Hasil untuk "q-fin.CP"

D. Fasshauer, R B Sutton, A. Brunger et al.

Jon C. R. Bennett, Hui Zhang

B. Song, S. Noda, T. Asano et al.

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

This work summarizes and puts in an overall perspective studies done within the compact muon solenoid (CMS) concerning the discovery potential for squarks and gluinos, sleptons, charginos and neutralinos, supersymmetric (SUSY) dark matter, lightest Higgs, sparticle mass determination methods and the detector design optimization in view of SUSY searches. It represents the status of our understanding of these subjects as of summer 1997. As a benchmark we used the minimal supergravity-inspired supersymmetric standard model (mSUGRA) with a stable lightest supersymmetric particle (LSP). Discovery of supersymmetry at the large hadron collider should be relatively straightforward. It may occur through the observation of large excesses of events in missing ET plus jets, or with one or more isolated leptons. An excess of trilepton events or isolated dileptons with missing ET, exhibiting a characteristic signature in the l+l− invariant mass distribution, could also be the first manifestation of SUSY production. Squarks and gluinos can be discovered for masses in excess of 2 TeV. Charginos and neutralinos can be discovered from an excess of events in dilepton or trilepton final states. Inclusive searches can give early indications from their copious production in squark and gluino cascade decays. Indirect evidence for sleptons can also be obtained from inclusive dilepton studies. Isolation requirements and a jet veto would allow detection of both the direct chargino/neutralino production and the directly produced sleptons. Squark and gluino production may also represent a copious source of Higgs bosons through cascade decays. The lightest SUSY Higgs h → b may be reconstructed with a signal/background ratio of order 1 thanks to hard cuts on ETmiss justified by escaping LSPs. The LSP of SUSY models with conserved R-parity represents a very good candidate for cosmological dark matter. The region of parameter space where this is true is well covered by our searches, at least for tanβ = 2. If supersymmetry exists at the electroweak scale, it could hardly escape detection in CMS and the study of supersymmetry will form a central part of our physics program.

Kristine H. Luce, J. Crowther

M. Morris, J. Teevan, Katrina Panovich

P. Bolton, Hui Chen, Neng Wang

F. Yeh, V. Wedeen, W. Tseng

Lena Mamykina, Bella Manoim, Manas Mittal et al.

Timothy Erickson, Toni M. Whited

X. Yi, Qifan Yang, K. Yang et al.

Frequency combs are having a broad impact on science and technology because they provide a way to coherently link radio/microwave-rate electrical signals with optical-rate signals derived from lasers and atomic transitions. Integrating these systems on a photonic chip would revolutionize instrumentation, time keeping, spectroscopy, navigation, and potentially create new mass-market applications. A key element of such a system-on-a-chip will be a mode-locked comb that can be self-referenced. The recent demonstration of soliton mode locking in crystalline and silicon nitride microresonators has provided a way to both mode lock and generate femtosecond time-scale pulses. Here, soliton mode locking is demonstrated in high-Q silica resonators. The resonators produce low-phase-noise soliton pulse trains at readily detectable pulse rates—two essential properties for the operation of frequency combs. A method for the long-term stabilization of the solitons is also demonstrated, and is used to test the theoretical dependence of the comb power, efficiency, and soliton existence power on the pulse width. The influence of the Raman process on the soliton existence power and efficiency is also observed. The resonators are microfabricated on silicon chips and feature reproducible modal properties required for soliton formation. A low-noise and detectable pulse rate soliton frequency comb on a chip is a significant step towards a fully integrated frequency comb system.

I. Selesnick

Q. An, R. Asfandiyarov, P. Azzarello et al.

DAMPE satellite has directly measured the cosmic ray proton spectrum from 40 GeV to 100 TeV and revealed a new feature at about 13.6 TeV. The precise measurement of the spectrum of protons, the most abundant component of the cosmic radiation, is necessary to understand the source and acceleration of cosmic rays in the Milky Way. This work reports the measurement of the cosmic ray proton fluxes with kinetic energies from 40 GeV to 100 TeV, with 2 1/2 years of data recorded by the DArk Matter Particle Explorer (DAMPE). This is the first time that an experiment directly measures the cosmic ray protons up to ~100 TeV with high statistics. The measured spectrum confirms the spectral hardening at ~300 GeV found by previous experiments and reveals a softening at ~13.6 TeV, with the spectral index changing from ~2.60 to ~2.85. Our result suggests the existence of a new spectral feature of cosmic rays at energies lower than the so-called knee and sheds new light on the origin of Galactic cosmic rays.

Ding Ding, Xiaocong Fan, Yihuan Zhao et al.

Abstract High energy consumption has become a growing concern in the operation of complex cloud data centers due to the ever-expanding size of cloud computing facilities and the ever-increasing number of users. It is critical to find viable solutions to cloud task scheduling so that cloud resources can be utilized in an energy-efficient way while still meeting diverse user requirements in real time. In this research we propose a Q-learning based task scheduling framework for energy-efficient cloud computing (QEEC). QEEC has two phases. In the first phase a centralized task dispatcher is used to implement the M/M/S queueing model, by which the arriving user requests are assigned to each server in a cloud. In the second phase a Q-learning based scheduler on each server first prioritizes all the requests by task laxity and task life time, then uses a continuously-updating policy to assign tasks to virtual machines, applying incentives to reward the assignments that can minimize task response time and maximize each server’s CPU utilization. We have conducted simulation experiments, which have confirmed that implementing a M/M/S queueing system in a cloud can help to reduce the average task response time. The experiments have also demonstrated that the QEEC approach is the most energy-efficient as compared to other task scheduling policies, which can be largely credited to the M/M/S queueing model and the Q-learning strategy implemented in QEEC.

A. Banerjee, A. Pradhan, Takol Tangphati et al.

Following the recent theory of f(Q) gravity, we continue to investigate the possible existence of wormhole geometries, where Q is the non-metricity scalar. Recently, the non-metricity scalar and the corresponding field equations have been studied for some spherically symmetric configurations in Mustafa (Phys Lett B 821:136612, 2021) and Lin and Zhai (Phys Rev D 103:124001, 2021). One can note that field equations are different in these two studies. Following Lin and Zhai (2021), we systematically study the field equations for wormhole solutions and found the violation of null energy conditions in the throat neighborhood. More specifically, considering specific choices for the f(Q) form and for constant redshift with different shape functions, we present a class of solutions for static and spherically symmetric wormholes. Our survey indicates that wormhole solutions could not exist for specific form function f(Q)=Q+αQ2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f(Q)= Q+ \alpha Q^2$$\end{document}. To summarize, exact wormhole models can be constructed with violation of the null energy condition throughout the spacetime while being ρ≥0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\rho \ge 0$$\end{document} and vice versa.

Mihai Cucuringu, Kang Li, Chao Zhang

This study focuses on forecasting intraday trading volumes, a crucial component for portfolio implementation, especially in high-frequency (HF) trading environments. Given the current scarcity of flexible methods in this area, we employ a suite of machine learning (ML) models enriched with numerous HF predictors to enhance the predictability of intraday trading volumes. Our findings reveal that intraday stock trading volume is highly predictable, especially with ML and considering commonality. Additionally, we assess the economic benefits of accurate volume forecasting through Volume Weighted Average Price (VWAP) strategies. The results demonstrate that precise intraday forecasting offers substantial advantages, providing valuable insights for traders to optimize their strategies.

V. M. Belyaev

Here we demonstrate how we can use Small Volatility Approximation in calibration of Multi-Factor HJM model with deterministic correlations, factor volatilities and mean reversals. It is noticed that quality of this calibration is very good and it does not depend on number of factors.

K. Churruca, Kristiana Ludlow, W. Wu et al.

Background Q-methodology is an approach to studying complex issues of human ‘subjectivity’. Although this approach was developed in the early twentieth century, the value of Q-methodology in healthcare was not recognised until relatively recently. The aim of this review was to scope the empirical healthcare literature to examine the extent to which Q-methodology has been utilised in healthcare over time, including how it has been used and for what purposes. Methods A search of three electronic databases (Scopus, EBSCO-CINAHL Complete, Medline) was conducted. No date restriction was applied. A title and abstract review, followed by a full-text review, was conducted by a team of five reviewers. Included articles were English-language, peer-reviewed journal articles that used Q-methodology (both Q-sorting and inverted factor analysis) in healthcare settings. The following data items were extracted into a purpose-designed Excel spreadsheet: study details (e.g., setting, country, year), reasons for using Q-methodology, healthcare topic area, participants (type and number), materials (e.g., ranking anchors and Q-set), methods (e.g., development of the Q-set, analysis), study results, and study implications. Data synthesis was descriptive in nature and involved frequency counting, open coding and the organisation by data items. Results Of the 2,302 articles identified by the search, 289 studies were included in this review. We found evidence of increased use of Q-methodology in healthcare, particularly over the last 5 years. However, this research remains diffuse, spread across a large number of journals and topic areas. In a number of studies, we identified limitations in the reporting of methods, such as insufficient information on how authors derived their Q-set, what types of analyses they performed, and the amount of variance explained. Conclusions Although Q-methodology is increasingly being adopted in healthcare research, it still appears to be relatively novel. This review highlight commonalities in how the method has been used, areas of application, and the potential value of the approach. To facilitate reporting of Q-methodological studies, we present a checklist of details that should be included for publication.

Florian Kittelmann, Pavel Sulimov, Kurt Stockinger

Classical and learned query optimizers (LQOs) use cardinality estimations as one of the critical inputs for query planning. Thus, accurately predicting the cardinality of arbitrary queries plays a vital role in query optimization. A recent boom in novel deep learning methods stimulated not only the rise of LQOs but also contributed to the appearance of learned cardinality estimators (LCEs). However, the majority of them are based on classical neural networks, ignoring that multivariate correlations between attributes across different tables could be naturally represented via entanglements in quantum circuits. In this paper, we introduce QardEst - Quantum Cardinality Estimator - a novel quantum neural network approach to estimate the cardinality of join queries. Our experiments conducted with a similar number of trainable parameters suggest that quantum neural networks executed on a quantum simulator outperform classical neural networks in terms of mean squared error as well as the q-error.

C. Carlet, Sihem Mesnager, Chunming Tang et al.