Explicit Visual Prompting for Universal Foreground Segmentations

Foreground segmentation is a fundamental problem in computer vision, which includes salient object detection, forgery detection, defocus blur detection, shadow detection, and camouflage object detection. Previous works have typically relied on domain-specific solutions to address accuracy and robustness issues in those applications. In this paper, we present a unified framework for a number of foreground segmentation tasks without any task-specific designs. We take inspiration from the widely-used pre-training and then prompt tuning protocols in NLP and propose a new visual prompting model, named Explicit Visual Prompting (EVP). Different from the previous visual prompting which is typically a dataset-level implicit embedding, our key insight is to enforce the tunable parameters focusing on the explicit visual content from each individual image, i.e., the features from frozen patch embeddings and high-frequency components. Our method freezes a pre-trained model and then learns task-specific knowledge using a few extra parameters. Despite introducing only a small number of tunable parameters, EVP achieves superior performance than full fine-tuning and other parameter-efficient fine-tuning methods. Experiments in fourteen datasets across five tasks show the proposed method outperforms other task-specific methods while being considerably simple. The proposed method demonstrates the scalability in different architectures, pre-trained weights, and tasks. The code is available at: https://github.com/NiFangBaAGe/Explicit-Visual-Prompt.Comment: arXiv admin note: substantial text overlap with arXiv:2303.1088

A highly sensitive silicon nanowire array sensor for joint detection of tumor markers CEA and AFP

Liver cancer is one of the malignant tumors with the highest fatality rate and increasing incidence, which has no effective treatment plan. Early diagnosis and early treatment of liver cancer play a vital role in prolonging the survival period of patients and improving the cure rate. Carcinoembryonic antigen (CEA) and alpha-fetoprotein (AFP) are two crucial tumor markers for liver cancer diagnosis. In this work, we firstly proposed a wafer-level, highly controlled silicon nanowire (SiNW) field-effect transistor (FET) joint detection sensor for highly sensitive and selective detection of CEA and AFP. The SiNWs-FET joint detection sensor possesses 4 sensing regions. Each sensing region consists of 120 SiNWs arranged in a 15 × 8 array. The SiNW sensor was developed by using a wafer-level and highly controllable top-down manufacturing technology to achieve the repeatability and controllability of device preparation. To identify and detect CEA/AFP, we modified the corresponding CEA antibodies/AFP antibodies to the sensing region surface after a series of surface modification processes, including O2 plasma treatment, soaking in 3-aminopropyltriethoxysilane (APTES) solution, and soaking in glutaraldehyde (GA) solution. The experimental results showed that the SiNW array sensor has superior sensitivity with a real-time ultralow detection limit of 0.1 fg ml−1 (AFP in 0.1× PBS) and 1 fg ml−1 (CEA in 0.1× PBS). Also, the logarithms of the concentration of CEA (from 1 fg ml−1 to 10 pg ml−1) and AFP (from 0.1 fg ml−1 to 100 pg ml−1) achieved conspicuously linear relationships with normalized current changes. The R2 of AFP in 0.1× PBS and R2 of CEA in 0.1× PBS were 0.99885 and 0.99677, respectively. Furthermore, the sensor could distinguish CEA/AFP from interferents at high concentrations. Importantly, even in serum samples, our sensor could successfully detect CEA/AFP. This demonstrates the promising clinical development of our sensor

A Protective Role by Interleukin-17F in Colon Tumorigenesis

Interleukin-17F (IL-17F), produced by Th17 cells and other immune cells, is a member of IL-17 cytokine family with highest homology to IL-17A. IL-17F has been shown to have multiple functions in inflammatory responses. While IL-17A plays important roles in cancer development, the function of IL-17F in tumorigenesis has not yet been elucidated. In the current study, we found that IL-17F is expressed in normal human colonic epithelial cells, but this expression is greatly decreased in colon cancer tissues. To examine the roles of IL-17F in colon cancer, we have used IL-17F over-expressing colon cancer cell lines and IL-17F-deficient mice. Our data showed decreased tumor growth of IL-17F-transfected HCT116 cells comparing to mock transfectants when transplanted in nude mice. Conversely, there were increased colonic tumor numbers and tumor areas in Il-17f−/− mice than those from wild-type controls after colon cancer induction. These results indicate that IL-17F plays an inhibitory role in colon tumorigenesis in vivo. In IL-17F over-expressing tumors, there was no significant change in leukocyte infiltration; instead, we found decreased VEGF levels and CD31+ cells. While the VEGF levels were increased in the colon tissues of Il-17f−/− mice with colon cancer. Together, our findings demonstrate a protective role for IL-17F in colon cancer development, possibly via inhibiting tumor angiogenesis

Fine-grained breast cancer classification with bilinear convolutional neural networks (BCNNS)

Classification of histopathological images of cancer is challenging even for well- trained professionals, due to the fine-grained variability of the disease. Deep Convolutional Neural Networks (CNNs) showed great potential for classification of a number of the highly variable fine-grained objects. In this study, we introduce a Bilinear Convolutional Neural Networks (BCNNs) based deep learning method for fine-grained classification of breast cancer histopathological images. We evaluated our model by comparison with several deep learning algorithms for fine-grained classification. We used bilinear pooling to aggregate a large number of orderless features without taking into consideration the disease location. The experimental results on BreaKHis, a publicly available breast cancer dataset, showed that our method is highly accurate with 99.24% and 95.95% accuracy in binary and in fine- grained classification, respectively

The use of 3-(2-pyridyl)-5, 6-diphenyl-1, 2, 4-triazine as precolumn derivatizing reagent in HPLC determination for Fe(II) in natural samples

The 3-(2-pyridyl)-5,6-diphenyl-1,2,4-triazine (PDT) was used for the first time as a precolumn derivatizing reagent applied in the high-performance liquid chromatography (HPLC) method with UV-detection for the Fe (II) determination. The Fe (II) reacts with PDT to form a magenta colored chelate in the presence of sodium dodecyl sulfate (SDS) and acetic acid-sodium acetate buffer solution medium of pH 4.65. The selection of maximum absorbance detect wavelength and the optimum composition of the organic modifier in the mobile phase were investigated in detail for the quantitative determination of Fe (II) using HPLC system. The formed Fe(II)-PDT chelate was satisfactorily separated from PDT on a Agilent Shim-pack ODS column (Eclipse XDB-C8,4.6x150mm) by isocratic elution with acetonitrile and 0.02 mol L-1 acetic acid-sodium acetate buffer solution (pH 4.65, containing 0.02% of SDS and 60x10-3 mol L-1 NaClO4) as mobile phase at a flow rate of 1 mL min-1, and monitored with a multiple wavelength detector. The detection limit (S/N =3) is 0.35 ng ml-1. Due to the excellent separation ability of HPLC, the innovative introduction of PDT as the precolumn derivatizing reagent, and the proper selection of the detect wavelength, the sensitivity of our newly developed HPLC method was enhanced remarkably compared to the common spectrophotometric methods. The developed HPLC method was successfully applied to the determination of Fe(II) in lake water samples

A Robust Operation Method with Advanced Adiabatic Compressed Air Energy Storage for Integrated Energy System under Failure Conditions

Integrated energy system (IES) is an important direction for the future development of the energy industry, and the stable operation of the IES can ensure heat and power supply. This study established an integrated system composed of an IES and advanced adiabatic compressed air energy storage (AA-CAES) to guarantee the robust operation of the IES under failure conditions. Firstly, a robust operation method using the AA-CAES is formulated to ensure the stable operation of the IES. The method splits the energy release process of the AA-CAES into two parts: a heat-ensuring part and a power-ensuring part. The heat-ensuring part uses the high-temp tank to maintain the balance of the heat subnet of the IES, and the power-ensuring part uses the air turbine of the first stage to maintain the balance of the power subnet. Moreover, another operation method using a spare gas boiler is formulated to compare the income of the IES with two different methods under failure conditions. The results showed that the AA-CAES could guarantee the balance of heat subnet and power subnet under steady conditions, and the dynamic operation income of the IES with the AA-CAES method was a bit higher than the income of the IES with the spare gas boiler method

A Robust Operation Method with Advanced Adiabatic Compressed Air Energy Storage for Integrated Energy System under Failure Conditions

Integrated energy system (IES) is an important direction for the future development of the energy industry, and the stable operation of the IES can ensure heat and power supply. This study established an integrated system composed of an IES and advanced adiabatic compressed air energy storage (AA-CAES) to guarantee the robust operation of the IES under failure conditions. Firstly, a robust operation method using the AA-CAES is formulated to ensure the stable operation of the IES. The method splits the energy release process of the AA-CAES into two parts: a heat-ensuring part and a power-ensuring part. The heat-ensuring part uses the high-temp tank to maintain the balance of the heat subnet of the IES, and the power-ensuring part uses the air turbine of the first stage to maintain the balance of the power subnet. Moreover, another operation method using a spare gas boiler is formulated to compare the income of the IES with two different methods under failure conditions. The results showed that the AA-CAES could guarantee the balance of heat subnet and power subnet under steady conditions, and the dynamic operation income of the IES with the AA-CAES method was a bit higher than the income of the IES with the spare gas boiler method

Convolutional Two-Stream Network Using Multi-Facial Feature Fusion for Driver Fatigue Detection

Road traffic accidents caused by fatigue driving are common causes of human casualties. In this paper, we present a driver fatigue detection algorithm using two-stream network models with multi-facial features. The algorithm consists of four parts: (1) Positioning mouth and eye with multi-task cascaded convolutional neural networks (MTCNNs). (2) Extracting the static features from a partial facial image. (3) Extracting the dynamic features from a partial facial optical flow. (4) Combining both static and dynamic features using a two-stream neural network to make the classification. The main contribution of this paper is the combination of a two-stream network and multi-facial features for driver fatigue detection. Two-stream networks can combine static and dynamic image information, while partial facial images as network inputs can focus on fatigue-related information, which brings better performance. Moreover, we applied gamma correction to enhance image contrast, which can help our method achieve better results, noted by an increased accuracy of 2% in night environments. Finally, an accuracy of 97.06% was achieved on the National Tsing Hua University Driver Drowsiness Detection (NTHU-DDD) dataset