Standardized report template for indeterminate renal masses at CT and MRI: a collaborative product of the SAR Disease-Focused Panel on. Read the latest articles of Physics Reports at cialispreisvergleich.top, Elsevier's leading platform of peer-reviewed scholarly literature. Purpose: To determine the need for a standardized renal mass reporting template by analyzing reports of indeterminate renal masses and.
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High activity of sequential low dose chemo-modulating Temozolomide in combination with Fotemustine in metastatic melanoma. Vemurafenib-associated gingival hyperplasia in patient with metastatic melanoma. J Am Acad Dermatol. Idiopathic gingival enlargement and its management. J Indian Soc Periodontol. Idiopathic fibrous hyperplasia of the palate.
Article Versions 1 version 1. This is an open access article distributed under the terms of the Creative Commons Attribution Licence , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Track an article to receive email alerts on any updates to this article. Approved with reservations Key revisions are required to address specific details and make the paper fully scientifically sound. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions.
How to cite this report:. The direct URL for this report is: This Case Report is of scientific interest and well written. A more detailed explanation of therapy would be appreciated. About the thermal printed palate plaque for instance: Is it a mold or 3D printed? Is it placed immediately after surgery and left in place 24 hours a day? Is thermal plaque therapy a novel approach or are there any references about this kind of therapy?
Yes Are enough details provided of any physical examination and diagnostic tests, treatment given and outcomes? Yes Is sufficient discussion included of the importance of the findings and their relevance to future understanding of disease processes, diagnosis or treatment?
Yes Is the case presented with sufficient detail to be useful for other practitioners? Salval A and Ciancio F. I must congratulate the authors on the quality and quantity of the photographic material. The case is really well documented with clinical pictures, histologic images and radiologic findings. I have personally treated a couple of these cases but often experienced short to medium term recurrence. The alternative treatment described by the authors the use of a guiding plaque after removal is interesting and seems to warrant better long term outcome.
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Peer Review Status Referee Status: Alongside their report, referees assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested. Approved with reservations - key revisions are required to address specific details and make the paper fully scientifically sound. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions. Scaling deep learning is very tricky because the best performing optimizer, stochastic gradient descent SGD , is mostly sequential.
Model parallelism can be achieved by processing the elements of a minibatch in parallel — however, the best size of the minibatch is determined by the statistical properties of the process and is thus limited. However, when one ignores the quality or convergence in general , the model-parallel SGD will scale wonderfully to any size system out there!
Weak scaling by adding more data can benefit this further, after all we can process all that data in parallel. In practice, unfortunately, test accuracy matters, not how much data one processed. The SGD optimization method optimizes the function that the network represents to the dataset used for learning.
This minimizes the so called training error. However, it is not clear whether the training error is a useful metric. After all, the network could just learn all examples without any capability to work on unseen examples. This is a classic case of overfitting. Thus, real-world users typically report test accuracy of an unseen dataset because machine learning is not optimization!
Yet, when scaling deep learning computations, one must tune many so called hyperparameters batch size, learning rate, momentum, … to enable convergence of the model. It may not be clear whether the best setting of those parameters benefits the test accuracy as well. In fact, there is evidence that careful tuning of hyperparameters may decrease the test accuracy by overfitting to a specific problem.
Of course, hyperparameters heavily depend in the dataset and the network used for training. Thus, optimizing the parameters for a specific task will enable you to achieve highest performance.
Thus, after consuming millions of compute hours to tune specific hyperparameters, one simply reports the number of the fastest run! A classic one, but very popular in deep learning: Oh, and if you have specialized hardware then make sure to never compare to the latest available GPU but pick one from some years back.
Another classic that seems to be very popular. For example, run the operations processing layers, communicating, updating gradients in isolation and only report scaling numbers of those.
This elegantly avoids questions about the test accuracy, after all, one just worries about a part of the calculation, no? Each nationalism is different, drawing on countries' histories, traditions, mores. In vetoing Britain's application to the Common Market in , Charles de Gaulle who had lived in England and spoke English said that Britain had "in all her doings very marked and very original habits and traditions.
They always were a bad fit. Nationalism sits ill with transnational elites confident that the "arc of history," as Barack Obama says, bends toward greater submergence of national identities to international control: Centralizing power in the hands of well-educated liberal-minded experts is the wave of the future. But this is a 20th-century, and increasingly obsolete, view. In the industrial age, with its massive factories and masses of workers, centralization seemed inevitable and beneficial.
In the information age, when your small phone contains more information than a Carnegie library and performs more functions than a Cold War UNIVAC, centralization blocks creative innovation and adaptive flexibility.
Transnational institutions have plainly failed to inspire the fellow feeling and genuine enthusiasm that nations inspire in most people. The EU long since achieved its initial purpose of preventing another European war. But even with an anthem by Beethoven it has failed to match the feelings of passionate attachment symbolized by the Union Jack or the French tricolor. The elites' howls of bigotry and racism aren't accurate descriptions of those who failed to follow their lead.
They're laments of their own failure to persuade them to do so. Views expressed in this column are those of the author, not those of Rasmussen Reports.
Rasmussen Reports is a media company specializing in the collection, publication and distribution of public opinion information. We conduct public opinion polls on a variety of topics to inform our audience on events in the news and other topics of interest.
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Vulnerability Reporting for the Masses. Greg Howard. Oct 02, Don't just rely on vulnerability counts to understand your exposure to threats and. PDF download for Metastatic disease masquerading as small intestinal tumoural masses: two case reports and, Article Information. Analyze up to 10 years of full 10K Annual Reports and Quarterly 10Q SEC filings for Motivating The Masses Inc (MNMT) using our online tools to quickly find.