Loop interaction in the visible emission corona: Morphological details

Coronagraph observations of two post flare loop systems, recorded photographically in the emissions of Fe 14 (5303 A) and Fe 10 (6374 A), show occasional enhancements at the intersections of some loops. The brightness of such enhancements in the green line gradually increases to a maximum value several times greater than that of the legs of the loops and then declines with a typical lifetime approx. 30 to 60 min. In red line emission the loop systems are usually very faint, but show the same overall type of enhancement, with a lag in maximum brightness relative to that of the green line approx. 10 min. The electron density, derived from the cooling time, is approx. 10 to the 12th power/cu cm

LLTI: Low-Latency Threshold Implementations

With the enormous increase in portable cryptographic devices, physical attacks are becoming similarly popular. One of the most common physical attacks is Side-Channel Analysis (SCA), extremely dangerous due to its non-invasive nature. Threshold Implementations (TI) was proposed as the first countermeasure to provide provable security in masked hardware implementations. While most works on hardware masking are focused on optimizing the area requirements, with the newer and smaller technologies area is taking a backseat, and low-latency is gaining importance. In this work, we revisit the scheme proposed by Arribas et al. in TCHES 2018 to secure unrolled implementations. We formalize and expand this methodology, to devise a masking scheme, derived from TI, designed to secure hardware implementations optimized for latency named Low-Latency Threshold Implementations (LLTI). By applying the distributive property and leveraging a divide-and-conquer strategy, we split a non-linear operation in layers which are masked separately. The result is a more efficient scheme than the former TI for any operation of algebraic degree greater than two, achieving great optimizations both in terms of speed and area. We compare the performance of first-order LLTI with first-order TI in securing a cubic gate and a degree-7 AND gate without using any registers in between. We achieve a 137% increase in maximum frequency and a 60% reduction in area for the cubic gate, and 3131 times reduction in area in the case of a degree-7 AND gate compared to TI. To further illustrate the power of our scheme we take a low-latency PRINCE implementation from the literature and, by simply changing the secure S-box with the LLTI version, we achieve a 46% max. frequency improvement and a 38% area reduction. Moreover, we apply LLTI to a secure a low-latency AES implementation and compare it with the TI version, achieving a 6.9 times max. freq. increase and a 47.2% area reduction

DEFormer: DCT-driven Enhancement Transformer for Low-light Image and Dark Vision

The goal of low-light image enhancement is to restore the color and details of the image and is of great significance for high-level visual tasks in autonomous driving. However, it is difficult to restore the lost details in the dark area by relying only on the RGB domain. In this paper we introduce frequency as a new clue into the network and propose a novel DCT-driven enhancement transformer (DEFormer). First, we propose a learnable frequency branch (LFB) for frequency enhancement contains DCT processing and curvature-based frequency enhancement (CFE). CFE calculates the curvature of each channel to represent the detail richness of different frequency bands, then we divides the frequency features, which focuses on frequency bands with richer textures. In addition, we propose a cross domain fusion (CDF) for reducing the differences between the RGB domain and the frequency domain. We also adopt DEFormer as a preprocessing in dark detection, DEFormer effectively improves the performance of the detector, bringing 2.1% and 3.4% improvement in ExDark and DARK FACE datasets on mAP respectively.Comment: submit to ICRA202

DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior

We present DreamCraft3D, a hierarchical 3D content generation method that produces high-fidelity and coherent 3D objects. We tackle the problem by leveraging a 2D reference image to guide the stages of geometry sculpting and texture boosting. A central focus of this work is to address the consistency issue that existing works encounter. To sculpt geometries that render coherently, we perform score distillation sampling via a view-dependent diffusion model. This 3D prior, alongside several training strategies, prioritizes the geometry consistency but compromises the texture fidelity. We further propose Bootstrapped Score Distillation to specifically boost the texture. We train a personalized diffusion model, Dreambooth, on the augmented renderings of the scene, imbuing it with 3D knowledge of the scene being optimized. The score distillation from this 3D-aware diffusion prior provides view-consistent guidance for the scene. Notably, through an alternating optimization of the diffusion prior and 3D scene representation, we achieve mutually reinforcing improvements: the optimized 3D scene aids in training the scene-specific diffusion model, which offers increasingly view-consistent guidance for 3D optimization. The optimization is thus bootstrapped and leads to substantial texture boosting. With tailored 3D priors throughout the hierarchical generation, DreamCraft3D generates coherent 3D objects with photorealistic renderings, advancing the state-of-the-art in 3D content generation. Code available at https://github.com/deepseek-ai/DreamCraft3D.Comment: Project Page: https://mrtornado24.github.io/DreamCraft3D