Extensive sampling of neural activity during rich cognitive phenomena is critical for robust understanding of brain function. Here we present the Natural Scenes Dataset (NSD), in which high-resolution functional magnetic resonance imaging responses to tens of thousands of richly annotated natural scenes were measured while participants performed a continuous recognition task. To optimize data quality, we developed and applied novel estimation and denoising techniques. Simple visual inspections of the NSD data reveal clear representational transformations along the ventral visual pathway. Further exemplifying the inferential power of the dataset, we used NSD to build and train deep neural network models that predict brain activity more accurately than state-of-the-art models from computer vision. NSD also includes substantial resting-state and diffusion data, enabling network neuroscience perspectives to constrain and enhance models of perception and memory. Given its unprecedented scale, quality and breadth, NSD opens new avenues of inquiry in cognitive neuroscience and artificial intelligence. The authors measured high-resolution fMRI activity from eight individuals who saw and memorized thousands of annotated natural images over 1 year. This massive dataset enables new paths of inquiry in cognitive neuroscience and artificial intelligence.

How does google understand how to translate '今日はどうですか？' to'How are you doing today?' or vice versa? How do we get to predict a disease spread such as COVID-19 way into the future beforehand? How do automatic Text generation or Text Summarization mechanisms work? The answer is Recurrent Neural Networks. RNNs have been the solution to deal with most problems in Natural language Processing and not only NLP but in Bio-informatics, Financial Forecasting, Sequence modelling etc.

Infectious diseases pose a threat to human life and could affect the whole world in a very short time. Corona-2019 virus disease (COVID-19) is an example of such harmful diseases. COVID-19 is a pandemic of an emerging infectious disease, called coronavirus disease 2019 or COVID-19, caused by the coronavirus SARS-CoV-2, which first appeared in December 2019 in Wuhan, China, before spreading around the world on a very large scale. The continued rise in the number of positive COVID-19 cases has disrupted the health care system in many countries, creating a lot of stress for governing bodies around the world, hence the need for a rapid way to identify cases of this disease. Medical imaging is a widely accepted technique for early detection and diagnosis of the disease which includes different techniques such as Chest X-ray (CXR), Computed Tomography (CT) scan, etc.

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Research suggests that many more women could benefit from targeted breast cancer treatments – we just need to find them.

The Royal Surrey Foundation Trust treated Emma McCormick, 44, using adaptive radiotherapy after she was diagnosed with the cancer last April and was referred to St Luke's Cancer Centre. The treatment, called Ethos, involves a machine, created by healthcare company Varian, which uses artificial intelligence to deliver a prescription dose to tumours. The AI technology uses daily CT scans to target the specific areas that need radiotherapy, which helps avoid damage to healthy tissue and limit side-effects. Patients are required only to lay still on a flat surface inside the machine for the duration of the treatment. There is a screen above the machine which shows different images, and medical staff can play music to make the treatment more comfortable.

You've got to have more ingenuity. The adjustments have been daily, as have been the complaints. The COVID-19 pandemic has affected moods, behaviors and businesses in likely equal measure. Yet, on the surface, fast food companies have done very well over the last few years. At one time, it seemed like they were the only option. They were certainly the best, easiest and most familiar option.

While mammograms are currently the gold standard in breast cancer screening, swirls of controversy exist: advocates argue for the ability to save lives, (women aged 60 to 69 had a 33 percent lower risk of dying compared to those who didn't get mammograms), and another camp argues about costly and potentially traumatic false positives (a meta-analysis of three randomized trials found a 19 percent over-diagnosis rate from mammography). Even with some saved lives, and some overtreatment and overscreening, current guidelines are still a catch all: women aged 45 to 54 should get mammograms every year. While personalized screening has long been thought of as the answer, tools that can leverage the troves of data to do this lag behind. This led scientists from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and Jameel Clinic for Machine Learning and Health to ask: Can we use machine learning to provide personalized screening? Out of this came Tempo, a technology for creating risk-based screening guidelines.

Video management platforms, equipped with technologies such as artificial intelligence (AI) and machine learning, are vastly expanding capabilities in the area of urban surveillance and public safety, according to research. The Covid-19 pandemic has spurred the use of technologies, such as crowd monitoring, which ABI Research believes are here to stay. It adds that other developments in urban surveillance from live video feeds to bodycams, will be assisted by the introduction of 5G. In its report, Urban Surveillance Technologies and Public Safety Strategies, global technology intelligence firm, ABI Research, forecasts a compound annual growth rate of 11.6 per cent with 1.4 billion closed-circuit television (CCTV) surveillance cameras in urban areas worldwide in 2030. "Currently, the main use of CCTV in public safety is to aid authorities to solve crimes retroactively," said Lindsey Vest, smart cities and smart spaces research analyst at ABI Research.

Neuralink, the brain chip company co-founded in 2016 by the Tesla CEO, posted a notice looking for clinical trial director to work with the start-up's first trial participants. The company is looking to build interfaces between human brains and external electronic devices. According to Musk, the first version of this product is able to transmit information on the muscular activity of the person wearing it via Bluetooth technology. In other words, with each of your movements, the chip can identify the location of your muscles. The chips would both record and stimulate brain activity with the goal of helping those with serious spinal-cord injuries and neurological disorders.