Simulation of charged particle trajectories in the neutron decay correlation experiment abBA

The proposed neutron decay correlation experiment, abBA, will directly detect the direction of emission of decay protons and electrons as well as providing spectroscopic information for both particles. In order to provide this information, the abBA experiment incorporates spatially varying electric and magnetic fields. We report on detailed simulations of the decay particle trajectories in order to assess the impact of various systematic effects on the experimental observables. These include among others; adiabaticity of particle orbits, tracking of orbits, reversal of low energy protons due to inhomogeneous electric field, and accuracy of proton time of flight measurements. Several simulation methods were used including commercial software (Simion), custom software, as well as analytical tools based on the use of adiabatic invariants. Our results indicate that the proposed field geometry of the abBA spectrometer will be substantially immune to most systematic effects and that transport calculations using adiabatic invariants agree well with solution of the full equations of motion

Recursive Neural Networks Can Learn Logical Semantics

Tree-structured recursive neural networks (TreeRNNs) for sentence meaning have been successful for many applications, but it remains an open question whether the fixed-length representations that they learn can support tasks as demanding as logical deduction. We pursue this question by evaluating whether two such models---plain TreeRNNs and tree-structured neural tensor networks (TreeRNTNs)---can correctly learn to identify logical relationships such as entailment and contradiction using these representations. In our first set of experiments, we generate artificial data from a logical grammar and use it to evaluate the models' ability to learn to handle basic relational reasoning, recursive structures, and quantification. We then evaluate the models on the more natural SICK challenge data. Both models perform competitively on the SICK data and generalize well in all three experiments on simulated data, suggesting that they can learn suitable representations for logical inference in natural language

A large annotated corpus for learning natural language inference

Understanding entailment and contradiction is fundamental to understanding natural language, and inference about entailment and contradiction is a valuable testing ground for the development of semantic representations. However, machine learning research in this area has been dramatically limited by the lack of large-scale resources. To address this, we introduce the Stanford Natural Language Inference corpus, a new, freely available collection of labeled sentence pairs, written by humans doing a novel grounded task based on image captioning. At 570K pairs, it is two orders of magnitude larger than all other resources of its type. This increase in scale allows lexicalized classifiers to outperform some sophisticated existing entailment models, and it allows a neural network-based model to perform competitively on natural language inference benchmarks for the first time.Comment: To appear at EMNLP 2015. The data will be posted shortly before the conference (the week of 14 Sep) at http://nlp.stanford.edu/projects/snli

Review of the physical and mechanical properties and potential applications of the B2 compound NiAl: Unabridged version of a paper published in International materials review

Considerable work has been performed on NiAl over the last three decades, with an extremely rapid growth in research on this intermetallic occurring in the last few years due to recent interest in this material for electronic and high temperature structural applications. However, many physical properties and the controlling fracture and deformation mechanisms over certain temperature regimes are still in question. Part of this problem lies in the incomplete characterization of many of the alloys previously investigated. Fragmentary data on processing conditions, chemistry, microstructure and the apparent difficulty in accurately measuring composition has made direct comparison between individual studies sometimes tenuous. Therefore, the purpose of this review is to summarize all available mechanical and pertinent physical properties on NiAl, stressing the most recent investigations, in an attempt to understand the behavior of NiAl and its alloys over a broad temperature range

Increasing trap stiffness with position clamping in holographic optical tweezers

We present a holographic optical tweezers system capable of position clamping multiple particles. Moving an optical trap in response to the trapped object's motion is a powerful technique for optical control and force measurement. We have now realised this experimentally using a Boulder Nonlinear Systems Spatial Light Modulator (SLM) with a refresh rate of 203Hz. We obtain a reduction of 44% in the variance of the bead's position, corresponding to an increase in effective trap stiffness of 77%. This reduction relies on the generation of holograms at high speed. We present software capable of calculating holograms in under 1ms using a graphics processor unit. © 2009 Optical Society of America

Scaling Behavior of the Landau Gauge Overlap Quark Propagator

The properties of the momentum space quark propagator in Landau gauge are examined for the overlap quark action in quenched lattice QCD. Numerical calculations are done on three lattices with different lattice spacings and similar physical volumes to explore the approach of the quark propagator towards the continuum limit. We have calculated the nonperturbative momentum-dependent wavefunction renormalization function $Z(p^2)$ and the nonperturbative mass function $M(p^2)$ for a variety of bare quark masses and extrapolate to the chiral limit. We find the behavior of $Z(p^2)$ and $M(p^2)$ are in good agreement for the two finer lattices in the chiral limit. The quark condensate is also calculated.Comment: 3 pages, Lattice2003(Chiral fermions

A unifying explanation of complex frequency spectra of gamma Dor, SPB and Be stars: combination frequencies and highly non-sinusoidal light curves

There are many Slowly Pulsating B (SPB) stars and γ Dor stars in the Kepler mission data set. The light curves of these pulsating stars have been classified phenomenologically into stars with symmetric light curves and with asymmetric light curves. In the same effective temperature ranges as the γ Dor and SPB stars, there are variable stars with downward light curves that have been conjectured to be caused by spots. Among these phenomenological classes of stars, some show ‘frequency groups’ in their amplitude spectra that have not previously been understood. While it has been recognized that non-linear pulsation gives rise to combination frequencies in a Fourier description of the light curves of these stars, such combination frequencies have been considered to be a only a minor constituent of the amplitude spectra. In this paper, we unify the Fourier description of the light curves of these groups of stars, showing that many of them can be understood in terms of only a few base frequencies, which we attribute to g-mode pulsations, and combination frequencies, where sometimes a very large number of combination frequencies dominate the amplitude spectra. The frequency groups seen in these stars are thus tremendously simplified. We show observationally that the combination frequencies can have amplitudes greater than the base frequency amplitudes, and we show theoretically how this arises. Thus for some γ Dor and SPB stars, combination frequencies can have the highest observed amplitudes. Among the B stars are pulsating Be stars that show emission lines in their spectra from occasional ejection of material into a circumstellar disc. Our analysis gives strong support to the understanding of these pulsating Be stars as rapidly rotating SPB stars, explained entirely by g-mode pulsations