84,903 research outputs found
QuesNet: A Unified Representation for Heterogeneous Test Questions
Understanding learning materials (e.g. test questions) is a crucial issue in
online learning systems, which can promote many applications in education
domain. Unfortunately, many supervised approaches suffer from the problem of
scarce human labeled data, whereas abundant unlabeled resources are highly
underutilized. To alleviate this problem, an effective solution is to use
pre-trained representations for question understanding. However, existing
pre-training methods in NLP area are infeasible to learn test question
representations due to several domain-specific characteristics in education.
First, questions usually comprise of heterogeneous data including content text,
images and side information. Second, there exists both basic linguistic
information as well as domain logic and knowledge. To this end, in this paper,
we propose a novel pre-training method, namely QuesNet, for comprehensively
learning question representations. Specifically, we first design a unified
framework to aggregate question information with its heterogeneous inputs into
a comprehensive vector. Then we propose a two-level hierarchical pre-training
algorithm to learn better understanding of test questions in an unsupervised
way. Here, a novel holed language model objective is developed to extract
low-level linguistic features, and a domain-oriented objective is proposed to
learn high-level logic and knowledge. Moreover, we show that QuesNet has good
capability of being fine-tuned in many question-based tasks. We conduct
extensive experiments on large-scale real-world question data, where the
experimental results clearly demonstrate the effectiveness of QuesNet for
question understanding as well as its superior applicability
Procedural function-based modelling of volumetric microstructures
We propose a new approach to modelling heterogeneous objects containing internal volumetric structures with size of details orders of magnitude smaller than the overall size of the object. The proposed function-based procedural representation provides compact, precise, and arbitrarily parameterised models of coherent microstructures, which can undergo blending, deformations, and other geometric operations, and can be directly rendered and fabricated without generating any auxiliary representations (such as polygonal meshes and voxel arrays). In particular, modelling of regular lattices and cellular microstructures as well as irregular porous media is discussed and illustrated. We also present a method to estimate parameters of the given model by fitting it to microstructure data obtained with magnetic resonance imaging and other measurements of natural and artificial objects. Examples of rendering and digital fabrication of microstructure models are presented
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Transferring Unit Cell Based Tissue Scaffold Design to Solid Freeform Fabrication
Designing for the freeform fabrication of heterogeneous tissue scaffold is always a challenge in
Computer Aided Tissue Engineering. The difficulties stem from two major sources: 1)
limitations in current CAD systems to assembly unit cells as building blocks to form complex
tissue scaffolds, and 2) the inability to generate tool paths for freeform fabrication of unit cell
assemblies. To overcome these difficulties, we have developed an abstract model based on
skeletal representation and associated computational methods to assemble unit cells into an
optimized structure. Additionally we have developed a process planning technique based on
internal architecture pattern of unit cells to generate tool paths for freeform fabrication of tissue
scaffold. By modifying our optimization process, we are able to transfer an optimized design to
our fabrication system via our process planning technique.Mechanical Engineerin
Exploiting conceptual spaces for ontology integration
The widespread use of ontologies raises the need to integrate distinct conceptualisations. Whereas the symbolic approach of established representation standards ā based on first-order logic (FOL) and syllogistic reasoning ā does not implicitly represent semantic similarities, ontology mapping addresses this problem by aiming at establishing formal relations between a set of knowledge entities which represent the same or a similar meaning in distinct ontologies. However, manually or semi-automatically identifying similarity relationships is costly. Hence, we argue, that representational facilities are required which enable to implicitly represent similarities. Whereas Conceptual Spaces (CS) address similarity computation through the representation of concepts as vector spaces, CS rovide neither an implicit representational mechanism nor a means to represent arbitrary relations between concepts or instances. In order to overcome these issues, we propose a hybrid knowledge representation approach which extends FOL-based ontologies with a conceptual grounding through a set of CS-based representations. Consequently, semantic similarity between instances ā represented as members in CS ā is indicated by means of distance metrics. Hence, automatic similarity detection across distinct ontologies is supported in order to facilitate ontology integration
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Blending the physical and the digital through conceptual spaces
The rise of the Internet facilitates an ever increasing growth of virtual, i.e. digital spaces which co-exist with the physical environment, i.e. the physical space. In that, the question arises, how physical and digital space can interact synchronously. While sensors provide a means to continuously observe the physical space, several issues arise with respect to mapping sensor data streams to digital spaces, for instance, structured linked data, formally represented through symbolic Semantic Web (SW) standards such as OWL or RDF. The challenge is to bridge between symbolic knowledge representations and the measured data collected by sensors. In particular, one needs to map a given set of arbitrary sensor data to a particular set of symbolic knowledge representations, e.g. ontology instances. This task is particularly challenging due to the vast variety of possible sensor measurements. Conceptual Spaces (CS) provide a means to represent knowledge in geometrical vector spaces in order to enable computation of similarities between knowledge entities by means of distance metrics. We propose an approach which allows to refine symbolic concepts as CS and to ground ontology instances to so-called prototypical members which are vectors in the CS. By computing similarities in terms of spatial distances between a given set of sensor measurements and a finite set of CS members, the most similar instance can be identified. In that, we provide a means to bridge between the physical space, as observed by sensors, and the digital space made up of symbolic representations
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