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Our team mainly works on leopards and other terrestrial mammals in protected areas and other forests of Karnataka. Our research focuses on establishing the baseline population of leopards in both forests and human-dominated landscapes, and further monitoring the same areas periodically to assess changes in the population.

We survey an area of interest using camera-traps which capture images of wildlife with minimal intrusion. Camera-traps are remotely triggered, motion-sensing cameras that capture a photo every time the infrared beam is cut either by an animal or a person. They are relatively light, easy to use, and low-fuss on the field as we don’t need to carry a laptop just to download data from each camera-trap. Each unit has a protected USB slot where a pen drive can be inserted and we can instantly download the data onto the pen drive. However, each unit does have to be tethered firmly to a tree or a pole lest curious young elephants tear them away during play, or poachers steal them. It is interesting to note that the unsuccessful parties get captured on the very camera-traps they try to steal, or on the one installed right opposite (which they miss spotting).

Elephant calves are full of curiosity and enjoy interacting with things on the ground that they can touch and feel. This little one is having a good time stripping the camera-trap away from the sapling it was tethered to.
Photo Credit: Sanjay Gubbi

We can easily programme the camera-traps for trigger sensitivity and frequency of captures as per our requirement. The infrared sensor detects the motion of the animal thus, triggering the camera to capture a photo. The quality of the photographs is sufficient to differentiate the patterns on animals such as leopards and tigers which is what we’re primarily concerned with. However, we do enjoy our share of entertaining photographs of macaques posing for pond-side selfies, or dholes that resemble flying corgis.

We get several thousands of photographs from each study site which we initially used to manually sort and analyse depending on the species photographed. The effort of sorting the photographs alone often required an enormous amount of manual work, and usually took us several months in a year. Apart from the large amount of resources it consumed, it was a hindrance to working in more sites. With the leopard being a widespread species, working in a larger number of sites was critical to establish benchmark data for as many areas as possible. If we couldn’t sort photos from one site in a manageable frame of time, how would we extend the study beyond?

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We photo-captured this dhole in the middle of a sprint. We assure you, this is not a flying Corgi, however much it may resemble one.
Photo Credit: Sanjay Gubbi

Given the large-scale of data and number of photos to sift through, we collaborated with Mr. Ramprasad, the former chief technologist for AI at Wipro who helped design a programme that could do the image sorting for us.

The software uses a convolutional neural network (CNN), which is a framework that enables machine-learning algorithms to work together to analyse images. This kind of work falls under an interdisciplinary field called ‘computer vision’ which deals with training machines to identify and classify images much like a human would. The CNN classifier needs to be trained to recognize the features, colours, shapes, sizes, and unique patterns associated with leopards and other animals. We fed thousands of images to train the classifier to recognize leopards from our field sites with a certain measure of accuracy.

In the first stage of analysis, the software helps us immensely by removing all the ‘noise’ – all irrelevant images without the target wild animals, or those with humans or livestock. Camera-traps are often triggered by the slightest motion of even falling leaves, leading to a large portion of the images being false captures. As an estimate from our largest site in 2018, out of a total of 2,99,364 images captured, only about 6% (17,888) of the images obtained were of mammals, with the rest of the 94% being humans, livestock, other species and false triggers.

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Most photographs we get are of animals walking by – half blurred or partial. This leopard was kind enough to sit and pose for our camera-trap.
Photo Credit: Sanjay Gubbi

For the second stage, we trained the classifier to identify and segregate the animal images as per the mammalian species we focus on. The classifier currently operates at an accuracy of around 90% for big cat (leopards and tigers) identification. Its accuracy will go up by learning more characteristics of those target species as we feed more photographs from similar habitats into the software. This accuracy is highly useful as many images we obtain are partials with only some body parts, or with obscured patterns, at different angles, or captured at night or in poor lighting. Currently, the accuracy of the classifier for certain distinct species such as leopards, tigers, and porcupines is higher than other species such as sambar deer, dhole, etc. We can remedy this by training it with more and diverse images of these species.

To date, we’ve used this software to sort through more than 1.6 million photographs to identify 363 leopard individuals. With this software, our workload has reduced from months to hours. The monumental effort we would have otherwise put into sifting through these many images manually has been cut down hugely. To put into perspective, the classifier can process up to 60,000 images in nearly half the time required by three researchers working full-time for three weeks, saving us a lot of valuable time and effort.

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Tiger and leopard individuals can be differentiated based on the unique patterns on their bodies. Notice how the stripes differ among the tigers along the flanks, belly, undersides and the legs. The rosettes differ between the leopards in the shapes, and the way they are clustered all over the body.
Photo Credit: Sanjay Gubbi

The final step for us is to identify individual leopards and tigers to estimate their population using appropriate statistical methodology. For animals that have marks or patterns on their body like the leopard or tiger, we can identify individuals by matching these marks or patterns as they are unique to an individual just like fingerprints in humans.

We compare the images of leopards and tigers that have been validated and extracted by the classifier by using another software called Wild-ID which pulls out images with similar patterns for us to match. These automated matches do have some margin of error thus, we validate the final set of images manually. However, this software still cuts down our effort of going through nearly 900 images to identify around 70 individuals to find the initial matches. Looking through hundreds of images of patterned animals can be extremely strenuous for the eyes, further bringing in the chances of human error.

We have been working towards incorporating technology and relevant software into different aspects of our work, to cut down the manual effort and get quicker results. The aim is to minimise error, maximise efficiency while also optimising the human-effort component that goes into implementing a research study on such a large scale.

Amrita Menon is interested in conservation biology and population ecology. She is currently working as a research affiliate on the leopard conservation project in Karnataka with the Western Ghats Programme at NCF.

Sanjay Gubbi is a conservation biologist whose work focuses on the conservation of large carnivores like tigers and leopards. He currently works as a Scientist and Programme Head with the Western Ghats Programme at Nature Conservation Foundation.

Phalguni Ranjan is a marine biologist working as a science and conservation communicator with the Western Ghats Programme at NCF.

This series is an initiative by the Nature Conservation Foundation, under their programme Nature Communication to encourage nature content in all Indian languages. If you’re interested in writing on nature and birds, please fill up this form.

How to find the best deals during online sales? We discussed this on Orbital, our weekly technology podcast, which you can subscribe to via Apple Podcasts, Google Podcasts, or RSS, download the episode, or just hit the play button below.

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How to install the FreeIPA identity and authorization solution on CentOS 8


Jack Wallen walks you through the process of installing an identity and authorization platform on CentOS 8.

Image: CentOS

FreeIPA is an open source identity and authorization platform that provides centralized authorization for Linux, macOS, and Windows. This solution is based on the 389 Directory Server and uses Kerberos, SSSD, Dogtag, NTP, and DNS. The installation isn’t terribly challenging, and you’ll find a handy web-based interface that makes the platform easy to administer.

I’m going to walk you through the steps of getting FreeIPA up and running on CentOS 8. 

SEE: CentOS: A how-to guide (free PDF) (TechRepublic) 

What you’ll need

How to set your hostname

The first thing you must do is set your hostname. I’m going to be demonstrating with a LAN-only FQDN (which then must be mapped in /etc/hosts on any client machine that wants to access the server). 

Set your hostname with the command:

sudo hostnamectl set-hostname HOSTNAME

Where HOSTNAME is the FQDN of the server.

After you’ve set the hostname, you must add an entry in the server’s hosts file. Issue the command:

sudo nano /etc/hosts

Add a line at the bottom like this:


Where SERVER_IP is the IP address of the server and HOSTNAME is the FQDN of the server.

Save and close the file.

How to install FreeIPA

The installation of FreeIPA starts with enabling the idm:DL1 repository with the command:

sudo module enable idm:DL1

When that command completes, sync the repository with the command:

sudo dnf distro-sync

Install FreeIPA with the command:

sudo dnf install ipa-server ipa-server-dns -y

How to set up FreeIPA Server

Next you have to run the configuration script for FreeIPA Server. To do that, issue the command:

sudo ipa-server-install

The first question you must answer is whether or not you want to install BIND for DNS. Accept the default (no) by pressing Enter on your keyboard. You must then confirm the domain and realm name, which will both be detected by the script. Once you’ve confirmed those entries, you’ll need to set a directory manager password, an IPA admin password for the web interface, and then accept the default (no) for the installation of chrony. 

After you’ve taken care of the above, you’ll be presented with the details of your installation (Figure A).

Figure A


The details of my installation of FreeIPA Server.

Type y and hit Enter on your keyboard. The configuration will begin. This does take a bit of time, so either sit back and watch the text fly by or set about to take care of another task.

When the configuration completes, you’re ready to continue on.

How to access the web interface

Open a browser and point it to https://SERVER_IP (where SERVER IP is the IP address of the hosting server). You should be prompted for a username and password (Figure B). The username is admin and the password is the one you set for IPA admin during the configuration. 

Figure B


The FreeIPA login screen.

Upon successful login, you’ll find yourself at the FreeIPA main window, where you can begin managing your centralized authentication server (Figure C).

Figure C


The FreeIPA main window is ready to work.

And that’s all there is to getting FreeIPA installed on CentOS. You can now spend some time adding users and other bits to make your identity and authorization solution work for your business.

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Targeting Aging is the Way to Treat Diseases of Aging

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Near all work to date on the treatment of age-related disease has failed to consider or target underlying mechanisms of aging, the molecular damage that accumulates to cause pathology. It has instead involved one or another attempt to manipulate the complicated, disrrayed state of cellular metabolism in late stage disease, chasing proximate causes of pathology that are far downstream of the mechanisms of aging. This strategy has largely failed, and where it has succeeded has produced only modest benefits. Consider that statins, widely thought to be a major success in modern medicine, do no more than somewhat reduce and delay mortality due to atherosclerosis. They are not a cure. The mechanisms of aging are why age-related diseases such as atherosclerosis exist. They are the root cause of these diseases. Attempted therapies that continue to fail to target the mechanisms of aging will continue to fail to deliver meaningful benefits to patients. This must change.

Targeting Aging is the Way to Treat Diseases of Aging

Image credit: Pixabay (Free Pixabay license)

Aging doesn’t kill people – diseases kill people. Right? In today’s world, and in a country like the United States, most people die of diseases such as heart attack and stroke, cancer, and Alzheimer’s. These diseases tend to be complex, challenging, difficult, and extremely ugly to experience. And they are by nature chronic, caused by multifactorial triggers and predispositions and lifestyle choices. What we are only now beginning to understand is that the diseases that ultimately kill us are inseparable from the aging process itself. Aging is the root cause. This means that studying these diseases without taking aging into account could be dangerously misleading … and worst of all, impede real progress.

Take Alzheimer’s disease. To truly treat a disease like Alzheimer’s, we would need to identify and understand the biological targets and mechanisms that trigger the beginning of the disease, allowing us to intervene early – ideally, long before the onset of disease, to prevent any symptoms from happening. But in the case of diseases like Alzheimer’s, the huge problem is that we actually understand very little about those early targets and mechanisms. The biology underlying such diseases is incredibly complex. We aren’t sure what the cause is, we know for sure there isn’t only one target to hit, and all prior attempts to hit any targets at all have failed. When you start to think about how much of what we think we know about Alzheimer’s comes from very broken models – for example, mice, which don’t get Alzheimer’s naturally – it becomes totally obvious why we’re at a scientific stalemate in developing treatments for the disease, and that we’ve likely been coming at this from the wrong direction entirely.

The biggest risk factor for Alzheimer’s isn’t your APOE status; it’s your age. People in their twenties don’t get Alzheimer’s. But after you hit the age of 65, your risk of Alzheimer’s doubles every five years, with your risk reaching nearly one out of three by the time you’re 85. What if going after this one biggest risk factor is the best vector of attack? Maybe even the only way to truly address it? This isn’t about the vanity of staying younger, about holding on to your good looks or your ability to run an 8 minute mile. It’s about the only concrete possibility we have to cure these diseases. Instead of choosing targets for a single specific disease, i.e. a specific condition that arises in conjunction with aging, we can get out in front of disease by choosing targets that promote health. And we can identify these by looking at disease through the lens of the biology of aging.


Source: Fight Aging!

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The Mandalorian Season 1 Recap Distills the Star Wars Series Into 89 Seconds

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Before The Mandalorian season 2 premieres Friday afternoon on Disney+ Hotstar (and Friday midnight on Disney+ in the US), Disney and Lucasfilm have given us an official 89-second recap of The Mandalorian season 1. That’s very brief, but it speaks to the fact that The Mandalorian wasn’t a narratively-heavy show on its debut last year.

Everything You Need to Know About The Mandalorian Season 2

The Mandalorian season 1 recap touches upon Mando’s (Pedro Pascal) profession (he’s a bounty hunter), his newest target (Baby Yoda), the people he meets along the way — Cara Dune (Gina Carano), Greef Karga (Carl Weathers), and Kuiil (voiced by Nick Nolte) — and the consequences of his decision to bring Baby Yoda under his wing.

“You have something I want. It means more to me than you will ever know,” the darksaber-wielding villain Moff Gideon (Giancarlo Esposito) says deep into The Mandalorian season 1 recap, as we are given a reminder of the Star Wars series’ action-heavy side. Gideon then declares: “It will be mine.”

The season 1 recap wraps by setting up The Mandalorian season 2, as tribe leader The Armorer (Emily Swallow) instructs Mando to reunite Baby Yoda “with its own kind”. Mando wonders: “You expect me to search the galaxy for the home of this creature?” Well, yes, otherwise what would we do in season 2, Mando.

In addition to Pascal, Carano, Weathers, and Esposito, The Mandalorian season 2 also stars Omid Abtahi as Dr. Pershing, Horatio Sanz as Mythrol, Rosario Dawson as Ahsoka Tano, Katee Sackhoff as Bo-Katan Kryze, Temuera Morrison as Boba Fett, Timothy Olyphant as former slave Cobb Vanth, Michael Biehn as a rival bounty hunter, and Sasha Banks in an undisclosed role.

Jon Favreau (The Lion King, Iron Man) created The Mandalorian and serves as showrunner and head writer on the Star Wars series. Favreau and Weathers are among the directors on season 2 alongside Dave Filoni, Rick Famuyiwa, Bryce Dallas Howard, Peyton Reed, and Robert Rodriguez.

The Mandalorian season 2 premieres October 30 on Disney+ Hotstar in India. Episodes will air weekly.

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