Deep learning based image enhancement models have largely improved the readability of fundus images in order to decrease the uncertainty of clinical observations and the risk of misdiagnosis. However, due to the difficulty of acquiring paired real fundus images at different qualities, most existing methods have to adopt synthetic image pairs as training data. The domain shift between the synthetic and the real images inevitably hinders the generalization of such models on clinical data. In this work, we propose an end-to-end optimized teacher-student framework to simultaneously conduct image enhancement and domain adaptation. The student network uses synthetic pairs for supervised enhancement, and regularizes the enhancement model to reduce domain-shift by enforcing teacher-student prediction consistency on the real fundus images without relying on enhanced ground-truth. Moreover, we also propose a novel multi-stage multi-attention guided enhancement network (MAGE-Net) as the backbones of our teacher and student network. Our MAGE-Net utilizes multi-stage enhancement module and retinal structure preservation module to progressively integrate the multi-scale features and simultaneously preserve the retinal structures for better fundus image quality enhancement. Comprehensive experiments on both real and synthetic datasets demonstrate that our framework outperforms the baseline approaches. Moreover, our method also benefits the downstream clinical tasks.

Sydney, Australia Xinhua A global study released on Wednesday has found that three out of five people, about 61 percent, are wary about trusting artificial intelligence (AI) systems, reporting either ambivalence or an unwillingness to trust. Researchers from the University of Queensland and KPMG Australia surveyed more than 17,000 people from 17 countries across the world, such as Australia, China, France and the United States, which were regarded by the study as leaders in AI activity and readiness within their region. According to the study, only 39 percent said they are willing to trust AI systems, while a third of people worldwide reported high acceptance. Though about 85 percent believed the use of AI will result in a wide range of benefits, including improved efficiency, innovation, reduced costs and better use of resources, 73 percent of the respondents also voiced concern over potential risks from AI use. Among the nine listed risks, cybersecurity is the dominant concern raised by 84 percent of people, followed by manipulation or harmful use of AI and job loss due to automation.

In a Brooklyn lab stuffed with 3D printers and a makeshift pickleball court, employees at a brain interface startup called Synchron are working on technology designed to transform daily life for people with paralysis. The Synchron Switch is implanted through the blood vessels to allow people with no or very limited physical mobility to operate technology such as cursors and smart home devices using their mind. So far, the nascent technology has been used on three patients in the U.S. and four in Australia. "I've seen moments between patient and partner, or patient and spouse, where it's incredibly joyful and empowering to have regained an ability to be a little bit more independent than before," Synchron CEO Tom Oxley told CNBC in an interview. "It helps them engage in ways that we take for granted."

Five days after Russia launched its full-scale invasion of Ukraine, a year ago this week, US-based facial recognition company Clearview AI offered the Ukrainian government free access to its technology, suggesting that it could be used to reunite families, identify Russian operatives, and fight misinformation. Soon afterward, the Ukraine government revealed it was using the technology to scan the faces of dead Russian soldiers to identify their bodies and notify their families. By December 2022, Mykhailo Fedorov, Ukraine's vice prime minister and minister of digital transformation, was tweeting a picture of himself with Clearview AI's CEO Hoan Ton-That, thanking the company for its support. Accounting for the dead and letting families know the fate of their relatives is a human rights imperative written into international treaties, protocols, and laws like the Geneva Conventions and the International Committee of the Red Cross' (ICRC) Guiding Principles for Dignified Management of the Dead. It is also tied to much deeper obligations.

AI in pharma refers to the use of automated algorithms to jobs that normally need human intelligence. Large datasets including disease patterns can be successfully identified by AI solutions in pharma, and they can also assist in understanding which medication formulations would be most effective for treating certain ailments. The artificial intelligence (AI) in the pharma market covered in the report is segmented by technology into context-aware processing, natural language processing, querying method, and deep learning; by drug type into the small molecule, large molecules; by application into diagnosis, clinical trial research, drug discovery, research and development, epidemic prediction. The artificial intelligence (AI) in pharma market research report is one of a series of new reports from The Business Research Company that provides artificial intelligence (AI) in pharma market statistics, including artificial intelligence (AI) in pharma industry global market size, regional shares, competitors with artificial intelligence (AI) in pharma market share, detailed artificial intelligence (AI) in pharma market segments, market trends and opportunities, and any further data you may need to thrive in the artificial intelligence (AI) in pharma industry. This artificial intelligence (AI) in pharma market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenario of the industry.

Without urgent investment in building a sovereign capability in locally developed artificial intelligence tools, Australia risks ceding control of its strategic systems and technology to foreign Interests, a group of eminent scientists have warned. An open letter from 14 of the nation's leading experts on AI and robotics published on Wednesday called for the urgent development of national research and education strategies to build locally-owned and operated AI capacity. The so-called Kingston AI Group, which includes professors from eight universities, says Australia must build a domestic pipeline of top AI talent in order to reduce the significant risks and maximise the potential economic and security benefits of this rapidly emerging technology. "A failure to deliver the AI workforce Australia needs will harm our future economic growth, shrink our economic complexity, and weaken our sovereign control in key industry sectors," the authors of the paper say. "If done strategically, a major investment would result in Australia becoming one of the leading countries in AI."

The recently emerged spectral clustering surpasses conventional clustering methods by detecting clusters of any shape without the convexity assumption. Unfortunately, with a computational complexity of $O(n^3)$, it was infeasible for multiple real applications, where $n$ could be large. This stimulates researchers to propose the approximate spectral clustering (ASC). However, most of ASC methods assumed that the number of clusters $k$ was known. In practice, manual setting of $k$ could be subjective or time consuming. The proposed algorithm has two relevance metrics for estimating $k$ in two vital steps of ASC. One for selecting the eigenvectors spanning the embedding space, and the other to discover the number of clusters in that space. The algorithm used a growing neural gas (GNG) approximation, GNG is superior in preserving input data topology. The experimental setup demonstrates the efficiency of the proposed algorithm and its ability to compete with similar methods where $k$ was set manually.

Much of named entity recognition (NER) research focuses on developing dataset-specific models based on data from the domain of interest, and a limited set of related entity types. This is frustrating as each new dataset requires a new model to be trained and stored. In this work, we present a ``versatile'' model -- the Prompting-based Unified NER system (PUnifiedNER) -- that works with data from different domains and can recognise up to 37 entity types simultaneously, and theoretically it could be as many as possible. By using prompt learning, PUnifiedNER is a novel approach that is able to jointly train across multiple corpora, implementing intelligent on-demand entity recognition. Experimental results show that PUnifiedNER leads to significant prediction benefits compared to dataset-specific models with impressively reduced model deployment costs. Furthermore, the performance of PUnifiedNER can achieve competitive or even better performance than state-of-the-art domain-specific methods for some datasets. We also perform comprehensive pilot and ablation studies to support in-depth analysis of each component in PUnifiedNER.

Data scarcity is a problem that occurs in languages and tasks where we do not have large amounts of labeled data but want to use state-of-the-art models. Such models are often deep learning models that require a significant amount of data to train. Acquiring data for various machine learning problems is accompanied by high labeling costs. Data augmentation is a low-cost approach for tackling data scarcity. This paper gives an overview of current state-of-the-art data augmentation methods used for natural language processing, with an emphasis on methods for neural and transformer-based models. Furthermore, it discusses the practical challenges of data augmentation, possible mitigations, and directions for future research.

Social Internet of Things (SIoT), a promising and emerging paradigm that injects the notion of social networking into smart objects (i.e., things), paving the way for the next generation of Internet of Things. However, due to the risks and uncertainty, a crucial and urgent problem to be settled is establishing reliable relationships within SIoT, that is, trust evaluation. Graph neural networks for trust evaluation typically adopt a straightforward way such as one-hot or node2vec to comprehend node characteristics, which ignores the valuable semantic knowledge attached to nodes. Moreover, the underlying structure of SIoT is usually complex, including both the heterogeneous graph structure and pairwise trust relationships, which renders hard to preserve the properties of SIoT trust during information propagation. To address these aforementioned problems, we propose a novel knowledge-enhanced graph neural network (KGTrust) for better trust evaluation in SIoT. Specifically, we first extract useful knowledge from users' comment behaviors and external structured triples related to object descriptions, in order to gain a deeper insight into the semantics of users and objects. Furthermore, we introduce a discriminative convolutional layer that utilizes heterogeneous graph structure, node semantics, and augmented trust relationships to learn node embeddings from the perspective of a user as a trustor or a trustee, effectively capturing multi-aspect properties of SIoT trust during information propagation. Finally, a trust prediction layer is developed to estimate the trust relationships between pairwise nodes. Extensive experiments on three public datasets illustrate the superior performance of KGTrust over state-of-the-art methods.