Creation of nanomaterials with specific morphology remains a complex experimental process, even though there is a growing demand for these materials in various industry sectors. This study explores the potential of AI to predict the morphology of nanoparticles within the data availability constraints. For that, we first generated a new multi-modal dataset that is double the size of analogous studies. Then, we systematically evaluated performance of classical machine learning and large language models in prediction of nanomaterial shapes and sizes. Finally, we prototyped a text-to-image system, discussed the obtained empirical results, as well as the limitations and promises of existing approaches.

A tubular, soft robot controlled by light can pump liquids, unscrew bolts and travel through pipes. The robot can also be designed to bend in the direction of a light source, like a plant tilting towards the sun. In some materials, molecules can gain energy from light, which makes the material expand or contract. Jiu-an Lv at Westlake University in China used this effect to create prototypes of a soft robot made up of a tube 15 to 40 millimetres long. He and his colleagues wound filaments of a light-sensitive elastic material into an arrangement inspired by an elephant's trunk, which has muscle fibres layered together in such a way that they can assume many different shapes depending on which ones get tense.

Patch-based appearance models are used in a wide range of computer vision ap- plications. To learn such models it has previously been necessary to specify a suitable set of patch sizes and shapes by hand. In the jigsaw model presented here, the shape, size and appearance of patches are learned automatically from the repeated structures in a set of training images. By learning such irregularly shaped'jigsaw pieces', we are able to discover both the shape and the appearance of object parts without supervision. When applied to face images, for example, the learned jigsaw pieces are surprisingly strongly associated with face parts of different shapes and scales such as eyes, noses, eyebrows and cheeks, to name a few.

Outlier detection, which is the process of identifying extreme values in data, has many applications across a wide variety of industries including finance, insurance, cybersecurity and healthcare. In finance, for example, it can detect malicious events like credit card fraud. In insurance, it can identify forged or fabricated documents. In cybersecurity, it is used for identifying malicious behaviors like password theft and phishing. Finally, outlier detection has been used for rare disease detection in a healthcare context.

Today you'll learn about a very powerful library called Numpy. We'll learn about Numpy Array(np array for short) and operations on them, along with what makes them better than the pre-existing data structures. The most important entity in the whole NumPy package is the Numpy Array. If you've worked with Python before you must be familiar with the data structure called Lists. List are containers that can store any kind of data in it.

Scientists from MIT's Computer Science and Artificial Intelligence Laboratory ( CSAIL) and the University of Calgary have developed a modular robot system that can morph into different shapes. ElectroVoxels don't have any motors or moving parts. Instead, they use electromagnets to shift around each other. Each edge of an ElectroVoxel cube is an electromagnetic ferrite core wrapped with copper wire. The length of each ElectroVoxel side is around 60 millimeters.

The inside of a tokamak--the doughnut-shaped vessel designed to contain a nuclear fusion reaction--presents a special kind of chaos. Hydrogen atoms are smashed together at unfathomably high temperatures, creating a whirling, roiling plasma that's hotter than the surface of the sun. Finding smart ways to control and confine that plasma will be key to unlocking the potential of nuclear fusion, which has been mooted as the clean energy source of the future for decades. At this point, the science underlying fusion seems sound, so what remains is an engineering challenge. "We need to be able to heat this matter up and hold it together for long enough for us to take energy out of it," says Ambrogio Fasoli, director of the Swiss Plasma Center at École Polytechnique Fédérale de Lausanne in Switzerland. That's where DeepMind comes in.

The inside of a tokamak--the donut-shaped vessel designed to contain a nuclear fusion reaction--presents a special kind of chaos. Hydrogen atoms are smashed together at unfathomably high temperatures, creating a whirling, roiling plasma that's hotter than the surface of the sun. Finding smart ways to control and confine that plasma will be key to unlocking the potential of nuclear fusion, which has been mooted as the clean energy source of the future for decades. At this point, the science underlying fusion seems sound, so what remains is an engineering challenge. "We need to be able to heat this matter up and hold it together for long enough for us to take energy out of it," says Ambrogio Fasoli, director of the Swiss Plasma Center at École Polytechnique Fédérale de Lausanne. That's where DeepMind comes in.

Presenting a real picture as a set of numbers by managing and storing it through a computer is known as a digital image. The above definition states that a set of numbers represents a digital image. Well, then we don't know what is meant by a set of numbers. The set of numbers is referred to as the picture element, which we know as pixels. The imaging device records a number or a small set of numbers that describe some of the properties of that pixel, such as its brightness, light intensity, or color for each pixel, and these numbers are arranged in rows and columns to match the vertical and horizontal position of the pixel.

A soft plastic'robot' that can take on different shapes is being developed in a NASA laboratory to be used one day for exploring the moon and beyond. It has been made using 3D printing technology with a mould and liquid plastic that cools to form the final structure. It has been designed with internal air pockets that can be inflated and deflated to create different shapes, sizes and strengths out of silicon. Their plasticity means they are more resistance to hard hits and can take on multiple functions, even joining together to form temporary mega structures. A soft plastic'robot' (pictured) that can take on different shapes is being developed in a NASA laboratory to be used one day for exploring the moon and beyond Two interns, Chuck Sullivan and Jack Fitzpatrick,working in NASA's Langley's Makerspace Lab, have created a small plastic device that could be the prototype of a future'collapsible' space robot.