Some comments on the universal constant in DSR

Deformed Special Relativity is usually presented as a deformation of Special Relativity accommodating a new universal constant, the Planck mass, while respecting the relativity principle. In order to avoid some fundamental problems (e.g. soccer ball problem), we argue that we should switch point of view and consider instead the Newton constant $G$ as the universal constant.Comment: 12 pages, Proceedings of DICE2006 (Piombino, Italy

Incipient Separation in Shock Wave Boundary Layer Interactions as Induced by Sharp Fin

The incipient separation induced by the shock wave turbulent boundary layer interaction at the sharp fin is the subject of present study. Existing theories for the prediction of incipient separation, such as those put forward by McCabe (1966) and Dou and Deng (1992), can have thus far only predicting the direction of surface streamline and tend to over-predict the incipient separation condition based on the Stanbrook's criterion. In this paper, the incipient separation is firstly predicted with Dou and Deng (1992)'s theory and then compared with Lu and Settles (1990)' experimental data. The physical mechanism of the incipient separation as induced by the shock wave/turbulent boundary layer interactions at sharp fin is explained via the surface flow pattern analysis. Furthermore, the reason for the observed discrepancy between the predicted and experimental incipient separation conditions is clarified. It is found that when the wall limiting streamlines behind the shock wave becomes\ aligning with one ray from the virtual origin as the strength of shock wave increases, the incipient separation line is formed at which the wall limiting streamline becomes perpendicular to the local pressure gradient. The formation of this incipient separation line is the beginning of the separation process. The effects of Reynolds number and the Mach number on incipient separation are also discussed. Finally, a correlation for the correction of the incipient separation angle as predicted by the theory is also given.Comment: 34 pages; 9 figure

A YOLOV8-based approach for steel plate surface defect detection

Hot-rolled steel strips are a commonly used product in both production and daily life. However, the manufacturing process inevitably leads to the occurrence of surface defects. To solve this problem, Our method uses YOLOV8 and squeeze-and-excitation (SE) attention mechanism to detect surface defects in hot-rolled steel strips. Our method balances accuracy and real-time performance, while detecting four common surface defects. The method has an average accuracy of 90,9 % and a maximum accuracy of 98,5 % for detecting a single category of surface defects. Experimental results confirm good performance of our proposed method in classifying and localizing surface defects in hot-rolled steel strips, and has the potential for broad application and promotion

A YOLOV8-based approach for steel plate surface defect detection

Hot-rolled steel strips are a commonly used product in both production and daily life. However, the manufacturing process inevitably leads to the occurrence of surface defects. To solve this problem, Our method uses YOLOV8 and squeeze-and-excitation (SE) attention mechanism to detect surface defects in hot-rolled steel strips. Our method balances accuracy and real-time performance, while detecting four common surface defects. The method has an average accuracy of 90,9 % and a maximum accuracy of 98,5 % for detecting a single category of surface defects. Experimental results confirm good performance of our proposed method in classifying and localizing surface defects in hot-rolled steel strips, and has the potential for broad application and promotion

ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo Systems

In this paper we present ActiveStereoNet, the first deep learning solution for active stereo systems. Due to the lack of ground truth, our method is fully self-supervised, yet it produces precise depth with a subpixel precision of $1/30th$ of a pixel; it does not suffer from the common over-smoothing issues; it preserves the edges; and it explicitly handles occlusions. We introduce a novel reconstruction loss that is more robust to noise and texture-less patches, and is invariant to illumination changes. The proposed loss is optimized using a window-based cost aggregation with an adaptive support weight scheme. This cost aggregation is edge-preserving and smooths the loss function, which is key to allow the network to reach compelling results. Finally we show how the task of predicting invalid regions, such as occlusions, can be trained end-to-end without ground-truth. This component is crucial to reduce blur and particularly improves predictions along depth discontinuities. Extensive quantitatively and qualitatively evaluations on real and synthetic data demonstrate state of the art results in many challenging scenes.Comment: Accepted by ECCV2018, Oral Presentation, Main paper + Supplementary Material

Measurements of a fast nuclear spin dynamics in a single InAs quantum dot with positively charged exciton

By using highly time-resolved spectroscopy with an alternative {\sigma}+/{\sigma} - laser pulse modulation technique, we are able to measure the fast buildup and decay times of the dynamical nuclear spin polarization (DNSP) at 5 K for a single InAs quantum dot (QD) with positively charged exciton. It is shown that the nuclear dipole-dipole interaction can efficiently depolarize DNSP with a typical time constant of 500 {\mu}s in the absence of external magnetic field. By using an external field of 8 mT to suppress the nuclear dipolar interaction, the decay time turns to be mainly induced by interaction with unpaired electron and extends to about 5 ms. In addition, it is found that the time constant of hole-induced depolarization of nuclear spin is about 112 ms.Comment: 5 pages, 3 figure

NASA: Neural Articulated Shape Approximation

Efficient representation of articulated objects such as human bodies is an important problem in computer vision and graphics. To efficiently simulate deformation, existing approaches represent 3D objects using polygonal meshes and deform them using skinning techniques. This paper introduces neural articulated shape approximation (NASA), an alternative framework that enables efficient representation of articulated deformable objects using neural indicator functions that are conditioned on pose. Occupancy testing using NASA is straightforward, circumventing the complexity of meshes and the issue of water-tightness. We demonstrate the effectiveness of NASA for 3D tracking applications, and discuss other potential extensions.Comment: ECCV 202

A comparative study of magnetic behaviors in TbNi2, TbMn2 and TbNi2Mn

All TbNi2, TbMn2, and TbNi2Mn compounds exhibit the cubic Laves phase with AB2-type structure in spite of the fact that the ratio of the Tb to transition-metal components in TbNi2Mn is 1:3. Rietveld refinement indicates that in TbNi2Mn the Mn atoms are distributed on both the A (8a) and B (16d) sites. The values of the lattice constants were measured to be a = 14.348 Å (space group F-43 m), 7.618 Å, and 7.158 Å (space group Fd-3 m) for TbNi2, TbMn2, and TbNi2Mn, respectively. The magnetic transition temperatures TC were found to be TC = 38 K and TC = 148 K for TbNi2 and TbNi2Mn, respectively, while two magnetic phase transitions are detected for TbMn2 at T1 = 20 K and T2 = 49 K. Clear magnetic history effects in a low magnetic field are observed in TbMn2 and TbNi2Mn. The magnetic entropy changes have been obtained