DCRL-KG: Distributed Multi-Modal Knowledge Graph Retrieval Platform Based on Collaborative Representation Learning

The knowledge graph with relational abundant information has been widely used as the basic data support for the retrieval platforms.Image and text descriptions added to the knowledge graph enrich the node information,which accounts for the advantage of the multi-modal knowledge graph.In the field of cross-modal retrieval platforms,multi-modal knowledge graphs can help to improve retrieval accuracy and efficiency because of the abundant relational infor-mation provided by knowledge graphs.The representation learning method is sig-nificant to the application of multi-modal knowledge graphs.This paper proposes a distributed collaborative vector retrieval platform(DCRL-KG)using the multi-modal knowledge graph VisualSem as the foundation to achieve efficient and high-precision multimodal data retrieval.Firstly,use distributed technology to classify and store the data in the knowledge graph to improve retrieval efficiency.Secondly,this paper uses BabelNet to expand the knowledge graph through multi-ple filtering processes and increase the diversification of information.Finally,this paper builds a variety of retrieval models to achieve the fusion of retrieval results through linear combination methods to achieve high-precision language retrieval and image retrieval.The paper uses sentence retrieval and image retrieval experi-ments to prove that the platform can optimize the storage structure of the multi-modal knowledge graph and have good performance in multi-modal space.

真学习还是假参与?——Presentation在高校课堂教学应用中的问题与改进

Presentation是目前国内高校教学改革中常用的教学形式之一,在各类学科教学中均有不同程度的应用。这种以增强教学双主体互动为主要特征的教学形式,在实际教学中呈现出学生课堂参与深度不足、学习收获不系统、课堂汇报“一言堂”等问题。学生学习是为了完成任务而非获得知识、学生习惯于服从教师权威而非批判性思考、教师教学准备不足与指导环节缺失,以及教学评价的评价主体与形式单一是造成Presenta-tion应用效果不佳的重要因素。提高Presentation的应用效果,需要强化对教学过程的管理,保障Presentation规范实施;改革学业评价方式,促进学生主动思考与合作学习;拓展教学资源供给,为学生自主与合作学习提供更多支持;鼓励教师开展教学行动研究,不断提高教学专业化水平。

A Hand Features Based Fusion Recognition Network with Enhancing Multi-Modal Correlation

Fusing hand-based features in multi-modal biometric recognition enhances anti-spoofing capabilities.Additionally,it leverages inter-modal correlation to enhance recognition performance.Concurrently,the robustness and recognition performance of the system can be enhanced through judiciously leveraging the correlation among multimodal features.Nevertheless,two issues persist in multi-modal feature fusion recognition:Firstly,the enhancement of recognition performance in fusion recognition has not comprehensively considered the inter-modality correlations among distinct modalities.Secondly,during modal fusion,improper weight selection diminishes the salience of crucial modal features,thereby diminishing the overall recognition performance.To address these two issues,we introduce an enhanced DenseNet multimodal recognition network founded on feature-level fusion.The information from the three modalities is fused akin to RGB,and the input network augments the correlation between modes through channel correlation.Within the enhanced DenseNet network,the Efficient Channel Attention Network(ECA-Net)dynamically adjusts the weight of each channel to amplify the salience of crucial information in each modal feature.Depthwise separable convolution markedly reduces the training parameters and further enhances the feature correlation.Experimental evaluations were conducted on four multimodal databases,comprising six unimodal databases,including multispectral palmprint and palm vein databases from the Chinese Academy of Sciences.The Equal Error Rates(EER)values were 0.0149%,0.0150%,0.0099%,and 0.0050%,correspondingly.In comparison to other network methods for palmprint,palm vein,and finger vein fusion recognition,this approach substantially enhances recognition performance,rendering it suitable for high-security environments with practical applicability.The experiments in this article utilized amodest sample database comprising 200 individuals.The subsequent phase involves preparing for the extension of the method to larger databases.

A Comprehensive Survey on Deep Learning Multi-Modal Fusion:Methods,Technologies and Applications

Multi-modal fusion technology gradually become a fundamental task in many fields,such as autonomous driving,smart healthcare,sentiment analysis,and human-computer interaction.It is rapidly becoming the dominant research due to its powerful perception and judgment capabilities.Under complex scenes,multi-modal fusion technology utilizes the complementary characteristics of multiple data streams to fuse different data types and achieve more accurate predictions.However,achieving outstanding performance is challenging because of equipment performance limitations,missing information,and data noise.This paper comprehensively reviews existing methods based onmulti-modal fusion techniques and completes a detailed and in-depth analysis.According to the data fusion stage,multi-modal fusion has four primary methods:early fusion,deep fusion,late fusion,and hybrid fusion.The paper surveys the three majormulti-modal fusion technologies that can significantly enhance the effect of data fusion and further explore the applications of multi-modal fusion technology in various fields.Finally,it discusses the challenges and explores potential research opportunities.Multi-modal tasks still need intensive study because of data heterogeneity and quality.Preserving complementary information and eliminating redundant information between modalities is critical in multi-modal technology.Invalid data fusion methods may introduce extra noise and lead to worse results.This paper provides a comprehensive and detailed summary in response to these challenges.

Chlorfenapyr poisoning:mechanisms,clinical presentations,and treatment strategies

BACKGROUND:Chlorfenapyr is used to kill insects that are resistant to organophosphorus insecticides.Chlorfenapyr poisoning has a high mortality rate and is difficult to treat.This article aims to review the mechanisms,clinical presentations,and treatment strategies for chlorfenapyr poisoning.DATA RESOURCES:We conducted a review of the literature using PubMed,Web of Science,and SpringerLink from their beginnings to the end of October 2023.The inclusion criteria were systematic reviews,clinical guidelines,retrospective studies,and case reports on chlorfenapyr poisoning that focused on its mechanisms,clinical presentations,and treatment strategies.The references in the included studies were also examined to identify additional sources.RESULTS:We included 57 studies in this review.Chlorfenapyr can be degraded into tralopyril,which is more toxic and reduces energy production by inhibiting the conversion of adenosine diphosphate to adenosine triphosphate.High fever and altered mental status are characteristic clinical presentations of chlorfenapyr poisoning.Once it occurs,respiratory failure occurs immediately,ultimately leading to cardiac arrest and death.Chlorfenapyr poisoning is diflcult to treat,and there is no specific antidote.CONCLUSION:Chlorfenapyr is a new pyrrole pesticide.Although it has been identified as a moderately toxic pesticide by the World Health Organization(WHO),the mortality rate of poisoned patients is extremely high.There is no specific antidote for chlorfenapyr poisoning.Therefore,based on the literature review,future efforts to explore rapid and effective detoxification methods,reconstitute intracellular oxidative phosphorylation couplings,identify early biomarkers of chlorfenapyr poisoning,and block the conversion of chlorfenapyr to tralopyril may be helpful for emergency physicians in the diagnosis and treatment of this disease.

Towards trustworthy multi-modal motion prediction:Holistic evaluation and interpretability of outputs

Predicting the motion of other road agents enables autonomous vehicles to perform safe and efficient path planning.This task is very complex,as the behaviour of road agents depends on many factors and the number of possible future trajectories can be consid-erable(multi-modal).Most prior approaches proposed to address multi-modal motion prediction are based on complex machine learning systems that have limited interpret-ability.Moreover,the metrics used in current benchmarks do not evaluate all aspects of the problem,such as the diversity and admissibility of the output.The authors aim to advance towards the design of trustworthy motion prediction systems,based on some of the re-quirements for the design of Trustworthy Artificial Intelligence.The focus is on evaluation criteria,robustness,and interpretability of outputs.First,the evaluation metrics are comprehensively analysed,the main gaps of current benchmarks are identified,and a new holistic evaluation framework is proposed.Then,a method for the assessment of spatial and temporal robustness is introduced by simulating noise in the perception system.To enhance the interpretability of the outputs and generate more balanced results in the proposed evaluation framework,an intent prediction layer that can be attached to multi-modal motion prediction models is proposed.The effectiveness of this approach is assessed through a survey that explores different elements in the visualisation of the multi-modal trajectories and intentions.The proposed approach and findings make a significant contribution to the development of trustworthy motion prediction systems for autono-mous vehicles,advancing the field towards greater safety and reliability.

Recent Advances for Global Perspectives on Etiology, Pathophysiology, Clinical Presentations, and Management of Moyamoya Disease

Moyamoya disease (MMD) is a condition characterized by the gradual narrowing and blockage of blood vessels in the brain, specifically those in the circle of Willis and the arteries that supply it. This results in reduced blood flow and oxygen to the brain, leading to progressive symptoms and potential complications. The underlying pathophysiological mechanism remains elucidated. However, recent studies have highlighted numerous etiologic factors: abnormal immune complex responses, susceptibility genes, branched-chain amino acids, antibodies, heritable diseases, and acquired diseases, which may be the great potential triggers for the development of moyamoya disease. Its clinical presentation has varying degrees from transient asymptomatic events to significant neurological deficits. Moyamoya disease (MMD) shows different patterns in children and adults. Children with MMD are more susceptible to ischemic events due to decreased blood flow to the brain. Conversely, adults with MMD are more prone to hemorrhagic events involving brain bleeding. Children with MMD may experience a range of symptoms including motor impairments, sensory issues, seizures, headaches, dizziness, cognitive delays, or ongoing neurological problems. Although adults may present with similar clinical symptoms as children, they are more prone to experiencing sudden onset intraventricular, subarachnoid, or intracerebral hemorrhages. One of the challenges in moyamoya disease is the potential for misdiagnosis or delayed diagnosis, particularly when physicians fail to consider MMD as a possible cause in stroke patients. This review aims to provide a comprehensive overview of recent global studies on the pathophysiology of MMD, along with advancements in its management. Additionally, the review will delve into various surgical treatment options for MMD, as well as its rare occurrence alongside atrioventricular malformations. Exciting prospects include the use of autologous bone marrow transplant and the potential role of Connexin 43 protein treatment in the development of moyamoya disease.

Multi-dimension and multi-modal rolling mill vibration prediction model based on multi-level network fusion

Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.

Multi-modal knowledge graph inference via media convergence and logic rule

Media convergence works by processing information from different modalities and applying them to different domains.It is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective.To address the issue,an inference method based on Media Convergence and Rule-guided Joint Inference model(MCRJI)has been pro-posed.The authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction.First,a multi-headed self-attention approach is used to obtain the attention of different media features of entities during semantic synthesis.Second,logic rules of different lengths are mined from knowledge graph to learn new entity representations.Finally,knowledge graph inference is performed based on representing entities that converge multi-media features.Numerous experimental results show that MCRJI outperforms other advanced baselines in using multi-media features and knowledge graph inference,demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi-media features.

Research on Multi-modal In-Vehicle Intelligent Personal Assistant Design

Intelligent personal assistants play a pivotal role in in-vehicle systems,significantly enhancing life efficiency,driving safety,and decision-making support.In this study,the multi-modal design elements of intelligent personal assistants within the context of visual,auditory,and somatosensory interactions with drivers were discussed.Their impact on the driver’s psychological state through various modes such as visual imagery,voice interaction,and gesture interaction were explored.The study also introduced innovative designs for in-vehicle intelligent personal assistants,incorporating design principles such as driver-centricity,prioritizing passenger safety,and utilizing timely feedback as a criterion.Additionally,the study employed design methods like driver behavior research and driving situation analysis to enhance the emotional connection between drivers and their vehicles,ultimately improving driver satisfaction and trust.

Generative Multi-Modal Mutual Enhancement Video Semantic Communications

Recently,there have been significant advancements in the study of semantic communication in single-modal scenarios.However,the ability to process information in multi-modal environments remains limited.Inspired by the research and applications of natural language processing across different modalities,our goal is to accurately extract frame-level semantic information from videos and ultimately transmit high-quality videos.Specifically,we propose a deep learning-basedMulti-ModalMutual Enhancement Video Semantic Communication system,called M3E-VSC.Built upon a VectorQuantized Generative AdversarialNetwork(VQGAN),our systemaims to leverage mutual enhancement among different modalities by using text as the main carrier of transmission.With it,the semantic information can be extracted fromkey-frame images and audio of the video and performdifferential value to ensure that the extracted text conveys accurate semantic information with fewer bits,thus improving the capacity of the system.Furthermore,a multi-frame semantic detection module is designed to facilitate semantic transitions during video generation.Simulation results demonstrate that our proposed model maintains high robustness in complex noise environments,particularly in low signal-to-noise ratio conditions,significantly improving the accuracy and speed of semantic transmission in video communication by approximately 50 percent.

MMDistill:Multi-Modal BEV Distillation Framework for Multi-View 3D Object Detection

Multi-modal 3D object detection has achieved remarkable progress,but it is often limited in practical industrial production because of its high cost and low efficiency.The multi-view camera-based method provides a feasible solution due to its low cost.However,camera data lacks geometric depth,and only using camera data to obtain high accuracy is challenging.This paper proposes a multi-modal Bird-Eye-View(BEV)distillation framework(MMDistill)to make a trade-off between them.MMDistill is a carefully crafted two-stage distillation framework based on teacher and student models for learning cross-modal knowledge and generating multi-modal features.It can improve the performance of unimodal detectors without introducing additional costs during inference.Specifically,our method can effectively solve the cross-gap caused by the heterogeneity between data.Furthermore,we further propose a Light Detection and Ranging(LiDAR)-guided geometric compensation module,which can assist the student model in obtaining effective geometric features and reduce the gap between different modalities.Our proposed method generally requires fewer computational resources and faster inference speed than traditional multi-modal models.This advancement enables multi-modal technology to be applied more widely in practical scenarios.Through experiments,we validate the effectiveness and superiority of MMDistill on the nuScenes dataset,achieving an improvement of 4.1%mean Average Precision(mAP)and 4.6%NuScenes Detection Score(NDS)over the baseline detector.In addition,we also present detailed ablation studies to validate our method.

Clinical Presentation and Treatment Outcomes of Pregnancy-Related Acute Kidney Injury among Pregnant Women Admitted at the Benjamin Mkapa Hospital in Tanzania

Background: Globally, PRAKI is among the leading causes of death in pregnant women. The prevalence, causes and outcome of this condition vary among countries due to differences in environmental, socioeconomic, and health delivery systems. The common causes that have been reported in several studies are PIH, Haemorrhages and Sepsis while the outcomes may be either complete renal recovery, progression to CKD and hence dialysis dependency or death. This study aimed at determining clinical presentation and treatment outcomes of Pregnancy-Related Acute Kidney Injury in Pregnant women admitted at the Benjamin Mkapa Hospital, Dodoma, Tanzania. Results: Out of 4007 pregnant women who were admitted to the maternity ward 51 pregnant women were found to have PRAKI. Of those with PRAKI, 74.5% were between 21 to 25 years. The leading causes of PRAKI were PPH 12 (23.53%), Eclampsia 12 (23.53%), and pre-eclampsia 12 (23.5%). Hemodialysis therapy was provided to 22 (43.1%) patients, 15 (29.4%) individuals recovered spontaneously with medical management and 14 (27.5%) missed haemodialysis therapy due to various reasons. The mortality due to PRAKI was 17 (33.3%). Conclusion and Recommendation: Pre-eclampsia/eclampsia and post-partum haemorrhage were found to be the main causes of PRAKI. The mortality related to PRAKI is high and Hemodialysis therapy is vital help to prevent deaths for pregnant women with PRAKI. Pregnant women who develop acute kidney injury should be followed closely and a nephrologist should be consulted early. Early referral should be done by the lower level facilities for all at-risk pregnant women to a specialized multidisciplinary health facility.

Cultivate Critical Thinking Ability via Students’ Presentations in College English Classes

In the new era when demands for flexible intellectual skills are increasing,strengthening the cultivation of critical thinking ability of students has become one of the key purposes of higher education.This paper intends to discuss how to cultivate students’critical thinking ability through English classroom presentations.It points out that during the whole process,teachers’guidance plays an indispensable role.Only when both teachers and students are aware that classroom presentations are a golden opportunity for the cultivation of students’critical thinking ability and carry this awareness into their English classes,can English presentations be brought into full play.

Unsupervised multi-modal image translation based on the squeeze-and-excitation mechanism and feature attention module

The unsupervised multi-modal image translation is an emerging domain of computer vision whose goal is to transform an image from the source domain into many diverse styles in the target domain.However,the multi-generator mechanism is employed among the advanced approaches available to model different domain mappings,which results in inefficient training of neural networks and pattern collapse,leading to inefficient generation of image diversity.To address this issue,this paper introduces a multi-modal unsupervised image translation framework that uses a generator to perform multi-modal image translation.Specifically,firstly,the domain code is introduced in this paper to explicitly control the different generation tasks.Secondly,this paper brings in the squeeze-and-excitation(SE)mechanism and feature attention(FA)module.Finally,the model integrates multiple optimization objectives to ensure efficient multi-modal translation.This paper performs qualitative and quantitative experiments on multiple non-paired benchmark image translation datasets while demonstrating the benefits of the proposed method over existing technologies.Overall,experimental results have shown that the proposed method is versatile and scalable.

Thinking on Improving Presentation Teaching-Based on the Empirical Analysis of Presentation in College English Classes of Junior Non-English Majors in Universities of Science and Engineering

Based on the empirical investigation of presentation in college English classes for junior non-English majors in science and engineering universities,the author explains her own thinking on how to improve presentation teaching from four aspects:setting teaching objectives,selecting teaching contents,using teaching methods,and evaluating and implementing methods.

The Aesthetic Presentation of Chinese Classical Music in Piano Performance

Piano art has a certain sense of musical beauty,and the performance of piano works can fully reflect the artistic and aesthetic connotations of music works.In piano performance,the aesthetic presentation of Chinese classical music can further enhance the performance effect and give the audience and listeners a better artistic experience.Therefore,this paper analyzes the artistic characteristics of Chinese classical music and the characteristics of piano performance and discusses the aesthetic presentation and effect of Chinese classical music in piano performance.

Research on Heterogeneous Information Network Link Prediction Based on Representation Learning

A heterogeneous information network,which is composed of various types of nodes and edges,has a complex structure and rich information content,and is widely used in social networks,academic networks,e-commerce,and other fields.Link prediction,as a key task to reveal the unobserved relationships in the network,is of great significance in heterogeneous information networks.This paper reviews the application of presentation-based learning methods in link prediction of heterogeneous information networks.This paper introduces the basic concepts of heterogeneous information networks,and the theoretical basis of representation learning,and discusses the specific application of the deep learning model in node embedding learning and link prediction in detail.The effectiveness and superiority of these methods on multiple real data sets are demonstrated by experimental verification.

Methods for Derivation of Density Matrix of Arbitrary Multi-Mode Gaussian States from Its Phase Space Representation

We present a method for derivation of the density matrix of an arbitrary multi-mode continuous variable Gaussian entangled state from its phase space representation.An explicit computer algorithm is given to reconstruct the density matrix from Gaussian covariance matrix and quadrature average values.As an example,we apply our method to the derivation of three-mode symmetric continuous variable entangled state.Our method can be used to analyze the entanglement and correlation in continuous variable quantum network with multi-mode quantum entanglement states.

Presentation在“电磁场与电磁波”教学中的应用研究

本文探讨了Presentation教学法在专业基础课"电磁场与电磁波"课堂教学中的适用性和特点。教学实践结果表明:课堂中采用Presentation教学法,调动了学生学习的自主性和积极性,实现了良好的教学效果,为学生后续课程的学习奠定了良好基础。