Momentum dependence of the symmetry potential and nuclear reactions induced by neutron-rich nuclei at RIA

Effects of the momentum-dependence of the symmetry potential in nuclear reactions induced by neutron-rich nuclei at RIA energies are studied using an isospin- and momentum-dependent transport model. It is found that symmetry potentials with and without the momentum-dependence but corresponding to the same density-dependent symmetry energy $E_{sym}(\rho)$ lead to significantly different predictions on several $E_{sym}(\rho)$-sensitive experimental observables. The momentum-dependence of the symmetry potential is thus critically important for investigating accurately the equation of state (${\rm EOS}$) and novel properties of dense neutron-rich matter at RIA.Comment: Rapid Communication, Phys. Rev. C in pres

On the choice of colliding beams to study deformation effects in relativistic heavy ion collisions

It has been suggested that collisions between deformed shapes will lead to interesting effects on various observables such as K production and elliptic flow. Simple formulae can be written down which show how to choose the colliding beams which will maximise the effects of deformation.Comment: 2 pages, this version supersedes the previous on

A Transport Model for Nuclear Reactions Induced by Radioactive Beams

Major ingredients of an isospin and momentum dependent transport model for nuclear reactions induced by radioactive beams are outlined. Within the IBUU04 version of this model we study several experimental probes of the equation of state of neutron-rich matter, especially the density dependence of the nuclear symmetry energy. Comparing with the recent experimental data from NSCL/MSU on isospin diffusion, we found a nuclear symmetry energy of $)\approx 31.6(\rho /\rho_{0})^{1.05}$ at subnormal densities. Predictions on several observables sensitive to the density dependence of the symmetry energy at supranormal densities accessible at GSI and the planned Rare Isotope Accelerator (RIA) are also made.Comment: 10 pages. Talk given at the 2nd Argonne/MSU/JINA/INT RIA Workshop at MSU, March 9-12, 2005 to be published in the Proceedings by the American Institute of Physic

Decay2Distill: Leveraging spatial perturbation and regularization for self-supervised image denoising

Unpaired image denoising has achieved promising development over the last few years. Regardless of the performance, methods tend to heavily rely on underlying noise properties or any assumption which is not always practical. Alternatively, if we can ground the problem from a structural perspective rather than noise statistics, we can achieve a more robust solution. with such motivation, we propose a self-supervised denoising scheme that is unpaired and relies on spatial degradation followed by a regularized refinement. Our method shows considerable improvement over previous methods and exhibited consistent performance over different data domains

Bone histomorphometric study of young rats following oestrogen deficiency

Osteoporosis is a global problem which results in increased fractures risk. The reports from earlier studies were inconsistent with the aging factor as well as the time which is needed to induce bone loss post-ovariectomy. This study aimed to determine the short-term effects of estrogen deficiency on bone structural histomorphometric parameters in young rats. 30 Sprague-Dawley female rats weighing 250 to 300 g were assigned to baseline, sham-operated and ovariectomy groups. The baseline group (n = 10) was sacrificed immediately. Sham-operated rats (SO, n = 10) underwent sham operation while ovariectomised group (OVX, n =10) underwent bilateral ovariectomy. All the rats were sacrificed 6 weeks post-ovariectomy. Following sacrifice, the right femora were dissected and subjected to the histomorphometric analysis using modified Von Kossa method. Bone volume/tissue volume (BV/TV) and trabecular number (Tb.N) reduced significantly, while trabecular separation (Tb.Sp) increased significantly in the ovariectomized rats, compared to the baseline and sham groups 6 weeks postovariectomy (P<0.001). In the other hand, trabecular thickness (Tb.Th) was consistent among the groups (P=0.41). Estrogen deficiency resulted in marked decline in BV/TV which most probably attributed to a reduction in Tb.N. In contrast, Tb.Th was found to be preserved following estrogen loss. Hence, the period of 6 weeks post-ovariectomy was sufficient to induce osteoporosis in ovariectomized rats without affecting Tb.Th.Key words: Bone histomorphometry, menopause, estrogen deficiency, osteoporosis, ovariectomy, trabecular bone

EDEN: Multimodal Synthetic Dataset of Enclosed GarDEN Scenes

Multimodal large-scale datasets for outdoor scenes are mostly designed for urban driving problems. The scenes are highly structured and semantically different from scenarios seen in nature-centered scenes such as gardens or parks. To promote machine learning methods for nature-oriented applications, such as agriculture and gardening, we propose the multimodal synthetic dataset for Enclosed garDEN scenes (EDEN). The dataset features more than 300K images captured from more than 100 garden models. Each image is annotated with various low/high-level vision modalities, including semantic segmentation, depth, surface normals, intrinsic colors, and optical flow. Experimental results on the state-of-the-art methods for semantic segmentation and monocular depth prediction, two important tasks in computer vision, show positive impact of pre-training deep networks on our dataset for unstructured natural scenes. The dataset and related materials will be available at https://lhoangan.github.io/eden.Comment: Accepted for publishing at WACV 202

Breakdown of Universality in Random Matrix Models

We calculate smoothed correlators for a large random matrix model with a potential containing products of two traces \tr W_1(M) \cdot \tr W_2(M) in addition to a single trace \tr V(M). Connected correlation function of density eigenvalues receives corrections besides the universal part derived by Brezin and Zee and it is no longer universal in a strong sense.Comment: 16 pages, LaTex, references and footnote adde

ShadingNet: Image Intrinsics by Fine-Grained Shading Decomposition

In general, intrinsic image decomposition algorithms interpret shading as one unified component including all photometric effects. As shading transitions are generally smoother than reflectance (albedo) changes, these methods may fail in distinguishing strong photometric effects from reflectance variations. Therefore, in this paper, we propose to decompose the shading component into direct (illumination) and indirect shading (ambient light and shadows) subcomponents. The aim is to distinguish strong photometric effects from reflectance variations. An end-to-end deep convolutional neural network (ShadingNet) is proposed that operates in a fine-to-coarse manner with a specialized fusion and refinement unit exploiting the fine-grained shading model. It is designed to learn specific reflectance cues separated from specific photometric effects to analyze the disentanglement capability. A large-scale dataset of scene-level synthetic images of outdoor natural environments is provided with fine-grained intrinsic image ground-truths. Large scale experiments show that our approach using fine-grained shading decompositions outperforms state-of-the-art algorithms utilizing unified shading on NED, MPI Sintel, GTA V, IIW, MIT Intrinsic Images, 3DRMS and SRD datasets.Comment: Submitted to International Journal of Computer Vision (IJCV