Surface dynamics of noisy viscoelastic films by adiabatic approximation

Surface dynamics is sometimes used to determine the rheological properties of soft materials. In typical data analyses, surface capillary waves are included without incorporating thermal noise. A phenomenological expression for the time-dependent power spectral density has been proposed to account for thermal noise and shown to agree well with experiment. In this paper, we investigate the surface dynamics of viscoelastic films with thermal noise by using an adiabatic approximation involving fast quasi-equilibrium elastic vibrations to derive the power spectral density. Our result justifies the use of the phenomenological expression.     

Asymmetric synthesis of (triaryl)methylamines by rhodium-catalyzed addition of arylboroxines to cyclic N-sulfonyl ketimines

Asymmetric addition of arylboroxines to cyclic N-sulfonyl ketimines proceeded in the presence of a rhodium catalyst coordinated with a chiral diene ligand to give high yields of benzosultams, where a triaryl-substituted stereogenic carbon center was created with high enantioselectivity (93-99% ee). The chiral benzosultams were transformed into the chiral (triaryl)methylamines by breaking the cyclic structure.     

Adjusted support vector machines based on a new loss function

Support vector machine (SVM) has attracted considerable attentions recently due to its successful applications in various domains. However, by maximizing the margin of separation between the two classes in a binary classification problem, the SVM solutions often suffer two serious drawbacks. First, SVM separating hyperplane is usually very sensitive to training samples since it strongly depends on support vectors which are only a few points located on the wrong side of the corresponding margin boundaries. Second, the separating hyperplane is equidistant to the two classes which are considered equally important when optimizing the separating hyperplane location regardless the number of training data and their dispersions in each class. In this paper, we propose a new SVM solution, adjusted support vector machine (ASVM), based on a new loss function to adjust the SVM solution taking into account the sample sizes and dispersions of the two classes. Numerical experiments show that the ASVM outperforms conventional SVM, especially when the two classes have large differences in sample size and dispersion.     

Flux-limited nonequilibrium electron energy transport in warm dense gold

An abrupt change in energy transport has been observed in femtosecond laser heated gold when the absorbed laser flux exceeds ~7×10(12) W/cm(2). Below this value, the absorbed flux is carried by ballistic motion of nonthermal electrons produced in interband excitation. Above this value energy transport appears to include ballistic transport by nonthermal electrons and heat diffusion by thermalized hot electrons. The ballistic component is limited to a flux of ~7×10(12) W/cm(2). This offers a unique benchmark for comparison with theory on nonequilibrium electron transport.     

Magnetic-field-induced insulating phases at large r s

Exploring a two-dimensional hole system in the large r(s) regime we found a surprisingly rich phase diagram. At the highest densities, beside the nu =1/3, 2/3, and 2/5 fractional quantum Hall states, we observe both of the previously reported high field insulating and reentrant insulating phases. As the density is lowered, the reentrant insulating phase initially strengthens, then it unexpectedly starts weakening until it completely disappears. The intricate behavior of the insulating phases can be explained by a nonmonotonic melting line in the nu-r(s) phase space.     

Wavelet basis selection and feature extraction for shift invariant ultrasound foreign body classification

This paper proposes a novel technique for automatic ultrasound non-destructive foreign body (FB) detection and classification. A signal registration process is introduced to eliminate shift variations commonly encountered in ultrasound signals. Information theory based methods are then developed for wavelet basis selection and feature extraction to facilitate robust FB classification. Probabilistic neural networks are used for FB classification. Experimental results confirm that the wavelet basis selected by the proposed method improves the FB classification accuracy. It is concluded that low order wavelet bases have better ability to distinguish classes with great similarities than their higher order counterparts, while the reverse is true for more divergent classes.