Low-Energy Effective Field Theory of Lepton-Proton Bremsstrahlung

We calculate the cross section for the lepton-proton bremsstrahlung process $l+p\to l^\prime +p+\gamma$ in effective field theory. This process corresponds to an undetected background signal for the proposed MUSE experiment at PSI. MUSE is designed to measure elastic scattering of low-energy electrons and muons off a proton target in order to extract a precise value for the proton's r.m.s. radius. We show that the commonly used {\it peaking approximation}, which is used to evaluate the {\it radiative tail} for the elastic cross section, is not applicable for muon proton scattering at the low-energy MUSE kinematics. We also correct a misprint in a commonly cited review article.Comment: 16 pages, 7 figure

Graph-Based Weakly-Supervised Methods for Information Extraction & Integration

The variety and complexity of potentially-related data resources available for querying --- webpages, databases, data warehouses --- has been growing ever more rapidly. There is a growing need to pose integrative queries across multiple such sources, exploiting foreign keys and other means of interlinking data to merge information from diverse sources. This has traditionally been the focus of research within Information Extraction (IE) and Information Integration (II) communities, with IE focusing on converting unstructured sources into structured sources, and II focusing on providing a unified view of diverse structured data sources. However, most of the current IE and II methods, which can potentially be applied to the pro blem of integration across sources, require large amounts of human supervision, often in the form of annotated data. This need for extensive supervision makes existing methods expensive to deploy and difficult to maintain. In this thesis, we develop techniques that generalize from limited human input, via weakly-supervised methods for IE and II. In particular, we argue that graph-based representation of data and learning over such graphs can result in effective and scalable methods for large-scale Information Extraction and Integration. Within IE, we focus on the problem of assigning semantic classes to entities. First we develop a context pattern induction method to extend small initial entity lists of various semantic classes. We also demonstrate that features derived from such extended entity lists can significantly improve performance of state-of-the-art discriminative taggers. The output of pattern-based class-instance extractors is often high-precision and low-recall in nature, which is inadequate for many real world applications. We use Adsorption, a graph based label propagation algorithm, to significantly increase recall of an initial high-precision, low-recall pattern-based extractor by combining evidences from unstructured and structured text corpora. Building on Adsorption, we propose a new label propagation algorithm, Modified Adsorption (MAD), and demonstrate its effectiveness on various real-world datasets. Additionally, we also show how class-instance acquisition performance in the graph-based SSL setting can be improved by incorporating additional semantic constraints available in independently developed knowledge bases. Within Information Integration, we develop a novel system, Q, which draws ideas from machine learning and databases to help a non-expert user construct data-integrating queries based on keywords (across databases) and interactive feedback on answers. We also present an information need-driven strategy for automatically incorporating new sources and their information in Q. We also demonstrate that Q\u27s learning strategy is highly effective in combining the outputs of ``black box\u27\u27 schema matchers and in re-weighting bad alignments. This removes the need to develop an expensive mediated schema which has been necessary for most previous systems

Estimation of pKBH+ of Some Mesoionic Thiones

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Research Notes : India : Environmental impact on different characteristics of soybean

The leguminous protein and oil rich crop soybean requires specific environrnent for its successful growth and yield. The variation in yield levels and restricted adaptation are mostly due to photoperiodic requirements (Weber and Moorthy, 1952) and probably due to thermal sensitivity (Leng, 1968). The nature and magnitude of variability present in an individual or a population is due to both genetic and nongenetic causes

Comparison of Different Supervisied Classifiers in Detection of Microaneurysms

Diabetes is a rapidly increasing illness around the world. It can further cause diabetic rethinopathy(DR). If not treated properly it can make a person blind. Therefore a early detection system for (DR) is required which can be done by detecting abnormalities in eye known as microaneurysms. The main objective of this paper is to find out how different supervised classifiers responds to our morphological operation algorithm of detection of microaneurysms. The performances of the classifiers are examined by the images obtained from databse DIARETDB1 which also gives ground truths. DOI: 10.17762/ijritcc2321-8169.160413

A drug utilization study of topical corticosteroids in the department of dermatology in a tertiary care teaching hospital in upper Assam

Background: Topical corticosteroids form an important group of drugs in dermatology and are among the most commonly prescribed medications. However, despite their efficacy, they are associated with various adverse effects and as majority of the skin conditions are chronic, there is a need to ensure that there is rationality in drug use. Thus, this study was conducted with an aim to analyze the drug utilization pattern of topical corticosteroids.Methods: The study was a hospital-based, prospective and observational study and conducted for a period of 12 months. The method of data collection was done based on one-on-one consultation with patients. Data collected were recorded prospectively in a specially designed proforma. Results were then entered and analyzed using Microsoft excel.Results: In the study, it was observed that 48% of the patients were males while 52% were females. Most patients belonged to the age group of 21-30 years (30%). Scabies (30%) was the most common dermatological condition. With regard to the prescribing frequency of different topical corticosteroids, mometasone furoate (31.4%) was the preferred choice in most patients. H1 antihistaminic drugs like levocetirizine (81.6%) were the most prescribed along with topical corticosteroids, followed by emollients (36.4%) and permethrin (29.2%). The average number of drugs per prescription was 3.6 and all drugs were prescribed by their generic names.Conclusions: Periodic monitoring of the drug utilization pattern in the form of prescription auditing is an effective tool to constitute guidelines for improving the utilization pattern

Implications of surface noise for the motional coherence of trapped ions

Electric noise from metallic surfaces is a major obstacle towards quantum applications with trapped ions due to motional heating of the ions. Here, we discuss how the same noise source can also lead to pure dephasing of motional quantum states. The mechanism is particularly relevant at small ion-surface distances, thus imposing a new constraint on trap miniaturization. By means of a free induction decay experiment, we measure the dephasing time of the motion of a single ion trapped 50~$\mu$m above a Cu-Al surface. From the dephasing times we extract the integrated noise below the secular frequency of the ion. We find that none of the most commonly discussed surface noise models for ion traps describes both, the observed heating as well as the measured dephasing, satisfactorily. Thus, our measurements provide a benchmark for future models for the electric noise emitted by metallic surfaces.Comment: (5 pages, 4 figures

AAUOKHYB -1 : An excellent Okra hybrid developed by Assam Agricultural University (AAU) suitable for diverse agro-climatic situation of North East India

A hybrid development programme on okra (Abelmoschus esculentus) was undertaken at the Assam Agricultural University (AAU), Jorhat. The work included collection and evaluation of okra germplasm, identification of parents for hybridization, selfing of identified parental lines, making crosses and production of hybrid seeds, testing of hybrids for yield and resistance to Yellow Vein Mosaic Virus (YVMV) disease, adaptability under diverse agro-climatic condition and testing in all India trials. The programme resulted in development and identification of excellent Okra hybrid AAUOKHYB-1 in 2017.  The performance of the hybrid was very good under diverse agro-climatic conditions yielding about 162.0 q/ha fruit yield which is 32.5% higher than the best commercial check Arka Anamika. The hybrid showed resistant (R) reaction to YVMV disease. A wide range of heterosis upto 43% was exhibited by the hybrid in different vegetable research centers of the country as compared to the best check variety