Structural Descriptions in Human-Assisted Robot Visual Learning

The paper presents an approach to using structural descriptions, obtained through a human-robot tutoring dialogue, as labels for the visual object models a robot learns. The paper shows how structural descriptions enable relating models for different aspects of one and the same object, and how being able to relate descriptions for visual models and discourse referents enables incremental updating of model descriptions through dialogue (either robot- or human-initiated). The approach has been implemented in an integrated architecture for human-assisted robot visual learning

A Novel Azeotropic Mixture for Solvent Extraction of Edible Oils

Rosana G. Moreira, Editor-in-Chief; Texas A&M UniversityThis is a paper from International Commission of Agricultural Engineering (CIGR, Commission Internationale du Genie Rural) E-Journal Volume 8 (2006): A Novel Azeotropic Mixture for Solvent Extraction of Edible Oils. Manuscript FP 06 005. Vol. VIII. April, 2006

Introducing wearable haptics for rendering velocity feedback in VR serious games for neuro-rehabilitation of children

Rehabilitation in virtual reality offers advantages in terms of flexibility and parametrization of exercises, repeatability, and continuous data recording and analysis of the progress of the patient, also promoting high engagement and cognitive challenges. Still, most of the proposed virtual settings provide a high quality, immersive visual and audio feedback, without involving the sense of touch. In this paper, we show the design, implementation, and first evaluation of a gaming scenario for upper limb rehabilitation of children with cerebral palsy. In particular, we took care to introduce haptic feedback as a useful source of sensory information for the proposed task, considering—at the same time—the strict constraints for haptic wearable devices to comply with patient’s comfort, residual motor abilities, and with the embedded tracking features of the latest VR technologies. To show the potential of haptics in a rehabilitation setup, the proposed device and rendering method have been used to improve the velocity control of upper limb movements during the VR exercise, given its importance as a motor recovery metric. Eight healthy participants were enrolled, and results showed that haptic feedback can lead to lower speed tracking errors and higher movement smoothness, making the proposed setup suitable to be used in a rehabilitation context as a way to promote movement fluidity during exercises

Assessing Capsule Networks with Biased Data

Machine learning based methods achieves impressive results in object classification and detection. Utilizing representative data of the visual world during the training phase is crucial to achieve good performance with such data driven approaches. However, it not always possible to access bias-free datasets thus, robustness to biased data is a desirable property for a learning system. Capsule Networks have been introduced recently and their tolerance to biased data has received little attention. This paper aims to fill this gap and proposes two experimental scenarios to assess the tolerance to imbalanced training data and to determine the generalization performance of a model with unfamiliar affine transformations of the images. This paper assesses dynamic routing and EM routing based Capsule Networks and proposes a comparison with Convolutional Neural Networks in the two tested scenarios. The presented results provide new insights into the behaviour of capsule networks

FRET Dyes Significantly Affect SAXS Intensities of Proteins

Structural analyses in biophysics aim at revealing a relationship between a molecule's dynamic structure and its physiological function. Förster resonance energy transfer (FRET) and small‐angle X‐ray scattering (SAXS) are complementary experimental approaches to this. Their concomitant application in combined studies has recently opened a lively debate on how to interpret FRET measurements in the light of SAXS data with the popular example of the radius of gyration, commonly derived from both FRET and SAXS. There still is a lack of understanding in how to mutually relate and interpret quantities equally obtained from FRET or SAXS, and to what extent FRET dyes affect SAXS intensities in combined applications. In the present work, we examine the interplay of FRET and SAXS from a computational simulation perspective. Molecular simulations are a valuable complement to experimental approaches and supply instructive information on dynamics. As FRET depends not only on the mutual separation but also on the relative orientations, the dynamics, and therefore also the shapes of the dyes, we utilize a novel method for simulating FRET‐dye‐labeled proteins to investigate these aspects in atomic detail. We perform structure‐based simulations of four different proteins with and without dyes in both folded and unfolded conformations. In‐silico derived radii of gyration are different with and without dyes and depend on the chosen dye pair. The dyes apparently influence the dynamics of unfolded systems. We find that FRET dyes attached to a protein have a significant impact on theoretical SAXS intensities calculated from simulated structures, especially for small proteins. Radii of gyration from FRET and SAXS deviate systematically, which points to further underlying mechanisms beyond prevalent explanation approaches

Automatic Selection of the Optimal Local Feature Detector

A large number of different local feature detectors have been proposed in the last few years. However, each feature detector has its own strengths and weaknesses that limit its use to a specific range of applications. In this paper is presented a tool capable of quickly analysing input images to determine which type and amount of transformation is applied to them and then selecting the optimal feature detector, which is expected to perform the best. The results show that the performance and the fast execution time render the proposed tool suitable for real-world vision applications

Detection of Human Rights Violations in Images: Can Convolutional Neural Networks Help?

After setting the performance benchmarks for image, video, speech and audio processing, deep convolutional networks have been core to the greatest advances in image recognition tasks in recent times. This raises the question of whether there are any benefit in targeting these remarkable deep architectures with the unattempted task of recognising human rights violations through digital images. Under this perspective, we introduce a new, well-sampled human rights-centric dataset called Human Rights Understanding (HRUN). We conduct a rigorous evaluation on a common ground by combining this dataset with different state-of-the-art deep convolutional architectures in order to achieve recognition of human rights violations. Experimental results on the HRUN dataset have shown that the best performing CNN architectures can achieve up to 88.10% mean average precision. Additionally, our experiments demonstrate that increasing the size of the training samples is crucial for achieving an improvement on mean average precision principally when utilising very deep networks