Local probing of ferroelectric and ferroelastic switching through stress-mediated piezoelectric spectroscopy

Strain effects have a significant role in mediating classic ferroelectric behavior such as polarization switching and domain wall dynamics. These effects are of critical relevance if the ferroelectric order parameter is coupled to strain and is therefore, also ferroelastic. Here, switching spectroscopy piezoresponse force microscopy (SS-PFM) is combined with control of applied tip pressure to exert direct control over the ferroelastic and ferroelectric switching events, a modality otherwise unattainable in traditional PFM. As a proof of concept, stress-mediated SS-PFM is applied toward the study of polarization switching events in a lead zirconate titanate thin film, with a composition near the morphotropic phase boundary with co-existing rhombohedral and tetragonal phases. Under increasing applied pressure, shape modification of local hysteresis loops is observed, consistent with a reduction in the ferroelastic domain variants under increased pressure. These experimental results are further validated by phase field simulations. The technique can be expanded to explore more complex electromechanical responses under applied local pressure, such as probing ferroelectric and ferroelastic piezoelectric nonlinearity as a function of applied pressure, and electro-chemo-mechanical response through electrochemical strain microscopy.

The Complexity of MAP Inference in Bayesian Networks Specified Through Logical Languages

We study the computational complexity of finding maximum a posteriori configurations in Bayesian networks whose probabilities are specified by logical formulas. This approach leads to a fine grained study in which local information such as context-sensitive independence and determinism can be considered. It also allows us to characterize more precisely the jump from tractability to NP-hardness and beyond, and to consider the complexity introduced by evidence alone.

Learning Bayesian Networks with Thousands of Variables

We present a method for learning Bayesian networks from data sets containing thousands of variables without the need for structure constraints. Our approach is made of two parts. The first is a novel algorithm that effectively explores the space of possible parent sets of a node. It guides the exploration towards the most promising parent sets on the basis of an approximated score function that is computed in constant time. The second part is an improvement of an existing ordering-based algorithm for structure optimization. The new algorithm provably achieves a higher score compared to its original formulation. Our novel approach consistently outperforms the state of the art on very large data sets.

Learning Bounded Tree-Width Bayesian Networks via Sampling

Learning Bayesian networks with bounded tree-width has attracted much attention recently, because low tree-width allows exact inference to be performed efficiently. Some existing methods [12, 14] tackle the problem by using k-trees to learn the optimal Bayesian network with tree-width up to k. In this paper, we propose a sampling method to efficiently find representative k-trees by introducing an Informative score function to characterize the quality of a k-tree. The proposed algorithm can efficiently learn a Bayesian network with tree-width at most k. Experiment results indicate that our approach is comparable with exact methods, but is much more computationally efficient.

Probing a 3,4'-bis-guanidinium diaryl derivative as an allosteric inhibitor of the Ras pathway

Mutations in the Ras-pathway occur in 40–45% of colorectal cancer patients and these are refractory to treatment with anti-EGFR-targeted therapies. With this in mind, we have studied novel guanidinium- based compounds with demonstrated ability to inhibit protein kinases. We have performed docking stud- ies with several proteins involved in the Ras-pathway and evaluated 3,40-bis-guanidinium derivatives as inhibitors of B-Raf. Compound 3, the most potent in this series, demonstrated strong cytotoxicity in WTB-Raf colorectal cancer cells and also cells with V600EB-Raf mutations. Cell death was induced by apop- tosis, detected by cleavage of PARP. Compound 3 also potently inhibited ERK1/2 signalling, inhibited EGFR activation, as well as Src, STAT3 and AKT phosphorylation. Mechanistically, compound 3 did not inhibit ATP binding to B-Raf, but direct assay of B-Raf activity was inhibited in vitro. Summarizing, we have identified a novel B-Raf type-III inhibitor that exhibits potent cellular cytotoxicity

An Ultrasonic Relaxation Study of 1-Alkyl-3-Methylmidazolium-Based Room Temperature Ionic Liquids: Probing the Role of Alkyl Chain Length in the Cation

Ultrasound absorption spectra of four 1-alkyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide were determined as a function of the alkyl chain length on the cation from 1-propyl- to 1-hexyl- from 293.15 to 323.15 K at ambient pressure. Herein, the ultrasound absorption measurements were carried out using a standard pulse technique within a frequency range from 10 to 300 MHz. Additionally the speed of sound, density and viscosity have been measured. The presence of strong dissipative processes during the ultrasound wave propagation was found experimentally, i.e. relaxation processes in the megahertz range were observed for all compounds over the whole temperature range. The relaxation spectra (both relaxation amplitude and relaxation frequency) were shown to be dependent on the alkyl side chain length of the 1-alkyl-3-methylimidazolium ring. In most cases, a single Debye model described the absorption spectra very well. However, a comparison of the determined spectra with the spectra of a few other imidazolium-based ionic liquids reported in the literature (in part recalculated in this work) shows that the complexity of the spectra increases rapidly with the elongation of the alkyl chain length on the cation. This complexity indicates that both the volume viscosity and the shear viscosity are involved in relaxation processes even in relatively low frequency ranges. As a consequence, the sound velocity dispersion is present at relatively low megahertz frequencies.

Probing confinement resonances by photoionizing Xe inside a C60+ molecular cage

Double photoionization accompanied by loss of n C atoms (n=0, 2, 4, 6) was investigated by merging beams of Xe@C60+ ions and synchrotron radiation and measuring the yields of product ions. The giant 4d dipole resonance of the caged Xe atom has a prominent signature in the cross section for these product channels, which together account for 6.2 ± 1.4 of the total Xe 4d oscillator strength of 10. Compared to that for a free Xe atom, the oscillator strength is redistributed in photon energy due to multipath interference of outgoing Xe 4d photoelectron waves that may be transmitted or reflected by the spherical C60+ molecular cage, yielding so-called confinement resonances. The data are compared with an earlier measurement and with theoretical predictions for this single-molecule photoelectron interferometer system. Relativistic R-matrix calculations for the Xe atom in a spherical potential shell representing the fullerene cage show the sensitivity of the interference pattern to the molecular geometry. © 2013 American Physical Society.

The Rossiter-McLaughlin effect reloaded: Probing the 3D spin-orbit geometry, differential stellar rotation, and the spatially-resolved stellar spectrum of star-planet systems

When a planet transits its host star, it blocks regions of the stellar surface from view; this causes a distortion of the spectral lines and a change in the line-of-sight (LOS) velocities, known as the Rossiter-McLaughlin (RM) effect. Since the LOS velocities depend, in part, on the stellar rotation, the RM waveform is sensitive to the star-planet alignment (which provides information on the system’s dynamical history). We present a new RM modelling technique that directly measures the spatially-resolved stellar spectrum behind the planet. This is done by scaling the continuum flux of the (HARPS) spectra by the transit light curve, and then subtracting the infrom the out-of-transit spectra to isolate the starlight behind the planet. This technique does not assume any shape for the intrinsic local profiles. In it, we also allow for differential stellar rotation and centre-to-limb variations in the convective blueshift. We apply this technique to HD 189733 and compare to 3D magnetohydrodynamic (MHD) simulations. We reject rigid body rotation with high confidence (>99% probability), which allows us to determine the occulted stellar latitudes and measure the stellar inclination. In turn, we determine both the sky-projected (λ ≈ −0.4 ± 0.2◦) and true 3D obliquity (ψ ≈ 7+12 −4 ◦ ). We also find good agreement with the MHD simulations, with no significant centre-to-limb variations detectable in the local profiles. Hence, this technique provides a new powerful tool that can probe stellar photospheres, differential rotation, determine 3D obliquities, and remove sky-projection biases in planet migration theories. This technique can be implemented with existing instrumentation, but will become even more powerful with the next generation of high-precision radial velocity spectrographs.

Learning Bayesian Networks with Bounded Tree-Width via Guided Search

Bounding the tree-width of a Bayesian network can reduce the chance of overfitting, and allows exact inference to be performed efficiently. Several existing algorithms tackle the problem of learning bounded tree-width Bayesian networks by learning from k-trees as super-structures, but they do not scale to large domains and/or large tree-width. We propose a guided search algorithm to find k-trees with maximum Informative scores, which is a measure of quality for the k-tree in yielding good Bayesian networks. The algorithm achieves close to optimal performance compared to exact solutions in small domains, and can discover better networks than existing approximate methods can in large domains. It also provides an optimal elimination order of variables that guarantees small complexity for later runs of exact inference. Comparisons with well-known approaches in terms of learning and inference accuracy illustrate its capabilities.