Some modifications to the SNIP journal impact indicator

The SNIP (source normalized impact per paper) indicator is an indicator of the citation impact of scientific journals. The indicator, introduced by Henk Moed in 2010, is included in Elsevier's Scopus database. The SNIP indicator uses a source normalized approach to correct for differences in citation practices between scientific fields. The strength of this approach is that it does not require a field classification system in which the boundaries of fields are explicitly defined. In this paper, a number of modifications that will be made to the SNIP indicator are explained, and the advantages of the resulting revised SNIP indicator are pointed out. It is argued that the original SNIP indicator has some counterintuitive properties, and it is shown mathematically that the revised SNIP indicator does not have these properties. Empirically, the differences between the original SNIP indicator and the revised one turn out to be relatively small, although some systematic differences can be observed. Relations with other source normalized indicators proposed in the literature are discussed as well

SINDA-NASTRAN interfacing program theoretical description and user's manual

The task of converting SINDA finite difference thermal model temperature results into NASTRAN finite element model thermal loads can be very labor intensive if there is not one node-to-one element, or systematic node-to-element. correlation between models. This paper describes the SINDA-NASTRAN Interfacing Program (SNIP), a FORTRAN computer code that generates NASTRAN structural model thermal load cards given by SINDA (or similar thermal model) temperature results and thermal model geometric data. SNIP generates NASTRAN thermal load cards for NASTRAN plate, shell, bar, and beam elements. The paper describes the interfacing procedures used by SNIP, and discusses set-up and operation of the program. Sample cases are included to demonstrate use of the program and show its performance under a variety of conditions. SNIP can provide structural model thermal loads that accurately reflect thermal model results while reducing the time required to interface thermal and structural models when compared to other methods

The revised SNIP indicator of Elsevier's Scopus

The modified SNIP indicator of Elsevier, as recently explained by Waltman et al. (2013) in this journal, solves some of the problems which Leydesdorff & Opthof (2010 and 2011) indicated in relation to the original SNIP indicator (Moed, 2010 and 2011). The use of an arithmetic average, however, remains unfortunate in the case of scientometric distributions because these can be extremely skewed (Seglen, 1992 and 1997). The new indicator cannot (or hardly) be reproduced independently when used for evaluation purposes, and remains in this sense opaque from the perspective of evaluated units and scholars.Comment: Letter to the Editor of the Journal of Informetrics (2013; in press

Sinonasal inverted papilloma - malignant transformation and non-sinonasal malignancies

Objectives To assess malignant transformation rate, non-sinonasal malignancies, and factors contributing to recurrence in patients treated for sinonasal inverted papilloma (SNIP). Study Design Retrospective study. Methods We retrospectively reviewed medical records of all patients treated for SNIP (n = 296) between the years 1984-2014 at Helsinki University Hospital. Data from the Finnish Cancer Registry confirmed the number of those patients with sinonasal and non-sinonasal malignancies. Results Only 2 of 296 (0.7%) patients primarily diagnosed with benign SNIP developed sinonasal cancer in a mean follow-up of 5.8 years. The most common non-sinonasal cancer sites were similar to those reported for the whole Finnish population. None of the patients presented with an HPV-associated non-sinonasal malignancy. The recurrence rate among patients who underwent attachment-oriented surgery was significantly lower compared to those operated on with other approaches (40.2% vs. 56.6%, p = 0.006). Dysplasia in SNIP was associated with a higher recurrence rate (p < 0.001). Conclusions Malignant transformation of SNIP was rare. Patients with SNIP were not prone to HPV-associated non-sinonasal malignancies. Endoscopic resection and attachment-oriented surgery have become predominant approaches in the treatment of SNIP; meanwhile, the total number of SNIP recurrences has decreased. Level of Evidence 3 Laryngoscope, 2022Peer reviewe

An Analysis of Scale Invariance in Object Detection - SNIP

An analysis of different techniques for recognizing and detecting objects under extreme scale variation is presented. Scale specific and scale invariant design of detectors are compared by training them with different configurations of input data. By evaluating the performance of different network architectures for classifying small objects on ImageNet, we show that CNNs are not robust to changes in scale. Based on this analysis, we propose to train and test detectors on the same scales of an image-pyramid. Since small and large objects are difficult to recognize at smaller and larger scales respectively, we present a novel training scheme called Scale Normalization for Image Pyramids (SNIP) which selectively back-propagates the gradients of object instances of different sizes as a function of the image scale. On the COCO dataset, our single model performance is 45.7% and an ensemble of 3 networks obtains an mAP of 48.3%. We use off-the-shelf ImageNet-1000 pre-trained models and only train with bounding box supervision. Our submission won the Best Student Entry in the COCO 2017 challenge. Code will be made available at \url{http://bit.ly/2yXVg4c}.Comment: CVPR 2018, camera ready versio