scholarly journals Universal Bound to the Amplitude of the Vortex Nernst Signal in Superconductors

2021 ◽  
Vol 126 (7) ◽  
Author(s):  
Carl Willem Rischau ◽  
Yuke Li ◽  
Benoît Fauqué ◽  
Hisashi Inoue ◽  
Minu Kim ◽  
...  
Keyword(s):  
2015 ◽  
Vol 24 (12) ◽  
pp. 1544022 ◽  
Author(s):  
Carlos A. R. Herdeiro ◽  
Eugen Radu

Kerr black holes (BHs) have their angular momentum, [Formula: see text], bounded by their mass, [Formula: see text]: [Formula: see text]. There are, however, known BH solutions violating this Kerr bound. We propose a very simple universal bound on the rotation, rather than on the angular momentum, of four-dimensional, stationary and axisymmetric, asymptotically flat BHs, given in terms of an appropriately defined horizon linear velocity, [Formula: see text]. The [Formula: see text] bound is simply that [Formula: see text] cannot exceed the velocity of light. We verify the [Formula: see text] bound for known BH solutions, including some that violate the Kerr bound, and conjecture that only extremal Kerr BHs saturate the [Formula: see text] bound.


Author(s):  
Richard C. H. Webb

AbstractWe give a universal bound for the bounded geodesic image theorem of Masur–Minsky. The proof uses elementary techniques. We also give a universal bound for a stronger version of subsurface projection, this demonstrates good control over many standard subsurface projections simultaneously.


2019 ◽  
Vol 70 (2) ◽  
pp. 473-482
Author(s):  
Wei Lu ◽  
Jing Mao ◽  
Chuanxi Wu

Positivity ◽  
2019 ◽  
Vol 24 (4) ◽  
pp. 837-854
Author(s):  
Nedra Belhaj Rhouma ◽  
Mouldi Seddik

2012 ◽  
Vol 85 (2) ◽  
Author(s):  
Yujun Wang ◽  
Chris H. Greene
Keyword(s):  

2007 ◽  
Vol 98 (3) ◽  
pp. 1064-1072 ◽  
Author(s):  
Stefano Panzeri ◽  
Riccardo Senatore ◽  
Marcelo A. Montemurro ◽  
Rasmus S. Petersen

Information Theory enables the quantification of how much information a neuronal response carries about external stimuli and is hence a natural analytic framework for studying neural coding. The main difficulty in its practical application to spike train analysis is that estimates of neuronal information from experimental data are prone to a systematic error (called “bias”). This bias is an inevitable consequence of the limited number of stimulus-response samples that it is possible to record in a real experiment. In this paper, we first explain the origin and the implications of the bias problem in spike train analysis. We then review and evaluate some recent general-purpose methods to correct for sampling bias: the Panzeri-Treves, Quadratic Extrapolation, Best Universal Bound, Nemenman-Shafee-Bialek procedures, and a recently proposed shuffling bias reduction procedure. Finally, we make practical recommendations for the accurate computation of information from spike trains. Our main recommendation is to estimate information using the shuffling bias reduction procedure in combination with one of the other four general purpose bias reduction procedures mentioned in the preceding text. This provides information estimates with acceptable variance and which are unbiased even when the number of trials per stimulus is as small as the number of possible discrete neuronal responses.


2019 ◽  
Vol 6 (4) ◽  
pp. 719-729 ◽  
Author(s):  
Man-Hong Yung ◽  
Xun Gao ◽  
Joonsuk Huh

ABSTRACT In linear optics, photons are scattered in a network through passive optical elements including beam splitters and phase shifters, leading to many intriguing applications in physics, such as Mach–Zehnder interferometry, the Hong–Ou–Mandel effect, and tests of fundamental quantum mechanics. Here we present the fundamental limit in the transition amplitudes of bosons, applicable to all physical linear optical networks. Apart from boson sampling, this transition bound results in many other interesting applications, including behaviors of Bose–Einstein condensates (BEC) in optical networks, counterparts of Hong–Ou–Mandel effects for multiple photons, and approximating permanents of matrices. In addition, this general bound implies the existence of a polynomial-time randomized algorithm for estimating the transition amplitudes of bosons, which represents a solution to an open problem raised by Aaronson and Hance (Quantum Inf Comput 2012; 14: 541–59). Consequently, this bound implies that computational decision problems encoded in linear optics, prepared and detected in the Fock basis, can be solved efficiently by classical computers within additive errors. Furthermore, our result also leads to a classical sampling algorithm that can be applied to calculate the many-body wave functions and the S-matrix of bosonic particles.


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