Detection Performance Research Articles

ENHANCED TRANSFORMER-BASED DEEP KERNEL FUSED SELF ATTENTION MODEL FOR LUNG NODULE SEGMENTATION AND CLASSIFICATION

Lung cancer remains a leading cause of mortality globally, with outcomes heavily dependent on the timeliness and accuracy of diagnosis. Traditional medical imaging techniques, while foundational in detecting lung nodules, often falter in distinguishing malignant from benign lesions with high precision, largely due to their inability to contextualize the complex spatial relationships within the images. Precise segmentation and classification of lung nodules is crucial for the early detection of lung cancer. This paper presents a novel deep learning model that incorporates a transformer block to improve the performance of lung cancer detection and classification. From performance evaluation, it is evident that our proposed model has an average accuracy of 93%. 05%, which is superior to the existing D3DR _ MKCA model with a mean accuracy of 91.53%. These findings are especially important for the identification of Adenocarcinoma and Small Cell Carcinoma, as improvements in the precision and recall factors have been achieved in these cases.

Diagnostic Performance of Faecal Immunochemical Testing (FIT) in Patients with Lynch Syndrome Scheduled for Colonoscopic Surveillance.

Lynch syndrome (LS) carries a substantial lifetime risk of colorectal cancer which is currently mitigated by biennial colonoscopy surveillance. Paramount to the surveillance programme is the removal of adenomas before malignant transformation but there is an associated service burden and morbidity of repeated endoscopy. We investigated if faecal immunochemical testing (FIT) for faecal haemoglobin has the diagnostic performance to replace colonoscopy. In this retrospective cohort study, patients due to undergo planned surveillance for LS between November 2020 and April 2022 were sent two FIT kits prior to colonoscopy. Test diagnostic performance of colorectal cancer (CRC), advanced and non-advanced adenoma detection was calculated for single and double FIT strategies. A faecal-Hb of 10 µg Hb/g was considered positive. In total, 78 patients, with 45 (57.7%) female, median age 52 years (IQR 41-63), completed at least one FIT and colonoscopy. The median time from FIT to colonoscopy was 47 days. A single FIT was positive in 7/30 cases of adenoma (2/3 advanced, 5/27 non-advanced). A total of 64 (82.1% of FIT1T returners) completed a second FIT. Using the greatest of the two FITs (FIT2TMAX) 8/26 (2/3 advanced, 4/23 non-advanced), patients with adenomas were identified. There were no cases of CRC. The sensitivity for adenoma detection was 23.3% and 23.1%, respectively. In patients with LS awaiting colonoscopy, FIT has a low sensitivity for detecting adenomas and advanced adenomas. This is not improved by the addition of a second FIT test.

Noise robust damage detection of laminated composites using multichannel wavelet-enhanced deep learning model

This paper presents a noise-robust damage detection framework for composite structures via a commonly used vibration-based non-destructive testing (NDT) method. Recently, deep learning-based models have shown promising performance in the autonomous damage detection of laminated composites; however, the poor noise robustness of these models has plagued data-driven damage detection. Moreover, none of the existing studies on damage detection in laminated composites focus on noise-robust deep learning models with high generalization ability. Therefore, this study proposes a hybrid deep learning framework called a multi-channel convolutional autoencoder-support vector machine (MC-CAE-SVM) based on empirical mode decomposition (EMD) and correlation analysis for noise-robust damage detection. This framework aims to first decompose the vibrational signal from multiple health states into intrinsic mode functions (IMFs). Secondly, highly correlated IMFs were extracted using correlation analysis to remove noisy IMFs. Finally, these IMFs were transformed into a time-frequency representation using continuous wavelet transform (CWT) and input to the MC-CAE-SVM model for feature learning and damage detection. Additionally, the accuracy and sensitivity of the model to damage are enhanced by optimizing the MC-CAE-SVM model hyperparameters. Moreover, anti-noise analysis is performed to check the noise-robustness of the proposed model by incorporating noise at various levels. The results showed that the proposed model can provide better damage detection performance compared to conventional models with excellent noise robustness.

Scale Selection and Machine Learning-based Cell Segmentation and Tracking in Time Lapse Microscopy.

Monitoring and tracking of cell motion is a key component for understanding disease mechanisms and evaluating the effects of treatments. Time-lapse optical microscopy has been commonly employed for studying cell cycle phases. However, usual manual cell tracking is very time consuming and has poor reproducibility. Automated cell tracking techniques are challenged by variability of cell region intensity distributions and resolution limitations. In this work, we introduce a comprehensive cell segmentation and tracking methodology. A key contribution of this work is that it employs multi-scale space-time interest point detection and characterization for automatic scale selection and cell segmentation. Another contribution is the use of a neural network with class prototype balancing for detection of cell regions. This work also offers a structured mathematical framework that uses graphs for track generation and cell event detection. We evaluated cell segmentation, detection, and tracking performance of our method on time-lapse sequences of the Cell Tracking Challenge (CTC). We also compared our technique to top performing techniques from CTC. Performance evaluation results indicate that the proposed methodology is competitive with these techniques, and that it generalizes very well to diverse cell types and sizes, and multiple imaging techniques.

Staging Accuracy and Prognostic Value of Prostate-Specific Membrane Antigen PET/CT Strongly Depends on Lymph Node Tumor Burden.

Objectives: To explore the factors affecting the lymph node metastasis (LNM) detection performance of prostate-specific membrane antigen positron emission tomography/computed tomography (PSMA PET/CT) and to evaluate its prognostic value for biochemical recurrence after radical prostatectomy (RP). Methods: Patients who had intermediate- or high-risk prostate cancer and underwent robot-assisted (RA)RP between 2017 and 2021 were included. Initial lymph node staging was carried out using PSMA PET/CT. Sensitivity, specificity, and positive (PPV) and negative (NPV) predictive values were calculated. A cut-off value for LNM tumor deposit size that maximizes specificity was investigated and a post hoc specificity analysis was carried out. In survival analysis for biochemical progression-free survival (bPFS) after RP, Kaplan-Meier curves of molecular imaging (mi)N0 and miN1 patients were compared using the log-rank test and separate Cox regression models were developed to reveal the significance of PSMA PET/CT staging in pre- and post-surgery settings. Results: In 583 patients with a prevalence of pathology-proven LNM of 27.4%, overall sensitivity, specificity, PPV, and NPV of PSMA PET/CT per patient were 26.3% [95%CI 18.9-35.5], 93.9% [95%CI 84.9-100], 61.8% [95%CI 44.5-83.5], and 77.1% [95%CI 69.7-85.1], respectively. PSMA PET/CT showed a better sensitivity as LNM tumor deposit size increased (p = 0.003 OR 2.4 [95%CI 1.3-4.4]) and a better specificity in pT3-4 tumors (96.1%) versus pT2 (91.1%, p = 0.024 OR 2.7 [95%CI 1.1-6.3]). After adjustment according to 5.5 mm LNM tumor deposit size, which showed the best discriminative performance (AUC: 0.905 [95%CI 0.804-1.000, p < 0.001]), overall sensitivity tripled (90.2%, p < 0.001). The 1-year bPFS was 56.0% and 83.3% for miN1 and miN0 patients, respectively (p < 0.001). Whereas miN0pN1 was not, miN1pN1 disease was independently associated with decreased bPFS (HR:2.1 95%CI 1.3-3.4, p < 0.001). Conclusions: PSMA PET/CT has a lymph node tumor burden-dependent and cohort-driven diagnostic ability but consequently a strong independent prognostic value for predicting biochemical recurrence after RARP.

Research on Microscale Vehicle Logo Detection Based on Real-Time DEtection TRansformer (RT-DETR).

Vehicle logo detection (VLD) is a critical component of intelligent transportation systems (ITS), particularly for vehicle identification and management in dynamic traffic environments. However, traditional object detection methods are often constrained by image resolution, with vehicle logos in existing datasets typically measuring 32 × 32 pixels. In real-world scenarios, the actual pixel size of vehicle logos is significantly smaller, making it challenging to achieve precise recognition in complex environments. To address this issue, we propose a microscale vehicle logo dataset (VLD-Micro) that improves the detection of distant vehicle logos. Building upon the RT-DETR algorithm, we propose a lightweight vehicle logo detection algorithm for long-range vehicle logos. Our approach enhances both the backbone and the neck network. The backbone employs ResNet-34, combined with Squeeze-and-Excitation Networks (SENetV2) and Context Guided (CG) Blocks, to improve shallow feature extraction and global information capture. The neck network employs a Slim-Neck architecture, incorporating an ADown module to replace traditional downsampling convolutions. Experimental results on the VLD-Micro dataset show that, compared to the original model, our approach reduces the number of parameters by approximately 37.6%, increases the average accuracy (mAP@50:95) by 1.5%, and decreases FLOPS by 36.7%. Our lightweight network significantly improves real-time detection performance while maintaining high accuracy in vehicle logo detection.

Auxiliary Feature Fusion and Noise Suppression for HOI Detection

In recent years, one-stage HOI (Human–Object Interaction) detection methods tend to divide the original task into multiple sub-tasks by using a multi-branch network structure. However, there is no sufficient attention to information communication between these branches. The inference approach in the cascaded structure is singular, while fully parallel methods will disrupt the associations between different pieces of information. Besides, noise interference may occur during the fusion of different features and thus affect the detection performance. To address these issues, this article proposes a one-stage three-branch parallel HOI detection method, which treats HOI as three separate sub-tasks (human detection, object detection, and interaction detection) and leverages three distinct reasoning relationships to generate richer relational information. Firstly , an auxiliary feature fusion (AFF) module is introduced, which integrates features originally extracted independently to form fused features enriched with supplementary information. This approach strengthens communication between branches in the network while handling the three sub-tasks concurrently, thereby facilitating the exchange of more contextual information. Secondly , to mitigate noise interference generated during the fusion process, a fusion noise suppression (FNS) module is introduced, which effectively suppresses noise and enhances the model’s performance in interaction detection tasks. Finally , experiments are conducted on two major benchmark datasets, and experimental results show that our HOI detection method is superior to previous methods. Also, ablation studies confirm the effectiveness of all the components in our proposed method.

Theoretical Study of Gas Sensing toward Acetone by a Single-Atom Transition Metal (Sc, Ti, V, and Cr)-Doped InP3 Monolayer.

Acetone (C3H6O) gas in the exhaled breath of diabetic patients can be used as an important biomarker for the painless and noninvasive diagnosis of diabetes mellitus. In this paper, based on the density functional theory (DFT), the adsorption behaviors of pristine and single-atom transition metal (X = Sc, Ti, V, and Cr)-doped InP3 surfaces (denoted as X-InP3) toward C3H6O molecule were examined to explore the potential of these two-dimensional (2D) materials as a sensitive sensor for acetone gas. The calculation results indicate the unfavorable detection property for the pristine 2D-InP3 surface upon acetone with an unsatisfied gas response (12.4%). The introduction of a single-atom transition metal (Sc, Ti, V, and Cr) into the InP3 layer has significantly improved the adsorption capacity toward the C3H6O molecule. Owing to the high gas response values (-98.0%, 393.3%, and 393.3%), the Ti-InP3, V-InP3, and Cr-InP3 layers show their superiority in C3H6O detection at room temperature, in which Ti-InP3 achieves recycle use through heating at 698 K. Sc-InP3 is unsuitable for C3H6O sensing with a poor response (8.1%). Our work first gives a theoretical predication about the adsorption and sensitive detection performance of pristine and single-atom transition metal (Sc, Ti, V, and Cr)-doped InP3 upon acetone, which may provide an emerging kind of sensing material for the noninvasive diagnosis of diabetes mellitus indicated by acetone gas.

An improved YOLOv8n-IRP model for natural rubber tree tapping surface detection and tapping key point positioning.

Aiming at the problem that lightweight algorithm models are difficult to accurately detect and locate tapping surfaces and tapping key points in complex rubber forest environments, this paper proposes an improved YOLOv8n-IRP model based on the YOLOv8n-Pose. First, the receptive field attention mechanism is introduced into the backbone network to enhance the feature extraction ability of the tapping surface. Secondly, the AFPN structure is used to reduce the loss and degradation of the low-level and high-level feature information. Finally, this paper designs a dual-branch key point detection head to improve the screening ability of key point features in the tapping surface. In the detection performance comparison experiment, the YOLOv8n-IRP improves the D_mAP50 and P_mAP50 by 1.4% and 2.3%, respectively, over the original model while achieving an average detection success rate of 87% in the variable illumination test, which demonstrates enhanced robustness. In the positioning performance comparison experiment, the YOLOv8n-IRP achieves an overall better localization performance than YOLOv8n-Pose and YOLOv5n-Pose, realizing an average Euclidean distance error of less than 40 pixels. In summary, YOLOv8n-IRP shows excellent detection and positioning performance, which not only provides a new method for the key point localization of the rubber-tapping robot but also provides technical support for the unmanned rubber-tapping operation of the intelligent rubber-tapping robot.

A split organic photophotochemical transistor/vision sensing platform based on MNZ composite and ZIF-67/CuCoO nanospheres for ultra-sensitive detection of CEA

In this study, a novel organic photophotochemical transistor (OPECT) biosensing platform was proposed for dual-mode detection of CEA. The dual-mode detecting system is achieved benefit from the exceptional photoelectric performance of MIL-53(Fe) -NH2@ZnIn2S4 (MNZ) and the peroxidase enzyme (POD) activity of ZIF-67/Cu0.76Co2.24O4 (ZIF-67/CuCoO). Ab2- ZIF-67/CuCoO probe was immobilized on a 96-well plate by enzyme-linked immunosorbent assay, which accelerated the oxidation of 3,3,5,5-tetramethylbenzidine (TMB) by hydrogen peroxide and thus successfully realized visual detection of CEA. Additionally, in OPECT biosensing mode, the oxidized form of TMB (oxTMB·) serves as a consumptive agents of the electron donor AA, which cause the photocurrent change of MNZ heterojunction, leading to a decrease in the channel current of poly (ethylenedioxythiophene): poly (styrene sulfonate) (PEDOT: PSS) organic transistor. The integration of nanobiocatalysts and the OPECT system demonstrates excellent detection performance for CEA, with a detection limit as low as 3.24 fg mL−1 and expanded their prospective applications for clinical detection of nucleic acids, proteins, and other tumor markers.

A Feature-Driven Inception Dilated Network for Infrared Image Super-Resolution Reconstruction

Image super-resolution (SR) algorithms based on deep learning yield good visual performances on visible images. Due to the blurred edges and low contrast of infrared (IR) images, methods transferred directly from visible images to IR images have a poor performance and ignore the demands of downstream detection tasks. Therefore, an Inception Dilated Super-Resolution (IDSR) network with multiple branches is proposed. A dilated convolutional branch captures high-frequency information to reconstruct edge details, while a non-local operation branch captures long-range dependencies between any two positions to maintain the global structure. Furthermore, deformable convolution is utilized to fuse features extracted from different branches, enabling adaptation to targets of various shapes. To enhance the detection performance of low-resolution (LR) images, we crop the images into patches based on target labels before feeding them to the network. This allows the network to focus on learning the reconstruction of the target areas only, reducing the interference of background areas in the target areas’ reconstruction. Additionally, a feature-driven module is cascaded at the end of the IDSR network to guide the high-resolution (HR) image reconstruction with feature prior information from a detection backbone. This method has been tested on the FLIR Thermal Dataset and the M3FD Dataset and compared with five mainstream SR algorithms. The final results demonstrate that our method effectively maintains image texture details. More importantly, our method achieves 80.55% mAP, outperforming other methods on FLIR Dataset detection accuracy, and with 74.7% mAP outperforms other methods on M3FD Dataset detection accuracy.

Image tampering detection based on RDS-YOLOv5 feature enhancement transformation

Malicious image tampering has gradually become another way to threaten social stability and personal safety. Timely detection and precise positioning can help reduce the occurrence of risks and improve the overall safety of society. Due to the limitations of highly targeted dataset training and low-level feature extraction efficiency, the generalization and actual performance of the recent tampered detection technology have not yet reached expectations. In this study, we propose a tampered image detection method based on RDS-YOLOv5 feature enhancement transformation. Firstly, a multi-channel feature enhancement fusion algorithm is proposed to enhance the tampering traces in tampered images. Then, an improved deep learning model named RDS-YOLOv5 is proposed for the recognition of tampered images, and a nonlinear loss metric of aspect ratio was introduced into the original SIOU loss function to better optimize the training process of the model. Finally, RDS-YOLOv5 is trained by combining the features of the original image and the enhancement image to improve the robustness of the detection model. A total of 6187 images containing three forms of tampering: splice, remove, and copy-move were used to comprehensively evaluate the proposed algorithm. In ablation test, compared with the original YOLOv5 model, RDS-YOLOv5 achieved a performance improvement of 6.46%, 5.13%, and 3.15% on F1-Score, mAP50 and mAP95, respectively. In comparative experiments, using SRIOU as the loss function significantly improved the model’s ability to search for the real tampered regions by 2.54%. And the RDS-YOLOv5 model trained by the fusion dataset further improved the comprehensive detection performance by about 1%.

Evaluation of satellite-derived precipitation datasets: a case study in the Ten Tributaries region of the Yellow River Basin

ABSTRACT Satellite-derived precipitation datasets are essential components of hydrological simulations, particularly in data-scarce regions of western China. However, a comprehensive assessment of their accuracy and reliability is required. Here, the accuracy of two high-resolution satellite-derived precipitation datasets, Integrated Multi-satellite Retrievals for GPM – Final (IMERG-F) and Gauge-Adjusted Global Satellite Mapping of Precipitation (GSMaP-Gauge), was evaluated across the Ten Tributaries region of the Yellow River Basin in western China using four quantitative metrics and three categorical scoring indicators. This evaluation sought to ascertain the retrieval accuracy of these products on both the daily scale and hourly scale of heavy precipitation events, and investigated their inversion error characteristics across various spatiotemporal scales. Both datasets effectively captured the spatiotemporal patterns of annual average precipitation within the study area. Notably, the daily-scale accuracy of these satellite-derived precipitation products surpassed their hourly and half-hourly counterparts. Both GPM-IMERG and GSMaP-Gauge adeptly reproduced most precipitation events in the Ten Tributaries region, with peak detection performance observed in the central and southern zones, providing a reliable data source for drought monitoring and hydrological modeling. Overall, compared with GPM-IMERG, GSMaP-Gauge displayed superior inversion accuracy across diverse spatiotemporal scales.

Automated Recognition of Submerged Body-like Objects in Sonar Images Using Convolutional Neural Networks

The Police Robot for Inspection and Mapping of Underwater Evidence (PRIME) is an uncrewed surface vehicle (USV) currently being developed for underwater search and recovery teams to assist in crime scene investigation. The USV maps underwater scenes using sidescan sonar (SSS). Test exercises use a clothed mannequin lying on the seafloor as a target object to evaluate system performance. A robust, automated method for detecting human body-shaped objects is required to maximise operational functionality. The use of a convolutional neural network (CNN) for automatic target recognition (ATR) is proposed. SSS image data acquired from four different locations during previous missions were used to build a dataset consisting of two classes, i.e., a binary classification problem. The target object class consisted of 166 196 × 196 pixel image snippets of the underwater mannequin, whereas the non-target class consisted of 13,054 examples. Due to the large class imbalance in the dataset, CNN models were trained with six different imbalance ratios. Two different pre-trained models (ResNet-50 and Xception) were compared, and trained via transfer learning. This paper presents results from the CNNs and details the training methods used. Larger datasets are shown to improve CNN performance despite class imbalance, achieving average F1 scores of 97% in image classification. Average F1 scores for target vs background classification with unseen data are only 47% but the end result is enhanced by combining multiple weak classification results in an ensemble average. The combined output, represented as a georeferenced heatmap, accurately indicates the target object location with a high detection confidence and one false positive of low confidence. The CNN approach shows improved object detection performance when compared to the currently used ATR method.

Traffic flow parameter estimation for complex environments based on improved YOLOv8

Estimation of traffic flow parameters based on computer vision is still a very popular challenge. In particular, the detection problems such as occlusion, small targets, luminance variations, and lighting jitter, etc., are caused by dense traffic scenes and complex weather conditions. Previous studies about detecting and tracking methods focused on daytime and nighttime or single weather condition, but the performance of this model will decline dramatically due to complex conditions. In this paper, a framework for estimating traffic flow parameters in complex environments is presented, in which Ghost-YOLOv8 vehicle detection model is proposed. GhostConv is used to replace part of Conv and a new C2fGhost module is designed to replace part of C2f, reducing the number of parameters and improving the detection performance of the model; The global attention mechanism module is added to the neck network, which strengthens the semantic and location information in the features and improves the model’s ability of feature fusion; In view of the loss of semantic information caused by different scales in detecting small targets, a small target detection layer is added to enhance a combination of deep and shallow semantic information; The GIoU bounding loss function is used instead of the original loss function, which improves the performance of the network’s bounding box regression. Then, combining DeepSORT tracking algorithm and virtual line counting method, the proposed framework estimates traffic flow parameters, including volume, speed, and density in complex environments. The experimental results show that compared with the original model in the UA-DETRAC dataset, the improved YOLOv8 model’s the accuracy is increased by 1.4%, and the parameter number and model size are reduced by 0.229G and 0.2MB respectively. In addition, the framework has good robustness, reaching 97.56% accuracy when estimating average traffic flow parameters. It is sufficient to overcome complex weather conditions and effectively help control traffic for intelligent transportation systems and traffic management to provide good data support. In conclusion, it shows that the model can reduce the number and size of model parameters and improve the detection precision as well as meeting the requirements of edge computing devices and having better real-time performance, so it has practical application value.

Miniaturized optical system of a laser seeker based on a dynamic and static performance co-design

The laser semi-active seeker, crucial for miniaturizing guided munitions, offers high precision, anti-jamming, and rapid response. Current research focuses on system response, spot parameters, and angular velocity accuracy. With small-caliber munitions on the rise, further miniaturization and weight reduction are essential. This paper proposes a design for an optical system that balances dynamic imaging quality and miniaturized static structure. Using quadrant detector principles, factors affecting detection performance are analyzed to determine the optimal spot size and imaging range. Based on imaging requirements and the optical properties of a dual-reflector structure, we determine the design parameters of the system to achieve a combination of dynamic and static performance. Evaluations using ZEMAX and MATLAB confirm that the system achieves ±4∘ linear and ±8∘ full fields of view, 0.12% distortion, 4.52 mrad angular accuracy, a 15.72 mm axial length, and has the possibility of actual production. The system is compact, with excellent spot roundness, stability, and uniform energy distribution, providing a reference for future guided weapon research.

Electronic Health Record Phenotyping of Pediatric Suicide-Related Emergency Department Visits

Suicide is a leading cause of death among young people. Accurate detection of self-injurious thoughts and behaviors (SITB) underpins equity in youth suicide prevention. To compare methods of detecting SITB using structured electronic health information and measure algorithmic performance across demographics. This cross-sectional study used medical records among youths aged 6 to 17 years with at least 1 mental health-related emergency department (ED) visit in 2017 to 2019 to an academic health system in Southern California serving 787 000 unique individuals each year. Analyses were conducted between January and September 2023. Multiexpert electronic health record review ascertained the presence of SITB using the Columbia Classification Algorithm of Suicide Assessment. Random forest classifiers with nested cross-validation were developed using (1) International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes for nonfatal suicide attempt and self-harm and chief concern and (2) all available structured data, including diagnoses, medications, and laboratory tests. Detection performance was assessed overall and stratified by age group, sex, and race and ethnicity. The sample comprised 2702 unique youths with an MH-related ED visit (1384 youths who identified as female [51.2%]; 131 Asian [4.8%], 266 Black [9.8%], 719 Hispanic [26.6%], 1319 White [48.8%], and 233 other race [8.6%]; median [IQR] age, 14 [12-16] years), including 898 children and 1804 adolescents. Approximately half of visits were related to SITB (1286 visits [47.6%]). Sensitivity of SITB detection using only codes and chief concern varied by age group and increased until age 15 years (6-9 years: 59.3% [95% CI, 48.5%-69.5%]; 10-12 years: 69.0% [95% CI, 63.8%-73.9%]; 13-15 years: 88.4% [95% CI, 85.1%-91.2%]; 16-17 years: 83.1% [95% CI, 79.1%-86.6%]), while specificity remained constant. The area under the receiver operating characteristic curve (AUROC) was lower among preadolescents (0.841 [95% CI, 0.815-0.867]) and male (0.869 [95% CI, 0.848-0.890]), Black (0.859 [95% CI, 0.813-0.905]), and Hispanic (0.861 [95% CI, 0.831-0.891]) youths compared with adolescents (0.925 [95% CI, 0.912-0.938]), female youths (0.923 [95% CI, 0.909-0.937]), and youths of other races and ethnicities (eg, White: 0.901 [95% CI, 0.884-0.918]). Augmented classification (ie, using all available structured data) outperformed classification with codes and chief concern alone (AUROC, 0.975 [95% CI, 0.968-0.980] vs 0.894 [95% CI, 0.882-0.905]; P < .001). In this study, diagnostic codes and chief concern underestimated SITB prevalence, particularly among minoritized youths. These results suggest that priority on algorithmic fairness in suicide prevention strategies must extend to accurate detection of youths with suicide-related emergencies.

MSSA: Multi-Representation Semantics-Augmented Set Abstraction for 3D Object Detection

Accurate recognition and localization of 3D objects is a fundamental research problem in 3D computer vision. Benefiting from transformation-free point cloud processing and flexible receptive fields, point-based methods have become accurate in 3D point cloud modeling, but still fall behind voxel-based competitors in 3D detection. We observe that the set abstraction module, commonly utilized by point-based methods for downsampling points, tends to retain excessive irrelevant background information, thus hindering the effective learning of features for object detection tasks. To address this issue, we propose MSSA, a Multi-representation Semantics-augmented Set Abstraction for 3D object detection. Specifically, we first design a backbone network to encode different representation features of point clouds, which extracts point-wise features through PointNet to preserve fine-grained geometric structure features, and adopts VoxelNet to extract voxel features and BEV features to enhance the semantic features of key points. Second, to efficiently fuse different representation features of keypoints, we propose a Point feature-guided Voxel feature and BEV feature fusion (PVB-Fusion) module to adaptively fuse multi-representation features and remove noise. At last, a novel Multi-representation Semantic-guided Farthest Point Sampling (MS-FPS) algorithm is designed to help set abstraction modules progressively downsample point clouds, thereby improving instance recall and detection performance with more important foreground points. We evaluate MSSA on the widely used KITTI dataset and the more challenging nuScenes dataset. Experimental results show that compared to PointRCNN, our method improves the AP of “moderate” level for three classes of objects by 7.02%, 6.76%, and 5.44%, respectively. Compared to the advanced point-voxel-based method PV-RCNN, our method improves the AP of “moderate” level by 1.23%, 2.84%, and 0.55% for the three classes, respectively.

Gradient correlation based detection of adversarial attacks on vehicular networks

The controller area network (CAN) bus, which controls real-time communication and data transmission among vehicle electronic control units, lacks security mechanisms and is highly vulnerable to attacks. Existing in-vehicle network intrusion detection systems (IDSs) typically rely on deep learning models for detection, which are susceptible to interference from adversarial attacks owing to the vulnerability of the models themselves, thereby compromising the detection performance. In this study, we propose an adversarial attack detection method based on gradient correlation that achieves a high accuracy rate using a linear approach to detect adversarial samples. The experimental results show that the proposed model does not require retraining of the original detection model and demonstrates better detection performance for multiple adversarial attacks.

Unraveling Plasmonic Tilted Fiber Bragg Gratings (TFBG): A Journey From “Anomalous Resonances” to Refined Refractometry

AbstractPlasmonic tilted fiber Bragg gratings (TFBGs) have emerged as versatile tools for refractometric analyses and biochemical sensing. Their applications have significantly blossomed these last years, from proteins and cellular bioassays to operando monitoring in batteries, to cite just a few. They are widely recognized for their cutting‐edge performance and low limits of detection, arising from their dense multimodal spectral nature featuring tens of narrowband cladding mode resonances. Their comb‐like spectrum is so rich that numerous demodulation techniques have been reported, without benchmark of their relative performance while they possess important distinctions. This review highlights developments in detangling techniques from the pioneering works based on single‐peak analysis up to the most recent approaches involving Fourier analysis, the implementation of machine learning, and cascaded spectral decomposition processes. To fairly compare the different techniques of the literature, we implemented each analysis on original experimental refractometric calibrations, revealing the assets of the most updated methods. This paper therefore reviews these demodulation techniques based on the same datasets, obtained under the same conditions. We show and discuss the results obtained from bioassays and pinpoint the importance of advanced analytical methodologies to maximize the reproducibility, reliability and performance of plasmonic‐based TFBGs biosensors.