Recently, there has been an explosion of mobile applications that perform computationally intensive tasks such as video streaming, data mining, virtual reality, augmented reality, image processing, video processing, face recognition, and online gaming. However, user devices (UDs), such as tablets and smartphones, have a limited ability to perform the computation needs of the tasks. Mobile edge computing (MEC) has emerged as a promising technology to meet the increasing computing demands of UDs. Task offloading in MEC is a strategy that meets the demands of UDs by distributing tasks between UDs and MEC servers. Deep reinforcement learning (DRL) is gaining attention in task-offloading problems because it can adapt to dynamic changes and minimize online computational complexity. However, the various types of continuous and discrete resource constraints on UDs and MEC servers pose challenges to the design of an efficient DRL-based task-offloading strategy. Existing DRL-based task-offloading algorithms focus on the constraints of the UDs, assuming the availability of enough storage resources on the server. Moreover, existing multiagent DRL (MADRL)--based task-offloading algorithms are homogeneous agents and consider homogeneous constraints as a penalty in their reward function. We proposed a novel combinatorial client-master MADRL (CCM\_MADRL) algorithm for task offloading in MEC (CCM\_MADRL\_MEC) that enables UDs to decide their resource requirements and the server to make a combinatorial decision based on the requirements of the UDs. CCM\_MADRL\_MEC is the first MADRL in task offloading to consider server storage capacity in addition to the constraints in the UDs. By taking advantage of the combinatorial action selection, CCM\_MADRL\_MEC has shown superior convergence over existing MADDPG and heuristic algorithms.

The goal of this paper is to develop a multilingual classifier and conditional probability estimator of occupation codes for online job advertisements according in accordance with the International Standard Classification of Occupations (ISCO) extended with the Polish Classification of Occupations and Specializations (KZiS), which is analogous to the European Classification of Occupations. In this paper, we utilise a range of data sources, including a novel one, namely the Central Job Offers Database, which is a register of all vacancies submitted to Public Employment Offices. Their staff members code the vacancies according to the ISCO and KZiS. A hierarchical multi-class classifier has been developed based on the transformer architecture. The classifier begins by encoding the jobs found in advertisements to the widest 1-digit occupational group, and then narrows the assignment to a 6-digit occupation code. We show that incorporation of the hierarchical structure of occupations improves prediction accuracy by 1-2 percentage points, particularly for the hand-coded online job advertisements. Finally, a bilingual (Polish and English) and multilingual (24 languages) model is developed based on data translated using closed and open-source software. The open-source software is provided for the benefit of the official statistics community, with a particular focus on international comparability.

Implicit bias describes the phenomenon where optimization-based training algorithms, without explicit regularization, show a preference for simple estimators even when more complex estimators have equal objective values. Multiple works have developed the theory of implicit bias for binary classification under the assumption that the loss satisfies an exponential tail property. However, there is a noticeable gap in analysis for multiclass classification, with only a handful of results which themselves are restricted to the cross-entropy loss. In this work, we employ the framework of Permutation Equivariant and Relative Margin-based (PERM) losses [Wang and Scott, 2024] to introduce a multiclass extension of the exponential tail property. This class of losses includes not only cross-entropy but also other losses. Using this framework, we extend the implicit bias result of Soudry et al. [2018] to multiclass classification. Furthermore, our proof techniques closely mirror those of the binary case, thus illustrating the power of the PERM framework for bridging the binary-multiclass gap.

Meta announced Monday that it would allow US national security agencies and defense contractors to use its open-source artificial intelligence model, Llama. The announcement came days after Reuters reported an older version of Llama had been used by researchers to develop defense applications for the military wing of the Chinese government. Meta's policies typically prohibit the use of its open-source large language model for "military, warfare, nuclear industries or applications, [and] espionage". The company is making an exception for US agencies and contractors as well as similar national security agencies in the UK, Canada, Australia and New Zealand, according to Bloomberg. "These kinds of responsible and ethical uses of open source AI models like Llama will not only support the prosperity and security of the United States, they will also help establish US open-source standards in the global race for AI leadership," Nick Clegg, Meta's president of global affairs, wrote in a blog post.

It speaks a bit of Welsh, can recite the building regulations, refuses to say whether Rishi Sunak is better than Keir Starmer and won't explain the UK corporation tax regime. The government is launching an artificial intelligence chatbot to help businesses chart the 700,000 page labyrinth that is the Gov.UK website and it looks like users can expect varied results. The experimental system will be tested by up to 15,000 business users before wider availability, possibly next year. Before you get started it warns: "The biggest limitation of AI tools like me is a problem known as'hallucination'. This means we sometimes make up false information or facts but present them to you confidently."

Today's best AI models, like OpenAI's ChatGPT and Anthropic's Claude, come with conditions: their creators control the terms on which they are accessed to prevent them being used in harmful ways. This is in contrast with'open' models, which can be downloaded, modified, and used by anyone for almost any purpose. A new report by non-profit research organization Epoch AI found that open models available today are about a year behind the top closed models. "The best open model today is on par with closed models in performance, but with a lag of about one year," says Ben Cottier, lead researcher on the report. Meta's Llama 3.1 405B, an open model released in July, took about 16 months to match the capabilities of the first version of GPT-4.

Today in the newsletter: X's final form, learnings from a packed week of earnings, and niche online Halloween costumes. Thank you for joining me. With the US election, X's transformation into Elon Musk's weapon reaches its peak. He has succeeded in bending his social network to his will. Last week, Musk deputized his followers to report any "potential instances of voter fraud and irregularities", tweeting about and linking to a forum within X called the "election integrity community".

Fox News chief political anchor Bret Baier has the latest on the pros and cons of the bombshell developments on'Special Report.' It is said that predicting the future isn't magic. If that's the case, perhaps we should ask AI when Congress might pass a bill to regulate the emerging technology – before it spirals out of control. There's a push by Congressional leaders to approve a bill regulating AI when lawmakers return to Washington after the election. But the path to passage - and developing a consensus on establishing guardrails for AI - is far from certain.

Autonomous surface vessels (ASVs) are becoming increasingly significant in enhancing the safety and sustainability of maritime operations. To ensure the reliability of modern control algorithms utilized in these vessels, digital twins (DTs) provide a robust framework for conducting safe and effective simulations within a virtual environment. Digital twins are generally classified on a scale from 0 to 5, with each level representing a progression in complexity and functionality: Level 0 (Standalone) employs offline modeling techniques; Level 1 (Descriptive) integrates sensors and online modeling to enhance situational awareness; Level 2 (Diagnostic) focuses on condition monitoring and cybersecurity; Level 3 (Predictive) incorporates predictive analytics; Level 4 (Prescriptive) embeds decision-support systems; and Level 5 (Autonomous) enables advanced functionalities such as collision avoidance and path following. These digital representations not only provide insights into the vessel's current state and operational efficiency but also predict future scenarios and assess life endurance. By continuously updating with real-time sensor data, the digital twin effectively corrects modeling errors and enhances decision-making processes. Since DTs are key enablers for complex autonomous systems, this paper introduces a comprehensive methodology for establishing a digital twin framework specifically tailored for ASVs. Through a detailed literature survey, we explore existing state-of-the-art enablers across the defined levels, offering valuable recommendations for future research and development in this rapidly evolving field.

High-speed flight vehicles, which travel much faster than the speed of sound, are crucial for national defense and space exploration. However, accurately predicting their behavior under numerous, varied flight conditions is a challenge and often prohibitively expensive. The proposed approach involves creating smarter, more efficient machine learning models (also known as surrogate models or meta models) that can fuse data generated from a variety of fidelity levels -- to include engineering methods, simulation, wind tunnel, and flight test data -- to make more accurate predictions. These models are able to move the bulk of the computation from high performance computing (HPC) to single user machines (laptop, desktop, etc.). The project builds upon previous work but introduces code improvements and an informed perspective on the direction of the field. The new surrogate modeling framework is now modular and, by design, broadly applicable to many modeling problems. The new framework also has a more robust automatic hyperparameter tuning capability and abstracts away most of the pre- and post-processing tasks. The Gaussian process regression and deep neural network-based models included in the presented framework were able to model two datasets with high accuracy (R^2>0.99). The primary conclusion is that the framework is effective and has been delivered to the Air Force for integration into real-world projects. For future work, significant and immediate investment in continued research is crucial. The author recommends further testing and refining modeling methods that explicitly incorporate physical laws and are robust enough to handle simulation and test data from varying resolutions and sources, including coarse meshes, fine meshes, unstructured meshes, and limited experimental test points.