Gene Expression Barcode Values Reveal a Potential Link between Parkinson\u27s Disease and Gastric Cancer

Gastric cancer is a disease that develops from the lining of the stomach, whereas Parkinson\u27s disease is a long-term degenerative disorder of the central nervous system that mainly affects the motor system. Although these two diseases seem to be distinct from each other, increasing evidence suggests that they might be linked. To explore the linkage between these two diseases, differentially expressed genes between the diseased people and their normal controls were identified using the barcode algorithm. This algorithm transforms actual gene expression values into barcode values comprised of 1\u27s (expressed genes) and 0\u27s (silenced genes). Once the overlapped differentially expressed genes were identified, their biological relevance was investigated. Thus, using the gene expression profiles and bioinformatics methods, we demonstrate that Parkinson\u27s disease and gastric cancer are indeed linked. This research may serve as a pilot study, and it will stimulate more research to investigate the relationship between gastric cancer and Parkinson\u27s disease from the perspective of gene profiles and their functions

Calibration-based Dual Prototypical Contrastive Learning Approach for Domain Generalization Semantic Segmentation

Prototypical contrastive learning (PCL) has been widely used to learn class-wise domain-invariant features recently. These methods are based on the assumption that the prototypes, which are represented as the central value of the same class in a certain domain, are domain-invariant. Since the prototypes of different domains have discrepancies as well, the class-wise domain-invariant features learned from the source domain by PCL need to be aligned with the prototypes of other domains simultaneously. However, the prototypes of the same class in different domains may be different while the prototypes of different classes may be similar, which may affect the learning of class-wise domain-invariant features. Based on these observations, a calibration-based dual prototypical contrastive learning (CDPCL) approach is proposed to reduce the domain discrepancy between the learned class-wise features and the prototypes of different domains for domain generalization semantic segmentation. It contains an uncertainty-guided PCL (UPCL) and a hard-weighted PCL (HPCL). Since the domain discrepancies of the prototypes of different classes may be different, we propose an uncertainty probability matrix to represent the domain discrepancies of the prototypes of all the classes. The UPCL estimates the uncertainty probability matrix to calibrate the weights of the prototypes during the PCL. Moreover, considering that the prototypes of different classes may be similar in some circumstances, which means these prototypes are hard-aligned, the HPCL is proposed to generate a hard-weighted matrix to calibrate the weights of the hard-aligned prototypes during the PCL. Extensive experiments demonstrate that our approach achieves superior performance over current approaches on domain generalization semantic segmentation tasks.Comment: Accepted by ACM MM'2

Évaluation de la qualité des images obtenues par synthèse de vues 3D

Depth-Image-Based Rendering (DIBR) is a fundamental technology in several 3D-related applications, such as Free viewpoint video (FVV), Virtual Reality (VR) and Augmented Reality (AR). However, new challenges have also been brought in assessing the quality of DIBR-synthesized views since this process induces some new types of distortions, which are inherently different from the distortions caused by video coding. This work is dedicated to better evaluate the quality of DIBRsynthesized views in immersive multimedia. In chapter 2, we propose a completely No-reference (NR) metric. The principle of the first NR metrics NIQSV is to use a couple of opening and closing morphological operations to detect and measure the distortions, such as “blurry regions” and “crumbling”. In the second NR metric NIQSV+, we improve NIQSV by adding a “black hole” and a “stretching” detection. In chapter 3, we propose two Fullreference metrics to handle the geometric distortions by using a dis-occlusion mask and a multi-resolution block matching methods.In chapter 4, we present a new DIBR-synthesized image database with its associated subjective scores. This work focuses on the distortions only induced by different DIBR synthesis methods which determine the quality of experience (QoE) of these DIBR related applications. In addition, we also conduct a benchmark of the state-of-the-art objective quality assessment metrics for DIBR-synthesized views on this database. The chapter 5 concludes the contributions of this thesis and gives some directions of future work.Depth-Image-Based Rendering (DIBR) est une technologie fondamentale dans plusieurs applications liées à la 3D, telles que la vidéo en mode point de vue libre (FVV), la réalité virtuelle (VR) et la réalité augmentée (AR). Cependant, l'évaluation de la qualité des vues synthétisées par DIBR a également posé de nouveaux problèmes, car ce processus induit de nouveaux types de distorsions, qui sont intrinsèquement différentes des distorsions provoquées par le codage vidéo. Ce travail est destiné à mieux évaluer la qualité des vues synthétisées par DIBR en multimédia immersif. Au chapitre 2, nous proposons deux métriques complètements sans référence (NR). Le principe de la première métrique NR NIQSV consiste à utiliser plusieurs opérations morphologiques d’ouverture et de fermeture pour détecter et mesurer les distorsions, telles que les régions floues et l’effritement. Dans la deuxième métrique NR NIQSV+, nous améliorons NIQSV en ajoutant un détecteur de “black hole” et une détection “stretching”.Au chapitre 3, nous proposons deux métriques de référence complète pour traiter les distorsions géométriques à l'aide d'un masque de désocclusion et d'une méthode de correspondance de blocs multi-résolution. Au chapitre 4, nous présentons une nouvelle base de données d'images synthétisée par DIBR avec ses scores subjectifs associés. Ce travail se concentre sur les distorsions uniquement induites par différentes méthodes de synthèse de DIBR qui déterminent la qualité d’expérience (QoE) de ces applications liées à DIBR. En outre, nous effectuons également une analyse de référence des mesures d'évaluation de la qualité objective de pointe pour les vues synthétisées par DIBR sur cette base de données. Le chapitre 5 conclut les contributions de cette thèse et donne quelques orientations pour les travaux futurs

A full-reference Image Quality Assessment metric for 3D Synthesized Views

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Performance comparison of objective metrics on free-viewpoint videos with different depth coding algorithms

International audienceThe popularity of 3D applications has brought out new challenges in the creation, compression and transmission of 3D content due to the large size of 3D data and the limitation of transmission. Several compression standards, such as, Multiview-HEVC and 3D-HEVC have been proposed to compress the 3D content aiding by view synthesis technologies, among which the most commonly used algorithm is Depth-Image-Based-Rendering (DIBR), but the quality assessment of DIBR-synthesized view is very challenging owing to its new types of distortions induced by inaccurate depth map which the conventional 2D quality metrics may fail to assess. In this paper, we test the performance of existing objective metrics on free-viewpoint video with different depth coding algorithms. Results show that all the existing objective metrics perform not well on this database including the full-reference and the no-reference. There is certainly room for further improvement for the algorithms

SC-IQA: Shift compensation based image quality assessment for DIBR-synthesized views

International audienceDepth-image-based-rendering (DIBR) has been used to generate the virtual views for Multi-view videos and Free-viewpoint videos. However, the quality assessment of DIBR-synthesized views is very challenging owing to the new types of distortions induced by inaccurate depth maps, dis-occlusions and image inpainting methods. There exist a large number of object shifts and geometric distortions in the synthesized view which the traditional 2D quality metrics may fail to assess. In this paper, we propose a shift compensation based image quality assessment metric (SC-IQA) for DIBR-synthesized views. Firstly, the global geometric shift is compensated roughly by an SURF + RANSAC homography approach. Then, a multi-resolution block matching method, which performs a more accurate matching, is used to precisely compensate the shift and penalize the local geometric distortion as well. In addition, a visual saliency map is also used as a weighting function. To calculate the final overall quality scores, only the worst blocks are utilized since the biggest distortions have the most effects on the overall perceptual quality. The results show that the proposed metric significantly outperforms the state-of-the-art synthesized view dedicated metrics and the conventional 2D IQA metrics

NIQSV: A no reference image quality assessment metric for 3D synthesized views

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NIQSV+: A No-Reference Synthesized View Quality Assessment Metric

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