Autoencoders are composed of coding and decoding units, hence they hold the inherent potential of high-performance data compression and signal compressed sensing. The main disadvantages of current autoencoders comprise the following several aspects: the research objective is not data reconstruction but feature representation; the performance evaluation of data recovery is neglected; it is hard to achieve lossless data reconstruction by pure autoencoders, even by pure deep learning. This paper aims for image reconstruction of autoencoders, employs cascade decoders-based autoencoders, perfects the performance of image reconstruction, approaches gradually lossless image recovery, and provides solid theory and application basis for autoencoders-based image compression and compressed sensing. The proposed serial decoders-based autoencoders include the architectures of multi-level decoders and the related optimization algorithms. The cascade decoders consist of general decoders, residual decoders, adversarial decoders and their combinations. It is evaluated by the experimental results that the proposed autoencoders outperform the classical autoencoders in the performance of image reconstruction.

Abstract: Scientific machine learning (SciML) is the burgeoning field combining scientific knowledge with machine learning for data-efficient predictive modeling. We will introduce SciML as the key to effective learning in many engineering applications, such as improving the fidelity of climate models to accelerating clinical trials. This will lead us to the question on the frontier of SciML: what about stiffness? Stiffness is a pervasive quality throughout engineering systems and the most common cause of numerical difficulties in simulation. We will see that handling stiffness in learning, and thus real-world models, requires new training techniques.

At a time when the US and China are divided on everything from economics to human rights, artificial intelligence is still a point of particular friction. With the potential to revolutionize everything from food production and health care to financial markets and surveillance, it's a technology that sparks both optimism and paranoia. One of the field's most influential figures is Andrew Chi-Chih Yao, whose education and professional life have straddled the world's two biggest economies. China-born and Harvard-trained, Yao is his country's only recipient of the Turing Award, computer science's equivalent of a Nobel Prize. After almost 40 years in the US, he returned to China in 2004.

At a time when the US and China are divided on everything from economics to human rights, artificial intelligence is still a point of particular friction. With the potential to revolutionise everything from food production and health care to financial markets and surveillance, it's a technology that sparks both optimism and paranoia. One of the field's most influential figures is Andrew Chi-Chih Yao, whose education and professional life have straddled the world's two biggest economies. China-born and Harvard-trained, Yao is his country's only recipient of the Turing Award, computer science's equivalent of a Nobel Prize. After almost 40 years in the US, he returned to China in 2004.

Caroline Grace has always enjoyed vintage technology. An IT tech in the Mid-Ohio Valley, they collect retro games, laser discs and cassette tapes, but mostly, vinyl records. Their collection is in the thousands, and hundreds of those are video game soundtracks. "I've been a big fan of games all my life," says Grace. "Some of my earliest memories are playing games like Wonder Boy III: The Dragon's Trap and Goof Troop with my dad and brother. I get positive feelings from listening to the Wonder Boy III music now. I have a lot of pleasant memories of playing it with my family back in the day."

More and more businesses are moving into an era wherein artificial intelligence (AI) is a component of every new initiative. One such tool called emotion AI analyzes facial expressions based on a person's faceprint to find their goals, attitudes, and interior emotions. The "basic emotions" theory, which asserts that people all over the world express the same six basic internal emotional states (happiness, surprise, fear, disgust, anger, and sadness) through their facial expressions, which are influenced by our biological and evolutionary origins, is the foundation of this application, which is also known as emotion AI or affective computing. This idea sounds logical on the surface because nonverbal communication heavily relies on facial expressions. Emotion AI is an emerging technology that "allows a computer and systems to identify, process, and simulate human feelings and emotions," according to a recent report by tech industry research firm AIMultiple.

To the right, search space, where all states with the same initial-state distance (g) and estimated goal distance (h) are represented by a single binary decision diagram (to the left), and only those whose g h solution cost need to be considered. Daniel Fišer, Álvaro Torralba and Joerg Hoffmann won an outstanding paper runners-up award at AAAI 2022 for their paper Operator-potential heuristics for symbolic search. Here, Álvaro tells us more about the field of optical planning, their methodology, and how potential heuristics can be used in symbolic search with very positive results. At a very general level, the research is on automated planning. This is a sub-area of AI where we try to answer the question: what is the best way to act given our knowledge of the world?

FICO, the leading provider of analytics and decision management technology, together with Google and academics at UC Berkeley, Oxford, Imperial, UC Irvine and MIT, have announced the winners of the first xML Challenge at the 2018 NeurIPS workshop on Challenges and Opportunities for AI in Financial Services. Participants were challenged to create machine learning models with both high accuracy and explainability using a real-world dataset provided by FICO. Sanjeeb Dash, Oktay Gu nlu k and Dennis Wei, representing IBM Research, were this year's challenge winners. The winning team received the highest score in an empirical evaluation method that considered how useful explanations are for a data scientist with the domain knowledge in the absence of model prediction, as well as how long it takes for such a data scientist to go through the explanations. For their achievements, the IBM team earned a $5,000 prize.

Francesco Petruccione was born in 1961 in Genova (Italy). He studied Physics at the University of Freiburg i. Br. and received his PhD in 1988. He was conferred the "Habilitation" degree (Dr. In 2004 he was appointed Professor of Theoretical Physics at the University of KwaZulu-Natal (UKZN), in Durban (South Africa). In 2005 he was awarded an Innovation Fund grant to set up a Centre for Quantum Technology.

"How big is the universe?" asks Alicia Nash as her face beamed with curiosity and allure. I know because all the data indicates it's infinite," answers John Forbes Nash Jr. with confidence even though there is no evidence to support his statement. "I don't; I just believe it," he says with a rather innocent smile. Though Ron Howard's A Beautiful Mind focused loosely on Nobel prize winner John Forbes Nash's battle with schizophrenia, it did point to his unique ability to see patterns where no patterns exist. He viewed the world in a different light, and that was all he needed to make his mark in history. NICE Actimize is a software company that helps its customers in combating financial crimes.