http://www.weeklystandard.com/blogs/war-al-qaeda-islamic-state-syria_806069.html

At War With Al Qaeda, Islamic State in Syria12:05 PM, SEP 23, 2014 • BY THOMAS JOSCELYN

Airstrikes in Syria for the first time overnight. Much of the public discourse in the weeks leading up to the bombings focused on the Islamic State, a former branch of al Qaeda that has captured a significant amount territory across both Iraq and Syria. But the bombings are not just intended to weaken the Islamic State. U.S. bombers are also targeting positions controlled by Jabhat al Nusrah, al Qaeda’s official branch in Syria. 

This is the right course for the Obama administration to pursue.

With its stunningly effective military tactics and mass killings, the Islamic State has garnered most of the headlines since earlier this year. The recorded beheadings of James Foley and Steven Sotloff played no small part in shaping the American public’s opinion that something must be done.

But Jabhat al Nusrah, which has been openly at odds with the Islamic State since last year, is also a threat.

Consider all of the following.

Al Qaeda operatives embedded within Jabhat al Nusrah are tasked with targeting the U.S. and Western interests. The Obama administration says they were plotting “imminent” attacks.

In recent days, U.S. officials openly worried about al Qaeda operatives, known as the “Khorasan group,” who had been dispatched to Syria by al Qaeda’s senior leaders to plan attacks against the West. The Khorasan group, which is named after al Qaeda’s Khorasan shura, or advisory, council is embedded within Jabhat al Nusrah.

A statement released by CENTCOM notes that the U.S. airstrikes in Syria targeted "a network of seasoned al Qaeda veterans - sometimes referred to as the Khorasan Group - who have established a safe haven in Syria to develop external attacks, construct and test improvised explosive devices and recruit Westerners to conduct operations." The strikes are intended "to disrupt the imminent attack plotting against the United States and Western interests."

http://www.nytimes.com/2014/09/22/opinion/a-cancer-battle-we-can-win.html

A Cancer Battle We Can Win By ANDREA McKEE and ANDREW SALNERSEPT. 21, 2014

THE war against cancer can be confusing, with providers, insurers and policy makers debating the effectiveness of treatments, prevention programs and research. But there is one significant victory within our grasp. There is, increasingly, a consensus that CT screening for lung cancer can save thousands of lives each year.

Lung cancer, the No. 1 cancer killer, claims the lives of approximately 435 people in the United States every day. In fact, more women die of lung cancer each year than breast, ovarian and uterine cancers combined. While lung cancer is curable with surgery in its early stages, most people are given diagnoses of lung cancer after symptoms develop, when the disease is often advanced and resistant to treatment.

Now, however, there is good evidence that we can reduce the number of people who die of this devastating disease. A recent study called the National Lung Screening Trial proved that we do that by using a low-dose CT scan to detect early stage lung cancer. The study showed that in older people, both current and former heavy smokers, annual screening reduced the number of deaths from lung cancer by 20 percent.

Dozens of medical organizations, including the United States Preventive Services Task Force, now recommend CT lung screening for high-risk individuals. Approximately nine million Americans meet the task force’s criteria for high risk: current smokers between 55 and 80 who have smoked, on average, at least one pack of cigarettes a day for 30 years, or former smokers in that age range who smoked that much and quit within the last 15 years. The recommendation carries significant weight. And the screenings will be more affordable for those who want them because the Affordable Care Act requires that all private insurers cover CT lung screening for those at high risk with no co-pay, starting in January 2015.

By November of this year, the federal agency that administers Medicare will decide whether it should provide CT lung screening coverage for the Medicare beneficiaries at high risk, estimated to be between three and four million people. Since a CT lung screening exam can cost several hundred dollars, that coverage would ensure that millions of high-risk Americans over 65 would get lifesaving intervention regardless of income level.

Full paper (fresh off the press): http://arxiv.org/pdf/1409.4842v1.pdfSummary: http://googleresearch.blogspot.com/2014/09/building-deeper-understanding-of-images.html

Building a deeper understanding of images Posted by Christian Szegedy, Software Engineer

The ImageNet large-scale visual recognition challenge (ILSVRC) is the largest academic challenge in computer vision, held annually to test state-of-the-art technology in image understanding, both in the sense of recognizing objects in images and locating where they are. Participants in the competition include leading academic institutions and industry labs. In 2012 it was won by DNNResearch using the convolutional neural network approach described in the now-seminal paper by Krizhevsky et al.[4]

In this year’s challenge, team GoogLeNet (named in homage to LeNet, Yann LeCun's influential convolutional network) placed first in the classification and detection (with extra training data) tasks, doubling the quality on both tasks over last year's results. The team participated with an open submission, meaning that the exact details of its approach are shared with the wider computer vision community to foster collaboration and accelerate progress in the field.

The competition has three tracks: classification, classification with localization, and detection. The classification track measures an algorithm’s ability to assign correct labels to an image. The classification with localization track is designed to assess how well an algorithm models both the labels of an image and the location of the underlying objects. Finally, the detection challenge is similar, but uses much stricter evaluation criteria. As an additional difficulty, this challenge includes a lot of images with tiny objects which are hard to recognize. Superior performance in the detection challenge requires pushing beyond annotating an image with a “bag of labels” -- a model must be able to describe a complex scene by accurately locating and identifying many objects in it. As examples, the images in this post are actual top-scoring inferences of the GoogleNet detection model on the validation set of the detection challenge.

This work was a concerted effort by Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Drago Anguelov, Dumitru Erhan, Andrew Rabinovich and myself. Two of the team members -- Wei Liu and Scott Reed -- are PhD students who are a part of the intern program here at Google, and actively participated in the work leading to the submissions. Without their dedication the team could not have won the detection challenge.http://freebeacon.com/issues/hezbollah-launches-drone-strike-near-syrian-border/

Hezbollah Launches Drone Strike Near Syrian BorderStrike is Hezbollah's first use of weaponized drone

Hezbollah fighters / AP

BY: Washington Free Beacon StaffSeptember 22, 2014 1:55 pm

Lebanese Shiite terrorist group Hezbollah has only used unmanned aerial vehicles for reconnaissance purposes–until now.

On Sunday, Hezbollah used unmanned aerial vehicles to bomb al Qaeda-linked militants in northeast Lebanon, the first time the group has used drones with attack capabilities. The group used drones to bomb Nusra Front targets in Arsal, a northeastern Lebanese town nine miles from the Syrian border, according to Iranian news agency Fars.

The report, which includes a video that shows the purported drone strike, shows Hezbollah using a weaponized drone, the first non-intelligence drone used by the group.

The story has some account of the working of C1/C2 and unexpected queries:

http://www.buzzfeed.com/xtinehlee/i-had-a-stroke-at-33#31zuvqd

There was a cascade of input — triangles and sky and gravel sound and music on the radio and wind and the feeling of rough cloth near my hands. I could not make sense of it all; I did not know the small triangles were trees, the larger ones mountains, the sound tires crunching snow and Snow Patrol, the jacket Gore-Tex, and that my wrists were the things attached to things called my hands.

Numbers became squiggles, colors lost their names, food lost flavor, music had no melody.

http://www.nytimes.com/2014/09/23/world/middleeast/us-and-allies-hit-isis-targets-in-syria.html?_r=0

Airstrikes by U.S. and Allies Hit ISIS Targets in SyriaBy HELENE COOPER and ERIC SCHMITTSEPT. 22, 2014

WASHINGTON — The United States and allies launched airstrikes against Sunni militants in Syria early Tuesday, unleashing a torrent of cruise missiles and precision-guided bombs from the air and sea on the militants’ de facto capital of Raqqa and along the porous Iraq border.

American fighter jets and armed Predator and Reaper drones, flying alongside warplanes from several Arab allies, struck a broad array of targets in territory controlled by the militants, known as the Islamic State. American defense officials said the targets included weapons supplies, depots, barracks and buildings the militants use for command and control. Tomahawk cruise missiles were fired from United States Navy ships in the region.

http://www.technologyreview.com/news/530871/robots-that-learn-through-repetition-not-programming/

Robots That Learn Through Repetition, Not ProgrammingComputing News

Robots That Learn Through Repetition, Not Programming

A startup says getting a robot to do things should be less about writing code and more like animal training.

By Tom Simonite on September 22, 2014

Why It Matters

Making it easier to give robots intelligent behavior could make them cheaper and more widely used.

In an onstage demonstration this week, Todd Hylton of Brain Corporation used gestures to train a wheeled robot to come when he beckoned to it.

Eugene Izhikevich thinks you shouldn’t have to write code in order to teach robots new tricks. “It should be more like training a dog,” he says.  “Instead of programming, you show it consistent examples of desired behavior.”

Izhikevich’s startup, Brain Corporation, based in San Diego, has developed an operating system for robots called BrainOS to make that possible. To teach a robot running the software to pick up trash, for example, you would use a remote control to repeatedly guide its gripper to perform that task. After just minutes of

repetition, the robot would take the initiative and start doing the task for itself. “Once you train it, it’s fully autonomous,” says Izhikevich, who is cofounder and CEO of the company.