Most of the successful deep neural network architectures are structured, often consisting of elements like convolutional neural networks and gated recurrent neural networks. Recently, graph neural networks (GNNs) have been successfully applied to graph-structured data such as point cloud and molecular data. These networks often only consider pairwise dependencies, as they operate on a graph structure. We generalize the GNN into a factor graph neural network (FGNN) providing a simple way to incorporate dependencies among multiple variables. We show that FGNN is able to represent Max-Product belief propagation, an approximate inference method on probabilistic graphical models, providing a theoretical understanding on the capabilities of FGNN and related GNNs. Experiments on synthetic and real datasets demonstrate the potential of the proposed architecture.

With the development of machine learning techniques, the attention of research has been moved from single-modal learning to multi-modal learning, as real-world data exist in the form of different modalities. However, multi-modal models often carry more information than single-modal models and they are usually applied in sensitive scenarios, such as medical report generation or disease identification. Compared with the existing membership inference against machine learning classifiers, we focus on the problem that the input and output of the multi-modal models are in different modalities, such as image captioning. This work studies the privacy leakage of multi-modal models through the lens of membership inference attack, a process of determining whether a data record involves in the model training process or not.

This paper discusses the effectiveness of leveraging Chatbot: Generative Pre-trained Transformer (ChatGPT) versions 3.5 and 4 for analyzing research papers for effective writing of scientific literature surveys. The study selected the \textit{Application of Artificial Intelligence in Breast Cancer Treatment} as the research topic. Research papers related to this topic were collected from three major publication databases Google Scholar, Pubmed, and Scopus. ChatGPT models were used to identify the category, scope, and relevant information from the research papers for automatic identification of relevant papers related to Breast Cancer Treatment (BCT), organization of papers according to scope, and identification of key information for survey paper writing. Evaluations performed using ground truth data annotated using subject experts reveal, that GPT-4 achieves 77.3\% accuracy in identifying the research paper categories and 50\% of the papers were correctly identified by GPT-4 for their scopes. Further, the results demonstrate that GPT-4 can generate reasons for its decisions with an average of 27\% new words, and 67\% of the reasons given by the model were completely agreeable to the subject experts.

For this experiment, the researchers relied on 61 hours of video from a helmet camera worn by a child who lives near Adelaide, Australia. That child, Sam, wore the camera off and on for one and a half years, from the time he was six months old until a little after his second birthday. The camera captured the things Sam looked at and paid attention to during about 1% of his waking hours. It recorded Sam's two cats, his parents, his crib and toys, his house, his meals, and much more. "This data set was totally unique," Lake says.

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ADELAIDE, AUSTRALIA--Cosmos Magazine reports that Daryl Wesley of Flinders University and Mimal and Marrku Traditional Owners of the Wilton River area used machine learning to analyze changes in rock art styles in northern Australia's Arnhem Land. The computer was supplied with information of more than 1,000 types of objects and a mathematical model to determine how similar two images are to one another. The model was then applied to images of the rock art. "One amazing outcome is that the machine learning approach ordered the styles in the same chronology that archaeologists have ordered them in by inspecting which appear on top of which," said team member Jarrad Kowlessar of Flinders University. Styles of artwork that are closer to each other in age are also closer to each other in appearance, he explained.

Adelaide (Australia): Researchers at the University of South Australia have developed a cost-effective new technique to monitor soil moisture using a standard digital camera and machine learning technology. The United Nations predicts that by 2050 many areas of the planet may not have enough fresh water to meet the demands of agriculture if we continue our current patterns of use. One solution to this global dilemma is the development of more efficient irrigation, central to which is precision monitoring of soil moisture, allowing sensors to guide'smart' irrigation systems to ensure water is applied at the optimum time and rate. Current methods for sensing soil moisture are problematic -- buried sensors are susceptible to salts in the substrate and require specialised hardware for connections, while thermal imaging cameras are expensive and can be compromised by climatic conditions such as sunlight intensity, fog, and clouds. Researchers from The University of South Australia and Baghdad's Middle Technical University have developed a cost-effective alternative that may make precision soil monitoring simple and affordable in almost any circumstance.

Zygmunt received his PhD degree in Computer Science from the University of Adelaide, Australia in 2013, and his MSc degree in Computer Science from the University of KwaZulu-Natal, South Africa in 2009. He is a senior research fellow at the Australian Institute for Machine Learning. His research lies at the interface of computer vision, machine learning, and challenging industry problems. He develops algorithms that allow computers to perform tasks typically associated with human intelligence. In the last couple of years, his work has focused on the application of machine learning and image processing techniques for the development of smart medical devices.

Elon Musk is reportedly launching an investigation into an employee who sabotaged the company. Elon Musk, Chief Executive Officer of Space Exploration Technologies Corporation, speaks on the final day of the 68th International Astronautical Congress in Adelaide, Australia, on Sept. 29, 2017. Elon Musk has called lidar a crutch. The Tesla CEO believes he can build self-driving and semi-autonomous cars without relying on the technology, which uses lasers to help the cars map and navigate their surroundings. Instead, Tesla has looked to cameras and radar -- without lidar -- to do much of the work needed for its Autopilot driver assistance system.

Tesla founder Elon Musk believes he can rebuild Puerto Rico's power grid. Tesla CEO Elon Musk speaks during a news conference at the Adelaide Oval in Adelaide, Australia on July 7, 2017. Tesla will partner with French renewable energy developer Neoen to build the world's biggest Lithium IIon Battery, a 100MW battery that will be built in James Town, the South Australian government announced on the day. SAN FRANCISCO -- Elon Musk has so many irons in the fire, you can't see the fire. The Tesla and SpaceX CEO tweeted Friday that he is delaying the unveiling of a self-driving truck in order to focus his attention on smoothing out Model 3 production issues and helping devastated Puerto Rico switch over to solar power.