Automated lfare forecasting using a statistical learning technique

We present a new method for automatically forecasting the occurrence of solar flares based on photospheric magnetic measurements. The method is a cascading combination of an ordinal logistic regression model and a support vector machine classifier. The predictive variables are three photospheric magnetic parameters, i.e., the total unsigned magnetic flux, length of the strong-gradient magnetic polarity inversion line, and total magnetic energy dissipation. The output is true or false for the occurrence of a certain level of flares within 24 hours. Experimental results, from a sample of 230 active regions between 1996 and 2005, show the accuracies of a 24- hour flare forecast to be 0.86, 0.72, 0.65 and 0.84 respectively for the four different levels. Comparison shows an improvement in the accuracy of X-class flare forecasting.

Traffic System Reliability Comparison Between Digital Driving and Conventional Driving

Driver behavior modeling is becoming increasingly important in the study of traffic safety and devel- opment of cognitive vehicles. An algorithm for dealing with reliability for both digital driving and conventional driving has been developed in this paper. Problems of digital driving error classification, digital driving error probability quantification and digital driving reliability simulation have been addressed using a comparison re- search method. Simulation results show that driving reliability analysis discussed here is capable of identifying digital driving behavior characteristics and achieving safety assessment of intelligent transportation system.

Natural Scene Classification Inspired by Visual Perception and Cognition Mechanisms

The process of human natural scene categorization consists of two correlated stages: visual perception and visual cognition of natural scenes.Inspired by this fact,we propose a biologically plausible approach for natural scene image classification.This approach consists of one visual perception model and two visual cognition models.The visual perception model,composed of two steps,is used to extract discriminative features from natural scene images.In the first step,we mimic the oriented and bandpass properties of human primary visual cortex by a special complex wavelets transform,which can decompose a natural scene image into a series of 2D spatial structure signals.In the second step,a hybrid statistical feature extraction method is used to generate gist features from those 2D spatial structure signals.Then we design a cognitive feedback model to realize adaptive optimization for the visual perception model.At last,we build a multiple semantics based cognition model to imitate human cognitive mode in rapid natural scene categorization.Experiments on natural scene datasets show that the proposed method achieves high efficiency and accuracy for natural scene classification.

Deep Gradient Learning for Efficient Camouflaged Object Detection

This paper introduces deep gradient network(DGNet),a novel deep framework that exploits object gradient supervision for camouflaged object detection(COD).It decouples the task into two connected branches,i.e.,a context and a texture encoder.The es-sential connection is the gradient-induced transition,representing a soft grouping between context and texture features.Benefiting from the simple but efficient framework,DGNet outperforms existing state-of-the-art COD models by a large margin.Notably,our efficient version,DGNet-S,runs in real-time(80 fps)and achieves comparable results to the cutting-edge model JCSOD-CVPR21 with only 6.82%parameters.The application results also show that the proposed DGNet performs well in the polyp segmentation,defect detec-tion,and transparent object segmentation tasks.The code will be made available at https://github.com/GewelsJI/DGNet.

Impact of hypothermia on the biomechanical effect of epithelium-off corneal cross-linking

Background:The corneal cross-linking(CXL)photochemical reaction is essentially dependent on oxygen and hypothermia,which usually leads to higher dissolved oxygen levels in tissues,with potentially greater oxygen availability for treatment.Here,we evaluate whether a reduction of corneal temperature during CXL may increase oxygen availability and therefore enhance the CXL biomechanical stiffening effect in ex vivo porcine corneas.Methods:One hundred and twelve porcine corneas had their epithelium manually debrided before being soaked with 0.1%hypo-osmolaric riboflavin.These corneas were equally assigned to one of four groups.Groups 2 and 4 underwent accelerated epithelium-off CXL using 9 mW/cm^(2) irradiance for 10 min,performed either in a cold room temperature(group 2,4℃)or at standard room temperature(group 4,24℃).Groups 1 and 3 served as non-cross-linked,temperature-matched controls.Using a stress-strain extensometer,the elastic moduli of 5-mm wide corneal strips were analyzed as an indicator of corneal stiffness.Results:Accelerated epithelium-off CXL led to significant increases in the elastic modulus between 1%and 5%of strain when compared to non-cross-linked controls(P<0.05),both at 4℃(1.40±0.22 vs.1.23±0.18 N/mm)and 24 C(1.42±0.15 vs.1.19±0.11 N/mm).However,no significant difference was found between control groups(P=0.846)or between groups in which CXL was performed at low or standard room temperature(P=0.969).Conclusions:Although initial oxygen availability should be increased under hypothermic conditions,it does not appear to play a significant role in the biomechanical strengthening effect of accelerated epithelium-off CXL protocols in ex vivo porcine corneas.

Video Polyp Segmentation: A Deep Learning Perspective

We present the first comprehensive video polyp segmentation(VPS)study in the deep learning era.Over the years,developments in VPS are not moving forward with ease due to the lack of a large-scale dataset with fine-grained segmentation annotations.To address this issue,we first introduce a high-quality frame-by-frame annotated VPS dataset,named SUN-SEG,which contains 158690colonoscopy video frames from the well-known SUN-database.We provide additional annotation covering diverse types,i.e.,attribute,object mask,boundary,scribble,and polygon.Second,we design a simple but efficient baseline,named PNS+,which consists of a global encoder,a local encoder,and normalized self-attention(NS)blocks.The global and local encoders receive an anchor frame and multiple successive frames to extract long-term and short-term spatial-temporal representations,which are then progressively refined by two NS blocks.Extensive experiments show that PNS+achieves the best performance and real-time inference speed(170 fps),making it a promising solution for the VPS task.Third,we extensively evaluate 13 representative polyp/object segmentation models on our SUN-SEG dataset and provide attribute-based comparisons.Finally,we discuss several open issues and suggest possible research directions for the VPS community.Our project and dataset are publicly available at https://github.com/GewelsJI/VPS.

3D object retrieval based on histogram of local orientation using one-shot score support vector machine

In this paper, a content based descriptor is pro- posed to retrieve 3D models, which employs histogram of local orientation （HLO） as a geometric property of the shape. The proposed 3D model descriptor scheme consists of three steps. In the first step, Poisson equation is utilized to define a 3D model signature. Next, the local orientation is calculated for each voxel of the model using Hessian matrix. As the final step, a histogram-based 3D model descriptor is extracted by accumulating the values of the local orientation in bins. Due to efficiency of Poisson equation in describing the models with various structures, the proposed descriptor is capable of discriminating these models accurately. Since, the inner vox- els have a dominant contribution in the formation of the de- scriptor, sufficient robustness against noise can be achieved. This is because the noise mostly influences the boundary vox- els. Furthermore, we improve the retrieval performance us- ing support vector machine based one-shot score （SVM-OSS） similarity measure, which is more efficient than the conven- tional methods to compute the distance of feature vectors. The rotation normalization is performed employing the prin- cipal component analysis. To demonstrate the applicability of HLO, we implement experimental evaluations of precision- recall curve on ESB, PSB and WM-SHREC databases of 3D models. Experimental results validate the effectiveness of the proposed descriptor compared to some current methods.

SRNET:A Shallow Skip Connection Based Convolutional Neural Network Design for Resolving Singularities

Convolutional neural networks(CNNs)have shown tremendous progress and performance in recent years.Since emergence,CNNs have exhibited excellent performance in most of classification and segmentation tasks.Currently,the CNN family includes various architectures that dominate major vision-based recognition tasks.However,building a neural network(NN)by simply stacking convolution blocks inevitably limits its optimization ability and introduces overfitting and vanishing gradient problems.One of the key reasons for the aforementioned issues is network singularities,which have lately caused degenerating manifolds in the loss landscape.This situation leads to a slow learning process and lower performance.In this scenario,the skip connections turned out to be an essential unit of the CNN design to mitigate network singularities.The proposed idea of this research is to introduce skip connections in NN architecture to augment the information flow,mitigate singularities and improve performance.This research experimented with different levels of skip connections and proposed the placement strategy of these links for any CNN.To prove the proposed hypothesis,we designed an experimental CNN architecture,named as Shallow Wide ResNet or SRNet,as it uses wide residual network as a base network design.We have performed numerous experiments to assess the validity of the proposed idea.CIFAR-10 and CIFAR-100,two well-known datasets are used for training and testing CNNs.The final empirical results have shown a great many of promising outcomes in terms of performance,efficiency and reduction in network singularities issues.

Facial-sketch Synthesis:A New Challenge

This paper aims to conduct a comprehensive study on facial-sketch synthesis(FSS).However,due to the high cost of obtaining hand-drawn sketch datasets,there is a lack of a complete benchmark for assessing the development of FSS algorithms over the last decade.We first introduce a high-quality dataset for FSS,named FS2K,which consists of 2104 image-sketch pairs spanning three types of sketch styles,image backgrounds,lighting conditions,skin colors,and facial attributes.FS2K differs from previous FSS datasets in difficulty,diversity,and scalability and should thus facilitate the progress of FSS research.Second,we present the largest-scale FSS investigation by reviewing 89 classic methods,including 25 handcrafted feature-based facial-sketch synthesis approaches,29 general translation methods,and 35 image-to-sketch approaches.In addition,we elaborate comprehensive experiments on the existing 19 cutting-edge models.Third,we present a simple baseline for FSS,named FSGAN.With only two straightforward components,i.e.,facialaware masking and style-vector expansion,our FSGAN surpasses the performance of all previous state-of-the-art models on the proposed FS2K dataset by a large margin.Finally,we conclude with lessons learned over the past years and point out several unsolved challenges.Our code is available at https://github.com/DengPingFan/FSGAN.

Impact of hypothermia on the biomechanical effect of epithelium-off corneal cross-linking

Background:The corneal cross-linking(CXL)photochemical reaction is essentially dependent on oxygen and hypothermia,which usually leads to higher dissolved oxygen levels in tissues,with potentially greater oxygen availability for treatment.Here,we evaluate whether a reduction of corneal temperature during CXL may increase oxygen availability and therefore enhance the CXL biomechanical stiffening effect in ex vivo porcine corneas.Methods:One hundred and twelve porcine corneas had their epithelium manually debrided before being soaked with 0.1%hypo-osmolaric riboflavin.These corneas were equally assigned to one of four groups.Groups 2 and 4 underwent accelerated epithelium-off CXL using 9 mW/cm^(2) irradiance for 10 min,performed either in a cold room temperature(group 2,4℃)or at standard room temperature(group 4,24℃).Groups 1 and 3 served as non-crosslinked,temperature-matched controls.Using a stress-strain extensometer,the elastic moduli of 5-mm wide corneal strips were analyzed as an indicator of corneal stiffness.Results:Accelerated epithelium-off CXL led to significant increases in the elastic modulus between 1 and 5%of strain when compared to non-cross-linked controls(P<0.05),both at 4℃(1.40±0.22 vs 1.23±0.18 N/mm)and 24℃(1.42±0.15 vs 1.19±0.11 N/mm).However,no significant difference was found between control groups(P=0.846)or between groups in which CXL was performed at low or standard room temperature(P=0.969).Conclusions:Although initial oxygen availability should be increased under hypothermic conditions,it does not appear to play a significant role in the biomechanical strengthening effect of epithelium-off CXL accelerated protocols in ex vivo porcine corneas.