In Reinforcement Learning (RL), training a policy from scratch with online experiences can be inefficient because of the difficulties in exploration. Recently, offline RL provides a promising solution by giving an initialized offline policy, which can be refined through online interactions. However, existing approaches primarily perform offline and online learning in the same task, without considering the task generalization problem in offline-to-online adaptation. In real-world applications, it is common that we only have an offline dataset from a specific task while aiming for fast online-adaptation for several tasks. To address this problem, our work builds upon the investigation of successor representations for task generalization in online RL and extends the framework to incorporate offline-to-online learning. We demonstrate that the conventional paradigm using successor features cannot effectively utilize offline data and improve the performance for the new task by online fine-tuning. To mitigate this, we introduce a novel methodology that leverages offline data to acquire an ensemble of successor representations and subsequently constructs ensemble Q functions. This approach enables robust representation learning from datasets with different coverage and facilitates fast adaption of Q functions towards new tasks during the online fine-tuning phase. Extensive empirical evaluations provide compelling evidence showcasing the superior performance of our method in generalizing to diverse or even unseen tasks.

The paper focuses on the marginalization of indigenous language communities in the face of rapid technological advancements. We highlight the cultural richness of these languages and the risk they face of being overlooked in the realm of Natural Language Processing (NLP). We aim to bridge the gap between these communities and researchers, emphasizing the need for inclusive technological advancements that respect indigenous community perspectives. We show the NLP progress of indigenous Latin American languages and the survey that covers the status of indigenous languages in Latin America, their representation in NLP, and the challenges and innovations required for their preservation and development. The paper contributes to the current literature in understanding the need and progress of NLP for indigenous communities of Latin America, specifically low-resource and indigenous communities in general.

Data-centric learning focuses on enhancing algorithmic performance from the perspective of the training data [Oala et al., 2023]. In contrast to model-centric learning, which designs novel algorithms or optimization techniques for performance improvement with fixed training data, data-centric learning operates with a fixed learning algorithm while modifying the training data through trimming, augmenting, or other methods aligned with improving utility [Zha et al., 2023]. Data-centric learning holds significant potential in many areas such as model interpretation, subset training set selection, data generation, noisy label detection, active learning, and others [Chhabra et al., 2024, Kwon et al., 2024]. The essence of data-centric learning lies in estimating data influence, also known as data valuation [Hammoudeh and Lowd, 2022], in the context of a learning task. Intuitively, the impact of an individual data sample can be measured by assessing the change in learning utility when training with and without that specific sample. This leave-one-out influence [Cook and Weisberg, 1982] provides a rough gauge of the relative data influence of the specific sample on the otherwise full fixed training set. On the other hand, Shapley value [Ghorbani and Zou, 2019, Jia et al., 2019], originating from cooperative game theory, quantifies the increase in value when a group of samples collaborates to achieve the learning goal. Unlike leave-one-out influence, Shapley value represents the weighted average utility change resulting from adding the point to different training subsets. Despite the absence of assumptions on the learning model, the aforementioned retraining-based methods incur significant computational costs, especially for large-scale data analysis and deep models [Hammoudeh and Lowd, 2022].

"Very creepy," was April's first thought when she saw her face on a generative AI website. April is one half of the Maddison twins. She and her sister Amelia make content for OnlyFans, Instagram and other platforms, but they also existed as a custom generative AI model – made without their consent. "It was really weird to see our faces, but not really our faces," she says. Deepfakes – the creation of realistic but false imagery, video and audio using artificial intelligence – is on the political agenda after the federal government announced last week it would introduce legislation to ban the creation and sharing of deepfake pornography as part of measures to combat violence against women.

Law enforcement in the United States, United Kingdom, and Australia this week named a Russian national as the person behind LockBitSupp, the pseudonym of the leader of the LockBit ransomware gang that the US says is responsible for extracting 500 million from its victims. Dmitry Yuryevich Khoroshev has been sanctioned and charged with 26 criminal counts in the US, which combined could result in a prison sentence of 185 years. That is, if he's ever arrested and successfully prosecuted--an extremely rare event for suspects who live in Russia. Elsewhere in the world of cybercrime, WIRED's Andy Greenberg interviewed a representative of Cyber Army of Russia, a group of hackers who have targeted water utilities in the US and Europe and are said to have ties to the notorious Russian military hacking unit known as Sandworm. The responses from Cyber Army of Russia were littered with pro-Kremlin talking points--and some curious admissions.

The escalating frequency and severity of natural disasters, exacerbated by climate change, underscore the critical role of insurance in facilitating recovery and promoting investments in risk reduction. This work introduces a novel Adaptive Robust Optimization (ARO) framework tailored for the calculation of catastrophe insurance premiums, with a case study applied to the United States National Flood Insurance Program (NFIP). To the best of our knowledge, it is the first time an ARO approach has been applied to for disaster insurance pricing. Our methodology is designed to protect against both historical and emerging risks, the latter predicted by machine learning models, thus directly incorporating amplified risks induced by climate change. Using the US flood insurance data as a case study, optimization models demonstrate effectiveness in covering losses and produce surpluses, with a smooth balance transition through parameter fine-tuning. Among tested optimization models, results show ARO models with conservative parameter values achieving low number of insolvent states with the least insurance premium charged. Overall, optimization frameworks offer versatility and generalizability, making it adaptable to a variety of natural disaster scenarios, such as wildfires, droughts, etc. This work not only advances the field of insurance premium modeling but also serves as a vital tool for policymakers and stakeholders in building resilience to the growing risks of natural catastrophes.

Real-world time series often exhibit complex interdependencies that cannot be captured in isolation. Global models that model past data from multiple related time series globally while producing series-specific forecasts locally are now common. However, their forecasts for each individual series remain isolated, failing to account for the current state of its neighbouring series. Multivariate models like multivariate attention and graph neural networks can explicitly incorporate inter-series information, thus addressing the shortcomings of global models. However, these techniques exhibit quadratic complexity per timestep, limiting scalability. This paper introduces the Context Neural Network, an efficient linear complexity approach for augmenting time series models with relevant contextual insights from neighbouring time series without significant computational overhead. The proposed method enriches predictive models by providing the target series with real-time information from its neighbours, addressing the limitations of global models, yet remaining computationally tractable for large datasets.

The pitch contours of Mandarin two-character words are generally understood as being shaped by the underlying tones of the constituent single-character words, in interaction with articulatory constraints imposed by factors such as speech rate, co-articulation with adjacent tones, segmental make-up, and predictability. This study shows that tonal realization is also partially determined by words' meanings. We first show, on the basis of a Taiwan corpus of spontaneous conversations, using the generalized additive regression model, and focusing on the rise-fall tone pattern, that after controlling for effects of speaker and context, word type is a stronger predictor of pitch realization than all the previously established word-form related predictors combined. Importantly, the addition of information about meaning in context improves prediction accuracy even further. We then proceed to show, using computational modeling with context-specific word embeddings, that token-specific pitch contours predict word type with 50% accuracy on held-out data, and that context-sensitive, token-specific embeddings can predict the shape of pitch contours with 30% accuracy. These accuracies, which are an order of magnitude above chance level, suggest that the relation between words' pitch contours and their meanings are sufficiently strong to be functional for language users. The theoretical implications of these empirical findings are discussed.

Social robotics researchers are increasingly interested in multi-party trained conversational agents. With a growing demand for real-world evaluations, our study presents Large Language Models (LLMs) deployed in a month-long live show at the Edinburgh Festival Fringe. This case study investigates human improvisers co-creating with conversational agents in a professional theatre setting. We explore the technical capabilities and constraints of on-the-spot multi-party dialogue, providing comprehensive insights from both audience and performer experiences with AI on stage. Our human-in-the-loop methodology underlines the challenges of these LLMs in generating context-relevant responses, stressing the user interface's crucial role. Audience feedback indicates an evolving interest for AI-driven live entertainment, direct human-AI interaction, and a diverse range of expectations about AI's conversational competence and utility as a creativity support tool. Human performers express immense enthusiasm, varied satisfaction, and the evolving public opinion highlights mixed emotions about AI's role in arts.

The exploration of high-speed movement by robots or road traffic agents is crucial for autonomous driving and navigation. Trajectory prediction at high speeds requires considering historical features and interactions with surrounding entities, a complexity not as pronounced in lower-speed environments. Prior methods have assessed the spatio-temporal dynamics of agents but often neglected intrinsic intent and uncertainty, thereby limiting their effectiveness. We present the Denoised Endpoint Distribution model for trajectory prediction, which distinctively models agents' spatio-temporal features alongside their intrinsic intentions and uncertainties. By employing Diffusion and Transformer models to focus on agent endpoints rather than entire trajectories, our approach significantly reduces model complexity and enhances performance through endpoint information. Our experiments on open datasets, coupled with comparison and ablation studies, demonstrate our model's efficacy and the importance of its components. This approach advances trajectory prediction in high-speed scenarios and lays groundwork for future developments.