Local generation of hydrogen for enhanced photothermal therapy.

By delivering the concept of clean hydrogen energy and green catalysis to the biomedical field, engineering of hydrogen-generating nanomaterials for treatment of major diseases holds great promise. Leveraging virtue of versatile abilities of Pd hydride nanomaterials in high/stable hydrogen storage, self-catalytic hydrogenation, near-infrared (NIR) light absorption and photothermal conversion, here we utilize the cubic PdH0.2 nanocrystals for tumour-targeted and photoacoustic imaging (PAI)-guided hydrogenothermal therapy of cancer. The synthesized PdH0.2 nanocrystals have exhibited high intratumoural accumulation capability, clear NIR-controlled hydrogen release behaviours, NIR-enhanced self-catalysis bio-reductivity, high NIR-photothermal effect and PAI performance. With these unique properties of PdH0.2 nanocrystals, synergetic hydrogenothermal therapy with limited systematic toxicity has been achieved by tumour-targeted delivery and PAI-guided NIR-controlled release of bio-reductive hydrogen as well as generation of heat. This hydrogenothermal approach has presented a cancer-selective strategy for synergistic cancer treatment

CGraph : a correlations-aware approach for efficient concurrent iterative graph processing

With the fast growing of iterative graph analysis applications, the graph processing platform has to efficiently handle massive Concurrent iterative Graph Processing (CGP) jobs. Although it has been extensively studied to optimize the execution of a single job, existing solutions face high ratio of data access cost to computation for the CGP jobs due to significant cache interference and memory wall, which incurs low throughput. In this work, we observed that there are strong spatial and temporal correlations among the data accesses issued by different CGP jobs because these concurrently running jobs usually need to repeatedly traverse the shared graph structure for the iterative processing of each vertex. Based on this observation, this paper proposes a correlations-aware execution model, together with a core-subgraph based scheduling algorithm, to enable these CGP jobs to efficiently share the graph structure data in cache/memory and their accesses by fully exploiting such correlations. It is able to achieve the efficient execution of the CGP jobs by effectively reducing their average ratio of data access cost to computation and therefore delivers a much higher throughput. In order to demonstrate the efficiency of the proposed approaches, a system called CGraph is developed and extensive experiments have been conducted. The experimental results show that CGraph improves the throughput of the CGP jobs by up to 2.31 times in comparison with the existing solutions

A numerically stable fragile watermarking scheme for authenticating 3D models

International audienceThis paper analyzes the numerically instable problem in the current 3D fragile watermarking schemes. Some existing fragile watermarking schemes apply the floating-point arithmetic to embed the watermarks. However, these schemes fail to work properly due to the numerically instable problem, which is common in the floating-point arithmetic. This paper proposes a numerically stable fragile watermarking scheme. The scheme views the mantissa part of the floating-point number as an unsigned integer and operates on it by the bit XOR operator. Since there is no numerical problem in the bit operation, this scheme is numerically stable. The scheme can control the watermark strength through changing the embedding parameters. This paper further discusses selecting appropriate embedding parameters to achieve good performance in terms of the perceptual invisibility and the ability to detect unauthorized attacks on the 3D models. The experimental results show that the proposed public scheme could detect attacks such as adding noise, adding/deleting faces, inserting/removing vertices, etc. The comparisons with the existing fragile schemes show that this scheme is easier to implement and use

Constructing G1 quadratic Bezier curves with arbitrary endpoint tangent vectors

International audienceQuadratic Bézier curves are important geometric entities in many applications. However, it was often ignored by the literature the fact that a single segment of a quadratic Bézier curve may fail to fit arbitrary endpoint unit tangent vectors. The purpose of this paper is to provide a solution to this problem, i.e., constructing G1 quadratic Bézier curves satisfying given endpoint (positions and arbitrary unit tangent vectors) conditions. Examples are given to illustrate the new solution and to perform comparison between the G1 quadratic Bézier cures and other curve schemes such as the composite geometric Hermite curves and the biarcs

Pick the Best Pre-trained Model: Towards Transferability Estimation for Medical Image Segmentation

Transfer learning is a critical technique in training deep neural networks for the challenging medical image segmentation task that requires enormous resources. With the abundance of medical image data, many research institutions release models trained on various datasets that can form a huge pool of candidate source models to choose from. Hence, it's vital to estimate the source models' transferability (i.e., the ability to generalize across different downstream tasks) for proper and efficient model reuse. To make up for its deficiency when applying transfer learning to medical image segmentation, in this paper, we therefore propose a new Transferability Estimation (TE) method. We first analyze the drawbacks of using the existing TE algorithms for medical image segmentation and then design a source-free TE framework that considers both class consistency and feature variety for better estimation. Extensive experiments show that our method surpasses all current algorithms for transferability estimation in medical image segmentation. Code is available at https://github.com/EndoluminalSurgicalVision-IMR/CCFVComment: MICCAI2023(Early Accepted

LogUAD: Log unsupervised anomaly detection based on word2Vec

System logs record detailed information about system operation and are important for analyzing the system\u27s operational status and performance. Rapid and accurate detection of system anomalies is of great significance to ensure system stability. However, large-scale distributed systems are becoming more and more complex, and the number of system logs gradually increases, which brings challenges to analyze system logs. Some recent studies show that logs can be unstable due to the evolution of log statements and noise introduced by log collection and parsing. Moreover, deep learning-based detection methods take a long time to train models. Therefore, to reduce the computational cost and avoid log instability we propose a new Word2Vec-based log unsupervised anomaly detection method (LogUAD). LogUAD does not require a log parsing step and takes original log messages as input to avoid the noise. LogUAD uses Word2Vec to generate word vectors and generates weighted log sequence feature vectors with TF-IDF to handle the evolution of log statements. At last, a computationally efficient unsupervised clustering is exploited to detect the anomaly. We conducted extensive experiments on the public dataset from Blue Gene/L (BGL). Experimental results show that the F1-score of LogUAD can be improved by 67.25% compared to LogCluster

A point-in-polygon method based on a quasi-closest point

International audienceThis paper presents a numerically stable solution to a point-in-polygon problem by combining the orientation method and the uniform subdivision technique. We define first a quasi-closest point that can be locally found through the uniform subdivision cells, and then we provide the criteria for determining whether a point lies inside a polygon according to the quasi-closest point. For a large number of points to be tested against the same polygon, the criteria are employed to determine the inclusion property of an empty cell as well as a test point. The experimental tests show that the new method resolves the singularity of a test point on an edge without loss of efficiency. The GIS case study also demonstrates the capability of the method to identify which region contains a test point in a map

Filling n-sided regions with G1 triangular Coons B-spline patches

International audienceFilling n-sided regions is an essential operation in shape and surface modeling. Positional and tangential continuities are highly required in designing and manufacturing. We propose a method for filling n-sided regions with untrimmed triangular Coons B-spline patches, preserving G1 continuity exactly. The algorithm first computes a central point, a central normal, the central, and the corner derivative vectors. Then the region is split into n triangular areas by connecting the central point to each corner of the boundary. These inner curves and all cross-boundary derivatives are computed fulfilling G1 compatibility conditions. And finally, the triangular patches are generated in the Coons B-spline form, one boundary of which is regressed to the central vertex. Neither positional nor tangential error is introduced by this method. And only one degree elevation is needed