Deep Projective 3D Semantic Segmentation

Semantic segmentation of 3D point clouds is a challenging problem with numerous real-world applications. While deep learning has revolutionized the field of image semantic segmentation, its impact on point cloud data has been limited so far. Recent attempts, based on 3D deep learning approaches (3D-CNNs), have achieved below-expected results. Such methods require voxelizations of the underlying point cloud data, leading to decreased spatial resolution and increased memory consumption. Additionally, 3D-CNNs greatly suffer from the limited availability of annotated datasets. In this paper, we propose an alternative framework that avoids the limitations of 3D-CNNs. Instead of directly solving the problem in 3D, we first project the point cloud onto a set of synthetic 2D-images. These images are then used as input to a 2D-CNN, designed for semantic segmentation. Finally, the obtained prediction scores are re-projected to the point cloud to obtain the segmentation results. We further investigate the impact of multiple modalities, such as color, depth and surface normals, in a multi-stream network architecture. Experiments are performed on the recent Semantic3D dataset. Our approach sets a new state-of-the-art by achieving a relative gain of 7.9 %, compared to the previous best approach.Comment: Submitted to CAIP 201

Chapter 17- Mentoring Redesigned to Attract Entry-Level Students

Competitive and highly structured mentoring relationships between undergraduate students and professional researchers are often life-changing. However, such mentoring programs often have rigid qualifications and attract students who are already advanced in their educational and professional planning. The University of New Mexico (UNM) developed a program to shift the paradigm to attract entry-level students for whom “professional research” was still a new and daunting concept. By pairing these students with engineers and scientists at the Air Force Research Laboratory and Sandia National Laboratory, UNM was able to engage students in structured, low-stakes mentoring that helped shape their current understanding of research, and illuminated career pathways and opportunities in their chosen academic disciplines. The Science, Technology, Engineering, and Math (STEM) Collaborative Center staff recruited entry-level UNM students as mentees and recruited engineers and scientists as mentors. UNM then matched mentor-mentee pairs using an interest form, hosted introductory events for pairs to meet on campus, and followed up with mentors and mentees to provide support and promote ongoing conversations. Students who participated in this program were more likely than their peers to persist at UNM

Fire and grazing determined grasslands of central Madagascar represent ancient assemblages

The ecology of Madagascar's grasslands is under-investigated and the dearth of ecological understanding of how disturbance by fire and grazing shapes these grasslands stems from a perception that disturbance shaped Malagasy grasslands only after human arrival. However, worldwide, fire and grazing shape tropical grasslands over ecological and evolutionary timescales, and it is curious Madagascar should be a global anomaly. We examined the functional and community ecology of Madagascar's grasslands across 71 communities in the Central Highlands. Combining multivariate abundance models of community composition and clustering of grass functional traits, we identified distinct grass assemblages each shaped by fire or grazing. The fire-maintained assemblage is primarily composed of tall caespitose species with narrow leaves and low bulk density. By contrast, the grazer-maintained assemblage is characterized by mat-forming, high bulk density grasses with wide leaves. Within each assemblage, levels of endemism, diversity and grass ages support these as ancient assemblages. Grazer-dependent grasses can only have co-evolved with a now-extinct megafauna. Ironically, the human introduction of cattle probably introduced a megafaunal substitute facilitating modern day persistence of a grazer-maintained grass assemblage in an otherwise defaunated landscape, where these landscapes now support the livelihoods of millions of people

Pathwise Sensitivity Analysis in Transient Regimes

The instantaneous relative entropy (IRE) and the corresponding instanta- neous Fisher information matrix (IFIM) for transient stochastic processes are pre- sented in this paper. These novel tools for sensitivity analysis of stochastic models serve as an extension of the well known relative entropy rate (RER) and the corre- sponding Fisher information matrix (FIM) that apply to stationary processes. Three cases are studied here, discrete-time Markov chains, continuous-time Markov chains and stochastic differential equations. A biological reaction network is presented as a demonstration numerical example

Laser Applications

Contains research objectives and reports on three research projects.Joint Services Electronics Programs (U. S. Army, U. S. Navy, and U. S. Air Force) under Contract DAAB07-71-C-0300U. S. Air Force Office of Scientific Research (Contract F44620-71-C-0051)Naval Air Systems Comman

Activation of Microglial Poly(ADP-Ribose)-Polymerase-1 by Cholesterol Breakdown Products during Neuroinflammation: a Link between Demyelination and Neuronal Damage

Multiple sclerosis (MS) is a chronic demyelinating disease in which it has only recently been suggested that damage to neuronal structures plays a key role. Here, we uncovered a link between the release of lipid breakdown products, found in the brain and cerebrospinal fluid (CSF) of MS patients as well as in experimental autoimmune encephalomyelitis, and neuronal damage mediated by microglial activation. The concentrations of the breakdown product 7-ketocholesterol detected in the CSF of MS patients were capable of inducing neuronal damage via the activation and migration of microglial cells in living brain tissue. 7-ketocholesterol rapidly entered the nucleus and activated poly(ADP-ribose)-polymerase (PARP)-1, followed by the expression of migration-regulating integrins CD11a and intercellular adhesion molecule 1. These findings reveal a novel mechanism linking demyelination and progressive neuronal damage, which might represent an underlying insidious process driving disease beyond a primary white matter phenomenon and rendering the microglial PARP-1 a possible antiinflammatory therapeutic target

High-resolution 3-D imaging of surface damage sites in fused silica with Optical Coherence Tomography

In this work, we present the first successful demonstration of a non-contact technique to precisely measure the 3D spatial characteristics of laser induced surface damage sites in fused silica for large aperture laser systems by employing Optical Coherence Tomography (OCT). What makes OCT particularly interesting in the characterization of optical materials for large aperture laser systems is that its axial resolution can be maintained with working distances greater than 5 cm, whether viewing through air or through the bulk of thick optics. Specifically, when mitigating surface damage sites against further growth by CO{sub 2} laser evaporation of the damage, it is important to know the depth of subsurface cracks below the damage site. These cracks are typically obscured by the damage rubble when imaged from above the surface. The results to date clearly demonstrate that OCT is a unique and valuable tool for characterizing damage sites before and after the mitigation process. We also demonstrated its utility as an in-situ diagnostic to guide and optimize our process when mitigating surface damage sites on large, high-value optics