The Photo-Oxidation of Poly alpha Methyl Styrene, Poly Para Methyl Styrene, Poly 2,5 Dimethyl Styrene, Poly 2,4,6 Trimethyl Styrene and Polystyrene

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A machine learning study to identify spinodal clumping in high energy nuclear collisions

The coordinate and momentum space configurations of the net baryon number in heavy ion collisions that undergo spinodal decomposition, due to a first-order phase transition, are investigated using state-of-the-art machine-learning methods. Coordinate space clumping, which appears in the spinodal decomposition, leaves strong characteristic imprints on the spatial net density distribution in nearly every event which can be detected by modern machine learning techniques. On the other hand, the corresponding features in the momentum distributions cannot clearly be detected, by the same machine learning methods, in individual events. Only a small subset of events can be systematically differ- entiated if only the momentum space information is available. This is due to the strong similarity of the two event classes, with and without spinodal decomposition. In such sce- narios, conventional event-averaged observables like the baryon number cumulants signal a spinodal non-equilibrium phase transition. Indeed the third-order cumulant, the skewness, does exhibit a peak at the beam energy (Elab = 3–4 A GeV), where the transient hot and dense system created in the heavy ion collision reaches the first-order phase transition

A Hybrid Quantum Encoding Algorithm of Vector Quantization for Image Compression

Many classical encoding algorithms of Vector Quantization (VQ) of image compression that can obtain global optimal solution have computational complexity O(N). A pure quantum VQ encoding algorithm with probability of success near 100% has been proposed, that performs operations 45sqrt(N) times approximately. In this paper, a hybrid quantum VQ encoding algorithm between classical method and quantum algorithm is presented. The number of its operations is less than sqrt(N) for most images, and it is more efficient than the pure quantum algorithm. Key Words: Vector Quantization, Grover's Algorithm, Image Compression, Quantum AlgorithmComment: Modify on June 21. 10pages, 3 figure

Scanning Electron Microscopy of High-Modulus Polyethylene Fibres

Scanning electron microscopy (SEM) examination of high modulus polyethylene (HMPE) fibres gives rise to a number of artifacts which are here recognized. Antistatic agents may be successfully used for the observation of the woven fibres, but only in conjunction with an intermediate metallic coating. For isolated threads superior results are obtained with the metallic coating alone. New SEM evidence suggests that the high density of surface cracks produced by plasma treatment of HMPE fibres is associated with an aging process. This can also be activated by mechanical energy or storage at room conditions

Adaptive route selection for dynamic route guidance system based on fuzzy-neural approaches

The objective of this work is to model the driver behaviour in the area of route selection. The research focus on an optimum route search function in a typical in-car navigation system or dynamic route guidance (DRG) system. In this work, we want to emphasize the need to orientate the route selection method on the driver's preference. Each route candidate has a set of attributes. A fuzzy-neural approach is used to represent the correlation of the attributes with the driver's route selection. A recommendation or route ranking can be provided to the driver. Based on a training of the fuzzy-neural net on the driver's choice, the route selection function can be made adaptive to the decision-making of the driver.published_or_final_versio

Quantum integrable system with two color components in two dimensions

The Davey-Stewartson 1(DS1) system[9] is an integrable model in two dimensions. A quantum DS1 system with 2 colour-components in two dimensions has been formulated. This two-dimensional problem has been reduced to two one-dimensional many-body problems with 2 colour-components. The solutions of the two-dimensional problem under consideration has been constructed from the resulting problems in one dimensions. For latters with the $\delta$-function interactions and being solved by the Bethe ansatz, we introduce symmetrical and antisymmetrical Young operators of the permutation group and obtain the exact solutions for the quantum DS1 system. The application of the solusions is discussed.Comment: 14 pages, LaTeX fil

Magnetically Robust Non-Fermi Liquid Behavior in Heavy Fermion Systems with f^2-Configuration: Competition between Crystalline-Electric-Field and Kondo-Yosida Singlets

We study a magnetic field effect on the Non-Fermi Liquid (NFL) which arises around the quantum critical point (QCP) due to the competition between the f^2-crystalline-electric-field singlet and the Kondo-Yosida singlet states by using the numerical renormalization ground method. We show the characteristic temperature T_F^*, corresponding to a peak of a specific heat, is not affected by the magnetic field up to H_z^* which is determined by the distance from the QCP or characteristic energy scales of each singlet states. As a result, in the vicinity of QCP, there are parameter regions where the NFL is robust against the magnetic field, at an observable temperature range T > T_F^*, up to H_z^* which is far larger than T_F^* and less than min(T_{K2}, $Delta).Comment: 8 pages, 9 figur

CURE: Flexible Categorical Data Representation by Hierarchical Coupling Learning

© 1989-2012 IEEE. The representation of categorical data with hierarchical value coupling relationships (i.e., various value-to-value cluster interactions) is very critical yet challenging for capturing complex data characteristics in learning tasks. This paper proposes a novel and flexible coupled unsupervised categorical data representation (CURE) framework, which not only captures the hierarchical couplings but is also flexible enough to be instantiated for contrastive learning tasks. CURE first learns the value clusters of different granularities based on multiple value coupling functions and then learns the value representation from the couplings between the obtained value clusters. With two complementary value coupling functions, CURE is instantiated into two models: coupled data embedding (CDE) for clustering and coupled outlier scoring of high-dimensional data (COSH) for outlier detection. These show that CURE is flexible for value clustering and coupling learning between value clusters for different learning tasks. CDE embeds categorical data into a new space in which features are independent and semantics are rich. COSH represents data w.r.t. an outlying vector to capture complex outlying behaviors of objects in high-dimensional data. Substantial experiments show that CDE significantly outperforms three popular unsupervised encoding methods and three state-of-the-art similarity measures, and COSH performs significantly better than five state-of-the-art outlier detection methods on high-dimensional data. CDE and COSH are scalable and stable, linear to data size and quadratic to the number of features, and are insensitive to their parameters