Survival analysis (SA), or equivalently time-to-event analysis, comprises a set of techniques enabling the unbiased estimation of the distribution of outcome variables that are partially censored, truncated, or both. Usually, the outcome is given by the time until the occurrence of an event such as death, system failure, or time to remission. Non-parametric methods like the Kaplan-Meier estimator [1] are baseline tools still used today, yet semi-parametric methods received the most attention historically, in particular the Cox proportional hazards regression model [2] and its extensions. Since the early 2000s, Machine Learning (ML) methods have been successfully adapted to survival tasks: e.g., Random Survival Forest [3] and boosting-based methods [4]. These methods often outperform traditional statistical models in terms of predictive power [5] (see [6] and [7] for detailed discussions). Neural networks (NNs) had already been applied to survival tasks in the 1990s [8, 9], but were shallow and restricted to the most standard survival settings.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. An artificial intelligence system can't be registered as the inventor of a patent, Britain's Supreme Court ruled Wednesday in a decision that denies machines the same status as humans. The U.K.'s highest court concluded that "an inventor must be a person" to apply for patents under the current law. The decision was the culmination of American technologist Stephen Thaler's long-running British legal battle to get his AI, dubbed DABUS, listed as the inventor of two patents.

AI may or may not take people's jobs in years to come, but in the meantime, there's one thing they cannot obtain: patents. Dr. Stephen Thaler has spent years trying to get patents for two inventions created by his AI "creativity machine" DABUS. Now, the United Kingdom's Supreme Court has rejected his appeal to approve these patents when listing DABUS as the inventor, Reuters reports. The court's rationale stems from a provision in UK patent law that states, "an inventor must be a natural person." The ruling stipulated that the appeal was unconcerned with whether this should change in the future. "The judgment establishes that UK patent law is currently wholly unsuitable for protecting inventions generated autonomously by AI machines," Thaler's lawyers said in a statement.

The United States has imposed sanctions on a network accused of evading trade restrictions to supply Iran with components to build drones. The US Treasury Department on Tuesday announced the measures have been implemented against a web of foreign front companies that have been sending Tehran sensitive equipment. Iranian-built unmanned aerial vehicles (UAVs) are being used in the wars in the Middle East and Ukraine. The sanctions target 10 entities and four individuals. The firms affected include "intermediary companies, front companies, and logistics businesses" based in Iran, Malaysia, Hong Kong and Indonesia.

Visual question answering is a multimodal task that requires the joint comprehension of visual and textual information. However, integrating visual and textual semantics solely through attention layers is insufficient to comprehensively understand and align information from both modalities. Intuitively, object attributes can naturally serve as a bridge to unify them, which has been overlooked in previous research. In this paper, we propose a novel VQA approach from the perspective of utilizing object attribute, aiming to achieve better object-level visual-language alignment and multimodal scene understanding. Specifically, we design an attribute fusion module and a contrastive knowledge distillation module. The attribute fusion module constructs a multimodal graph neural network to fuse attributes and visual features through message passing. The enhanced object-level visual features contribute to solving fine-grained problem like counting-question. The better object-level visual-language alignment aids in understanding multimodal scenes, thereby improving the model's robustness. Furthermore, to augment scene understanding and the out-of-distribution performance, the contrastive knowledge distillation module introduces a series of implicit knowledge. We distill knowledge into attributes through contrastive loss, which further strengthens the representation learning of attribute features and facilitates visual-linguistic alignment. Intensive experiments on six datasets, COCO-QA, VQAv2, VQA-CPv2, VQA-CPv1, VQAvs and TDIUC, show the superiority of the proposed method.

The goal of this work is to develop accurate Machine Learning (ML) models for predicting the assembly axial neutron flux profiles in the SAFARI-1 research reactor, trained by measurement data from historical cycles. The data-driven nature of ML models makes them susceptible to uncertainties which are introduced by sources such as noise in training data, incomplete coverage of the domain, extrapolation and imperfect model architectures. To this end, we also aim at quantifying the approximation uncertainties of the ML model predictions. Previous work using Deep Neural Networks (DNNs) has been successful for fuel assemblies in SAFARI-1, however, not as accurate for control follower assemblies. The aim of this work is to improve the ML models for the control assemblies by a combination of supervised and unsupervised ML algorithms. The $k$-means and Affinity Propagation unsupervised ML algorithms are employed to identify clusters in the set of the measured axial neutron flux profiles. Then, regression-based supervised ML models using DNN (with prediction uncertainties quantified with Monte Carlo dropout) and Gaussian Process (GP) are trained for different clusters and the prediction uncertainty is estimated. It was found that applying the proposed procedure improves the prediction accuracy for the control assemblies and reduces the prediction uncertainty. Flux shapes predicted by DNN and GP are very close, and the overall accuracy became comparable to the fuel assemblies. The prediction uncertainty is however smaller for GP models.

Since their original release, Transformer-based LLMs have been shown to be remarkably proficient on a wide array of important machine learning tasks. Their underlying Transformer architecture has become state-of-the-art for modeling and reasoning about natural language, and has shown promise in domains such as computer vision [Dosovitskiy et al., 2020] and reinforcement learning [Chen et al., 2021] as well. Contemporary instantiations of Transformer architectures are infamously large, typically requiring tremendous compute resources for both training and inference. This is by design, as Transformers trained with more parameters or data are demonstrably more capable than their slimmer predecessors--often by a significant margin [Brown et al., 2020, Touvron et al., 2023]. Still, a growing body of work suggests that Transformerbased models, and neural networks more generally, do not require all fitted parameters to retain their learned hypotheses.

Knowledge represented in Large Language Models (LLMs) is quite often incorrect and can also become obsolete over time. Updating knowledge via fine-tuning is computationally resource-hungry and not reliable, and so knowledge editing (KE) has developed as an effective and economical alternative to inject new knowledge or to fix factual errors in LLMs. Although there has been considerable interest in this area, current KE research exclusively focuses on the monolingual setting, typically in English. However, what happens if the new knowledge is supplied in one language, but we would like to query the LLM in a different language? To address the problem of multilingual knowledge editing, we propose Retrieval-augmented Multilingual Knowledge Editor (ReMaKE) to update new knowledge in LLMs. ReMaKE can perform model-agnostic knowledge editing in multilingual settings. ReMaKE concatenates the new knowledge retrieved from a multilingual knowledge base with prompts. Our experimental results show that ReMaKE outperforms baseline knowledge editing methods by a significant margin and is the first KE method to work in a multilingual setting. We provide our multilingual knowledge editing dataset (MzsRE) in 12 languages, which along with code, and additional project information is available at https://github.com/Vicky-Wil/ReMaKE.

This review paper provides an integrated perspective of Explainable Artificial Intelligence techniques applied to Brain-Computer Interfaces. BCIs use predictive models to interpret brain signals for various high-stake applications. However, achieving explainability in these complex models is challenging as it compromises accuracy. The field of XAI has emerged to address the need for explainability across various stakeholders, but there is a lack of an integrated perspective in XAI for BCI (XAI4BCI) literature. It is necessary to differentiate key concepts like explainability, interpretability, and understanding in this context and formulate a comprehensive framework. To understand the need of XAI for BCI, we pose six key research questions for a systematic review and meta-analysis, encompassing its purposes, applications, usability, and technical feasibility. We employ the PRISMA methodology -- preferred reporting items for systematic reviews and meta-analyses to review (n=1246) and analyze (n=84) studies published in 2015 and onwards for key insights. The results highlight that current research primarily focuses on interpretability for developers and researchers, aiming to justify outcomes and enhance model performance. We discuss the unique approaches, advantages, and limitations of XAI4BCI from the literature. We draw insights from philosophy, psychology, and social sciences. We propose a design space for XAI4BCI, considering the evolving need to visualize and investigate predictive model outcomes customised for various stakeholders in the BCI development and deployment lifecycle. This paper is the first to focus solely on reviewing XAI4BCI research articles. This systematic review and meta-analysis findings with the proposed design space prompt important discussions on establishing standards for BCI explanations, highlighting current limitations, and guiding the future of XAI in BCI.

Scalable load balancing algorithms are of great interest in cloud networks and data centers, necessitating the use of tractable techniques to compute optimal load balancing policies for good performance. However, most existing scalable techniques, especially asymptotically scaling methods based on mean field theory, have not been able to model large queueing networks with strong locality. Meanwhile, general multi-agent reinforcement learning techniques can be hard to scale and usually lack a theoretical foundation. In this work, we address this challenge by leveraging recent advances in sparse mean field theory to learn a near-optimal load balancing policy in sparsely connected queueing networks in a tractable manner, which may be preferable to global approaches in terms of communication overhead. Importantly, we obtain a general load balancing framework for a large class of sparse bounded-degree topologies. By formulating a novel mean field control problem in the context of graphs with bounded degree, we reduce the otherwise difficult multi-agent problem to a single-agent problem. Theoretically, the approach is justified by approximation guarantees. Empirically, the proposed methodology performs well on several realistic and scalable network topologies. Moreover, we compare it with a number of well-known load balancing heuristics and with existing scalable multi-agent reinforcement learning methods. Overall, we obtain a tractable approach for load balancing in highly localized networks. The aforementioned decentralized queueing systems with underlying graph structure can be modelled as various variants of multi-agent (partially-observable) Markov decision processes (MDP) [1, 2].