Ford Motor China Co.,ltd, Shanghai, 200082, China Abstract The comprehensiveness of vehicle-to-everything (V2X) recognition enriches and holistically shapes the global Birds-Eye-View (BEV) perception, incorporating rich semantics and integrating driving scene information, thereby serving features of trajectory prediction, decision-making and driving planning. Utilizing V2X message sets to form BEV format proves to be an effective perception method for connected and automated vehicles (CAVs). Specifically, MAP, SPAT and RSI data contributes to the achievement of road connectivity, synchronized traffic signal navigation and obstacle warning. Moreover, using time-sequential BSMs information from multiple vehicles allows for the perception of current state and the prediction of future trajectories. Therefore, this paper develops a comprehensive autonomous driving model that relies on BEV-V2X perception, Interacting Multiple model Unscented Kalman Filter (IMM-UKF)-based trajectory prediction, and deep reinforcement learning (DRL)-based decision making and planing. We establish a DRL environment with reward-shaping methods to formulate a unified set of optimal driving behaviors that encompass obstacle avoidance, lane changes, overtaking, turning maneuver, and synchronized traffic signal navigation. Consequently, a complex traffic intersection scenario was simulated, and the well-trained model was applied for driving control. The observed driving behavior closely resembled that of an experienced driver, exhibiting anticipatory actions and revealing notable operational highlights of driving policy. Ford Motor China Co.,ltd, Shanghai, 200082, China I. INTRODUCTION Cooperative Intelligent Transport Systems (C-ITS) based on vehicle-to-everything (V2X) communication has considerably developed in recent years, bringing us closer to utilizing V2X for autonomous driving and assistance guidance [1-2]. Due to the numerous substantial benefits provided by C-ITS, governmental authorities worldwide have initiated the allocation of dedicated spectrum for V2X technologies, on a license-exempt basis in the 5.9 GHz band for the cellular based Cellular-V2X (C-V2X) PC5 technology [3-4]. The characteristics of 5G technologies including enhanced Mobile Broadband (eMBB) and massive Machine Type Communications (mMTC) are pivotal in vehicular communications [5-6]. Furthermore, facilitating Ultra-Reliable and Low-Latency Communications (URLLC) of 5G is a fundamental element of advanced V2X applications [7-8].

What can we learn about law and legal argumentation from court judgments alone? Contemporary research addresses empirical legal questions (e.g., which arguments are used) or legal NLP questions (e.g., predicting case outcomes) by relying on the availability of the final'products' of each case, the court decisions (Habernal et al, 2023; Medvedeva et al, 2020). The European Court of Human Rights (ECHR) is a prominent data source, as its decisions are freely available in a large amount, along with the metadata of the violated articles and other attributes. This makes ECHR a popular choice among NLP researchers (Aletras et al, 2016; Chalkidis et al, 2020). However, whether or not the legal arguments in ECHR's cases are created as a part of legal deliberation or are created post-hoc after reaching a decision remains an open (and partly controversial) question. In order to better understand the legal argument mechanics, that is which arguments of the parties were presented, discussed, or questioned, and thus might have influenced the case outcome, we must take the oral hearings into account. We witness that the availability of oral hearing transcripts of the U.S. Supreme Court enables further legal research (Ashley et al, 2007). However, empirical research into the interplay of arguments at the court hearings and the final judgments has been so far impossible for the ECHR, as there are no hearing transcripts available.

Verizon Media (VZM) native advertising is one of VZM largest and fastest growing businesses, reaching a run-rate of several hundred million USDs in the past year. Driving the VZM native models that are used to predict event probabilities, such as click and conversion probabilities, is OFFSET - a feature enhanced collaborative-filtering based event-prediction algorithm. In this work we focus on the challenge of predicting click-through rates (CTR) when we are aware that some of the clicks have short dwell-time and are defined as accidental clicks. An accidental click implies little affinity between the user and the ad, so predicting that similar users will click on the ad is inaccurate. Therefore, it may be beneficial to remove clicks with dwell-time lower than a predefined threshold from the training set. However, we cannot ignore these positive events, as filtering these will cause the model to under predict. Previous approaches have tried to apply filtering and then adding corrective biases to the CTR predictions, but did not yield revenue lifts and therefore were not adopted. In this work, we present a new approach where the positive weight of the accidental clicks is distributed among all of the negative events (skips), based on their likelihood of causing accidental clicks, as predicted by an auxiliary model. These likelihoods are taken as the correct labels of the negative events, shifting our training from using only binary labels and adopting a binary cross-entropy loss function in our training process. After showing offline performance improvements, the modified model was tested online serving VZM native users, and provided 1.18% revenue lift over the production model which is agnostic to accidental clicks.

Knowledge graphs (KGs) comprise entities interconnected by relations of different semantic meanings. KGs are being used in a wide range of applications. However, they inherently suffer from incompleteness, i.e. entities or facts about entities are missing. Consequently, a larger body of works focuses on the completion of missing information in KGs, which is commonly referred to as link prediction (LP). This task has traditionally and extensively been studied in the transductive setting, where all entities and relations in the testing set are observed during training. Recently, several works have tackled the LP task under more challenging settings, where entities and relations in the test set may be unobserved during training, or appear in only a few facts. These works are known as inductive, few-shot, and zero-shot link prediction. In this work, we conduct a systematic review of existing works in this area. A thorough analysis leads us to point out the undesirable existence of diverging terminologies and task definitions for the aforementioned settings, which further limits the possibility of comparison between recent works. We consequently aim at dissecting each setting thoroughly, attempting to reveal its intrinsic characteristics. A unifying nomenclature is ultimately proposed to refer to each of them in a simple and consistent manner.

Just like the previous generation of task-tuned models, large language models (LLMs) that are adapted to tasks via prompt-based methods like in-context-learning (ICL) perform well in some setups but not in others. This lack of consistency in prompt-based learning hints at a lack of robust generalisation. We here introduce the ICL consistency test -- a contribution to the GenBench collaborative benchmark task (CBT) -- which evaluates how consistent a model makes predictions across many different setups while using the same data. The test is based on different established natural language inference tasks. We provide preprocessed data constituting 96 different 'setups' and a metric that estimates model consistency across these setups. The metric is provided on a fine-grained level to understand what properties of a setup render predictions unstable and on an aggregated level to compare overall model consistency. We conduct an empirical analysis of eight state-of-the-art models, and our consistency metric reveals how all tested LLMs lack robust generalisation.

With the rapid advancements of sensor technology and deep learning, autonomous driving systems are providing safe and efficient access to intelligent vehicles as well as intelligent transportation. Among these equipped sensors, the radar sensor plays a crucial role in providing robust perception information in diverse environmental conditions. This review focuses on exploring different radar data representations utilized in autonomous driving systems. Firstly, we introduce the capabilities and limitations of the radar sensor by examining the working principles of radar perception and signal processing of radar measurements. Then, we delve into the generation process of five radar representations, including the ADC signal, radar tensor, point cloud, grid map, and micro-Doppler signature. For each radar representation, we examine the related datasets, methods, advantages and limitations. Furthermore, we discuss the challenges faced in these data representations and propose potential research directions. Above all, this comprehensive review offers an in-depth insight into how these representations enhance autonomous system capabilities, providing guidance for radar perception researchers. To facilitate retrieval and comparison of different data representations, datasets and methods, we provide an interactive website at https://radar-camera-fusion.github.io/radar.

Recent advances in deep learning, and especially the invention of encoder-decoder architectures, has significantly improved the performance of abstractive summarization systems. The majority of research has focused on written documents, however, neglecting the problem of multi-party dialogue summarization. In this paper, we present a dataset of French political debates for the purpose of enhancing resources for multi-lingual dialogue summarization. Our dataset consists of manually transcribed and annotated political debates, covering a range of topics and perspectives. We highlight the importance of high quality transcription and annotations for training accurate and effective dialogue summarization models, and emphasize the need for multilingual resources to support dialogue summarization in non-English languages. We also provide baseline experiments using state-of-the-art methods, and encourage further research in this area to advance the field of dialogue summarization. Our dataset will be made publicly available for use by the research community.

We face up to the challenge of explainability in Multimodal Artificial Intelligence (MMAI). At the nexus of neuroscience-inspired and quantum computing, interpretable and transparent spin-geometrical neural architectures for early fusion of large-scale, heterogeneous, graph-structured data are envisioned, harnessing recent evidence for relativistic quantum neural coding of (co-)behavioral states in the self-organizing brain, under competitive, multidimensional dynamics. The designs draw on a self-dual classical description - via special Clifford-Lipschitz operations - of spinorial quantum states within registers of at most 16 qubits for efficient encoding of exponentially large neural structures. Formally 'trained', Lorentz neural architectures with precisely one lateral layer of exclusively inhibitory interneurons accounting for anti-modalities, as well as their co-architectures with intra-layer connections are highlighted. The approach accommodates the fusion of up to 16 time-invariant interconnected (anti-)modalities and the crystallization of latent multidimensional patterns. Comprehensive insights are expected to be gained through applications to Multimodal Big Data, under diverse real-world scenarios.

Labeling neural network submodules with human-legible descriptions is useful for many downstream tasks: such descriptions can surface failures, guide interventions, and perhaps even explain important model behaviors. To date, most mechanistic descriptions of trained networks have involved small models, narrowly delimited phenomena, and large amounts of human labor. Labeling all human-interpretable sub-computations in models of increasing size and complexity will almost certainly require tools that can generate and validate descriptions automatically. Recently, techniques that use learned models in-the-loop for labeling have begun to gain traction, but methods for evaluating their efficacy are limited and ad-hoc. How should we validate and compare open-ended labeling tools? This paper introduces FIND (Function INterpretation and Description), a benchmark suite for evaluating the building blocks of automated interpretability methods. FIND contains functions that resemble components of trained neural networks, and accompanying descriptions of the kind we seek to generate. The functions span textual and numeric domains, and involve a range of real-world complexities. We evaluate methods that use pretrained language models (LMs) to produce descriptions of function behavior in natural language and code. Additionally, we introduce a new interactive method in which an Automated Interpretability Agent (AIA) generates function descriptions. We find that an AIA, built from an LM with black-box access to functions, can infer function structure, acting as a scientist by forming hypotheses, proposing experiments, and updating descriptions in light of new data. However, AIA descriptions tend to capture global function behavior and miss local details. These results suggest that FIND will be useful for evaluating more sophisticated interpretability methods before they are applied to real-world models.

Attention mechanism is a central component of the transformer architecture which led to the phenomenal success of large language models. However, the theoretical principles underlying the attention mechanism are poorly understood, especially its nonconvex optimization dynamics. In this work, we explore the seminal softmax-attention model $f(\boldsymbol{X})=\langle \boldsymbol{Xv}, \texttt{softmax}(\boldsymbol{XWp})\rangle$, where $\boldsymbol{X}$ is the token sequence and $(\boldsymbol{v},\boldsymbol{W},\boldsymbol{p})$ are trainable parameters. We prove that running gradient descent on $\boldsymbol{p}$, or equivalently $\boldsymbol{W}$, converges in direction to a max-margin solution that separates $\textit{locally-optimal}$ tokens from non-optimal ones. This clearly formalizes attention as an optimal token selection mechanism. Remarkably, our results are applicable to general data and precisely characterize $\textit{optimality}$ of tokens in terms of the value embeddings $\boldsymbol{Xv}$ and problem geometry. We also provide a broader regularization path analysis that establishes the margin maximizing nature of attention even for nonlinear prediction heads. When optimizing $\boldsymbol{v}$ and $\boldsymbol{p}$ simultaneously with logistic loss, we identify conditions under which the regularization paths directionally converge to their respective hard-margin SVM solutions where $\boldsymbol{v}$ separates the input features based on their labels. Interestingly, the SVM formulation of $\boldsymbol{p}$ is influenced by the support vector geometry of $\boldsymbol{v}$. Finally, we verify our theoretical findings via numerical experiments and provide insights.