VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models

Diffusion models have shown impressive results in text-to-image synthesis. Using massive datasets of captioned images, diffusion models learn to generate raster images of highly diverse objects and scenes. However, designers frequently use vector representations of images like Scalable Vector Graphics (SVGs) for digital icons or art. Vector graphics can be scaled to any size, and are compact. We show that a text-conditioned diffusion model trained on pixel representations of images can be used to generate SVG-exportable vector graphics. We do so without access to large datasets of captioned SVGs. By optimizing a differentiable vector graphics rasterizer, our method, VectorFusion, distills abstract semantic knowledge out of a pretrained diffusion model. Inspired by recent text-to-3D work, we learn an SVG consistent with a caption using Score Distillation Sampling. To accelerate generation and improve fidelity, VectorFusion also initializes from an image sample. Experiments show greater quality than prior work, and demonstrate a range of styles including pixel art and sketches. See our project webpage at https://ajayj.com/vectorfusion .Comment: Project webpage: https://ajayj.com/vectorfusio

A Novel Approach For Information Security With Automatic Variable Key Using Fibonacci Q-Matrix

Information security is essential nowadays. Large number of cipher generation and decryption algorithms exists and are being evolved due to increasing demand of users and e-commerce services. In this paper we propose a new approach for secure information transmission over communication channel with key variability concept in symmetric key algorithms using Fibonacci Qmatrix. Proposed approach will not only enhance the security of information but also saves computation time and reduces power requirements that will find it’s suitability for future hand held devices and online transaction processing

A Low Complexity Architecture for Online On-chip Detection and Identification of f-QRS Feature for Remote Personalized Health Care Applications

This paper introduces a novel low complexity highly accurate on-chip architecture for the detection of fragmented QRS (f-QRS) feature including notches and local extrema in the QRS complexes and subsequently identifies its various morphologies (Notched S, rsR', RsR' without elevation etc.) under the real-time environment targeting remote personalized health care. The proposed architecture uses the outcome of recently proposed Hybrid feature extraction algorithm (HFEA) [1] Level 3 detailed coefficients and detects and identifies the fragmentation feature from the QRS complex based on the criteria of the positions, and the magnitudes of the extrema (maxima and minima) and notches from the wavelet coefficients with no extra cost in terms of arithmetic complexity. To verify the proposed architecture 100 patients were randomly selected from the MIT-BIH Physio Net PTB database and their ECG was examined by two experienced cardiologists individually and the results were compared with those obtained from the architecture output wherein we have achieved 95 % diagnostic matching

Feature Matching with Improved SIRB using RANSAC

In this paper we suggest to improve the SIRB SIFT Scale-Invariant Feature Transform and ORB Oriented FAST and Rotated BRIEF algorithm by incorporating RANSAC to enhance the matching performance We use multi-scale space to extract the features which are impervious to scale rotation and affine variations Then the SIFT algorithm generates feature points and passes the interest points to the ORB algorithm The ORB algorithm generates an ORB descriptor where Hamming distance matches the feature points We propose to use RANSAC Random Sample Consensus to cut down on both the inliers in the form of noise and outliers drastically to cut down on the computational time taken by the algorithm This postprocessing step removes redundant key points and noises This computationally effective and accurate algorithm can also be used in handheld devices where their limited GPU acceleration is not able to compensate for the computationally expensive algorithms like SIFT and SURF Experimental results advocate that the proposed algorithm achieves good matching improves efficiency and makes the feature point matching more accurate with scale in-variance taken into consideratio

Association between angiotensin converting enzyme inhibitor or angiotensin receptor blocker use prior to major elective surgery and the risk of acute dialysis

Background: Some studies but not others suggest angiotensin converting enzyme inhibitor (ACEi) or angiotensin receptor blocker (ARB) use prior to major surgery associates with a higher risk of postoperative acute kidney injury (AKI) and death. Methods: We conducted a large population-based retrospective cohort study of patients aged 66 years or older who received major elective surgery in 118 hospitals in Ontario, Canada from 1995 to 2010 (n = 237,208). We grouped the cohort into ACEi/ARB users (n = 101,494) and non-users (n = 135,714) according to whether the patient filled at least one prescription for an ACEi or ARB (or not) in the 120 days prior to surgery. Our study outcomes were acute kidney injury treated with dialysis (AKI-D) within 14 days of surgery and all-cause mortality within 90 days of surgery. Results: After adjusting for potential confounders, preoperative ACEi/ARB use versus non-use was associated with 17% lower risk of post-operative AKI-D (adjusted relative risk (RR): 0.83; 95% confidence interval (CI): 0.71 to 0.98) and 9% lower risk of all-cause mortality (adjusted RR: 0.91; 95% CI: 0.87 to 0.95). Propensity score matched analyses provided similar results. The association between ACEi/ARB and AKI-D was significantly modified by the presence of preoperative chronic kidney disease (CKD) (P value for interaction < 0.001) with the observed association evident only in patients with CKD (CKD - adjusted RR: 0.62; 95% CI: 0.50 to 0.78 versus No CKD: adjusted RR: 1.00; 95% CI: 0.81 to 1.24). Conclusions: In this cohort study, preoperative ACEi/ARB use versus non-use was associated with a lower risk of AKI-D, and the association was primarily evident in patients with CKD. Large, multi-centre randomized trials are needed to inform optimal ACEi/ARB use in the peri-operative setting

Combination low-intensity chemotherapy plus inotuzumab ozogamicin, blinatumomab and rituximab for pediatric patients with relapsed/refractory B-cell acute lymphoblastic leukemia

Not available

Transient tissue priming via ROCK inhibition uncouples pancreatic cancer progression, sensitivity to chemotherapy, and metastasis

The emerging standard of care for patients with inoperable pancreatic cancer is a combination of cytotoxic drugs gemcitabine and Abraxane, but patient response remains moderate. Pancreatic cancer development and metastasis occur in complex settings, with reciprocal feedback from microenvironmental cues influencing both disease progression and drug response. Little is known about how sequential dual targeting of tumor tissue tension and vasculature before chemotherapy can affect tumor response. We used intravital imaging to assess how transient manipulation of the tumor tissue, or "priming," using the pharmaceutical Rho kinase inhibitor Fasudil affects response to chemotherapy. Intravital Förster resonance energy transfer imaging of a cyclin-dependent kinase 1 biosensor to monitor the efficacy of cytotoxic drugs revealed that priming improves pancreatic cancer response to gemcitabine/Abraxane at both primary and secondary sites. Transient priming also sensitized cells to shear stress and impaired colonization efficiency and fibrotic niche remodeling within the liver, three important features of cancer spread. Last, we demonstrate a graded response to priming in stratified patient-derived tumors, indicating that fine-tuned tissue manipulation before chemotherapy may offer opportunities in both primary and metastatic targeting of pancreatic cancer

Herschel-ATLAS/GAMA: SDSS cross-correlation induced by weak lensing

We report a highly significant (>10σ) spatial correlation between galaxies with S350 μm ≥ 30 mJy detected in the equatorial fields of the Herschel Astrophysical Terahertz Large Area Survey (H-ATLAS) with estimated redshifts ≳ 1.5, and Sloan Digital Sky Survey (SDSS) or Galaxy And Mass Assembly (GAMA) galaxies at 0.2 ≤ z ≤ 0.6. The significance of the cross-correlation is much higher than those reported so far for samples with non-overlapping redshift distributions selected in other wavebands. Extensive, realistic simulations of clustered sub-mm galaxies amplified by foreground structures confirm that the cross-correlation can be explained by weak gravitational lensing (μ < 2). The simulations also show that the measured amplitude and range of angular scales of the signal are larger than can be accounted for by galaxy–galaxy weak lensing. However, for scales ≲ 2 arcmin, the signal can be reproduced if SDSS/GAMA galaxies act as signposts of galaxy groups/clusters with halo masses in the range 1013.2–1014.5 M⊙. The signal detected on larger scales appears to reflect the clustering of such haloes