Embedding based Knowledge Graph (KG) Completion has gained much attention over the past few years. Most of the current algorithms consider a KG as a multidirectional labeled graph and lack the ability to capture the semantics underlying the schematic information. In a separate development, a vast amount of information has been captured within the Large Language Models (LLMs) which has revolutionized the field of Artificial Intelligence. KGs could benefit from these LLMs and vice versa. This vision paper discusses the existing algorithms for KG completion based on the variations for generating KG embeddings. It starts with discussing various KG completion algorithms such as transductive and inductive link prediction and entity type prediction algorithms. It then moves on to the algorithms utilizing type information within the KGs, LLMs, and finally to algorithms capturing the semantics represented in different description logic axioms. We conclude the paper with a critical reflection on the current state of work in the community and give recommendations for future directions.

For the performance modeling of power converters, the mainstream approaches are essentially knowledge-based, suffering from heavy manpower burden and low modeling accuracy. Recent emerging data-driven techniques greatly relieve human reliance by automatic modeling from simulation data. However, model discrepancy may occur due to unmodeled parasitics, deficient thermal and magnetic models, unpredictable ambient conditions, etc. These inaccurate data-driven models based on pure simulation cannot represent the practical performance in physical world, hindering their applications in power converter modeling. To alleviate model discrepancy and improve accuracy in practice, this paper proposes a novel data-driven modeling with experimental augmentation (D2EA), leveraging both simulation data and experimental data. In D2EA, simulation data aims to establish basic functional landscape, and experimental data focuses on matching actual performance in real world. The D2EA approach is instantiated for the efficiency optimization of a hybrid modulation for neutral-point-clamped dual-active-bridge (NPC-DAB) converter. The proposed D2EA approach realizes 99.92% efficiency modeling accuracy, and its feasibility is comprehensively validated in 2-kW hardware experiments, where the peak efficiency of 98.45% is attained. Overall, D2EA is data-light and can achieve highly accurate and highly practical data-driven models in one shot, and it is scalable to other applications, effortlessly.

Figure 1: We present AnyTeleop, a vision-based teleoperation system for a variety of scenarios to solve a wide range of manipulation tasks. AnyTeleop can be used for various robot arms with different robot hands. It also supports teleoperation within different realities, such as IsaacGym (top row), and SAPIEN simulator (middle row), and real world (bottom rows). Abstract--Vision-based teleoperation offers the possibility experiments, AnyTeleop can outperform a previous system that to endow robots with human-level intelligence to physically was designed for a specific robot hardware with a higher interact with the environment, while only requiring low-cost success rate, using the same robot. However, current vision-based teleoperation AnyTeleop leads to better imitation learning performance, systems are designed and engineered towards a particular robot compared with a previous system that is particularly designed model and deploy environment, which scales poorly as the pool for that simulator. of the robot models expands and the variety of the operating environment increases. They can adapt Reality (VR) devices [4, 17, 15], wearable gloves [29, 30], to new robots given only the kinematic model, i.e., URDF handheld controller [47, 48, 20], haptic sensors [12, 23, files. Second, we develop a web-based viewer compatible 52, 55], or motion capture trackers [68]. Fortunately, recent with standard browsers, to achieve simulator-agnostic visualization developments in vision-based teleoperation [2, 24, 16, 26, and enable remote teleoperation across the internet.

The ability to quickly learn a new task with minimal instruction - known as few-shot learning - is a central aspect of intelligent agents. Classical few-shot benchmarks make use of few-shot samples from a single modality, but such samples may not be sufficient to characterize an entire concept class. In contrast, humans use cross-modal information to learn new concepts efficiently. In this work, we demonstrate that one can indeed build a better ${\bf visual}$ dog classifier by ${\bf read}$ing about dogs and ${\bf listen}$ing to them bark. To do so, we exploit the fact that recent multimodal foundation models such as CLIP are inherently cross-modal, mapping different modalities to the same representation space. Specifically, we propose a simple cross-modal adaptation approach that learns from few-shot examples spanning different modalities. By repurposing class names as additional one-shot training samples, we achieve SOTA results with an embarrassingly simple linear classifier for vision-language adaptation. Furthermore, we show that our approach can benefit existing methods such as prefix tuning, adapters, and classifier ensembling. Finally, to explore other modalities beyond vision and language, we construct the first (to our knowledge) audiovisual few-shot benchmark and use cross-modal training to improve the performance of both image and audio classification.

Data scarcity has been the main factor that hinders the progress of event extraction. To overcome this issue, we propose a Self-Training with Feedback (STF) framework that leverages the large-scale unlabeled data and acquires feedback for each new event prediction from the unlabeled data by comparing it to the Abstract Meaning Representation (AMR) graph of the same sentence. Specifically, STF consists of (1) a base event extraction model trained on existing event annotations and then applied to large-scale unlabeled corpora to predict new event mentions as pseudo training samples, and (2) a novel scoring model that takes in each new predicted event trigger, an argument, its argument role, as well as their paths in the AMR graph to estimate a compatibility score indicating the correctness of the pseudo label. The compatibility scores further act as feedback to encourage or discourage the model learning on the pseudo labels during self-training. Experimental results on three benchmark datasets, including ACE05-E, ACE05-E+, and ERE, demonstrate the effectiveness of the STF framework on event extraction, especially event argument extraction, with significant performance gain over the base event extraction models and strong baselines. Our experimental analysis further shows that STF is a generic framework as it can be applied to improve most, if not all, event extraction models by leveraging large-scale unlabeled data, even when high-quality AMR graph annotations are not available.

The more lead that people are exposed to in childhood or in the uterus, the more likely they are to engage in criminal behaviour as teenagers or adults, according to a review of 17 studies. "The evidence shows an excess risk for criminal behaviour years later," says Maria Jose Talayero at the George Washington University in Washington DC. Lead exposure has fallen in many countries, mainly due to the removal of lead additives from petrol (gasoline). However, there is no safe level – any amount of exposure is thought to be harmful. It is estimated that 1 in 3 children globally have blood lead levels above 5 micrograms per decilitre, which can result in decreased intelligence, behavioural difficulties and learning problems.

Technology never exists in a vacuum, and the rise of cryptocurrency in the last two or three years shows that. While plenty of people were making extraordinary amounts of money from investing in bitcoin and its competitors, there was consternation about the impact those get-rich-quick speculators had on the environment. Mining cryptocurrency was environmentally taxing. The core principle behind it was that you had to expend effort to get rich. To mint a bitcoin or another cryptocurrency, you had to first "mine" it.

In the initial phase of AI research following the second AI winter, the focus was on identifying new areas where AI could outperform humans, with famous examples including chess [Silver et al., 2018], Go [Silver et al., 2016], and Starcraft [Vinyals et al., 2019]. While this was a limited application to games, it set the tone for research to prioritize building AI agents with superhuman capabilities. However, over the last decade, the research community has witnessed a shift towards a human-centric approach that aims to leverage AI to aid humans in everyday tasks and relieve them of repetitive duties [Xu, 2019, Riedl, 2019, Shneiderman, 2021]. The interaction between humans and machines is a crucial aspect of human-centric AI [Mikolov et al., 2016], and it should take place in domains where humans are already familiar and require little to no training. Therefore, applications that involve niche practices, such as coding and mathematics, should be avoided in favor of language-based applications. In particular, human-machine communication should be grounded in natural language, which presents the challenge of teaching artificial agents to communicate in multiple languages. Recent advances in natural language processing (NLP) have led to the emergence of the transformer architecture [Vaswani et al., 2017], which has become the preferred approach for language-based applications, as exemplified by Language Models (LMs) such as GPT3 [Brown et al., 2020], LLaMA [Touvron et al., 2023], and Lamda [Thoppilan et al., 2022]. One of the challenges for language model architectures is their focus on predicting the next word in a sentence rather than comprehending the broader context and purpose of language usage. While humans use language as a tool for coordination and communication to thrive in a shared environment, artificial intelligence may struggle to understand the subtleties and complexities of language fully.

Pauli spin blockade (PSB) can be employed sive; in the few-charges regime it can be found in as a great resource for spin qubit unexpected gate voltage locations or it might be initialisation and readout even at elevated absent, and in the multi-charge regime it has to temperatures but it can be difficult to be found like the proverbial needle in a haystack. We present a machine learning Its detection is challenging even for experienced algorithm capable of automatically identifying human experimenters since evidence for PSB is PSB using charge transport measurements. Those by training the algorithm with simulated details are affected by fluctuations in the disorder data and by using cross-device validation. The an essential step for realising fully scarcity of available data makes reliable automation automatic qubit tuning, is expected to be tough. In addition, PSB data tends to be employable across all types of quantum dot unbalanced, meaning that there are many more devices. Measurements promising candidates for scalable quantum computation exhibiting PSB are therefore rare in an and simulation [1-3]. They can achieve already scarce body of data. An automatic approach universal quantum computation [4] with gates would also allow us to gather sufficient reaching high fidelity [5, 6].

Cultural heritage is an arena of international relations that interests all states worldwide. The inscription process on the UNESCO World Heritage List and the UNESCO Representative List of the Intangible Cultural Heritage of Humanity often leads to tensions and conflicts among states. This research addresses these challenges by developing automatic tools that provide valuable insights into the decision-making processes regarding inscriptions to the two lists mentioned above. We propose innovative topic modelling and tension detection methods based on UNESCO's summary records. Our analysis achieved a commendable accuracy rate of 72% in identifying tensions. Furthermore, we have developed an application tailored for diplomats, lawyers, political scientists, and international relations researchers that facilitates the efficient search of paragraphs from selected documents and statements from specific speakers about chosen topics. This application is a valuable resource for enhancing the understanding of complex decision-making dynamics within international heritage inscription procedures.