296 research outputs found
Learning morphological phenomena of Modern Greek an exploratory approach
This paper presents a computational model for the description of concatenative morphological phenomena of modern Greek (such as inflection, derivation and compounding) to allow learners, trainers and developers to explore linguistic processes through their own constructions in an interactive open‐ended multimedia environment. The proposed model introduces a new language metaphor, the ‘puzzle‐metaphor’ (similar to the existing ‘turtle‐metaphor’ for concepts from mathematics and physics), based on a visualized unification‐like mechanism for pattern matching. The computational implementation of the model can be used for creating environments for learning through design and learning by teaching
Replay: multi-modal multi-view acted videos for casual holography
We introduce Replay, a collection of multi-view, multimodal videos of humans interacting socially. Each scene
is filmed in high production quality, from different viewpoints with several static cameras, as well as wearable
action cameras, and recorded with a large array of microphones at different positions in the room. Overall, the dataset contains over 4000 minutes of footage and over 7 million timestamped high-resolution frames annotated with camera poses and partially with foreground masks. The Replay dataset has many potential applications, such as novelview synthesis, 3D reconstruction, novel-view acoustic synthesis, human body and face analysis, and training generative models. We provide a benchmark for training and evaluating novel-view synthesis, with two scenarios of different difficulty. Finally, we evaluate several baseline state-of-theart methods on the new benchmark
Text-to-4D dynamic scene generation
We present MAV3D (Make-A-Video3D), a method for generating three-dimensional dynamic scenes from text descriptions. Our approach uses a 4D dynamic Neural Radiance Field (NeRF), which is optimized for scene appearance, density, and motion consistency by querying a Text-to-Video (T2V) diffusion-based model. The dynamic video output generated from the provided text can be viewed from any camera location and angle, and can be composited into any 3D environment. MAV3D does not require any 3D or 4D data and the T2V model is trained only on Text-Image pairs and unlabeled videos. We demonstrate the effectiveness of our approach using comprehensive quantitative and qualitative experiments and show an improvement over previously established internal baselines. To the best of our knowledge, our method is the first to generate 3D dynamic scenes given a text description. Generated samples can be viewed at make-a-video3d.github.i
Can disordered mobile phone use be considered a behavioral addiction? An update on current evidence and a comprehensive model for future research
Despite the many positive outcomes, excessive mobile phone use is now often associated with potentially harmful and/or disturbing behaviors (e.g., symptoms of deregulated use, negative impact on various aspects of daily life such as relationship problems, and work intrusion). Problematic mobile phone use (PMPU) has generally been considered as a behavioral addiction that shares many features with more established drug addictions. In light of the most recent data, the current paper reviews the validity of the behavioral addiction model when applied to PMPU. On the whole, it is argued that the evidence supporting PMPU as an addictive behavior is scarce. In particular, it lacks studies that definitively show behavioral and neurobiological similarities between mobile phone addiction and other types of legitimate addictive behaviors. Given this context, an integrative pathway model is proposed that aims to provide a theoretical framework to guide future research in the field of PMPU. This model highlights that PMPU is a heterogeneous and multi-faceted condition
Negotiated economic grid brokering for quality of service
We demonstrate a Grid broker's job submission system and its selection process for finding the provider that is most likely to be able to complete work on time and on budget. We compare several traditional site selection mechanisms with an economic and Quality of Service (QoS) oriented approach. We show how a greater profit and QoS can be achieved if jobs are accepted by the most appropriate provider. We particularly focus upon the benefits of a negotiation process for QoS that enables our selection process to occur
Fully Trainable and Interpretable Non-Local Sparse Models for Image Restoration
Non-local self-similarity and sparsity principles have proven to be powerful
priors for natural image modeling. We propose a novel differentiable relaxation
of joint sparsity that exploits both principles and leads to a general
framework for image restoration which is (1) trainable end to end, (2) fully
interpretable, and (3) much more compact than competing deep learning
architectures. We apply this approach to denoising, jpeg deblocking, and
demosaicking, and show that, with as few as 100K parameters, its performance on
several standard benchmarks is on par or better than state-of-the-art methods
that may have an order of magnitude or more parameters.Comment: ECCV 202
Learning to Recognize 3D Human Action from A New Skeleton-based Representation Using Deep Convolutional Neural Networks
Recognizing human actions in untrimmed videos is an important challenging task. An effective 3D motion representation and a powerful learning model are two key factors influencing recognition performance. In this paper we introduce a new skeletonbased
representation for 3D action recognition in videos. The key idea of the proposed representation is to transform 3D joint coordinates of the human body carried in skeleton sequences into RGB images via a color encoding process. By normalizing the
3D joint coordinates and dividing each skeleton frame into five parts, where the joints are concatenated according to the order of their physical connections, the color-coded representation is able to represent spatio-temporal evolutions of complex 3D motions,
independently of the length of each sequence. We then design and train different Deep Convolutional Neural Networks (D-CNNs) based on the Residual Network architecture (ResNet) on the obtained image-based representations to learn 3D motion features
and classify them into classes. Our method is evaluated on two widely used action recognition benchmarks: MSR Action3D and NTU-RGB+D, a very large-scale dataset for 3D human action recognition. The experimental results demonstrate that the proposed
method outperforms previous state-of-the-art approaches whilst requiring less computation for training and prediction.This research was carried out at the Cerema Research Center
(CEREMA) and Toulouse Institute of Computer Science Research
(IRIT), Toulouse, France. Sergio A. Velastin is grateful for funding
received from the Universidad Carlos III de Madrid, the European
Union’s Seventh Framework Programme for Research, Technological
Development and demonstration under grant agreement
N. 600371, el Ministerio de Economia, Industria y Competitividad
(COFUND2013-51509) el Ministerio de Educación, cultura y
Deporte (CEI-15-17) and Banco Santander
Associations between clinical canine leishmaniosis and multiple vector-borne co-infections: a case-control serological study
Dogs that have clinical leishmaniosis (ClinL), caused by the parasite Leishmania infantum, are commonly co-infected with other pathogens, especially vector-borne pathogens (VBP). A recent PCR-based study found that ClinL dogs are more likely to be additionally infected with the rickettsial bacteria Ehrlichia canis. Further information on co-infections in ClinL cases with VBP, as assessed by serology, is required. The research described in this report determined if dogs with ClinL are at higher risk of exposure to VBP than healthy control dogs using a case-control serology study
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