With the advancement of large language models, language-based forecasting has recently emerged as an innovative approach for predicting human mobility patterns. The core idea is to use prompts to transform the raw mobility data given as numerical values into natural language sentences so that the language models can be leveraged to generate the description for future observations. However, previous studies have only employed fixed and manually designed templates to transform numerical values into sentences. Since the forecasting performance of language models heavily relies on prompts, using fixed templates for prompting may limit the forecasting capability of language models. In this paper, we propose a novel framework for prompt mining in language-based mobility forecasting, aiming to explore diverse prompt design strategies. Specifically, the framework includes a prompt generation stage based on the information entropy of prompts and a prompt refinement stage to integrate mechanisms such as the chain of thought. Experimental results on real-world large-scale data demonstrate the superiority of generated prompts from our prompt mining pipeline. Additionally, the comparison of different prompt variants shows that the proposed prompt refinement process is effective. Our study presents a promising direction for further advancing language-based mobility forecasting.

Multi-Source cross-lingual transfer learning deals with the transfer of task knowledge from multiple labelled source languages to an unlabeled target language under the language shift. Existing methods typically focus on weighting the predictions produced by language-specific classifiers of different sources that follow a shared encoder. However, all source languages share the same encoder, which is updated by all these languages. The extracted representations inevitably contain different source languages' information, which may disturb the learning of the language-specific classifiers. Additionally, due to the language gap, language-specific classifiers trained with source labels are unable to make accurate predictions for the target language. Both facts impair the model's performance. To address these challenges, we propose a Disentangled and Adaptive Network (DA-Net). Firstly, we devise a feedback-guided collaborative disentanglement method that seeks to purify input representations of classifiers, thereby mitigating mutual interference from multiple sources. Secondly, we propose a class-aware parallel adaptation method that aligns class-level distributions for each source-target language pair, thereby alleviating the language pairs' language gap. Experimental results on three different tasks involving 38 languages validate the effectiveness of our approach.

Extensive research has been conducted on predicting air pollution concentrations using various modelling frameworks [1, 2, 3, 4, 5, 6, 7, 8, 9]. However, leveraging air pollution concentration data should not be seen as a unilateral process where predictions are simply delivered to stakeholders without further engagement. Instead, an iterative approach that considers the practical use and outcomes of these predictions is crucial for refining and directing future research concerning air pollution. In response to this need, our work introduces Environmental Insights, an open-source Python package designed to facilitate active engagement with air pollution issues. This package enables stakeholders to download, analyse, and visualise air pollution concentration data, thereby offering a unified platform for exploring potential air pollution futures. Environmental Insights aims to disseminate and democratise access to air pollution data, breaking down barriers for individuals and communities without extensive resources or technical expertise. By empowering a broader audience to engage with air pollution data, the package also seeks to amplify public pressure on policymakers for meaningful air quality improvements in areas of significant concern to the community.

Artificial intelligence has demonstrated immense potential in scientific research. Within molecular science, it is revolutionizing the traditional computer-aided paradigm, ushering in a new era of deep learning. With recent progress in multimodal learning and natural language processing, an emerging trend has targeted at building multimodal frameworks to jointly model molecules with textual domain knowledge. In this paper, we present the first systematic survey on multimodal frameworks for molecules research. Specifically,we begin with the development of molecular deep learning and point out the necessity to involve textual modality. Next, we focus on recent advances in text-molecule alignment methods, categorizing current models into two groups based on their architectures and listing relevant pre-training tasks. Furthermore, we delves into the utilization of large language models and prompting techniques for molecular tasks and present significant applications in drug discovery. Finally, we discuss the limitations in this field and highlight several promising directions for future research.

One way to avoid global smoothness of f is to use local Lipschitz continuity of the gradient ("local smoothness"). Local We develop new sub-optimality bounds for gradient smoothness uses different Lipschitz constants for different descent (GD) that depend on the conditioning neighbourhoods, thus avoiding global assumptions and obtaining of the objective along the path of optimization, improved rates. However, such analyses typically require rather than on global, worst-case constants. Key the iterates to be bounded, in which case local smoothness to our proofs is directional smoothness, a measure reduces to L-smoothness over a compact set (Malitsky of gradient variation that we use to develop upperbounds & Mishchenko, 2020). Boundedness can be enforced in a on the objective. Minimizing these upperbounds variety of ways: Zhang & Hong (2020) break optimization requires solving implicit equations to obtain into stages, Patel & Berahas (2022) develop a stopping-time a sequence of strongly adapted step-sizes; framework, and Lu & Mei (2023) use line-search and a modified we show that these equations are straightforward update. These approaches either modify the underlying to solve for convex quadratics and lead to new optimization algorithm, require local smoothness oracles guarantees for two classical step-sizes. For general (Park et al., 2021), or rely on highly complex arguments.

We develop provably safe and convergent reinforcement learning (RL) algorithms for control of nonlinear dynamical systems, bridging the gap between the hard safety guarantees of control theory and the convergence guarantees of RL theory. Recent advances at the intersection of control and RL follow a two-stage, safety filter approach to enforcing hard safety constraints: model-free RL is used to learn a potentially unsafe controller, whose actions are projected onto safe sets prescribed, for example, by a control barrier function. Though safe, such approaches lose any convergence guarantees enjoyed by the underlying RL methods. In this paper, we develop a single-stage, sampling-based approach to hard constraint satisfaction that learns RL controllers enjoying classical convergence guarantees while satisfying hard safety constraints throughout training and deployment. We validate the efficacy of our approach in simulation, including safe control of a quadcopter in a challenging obstacle avoidance problem, and demonstrate that it outperforms existing benchmarks.

The recent surge in research focused on generating synthetic data from large language models (LLMs), especially for scenarios with limited data availability, marks a notable shift in Generative Artificial Intelligence (AI). Their ability to perform comparably to real-world data positions this approach as a compelling solution to low-resource challenges. This paper delves into advanced technologies that leverage these gigantic LLMs for the generation of task-specific training data. We outline methodologies, evaluation techniques, and practical applications, discuss the current limitations, and suggest potential pathways for future research.

Advanced language-processing and machine-learning techniques promise massive efficiency improvements in the previously widely manual field of patent and technical knowledge management. This field presents large-scale and complex data with very precise contents and language representation of those contents. Particularly, patent texts can differ from mundane texts in various aspects, which entails significant opportunities and challenges. This paper presents a systematic overview of patent-related tasks and popular methodologies with a special focus on evolving and promising techniques. Language processing and particularly large language models as well as the recent boost of general generative methods promise to become game changers in the patent field. The patent literature and the fact-based argumentative procedures around patents appear almost as an ideal use case. However, patents entail a number of difficulties with which existing models struggle. The paper introduces fundamental aspects of patents and patent-related data that affect technology that wants to explore or manage them. It further reviews existing methods and approaches and points out how important reliable and unbiased evaluation metrics become. Although research has made substantial progress on certain tasks, the performance across many others remains suboptimal, sometimes because of either the special nature of patents and their language or inconsistencies between legal terms and the everyday meaning of terms. Moreover, yet few methods have demonstrated the ability to produce satisfactory text for specific sections of patents. By pointing out key developments, opportunities, and gaps, we aim to encourage further research and accelerate the advancement of this field.

Optimizing the performance of many objectives (instantiated by tasks or clients) jointly with a few Pareto stationary solutions (models) is critical in machine learning. However, previous multi-objective optimization methods often focus on a few number of objectives and cannot scale to many objectives that outnumber the solutions, leading to either subpar performance or ignored objectives. We introduce Many-objective multi-solution Transport (MosT), a framework that finds multiple diverse solutions in the Pareto front of many objectives. Our insight is to seek multiple solutions, each performing as a domain expert and focusing on a specific subset of objectives while collectively covering all of them. MosT formulates the problem as a bi-level optimization of weighted objectives for each solution, where the weights are defined by an optimal transport between the objectives and solutions. Our algorithm ensures convergence to Pareto stationary solutions for complementary subsets of objectives. On a range of applications in federated learning, multi-task learning, and mixture-of-prompt learning for LLMs, MosT distinctly outperforms strong baselines, delivering high-quality, diverse solutions that profile the entire Pareto frontier, thus ensuring balanced trade-offs across many objectives.

The advancement of Parkinson's Disease (PD) diagnosis through speech analysis is hindered by a notable lack of publicly available, diverse language datasets, limiting the reproducibility and further exploration of existing research. In response to this gap, we introduce a comprehensive corpus from 108 native Castilian Spanish speakers, comprising 55 healthy controls and 53 individuals diagnosed with PD, all of whom were under pharmacological treatment and recorded in their medication-optimized state. This unique dataset features a wide array of speech tasks, including sustained phonation of the five Spanish vowels, diadochokinetic tests, 16 listen-and-repeat utterances, and free monologues. The dataset emphasizes accuracy and reliability through specialist manual transcriptions of the listen-and-repeat tasks and utilizes Whisper for automated monologue transcriptions, making it the most complete public corpus of Parkinsonian speech, and the first in Castillian Spanish. NeuroVoz is composed by 2,903 audio recordings averaging $26.88 \pm 3.35$ recordings per participant, offering a substantial resource for the scientific exploration of PD's impact on speech. This dataset has already underpinned several studies, achieving a benchmark accuracy of 89% in PD speech pattern identification, indicating marked speech alterations attributable to PD. Despite these advances, the broader challenge of conducting a language-agnostic, cross-corpora analysis of Parkinsonian speech patterns remains an open area for future research. This contribution not only fills a critical void in PD speech analysis resources but also sets a new standard for the global research community in leveraging speech as a diagnostic tool for neurodegenerative diseases.