Many applications need access to background knowledge about how different concepts and entities are related. Although Knowledge Graphs (KG) and Large Language Models (LLM) can address this need to some extent, KGs are inevitably incomplete and their relational schema is often too coarse-grained, while LLMs are inefficient and difficult to control. As an alternative, we propose to extract relation embeddings from relatively small language models. In particular, we show that masked language models such as RoBERTa can be straightforwardly fine-tuned for this purpose, using only a small amount of training data. The resulting model, which we call RelBERT, captures relational similarity in a surprisingly fine-grained way, allowing us to set a new state-of-the-art in analogy benchmarks. Crucially, RelBERT is capable of modelling relations that go well beyond what the model has seen during training. For instance, we obtained strong results on relations between named entities with a model that was only trained on lexical relations between concepts, and we observed that RelBERT can recognise morphological analogies despite not being trained on such examples. Overall, we find that RelBERT significantly outperforms strategies based on prompting language models that are several orders of magnitude larger, including recent GPT-based models and open source models.

Collaborative perception can substantially boost each agent's perception ability by facilitating communication among multiple agents. However, temporal asynchrony among agents is inevitable in the real world due to communication delays, interruptions, and clock misalignments. This issue causes information mismatch during multi-agent fusion, seriously shaking the foundation of collaboration. To address this issue, we propose CoBEVFlow, an asynchrony-robust collaborative perception system based on bird's eye view (BEV) flow. The key intuition of CoBEVFlow is to compensate motions to align asynchronous collaboration messages sent by multiple agents. To model the motion in a scene, we propose BEV flow, which is a collection of the motion vector corresponding to each spatial location. Based on BEV flow, asynchronous perceptual features can be reassigned to appropriate positions, mitigating the impact of asynchrony. CoBEVFlow has two advantages: (i) CoBEVFlow can handle asynchronous collaboration messages sent at irregular, continuous time stamps without discretization; and (ii) with BEV flow, CoBEVFlow only transports the original perceptual features, instead of generating new perceptual features, avoiding additional noises. To validate CoBEVFlow's efficacy, we create IRregular V2V(IRV2V), the first synthetic collaborative perception dataset with various temporal asynchronies that simulate different real-world scenarios. Extensive experiments conducted on both IRV2V and the real-world dataset DAIR-V2X show that CoBEVFlow consistently outperforms other baselines and is robust in extremely asynchronous settings. The code is available at https://github.com/MediaBrain-SJTU/CoBEVFlow.

Over the past two decades, the availability of high-performance computing and the development of neural networks of larger capacity have conspired to fuel a revolution in the way we think at the optimisation of complex systems. When the dimensionality of the space of relevant design parameters exceeds a few units, and brute-force scans cease be a viable option for its exploration. We nowadays, have the option of letting automated systems find their way to configurations that correspond to advantageous extrema of carefully specified objective functions. The engine under the hood of these optimisation searches is automatic differentiation, which allows computer programs to keep track of the gradient of the objective function, through the chain rule of differential calculus, as computer code performs arbitrarily complex successions of operations to model the behaviour of the system. Crucial to a successful optimisation of the system is the inclusion in the model of all relevant effects that have an impact on the precision of the inference that the data generated by the system may produce. An incomplete description of the inference itself, or a mock up of the reconstruction techniques performing the dimensionality reduction step which translates raw data into high-level features informing the objective function, are likely to prevent the identification of designs that maximise the true objective, as they introduce a misalignment.

The demo explains how AI is used in the app and its features. Cross-border data flow will play a vital role in shaping the international artificial intelligence landscape, but fear of balkanized technology shouldn't blind Western countries to China's long-standing ambitions and approach, experts argued. "No one wants a balkanized world, and China doesn't, either," Nate Picarsic, senior fellows focusing on China policy at the Foundation for Defense of Democracies (FDD), told Fox News Digital. "But we shouldn't be leaving them in the driver's seat and defining the terms of all of these new realms just in defense of the global system." "We have to be clear eyed about what they're trying to do, defend our interests, have teeth and guardrails to make sure that they're playing by the rules… otherwise, we end up in an AI and data environment that is defined by Chinese norms and standards, because that's what their ambition is," he added.

This paper presents a comprehensive review of the current state of the art in quantum navigation systems, with a specific focus on their application in maritime navigation. Quantum technologies have the potential to revolutionise navigation and positioning systems due to their ability to provide highly accurate and secure information. The review covers the principles of quantum navigation and highlights the latest developments in quantum-enhanced sensors, atomic clocks, and quantum communication protocols. The paper also discusses the challenges and opportunities of using quantum technologies in maritime navigation, including the effects that the maritime environment and the specificity of marine applications can have on the performance of quantum sensors. Finally, the paper concludes with a discussion on the future of quantum navigation systems and their potential impact on the maritime industry. This review aims at providing a valuable resource for researchers and engineers interested in the development and deployment of quantum navigation systems.

Despite its centrality in the philosophy of cognitive science, there has been little prior philosophical work engaging with the notion of representation in contemporary NLP practice. This paper attempts to fill that lacuna: drawing on ideas from cognitive science, I introduce a framework for evaluating the representational claims made about components of neural NLP models, proposing three criteria with which to evaluate whether a component of a model represents a property and operationalising these criteria using probing classifiers, a popular analysis technique in NLP (and deep learning more broadly). The project of operationalising a philosophically-informed notion of representation should be of interest to both philosophers of science and NLP practitioners. It affords philosophers a novel testing-ground for claims about the nature of representation, and helps NLPers organise the large literature on probing experiments, suggesting novel avenues for empirical research.

Recent transfer learning (TL) approaches in industrial intelligent fault diagnosis (FD) mostly follow the "pre-train and fine-tuning" paradigm to address data drift, which emerges from variable working conditions. However, we find that this approach is prone to the phenomenon known as catastrophic forgetting. Furthermore, performing frequent models fine-tuning on the resource-constrained edge nodes can be computationally expensive and unnecessary, given the excellent transferability demonstrated by existing models. In this work, we propose the Drift-Aware Weight Consolidation (DAWC), a method optimized for edge deployments, mitigating the challenges posed by frequent data drift in the industrial Internet of Things (IIoT). DAWC efficiently manages multiple data drift scenarios, minimizing the need for constant model fine-tuning on edge devices, thereby conserving computational resources. By detecting drift using classifier confidence and estimating parameter importance with the Fisher Information Matrix, a tool that measures parameter sensitivity in probabilistic models, we introduce a drift detection module and a continual learning module to gradually equip the FD model with powerful generalization capabilities. Experimental results demonstrate that our proposed DAWC achieves superior performance compared to existing techniques while also ensuring compatibility with edge computing constraints. Additionally, we have developed a comprehensive diagnosis and visualization platform.

While speech emotion recognition (SER) research has made significant progress, achieving generalization across various corpora continues to pose a problem. We propose a novel domain adaptation technique that embodies a multitask framework with SER as the primary task, and contrastive learning and information maximisation loss as auxiliary tasks, underpinned by fine-tuning of transformers pre-trained on large language models. Empirical results obtained through experiments on well-established datasets like IEMOCAP and MSP-IMPROV, illustrate that our proposed model achieves state-of-the-art performance in SER within cross-corpus scenarios.

Industrial artificial intelligence (IAI) stresses the application of artificial intelligence techniques to industries, with some inherent challenges, such as uncertainties in sensory signals, real-time data processing, high modelling accuracy, and the interpretability of predictive models and results [1-7]. Recently, the IAI concept has received considerable attention worldwide due to the availability of cheaper sensors for data acquisition, powerful computing facilities and advanced algorithms that perform speedily at lower computational cost, larger storage devices and cloud computing technology for data management, and faster communication systems for sharing and delivering data. Although the IAI concept is not well-defined so far, the development of advanced machine learning algorithms is strongly expected so that they can meet these requirements of IAI. Machine learning has been a very active research area in AI over the past decades, and significant efforts in building predictive learner models have been made [8]. Among these approaches, the most popular and widely used ones include multilayer perceptrons with error-backpropagation algorithms (MLPs) [9], support vector machines (SVMs) [10], Bayesian networks (BNs) [11], and adaptive neuro-fuzzy inference systems (ANFIS) [12].

Quality of Service (QoS) prediction is an essential task in recommendation systems, where accurately predicting unknown QoS values can improve user satisfaction. However, existing QoS prediction techniques may perform poorly in the presence of noise data, such as fake location information or virtual gateways. In this paper, we propose the Probabilistic Deep Supervision Network (PDS-Net), a novel framework for QoS prediction that addresses this issue. PDS-Net utilizes a Gaussian-based probabilistic space to supervise intermediate layers and learns probability spaces for both known features and true labels. Moreover, PDS-Net employs a condition-based multitasking loss function to identify objects with noise data and applies supervision directly to deep features sampled from the probability space by optimizing the Kullback-Leibler distance between the probability space of these objects and the real-label probability space. Thus, PDS-Net effectively reduces errors resulting from the propagation of corrupted data, leading to more accurate QoS predictions. Experimental evaluations on two real-world QoS datasets demonstrate that the proposed PDS-Net outperforms state-of-the-art baselines, validating the effectiveness of our approach.