Lifeboat Foundation

An interdisciplinary team of clinicians and scientists has published a consensus paper recommending appropriate quantitative imaging techniques for coronary artery stenosis and atherosclerosis related treatment and procedural planning. The paper has been published in Nature Reviews Cardiology.

Quantitative imaging has become increasingly important for the diagnosis of coronary artery disease (CAD) over the past five years. This is because new quantitative techniques can detect narrowed coronary arteries (coronary artery stenoses) and atherosclerosis, which play a major role in CHD patients.

It is important to correctly diagnose and accurately assess the severity of coronary artery stenosis or the extent of atherosclerotic burden for the selection of appropriate measures of therapy and the related further course of the disease. However, the complexity and variety of different quantitative imaging modalities, such as computed tomography (CT), magnetic resonance imaging (MRI), invasive coronary angiography (ICA), intravascular ultrasound (IVUS), and optical coherence tomography (OCT), necessitated a comprehensive clinical consensus.

The term ‘quantum computer’ gets usually tossed around in the context of hyper-advanced, state-of-the-art computing devices, but much as how a 19th century mechanical computer, a discrete computer created from individual transistors, and a human being are all computers, the important quantifier is how fast and accurate the system is at the task, whether classical or quantum computing. This is demonstrated succinctly by [Davide ‘dakk’ Gessa] with 200 lines of BASIC code on a Commodore 64 (GitHub), implementing a range of quantum gates.

Much like a transistor in classical computing, the qubit forms the core of quantum computing, and we have known for a long time that a qubit can be simulated, even on something as mundane as an 8-bit MPU. Ergo [Davide]’s simulations of various quantum gates on a C64, ranging from Pauli-X, Pauli-Y, Pauli-Z, Hadamard, CNOT and SWAP, all using a two-qubit system running on a system that first saw the light of day in the early 1980s.

Naturally, the practical use of simulating a two-qubit system on a general-purpose MPU running at a blistering ~1 MHz is quite limited, but as a teaching tool it’s incredibly accessible and a fun way to introduce people to the world of quantum computing.

Quantum computing has long been heralded as the next frontier in computing. However, despite their immense potential, quantum computers today still make too many errors to be useful.

While it may become possible to correct these errors in the future, there is still a long way to go to reach fault tolerance. For now, the best strategy is to minimize errors and mitigate their impact on quantum computations by devising methods that can work with the existing quantum hardware.

Continue reading “QEDMA Quantum Computing: Shaping the Future of Quantum Operating Systems” »

In this informative and engaging video, we explore the fascinating world of quantum computing and its untapped potential. We delve into the challenges of building quantum computers and how artificial intelligence can help us overcome these challenges.

Whether you’re an AI or quantum computing enthusiast, or simply curious about the future of technology, this video is a must-watch. Join us as we unlock the potentials of quantum computing with AI and discover the limitless possibilities that lie ahead.

Continue reading “Unlocking The Potentials of Quantum Computing With AI” »

Priyanjali Gupta, an engineering student from Tamil Nadu’s Vellore Institute of Technology (VIT), has created an AI model which can translate American sign language to English in real-time. A third-year computer science student, Priyanjali Gupta is specialising in Data Science and developed the new model using Tensorflow object detection API and it is able to translate the signs using transfer learning from a pre-trained model named ssd_mobilenet.

Gupta has shared her creation on LinkedIn wherein she demonstrated the capabilities of her AI model in a demo video. According to her Github post, “The dataset is made manually by running the Image Collection Python file that collects images from your webcam for or all the mentioned below signs in the American Sign Language: Hello, I Love You, Thank you, Please, Yes and No”, She also displayed the same in her demo clip.

New work from Carnegie Mellon University has enabled robots to learn household chores by watching videos of people performing everyday tasks in their homes.

The research could help improve the utility of robots in the home, allowing them to assist people with tasks like cooking and cleaning. Two robots successfully learned 12 tasks including opening a drawer, oven door and lid; taking a pot off the stove; and picking up a telephone, vegetable or can of soup.

“The robot can learn where and how humans interact with different objects through watching videos,” said Deepak Pathak, an assistant professor in the Robotics Institute at CMU’s School of Computer Science. “From this knowledge, we can train a model that enables two robots to complete similar tasks in varied environments.”

Anthropology and archaeology are revealing that a human society can take myriad forms, which can teach us how to build a modern society that is more equal, resilient and stable.

By Michael Marshall

EcoRunner Team Delft.

Range anxiety is not a term that you hear quite often these days. Electric vehicle (EV) manufacturers have ensured that their cars are now adept at traveling long distances on a single charge. However, charging times still need to catch up and offer a quicker way to get back on the road again.

One researcher said he’s concerned about the “existential dangers” of artificial intelligence for humanity.