1,645 research outputs found
Two-channel point-contact tunneling theory of superconductors
We introduce a two-channel tunneling model to generalize the widely used BTK
theory of point-contact conductance between a normal metal contact and
superconductor. Tunneling of electrons can occur via localized surface states
or directly, resulting in a Fano resonance in the differential conductance
. We present an analysis of within the two-channel model when
applied to soft point-contacts between normal metallic silver particles and
prototypical heavy-fermion superconductors CeCoIn and CeRhIn at high
pressures. In the normal state the Fano line shape of the measured is well
described by a model with two tunneling channels and a large
temperature-independent background conductance. In the superconducting state a
strongly suppressed Andreev reflection signal is explained by the presence of
the background conductance. We report Andreev signal in CeCoIn consistent
with standard -wave pairing, assuming an equal mixture of
tunneling into [100] and [110] crystallographic interfaces. Whereas in
CeRhIn at 1.8 and 2.0 GPa the signal is described by a -wave
gap with reduced nodal region, i.e., increased slope of the gap opening on the
Fermi surface. A possibility is that the shape of the high-pressure Andreev
signal is affected by the proximity of a line of quantum critical points that
extends from 1.75 to 2.3 GPa, which is not accounted for in our description of
the heavy-fermion superconductor.Comment: 13 pages, 13 figure
Theory of a Magnetically-Controlled Quantum-Dot Spin Transistor
We examine transport through a quantum dot coupled to three ferromagnetic
leads in the regime of weak tunnel coupling. A finite source-drain voltage
generates a nonequilibrium spin on the otherwise non-magnetic quantum dot. This
spin accumulation leads to magnetoresistance. A ferromagnetic but current-free
base electrode influences the quantum-dot spin via incoherent spin-flip
processes and coherent spin precession. As the dot spin determines the
conductance of the device, this allows for a purely magnetic transistor-like
operation. We analyze the effect of both types of processes on the electric
current in different geometries.Comment: 7 pages, 6 figure
Theory of spin Hall magnetoresistance
We present a theory of the spin Hall magnetoresistance (SMR) in multilayers
made from an insulating ferromagnet F, such as yttrium iron garnet (YIG), and a
normal metal N with spin-orbit interactions, such as platinum (Pt). The SMR is
induced by the simultaneous action of spin Hall and inverse spin Hall effects
and therefore a non-equilibrium proximity phenomenon. We compute the SMR in
FN and FNF layered systems, treating N by spin-diffusion theory with
quantum mechanical boundary conditions at the interfaces in terms of the
spin-mixing conductance. Our results explain the experimentally observed spin
Hall magnetoresistance in NF bilayers. For FNF spin valves we predict
an enhanced SMR amplitude when magnetizations are collinear. The SMR and the
spin-transfer torques in these trilayers can be controlled by the magnetic
configuration
Meta-Learning Probabilistic Inference For Prediction
This paper introduces a new framework for data efficient and versatile
learning. Specifically: 1) We develop ML-PIP, a general framework for
Meta-Learning approximate Probabilistic Inference for Prediction. ML-PIP
extends existing probabilistic interpretations of meta-learning to cover a
broad class of methods. 2) We introduce VERSA, an instance of the framework
employing a flexible and versatile amortization network that takes few-shot
learning datasets as inputs, with arbitrary numbers of shots, and outputs a
distribution over task-specific parameters in a single forward pass. VERSA
substitutes optimization at test time with forward passes through inference
networks, amortizing the cost of inference and relieving the need for second
derivatives during training. 3) We evaluate VERSA on benchmark datasets where
the method sets new state-of-the-art results, handles arbitrary numbers of
shots, and for classification, arbitrary numbers of classes at train and test
time. The power of the approach is then demonstrated through a challenging
few-shot ShapeNet view reconstruction task
Soft-Collinear Messengers: A New Mode in Soft-Collinear Effective Theory
It is argued that soft-collinear effective theory for processes involving
both soft and collinear partons, such as exclusive B-meson decays, should
include a new mode in addition to soft and collinear fields. These
"soft-collinear messengers" can interact with both soft and collinear particles
without taking them far off-shell. They thus can communicate between the soft
and collinear sectors of the theory. The relevance of the new mode is
demonstrated with an explicit example, and the formalism incorporating the
corresponding quark and gluon fields into the effective Lagrangian is
developed.Comment: 22 pages, 5 figures. Extended Section 6, clarifying the relevance of
different types of soft-collinear interaction
Optimized broad-histogram simulations for strong first-order phase transitions: Droplet transitions in the large-Q Potts model
The numerical simulation of strongly first-order phase transitions has
remained a notoriously difficult problem even for classical systems due to the
exponentially suppressed (thermal) equilibration in the vicinity of such a
transition. In the absence of efficient update techniques, a common approach to
improve equilibration in Monte Carlo simulations is to broaden the sampled
statistical ensemble beyond the bimodal distribution of the canonical ensemble.
Here we show how a recently developed feedback algorithm can systematically
optimize such broad-histogram ensembles and significantly speed up
equilibration in comparison with other extended ensemble techniques such as
flat-histogram, multicanonical or Wang-Landau sampling. As a prototypical
example of a strong first-order transition we simulate the two-dimensional
Potts model with up to Q=250 different states on large systems. The optimized
histogram develops a distinct multipeak structure, thereby resolving entropic
barriers and their associated phase transitions in the phase coexistence region
such as droplet nucleation and annihilation or droplet-strip transitions for
systems with periodic boundary conditions. We characterize the efficiency of
the optimized histogram sampling by measuring round-trip times tau(N,Q) across
the phase transition for samples of size N spins. While we find power-law
scaling of tau vs. N for small Q \lesssim 50 and N \lesssim 40^2, we observe a
crossover to exponential scaling for larger Q. These results demonstrate that
despite the ensemble optimization broad-histogram simulations cannot fully
eliminate the supercritical slowing down at strongly first-order transitions.Comment: 11 pages, 12 figure
Exploiting transient protein states for the design of small-molecule stabilizers of mutant p53
The destabilizing p53 cancer mutation Y220C creates an extended crevice on the surface of the protein that can be targeted by small-molecule stabilizers. Here, we identify different classes of small molecules that bind to this crevice and determine their binding modes by X-ray crystallography. These structures reveal two major conformational states of the pocket and a cryptic, transiently open hydrophobic subpocket that is modulated by Cys220. In one instance, specifically targeting this transient protein state by a pyrrole moiety resulted in a 40-fold increase in binding affinity. Molecular dynamics simulations showed that both open and closed states of this subsite were populated at comparable frequencies along the trajectories. Our data extend the framework for the design of high-affinity Y220C mutant binders for use in personalized anticancer therapy and, more generally, highlight the importance of implementing protein dynamics and hydration patterns in the drug-discovery process
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