Miracle-dz/UniFAR-FTUs huggingface dataset:
OPPORTUNITIES AND CHALLENGES OF SECONDARY VOCATIONAL MAKER EDUCATION UNDER THE BACKGROUND OF NEW GENERATION INFORMATION TECHNOLOGY

Abstract. Maker education integrates innovative spirit with education and teaching. Vocational education shoulders the responsibility of training talents for all kinds of occupations. This study aims to explore the feasibility and necessity of developing maker education in secondary vocational schools. By using literature research method, this paper searched and visualized the literature with “maker education” as the key word in CNKI database, and analyzed the development status of maker education in China. The results show that there are relatively few research results on the application and practice of maker education mode, especially in the field of secondary vocational education. On the basis of previous studies, combined with the current situation of secondary vocational, this paper analyzes that secondary vocational maker education has many opportunities in policy and technology, but also faces challenges such as the concept needs to be improved, the curriculum system is not perfect, the teachers are weak, and maker space needs to be improved. Finally, this study confirms that the deep integration of maker education and secondary vocational education is an inevitable trend, and provides insights to promote the development of secondary vocational maker education.
Diagnosis of fasciolosis in cattle slaughtered at the slaughterhouse of the urban commune of Dédougou in Burkina Faso

Fasciolosis is a major zoonosis of ruminants which causes great economic losses to breeders. Given the economic and public health importance of this pathology, it is necessary to establish the current situation in a perspective of animal and ruminant meat consumer health protection. Therefore, the present study was conducted at the slaughterhouse of Dédougou in order to identify the species of fluke that caused this pathology in the Boucle du Mouhoun region and to estimate the prevalence, the risk factors and the economic losses associated with this pathology. For this purpose, 820 cattle and 6360 small ruminants were diagnosed through post-mortem examination over a five-month period from March to August 2018. Out of the 6360 small ruminant livers examined, no cases of infestation were observed unlike cattle in which the prevalence of infestation was 2.07%. These infestations were all due to Fasciola gigantica . Regarding the risk factors, a slight effect of the month and a high influence of animals' origin were observed. March, with a prevalence of 0.25%, was the most favorable infestation month ( P -value=0.05) and Sourou (19.51%), was the highest infestation risk area ( P -value<0.0001). The estimated financial loss associated with the infestations was XOF 22,200.29 per infested animal. The situation is particularly worrying insofar as some populations, especially rural populations, consume meat from uncontrolled slaughter areas. Appropriate precautions should therefore be taken to ensure both consumer and animal health, given the zoonotic nature of the disease.
Posttranscriptional Regulation by Copper with a New Upstream Open Reading Frame

Copper is a transition metal necessary for living beings but also extremely toxic. Bacteria thus tightly control its homeostasis with transcriptional regulators. ABSTRACT Copper is essential to most living beings but also highly toxic and as such is an important player at the host-pathogen interface. Bacteria have thus developed homeostatic mechanisms to tightly control its intracellular concentration. Known Cu export and import systems are under transcriptional control, whereas posttranscriptional regulatory mechanisms are yet to be characterized. We identified a three-gene operon, bp2923-bfrG-bp2921, downregulated by copper and notably encoding a TonB-dependent transporter in Bordetella pertussis. We show here that the protein encoded by the first gene, which is a member of the DUF2946 protein family, represents a new type of upstream Open Reading Frame (uORF) involved in posttranscriptional regulation of the downstream genes. In the absence of copper, the entire operon is transcribed and translated. Perception of copper by the nascent bp2923-coded protein via its conserved CXXC motif triggers Rho-dependent transcription termination between the first and second genes by relieving translation arrest on a conserved C-terminal RAPP motif. Homologs of bp2923 are widespread in bacterial genomes, where they head operons predicted to participate in copper homeostasis. This work has thus unveiled a new mode of genetic regulation by a transition metal and identified a regulatory function for a member of an uncharacterized family of bacterial proteins that we have named CruR, for copper-responsive upstream regulator. IMPORTANCE Copper is a transition metal necessary for living beings but also extremely toxic. Bacteria thus tightly control its homeostasis with transcriptional regulators. In this work, we have identified in the whooping cough agent Bordetella pertussis a new control mechanism mediated by a small protein called CruR, for copper-responsive upstream regulator. While being translated by the ribosome CruR is able to perceive intracellular copper, which shuts down the transcription of downstream genes of the same operon, coding for a copper uptake system. This mechanism limits the import of copper in conditions where it is abundant for the bacterium. This is the first report of “posttranscriptional regulation” in response to copper. Homologs of CruR genes head many operons harboring copper-related genes in various bacteria, and therefore the regulatory function unveiled here is likely a general property of this new protein family.
A framework for automatic architectural synthesis in conceptual design phase

ABSTRACT Architectural synthesis is a key phase of the conceptual design process. In the constant evolution of complex systems, architects have found it difficult to carry out an exhaustive search of all the feasible architectural solutions for a set of given requirements. This paper proposes a computational synthesis framework that automatically generates, from a set of formatted requirements and a sub-systems database, all architectures that are requirement consistent, minimalist regarding the number of components and non-redundant. The resolution principles are based on graph theory representations and Constraint Programming solving techniques. Key features of the framework and an application example are also detailed. Abbreviations: AO: architectural object; BPMN: business process model and notation; CSP: constraint satisfaction problem; DSM: design structure matrix; SE: systems engineering.
Spatial-Temporal Attention Networks for Vehicle Trajectory Prediction

Predicting the future trajectory of vehicles is essential to the safety of autonomous driving. However, due to the uncertainty of the future behavior of vehicles and the complexity of interactions between vehicles, reasonable and accurate trajectory prediction is still one of the huge challenges faced by autonomous driving. In this paper, we present a Spatial-Temporal Attention Networks (STAN) for the prediction of the future trajectory of vehicles. STAN uses Transformer network to extract the historical trajectory features of vehicles, uses Graph Attention Network (GAT) to extract the spatial interactions features between vehicles, and captures the temporal correlations of interactions through Transformer. The experimental results on the NGSIM US-101 dataset show that our model has achieved competitive results compared with some existing works.
Design and Generalization of Enterprise Knowledge Graph

Knowledge Graph is widely used in artificial intelligence fields such as intelligent search, intelligent recommendation and intelligent question answering, and EKG (Enterprise Knowledge Graph) is an important foundation for enterprises to build intelligent platforms. This paper proposes the design idea of constructing EKG based on the five dimensions of human, financial, material, time and information. First, the EKG framework is built by applying component microservices, pre-construction and business orchestration; Mining and analyzing prediction methods, and finally designing an intelligent visual EKG with auxiliary decision-making functions.
DeepTpNet: A Deep Learning Approach to Identify Cancer Subtypes Based on Multi-omics Data

Cancer is an extremely complex and lethal genetic disease. Cancer subtypes can be used to guide the early diagnosis, personalized treatment of cancer and improve the treatment effect. The rapid development of high-throughput sequencing technology has made it possible to distinguish cancer subtypes through multi-omics data, but existing methods still have many limitations due to the complexity and diversity of omics data. We developed a deep learning method to identify cancer subtypes based on multi-omics data, named DeepTpNet. DeepTpNet can accurately describe multi-omics data with different distributions, extract latent variables of multi-omics data, and use Gaussian Mixed Model(GMM) method to identify cancer subtypes. We trained DeepTpNet on 4 cancer datasets and conducted comparative experiments with existing methods, and our model is better than existing methods. The results of DeepTpNet show that it is able to identify more powerful subtypes, providing a new framework for identifying cancer.
Autonomous Driving-Oriented Cognitive Map Lane Generation and Location Recognition Algorithm

In order to solve the challenges of high-precision maps based on lidar 3D point clouds, which are expensive on-vehicle sensors and high computing power requirements, the academic community has proposed a cognitive map solution with low cost, small amount of data, and moderate positioning accuracy. The current cognitive map mainly relies on the geometric relationship between road signs and lane lines to complete vehicle positioning, and the local positioning accuracy needs to be further improved. This paper proposes a real-time vectorized map modeling solution, using a vectorized map instead of a discrete point map to support a vehicle positioning solution based on road geometry, thereby improving the local positioning accuracy of the vehicle. At the same time, in order to solve the problem of vehicle global positioning, this paper adopts a location recognition algorithm based on the perspective of the vehicle to complete the global location estimation of the vehicle on a large scale only by relying on the vision, so as to serve the navigation function.
On the RTL Implementation of FINN Matrix Vector Unit

Field-programmable gate array (FPGA)–based accelerators are becoming increasingly popular for deep neural network (DNN) inference due to their ability to scale performance with increasing degrees of specialization with dataflow architectures or custom data type precision. In order to reduce the barrier for software engineers and data scientists to adopt FPGAs, C++- and OpenCL-based design entries with high-level synthesis (HLS) have been introduced. They provide higher abstraction compared with register-transfer level (RTL)–based design. HLS offers faster development time, better maintainability, and more flexibility in code exploration when evaluating several options for multi-dimension tensors, convolutional layers, or different degrees of parallelism. For this reason, HLS has been adopted by DNN accelerator generation frameworks such as FINN and hls4ml. In this article, we present an alternative backend library for FINN, leveraging RTL. We investigate and evaluate, across a spectrum of design dimensions, the pros and cons of an RTL-based implementation versus the original HLS variant. We show that for smaller design parameters, RTL produces significantly smaller circuits as compared with HLS. For larger circuits, however, the look-up table (LUT) count of RTL-based design is slightly higher, up to around 15%. On the other hand, HLS consistently requires more flip-flops (FFs; with an orders-of-magnitude difference for smaller designs) and block RAMs (BRAMs; 2× more). This also impacts the critical path delay, with RTL producing significantly faster circuits, up to around 80%. RTL also benefits from at least a 10× reduction in synthesis time. Finally, the results were validated in practice using two real-world use cases, one of a multi-layer perceptron (MLP) used in network intrusion detection and the other a convolution network called ResNet, used in image recognition. Overall, since HLS frameworks code-generate the hardware design, the benefits of the ease in the design entry is less important. As such, the gained benefits in synthesis time together with some design-dependent resource benefits make the RTL abstraction an attractive alternative.
A SuperPoint-based Method for Automatic Stitching of Corneal Nerve Microscopy Images

This paper presents an end-to-end image stitching method for a large number of in vivo corneal confocal microscopy images. We firstly learn a feature point detector for self-labeling from a randomly generated synthetic shape dataset. And then the self-labeling detector is utilized to the confocal microscopic images to match the nerve structure. Finally the microscopic image data with pseudo-ground truth feature points is utilized to the training model again to obtain the stable and repeatable feature points. Images registration is carried out using the Brute-Force matching and the adapted affine transform. In the experiments, the effectiveness of the method is verified by comparing with the classical feature detection algorithm and existing stitching methods. A large scale of corneal confocal microscopy is obtained with accurate joint of corneal nerves.
CDE-Net: A semi-supervised nucleus instance segmentation method based on center deviation estimation

∗ Cell nucleus segmentation is an important step in the diagnosis of tumor malignancy. In the case of out-of-focus, the cell nucleus boundary tends to be blurred, which increases the difficulty of cell segmentation and marking. Therefore, in the case of marking only some core instances, it is very important to realize the segmentation of core instances. In this paper, we propose a semi-supervised nucleus instance segmentation method based on anchor bias prediction that is achieved by our proposed center deviation estimation network (CDE-Net). By introducing the CDE branch to predict the offset degree of the anchor frame, and weighting the confidence of the anchor frame, more accurate prediction results can be obtained. The model adopts the semi-supervised training method to progressively learn the CDE-Net model, which alleviates the problem of imbalance between positive and negative samples caused by only marking part of the nuclear samples. The experimental results show that CDE-Net is superior to Mask R-CNN in many evaluation indicators, and achieves SOTA on the partially labelled MoNuSeg data set
Double Auctions and Transaction Costs

Transaction costs are omnipresent in markets but are often omitted in economic models. We show that the presence of transaction costs can fundamentally alter incentive and welfare properties of Double Auctions, a canonical market organization. We further show that transaction costs can be categorized into two types. Double Auctions with homogeneous transaction costs---a category that includes fixed fees and price based fees---preserve the key advantages of Double Auctions without transaction costs: markets with homogeneous transaction costs are asymptotically strategyproof, and there is no efficiency-loss due to strategic behavior. In contrast, double auctions with heterogeneous transaction costs---such as spread fees---lead to complex strategic behavior (price guessing) and may result in severe market failures. Allowing for aggregate uncertainty, we extend these insights to market organizations other than Double Auctions.
Immediate and Sustained Effects of Neurofeedback and Working Memory Training on Cognitive Functions in Children and Adolescents with ADHD: A Multi-Arm Pragmatic Randomized Controlled Trial

Objective: To evaluate the effects of neurocognitive training methods on targeted cognitive functions in children and adolescent with ADHD. Method: A pragmatic four-arm randomized controlled trial compared two types of neurofeedback (Slow Cortical Potential and Live Z-score) and Working-memory training (WMT) with treatment as usual. N = 202 participants with ADHD aged 9 to 17 years were included. A battery of cognitive function tests was completed pretreatment, posttreatment, and after 6-months. Results: The effects of WMT on spatial and verbal working-memory were superior to neurofeedback and treatment as usual at posttreatment, but only partially sustained at follow-up. No other consistent effects were observed. We found no clear indications that effects were moderated by ADHD presentation, ongoing medication, age, or sex. Conclusion: The sustained effects of neurocognitive training on cognitive functioning in children and adolescents with ADHD may be limited. Future research should focus on more personalized forms of neurocognitive training.
Removal Rate Prediction of Multiway Material Data Using the Deep Learning Approach

Chemical Mechanical Polishing (CMP) process is one of the most critical process steps in advanced packaging manufacturing. The material removal rate (MRR) value, which affects polishing accuracy, can be predicted by data-driven method. There is no general study of predicting MRR value based on different deep learning neural networks. In this paper, multiway data are directly employed as the input of three types of deep learning neural network to predict MRR value. Afterwards, features extracted by deep learning neural network are applied as the input of ensemble machine learning model. The proposed approaches are verified by the public dataset in 2016 PHM Challenge. Experiments results show the deep learning neural networks outperform traditional machine learning model, and integration model between deep learning model and traditional machine learning model pursues better performance.
Attention-based Deep Learning Methods for Document Layout Analysis

∗ Deep learning has unique advantages in document layout analysis. In this paper, we propose to use the attention mechanism to improve the deep learning based methods for document layout analysis, and conducted comprehensive experiments to evaluate the effects of different models/modules/backbones. Firstly, the evaluated backbones include ResNet-50 and ResNet-101 for their high performance, and the popularly used Fast RCNN and Faster RCNN based methods. Secondly, for the loss function, the Focal loss function is also adopted to improve the performance of document layout analysis. Most importantly, we propose to integrate the attention mechanism into the deep learning-based methods to improve the performance. Specifically, when calculating the residual using this ResNet network, we use the attention to perform a dot product operation on the degraded accuracy to correct the residual error of the network calculation, which obtains a higher detection accuracy. We conducted experiments on the PubLayNet, DocBank and ICDAR-2013 datasets for evaluation. Experimental results show that, with the attention mechanism, the basic deep learning-based methods has been significantly improved in the task of document layout analysis, demonstrating the effectiveness of the proposed method. The focal loss that is designed to overcome the problem of imbalance number of training samples is also effective for document layout analysis.
Optimization of task unloading strategy based on Game Theory in cloud edge collaborative system

Task unloading can reduce task completion time and save system energy consumption by migrating processing tasks on terminal devices to edge servers, which is the key research content of edge computing. At present, most of the research on edge computing task unloading is edge end centralized architecture, without considering the powerful computing power and resources of cloud center. In order to make full use of cloud and edge server resources, this paper proposes an optimization scheme of coarse-grained computing task unloading strategy under cloud edge cooperation. Considering the heterogeneity of mobile devices and the interference between channels, in order to minimize the system delay and energy consumption, the task unloading decision of multiple rational end users is established as a task migration model based on game theory, and an improved particle swarm optimization algorithm is proposed to obtain the optimal task unloading strategy set. Simulation results show that the proposed scheme can significantly reduce the system overhead cost, and can expand the scale with the increase of the number of mobile devices.
Evoking Whitehead’s Dictum

Edsger Dijkstra’s work on formal derivation of programs applies White­ head’s dictum above to programming. Instead of mentally simulating program execution—what is called operational reasoning—Dijkstra showed how program­ mers could derive a program by manipulating assertions about program variables. A correctness proof would be produced as a side effect of these manipulations. In his illustrations, however, Dijkstra did not hesitate to work outside of the for­ malism. His derivations were invariably compelling, so it was easy to overlook that some parts were not formalized. Where should we draw the line about what needs to be formalized? Most would agree that formalism makes ideas more precise but also can make those ideas less accessible to a broader audience. Therefore, the use of formalism makes sense when the additional effort required by the author and readers will yield benefits—because precision is important or because formal manipulations (manual or automated) would be helpful for drawing conclusions from a statement or for increasing our assurance in the validity of a statement. The formalisms he was using undoubtedly also influenced Dijkstra’s decisions about what to formalize. Evoking Whitehead’s Dictum
Infrared and visible image fusion based on edge-preserving filter and weighted least square optimization

Infrared (IR) and visible (VI) image fusion play an important role in improving ability to scene perception and target detection, however, due to different imaging principles, significant feature differences of images make it very difficult to extract and integrate feature information effectively, especially in complex scenes where the target feature has different scales and contrast. Therefore, this paper proposes an image fusion method based on scale-aware edge-preserving filter and weighted least square optimization, aiming to extract features at different scales more accurately. First, we designed a hybrid feature decomposition method based on the scale-aware structure-preserving filter and Gaussian filter. The proposed method separated source images into region, structure, and texture layers, and thus achieved a finer-scale division than traditional multiscale decomposition methods. Then, according to the characteristics of infrared and visible images in the region layer and texture layer, the weighted least squares optimization framework is used combing with visual saliency map and scale-aware mechanism respectively, to obtain better visual expression effect. Experimental results indicated that the proposed method could achieve better subjective and objective results than current state-of-the-art methods.