SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection

Vision-based vehicle detection approaches achieve incredible success in recent years with the development of deep convolutional neural network (CNN). However, existing CNN based algorithms suffer from the problem that the convolutional features are scale-sensitive in object detection task but it is common that traffic images and videos contain vehicles with a large variance of scales. In this paper, we delve into the source of scale sensitivity, and reveal two key issues: 1) existing RoI pooling destroys the structure of small scale objects, 2) the large intra-class distance for a large variance of scales exceeds the representation capability of a single network. Based on these findings, we present a scale-insensitive convolutional neural network (SINet) for fast detecting vehicles with a large variance of scales. First, we present a context-aware RoI pooling to maintain the contextual information and original structure of small scale objects. Second, we present a multi-branch decision network to minimize the intra-class distance of features. These lightweight techniques bring zero extra time complexity but prominent detection accuracy improvement. The proposed techniques can be equipped with any deep network architectures and keep them trained end-to-end. Our SINet achieves state-of-the-art performance in terms of accuracy and speed (up to 37 FPS) on the KITTI benchmark and a new highway dataset, which contains a large variance of scales and extremely small objects.Comment: Accepted by IEEE Transactions on Intelligent Transportation Systems (T-ITS

Rethinking annotation granularity for overcoming deep shortcut learning: A retrospective study on chest radiographs

Deep learning has demonstrated radiograph screening performances that are comparable or superior to radiologists. However, recent studies show that deep models for thoracic disease classification usually show degraded performance when applied to external data. Such phenomena can be categorized into shortcut learning, where the deep models learn unintended decision rules that can fit the identically distributed training and test set but fail to generalize to other distributions. A natural way to alleviate this defect is explicitly indicating the lesions and focusing the model on learning the intended features. In this paper, we conduct extensive retrospective experiments to compare a popular thoracic disease classification model, CheXNet, and a thoracic lesion detection model, CheXDet. We first showed that the two models achieved similar image-level classification performance on the internal test set with no significant differences under many scenarios. Meanwhile, we found incorporating external training data even led to performance degradation for CheXNet. Then, we compared the models' internal performance on the lesion localization task and showed that CheXDet achieved significantly better performance than CheXNet even when given 80% less training data. By further visualizing the models' decision-making regions, we revealed that CheXNet learned patterns other than the target lesions, demonstrating its shortcut learning defect. Moreover, CheXDet achieved significantly better external performance than CheXNet on both the image-level classification task and the lesion localization task. Our findings suggest improving annotation granularity for training deep learning systems as a promising way to elevate future deep learning-based diagnosis systems for clinical usage.Comment: 22 pages of main text, 18 pages of supplementary table

A cuproptosis-related lncRNA signature-based prognostic model featuring on metastasis and drug selection strategy for patients with lung adenocarcinoma

Introduction: Lung adenocarcinoma is a common cause of mortality in patients with cancer. Recent studies have indicated that copper-related cell death may not occur in the same way as previously described. Long non-coding RNAs (lncRNAs) play a key role in the occurrence and development of tumors; however, the relationship between cuproptosis and lncRNAs in tumorigenesis and lung adenocarcinoma (LUAD) treatment has not been well established. Our study aimed to construct a model to analyze the prognosis of lung adenocarcinoma in patients using a carcinogenesis-related lncRNA (CR) signature.Methods: The transcriptional profiles of 507 samples from The Cancer Genome Atlas were assessed. Cox regression and co-expression analyses, and the least absolute shrinkage and selection operator (LASSO) were used to filter the CR and develop the model. The expression status of the six prognostic CRs was used to classify all samples into high- and low-risk groups. The overall disease-free survival rate was compared between the two groups. The Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes were used to identify the pathways and mechanisms involved in this model. Subsequently, immunotherapy response, sensitivity, and correlation analyses for several anti-tumor medications were performed. In vitro experiments, including qPCR, were conducted in nine lung adenocarcinoma cell lines and 16 pairs of lung adenocarcinoma and para-carcinoma tissues.Results: After confirmation using the ROC curve, patients in the low-risk category benefited from both overall and disease-free survival. Gene Ontology analysis highlighted cell movement in the model. In the in vitro experiments, qPCR results showed the expression levels of six CRs in 16 pairs of carcinoma and para-carcinoma tissues, which were in accordance with the results of the model. AL138778.1 is a protective factor that can weaken the invasion and migration of A549 cells, and AL360270.1 is a hazardous factor that promotes the invasion and migration of A549 cells. According to this model, targeted treatments such as axitinib, gefitinib, linsitinib, pazopanib, and sorafenib may be more appropriate for low-risk patients.Conclusion: Six CR profiles (AL360270.1, AL138778.1, CDKN2A-DT, AP003778.1, LINC02718, and AC034102.8) with predictive values may be used to evaluate the prognosis of patients with lung adenocarcinoma undergoing therapy

Synergy of slippery surface and pulse flow: An anti-scaling solution for direct contact membrane distillation

Recent progress on mitigating scaling on hydrophobic membrane distillation (MD) membrane focuses on the design of superhydrophobic/omniphobic surface and process optimization. However, the rationale for scaling resistance is not yet complete. We attempted in this work to unravel the correlation of scaling resistance based on the synergy of slippery surface (via chem-physical engineering) and pulse flow (process engineering). Superhydrophobic micro-pillared polyvinylidene fluoride (MP-PVDF) and CF4 plasma modified MP-PVDF (CF4-MP-PVDF) were utilized as the model membranes. We proposed rheometry as a simple quantitative measure for the wetting state in a hydrodynamic environment. Results showed that MP-PVDF possessed pinned wetting and prone to scaling (2000 mg/L CaSO4 solution) in both steady and pulse flow. In contrast, the CF4-MP-PVDF showed suspended wetting and excellent scaling resistance (at water recovery of 60%, the CF4-MP-PVDF surface was still clean without any crystals) under pulse flow, but not at steady flow. At steady flow, feed over-pressure changes the suspended wetting to pinned wetting by pushing the water-gas interface into the pillars, thereby resulting in scaling for CF4-MP-PVDF. At pulse flow, rhythmic fluctuation in the water-gas interface for CF4-MP-PVDF led to sustained scaling resistance. For the first time, we experimentally demonstrated a scaling resistance in DCMD via engineering surface wetting state and process. We envision that this rationale would pave the forward-looking strategy for a robust stable MD process in the near future

Slippery for scaling resistance in membrane distillation: a novel porous micropillared superhydrophobic surface

Scaling in membrane distillation (MD) is a key issue in desalination of concentrated saline water, where the interface property between the membrane and the feed become critical. In this paper, a slippery mechanism was explored as an innovative concept to understand the scaling behavior in membrane distillation for a soluble salt, NaCl. The investigation was based on a novel design of a superhydrophobic polyvinylidene fluoride (PVDF) membrane with micro-pillar arrays (MP-PVDF) using a micromolding phase separation (μPS) method. The membrane showed a contact angle of 166.0 ± 2.3° and the sliding angle of 15.8 ± 3.3°. After CF4 plasma treatment, the resultant membrane (CF4-MP-PVDF) showed a reduced sliding angle of 3.0o. In direct contact membrane distillation (DCMD), the CF4-MP-PVDF membrane illustrated excellent anti-scaling in concentrating saturated NaCl feed. Characterization of the used membranes showed that aggregation of NaCl crystals occurred on the control PVDF and MP-PVDF membranes, but not on the CF4-MP-PVDF membrane. To understand this phenomenon, a “slippery” theory was introduced and correlated the sliding angle to the slippery surface of CF4-MP-PVDF and its anti-scaling property. This work proposed a well-defined physical and theoretical platform for investigating scaling problems in membrane distillation and beyond

Unprecedented scaling/fouling resistance of omniphobic polyvinylidene fluoride membrane with silica nanoparticle coated micropillars in direct contact membrane distillation

Recent development of omniphobic membranes shows promise in scaling/fouling mitigation in membrane distillation (MD), however, the fundamental understanding is still under dispute. In this paper, we report a novel omniphobic micropillared membrane coated by silica nanoparticles (SiNPs) (SiNPs-MP-PVDF) with dual-scale roughness prepared by a micromolding phase separation (μPS) and electrostatic attraction. This membrane was used as a model for analysis of scaling behavior by calcium sulfate (CaSO4) scaling and fouling behavior by protein casein in comparison with commercial (C-PVDF) and micropillared (MP-PVDF) membranes. Unprecedented scaling/fouling resistance to CaSO4 and casein was observed in direct contact membrane distillation (DCMD) for SiNPs-MP-PVDF membrane. Similar scaling and fouling occurred for commercial PVDF and micropillared PVDF membranes. The observation corresponds well to the wetting state of all membranes as SiNPs-MP-PVDF shows suspended wetting, but MP-PVDF shows pinned wetting. From a hydrodynamic view, the suspended wetting attributes a slippery surface which reduces the direct contact of foulants to solid membrane part and leads to significantly reduced fouling and scaling. However, a pinned (or metastable) wetting state leads to a stagnant interfacial layer that is prone to severe fouling and scaling. This work highlights that both scaling and fouling resistance are indeed of suspended wetting state and slippage origin