Paradigm

About: Paradigm is a based out in . It is known for research contribution in the topics: Agile software development & Search algorithm. The organization has 851 authors who have published 955 publications receiving 25827 citations. The organization is also known as: paradigmatic.

Transcatheter aortic-valve replacement with a self-expanding valve in low-risk patients

Abstract: Background Transcatheter aortic-valve replacement (TAVR) is an alternative to surgery in patients with severe aortic stenosis who are at increased risk for death from surgery; less is know...

Surgical or Transcatheter Aortic-Valve Replacement in Intermediate-Risk Patients

Abstract: BackgroundAlthough transcatheter aortic-valve replacement (TAVR) is an accepted alternative to surgery in patients with severe aortic stenosis who are at high surgical risk, less is known about comparative outcomes among patients with aortic stenosis who are at intermediate surgical risk. MethodsWe evaluated the clinical outcomes in intermediate-risk patients with severe, symptomatic aortic stenosis in a randomized trial comparing TAVR (performed with the use of a self-expanding prosthesis) with surgical aortic-valve replacement. The primary end point was a composite of death from any cause or disabling stroke at 24 months in patients undergoing attempted aortic-valve replacement. We used Bayesian analytical methods (with a margin of 0.07) to evaluate the noninferiority of TAVR as compared with surgical valve replacement. ResultsA total of 1746 patients underwent randomization at 87 centers. Of these patients, 1660 underwent an attempted TAVR or surgical procedure. The mean (±SD) age of the patients was 7...

Empirical Characteristics of Dynamic Trading Strategies: The Case of Hedge Funds

Abstract: This article presents some new results on an unexplored dataset on hedge fund performance. The results indicate that hedge funds follow strategies that are dramatically different from mutual funds, and support the claim that these strategies are highly dynamic. The article finds five dominant investment styles in hedge funds, which when added to Sharpe’s (1992) asset class factor model can provide an integrated framework for style analysis of both buy-and-hold and dynamic trading strategies.

Kisspeptin directly stimulates gonadotropin-releasing hormone release via G protein-coupled receptor 54

Abstract: We have recently described a molecular gatekeeper of the hypothalamic-pituitary-gonadal axis with the observation that G protein-coupled receptor 54 (GPR54) is required in mice and men for the pubertal onset of pulsatile luteinizing hormone (LH) and follicle-stimulating hormone (FSH) secretion to occur. In the present study, we investigate the possible central mode of action of GPR54 and kisspeptin ligand. First, we show that GPR54 transcripts are colocalized with gonadotropin-releasing hormone (GnRH) neurons in the mouse hypothalamus, suggesting that kisspeptin, the GPR54 ligand, may act directly on these neurons. Next, we show that GnRH neurons seem anatomically normal in gpr54–/– mice, and that they show projections to the median eminence, which demonstrates that the hypogonadism in gpr54–/– mice is not due to an abnormal migration of GnRH neurons (as occurs with KAL1 mutations), but that it is more likely due to a lack of GnRH release or absence of GnRH neuron stimulation. We also show that levels of kisspeptin injected i.p., which stimulate robust LH and FSH release in wild-type mice, have no effect in gpr54–/– mice, and therefore that kisspeptin acts directly and uniquely by means of GPR54 signaling for this function. Finally, we demonstrate by direct measurement, that the central administration of kisspeptin intracerebroventricularly in sheep produces a dramatic release of GnRH into the cerebrospinal fluid, with a parallel rise in serum LH, demonstrating that a key action of kisspeptin on the hypothalamo-pituitary-gonadal axis occurs directly at the level of GnRH release. The localization and GnRH release effects of kisspeptin thus define GPR54 as a major control point in the reproductive axis and suggest kisspeptin to be a neurohormonal effector.

Generalizing from a Few Examples: A Survey on Few-shot Learning

Abstract: Machine learning has been highly successful in data-intensive applications but is often hampered when the data set is small. Recently, Few-shot Learning (FSL) is proposed to tackle this problem. Using prior knowledge, FSL can rapidly generalize to new tasks containing only a few samples with supervised information. In this article, we conduct a thorough survey to fully understand FSL. Starting from a formal definition of FSL, we distinguish FSL from several relevant machine learning problems. We then point out that the core issue in FSL is that the empirical risk minimizer is unreliable. Based on how prior knowledge can be used to handle this core issue, we categorize FSL methods from three perspectives: (i) data, which uses prior knowledge to augment the supervised experience; (ii) model, which uses prior knowledge to reduce the size of the hypothesis space; and (iii) algorithm, which uses prior knowledge to alter the search for the best hypothesis in the given hypothesis space. With this taxonomy, we review and discuss the pros and cons of each category. Promising directions, in the aspects of the FSL problem setups, techniques, applications, and theories, are also proposed to provide insights for future research.1