We introduce squared families, which are families of probability densities obtained by squaring a linear transformation of a statistic. Squared families are singular, however their singularity can easily be handled so that they form regular models. After handling the singularity, squared families possess many convenient properties. Their Fisher information is a conformal transformation of the Hessian metric induced from a Bregman generator. The Bregman generator is the normalising constant, and yields a statistical divergence on the family. The normalising constant admits a helpful parameter-integral factorisation, meaning that only one parameter-independent integral needs to be computed for all normalising constants in the family, unlike in exponential families. Finally, the squared family kernel is the only integral that needs to be computed for the Fisher information, statistical divergence and normalising constant. We then describe how squared families are special in the broader class of $g$-families, which are obtained by applying a sufficiently regular function $g$ to a linear transformation of a statistic. After removing special singularities, positively homogeneous families and exponential families are the only $g$-families for which the Fisher information is a conformal transformation of the Hessian metric, where the generator depends on the parameter only through the normalising constant. Even-order monomial families also admit parameter-integral factorisations, unlike exponential families. We study parameter estimation and density estimation in squared families, in the well-specified and misspecified settings. We use a universal approximation property to show that squared families can learn sufficiently well-behaved target densities at a rate of $\mathcal{O}(N^{-1/2})+C n^{-1/4}$, where $N$ is the number of datapoints, $n$ is the number of parameters, and $C$ is some constant.

Node importance estimation, a classical problem in network analysis, underpins various web applications. Previous methods either exploit intrinsic topological characteristics, e.g., graph centrality, or leverage additional information, e.g., data heterogeneity, for node feature enhancement. However, these methods follow the supervised learning setting, overlooking the fact that ground-truth node-importance data are usually partially labeled in practice. In this work, we propose the first semi-supervised node importance estimation framework, i.e., EASING, to improve learning quality for unlabeled data in heterogeneous graphs. Different from previous approaches, EASING explicitly captures uncertainty to reflect the confidence of model predictions. To jointly estimate the importance values and uncertainties, EASING incorporates DJE, a deep encoder-decoder neural architecture. DJE introduces distribution modeling for graph nodes, where the distribution representations derive both importance and uncertainty estimates. Additionally, DJE facilitates effective pseudo-label generation for the unlabeled data to enrich the training samples. Based on labeled and pseudo-labeled data, EASING develops effective semi-supervised heteroscedastic learning with varying node uncertainty regularization. Extensive experiments on three real-world datasets highlight the superior performance of EASING compared to competing methods. Codes are available via https://github.com/yankai-chen/EASING.

Humor plays a significant role in daily language communication. With the rapid development of large language models (LLMs), natural language processing has made significant strides in understanding and generating various genres of texts. However, most LLMs exhibit poor performance in generating and processing Chinese humor. In this study, we introduce a comprehensive Chinese humor-related dataset, the Chinese Fun Set (CFunSet). This dataset aggregates existing Chinese humor datasets and includes over 20,000 jokes collected from Tieba-JokeBar, a Chinese online platform known for joke sharing. The resulting corpus comprises more than 160,000 entries. Leveraging CFunSet, we developed the Chinese Fun Model (CFunModel), the first large language model designed to handle various Chinese humor-related tasks including Crosstalk Response Selection, Humor Recognition, Joke Generation, etc. Experimental results demonstrate that CFunModel outperforms popular large language models in these tasks. Our CFunSet is available at https://huggingface.co/datasets/ZhenghanYU/CFunSet and CFunModel is available at https://huggingface.co/ZhenghanYU/CFunModel. A demostration video of our work is available at https://youtu.be/MOsISOJ66Ms.

This article reports on the third iteration of a survey of computerized tools and technologies taught as part of postgraduate translation training programmes. While the survey was carried out under the aegis of the EMT Network, more than half of responses are from outside that network. The results show the responsiveness of programmes to innovations in translation technology, with increased compulsory inclusion of machine translation, post-editing, and quality evaluation, and a rapid response to the release of generative tools. The flexibility required during the Covid-19 pandemic has also led to some lasting changes to programmes. While the range of tools being taught has continued to expand, programmes seem to be consolidating their core offering around cloud-based software with cost-free academic access. There has also been an increase in the embedding of professional contexts and workflows associated with translation technology. Generic file management and data security skills have increased in perceived importance, and legal and ethical issues related to translation data have also become more prominent. In terms of course delivery the shift away from conventional labs identified in EMT2017 has accelerated markedly, no doubt partly driven by the pandemic, accompanied by a dramatic expansion in the use of students' personal devices.

--Convolutional neural networks (CNNs) have succeeded remarkably in various computer vision tasks. However, they are not intrinsically explainable. While feature-level understanding of CNNs reveals where the models looked, concept-based explainability methods provide insights into what the models saw. However, their assumption of linear reconstructability of image activations fails to capture the intricate relationships within these activations. Their Fidelity-only approach to evaluating global explanations also presents a new concern. For the first time, we address these limitations with the novel Transformative Nonlinear Concept Explainer (TraNCE) for CNNs. Unlike linear reconstruction assumptions made by existing methods, TraNCE captures the intricate relationships within the activations. This study presents three original contributions to the CNN explain-ability literature: (i) An automatic concept discovery mechanism based on variational autoencoders (V AEs). This transformative concept discovery process enhances the identification of meaningful concepts from image activations. Based on the investigations on publicly available datasets, we prove that a valid decomposition of a high-dimensional image activation should follow a non-linear reconstruction, contributing to the explainer's efficiency. We also demonstrate quantitatively that, besides accuracy, consistency is crucial for the meaningfulness of concepts and human trust. The code is available at https://github.com/daslimo/TrANCE ONVOLUTIONAL neural networks (CNNs) are widely used in computer vision, achieving notable success in visual classification tasks [1], [2]. However, understanding them at a human level remains a major challenge in artificial intelligence (AI), raising significant concerns about their explainability, especially in promoting ethical AI [3]- [5].

Appropriate traffic state representation is crucial for learning traffic signal control policies. However, most of the current traffic state representations are heuristically designed, with insufficient theoretical support. In this paper, we (1) develop a flexible, efficient, and theoretically grounded method, namely generalized phase pressure (G2P) control, which takes only simple lane features into consideration to decide which phase to be actuated; 2) extend the pressure control theory to a general form for multi-homogeneous-lane road networks based on queueing theory; (3) design a new traffic state representation based on the generalized phase state features from G2P control; and 4) develop a reinforcement learning (RL)-based algorithm template named G2P-XLight, and two RL algorithms, G2P-MPLight and G2P-CoLight, by combining the generalized phase state representation with MPLight and CoLight, two well-performed RL methods for learning traffic signal control policies. Extensive experiments conducted on multiple real-world datasets demonstrate that G2P control outperforms the state-of-the-art (SOTA) heuristic method in the transportation field and other recent human-designed heuristic methods; and that the newly proposed G2P-XLight significantly outperforms SOTA learning-based approaches. Our code is available online.

Time series classification is usually regarded as a distinct task from tabular data classification due to the importance of temporal information. However, in this paper, by performing permutation tests that disrupt temporal information on the UCR time series classification archive, the most widely used benchmark for time series classification, we identify a significant proportion of datasets where temporal information has little to no impact on classification. Many of these datasets are tabular in nature or rely mainly on tabular features, leading to potentially biased evaluations of time series classifiers focused on temporal information. To address this, we propose UCR Augmented, a benchmark based on the UCR time series classification archive designed to evaluate classifiers' ability to extract and utilize temporal information. Testing classifiers from seven categories on this benchmark revealed notable shifts in performance rankings. Some previously overlooked approaches perform well, while others see their performance decline significantly when temporal information is crucial. UCR Augmented provides a more robust framework for assessing time series classifiers, ensuring fairer evaluations. Our code is available at https://github.com/YunruiZhang/

--Proprioception is essential for coordinating human movements and enhancing the performance of assistive robotic devices. Skin stretch feedback, which closely aligns with natural proprioception mechanisms, presents a promising method for conveying proprioceptive information. T o better understand the impact of interference on skin stretch perception, we conducted a user study with 30 participants that evaluated the effect of two simultaneous skin stretches on user perception. We observed that when participants experience simultaneous skin stretch stimuli, a masking effect occurs which deteriorates perception performance in the collocated skin stretch configurations. However, the perceived workload stays the same. These findings show that interference can affect the perception of skin stretch such that multi-channel skin stretch feedback designs should avoid locating modules in close proximity. I. INTRODUCTION Proprioception, the sense of limb position relative to the body [1], is crucial for coordinating human movements.

DATE SUBMITTED: March, 202 5 Words: 9, 22 0 2 Abstract H umanoid robots are a form of embodied artificial intelligence (AI) that look s and act s more and more like humans. Powered by generative AI and advances in robotics, humanoid robots can speak and interact with humans rather naturally but are still easily recognizable as robots. But how will we treat humanoids when they seem indistinguishable from humans in appearance and mind? We find a tendency (called "anti - robot" speciesism) to deny such robots humanlike capabilities, driven by motivations to accord members of the human species preferential treatment . Six experiments show that robots are denied humanlike attributes, simply because they are not biological beings and because humans want to avoid feelings of cognitive dissonance when utilizing such robots for unsavory tasks . Th us, pe ople do not rationally attribute capabilities to perfectly human like robots but deny them capabilities as it suits them . Keywords: robots, artificial intelligence, humanoids, speciesism, cognitive dissonance 3 In recent years, n ew artificial intelligen ce (AI) technologies have been introduced into the marketplace that have the potential to radically change people's work and lives . This paper examines how people might react to robots that seem be " perfectly human like " . With major companies like Amazon and Nvidia planning mass production of such robots, we are entering an era where the line between human and non - human entities is increasingly blurred. Our findings suggest that the advent of such robots will not lead people to rationally conclude that these robots are as capable as humans in performing some tasks . Rather, people will deny these robots humanlike attributes, driven by their motivation to prioritize their own species and to avoid feelings of cognitive dissonance from utilizing such robots for unsavory tasks. Aversion to Robots and AI People are often averse to robots. P sychological research has explained this effect by arguing that such "almost humanlike" robots appear as aesthetically dis pleasing, and that they remind people of zombies, death, or disease (Kätsyri et al., 2015; Mori, 1970; Wang et al., 2015) . Other psychological explanations focus on how people perceive robot minds, sometimes referred to as the "uncanny valley of mind" (Müller et al., 2021; Stein & Ohler, 2017) . These theories suggest that humanoid robots can be unsettling because they remind people of the human ability to experience feelings, even though these robots are not seen as having such capabilities (Gray & Wegner, 2012; Smith et al., 2021) .

Abstract--Deep Reinforcement Learning (DRL) holds significant promise for achieving human-like Autonomous Vehicle (AV) capabilities, but suffers from low sample efficiency and challenges in reward design. Model-Based Reinforcement Learning (MBRL) offers improved sample efficiency and generalizability compared to Model-Free Reinforcement Learning (MFRL) in various multi-agent decision-making scenarios. Nevertheless, MBRL faces critical difficulties in estimating uncertainty during the model learning phase, thereby limiting its scalability and applicability in real-world scenarios. Additionally, most Connected Autonomous Vehicle (CAV) studies focus on single-agent decision-making, while existing multi-agent MBRL solutions lack computationally tractable algorithms with Probably Approximately Correct (P AC) guarantees, an essential factor for ensuring policy reliability with limited training data. T o address these challenges, we propose MA-PMBRL, a novel Multi-Agent Pessimistic Model-Based Reinforcement Learning framework for CAVs, incorporating a max-min optimization approach to enhance robustness and decision-making. T o mitigate the inherent subjectivity of uncertainty estimation in MBRL and avoid incurring catastrophic failures in AV, MA-PMBRL employs a pessimistic optimization framework combined with Projected Gradient Descent (PGD) for both model and policy learning. MA-PMBRL also employs general function approximations under partial dataset coverage to enhance learning efficiency and system-level performance. By bounding the suboptimality of the resulting policy under mild theoretical assumptions, we successfully establish P AC guarantees for MA-PMBRL, demonstrating that the proposed framework represents a significant step toward scalable, efficient, and reliable multi-agent decision-making for CAVs. Multi-Agent Reinforcement Learning (MARL) has emerged as a promising approach for enabling CA Vs to execute complex tasks autonomously . R. Wen and R. Li are with the College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310058, China (email: {wenruoqi, lirongpeng }@zju.edu.cn). X. Xu is with the Information and Communication Branch of State Grid Hebei Electric Power Co., Ltd, China (e-mail:hsuxing@zju.edu.cn). Z. Zhao is with Zhejiang Lab, Hangzhou 311121, China, and also with the College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310058, China (email: zhaozf@zhejianglab.com). However, the costly requirement for sufficient data through extensive real-world interactions makes MFRL stuck in unstable learning and high computational overhead, thus making it less competent in autonomous driving scenarios.