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One of the Caribbean’s most engrossing natural spectacles happens every summer and autumn, just after sunset, when shallow waters transform into a fantastic green light show. If you ever get a chance to see it, never forget that you’re watching a giant, raunchy worm orgy happening in real time. And thanks to a new study published in PLOS ONE, we now know a few more secrets about how this marine sex party gets started—particularly that the party always starts on time, and that it hinges on a very specific, unique enzyme that gives the Bermuda fireworms (Odontosyllis enopla) their fiery glow.

Mark Siddall, a curator in the American Museum of Natural History’s Division of Invertebrate Zoology and a coauthor of the new study, first observed the Bermuda worms’ mating ritual up close back in 1995. “I was amazed,” he says. “It took me a few minutes to believe what I was seeing.’ He was immediately interested to learn more about how it worked and what sorts of genes conferred the species with such a remarkable luminescence. It would take two decades until he and his colleagues could finally get around to running an actual study.

Bermuda fireworms’ luminescence was first documented by Christopher Columbus and his crew in 1492, as “looking like the flame of a small candle alternately raised and lowered.” Scientists would identify the spectacle as a mating ritual in the 1930s, as occurring in the summer and autumn, at an eerily prompt 55 minutes after sunset, three nights after a full moon. It’s the females that light up and attract males.

“Imagine looking out over the water,” says Siddall. “It’s dark. The sun has set almost an hour prior. On a clear night the moon is up behind you, looking very full. Then you see something faint, down in the water waist deep, up close and personal, it’s unmistakable. The female worms, which are maybe an inch and a half long or so, are swimming in tight circles of maybe four inches in diameter, and they’re shedding light. They themselves are aglow but the glow surrounds them and trails behind them like luminous blue-green milk. Suddenly, and much fainter appears a tiny comet of light, streaking in a dead-straight trajectory on an angle from the bottom, a male homing in on a mate with incredible precision. And then it’s over. She’s gone.”

The glow is caused by an enzyme caused luciferase. The team ran an analysis of a dozen female worms’ transcriptomes—the complete set of RNA—and found the luciferase the Bermuda worms produce is unlike anything found in any other organism to date. It’s a completely distinct form, created by a gene family that is entirely unique to this species.

“Firefly luciferases clearly evolved from oxygenases with other functions that have nothing to with light,” says Siddall. “But these luciferases in Odontosyllis species, at the protein level, look like nothing else ever sequenced before.”

The team also identified genes that play a bigger role in the mating performance. The worms actually undergo a kind of metamorphosis called “epitoky,” which means the species is responding to signals that indicate what time of the day and month it is. Besides bioluminescence, the epitoky phase includes the enlarging of male worms’ eyes (so they can see the female glow more clearly), and modifications to organs that store and release sex cells.

Glowing before copulation might be kinky, but it’s also exceedingly useful as a mating ritual. The ocean is huge, and that makes it much harder for small organisms to find one-another. “If you’re going to toss your eggs and sperm into the water, it’s best to do it at the same time as others of your species,” says Siddall. “Now, if you can get the timing right, and be in the same place, so much the better.” Clockwork luminescence in the dark helps fulfill both of these conditions, greatly increasing the chance of successful reproduction.

There are also some practical applications for studying such a niche behavior by a niche species, like in the artificial production of light-producing enzymes. The Bermuda worm luciferase might prove to be more efficient to emulate than other kinds of proteins.

“The mating displays of these invertebrates are visually spectacular,” says James B. Wood, a marine biologist and president of the Coral Sea Aquarium in Florida who was not involved with the study. He thinks the results are certainly exciting, even if they aren’t all that shocking. “As a former faculty member at the Bermuda Institute of Ocean Sciences, I had the pleasure of introducing many students to this amazing natural fireworks show. The worms are more punctual than most people! Who knows what other secrets they keep?”

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Can This Game Turn You Vegan?

The gaming world’s depiction of human violence is usually characterized as “glorifying,” however the internet definitely has an exception to that rule now with “That Cow Game”–a minimalist slaughterhouse simulator where you play the cow.

The big change? The cow is the foreman and the people are being slaughtered.

Alexey Botkov, a game creator from New Zealand and part of the Frogshark game studio, participated in a Ludum Dare competition where designers had 48 hours to create a game by themselves with the theme: “You are the monster.”

When you consider the Jekyll-and-Hyde games that give you a sinister purpose, or make you an anti-hero in pursuit of a greater evil than yourself, Botkov’s game is definitely a foray into something new. “The idea for the project sparked from a multitude of conversations I’ve had with my friends about meat production, factory farming, [and] humanity’s relationship with animals in the modern day,” he says.“There are almost 3000 submissions and in a lot of them you control a monster of some kind. I wanted the theme to carry a self-reflective quality for the player instead of a literal representation in the game.”

The visual style isn’t anything too graphic, either. The arterial spray from the bodies is heavily pixelated and cartoonish. Your character is reminiscent of a Minecraft cow as you move around a minimalistic slaughterhouse. Botkov intentionally made the humans the only flexible and moving forms. “The contrast in the imagery is brought forth by the fleshy form of the humans surrounded by all the blocky grey machinery. Some Carnivore predators often play with their food, and so do children. In the game you can walk in and headbutt the bodies, they will flail around and it’s fun to do in a perverse kind of way. I wanted that to be the conflicting feeling. It feels somehow wrong, but it’s fun, and it’s not an uncommon feeling. People enjoy the taboo,” says Botkov, “playing with fire, getting away with things, and I find that somewhat relevant.”

That Cow Game

Because of the competition, Botkov was working within a short time frame, so the game certainly could have gone farther given time. He offers a bit of insight on what would have come from another few hours.

“If I was to add anything it would be sound and a variety of voices coming from the humans as you bump into them,” he says. I also didn’t intend for this to be a game in a traditional sense where you had to do stuff or achieve anything, but rather a play space that you can navigate and decide the meaning of.”

That absence of things actually does work in Botkov’s favor. As the cow (the main character), you’re really only tasked with walking back and forth along the assembly line for as long as it takes you to realize there’s nothing you can do to affect the process. Whether that moment was intentional or not, it left an unsettling feeling hanging in the air. I kept wondering: Was there something I can do? Am I missing something to stop this?

That Cow Game

There was even a small sense of panic as I moved back and forth along the line. From decades of gaming experience, I had a sense of dread that something was coming, whether in the form of a sudden zombie-like uprising of the meat, or some sort of jump scare.

Nothing came. The meat mill just kept moving.

The question you’re probably asking at this point is whether Botkov is vegetarian, or vegan.

“I eat meat and I am a monster, really,” he says. “More so because I’m aware of the issues, yet I’m still complicit. I guess [with the game] I’m questioning my own relationship with the whole thing and trying to figure out what my values are.”

If that was his purpose, he seems to have succeeded. Botkov told us it’s forced a lot of discussions about the topic, and that it’s been a useful vehicle for reflection. “It can be hard to look at oneself and one’s actions in search of understanding. Creating art though, you have a thing that you’ve made, and it means something – you can ask those questions and seek your own truths that come from a mysterious place. One thing I can say is that I know more, now having made the game, than I did before, when it was still a thought in my mind.”

Download the game here.

Can You Lie To Your Deep Learning Model?

Can you fool your deep learning model?

What does lying to your deep learning model even entail? This question we’re sure most of you haven’t even considered in your learning or professional journey. But as we’ll see in this article, it’s an important question to answer.

But before we jump into our final installment in this series, let’s quickly recap we’ve learned thus far.

What We’ve Covered in this Series

In part 1, we injected noise into the CIFAR-10 dataset, trained models on that polluted data, and ran a pair of experiments. It shouldn’t come as a surprise that poor data produced poor model performance but what was far more interesting was that certain classes were much more impacted than others. Images of frogs and trucks were easy for our model to learn and the “lies” we told our model didn’t drastically impair its accuracy while noisy labels in cat data were significantly more detrimental.

Having learned that pollution affects classes rather differently in part 1, what we learned next in part 2 was that class sensitivity was not model specific. In other words, the same classes were consistently affected in consistent ways across different models, supporting the hypothesis that class sensitivity isn’t model-dependent but data-dependent. In essence: bad cat data affected each model more drastically than bad frog and truck data across the board.

In this article, we’re going to build upon those lessons. We’re going to start by comparing the impact of data noise and data volume.

Expressly, we’d like to understand which is more hostile to accuracy: polluted information or reduced quantity? We compared our custom-built CNN model with popular and off-the-shelf deep learning architectures such as ResNet18, UnResNet18 (ResNet18 without skip connections), GoogLeNet, and LeNet.

Comparing our Deep Learning Model to Popular Industry Models

Here’s what we discovered with our own model:

And here’s how it compares to some popular models in the industry today: 

Figure 1: Impact of labeling noise and data reduction on model accuracy, for 5 different models: shallow custom CNN, LeNet, UnResNet18, GoogleNet, and ResNet18.

As you can see, regardless of the model we used, bad data was more detrimental than less data. To put it another way: labeling noise impacts model performance more than volume reduction.

And that makes sense. After all, reducing data volume reduces the amount of good data while polluting data also reduces the amount of good data but replaces it with harmful labels.

Figure 2: Relation between data volume and labeling noise

Now that we have seen the impact of labeling noise and data volume reduction on the overall accuracy of the model, let us see how the accuracy of each class in CIFAR-10 gets impacted by those factors.

Impact of Labeling Noise and Data Volume Reduction on the Accuracy of Each Class

To measure this, we’re using a measure we named the Impact Index.

First, we noted the True Positive Rate (TPR) score for each class across multiple levels in our experiment. The levels here are the same percentages reflected in figure 1, namely the percentage of noise or data reduction at 5 percent intervals. The TPRn_norm and TPRd_norm reflect the change from the baseline with zero percent pollution (i.e. scores above a value of 1 are improvements in true positive rate, scores below are poorer performance).

Looking at our airplane class, those figures look like this:

Table 1: Change in TPR for the airplane class using our custom model

Impact Index Y-X is simply the measure of the distance between the baseline and the noise induction score (Y) minus the distance between the baseline and the data reduction score (X). If you’re more of a visual person, think of it like this:

Figure 3: An illustration of how Impact Index Y-X is calculated

Put simply, Impact Index Y-X is a shorthand for how much more pollution harmed a model than data reduction did. 

What Can We Do With This Measurement?

Well, in our previous pieces, we’ve proven that polluted data hurt model accuracy more than less data but also that certain classes are more affected than others. We’ve also learned that those classes are affected regardless of the model used.

With this measurement, we can actually quantify how much each model is affected at each stage of our experiment. Here’s how each model fared at every 5% interval: 

Figure 4: A visualization of Impact Index Y-X over each class in each model

Interestingly, in our custom model, the reduction was seemingly beneficial to the truck, ship, deer, and frog classes, even at 30% reduction. Noise, on the other hand, was never beneficial at 30%.

You can see that the cat, bird, deer, and dog classes were affected most, regardless of the model used. That said, the last thing that stuck out to us is that ‘bird’ was one of the most affected classes.

It’s also abundantly clear that the LeNet model was the weakest performing for this class, regardless of the amount of pollution we injected. Its performance with just 5% noise is worse than at 30% in any other model:

Figure 5: Impact Index Factor for Class Bird

And while that underscores what we found in parts 1 and 2 of this series – that the relative sensitivity of data classes is largely model-agnostic – it’s also worth underscoring that even though data quality is the main driver of model accuracy, the model you choose matters too. 

Some models, like LeNet, are very sensitive to noise while other models can cope with it a little better. And of course, no matter what, your data quality is the truest driver of accuracy.

Final Thoughts

So, what did we learn about lying to your models? For starters, we learned that different classes are affected differently and different methods of “lying” affect those classes differently as well. There are certain lies that are “easier” to tell than others, and some models are harder to fool.

We learned that bad labels are much more detrimental than less data. And it follows that models trained on bad data are harder to fix than ones that just need more of it.

This is all to say: no matter how much time you spend deciding which models to use for your project, make sure you dedicate serious energy to making sure you’re giving it the right data. 

And if that means it’s less data than you really wanted, that’s fine. Avoiding noise sets you up for more success later. After all, it’s usually a lot easier to get more quality data later than to work with the pollution you didn’t catch earlier.

About the Authors

Jennifer Prendki – Founder and CEO, Alectio

Prior to founding Alectio, she was the VP of Machine Learning at Figure Eight, one of the industry leaders in data labeling (recently acquired by Appen).

She also headed Machine Learning at Atlassian and various Data Science initiatives on the Search team at Walmart Labs. She is known for her active support of women in STEM and Technology.

Akanksha Devkar – Machine Learning Engineer, Alectio

Akanksha is a Machine Learning Engineer at Alectio focusing on developing Active Learning strategies and other Data Curation algorithms. When not training neural networks on the machine, she is mostly firing her neurons in having thought experiments. She lives to eat and loves to star-gaze.

Need help curating your dataset or diagnosing your model? Contact us at Alectio! 🙂

Related

How To Set Up And Use Google Assistant On Pixel Watch

In a world where everything is connected and more accessories are gaining “smart” integrations, it’s really transformed the way we interact with our devices. Leading the way is Google Assistant, which is available everywhere, and can handle everything from asking it basic questions to controlling your smart home devices.

How to Set Up Google Assistant on Pixel Watch

So when Google announced and subsequently released, the Pixel Watch, providing quick and easy access to Google Assistant was a no-brainer. However, unlike the Galaxy Watch 4 and Galaxy Watch 5, you don’t have to jump through a bunch of hoops to get rid of Bixby or anything. Instead, you’ll just need to go through a few basic steps in order to set up Google Assistant on Pixel Watch.

Press and hold the side button next to the crown on your Pixel Watch.

When prompted, tap the Get Started button.

Tap Open on phone to activate.

From your paired Android phone, open the Google Pixel Watch app.

Tap the Set up button.

When prompted, tap the Activate button.

Follow the on-screen steps to finish the setup process.

If prompted, set up your Hey Google or Ok Google “hotword”.

In some instances, when going through the process to set up Google Assistant on Pixel Watch, you’ll also be prompted to add “Voice Match”. If you are already using Google Assistant on other devices, this is likely already set up. But it essentially makes it so your device will recognize when you are making the request, helping to cut down on the potential for others to accidentally active Assistant when saying the “hotword”.

How to Use Google Assistant on Pixel Watch

Now that you have finished setting everything up, it’s now just a matter of knowing how to use Google Assistant on Pixel Watch. Perhaps surprisingly, there are actually three different methods for doing so:

Press and hold the side button next to the crown on your Pixel Watch.

Tap the screen on your Pixel Watch or push any button, then say Hey Google or Ok Google.

If using a watch face with complications, add Assistant to the visible complications. Once completed, tap the Assistant complication.

With the second option, Google points out that you can’t use the hotword to activate Assistant if your Watch’s screen is not on. This was likely done in an effort to improve battery life and cut down on the potential battery drain when microphones are constantly listening for the prompt.

How To Turn Off Always-listening Google Assistant

Speaking of cutting down on potential battery drain, you might find yourself only using the Pixel Watch’s side button or an Assistant complication. With these options, you can still use Google Assistant on Pixel Watch, removing the need to have your smartwatch “always listening” for the correct hotword. Here’s how you can turn off the always-listening Google Assistant on Pixel Watch:

From your Pixel Watch, swipe down on the watch face to reveal the Quick Settings panel.

Tap the Settings (cog icon) button.

Scroll down and tap Google.

Tap Assistant.

Tap the toggle next to Hey Google to turn off the hotword functionality.

Moving forward, you’ll now “only” have the two aforementioned options. If you want to use Google Assistant, just press the side button or tap the Assistant complication.

How This Aqara Radiator Valve Can Help You Save On Electricity Bills

The best way to target your heating spend while keeping yourself comfortable is only to heat rooms when you’re in them. Aqara’s radiator thermostat E1 can help you to do that.

A radiator thermostat like Aqara’s is something you can install yourself, in just five minutes. It attaches in place of the ordinary valve on your radiator and lets you control the temperature of that specific radiator using an app on your phone.

Once it’s on, it’ll give you granular control over your heating.

It’s much easier to control the temperature when you can see it clearly on your phone screen, instead of crawling around on the floor trying to figure out what the lines on your radiator valve mean.

And you can schedule heating on that radiator as well. If the Aqara radiator thermostat is in your bedroom, even when you want the heat on in the rest of the house, you can have it switched off in your room. You can then schedule the radiator to come to life as you’re heading up to bed, or to warm you up first thing in the morning.

Aqara’s radiator thermostat also has a grouping feature that allows users to sync multiple radiators for easy scheduling around the home. Geofencing is also supported. That means if you go out and leave your radiator on, it will automatically turn down when you go outside. It can also be used to warm up your home just in time for your return.

Users can integrate the Aqara thermostat into their smart home setups, as it is compatible with all the key smart assistants and smart home platforms: Apple HomeKit, Alexa, Google Assistant and IFTTT. It’s also Matter ready.

And you’ll get even more from the Aqara radiator thermostat if you use it in conjunction with other smart home devices. For example, use it with a motion sensor to switch off the radiator when everyone has left the room, or schedule it to turn down at the same time a curtain or roller shade driver opens to let in sunshine at noon.

Aqara’s door and window sensor will alert you if a door or window is left open. Even better, it can control other devices on the same network, so if it senses an open window, it can turn down your radiators, so you don’t waste money heating up your back garden.  

And for even more control, if you use the Aqara radiator thermostat alongside the brand’s temperature and humidity sensor, you can use that sensor to regulate the temperature of your radiator.

Most TRVs rely on internal sensors to measure temperature. As you can imagine, given that they’re so close to the radiator itself, this may not give you a very precise picture of how warm the room is. So, if you want the radiator to come on when the room temperature dips below a certain threshold, or switch off when it gets warm enough, this is the most accurate way to do that.

If you have concerns over damp in rooms you don’t heat often, or where clothes are drying, this sensor can help there as well. Not only can you monitor home humidity levels on your phone via the sensor, but you could set up a smart dehumidifier – or an ordinary dehumidifier with a smart plug – to come on at a given threshold to control damp.

Aqara also makes a TVOC air quality monitor with an E-ink display, which not only shows temperature and humidity, but also tracks indoor air pollution. During the winter, when all your doors and windows are sealed shut to save heat, your home air quality can suffer. The TVOC sensor will let you track your air quality in real time. It measures VOCs in the air: these are all the gaseous pollutants that come from cleaning products, paint, glue, new furniture and personal care products and they can be bad for your health. Monitoring your home air quality for them is especially important if you’re decorating or renovating your home.

The TVOC air quality monitor can also help you to keep an eye on your home heating in the longer-term, with easy-to-view historical data tracking. To use any Aqara products, you’ll need to get an Aqara Smart Hub, which lets the devices talk to each other. There are several options, from the budget-friendly Smart Hub E1 to the comprehensive Smart Hub M2, which can connect up to 128 Aqara devices. You can buy all of Aqara’s products from its UK and US Amazon stores.

Nutrition Tracking Can Put You On The Path To Meet Your Fitness Goals

When you first start working out consistently, it’s not unusual to go through a period of noticeable changes followed by a sudden plateau where progress seems to slam to a halt. It’s very common, but if you want to get over that frustrating phase, taking note of your calorie and nutritional intake can help.

When I hit my plateau, I spent a week monitoring what I ate and discovered that, regardless of how healthy my diet was, I was eating enough to sustain two men. Tracking provided the data I needed to make better decisions, which allowed me to enjoy steady progress.

Whether your fitness goal is fat loss or muscle gain, nutrition tracking is easy, and you can count on several tools to make the best of your journey. 

How the body burns fat and gains muscle

You require a specific number of calories to function and if you hit it every day, your body will remain exactly the same in terms of muscle and fat. This number is known as your maintenance caloric intake, and it depends on parameters like your height, weight, genetics, and daily activity levels. Adult men will typically fall somewhere between 2,000 and 3,000 calories, says the US Department of Agriculture, while women commonly require between 1,600 and 2,400.

[Related: There are only two supplements proven to help you build muscle]

If you’re exercising consistently and vigorously, your body will only be able to build muscle if you give it enough extra energy to do so. This means eating more calories than your maintenance level, which will result in a caloric surplus. (If you want to dig deeper into how to get buff, we have a complete beginner’s guide on how to get those muscle gains.) To reduce fat, you need to go in the opposite direction and aim for a caloric deficit, which entails eating fewer calories than your maintenance rate. To enjoy steady and safe progress, experts recommend that your surplus or deficit be around 500 calories.

How to track calories (more or less) accurately

People used to count calories with pen and paper, but luckily these days we have nifty apps that make the process considerably more convenient. Online platforms like Calculator.net’s Calorie calculator use factors like your age, height, weight, and daily activity levels to provide your maintenance rate as well as some general parameters for muscle gain and weight loss. Once you have those numbers, you simply tally up the caloric content of the food you eat on a daily basis and adjust your diet according to your fitness goals. If you want to have something on your phone, apps like MyPlate (available for Android and iOS) and MyFitnessPal (available for Android and iOS) can be helpful. These tools will determine your approximate maintenance rate and set a caloric budget for you. 

Keep in mind that no matter the app or method you use, the numbers you see in these tools are only approximations. The formulas these platforms use to calculate numbers like your maintenance rate, for example, are based on general statistics that leave little room for individuality, and may not consider factors that make your body different from the norm. This also applies to the apps’ massive database of food data, as the caloric value you see on labels and packaging can be up to 20 percent inaccurate, says the US Food and Drug Administration, so be careful not to get too attached to the exact number. 

And then there’s the body’s ability to absorb only a fraction of the available calories, which may be anything between 20 and 90 percent, says Michael S. Parker, a certified fitness nutrition specialist and founder of Forge Fitness. This is because our bodies just don’t digest the calories of some foods as well as others.

Instead of trying to make these numbers fit perfectly, Parker recommends using calorie tracking as a rough set of guidelines to help you learn about the energy value in various foods and how much you’re actually eating. From there you can stop tracking and make wise eating decisions when you’re hungry. 

Going beyond calories

The average fitness noob doesn’t need to know much beyond the concepts of surplus, maintenance, and deficit. But as you get more serious about exercising, you might benefit from tracking macronutrients, also known simply as macros. These account for the three largest nutrient categories and Parker explains that each of them has a role: Protein is essential for building muscle, while carbohydrates aid in performance, and dietary fat helps with hormone regulation and other essential bodily functions. 

How much of each macro you should eat depends on factors like your basal metabolic rate, sex, age, and weight. But for muscle building, the International Society of Sports Nutrition recommends consuming 1.4 to 2 grams of protein per kilogram of body weight per day. They also recommend 4 to 7 grams per kilo per day of carbs for weight training athletes to optimize strength performance and muscle building. You should devote the rest of your daily calorie budget to dietary fat. Nutrition tracking apps can monitor your macros and do all the math for you, so you can tackle multiple goals at the same time. For example, you’ll be able to prioritize protein to maintain muscle mass while leaving enough of a deficit in your calorie budget to enable fat burning. 

Health and safety are more important than any fitness goal

You should never use overuse caloric deficit in an attempt to lose weight faster. Losing fat—and keeping it off—is safest and most effective when you do it gradually. A deficit of around 500 calories a day will burn fat at a rate of up to one pound per week, which research shows is a safe and sustainable pace.

But counting calories is a slippery slope and people who hyper-fixate on recording everything they eat run the risk of developing eating disorders.

“Tracking nutrition can easily turn into something that is unhealthy,” says Katherine Metzelaar, a registered dietitian and founder of Bravespace Nutrition, an organization that helps patients recover from eating disorders and challenges relating to body image. “I would not recommend someone track [their food] if they have a history of dieting, disordered eating, or an eating disorder.” 

[Related: Anorexia may be more complicated than we thought]

But when done safely, food tracking can provide valuable insight into your body’s nutrition which will be helpful to continue making fitness progress. So Metzelaar is adamant about recommending approaching this method cautiously and tracking your food for no more than three days at a time. 

Keep in mind that in your fitness journey, you’re not going to see changes overnight. Building muscle and losing fat is the result of introducing healthy eating and exercise habits into your lifestyle on a sustainable basis. Tracking your nutrition is definitely not a silver bullet solution, but it can help set you on the path to that sustainability.

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