Statistically Locked-in Transport Through Periodic Potential Landscapes

Classical particles driven through periodically modulated potential energy landscapes are predicted to follow a Devil's staircase hierarchy of commensurate trajectories depending on the orientation of the driving force. Recent experiments on colloidal spheres flowing through arrays of optical traps do indeed reveal such a hierarchy,but not with the predicted structure. The microscopic trajectories, moreover,appear to be random, with commensurability emerging only in a statistical sense. We introduce an idealized model for periodically modulated transport in the presence of randomness that captures both the structure and statistics of such statistically locked-in states.Comment: REVTeX with EPS figures, 4 pages, 4 figure

Implicit large eddy simulations of anisotropic weakly compressible turbulence with application to core-collapse supernovae

(Abridged) In the implicit large eddy simulation (ILES) paradigm, the dissipative nature of high-resolution shock-capturing schemes is exploited to provide an implicit model of turbulence. Recent 3D simulations suggest that turbulence might play a crucial role in core-collapse supernova explosions, however the fidelity with which turbulence is simulated in these studies is unclear. Especially considering that the accuracy of ILES for the regime of interest in CCSN, weakly compressible and strongly anisotropic, has not been systematically assessed before. In this paper we assess the accuracy of ILES using numerical methods most commonly employed in computational astrophysics by means of a number of local simulations of driven, weakly compressible, anisotropic turbulence. We report a detailed analysis of the way in which the turbulent cascade is influenced by the numerics. Our results suggest that anisotropy and compressibility in CCSN turbulence have little effect on the turbulent kinetic energy spectrum and a Kolmogorov $k^{-5/3}$ scaling is obtained in the inertial range. We find that, on the one hand, the kinetic energy dissipation rate at large scales is correctly captured even at relatively low resolutions, suggesting that very high effective Reynolds number can be achieved at the largest scales of the simulation. On the other hand, the dynamics at intermediate scales appears to be completely dominated by the so-called bottleneck effect, \ie the pile up of kinetic energy close to the dissipation range due to the partial suppression of the energy cascade by numerical viscosity. An inertial range is not recovered until the point where relatively high resolution $\sim 512^3$, which would be difficult to realize in global simulations, is reached. We discuss the consequences for CCSN simulations.Comment: 17 pages, 9 figures, matches published versio

Stellar iron core collapse in {3+1} general relativity and the gravitational wave signature of core-collapse supernovae

I perform and analyse the first ever calculations of rotating stellar iron core collapse in {3+1} general relativity that start out with presupernova models from stellar evolutionary calculations and include a microphysical finite-temperature nuclear equation of state, an approximate scheme for electron capture during collapse and neutrino pressure effects. Based on the results of these calculations, I obtain the to-date most realistic estimates for the gravitational wave signal from collapse, bounce and the early postbounce phase of core collapse supernovae.thesi

Classical Structured Prediction Losses for Sequence to Sequence Learning

There has been much recent work on training neural attention models at the sequence-level using either reinforcement learning-style methods or by optimizing the beam. In this paper, we survey a range of classical objective functions that have been widely used to train linear models for structured prediction and apply them to neural sequence to sequence models. Our experiments show that these losses can perform surprisingly well by slightly outperforming beam search optimization in a like for like setup. We also report new state of the art results on both IWSLT'14 German-English translation as well as Gigaword abstractive summarization. On the larger WMT'14 English-French translation task, sequence-level training achieves 41.5 BLEU which is on par with the state of the art.Comment: 10 pages, NAACL 201

Structural and functional analysis of mutant MATal homeodomains by multidimensional nmr spectroscopy

Homeodomain proteins are transcription factors that contain a conserved 60- residue sequence, beginning with an N-terminal unstructured arm, followed by an alpha helix, a loop, and a helix-tum-helix. The yeast protein MATa1 is unusual among homeodomains in that, as a monomer, it binds very poorly to its DNA operator. However, the a1- α2 heterodimer binds to the hsg operator with 3000 times the affinity it has for nonspecific DNA. Studies have shown that most of the heterodimer\u27s binding specificity is due to a1 rather than α2 (1,2). To identify the structural changes that transform al into a strong, sequence-specific DNA binding protein, a single-point mutant (s25y) and a double-point mutant (q24r/s25y) were studied. EMSA studies showed that both mutants bind to DNA with greater affinity than wild type al does. Analysis of 2-D 15N-HSQC and 3-D 15N-NOESY spectra showed that significant changes in the chemical shifts of the backbone amide groups of loop 1 and helix 3 occur upon mutation. The NOESY spectrum was also used to identify NOEs between amide protons of sequential residues, indicating where alpha helical conformations occurred. The NOEs showed that the third helix is extended in the a1 mutants. Finally, titration experiments were performed by adding aliquots of the 19- residue a2 tail peptide to al and to each al mutant, and then recording HSQC spectra. These showed that chemical shift changes which occur in wild type al upon a2 tail binding are diminished in the al mutants