Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedback

Becoming a (super) hero is almost every kid's dream. During their sheltered childhood, they do whatever it takes to grow up to be one. Work hard, play hard -- all day long. But as they're getting older, distractions are more and more likely to occur. They're getting off track. They start discovering what is feared as simple math. Finally, they end up as a researcher, writing boring, non-impressive papers all day long because they only rely on simple mathematics. No top-tier conferences, no respect, no groupies. Life's over. To finally put an end to this tragedy, we propose a fundamentally new algorithm, dubbed zero2hero, that turns every research paper into a scientific masterpiece. Given a LaTeX document containing ridiculously simple math, based on next-generation large language models, our system automatically over-complicates every single equation so that no one, including yourself, is able to understand what the hell is going on. Future reviewers will be blown away by the complexity of your equations, immediately leading to acceptance. zero2hero gets you back on track, because you deserve to be a hero$^{\text{TM}}$. Code leaked at \url{https://github.com/mweiherer/zero2hero}.

The above scenario may not be the stuff of sci-fi blockbusters, but in its own waypoints to an exciting shift in the relationship between citizens and governments. To some degree, we are already seeing this transformation taking shape--government agencies using machines to crunch data, say, or to improve citizen outreach programs. In our daily lives, meanwhile, AI makes countless decisions on our behalf-- though these are rarely more urgent than what to watch on Netflix tonight. The idea that a smart network might, without prompting, take decisive action in order to save human lives, however, is potentially a very big deal. This might be a way off, but the prospect of AI-enhanced government has led to what one observer describes as "a global race among nations," with several Gulf countries--particularly Saudi Arabia and the UAE-- very much in the running.

Tourism is one of the most important economic sectors in the world (Hollenhorst The Bidirectional Encoder Representations et al., 2014), and its services have many from Transformers (BERT) is currently the characteristics that distinguish them from most important and state-of-the-art natural other products. Services are not tangible language model (Tenney et al., 2019) since and cannot be tested in advance, which is its launch in 2018 by Google. BERT Large, why the customer assumes an increased which is based on a Transformer risk before starting the trip. The service is architecture, is considered one of the most co-created together with the customer, so powerful language models with 24 layers, the customer is an active co-creator of the 16 attention heads, and 340 million service. Services are subject to the unoactu parameters (Lan et al. 2019). BERT is a principle, which means they are pretrained model and can be fine-tuned to produced at the same time as they are perform numerous downstream tasks such consumed, and they are considered as text classification, question answering, bilateral, i.e. a reciprocal relationship sentiment analysis, extractive between persons (Chehimi, 2014). In summarization, named entity recognition, addition, tourism services are relatively or sentence similarity (Egger, 2022). The expensive compared to everyday products model was pretrained on a huge English and have an intercultural dimension.

For all the progress that's been made in the field of artificial intelligence, the world's most flexible, efficient information processor remains the human brain. Although we can quickly make decisions based on incomplete and changing information, many of today's artificial intelligence systems only work after being trained on well-labeled data, and when new information is available, a complete retraining is often required to incorporate it. Now the startup Nara Logics, co-founded by an MIT alumnus, is trying to take artificial intelligence to the next level by more closely mimicking the brain. The company's AI engine uses recent discoveries in neuroscience to replicate brain structure and function at the circuit level. The result is an AI platform that holds a number of advantages over traditional neural network-based systems.

Even if there has been a lot of development in artificial intelligence, the human brain is the most complex and dynamic knowledge processing to date. There is a significant lag time-period when new and accurate knowledge becomes accessible and when used artificial intelligence systems are updated. Still, it is not needed for newly generated and newly built artificial intelligence systems to be retrained. Now, the Cambridge, Massachusetts-based company Nara Logics, which a 2010 MIT graduate created, is working to advance artificial intelligence by focusing on the functionality of the brain. New developments in neuroscience are used in artificial intelligence to imitate the circuit work and simulate it correctly.

For all the progress that's been made in the field of artificial intelligence, the world's most flexible, efficient information processor remains the human brain. Although we can quickly make decisions based on incomplete and changing information, many of today's artificial intelligence systems only work after being trained on well-labeled data, and when new information is available, a complete retraining is often required to incorporate it. Now the startup Nara Logics, co-founded by an MIT alumnus, is trying to take artificial intelligence to the next level by more closely mimicking the brain. The company's AI engine uses recent discoveries in neuroscience to replicate brain structure and function at the circuit level. The result is an AI platform that holds a number of advantages over traditional neural network-based systems.

For all the progress that's been made in the field of artificial intelligence, the world's most flexible, efficient information processor remains the human brain. Although we can quickly make decisions based on incomplete and changing information, many of today's artificial intelligence systems only work after being trained on well-labeled data, and when new information is available, a complete retraining is often required to incorporate it. Now the startup Nara Logics, co-founded by an MIT alumnus, is trying to take artificial intelligence to the next level by more closely mimicking the brain. The company's AI engine uses recent discoveries in neuroscience to replicate brain structure and function at the circuit level. The result is an AI platform that holds a number of advantages over traditional neural network-based systems.

Background: Providing appropriate care for people suffering from COVID-19, the disease caused by the pandemic SARS-CoV-2 virus is a significant global challenge. Many individuals who become infected have pre-existing conditions that may interact with COVID-19 to increase symptom severity and mortality risk. COVID-19 patient comorbidities are likely to be informative about individual risk of severe illness and mortality. Accurately determining how comorbidities are associated with severe symptoms and mortality would thus greatly assist in COVID-19 care planning and provision. Methods: To assess the interaction of patient comorbidities with COVID-19 severity and mortality we performed a meta-analysis of the published global literature, and machine learning predictive analysis using an aggregated COVID-19 global dataset. Results: Our meta-analysis identified chronic obstructive pulmonary disease (COPD), cerebrovascular disease (CEVD), cardiovascular disease (CVD), type 2 diabetes, malignancy, and hypertension as most significantly associated with COVID-19 severity in the current published literature. Machine learning classification using novel aggregated cohort data similarly found COPD, CVD, CKD, type 2 diabetes, malignancy and hypertension, as well as asthma, as the most significant features for classifying those deceased versus those who survived COVID-19. While age and gender were the most significant predictor of mortality, in terms of symptom-comorbidity combinations, it was observed that Pneumonia-Hypertension, Pneumonia-Diabetes and Acute Respiratory Distress Syndrome (ARDS)-Hypertension showed the most significant effects on COVID-19 mortality. Conclusions: These results highlight patient cohorts most at risk of COVID-19 related severe morbidity and mortality which have implications for prioritization of hospital resources.

Agencies should look to early artificial intelligence adopters in government and industry when crafting strategies for adopting such technologies, according to a new report. Deloitte surveyed about 1,100 executives from U.S. organizations using AI in the third quarter of 2018 -- 10% of them from the public sector -- and found 74% of respondents felt the technologies would be "very" or "critically" important within two years. But government is lagging behind its peers in adopting the new technologies, according to the study. Bill Eggers, executive director of Deloitte's Center for Government Insights, said this reflects agencies' investment and strategizing around AI. "Governments were on the lower end of the AI maturity curve compared to other industries, and it's certainly no surprise that financial services and technology companies were the higher end," Eggers told FedScoop. "The reason why this might be is both a skills gap issue, but also the public sector is investing the least in AI of all the different industries that we looked at."