10 research outputs found
Enhancing planetary imagery with the holistic attention network algorithm
No full text&lt;p&gt;The recent developments in computer vision research in the field of Single Image Super Resolution (SISR)&lt;/p&gt;&lt;p&gt;can help improve the satellite imagery data quality and, thus, find application in planetary exploration.&lt;/p&gt;&lt;p&gt;The aim of this study is to enhance planetary surface imagery, in planetary bodies that there are&lt;/p&gt;&lt;p&gt;available data but in a low resolution. Here, we have applied the holistic attention network (HAN)&lt;/p&gt;&lt;p&gt;algorithm to a set of images of Saturn&amp;#8217;s moon Titan from the Titan Radar Mapper instrument in its&lt;/p&gt;&lt;p&gt;Synthetic Aperture Radar (SAR) mode, which was on board the Cassini spacecraft. HAN can find&lt;/p&gt;&lt;p&gt;correlations among hierarchical layers, channels of each layer, and all positions of each channel, which&lt;/p&gt;&lt;p&gt;can be interpreted as an application and intersection of previously known models. The algorithm used&lt;/p&gt;&lt;p&gt;in our case-study was trained on 5000 grayscale images from HydroSHED Earth surface imagery dataset&lt;/p&gt;&lt;p&gt;resampled over different resolutions. Our experimental setup was to generate High Resolution (HR)&lt;/p&gt;&lt;p&gt;imagery from eight times lower resolution (x8 scale). We followed the standard workflow for this&lt;/p&gt;&lt;p&gt;purpose, which is to first train the network enhancing x2 scale to HR, then x4 scale to x2 scale, and&lt;/p&gt;&lt;p&gt;finally x8 scale to x4 scale, using subsequently the results of the previous training. The promising results&lt;/p&gt;&lt;p&gt;open a path for further applications of the trained model to improve the imagery data quality, and aid&lt;/p&gt;&lt;p&gt;in the detection and analysis of planetary surface features.&lt;/p&gt;</jats:p
Statistical machine translation
No full textAutomatic translation from one human language to another using computers, better known as machine translation (MT), is a longstanding goal of computer science. In order to be able to perform such a task, the computer must \know &quot; the two languages|synonyms for words and phrases, grammars of the two languages, and semantic or world knowledge. One way to incorporate such knowledge into a computer is to use bilingual experts to hand-craft the necessary information into the computer program. Another is to let the computer learn some of these things automatically by examining large amounts of parallel text: documents which are translations of each other. The Canadian government produces one such resource, for example, in the form of parliamentary proceedings which are recorded in both English and French. Recently, statistical data analysis has been used to gather MT knowledge automatically from parallel bilingual text. Unfortunately, these techniques and tools have not been disseminated to the scienti c community invery usable form, and new follow-on ideas have developed sporadically. In a six-week summer workshop at Johns Hopkins University, we constructed a basic statistical MT toolkit (called Egypt) intended for distribution to interested researchers. We also used the toolkit as a platform for experimentation during the workshop. Our experiments included working with distant language pairs (such as Czech/English), rapidly porting to new language pairs, managing with small bilingual data sets, speeding up algorithms for decoding and bilingual and text training, and incorporating morphology, syntax, dictionaries, and cognates. Late in the workshop, we built an MT system for a new language pair (Chinese/English) in a single day. We describe both the toolkit and the experiments in this report.
International Consensus Statement on Allergy and Rhinology: Allergic Rhinitis
No full textCritical examination of the quality and validity of available allergic rhinitis (AR) literature is necessary to improve understanding and to appropriately translate this knowledge to clinical care of the AR patient. To evaluate the existing AR literature, international multidisciplinary experts with an interest in AR have produced the International Consensus statement on Allergy and Rhinology: Allergic Rhinitis (ICAR:AR). Using previously described methodology, specific topics were developed relating to AR. Each topic was assigned a literature review, evidence-based review (EBR), or evidence-based review with recommendations (EBRR) format as dictated by available evidence and purpose within the ICAR:AR document. Following iterative reviews of each topic, the ICAR:AR document was synthesized and reviewed by all authors for consensus. The ICAR:AR document addresses over 100 individual topics related to AR, including diagnosis, pathophysiology, epidemiology, disease burden, risk factors for the development of AR, allergy testing modalities, treatment, and other conditions/comorbidities associated with AR. This critical review of the AR literature has identified several strengths; providers can be confident that treatment decisions are supported by rigorous studies. However, there are also substantial gaps in the AR literature. These knowledge gaps should be viewed as opportunities for improvement, as often the things that we teach and the medicine that we practice are not based on the best quality evidence. This document aims to highlight the strengths and weaknesses of the AR literature to identify areas for future AR research and improved understandin
