OOP: Object-Oriented-Priority for Motion Saliency Maps

Belardinelli A, Schneider WX, Steil JJ. OOP: Object-Oriented-Priority for Motion Saliency Maps. In: Workshop on Brain Inspired Cognitive Systems. 2010: 370-381

Embodied neuromorphic intelligence

The design of robots that interact autonomously with the environment and exhibit complex behaviours is an open challenge that can benefit from understanding what makes living beings fit to act in the world. Neuromorphic engineering studies neural computational principles to develop technologies that can provide a computing substrate for building compact and low-power processing systems. We discuss why endowing robots with neuromorphic technologies – from perception to motor control – represents a promising approach for the creation of robots which can seamlessly integrate in society. We present initial attempts in this direction, highlight open challenges, and propose actions required to overcome current limitations

Dislocation Density-Based Finite Element Method Modeling of Ultrasonic Consolidation

A dislocation density-based constitutive model has been developed and implemented into a crystal plasticity quasi-static finite element framework. This approach captures the statistical evolution of dislocation structures and grain fragmentation at the bonding interface when sufficient boundary conditions pertaining to the Ultrasonic Consolidation (UC) process are prescribed. The hardening is incorporated using statistically stored and geometrically necessary dislocation densities (SSDs and GNDs), which are dislocation analogs of isotropic and kinematic hardening, respectively. Since the macroscopic global boundary conditions during UC involves cyclic sinosuidal simple shear loading along with constant normal pressure, the cross slip mechanism has been included in the evolution equation for SSDs. The inclusion of cross slip promotes slip irreversibility, dislocation storage, and hence, cyclic hardening during the UC. The GND considers strain-gradient and thus renders the model size-dependent. The model is calibrated using experimental data from published refereed literature for simple shear deformation of single crystalline pure aluminum alloy and uniaxial tension of polycrystalline Aluminum 3003-H18 alloy. The model also incorporates various local and global effects such as (1) friction, (2) thermal softening, (3) acoustic softening, (4) surface texture of the sonotrode and initial mating surfaces, and (6) presence of oxide-scale at the mating surfaces, which further contribute significantly specifically to the grain substructure evolution at the interface and to the anisotropic bulk deformation away from the interface during UC in general. The model results have been predicted for Al-3003 alloy undergoing UC. A good agreement between the experimental and simulated results has been observed for the evolution of linear weld density and anisotropic global strengths macroscopically. Similarly, microscopic observations such as embrittlement due to grain substructure evolution at the UC interface have been also demonstrated by the simulation. In conclusion, the model was able to predict the effects of macroscopic global boundary conditions on bond quality. It has been found that the normal pressure enhances good bonding characteristics at the interface, though beyond a certain magnitude enhances dynamic failure. Similarly, lower oscillation amplitudes result in a lower rate of gap closure, whereas higher oscillation amplitude results in an enhanced rate of gap relaxation at the interface. Henceforth, good bonding characteristics between the constituent foils are found at an optimum oscillation amplitude. A similar analogy is also true for weld speed where the longitudinal locations behind the sonotrode rip open when higher weld speeds are implemented in the UC machine, leading to lower linear weld density and poor bonding characteristics

Leveraging Supervoxels for Medical Image Volume Segmentation With Limited Supervision

The majority of existing methods for machine learning-based medical image segmentation are supervised models that require large amounts of fully annotated images. These types of datasets are typically not available in the medical domain and are difficult and expensive to generate. A wide-spread use of machine learning based models for medical image segmentation therefore requires the development of data-efficient algorithms that only require limited supervision. To address these challenges, this thesis presents new machine learning methodology for unsupervised lung tumor segmentation and few-shot learning based organ segmentation. When working in the limited supervision paradigm, exploiting the available information in the data is key. The methodology developed in this thesis leverages automatically generated supervoxels in various ways to exploit the structural information in the images. The work on unsupervised tumor segmentation explores the opportunity of performing clustering on a population-level in order to provide the algorithm with as much information as possible. To facilitate this population-level across-patient clustering, supervoxel representations are exploited to reduce the number of samples, and thereby the computational cost. In the work on few-shot learning-based organ segmentation, supervoxels are used to generate pseudo-labels for self-supervised training. Further, to obtain a model that is robust to the typically large and inhomogeneous background class, a novel anomaly detection-inspired classifier is proposed to ease the modelling of the background. To encourage the resulting segmentation maps to respect edges defined in the input space, a supervoxel-informed feature refinement module is proposed to refine the embedded feature vectors during inference. Finally, to improve trustworthiness, an architecture-agnostic mechanism to estimate model uncertainty in few-shot segmentation is developed. Results demonstrate that supervoxels are versatile tools for leveraging structural information in medical data when training segmentation models with limited supervision

C2D Spitzer-IRS spectra of disks around T Tauri stars: IV. Crystalline silicates

Aims. Dust grains in the planet-forming regions around young stars are expected to be heavily processed due to coagulation, fragmentation, and crystallization. This paper focuses on the crystalline silicate dust grains in protoplanetary disks for a statistically significant number of TTauri stars (96). Methods. As part of the cores to disks (c2d) legacy program, we obtained more than a hundred Spitzer/IRS spectra of TTauri stars, over a spectral range of 5-35 μm where many silicate amorphous and crystalline solid-state features are present. At these wavelengths, observations probe the upper layers of accretion disks up to distances of a dozen AU from the central object. Results. More than 3/4 of our objects show at least one crystalline silicate emission feature that can be essentially attributed to Mg-rich silicates. The Fe-rich crystalline silicates are largely absent in the c2d IRS spectra. The strength and detection frequency of the crystalline features seen at λ > 20 μm correlate with each other, while they are largely uncorrelated with the observational properties of the amorphous silicate 10 μm feature. This supports the idea that the IRS spectra essentially probe two independent disk regions: a warm zone (≤1 AU) emitting at ~ 10 μm and a much colder region emitting at λ > 20 μm (≤10 AU). We identify a crystallinity paradox, as the long-wavelength (λ > 20 m) crystalline silicate features are detected 3.5 times more frequently (~55% vs. ~15%) than the crystalline features arising from much warmer disk regions (λ ~ 10 μm). This suggests that the disk has an inhomogeneous dust composition within ~10 AU. The analysis of the shape and strength of both the amorphous 10 μm feature and the crystalline feature around 23 μm provides evidence for the prevalence of μm-sized (amorphous and crystalline) grains in upper layers of disks. Conclusions. The abundant crystalline silicates found far from their presumed formation regions suggest efficient outward radial transport mechanisms in the disks around TTauri stars. The presence of μm-sized grains in disk atmospheres, despite the short timescales for settling to the midplane, suggests efficient (turbulent) vertical diffusion, probably accompanied by grain-grain fragmentation to balance the expected efficient growth. In this scenario, the depletion of submicron-sized grains in the upper layers of the disks points toward removal mechanisms such as stellar winds or radiation pressure

A Posture Sequence Learning System for an Anthropomorphic Robotic Hand

The paper presents a cognitive architecture for posture learning of an anthropomorphic robotic hand. Our approach is aimed to allow the robotic system to perform complex perceptual operations, to interact with a human user and to integrate the perceptions by a cognitive representation of the scene and the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces and to perform complex interaction with the human operator

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

Relevance of accurate Monte Carlo modeling in nuclear medical imaging

Monte Carlo techniques have become popular in different areas of medical physics with advantage of powerful computing systems. In particular, they have been extensively applied to simulate processes involving random behavior and to quantify physical parameters that are difficult or even impossible to calculate by experimental measurements. Recent nuclear medical imaging innovations such as single-photon emission computed tomography (SPECT), positron emission tomography (PET), and multiple emission tomography (MET) are ideal for Monte Carlo modeling techniques because of the stochastic nature of radiation emission, transport and detection processes. Factors which have contributed to the wider use include improved models of radiation transport processes, the practicality of application with the development of acceleration schemes and the improved speed of computers. This paper presents derivation and methodological basis for this approach and critically reviews their areas of application in nuclear imaging. An overview of existing simulation programs is provided and illustrated with examples of some useful features of such sophisticated tools in connection with common computing facilities and more powerful multiple-processor parallel processing systems. Current and future trends in the field are also discussed