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Innovating Neural Machine Translation with Arul Menezes - Transcript
TWIMLai
Sam Charrington: [00:00:00] Welcome to The TWIML AI Podcast. I'm your host, Sam Charrington. Before we jump into the interview, I'd like to take a moment to thank Microsoft for their support of the show and their sponsorship of this series of episodes highlighting just a few of the fundamental innovations behind Azure Cognitive Services. Cognitive Services is a portfolio of domain-specific capabilities that brings AI within the reach of every developer without requiring machine learning expertise. All it takes is an API call to embed the ability to see, hear, speak, search, understand and accelerate decision making into your apps.
Visit aka.ms/cognitive to learn how customers like Volkswagen, Uber and the BBC have used Azure Cognitive Services to embed services like realtime translation, facial recognition, and natural language understanding to create robust and intelligent user experiences in their apps. While you're there, you can take advantage of the $200 credit to start building your own intelligent applications when you open and Azure free account. That link again is aka.ms/cognitive. And now, on to the show.
All right, everyone, I am here with Arul Menezes. Arul is a distinguished engineer at Microsoft. Arul, welcome to the TWIML AI podcast.
Arul Menezes: [00:01:43] Thank you, Sam. I'm delighted to be here.
Sam Charrington: [00:01:45] I'm really looking forward to our chat, which will focus on some of the work you're doing in the machine translation space. To get us started, I'd love to have you introduce yourself and share a little bit about your background. How did you come to work in NLP and, and translation? And tell us a little bit about your story.
Arul Menezes: [00:02:03] Yeah, so I've actually been at Microsoft 30 years at this point.
Sam Charrington: [00:02:07] Wow.
Arul Menezes: [00:02:07] I, yeah, I know. God, it's a long time. I was actually in a PhD program. I came here for the summer, loved it so much I never went back. So I worked at Microsoft in the various engineering teams for a while, and then eventually I drifted back into research and I joined the natural language processing team in Microsoft Research, and I started the machine translation project, and I've been doing that ever since, so I've been doing machine translation for, like, 20 years now, and it's been, it's been a great ride because it's just a fascinating field. So many interesting challenges and we have made so much progress from when we started, you know, and we've gone through so many evolutions of technology. It's been, it's been a great ride, yeah.
Sam Charrington: [00:02:49] Yeah, there are some pretty famous examples of, you know, how the introduction of deep learning has changed machine translation. I'm assuming that your experience there i- is no different.
Arul Menezes: [00:03:04] Yeah.
Sam Charrington: [00:03:04] Can you share a little bit about how the, the evolution that you've seen over the years?
Arul Menezes: [00:03:08] Sure. Sure. I mean, historically, you know, machine translation is something people s- tried to do, you know, in the '50s. It was one of the first things they wanted to do with computers, you know, along with simulating sort of nuclear sort of bombs. But for the longest time, it was very, very hard to make progress, so all the way through, I would say, the late '90s, early 2000s, we were still in sort of rule based and knowledge sort of engineered approaches, but then the first real breakthrough that came in the late '90s, well actually starting a little earlier in terms of some papers published at IBM, but really taking off in the late '90s and early 2000s was statistical machine translation, where for the first time, you know, we were able to take advantage of, like, large amounts of previously translated data, right?
So you take documents and web pages and things that, that have previously been translated by people and you get these parallel texts, which is, let's say, English and French, and you align documents and sentences, and then eventually words and phrases so you can learn these translations, and so with statistical machine translation, we were learning from data for the very first time, instead of having people hand code it.
And it worked, actually, astonishingly well compared to what we were doing before. But eventually, we ran into the limits of the technology, because while we had the data, we didn't have the techniques to do a good job of learning what that data was telling us because, you know, the machine learning techniques that we had back then just weren't good enough at… They were good at memorizing, right? If you said something exactly the way they had seen in the data, they would do a good job of translating it. But they were terrible at generalizing from what they saw in the data, and that's where neural models come in.
Like, neural models are amazing at generalizing, you know. People always talk about how some of the latest models, you know, you can probe them to figure out what was in their training data and get them to reproduce what was in their training data. But what we forget is it takes work to actually make them do that, because most of the time, they're generalizing. They're paraphrasing. They're not just replicating their training data, and that's something we were not able to do before.
So if you look at the evolution over the last 20 years of machine translation, we had our statistical machine translation, which did really well for a while, but then eventually plateaued. Then, you know, we had sort of the advent of neural networks, and the first thing that people tried to do was, you know, we did feedforward neural networks. We tried to shoehorn them into the framework we already had and combine feedforward networks and statistical techniques, and that worked okay. You got a few incremental improvements. But it wasn't until we had the sort of pure neural LSTM models that we, for the first time, were really capturing the power of neural models, right?
So what an LSTM model would do would be, you know, you have this encoder that you feed the source language sentence in, and it basically embeds the meaning of that entire sentence in the LSTM state. And then you feed that through a decoder that is now generating a fluent sentence, sort of based on this very abstracted embedded understanding of what the source language said. And so that's very different from the way we were doing it, just sort of copying words and phrases that we'd memorized.
So that was the first revolution, and, and it gave us amazing results, actually, compared to what we were doing before. And then, of course, along, after that came transformers, which sort of take that whole encoder/decoder architecture, but take it to the next level. Instead of having the meaning of the entire source sentence be encoded into a single LSTM state, which may work well for short sentences but gets, you know, worse as you get to longer sentences. In a transformer, you know, we have the self attention that's basically looking at every word in the source and every word in the target, and so you have, like, full context available to the model at any point in time.
So that's where we stand today is, you know, transformers are the state of the art, but of course there's lots of really cool, interesting variations and things we're doing, which I think we're going to talk about at some point.
Sam Charrington: [00:07:25] And, and when you talk about transformers being the state of the art, is that what is powering the current kind of production Azure machine translation service? Or is that the state of the art in research and, you know, there's some combination of the various techniques you mentioned that is powering the live service?
Arul Menezes: [00:07:46] So the live service is very much powered by transformers. We have, you know, all 180 language pairs or something that we support powered by transformers running in production. Now, one thing we do do is that we take advantage of what's called knowledge distillation, right, to take the knowledge that's embedded in these very large transformers that we train offline and then condense that or distill that into smaller, still transformers, but smaller, shallower, and narrower models that we use in production, right?
So we typically go through multiple stages of these teacher models before we get to the student, so our pipeline's actually fairly complex. We take the parallel data, which I mentioned earlier, which is sort of the lifeblood of machine translation. This is the previously translated human text. And we train, like, a first teacher based on that data. Then we typically do what's called back translation, which is a technique in machine translation to take advantage of monolingual data, so data that's not parallel, so it's not translated source and target. It's just in one language, typically the target language. And what we do there is we want to take advantage of this monolingual data to teach the model more about the syntax and the, you know, semantics of the target language so it gets more fluent.
And the way we incorporate that data into a machine translation model is through something called back translation, where we take the, the target language data, we translate it back to the source using one of our models, and then we use it to train the model in the other direction. So this is a little complicated. So basically, if you're training an English to French model…
Sam Charrington: [00:09:28] Mm-hmm [affirmative].
Arul Menezes: [00:09:29] … in addition to the parallel English-French data, you also take some French monolingual data, you translate it back to English using your other direction translation system, the French to English system…
Arul Menezes: [00:09:41] … and then you put that synthetic data back into training your English-French system.
Sam Charrington: [00:09:45] Okay.
Arul Menezes: [00:09:45] So, so that's-
Sam Charrington: [00:09:47] [crosstalk 00:09:49] essentially a, a data augmentation technique?
Arul Menezes: [00:09:51] It is, yeah, it's a data augmentation technique, and it works, like, incredibly well, actually. Adds several points to our metric. The metric we use in machine translation is called a blue score. I mean, there are other metrics and I, I mean, I could talk about that at some point if we want to get into it, but, you know, we get several points of blue score out of the back translation.
And then, so that's our final sort of teacher model, which is typically huge, and then what we do is we use that model to teach the student model. And the way we do that is essentially we run, like, a huge amount of text through this teacher model, and then we take the data generated by the teacher and we train the student on it. And the reason that works is because unlike sort of natural data that we train the teacher on, which can be confusing, contradictory, diverse, the, the data generated by the teacher is very uniform and it's very standardized, and so you can use a much simpler student model to learn all of that knowledge from the teacher, because it's a simpler learning problem.
And having done that, that model runs, like, super fast and we can h- host in production and translate, like, trillions of words, you know, so yeah.
Sam Charrington: [00:10:59] And so the, the student teacher part of the process is kind of interesting to explore a little bit further. Are you essentially trying to do something your- the task that you're trying to, or goal that you're trying to achieve with that is model compression.
Arul Menezes: [00:11:13] Right.
Sam Charrington: [00:11:14] Very different approach to it than, like, pruning or…
Sam Charrington: [00:11:18] … you know, some of the other ways you might approach compression.
Arul Menezes: [00:11:20] Yeah, right. So we do, like, we do a lot of different things to model compression, right? So one of the things we do is we, we do quantization, for example, within all our models in eight bits. We've experimented with less than eight bits. It's not quite as effective, but, you know we, we do that. We do some other, like, pruning techniques as well, but the biggest one is the knowledge distillation, and what you're trying to do there is get a smaller model to basically mimic the behavior of the big teacher model just running a lot cheaper.
And by combining all the techniques, we published a paper last year on this at a workshop, and from our big teacher with all of the knowledge distillation, the compression, the quantization and, and so on, we're running something like 250 times faster…
Arul Menezes: [00:12:06] … on the student than the teacher with, I mean, there is a small loss in quality, right? But we lose maybe half a blue point, not too much, and in some cases not even any. We can, like, actually maintain the quality as is, so…
Sam Charrington: [00:12:20] The, my next question for you, it, the way you describe the process, and in particular the idea that the teacher is outputting more consistent examples than what is found in the training data…
Arul Menezes: [00:12:35] Mm-hmm [affirmative], right.
Sam Charrington: [00:12:35] My next question was, or the intuition that I had was that that would cause the student to be far less effective at generalizing and would make it perform worse, but it sounds like that's not the case in practice.
Arul Menezes: [00:12:51] So the key to that is to make sure that the data that you feed through the teacher to teach the student is diverse enough to cover all the situations that you may encounter, right? So the students are a little weird, I mean, and I think you're sort of hinting at that. We do, for example over-fit the student to the training data, which is something that you typically wouldn't do in your teacher model, because you, in fact, are trying to make the teacher match the student as much as possible.
Arul Menezes: [00:13:18] So some of the things that you do to make the, to the teachers better at generalization, you don't do in the student. And in fact, if you look at the student distributions, they're much sharper than the teacher distributions, because they have overfit to the data that they've seen. But, you know, there's a little evidence that you could get into some corner cases that are brittle, like you know there's this problem of neural hallucination that all of the neural models are subject to where, you know, occasionally they'll just output something that is completely off the wall, unrelated to anything that they've seen. And there's some evidence that there's a little bit of amplification of that going on. Like if it's… You know, the teachers are also subject to hallucination, but maybe at a very, very low frequency, and that maybe that's being amplified a little bit in the student.
So w- we're, you know, we're working on managing that, but yeah, so there's, there's, you know, it's a trade-off. Like, the students have lower capacity, but that's what enables us to run them, and we, you know, we run them on CPU. We don't, we, we don't use GPUs in production inference. We use, of course, all models are trained and, and all the knowledge [inaudible 00:14:26] are done on GPUs, but in, but in production we're just using CPUs.
Sam Charrington: [00:14:26] And is, is that primarily based on the cost benefit analysis, or is it based on a latency envelope that you have to work with and not needing, not wanting a kind of batch…
Sam Charrington: [00:14:38] … Inference requests?
Arul Menezes: [00:14:39] Yeah, that, that's exactly right. I mean, you know, latency is a big concern. Our API's a real- real-time API, and so, you know, latency is the biggest driving factor. And honestly, if you do inference on GPUs, you know, you get some latency benefit, but the big benefit is on large batches. And so unless you have a matched batch translation API, you can't really take advantage of the full capacity of your, of your GPU, so, you know, in a real-time API.
Sam Charrington: [00:15:05] Mm-hmm [affirmative]. And are both the teacher and the student models transformers for you?
Arul Menezes: [00:15:12] Yeah, they are. They are. Yeah, the, the students are transformer large or a little bit larger, and then the s- sorry, that's the teachers, and then the students, they're very highly optimized transformer. I mean, they, we start with transformer base, but then we do a lot of really strange stuff. I would refer you to the paper, actually. [laughs]
Sam Charrington: [00:15:30] Okay. When you were describing the data augmentation technique that you use…
Sam Charrington: [00:15:37] … it kind of called to mind ideas about incorporating a GAN type of approach where you're doing the pass-back translation and then, you know, maybe there's some GAN that is trying to…
Sam Charrington: [00:15:47] … figure out if the results going backwards…
Sam Charrington: [00:15:51] … is like a human translation. Is there a role for that kind of technique? Is that something that comes up in the research?
Arul Menezes: [00:15:57] Yeah, so we've, we've looked at GANs. There were some exciting results, but but in the end, I mean, I think we have some okay research results. We haven't seen much benefit, but more broadly, in terms of data augmentation, we're using it all over the place, right? So it's we have the back translation, but there are a lot of phenomenon that we want to address in machine translation that is maybe not well represented in the data, and so we use data augmentation pretty heavily to cover those cases, right?
To give you a simple example, when you translate a sentence and you get a particular translation and then you go in and let's say you remove the period at the end of the sentence, sometimes it changes the translation entirely. They may both be perfectly good translations, right? But they're different. So one way to look at it is, well, they're good, both good translations, but people don't like that. So if you look at our customers, and we're very sensitive to what our users, the feedback we get from our users. So one of the feedback we got was that, you know, we want a little more stability in our translation. So, you know, just because I lost a period at the end of the sentence, I shouldn't get a drastically different translation. And so, you know, it's very easy to augment the data and say, well, you know, stochastically I'm going to, like, delete the period on my sentences, and so then the model learns to basically be robust whether there's a period or not.
Now, of course, you know, that's different than a question mark. You definitely want to leave the question mark in because that changes the meaning of the whole…
Arul Menezes: [00:17:23] … sentence. But, you know, things like that, punctuation, the period, commas, things like that. Maybe, you know, capitalization, for example. One of the the other examples would be like an all caps sentence. You know, you take the whole sentence and you change it to all caps. Well, you get a totally different translation, right? So we, again, generate some synthetic all caps data so that the model learns to do a good job of translating that as well.
And then there's, you know, there's all these, like, I, I would call them, you know, long-tail phenomenon that and, you know we feel that data augmentation's a good way to address some of these, yeah.
Sam Charrington: [00:17:53] Your examples are really interesting to me because I'm refer- I'm comparing them to, like, your textbook NLP types of examples where the first thing you're doing is making everything lowercase and getting rid of all of your punctuation.
Sam Charrington: [00:18:05] Sounds like that does not work for translation.
Arul Menezes: [00:18:08] No, because there's a lot of information in casing and punctuation, right? Like, I mean, if you want to handle names, for example, you need to pay attention to the case of the input. Like, everything in the input has information, and so actually even the punctuation, right? Like, sometimes if you take the period off the end of the sentence, it should change things because it may be a noun phrase rather than an actual sentence, right? So it's not so much we're preprocessing the data and trying to be clever. It's about exposing the model to different variations so that the model can figure things out for itself.
Sam Charrington: [00:18:41] Mm-hmm [affirmative]. One of the questions this prompts is, like, the unit of, you know, work or the unit of thing that you're trying to translate.
Sam Charrington: [00:18:50] You know, translating a word being different from translating a sentence…
Arul Menezes: [00:18:54] Sure.
Sam Charrington: [00:18:54] … being different from translating a, an entire document.
Sam Charrington: [00:18:57] Sounds like most of what we've been talking about is kind of phrase by phrase now relative to the word by word that, you know, we were doing 20 years ago. Are you also looking at the entire document? Are you able to get information from a broader context to impact the translations?
Arul Menezes: [00:19:18] Yeah, so that's, that's a very good question, Sam. Yeah, so the context matters a lot, right? So one of the reasons why neural models are so great at translating now is because they, they are looking at the whole sentence context and they're translating the entire conte- the sentence and every w- they, they're basically sort of figuring out the meaning of every word and phrase in the context of the whole sentence, which is something we couldn't do with statistical machine translation before.
So now the next step is to expand that context to beyond the sentence, right? So there are a lot of phenomenon that it's impossible to translate well without context beyond the sentence. Like, in many languages, unless you have document-level context or paragraph-level context, you can't generate the right pronouns because you don't actually know. The sentence doesn't have enough clues to let you know what is the gender of the subject or the object or the person you're talking about in that sentence. Beyond just the pronouns, it's also like you know, the senses of words and you know, disambiguating those. So, we're, we're actually moving towards translating at the whole document level context, or at least, you know, very large, multi-sentence fragments. And then there, we'll be able to use, you know, the, the, the, the context of the entire document to translate each individual sentence.
And we actually have some really great research results based on translating at the document level. Yeah, so we're pretty excited about that. That model is not in production yet.
Arul Menezes: [00:20:48] But it's something that we're working on. We did ship a document-level API. I think it's in public preview right now. Which addresses sort of the other half of the problem, which is, you know, people have documents. They've got formatting. You know, it's in PDF. It's in Word. It's in PowerPoint, whatever, and HTML, and it's a hassle getting all the text out of that…
Sam Charrington: [00:21:11] Yeah.
Arul Menezes: [00:21:11] … getting it translated, and then worse still trying to reassemble the document and reconstruct the formatting of that document on the translated thing. So we've made that easy. We just shipped this API. Just give us your PDF. We'll tear it apart, we'll do the translation, we'll put it back together, and we'll preserve the format. And you know, especially for PDF, that is actually really hard. Doing the format preservation is tricky. But we're pretty excited about that API.
And so, then, that's the place where our document level neural model would fit right in, right? Because now we have, the user's giving us the whole document. We can not only handle all the stuff about the formatting and all that. We can go one better. We can actually use the whole document context to give you better quality translations.
Sam Charrington: [00:21:53] Mm-hmm [affirmative]. Can you give us an overview of some of the techniques that go into looking at the entire document when building the, the model?
Arul Menezes: [00:22:03] Yeah, so there's, I mean, right now, as I said, we haven't actually shipped this, so we're looking at a, a bunch of variations. You know, there's several things that people have looked at, like mo- you know, there are hierarchical models where you do the, you run transformers at the sentence level, and then you run a second level to sort of, like, collect the sentence level information into, like, a document level context vector, and then you feed that back into translating each sentence.
We're finding that actually, if you just make it super simple and treat the whole thing as, as if it were a giant sentence, in effect, you get really good results. You do have to deal with the performance issues, right, because transformers are n-squared in the size of the input and the output, so instead of, you know, handling, you know, a 25-word sentence, if we're not translating a thousand-word para- you know, document or paragraph, then the, you know, you've got, like, an n-squared problem in terms of the performance, right? It's going to be that much more expensive, but we have, we have things that we're looking at to make that faster as well, so we're pretty optimistic we can do that, and and I think we can do that with just letting the transformer figure it out for itself rather than trying to be very clever about all this hierarchical stuff.
Sam Charrington: [00:23:10] Nice. Nice. Let's talk a little bit about the role of different languages. You know, we, we've already talked about how you can use back translation to help augment the performance of your translation of a language in, in one direction or the translation between a couple of language pairs.
Sam Charrington: [00:23:28] Are there ways to take advantage of the other 130 or so languages…
Sam Charrington: [00:23:33] … that y- that you support when you're building the n-plus-1th model for a given language?
Arul Menezes: [00:23:38] Absolutely. Absolutely. That's been one of the most exciting things, I would say, that came out of sort of transformers and, and neural models in general is the ability to do this sort of transfer learning between languages, right? And the reason we can do that is because transformers or neural models in general are representing the meanings of words and sentences and phrases as embeddings in, you know, the space, and by training on multiple languages together, you can actually get the representations of these languages to merge and have the similar concepts be represented through relative points in spa- in that space, right?
So, as a practical matter, we've basically found that if we group languages by family, right, and take, so for example we took all our Indian languages and we put them together and we trained one joint model a- across all of the languages and now we're talking, you know, languages where you have a very different amount of data. You have Hindi, where we have quite a lot of data, and then we have, like, Assamese, which was, I think, the last one that we shipped, that has probably, like, two orders of magnitude less data. And the, the wonderful thing is that by training them jointly, the Assamese model learns from the huge amount of data that we have for Hindi and does, like, dramatically better than if we had just trained on Assamese by itself.
In fact, we have done those experiments and, you know, for the smaller languages, we can get, like, five, 10 blue points, which is, like, a crazy level of improvement just from the transfer learning and multilingual.
We also do that with, like, Arabic all of our Middle Eastern languages. So we're just, like, grouping more and more language families together and getting huge benefits out of this.
Sam Charrington: [00:25:31] And when you're grouping the, the language families, have you ever, do you experiment with going across language families and seeing if there's some improvement,
Sam Charrington: [00:25:42] … improvement there?
Arul Menezes: [00:25:43] Yeah, so we, you know, we've trained models that are, like, 50 or 100 languages in them. What you run into is, you know, as you add languages, you have to increase the size of your vocabulary to accommodate all of these languages, and you have to increase the size of the model, because at some point you run into model capacity a little bit. So you can have a model that does a ni- a really nice job of learning from 50 or 100 languages, but it gets to be a really huge model, and so in terms of cost effectiveness, we've found that, like, you get, like, almost all of the benefit of the transfer learning at, like, a much reduced cost by just grouping 10, 15 languages at a time. And if they're related, it's better. But actually, even if they're unrelated, it still works. [laughs] It's quite amazing how well it works even if the languages are not related, yeah.
Sam Charrington: [00:26:32] We may think of it as, like, a computation test of Chomsky's universal grammar and, you know, these ideas that suggest that all languages have these common elements.
Sam Charrington: [00:26:42] I- if you are able to train these models across languages and im- improve them, that would seem to support those kinds of theories.
Arul Menezes: [00:26:48] I mean, definitely the models do a really good job of bringing related concepts together in the, in the embedding space, right?
Sam Charrington: [00:26:56] Would you consider this, you, you referenced this as, like, multilingual transfer learning. Would you also think of it as a type of multitask learning as well, or is, is that not technically what you're doing in this task?
Arul Menezes: [00:27:09] So we're also doing, in addition to multilingual just machine translation, we're also doing multilingual multitask learning, and what we're doing there is we are combining the sort of so there's let me back up a bit. There's been this whole line of research based on models like BERT, right? Pretrained language models where, if you look at BERT, it's actually the encoder half of a machine translation model, but it's trained on monolingual data. It's trained on a, on a single language data on this objective that's a reconstruction objective where, you know, you're given a, a, a sentence where you have couple of words or phrases blanked out. You need to predict that, right?
And then you have multilingual BERT where you take multiple separate monolingual corpora, right, so it's like a bunch of English text, a bunch of French text, and all of it, and you train them jointly in the same model. And it does a pretty good job of actually pulling the representations of those things together. So that's one line of research that's sort of really driven a revolution in, like, the whole natural language understanding field, right? So for example, today if you want to train a named entity tagger, you wouldn't start from scratch on your ta- on your named entity data. You would start with a pretrained model.
So one of the things that we're very excited about is we have this project that we call [ZICOR 00:28:43] where we're bringing the machine translation work and this sort of pretrained language model, BERT-style work together, right? And we train, we're training this multitask, multilingual model that's, architecturally, it's just a machine translation model, right? But in addition to training it on the parallel data, let's say the English-French data and the English-German data and conversely the German-English data and the French-English data and, you know, 10 or 15 or 50 or 100 other languages. In addition, we have a separate task where we have the BERT tasks, where we take monolingual data and we, we have it reconstruct, you know, the, the missing words. And we also have what's called a denoising autoencoder task, which is where you give it a scrambled sentence, and then it has to output the unscrambled sentence through the decoder.
And then now you have these three tasks, and we train them in rotation on the same model, so they're sharing parameters. So the model has to figure out how to use the same parameters to do a good job of the BERT task, to do a good job of the denoising autoencoder task, as well as to do a good job of the machine translation task. And this, we find, leads to, like, much better representation that work for better natural language understanding quality, but also better machine translation quality.
Sam Charrington: [00:29:43] Nice. And the, the BERT task in this example is within the same language, as opposed to…
Sam Charrington: [00:29:49] … across the target, to the target language?
Arul Menezes: [00:29:53] Yeah, there's actually, like, a whole family of tasks, right? I mean, people have come up with, I mean, we've, we've experimented with, like, 20, 25 tasks. Like, so you can do a monolingual mass language model task, which is the BERT task, but you can do a cross-lingual ma- mass language task as well, and you can do the denoising autoencoder task monolingually, where you have to reconstruct the same language, but you can also do that cross-lingually where you have to reconstruct sort of a scrambled foreign language task, so there's, like, a real, like, sort of stone soup approach where people are just throwing in all kinds of tasks, and they all help a little bit.
But we need to figure out, like, what's the minimal set that you need? Because, you know, it's work. It's computational expense to train these huge models on all these tasks, so if we can find the minimal set that works, that would be ideal. And so far, what we're working with is, like, a denoising autoencoder, a mass language model, and a machine translation task.
Sam Charrington: [00:30:49] Very, very cool. Very cool. So I think one of the things that, you know, often users of this kind of machine translation services experiences that, you know, they weren't great in the general case, but when you start to try to apply them to specific domains, it's a lot more challenging, you know, and kind of the technical conversations or translating, you know, medical conversations or, you know, construction or what have you. Is there anything that you're doing to make the domain-specific performance better for these kinds of systems?
Arul Menezes: [00:31:26] Yeah, definitely you know, domain performance in specialized domains is a real challenge. We're doing several things to get better there, right? So the, the first thing is that the quality is really determined by the availability of data, right? So in the domains like, let's say news or web pages where we have a ton of data, you know, we're doing really, really well. And then if you go into a more specialized domain like, let's say, medical or legal where we don't have as much data, we're maybe not doing quite as well. And so one of the things we're doing is we're now taking the same neural models that are good at translation and we're using them to identify parallel data in these domains that we can find on the web that we maybe weren't finding before, and we can do that because these models, you know, because the representations are shared in the multilingual models, they are actually very good at identifying potential training data that, that is translations of each other. So that's one thing we're doing.
The other thing we're doing, of course, is the same kind of transfer learning approach that we're using cross-lingually applies within domains, as well, right? So if you have a small amount of medical domain data, you don't want to, like, just train a l- a model that's based just on that you know, small data. What we're doing instead is we're taking, you know, our huge model that's trained on a ton of, like, general data across a bunch of domains, and then you fine-tune it for the specific domains that you're interested in. And we actually have a product called Customer Translator that we have, like, you know, thousands of customers using, where they are using this approach to customize the machine translation to their company or their application needs, right?
So let's say you're a car company or something and you have a bunch of data that's about, like, automotive manuals, right? So you come to our website, you log in, you create your account, etc., you upload this data, and then what we do is we take your small amount of domain-specific data, we take our large model, and then we fine-tune it to that data, and now you have the model that does, like, you know, sometimes dramatically, again, 10, 15, 20 blue points better than the baseline because, you know, we've learned the vocabulary and the specifics of your domain, but we're still leveraging, we're standing on this platform of, like, the broad general domain quality.
So that's been extremely popular and valuable, actually. We just shipped a new version of that based on transformers a couple of months ago.
Sam Charrington: [00:33:45] And in that case, the user is presumably bringing translated documents so that, that you're able to train or fine tune all, with both source and target translations?
Arul Menezes: [00:33:56] Yeah, that's exactly right. I mean, a lot of the companies that we work with have some data, right? Like, let's say they had a previous version of their vehicle or, you know whatever and they had manuals that were translated. In Microsoft's case, for example, you know, we have, let's say the manuals for Microsoft Word going back, you know, a couple of decades, and this is the kind of data you can use to customize it so that anything, any new content that you want to translate can have, like, a very consistent, like, vocabulary and, and tone and so on, yeah.
Sam Charrington: [00:34:28] Mm-hmm [affirmative]. And then in that first example or the first technique that you mentioned, that sounds really interesting. So you've got this index of the web and Bing…
Sam Charrington: [00:34:37] … you know, for example, or maybe you have a separate one, but you go- have this mechanism to kind of crawl the web and…
Sam Charrington: [00:34:44] It sounds like the idea is that you can use the model to identify, hey, I've got these two documents.
Sam Charrington: [00:34:52] They look really similar, but there's a, a high percentage of words that I don't know…
Arul Menezes: [00:34:57] Yeah, right.
Sam Charrington: [00:34:57] … that occupy similar positions in the same documents.
Sam Charrington: [00:35:00] And then you have someone translate- oh, well actually, then once you know that, you can just align them, so to speak, and you've got more domain-specific document to add to your training set? Is that the general idea?
Arul Menezes: [00:35:13] Yeah. I mean, it, it's like you're trying to find two very similar-looking needles in a very, very, very large haystack, right? [laughs]
Arul Menezes: [00:35:21] And so, so you have to have a magnet that finds exactly those two needles and rejects everything else. So the, the cross-lingual embedding space is pretty key here, right? So you're basically, in principle, if you embedded every single sentence or document on the web and then were able to look at every single document and find all of its very similarly close embeddings, you'd be done. But, you know, that's [laughs] that's,
Sam Charrington: [00:35:47] easier said than done?
Arul Menezes: [00:35:48] Easier said than done, right? So that's the kind of thing that we're trying to do at scale, right, is, like, you got these, you know, trillions of documents and, you know, we want to find the matching one, so you need to do it efficiently, and so there's a lot of, like, clever engineering that goes into, like, indexing this stuff and, and, like, computing the embeddings efficiently. And, of course, also, you know, we're not really trying to match every page in the web to every other page in the web, because you have, you know, a lot of clues that says whe- you know, if I have a document here, you know, is it likely I'd have a translated document somewhere? It's going to be either in the same, like, top-level domain or, or related sites, things like that. So there are, there are ways to constrain that search.
Sam Charrington: [00:36:27] Mm-hmm [affirmative]. Our conversation thus far has focused primarily on text translation. Are you also involved in voice translation?
Arul Menezes: [00:36:38] Yeah, so we actually have been doing speech translation for a while. Several years ago we shipped a feature for speech translation in Skype called Skype Translator. It was, you know, really well received, super exciting. A lot of people use it even today, right? Especially, you know, people talking to their relatives in another country, and, you know, there's a lot of interesting challenges in speech translation because it's not that you just take the output of a speech recognition system and then just pass it to machine translation, right? There's a, there's a real mismatch in what comes out of speech recognition and what is needed to do a good job of translation, because of course translation's expecting, like, you know, well-formatted text, capitalization, punctuation, br- sentence breaks, things like that.
So we put a, we put a lot of effort into bridging that gap, you know, post-processing the output of speech recognition so that we have, you know, h- really accurate sentence boundaries. So that, that matters a lot. I mean, you break the sentence in the middle and you try to translate… Like, if you break a sentence in the middle, the speech recognition there is okay, because as a human reading it, you know, there's a period in there. You just ignore it and move on. But the machine doesn't know that, and so when you're trying to translate, you've got these two separate sentences and then it does a terrible job of it. So doing, getting the sentence breaks right, getting punctuation right and so on is really important, and so, so that's what we've been doing.
We actually have a project going on now with the European Parliament where they are going to be using our technology, well, it's, it, there, there's three contestants or three bidders in this project, and so there's an evaluation that will happen in a few months, but we're hoping that they'll adopt our technology for live transcription and translation of the European Parliament sessions in all 24 languages of the European Parliament, which is super exciting.
Sam Charrington: [00:38:26] Oh, wow. Wow.
Sam Charrington: [00:38:28] So when you think about kind of where we are with, you know, transformers and some of the innovations that we've talked about and, you know, relative to your 20, 30 years in the space and I'm curious what you're most excited about and, and where you see it going.
Arul Menezes: [00:38:44] Yeah, I mean, the pace of innovation has just been amazing. There's so many things that are happening that, like, you know, would be a really dramatic impact, right? So one is just much larger models, right? As we scale up the model, we see continual improvements. And so as the hardware and the, you know, our ability to serve up these larger models keeps growing, the quality will also keep growing, right?
The architect of these large models also matters, right? Like, it's not just matter of taking the smaller model and scaling it up exactly as is, so there are things like mixture of experts models that, for example, allow you to scale the number of parameters without the cost scaling as linearly, right, because you have parts of the model that specialize in different parts of the problem.
And then, you know, multilingual is definitely the future. Pretrained models is definitely the future, right? So, so, like, if you put that all together, like pretrained, multilingual, multitask trained, maybe with mixture of experts, huge models, and then we would specialize them for individual language pairs or groups of languages and then distill them do- down to something we can ship. So that's one area that there's a lot of innovation happening.
The other thing is that, you know, 10 years ago, people were just amazed that translation worked at all, right?
Sam Charrington: [00:40:07] [laughs]
Arul Menezes: [00:40:08] And now we're doing a really good job and expectations have risen, so you get to the point where a lot of sort of smaller, let's call them long-tail problems start to matter a lot, right? So if you look at translation of names, we probably get them 99% right, right? But a few years ago it would have been fine to say, "Hey, we're 97% accurate on names." But maybe now that's not good enough, right? Like, screwing up 1% of the names is not acceptable, so, you know, how do we get that last 1% of names a- and, you know, I'm just making up the nu- it, it may be 99.9%. You're still going to have upset customers if you get, you know, 0.1% of your names or your numbers, numbers are even worse, right?
Arul Menezes: [00:40:48] Like, if you misstate a number even, like, 0.1% of the time, it could have catastrophic consequences, right? So that's an important area. I mentioned neural hallucination before. That's something we see where, again, may happen only 0.1% of the time, but if you get, like, a completely unrelated sentence that has nothing to do with your input but is really fluent, it's pretty deceptive, right? Like, because especially if I'm just putting my faith in this translation that, and I don't understand the source language at all, you'd be like, "Well, sounds okay," and move on. But maybe it says something completely different from what the source said, right? And so that's, that's a challenge.
Arul Menezes: [00:41:25] Yeah, I mean, there's lots of really cool things happening in this space.
Sam Charrington: [00:41:30] Awesome. Awesome. Well, Arul, thanks so much for taking some time to share a bit about what you're up to. Very cool stuff.
Arul Menezes: [00:41:38] Thank you. You're welcome, Sam.
Sam Charrington: [00:41:40] Thank you.
Arul Menezes: [00:41:40] Happy to be on, on the show. Take care. Bye.
Sam Charrington: [00:41:43] Thank you. All right, everyone, that's our show for today. To learn more about today's guest or the topics mentioned in this interview, visit TWIMLAI.com. Of course, if you like what you hear on the podcast, please subscribe, rate, and review the show on your favorite pod catcher. Thanks so much for listening, and catch you next time.
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Promoting safe parties / We take safety seriously!
Dunedin Equinox is a not-for-profit society based in Dunedin, New Zealand.
We have dedicated ourselves over the years to celebrate the sun crossing the celestial equator in the best way we know how, by bringing together a diverse range of people with one thing in common, the love of dancing, laughing and performing with one another.
With your help we can continue to grow and develop, and make Dunedin Equinox everything it can be!
Dunedin Equinox is a safe, supportive, non-threatening space that encourages a willingness to learn, respect, and mental and physical safety. As a collective, we strive to provide an open, safe and nurturing environment that encourages everyone to feel a sense of participation and belonging.
First held at Buckland's Crossing Reserve, Dunedin Equinox (originally Spring Equinox) has expanded to throwing multiple festivals a year and has developed a well deserved reputation for good people and outstanding music.
We love volunteers! Our parties wouldn't exist without a dedicated crew of volunteers behind the scenes. If you would like to get involved please message the Dunedin Equinox page on facebook.
The latest information from our community.


text:
No Doubt Announce Summer Tour With Paramore
PrevPreviousCaptures From No Doubt Summer Tour Annoucement
NextCONFIRMED: No Los Angeles Dates At The MomentNext
I know, I know, it's getting old, but I do like to post articles from official sources rather than just No Doubt. The following article was found on MTV this morning, I guess the cat is officially out the bag now.
And eww…let's pray we never see the phrase "paradoubt" anymore.
No Doubt have finally announced the dates for their much-anticipated summer tour with Paramore. The trek will kick off with a pair of festival appearances — May 3 at the Bamboozle Festival in East Rutherford, New Jersey, and May 16 at the Mandalay Bay Events Center in Las Vegas, for Tiger Woods' annual Tiger Jam charity event. After No Doubt work off the rust that comes with a four-year hiatus, they'll kick into high gear on May 19 at the Save Mart Center in Fresno, California.
All in all, the Paradoubt (we just made that up!) tour will make 44 stops across North America before wrapping on August 1 in No Doubt's Orange County, California, home turf. Tickets for most dates will go on sale March 7, though members of No Doubt's Tour Club will be given an exclusive 72-hour pre-sale, which starts Thursday at 4 p.m. ET. Members of Paramore's fan club get their crack at tickets on May 2.
For more information on pre-sales, head over to NoDoubt.com or Paramore.net.
In addition to Paramore, the Sounds, Janelle Monáe and Bedouin Soundclash will serve as support on select dates.
And thanks to Jackie for pointing out an error in the article, Paramore's presale starts March 2, not May 2.
0 Replies to "No Doubt Announce Summer Tour With Paramore"
Just so you know, there's a typo in the article that states paramore fans will get their pre sale tickets on may 2nd – it should be march 2nd.
If we're going to combine band names, I much prefer "No Paramore" to "Paradoubt" 😉

redpajama_set_name: RedPajamaCommonCrawl


Mr. Wagman has spent his entire career in the CPA arena.
Prior to joining Whitman Business Advisors, he practiced Public Accounting for over 25 years and was elected Managing Partner for a 125 person regional CPA firm. While with the CPA firm he was very active in the New York State Society of CPAs, serving and chairing several committees. He then changed careers and joined a Consulting firm that provided talent acquisition, M&A and Practice Management Consulting for Big 4, regional and local CPA firms. At Whitman Business Advisors LLC Barry will focus on M&A and Talent Acquisition for CPA firms of all sizes.
Barry holds a BS in Accounting from New York University and lives in Long Island with his wife Nina. He enjoys his weekends spending time with his four daughters and his nine grandchildren.


Join a team of international volunteers in hands-on conservation projects that protect, preserve and restore the distinct and varied ecosystems of Australia.
Twin Work & Volunteer has been sending volunteers to Australia for a number of years – it's one of the world's most popular destinations, especially for lovers of wildlife and spectacular landscapes. This programme encompasses a range of conservation projects, that can include planting a new forest in a week, creating wildlife habitats for an endangered species, building boardwalks, or removing invasive species to restore the natural balance to an area.
You will be managed by professional team leaders who are qualified to ensure your safety and welfare whilst volunteering in Australia. Your daily tasks will involve travelling in groups of 6 - 10 to complete conservation projects in urban, regional and remote areas.
Volunteers are able to choose from a selection of starting locations around Australia, and each week they'll be assigned to a range of different conservation projects such as tree planting, track building, flora and fauna surveys, weed control and heritage restoration projects. Please scroll down to the 'Location' section to find out more about each of the project destinations available in Australia.
Your volunteering project will begin on a Friday at 11am.
Choose the duration of your programme up to the maximum that your tourist visa will allow (normally 3 - 6 months).
The programme finishes around 4pm on a Friday.
You should book your return flight home on the next day (Saturday).
As you will be assigned to projects on arrival, we recommend that you choose a starting location based on your preferred climate and any travel plans you have before or after volunteering.
Open to volunteers aged 18 to 70, with the majority of volunteers being aged 18 - 35.
There are no specific skills required to join the programme, just a willingness to get your hands dirty and to contribute to the experience within your team.
Suitable for people who enjoy outdoor activities, and have a reasonable level of fitness.
Your team leader will provide full training on-site to give you any specific skills you need (such as species identification, or construction skills).
All briefings, safety and project instructions are given in English.
Your volunteering project will begin on the Friday at 11am.
This programme enables volunteers to protect, preserve and restore the Australian environment. The types of projects you'll work in will depend on the location, the season and the conservation priorities.
Following your arrival at your chosen centre, you'll be introduced to your Team Leader, and assigned a team of approximately six to ten volunteers. The Leader will provide all training required on-site and will explain the conservation aims of the projects.
Projects normally run five days a week, with one or two days free for relaxing or sightseeing (see *Free Time* tab). Working hours are from 8am - 4pm daily, although this varies on some projects when required by seasonal conditions or project activities.
This is a great way to see Australia, as the team travels to a different project location every week or two. You will be able to incorporate as much variety into your chosen programmes as possible, whilst achieving the essential conservation outcomes.
Australian Conservation Volunteer projects are managed in conjunction with project partners that may include regional councils, national parks, museums, landcare groups, conservation departments and other national conservation agencies.
All of the projects can be broadly categorised into the following key focus areas: Flora & Fauna, Landcare, Parks & Reserves, Coasts & Waterways, Healthy Communities, Heritage, Disaster Relief.
While you'll be taking part on conservation projects from Monday to Friday (although project days may vary), weekends are generally free time for you to relax and sightsee in the local area.
Whether your weekend is spent in a capital city, or a regional area, local staff will be able to advise you on how to make the most of your time. You might choose to spend time with other volunteer friends, or strike out on your own to do something you've always wanted.
A great wild experience - this could include watching thousands of fairy penguins come ashore on Phillip Island (Victoria), a day trip to the Great Barrier Reef in Queensland, a bushwalk through ancient rainforests in Tasmania's World Heritage area, or any number of great outdoor experiences.
Learn to surf! There are plenty of surf schools, selling lessons lasting from a couple of hours to a day. With 50,000 kilometres of coastline, and over 10,000 beaches, you're sure to find the perfect spot – just remember to check on beach safety rules before you swim.
A day at the cricket – with major venues in all the capital cities, cricket is an integral part of Australia's lifestyle. Played at all levels, from the backyard to the Melbourne Cricket Ground (MCG), cricket is a national obsession. Can't get to a match? Watch it on the television instead!
Try a cultural tour – most major cities have companies offering aboriginal tours, as well as in the more obvious outback locations for rock art and bush tucker.
Arts and culture are part of the fabric of Australia. Check out some of the major cultural festivals, or enjoy a visit to one of the capital city galleries or the thriving regional galleries around the country – many offer free entry.
There is a thriving music scene in Australia – ask local staff for suggestions. You can find everything from pub rock, jazz and blues to hip-hop, indigenous music or classical.
Enjoy Christmas at the beach! December 25th falls nicely into Australia's summer time – as well as the beach, many people enjoy camping trips or just relaxing at home.
All accommodation and meals are included during the programme; three meals a day, seven days a week.
Accommodation: This varies according to the project and location, and volunteers enjoy the chance to travel to a range of locations as part of this programme. Typical accommodation can include caravans, hostels, shearers' quarters, bunkhouses, and camping (tents will be supplied). Volunteers should bring a sleeping mat as well as a sleeping bag, and be prepared to share a room.
Food: All meals are included. For breakfast, the project supplies cereals, bread or toast, tea and coffee. Lunch is normally a self-made sandwich and fruit; in the evening you'll be able to enjoy a meal prepared by the group, typically something like pasta, a roast or a vegetarian option.
Be prepared to help with the preparation of meals and cleaning up of dishes, plus some domestic duties as required. The project can cater for vegetarians but if you have very specific dietary requirements (food allergies, for example), you may need to provide your own food.
Please note that you only require a tourist visa to participate, though a Working Holiday Visa is fine too. To apply for your visa, please see www.immi.gov.au.
All transport during the project is included. The team travels together in a minibus or four-wheel drive vehicle driven by the team leader.
DEPARTURE: Your entire volunteering programme finishes around 4pm on a Friday, so book your return flight home for the next day (Saturday).
For American and Canadian participants: We offer insurance options with International Volunteer Card (IVC).
The conservation volunteer projects take place in different locations across Australia. Volunteers will be able to spend their time away from major cities and see parts of Australia that are often well off the main tourist tracks.
Help scientists and indigenous rangers to bait and monitor the invasive ant species in Nhulunbuy, East Arnhem Land.
Work on marine debris surveys in Talc Head (a remote tropical paradise) – help survey and remove debris from beaches. Protect local sea turtle nesting areas.
Assist with track clearing to the historic headland » Help protect Uluru (Ayers Rock) - the World Heritage Area through invasive weed removal at the Mutitjulu Waterhole.
Help establish a second population of wombats at St George in south east Queensland.
Protect the new wombat population from dingoes and foxes.
Help remove invasive weeds in Fraser Island (World Heritage-listed).
Help upgrade walking tracks and trails in National Park areas.
Help assess the state of local koala populations and their habitats in Springsure Koala Reserve.
Help with mass tree planting in the semi-tropical environment of the Stuart Creek floodplains.
Help the endangered pygmy bluetongue lizard populations.
Work in Brookfield Conservation Park - habitat for the hairy-nosed wombat, fattailed dunnart and red kangaroo – gather data, remove invasive plants, and collect seeds for revegetation projects.
Assist with the environmental program at Aroona Dam Sanctuary - locate and remove the invasive Tobacco Bush weed.
Work on a range of urban, semi-urban, and beach activities around Adelaide.
Vegetation surveys, track building, maintenance projects and beach clean ups in Musselroe Bay & Tomahawk.
Maintenance of a wetland area and creek restoration in the coastal region of Nawrantapu National Park.
Work in the bushlands and rehabilitate habitats for native birds and wildlife, as well as endangered plants in Launceston.
Tree planting and track construction projects across Tasmania.
Help a long-term project of restoring degraded land and turn it into a haven for wildlife.
Help with a variety of conservation activities including, erosion control and dune restoration, removal of barriers to encourage wildlife movement and maintenance of the wildlife breeding pens at the Peron Captive Breeding Centre.
Help enhance biodiversity at Centenary Park - wetland planting helps stabilise the banks of the lake, improve water quality, create habitat for water birds and increase biodiversity in the wetlands.
Once you have commenced volunteering you'll be free to travel on independently around Australia or return home.
Experience fantastic dolphin and whale sightings. Raise awareness about wildlife conservation in the beautiful Garden Route region of South Africa.
Take part in a journey of discovery that provides you with in-depth Game Ranger training & gives you the opportunity to discover the natural world of the Big 5 whilst travelling on your Gap Year or Career Break.
Head to Sodwana in South Africa to volunteer with sea turtles! Learn about the conservation of these fascinating animals and help spread the importance of turtle protection in local communities.


text:
Pakistan Agriculture News
ECC approves 50pc of export package incentive for eligible textile, non-textile sectors
October 12, 2017, 4:11 am October 24, 2017 924
ISLAMABAD: In order to promote exports, the Economic Coordination Committee (ECC) on Friday approved a proposal that 50 percent of the export package incentive for eligible textile and non-textile sectors, announced in Prime Minister's Export Package, be provided on the same terms as for the period January to June 2017 without condition of increment.
A meeting of ECC of the Cabinet chaired by Prime Minister Shahid Khaqan Abbasi approved that the remaining 50 percent of the rate of incentive would be provided if the exporter achieves an increase of 10 percent or more in exports as compared to the corresponding period of the last year.
It was also approved that an additional two percent drawback would be provided for export to non-traditional markets. Besides, expeditious settlement of payments claims by the State Bank of Pakistan was also approved.
The meeting approved a proposal submitted by the Commerce Ministry to allocate additional quantity of 12 million kilograms of surplus tobacco to all the tobacco companies and dealers on pro-rata basis.
The ECC also approved a proposal to deregulate the margins on High-Speed Diesel for the Oil Marketing Companies (OMC) and dealers under the policy of liberalization and deregulation.
The impact of the policy would be reviewed after three months.
It was also decided that OMCs would add Fuel Marker in HSD within six months at depot stage to avoid adulteration.
It was decided that OGRA would develop a mechanism to monitor the OMCs commercial stock position, the dealers inventory system and Fuel Marker System.
The ECC provided a provisional approval of the issuance of Government of Pakistan's sovereign guarantee for Rs. 39,000 million for construction of 2X660MW Coal Power Project Jamshoro, subject a third part evaluation especially pertaining to demand and supply situation.
READ MORE: Low kinnow yield expected this year
The ECC also extended the period of provision of subsidy to agricultural tube-well consumers in Balochistan till December 31 2017 subject to commitment of past payments by all concerned/stakeholders on same terms and conditions as approved earlier by the ECC on June 17, 2015.
The approval is linked with a comprehensive review of solarization of the tube-wells to be undertaken on a priority basis in order to save electricity bills and the subsidy being provided by the federal and the provincial governments.
The need to put in place efficient irrigation methods likes drip-irrigation were also emphasized by the meeting.
The ECC approved a summary for extending the period of applicability of reduced rate of 0.4 percent advance income tax on banking transactions of non-filers under section 236P of the Income Tax Ordinance 2001 upto December 2017.
Various measures for rationalization of imports and reducing the import bill were also suggested by Commerce division and Federal Board of Revenue.
The detailed lists of import items would be reviewed and finalized.
Courtesy: Business Recorder


text:
Alexis Lynne Jordan (Columbia (South Carolina), 7 april 1992) is een Amerikaans zangeres en actrice. Ze had in 2011 in Nederland een nummer 1-hit met Happiness.

Biografie
Jordan schreef al op vroege leeftijd muziek en vertrok op haar elfde naar Californië. Op haar twaalfde mocht ze spelen voor Smokey Robinson, die een hommage bracht aan Stevie Wonder. In 2006 deed ze op 14-jarige leeftijd mee aan America's Got Talent, waar ze Whitney Houstons I Have Nothing zong tijdens de auditieronden. Later moest ze de wedstrijd verlaten. Om een grotere kans te maken in het muziekcircuit verhuisde ze naar Atlanta, Georgia. Desondanks verdween de zangeres naar de achtergrond, maar ze volhardde en nam filmpjes op die geplaatst werden op YouTube. Haar filmpjes bleken in de smaak te vallen bij het grotere publiek, maar ook bij het productiehuis Stargate en Jay-Z.

In september 2010 kwam haar debuutsingle Happiness uit, die goed verkocht in de Verenigde Staten en tevens haar internationale doorbraak betekende. In Australië verkocht de single meer dan 140.000 exemplaren en ook in andere landen (waaronder het Verenigd Koninkrijk, Noorwegen, België en Polen) werd het nummer een succes. In de Nederlandse Top 40 stond Happiness tien weken op de eerste plaats en werd het de grootste hit van 2011.

Het succes van haar opvolgende singles Good girl en Hush hush bleef bescheiden. Wel scoorde ze in 2011 in diverse landen nog een grote hit met Got 2 luv U, een samenwerking met Sean Paul.

Discografie

Albums

|-
|align="left"|Alexis Jordan||25-02-2011||30-04-2011||21||23||
|}

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|align="left"|Alexis Jordan||2011||13-08-2011||87||3||
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Singles

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|align="left"|Happiness||14-06-2010||05-03-2011||1(10wk)||30|| Nr. 1 in de Single Top 100 / Hit van het jaar 2011
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|align="left"|Good girl||18-02-2011||18-06-2011||25||7|| Nr. 53 in de Single Top 100
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|align="left"|Got 2 luv U||11-07-2011||03-09-2011||6||20|| met Sean Paul /Nr. 9 in de Single Top 100 / Alarmschijf
|-
|align="left"|Hush hush||29-08-2011||10-09-2011||12||11|| Nr. 22 in de Single Top 100 / Alarmschijf
|-
|align="left"|Acid rain||2013||23-02-2013||tip7||-|| Nr. 88 in de Single Top 100
|}

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|align="left"|Happiness||2010||12-03-2011||3||16|| Nr. 3 in de Radio 2 Top 30 / Goud
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|align="left"|Good girl||2011||11-06-2011||tip9||-||
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|align="left"|Got 2 luv U||2011||27-08-2011||13||19|| met Sean Paul
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|align="left"|Hush hush||2011||17-09-2011||tip5||-||
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|align="left"|Acid rain||2013||02-03-2013||tip5||-||
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Radio 2 Top 2000

Videografie

Videoclips

Films
2011: Justice (een korte film waarin ze superheld Swift Justice speelde)
2011: Honey 2 (het vervolg op de dansfilm Honey waarin ze verschijnt als zichzelf)

Externe link
 Alexis Jordan

Amerikaans zanger
Amerikaans acteur

redpajama_set_name: RedPajamaWikipedia


CAIRO (Reuters) - The Egyptian militant group Ajnad Misr claimed responsibility on Saturday for a blast that killed one police officer in Cairo.
Islamist militants have stepped up attacks on members of the security forces, killing hundreds, since the army toppled Egypt's first freely elected President Mohamed Mursi in July.
The insurgency threatens national security ahead of a presidential election in May, as well as the vital tourist industry.
Al Qaeda leader Ayman al-Zawahri gave his blessing, in a recording released on an Islamist website on Friday night, to Jihadists in carrying out attacks against the army and security forces in Egypt. Al Qaeda is not believed to be linked to Ajnad Misr.


Maintaining Guantanamo is fast becoming silly.
The United States transferred two Libyan detainees, Omar Khalifa Mohammed Abu Bakr and Salem Abdul Salem Ghereby, from Guantanamo Bay to Senegal on Monday.
The Department of Defense announced today the transfer of Ali Ahmad Muhammad al-Razihi, Khalid Abd-al-Jabbar Muhammad Uthman al-Qadasi, Adil Said al-Hajj Ubayd al-Busays, Sulayman Awad Bin Uqayl al-Nahdi, and Fahmi Salem Said al-Asani from the detention facility at Guantanamo Bay to the Government of the United Arab Emirates.
Wherein I eat crow, I double down on part of my criticism of the New York Times editorial page, and Shaker Aamer's lawyer responds to my post.
A long-time Guantanamo detainee goes back to Britain, though he is not British, where he is a cause celebre for no apparent reason.


Poor Oliver. Not 24 hours after returning home from vacation, I left on a work trip. He had to snuggle with The Boy. He had to push under the covers at night with The Boy. He had to get breakfast this morning from The Boy.
Perhaps I should say poor Boy.
Travel is over and I am happy to be home. Oliver is behind me on the guest bed pummeling poor Mr. Bear. A week ago that bear had some structure. He's now reduced to a puddle of fake fur and a little bear-sized sweater. That's a whole lot of kneading in seven days.
Oliver is ready to go back to the couch. He has the need to knead me some more. And Obi wouldn't object to another foot snuggle. He's no mama's boy…but he still likes having me back. He guesses. If you need him to say something like that. And if I need to smooth his brow and kiss his head…well, that might be okay.
For Thunder Thursday, we bring you Kelly's…Mustang Sally.
Sometimes you gotta slow your Mustang down.
All siblings love bunk beds, right?


Fans celebrate during an exhibition game between C.F. Pachuca and Club America at H-E-B Park Wednesday, July 4, 2018, in McAllen.
EDINBURG — On Wednesday night at H-E-B Park, the sea of orange seats was transformed into a wave of yellow jerseys.
The Mexico City-based Club América had its supporters out in abundance in those yellow jerseys, but Pachuca had a few supporters, as well. The two Liga MX teams met as part of the Tour Aguila, and América came out on top 3-2.
While the rest of the nation was gathered to celebrate America's independence, the Rio Grande Valley gathered to support Mexican futbol.
C.F. Pachuca's Jorge Daniel Hernández (16) and Club America's Henry Martín (21) battle for the ball in an exhibition game at H-E-B Park Wednesday, July 4, 2018, in McAllen.
C.F. Pachuca's Víctor Guzmán, left, advances the ball in a game with Club America in an exhibition game at H-E-B Park Wednesday, July 4, 2018, in McAllen.
The game was sold out with an official attendance of 10,978, marking the fourth sellout in H-E-B Park history. Delia's Tamales even debuted a food truck for the occasion.
Banners and flags waved in the wind, mostly for América. One giant América flag that was unfurled at the start of the game covered almost two entire sections of seats on the east side of the stadium.
The first goal came when Perez charged out to challenge the run of América's Jeremy Menez. Perez's tackle tripped up Menez in the box, and the referee called for a penalty kick.
América striker Cecilio Dominguez took the PK and put it in the lower-left corner, beating Perez to give América a 1-0 lead at 32:44 of the first half.
When the goal went in, the largely pro-América crowd lost its collective mind. Cheers echoed throughout the packed stadium.
América's second goal took the skill level up a notch, as Dominguez played a little give-and-go with Jorge Sanchez. The key to the play was a nasty back-heel flick by Sanchez that found Dominguez alone in the box. From there, Dominguez just needed a deft touch to tuck the ball inside the right post, past Perez.
In the 49th minute, Oswaldo Leon received a picturesque pass from Dominguez at the top of the box and put the ball home past Pachuca keeper Alfonso Blanco, who was subbed on for Perez at the half.
Raul Lopez got Pachuca on the board in the 59th minute. He hit a curling shot from about 22 yards out that went across the mouth of the goal and into the upper-left corner, beating Oscar Jimenez, who was subbed on as keeper for América at halftime.
About 10 minutes later, Juan Lopez capitalized on a giveaway by América to cut the lead to 3-2. Lopez got the ball in the box and buried a shot to the right of Jimenez.
Drama started early, as Perez had to charge out of his box seven minutes in to challenge the run made by América's Dominguez. Dominguez got the ball through and into the goal, but just as the stadium began to erupt, the sight of the official holding up his offside flag quieted the crowd.
In the 23rd minute, América first-team goalie Agustin Marchesin faced a tough shot in the box from the Pachuca attack, but he stuck out his right foot and got a toe on the low shot to keep the game scoreless.
A minute later, Menez missed a shot just to the left of Perez on the counterattack.
Both teams are preparing for the start of the season later in July and will begin the Copa MX at the start of August.


By a weird stroke of luck or God's planning, I'm enrolled in three different Bible studies right now. For 15 years I've been attending and serving in a women's class, and in that class we are studying the Book of Romans this year.
In addition, I'm helping out in the high school Bible study that the Apple of my Eye teaches. So I'm doing those lessons too.
Now, you'd think ~ and so would I ~ that that would be a pretty full schedule, what with also being a mom and a wife and a teacher… But then I got an invitation from a friend. She and another friend were getting a group together to do a small group Bible study on the Book of Revelation. She's a great one to study the Bible with, and Revelation is such an important book to study, that I didn't want to say no. So I prayed and talked to my hubby and we decided that I should give it a try, and I could always drop it if it was too much for my schedule. And I've missed a few weeks, but I've managed to be there most of the time.
So I'm studying Romans and Matthew and Revelation. And though it's tough to get all the homework done every week, I'm enjoying the learning.
And my favorite part about the combined studying is that almost every week, I realize that I'm studying something in one of the books that coincides with something I'm studying in one of the other books. For instance, one week we studied Matthew 8 and the account of Jesus healing a demon-possessed man. The demons recognized Jesus, and begged Him not to send them into the abyss. That same week, we were studying Revelation 9, and reading about that abyss. Nice "coincidence," eh?
I knew that would happen… that my learning would collide with my other learning, but I still love it when it happens, because often I'm feeling that one book is enlightened by another.
This week was an especially good one. Chapter 11 of Matthew, verses 28-30: "Come to me, all you who labor and are heavy-laden, and I will give you rest. Take My yoke upon you and learn from Me, for I am gentle and lowly in heart, and you will find rest for your souls. For My yoke is easy and My burden is light." It's a classic, isn't it? I mean, who doesn't love the idea of rest, and as easy burden? And I've always read that verse in a sort of overview way. That life with Jesus is not as hard as life without Him. Which is true, and a beautiful, reassuring truth.
But my study in Romans has shed amazing new light on that verse. We are several chapter into Romans, and the most important point of Paul's letter is that salvation comes through God's grace, not by our obedience to the law. Which ~ think about it ~ is awesome news because we simply cannot succeed in obeying the law. But through our faith in Jesus as Savior, we are credited with righteousness. That's grace: salvation in exchange for faith.
And that, my friends, is an easy yoke.


I hope all of our readers had a great time on Superbowl Sunday. I sure did. One of my friends had invited me to a Superbowl party but got sick at the last minute. So after he called, I contacted Susan Dean and she and I went to the Coachman's Inn, 3240 S. Eastern Ave. The new owners, longtime local attorney Bucky Buchanan and Chip Lightman were putting on a great party with a free barbeque buffet and free beer.
We were greeted by the manager, Ray Mackaly who gave us raffle tickets and showed us to one of the last remaining tables. I won the second raffle held just after the game started and my prizes were a Superbowl XL11 t-shirt and a program. Later on Susan also won a t-shirt. We got completely full with one trip through the buffet. They even came to the tables with more beer in pitchers for those who wanted it. Of course their usual bill of fare was also available.
Everyone who's ever been to the Coachman's knows the food, service and ambiance are great and that continues. Bucky made the rounds visiting everyone and manager Ray cracked jokes keeping us happy and comfortable. We watched the game on several TV's placed strategically for the viewing pleasure of all.
We got to meet several new people and told them about Very Vintage Vegas and our blog site. I'm sending out a special hello to Christine Nichols, a terrific young girl we met who was there with her friend, Daniel. We all enjoyed the game and the half-time entertainment with Tom Petty. Most of us were happy that the Giants won.


It's too hot for a walk. This is an objective truth.
Bruiser's desire for a walk literally makes him blurry.
But Bruiser is quite clear that he needs a walk. At 10 p.m., the time is now. We held him off for maybe 45 minutes after that, just to let the temperature fall a few more degrees.
there's AC in that house. just fyi.
So we gather our character and our fortitude and the leash.
We take a short route. Bruiser isn't as young as he once was (unlike the rest of us, whose youth springs eternal, although a mite grouchy with the heat, not that anyone's counting).
the moon, acting predictably beautiful.
Full, or almost full, moon--it's a beautiful night. Point conceded, Bruiser. Now let's get in there and make the most of that swamp cooler.


Rosie James is a textile artist from Rochester, England. She captures human nature using machine and hand embroidery.
Rosie has a terrific eye for detail and her technical skill in recreating human behaviour in stitched form is remarkable, catching snapshots of people on the move, and minding their own business.
Her pieces are large, often over a metre in length, and combined different textiles with the stitching to create depth and interest. She leaves messages and hints about the subjects of pieces throughout her work, allowing you to uncover more narrative as you explore.
I've seen the piece above for real, and you have to take your time to take it all in. It's great work to look at and reflects a true affection for human nature.
As well creating these fantastic works, Rosie is heavily involved in teaching textiles and is one of the artists in residence at the University for the Creative Arts in Farnham. If you are interested in seeing some of Rosie's work, UCA Farnham have an exhibition running from 15th to 21st May featuring the work of all their AIRs. Rosie will be featuring some new works that move away from crowds into a different space – you'll have to go to the show to see what I'm talking about!
That is breathtaking. I wish I could go to the show and see it for real.
Boo! Rosie lives in the town next to me and I really wanted to go see her work, but I'm out of the country by then. I will just have to enjoy her work online instead!
She really can draw with thread exceptionally well.
absolutely breathtaking. totally experiencing mixed feelings of awe and envy right now!
I have just come back from the show in Paris at Bibliothèque Forney, and have discovered Rosie Jame's work and want to express my admiration. I do love it.


text:
Q: Why user defined exception classes are preferred/important in java? When we have System defined exception classes in Java, then why there is need to make user defined exception classes? Because my teacher told me to make Exception classes in my project.
Any elaborate example will be good.

A: User defined exceptions can be much more descriptive.
Example :
public void setName (String name)
    throws NameException
{
    if (name == null || name == "")
        throw new MissingNameException ();
    if (name.length > 30)
        throw new InvalidNameException (name); // this exception can contain a message that explains why the
                                               // name is invalid and what a valid name looks like
    _name = name;
}

The alternative to creating your own exception is to use a predefined exception such as InvalidArgumentException, but that would give less information to the method that catches this exception (since it can be thrown by many methods in many classes).
Defining a class hierarchy of exceptions gives the user of your classes better control when handling the exceptions.

A: Because the existing exceptions may not be suitable for your situation. By creating your own exception you can make it easier to understand why the exception is thrown and it will stand out better than if you were using an existing exception.

A: There are a few reasons to have user defined exceptions:

*

*You want to pass along extra information such as error codes.  For example, if you are a database vendor, you can add extra methods to include your internal error codes.

*To add more specific Exception types so you don't need to rely on parsing the exception message which could change over time.  For example, Hibernate has ConstraintViolationException.  You know immediately that you have a problem with some kind of constraint on the database (probably foreign key constraint).

*You can handle different Exceptions differently with different catch blocks.

Having said that, in general you should prefer to use the built in Exceptions or at least have your custom Exceptions extend the built-ins so users don't have to care about your special classes unless they want to.
For more info about why you should prefer the built-in exceptions see Effective Java Item 60: Favor the use of standard exceptions

redpajama_set_name: RedPajamaStackExchange


Nollywood film star Ebube Nwagbo is officially out with her new clothing line named Poshedupbyeb. The actress in her excitement shared her journey on this development on social media.
Ebube also says she's been working on the project for over a year and the clothes are inspired by her fashion sense and accessories.


Raise your hand if you've been personally victimized by the MMFF! After the show-stopper that was Die Beautiful, starring beloved TEAM cover kween Paolo Ballesteros and his equally iconic co-star Christian Bables, we thought we had to say goodbye to the end of a very short era. Gone were the days of the kind of cinematic drama that set our hearts ablaze, and as funny as Vice Ganda movies happen to be every Christmas, we can't help but feel something missing.
While Die Beautiful's producer, Jun Robles, came through for our drag and trans sisters alike, Celeste Legaspi (Minsan ang Minahal ay Ako, Basta't Mahal Kita) has co-produced Ang Larawan, an adaptation of the Nick Joaquin stage play which definitely rings familiar to more musical homos. If the star-studded cast hasn't already gotten your attention with the likes of Rachel Alejandro and Joanna Ampil joining Celeste herself, we ought to mention that our favorite indie cutie, Paulo Avelino, sings his heart out in a loose tie as well.
Alejandro and Ampil play Paula and Candida Marasigan respectively, sisters on the verge of financial despair after their renowned father, Don Lorenzo Marasigan, has been ill for years, unable to create another masterpiece after the beloved larawan he bequeathed to the two daughters. They are then hounded by three separate parties to sell the piece in order to amass a fortune that will sustain them and their father for the rest of their lives. These include their older siblings, Pepang and Manolo (Nonie Buencamino and Menchu Lauchengco), a renowned senator named Don Perico (Robert Arevalo), and the vaudeville musician who wishes to come between them, Tony Javier (Paulo Avelino).
"Fate probably decides our lives, but Paula says no…for me parang woman empowerment nga eh," Rachel says of her character, alluding to her seeming lack of agency.
Ang Larawan does not only portray the lack of agency among the oppressed and the means necessary to reclaim this agency, but ultimately aims to exist as a means to showcase pure Filipino talent, independent difficulties notwithstanding. "Celeste is a perfectionist, so kapag may naririnig siyang off, oh. Stop agad. Alam niya kaagad eh," Paulo says of the film's co-producer and acting vocal coach, who oversaw every rehearsal and retake throughout the course of a year. In a genre known for intimate settings and strictly millennial motifs, Ang Larawan works within the format to its greatest capacity, and not just in spite of it. "Nagbukas din siya ng ibang mundo sa trabaho namin na hindi ko pa nata-tackle," Paulo adds, on the hopeful note that this could pave the way for more local movie musicals produced.
After all, who better to tell our stories than our artists themselves, representing Filipino struggles in mediums untapped by the artists of our past? "Unti-unti nating nabebenta ang ating kultura sa iba at hindi namin nakikilala kung sino talaga tayo," Paulo says, and perhaps he's right. But with the way Filipino indie films are coming along, we hope he won't be right for long.


text:
Articles Posted in Orthopedics & Sports Medicine
NFL settlements raise new questions on head trauma and Parkinson's and ALS
The National Football League, which long has resisted the growing reality that game-related head blows can cause major harms to its players, may be providing yet new and unintended warnings about the sustained damages of concussions.
The Los Angeles Times reported that pro football's pay-outs, as part of its billion-dollar head-injuries settlement with NFL players and their union, have been surprisingly high in cases where retirees have claimed damages due to Parkinson's and ALS.
Parkinson's, the newspaper noted, is a "progressive movement disorder that produces tremors, impaired movement, and slurred speech." It is "marked by the buildup of proteins called Lewy bodies in brain cells." ALS, also known as amyotrophic lateral sclerosis or Lou Gehrig's disease, is a condition affecting "nerve cells in the brain and spinal cord and ultimately results in a fatal inability to initiate and control muscle movement."
Posted in: Brain Injury, Communication, Disclosure, Medical Malpractice, Orthopedics & Sports Medicine, Preventive Care, Primary Care and Statistics
Updated: August 11, 2018 12:47 pm
As Big Pharma rivals battled to hype opioids, watchdogs dozed. They slumber still.
When Big Pharma pursues rapacious profits and regulators snooze, patients suffer terrible consequences, as new revelations about the opioid crisis show.
Kaiser Health News Service , via the Washington Post, and The New York Times both have done excellent investigative digging into drug makers' role in fueling the prescription painkiller mess that authorities estimate claims 116 lives a day due to overdoses.
Fred Schulte, writing for the independent, nonpartisan Kaiser service, reported that rival makers — seeing how much money Purdue Pharma was making with its powerful and addictive OxyContin drug and that it was encountering law enforcement and regulatory challenges — stepped in with "similarly dangerous painkillers, such as fentanyl, morphine and methadone."
Posted in: Accessibility of Healthcare, Addiction, Cancer, Clinical guidelines, Communication, Conflicts of Interest, Disclosure, Ethics, Medical Malpractice, Medications, Orthopedics & Sports Medicine, Pain, Preventive Care, Primary Care, Product Safety and Research Studies
Updated: August 4, 2018 3:36 pm
Wrestler scandal fuels rising concern on how schools, teams protect kids
Soon, many young people will be back to school and signing up for sports teams. Many will have to undergo physical exams before they can play. And it's a tragic reality that grown-ups may need to think a lot how to protect young people from sexual predators who also are doctors.
That's because Ohio State University, sadly, has joined the University of Southern California, Michigan State-USA Gymnastics, and Penn State University in the notoriety of dealing with a sexual abuse scandal involving adults and students. In the case of the Buckeyes, it's Big Ten male wrestlers.
OSU said it had hired an outside law firm to investigate the allegations against Richard Strauss, who had blue-chip credentials and served as the team doctor to university wrestlers roughly from 1979 to 1997. The doctor killed himself in 2005. Lawyers interviewed more than 200 one-time OSU students, with 100 of them accusing Strauss of sexual misconduct, "including former athletes from 14 different sports teams."
Posted in: Communication, Conflicts of Interest, Disclosure, Doctor-Patient Relationship, Ethics, Medical Malpractice, Orthopedics & Sports Medicine, Patient Privacy, Preventive Care and Primary Care
NHL sells its players short by failing to acknowledge harms of head blows
The 2018 Stanley Cup may rest for a bit as the pride and joy of enthusiasts in the nation's capital and of its title-winning team. But as fans of the pro and amateur game look to the future, they may have reason to be downcast about hockey's most important component: its players.
Author Ken Dryden (photo above, left) has important things to say about them, because he was a goaltender for the Montreal Canadiens and has been enshrined in the Hockey Hall of Fame. The onetime Canadian parliamentarian has pointed out that owners and bosses in the National Hockey League, as illustrated by videotapes of their sworn testimony in a long-running court case, are locking arms and taking a counter-factual position on the damages that players may suffer due to blows to the head they receive in games.
In "infuriating" fashion, NHL Commissioner Gary Bettman (photo above, right), Boston Bruins owner and chairman of the league's Board of Governors Jeremy Jacobs, other team owners, senior league executives and doctors are playing ostriches, Dryden wrote in a recent Op-Ed in the Washington Post. They're sticking their head in the sand, insisting that hockey has no issue at all with "chronic traumatic encephalopathy, or CTE. That's a degenerative brain disease that has been found in athletes including professional hockey and football players, as well as soldiers and others who have suffered repeated brain injuries. Symptoms of CTE include cognitive impairment, depression, emotional instability and suicidal thoughts."
Posted in: Brain Injury, Communication, Disclosure, Ethics, Medical Malpractice, Mental Disability, Orthopedics & Sports Medicine, Preventive Care and Primary Care
Let's bust this myth now: Don't expect insurers to help cut medical bills
Patients who expect their health insurer will work in their interests to contain costs by medical providers might just as well hope for assistance from leprechauns, unicorns, or the tooth fairy.
Marshall Allen, a reporter for the Pulitzer Prize-winning investigative site ProPublica, has just penned a strong myth-busting piece, cross-posted on the National Public Radio site, about health insurers, writing:
The United States spends more per person on health care than any other country does. A lot more. As a country, by many measures, we are not getting our money's worth. Tens of millions remain uninsured. And millions are in financial peril: About 1 in 5 is currently being pursued by a collection agency over medical debt. Health care costs repeatedly top the list of consumers' financial concerns. Experts frequently blame this on the high prices charged by doctors and hospitals. But less scrutinized is the role insurance companies — the middlemen between patients and those providers — play in boosting our health care tab. Widely perceived as fierce guardians of health care dollars, insurers, in many cases, aren't. In fact, they often agree to pay high prices, then, one way or another, pass those high prices on to patients — all while raking in healthy profits.
Posted in: Accessibility of Healthcare, Communication, Conflicts of Interest, Disclosure, Ethics, Hospitals, Insurance, Medical Device Safety, Medical Malpractice, Orthopedics & Sports Medicine, Preventive Care and Primary Care
Overrun by sports performance chatter? Can questions on MBI bust the bunk?
If you can get your favorite sports fans peeled away from the latest broadcast pro event ─ whether it's the basketball playoffs, hockey championship series, golf tourneys, or the heating up baseball season ─ a conversation of sorts could be sparked by dropping numbers on them. See what kind of rise you can get by telling them their data-driven obsession with improving their own athletic performance may be built on shoddy calculation.
In the "Moneyball," statistics' crazy world of contemporary sports and athletic fandom, that statement could be heretical. But the numbers-driven folks at the web site "528" deserve credit for digging into a popular but dubious approach employed by researchers in sports medical science: Magnitude-based inference, aka MBI. Their article's worth a read, especially for wonks and the numerically inclined. For those who are less so, here's a taste of what's at stake, as 528 reported:
At first blush, the studies look reasonable enough. Low-intensity stretching seems to reduce muscle soreness. Beta-alanine supplements may boost performance in water polo players. Isokinetic strength training could improve swing kinematics in golfers. Foam rollers can reduce muscle soreness after exercise. The problem: All of these studies shared a statistical analysis method unique to sports science. And that method is severely flawed.
Posted in: Accessibility of Healthcare, Advertising, Communication, Conflicts of Interest, Disclosure, Medical Error, Medical Malpractice, Misdiagnosis, Nutrition, Orthopedics & Sports Medicine, Preventive Care, Primary Care and Research Studies
Winter Games and Super Bowl offer health takeaways, too
The Winter Olympic Games and the Super Bowl can offer fans not just exciting sports spectacles but also important health insights and information— everything from the risks of viruses and the value of hand washing to the dangers of head blows and why Americans may be slowly changing their minds about how they feel about violent recreations.
Let's start with what can happen when you put more than 2,000 elite athletes from 92 nations in a village setting in Pyeongchang, South Korea. It's no surprise that contagious illnesses can break out, and in this case the noxious norovirus. More than 100 cases of the highly infectious viral illness at the Olympic site have been confirmed already, and 1,200 people — many of them security guards for the Games — have been quarantined with disease symptoms. (The South Korean military has sent in forces to assist with security, in place of the quarantined guards).
Norovirus, aka the winter vomiting disease, is a gastrointestinal bug with other symptoms including diarrhea, nausea, and stomach pain, according to the according to the CDC. Its symptoms typically start 12 to 48 hours after patients come in contact with the virus. Symptoms might also include headache and body aches. Fever is uncommon. The sickness is highly contagious, spreading when viral particles get aerosolized over large areas. Hygiene becomes key in outbreaks, as public health experts have emphasized and global cruise lines have discovered.
Posted in: Accessibility of Healthcare, Brain Injury, Communication, Infections, Medical Malpractice, Orthopedics & Sports Medicine, Preventive Care and Primary Care
Updated: February 11, 2018 8:52 am
Gymnastics scandal offers a tragic reminder of challenges in protecting kids
His basic credentials would come under fire, but they were sufficient for the "doctor" to insinuate himself into major institutions, and, worse, into the lives of hundreds of girls and young women on whom he inflicted a tragic toll. His combination of enthusiasm — he was a rah-rah kind of guy— extreme controlling conduct, and horrific "treatments" never seemed to set off the red flags they should have.
Instead, Larry Nassar — an osteopath who served as an athletics and team caregiver for USA Gymnastics and Michigan State University — got away for years with abusing adolescent females put under his sway. He purportedly provided medical services to them, many in exclusive and demanding athletic camps where young participants were cut off from their friends, family, coaches, and personal physicians. He "treated" aspiring Olympians, at all hours of the night and day, alone and without any other adults around, in their bedrooms, on their beds — not in medical offices or athletic training facilities.
He enthusiastically told his patients, many of whom excelled at their sport because of their willingness to please adults and to be coached, that he could deal with their pains and injuries with what he termed pelvic manipulations in which he digitally penetrated them in their private parts. Without medical cause or justification, he conducted repeated and invasive "exams" of girls and young women's genitals.
Posted in: Communication, Conflicts of Interest, Doctor-Patient Relationship, Ethics, Gynecology, Medical Malpractice and Orthopedics & Sports Medicine
Updated: January 30, 2018 8:05 pm
Repeated, lesser head hits, and not just concussions, linked to brain harms
As the science keeps getting deeper, the news keeps getting worse about the harms that can be inflicted by repeated blows to the head in sports — and in life.
The path-breaking medical scientists at Boston University and elsewhere, who have helped to establish how concussions, notably in football, may lead to the onset of the neurodegenerative disease chronic traumatic encephalopathy or CTE, have told the Washington Post that their latest study may show that, "It's really the hit that counts."
Posted in: Brain Injury, Clinical guidelines, Communication, Disclosure, Medical Malpractice, Mental Disability, Mental Illness, Orthopedics & Sports Medicine, Preventive Care, Product Safety and Research Studies
Safety and injury prevention should be part of our new year exercise plans
Millions of Americans may be hitting the gym as part of their new year resolve to get fitter. They also need to exercise caution and common sense to avoid injuries that could leave them in worse shape.
As the Washington Post reported, the 2018 health club crush will result in "hundreds of thousands of [exercisers] stumbling on treadmills, falling off exercise balls, getting snapped in the face by resistance bands, dropping weights on their toes and wrenching their backs by lifting too much weight."
Further, the newspaper added:
Posted in: Communication, Medical Malpractice, Nutrition, Orthopedics & Sports Medicine, Preventive Care, Primary Care, Product Safety, Self-care and Training
Updated: January 8, 2018 9:33 am