Relaxation of quantum states under energy perturbations

The energy-based stochastic extension of the Schrodinger equation is perhaps the simplest mathematically rigourous and physically plausible model for the reduction of the wave function. In this article we apply a new simulation methodology for the stochastic framework to analyse formulae for the dynamics of a particle confined to a square-well potential. We consider the situation when the width of the well is expanded instantaneously. Through this example we are able to illustrate in detail how a quantum system responds to an energy perturbation, and the mechanism, according to the stochastic evolutionary law, by which the system relaxes spontaneously into one of the stable eigenstates of the Hamiltonian. We examine in particular how the expectation value of the Hamiltonian and the probability distribution for the position of the particle change in time. An analytic expression for the typical timescale of relaxation is derived. We also consider the small perturbation limit, and discuss the relation between the stochastic framework and the quantum adiabatic theorem

Dry matter yields and quality of organic lupin/cereal mixtures for wholecrop forage

In view of climate change predictions and the general desirability of increasing the amount of home grown protein, a case exists for the investigation of lupins and lupin/cereal bicrop combinations as wholecrop forage on organic farms. A replicated randomised block trial is described which took place at the Royal Agricultural College, Cirencester, in 2005. This involved spring sown blue, white and yellow lupins, millet, wheat and triticale and lupin/cereal bi-crops. Data for dry matter yields for wholecrop silage, crude protein, MAD fi bre content and estimated ME, are presented for a single harvest. It is concluded that white lupins and white lupin bi-crops with spring wheat or triticale offer the best prospects for a viable wholecrop forage crop in an organic situation

Scotin, a novel p53-inducible proapoptotic protein located in the ER and the nuclear membrane

p53 is a transcription factor that induces growth arrest or apoptosis in response to cellular stress. To identify new p53-inducible proapoptotic genes, we compared, by differential display, the expression of genes in spleen or thymus of normal and p53 nullizygote mice after γ-irradiation of whole animals. We report the identification and characterization of human and mouse Scotin homologues, a novel gene directly transactivated by p53. The Scotin protein is localized to the ER and the nuclear membrane. Scotin can induce apoptosis in a caspase-dependent manner. Inhibition of endogenous Scotin expression increases resistance to p53-dependent apoptosis induced by DNA damage, suggesting that Scotin plays a role in p53-dependent apoptosis. The discovery of Scotin brings to light a role of the ER in p53-dependent apoptosis

Sub-basin and temporal variability of macroinvertebrate assemblages in Alpine streams: when and where to sample?

Can be viewed at https://rdcu.be/be8n

Membrane Proteins Are Dramatically Less Conserved than Water-Soluble Proteins across the Tree of Life.

Membrane proteins are crucial in transport, signaling, bioenergetics, catalysis, and as drug targets. Here, we show that membrane proteins have dramatically fewer detectable orthologs than water-soluble proteins, less than half in most species analyzed. This sparse distribution could reflect rapid divergence or gene loss. We find that both mechanisms operate. First, membrane proteins evolve faster than water-soluble proteins, particularly in their exterior-facing portions. Second, we demonstrate that predicted ancestral membrane proteins are preferentially lost compared with water-soluble proteins in closely related species of archaea and bacteria. These patterns are consistent across the whole tree of life, and in each of the three domains of archaea, bacteria, and eukaryotes. Our findings point to a fundamental evolutionary principle: membrane proteins evolve faster due to stronger adaptive selection in changing environments, whereas cytosolic proteins are under more stringent purifying selection in the homeostatic interior of the cell. This effect should be strongest in prokaryotes, weaker in unicellular eukaryotes (with intracellular membranes), and weakest in multicellular eukaryotes (with extracellular homeostasis). We demonstrate that this is indeed the case. Similarly, we show that extracellular water-soluble proteins exhibit an even stronger pattern of low homology than membrane proteins. These striking differences in conservation of membrane proteins versus water-soluble proteins have important implications for evolution and medicine

Low-resource Multi-task Audio Sensing for Mobile and Embedded Devices via Shared Deep Neural Network Representations

Continuous audio analysis from embedded and mobile devices is an increasingly important application domain. More and more, appliances like the Amazon Echo, along with smartphones and watches, and even research prototypes seek to perform multiple discriminative tasks simultaneously from ambient audio; for example, monitoring background sound classes (e.g., music or conversation), recognizing certain keywords (‘Hey Siri’ or ‘Alexa’), or identifying the user and her emotion from speech. The use of deep learning algorithms typically provides state-of-the-art model performances for such general audio tasks. However, the large computational demands of deep learning models are at odds with the limited processing, energy and memory resources of mobile, embedded and IoT devices. In this paper, we propose and evaluate a novel deep learning modeling and optimization framework that speci cally targets this category of embedded audio sensing tasks. Although the supported tasks are simpler than the task of speech recognition, this framework aims at maintaining accuracies in predictions while minimizing the overall processor resource footprint. The proposed model is grounded in multi-task learning principles to train shared deep layers and exploits, as input layer, only statistical summaries of audio lter banks to further lower computations. We nd that for embedded audio sensing tasks our framework is able to maintain similar accuracies, which are observed in comparable deep architectures that use single-task learning and typically more complex input layers. Most importantly, on an average, this approach provides almost a 2.1⇥ reduction in runtime, energy, and memory for four separate audio sensing tasks, assuming a variety of task combinations.Microsoft Researc

Metric approach to quantum constraints

A new framework for deriving equations of motion for constrained quantum systems is introduced, and a procedure for its implementation is outlined. In special cases the framework reduces to a quantum analogue of the Dirac theory of constrains in classical mechanics. Explicit examples involving spin-1/2 particles are worked out in detail: in one example our approach coincides with a quantum version of the Dirac formalism, while the other example illustrates how a situation that cannot be treated by Dirac's approach can nevertheless be dealt with in the present scheme.Comment: 13 pages, 1 figur

Algebras for parameterised monads

Parameterised monads have the same relationship to adjunctions with parameters as monads do to adjunctions. In this paper, we investigate algebras for parameterised monads. We identify the Eilenberg-Moore category of algebras for parameterised monads and prove a generalisation of Beck’s theorem characterising this category. We demonstrate an application of this theory to the semantics of type and effect systems

CC255 Energy Uses in Nebraska Agriculture

Campaign Circular 255: This circular includes information about energy uses in Nebraska Agriculture including machine operations, irrigation, crop drying, and electrical energy use in general