In an era where data privacy is paramount, the challenge of sharing information without compromising sensitive details has become more relevant than ever. Here, we consider the framework known as Controlled Query Evaluation (CQE), an innovative approach that safeguards confidentiality while still providing maximal query answers. We present an extension of this framework that enhances its expressivity through the use of rich forms of data protection rules. We explore the practical importance of these rules and some of the technical underpinnings that make this system effective. We then study some computational properties when data are managed through ontologies specified in DL-LiteR, a popular language designed for efficient reasoning in data-intensive applications.

The capacity to reason logically is a hallmark of human cognition. Humans excel at integrating multimodal information for logical reasoning, as exemplified by the Visual Question Answering (VQA) task, which is a challenging multimodal task. VQA tasks and large vision-and-language models aim to tackle reasoning problems, but the accuracy, consistency and integrity of the generated answers is hard to evaluate in the absence of a VQA dataset that can offer formal, comprehensive and systematic complex logical reasoning questions. To address this gap, we present LoRA, a novel Logical Reasoning Augmented VQA dataset that requires formal and complex description logic reasoning based on a food-and-kitchen knowledge base. Our main objective in creating LoRA is to enhance the complex and formal logical reasoning capabilities of VQA models, which are not adequately measured by existing VQA datasets. We devise strong and flexible programs to automatically generate 200,000 diverse description logic reasoning questions based on the SROIQ Description Logic, along with realistic kitchen scenes and ground truth answers. We fine-tune the latest transformer VQA models and evaluate the zero-shot performance of the state-of-the-art large vision-and-language models on LoRA. The results reveal that LoRA presents a unique challenge in logical reasoning, setting a systematic and comprehensive evaluation standard.

Pretraining a language model (LM) on text has been shown to help various downstream NLP tasks. Recent works show that a knowledge graph (KG) can complement text data, offering structured background knowledge that provides a useful scaffold for reasoning. However, these works are not pretrained to learn a deep fusion of the two modalities at scale, limiting the potential to acquire fully joint representations of text and KG.

We investigated the effectiveness of Digit Embedding. We can see that the numbers in the (100090-100140) range are clustered together. We investigated with more ranges. Figure 3 shows the 2-d embedding of decimal numbers in the range [1.0, 10.0] and [20.0-31.0]. And the numbers with larger differences like (1.6478, 30.7010), (5.339, 30.5113) are far from Please note that in this setup, the Digit Embedding is still applied.

The goal is to capture metadata automatically in a maximally interoperable way so that it can be extended and immediately used for publication, archiving, or by 3rd party systems (such as external repositories or workflow runtimes). Metadata is stored in a hidden directory in each project and users are not expected to manipulate it directly. It can be changed through Renku CLI commands or through the web user interface. If a project is pushed to a Renku-enabled git repository server, the Renku knowledge graph (KG) picks up and indexes the project's metadata in the global KG for that instance. RDF is defined as a graph of facts expressing relationships between resources by using semantic ontologies to precisely define the meaning of entities and their properties.

All ontology-based individual objects are housed within the blender file. For rendering an object in the final image, it is positioned in predefined locations on the scene. Specifically, we designated ten empty positions, such as varied table locations in the scene. To make this object visible in the final image, the script will copy this object and place it in one of the ten positions. Each question and answer group has a unique list of corresponding visuals used for image creation.

Unsupervised learning of latent variable models (LVMs) is widely used to represent data in machine learning. When such models reflect the ground truth factors and the mechanisms mapping them to observations, there is reason to expect that they allow generalization in downstream tasks. It is however well known that such identifiability guaranties are typically not achievable without putting constraints on the model class. This is notably the case for nonlinear Independent Component Analysis, in which the LVM maps statistically independent variables to observations via a deterministic nonlinear function. Several families of spurious solutions fitting perfectly the data, but that do not correspond to the ground truth factors can be constructed in generic settings. However, recent work suggests that constraining the function class of such models may promote identifiability. Specifically, function classes with constraints on their partial derivatives, gathered in the Jacobian matrix, have been proposed, such as orthogonal coordinate transformations (OCT), which impose orthogonality of the Jacobian columns. In the present work, we prove that a subclass of these transformations, conformal maps, is identifiable and provide novel theoretical results suggesting that OCTs have properties that prevent families of spurious solutions to spoil identifiability in a generic setting.