Boundary of Quantum Evolution under Decoherence

Relaxation effects impose fundamental limitations on our ability to coherently control quantum mechanical phenomena. In this letter, we establish physical limits on how closely can a quantum mechanical system be steered to a desired target state in the presence of relaxation. In particular, we explicitly compute the maximum coherence or polarization that can be transferred between coupled nuclear spins in the presence of very general decoherence mechanisms that include cross-correlated relaxation. We give analytical expressions for the control laws (pulse sequences) which achieve these physical limits and provide supporting experimental evidence. Exploitation of cross-correlation effects has recently led to the development of powerful methods in NMR spectroscopy to study very large biomolecules in solution. We demonstrate with experiments that the optimal pulse sequences provide significant gains over these state of the art methods, opening new avenues for spectroscopy of much larger proteins. Surprisingly, in spite of very large relaxation rates, optimal control can transfer coherence without any loss when cross-correlated relaxation rates are tuned to auto-correlated relaxation rates

Broadband Relaxation-Optimized Polarization Transfer in Magnetic Resonance

Many applications of magnetic resonance are limited by rapid loss of spin coherence caused by large transverse relaxation rates. In nuclear magnetic resonance (NMR) of large proteins, increased relaxation losses lead to poor sensitivity of experiments and increased measurement time. In this paper we develop broadband relaxation optimized pulse sequences (BB-CROP) which approach fundamental limits of coherence transfer efficiency in the presence of very general relaxation mechanisms that include cross-correlated relaxation. These broadband transfer schemes use new techniques of chemical shift refocusing (STAR echoes) that are tailored to specific trajectories of coupled spin evolution. We present simulations and experimental data indicating significant enhancement in the sensitivity of multi-dimensional NMR experiments of large molecules by use of these methods

Efficient Linear Programming for Dense CRFs

The fully connected conditional random field (CRF) with Gaussian pairwise potentials has proven popular and effective for multi-class semantic segmentation. While the energy of a dense CRF can be minimized accurately using a linear programming (LP) relaxation, the state-of-the-art algorithm is too slow to be useful in practice. To alleviate this deficiency, we introduce an efficient LP minimization algorithm for dense CRFs. To this end, we develop a proximal minimization framework, where the dual of each proximal problem is optimized via block coordinate descent. We show that each block of variables can be efficiently optimized. Specifically, for one block, the problem decomposes into significantly smaller subproblems, each of which is defined over a single pixel. For the other block, the problem is optimized via conditional gradient descent. This has two advantages: 1) the conditional gradient can be computed in a time linear in the number of pixels and labels; and 2) the optimal step size can be computed analytically. Our experiments on standard datasets provide compelling evidence that our approach outperforms all existing baselines including the previous LP based approach for dense CRFs.Comment: 24 pages, 10 figures and 4 table

Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials

Dense conditional random fields (CRFs) have become a popular framework for modelling several problems in computer vision such as stereo correspondence and multi-class semantic segmentation. By modelling long-range interactions, dense CRFs provide a labelling that captures finer detail than their sparse counterparts. Currently, the state-of-the-art algorithm performs mean-field inference using a filter-based method but fails to provide a strong theoretical guarantee on the quality of the solution. A question naturally arises as to whether it is possible to obtain a maximum a posteriori (MAP) estimate of a dense CRF using a principled method. Within this paper, we show that this is indeed possible. We will show that, by using a filter-based method, continuous relaxations of the MAP problem can be optimised efficiently using state-of-the-art algorithms. Specifically, we will solve a quadratic programming (QP) relaxation using the Frank-Wolfe algorithm and a linear programming (LP) relaxation by developing a proximal minimisation framework. By exploiting labelling consistency in the higher-order potentials and utilising the filter-based method, we are able to formulate the above algorithms such that each iteration has a complexity linear in the number of classes and random variables. The presented algorithms can be applied to any labelling problem using a dense CRF with sparse higher-order potentials. In this paper, we use semantic segmentation as an example application as it demonstrates the ability of the algorithm to scale to dense CRFs with large dimensions. We perform experiments on the Pascal dataset to indicate that the presented algorithms are able to attain lower energies than the mean-field inference method

Microstructures of negative and positive azeotropes

Azeotropes famously impose fundamental restrictions on distillation processes, yet their special thermodynamic properties make them highly desirable for a diverse range of industrial and technological applications. Using neutron diffraction, we investigate the structures of two prototypical azeotropes, the negative acetone–chloroform and the positive benzene–methanol azeotrope. C–H⋯O hydrogen bonding is the dominating interaction in the negative azeotrope but C–Cl⋯O halogen bonding contributes as well. Hydrogen-bonded chains of methanol molecules, which are on average longer than in pure methanol, are the defining structural feature of the positive azeotrope illustrating the fundamentally different local mixing in the two kinds of azeotropes. The emerging trend for both azeotropes is that the more volatile components experience the more pronounced structural changes in their local environments as the azeotropes form. The mixing of the acetone–chloroform azeotrope is essentially random above 20 Å, where the running Kirkwood–Buff integrals of our structural model converge closely to the ones expected from thermodynamic data. The benzene–methanol azeotrope on the other hand displays extended methanol-rich regions and consequently the running Kirkwood–Buff integrals oscillate up to at least 60 Å. Our study provides the first experimental insights into the microstructures of azeotropes and a direct link with their thermodynamic properties. Ultimately, this will provide a route for creating tailored molecular environments in azeotropes to improve and fine-tune their performances

Silent synapses generate sparse and orthogonal action potential firing in adult-born hippocampal granule cells.

In adult neurogenesis young neurons connect to the existing network via formation of thousands of new synapses. At early developmental stages, glutamatergic synapses are sparse, immature and functionally 'silent', expressing mainly NMDA receptors. Here we show in 2- to 3-week-old young neurons of adult mice, that brief-burst activity in glutamatergic fibers is sufficient to induce postsynaptic AP firing in the absence of AMPA receptors. The enhanced excitability of the young neurons lead to efficient temporal summation of small NMDA currents, dynamic unblocking of silent synapses and NMDA-receptor-dependent AP firing. Therefore, early synaptic inputs are powerfully converted into reliable spiking output. Furthermore, due to high synaptic gain, small dendritic trees and sparse connectivity, neighboring young neurons are activated by different distinct subsets of afferent fibers with minimal overlap. Taken together, synaptic recruitment of young neurons generates sparse and orthogonal AP firing, which may support sparse coding during hippocampal information processing

QCD corrections to the Wilson coefficients C9 and C10 in two-Higgs doublet models

In this letter we present the analytic results for the two-loop corrections to the Wilson coefficients C_9(mu_W) and C_10(mu_W) in type-I and type-II two-Higgs-doublet models at the matching scale mu_W. These corrections are important ingredients for next-to-next-to-leading logarithmic predictions of various observables related to the decays B -> X_s l^+ l^- in these models. In scenarios with moderate values of tan(beta) neutral Higgs boson contributions can be safely neglected for e,mu. Therefore we concentrate on the contributions mediated by charged Higgs bosons.Comment: 12 pages, 3 figure

Insights Into Today’s Real Estate Market - A Focus on Switzerland

Real estate is at the confluence of several major transformations: higher inflation, higher interest rates, and increasing ESG risks. In this Roundup, experts from academia, industry, and regulation discuss how investors and households navigate new difficult tradeoffs in the Swiss context. Will macroeconomic uncertainties affect prices and impact the financial sector? What is the proper place of real estate in an investor's portfolio? Should homeowners rethink their financing options? And will increasingly strict energy regulations impact the market