Sedatech UC03109F1I8HE Mini Evolution Passive Cooling Desktop PC
CPU Intel Core i7-4785T 4×2.2Ghz (max 3.2Ghz) Asus Rock motherboard Haswell Generation low power Intel i7 processor (TDP 35W) with Turbo Boost Technology (up to 3.2Ghz) 120Gb SSD Drive Completely silent fanless case which looks like an unbadged Akasa Euler Mini ITX Case Low power consumption and small dimensions (W x H x D): 22,8 x 6,15 x 18,7 With Windows 8.1 64-bit EN (DVD + booklet + activation key) plus drivers disc
Perfect silent HTPC
Wiped clean with latest Ubuntu 19.10 but it does come with the original Windows 8.1 cd and key
Todd Whitney is a podcast producer, editor, and sound designer exploring audio in a variety of spaces. Since 2012, he’s produced stories for NPR’s Tell Me More with Michel Martin and reported on waste, cultures, and change for KALW’s Crosscurrents. He’s also directed audio fiction and produced food travelogues for Audible Originals. Beyond the podcast space, he’s an avid hacker-maker, whose current work centers around building soil moisture sensors.
Imagine losing your child in their first year of life and having no idea what caused it. This is the heartbreaking reality for thousands of families each year who lose a child to Sudden Unexpected Infant Death (SUID). Despite decades-long efforts to prevent SUID, it remains the leading cause of death for children between one month and one year of age in developed nations. In the U.S. alone, 3,600 children die unexpectedly of SUID each year.
For years, researchers hypothesized that infants who died due to SUID in the earliest stages of the life differed from those dying of SUID later. Now, for the first time, we know, thanks to the single largest study ever undertaken on the subject, this is statistically the case.
Working in collaboration with Tatiana Anderson and Jan-Marino Ramirez at Seattle Children’s Research Institute and Edwin Mitchell at University of Auckland, we analyzed the Center for Disease Control (CDC) data on every child born in the U.S. over a decade, including over 41 million births and 37,000 SUID deaths. We compared all possible groups by the age at the time of death to understand if these populations were different.”
In our study published today in Pediatrics, a leading pediatric journal, we found that SUID deaths during the first week of life, were statistically different from all other SUID deaths that occur between the first week and first year of life. SUID cases in the first week of life have been called SUEND, which stands for Sudden Unexpected Early Neonatal Death. We have called SUID deaths between 7-364 days postperinatal SUID.
The two groups – SUEND and postperinatal SUID – differed by several factors such as birth order, maternal age and marital status. For postperinatal deaths, the risk of SUID progressively while the opposite was true for SUEND deaths where firstborn children were more at risk. Postperinatal SUID rates were higher for unmarried, young mothers (between 15-24 years old) at birth, while unmarried, young mothers of the same age showed a decreased risk of SUEND death. The two groups also had different distributions of birthweight and pregnancy length.
Our study concluded that SUID deaths in the first week differed from postperinatal SUID deaths and that the two groups should be considered separately in future research. Considering these two as different causes may help uncover independent underlying physiological mechanisms and/or genetic factors.
This research is part of Microsoft’s AI for Good initiative, a $125 million five-year program where we utilize AI to help tackle some of the world’s greatest challenges and helping some of the world’s most vulnerable populations. For this research, we leveraged our machine learning, cloud-computing capabilities and advanced modelling techniques powered by AI to analyze the data.
By pairing our capabilities and data scientists with Seattle Children’s medical research expertise, we’re continuing to make progress on identifying the cause of SUID. Earlier this year, we published a study that estimated approximately 22% of SUID deaths in the U.S. were attributable to maternal cigarette-smoking during pregnancy, giving us further evidence that, through our collaboration with experts in varying disciplines, we are getting to the root of this problem and making remarkable advances.
We hope our progress in piecing together the SUID puzzle ultimately saves lives, and gives parents and researchers hope for the future.
I’ve got a Vaio Z56VRG, C2D T9900, 8GB RAM, 256GB SSD, 1600×900 IPS screen. Windows 10 Pro which runs decently well on that machine, clean of all the unnecessary stuff, will all the Vaio utilities installed. Ready to roll, was surprised how easy it was to get all the bits working despite the venerable age.
Looking for £80-100
EDIT: battery is also fine, I bet you can get 3-4 hours out of it. Rated for 59mWh, but I haven’t done any battery tests on it. It does hold charge when I unplugged it
Two very reasonably priced (ie cheap) laptops. Identical spec
HP-Probook 6470b laptops Intel i5 CPU 4GB RAM (plus spare SODIMM slot) 500GB hard disk (one has Hitachi, one has a Seagate) Intel mobile graphics 3 x USB ports eSATA DisplayPort, VGA out LAN port, modem port, wifi (centrino chipset) Optical drive (DVD) and SD slot 2 x PSU (1 ea)
Installed Ubuntu 19.10 with latest updates as of today Condition: Used, ex-Corporate laptops. I’d describe as B grade – your milage mary vary. No dead pixels. One has a couple of slight dents in lid from where I carted it about as a pentest machine. Batteries fully charged. @gibbo52 has first refusal
To learn more about WebViews, how they work, and more about options like Evergreen (WebView content is rendered by the Microsoft Edge browser instance on the user’s computer) vs. Bring Your Own (WebView content is rendered by a separate instance of the Microsoft Edge browser downloaded with the application) check out our developer documentation.
WebView2 API Sample
Recently, we built and launched a sample application (we call it WebView2 API Sample) using the WebView2 APIs to create an interactive application that demonstrates WebView2’s functionalities. The WebView2 API Sample is intended to be the most comprehensive guide available and will be updated regularly as we add more features to our SDK.
You can build and play around with the WebView2 API Sample by downloading or cloning it from our WebView2 Samples repository. To learn more about the sample’s source code and functionality, read our WebView2 API Sample guide. As you develop your own applications, we recommend referencing the source code for suggested API patterns for WebView2 workflows.
Build your own WebView2 application
You can learn more about WebView2 through our documentation, get started using our getting-started guide, and checkout more examples in our samples repository.
Tell us what you plan to build with WebView2 and please reach out with any thoughts or feedback through our feedback repo.
– Palak Goel, Program Manager, WebView
There are 79 million businesses worldwide who meet the “small or medium business” (SMB) definition of having 300 or fewer employees, and those businesses represent 95 percent of all the companies on earth—which amounts to a staggering 63 percent of the world’s workforce. As gigantic as those figures might be, they’re belied by other numbers that cast a shadow across worldwide employment: Last year, 55 percent of SMBs weathered cyberattacks, 52 percent of these breaches were caused by human error, and, in a quarter of these cases, sensitive customer data was breached. The average cyberattack will cost an SMB U.S. $190,000 and, after a ransomware attack, only one-third of SMBs can remain profitable.
This year, these numbers will only increase because 90 percent of SMBs do not currently have any data protection.
In an era where nearly every company is, in some regard, a technology company, the upcoming end of support for Windows 7 on January 14, 2020 only adds to the pressure on these businesses.
We considered our responsibility to this community
During my keynote at Microsoft Ignite, I spoke at length about the challenges associated with app compatibility, and I shared how Microsoft has taken on a responsibility for compatibility. The reasoning behind this is simple: Among other reasons, when more organizations are operating modern infrastructures, it’s much simpler to keep attacks from spreading throughout the world. Similarly, as my team looked at the needs of the SMB community, we considered our responsibility to their security posture. After some analysis, we discovered a way to help them that didn’t exist within the enterprise offering of Microsoft 365 (a product we had fine-tuned to the needs of large companies).
The answer was Microsoft 365 Business, and I believe it offers SMBs the best possible opportunity to be secure and productive at the lowest possible cost. Microsoft 365 Business offers the same security tools used by many banks, governments, and multi-national corporations, as well as the very same productivity tools in Office 365.
Recently, we’ve undertaken an effort to think and talk about this topic differently.
While many SMBs don’t have the resources to hire a Chief Security Officer (CSO) of their own, I think this community can use Microsoft 365 Business like a CSO. I encourage you to spend a few minutes at YourNewCSO to learn how to use these resources right away. No matter where you are on your security journey, the site and these eight quick (and funny?) videos will show you steps to better secure your business.
Our data clearly demonstrates that combining security with a huge boost in productivity is the type of innovation that will set an SMB apart in a competitive environment. A recent study of two customers by Qualtrics found that employees using modern tools were 50 percent more likely to say they could better serve their customers, and they were 121 percent more likely to feel valued by their company—a sentiment that is directly tied to improved productivity, loyalty, and a positive organizational culture.
Fully use what you already have
Rather than simply try to sell something throughout this post, I’d like to point out some ways SMBs who already own Office 365 can improve their security without spending any additional money. Included below are seven steps to improve your security at no extra cost—you can also read how to do it or watch this quick overview.
Store files in OneDrive for Business, and the cloud becomes your backup. No more manual PC backups, which saves you time and money. Even better, if you are hacked and are regularly saving your documents to OneDrive, you can simply revert your files back to before the hack occurred.
Stop email auto-forwarding.
As we found from talking with hundreds of SMBs, creating a culture of security is one of the biggest first steps you can take. Right now is the time to educate your employees about how to identify security threats (e.g., don’t click that suspicious link, and if you do, please let someone know), and with Windows 7 very quickly reaching end of support, use this as an opportunity to move to our best available, most secure platform. Microsoft 365 Business can help.
Office 365 security tips
Seven security features in Office 365 you can use to secure your organization.
Why move from Office 365 to Microsoft 365 Business
Office 365 provides the suite of productivity tools you know and love, including capabilities like Exchange Online, SharePoint Online, and OneDrive for Business. But when you move to Microsoft 365 Business, you get that power of Office 365 as well as a comprehensive, cloud-based security solution that lets you defend your business against advanced threats. Microsoft 365 helps you to protect against cyberthreats with sophisticated phishing and ransomware protection; lets you control access to sensitive information, using encryption to keep data from being accidentally shared with someone not authorized to see it; and enables you to secure the devices that connect to your data, helping keep iOS, Android, Windows, and Mac devices secure and up-to-date. Microsoft 365 Business is fully integrated with Office 365, so you have one place for administration, billing, and 24×7 support.
In addition to visiting YourNewCSO, consider the value of insuring yourself against a cyberattack. I’m excited to announce that, starting today, we’re piloting a new program in the U.S. in collaboration with AXA XL (a global insurer) and Slice Labs (on-demand insurance platform) to offer a free cybersecurity health check and support AXA XL’s provision of cyber insurance for qualified customers that use Microsoft 365 Business, Office 365 Business, and Office 365 Business Premium.
With your permission, AXA XL will assess your organization’s security and offer their services to qualifying customers, potentially with a discount. You can find more information about the collaboration in the AXA XL and Slice Labs press release, and you can read more about their offering and purchase insurance.
It’s one thing for a Microsoft researcher to use all the available bells and whistles, plus Azure’s powerful computing infrastructure, to develop an AI-based machine translation model that can perform as well as a person on a narrow research benchmark with lots of data. It’s quite another to make that model work in a commercial product.
To tackle the human parity challenge, three research teams used deep neural networks and applied other cutting-edge training techniques that mimic the way people might approach a problem to provide more fluent and accurate translations. Those included translating sentences back and forth between English and Chinese and comparing results, as well as repeating the same translation over and over until its quality improves.
“In the beginning, we were not taking into account whether this technology was shippable as a product. We were just asking ourselves if we took everything in the kitchen sink and threw it at the problem, how good could it get?” Menezes said. “So we came up with this research system that was very big, very slow and very expensive just to push the limits of achieving human parity.”
“Since then, our goal has been to figure out how we can bring this level of quality — or as close to this level of quality as possible — into our production API,” Menezes said.
Someone using Microsoft Translator types in a sentence and expects a translation in milliseconds, Menezes said. So the team needed to figure out how to make its big, complicated research model much leaner and faster. But as they were working to shrink the research system algorithmically, they also had to broaden its reach exponentially — not just training it on news articles but on anything from handbooks and recipes to encyclopedia entries.
To accomplish this, the team employed a technique called knowledge distillation, which involves creating a lightweight “student” model that learns from translations generated by the “teacher” model with all the bells and whistles, rather than the massive amounts of raw parallel data that machine translation systems are generally trained on. The goal is to engineer the student model to be much faster and less complex than its teacher, while still retaining most of the quality.
In one example, the team found that the student model could use a simplified decoding algorithm to select the best translated word at each step, rather than the usual method of searching through a huge space of possible translations.
The researchers also developed a different approach to dual learning, which takes advantage of “round trip” translation checks. For example, if a person learning Japanese wants to check and see if a letter she wrote to an overseas friend is accurate, she might run the letter back through an English translator to see if it makes sense. Machine learning algorithms can also learn from this approach.
In the research model, the team used dual learning to improve the model’s output. In the production model, the team used dual learning to clean the data that the student learned from, essentially throwing out sentence pairs that represented inaccurate or confusing translations, Menezes said. That preserved a lot of the technique’s benefit without requiring as much computing.
With lots of trial and error and engineering, the team developed a recipe that allowed the machine translation student model — which is simple enough to operate in a cloud API — to deliver real-time results that are nearly as accurate as the more complex teacher, Menezes said.
Improving search with multi-task learning
In the rapidly evolving AI landscape, where new language understanding models are constantly introduced and improved upon by others in the research community, Bing’s search experts are always on the hunt for new and promising techniques. Unlike the old days, in which people might type in a keyword and click through a list of links to get to the information they’re looking for, users today increasingly search by asking a question — “How much would the Mona Lisa cost?” or “Which spider bites are dangerous?” — and expect the answer to bubble up to the top.
“This is really about giving the customers the right information and saving them time,” said Rangan Majumder, partner group program manager of search and AI in Bing. “We are expected to do the work on their behalf by picking the most authoritative websites and extracting the parts of the website that actually shows the answer to their question.”
To do this, not only does an AI model have to pick the most trustworthy documents, but it also has to develop an understanding of the content within each document, which requires proficiency in any number of language understanding tasks.
Last June, Microsoft researchers were the first to develop a machine learning model that surpassed the estimate for human performance on the General Language Understanding Evaluation (GLUE) benchmark, which measures mastery of nine different language understanding tasks ranging from sentiment analysis to text similarity and question answering. Their Multi-Task Deep Neural Network (MT-DNN) solution employed both knowledge distillation and multi-task learning, which allows the same model to train on and learn from multiple tasks at once and to apply knowledge gained in one area to others.
Bing’s experts this fall incorporated core principles from that research into their own machine learning model, which they estimate has improved answers in up to 26 percent of all questions sent to Bing in English markets. It also improved caption generation — or the links and descriptions lower down on the page — in 20 percent of those queries. Multi-task deep learning led to some of the largest improvements in Bing question answering and captions, which have traditionally been done independently, by using a single model to perform both.
For instance, the new model can answer the question “How much does the Mona Lisa cost?” with a bolded numerical estimate: $830 million. In the answer below, it first has to know that the word cost is looking for a number, but it also has to understand the context within the answer to pick today’s estimate over the older value of $100 million in 1962. Through multi-task training, the Bing team built a single model that selects the best answer, whether it should trigger and which exact words to bold.
Earlier this year, Bing engineers open sourced their code to pretrain large language representations on Azure. Building on that same code, Bing engineers working on Project Turing developed their own neural language representation, a general language understanding model that is pretrained to understand key principles of language and is reusable for other downstream tasks. It masters these by learning how to fill in the blanks when words are removed from sentences, similar to the popular children’s game Mad Libs.
You take a Wikipedia document, remove a phrase and the model has to learn to predict what phrase should go in the gap only by the words around it,” Majumder said. “And by doing that it’s learning about syntax, semantics and sometimes even knowledge. This approach blows other things out of the water because when you fine tune it for a specific task, it’s already learned a lot of the basic nuances about language.”
To teach the pretrained model how to tackle question answering and caption generation, the Bing team applied the multi-task learning approach developed by Microsoft Research to fine tune the model on multiple tasks at once. When a model learns something useful from one task, it can apply those learnings to the other areas, said Jianfeng Gao, partner research manager in the Deep Learning Group at Microsoft Research.
For example, he said, when a person learns to ride a bike, she has to master balance, which is also a useful skill in skiing. Relying on those lessons from bicycling can make it easier and faster to learn how to ski, as compared with someone who hasn’t had that experience, he said.
“In some sense, we’re borrowing from the way human beings work. As you accumulate more and more experience in life, when you face a new task you can draw from all the information you’ve learned in other situations and apply them,” Gao said.
Like the Microsoft Translator team, the Bing team also used knowledge distillation to convert their large and complex model into a leaner model that is fast and cost-effective enough to work in a commercial product.
And now, that same AI model working in Microsoft Search in Bing is being used to improve question answering when people search for information within their own company. If an employee types a question like “Can I bring a dog to work”? into the company’s intranet, the new model can recognize that a dog is a pet and pull up the company’s pet policy for that employee — even if the word dog never appears in that text. And it can surface a direct answer to the question.
“Just like we can get answers for Bing searches from the public web, we can use that same model to understand a question you might have sitting at your desk at work and read through your enterprise documents and give you the answer,” Majumder said.
Top image: Microsoft investments in natural language understanding research are improving the way Bing answers search questions like “How much does the Mona Lisa cost?” Image by Musée du Louvre/Wikimedia Commons.
Jennifer Langston writes about Microsoft research and innovation. Follow her onTwitter.
The condition is what I’d call well used but reasonable. There are scratches here and there, and there’s discolouration around the mouse pad. But apart from that, condition is pretty decent.
The charger is also temperamental. It works fine, but certain plug sockets or extension leads cause it to drop out. This could just be the electrics in my house though… But it’s something I’d make perfectly clear to any potential buyer beforehand.
Pictures attached. Would you still be interested given the above notes?