Despite the number of works published in recent years, vehicle localization remains an open, challenging problem. While map-based localization and SLAM algorithms are getting better and better, they remain a single point of failure in typical localization pipelines. This paper proposes a modular localization architecture that fuses sensor measurements with the outputs of off-the-shelf localization algorithms. The fusion filter estimates model uncertainties to improve odometry in case absolute pose measurements are lost entirely. The architecture is validated experimentally on a real robot navigating autonomously proving a reduction of the position error of more than 90% with respect to the odometrical estimate without uncertainty estimation in a two-minute navigation period without position measurements.

In online video platforms, reading or writing comments on interesting videos has become an essential part of the video watching experience. However, existing video recommender systems mainly model users' interaction behaviors with videos, lacking consideration of comments in user behavior modeling. In this paper, we propose a novel recommendation approach called LSVCR by leveraging user interaction histories with both videos and comments, so as to jointly conduct personalized video and comment recommendation. Specifically, our approach consists of two key components, namely sequential recommendation (SR) model and supplemental large language model (LLM) recommender. The SR model serves as the primary recommendation backbone (retained in deployment) of our approach, allowing for efficient user preference modeling. Meanwhile, we leverage the LLM recommender as a supplemental component (discarded in deployment) to better capture underlying user preferences from heterogeneous interaction behaviors. In order to integrate the merits of the SR model and the supplemental LLM recommender, we design a twostage training paradigm. The first stage is personalized preference alignment, which aims to align the preference representations from both components, thereby enhancing the semantics of the SR model. The second stage is recommendation-oriented fine-tuning, in which the alignment-enhanced SR model is fine-tuned according to specific objectives. Extensive experiments in both video and comment recommendation tasks demonstrate the effectiveness of LSVCR. Additionally, online A/B testing on the KuaiShou platform verifies the actual benefits brought by our approach. In particular, we achieve a significant overall gain of 4.13% in comment watch time.

Defining and thereby separating the intelligent "agent" from its embodied "environment", which then provides feedback to the agent, is crucial to model intelligent behaviour. Popular approaches, like reinforcement learning (RL), heavily employ such models containing agent-environment loops, which boils down the problem to agent(s) trying to maximise reward in the given uncertain environment Sutton and Barto [2018]. Active inference has emerged in neuroscience as a biologically plausible framework Friston [2010], which adopts a different approach to modelling intelligent behaviour compared to other contemporary methods like RL. In the active inference framework, an agent accumulates and maximises the model evidence during its lifetime to perceive, learn, and make decisions Da Costa et al. [2020], Sajid et al. [2021], Millidge et al. [2020]. However, maximising the model evidence becomes challenging when the agent encounters a highly'entropic' observation (i.e. an unexpected observation) concerning the agent's generative (world) model Da Costa et al. [2020], Sajid et al. [2021], Millidge et al. [2020]. This seemingly intractable objective of maximising model evidence (or minimising the entropy of encountered observations) is achievable by minimising an upper bound on the entropy of observations, called variational free energy Da Costa et al. [2020], Sajid et al. [2021]. Given this general foundation, active inference Friston et al. [2017] offers excellent flexibility in defining the generative model structure for a given problem and has attracted much attention in various domainsKuchling et al. [2020], Deane et al. [2020]. In this work, we develop an efficient decision-making scheme based on active inference by combining'planning' and'learning from experience'.

The analysis of dynamic organelles remains a formidable challenge, though key to understanding biological processes. We introduce Nellie, an automated and unbiased pipeline for segmentation, tracking, and feature extraction of diverse intracellular structures. Nellie adapts to image metadata, eliminating user input. Nellie's preprocessing pipeline enhances structural contrast on multiple intracellular scales allowing for robust hierarchical segmentation of sub-organellar regions. Internal motion capture markers are generated and tracked via a radius-adaptive pattern matching scheme, and used as guides for sub-voxel flow interpolation. Nellie extracts a plethora of features at multiple hierarchical levels for deep and customizable analysis. Nellie features a Napari-based GUI that allows for code-free operation and visualization, while its modular open-source codebase invites customization by experienced users. We demonstrate Nellie's wide variety of use cases with two examples: unmixing multiple organelles from a single channel using feature-based classification and training an unsupervised graph autoencoder on mitochondrial multi-mesh graphs to quantify latent space embedding changes following ionomycin treatment.

We scrutinize the resilience of the partial-sharing online federated learning (PSO-Fed) algorithm against model-poisoning attacks. PSO-Fed reduces the communication load by enabling clients to exchange only a fraction of their model estimates with the server at each update round. Partial sharing of model estimates also enhances the robustness of the algorithm against model-poisoning attacks. To gain better insights into this phenomenon, we analyze the performance of the PSO-Fed algorithm in the presence of Byzantine clients, malicious actors who may subtly tamper with their local models by adding noise before sharing them with the server. Through our analysis, we demonstrate that PSO-Fed maintains convergence in both mean and mean-square senses, even under the strain of model-poisoning attacks. We further derive the theoretical mean square error (MSE) of PSO-Fed, linking it to various parameters such as stepsize, attack probability, number of Byzantine clients, client participation rate, partial-sharing ratio, and noise variance. We also show that there is a non-trivial optimal stepsize for PSO-Fed when faced with model-poisoning attacks. The results of our extensive numerical experiments affirm our theoretical assertions and highlight the superior ability of PSO-Fed to counteract Byzantine attacks, outperforming other related leading algorithms.

Given the uncertainty surrounding how existing explainability methods for autonomous vehicles (AVs) meet the diverse needs of stakeholders, a thorough investigation is imperative to determine the contexts requiring explanations and suitable interaction strategies. A comprehensive review becomes crucial to assess the alignment of current approaches with the varied interests and expectations within the AV ecosystem. This study presents a review to discuss the complexities associated with explanation generation and presentation to facilitate the development of more effective and inclusive explainable AV systems. Our investigation led to categorising existing literature into three primary topics: explanatory tasks, explanatory information, and explanatory information communication. Drawing upon our insights, we have proposed a comprehensive roadmap for future research centred on (i) knowing the interlocutor, (ii) generating timely explanations, (ii) communicating human-friendly explanations, and (iv) continuous learning. Our roadmap is underpinned by principles of responsible research and innovation, emphasising the significance of diverse explanation requirements. To effectively tackle the challenges associated with implementing explainable AV systems, we have delineated various research directions, including the development of privacy-preserving data integration, ethical frameworks, real-time analytics, human-centric interaction design, and enhanced cross-disciplinary collaborations. By exploring these research directions, the study aims to guide the development and deployment of explainable AVs, informed by a holistic understanding of user needs, technological advancements, regulatory compliance, and ethical considerations, thereby ensuring safer and more trustworthy autonomous driving experiences.

End-to-end autonomous driving has witnessed remarkable progress. However, the extensive deployment of autonomous vehicles has yet to be realized, primarily due to 1) inefficient multi-modal environment perception: how to integrate data from multi-modal sensors more efficiently; 2) non-human-like scene understanding: how to effectively locate and predict critical risky agents in traffic scenarios like an experienced driver. To overcome these challenges, in this paper, we propose a Multi-Modal fusion transformer incorporating Driver Attention (M2DA) for autonomous driving. To better fuse multi-modal data and achieve higher alignment between different modalities, a novel Lidar-Vision-Attention-based Fusion (LVAFusion) module is proposed. By incorporating driver attention, we empower the human-like scene understanding ability to autonomous vehicles to identify crucial areas within complex scenarios precisely and ensure safety. We conduct experiments on the CARLA simulator and achieve state-of-the-art performance with less data in closed-loop benchmarks. Source codes are available at https://anonymous.4open.science/r/M2DA-4772.

Predicting electric vehicle (EV) charging events is crucial for load scheduling and energy management, promoting seamless transportation electrification and decarbonization. While prior studies have focused on EV charging demand prediction, primarily for public charging stations using historical charging data, home charging prediction is equally essential. However, existing prediction methods may not be suitable due to the unavailability of or limited access to home charging data. To address this research gap, inspired by the concept of non-intrusive load monitoring (NILM), we develop a home charging prediction method using historical smart meter data. Different from NILM detecting EV charging that has already occurred, our method provides predictive information of future EV charging occurrences, thus enhancing its utility for charging management. Specifically, our method, leverages a self-attention mechanism-based transformer model, employing a ``divide-conquer'' strategy, to process historical meter data to effectively and learn EV charging representation for charging occurrence prediction. Our method enables prediction at one-minute interval hour-ahead. Experimental results demonstrate the effectiveness of our method, achieving consistently high accuracy of over 96.81\% across different prediction time spans. Notably, our method achieves high prediction performance solely using smart meter data, making it a practical and suitable solution for grid operators.

Measuring nonlinear feature interaction is an established This paper investigates Shapley interactions in a number of approach to understanding complex patterns tasks and architectures. We use Shapley interactions as a of attribution in many models. In this paper, case study to illustrate the importance of grounding model we use Shapley Taylor interaction indices interpretations in the underlying structure of the data and the (STII) to analyze the impact of underlying data target models. To this end, we draw connections between structure on model representations in a variety of interaction metrics and various structural properties of the modalities, tasks, and architectures. Considering data in each setting: syntax, tokenization, and idiomatic linguistic structure in masked and auto-regressive expressions in masked and autoregressive language models language models (MLMs and ALMs), we find (MLMs and ALMs, respectively); phoneme articulation differences that STII increases within idiomatic expressions in speech models; and distinctions between edges, and that MLMs scale STII with syntactic distance, foreground, and background pixels in image classifiers. After relying more on syntax in their nonlinear structure introducing our approach to Shapley interactions, we than ALMs do. Our speech model findings apply them in a variety of settings and find the following.

Named Entity Recognition (NER) is a fundamental task to extract key information from texts, but annotated resources are scarce for dialects. This paper introduces the first dialectal NER dataset for German, BarNER, with 161K tokens annotated on Bavarian Wikipedia articles (bar-wiki) and tweets (bar-tweet), using a schema adapted from German CoNLL 2006 and GermEval. The Bavarian dialect differs from standard German in lexical distribution, syntactic construction, and entity information. We conduct in-domain, cross-domain, sequential, and joint experiments on two Bavarian and three German corpora and present the first comprehensive NER results on Bavarian. Incorporating knowledge from the larger German NER (sub-)datasets notably improves on bar-wiki and moderately on bar-tweet. Inversely, training first on Bavarian contributes slightly to the seminal German CoNLL 2006 corpus. Moreover, with gold dialect labels on Bavarian tweets, we assess multi-task learning between five NER and two Bavarian-German dialect identification tasks and achieve NER SOTA on bar-wiki. We substantiate the necessity of our low-resource BarNER corpus and the importance of diversity in dialects, genres, and topics in enhancing model performance.