8,248 research outputs found
GS-SCZ, GS-Ht <i>preserved</i> modules correlated with genomic scores and significant enrichment in PGC3 priority genes in LBD and PITT samples.
No full textGS-SCZ, GS-Ht preserved modules correlated with genomic scores and significant enrichment in PGC3 priority genes in LBD and PITT samples.</p
Analysis of steroid estrogens in water using liquid chromatography/tandem mass spectrometry with chemical derivatization
No full textThermogravimetric data of FPUF and the code of K-GS model
No full texta mathematical model was proposed to solve the kinetic parameters of solid material pyrolysis in the inert atmosphere by combining the Kissinger method and the global search algorithm (GS). The model was validated by the mechanism of the one-step pyrolysis of cellulose in the nitrogen atmosphere and applied to the pyrolysis kinetics analysis of FPUF. The results show that the model can effectively and accurately describe the solid pyrolysis process, and the results can provide basic data for the flame combustion numerical simulation of FPUF
Specific Oral Medications Decrease the Need for Surgery in Adhesive Partial Small Bowel Obstruction.
No full textDifferentially Private GAN for Time Series
No full textGenerative Adversarial Networks (GANs) are a modern solution aiming to encourage public sharing of data, even if the data contains inherently private information, by generating synthetic data that looks like, but is not equal to, the data the GAN was trained on. However, GANs are prone to remembering samples from the training data, therefore additional care is needed to guarantee privacy. Differentially Private (DP) GANs offer a solution to this problem by protecting user privacy through a mathematical guarantee, achieved by adding carefully constructed noise at specific points in the training process. A state-of-the-art example of such a GAN is Gradient Sanitized Wasserstein GAN, (GS-WGAN), \cite{chen2021gswgan}. This model is shown to create higher quality synthetic images than other DP GANs. To extend the applicability of GS-WGAN we first reproduce and extend the evaluation, verifying that the model outperforms DP-CGAN by an average of 40\% when assessed across three qualitative metrics and two datasets. Secondly we propose improvements to the architecture and training procedure to make GS-WGAN applicable on timeseries data. The experimental results show that GS-WGAN is fit for generating synthetic timeseries through promising experimental results.[1] D. Chen, T. Orekondy, and M. Fritz, “Gs-wgan: A gradient-sanitized approach for learning differentially private generators,” 2021CSE3000 Research ProjectComputer Science and Engineerin
GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators
No full textThe wide-spread availability of rich data has fueled the growth of machine learning applications in numerous domains. However, growth in domains with highly-sensitive data (e.g., medical) is largely hindered as the private nature of data prohibits it from being shared. To this end, we propose Gradient-sanitized Wasserstein Generative Adversarial Networks (GS-WGAN), which allows releasing a sanitized form of the sensitive data with rigorous privacy guarantees. In contrast to prior work, our approach is able to distort gradient information more precisely, and thereby enabling training deeper models which generate more informative samples. Moreover, our formulation naturally allows for training GANs in both centralized and federated (i.e., decentralized) data scenarios. Through extensive experiments, we find our approach consistently outperforms state-of-the-art approaches across multiple metrics (e.g., sample quality) and datasets
EBUS-GS and VBN for GGO lesions
Get PDFBackground: Endobronchial ultrasonography with guide sheath (EBUS-GS) could be useful for diagnosing ground-glass opacity (GGO) predominant-type lesions in the peripheral lung. Furthermore, several studies have reported that transbronchial biopsy using EBUS-GS and virtual bronchoscopic navigation (VBN) was safe and effective for diagnosing small peripheral lung lesions. Our objectives were to diagnose solitary peripheral GGO predominant-type lesions by transbronchial biopsy using EBUS-GS and VBN under radiographic fluoroscopic guidance, and to evaluate the clinical factors associated with diagnostic yield. Methods: The medical records of 169 patients with GGO predominant-type lesions who underwent transbronchial biopsy using EBUS-GS and VBN under radiographic fluoroscopic guidance were retrospectively reviewed. Results: Endobronchial ultrasonography images could be obtained for 156 (92%) of 169 GGO predominant-type lesions, and 116 (69%) were successfully diagnosed by this method (20 of 31 pure GGO lesions [65%]; 96 of 138 mixed GGO predominant-type lesions [70%]). The mean size of diagnosed lesions was significantly larger than that of nondiagnosed lesions (22 mm versus 18 mm, p < 0.01). Regarding diagnostic yield based on computed tomography sign, cases with presence of a bronchus leading directly to a lesion had significantly higher diagnostic yield than the other lesions (p < 0.01). Conclusions: The addition of VBN to EBUS-GS could be useful in clinical practice for diagnosing GGO predominant-type lesions in the peripheral lung
Glutamine deprivation up-regulates GS expression.
No full text<p>A) GS immune-positive hippocampal neurons were detected in AD patients but not in control samples. Immunopositive astrocytes are found in both. B) GS levels in AD frontal cortex are significantly higher than in control samples. One representative blot at two different exposure times is shown. C) Quantification and statistics of 12 AD and 8 control cases. GS levels in controls were arbitrarily set as 100%. Error bars denote standard deviations. P values were calculated via Student's T-test. D) Removing glutamine from culture medium or inhibiting GS activity with MSO induces GS expression in primary neurons. E) Removing glutamine from the culture medium promotes GS expression in N2a cells.</p
Specific stabilization of DNA triple helices by indolo[2,1-b]quinazolin-6,12-dione derivatives
No full textGaussianEnhancer++: A General GS-Agnostic Rendering Enhancer
No full textGaussian Splatting (GS) methods, including 3DGS and 2DGS, have demonstrated significant effectiveness in real-time novel view synthesis (NVS), establishing themselves as a key technology in the field of computer graphics. However, GS-based methods still face challenges in rendering high-quality image details. Even when utilizing advanced frameworks, their outputs may display significant rendering artifacts when relying solely on a few input views, such as noise and blurriness. A reasonable approach is to conduct post-processing in order to restore clear details. Therefore, we propose GaussianEnhancer, a general GS-agnostic post-processor that employs a degradation-driven view blending method to improve the rendering quality of GS models. Specifically, we design a degradation modeling method tailored to the GS style and construct a large-scale training dataset to effectively simulate the native rendering artifacts of GS, enabling efficient training. In addition, we present a spatial information fusion framework that includes view fusion and depth modulation modules. This framework successfully integrates related images and leverages depth information from the target image to enhance rendering details. Through our GaussianEnhancer, we effectively eliminate the rendering artifacts of GS models and generate highly realistic image details. Based on GaussianEnhancer, we introduce GaussianEnhancer++, which features an enhanced GS-style degradation simulator, leading to improved enhancement quality. Furthermore, GaussianEnhancer++ can generate ultra-high-resolution outputs from noisy low resolution GS rendered images by leveraging the symmetry between high-resolution and low resolution images. Extensive experiments demonstrate the excellent restoration ability of GaussianEnhancer++ on various novel view synthesis benchmarks
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