The impact of intergovernmental grants-in-aid on public school expenditure under the segregated school system

This dissertation investigated the fiscal behavior of local public school districts under the segregated schools system in Maryland with the emphasis on the relative response of school expenditure to intergovernmental grants-in-aid between black and white schools. The median voter model was employed to derive theoretical demand specifications for both no-nonresidential-property-tax-shifting and partial-shifting assumptions. The theoretical model derived here is unique in the sense that inter-racial factors are included to determine the demand for education in black and white schools, since the provision of public education services was decided simultaneously by the median voter who was presumed to be white;A pooled cross-section and time series data set from 1929 through 1955 was utilized for empirical estimates of both theoretical models and ad hoc adjusted models. The methods of ordinary least squares and nonlinear least squares were used to obtain unbiased estimates for both no tax shifting and partial tax shifting models, respectively. The simulation of the effect of nonexistence of government policy (grants) was also performed;The results showed that the expenditure or demand for education in black school districts was much more responsive to intergovernmental grants than in white school districts. Also, the ratio of per pupil expenditure between black and white was one-half in 1929, but almost one in 1955. In addition, simulation results showed that grants had a greater effect on spending for black schools than for white schools. Hence, the equalization of expenditure levels or the reduction of the gap in economic and social well-being between blacks and whites could have been achieved much faster had there been a way to allocate more of available state and federal grants-in-aid to black schools so as to stimulate the voter\u27s spending on black schools

DiffRef3D: A Diffusion-based Proposal Refinement Framework for 3D Object Detection

Denoising diffusion models show remarkable performances in generative tasks, and their potential applications in perception tasks are gaining interest. In this paper, we introduce a novel framework named DiffRef3D which adopts the diffusion process on 3D object detection with point clouds for the first time. Specifically, we formulate the proposal refinement stage of two-stage 3D object detectors as a conditional diffusion process. During training, DiffRef3D gradually adds noise to the residuals between proposals and target objects, then applies the noisy residuals to proposals to generate hypotheses. The refinement module utilizes these hypotheses to denoise the noisy residuals and generate accurate box predictions. In the inference phase, DiffRef3D generates initial hypotheses by sampling noise from a Gaussian distribution as residuals and refines the hypotheses through iterative steps. DiffRef3D is a versatile proposal refinement framework that consistently improves the performance of existing 3D object detection models. We demonstrate the significance of DiffRef3D through extensive experiments on the KITTI benchmark. Code will be available

The behaviour of stacking fault energy upon interstitial alloying

Stacking fault energy is one of key parameters for understanding the mechanical properties of face-centered cubic materials. It is well known that the plastic deformation mechanism is closely related to the size of stacking fault energy. Although alloying is a conventional method to modify the physical parameter, the underlying microscopic mechanisms are not yet clearly established. Here, we propose a simple model for determining the effect of interstitial alloying on the stacking fault energy. We derive a volumetric behaviour of stacking fault energy from the harmonic approximation to the energy-lattice curve and relate it to the contents of interstitials. The stacking fault energy is found to change linearly with the interstitial content in the usual low concentration domain. This is in good agreement with previously reported experimental and theoretical data.111Ysciescopu

Toward an Evaluation Model of User Experiences on Virtual Reality Indoor Bikes

This paper deals with deriving a model or framework to evaluate user experiences (UX) of virtual reality (VR) systems, especially, VR indoor bikes which are under construction. Recently, VR is one of the most appealing areas attracting people’s interests around the world. Many products armed with it increasingly emerge on the market, and it is expected that the use of VR systems will continue to increase sharply in the future. However, UX of such products cannot be evaluated appropriately at the moment due to a lack of proper evaluation models. In a broad sense, UX that may stem from human machine interface in ergonomics covers affect, usability, and user value in spite of some differences in definition among the researchers. While evaluations of UX on the products without VR have been overall justifiably performed, UX has been evaluated neither systematically nor strictly on the products with VR. Through the analyses of expert reviews, we newly identify an additional component and its elements, and modify some elements of the three existing components for evaluating UX on the VR systems. As a result, we propose a comprehensive evaluation model of UX, which consists of four factors: usability, affect, user value, and presence feeling. In addition, we determine the components and their elements for specific VR indoor bikes similarly through the analyses of expert surveys and focus-group discussions, which results in developing a questionnaire for users. Finally, along with the questionnaire, we propose a specific evaluation model for VR indoor bikes

PG-RCNN: Semantic Surface Point Generation for 3D Object Detection

One of the main challenges in LiDAR-based 3D object detection is that the sensors often fail to capture the complete spatial information about the objects due to long distance and occlusion. Two-stage detectors with point cloud completion approaches tackle this problem by adding more points to the regions of interest (RoIs) with a pre-trained network. However, these methods generate dense point clouds of objects for all region proposals, assuming that objects always exist in the RoIs. This leads to the indiscriminate point generation for incorrect proposals as well. Motivated by this, we propose Point Generation R-CNN (PG-RCNN), a novel end-to-end detector that generates semantic surface points of foreground objects for accurate detection. Our method uses a jointly trained RoI point generation module to process the contextual information of RoIs and estimate the complete shape and displacement of foreground objects. For every generated point, PG-RCNN assigns a semantic feature that indicates the estimated foreground probability. Extensive experiments show that the point clouds generated by our method provide geometrically and semantically rich information for refining false positive and misaligned proposals. PG-RCNN achieves competitive performance on the KITTI benchmark, with significantly fewer parameters than state-of-the-art models. The code is available at https://github.com/quotation2520/PG-RCNN.Comment: Accepted by ICCV 202

Controlling the Magnetic Anisotropy of the van der Waals Ferromagnet Fe3GeTe2 through Hole Doping.

Identifying material parameters affecting properties of ferromagnets is key to optimized materials that are better suited for spintronics. Magnetic anisotropy is of particular importance in van der Waals magnets, since it not only influences magnetic and spin transport properties, but also is essential to stabilizing magnetic order in the two-dimensional limit. Here, we report that hole doping effectively modulates the magnetic anisotropy of a van der Waals ferromagnet and explore the physical origin of this effect. Fe3-xGeTe2 nanoflakes show a significant suppression of the magnetic anisotropy with hole doping. Electronic structure measurements and calculations reveal that the chemical potential shift associated with hole doping is responsible for the reduced magnetic anisotropy by decreasing the energy gain from the spin-orbit induced band splitting. Our findings provide an understanding of the intricate connection between electronic structures and magnetic properties in two-dimensional magnets and propose a method to engineer magnetic properties through doping

Effect of freshwater discharge from Namgang Dam on ichthyoplankton assemblage structure in Jinju Bay, Korea

The movement of fish eggs and larvae in bay and estuarine systems is affected by freshwater discharge. In this study, the assemblage structures of ichthyoplankton eggs and larvae were assessed for the first time in Jinju Bay, South Korea, to identify the spawning and nursery functions of the bay. Fish eggs and larvae and several environmental parameters were sampled monthly from April 2015 to March 2016 inside and outside of the bay. Within the bay we collected eggs and larvae from 25 and 35 species, respectively, indicating greater diversity than outside the bay, where we collected eggs and larvae of 20 and 28 species, respectively. Fluctuations in water temperature and salinity were larger inside than outside of the bay, and chlorophyll-a concentration was higher within the bay, likely due to discharge from the Namgang Dam, which causes water to flow from the inside to the outside of the bay. This process influences fish larva abundance, such that more larvae are found outside than inside the bay. We also found that 28 fish species use Jinju Bay as a spawning ground. For some species, the timing of egg and larva appearance differed inside and outside of the bay, suggesting that the timing of spawning may differ between the two environments