z-component of total spin and rise with the increase of angular momenta

Deep Reinforcement Learning-based Image Captioning with Embedding Reward

Image captioning is a challenging problem owing to the complexity in understanding the image content and diverse ways of describing it in natural language. Recent advances in deep neural networks have substantially improved the performance of this task. Most state-of-the-art approaches follow an encoder-decoder framework, which generates captions using a sequential recurrent prediction model. However, in this paper, we introduce a novel decision-making framework for image captioning. We utilize a "policy network" and a "value network" to collaboratively generate captions. The policy network serves as a local guidance by providing the confidence of predicting the next word according to the current state. Additionally, the value network serves as a global and lookahead guidance by evaluating all possible extensions of the current state. In essence, it adjusts the goal of predicting the correct words towards the goal of generating captions similar to the ground truth captions. We train both networks using an actor-critic reinforcement learning model, with a novel reward defined by visual-semantic embedding. Extensive experiments and analyses on the Microsoft COCO dataset show that the proposed framework outperforms state-of-the-art approaches across different evaluation metrics

Aharonov-Bohm phase operations on a double-barrier nanoring charge qubit

We present a scheme for charge qubit implementation in a double-barrier nanoring. The logical states of the qubit are encoded in the spatial wavefunctions of the two lowest energy states of the system. The Aharonov-Bohm phase introduced by magnetic flux, instead of tunable tunnelings, along with electric fields can be used for implementing the quantum gate operations. During the operations, the external fields should be switched smoothly enough to avoid the errors caused by the transition to higher-lying states. The structure and field effects on the validity of the qubit are also studied.Comment: 6 pages, 7 figure

Therapeutic benefit of aripiprazole-olanzapine combination in the treatment of senile Alzheimer’s disease complicated by mental disorders

Purpose: To determine the clinical efficacy of aripiprazole-olanzapine combination treatment in elderly Alzheimer’s disease complicated with mental disorders. Methods: Ninety-two elderly patients with Alzheimer’s disease and mental disorders who were admitted to Binzhou People's Hospital, were enrolled in the study. They were randomized into control and study groups. Control group was treated with olanzapine, while the study group was treated with aripiprazole as an adjuvant therapy in addition to olanzapine. The clinical efficacy, scores on different scales (MMSE, ADAS-cog, CDR, ADL, NPI and CMAI), and incidence of adverse reactions were determined. Results: The overall degree of response was significantly higher in the study group than in the control group (p < 0.05). There were no significant differences in MMSE, ADAS-cog, CDR, ADL, NPI and CMAI scores between the two groups before treatment (p > 0.05). The MMSE score of the study group was significantly higher than that of the control group, and the scores in the other scales in the study group were significantly lower after treatment (p < 0.05). The study group had significantly lower incidence of adverse reactions than control group (p < 0.05). Conclusion: Aripiprazole-olanzapine combination has significant therapeutic benefit in the treatment of elderly Alzheimer’s disease patients complicated with mental disorders. It promotes recovery of neurological function, as well as produces a lower incidence of adverse reactions. Keywords: Aripiprazole, Olanzapine, Alzheimer’s disease, Mental disorder

Context-aware service discovery and service composition over smart phones

One of the advantages of smart phones is the ubiquity of sensing and computing power. The smart phone consists of a mobile computing platform. The Web browsers installed on the platform make it possible to surf the Internet through mobile broadband and Wi-Fi. An important aspect of smart phones is that they have application programming interfaces, which is able to take advantage of third-party applications. Different from any desktop applications, the smart phone applications could be highly adaptive to contexts, i.e. according to context information, e.g. location, identity, and time, the applications are tuned to satisfy particular requirements in the contexts. On the other sense, service composition is a way to plan a business process to fulfill business goals that cannot be achieved by individual business services. Service composition can be modeled as a AI planning problem. Based on the initial context and the goal context, planning-based service composition launches a goal-oriented composition procedure to generate a plan. Service composition over smart phones can be context-awareness. In this thesis, we want to investigate context based service discovery and service composition over smart phones. We propose a constraint-based context model. We include non-electronic services into service composition, which extends the scope of services considered in existing service composition research. Moreover, our composition algorithm suits mobile computation power because the service composition can adjust to the computation power of mobile phones easily. As a motivating example, we build an entertainment planner over an Android phone

A robust and efficient statistical method for genetic association study using case and control samples from multiple cohorts

BACKGROUND: The theoretical basis of genome-wide association studies (GWAS) is statistical inference of linkage disequilibrium (LD) between any polymorphic marker and a putative disease locus. Most methods widely implemented for such analyses are vulnerable to several key demographic factors and deliver a poor statistical power for detecting genuine associations and also a high false positive rate. Here, we present a likelihood-based statistical approach that accounts properly for non-random nature of case–control samples in regard of genotypic distribution at the loci in populations under study and confers flexibility to test for genetic association in presence of different confounding factors such as population structure, non-randomness of samples etc. RESULTS: We implemented this novel method together with several popular methods in the literature of GWAS, to re-analyze recently published Parkinson’s disease (PD) case–control samples. The real data analysis and computer simulation show that the new method confers not only significantly improved statistical power for detecting the associations but also robustness to the difficulties stemmed from non-randomly sampling and genetic structures when compared to its rivals. In particular, the new method detected 44 significant SNPs within 25 chromosomal regions of size < 1 Mb but only 6 SNPs in two of these regions were previously detected by the trend test based methods. It discovered two SNPs located 1.18 Mb and 0.18 Mb from the PD candidates, FGF20 and PARK8, without invoking false positive risk. CONCLUSIONS: We developed a novel likelihood-based method which provides adequate estimation of LD and other population model parameters by using case and control samples, the ease in integration of these samples from multiple genetically divergent populations and thus confers statistically robust and powerful analyses of GWAS. On basis of simulation studies and analysis of real datasets, we demonstrated significant improvement of the new method over the non-parametric trend test, which is the most popularly implemented in the literature of GWAS

Mathematical modelling for multiproduct EPQ problem featuring delayed differentiation, expedited rate, and scrap

The client requirements of present-day markets emphasize product quality, variety, and rapid response. To gain competitive advantages in marketplaces and meet customer needs, manufacturers today seek the most economical and fastest fabrication schemes and strategies to produce their various goods, especially when commonality exists within these multiple end products. Inspired by the above viewpoints, this study uses a mathematical modelling approach for solving a multiproduct economic production quantity (EPQ) problem featuring scrap, delayed differentiation, and expedited rate on the fabrication of the common part. We build a two-stage multiproduct fabrication scheme. Stage one uses an accelerated rate to produce all necessary common parts for multi-item to shorten its uptime, while stage two fabricates finished products sequentially using a rotation cycle rule. Inevitable random scraps produced in both stages are identified and removed to achieve the anticipated quality. We determined the optimal cost-minimization operating cycle length and used a numerical example to show our model’s capability and to explore collective and individual impacts of scrap, expedited-rate, and postponement strategies on various performances of the studied problem (such as uptime of common part, utilization, rotation cycle time, total system cost, and individual cost contributor, etc.) Our model can offer an optimization solution and in-depth managerial insights for fabrication and operations planning in a wide variety of present-day industries, such as automotive, household goods, clothing, etc