Australian police say a shooting at Bondi Beach, which killed 12 people - including one gunman - targeted the Jewish community on the first day of Hanukkah. Twenty-nine people were injured, with a second gunman in critical condition. Drone footage appears to show a gunman firing from a bridge in a nearby carpark. In the wake of a recent fatal shark attack, the BBC is off the coast in Sydney to learn how authorities are trying to protect people. The BBC's Katy Watson was in the courtroom as Erin Patterson was sentenced to life.

Breakthroughs, discoveries, and DIY tips sent every weekday. When you think of koalas (Phascolarctos cinereus), chances are that words like cute or fluffy come to mind--not cryptic or stealthy. And yet, researchers in southeastern Australia have just discovered hundreds of previously undocumented koalas living surprisingly close to the city of Newcastle. The team conducted what they claim to be the largest and most accurate peer-reviewed koala survey to date. As detailed in a study published this month in the journal Biological Conversation, the survey estimates that a population of 4,357 koalas across 166,302 acres of land is living in the state of New South Wales.

Understanding future weather changes at regional and local scales is crucial for planning and decision-making, particularly in the context of extreme weather events, as well as for broader applications in agriculture, insurance, and infrastructure development. However, the computational cost of downscaling Global Climate Models (GCMs) to the fine resolutions needed for such applications presents a significant barrier. Drawing on advancements in weather forecasting models, this study introduces a cost-efficient downscaling method using a pretrained Earth Vision Transformer (Earth ViT) model. Initially trained on ERA5 data to downscale from 50 km to 25 km resolution, the model is then tested on the higher resolution BARRA-SY dataset at a 3 km resolution. Remarkably, it performs well without additional training, demonstrating its ability to generalize across different resolutions. This approach holds promise for generating large ensembles of regional climate simulations by downscaling GCMs with varying input resolutions without incurring additional training costs. Ultimately, this method could provide more comprehensive estimates of potential future changes in key climate variables, aiding in effective planning for extreme weather events and climate change adaptation strategies.

Satirical news is real news combined with a humorous comment or exaggerated content, and it often mimics the format and style of real news. However, satirical news is often misunderstood as misinformation, especially by individuals from different cultural and social backgrounds. This research addresses the challenge of distinguishing satire from truthful news by leveraging multilingual satire detection methods in English and Arabic. We explore both zero-shot and chain-of-thought (CoT) prompting using two language models, Jais-chat(13B) and LLaMA-2-chat(7B). Our results show that CoT prompting offers a significant advantage for the Jais-chat model over the LLaMA-2-chat model. Specifically, Jais-chat achieved the best performance, with an F1-score of 80\% in English when using CoT prompting. These results highlight the importance of structured reasoning in CoT, which enhances contextual understanding and is vital for complex tasks like satire detection.

The growing integration of digitized infrastructure with Internet of Things (IoT) networks has transformed the management and optimization of building energy consumption. By leveraging IoT-based monitoring systems, stakeholders such as building managers, energy suppliers, and policymakers can make data-driven decisions to improve energy efficiency. However, accurate energy forecasting and analytics face persistent challenges, primarily due to the inherent physical constraints of buildings and the diverse, heterogeneous nature of IoT-generated data. In this study, we conduct a comprehensive benchmarking of two publicly available IoT datasets, evaluating the performance of time series foundation models in the context of building energy analytics. Our analysis shows that single-modal models demonstrate significant promise in overcoming the complexities of data variability and physical limitations in buildings, with future work focusing on optimizing multi-modal models for sustainable energy management.

In the age of information overload, professionals across various fields face the challenge of navigating vast amounts of documentation and ever-evolving standards. Ensuring compliance with standards, regulations, and contractual obligations is a critical yet complex task across various professional fields. We propose a versatile conversational AI assistant framework designed to facilitate compliance checking on the go, in diverse domains, including but not limited to network infrastructure, legal contracts, educational standards, environmental regulations, and government policies. By leveraging retrieval-augmented generation using large language models, our framework automates the review, indexing, and retrieval of relevant, context-aware information, streamlining the process of verifying adherence to established guidelines and requirements. This AI assistant not only reduces the manual effort involved in compliance checks but also enhances accuracy and efficiency, supporting professionals in maintaining high standards of practice and ensuring regulatory compliance in their respective fields. We propose and demonstrate AuditNet, the first conversational AI security assistant designed to assist IoT network security experts by providing instant access to security standards, policies, and regulations.

Founded in 2016 with only a handful of individuals, Quantexa was built with a purpose that through a greater understanding of context, better decisions can be made. We connect the dots within our Customers data using dynamic entity resolution and advanced network analytics to create context, empowering businesses to see the bigger picture and drive real value from their data. Due to the continuous success and high demand from our customers, we are looking for a Data Engineer with a proven track record to join the Quantexa family. What does a Data Engineer role at Quantexa look like? In order to be a successful data Engineer at Quantexa, you'll need to be comfortable dealing with both internal and external stakeholders You will be managing, transforming and cleansing high volume data, helping our Tier 1 clients solve business problems in the area of fraud, compliance and financial crime.

Founded in Sydney in 2017, we saw the need to provide short term, flexible leases of electric bikes to people who want to make money from food delivery. We are doubling in size every 6 months! The problems you solve and the impact you have will be key to helping Zoomo lead the transformation of last-mile logistics as we scale across global markets. We are transitioning billions of urban delivery miles from bikes, cars and trucks to smart e-bikes, starting with food delivery. At Zoomo, we celebrate diversity and are committed to creating an inclusive environment and equal opportunities.

Join Simple Machines and be part of our mission to help clients unlock the full potential of their data through the creation of cutting-edge business driven data platforms and software solutions. As we continue to expand our team across Australia and New Zealand, we are seeking Data Engineers who are unafraid to challenge the status quo and push boundaries. We are looking for a consultant-minded individual who is passionate about working closely with clients to solve complex problems and lead them on a journey from discovery to technical implementation. You have a knack for understanding client needs and delivering market-leading solutions. Simple Machines is not your average consultancy or software engineering firm.

A key question in a number of court cases is whether a speaker on an audio recording is a particular known speaker, for example, whether a speaker on a recording of an intercepted telephone call is the defendant. In most English-speaking countries, expert testimony is only admissible in a court of law if it will potentially assist the judge or the jury to make a decision. If the judge or the jury's speaker identification were equally accurate or more accurate than a forensic scientist's forensic voice comparison, then the forensic-voice-comparison testimony would not be admissible. In a research paper published in the journal Forensic Science International, a multidisciplinary international team of researchers has reported the first set of results from a comprehensive study that compares the accuracy of speaker-identification by individual listeners (like judges or jury members) with the accuracy of a forensic-voice-comparison system that is based on state-of-the-art automatic-speaker-recognition technology, and that does so using recordings that reflect the conditions of an actual case. The questioned-speaker recording was of a telephone call with background office noise, and the known-speaker recording was of a police interview conducted in echoey room with background ventilation-system noise.