April 08, 2014
WINTER BAT SURVEYS REVEAL NO GOOD NEWS AT NH HIBERNACULA
CONCORD, N.H. -- Recent surveys for bats in New Hampshire hibernacula, places where bats spend the winter, resulted in biologists finding a total of only 28 bats, with two formerly very common species missing.
"White-nose syndrome has decimated little brown bats and northern long-eared bats," said New Hampshire Fish and Game biologist Emily Preston. "Little brown bats were the most common species in the state before 2009."
White-nose syndrome is a fungal infection that infects bats during the winter, while they are hibernating. It damages the thin tissue of the wing. That tissue helps the bats with temperature regulation, air exchange, water retention and flight. "Since bats are hibernating, their immune system is suppressed, so they cannot fight the fungus," said Preston. The fungus causes the bats to wake up from hibernation much more frequently and stay awake longer. This burns through their stored fat too quickly, often times not leaving enough to survive the winter.
White-nose syndrome was first documented in New Hampshire in 2009 during normal winter surveys conducted by Preston, researchers Dr. Scott Reynolds and Dr. Jacques Veilleux, as well as US Fish and Wildlife Service biologist Susi von Oettingen.
One survey this winter found that a mine that previously had 514 bats with three species in 2009, now had no bats. Dr. Veilleux shook his head and said, "I can't believe we've lost so many bats, so quickly. I had some small hope that we might see a bit of a recovery this year; it's sad that we have experienced a near complete loss of our wintering bats, and really sad to think I likely won't be around to witness a possible recovery of these populations." Since bats generally have only one pup a year, it will take many decades for the population to rebound, if they ever do.
Overall, over 90% of little brown and northern long-eared bats in the Northeast have been killed by this disease. The disease has now spread to 23 states and 5 Canadian provinces, killing millions of bats.
Not finding little brown or northern long-eared bats in hibernacula does not mean those species are gone from the state. N.H.'s summer bats fly to hibernacula in Vermont and New York also. "We monitor a couple of little brown bat maternity colonies. These are places where female bats raise their pups. So far, these still have bats and they are producing pups," said Preston. Maternity colonies in barns and homes are also monitored by homeowners under a citizen science program run by N.H. Fish and Game. Homeowners simply go out at dusk and count the number of bats that exit when it gets dark enough. The survey takes about 20 minutes and details on how to do this, as well as more information on N.H. bats, are found on the Fish and Game website at http://www.wildlife.state.nh.us/Wildlife/Nongame/bats.html.
Never forget a face
New algorithm uses subtle changes to make a face more memorable without changing a person’s overall appearance.
CAMBRIDGE, Mass. -- Do you have a forgettable face? Many of us go to great lengths to make our faces more memorable, using makeup and hairstyles to give ourselves a more distinctive look.
Now your face could be instantly transformed into a more memorable one without the need for an expensive makeover, thanks to an algorithm developed by researchers in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL).
The algorithm, which makes subtle changes to various points on the face to make it more memorable without changing a person’s overall appearance, was unveiled earlier this month at the International Conference on Computer Vision in Sydney.
“We want to modify the extent to which people will actually remember a face,” says lead author Aditya Khosla, a graduate student in the Computer Vision group within CSAIL. “This is a very subtle quality, because we don’t want to take your face and replace it with the most memorable one in our database, we want your face to still look like you.”
More memorable — or less
The system could ultimately be used in a smartphone app to allow people to modify a digital image of their face before uploading it to their social networking pages. It could also be used for job applications, to create a digital version of an applicant’s face that will more readily stick in the minds of potential employers, says Khosla, who developed the algorithm with CSAIL principal research scientist Aude Oliva, the senior author of the paper, Antonio Torralba, an associate professor of electrical engineering and computer science, and graduate student Wilma Bainbridge.
Conversely, it could also be used to make faces appear less memorable, so that actors in the background of a television program or film do not distract viewers’ attention from the main actors, for example.
To develop the memorability algorithm, the team first fed the software a database of more than 2,000 images. Each of these images had been awarded a “memorability score,” based on the ability of human volunteers to remember the pictures. In this way the software was able to analyze the information to detect subtle trends in the features of these faces that made them more or less memorable to people.
The researchers then programmed the algorithm with a set of objectives — to make the face as memorable as possible, but without changing the identity of the person or altering their facial attributes, such as their age, gender, or overall attractiveness. Changing the width of a nose may make a face look much more distinctive, for example, but it could also completely alter how attractive the person is, and so would fail to meet the algorithm’s objectives.
When the system has a new face to modify, it first takes the image and generates thousands of copies, known as samples. Each of these samples contains tiny modifications to different parts of the face. The algorithm then analyzes how well each of these samples meets its objectives.
Once the algorithm finds a sample that succeeds in making the face look more memorable without significantly altering the person’s appearance, it makes yet more copies of this new image, with each containing further alterations. It then keeps repeating this process until it finds a version that best meets its objectives.
“It’s really like applying an elastic mesh onto the photograph that slightly modifies the face,” Oliva says. “So the face still looks like you, but maybe with a bit of lifting.”
The team then selected photographs of 500 people and modified them to produce both a memorable and forgettable version of each. When they tested these images on a group of volunteers, they found that the algorithm succeeded in making the faces more or less memorable, as required, in around 75 percent of cases.
Familiarity breeds likability
Making a face appear familiar can also make it seem more likable, Oliva says. She and Bainbridge have published a complimentary paper in the journal Cognitive Science and Social Psychology on the attributes that make a face memorable. The first time we see a face, we tend to “tag” it with attributes based on appearance, such as intelligence, kindness, or coldness. “If we tag a person with familiarity, because we think this is a face we have seen before, we have a tendency to like it more, and for instance to think the person is more trustworthy,” she says.
The team is now investigating the possibility of adding other attributes to their model, so that it could modify faces to be both more memorable and to appear more intelligent or trustworthy, for example. “So you could imagine having a system that would be able to change the features of your face to make you whatever you would wish for, but always in a very subtle way,” Oliva says.