浅评Detective、Marvel漫画公司超能英雄角色的特性与发展

近年,超人、蝙蝠侠、蜘蛛侠、X战警、绿巨人、钢铁侠、神奇四侠等超能英雄角色频频出现在电影屏幕中,并在全球拥有巨大的票房。文章分析了超能英雄们设定方面的共同特点、创作的社会背景、及演变趋势,以寻找出超人们魅力永存的根源。

The Traits of Famous Fictional Detective Sherlock Holmes: On reading The Adventure of the Speckled Band

Sherlock Holmes is a fictional detective created by Sir Arthur Conan Doyle, the Scottish author and physician. As a London-based "consulting detective" whose abilities border on the fantastic, Holmes is famous for his astute logical reasoning, his ability to adopt almost any disguise, and his use of forensic science skills to solve difficult cases. The paper tries to analyze the characteristics of the Holmes and how did Holmes observe evidences and analyze clues.

Detective Story is "A Kind of Intellectual Game"

Compared with other kinds of fiction,detective story is a kind of fiction with different characteristics,it involves a process of thinking,analysis,inference,interaction.This passage mainly discusses detective story's characteristics as a kind of intellectual game and reader's psychology during reading.

The Shifting Relationship Between Author and Reader in Detective Genres

This paper,in the frame of Barthes and Foucault's ideas about the"Author",explores the complicated relationship between the author and reader by comparing the classical detective story,Edgar Allen Poe's The Murder in the Rue Morgue,and the metaphysical detective story,Paul Auster'City of Glass and Umberto Eco's The Name of the Rose.These two stories investigate the perspectives;the story of crime and the story of investigation.

Seeing Returned Colonials and Poor Whites:Retributive Ghosts in Conan Doyle’s Detective Stories

In Conan Doyle’s detective stories mainly including“The Resident Patient,”“The Gloria Scott,”“The Adventure of Blanched Soldier,”and“The Crooked Man,”featuring the master sleuth character Sherlock Holmes,he depicts the return of the colonials from British colonies,mostly India,with physically deformed or ravaged body and traumatic past that haunt and trouble his characters’present life.Doyle allegorically uses returned colonials or poor whites who turn into figures of retributive ghosts that function as pathetic memories and inner fears from British colonies.The seeing of ghostly figures and haunting past events delineated in these stories cause characters’sense of uncanny horror and remind them of their past trauma.These monstrous returned colonials or poor whites often create a fear and a social menace that must be appropriately dealt with when the master sleuth is commissioned to pin down the truth of client’s cases.Why are these bodies of ghostly figures so“irregular”and ravaged?What do these deformities signify?How can returned colonial’s or poor white’s traumatic past be related to retributive ghost?This paper attempts to probe into these issues in order to find out possible answers.

Railway, Mobility, and Horror： Conan Doyle＇s Mystery and Detective Stories

Sir Arthur Conan Doyle wrote many mystery and detective stories from 1890s to 1910s, years saw the advancement of powerful modem science and technology, especially inventions of transportation means or machines that accelerate mobility power in late-Victorian and Edwardian society. In some of these mystery or detective stories especially featuring the well-known sleuth Sherlock Holmes, Doyle tended to integrate an early subject＇s experience of shrunken space and reduced time into an unknown fear by delineating his characters who perceive horror and nervousness while facing or riding on a railway transportation, including mainly the steam railway in mysterious tales like ＂The Lost Special＂ and ＂The Man with the Watches＂ as well as in detective stories like ＂The Adventure of the Engineer＇s Thumb＂, ＂The Adventure of Bruce-Partington Plan＂, ＂Valley of Fear＂ and several others. How can this spatiotemporal mobility be connected to mysterious affairs which lead Doyle＇s quasi-detective characters and police power to spring into investigative action？ Railway, mobility, and horror are woven together into a driving force that facilitates our geographical and forensic exploration of Doyle＇s stories.

Formulas of Edgar Allan Poe’s Detective Story

Allan Poe has been deemed as the founder of modern detective story. This paper mainly talks about his contributions tomake this new genre a formal sub-genre of literature. Techniques he used in his short stories, locked-room murder and the arm-chair detective, have become the classical conventions of detective story. The eccentric but brilliant protagonist, Auguste Dupin inhis story, has become a model of the later detectives. Poe has also contributed to define the detective story as some kind of intellec-tual game, the plot of which concentrates on the process of investigation.

An Analysis of Agencies in the Field of Taiwan Residents Detective Novels Production

Since 2000 A.D.,lots of translated detective novels have being published in Taiwan,which demonstrates that detective novel is popular in Taiwan,but there are seldom local detective novels to be published.Through the theory of field of cultural production by Pierre Bourdieu,the paper analyzed how the creators and cultural intermediaries’form of capitals and aesthetics construct the mechanism of the publishing industry,and how the market of detective novels in Taiwan are dominated by foreign products.The study adopted second documentary analysis and in-depth interview.The former is to calculate the published detective novels from 2001 to September 2015 sold in the dominant on-line bookstore,Books.com.tw,in Taiwan,while the latter is to interview 15 related agencies included writers,editors,translators,and a manager of bookstore.The results contain three following issues.Firstly,local production has re-started since 1980’s after a long-time decline.Considering the large cost to cultivate local writers,Taiwan Residents publishers prefer to produce well-known foreign works.Secondly,literary awards are the vital way in the production of local works.The writers receive symbolic capital through awards,and even obtain more opportunities to publish their works or cooperate with other related organization,which means the acquirement of social capital.Finally,the market of local detective novels is forced to be the field of restricted production as a result of supplanted by translated novels.As a consequence,the production of local detective novels becomes popular literature of niche market.

Exploration on the Pattern of Image in Edgar Allan Poe's Detective Story

Despite of only producing five ratiocinative tales in the whole life,Edgar Allan Poe is acknowledged as the "father of the detective story".In those tales,Poe portrays the hero Dupin who is the first detective image in the history of the western literature vividly.Based on the stories in which Dupin appeared,concerns on the creation of Dupin,the analysis of his features and the function of the setting fellows,like friend and police,summarizing the traditional image pattern of detective stories created by Poe,revealing the great influence Poe had on the development of detective literature,even on the literature of the whole world.

Early identification of stroke through deep learning with multi-modal human speech and movement data

Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.

The Spyware Detective

一款间谍软件检测和清除工具，通过分析所有可能被感染的对象，包括内存中运行的进程、dll文件、可执行交件、注册表、cookle等．来检测任何可能存在的间谍软件：广告程序、木马、RAT程序、浏览器劫持程序、蠕虫等。

Wind Detectives

A new laser radar technology developed byChinese scientists could help Olympic sailors byaccurately measuring wind speed and

Plant detective

认识一下这些不寻常的植物吧。Bee orchid 蜜蜂兰 It takes six years for this plant to grow from a seed into a beautifu flower.这种植物从一颗种子生长到开出漂亮的花需要六年。

Summary Analysis of National Surveillance on Kashin-Beck Disease from 1990 to 2023

Objective To analyze the epidemiological characteristics and epidemic situation of children with Kashin-Beck disease(KBD)in China,and provide the basis for formulating prevention and control measures.Methods Fixed-point monitoring,moving-point monitoring,and full coverage of monitoring were promoted successively from 1990 to 2023.Some children(7-12 years old)underwent clinical and right-hand X-ray examinations every year.According to the KBD diagnosis criteria,clinical and X-ray assessments were used to confirm the diagnosis.Results In 1990,the national KBD detectable rate was 21.01%.X-ray detection decreased to below 10%in 2003 and below 5%in 2007.Between 2010 and 2018,the prevalence of KBD in children was less than 0.4%,which fluctuated at a low level,and has decreased to 0%since 2019.Spatial epidemiological analysis indicated a spatial clustering of adult patients prevalence rate in the KBD areas.Conclusion The evaluation results of the elimination of KBD in China over the last 5 years showed that all villages in the monitored areas have reached the elimination standard.While the adult KBD patients still need for policy consideration and care.

基于改进Detection Transformer的棉花幼苗与杂草检测模型研究

基于深度学习的目标检测技术在棉花幼苗与杂草检测领域已取得一定进展。论文提出了基于改进Detection Transformer的棉花幼苗与杂草检测模型,以提高杂草目标检测的准确率和效率。首先,引入了可变形注意力模块替代原始模型中的Transformer注意力模块,提高模型对特征图目标形变的处理能力。提出新的降噪训练机制,解决了二分图匹配不稳定问题。提出混合查询选择策略,提高解码器对目标类别和位置信息的利用效率。使用Swin Transformer作为网络主干,提高模型特征提取能力。通过对比原网络,论文提出的模型方法在训练过程中表现出更快的收敛速度,并且在准确率方面提高了6.7%。

IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

A network intrusion detection system is critical for cyber security against llegitimate attacks.In terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,etc.In terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal traffic.It is challenging to identify a specific attack due to complex features and data imbalance issues.To address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced data.First,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,etc.Second,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic anchors.Third,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority attacks.Fourth,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network traffic.Finally,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep features.Detailed experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and NSL-KDD.An explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

Esophageal cancer screening,early detection and treatment:Current insights and future directions

Esophageal cancer ranks among the most prevalent malignant tumors globally,primarily due to its highly aggressive nature and poor survival rates.According to the 2020 global cancer statistics,there were approximately 604000 new cases of esophageal cancer,resulting in 544000 deaths.The 5-year survival rate hovers around a mere 15%-25%.Notably,distinct variations exist in the risk factors associated with the two primary histological types,influencing their worldwide incidence and distribution.Squamous cell carcinoma displays a high incidence in specific regions,such as certain areas in China,where it meets the cost-effect-iveness criteria for widespread endoscopy-based early diagnosis within the local population.Conversely,adenocarcinoma(EAC)represents the most common histological subtype of esophageal cancer in Europe and the United States.The role of early diagnosis in cases of EAC originating from Barrett's esophagus(BE)remains a subject of controversy.The effectiveness of early detection for EAC,particularly those arising from BE,continues to be a debated topic.The variations in how early-stage esophageal carcinoma is treated in different regions are largely due to the differing rates of early-stage cancer diagnoses.In areas with higher incidences,such as China and Japan,early diagnosis is more common,which has led to the advancement of endoscopic methods as definitive treatments.These techniques have demonstrated remarkable efficacy with minimal complications while preserving esophageal functionality.Early screening,prompt diagnosis,and timely treatment are key strategies that can significantly lower both the occurrence and death rates associated with esophageal cancer.

Improvement of High-Speed Detection Algorithm for Nonwoven Material Defects Based on Machine Vision

Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.

A Hybrid Intrusion Detection Method Based on Convolutional Neural Network and AdaBoost

To solve the problem of poor detection and limited application range of current intrusion detection methods,this paper attempts to use deep learning neural network technology to study a new type of intrusion detection method.Hence,we proposed an intrusion detection algorithm based on convolutional neural network(CNN)and AdaBoost algorithm.This algorithm uses CNN to extract the characteristics of network traffic data,which is particularly suitable for the analysis of continuous and classified attack data.The AdaBoost algorithm is used to classify network attack data that improved the detection effect of unbalanced data classification.We adopt the UNSW-NB15 dataset to test of this algorithm in the PyCharm environment.The results show that the detection rate of algorithm is99.27%and the false positive rate is lower than 0.98%.Comparative analysis shows that this algorithm has advantages over existing methods in terms of detection rate and false positive rate for small proportion of attack data.