In the development of safety and mission-critical systems, including autonomous space robotic missions, complex behaviour is captured during the requirements elicitation phase. Requirements are typically expressed using natural language which is ambiguous and not amenable to formal verification methods that can provide robust guarantees of system behaviour. To support the definition of formal requirements, specification patterns provide reusable, logic-based templates. A suite of robotic specification patterns, along with their formalisation in NASA's Formal Requirements Elicitation Tool (FRET) already exists. These pre-existing requirement patterns are domain agnostic and, in this paper we explore their applicability for space missions. To achieve this we carried out a literature review of existing space missions and formalised their requirements using FRET, contributing a corpus of space mission requirements. We categorised these requirements using pre-existing specification patterns which demonstrated their applicability in space missions. However, not all of the requirements that we formalised corresponded to an existing pattern so we have contributed 5 new requirement specification patterns as well as several variants of the existing and new patterns. We also conducted an expert evaluation of the new patterns, highlighting their benefits and limitations.

Smart contracts are trustworthy, immutable, and automatically executed programs on the blockchain. Their execution requires the Gas mechanism to ensure efficiency and fairness. However, due to non-optimal coding practices, many contracts contain Gas waste patterns that need to be optimized. Existing solutions mostly rely on manual discovery, which is inefficient, costly to maintain, and difficult to scale. Recent research uses large language models (LLMs) to explore new Gas waste patterns. However, it struggles to remain compatible with existing patterns, often produces redundant patterns, and requires manual validation/rewriting. To address this gap, we present GasAgent, the first multi-agent system for smart contract Gas optimization that combines compatibility with existing patterns and automated discovery/validation of new patterns, enabling end-to-end optimization. GasAgent consists of four specialized agents, Seeker, Innovator, Executor, and Manager, that collaborate in a closed loop to identify, validate, and apply Gas-saving improvements. Experiments on 100 verified real-world contracts demonstrate that GasAgent successfully optimizes 82 contracts, achieving an average deployment Gas savings of 9.97%. In addition, our evaluation confirms its compatibility with existing tools and validates the effectiveness of each module through ablation studies. To assess broader usability, we further evaluate 500 contracts generated by five representative LLMs across 10 categories and find that GasAgent optimizes 79.8% of them, with deployment Gas savings ranging from 4.79% to 13.93%, showing its usability as the optimization layer for LLM-assisted smart contract development.

We introduce ICE-ID, a novel benchmark dataset for historical identity resolution, comprising 220 years (1703-1920) of Icelandic census records. ICE-ID spans multiple generations of longitudinal data, capturing name variations, demographic changes, and rich genealogical links. To the best of our knowledge, this is the first large-scale, open tabular dataset specifically designed to study long-term person-entity matching in a real-world population. We define identity resolution tasks (within and across census waves) with clearly documented metrics and splits. We evaluate a range of methods: handcrafted rule-based matchers, a ML ensemble as well as LLMs for structured data (e.g. transformer-based tabular networks) against a novel approach to tabular data called NARS (Non-Axiomatic Reasoning System) - a general-purpose AI framework designed to reason with limited knowledge and resources. Its core is Non-Axiomatic Logic (NAL), a term-based logic. Our experiments show that NARS is suprisingly simple and competitive with other standard approaches, achieving SOTA at our task. By releasing ICE-ID and our code, we enable reproducible benchmarking of identity resolution approaches in longitudinal settings and hope that ICE-ID opens new avenues for cross-disciplinary research in data linkage and historical analytics.

Many real-world graph learning tasks require handling dynamic graphs where new nodes and edges emerge. Dynamic graph learning methods commonly suffer from the catastrophic forgetting problem, where knowledge learned for previous graphs is overwritten by updates for new graphs. To alleviate the problem, continual graph learning methods are proposed. However, existing continual graph learning methods aim to learn new patterns and maintain old ones with the same set of parameters of fixed size, and thus face a fundamental tradeoff between both goals. In this paper, we propose Parameter Isolation GNN (PI-GNN) for continual learning on dynamic graphs that circumvents the tradeoff via parameter isolation and expansion. Our motivation lies in that different parameters contribute to learning different graph patterns. Based on the idea, we expand model parameters to continually learn emerging graph patterns. Meanwhile, to effectively preserve knowledge for unaffected patterns, we find parameters that correspond to them via optimization and freeze them to prevent them from being rewritten. Experiments on eight real-world datasets corroborate the effectiveness of PI-GNN compared to state-of-the-art baselines.

Eric lives in France and he owns a company named LBKE where he works as a developer and as a R&D consultant. If you think that static rendering is limited to generic, public content that is the same for every user of your website, you should definitely read this article. Segmented Rendering is a new pattern for the Jamstack that lets you personalize content statically, without any sort of client-side rendering or per-request Server-Side Rendering. Let's focus on a scenario very useful for blog owners: handling paid content. Wow, you just got promoted!

Training a machine learning model requires energy, time, and patience. Smart data scientists organize experiments and track trials on the historical data to deploy the best solution. Problems may arise when we pass newly available samples to our pre-build machine learning pipeline. In the case of predictive algorithms, the registered performances may diverge from the expected ones. The causes behind discrepancies are variegated.

Perhaps it was initially when cloud computing providers began to offer AI as a service. The cloud made it cheap and readily available to solutions developers. As a result, AI found its way into applications that did not require AI capabilities and the solution ended up less valuable. The car will stop just fine with stock brakes; high-end models just waste money and resources. These days we better understand the pragmatic use of AI--when it will prove worthwhile and when it will not.

Machine Learning (ML) is a well-known innovation that nearly everyone knows about. A study uncovers that 77% of devices that we presently use are utilizing ML. From a social event of SMART devices over Netflix proposition through products like Amazon's Alexa, and Google Home, artificial intelligence services are proclaiming cutting-edge innovative solutions for organizations and regular day to day existences. The year 2021 is ready to observe some significant ML and AI trends that would maybe reshape our economic, social, and industrial workings. As of now, the AI-ML industry is developing at a quick rate and gives sufficient advancement scope to companies to bring the vital change. According to Gartner, around 37% of all companies reviewed are utilizing some type of ML in their business and it is anticipated that around 80% of modern advances will be founded on AI and ML by 2022.

Graph neural networks (GNNs) have achieved strong performance in various applications. In the real world, network data is usually formed in a streaming fashion. The distributions of patterns that refer to neighborhood information of nodes may shift over time. The GNN model needs to learn the new patterns that cannot yet be captured. But learning incrementally leads to the catastrophic forgetting problem that historical knowledge is overwritten by newly learned knowledge. Therefore, it is important to train GNN model to learn new patterns and maintain existing patterns simultaneously, which few works focus on. In this paper, we propose a streaming GNN model based on continual learning so that the model is trained incrementally and up-to-date node representations can be obtained at each time step. Firstly, we design an approximation algorithm to detect new coming patterns efficiently based on information propagation. Secondly, we combine two perspectives of data replaying and model regularization for existing pattern consolidation. Specially, a hierarchy-importance sampling strategy for nodes is designed and a weighted regularization term for GNN parameters is derived, achieving greater stability and generalization of knowledge consolidation. Our model is evaluated on real and synthetic data sets and compared with multiple baselines. The results of node classification prove that our model can efficiently update model parameters and achieve comparable performance to model retraining. In addition, we also conduct a case study on the synthetic data, and carry out some specific analysis for each part of our model, illustrating its ability to learn new knowledge and maintain existing knowledge from different perspectives.

Chess has a reputation for cold logic, but Vladimir Kramnik loves the game for its beauty. "It's a kind of creation," he says. His passion for the artistry of minds clashing over the board, trading complex but elegant provocations and counters, helped him dethrone Garry Kasparov in 2000 and spend several years as world champion. Yet Kramnik, who retired from competitive chess last year, also believes his beloved game has grown less creative. He partly blames computers, whose soulless calculations have produced a vast library of openings and defenses that top-flight players know by rote.