The last time we used a scanning electron microscope (a SEM), it looked like something from a bad 1950s science fiction movie. These days SEMs, like the one at the IBM research center, look like computers with a big tank poised nearby. Interestingly, the SEM is so sensitive that it has to be in a quiet room to prevent sound from interfering with images.
As a demo of the machine’s impressive capability, [John Ott] loads two US pennies, one facing up and one face down. [John] notes that Lincoln appears on both sides of the penny and then proves the assertion correct using moderate magnification under the electron beam.
Although it’s generally accepted that synthesized voices which mimic real people’s voices (so-called ‘deepfakes’) can be pretty convincing, what does our brain really think of these mimicry attempts? To answer this question, researchers at the University of Zurich put a number of volunteers into fMRI scanners, allowing them to observe how their brains would react to real and a synthesized voices. The perhaps somewhat surprising finding is that the human brain shows differences in two brain regions depending on whether it’s hearing a real or fake voice, meaning that on some level we are aware of the fact that we are listening to a deepfake.
The detailed findings by [Claudia Roswandowitz] and colleagues are published in Communications Biology. For the study, 25 volunteers were asked to accept or reject the voice samples they heard as being natural or synthesized, as well as perform identity matching with the supposed speaker. The natural voices came from four male (German) speakers, whose voices were also used to train the synthesis model with. Not only did identity matching performance crater with the synthesized voices, the resulting fMRI scans showed very different brain activity depending on whether it was the natural or synthesized voice.
One of these regions was the auditory cortex, which clearly indicates that there were acoustic differences between the natural and fake voice, the other was the nucleus accumbens (NAcc). This part of the basal forebrain is involved in the cognitive processing of e.g. motivation, reward and reinforcement learning, which plays a key role in social, maternal and addictive behavior. Overall, the deepfake voices are characterized by acoustic imperfections, and do not elicit the same sense of recognition (and thus reward sensation) as natural voices do.
Until deepfake voices can be made much better, it would appear that we are still safe, for now.
The Guinness brewery has a long history of innovation, but did you know that it was the birthplace of the t-test? A t-test is usually what underpins a declaration of results being “statistically significant”. Scientific American has a fascinating article all about how the Guinness brewery (and one experimental brewer in particular) brought it into being, with ramifications far beyond that of brewing better beer.
William Sealy Gosset (aka ‘Student’), self-trained statistician. [source: user Wujaszek, wikipedia]Head brewer William Sealy Gosset developed the technique in the early 1900s as a way to more effectively monitor and control the quality of stout beer. At Guinness, Gosset and other brilliant researchers measured everything they could in their quest to optimize and refine large-scale brewing, but there was a repeated problem. Time and again, existing techniques of analysis were simply not applicable to their gathered data, because sample sizes were too small to work with.
While the concept of statistical significance was not new at the time, Gosset’s significant contribution was finding a way to effectively and economically interpret data in the face of small sample sizes. That contribution was the t-test; a practical and logical approach to dealing with uncertainty.
As mentioned, t-testing had ramifications and applications far beyond that of brewing beer. The basic question of whether to consider one population of results significantly different from another population of results is one that underlies nearly all purposeful scientific inquiry. (If you’re unclear on how exactly the t-test is applied and how it is meaningful, the article in the first link walks through some excellent and practical examples.)
Dublin’s Guinness brewery has a rich heritage of innovation so maybe spare them a thought the next time you indulge in statistical inquiry, or in a modern “nitro brew” style beverage. But if you prefer to keep things ultra-classic, there’s always beer from 1574, Dublin castle-style.
When I was a kid, the solar system was simple. There were nine planets and they all orbited in more-or-less circles around the sun. This same sun-and-a-handful-of-planets scheme repeated itself again and again throughout our galaxy, and these galaxies make up the universe. It’s a great story that’s easy to wrap your mind around, and of course it’s a great first approximation, except maybe that “nine planets” thing, which was just a fluke that we’ll examine shortly.
What’s happened since, however, is that telescopes have gotten significantly better, and many more bodies of all sorts have been discovered in the solar system which is awesome. But as a casual astronomy observer, I’ve given up hope of holding on to a simple mental model. The solar system is just too weird.
We take easy communications for granted these days. It’s no bother to turn on a lightbulb remotely via a radio link or sense the water level in your petunias, but how does a drilling rig sense data from the drill head whilst deep underground, below the sea bed? The answer is with mud pulse telemetry, about which a group of researchers have produced a study, specifically about modelling the signal impairments and strategies for maintaining the data rate and improving the signal quality.
If you’re still confused, mud pulse telemetry (MPT) works by sending a modulated pressure wave vertically through the column of mud inside the drilling tube. It’s essential to obtain real-time data during drilling operations on the exact angle and direction the drill bit is pointing (so it can be corrected) and details of geological formations so decisions can be made promptly. The goal is to reduce drilling time and, therefore, costs and minimize environmental impact — although some would strongly argue about that last point.
Electrohydrodynamics (EHD) involves the dynamics of electrically charged fluids, which effectively means making fluids move using nothing but electric fields, making it an attractive idea for creating a pump out of. This is the topic of a 2023 paper by [Michael Smith] and colleagues in Science, titled “Fiber pumps for wearable fluidic systems”. The ‘fiber pumps’ as they call the EHD pumps in this study are manufactured by twisting two helical, 80 µm thick copper electrodes around a central mandrel, along with TPU (thermoplastic polyurethane) before applying heat. This creates a tube where the two continuous electrodes are in contact with any fluids inside the tube.
For the fluid a dielectric fluid is required to create the ions, which was 3M Novec 7100, a methoxy-fluorocarbon. Because of the used voltage of 8 kV, a high electrical breakdown of the fluid is required. After ionization the required current is relatively low, with power usage reported as 0.9 W/m, with one meter of this pump generating a pressure of up to 100 kilopascals and a flowrate of 55 mL/minute. One major limitation is still that after 6 days of continuous pumping, the copper electrodes are rendered inert due to deposits, requiring the entire system to be rinsed. Among the applications the researchers see artificial muscles and flexible tubing in clothing to cool, heat and provide sensory feedback in VR applications.
While the lack of moving parts as with traditional pumps is nice, the limitations are still pretty severe. What is however interesting about this manufacturing method is that it is available to just about any hobbyist who happens to have some copper wiring, TPU filament and something that could serve as a mandrel lying around.
We’ve seen many graphical and animated explainers for the Fourier series. We suppose it is because it is so much fun to create the little moving pictures, and, as a bonus, it really helps explain this important concept. Even if you already understand it, there’s something beautiful and elegant about watching a mathematical formula tracing out waveforms.
[Andrei Ciobanu] has added his own take to the body of animations out there — or, at least, part one of a series — and we were impressed with the scope of it. The post starts with the basics, but doesn’t shy away from more advanced math where needed. Don’t worry, it’s not all dull. There’s mathematical flowers, and even a brief mention of Pink Floyd.
The Fourier series is the basis for much of digital signal processing, allowing you to build a signal from the sum of many sinusoids. You can also go in reverse and break a signal up into its constituent waves.
We were impressed with [Andrei’s] sinusoid Tetris, and it appears here, too. We’ve seen many visualizers for this before, but each one is a little different.
