With growing interest in Procedural Content Generation (PCG) it becomes increasingly important to develop methods and tools for evaluating and comparing alternative systems. There is a particular lack regarding the evaluation of generative pipelines, where a set of generative systems work in series to make iterative changes to an artifact. We introduce a novel method called Generative Shift for evaluating the impact of individual stages in a PCG pipeline by quantifying the impact that a generative process has when it is applied to a pre-existing artifact. We explore this technique by applying it to a very rich dataset of Minecraft game maps produced by a set of alternative settlement generators developed as part of the Generative Design in Minecraft Competition (GDMC), all of which are designed to produce appropriate settlements for a pre-existing map. While this is an early exploration of this technique we find it to be a promising lens to apply to PCG evaluation, and we are optimistic about the potential of Generative Shift to be a domain-agnostic method for evaluating generative pipelines.

Transfer learning has led to large gains in performance for nearly all NLP tasks while making downstream models easier and faster to train. This has also been extended to low-resourced languages, with some success. We investigate the properties of cross-lingual transfer learning between ten low-resourced languages, from the perspective of a named entity recognition task. We specifically investigate how much adaptive fine-tuning and the choice of transfer language affect zero-shot transfer performance. We find that models that perform well on a single language often do so at the expense of generalising to others, while models with the best generalisation to other languages suffer in individual language performance. Furthermore, the amount of data overlap between the source and target datasets is a better predictor of transfer performance than either the geographical or genetic distance between the languages.

Continuum robots with variable stiffness have gained wide popularity in the last decade. Layer jamming (LJ) has emerged as a simple and efficient technique to achieve tunable stiffness for continuum robots. Despite its merits, the development of a control-oriented dynamical model tailored for this specific class of robots remains an open problem in the literature. This paper aims to present the first solution, to the best of our knowledge, to close the gap. We propose an energy-based model that is integrated with the LuGre frictional model for LJ-based continuum robots. Then, we take a comprehensive theoretical analysis for this model, focusing on two fundamental characteristics of LJ-based continuum robots: shape locking and adjustable stiffness. To validate the modeling approach and theoretical results, a series of experiments using our \textit{OctRobot-I} continuum robotic platform was conducted. The results show that the proposed model is capable of interpreting and predicting the dynamical behaviors in LJ-based continuum robots.

This report describes trade-offs in the design of international governance arrangements for civilian artificial intelligence (AI) and presents one approach in detail. This approach represents the extension of a standards, licensing, and liability regime to the global level. We propose that states establish an International AI Organization (IAIO) to certify state jurisdictions (not firms or AI projects) for compliance with international oversight standards. States can give force to these international standards by adopting regulations prohibiting the import of goods whose supply chains embody AI from non-IAIO-certified jurisdictions. This borrows attributes from models of existing international organizations, such as the International Civilian Aviation Organization (ICAO), the International Maritime Organization (IMO), and the Financial Action Task Force (FATF). States can also adopt multilateral controls on the export of AI product inputs, such as specialized hardware, to non-certified jurisdictions. Indeed, both the import and export standards could be required for certification. As international actors reach consensus on risks of and minimum standards for advanced AI, a jurisdictional certification regime could mitigate a broad range of potential harms, including threats to public safety.

We call on the Document AI (DocAI) community to reevaluate current methodologies and embrace the challenge of creating more practically-oriented benchmarks. Document Understanding Dataset and Evaluation (DUDE) seeks to remediate the halted research progress in understanding visually-rich documents (VRDs). We present a new dataset with novelties related to types of questions, answers, and document layouts based on multi-industry, multi-domain, and multi-page VRDs of various origins, and dates. Moreover, we are pushing the boundaries of current methods by creating multi-task and multi-domain evaluation setups that more accurately simulate real-world situations where powerful generalization and adaptation under low-resource settings are desired. DUDE aims to set a new standard as a more practical, long-standing benchmark for the community, and we hope that it will lead to future extensions and contributions that address real-world challenges. Finally, our work illustrates the importance of finding more efficient ways to model language, images, and layout in DocAI.

In this work, we investigate the structure and representation capacity of sinusoidal MLPs - multilayer perceptron networks that use sine as the activation function. These neural networks (known as neural fields) have become fundamental in representing common signals in computer graphics, such as images, signed distance functions, and radiance fields. This success can be primarily attributed to two key properties of sinusoidal MLPs: smoothness and compactness. These functions are smooth because they arise from the composition of affine maps with the sine function. This work provides theoretical results to justify the compactness property of sinusoidal MLPs and provides control mechanisms in the definition and training of these networks. We propose to study a sinusoidal MLP by expanding it as a harmonic sum. First, we observe that its first layer can be seen as a harmonic dictionary, which we call the input sinusoidal neurons. Then, a hidden layer combines this dictionary using an affine map and modulates the outputs using the sine, this results in a special dictionary of sinusoidal neurons. We prove that each of these sinusoidal neurons expands as a harmonic sum producing a large number of new frequencies expressed as integer linear combinations of the input frequencies. Thus, each hidden neuron produces the same frequencies, and the corresponding amplitudes are completely determined by the hidden affine map. We also provide an upper bound and a way of sorting these amplitudes that can control the resulting approximation, allowing us to truncate the corresponding series. Finally, we present applications for training and initialization of sinusoidal MLPs. Additionally, we show that if the input neurons are periodic, then the entire network will be periodic with the same period. We relate these periodic networks with the Fourier series representation.

Atrial fibrillation (AF) is the most common cardiac arrhythmia and associated with a high risk for serious conditions like stroke. The use of wearable devices embedded with automatic and timely AF assessment from electrocardiograms (ECGs) has shown to be promising in preventing life-threatening situations. Although deep neural networks have demonstrated superiority in model performance, their use on wearable devices is limited by the trade-off between model performance and complexity. In this work, we propose to use lightweight convolutional neural networks (CNNs) with parameterised hypercomplex (PH) layers for AF detection based on ECGs. The proposed approach trains small-scale CNNs, thus overcoming the limited computing resources on wearable devices. We show comparable performance to corresponding real-valued CNNs on two publicly available ECG datasets using significantly fewer model parameters. PH models are more flexible than other hypercomplex neural networks and can operate on any number of input ECG leads.

Unsupervised outlier detection constitutes a crucial phase within data analysis and remains a dynamic realm of research. A good outlier detection algorithm should be computationally efficient, robust to tuning parameter selection, and perform consistently well across diverse underlying data distributions. We introduce One-Class Boundary Peeling, an unsupervised outlier detection algorithm. One-class Boundary Peeling uses the average signed distance from iteratively-peeled, flexible boundaries generated by one-class support vector machines. One-class Boundary Peeling has robust hyperparameter settings and, for increased flexibility, can be cast as an ensemble method. In synthetic data simulations One-Class Boundary Peeling outperforms all state of the art methods when no outliers are present while maintaining comparable or superior performance in the presence of outliers, as compared to benchmark methods. One-Class Boundary Peeling performs competitively in terms of correct classification, AUC, and processing time using common benchmark data sets.

In the simulation of physical systems, an increase in model complexity directly corresponds to an increase in the simulation time, posing substantial limitations to the use of such models for critical applications that depend on timesensitive decisions. Therefore, fast emulators learned by data-driven architectures and integrated in algorithms for the solution of forward and inverse problems are becoming increasingly successful. On one hand, several contributions in the literature have proposed architectures for physics-based emulators designed to limit the number of model evaluations during training. These include, for example, physics-informed neural networks (PINN) [1], deep operator networks (DeepONet) [2], and transformers-based architectures [3]. On the other hand, generative approaches have been the subject of significant recent research due to their flexibility to quantify uncertainty in the predicted outputs. Unlike traditional deep learning tasks, generative models focus on capturing a distributional characterization of the latent variables, providing an improved understanding, and a superior way to interact with a given system. Examples in this context include Gaussian Processes [4], Bayesian networks [5], generative adversarial networks (GAN) [6], diffusion models [7], optimal transport [8], normalizing flow [9, 10] and Variational Auto-Encoders (VAE) [11]. When using data-driven emulators in the context of inverse problems, other difficulties arise. Inverse problem are often ill-posed as a result of non-uniqueness of solutions, or of ill-conditioning due to high-dimensionality, data-sparsity, noise-corruption, and nonlinear response of the physical systems [12, 13, 14, 15, 16].

The development of large high-quality datasets and high-performing models have led to significant advancements in the domain of Extractive Question Answering (EQA). This progress has sparked considerable interest in exploring unanswerable questions within the EQA domain. Training EQA models with unanswerable questions helps them avoid extracting misleading or incorrect answers for queries that lack valid responses. However, manually annotating unanswerable questions is labor-intensive. To address this, we propose AGent, a novel pipeline that automatically creates new unanswerable questions by re-matching a question with a context that lacks the necessary information for a correct answer. In this paper, we demonstrate the usefulness of this AGent pipeline by creating two sets of unanswerable questions from answerable questions in SQuAD and HotpotQA. These created question sets exhibit low error rates. Additionally, models fine-tuned on these questions show comparable performance with those fine-tuned on the SQuAD 2.0 dataset on multiple EQA benchmarks.