Observations of neutral-current nu interactions on deuterium in the Sudbury Neutrino Observatory are reported. Using the neutral current (NC), elastic scattering, and charged current reactions and assuming the standard 8B shape, the nu(e) component of the 8B solar flux is phis(e) = 1.76(+0.05)(-0.05)(stat)(+0.09)(-0.09)(syst) x 10(6) cm(-2) s(-1) for a kinetic energy threshold of 5 MeV. The non-nu(e) component is phi(mu)(tau) = 3.41(+0.45)(-0.45)(stat)(+0.48)(-0.45)(syst) x 10(6) cm(-2) s(-1), 5.3sigma greater than zero, providing strong evidence for solar nu(e) flavor transformation. The total flux measured with the NC reaction is phi(NC) = 5.09(+0.44)(-0.43)(stat)(+0.46)(-0.43)(syst) x 10(6) cm(-2) s(-1), consistent with solar models.
A system for automatically reading symbols, preferably figures, which are hand-written on an information carrier in an arrangement of squares provided on the information carrier. The images of these symbols are converted by an image convertor of glass fiber bundles to fit a camera tube screen where they are scanned vertically, quantized, and encoded to determine the size and numerical locations of intersections of the scanning beam with the lines in each symbol in each rectangle. This information is then processed by being stored and first roughly classified according to the maximum number of these intersections per symbol, each of which classes are then more specifically classified by being further processed as to the location of the mergings of the intersections, if any, in the upper, lower, right, and/or left part of the symbols, as well as determining the shape, length and/or width of the lines in certain of the symbols for their specific recognition, or identification. This recognized information then may be used for punching a code into the information carrier. If desired, the processor of this information can be located remote from the viewer and punching apparatus.
A precise measurement of the proton flux in primary cosmic rays with rigidity (momentum/charge) from 1 GV to 1.8 TV is presented based on 300 million events. Knowledge of the rigidity dependence of the proton flux is important in understanding the origin, acceleration, and propagation of cosmic rays. We present the detailed variation with rigidity of the flux spectral index for the first time. The spectral index progressively hardens at high rigidities.
Linear spin wave theory provides the leading term in the calculation of the excitation spectra of long-range ordered magnetic systems as a function of . This term is acquired using the Holstein–Primakoff approximation of the spin operator and valid for small δS fluctuations of the ordered moment. We propose an algorithm that allows magnetic ground states with general moment directions and single-Q incommensurate ordering wave vector using a local coordinate transformation for every spin and a rotating coordinate transformation for the incommensurability. Finally we show, how our model can determine the spin wave spectrum of the magnetic C-site langasites with incommensurate order.
Quantum computing exploits quantum phenomena such as superposition and entanglement to realize a form of parallelism that is not available to traditional computing. It offers the potential of significant computational speed-ups in quantum chemistry, materials science, cryptography, and machine learning. The dominant approach to programming quantum computers is to provide an existing high-level language with libraries that allow for the expression of quantum programs. This approach can permit computations that are meaningless in a quantum context; prohibits succint expression of interaction between classical and quantum logic; and does not provide important constructs that are required for quantum programming. We present Q#, a quantum-focused domain-specific language explicitly designed to correctly, clearly and completely express quantum algorithms. Q# provides a type system; a tightly constrained environment to safely interleave classical and quantum computations; specialized syntax; symbolic code manipulation to automatically generate correct transformations of quantum operations; and powerful functional constructs which aid composition.
We demonstrate a novel distributed acoustic sensing (DAS) system based on phase-sensitive optical time-domain reflectometry (Φ-OTDR). Both the phase and the amplitude of the Rayleigh scattering (RS) light can be demodulated in real-time. The technique is based on I/Q demodulation and homodyne detection using a 90° optical hybrid. The theoretical analysis is given, and as a proof of the concept, the dynamic strain sensing is experimentally demonstrated, with a sensing range of 12.566 km and a spatial resolution of 10 m.
In Symmetric Teleparallel General Relativity, gravity is attributed to the non-metricity. The so-called “coincident gauge” is usually taken in this theory so that the affine connection vanishes and the metric is the only fundamental variable. This gauge choice was kept in many studies on the extensions of Symmetric Teleparallel General Relativity, such as the so-called f(Q) theory. In this paper, we point out that sometimes this gauge choice conflicts with the coordinate system we selected based on symmetry. To circumvent this problem, we formulate the f(Q) theory in a covariant way with which we can find suitable non-vanishing affine connection for a given metric. We also apply this method to two important cases: the static spherically symmetric spacetime and the homogeneous and isotropic expanding universe.
Q-learning suffers from overestimation bias, because it approximates the maximum action value using the maximum estimated action value. Algorithms have been proposed to reduce overestimation bias, but we lack an understanding of how bias interacts with performance, and the extent to which existing algorithms mitigate bias. In this paper, we 1) highlight that the effect of overestimation bias on learning efficiency is environment-dependent; 2) propose a generalization of Q-learning, called \emph{Maxmin Q-learning}, which provides a parameter to flexibly control bias; 3) show theoretically that there exists a parameter choice for Maxmin Q-learning that leads to unbiased estimation with a lower approximation variance than Q-learning; and 4) prove the convergence of our algorithm in the tabular case, as well as convergence of several previous Q-learning variants, using a novel Generalized Q-learning framework. We empirically verify that our algorithm better controls estimation bias in toy environments, and that it achieves superior performance on several benchmark problems.
Q Methodology is an approach to understanding subjectivity that combines qualitative and quantitative techniques (Brown, 1996 Ramlo:2016). Originally developed in the 1930s, it allows for a systematic investigation into the viewpoints or perspectives of the participants in the study (Watts & Stenner, 2012). A Q methodology study begins with the researcher assembling a set of statements related to the research topic. The statements are often drawn from participant interviews, but can also be derived from theories related to the research topic or other sources (Brown, 1996). The participants in the study are asked to rank and sort the statements in accordance with a predefined grid pattern (Figure 1). If the participants feel that the statement aligns with their opinion they are asked to place it more to the right (positive) side of the grid, while if they disagree with it they should place it more to the left (negative) side.
R. Aaij, A. Abdelmotteleb, C. Abellan Beteta
et al.
The first simultaneous test of muon-electron universality using B^{+}→K^{+}ℓ^{+}ℓ^{-} and B^{0}→K^{*0}ℓ^{+}ℓ^{-} decays is performed, in two ranges of the dilepton invariant-mass squared, q^{2}. The analysis uses beauty mesons produced in proton-proton collisions collected with the LHCb detector between 2011 and 2018, corresponding to an integrated luminosity of 9 fb^{-1}. Each of the four lepton universality measurements reported is either the first in the given q^{2} interval or supersedes previous LHCb measurements. The results are compatible with the predictions of the Standard Model.
We study the continuous-time counterpart of Q-learning for reinforcement learning (RL) under the entropy-regularized, exploratory diffusion process formulation introduced by Wang et al. (2020). As the conventional (big) Q-function collapses in continuous time, we consider its first-order approximation and coin the term ``(little) q-function". This function is related to the instantaneous advantage rate function as well as the Hamiltonian. We develop a ``q-learning"theory around the q-function that is independent of time discretization. Given a stochastic policy, we jointly characterize the associated q-function and value function by martingale conditions of certain stochastic processes, in both on-policy and off-policy settings. We then apply the theory to devise different actor-critic algorithms for solving underlying RL problems, depending on whether or not the density function of the Gibbs measure generated from the q-function can be computed explicitly. One of our algorithms interprets the well-known Q-learning algorithm SARSA, and another recovers a policy gradient (PG) based continuous-time algorithm proposed in Jia and Zhou (2022b). Finally, we conduct simulation experiments to compare the performance of our algorithms with those of PG-based algorithms in Jia and Zhou (2022b) and time-discretized conventional Q-learning algorithms.
Kaitlin Conway-Moore, Jack M. Birch, Alison R. McKinlay
et al.
Abstract Background Globally, there is increasing evidence of resistance to government-led public health interventions in areas such as vaccination, climate change mitigation, sexual and reproductive healthcare, and the implementation of non-pharmaceutical infection control measures. One potential explanation for this could be the documented global rise in populist attitudes, characterised by distrust of scientific, government and other perceived ‘elites.’ While the effect of such attitudes on engagement with COVID-19-related interventions has been extensively considered and researched, their association with the receipt of other public health interventions is currently underexplored. Methods To understand how populist-aligned views might influence the receipt of public health interventions addressing areas other than COVID-19, we systematically reviewed quantitative research published across thirteen bibliographic databases and relevant websites between 2008 and 2024. All studies were set in member countries of the Organisation for Economic Co-operation and Development (OECD). Results Across 30 included studies, the vast majority of which were cross-sectional, we found evidence that populist-aligned attitudes have a negative impact on the receipt of public health interventions including vaccinations, sexual and reproductive health care and preventive health care. We also found preliminary evidence of the negative role of populist-aligned attitudes on the receipt of disease screening related to HIV/AIDS and adherence to non-pharmaceutical interventions during times of public health emergency, such as the 2009 H1N1 pandemic. Conclusions Although providing limited evidence of causality, the findings from this review suggest the need for future policy in many OECD countries to focus on trust-building between the public and political, scientific, and medical establishments. They also indicate the need for mitigation strategies to overcome the potentially negative impact of populist-style hostility towards out-groups on attitudes related to pressing public health issues such as abortion and family planning, for example by drawing on empathy-centred approaches. Systematic review registration PROSPERO registration number CRD42024513124.