Blue-green algae sealed within a small container have powered a computer for six months. Similar photosynthetic power generators could run a range of small devices cheaply in the coming years, without the need for the rare and unsustainable materials used in batteries. Christopher Howe at the University of Cambridge and his colleagues built a small enclosure about the size of an AA battery out of aluminium and clear plastic. Inside, they placed a colony of a type of cyanobacteria called Synechocystis sp. PCC 6803 – commonly known as "blue-green algae" – which produce oxygen through photosynthesis when exposed to sunlight.

We present a multi-camera visual-inertial odometry system based on factor graph optimization which estimates motion by using all cameras simultaneously while retaining a fixed overall feature budget. We focus on motion tracking in challenging environments, such as narrow corridors, dark spaces with aggressive motions, and abrupt lighting changes. These scenarios cause traditional monocular or stereo odometry to fail. While tracking motion with extra cameras should theoretically prevent failures, it leads to additional complexity and computational burden. To overcome these challenges, we introduce two novel methods to improve multi-camera feature tracking. First, instead of tracking features separately in each camera, we track features continuously as they move from one camera to another. This increases accuracy and achieves a more compact factor graph representation. Second, we select a fixed budget of tracked features across the cameras to reduce back-end optimization time. We have found that using a smaller set of informative features can maintain the same tracking accuracy. Our proposed method was extensively tested using a hardware-synchronized device consisting of an IMU and four cameras (a front stereo pair and two lateral) in scenarios including: an underground mine, large open spaces, and building interiors with narrow stairs and corridors. Compared to stereo-only state-of-the-art visual-inertial odometry methods, our approach reduces the drift rate, relative pose error, by up to 80% in translation and 39% in rotation.

A plastic-degrading enzyme enhanced by amino acid changes designed by a machine-learning algorithm can depolymerise polyethylene terephthalate (PET) at least twice as fast and at lower temperatures than the next best engineered enzyme. Six years ago scientists sifting through debris of a plastic bottle recycling plant discovered a bacterium that can degrade PET. The organism has two enzymes that hydrolyse the polymer first into mono-(2-hydroxyethyl) terephthalate and then into ethylene glycol and terephthalic acid to use as an energy source. One enzyme in particular, PETase, has become the target of protein engineering efforts to make it stable at higher temperatures and boost its catalytic activity. A team around Hal Alper from the University of Texas at Austin in the US has created a PETase that can degrade 51 different PET products, including whole plastic containers and bottles.

The article contains an overview of AI and machine learning applied in Chemistry along with libraries like RDKit. Image Credits Introduction Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. One of the chief goals of chem

While most insect-inspired robots come with a simple tarsus such as a hemispherical foot tip, insect legs have complex tarsal structures and claws, which enable them to walk on complex terrain. Their sharp claws can smoothly attach and detach on plant surfaces by actuating a single muscle. Thus, installing insect-inspired tarsus on legged robots would improve their locomotion on complex terrain. This paper shows that the tendon-driven ball-socket structure provides the tarsus both flexibility and rigidity, which is necessary for the beetle to walk on a complex substrate such as a mesh surface. Disabling the tarsus' rigidity by removing the socket and elastic membrane of a tarsal joint, the claws could not attach to the mesh securely. Meanwhile, the beetle struggled to draw the claws out of the substrate when we turned the tarsus rigid by tubing. We then developed a cable-driven bio-inspired tarsus structure to validate the function of the tarsus as well as to show its potential application in the legged robot. With the tarsus, the robotic leg was able to attach and retract smoothly from the mesh substrate when performing a walking cycle.

Future generations of robots will work very differently from those that assemble entire vehicles or solder electronics onto circuit boards at lightning speed on factory floors today. They will leave the factory halls and start working with people, handing them a tool at the right moment or assisting them in assembling heavy components. They will appear in agriculture, helping harvest the fields or process the fruits. And they will increasingly be found in living rooms, supporting and entertaining people there or simply making them feel less alone. Of course, these robots will also look different from the enormous metallic contraptions found in today's industrial plants.

Estimating value and/or bid distributions from an observed sequence of auctions is a fundamental challenge in Econometrics with direct practical applic ations. For example, these fundamentals allow one to analyze the performance of an auction and make co unterfactual predictions about alternatives. The difficulty of this problem depends on the fo rmat of the auctions and the structure of the observed information from each one, as well as how the fundamentals of bidders are interrelated and vary across the sequence of observations. In this paper, we study a basic version of the afore-describe d estimation challenge, wherein the auction format and the bidder distributions stay fixed across observations, and the bidders have independent private values (which are independently resam pled across different observations). The auction formats that we consider are first-and second-pri ce auctions, as well as Dutch and English auctions. What will make our problem challenging is that (i) our bidders are ex ante asymmetric, drawing their independent private values from different distributions; (ii) we will make no parametric assumptions about these distributions; and (iii) we will only be observing the 1 identity of the winner and the price they paid but not the losi ng bids. Under this observational model and our independent private values assumption above, we can focus our attention on first-and second-price auctions, and our results automatically e xtend to Dutch and English auctions. In the above settings, we give computationally and sample ef ficient methods for estimating all agents' bid distributions and (under equilibrium assumpti ons) value distributions: In the case of first-price auctions, we provide finite-sample es timation guarantees under L evy, Kolmogorov and T otal V ariation distance with minimal assumptions. Under (a condition weaker than) a lower bound on the density of the bid dis tributions (although we actually do not need existence of densities), Theorem 2.2 shows that the bid distributions can be estimated to within ε in L evy distance, using 1/ ε

In brief A synthetic enzyme designed using machine-learning software can break down waste plastics in 24 hours, according to research published in Nature. Scientists at the University of Texas Austin studied the natural structure of PETase, an enzyme known to degrade polymer chains in polyethylene. Next, they trained a model to generate mutations of the enzyme that work fast at low temperatures, let the software loose, and picked from the output a variant they named FAST-PETase to synthesize. FAST stands for functional, active, stable, and tolerant. FAST-PETase, we're told, can break down plastic in as little as 24 hours at temperatures between 30 and 50 degrees Celsius.

Plastic waste dumped in landfill could be cleared sooner than expected, after engineers developed an enzyme that can break it down in just a few hours. Millions of tons of plastic is left abandoned every year, pilling up in landfills and pollution the land and waterways - typically taking centuries to degrade. A team from the University of Texas in Austin created a new enzyme variant that can supercharge recycling on a large scale, reducing the impact of plastic pollution. The work focusing on PET (polyethylene terephthalate), which is a polymer found in most consumer plastic including bottles, packaging and some textiles. The enzyme was able to complete a'circular process' of breaking down the plastic into smaller parts and chemically putting it back together in as little as 24 hours. They've called it FAST-PETase (functional, active, stable, and tolerant PETase), developed from a natural PETase that allows bacteria to degrade and modify plastic.

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