YOLO-BEV: Generating Bird's-Eye View in the Same Way as 2D Object Detection

Vehicle perception systems strive to achieve comprehensive and rapid visual interpretation of their surroundings for improved safety and navigation. We introduce YOLO-BEV, an efficient framework that harnesses a unique surrounding cameras setup to generate a 2D bird's-eye view of the vehicular environment. By strategically positioning eight cameras, each at a 45-degree interval, our system captures and integrates imagery into a coherent 3x3 grid format, leaving the center blank, providing an enriched spatial representation that facilitates efficient processing. In our approach, we employ YOLO's detection mechanism, favoring its inherent advantages of swift response and compact model structure. Instead of leveraging the conventional YOLO detection head, we augment it with a custom-designed detection head, translating the panoramically captured data into a unified bird's-eye view map of ego car. Preliminary results validate the feasibility of YOLO-BEV in real-time vehicular perception tasks. With its streamlined architecture and potential for rapid deployment due to minimized parameters, YOLO-BEV poses as a promising tool that may reshape future perspectives in autonomous driving systems

Effect of AFM nanoindentation loading rate on the characterization of mechanical properties of vascular endothelial cell

Vascular endothelial cells form a barrier that blocks the delivery of drugs entering into brain tissue for central nervous system disease treatment. The mechanical responses of vascular endothelial cells play a key role in the progress of drugs passing through the blood–brain barrier. Although nanoindentation experiment by using AFM (Atomic Force Microscopy) has been widely used to investigate the mechanical properties of cells, the particular mechanism that determines the mechanical response of vascular endothelial cells is still poorly understood. In order to overcome this limitation, nanoindentation experiments were performed at different loading rates during the ramp stage to investigate the loading rate effect on the characterization of the mechanical properties of bEnd.3 cells (mouse brain endothelial cell line). Inverse finite element analysis was implemented to determine the mechanical properties of bEnd.3 cells. The loading rate effect appears to be more significant in short-term peak force than that in long-term force. A higher loading rate results in a larger value of elastic modulus of bEnd.3 cells, while some mechanical parameters show ambiguous regulation to the variation of indentation rate. This study provides new insights into the mechanical responses of vascular endothelial cells, which is important for a deeper understanding of the cell mechanobiological mechanism in the blood–brain barrier

Mechanistic evaluation of long-term in-stent restenosis based on models of tissue damage and growth

Development and application of advanced mechanical models of soft tissues and their growth represent one of the main directions in modern mechanics of solids. Such models are increasingly used to deal with complex biomedical problems. Prediction of in-stent restenosis for patients treated with coronary stents remains a highly challenging task. Using a finite element method, this paper presents a mechanistic approach to evaluate the development of in-stent restenosis in an artery following stent implantation. Hyperelastic models with damage, verified with experimental results, are used to describe the level of tissue damage in arterial layers and plaque caused by such intervention. A tissue-growth model, associated with vessel damage, is adopted to describe the growth behaviour of a media layer after stent implantation. Narrowing of lumen diameter with time is used to quantify the development of in-stent restenosis in the vessel after stenting. It is demonstrated that stent designs and materials strongly affect the stenting-induced damage in the media layer and the subsequent development of in-stent restenosis. The larger the artery expansion achieved during balloon inflation the higher the damage introduced to the media layer, leading to an increased level of in-stent restenosis. In addition, the development of in-stent restenosis is directly correlated with the artery expansion during the stent deployment. The correlation is further used to predict the effect of a complex clinical procedure, such as stent overlapping, on the level of in-stent restenosis developed after percutaneous coronary intervention.</div

Evaluation of different b-values in DWI and 1H MRS for pancreatic cancer and pancreatitis: a rabbit model

Pancreatic cancer is a common malignant tumor with high incidence of metastasis. Currently, there is no absolute standard for the choice of b-value for diffusion-weighted imaging (DWI) for pancreatic cancer. The b-value is rarely reported in animal model study, especially in pancreatic cancer/mass pancreatitis rabbit models. The authors\u27 aim was to determine the different b-values to differentiate the diagnosis of pancreatic cancer and mass pancreatitis in rabbit models using DWI. When comparing the effect of different b-values in diagnostic process, the pathological results could be regarded as the gold standard. In this research, 30 healthy New Zealand rabbits were selected and divided into three groups by random number table method: group 1 (pancreatic cancer), group 2 (mass pancreatitis) and the control group (healthy). After DWI (three different b-values 333, 667, 1000 s/mm2, respectively) and MRI examination, the model rabbits were then killed. Afterward, the tumor mass was removed for biopsy, and occupation anatomy and tumor histopathology were examined. Fat-suppressing sequences of T2WI, DWI, ADC, difference of ADC (DADC), and MRS were used. The present study determined that the effective differential diagnosis of pancreatic cancer and pancreatitis was determined at low b-values (333 s/mm2) when performed DWI inspection in rabbit models

Experimental and computational studies of poly-L-lactic acid for cardiovascular applications: recent progress

Stents are commonly used in medical procedures to alleviate the symptoms of coronary heart disease, a prevalent modern society disease. These structures are employed to maintain vessel patency and restore blood flow. Traditionally stents are made of metals such as stainless steel or cobalt chromium; however, these scaffolds have known disadvantages. An emergence of transient scaffolds is gaining popularity, with the structure engaged for a required period whilst healing of the diseased arterial wall occurs. Polymers dominate a medical device sector, with incorporation in sutures, scaffolds and screws. Thanks to their good mechanical and biological properties and their ability to degrade naturally. Polylactic acid is an extremely versatile polymer, with its properties easily tailored to applications. Its dominance in the stenting field increases continually, with the first polymer scaffold gaining FDA approval in 2016. Still some challenges with PLLA bioresorbable materials remain, especially with regard to understanding their mechanical response, assessment of its changes with degradation and comparison of their performance with that of metallic drug-eluting stent. Currently, there is still a lack of works on evaluating both the pre-degradation properties and degradation performance of these scaffolds. Additionally, there are no established material models incorporating non-linear viscoelastic behaviour of PLLA and its evolution with in-service degradation. Assessing these features through experimental analysis accompanied by analytical and numerical studies will provide powerful tools for design and optimisation of these structures endorsing their broader use in stenting. This overview assesses the recent studies investigating mechanical and computational performance of poly(l-lactic) acid and its use in stenting applications

Domain Adaptive Code Completion via Language Models and Decoupled Domain Databases

Large Language Models (LLMs) have demonstrated remarkable performance in code completion. However, due to the lack of domain-specific knowledge, they may not be optimal in completing code that requires intensive domain knowledge for example completing the library names. Although there are several works that have confirmed the effectiveness of fine-tuning techniques to adapt language models for code completion in specific domains. They are limited by the need for constant fine-tuning of the model when the project is in constant iteration. To address this limitation, in this paper, we propose $k$NM-LM, a retrieval-augmented language model (R-LM), that integrates domain knowledge into language models without fine-tuning. Different from previous techniques, our approach is able to automatically adapt to different language models and domains. Specifically, it utilizes the in-domain code to build the retrieval-based database decoupled from LM, and then combines it with LM through Bayesian inference to complete the code. The extensive experiments on the completion of intra-project and intra-scenario have confirmed that $k$NM-LM brings about appreciable enhancements when compared to CodeGPT and UnixCoder. A deep analysis of our tool including the responding speed, storage usage, specific type code completion, and API invocation completion has confirmed that $k$NM-LM provides satisfactory performance, which renders it highly appropriate for domain adaptive code completion. Furthermore, our approach operates without the requirement for direct access to the language model's parameters. As a result, it can seamlessly integrate with black-box code completion models, making it easy to integrate our approach as a plugin to further enhance the performance of these models.Comment: Accepted by ASE202

An Object-Oriented Color Visualization Method with Controllable Separation for Hyperspectral Imagery

Publisher's version (útgefin grein)Most of the available hyperspectral image (HSI) visualization methods can be considered as data-oriented approaches. These approaches are based on global data, so it is difficult to optimize display of a specific object. Compared to data-oriented approaches, object-oriented visualization approaches show more pertinence and would be more practical. In this paper, an object-oriented hyperspectral color visualization approach with controllable separation is proposed. Using supervised information, the proposed method based on manifold dimensionality reduction methods can simultaneously display global data information, interclass information, and in-class information, and the balance between the above information can be adjusted by the separation factor. Output images are visualized after considering the results of dimensionality reduction and separability. Five kinds of manifold algorithms and four HSI data were used to verify the feasibility of the proposed approach. Experiments showed that the visualization results by this approach could make full use of supervised information. In subjective evaluations, t-distributed stochastic neighbor embedding (T-SNE), Laplacian eigenmaps (LE), and isometric feature mapping (ISOMAP) demonstrated a sharper detailed pixel display effect within individual classes in the output images. In addition, T-SNE and LE showed clarity of information (optimum index factor, OIF), good correlation (ρ), and improved pixel separability () in objective evaluation results. For Indian Pines data, T-SNE achieved the best results in regard to both OIF and, which were 0.4608 and 23.83, respectively. However, compared with other methods, the average computing time of this method was also the longest (1521.48 s).This research was funded by the National Natural Science Foundation of China, grant numbers 61275010 and 61675051. The authors would like to thank D. Landgrebe from Purdue University for providing the AVIRIS Indian Pines data set and Prof. P. Gamba from the University of Pavia for providing the ROSIS-3 University of Pavia data set. The authors would like to express their appreciation to Jon Qiaosen Chen from the University of Iceland and Di Chen for helping improve the language of the paper.Peer Reviewe