Accurate prediction of what types of patents that companies will apply for in the next period of time can figure out their development strategies and help them discover potential partners or competitors in advance. Although important, this problem has been rarely studied in previous research due to the challenges in modelling companies' continuously evolving preferences and capturing the semantic correlations of classification codes. To fill in this gap, we propose an event-based dynamic graph learning framework for patent application trend prediction. In particular, our method is founded on the memorable representations of both companies and patent classification codes. When a new patent is observed, the representations of the related companies and classification codes are updated according to the historical memories and the currently encoded messages. Moreover, a hierarchical message passing mechanism is provided to capture the semantic proximities of patent classification codes by updating their representations along the hierarchical taxonomy. Finally, the patent application trend is predicted by aggregating the representations of the target company and classification codes from static, dynamic, and hierarchical perspectives. Experiments on real-world data demonstrate the effectiveness of our approach under various experimental conditions, and also reveal the abilities of our method in learning semantics of classification codes and tracking technology developing trajectories of companies.

Many real-world data sets can be presented in the form of a matrix whose entries correspond to the interaction between two entities of different natures (number of times a web user visits a web page, a student's grade in a subject, a patient's rating of a doctor, etc.). We assume in this paper that the mentioned interaction is determined by unobservable latent variables describing each entity. Our objective is to estimate the conditional expectation of the data matrix given the unobservable variables. This is presented as a problem of estimation of a bivariate function referred to as graphon. We study the cases of piecewise constant and H\"older-continuous graphons. We establish finite sample risk bounds for the least squares estimator and the exponentially weighted aggregate. These bounds highlight the dependence of the estimation error on the size of the data set, the maximum intensity of the interactions, and the level of noise. As the analyzed least-squares estimator is intractable, we propose an adaptation of Lloyd's alternating minimization algorithm to compute an approximation of the least-squares estimator. Finally, we present numerical experiments in order to illustrate the empirical performance of the graphon estimator on synthetic data sets.

This paper investigates the potential privacy risks associated with forecasting models, with specific emphasis on their application in the context of smart grids. While machine learning and deep learning algorithms offer valuable utility, concerns arise regarding their exposure of sensitive information. Previous studies have focused on classification models, overlooking risks associated with forecasting models. Deep learning based forecasting models, such as Long Short Term Memory (LSTM), play a crucial role in several applications including optimizing smart grid systems but also introduce privacy risks. Our study analyzes the ability of forecasting models to leak global properties and privacy threats in smart grid systems. We demonstrate that a black box access to an LSTM model can reveal a significant amount of information equivalent to having access to the data itself (with the difference being as low as 1% in Area Under the ROC Curve). This highlights the importance of protecting forecasting models at the same level as the data.

The inclusion of physical information in machine learning frameworks has revolutionized many application areas. This involves enhancing the learning process by incorporating physical constraints and adhering to physical laws. In this work we explore their utility for reinforcement learning applications. We present a thorough review of the literature on incorporating physics information, as known as physics priors, in reinforcement learning approaches, commonly referred to as physics-informed reinforcement learning (PIRL). We introduce a novel taxonomy with the reinforcement learning pipeline as the backbone to classify existing works, compare and contrast them, and derive crucial insights. Existing works are analyzed with regard to the representation/ form of the governing physics modeled for integration, their specific contribution to the typical reinforcement learning architecture, and their connection to the underlying reinforcement learning pipeline stages. We also identify core learning architectures and physics incorporation biases (i.e., observational, inductive and learning) of existing PIRL approaches and use them to further categorize the works for better understanding and adaptation. By providing a comprehensive perspective on the implementation of the physics-informed capability, the taxonomy presents a cohesive approach to PIRL. It identifies the areas where this approach has been applied, as well as the gaps and opportunities that exist. Additionally, the taxonomy sheds light on unresolved issues and challenges, which can guide future research. This nascent field holds great potential for enhancing reinforcement learning algorithms by increasing their physical plausibility, precision, data efficiency, and applicability in real-world scenarios.

With advanced development in robotics and autonomous systems in the past decades, the opportunities and demands for more complex physical human-robot interaction (pHRI) in our everyday unconstrained environments are rising; thus, it is critical for robots to be adaptive, compliant, reactive, safe and easy to program [1, 2, 3]. In many cases, robots will need to acquire new skills to satisfy task requirements in an ever-changing environment. It is usually difficult for non-experts to program robots for complex motion tasks and even tedious for experts to reprogram them when task requirements change. A straightforward and intuitive approach for robots to develop new skills is through Learning from Demonstration (LfD) [4, 5, 6, 7, 8]. This paradigm allows robots to acquire skills, typically encoded or defined in literature as action policies, motion policies, or imitation policies, directly from motion examples provided by humans or even other robots, mirroring a teacher-student relationship. In recent years, significant progress has been made in using LfD to learn complex and diverse motion tasks.

Although initially emerging in psychology, many of the concerns raised, such as lack of open access to data, materials, and/or experimental design apply also to other (social) sciences. Among these are both Human Robot Interaction (HRI) and its related sub-field of Child Robot Interaction (CRI), where social and psychological relationships between humans and robots are often the focus of the research. Given its novelty and rapidly evolving progress, CRI in particular suffers from fragmented and heterogeneous literature, varying research goals, and a lack of standardised methods and metrics. Recent efforts have brought forth conversations related to replication specifically within CRI [51, 52], with authors appealing for more works that address the main challenges in HRI with children whilst still ensuring high-quality reporting and data sharing. However, clear open science guidelines on reproducibility in HRI and related sub-fields are still missing.

One of the most promising applications in the era of NISQ (Noisy Intermediate-Scale Quantum) computing is quantum machine learning. Quantum machine learning offers significant quantum advantages over classical machine learning across various domains. Specifically, generative adversarial networks have been recognized for their potential utility in diverse fields such as image generation, finance, and probability distribution modeling. However, these networks necessitate solutions for inherent challenges like mode collapse. In this study, we capitalize on the concept that the estimation of mutual information between high-dimensional continuous random variables can be achieved through gradient descent using neural networks. We introduce a novel approach named InfoQGAN, which employs the Mutual Information Neural Estimator (MINE) within the framework of quantum generative adversarial networks to tackle the mode collapse issue. Furthermore, we elaborate on how this approach can be applied to a financial scenario, specifically addressing the problem of generating portfolio return distributions through dynamic asset allocation. This illustrates the potential practical applicability of InfoQGAN in real-world contexts.

The rapid advancements in artificial intelligence (AI) have led to a growing trend of human-AI teaming (HAT) in various fields. As machines continue to evolve from mere automation to a state of autonomy, they are increasingly exhibiting unexpected behaviors and human-like cognitive/intelligent capabilities, including situation awareness (SA). This shift has the potential to enhance the performance of mixed human-AI teams over all-human teams, underscoring the need for a better understanding of the dynamic SA interactions between humans and machines. To this end, we provide a review of leading SA theoretical models and a new framework for SA in the HAT context based on the key features and processes of HAT. The Agent Teaming Situation Awareness (ATSA) framework unifies human and AI behavior, and involves bidirectional, and dynamic interaction. The framework is based on the individual and team SA models and elaborates on the cognitive mechanisms for modeling HAT. Similar perceptual cycles are adopted for the individual (including both human and AI) and the whole team, which is tailored to the unique requirements of the HAT context. ATSA emphasizes cohesive and effective HAT through structures and components, including teaming understanding, teaming control, and the world, as well as adhesive transactive part. We further propose several future research directions to expand on the distinctive contributions of ATSA and address the specific and pressing next steps.

Recent progress in deep learning and natural language processing has given rise to powerful models that are primarily trained on a cloze-like task and show some evidence of having access to substantial linguistic information, including some constructional knowledge. This groundbreaking discovery presents an exciting opportunity for a synergistic relationship between computational methods and Construction Grammar research. In this chapter, we explore three distinct approaches to the interplay between computational methods and Construction Grammar: (i) computational methods for text analysis, (ii) computational Construction Grammar, and (iii) deep learning models, with a particular focus on language models. We touch upon the first two approaches as a contextual foundation for the use of computational methods before providing an accessible, yet comprehensive overview of deep learning models, which also addresses reservations construction grammarians may have. Additionally, we delve into experiments that explore the emergence of constructionally relevant information within these models while also examining the aspects of Construction Grammar that may pose challenges for these models. This chapter aims to foster collaboration between researchers in the fields of natural language processing and Construction Grammar. By doing so, we hope to pave the way for new insights and advancements in both these fields.

This paper examines the current landscape of AI regulations, highlighting the divergent approaches being taken, and proposes an alternative contextual, coherent, and commensurable (3C) framework. The EU, Canada, South Korea, and Brazil follow a horizontal or lateral approach that postulates the homogeneity of AI systems, seeks to identify common causes of harm, and demands uniform human interventions. In contrast, the U.K., Israel, Switzerland, Japan, and China have pursued a context-specific or modular approach, tailoring regulations to the specific use cases of AI systems. The U.S. is reevaluating its strategy, with growing support for controlling existential risks associated with AI. Addressing such fragmentation of AI regulations is crucial to ensure the interoperability of AI. The present degree of proportionality, granularity, and foreseeability of the EU AI Act is not sufficient to garner consensus. The context-specific approach holds greater promises but requires further development in terms of details, coherency, and commensurability. To strike a balance, this paper proposes a hybrid 3C framework. To ensure contextuality, the framework categorizes AI into distinct types based on their usage and interaction with humans: autonomous, allocative, punitive, cognitive, and generative AI. To ensure coherency, each category is assigned specific regulatory objectives: safety for autonomous AI; fairness and explainability for allocative AI; accuracy and explainability for punitive AI; accuracy, robustness, and privacy for cognitive AI; and the mitigation of infringement and misuse for generative AI. To ensure commensurability, the framework promotes the adoption of international industry standards that convert principles into quantifiable metrics. In doing so, the framework is expected to foster international collaboration and standardization without imposing excessive compliance costs.