Hasil untuk "q-fin.PR"

Stephen D. Turner

Summary Genome-wide association studies (GWAS) have identified thousands of human trait-associated single nucleotide polymorphisms. Here, I describe a freely available R package for visualizing GWAS results using Q-Q and manhattan plots. The qqman package enables the flexible creation of manhattan plots, both genome-wide and for single chromosomes, with optional highlighting of SNPs of interest. Availability qqman is released under the GNU General Public License, and is freely available on the Comprehensive R Archive Network (http://cran.r-project.org/package=qqman). The source code is available on GitHub (https://github.com/stephenturner/qqman). Contact turner@virginia.edu

F. Shayan

While the mixed method approach has attracted much attention over the past few years in the field of educational research, including educational technology, Q-methodology, which encapsulates the advantages of both the quantitative and qualitative methods, has not yet entered the mainstream of scholarly educational technology research into human subjectivity and remains unfamiliar to most researchers. Based on more than 16 years of applying Q-methodology in a diverse range of studies, Watts and Stenner’s Doing Q Methodological Research is a step-by-step procedure that covers both the theory and pragmatism of gathering, analysing, interpreting, and publishing a Q-study.

K. Sørensen, S. van den Broucke, J. Pelikan et al.

BackgroundSeveral measurement tools have been developed to measure health literacy. The tools vary in their approach and design, but few have focused on comprehensive health literacy in populations. This paper describes the design and development of the European Health Literacy Survey Questionnaire (HLS-EU-Q), an innovative, comprehensive tool to measure health literacy in populations.MethodsBased on a conceptual model and definition, the process involved item development, pre-testing, field-testing, external consultation, plain language check, and translation from English to Bulgarian, Dutch, German, Greek, Polish, and Spanish.ResultsThe development process resulted in the HLS-EU-Q, which entailed two sections, a core health literacy section and a section on determinants and outcomes associated to health literacy. The health literacy section included 47 items addressing self-reported difficulties in accessing, understanding, appraising and applying information in tasks concerning decisions making in healthcare, disease prevention, and health promotion. The second section included items related to, health behaviour, health status, health service use, community participation, socio-demographic and socio-economic factors.ConclusionsBy illuminating the detailed steps in the design and development process of the HLS-EU-Q, it is the aim to provide a deeper understanding of its purpose, its capability and its limitations for others using the tool. By stimulating a wide application it is the vision that HLS-EU-Q will be validated in more countries to enhance the understanding of health literacy in different populations.

Anderson Cs, E. Heeley, Y. Huang et al.

G. Aad, T. Abajyan, B. Abbott et al.

F. An, J. Bai, A. Balantekin et al.

The Daya Bay Reactor Neutrino Experiment has measured a nonzero value for the neutrino mixing angle θ(13) with a significance of 5.2 standard deviations. Antineutrinos from six 2.9 GWth reactors were detected in six antineutrino detectors deployed in two near (flux-weighted baseline 470 m and 576 m) and one far (1648 m) underground experimental halls. With a 43,000 ton-GWth-day live-time exposure in 55 days, 10,416 (80,376) electron-antineutrino candidates were detected at the far hall (near halls). The ratio of the observed to expected number of antineutrinos at the far hall is R=0.940±0.011(stat.)±0.004(syst.). A rate-only analysis finds sin(2)2θ(13)=0.092±0.016(stat.)±0.005(syst.) in a three-neutrino framework.

M. Grabherr, B. Haas, Moran Yassour et al.

Massively parallel sequencing of cDNA has enabled deep and efficient probing of transcriptomes. Current approaches for transcript reconstruction from such data often rely on aligning reads to a reference genome, and are thus unsuitable for samples with a partial or missing reference genome. Here we present the Trinity method for de novo assembly of full-length transcripts and evaluate it on samples from fission yeast, mouse and whitefly, whose reference genome is not yet available. By efficiently constructing and analyzing sets of de Bruijn graphs, Trinity fully reconstructs a large fraction of transcripts, including alternatively spliced isoforms and transcripts from recently duplicated genes. Compared with other de novo transcriptome assemblers, Trinity recovers more full-length transcripts across a broad range of expression levels, with a sensitivity similar to methods that rely on genome alignments. Our approach provides a unified solution for transcriptome reconstruction in any sample, especially in the absence of a reference genome.

Di Xiao, M. Chang, Q. Niu

Ever since its discovery, the Berry phase has permeated through all branches of physics. Over the last three decades, it was gradually realized that the Berry phase of the electronic wave function can have a profound effect on material properties and is responsible for a spectrum of phenomena, such as ferroelectricity, orbital magnetism, various (quantum/anomalous/spin) Hall effects, and quantum charge pumping. This progress is summarized in a pedagogical manner in this review. We start with a brief summary of necessary background, followed by a detailed discussion of the Berry phase effect in a variety of solid state applications. A common thread of the review is the semiclassical formulation of electron dynamics, which is a versatile tool in the study of electron dynamics in the presence of electromagnetic fields and more general perturbations. Finally, we demonstrate a re-quantization method that converts a semiclassical theory to an effective quantum theory. It is clear that the Berry phase should be added as a basic ingredient to our understanding of basic material properties.

M. Adams, S. Celniker, R. Holt et al.

Zhuojun Liu, Yi Xu, Ye Lin et al.

Sharp electromagnetic resonances play an essential role in physics in general and optics in particular. The last decades have witnessed the successful developments of high-quality (Q) resonances in microcavities operating below the light line, which however is fundamentally challenging to access from free space. Alternatively, metasurface-based bound states in the continuum (BICs) offer a complementary solution of creating high-Q resonances in devices operating above the light line, yet the experimentally demonstrated Q factors under normal excitations are still limited. Here, we present the realizations of quasi-BIC under normal excitation with a record Q factor up to 18 511 by engineering the symmetry properties and the number of the unit cells in all-dielectric metasurface platforms. The high-Q quasi-BICs exhibit exceptionally high conversion efficiency for the third harmonic generation and even enable the second harmonic generation in Si metasurfaces. Such ultrasharp resonances achieved in this work may immediately boost the performances of BICs in a plethora of fundamental research and device applications, e.g., cavity QED, biosensing, nanolasing, and quantum light generations.

Jesse Clifton, Eric B. Laber

Q-learning, originally an incremental algorithm for estimating an optimal decision strategy in an infinite-horizon decision problem, now refers to a general class of reinforcement learning methods widely used in statistics and artificial intelligence. In the context of personalized medicine, finite-horizon Q-learning is the workhorse for estimating optimal treatment strategies, known as treatment regimes. Infinite-horizon Q-learning is also increasingly relevant in the growing field of mobile health. In computer science, Q-learning methods have achieved remarkable performance in domains such as game-playing and robotics. In this article, we ( a) review the history of Q-learning in computer science and statistics, ( b) formalize finite-horizon Q-learning within the potential outcomes framework and discuss the inferential difficulties for which it is infamous, and ( c) review variants of infinite-horizon Q-learning and the exploration-exploitation problem, which arises in decision problems with a long time horizon. We close by discussing issues arising with the use of Q-learning in practice, including arguments for combining Q-learning with direct-search methods; sample size considerations for sequential, multiple assignment randomized trials; and possibilities for combining Q-learning with model-based methods.

M. V. Rybin, K. Koshelev, Z. Sadrieva et al.

Recent progress in nanoscale optical physics is associated with the development of a new branch of nanophotonics exploring strong Mie resonances in dielectric nanoparticles with a high refractive index. The high-index resonant dielectric nanostructures form building blocks for novel photonic metadevices with low losses and advanced functionalities. However, unlike extensively studied cavities in photonic crystals, such dielectric resonators demonstrate low quality factors (Q factors). Here, we uncover a novel mechanism for achieving giant Q factors of subwavelength nanoscale resonators by realizing the regime of bound states in the continuum. In contrast to the previously suggested multilayer structures with zero permittivity, we reveal strong mode coupling and Fano resonances in homogeneous high-index dielectric finite-length nanorods resulting in high-Q factors at the nanoscale. Thus, high-index dielectric resonators represent the simplest example of nanophotonic supercavities, expanding substantially the range of applications of all-dielectric resonant nanophotonics and meta-optics.

M. S. Bin-Alam, O. Reshef, Y. Mamchur et al.

Plasmonic nanostructures hold promise for the realization of ultra-thin sub-wavelength devices, reducing power operating thresholds and enabling nonlinear optical functionality in metasurfaces. However, this promise is substantially undercut by absorption introduced by resistive losses, causing the metasurface community to turn away from plasmonics in favour of alternative material platforms (e.g., dielectrics) that provide weaker field enhancement, but more tolerable losses. Here, we report a plasmonic metasurface with a quality-factor (Q-factor) of 2340 in the telecommunication C band by exploiting surface lattice resonances (SLRs), exceeding the record by an order of magnitude. Additionally, we show that SLRs retain many of the same benefits as localized plasmonic resonances, such as field enhancement and strong confinement of light along the metal surface. Our results demonstrate that SLRs provide an exciting and unexplored method to tailor incident light fields, and could pave the way to flexible wavelength-scale devices for any optical resonating application. Metallic nanostructures are useful in many optical devices due to their nonlinear properties and responses to interaction with light. Here the authors demonstrate a metasurface of gold nanoparticle arrays with ultra-narrow surface lattice resonances of high quality-factor that operates in the telecommunication band.

G. Wei, Hui Gao, Yu Wei

The generalized Heronian mean and geometric Heronian mean operators provide two aggregation operators that consider the interdependent phenomena among the aggregated arguments. In this paper, the generalized Heronian mean operator and geometric Heronian mean operator under the q‐rung orthopair fuzzy sets is studied. First, the q‐rung orthopair fuzzy generalized Heronian mean (q‐ROFGHM) operator, q‐rung orthopair fuzzy geometric Heronian mean (q‐ROFGHM) operator, q‐rung orthopair fuzzy generalized weighted Heronian mean (q‐ROFGWHM) operator, and q‐rung orthopair fuzzy weighted geometric Heronian mean (q‐ROFWGHM) operator are proposed, and some of their desirable properties are investigated in detail. Furthermore, we extend these operators to q‐rung orthopair 2‐tuple linguistic sets (q‐RO2TLSs). Then, an approach to multiple attribute decision making based on q‐ROFGWHM (q‐ROFWGHM) operator is proposed. Finally, a practical example for enterprise resource planning system selection is given to verify the developed approach and to demonstrate its practicality and effectiveness.

Laura Hull, W. Mandy, Meng-Chuan Lai et al.

There currently exist no self-report measures of social camouflaging behaviours (strategies used to compensate for or mask autistic characteristics during social interactions). The Camouflaging Autistic Traits Questionnaire (CAT-Q) was developed from autistic adults’ experiences of camouflaging, and was administered online to 354 autistic and 478 non-autistic adults. Exploratory factor analysis suggested three factors, comprising of 25 items in total. Good model fit was demonstrated through confirmatory factor analysis, with measurement invariance analyses demonstrating equivalent factor structures across gender and diagnostic group. Internal consistency (α = 0.94) and preliminary test–retest reliability (r = 0.77) were acceptable. Convergent validity was demonstrated through comparison with measures of autistic traits, wellbeing, anxiety, and depression. The present study provides robust psychometric support for the CAT-Q.

John Schulman, P. Abbeel, Xi Chen

Two of the leading approaches for model-free reinforcement learning are policy gradient methods and $Q$-learning methods. $Q$-learning methods can be effective and sample-efficient when they work, however, it is not well-understood why they work, since empirically, the $Q$-values they estimate are very inaccurate. A partial explanation may be that $Q$-learning methods are secretly implementing policy gradient updates: we show that there is a precise equivalence between $Q$-learning and policy gradient methods in the setting of entropy-regularized reinforcement learning, that "soft" (entropy-regularized) $Q$-learning is exactly equivalent to a policy gradient method. We also point out a connection between $Q$-learning methods and natural policy gradient methods. Experimentally, we explore the entropy-regularized versions of $Q$-learning and policy gradients, and we find them to perform as well as (or slightly better than) the standard variants on the Atari benchmark. We also show that the equivalence holds in practical settings by constructing a $Q$-learning method that closely matches the learning dynamics of A3C without using a target network or $\epsilon$-greedy exploration schedule.

Peide Liu, Shyi-Ming Chen, Peng Wang

To be able to describe more complex fuzzy uncertainty information effectively, the concept of ${q}$ -rung orthopair fuzzy sets ( ${q}$ -ROFSs) was first proposed by Yager. The ${q}$ -ROFSs can dynamically adjust the range of indication of decision information by changing a parameter ${q}$ based on the different hesitation degree from the decision-makers, where ${q} {\ge } {1}$ , so they outperform the traditional intuitionistic fuzzy sets and Pythagorean fuzzy sets. In real decision-making problems, there is often an interaction phenomenon between attributes. For aggregating these complex fuzzy information, the Maclaurin symmetric mean (MSM) operator is more superior by considering interrelationships among attributes. In addition, the power average (PA) operator can reduce the effects of extreme evaluating data from some experts with prejudice. In this paper, we introduce the PA operator and the MSM operator based on ${q}$ -rung orthopair fuzzy numbers ( ${q}$ -ROFNs). Then, we put forward the ${q}$ -rung orthopair fuzzy power MSM ( ${q}$ -ROFPMSM) operator and the ${q}$ -rung orthopair fuzzy power weighed MSM ( ${q}$ -ROFPWMSM) operator of ${q}$ -ROFNs and present some of their properties. Finally, we present a novel multiple-attribute group decision-making (MAGDM) method based on the ${q}$ -ROFPWA and the ${q}$ -ROFPWMSM operators. The experimental results show that the novel MAGDM method outperforms the existing MAGDM methods for dealing with MAGDM problems.

Peide Liu, Peng Wang

The theory of $q$-rung orthopair fuzzy sets ($q$-ROFSs) proposed by Yager effectively describes fuzzy information in the real world. Because $q$-ROFSs contain the parameter $q$ and can adjust the range of expressed fuzzy information, they are superior to both intuitionistic and Pythagorean fuzzy sets. Archimedean T-norm and T-conorm (ATT) is an important tool used to generate operational rules based on the q-rung orthopair fuzzy numbers ($q$-ROFNs). In comparison, the Bonferroni mean (BM) operator has an advantage because it considers the interrelationships between the different attributes. Therefore, it is an important and meaningful innovation to extend the BM operator to the $q$-ROFNs based upon the ATT. In this paper, we first discuss $q$-rung orthopair fuzzy operational rules by using ATT. Furthermore, we extend BM operator to the $q$-ROFNs and propose the $q$-rung orthopair fuzzy Archimedean BM $(q\hbox{-}{ROFABM})$ operator and the q-rung orthopair fuzzy weighted Archimedean BM $(q\hbox{-}{ROFWABM})$ operator and study their desirable properties. Then, a new multiple-attribute decision-making (MADM) method is developed based on $q\hbox{-}{ROFWABM}$ operator. Finally, we use a practical example to verify effectiveness and superiority by comparing to other existing methods.

Divyansh Garg, Shuvam Chakraborty, Chris Cundy et al.

In many sequential decision-making problems (e.g., robotics control, game playing, sequential prediction), human or expert data is available containing useful information about the task. However, imitation learning (IL) from a small amount of expert data can be challenging in high-dimensional environments with complex dynamics. Behavioral cloning is a simple method that is widely used due to its simplicity of implementation and stable convergence but doesn't utilize any information involving the environment's dynamics. Many existing methods that exploit dynamics information are difficult to train in practice due to an adversarial optimization process over reward and policy approximators or biased, high variance gradient estimators. We introduce a method for dynamics-aware IL which avoids adversarial training by learning a single Q-function, implicitly representing both reward and policy. On standard benchmarks, the implicitly learned rewards show a high positive correlation with the ground-truth rewards, illustrating our method can also be used for inverse reinforcement learning (IRL). Our method, Inverse soft-Q learning (IQ-Learn) obtains state-of-the-art results in offline and online imitation learning settings, significantly outperforming existing methods both in the number of required environment interactions and scalability in high-dimensional spaces, often by more than 3x.

M. Todisco, Héctor Delgado, N. Evans