epsilon. Is a very small number to prevent any division by zero in the implementation (e.g. 10E-8). Further, learning rate decay can also be used with Adam. The paper uses a decay rate alpha = alpha/sqrt(t) updted each epoch (t) for the logistic regression demonstration. The Adam paper suggests: Good default settings for the tested machine learning problems are alpha=0.001, beta1=0.9, beta2=0.999 and epsilon=10−8 The TensorFlow documentation suggests some tuning of epsilon: The default value of 1e-8 for epsilon might not be a good default in general. For example, when training an Inception network on ImageNet a current good choice is 1.0 or 0.1. We can see that the popular deep learning libraries generally use the default parameters recommended by the paper. TensorFlow: learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08. Keras: lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0. Blocks: learning_rate=0.002, beta1=0.9, beta2=0.999, epsilon=1e-08, decay_factor=1. Lasagne: learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08 Caffe: learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08 MxNet: learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8 Torch: learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8

The diagram was generated with rails-erd

Domain Model

Given the disjoint vocabularies (Section2) andthe magnitude of improvement over BERT-Base(Section4), we suspect that while an in-domainvocabulary is helpful, SCIBERTbenefits mostfrom the scientific corpus pretraining.

We construct SCIVOCAB, a new WordPiece vo-cabulary on our scientific corpus using the Sen-tencePiece1library. We produce both cased anduncased vocabularies and set the vocabulary sizeto 30K to match the size of BASEVOCAB. The re-sulting token overlap between BASEVOCABandSCIVOCABis 42%, illustrating a substantial dif-ference in frequently used words between scien-tific and general domain texts

Although we could have constructed new WordPiece vocabulary based on biomedical corpora, we used the original vocabulary of BERTBASE for the following reasons: (i) compatibility of BioBERT with BERT, which allows BERT pre-trained on general domain corpora to be re-used, and makes it easier to interchangeably use existing models based on BERT and BioBERT and (ii) any new words may still be represented and fine-tuned for the biomedical domain using the original WordPiece vocabulary of BERT.

def _tokenize(self, text): split_tokens = [] if self.do_basic_tokenize: for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens): for sub_token in self.wordpiece_tokenizer.tokenize(token): split_tokens.append(sub_token) else: split_tokens = self.wordpiece_tokenizer.tokenize(text) return split_tokens

My initial experiments indicated that adding custom words to the vocab-file had some effects. However, at least on my corpus that can be described as "medical tweets", this effect just disappears after running the domain specific pretraining for a while. After spending quite some time on this, I have ended up dropping the custom vocab-files totally. Bert seems to be able to learn these specialised words by tokenizing them.

Since Bert does an excellent job in tokenising and learning this combinations, do not expect dramatic improvements by adding words to the vocab. In my experience adding very specific terms, like common long medical latin words, have some effect. Adding words like "footballs" will likely just have negative effects since the current vector is already pretty good.

As is the case in NLP applications in general, we begin by turning each input word into a vector using an embedding algorithm.

:It is fairly expensive (four days on 4 to 16 Cloud TPUs), but is a one-time procedure for each language

I do not understand what is the threat model of not allowing the root user to configure Firefox, since malware could just replace the entire Firefox binary.

Did you know accurate data reporting is often capped? Meaning once your website traffic reaches a certain limit, the data then becomes a guess rather than factual.This is where tools like Google Analytics becomes extremely limited and cashes in with their GA360 Premium suite. At Matomo, we believe all data should be reported 100% accurately, or else what’s the point?

But she's also addicted to her phone."

So it’s not surprising that Facebook is so coy about explaining why a certain user on its platform is seeing a specific advert. Because if the huge surveillance operation underpinning the algorithmic decision to serve a particular ad was made clear, the person seeing it might feel manipulated. And then they would probably be less inclined to look favorably upon the brand they were being urged to buy. Or the political opinion they were being pushed to form. And Facebook’s ad tech business stands to suffer.

Rojas-Lozano claimed that the second part of Google’s two-part CAPTCHA feature, which requires users to transcribe and type into a box a distorted image of words, letters or numbers before entering its site, is also used to transcribe words that a computer cannot read to assist with Google’s book digitization service. By not disclosing that, she argued, Google was getting free labor from its users.

threat model

Data Model

* Information-Processing Analysis : about the mental operations used by a person who has learned a complex skills

Dick and Carey Model

The Kemp Model of Instructional Design

The Web Annotation Data Model specification describes a structured model and format to enable annotations to be shared and reused across different hardware and software platforms.

Gujarat often produced growth faster than the national average, fewer regulations, better infrastructure and less corruption

Choose Your Plan

Clinic to view the NSAID value sets

Click to view the warfarin value sets

Concurrent use of both medications puts patients at a significant risk of bleeding that warrants appropriate management strategies

Algorithm

Warfarin is a vitamin K antagonist, which competitively inhibits a series of coagulation factors, as well as proteins C and S. These factors are biologically activated by the addition of carboxyl groups depending on vitamin K. Warfarin competitively inhibits this chemical reaction, thus depleting functional vitamin K reserves and hence reducing the synthesis of active coagulation factors.

Non-selective NSAIDs inhibit cyclooxygenase (COX) enzymes, COX-1 and COX-2 at different extent, leading varying effects on bleeding.(11, 12) COX-1 catalyzes the initial step in the formation of thromboxane (TxA2), and prostaglandins.(11, 12) TxA2 stimulates platelet aggregation.(13) Prostaglandins protect the gastrointestinal tract by increasing mucosal blood flow and the thickness of mucus layer,  stimulating bicarbonate secretion, and reducing gastric acid secretion.(

Misoprostol also has been demonstrated to help prevent gastric ulcer in those who receive NSAIDs.

Therefore, alternate management strategies such as utilizing proton pump inhibitors or misoprostol may help reduce bleeding events.

Thus, avoiding and/or limiting the use of NSAIDs is an ideal strategy to prevent serious complication from these medications.

Both corticosteroids and aldosterone antagonists have been shown to substantially increase the risk of upper gastrointestinal bleeding in patients on NSAIDs, with relative risks of 12.8 and 11 respectively compared to a risk of 4.3 with NSAIDs alone

Several risk factors for NSAID-related gastroduodenal bleeding are old age, a history of peptic ulcer disease, high dosages of NSAIDs, concomitant use of different NSAIDs.

The VKORC1 and CYP2C9 genotypes are the most important known genetic determinants of warfarin dosing. Warfarin targets VKORC1, an enzyme involved in vitamin K recycling. The variants CYP2C9*2 and *3, required with a lower dose of warfarin. The FDA-approved drug label for warfarin states that CYP2C9 and VKORC1 genotype information, when available, can assist in the selection of the initial dose of warfarin.

Concomitant NSAIDs occur with 24.3% of warfarin courses of therapy

In order for Google to be Google, it has to do evil. This is true for every major technology company. Apple, Facebook, Amazon, Tesla, Microsoft, Sony, Twitter, Samsung, Nintendo, Dell, HP, Toshiba -- every one of these organizations can't compete in the market without engaging in unethical, inhumane and invasive practices. It's a sliding scale: The larger the company, the more integrated it is in our everyday lives, the more evil it can be.

Take Facebook, for example. CEO Mark Zuckerberg will stand onstage at F8 and wax poetic about the beauty of connecting billions of people across the globe, while at the same time patenting technologies to determine users' social classes and enable discrimination in the lending process, and allowing housing advertisers to exclude racial and ethnic groups or families with women and children from their listings.

We can certainly understand why Google would prefer users not to install AdNauseam, as it directly opposes their core business model, but the Web Store’s Terms of Service do not (at least thus far) require extensions to endorse Google’s business model. Moreover, this is not the justification cited for the software’s removal.

We can certainly understand why Google would prefer users not to install AdNauseam, as it directly opposes their core business model, but the Web Store’s Terms of Service do not (at least thus far) require extensions to endorse Google’s business model.

Educators are feeling more and more pressure to provide educational content and teaching methods that keep pace with ongoing scientific and technical progress.

But in the notebook format it's much easier for the reader to experiment. Their exploration is scaffolded, they can make small modifications and see the results, even the answers to questions Norvig did not anticipate

On the other hand, a resource may be generic in that as a concept it is well specified but not so specifically specified that it can only be represented by a single bit stream. In this case, other URIs may exist which identify a resource more specifically. These other URIs identify resources too, and there is a relationship of genericity between the generic and the relatively specific resource.

The effect ofthe covariates on survival is to act multiplicatively on some unknown baseline hazardrate, which makes it difficult to model covariate effects that change over time. Secondly,if covariates are deleted from a model or measured with a different level of precision, theproportional hazards assumption is no longer valid. These weaknesses in the Cox modelhave generated interest in alternative models. One such alternative model is Aalen’s(1989) additive model. This model assumes that covariates act in an additive manneron an unknown baseline hazard rate. The unknown risk coefficients are allowed to befunctions of time, so that the effect of a covariate may vary over time.

Non-proportional hazards is a case of model misspecification.

The idea behind the model is that the log-hazard of an individual is a linear function of their static covariates and a population-level baseline hazard that changes over time.

separately. 1.1 Aims of the Model The

There are 22 response models in the system, including retrieval-based neural networks, generation-based neural networks, knowledge base question answering systems and template-based systems.Examples of candidate model responses are shown in Tabl

Without this, men fill one another's heads with noise and sounds;

So much of faculty development is one-size-fits-all andarranged according to preset schedules and locations - and by doing so, will consist-ently fail to meet the needs of those whose interests are marginal or different from themajority. Moreover, the understanding of“network”in the institutional sense fails toaccount for the individual level of the Personal Learning Network (PLN) where educa-tors can build connections and relationships that advance their ongoing learning out-side of institutional structures and boundaries

n sender/receiver, channel, code, messag

Denition of the evolution model by SPL main-tainer.

An HTML element is an individual component of an HTML document or web page, once this has been parsed into the Document Object Model.

Canada is the first case of the expansion of hospital medicine beyond the United States, and as of 2008, Canada had more than 100 hospital medicine programs.7 Currently, the estimated number of hospitalists in Canada has increased to ~3,000 (Colleen Savage, Administrator, Canadian SHM, personal communication, December 6, 2017). Yousef and Maslowski describe several drivers for the development of the hospitalist model, which are related to physicians, patients, and systems. Work–life balance and the desire for non-hospital work among primary care providers (PCPs) were leading physician factors. Two major patient-related factors were the increasing age and complexity of patients and the increasing number of “unattached” patients. Unattached patients are those who either do not have a PCP or their PCP does not have admitting hospital privileges. System drivers included PCP shortages, reduction in resident duty hours, higher need for health system efficiency, and cost reduction. Further, increasing health system complexity led to PCPs withdrawing from hospital care.7

“It doesn’t just help make hospitalists work better. It makes nursing better. It makes surgeons better. It makes pharmacy better.”

In the last 20 years, HM and technology have drastically changed the hospital landscape. But was HM pushed along by generational advances in computing power, smart devices in the shape of phones and tablets, and the software that powered those machines? Or was technology spurred on by having people it could serve directly in the hospital, as opposed to the traditionally fragmented system that preceded HM? “Bob [Wachter] and others used to joke that the only people that actually understand the computer system are the hospitalists,” Dr. Goldman notes. “Chicken or the egg, right?” adds Dr. Merlino of Press Ganey. “Technology is an enabler that helps providers deliver better care. I think healthcare quality in general has been helped by both.

“This has all been an economic move,” she says. “People sort of forget that, I think. It was discovered by some of the HMOs on the West Coast, and it was really not the HMOs, it was the medical groups that were taking risks—economic risks for their group of patients—that figured out if they sent … primary-care people to the hospital and they assigned them on a rotation of a week at a time, that they can bring down the LOS in the hospital. “That meant more money in their own pockets because the medical group was taking the risk.” Once hospitalists set up practice in a hospital, C-suite administrators quickly saw them gaining patient share and began realizing that they could be partners. “They woke up one day, and just like that, they pay attention to how many cases the orthopedist does,” she says. “[They said], ‘Oh, Dr. Smith did 10 cases last week, he did 10 cases this week, then he did no cases or he did two cases. … They started to come to the hospitalists and say, ‘Look, you’re controlling X% of my patients a day. We’re having a length of stay problem; we’re having an early-discharge problem.’ Whatever it was, they were looking for partners to try to solve these issues.” And when hospitalists grew in number again as the model continued to take hold and blossom as an effective care-delivery method, hospitalists again were turned to as partners. “Once you get to that point, that you’re seeing enough patients and you’re enough of a movement,” Dr. Gorman says, “you get asked to be on the pharmacy committee and this committee, and chairman of the medical staff, and all those sort of things, and those evolve over time.”

Hospitalists were seen as people to lead the charge for safety because they were already taking care of patients, already focused on reducing LOS and improving care delivery—and never to be underestimated, they were omnipresent, Dr. Gandhi says of her experience with hospitalists around 2000 at Brigham and Women’s Hospital in Boston. “At least where I was, hospitalists truly were leaders in the quality and safety space, and it was just a really good fit for the kind of mindset and personality of a hospitalist because they’re very much … integrators of care across hospitals,” she says. “They interface with so many different areas of the hospital and then try to make all of that work better.”

“When the IOM report came out, it gave us a focus and a language that we didn’t have before,” says Dr. Wachter, who served as president of SHM’s Board of Directors and to this day lectures at SHM annual meetings. “But I think the general sensibility that hospitalists are about improving quality and safety and patients’ experience and efficiency—I think that was baked in from the start.”

“My feeling at the time was this was a good idea,” Dr. Wachter says. “The trend toward our system being pushed to deliver better, more efficient care was going to be enduring, and the old model of the primary-care doc being your hospital doc … couldn’t possibly achieve the goal of producing the highest value.”

That type of optimism permeated nascent hospitalist groups. But it was time to start proving the anecdotal stories. Nearly two years to the day after the Wachter/Goldman paper published, a team led by Herbert Diamond, MD, published “The Effect of Full-Time Faculty Hospitalists on the Efficiency of Care at a Community Teaching Hospital” in the Annals of Internal Medicine.1 It was among the first reports to show evidence that hospitalists improved care

“The role of the hospitalist often is to take recommendations from a lot of different specialties and come up with the best plan for the patient,” says Tejal Gandhi, MD, MPH, CPPS, president and CEO of the National Patient Safety Foundation. “They’re the true patient advocate who is getting the cardiologist’s opinion, the rheumatologist’s opinion, and the surgeon’s opinion, and they come up with the best plan for the patient.”

Dr. Merlino says he’s proud of the specialists who rotated through the hospital rooms of AIDS patients. But so many disparate doctors with no “quarterback” to manage the process holistically meant consistency in treatment was generally lacking

Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift

“The ADDIE model consists of five steps: analysis, design, development, implementation, and evaluation. It is a strategic plan for course design and may serve as a blueprint to design IL assignments and various other instructional activities.”

It has been demonstrated that this formulation is almost equivalent to a SLIM model,[9] which is an item-item model based recommender

Approximate Fisher Information Matrix to Characterise the Training of Deep Neural Networks

But we by a love so much refined,

sublunary

Las aplicaciones aumentan el acceso a la justicia

exists as long as the feature is in development

Here’s a thought exercise. Barack Obama never once considered repealing George W. Bush’s Medicare Part D law