基于Contextual Transformer的自动驾驶单目3D目标检测

针对当前单目3D目标检测中存在的漏检和多尺度目标检测效果不佳的问题,提出了一种基于Contextual Transformer的自动驾驶单目3D目标检测算法(CM-RTM3D)。在ResNet-50网络中引入Contextual Transformer(CoT),构建ResNet-Transformer架构以提取特征。设计多尺度空间感知模块(MSP),通过尺度空间响应操作改善浅层特征的丢失情况,嵌入沿水平和竖直两个空间方向的坐标注意力机制(CA),使用softmax函数生成各尺度的重要性软权重。在偏移损失中采用Huber损失函数代替L1损失函数。实验结果表明:在KITTI自动驾驶数据集上,相较于RTM3D算法,该算法在简单、中等、困难三个难度级别下,AP3D分别提升了4.84、3.82、5.36个百分点,APBEV分别提升了4.75、6.26、3.56个百分点。

融合MobileNet与Contextual Transformer的人脸识别研究

FaceNet作为人脸识别的一大跨越,以其高精度、低硬件配置等优势被广泛应用于各个人脸识别相关领域。本文开源了首个餐厅支付场景下的中国人脸数据集CN-Face,该数据集拥有13000人的人脸图像,总计100000张。此外,本文以CA-SIA-WebFace作为训练集,利用改进后的MobileNet主干网络,采取不同的注意力机制添加方法,改变激活函数并且融入Contextual Transformer模块,大大降低了参数量和识别速度,显著提升了人脸识别精度。相较于原版FaceNet,在LFW测试集下,准确率达到98.79%,提升了2.74%,在CN-Face数据集中准确率达到95.22%,提升了1.35%。

Resilience, the 6th Vital Sign: Conceptualizing, Contextualizing, and Operationalizing All Six Vital Signs

There are five vital signs that healthcare providers assess: temperature, pulse, respiration, blood pressure, and pain. Normal levels for the five vital signs are published by the American Heart Association, and other specialty organizations, however, the sixth vital sign (resilience) which adopts the measure of immune resilience is suggested in this paper. Resilience is the ability of the immune system to respond to attacks and defend effectively against infections and inflammatory stressors, and psychological resilience is the capacity to resist, adapt, recover, thrive, and grow from a challenge or a stressor. Individuals with better optimal immune resilience had better health outcomes than those with minimal immune resilience. The purpose of this paper is to conceptualize, contextualize, and operationalize all six vital signs. We suggest measuring resilience subjectively and objectively. Subjectively, use a 5-item guided interview revised from the Connor-Davidson Resilience Scale (CDRC), a scale of 10 items. The revised CDRC scale is a 5-item scale. The scale is rated on a 5-point Likert scale from 0 (not true) to 4 (true all the time). The total score ranges from 0 to 20, with higher total scores indicating greater resilience. The scale demonstrated good construct validity and internal consistency (α = 0.85) during the development of the scale. The CD-RISC had a good Cronbach’s alpha level of 0.85. The Revised CD-RISC can be completed in 2 - 4 minutes. To measure resilience objectively, we suggest using Immune Resilience (IR) levels, the level of resilience to preserve and/or rapidly restore immune resilience functions that promote disease resistance and control inflammation and other inflammatory stress. IR levels are gauged with two peripheral blood metrics that quantify the balance between CD8 and CD4 T-cell levels and gene expression signatures tracking longevity-associated immunocompetence and mortality- or entropy-associated inflammation. IR deregulation is potentially reversible by decreasing inflammatory stress. IR metrics and mechanisms have utility as vital signs and biomarkers for measuring immune health and improving health outcomes.

The Impact of Contextual Teaching Method on Sichuan Folk Song Education and Students’Musical Expressiveness

This study explores the application of the contextual teaching method in Sichuan folk song education and its impact on students’musical expressiveness.By incorporating contextual teaching methods in music classes,this research investigates the effectiveness of this approach in enhancing students’understanding of Sichuan folk songs and improving their musical expressiveness and emotional expression.A mixed-method research approach is employed,utilizing classroom observations,questionnaires,interviews,and statistical analysis to assess the practical outcomes of contextual teaching in folk song education.

Research on the Influence of Anchor Attributes on Consumers’Online Behaviors in Social E-Commerce Platforms:The Moderating Effect of Platform Contextual Factors

As e-commerce continues to mature,the advantages of live streaming within the industry have become increasingly apparent,offering significant growth opportunities.Social e-commerce platforms,which are user-centered,integrate social networks with e-commerce by leveraging social interactions to drive product sales and enhance the overall consumer shopping experience.This type of e-commerce fosters engagement and promotes products by merging online communities with shopping behavior,creating a more interactive and dynamic marketplace.It not only retains the traditional e-commerce trading and marketing functions but also adds a social dimension,making live stream anchors crucial figures connecting consumers with products.These anchors can attract consumers with their appearance and charm,and use their expertise on live streaming platforms to guide consumers by recommending live content.They can also interact with their audiences and potentially influence them to purchase the recommended goods.It is evident that the attributes of anchors in live streaming rooms significantly impact consumers’online behavior.Therefore,researching how platform contextual factors regulate consumers’online behavior is of great practical significance.This study employs multilevel regression analysis to support its hypotheses using data.The findings indicate that contextual factors of the platform significantly influence online behavior,enhancing the positive relationship between user attachment and online activities.

Short Video Recommendation Algorithm Incorporating Temporal Contextual Information and User Context

With the popularity of 5G and the rapid development of mobile terminals,an endless stream of short video software exists.Browsing short-form mobile video in fragmented time has become the mainstream of user’s life.Hence,designing an efficient short video recommendation method has become important for major network platforms to attract users and satisfy their requirements.Nevertheless,the explosive growth of data leads to the low efficiency of the algorithm,which fails to distill users’points of interest on one hand effectively.On the other hand,integrating user preferences and the content of items urgently intensify the requirements for platform recommendation.In this paper,we propose a collaborative filtering algorithm,integrating time context information and user context,which pours attention into expanding and discovering user interest.In the first place,we introduce the temporal context information into the typical collaborative filtering algorithm,and leverage the popularity penalty function to weight the similarity between recommended short videos and the historical short videos.There remains one more point.We also introduce the user situation into the traditional collaborative filtering recommendation algorithm,considering the context information of users in the generation recommendation stage,and weight the recommended short-formvideos of candidates.At last,a diverse approach is used to generate a Top-K recommendation list for users.And through a case study,we illustrate the accuracy and diversity of the proposed method.

Aspect-Based Sentiment Classification Using Deep Learning and Hybrid of Word Embedding and Contextual Position

Aspect-based sentiment analysis aims to detect and classify the sentiment polarities as negative,positive,or neutral while associating them with their identified aspects from the corresponding context.In this regard,prior methodologies widely utilize either word embedding or tree-based rep-resentations.Meanwhile,the separate use of those deep features such as word embedding and tree-based dependencies has become a significant cause of information loss.Generally,word embedding preserves the syntactic and semantic relations between a couple of terms lying in a sentence.Besides,the tree-based structure conserves the grammatical and logical dependencies of context.In addition,the sentence-oriented word position describes a critical factor that influences the contextual information of a targeted sentence.Therefore,knowledge of the position-oriented information of words in a sentence has been considered significant.In this study,we propose to use word embedding,tree-based representation,and contextual position information in combination to evaluate whether their combination will improve the result’s effectiveness or not.In the meantime,their joint utilization enhances the accurate identification and extraction of targeted aspect terms,which also influences their classification process.In this research paper,we propose a method named Attention Based Multi-Channel Convolutional Neural Net-work(Att-MC-CNN)that jointly utilizes these three deep features such as word embedding with tree-based structure and contextual position informa-tion.These three parameters deliver to Multi-Channel Convolutional Neural Network(MC-CNN)that identifies and extracts the potential terms and classifies their polarities.In addition,these terms have been further filtered with the attention mechanism,which determines the most significant words.The empirical analysis proves the proposed approach’s effectiveness compared to existing techniques when evaluated on standard datasets.The experimental results represent our approach outperforms in the F1 measure with an overall achievement of 94%in identifying aspects and 92%in the task of sentiment classification.

A Personalized Adverse Drug Reaction Early Warning Method Based on Contextual Ontology and Rules Learning

Background: The fatality of adverse drug reactions (ADR) has become one of the major causes of the non-natural disease deaths globally, with the issue of drug safety emerging as a common topic of concern. Objective: The personalized ADR early warning method, based on contextual ontology and rule learning, proposed in this study aims to provide a reference method for personalized health and medical information services. Methods: First, the patient data is formalized, and the user contextual ontology is constructed, reflecting the characteristics of the patient population. The concept of ontology rule learning is then proposed, which is to mine the rules contained in the data set through machine learning to improve the efficiency and scientificity of ontology rule generation. Based on the contextual ontology of ADR, the high-level context information is identified and predicted by means of reasoning, so the occurrence of the specific adverse reaction in patients from different populations is extracted. Results: Finally, using diabetes drugs as an example, contextual information is identified and predicted through reasoning, to mine the occurrence of specific adverse reactions in different patient populations, and realize personalized medication decision-making and early warning of ADR.

Keyword Extraction for Contextual Advertising

Contextual advertising is a major revenue source for today＇s companies. Keyword extraction is a key step in this kind of advertising, through which appropriate advertising keywords are extracted from Web pages so that corresponding ads can be triggered. This paper describes a system that learns how to extract keywords from web pages for advertisement targeting. Firstly a text network for a single webpage is build, then PageRank is applied in the network to decide on the importance of a word, finally top-ranked words are selected as keywords of the webpage. The algorithm is tested on the corpus ofblog pages, and the experimental results prove practical and effective.

SSD Real-Time Illegal Parking Detection Based on Contextual Information Transmission

With the improvement of the national economic level,the number of vehicles is still increasing year by year.According to the statistics of National Bureau of Statics,the number is approximately up to 327 million in China by the end of 2018,which makes urban traffic pressure continues to rise so that the negative impact of urban traffic order is growing.Illegal parking-the common problem in the field of transportation security is urgent to be solved and traditional methods to address it are mainly based on ground loop and manual supervision,which may miss detection and cost much manpower.Due to the rapidly developing deep learning sweeping the world in recent years,object detection methods relying on background segmentation cannot meet the requirements of complex and various scenes on speed and precision.Thus,an improved Single Shot MultiBox Detector(SSD)based on deep learning is proposed in our study,we introduce attention mechanism by spatial transformer module which gives neural networks the ability to actively spatially transform feature maps and add contextual information transmission in specified layer.Finally,we found out the best connection layer in the detection model by repeated experiments especially for small objects and increased the precision by 1.5%than the baseline SSD without extra training cost.Meanwhile,we designed an illegal parking vehicle detection method by the improved SSD,reaching a high precision up to 97.3%and achieving a speed of 40FPS,superior to most of vehicle detection methods,will make contributions to relieving the negative impact of illegal parking.

Orthographic,Semantic,and Contextual Influences on Initial Processing and Learning of Novel Words During Reading:Evidence From Eye Movements

This study investigates how orthographic,semantic and contextual variables—including word length,concreteness,and contextual support—impact on the processing and learning of new words in a second language(L2)when first encountered during reading.Students learning English as a foreign language(EFL)were recruited to read sentences for comprehension,embedded with unfamiliar L2 words that occurred once.Immediately after this,they received a form recognition test,a meaning recall test,and a meaning recognition test.Eye-movement data showed significant effects of word length on both early and late processing of novel words,along with effects of concreteness only on late-processing eye-tracking measures.Informative contexts were read slower than neutral contexts,yet contextual support did not show any direct influence on the processing of novel words.Interestingly,initial learning of abstract words was better than concrete words in terms of form and meaning recognition.Attentional processing of novel L2 words,operationalized by total reading time,positively predicted L2 learners’recognition of new orthographic forms.Taken together,these results suggest:1)orthographic,semantic and contextual factors play distinct roles for initial processing and learning of novel words;2)online processing of novel words contributes to L2 learners’initial knowledge of unfamiliar lexical items acquired from reading.

Visual Relationship Detection with Contextual Information

Understanding an image goes beyond recognizing and locating the objects in it,the relationships between objects also very important in image understanding.Most previous methods have focused on recognizing local predictions of the relationships.But real-world image relationships often determined by the surrounding objects and other contextual information.In this work,we employ this insight to propose a novel framework to deal with the problem of visual relationship detection.The core of the framework is a relationship inference network,which is a recurrent structure designed for combining the global contextual information of the object to infer the relationship of the image.Experimental results on Stanford VRD and Visual Genome demonstrate that the proposed method achieves a good performance both in efficiency and accuracy.Finally,we demonstrate the value of visual relationship on two computer vision tasks:image retrieval and scene graph generation.

Retrieval of canopy biophysical variables from remote sensing data using contextual information

In order to improve the accuracy of biophysical parameters retrieved from remotely sensing data, a new algorithm was presented by using spatial contextual to estimate canopy variables from high-resolution remote sensing images. The developed algorithm was used for inversion of leaf area index (LAI) from Enhanced Thematic Mapper Plus (ETM+) data by combining with optimization method to minimize cost functions. The results show that the distribution of LAI is spatially consistent with the false composition imagery from ETM+ and the accuracy of LAI is significantly improved over the results retrieved by the conventional pixelwise retrieval methods, demonstrating that this method can be reliably used to integrate spatial contextual information for inverting LAI from high-resolution remote sensing images.

Analysis on the Relationship Between Contextual Research and Goal of Universality

In the linguistic field,there are disputes on the view of contextual research and the goal of universality.Some scholars believe that common features of linguistic phenomena are significant while others are in favor of the perception that it is more applicable and practical to carry out contextual researches.The author tends to analyze the reality and significance of contextual researches and with the goal of universality explained,the relationship between them and further suggestion will be discussed.

Contextual Adaptation of Language Choices -With Reference to Obama＇s 2015 State of the Union Address

An empirical research is done on how political Obama＇s 2015 State of the Union Address as the corpora sample speeches adapt to context in the framework of adaptation theory, taking This paper shows that language choices in the State of the Union Address are adaptive to all the levels of the context, including communicative context （language users, mental world, social world, and physical world） and linguistic context. It is confirmed one of the theoretical stances of adaptation theory that there is no language use without being adaptive to context.

Minimum detection efficiency for the loophole-free confirmation of quantum contextuality

Klyachko-Can-Binicioglu-Shumovsky （KCBS） inequality is a Bell-like inequality, the violation of which can be used to confirm the existence of quantum contextuality. However, the imperfection of detection efficiency may cause the so-called loophole in actual KCBS＇s experiments. We derive an alternative KCBS inequality to deal with the loophole in actual KCBS＇s experiments. We prove that if the experimental data violate this KCBS inequality, the loophole-free violation of the original KCBS inequality will occur. We show that the minimum detection efficiency needed for a loophole-free violation of the KCBS inequality is about 0.9738.

Cluster Analysis Based on Contextual Features Extraction for Conversational Corpus

Cluster analysis related to computational linguistics seldom concerned with Pragmatics level. Features of corpus on Pragmatics level related to specific situations, including backgrounds, titles and habits. To improve the accuracy of clustering for conversations collected from international students in Tsinghua University, it required contextual features. Here, we collected four-hundred conversations as a corpus and built it to Vector Space Model. With the Oxford-Duden Dictionary and other methods we modified the model and concluded into three groups. We testified our hypothesis through self-organizing map neural network. The result suggested that the modified model had a better outcome.

The Contextual Influence on the Application of Communicative Language Teaching to College English Course in Tongren University

This paper is divided into five parts.Part I is an introduction to the paper.In this part,I list two reasons why I carry out thestudy of the context of CLT application in Tongren University.Part II is the literature review of approach,CLT and Context.Part III ana-lyzes the learning and teaching context in Tongren University from three aspects—(1) the administrative policies.(2) the teachers of Eng-lish.(3) the students to support my view that the application condition of CLT is dominated by context in Tongren University.A conclu-sion is included in Part V.I had thought to give some suggestions on more widely applying CLT in Tongren University,but there is nospace on this paper.

Training a Quantum Neural Network to Solve the Contextual Multi-Armed Bandit Problem

Artificial intelligence has permeated all aspects of our lives today. However, to make AI behave like real AI, the critical bottleneck lies in the speed of computing. Quantum computers employ the peculiar and unique properties of quantum states such as superposition, entanglement, and interference to process information in ways that classical computers cannot. As a new paradigm of computation, quantum computers are capable of performing tasks intractable for classical processors, thus providing a quantum leap in AI research and making the development of real AI a possibility. In this regard, quantum machine learning not only enhances the classical machine learning approach but more importantly it provides an avenue to explore new machine learning models that have no classical counterparts. The qubit-based quantum computers cannot naturally represent the continuous variables commonly used in machine learning, since the measurement outputs of qubit-based circuits are generally discrete. Therefore, a continuous-variable (CV) quantum architecture based on a photonic quantum computing model is selected for our study. In this work, we employ machine learning and optimization to create photonic quantum circuits that can solve the contextual multi-armed bandit problem, a problem in the domain of reinforcement learning, which demonstrates that quantum reinforcement learning algorithms can be learned by a quantum device.

Born Rule and Noncontextual Probability

We present a new derivation of the Born rule from the assumption of noncontextual probability (NCP). Within the theorem we also demonstrate the continuity of probability with respect to the amplitudes, which has been suggested to be a gap in Zurek’s and Deutsch’s approaches, and we show that NCP is implicitly postulated also in their derivations. Finally, physical motivations of NCP are given based on an invariance principle with respect to a resolution change of measurements and with respect to the principle of no-faster-than-light signalling.