Role of internal gases and creep of Ag in controlling the critical current density of Ag-sheathed Bi2Sr2CaCu2Ox wires

High engineering critical current density JE of >500 A/mm2 at 20 T and 4.2 K can be regularly achieved in Ag-sheathed multifilamentary Bi2Sr2CaCu2Ox (Bi-2212) round wire when the sample length is several centimeters. However, JE(20 T) in Bi-2212 wires of several meters length, as well as longer pieces wound in coils, rarely exceeds 200 A/mm2. Moreover, long-length wires often exhibit signs of Bi-2212 leakage after melt processing that are rarely found in short, open-end samples. We studied the length dependence of JE of state-of-the-art powder-in-tube (PIT) Bi-2212 wires and gases released by them during melt processing using mass spectroscopy, confirming that JE degradation with length is due to wire swelling produced by high internal gas pressures at elevated temperatures [1,2]. We further modeled the gas transport in Bi-2212 wires and examined the wire expansion at critical stages of the melt processing of as-drawn PIT wires and the wires that received a degassing treatment or a cold-densification treatment before melt processing. These investigations showed that internal gas pressure in long-length wires drives creep of the Ag sheath during the heat treatment, causing wire to expand, lowering the density of Bi-2212 filaments, and therefore degrading the wire JE; the creep rupture of silver sheath naturally leads to the leakage of Bi-2212 liquid. Our work shows that proper control of such creep is the key to preventing Bi-2212 leakage and achieving high JE in long-length Bi-2212 conductors and coils

Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks

While the use of bottom-up local operators in convolutional neural networks (CNNs) matches well some of the statistics of natural images, it may also prevent such models from capturing contextual long-range feature interactions. In this work, we propose a simple, lightweight approach for better context exploitation in CNNs. We do so by introducing a pair of operators: gather, which efficiently aggregates feature responses from a large spatial extent, and excite, which redistributes the pooled information to local features. The operators are cheap, both in terms of number of added parameters and computational complexity, and can be integrated directly in existing architectures to improve their performance. Experiments on several datasets show that gather-excite can bring benefits comparable to increasing the depth of a CNN at a fraction of the cost. For example, we find ResNet-50 with gather-excite operators is able to outperform its 101-layer counterpart on ImageNet with no additional learnable parameters. We also propose a parametric gather-excite operator pair which yields further performance gains, relate it to the recently-introduced Squeeze-and-Excitation Networks, and analyse the effects of these changes to the CNN feature activation statistics.Comment: NeurIPS 201

Anomalous high energy dispersion in photoemission spectra from insulating cuprates

Angle resolved photoelectron spectroscopic measurements have been performed on an insulating cuprate Ca_2CuO_2Cl_2. High resolution data taken along the \Gamma to (pi,pi) cut show an additional dispersive feature that merges with the known dispersion of the lowest binding energy feature, which follows the usual strongly renormalized dispersion of ~0.35 eV. This higher energy part reveals a dispersion that is very close to the unrenormalized band predicted by band theory. A transfer of spectral weight from the low energy feature to the high energy feature is observed as the \Gamma point is approached. By comparing with theoretical calculations the high energy feature observed here demonstrates that the incoherent portion of the spectral function has significant structure in momentum space due to the presence of various energy scales.Comment: 5 pages, 3 figure

Algorithmic statistics: forty years later

Algorithmic statistics has two different (and almost orthogonal) motivations. From the philosophical point of view, it tries to formalize how the statistics works and why some statistical models are better than others. After this notion of a "good model" is introduced, a natural question arises: it is possible that for some piece of data there is no good model? If yes, how often these bad ("non-stochastic") data appear "in real life"? Another, more technical motivation comes from algorithmic information theory. In this theory a notion of complexity of a finite object (=amount of information in this object) is introduced; it assigns to every object some number, called its algorithmic complexity (or Kolmogorov complexity). Algorithmic statistic provides a more fine-grained classification: for each finite object some curve is defined that characterizes its behavior. It turns out that several different definitions give (approximately) the same curve. In this survey we try to provide an exposition of the main results in the field (including full proofs for the most important ones), as well as some historical comments. We assume that the reader is familiar with the main notions of algorithmic information (Kolmogorov complexity) theory.Comment: Missing proofs adde

Anti-shielding Effect and Negative Temperature in Instantaneously Reversed Electric Fields and Left-Handed Media

The connections between the anti-shielding effect, negative absolute temperature and superluminal light propagation in both the instantaneously reversed electric field and the left-handed media are considered in the present paper. The instantaneous inversion of the exterior electric field may cause the electric dipoles into the state of negative absolute temperature and therefore give rise to a negative effective mass term of electromagnetic field (i. e., the electromagnetic field propagating inside the negative-temperature medium will acquire an imaginary rest mass), which is said to result in the potential superluminality effect of light propagation in this anti-shielding dielectric. In left-handed media, such phenomena may also arise.Comment: 9 pages, Late

Magnetic properties and domain structure of (Ga,Mn)As films with perpendicular anisotropy

The ferromagnetism of a thin GaMnAs layer with a perpendicular easy anisotropy axis is investigated by means of several techniques, that yield a consistent set of data on the magnetic properties and the domain structure of this diluted ferromagnetic semiconductor. The magnetic layer was grown under tensile strain on a relaxed GaInAs buffer layer using a procedure that limits the density of threading dislocations. Magnetometry, magneto-transport and polar magneto-optical Kerr effect (PMOKE) measurements reveal the high quality of this layer, in particular through its high Curie temperature (130 K) and well-defined magnetic anisotropy. We show that magnetization reversal is initiated from a limited number of nucleation centers and develops by easy domain wall propagation. Furthermore, MOKE microscopy allowed us to characterize in detail the magnetic domain structure. In particular we show that domain shape and wall motion are very sensitive to some defects, which prevents a periodic arrangement of the domains. We ascribed these defects to threading dislocations emerging in the magnetic layer, inherent to the growth mode on a relaxed buffer

Screening of nuclear pairing in nuclear and neutron matter

The screening potential in the $^1S_0$ and $^3S_1$ pairing channels in neutron and nuclear matter in different approximations is discussed. It is found that the vertex corrections to the potential are much stronger in nuclear matter than in neutron matter.Comment: 11 pages, 8 figures, revtex4 styl