Hasil untuk "q-bio.CB"

A. Pusic, A. Klassen, A. Scott et al.

I. Ulbrich, M. Canagaratna, Q. Zhang et al.

Abstract. The organic aerosol (OA) dataset from an Aerodyne Aerosol Mass Spectrometer (Q-AMS) collected at the Pittsburgh Air Quality Study (PAQS) in September 2002 was analyzed with Positive Matrix Factorization (PMF). Three components – hydrocarbon-like organic aerosol OA (HOA), a highly-oxygenated OA (OOA-1) that correlates well with sulfate, and a less-oxygenated, semi-volatile OA (OOA-2) that correlates well with nitrate and chloride – are identified and interpreted as primary combustion emissions, aged SOA, and semivolatile, less aged SOA, respectively. The complexity of interpreting the PMF solutions of unit mass resolution (UMR) AMS data is illustrated by a detailed analysis of the solutions as a function of number of components and rotational forcing. A public web-based database of AMS spectra has been created to aid this type of analysis. Realistic synthetic data is also used to characterize the behavior of PMF for choosing the best number of factors, and evaluating the rotations of non-unique solutions. The ambient and synthetic data indicate that the variation of the PMF quality of fit parameter (Q, a normalized chi-squared metric) vs. number of factors in the solution is useful to identify the minimum number of factors, but more detailed analysis and interpretation are needed to choose the best number of factors. The maximum value of the rotational matrix is not useful for determining the best number of factors. In synthetic datasets, factors are "split" into two or more components when solving for more factors than were used in the input. Elements of the "splitting" behavior are observed in solutions of real datasets with several factors. Significant structure remains in the residual of the real dataset after physically-meaningful factors have been assigned and an unrealistic number of factors would be required to explain the remaining variance. This residual structure appears to be due to variability in the spectra of the components (especially OOA-2 in this case), which is likely to be a key limit of the retrievability of components from AMS datasets using PMF and similar methods that need to assume constant component mass spectra. Methods for characterizing and dealing with this variability are needed. Interpretation of PMF factors must be done carefully. Synthetic data indicate that PMF internal diagnostics and similarity to available source component spectra together are not sufficient for identifying factors. It is critical to use correlations between factor and external measurement time series and other criteria to support factor interpretations. True components with

M. Aguilar, G. Alberti, B. Alpat et al.

Q. Zhang, J. Jimenez, M. Canagaratna et al.

Daniel I. Kaufer, Jeffrey L. Cummings, Jeffrey L. Cummings et al.

Gavin Adrian Rummery, M. Niranjan

Scott Thomas, J. Reading, R. Shephard

T. Albrecht, P. Grutter, D. Horne et al.

Y. Akahane, T. Asano, B. Song et al.

Q. Ahmad, R. C. Allen, T. Andersen et al.

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.

Simon Watts, P. Stenner

T. Kippenberg, D. Armani, S. Spillane et al.

R. Koekoek, Rene F. Swarttouw

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.

M. Aguilar, D. Aisa, B. Alpat et al.

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.

Chun-Cheng Lin, Der-Jiunn Deng, Yen-Ling Chih et al.

Manufacturing is involved with complex job shop scheduling problems (JSP). In smart factories, edge computing supports computing resources at the edge of production in a distributed way to reduce response time of making production decisions. However, most works on JSP did not consider edge computing. Therefore, this paper proposes a smart manufacturing factory framework based on edge computing, and further investigates the JSP under such a framework. With recent success of some AI applications, the deep Q network (DQN), which combines deep learning and reinforcement learning, has showed its great computing power to solve complex problems. Therefore, we adjust the DQN with an edge computing framework to solve the JSP. Different from the classical DQN with only one decision, this paper extends the DQN to address the decisions of multiple edge devices. Simulation results show that the proposed method performs better than the other methods using only one dispatching rule.

De-Pin Zhao

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.

S. Carta, Anselmo Ferreira, Alessandro Sebastian Podda et al.

Abstract The stock market forecasting is one of the most challenging application of machine learning, as its historical data are naturally noisy and unstable. Most of the successful approaches act in a supervised manner, labeling training data as being of positive or negative moments of the market. However, training machine learning classifiers in such a way may suffer from over-fitting, since the market behavior depends on several external factors like other markets trends, political events, etc. In this paper, we aim at minimizing such problems by proposing an ensemble of reinforcement learning approaches which do not use annotations (i.e. market goes up or down) to learn, but rather learn how to maximize a return function over the training stage. In order to achieve this goal, we exploit a Q-learning agent trained several times with the same training data and investigate its ensemble behavior in important real-world stock markets. Experimental results in intraday trading indicate better performance than the conventional Buy-and-Hold strategy, which still behaves well in our setups. We also discuss qualitative and quantitative analyses of these results.

K. Parsons, D. Calic, M. Pattinson et al.

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.

Yanwei Jia, X. Zhou

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.