Stress-induced leakage current in dual-gate CMOSFETS with thin nitrided gate oxides

Master'sMASTER OF ENGINEERIN

Primary isolated intracranial Rosai–Dorfman disease: Report of a rare case and review of the literature

Background Intracranial involvement is an uncommon manifestation of Rosai–Dorfman disease (RDD) and had been rarely reported. In this study, we explore clinical characteristics, imageology manifestations and pathological features of primary intracranial RDD so as to improve the understanding for this disease. Methods One case (16-years-old boy) with primary intracranial RDD was analyzed and studied retrospectively by MRI features, histopathological observation and immunohistochemical staining, and the related literatures were reviewed. Results The case was single lesion and involved the dura of the left middle cranial fossa base, which was iso-hypo signal intensity on T1WI and hypointense on T2WI and FLAIR image. The lesion was a homogeneous contrast enhancement mass with dural tail sign and had peritumoral brain edema. Pathological analysis showed the lesion consisted of variable numbers of mature lymphocytes, plasma cells and neutrophils. The characteristic histiocytes were emperipolesis and positively expressed for S-100 and CD-68 and negatively expressed for CD-1a by immunohistochemical analysis. Based on clinical presentations and histological findings after surgical excision, a final diagnosis of primary intracranial RDD was made. Conclusion Primary intracranial RDD, especially located in the cranial base, is exceptionally rare, which hard to be distinguished with meningoma by imageology and clinical manifestations, but could be diagnosed by pathological and immunohistochemical examinations. Surgery is of the most importance treatment and prognosis is optimistic for this disease

Thermodynamic Cycle Analysis and Experimental Investigate on a Two-stage Vapor Injection Low Temperature Air Source Heat Pump with a Variable Displacement Ratio Rotary Compressor

Two-stage vapor injection compression cycle with flash tank was thermodynamically analyzed, the results showed that there existed the optimum theoretical displacement ratio of high stage to low stage corresponding to the maximum coefficient of performance(COP), the optimum displacement ratio and the volumetric heating capacity decreased with evaporation temperature decreasing. An optimum theoretical displacement ratio correlation for R290, R32 and R410A was given. A new type two-stage vapor injection low temperature air source heat pump (ASHP) was designed, which had a variable speed triple-cylinder rotary compressor with two cylinders in low stage and one cylinder in high stage. The experimental results of the new type ASHP showed that the heating capacity under 20℃/-20℃(inside room /outside room) could reach the rated heating capacity under 20℃/7℃, improving 96% compared to conventional one-stage ASHP, the heating capacity under 20℃/-30℃ could reach 80% of the rated one. COP of the new type ASHP could improve 5%~10% when the heating capacity was comparable to the conventional ASHP, and the heating capacity of the new type ASHP could improve 30%~50% when COP was comparable to the conventional ASHP

Uni3D: Exploring Unified 3D Representation at Scale

Scaling up representations for images or text has been extensively investigated in the past few years and has led to revolutions in learning vision and language. However, scalable representation for 3D objects and scenes is relatively unexplored. In this work, we present Uni3D, a 3D foundation model to explore the unified 3D representation at scale. Uni3D uses a 2D initialized ViT end-to-end pretrained to align the 3D point cloud features with the image-text aligned features. Via the simple architecture and pretext task, Uni3D can leverage abundant 2D pretrained models as initialization and image-text aligned models as the target, unlocking the great potential of 2D models and scaling-up strategies to the 3D world. We efficiently scale up Uni3D to one billion parameters, and set new records on a broad range of 3D tasks, such as zero-shot classification, few-shot classification, open-world understanding and part segmentation. We show that the strong Uni3D representation also enables applications such as 3D painting and retrieval in the wild. We believe that Uni3D provides a new direction for exploring both scaling up and efficiency of the representation in 3D domain.Comment: Code and Demo: https://github.com/baaivision/Uni3

Deciphering the causal association and co-disease mechanisms between psoriasis and breast cancer

BackgroundPrior research has indicated a link between psoriasis and the susceptibility to breast cancer (BC); however, a definitive causal relationship remains elusive. This study sought to elucidate the causal connection and shared underlying mechanisms between psoriasis and BC through bidirectional Mendelian randomization (MR) and bioinformatic approaches.MethodsWe employed a bidirectional MR approach to examine the potential causal connection between psoriasis and BC. Genetic data pertaining to psoriasis and BC were sourced from extensive published genome-wide association studies. The inverse -variance weighted or wald ratio served as the primary method for estimating causal effects. Sensitivity analysis of the MR results was applied with multiple methods. Leveraged datasets from the Gene Expression Omnibus and the Cancer Genome Atlas repositories to identify common differentially expressed genes, shedding light on the shared mechanisms underlying these two conditions.ResultsThe MR analysis revealed that when considering psoriasis as an exposure factor, the incidences of BC (OR=1.027) and estrogen receptor negative (ER-) BC (OR=1.054) were higher than in the general population. When using Her2+ BC as an exposure factor, the risk of psoriasis was 0.822 times higher (OR=0.822) than in the general population. Sensitivity analysis indicated that the results were robust. Transcriptome analysis showed that CXCL13 and CCL20 were activated in both BC and psoriasis. Both diseases were also linked to neutrophil chemotaxis, the IL-17 pathway, and the chemokine pathway.ConclusionThe results suggest that psoriasis may increase the risk of BC, especially ER- BC, while reverse MR suggests a decreased risk of psoriasis in Her2+ BC. Transcriptome analysis revealed a shared mechanism between psoriasis and BC

Graph ODE with Factorized Prototypes for Modeling Complicated Interacting Dynamics

This paper studies the problem of modeling interacting dynamical systems, which is critical for understanding physical dynamics and biological processes. Recent research predominantly uses geometric graphs to represent these interactions, which are then captured by powerful graph neural networks (GNNs). However, predicting interacting dynamics in challenging scenarios such as out-of-distribution shift and complicated underlying rules remains unsolved. In this paper, we propose a new approach named Graph ODE with factorized prototypes (GOAT) to address the problem. The core of GOAT is to incorporate factorized prototypes from contextual knowledge into a continuous graph ODE framework. Specifically, GOAT employs representation disentanglement and system parameters to extract both object-level and system-level contexts from historical trajectories, which allows us to explicitly model their independent influence and thus enhances the generalization capability under system changes. Then, we integrate these disentangled latent representations into a graph ODE model, which determines a combination of various interacting prototypes for enhanced model expressivity. The entire model is optimized using an end-to-end variational inference framework to maximize the likelihood. Extensive experiments in both in-distribution and out-of-distribution settings validate the superiority of GOAT

Hand-eye calibration method with a three-dimensional-vision sensor considering the rotation parameters of the robot pose

Hand-eye calibration is a fundamental step for a robot equipped with vision systems. However, this problem usually interacts with robot calibration because robot geometric parameters are not very precise. In this article, a new calibration method considering the rotation parameters of the robot pose is proposed. First, a constraint least square model is established assuming that each spherical center measurement of standard ball is equal in the robot base frame, which provides an initial solution. To further improve the solution accuracy, a nonlinear calibration model in the sensor frame is established. Since it can reduce one error accumulation process, a more accurate reference point can be used for optimization. Then, the rotation parameters of the robot pose whose slight errors cause large disturbance to the solution are selected by analyzing the coefficient matrices of the error items. Finally, the hand-eye transformation parameters are refined together with the rotation parameters in the nonlinear optimization solution. Some comparative simulations are performed between the modified least square method, constrained least square method, and the proposed method. The experiments are conducted on a 5-axis hybrid robot named TriMule to demonstrate the superior accuracy of the proposed method

GPT-4V(ision) as A Social Media Analysis Engine

Recent research has offered insights into the extraordinary capabilities of Large Multimodal Models (LMMs) in various general vision and language tasks. There is growing interest in how LMMs perform in more specialized domains. Social media content, inherently multimodal, blends text, images, videos, and sometimes audio. Understanding social multimedia content remains a challenging problem for contemporary machine learning frameworks. In this paper, we explore GPT-4V(ision)'s capabilities for social multimedia analysis. We select five representative tasks, including sentiment analysis, hate speech detection, fake news identification, demographic inference, and political ideology detection, to evaluate GPT-4V. Our investigation begins with a preliminary quantitative analysis for each task using existing benchmark datasets, followed by a careful review of the results and a selection of qualitative samples that illustrate GPT-4V's potential in understanding multimodal social media content. GPT-4V demonstrates remarkable efficacy in these tasks, showcasing strengths such as joint understanding of image-text pairs, contextual and cultural awareness, and extensive commonsense knowledge. Despite the overall impressive capacity of GPT-4V in the social media domain, there remain notable challenges. GPT-4V struggles with tasks involving multilingual social multimedia comprehension and has difficulties in generalizing to the latest trends in social media. Additionally, it exhibits a tendency to generate erroneous information in the context of evolving celebrity and politician knowledge, reflecting the known hallucination problem. The insights gleaned from our findings underscore a promising future for LMMs in enhancing our comprehension of social media content and its users through the analysis of multimodal information

Real-Time Evacuation Simulation in Mine Interior Model of Smoke and Action

Virtual human crowd models have been used in the simulation of building and urban evacuation, but have not yet applied to underground coal mine operations and escape situations with emphasis on smoke, fires and physiological behaviors. We explore this through a real-time simulation model, MIMOSA (Mine Interior Model Of Smoke and Action), which integrates an underground coal mine virtual environment, a fire and smoke propagation model, and a human physiology and behavior model. Each individual agent has a set of physiological parameters as variables of time and environment, simulating a miner’s physiological condition during normal operations as well as during emergencies due to fire and smoke. To obtain appropriate agent navigation in the mine environment, we have extended the HiDAC framework (High- Density Autonomous Crowds) navigation from a grid-based cell-portal graph to a geometrybased portal path and integrated a novel cellportal and shortest path visibility algorithm