Hasil untuk "q-bio.SC"

H. Srivastava, Choi Junesang

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. Caputo

Gavin Adrian Rummery, M. Niranjan

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

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

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.

Junling Hu, Michael P. Wellman

K. Svore, Alan Geller, M. Troyer et al.

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.

Zinan Wang, Li Zhang, Song Wang et al.

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.

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.

Andrea Skolik, S. Jerbi, V. Dunjko

Quantum machine learning (QML) has been identified as one of the key fields that could reap advantages from near-term quantum devices, next to optimization and quantum chemistry. Research in this area has focused primarily on variational quantum algorithms (VQAs), and several proposals to enhance supervised, unsupervised and reinforcement learning (RL) algorithms with VQAs have been put forward. Out of the three, RL is the least studied and it is still an open question whether VQAs can be competitive with state-of-the-art classical algorithms based on neural networks (NNs) even on simple benchmark tasks. In this work, we introduce a training method for parametrized quantum circuits (PQCs) that can be used to solve RL tasks for discrete and continuous state spaces based on the deep Q-learning algorithm. We investigate which architectural choices for quantum Q-learning agents are most important for successfully solving certain types of environments by performing ablation studies for a number of different data encoding and readout strategies. We provide insight into why the performance of a VQA-based Q-learning algorithm crucially depends on the observables of the quantum model and show how to choose suitable observables based on the learning task at hand. To compare our model against the classical DQN algorithm, we perform an extensive hyperparameter search of PQCs and NNs with varying numbers of parameters. We confirm that similar to results in classical literature, the architectural choices and hyperparameters contribute more to the agents’ success in a RL setting than the number of parameters used in the model. Finally, we show when recent separation results between classical and quantum agents for policy gradient RL can be extended to inferring optimal Q-values in restricted families of environments. This work paves the way towards new ideas on how a quantum advantage may be obtained for real-world problems in the future.

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.

Satwinder Singh, Naeem Tabassum, Tamer K. Darwish et al.

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

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.

Sierra Haile, Benjamin C. Balzer, Emily Egan et al.

This article focuses on current and emerging therapeutics for CADASIL (Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy). CADASIL is an inherited vascular disease that impairs blood flow in the small cerebral vessels of the brain, leading to strokes and other neurological deficits. The disease is caused by a mutation in the NOTCH3 gene located on chromosome 19. NOTCH3 encodes a transmembrane receptor expressed on vascular smooth muscle cells. In CADASIL, mutations in the NOTCH3 gene lead to the accumulation and deposition of the receptor, affecting the number of cysteine residues in its extracellular domain. These mutations result in the loss or gain of a cysteine residue within the epidermal growth factor-like repeat (EGFr) domains of the NOTCH protein. Beyond traditional symptomatic treatments for stroke, this work highlights advances in disease modifying approaches including gene editing, cell therapies, and immune-based interventions aimed at altering the course of CADASIL. It also examines ongoing clinical trials and recent patents related to these novel strategies. In addition to summarizing diagnostic methods and molecular mechanisms, the article emphasizes the translational potential of current research and the experimental models driving therapeutic development. The goal is to offer a comprehensive overview of CADASIL and emerging interventions that hold promise for improving long-term outcomes.