Hasil untuk "q-bio.MN"

V. Khachatryan, A. Sirunyan, A. Tumasyan et al.

New sets of parameters ("tunes") for the underlying-event (UE) modeling of the PYTHIA8, PYTHIA6 and HERWIG++ Monte Carlo event generators are constructed using different parton distribution functions. Combined fits to CMS UE proton-proton (pp) data at sqrt(s) = 7 TeV and to UE proton-antiproton (p p-bar) data from the CDF experiment at lower sqrt(s), are used to study the UE models and constrain their parameters, providing thereby improved predictions for proton-proton collisions at 13 TeV. In addition, it is investigated whether the values of the parameters obtained from fits to UE observables are consistent with the values determined from fitting observables sensitive to double-parton scattering processes. Finally, comparisons of the UE tunes to"minimum bias"(MB) events, multijet, and Drell-Yan (q q-bar to Z / gamma* to lepton-antilepton + jets) observables at 7 and 8 TeV are presented, as well as predictions for MB and UE observables at 13 TeV.

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.

Y. Shao, Zhengting Gan, E. Epifanovsky et al.

A summary of the technical advances that are incorporated in the fourth major release of the Q-Chem quantum chemistry program is provided, covering approximately the last seven years. These include developments in density functional theory methods and algorithms, nuclear magnetic resonance (NMR) property evaluation, coupled cluster and perturbation theories, methods for electronically excited and open-shell species, tools for treating extended environments, algorithms for walking on potential surfaces, analysis tools, energy and electron transfer modelling, parallel computing capabilities, and graphical user interfaces. In addition, a selection of example case studies that illustrate these capabilities is given. These include extensive benchmarks of the comparative accuracy of modern density functionals for bonded and non-bonded interactions, tests of attenuated second order Møller–Plesset (MP2) methods for intermolecular interactions, a variety of parallel performance benchmarks, and tests of the accuracy of implicit solvation models. Some specific chemical examples include calculations on the strongly correlated Cr2 dimer, exploring zeolite-catalysed ethane dehydrogenation, energy decomposition analysis of a charged ter-molecular complex arising from glycerol photoionisation, and natural transition orbitals for a Frenkel exciton state in a nine-unit model of a self-assembling nanotube.

The Ligo Scientific Collaboration, T. Abbott, T. Abbott et al.

The second Gravitational-Wave Transient Catalog reported on 39 compact binary coalescences observed by the Advanced LIGO and Advanced Virgo detectors between 1 April 2019 15:00 UTC and 1 October 2019 15:00 UTC. We present GWTC-2.1, which reports on a deeper list of candidate events observed over the same period. We analyze the final version of the strain data over this period with improved calibration and better subtraction of excess noise, which has been publicly released. We employ three matched-filter search pipelines for candidate identification, and estimate the astrophysical probability for each candidate event. While GWTC-2 used a false alarm rate threshold of 2 per year, we include in GWTC-2.1, 1201 candidates that pass a false alarm rate threshold of 2 per day. We calculate the source properties of a subset of 44 high-significance candidates that have an astrophysical probability greater than 0.5. Of these candidates, 36 have been reported in GWTC-2. If the 8 additional high-significance candidates presented here are astrophysical, the mass range of events that are unambiguously identified as binary black holes (both objects $\geq 3M_\odot$) is increased compared to GWTC-2, with total masses from $\sim 14 M_\odot$ for GW190924_021846 to $\sim 182 M_\odot$ for GW190426_190642. The primary components of two new candidate events (GW190403_051519 and GW190426_190642) fall in the mass gap predicted by pair instability supernova theory. We also expand the population of binaries with significantly asymmetric mass ratios reported in GWTC-2 by an additional two events (the mass ratio is less than $0.65$ and $0.44$ at $90\%$ probability for GW190403_051519 and GW190917_114630 respectively), and find that 2 of the 8 new events have effective inspiral spins $\chi_\mathrm{eff}>0$ (at $90\%$ credibility), while no binary is consistent with $\chi_\mathrm{eff}<0$ at the same significance.

Jicheng Jin, Xuefan Yin, Liangfu Ni et al.

Because of their ability to confine light, optical resonators1–3 are of great importance to science and technology, but their performance is often limited by out-of-plane-scattering losses caused by inevitable fabrication imperfections4,5. Here we theoretically propose and experimentally demonstrate a class of guided resonances in photonic crystal slabs, in which out-of-plane-scattering losses are strongly suppressed by their topological nature. These resonances arise when multiple bound states in the continuum—each carrying a topological charge6—merge in momentum space and enhance the quality factors Q of all nearby resonances in the same band. Using such resonances in the telecommunication regime, we experimentally achieve quality factors as high as 4.9 × 105—12 times higher than those obtained with standard designs—and this enhancement remains robust for all of our samples. Our work paves the way for future explorations of topological photonics in systems with open boundary conditions and for their application to the improvement of optoelectronic devices in photonic integrated circuits. Bound states in the continuum are merged in momentum space by varying the periodicity of the photonic crystal lattice, giving high-quality-factor guided resonances that are robust to out-of-plane scattering.

Xudong Zhang, Yilin Liu, Jiecai Han et al.

Ultracompact sources of circularly polarized light are important for classical and quantum optical information processing. Conventional approaches for generating chiral emission are restricted to excitation power ranges and fail to provide high-quality radiation with perfect polarization conversion. We used the physics of chiral quasi-bound states in the continuum to demonstrate the efficient and controllable emission of circularly polarized light from resonant metasurfaces. Exploiting intrinsic chirality and giant field enhancement, we revealed how to simultaneously modify and control spectra, radiation patterns, and spin angular momentum of photoluminescence and lasing without any spin injection. The superior characteristics of chiral emission and lasing promise multiple applications in nanophotonics and quantum optics. Description Another twist for metasurfaces Metasurfaces are specially designed arrays of dielectric components that transform the function of bulk optical components into thin films. Exploiting the physics of bulk states in the continuum for the highly efficient trapping of light, Zhang et al. demonstrate metasurfaces that operate as a source of chiral light (see the Perspective by Forbes). Using a dielectric metasurface doped with light-emitting molecules, they were able to produce chiral photoluminescence and lasing. This approach will be useful for the development of integrated optical devices. —ISO Cobalt carbonyl catalysts prove stable at lower gas pressure than previously thought.

W. Jin, Qifan Yang, L. Chang et al.

Driven by narrow-linewidth bench-top lasers, coherent optical systems spanning optical communications, metrology and sensing provide unrivalled performance. To transfer these capabilities from the laboratory to the real world, a key missing ingredient is a mass-produced integrated laser with superior coherence. Here, we bridge conventional semiconductor lasers and coherent optical systems using CMOS-foundry-fabricated microresonators with a high Q factor of over 260 million and finesse over 42,000. A five-orders-of-magnitude noise reduction in the pump laser is demonstrated, enabling a frequency noise of 0.2 Hz2 Hz−1 to be achieved in an electrically pumped integrated laser, with a corresponding short-term linewidth of 1.2 Hz. Moreover, the same configuration is shown to relieve the dispersion requirements for microcomb generation that have handicapped certain nonlinear platforms. The simultaneous realization of this high Q factor, highly coherent lasers and frequency combs using foundry-based technologies paves the way for volume manufacturing of a wide range of coherent optical systems. Using CMOS-ready ultra-high-Q microresonators, a highly coherent electrically pumped integrated laser with frequency noise of 0.2 Hz2 Hz−1, corresponding to a short-term linewidth of 1.2 Hz, is demonstrated. The device configuration is also found to relieve the dispersion requirements for microcomb generation that have limited certain nonlinear platforms.

A. Sirunyan, A. Tumasyan, W. Adam et al.

New sets of CMS underlying-event parameters (“tunes”) are presented for the pythia8 event generator. These tunes use the NNPDF3.1 parton distribution functions (PDFs) at leading (LO), next-to-leading (NLO), or next-to-next-to-leading (NNLO) orders in perturbative quantum chromodynamics, and the strong coupling evolution at LO or NLO. Measurements of charged-particle multiplicity and transverse momentum densities at various hadron collision energies are fit simultaneously to determine the parameters of the tunes. Comparisons of the predictions of the new tunes are provided for observables sensitive to the event shapes at LEP, global underlying event, soft multiparton interactions, and double-parton scattering contributions. In addition, comparisons are made for observables measured in various specific processes, such as multijet, Drell–Yan, and top quark-antiquark pair production including jet substructure observables. The simulation of the underlying event provided by the new tunes is interfaced to a higher-order matrix-element calculation. For the first time, predictions from pythia8 obtained with tunes based on NLO or NNLO PDFs are shown to reliably describe minimum-bias and underlying-event data with a similar level of agreement to predictions from tunes using LO PDF sets.

Beakcheol Jang, Myeonghwi Kim, Gaspard Harerimana et al.

Q-learning is arguably one of the most applied representative reinforcement learning approaches and one of the off-policy strategies. Since the emergence of Q-learning, many studies have described its uses in reinforcement learning and artificial intelligence problems. However, there is an information gap as to how these powerful algorithms can be leveraged and incorporated into general artificial intelligence workflow. Early Q-learning algorithms were unsatisfactory in several aspects and covered a narrow range of applications. It has also been observed that sometimes, this rather powerful algorithm learns unrealistically and overestimates the action values hence abating the overall performance. Recently with the general advances of machine learning, more variants of Q-learning like Deep Q-learning which combines basic Q learning with deep neural networks have been discovered and applied extensively. In this paper, we thoroughly explain how Q-learning evolved by unraveling the mathematical complexities behind it as well its flow from reinforcement learning family of algorithms. Improved variants are fully described, and we categorize Q-learning algorithms into single-agent and multi-agent approaches. Finally, we thoroughly investigate up-to-date research trends and key applications that leverage Q-learning algorithms.

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.

H. M. Srivastava, H. M. Srivastava, H. M. Srivastava

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.

L. C. R. Aaij, A. Abdelmotteleb, C. Beteta et al.

Conventional, hadronic matter consists of baryons and mesons made of three quarks and a quark–antiquark pair, respectively1,2. Here, we report the observation of a hadronic state containing four quarks in the Large Hadron Collider beauty experiment. This so-called tetraquark contains two charm quarks, a u¯\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline{{{{{u}}}}}$$\end{document} and a d¯\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline{{{{{d}}}}}$$\end{document} quark. This exotic state has a mass of approximately 3,875 MeV and manifests as a narrow peak in the mass spectrum of D0D0π+ mesons just below the D*+D0 mass threshold. The near-threshold mass together with the narrow width reveals the resonance nature of the state. The LHCb Collaboration reports the observation of an exotic, narrow, tetraquark state that contains two charm quarks, an up antiquark and a down antiquark.

L. C. R. Aaij, C. Beteta, T. Ackernley et al.

Using proton-proton collision data at centre-of-mass energies of s=7,8 and 13TeV recorded by the LHCb experiment at the Large Hadron Collider, corresponding to an integrated luminosity of 9fb-1, the invariant mass spectrum of J/ψ pairs is studied. A narrow structure around 6.9GeV/c2 matching the lineshape of a resonance and a broad structure just above twice the J/ψ mass are observed. The deviation of the data from nonresonant J/ψ-pair production is above five standard deviations in the mass region between 6.2 and 7.4GeV/c2, covering predicted masses of states composed of four charm quarks. The mass and natural width of the narrow X(6900) structure are measured assuming a Breit-Wigner lineshape.

M. Todisco, Héctor Delgado, N. Evans

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.

Yevgen Chebotar, Q. Vuong, A. Irpan et al.

In this work, we present a scalable reinforcement learning method for training multi-task policies from large offline datasets that can leverage both human demonstrations and autonomously collected data. Our method uses a Transformer to provide a scalable representation for Q-functions trained via offline temporal difference backups. We therefore refer to the method as Q-Transformer. By discretizing each action dimension and representing the Q-value of each action dimension as separate tokens, we can apply effective high-capacity sequence modeling techniques for Q-learning. We present several design decisions that enable good performance with offline RL training, and show that Q-Transformer outperforms prior offline RL algorithms and imitation learning techniques on a large diverse real-world robotic manipulation task suite. The project's website and videos can be found at https://qtransformer.github.io

Lavinia Heisenberg

Recent years have witnessed a rise in interest in the geometrical trinity of General Relativity and its extensions. This interest has been fuelled by novel insights into the nature of gravity, the possibility to address computational and conceptual questions -- such as the determination of black hole entropy or the definition of gravitational energy-momentum -- from a new perspective. In particular, $f(Q)$ gravity has also inspired numerous works on black holes, wormholes, and cosmology. In the latter case, $f(Q)$ models have the potential to elucidate phenomena in both early and late-time cosmology without necessitating the inclusion of dark energy, the inflaton field, or dark matter. Particularly noteworthy is the role of $f(Q)$ theories in addressing cosmological tensions, presenting exciting possibilities for reshaping our understanding of gravity and its manifestations in cosmology. The emergence of intriguing new black hole solutions and the potential existence of wormhole solutions suggest the presence of novel physics within the realm of strong gravity. These phenomena have become increasingly measurable only in recent times, opening up exciting avenues for further exploration and discovery. This review is tailored to students and researchers alike. It offers a self-contained and pedagogical introduction to metric-affine geometry--The mathematical foundation and indispensable tool upon which the geometrical trinity of General Relativity as well as its various extensions are built.