Miracle-dz/UniFAR-FTUs huggingface dataset:
A Recognition Algorithm Suitable for Complex Scenes and Various Types of License Plates

With the acceleration of intelligent transportation in the new era, license plate recognition technology has become more and more extensive. Although the existing license plate recognition technology can show good performance for specific scenes and traditional license plate recognition, it has poor recognition robustness and applicability in complex external environments such as arbitrary shooting angles, different lighting conditions, and different picture quality. To solve the above problems, this paper proposes an improved recognition algorithm suitable for complex scenes and various types of license plates. First, the SSD model is used for multi-target vehicle detection; the convolutional neural network (CNN) is further used for license plate location and correction; finally, the YOLOv2 model with modified output and reduced pooling layer is designed to realize the license plate character recognition. The algorithm in this paper is carried out on the surveillance image data of a road in a block of Beijing, which contains 3016 images with a total of 5315 vehicles to be detected. The experimental results show that the accuracy rate of the license plate recognition of the proposed method reaches 95.66%, and it has considerable detection performance for traditional license plates and new energy license plates. Compared with the classic algorithm HyperLPR, the recognition accuracy of the proposed algorithm in complex scenarios is improved by 13 %.
WFaceNets: A Series of Efficient Neural Networks for Face Validation Tasks

Although the algorithm based on deep convolutional neural network for face recognition has high accuracy, it needs huge computing resources. Besides, it is difficult to run offline on mobile devices. A series of network WFaceNets is proposed to maintain the accuracy of recognition and reduce the cost of computing resources. First of all, adjusted neural network structure achieved that a model with only about 1 million parameters hardly decreasing the feature learning ability to the aligned facial image. Secondly, for higher accuracy in face validation tasks, the Squeeze-and-Block structure is introduced at the bottleneck layer. SEBlock operates on the entire input feature, enabling the perception field of convolution network to cover the entire input feature, which is a beneficial semantic information supplement for the neural network. Finally, a new neural network structure is proposed, named WLayer, which is adaptive to calculate the importance of feature channels, so that the network can capture the features of key areas of the face. The experiment proved that based on 0.5 million face images as training samples, the accuracy was 99.35%, 99.28%, 99.25% and 99.12% in the LFW data set with 1.16m, 0.96m, 0.79m and 0.62m parameters, respectively.
AMINN: Attention-Based Multi-Information Neural Network for Emotion Recognition

Most of text emotion recognition methods are mainly based on the combination of simple word embedding and single network model, which cannot effectively capture the semantic emotional relationship in the sentence. To address this issue, we propose a neural network architecture called AMINN (Attention-Based Multi-Information Neural Network). We apply the attention mechanism to dynamically integrate the semantic emotional information to obtain key emotional information. The proposed method consists of pre-trained word embedding with fine-tuned training, two subnetworks and attention mechanism to capture the semantic emotional information in the text. The CNN sub-network captures the local semantic emotional information of the text, the BiLSTM subnetwork captures the contextual semantic emotional information of the text. The attention mechanism is used to fuse these two subnetworks. We conduct experiments on both Chinese(nlpcc2014) and English(SST) datasets to evaluate the effectiveness of our methods. Experimental results show that the proposed model is better than all baseline methods and is not limited by language. It pushes the recognition accuracy of single sentence positive/negative classification from 79% to 86%, and the f1 value reaches the highest value of 0.78. The recognition performance of fine-grained sentiment labels was also improved by 9.6%. These experimental results prove the superiority of our model.
STaRT-RWE: structured template for planning and reporting on the implementation of real world evidence studies

In alignment with the International Council of Harmonization’s strategic goals, a public-private consortium has developed a structured template for planning and reporting on the implementation of real world evidence (RWE) studies of the safety and effectiveness of treatments. The template serves as a guiding tool for designing and conducting reproducible RWE studies; set clear expectations for transparent communication of RWE methods; reduce misinterpretation of prose that lacks specificity; allow reviewers to quickly orient and find key information; and facilitate reproducibility, validity assessment, and evidence synthesis. The template is intended for use with studies of the effectiveness and safety of medical products and is compatible with multiple study designs, data sources, reporting guidelines, checklists, and bias assessment tools.
Acute Ischemic Stroke or Epileptic Seizure? Yield of CT Perfusion in a “Code Stroke” Situation

CTP patterns helped to differentiate acute ischemic stroke from epileptic seizure in a “code stroke“ situation. The results indicate that a hyperperfusion pattern, especially if not restricted to a vascular territory, may suggest reconsideration of intravenous thrombolytic therapy. BACKGROUND AND PURPOSE: The clinical differentiation between acute ischemic stroke and epileptic seizure may be challenging, and making the correct diagnosis could avoid unnecessary reperfusion therapy. We examined the accuracy of CTP in discriminating epileptic seizures from acute ischemic stroke without identified arterial occlusion. MATERIALS AND METHODS: We retrospectively identified consecutive patients in our emergency department who underwent CTP in the 4.5 hours following the development of an acute focal neurologic deficit who were discharged with a final diagnosis of acute ischemic stroke or epileptic seizure. RESULTS: Among 95 patients, the final diagnosis was epileptic seizure in 45 and acute ischemic stroke in 50. CTP findings were abnormal in 73% of the patients with epileptic seizure and 40% of those with acute ischemic stroke. Hyperperfusion was observed more frequently in the seizure group (36% versus 2% for acute ischemic stroke) with high specificity (98%) but low sensitivity (35%) for the diagnosis of epileptic seizure. Hypoperfusion was found in 38% of cases in each group and was not confined to a vascular territory in 24% of patients in the seizure group and 2% in the acute ischemic stroke group. The interobserver agreement was good (κ = 0.60) for hypo-, hyper-, and normoperfusion patterns and moderate (κ = 0.41) for the evaluation of vascular systematization. CONCLUSIONS: CTP patterns helped to differentiate acute ischemic stroke from epileptic seizure in a “code stroke” situation. Our results indicate that a hyperperfusion pattern, especially if not restricted to a vascular territory, may suggest reconsideration of intravenous thrombolysis therapy.
Understanding the performance of container execution environments

Many application server backends leverage container technologies to support workloads formed of short-lived, but potentially I/O-intensive, operations. The latency at which container-supported operations complete impacts both the users' experience and the throughput that the platform can achieve. This latency is a result of both the bootstrap and execution time of the containers and is impacted greatly by the performance of the I/O subsystem. Configuring appropriately the container environment and technology stack to obtain good performance is not an easy task, due to the variety of options, and poor visibility on their interactions. We present in this paper a benchmarking tool for the multi-parametric study of container bootstrap time and I/O performance, allowing us to understand such interactions within a controlled environment. We report the results obtained by evaluating a large number of environment configurations. Our conclusions highlight differences in support and performance between container runtime environments and I/O subsystems.
Plateau Forest Detection Based on Regional Correlation

Forest is one of the most important natural resources. Therefore, people are eager to acquire the information of forest resources quickly, accurately, and efficiently, and monitor the changes in time, to realize the scientific management and effective utilization of forest resources. The combination of remote sensing technology and image segmentation technology can effectively solve this problem. This paper proposed a convolutional neural network RC-Unet, which is based on Unet architecture. Based on the region correlation of forest, we firstly put forward an innovative theory, which use 8 pieces of surrounding images in Sudoku style to predict the center image. More information can be provided for forest detection in the middle area by simultaneously input this image and 8 surrounding images. Our experiments were performed on the Ziyuan-3 image data, in northeast of Qinghai province, which is belongs to the Sanjiangyuan region of China and is an important area for the concentrated protection of natural resources in China. The remote sensing database of plateau forest was established creatively. Comparing to decision tree and Unet, RC-Unet showed the best results in evaluation metrics included accuracy, recall and F1-score. Our finding has taken the advantage of specificity of forest, which promote the detection of forest areas in remote sensing.
An Automatic Glioma Segmentation System Based on A Separable Attention U-Net (SAUNet)

With the complicated structure of brains, glioma segmentation is a challenging task. To precisely segment gliomas, U-Net structure is adopted by most current methods. However, the computation complexity of U-Net based method is large. Therefore, a separable attention U-Net, which can reduce the computation complexity without decreasing the performance, is proposed in this paper. Firstly, data augmentation techniques are implemented to enlarge the database and thus avoid over-fitting; Moreover, the separable attention U-Net is constructed for glioma segmentation. The experimental results indicate that the dice similarity coefficient of the proposed separable attention U-Net can reach 0.879 with the parameter number of 4.29 M, which indicates that the proposed glioma segmentation method is of application significance. A single column document that allows authors to type their content into the pre-existing set of paragraph formatting styles applied to the sample placeholder text here. Throughout the document you will find further instructions on how to format your text.
Deep car detection by fusing grayscale image and weighted upsampled LiDAR depth

Recent advances have shown sensor-fusion’s vital role in accurate detection, especially for advanced driver assistance systems. We introduce a novel procedure for depth upsampling and sensor-fusion that together lead to an improved detection performance, compared to state-of-the-art results for detecting cars. Upsampling is generally based on combining data from an image to compensate for the low resolution of a LiDAR (Light Detector and Ranging). This paper, on the other hand, presents a framework to obtain dense depth map solely from a single LiDAR point cloud that makes it possible to use just one deep network for both LiDAR and image modalities. The produced full-depth map is added to the grayscale version of the image to produce a two-channel input for a deep neural network. The simple preprocessing structure is efficiently competent in filing cars’ shapes, which helps the fusion framework to outperforms the state-of-the-art on the KITTI object detection for the Car class.
GAN-Based Industrial Part Optical Image Generation Model with Controllable Constraints

The generation of industrial part optical image is particularly important for industrial vision inspection. However, the current digital measurement technology represented by visual measurement has many problems: a) the lighting debugging work requires a large amount of experience from visual engineers, b) the system development cycle is long and does not meet the flexibility requirements, c) the adaptability is poor, and it is difficult to adjust different parts quick response to differentiated testing. Therefore, this paper proposes an image generation model with controllable constraints added based on Generative Adversarial Networks, which can generate information such as illumination distribution and texture of the industrial part optical image. We use an image conversion model based on Generative Adversarial Networks, and add label vectors as controllable constraints to generate industrial part optical images with different texture and illumination distribution. In addition, a gradient penalty term is introduced in the loss function to stabilize model training. Experiments show that the gray distribution of the generated image fits the gray distribution of the real image better, and can reflect the real texture and brightness trend of the image. Compared with previous image acquisition and image rendering methods, our method can quickly obtain industrial part optical image under different conditions without tedious lighting debugging.
LRA-Net: local region attention network for 3D point cloud completion

Incomplete Point clouds obtained from one-side scanning always result in structural loss in 3D shape representations, thus many learning-based methods are proposed to restore complete point clouds from partial ones. However, most of them only utilize global features of inputs to generate outputs, which might lose details. In this paper, a new method that utilizes both global and local features is proposed. First, Local features are extracted from inputs and analyzed under the conditions interpreted by global features. Second, conditional local feature vectors are deeply fused with each other via graph convolution and self-attention. Third, deeply-fused features are decoded for generating coarse point clouds. Last, global features extracted from inputs and coarse outputs are combined to generate fine outputs with high-density. Our network is trained and tested on eight categories of objects in ModelNet. The results show that our network is able to overcome instability in local feature awareness, restore complete point clouds with more details and smoother shapes, and outperform most of those existing methods both intuitively and quantitatively. Our source codes will be available at: https://github.com/wuhang100/LRA-Net.
Almost indirect 8-bit convolution for QNNs

The implementations of the convolution operation in neural networks are usually based on convolution-to-GeMM (General Matrix Multiplication) transformation. However, this transformation requires a big intermediate buffer (called im2col or im2row), and its initialization is both memory and time-consuming. To overcome this problem, one may use the Indirect Convolution Algorithm. This algorithm replaces the im2row buffer with a much smaller buffer of pointers, called indirection buffer. However, it limits our flexibility in the choice of multiplication micro-kernel, making matrix multiplication slightly less efficient than in the classical GeMM algorithm. To overcome this problem, we propose the Almost Indirect Convolution Algorithm, which initializes small specifically ordered block of values, which is used in matrix multiplication, via indirection buffer, the same way GeMM Algorithms initializes one block from im2row buffer. Our approach allows us to combine computational efficiency and flexibility in shape of GeMM micro-kernels with a small memory footprint of the Indirect Convolution Algorithm. Experiments with convolutions of 8-bit matrices on ARM processors show that our convolution works 14-24% faster than Indirect for a small number of channels and 10-20% faster than classical GeMM-based. This proves that it is perfectly suitable for computing inference of 8-bit quantized networks on mobile devices.
LIDAR-based parking spot search algorithm

Autonomous driving considers issues related to a car driving in different real world situations. This work addresses a parking task and describes a new LIDAR-based parking spot search algorithm. The proposed approach was successfully validated in virtual experiments within the Gazebo simulator in a parking area with a perpendicular parking setup. HDBScan, OPTICS, and Gaussian Mixture clustering methods were compared for LIDAR data clustering in the parking spot search task, and the HDBScan clustering demonstrated best prediction and performance results.
Rescuing toleration

ABSTRACT Toleration has been recently attacked both on practical and on theoretical grounds. On practical grounds, confronting religious terrorism, many commentators have asked whether toleration can remain the general policy toward cultural and religious diversity. Theoretically, toleration has been questioned as to its analytical capacity in the realm of partisan politics. This paper aims at countering such criticisms, by means of a conceptual clarification especially focused on the notion of intolerance, intolerable and response to intolerance. The controversial cases arising in contemporary democracy are usually focused on the limits of toleration, hence on the intolerable, by stretching the interpretation of the self-defense and of the harm principle. The author argues that the stretching is often excessive and the resulting interpretations too contentious to provide solid grounds for the intolerable. Alternatively, issues of toleration can be examined from the point of view of tolerance/intolerance. This viewpoint can clarify issues at the descriptive level, sorting out who was tolerant and who was intolerant and what was intolerable, while disagreement may persist at the normative level, according to the favored justification of toleration.
Mapping and Validation of Stem Rust Resistance Loci in Spring Wheat Line CI 14275

Stem rust caused by Puccinia graminis f. sp. tritici (Pgt) remains a constraint to wheat production in East Africa. In this study, we characterized the genetics of stem rust resistance, identified QTLs, and described markers associated with stem rust resistance in the spring wheat line CI 14275. The 113 recombinant inbred lines, together with their parents, were evaluated at the seedling stage against Pgt races TTKSK, TRTTF, TPMKC, TTTTF, and RTQQC. Screening for resistance to Pgt races in the field was undertaken in Kenya, Ethiopia, and the United States in 2016, 2017, and 2018. One gene conferred seedling resistance to race TTTTF, likely Sr7a. Three QTL were identified that conferred field resistance. QTL QSr.cdl-2BS.2, that conferred resistance in Kenya and Ethiopia, was validated, and the marker Excalibur_c7963_1722 was shown to have potential to select for this QTL in marker-assisted selection. The QTL QSr.cdl-3B.2 is likely Sr12, and QSr.cdl-6A appears to be a new QTL. This is the first study to both detect and validate an adult plant stem rust resistance QTL on chromosome arm 2BS. The combination of field QTL QSr.cdl-2BS.2, QSr.cdl-3B.2, and QSr.cdl-6A has the potential to be used in wheat breeding to improve stem rust resistance of wheat varieties.
Research Advances on Therapeutic Approaches to Congenital Central Hypoventilation Syndrome (CCHS)

Congenital central hypoventilation syndrome (CCHS) is a genetic disorder of neurodevelopment, with an autosomal dominant transmission, caused by heterozygous mutations in the PHOX2B gene. CCHS is a rare disorder characterized by hypoventilation due to the failure of autonomic control of breathing. Until now no curative treatment has been found. PHOX2B is a transcription factor that plays a crucial role in the development (and maintenance) of the autonomic nervous system, and in particular the neuronal structures involved in respiratory reflexes. The underlying pathogenetic mechanism is still unclear, although studies in vivo and in CCHS patients indicate that some neuronal structures may be damaged. Moreover, in vitro experimental data suggest that transcriptional dysregulation and protein misfolding may be key pathogenic mechanisms. This review summarizes latest researches that improved the comprehension of the molecular pathogenetic mechanisms responsible for CCHS and discusses the search for therapeutic intervention in light of the current knowledge about PHOX2B function.
Dispersed Variable-Retention Harvesting Mitigates N Losses on Harvested Sites in Conjunction With Changes in Soil Microbial Community Structure

As an alternative to clear-cutting, variable-retention harvesting is now standard forest management practice on the coast of British Columbia and in temperate forests globally, due to the benefits associated with maintaining mature forest species and forest structural diversity. Although there is some evidence that variable-retention harvesting, particularly single-tree (dispersed) retention will mitigate the impacts of clear-cutting on soil microbial communities and nutrient cycling, findings have been inconsistent. We examined microbial community structure (phospholipid-fatty acid), and nutrient availability (PRSTM probes) in a large (aggregated) retention patch and over three harvesting treatments: dispersed retention, clear-cut and clear-cut edge 2 years after harvest. Unlike previous studies, we did not observe elevated nitrate in the harvested areas, instead ammonium was elevated. Availability of N and other nutrients were surprisingly similar between the dispersed-retention treatment and the retention patch. The microbial community, however, was different in the clear-cut and dispersed-retention treatments, mostly due to significantly lower abundance of fungi combined with an increase in bacteria, specifically Gram-negative bacteria. This was accompanied by lower δ13CPDB value of the Gram-negative PLFA's in these treatments, suggesting the decline in mycorrhizal fungal abundance may have allowed the dominant Gram-negative bacteria to access more of the recently photosynthesized C. This shift in the microbial community composition in the dispersed-retention treatment did not appear to have a major impact on microbial functioning and nutrient availability, indicating that this harvesting practice is more effective at maintaining generic microbial functions/processes. However, as Mn levels were twice as high in the retention patch compared to the harvested treatments, indicating the other “narrow” processes (i.e., those performed by a small number of specialized microorganisms), such as lignin degradation, catalyzed by Mn peroxidase, which concomitantly removes Mn from solution, may be more sensitive to harvesting regimes. The effect of harvesting on such narrow nutrient cycling processes requires further investigation.