Innovative organic farming in india

In this article general overview of research progress in the field of organic agriculture in India was given. This include revalidation of traditional organic practices, invent/discover and commercial production technology for organic inputs and research on organic systems. Some of the future directions of organic research in Indian was also discussed

Some applications of logic to feasibility in higher types

In this paper we demonstrate that the class of basic feasible functionals has recursion theoretic properties which naturally generalize the corresponding properties of the class of feasible functions. We also improve the Kapron - Cook result on mashine representation of basic feasible functionals. Our proofs are based on essential applications of logic. We introduce a weak fragment of second order arithmetic with second order variables ranging over functions from N into N which suitably characterizes basic feasible functionals, and show that it is a useful tool for investigating the properties of basic feasible functionals. In particular, we provide an example how one can extract feasible "programs" from mathematical proofs which use non-feasible functionals (like second order polynomials)

Persistent entanglement in a class of eigenstates of quantum Heisenberg spin glasses

The eigenstates of a quantum spin glass Hamiltonian with long-range interaction are examined from the point of view of localisation and entanglement. In particular, low particle sectors are examined and an anomalous family of eigenstates is found that is more delocalised but also has larger inter-spin entanglement. These are then identified as particle-added eigenstates from the one-particle sector. This motivates the introduction and the study of random promoted two-particle states, and it is shown that they may have large delocalisation such as generic ran- dom states and scale exactly like them. However, the entanglement as measured by two-spin concurrence displays different scaling with the total number of spins. This shows how for different classes of complex quantum states entanglement can be qualitatively different even if localisation measures such as participation ratio are not.Comment: 7 pages, 3 figures, 1 tabl

Detection of Metabolites by Proton Ex Vivo NMR, in Vivo MR Spectroscopy Peaks and Tissue Content Analysis: Biochemical-Magnetic Resonance Correlation: Preliminary Results

*Aim*: Metabolite concentrations by in vivo magnetic resonance spectroscopy and ex vivo NMR spectroscopy were compared with excised normal human tissue relaxation times and tissue homogenate contents.

*Hypothesis*: Biochemical analysis combined with NMR and MR spectroscopy defines better tissue analysis.

*Materials and Methods*: Metabolites were measured using peak area, amplitude and molecular weights of metabolites in the reference solutions. In normal brain and heart autopsy, muscle and liver biopsy tissue ex vivo NMR peaks and spin-lattice (T1) and spin-spin (T2) relaxation times, were compared with diseased tissue NMR data in meningioma brain, myocardial infarct heart, duchene-muscular-dystrophy muscle and diffused-liver-injury liver after respective in vivo proton MR spectroscopy was done. NMR data was compared with tissue homogenate contents and serum levels of biochemical parameters.

*Results*: The quantitation of smaller NMR visible metabolites was feasible for both ex vivo NMR and in vivo MR spectroscopy. Ex vivo H-1 NMR and in vivo MRS metabolite characteristic peaks (disease/normal data represented as fold change), T1 and T2, and metabolites in tissue homogenate and serum indicated muscle fibrosis in DMD, cardiac energy depletion in MI heart, neuronal dysfunction in meningioma brain and carbohydrate-lipid metabolic crisis in DLI liver tissues.

*Conclusion*: This preliminary report highlights the biochemical-magnetic resonance correlation as basis of magnetic resonance spectroscopic imaging data interpretation of disease

Quantum Coherence, Coherent Information and Information Gain in Quantum Measurement

A measurement is deemed successful, if one can maximize the information gain by the measurement apparatus. Here, we ask if quantum coherence of the system imposes a limitation on the information gain during quantum measurement. First, we argue that the information gain in a quantum measurement is nothing but the coherent information or the distinct quantum information that one can send from the system to apparatus. We prove that the maximum information gain from a pure state, using a mixed apparatus is upper bounded by the initial coherence of the system. Further, we illustrate the measurement scenario in the presence of environment. We argue that the information gain is upper bounded by the entropy exchange between the system and the apparatus. Also, to maximize the information gain, both the initial coherence of the apparatus, and the final entanglement between the system and apparatus should be maximum. Moreover, we find that for a fixed amount of coherence in the final apparatus state the more robust apparatus is, the more will be the information gain.Comment: 6 Pages, Comments are welcom

A Note on Batch and Incremental Learnability

AbstractAccording to Gold's criterion of identification in the limit, a learner, presented with data about a concept, is allowed to make a finite number of incorrect hypotheses before converging to a correct hypothesis. If, on the other hand, the learner is allowed to make only one conjecture which has to be correct, the resulting criterion of success is known as finite identification Identification in the limit may be viewed as an idealized model for incremental learning whereas finite identification may be viewed as an idealized model for batch learning. The present paper establishes a surprising fact that the collections of recursively enumerable languages that can be finite identified (batch learned in the ideal case) from both positive and negative data can also be identified in the limit (incrementally learned in the ideal case) from only positive data. It is often difficult to extract insights about practical learning systems from abstract theorems in inductive inference. However, this result may be seen as carrying a moral for the design of learning systems, as it yields, in theidealcase of no inaccuracies, an algorithm for converting batch systems that learn from both positive and negative data into incremental systems that learn from only positive data without any loss in learning power. This is achieved by the incremental system simulating the batch system in incremental fashion and using the heuristic of “localized closed-world assumption” to generate negative data

Open Content Licensing Initiatives

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A New Monte Carlo Method and Its Implications for Generalized Cluster Algorithms

We describe a novel switching algorithm based on a ``reverse'' Monte Carlo method, in which the potential is stochastically modified before the system configuration is moved. This new algorithm facilitates a generalized formulation of cluster-type Monte Carlo methods, and the generalization makes it possible to derive cluster algorithms for systems with both discrete and continuous degrees of freedom. The roughening transition in the sine-Gordon model has been studied with this method, and high-accuracy simulations for system sizes up to $1024^2$ were carried out to examine the logarithmic divergence of the surface roughness above the transition temperature, revealing clear evidence for universal scaling of the Kosterlitz-Thouless type.Comment: 4 pages, 2 figures. Phys. Rev. Lett. (in press