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Rishabh Singh - One of the best experts on this subject based on the ideXlab platform.

  • synthesis of data completion scripts using finite tree automata
    Conference on Object-Oriented Programming Systems Languages and Applications, 2017
    Co-Authors: Xinyu Wang, Isil Dillig, Rishabh Singh
    Abstract:

    In application domains that store data in a Tabular Format, a common task is to fill the values of some cells using values stored in other cells. For instance, such data completion tasks arise in the context of \emph{missing value imputation} in data science and \emph{derived data} computation in spreadsheets and relational databases. Unfortunately, end-users and data scientists typically struggle with many data completion tasks that require non-trivial programming expertise. This paper presents a synthesis technique for automating data completion tasks using \emph{programming-by-example (PBE)} and a very lightweight sketching approach. Given a \emph{formula sketch} (e.g., {\texttt{AVG}}($\texttt{?}_1$, $\texttt{?}_2$)) and a few input-output examples for each hole, our technique synthesizes a program to automate the desired data completion task. Towards this goal, we propose a domain-specific language (DSL) that combines spatial and relational reasoning over Tabular data and a novel synthesis algorithm that can generate DSL programs that are consistent with the input-output examples. The key technical novelty of our approach is a new version space learning algorithm that is based on \emph{finite tree automata} (FTA). The use of FTAs in the learning algorithm leads to a more compact representation that allows more sharing between programs that are consistent with the examples. We have implemented the proposed approach in a tool called \textsc{DACE} and evaluate it on 84 benchmarks taken from online help forums. We also illustrate the advantages of our approach by comparing our technique against two existing synthesizers, namely Prose and Sketch.

  • synthesis of data completion scripts using finite tree automata
    arXiv: Programming Languages, 2017
    Co-Authors: Xinyu Wang, Isil Dillig, Rishabh Singh
    Abstract:

    In application domains that store data in a Tabular Format, a common task is to fill the values of some cells using values stored in other cells. For instance, such data completion tasks arise in the context of missing value imputation in data science and derived data computation in spreadsheets and relational databases. Unfortunately, end-users and data scientists typically struggle with many data completion tasks that require non-trivial programming expertise. This paper presents a synthesis technique for automating data completion tasks using programming-by-example (PBE) and a very lightweight sketching approach. Given a formula sketch (e.g., AVG($?_1$, $?_2$)) and a few input-output examples for each hole, our technique synthesizes a program to automate the desired data completion task. Towards this goal, we propose a domain-specific language (DSL) that combines spatial and relational reasoning over Tabular data and a novel synthesis algorithm that can generate DSL programs that are consistent with the input-output examples. The key technical novelty of our approach is a new version space learning algorithm that is based on finite tree automata (FTA). The use of FTAs in the learning algorithm leads to a more compact representation that allows more sharing between programs that are consistent with the examples. We have implemented the proposed approach in a tool called DACE and evaluate it on 84 benchmarks taken from online help forums. We also illustrate the advantages of our approach by comparing our technique against two existing synthesizers, namely PROSE and SKETCH.

Tavis Read - One of the best experts on this subject based on the ideXlab platform.

  • a systematic review of non surgical treatments for lentigo maligna
    Journal of The European Academy of Dermatology and Venereology, 2016
    Co-Authors: Tavis Read, C Noonan, Michael David, Michael Wagels, Matthew Foote, Helmut Schaider
    Abstract:

    Lentigo maligna (LM) is the most common melanocytic malignancy of the head and neck. If left untreated, LM can progress to lentigo maligna melanoma (LMM). Complete surgical excision is the gold standard for treatment, however, due to the location, size, and advanced age of patients, surgery is not always acceptable. As a result, there is ongoing interest in alternative, less invasive treatment modalities. The objective was to provide a structured review of key literature reporting the use of radiotherapy, imiquimod and laser therapy for the management of LM in patients where surgical resection is prohibited. An independent review was conducted following a comprehensive search of the National Library of Medicine using MEDLINE and PubMed, Embase, Scopus, ScienceDirect and Cochrane Library databases. Data were presented in Tabular Format, and crude data pooled to calculate mean recurrence rates for each therapy. 29 studies met the inclusion criteria: radiotherapy 10; topical imiquimod 10; laser therapies 9. Radiotherapy demostrated recurrence rates of up to 31% (mean 11.5%), with follow-up durations of 1-96 months. Topical imiquimod recurrence rates were up to 50% (mean 24.5%), with follow-up durations of 2-49 months. Laser therapy yielded recurrence rates of up to 100% (mean 34.4%), and follow-up durations of 8-78 months. in each of the treatment series the I(2) value measuring statistical heterogeneity exceeded the accepted threshold of 50% and as such a meta-analysis of included data were inappropriate. For non-surgical patients with LM, radiotherapy and topical imiquimod were efficacious treatments. Radiotherapy produced superior complete response rates and fewer recurrences than imiquimod although both are promising non-invasive modalities. There was no consistent body of evidence regarding laser therapy although response rates of up to 100% were reported in low quality studies. A prospective comparative trial is indicated and would provide accurate data on the long-term efficacy and overall utility of these treatments.

Alexa J. Halford - One of the best experts on this subject based on the ideXlab platform.

  • dependence of emic wave parameters during quiet geomagnetic storm and geomagnetic storm phase times
    Journal of Geophysical Research, 2016
    Co-Authors: Alexa J. Halford, B J Fraser, S R Elkington, S K Morley, A A Chan
    Abstract:

    As electromagnetic ion cyclotron (EMIC) waves may play an important role in radiation belt dynamics, there has been a push to better include them into global simulations. How to best include EMIC wave effects is still an open question. Recently many studies have attempted to parameterize EMIC waves and their characteristics by geomagnetic indices. However, this does not fully take into account important physics related to the phase of a geomagnetic storm. In this paper we first consider how EMIC wave occurrence varies with the phase of a geomagnetic storm and the SYM-H, AE, and Kp indices. We show that the storm phase plays an important role in the occurrence probability of EMIC waves. The occurrence rates for a given value of a geomagnetic index change based on the geomagnetic condition. In this study we also describe the typical plasma and wave parameters observed in L and magnetic local time for quiet, storm, and storm phase. These results are given in a Tabular Format in the supporting inFormation so that more accurate statistics of EMIC wave parameters can be incorporated into modeling efforts.

Xinyu Wang - One of the best experts on this subject based on the ideXlab platform.

  • synthesis of data completion scripts using finite tree automata
    Conference on Object-Oriented Programming Systems Languages and Applications, 2017
    Co-Authors: Xinyu Wang, Isil Dillig, Rishabh Singh
    Abstract:

    In application domains that store data in a Tabular Format, a common task is to fill the values of some cells using values stored in other cells. For instance, such data completion tasks arise in the context of \emph{missing value imputation} in data science and \emph{derived data} computation in spreadsheets and relational databases. Unfortunately, end-users and data scientists typically struggle with many data completion tasks that require non-trivial programming expertise. This paper presents a synthesis technique for automating data completion tasks using \emph{programming-by-example (PBE)} and a very lightweight sketching approach. Given a \emph{formula sketch} (e.g., {\texttt{AVG}}($\texttt{?}_1$, $\texttt{?}_2$)) and a few input-output examples for each hole, our technique synthesizes a program to automate the desired data completion task. Towards this goal, we propose a domain-specific language (DSL) that combines spatial and relational reasoning over Tabular data and a novel synthesis algorithm that can generate DSL programs that are consistent with the input-output examples. The key technical novelty of our approach is a new version space learning algorithm that is based on \emph{finite tree automata} (FTA). The use of FTAs in the learning algorithm leads to a more compact representation that allows more sharing between programs that are consistent with the examples. We have implemented the proposed approach in a tool called \textsc{DACE} and evaluate it on 84 benchmarks taken from online help forums. We also illustrate the advantages of our approach by comparing our technique against two existing synthesizers, namely Prose and Sketch.

  • synthesis of data completion scripts using finite tree automata
    arXiv: Programming Languages, 2017
    Co-Authors: Xinyu Wang, Isil Dillig, Rishabh Singh
    Abstract:

    In application domains that store data in a Tabular Format, a common task is to fill the values of some cells using values stored in other cells. For instance, such data completion tasks arise in the context of missing value imputation in data science and derived data computation in spreadsheets and relational databases. Unfortunately, end-users and data scientists typically struggle with many data completion tasks that require non-trivial programming expertise. This paper presents a synthesis technique for automating data completion tasks using programming-by-example (PBE) and a very lightweight sketching approach. Given a formula sketch (e.g., AVG($?_1$, $?_2$)) and a few input-output examples for each hole, our technique synthesizes a program to automate the desired data completion task. Towards this goal, we propose a domain-specific language (DSL) that combines spatial and relational reasoning over Tabular data and a novel synthesis algorithm that can generate DSL programs that are consistent with the input-output examples. The key technical novelty of our approach is a new version space learning algorithm that is based on finite tree automata (FTA). The use of FTAs in the learning algorithm leads to a more compact representation that allows more sharing between programs that are consistent with the examples. We have implemented the proposed approach in a tool called DACE and evaluate it on 84 benchmarks taken from online help forums. We also illustrate the advantages of our approach by comparing our technique against two existing synthesizers, namely PROSE and SKETCH.

Sally A Camper - One of the best experts on this subject based on the ideXlab platform.

  • beyond big and small mice applications of mouse molecular genetics in endocrinology transgenic models in endocrinology edited by maria castro kluwer 2001 eur 210 00 us 189 00 gb 130 00 xii 265 pages isbn 0 7923 7344 8 transgenics in endocrinology edi
    Trends in Endocrinology and Metabolism, 2003
    Co-Authors: Lori T Raetzman, Sally A Camper
    Abstract:

    Twenty years ago, the very first transgenic mice captured the attention of scientists and the lay press alike when, thanks to the unregulated expression of the growth hormone transgene, they grew to almost double the size of normal mice. This illustrated profoundly that transgenes randomly integrated in the genome can be expressed and dramatically change the physiology of animals. Then came the targeted modification of endogenous mouse genes, using homologous recombination in embryonic stem cells, opening the door to the creation of recessive models of human disease and more controlled genetic alterations. Over the years, genetically engineered mice have been invaluable for testing endocrine paradigms, establishing gene function, studying cancer and establishing useful cell lines through targeted oncogenesis [1]. The applications of gene tailoring are now limited only by our imaginations. Methods are established for avoiding the tedious search for cis-acting regulatory sequences, producing tissue-specific and temporally regulated expression, generation of subtle changes, such as missense mutations, humanizing the mouse genome, and tagging rare cell types with fluorescent markers for cell sorting [2–4]. Presently, the generation and analysis of genetically engineered animals is a compelling approach that is feasible for all scientists, but given that these technologies have been in use for many years, newcomers might be daunted by the task of assimilating the vast literature relevant to their area of endocrinology. Two books, Transgenics in Endocrinology (TIE) and Transgenic Models in Endocrinology (TMIE), summarize the advances made using genetically engineered mice. TIE is more comprehensive, with nearly twice as many chapters as TMIE, and is generally more reference oriented, as most chapters have organized vast amounts of inFormation in accessible Tabular Format. The strength of TMIE is in the chapters that are reflective and future oriented, including the promise of viral vectors for altering gene expression and the utility of transgenic rats. TIE is part of the Contemporary Endocrinology series, and begins with a thorough introduction to genetic engineering. This brings the novice up to speed on the current technologies of transgenic and knockout mice. A review of sexual differentiation is followed by a review of Mullerian inhibiting substance and, in general, there is considerable emphasis on reproductive endocrinology. Aspects of ovarian and testicular function are covered with complete lists of transgenic mice that have defects in germ cell Formation, migration, differentiation and response to exogenous stimuli. The regulation of cell survival in reproductive tissues is addressed. Deregulated cell proliferation in the prostate and other endocrine tissues is achieved with transgenic mice that use tissuespecific promoters to drive expression of immortalizing oncogenes. These mice are used for understanding the consequences of excess proliferation as well as for establishing cell lines representative of various developmental stages [5]. Another important aspect of endocrinology reviewed in TIE is regulation of growth and metabolism. Bartke and co-workers catalog the important transgenic studies on central signaling pathways, growth hormone, insulin and the insulin growth factors. Both books contain in-depth discussions of the expression and function of pituitary hormones. Keri and Nilson, for example, contribute chapters on gonadotropins focused on their contributions to ovarian cancer susceptibility and defects in the genes themselves. Detailed and inFormative chapters on corticotrophins and pro-opiomelanocortin (POMC) are presented in both books. Low and colleagues present reviews on POMC that are less distinctive from one another, but still unique in their focus and coverage. The cis elements for hormonal regulation of POMC expression, pituitary tumors, POMC toxigenes, and POMC mutations are the focus of TIE, whereas TMIE is unique in its coverage of peptide processing and various POMC-derived peptide receptor genes. Several techniques are covered in TMIE, including the use of viral vectors for delivering genes to endocrine tissue. Also, the use of cell-type specific and inducible transgenes, with emphasis on their use in the anterior pituitary is discussed. Finally, the usefulness of transgenic rats is presented with suggestions for the design of dominant negative and antisense transgenes to circumvent the lack of gene targeting in rats. The use of transgenic rats is Corresponding author: L.T. Raetzman (raetzman@umich.edu). Update TRENDS in Endocrinology and Metabolism Vol.14 No.5 July 2003 204