First son Hunter Biden will be sentenced on Nov. 13, the week after the general election, after he was found guilty on charges in the criminal case focused on his purchase of a handgun in 2018. Judge Maryellen Noreika, in a court order Friday, set the sentencing date for Wednesday, Nov. 13, at 10:00 a.m. at the J. Caleb Boggs Federal Building in Wilmington, Delaware. President Biden's son will learn his fate 8 days after the 2020 presidential election. Hunter Biden was found guilty in June of making a false statement in the purchase of a gun, making a false statement related to information required to be kept by a federally licensed gun dealer, and possession of a gun by a person who is an unlawful user of or addicted to a controlled substance. He faces a total maximum prison time of 25 years for the three charges.

"That all-important macro data we have been hammering for months is finally starting to turn in an ominous direction," said Alex McGrath, chief investment officer at NorthEnd Private Wealth. Markets are now predicting a half a percent cut in interest rates at the Fed's next meeting in September, up from the quarter-point cut investors had been anticipating as of Thursday, according to CME FedWatch. The two-year Treasury yield, which is also reflective of short-term interest rate expectations, fell 20 basis points, to 3.96 percent. This week had already been a rocky one for Wall Street. The Federal Reserve's indication on Wednesday that it was moving closer to cutting interest rates in September prompted an accelerated market rally, and the S&P 500 rose 2 percent on comments by Jerome H. Powell, the Fed chair.

The new Labour government has shelved 1.3bn of funding pledged by the Conservatives for technology and artificial intelligence projects, putting the future of the UK's first next-generation supercomputer in doubt. The projects, announced last year, include 800m for the creation of an exascale supercomputer at the University of Edinburgh and a further 500m for the AI Research Resource, which funds computing power for AI. The government argues that these were "unfunded commitments". The Department for Science, Innovation and Technology said the funding had been promised by the previous government but had not been allocated in its spending plans. A spokesperson said: "We are absolutely committed to building technology infrastructure that delivers growth and opportunity for people across the UK. "The government is taking difficult and necessary spending decisions across all departments in the face of billions of pounds of unfunded commitments.

Less than three hours after the stabbing attack on Monday that led to the death of three children, an AI-generated image was shared on X by an account called Europe Invasion. It depicted bearded men in traditional Muslim dress outside the Houses of Parliament, one waving a knife, behind a crying child in a union jack T-shirt. The tweet, which has since been viewed 900,000 times, was captioned: "We must protect our children!" and shared by one of the most potent accounts for misinformation about the Southport stabbings. AI technology has been used in other ways, including an anti-immigration Facebook group that illustrated a call to attend a rally in Middlesbrough by generating an image of a large crowd at the town's cenotaph. Platforms like Suno – which employs AI to generate music complete with vocals and instruments – have been used to create online songs combining references to Southport with xenophobic content.

A mother and her daughter were killed by Russian shelling that hit the town of Nikopol in Ukraine's eastern Dnipropetrovsk region. Local governor Serhiy Lysak said private houses, a fire station, a college, a school and buses were damaged. Nikopol sits on the right bank of the Dnipro River Two people were injured by debris as Ukraine repelled a Russian drone attack on the region outside Kyiv. One of those hurt was Ilya Ponomaryov, a former Russian lawmaker who has lived in Ukraine for years and is a critic of the Kremlin. He wrote on Facebook that a drone exploded outside his front door, inflicting shrapnel wounds and causing a fire.

The intelligent reflection surface (IRS) and unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is widely used in temporary and emergency scenarios. Our goal is to minimize the energy consumption of the MEC system by jointly optimizing UAV locations, IRS phase shift, task offloading, and resource allocation with a variable number of UAVs. To this end, we propose a Flexible REsource Scheduling (FRES) framework by employing a novel deep progressive reinforcement learning which includes the following innovations: Firstly, a novel multi-task agent is presented to deal with the mixed integer nonlinear programming (MINLP) problem. The multi-task agent has two output heads designed for different tasks, in which a classified head is employed to make offloading decisions with integer variables while a fitting head is applied to solve resource allocation with continuous variables. Secondly, a progressive scheduler is introduced to adapt the agent to the varying number of UAVs by progressively adjusting a part of neurons in the agent. This structure can naturally accumulate experiences and be immune to catastrophic forgetting. Finally, a light taboo search (LTS) is introduced to enhance the global search of the FRES. The numerical results demonstrate the superiority of the FRES framework which can make real-time and optimal resource scheduling even in dynamic MEC systems.

Artificial intelligence (AI) has revolutionized decision-making processes and systems throughout society and, in particular, has emerged as a significant technology in high-impact scenarios of national interest. Yet, despite AI's impressive predictive capabilities in controlled settings, it still suffers from a range of practical setbacks preventing its widespread use in various critical scenarios. In particular, it is generally unclear if a given AI system's predictions can be trusted by decision-makers in downstream applications. To address the need for more transparent, robust, and trustworthy AI systems, a suite of tools has been developed to quantify the uncertainty of AI predictions and, more generally, enable AI to "self-assess" the reliability of its predictions. In this manuscript, we categorize methods for AI self-assessment along several key dimensions and provide guidelines for selecting and designing the appropriate method for a practitioner's needs. In particular, we focus on uncertainty estimation techniques that consider the impact of self-assessment on the choices made by downstream decision-makers and on the resulting costs and benefits of decision outcomes. To demonstrate the utility of our methodology for self-assessment design, we illustrate its use for two realistic national-interest scenarios. This manuscript is a practical guide for machine learning engineers and AI system users to select the ideal self-assessment techniques for each problem.

The rapid growth of Artificial Intelligence (AI) has underscored the urgent need for responsible AI practices. Despite increasing interest, a comprehensive AI risk assessment toolkit remains lacking. This study introduces our Responsible AI (RAI) Question Bank, a comprehensive framework and tool designed to support diverse AI initiatives. By integrating AI ethics principles such as fairness, transparency, and accountability into a structured question format, the RAI Question Bank aids in identifying potential risks, aligning with emerging regulations like the EU AI Act, and enhancing overall AI governance. A key benefit of the RAI Question Bank is its systematic approach to linking lower-level risk questions to higher-level ones and related themes, preventing siloed assessments and ensuring a cohesive evaluation process. Case studies illustrate the practical application of the RAI Question Bank in assessing AI projects, from evaluating risk factors to informing decision-making processes. The study also demonstrates how the RAI Question Bank can be used to ensure compliance with standards, mitigate risks, and promote the development of trustworthy AI systems. This work advances RAI by providing organizations with a valuable tool to navigate the complexities of ethical AI development and deployment while ensuring comprehensive risk management.

In order to deploy deep neural networks (DNNs) in high-stakes scenarios, it is imperative that DNNs provide inference robust to external perturbations - both intentional and unintentional. Although the resilience of DNNs to intentional and unintentional perturbations has been widely investigated, a unified vision of these inherently intertwined problem domains is still missing. In this work, we fill this gap by providing a survey of the state of the art and highlighting the similarities of the proposed approaches.We also analyze the research challenges that need to be addressed to deploy resilient and secure DNNs. As there has not been any such survey connecting the resilience of DNNs to intentional and unintentional perturbations, we believe this work can help advance the frontier in both domains by enabling the exchange of ideas between the two communities.

This paper introduces Semantic Haar-Adaptive Refined Pyramid Network (SHARP-Net), a novel architecture for semantic segmentation. SHARP-Net integrates a bottom-up pathway featuring Inception-like blocks with varying filter sizes (3x3$ and 5x5), parallel max-pooling, and additional spatial detection layers. This design captures multi-scale features and fine structural details. Throughout the network, depth-wise separable convolutions are used to reduce complexity. The top-down pathway of SHARP-Net focuses on generating high-resolution features through upsampling and information fusion using $1\times1$ and $3\times3$ depth-wise separable convolutions. We evaluated our model using our developed challenging Culvert-Sewer Defects dataset and the benchmark DeepGlobe Land Cover dataset. Our experimental evaluation demonstrated the base model's (excluding Haar-like features) effectiveness in handling irregular defect shapes, occlusions, and class imbalances. It outperformed state-of-the-art methods, including U-Net, CBAM U-Net, ASCU-Net, FPN, and SegFormer, achieving average improvements of 14.4% and 12.1% on the Culvert-Sewer Defects and DeepGlobe Land Cover datasets, respectively, with IoU scores of 77.2% and 70.6%. Additionally, the training time was reduced. Furthermore, the integration of carefully selected and fine-tuned Haar-like features enhanced the performance of deep learning models by at least 20%. The proposed SHARP-Net, incorporating Haar-like features, achieved an impressive IoU of 94.75%, representing a 22.74% improvement over the base model. These features were also applied to other deep learning models, showing a 35.0% improvement, proving their versatility and effectiveness. SHARP-Net thus provides a powerful and efficient solution for accurate semantic segmentation in challenging real-world scenarios.