Referencias | References

Referencias completas de vocabulario, eventos, crónicas, evidencias y otros contenidos utilizados en los proyectos relacionados con biotecnología y neurociencia de la KW Foundation.

Full references of vocabulary, events, chronicles, evidences and other contents used in KW Projects related to biotechnology and neuroscience.

Navegue por el glosario usando este índice.

Especial | A | B | C | D | E | F | G | H | I | J | K | L | M | N | Ñ | O | P | Q | R | S | T | U | V | W | X | Y | Z | TODAS

Página: (Anterior)   1  2  3  4  5  6  7  8  9  10  ...  105  (Siguiente)


Logo KW

AI-powered Google services [1747]

de System Administrator - viernes, 7 de abril de 2017, 15:08

The phone personalizes the machine-learning model locally, based on how it is used (A). Many users' updates are aggregated (B) to form a consensus change (C) to the shared model, after which the procedure is repeated. Image: Google

Android más inteligente: los servicios de Google que funcionan con AI mejorarán a medida que los utilice

Smarter Android: AI-powered Google services will get better as you use them

By Nick Heath

The tech giant is testing whether its mobile services could use an approach called Federated Learning to improve their machine-learning models.

Google is introducing a new way for its AI-powered services to improve as people use them.

The tech giant is testing whether its mobile services could use an approach called Federated Learning to refine their underlying machine-learning models.

For each Google service, a machine-learning model is downloaded to a mobile device. Federated Learning allows these models to improve by learning from data on the phone, and then to summarize any local changes as a small update. This update is then encrypted and sent back to the Google cloud, where it is averaged with other user updates to improve the shared backend model.

The continual refinement of the machine learning model stored on the phone benefits the end user, as improvements no longer depend solely on the improved machine learning models being downloaded to their phone.

Google says the approach also has the advantage of improving privacy, as all the training data remains on the device, and no individual updates are stored in the cloud. Updates will only be decrypted and averaged with those from other phones once hundreds or thousands of similar updates have been gathered.

"Federated Learning allows for smarter models, lower latency, and less power consumption, all while ensuring privacy," Google research scientists Brendan McMahan and Daniel Ramage said in a blog post.

"And this approach has another immediate benefit: in addition to providing an update to the shared model, the improved model on your phone can also be used immediately, powering experiences personalized by the way you use your phone."

Google is testing the Federated Learning approach in Gboard, a keyboard for Android handsets. In this instance, the machine learning model will remember which suggested inputs and information the user clicked on and use that data to improve future suggestions.

The blog post goes into some detail about the complexity of introducing the Federated Learning approach, including mentioning that the on-device training uses a miniature version of TensorFlow, Google's open-source software library for machine learning.

Google says that the Federated Learning approach can't be used to help solve every machine learning challenge, with exceptions including using labelled images to teach a machine to recognize the breed of dog in a photo.

In a somewhat similar move, last year Apple said it would approach machine learning in a way that respects personal data, by using what it called 'differential privacy'. This approach allows it to analyze customer data for trends without being able to identify any particular individuals: for example, to be able to spot trending words that need to be added to the QuickType keyboard suggestions.

Logo KW

Airbus Swears Its Pod/Car/Drone Is a Serious Idea Definitely [1734]

de System Administrator - viernes, 24 de marzo de 2017, 21:51


Airbus jura que su Pod/Car/Drone es una idea seria y definitiva

Airbus Swears Its Pod/Car/Drone Is a Serious Idea Definitely


AIR TRAVEL NEVER involves air travel alone. City to city transport usually goes something like car or cab to train to shuttle to the terminal where you catch a plane, only to reverse the process at the other end, often with a little running somewhere along the line for good measure. Airbus came up with a crazy idea to change all of that with Pop.Up, a conceptual two-passenger pod that clips to a set of wheels, hangs under a quadcopter, links with others to create a train, and even zips through a hyperloop tube.

This crazy concept blurs the once-firm lines between planes, trains, and automobiles to let people take to the skies when traffic backs up. In other words, it’s a flying car. A really cool flying car, cooked up with help from Italdesign and unveiled at the Geneva Motor Show, but still, a flying car. Farfetched, yes, but Airbus says it is taking the idea seriously. “Adding the third dimension to seamless multi-modal transportation networks will, without a doubt, improve the way we live, and how we get from A to B,” said Mathias Thomsen, general manager for urban air mobility at Airbus.

Around town, the carbon fiber pod couples with an electric ground module and rolls along on a rather conventional four wheels. It’s autonomous, of course, because everything is in the future. Don’t want to creep along in gridlocked traffic? Simply summon an eight-rotor air module that resembles a supersized consumer drone. Clip in, take off, and enjoy a range that, should this technology ever actually work, will max out at about 60 miles. Whatever mode to choose, the Pop.Up parks itself at a recharging station upon arrival.

It all sounds crazy, but some big names see it happening. Dubai, the most superlative of emirates, plans to put people-carrying drones in service later this year. Last year, Uber said it could launch a flying car service within a decade. Such things will help wealthy (and brave) commuters skip over traffic, but don’t solve the last mile problem unless your destination boasts a rooftop landing pad. That’s where the Pop.Up idea enjoys an edge. It eliminates a key point of friction in multi-modal transportation: changing from one mode to the next. You just chill out in your pod.

Wanna hear the really crazy part? This scheme isn’t as far-fetched as you might think.

“This is getting to be less of a technology challenge,” says Pat Anderson, who is developing similar vehicle concepts at Embry Riddle Aeronautical University. He believes autonomous software and electric propulsion will subject the aviation industry to radical changes. Say goodbye to wings on tubes, at least for short distances: batteries and rotors will win the day. That doesn’t mean there aren’t hurdles, not the least of which are federal regulations.

“We designed these regulations in the 50s and 60s, and they go largely unchanged, due to inertia,” Anderson says. The General Aviation Manufacturers Association is pushing hard to move certification of novel planes away from the current fixed federal standards, and into a less prescriptive model. That will mean new ideas can be approved more quickly, similar to the way that safety devices in cars are. The FAA has already gotten on board with the changes, for small aircraft, but designing and applying rules for an entirely new class of aircraft will take years.

As humans pack into increasingly dense global mega-cities, they’ll need new ideas for transport to avoid gridlock. But Airbus and Italdesign haven’t committed to any sort of timeframe for this one, so (unless you live in Dubai) gazing up longingly through your car’s sunroof is as close to zooming through the sacré bleu sky as you’re likely to get, for now.


Logo KW

Alan Parsons [278]

de System Administrator - lunes, 13 de enero de 2014, 20:04


Alan Parsons (Londres, 20 de diciembre de 1948) es ingeniero de sonido, productor, compositor, multi instrumentista e intérprete inglés.


Logo KW

Alcanzar los objetivos [516]

de System Administrator - sábado, 12 de julio de 2014, 16:55

Alcanzar los objetivos


La vida, son objetivos. Cuando los agotas, mueres. Si quieres tener una vida prolongada y estimulante deberás distribuir objetivos a lo largo de todo tu proyecto vital o correrás el riesgo de agotarlos demasiado temprano. Puedes imaginar estos objetivos como una riqueza que todos tenemos al nacer. Esta riqueza puedes distribuirla a lo largo de tu vida o derrocharla en los primeros años y agotarla. Si tienes demasiada prisa por conseguir un objetivo o si tratas de alcanzarlo con atajos, tal vez lo alcances pero no sabrás valorarlo, te pondrás metas cada vez más ambiciosas porque ninguna te llenará y derrocharás tu vida buscándole sentido y desearás que todo acabe. Y ello será porque te has dejado lo mejor en el camino, que es disfrutar con cada pequeño paso alcanzado. Con cada pequeño objetivo logrado.

Logo KW

Alexander Graham Bell [61]

de System Administrator - lunes, 3 de febrero de 2014, 16:54


Alexander Graham Bell (Edimburgo, Escocia, Reino Unido, 3 de marzo de 1847 – Beinn Bhreagh, Canadá, 2 de agosto de 1922) fue un científico e inventor británico. Contribuyó al desarrollo de las telecomunicaciones y la tecnología de la aviación. Padre, abuelo y hermano estuvieron asociados con el trabajo en locución y discurso (su madre y su esposa eran sordas), lo que influyó profundamente en el trabajo de Bell (investigación en la escucha y el habla). Esto le movió a experimentar con aparatos para el oído. Sus investigaciones le llevaron a intentar conseguir la patente del teléfono en América, obteniéndola en 1876, aunque el aparato ya había sido desarrollado anteriormente por el italiano Antonio Meucci, siendo éste reconocido como su inventor el 11 de junio de 2002.


Logo KW


de System Administrator - martes, 29 de julio de 2014, 21:11



Even before birth, concerned parents often fret over the possibility that their children may have underlying medical issues. Chief among these worries are rare genetic conditions that can drastically shape the course and reduce the quality of their lives. While progress is being made in genetic testing, diagnosis of many conditions occurs only after symptoms manifest, usually to the shock of the family.

A new algorithm, however, is attempting to identify specific syndromes much sooner by screening photos for characteristic facial features associated with specific genetic conditions, such as Down’s syndrome, Progeria, and Fragile X syndrome.

Researchers at the University of Oxford utilized machine learning to train the facial recognition software to identify features from a database of 2,878 images acquired via the web. Of these, 1,363 photos were of individuals with eight known developmental disorders while the more than 1,500 other images were of controls. The software maps faces and account for lighting, image quality, and other factors, just as many other photo applications utilized by Google and Facebook can. Individuals with similar traits are clustered together and the software improves as more individuals with related traits are identified.


The work was published in the journal eLife.

“A diagnosis of a rare genetic disorder can be a very important step. It can provide parents with some certainty and help with genetic counseling on risks for other children or how likely a condition is to be passed on,’ stated lead researcher Christoffer Nellåker in the release. ‘A diagnosis can also improve estimates of how the disease might progress, or show which symptoms are caused by the genetic disorder and which are caused by other clinical issues that can be treated.”

It’s believed that 30-40% of rare genetic disorders, which may affect up to 1 in 17 people, involve facial phenotypes or changes to the skull or face, making this a promising approach for detection.

Nellåker added, “A doctor should in future, anywhere in the world, be able to take a smartphone picture of a patient and run the computer analysis to quickly find out which genetic disorder the person might have.”

Learn more about the research at University of Oxford news, “Computer-aided diagnosis of rare genetic disorders from family snaps” or read the study here.

[Image credits: University of Oxford, eLife]

This entry was posted in GeneticsLongevity And Health and tagged aifacial featuresfacial recognitiongenetic disordersmachine learning,University of Oxford.


Logo KW

Algoritmo [58]

de System Administrator - jueves, 2 de enero de 2014, 20:54

En matemáticas, lógica, ciencias de la computación y disciplinas relacionadas, un algoritmo (del griego y latín, dixit algorithmus y éste, a su vez, del matemático persa Al-Juarismi) es un conjunto preestablecido de instrucciones o reglas bien definidas, ordenadas y finitas que permite realizar una actividad mediante pasos sucesivos que no generen dudas a quien deba realizar dicha actividad. Dados un estado inicial y una entrada, siguiendo los pasos sucesivos se llega a un estado final y se obtiene una solución. Los algoritmos son el objeto de estudio de la algoritmia.


Logo KW

ALM SaaS tools and services [618]

de System Administrator - viernes, 1 de agosto de 2014, 21:24

Five hot ALM SaaS tools and services

Explore various ALM cloud offerings

by Amy Reichert

The move to provide application lifecycle management tools via software as a service is on. The number of tools is currently somewhat limited, but this area should expand in the near future.

Unlike traditional software purchases, obtaining application lifecycle management (ALM) tools via software as a service (SaaS) carries no licensing or maintenance fees. Instead, ALM SaaS users typically pay a subscription to the service. The standard subscription includes the cost of maintenance, upgrades and support. Additionally, the software is not taking up space on your network but is accessible via the cloud. The advantages of an ALM cloud model are generally reduced cost, remote management of software updates, up-to-date security measures, customizable features, accessibility from nearly anywhere with an Internet connection and fast installation.

Buying into SaaS does not free software developers from concerns about security, data storage and uptime. In a traditional ALM software model of purchase and install, the IT organization assumes the ongoing responsibilities for covering these concerns. With the cloud, the ALM service provider can be expected to meet the minimum requirements of theservice-level agreement (SLA). Still, developers will have to ensure that the SLA covers their needs and verify that the provider keeps their end of the deal.

Here are five ALM cloud options for companies considering this service.

Reduce the costs of project management with HP ALM SaaS


Hewlett Packard (HP) offers ALM SaaS both directly and through their partners.

HP's services include load, performance and functional test management software via theirQuality Center ALM tool. Subscribers receive access for a specific user level in multiple languages, support site access, secure sockets layer (SSL) data transmission, customization and migration support. There is no fixed time period or maintenance fee.

The benefits of HP ALM SaaS are reduced cost and maintenance for testing tools. There is also less hassle over who is responsible for keeping the system running. The testing tools are accessible from anywhere there is an Internet connection, so offshore and onshore teams use the same instance.

Gain accessibility and versatility with JIRA ALM SaaS

Atlassian offers JIRA services equipped with ALM SaaS.

JIRA ALM SaaS has several add-on tools, including Zephyr for test case management. SynapseRT is an add-on that includes traceability functionality for mapping requirements to test cases. Additionally, Behave for JIRA is useful for Agile teams that want to merge test management with user story or feature acceptance. With Behave, testers can use story acceptance criteria to create automated tests with Cucumber. Testers can choose to write either manual or automated tests.

The JIRA tools are accessible to the full development team, which may reduce the total number of tools the team ends up using. Test cases can be exported from JIRA and used with Cucumber directly if needed.

Collaborate and share with TOMOS


TOMOS claims to be the lightweight tool for ALM SaaS that allows fluid collaboration between the whole development team, including managers.

TOMOS is a centralized Web 2.0 application that is accessible with an Internet connection. All hosting, technical support and software improvements are included. The tool provides a large number of features like version and build management, test case development, test execution and defect management. The service also includes a collaboration space to store documents, requirements, screenshots or any other project-related data and metrics dashboards so management gets informative visual updates on project status. Furthermore, TOMOS includes customizable reports and a social network for users to further collaborate and exchange information.

Microsoft shops embrace Visual Studio Online


Microsoft Visual Studio Online is the cloud-based SaaS version of Visual Studio and Microsoft's ALM management tool team foundation server. It currently operates with two source control systems: Team Foundation Version Control and Git.

The SaaS ALM offering includes the most recent features, is updated on a continuous basis and is built on the Windows Azure cloud. Users can access the service with an existing Microsoft account. The cloud offering is missing functions that are part of the traditional purchase, including lab management, structure query language reporting, business intelligence and SharePoint. Microsoft's ALM SaaS offering does feature an automatically scalable build farm and scalable load testing servers. Updates to code are typically delivered on a three-week cycle. If the development team is already comfortable using Visual Studio and Team Foundation Service, it makes sense to move to the cloud version and avoid unnecessary disruption or the need for re-training.

Enjoy integrated management with TraceCloud's SaaS ALM


TraceCloud is an AWS-based ALM tool that includes an integrated management system for requirements, defects and testing. It features a built-in change control board for approval tracking and configurable metrics dashboards for management at the project, release, baseline, folder and user level.

Because TraceCloud is a SaaS plug-and-play RESTful Web service built in Javascript object notation, it integrates well with existing systems. As a SaaS ALM offering, support and maintenance are included in the subscription fee. TraceCloud offers security with 128-bit SSL, as well as encryption and scalability with Amazon's elastic cloud infrastructure.

More on SaaS:

Mobile applications: Testing and monitoring using SaaS solutions
Developing applications on SaaS platforms: Factors to consider





Logo KW

Alternative Data [1719]

de System Administrator - jueves, 23 de febrero de 2017, 23:40

Alternative Data

Alternative data is information gathered from non-traditional information sources. Analysis of alternative data can provide insights beyond that which an industry's regular data sources are capable of providing.

The question of what constitutes alternative data varies from industry to industry.  In banking, for example, a lender may traditionally rely on an applicant’s credit score to assess risk and determine the probability that a loan will be paid back. When the applicant has no prior credit history, however, alternative data that illustrates the applicant’s history of meeting financial obligations, such as paying a cell phone bill on time each month, can be useful information. A bank that includes alternative data sources in risk assessment for a loan may also factor the applicant’s history of paying rent on time and whether or not the applicant consistently makes more than the required minimum monthly payment on credit card bills.

In recent years, the increase of data from mobile devices, satellitessensors and websites has led to large amounts of structuredsemistructured and unstructured data, also known as big data.  All that data has the potential to be mined for information and potentially help people make better data-driven decisions. In response to the demand for alternative data, some traditional research firms have branched out to become alt-data providers, selling corporate clients data from non-traditional sources and services to analyze that data..

Once a data source begins to be used on a regular basis, it is no longer considered to be an alternative source. Current use cases for alternative data include:

  • Precision agriculture – farmers can analyze time-series images taken by drones to make more accurate predictions about crop yields.
  • Healthcare – public healthcare workers can mine social media for specific keywords to fine-tune initiatives that track the spread of influenza.
  • Investment firms – analysts can use satellite imagery to analyze the number of cars in a shopping mall parking lot and predict the state of a local economy.
  • Military – autonomous robots can be used as additional eyes and ears to help improve situational awareness.


A Taxonomy For Alternative Data


 We’re witnessing a data revolution. You all know by now that we’ve produced more data in the last 2 years than we have throughout all of humanity. And the pace is only increasing. It begs the question: how do you extract actionable trading signals from this brave new world of noise? It starts with understanding the landscape. Here is Quandl’s take on the taxonomy of alternative data for finance.



Logo KW

Amazon Auto Scaling [471]

de System Administrator - miércoles, 9 de abril de 2014, 16:55

Cloud computing is an exciting suite of technologies that has come to dominate the discussion of computing for two main reasons:

• It can provide the flexibility to quickly deploy computing environments that are not only properly configured for current needs, but that can also expand or contract according to future needs.
• It can help an organization save money.

Cloud computing can deliver flexibility and cost savings because it is uniquely capable of being scaled to the “right size” for a particular environment, no matter how frequently usage of the environment expands or contracts. Taking advantage of this flexibility can be difficult; however, some companies try to do it manually, while others create a custom automated system. Either method has the potential of introducing new challenges into your environment:

• Depending on manual processes to start new servers, stop unneeded systems, or change allocated storage space is expensive, slow, and worst of all, error prone. Any savings from moving environments to the cloud can easily be erased by the costs of manual intervention.
• Creating an automated scaling system customized for your organization’s needs takes a long time, almost certainly costs more than planned for, and requires a risky test and deployment phase. Such a system also requires its own hardware, software, and support environment that scales itself for
expansion or contraction.

As with so many of its other services and products, Amazon Web Services (AWS) created Auto Scaling to solve its own scaling issues, and now provides the service to its customers for free. On its own, Auto Scaling monitors your environment to ensure that the desired systems stay running. What is even more powerful is that you can tie the Amazon CloudWatch monitoring service into Auto Scaling, which allows your environment to automatically scale up or down based on current conditions:

• As load increases, Amazon CloudWatch can call Auto Scaling to add new computing capacity.
• As load decreases, Amazon CloudWatch can trigger Auto Scaling to shed computing capacity and reduce cost.

This paper describes what Auto Scaling is, when to use it, and provides an example of setting up Auto Scaling.

Página: (Anterior)   1  2  3  4  5  6  7  8  9  10  ...  105  (Siguiente)