Cross Sections for He, Li, and Be Isotopes Produces in the a + a Reactions at 198.4 MeV

This research was sponsored by the National Science Foundation Grant NSF PHy 87-1440

PointPCA: Point Cloud Objective Quality Assessment Using PCA-Based Descriptors

Point clouds denote a prominent solution for the representation of 3-D photo-realistic content in immersive applications. Similarly to other imaging modalities, quality predictions for point cloud contents are vital for a wide range of applications, enabling trade-off optimizations between data quality and data size in every processing step from acquisition to consumption. In this work, we focus on use cases that consider human end-users consuming point cloud contents and, hence, we concentrate on visual quality metrics. In particular, we propose a set of perceptually-relevant descriptors based on Principal Component Analysis (PCA) decomposition that is applied to both geometry and texture data for full-reference point cloud quality assessment. Statistical features are derived from these descriptors to characterize local shape and appearance properties for both a reference and a distorted point cloud. They are subsequently compared to provide corresponding predictions of visual quality for the latter. As part of our method, a learning-based approach is proposed to fuse these individual quality predictors to a unified perceptual score. Various regression models are additionally evaluated for this task and shown to be effective in harnessing the predictors' strength. We validate the accuracy of the individual quality predictors, as well as the unified quality scores obtained after any regression model against subjectively-annotated datasets, and we show that non-linear regression models exhibit notable gains with respect to current literature. A software implementation of the proposed metric is made available at the following link: https://github.com/cwi-dis/pointpca.Comment: 10 pages, 4 figures, 3 table

Multi-Instance Multi-Label Learning

In this paper, we propose the MIML (Multi-Instance Multi-Label learning) framework where an example is described by multiple instances and associated with multiple class labels. Compared to traditional learning frameworks, the MIML framework is more convenient and natural for representing complicated objects which have multiple semantic meanings. To learn from MIML examples, we propose the MimlBoost and MimlSvm algorithms based on a simple degeneration strategy, and experiments show that solving problems involving complicated objects with multiple semantic meanings in the MIML framework can lead to good performance. Considering that the degeneration process may lose information, we propose the D-MimlSvm algorithm which tackles MIML problems directly in a regularization framework. Moreover, we show that even when we do not have access to the real objects and thus cannot capture more information from real objects by using the MIML representation, MIML is still useful. We propose the InsDif and SubCod algorithms. InsDif works by transforming single-instances into the MIML representation for learning, while SubCod works by transforming single-label examples into the MIML representation for learning. Experiments show that in some tasks they are able to achieve better performance than learning the single-instances or single-label examples directly.Comment: 64 pages, 10 figures; Artificial Intelligence, 201

190 MeV Proton-Induced Fission

This work was supported by the National Science Foundation Grants NSF PHY 78-22774 A03, NSF PHY 81-14339, and by Indiana Universit

Total Mass and Charge Distributions in the p + 27-Al Reaction at 180 MeV

This work was supported by the National Science Foundation Grants NSF PHY 78-22774 A03, NSF PHY 81-14339, and by Indiana Universit

A Global Study of the p+27-Al Reaction at 180 MeV

This work was supported by the National Science Foundation Grant NSF PHY 81-14339 and by Indiana Universit

On the origin of grain size effects in Ba(Ti0.96Sn0.04)O3 perovskite ceramics

Over the last 50 years, the study of grain size effects in ferroelectric ceramics has attracted great research interest. Although different theoretical models have been proposed to account for the variation in structure and properties of ferroelectrics with respect to the size of structural grains, the underlying mechanisms are still under debate. Here, we report the results of a study on the influence of grain size on the structural and physical properties of Ba(Ti0.96Sn0.04)O3 (BTS), a ferroelectric compound that represents a model perovskite system, where the effects of point defects, stoichiometry imbalance and phase transitions are minimized by chemical substitution. It was found that different microscopic mechanisms are responsible for the different grain size dependences observed in BTS. High permittivity is achieved in fine-grained BTS ceramics due to high domain wall density and polar nanoregions; high d33 is obtained in coarse-grained ceramics due to a high degree of domain alignment during poling; large electric field-induced strain in intermediate-grained ceramics is an outcome of a favorable interplay between constraints from grain boundaries and reversible reorientation of non-180° domains and polar nanoregions. These paradigms can be regarded as general guidelines for the optimization of specific properties of ferroelectric ceramics through grain size control

A bank of unscented Kalman filters for multimodal human perception with mobile service robots

A new generation of mobile service robots could be ready soon to operate in human environments if they can robustly estimate position and identity of surrounding people. Researchers in this field face a number of challenging problems, among which sensor uncertainties and real-time constraints. In this paper, we propose a novel and efficient solution for simultaneous tracking and recognition of people within the observation range of a mobile robot. Multisensor techniques for legs and face detection are fused in a robust probabilistic framework to height, clothes and face recognition algorithms. The system is based on an efficient bank of Unscented Kalman Filters that keeps a multi-hypothesis estimate of the person being tracked, including the case where the latter is unknown to the robot. Several experiments with real mobile robots are presented to validate the proposed approach. They show that our solutions can improve the robot's perception and recognition of humans, providing a useful contribution for the future application of service robotics

Phase evolution and electrical behaviour of samarium-substituted bismuth ferrite ceramics

Bi1-xSmxFeO3 (x = 0.15–0.18) ceramics with high density were produced using spark plasma sintering. The effects of composition, synthesis conditions and temperature on the phase evolution were studied, using XRD, TEM and dielectric spectroscopy. The coexistence of the ferroelectric R3c, antiferroelectric Pnam and paraelectric Pnma phases was revealed, with relative phase fractions affected by both calcination conditions and Sm concentration. Experiments on powdered samples calcined at different temperatures up to 950 °C suggest higher calcination temperatures promote Sm diffusion, allowing samples to reach compositional homogeneity. The structural transitions from the Pnam and R3c phases to the Pnma phase were comprehensively investigated, with phase transition temperatures clearly identified. The dielectric permittivity, electrical resistivity and breakdown strength were increased upon Sm-substitution, while ferroelectric switching was suppressed. The polarization-electric field loop became increasingly narrow with increasing Sm-content, but double hysteresis loops, which may reflect a reversible antiferroelectric to ferroelectric transformation, were not observed