[Paper Summary] Washington University Researchers propose a Deep Learning Model that automates Brain Tumor Classification

Biopsies are always the first call when it comes to diagnosing a case of brain cancer. Surgeons start by removing a thin layer of tissue from the tumor to find signs of disease closely under a microscope. Although biopsies are very presumptuous, the samples collected only represent a snatch of the whole tumor. MRI is a less bold but time-consuming process as radiologists have to manually map out the tumor area from the scan before the classification. 

Read More

[Paper Summary] An AI system trained by Loughborough University researchers recognizes the pre-movement patterns from an EEG

A group of researchers from the Intelligent Automation Center at Loughborough University has published a research paper focussed on possible results for training robots to ferret out the intention of arm movement before humans articulate the movement.

Read More

[Paper Summary] Stanford Researchers use Deep Learning to predict Biological Structures, like RNAs, more accurately than ever before

Determination of 3D structures of biological molecules, like RNA’s, is difficult and often requires millions of dollars for such extensive efforts. Stanford University researchers have devised a new deep learning algorithm called ARES (Atomic Rotationally Equivalent Scorer) for overcoming this challenge by computationally forecasting accurate structures. 

Read More

[Paper Summary] Google and Mayo Clinic Researchers propose a new AI Algorithm to improve Brain Stimulation devices to treat disease

lectrical simulation has the potential to widen treatment possibilities for millions of people with movement disorders, such as Parkinson’s disease, and epilepsy. In the future, this technology may help further treat psychiatric illness or even assist in recovery from brain injuries like stroke.

Read More

[Paper Summary] AI Researchers from ShanghaiTech and UC San Diego introduce SofGAN: A Portrait Image Generator with Dynamic Styling

Researchers in Shanghai and the United States have created a GAN-based portrait creation system that lets users build new faces with previously unattainable levels of control over specific features, including hair, eyes, spectacles, textures, and color.

Read More

[Paper Summary] This new Study shows Artificial Neural Networks (ANN) based on Human Brain Connectivity can perform Cognitive Tasks efficiently

Possibly a new breakthrough has been achieved in the domain of artificial intelligence. According to a new study, by a team of researchers from The Neuro (Montreal Neurological Institute-Hospital) and the Quebec Artificial Intelligence Institute, the artificial intelligence networks modelled on human brain connectivity are equipped to perform cognitive tasks efficiently and effectively. The study has been done via a sizable Open Science Repository by which the researchers tried to replicate and reconstruct the brain’s connectivity pattern. This was then applied to an artificial neural network (ANN) to achieve cognitive abilities like the human brain.

Read More

[Paper Summary] DGIST Team introduces, ‘DRANet’, an AI Neural Network module that can separate and convert Environmental Information

As a result of recent advances in Deep Learning (DL), deep learning neural networks (DNN) have been widely used to improve model performance in computer vision, natural language processing, and more. However, existing domain adaptation methods learn only associated features that share a domain. Thus domain gaps between data significantly degrade the existing model performance.

Read More

NVIDIA and King’s College London uses Cambridge-1 to build AI Models to generate synthetic brain images

Cambridge 1 AI supercomputer

NVIDIA and King’s College London have revealed new information about one of the first projects to be run on Cambridge-1, the UK’s most powerful supercomputer. The UK’s most powerful supercomputer, Cambridge-1, was announced in October last year and cost $100 million to build.

Read More

[Paper Summary] Griffith University Researchers design AI video surveillance system to detect Social Distancing Breaches

Researchers at Griffith University have developed an AI video surveillance system to monitor social distancing breaches in an airport without compromising privacy. The team eliminated the traditional need to store sensitive data on a central system by keeping image processing hedged to a local network of cameras.

Read More

[Paper Summary] Boston University Researchers propose a Machine Learning Algorithm for autonomous vehicles learning to drive by watching other cars

With self-driving cars that are powered by machine learning algorithms, vast amounts of driving data is required for them to function safely. However, if they could learn how to drive in the same way as babies do so—by watching and mimicking others around them—they would require far less compiled driving data. Eshed Ohn-Bar, a Boston University researcher is pushing for new ways of self driving cars to learn safe driving technique; by watching other drivers on the road and predicting their responses.

Read More

[Paper Summary] Scientists have created a new Tool ‘Storywrangler’ that can explore billions of Social Media messages in order to Predict Future Conflicts and Turmoil

Scientists have recently invented an instrument to divulge deeper into the billions of posts made on Twitter since 2008. The new tool is capable of providing an unprecedented, minute-by-minute view of popularity. The research was carried out by a team at the University of Vermont. The team calls the instrument the Storywrangler. 

Read More

[Paper Summary] Skoltech Researchers present a Machine Learning Framework involving Convolutional Neural Networks

Skoltech researchers and their partners in the U.S. have created a neural network that can help tweak semiconductor crystals to achieve superior properties for electronics. This is an exciting new direction of development with limitless possibilities for next-generation chips and solar cells. This study is published as a paper in the journal npj Computational Materials.

Read More

[Paper Summary] Researchers at Facebook AI, UC Berkeley, and Carnegie Mellon University Announced Rapid Motor Adaptation (RMA), An Artificial Intelligence (AI) Technique

To achieve success in the real world, walking robots must adapt to whatever surfaces they encounter, objects they carry, and conditions they are in, even if they’ve not been exposed to those conditions before. Moreover, to avoid falling and suffering damage, these adjustments must happen in fractions of a second.

Read More

[Paper Summary] Researchers from Facebook AI Research and UIUC Propose ‘MaskFormer’, A Mask Classification Model

In recent years, semantic segmentation has become an important tool for computer vision. One type of the technique is called per-pixel classification and the goal is to partition images into regions with different categories using deep learning techniques such as Fully Convolutional Networks (FCNs). Mask classification is another alternative way that separates the image partitioning and classifying aspects of segmentation. Instead a single pixel, mask-based methods predict binary masks with each associated to those assigned to one specific class.

Read More

[Paper Summary] Stanford’s AI Researchers introduce QA-GNN Model that jointly reasons with Language Models and Knowledge Graphs

painter is Italy.

In this research paper, published at NAACL 2021, researchers found that combining both LMs and KGs makes it possible to answer questions more effectively. Existing systems that use LM and KGs tend to be noisy, and the interactions between QA context and KG are not modeled.

Read More

[Paper Summary] A new study from Cambridge, Twitter, UCLA propose CW Networks (CWNs) with better Expressive Power than GNNs

A recent study from a multi-institutional research team introduces CW Networks (CWNs), a message-passing mechanism that produces state-of-the-art outcomes across a variety of molecular datasets while delivering superior expressivity than commonly utilized graph neural networks (GNNs).

Read More

[Paper Summary] Cornell and Harvard University Researchers develops Correlation Convolutional Neural Networks (CCNN): To determine which Correlations are most important

team of researchers from Cornell and Harvard University introduces a novel approach to parse quantum matter and make crucial data distinctions. This proposed technique will enable researchers to decipher the most perplexing phenomena in the subatomic realm.

Read More

[Paper Summary] Researchers from University of Sydney and Japan’s NIMS have discovered a way to create Artificial Networks of Nanowires

A team of researchers from the University of Sydney and Japan’s National Institute for Material Science have demonstrated that they can utilize a random network of nanowires to mimic the structure as well as the dynamics of the brain to solve simple tasks involving processing.

Read More

[Paper Summary] Stanford AI Lab introduces AGQA: A new benchmark for Compositional, Spatio-Temporal Reasoning

Designing machines capable of exhibiting a compositional understanding of visual events has been an important goal of the computer vision community. Stanford AI has recently introduced the benchmark,’ Action Genome Question Answering’ (AGQA). It measures temporal, spatial, and compositional reasoning via nearly two hundred million question answering pairs. The questions are complex, compositional, and annotated to allow definitive tests that find the types of questions that the models can and cannot answer.

Read More

[Paper Summary] A novel Caltech Algorithm allows Autonomous Systems navigate themselves by referring the Surrounding Terrain, summer or winter

Car and Drone

The process employed in the algorithm is called ‘visual terrain-relative navigation’ (VTRN), which was first developed in the 1960s that helped the autonomous devices compare the nearby terrain to high-resolution satellite images to locate themselves.

Read More
1 2