ScienceDaily: Computers & Math News

A computer-guided approach to design can propose more solutions and balance out human inexperience and design fixation.

Researchers have developed a trajectory-planning system for autonomous vehicles that enables them to travel from a starting point to a target location safely, even when there are many different uncertainties in the environment, such as unknown variations in the shapes, sizes, and locations of obstacles.

Researchers have developed soft robots that are capable of navigating complex environments, such as mazes, without input from humans or computer software.

Veterinarians and researchers have developed a technique to predict leptospirosis in dogs through artificial intelligence. Leptospirosis is a life-threatening bacterial disease dogs can get from drinking contaminated water.

Researchers found that people might not be 'addicted' to social media. Instead they get stuck in a state of dissociation, like what happens when you are reading a good book.

Digital pathology is an emerging field which deals with mainly microscopy images that are derived from patient biopsies. Because of the high resolution, most of these whole slide images (WSI) have a large size, typically exceeding a gigabyte (Gb). Therefore, typical image analysis methods cannot efficiently handle them. Seeing a need, researchers developed a novel artificial intelligence (AI) algorithm based on a framework called representation learning to classify lung cancer subtype based on lung tissue images from resected tumors.

A novel contract could help alleviate key sources of conflict between restaurants and food-delivery platforms.

Quantum physicists have answered a puzzling question at the forefront of research into iron-based superconductors: Why do electrons in iron selenide dance to a different tune when they move right and left rather than forward and back? In a new study, the researchers report the discovery of high-energy spin anisotropy that occurs concurrently with electronic nematicity in iron selenide.

Tactile sensation is an incredibly important part of how humans perceive their reality. Haptics or devices that can produce extremely specific vibrations that can mimic the sensation of touch are a way to bring that third sense to life. However, as far as haptics have come, humans are incredibly particular about whether or not something feels 'right,' and virtual textures don't always hit the mark. Now, researchers have developed a new method for computers to achieve that true texture -- with the help of human beings. Called a preference-driven model, the framework uses our ability to distinguish between the details of certain textures as a tool in order to give these virtual counterparts a tune-up.

New research fills a longstanding gap in carbon material science, potentially opening brand-new possibilities for electronics, optics and semiconducting material research.

Unlike fictional laser swords, real laser beams do not interact with each other when they cross -- unless the beams meet within a suitable material allowing for nonlinear light-matter interaction. In such a case, wave mixing can give rise to beams with changed colors and directions.

Researchers have reported a nano-sized neuromorphic memory device that emulates neurons and synapses simultaneously in a unit cell, another step toward completing the goal of neuromorphic computing designed to rigorously mimic the human brain with semiconductor devices.

Scientists discover superconductivity and charge density waves are intrinsically interconnected at the nanoscopic level, a new understanding that could help lead to the next generation of electronics and computers.

Scientists have been relentlessly working on understanding the fundamental mechanisms at the base of high-temperature superconductivity with the ultimate goal to design and engineer new quantum materials superconducting close to room temperature.

Using an artificial intelligence (AI) tool that emulates how humans visualize and is trained to recognize and classify images, investigators constructed a model that predicts the postoperative recurrence of Crohn disease with high accuracy by evaluating histological images. The AI tool also revealed previously unrecognized differences in adipose cells and significant differences in the extent of mast cell infiltration in the subserosa, or outer lining of the intestine, comparing patients with and without disease recurrence.