Low-Dose CT Using Denoising Diffusion Probabilistic Model for 20×\times Speedup

Low-dose computed tomography (LDCT) is an important topic in the field of radiology over the past decades. LDCT reduces ionizing radiation-induced patient health risks but it also results in a low signal-to-noise ratio (SNR) and a potential compromise in the diagnostic performance. In this paper, to improve the LDCT denoising performance, we introduce the conditional denoising diffusion probabilistic model (DDPM) and show encouraging results with a high computational efficiency. Specifically, given the high sampling cost of the original DDPM model, we adapt the fast ordinary differential equation (ODE) solver for a much-improved sampling efficiency. The experiments show that the accelerated DDPM can achieve 20x speedup without compromising image quality

Fatigue Detection for Ship OOWs Based on Input Data Features, from The Perspective of Comparison with Vehicle Drivers: A Review

Ninety percent of the world’s cargo is transported by sea, and the fatigue of ship officers of the watch (OOWs) contributes significantly to maritime accidents. The fatigue detection of ship OOWs is more difficult than that of vehicles drivers owing to an increase in the automation degree. In this study, research progress pertaining to fatigue detection in OOWs is comprehensively analysed based on a comparison with that in vehicle drivers. Fatigue detection techniques for OOWs are organised based on input sources, which include the physiological/behavioural features of OOWs, vehicle/ship features, and their comprehensive features. Prerequisites for detecting fatigue in OOWs are summarised. Subsequently, various input features applicable and existing applications to the fatigue detection of OOWs are proposed, and their limitations are analysed. The results show that the reliability of the acquired feature data is insufficient for detecting fatigue in OOWs, as well as a non-negligible invasive effect on OOWs. Hence, low-invasive physiological information pertaining to the OOWs, behaviour videos, and multisource feature data of ship characteristics should be used as inputs in future studies to realise quantitative, accurate, and real-time fatigue detections in OOWs on actual ships

Multi-Scale Object Detection Model for Autonomous Ship Navigation in Maritime Environment

Accurate detection of sea-surface objects is vital for the safe navigation of autonomous ships. With the continuous development of artificial intelligence, electro-optical (EO) sensors such as video cameras are used to supplement marine radar to improve the detection of objects that produce weak radar signals and small sizes. In this study, we propose an enhanced convolutional neural network (CNN) named VarifocalNet * that improves object detection in harsh maritime environments. Specifically, the feature representation and learning ability of the VarifocalNet model are improved by using a deformable convolution module, redesigning the loss function, introducing a soft non-maximum suppression algorithm, and incorporating multi-scale prediction methods. These strategies improve the accuracy and reliability of our CNN-based detection results under complex sea conditions, such as in turbulent waves, sea fog, and water reflection. Experimental results under different maritime conditions show that our method significantly outperforms similar methods (such as SSD, YOLOv3, RetinaNet, Faster R-CNN, Cascade R-CNN) in terms of the detection accuracy and robustness for small objects. The maritime obstacle detection results were obtained under harsh imaging conditions to demonstrate the performance of our network model

Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermark

Large language models (LLMs) have demonstrated powerful capabilities in both text understanding and generation. Companies have begun to offer Embedding as a Service (EaaS) based on these LLMs, which can benefit various natural language processing (NLP) tasks for customers. However, previous studies have shown that EaaS is vulnerable to model extraction attacks, which can cause significant losses for the owners of LLMs, as training these models is extremely expensive. To protect the copyright of LLMs for EaaS, we propose an Embedding Watermark method called EmbMarker that implants backdoors on embeddings. Our method selects a group of moderate-frequency words from a general text corpus to form a trigger set, then selects a target embedding as the watermark, and inserts it into the embeddings of texts containing trigger words as the backdoor. The weight of insertion is proportional to the number of trigger words included in the text. This allows the watermark backdoor to be effectively transferred to EaaS-stealer's model for copyright verification while minimizing the adverse impact on the original embeddings' utility. Our extensive experiments on various datasets show that our method can effectively protect the copyright of EaaS models without compromising service quality.Comment: Accepted by ACL 202

Research on bearing capacity of cross-type truss boom with variable cross-section of Crawler cranes

The web crossed truss boom is one of the commonly used truss boom structures of crawler cranes. However, the existing calculations fail to consider the limiting effect of the web members' bending resistance on the chord members, and cannot give full play to the load-bearing capacity of the existing structure. This paper takes the top section of the Crawler crane truss boom as the research object. The single-span truss theoretical model is established according to Timoshenko's elastic stability theory. And the theoretical critical load of the variable cross-section boom is obtained with full consideration of the limitation of the web member's bending resistance on the chord members. The finite element method simulation model is compared and verified. Compared with a large number of simulation experiments and theoretical calculations, it can be concluded that the theoretical calculations in this article are highly consistent with the simulation results, verified the assumptions that the web members' bending resistance help to improve the bending resistance of the chord members, and this will provide certain reference to the engineering designers

Self-assembling supramolecular dendrimer nanosystem for PET imaging of tumors

Bioimaging plays an important role in cancer diagnosis and treatment. However, imaging sensitivity and specificity still constitute key challenges. Nanotechnology-based imaging is particularly promising for overcoming these limitations because nanosized imaging agents can specifically home in on tumors via the "enhanced permeation and retention" (EPR) effect, thus resulting in enhanced imaging sensitivity and specificity. Here, we report an original nanosystem for positron emission tomography (PET) imaging based on an amphiphilic dendrimer, which bears multiple PET reporting units at the terminals. This dendrimer is able to self-assemble into small and uniform nanomicelles, which accumulate in tumors for effective PET imaging. Benefiting from the combined dendrimeric multivalence and EPR-mediated passive tumor targeting, this nanosystem demonstrates superior imaging sensitivity and specificity, with up to 14-fold increased PET signal ratios compared with the clinical gold reference 2-fluorodeoxyglucose ([18F]FDG). Most importantly, this dendrimer system can detect imaging-refractory low-glucose-uptake tumors that are otherwise undetectable using [18F]FDG. In addition, it is endowed with an excellent safety profile and favorable pharmacokinetics for PET imaging. Consequently, this dendrimer nanosystem constitutes an effective and promising approach for cancer imaging. Our study also demonstrates that nanotechnology based on self-assembling dendrimers provides a fresh perspective for biomedical imaging and cancer diagnosis

Motion Prediction Based TDMA Protocol in VANETs

In Vehicular Ad Hoc Networks (VANETs), the high mobility of vehicle nodes makes the network topology change frequently, reducing the forwarding efficiency of MAC protocol. In the existing enhanced TDMA-based MAC protocol, the farthest node in the current transmission range is chosen as the forwarding node to accelerate the multi-hop transmission. However, we use probabilistic model to show that there potentially exist better forwarding nodes, which could effectively improve transmission efficiency. Therefore, we propose a motion-prediction based TDMA protocol, which predicts the network topology in the next frame to select the better forwarding node. The test results of highway and urban scenarios show that the motion-prediction based TDMA protocol effectively reduces the number of hops in multi-hop transmission and decreases the broadcast delay by 50% to cover the whole network

Predicting Risk of Insulin Resistance in a Chinese Population with Polycystic Ovary Syndrome: Designing and Testing a New Predictive Nomogram

Background. This research is aimed at establishing and internally validating the risk nomogram of insulin resistance (IR) in a Chinese population of patients with polycystic ovary syndrome (PCOS). Methods. We developed a predictive model based on a training dataset of 145 PCOS patients, and data were collected between March 2018 and May 2019. The least absolute shrinkage and selection operator regression model was used to optimize function selection for the insulin resistance risk model. Multivariable logistic regression analysis was used to construct a prediction model integrating the function selected in the regression model of the least absolute shrinkage and selection operator. The predicting model’s characteristics of prejudice, disease, and lifestyle were analyzed using the C-index, the calibration diagram, and the study of the decision curve. External validity was assessed using the validation of bootstrapping. Results. Predictors contained in the prediction nomogram included occupation, disease durations (years), BMI, current use of metformin, and activities. With a C-index of 0.739 (95 percent confidence interval: 0.644–0.830), the model showed good differentiation and proper calibration. In the interval validation, a high C-index value of 0.681 could still be achieved. Examination of the decision curve found that the IR nomogram was clinically useful when the intervention was determined at the 11 percent IR potential threshold. Conclusion. This novel IR nomogram incorporates occupation, disease durations (years), BMI, current use of metformin, and activities. This nomogram could be used to promote the estimation of individual IR risk in patients with PCOS

Numerical investigations on drag coefficient of circular cylinder with two free ends in roller bearings

International audienceIn high speed roller bearings, the drag force due to the oil-air mixture present in the bearing cavity – that is acting against the roller movement – is usually computed with a two-dimensional model of flow around a cylinder of infinite length. However rollers are of finite length, and the flow is perturbed by the two free ends, the surrounding rings, the cage and other rolling elements. In this article, the Computational Fluid Dynamics (CFD) method is employed to analyze first the flow around one finite-length circular cylinder with two free ends in an open space. Then the model is changed to one finite cylinder and then several in-line circular cylinders sandwiched by two flat walls, which represents a simplified approach. The results indicate that both the flow pattern around the cylinder and its drag coefficient are modified in comparison with the two-dimensional model. Finally a relationship between the drag coefficient and the Reynolds number suitable for circular cylinder in roller bearings is proposed