Dual-Stream Diffusion Net for Text-to-Video Generation

With the emerging diffusion models, recently, text-to-video generation has aroused increasing attention. But an important bottleneck therein is that generative videos often tend to carry some flickers and artifacts. In this work, we propose a dual-stream diffusion net (DSDN) to improve the consistency of content variations in generating videos. In particular, the designed two diffusion streams, video content and motion branches, could not only run separately in their private spaces for producing personalized video variations as well as content, but also be well-aligned between the content and motion domains through leveraging our designed cross-transformer interaction module, which would benefit the smoothness of generated videos. Besides, we also introduce motion decomposer and combiner to faciliate the operation on video motion. Qualitative and quantitative experiments demonstrate that our method could produce amazing continuous videos with fewer flickers.Comment: 8pages, 7 figure

Integration and Industrialization of Film and Television Resources and its Significance for China’s Film and Television Work

The abundance of Film and Television resources in China is notable, yet the effective amalgamation and industrialization of these resources hold immense importance for the nation's film and television endeavors. Utilizing the full potential of policy, market, talent, and cultural assets, this research achieves the industrialization of Film and Television resources via a comprehensive amalgamation and integration of various capital, cooperation, and industry sectors. The findings of our study indicate that merging and mechanizing Film and Television resources in the realm of film and TV not only enhances their usage efficiency and financial worth, but also elevates their quality and expressive capacity. Consequently, this approach enables efficient education in Film and Television for the film and television sector in China. The research aids in comprehending the significance of incorporating Film and Television resource industrialization into film and TV projects, and also serves as a valuable methodological guide for diverse film and TV endeavors via these resources

The Prospect of Electronic Warfare in the 21st Century: An Analysis of Electronic Warfare Equipment Innovation and Its Strategic Impact Based on the Fusion of Quantum Communication and Artificial Intelligence

The rapid development of science and technology is driving the shape of electronic warfare to change dramatically, especially under the influence of quantum communication and artificial intelligence (AI) technology. This article comprehensively explores innovative approaches to the development of electronic warfare readiness in the 21st century, with a focus on the composite application of quantum communication technology and artificial intelligence technology in modern electronic warfare equipment. The non-reproducibility and anti-interference characteristics of quantum communication, as well as the rapid decision making and learning ability of artificial intelligence, are studied in this paper, and the impact of this technology fusion on the strategy and tactical execution of electronic warfare is further discussed. Using quantum communication, the communication security of electronic warfare system is greatly enhanced. The introduction of artificial intelligence can optimize tactical decisions and improve response speed. In addition, the paper also discusses the far-reaching impact of these technological integration on the global strategic security pattern, pointing out that it will promote the change of electronic tactics and bring new strategic competition focus. Finally, the paper puts forward some suggestions for the future research and development of electronic warfare equipment, emphasizing the need to find a balance between technical advantages and ethical regulations

On the Socialist Core and Its Modern and Contemporary Values in How the Steel was Tempered from the Perspective of Education

In-depth study of literary appreciation and its educational value for the book "How the Steel was Tempered", interpreted from a pedagogical perspective and in the context of socialist core values. Firstly, starting from the socialist core, the deep relationship between characterization, plot setting and theme is analyzed. Secondly, through the analysis of the growth experience of the hero, Paul Kochagin, the educational significance of his socialist spirit such as adhering to ideals and faiths, unremitting self-improvement and optimism is revealed. Finally, the paper discusses the value of the work in the contemporary content, emphasizing its positive effects on cultivating contemporary youth to take social responsibility, promoting the socialist core values, and upgrading the national moral quality. The research shows that "How the Steel was Tempered", with its profound socialist core and extensive educational value, is of great practical significance for us to deepen the education of socialist core values and guide the youth to establish a correct worldview and values

Determining acute ischemic stroke onset time using machine learning and radiomics features of infarct lesions and whole brain

Accurate determination of the onset time in acute ischemic stroke (AIS) patients helps to formulate more beneficial treatment plans and plays a vital role in the recovery of patients. Considering that the whole brain may contain some critical information, we combined the Radiomics features of infarct lesions and whole brain to improve the prediction accuracy. First, the radiomics features of infarct lesions and whole brain were separately calculated using apparent diffusion coefficient (ADC), diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) sequences of AIS patients with clear onset time. Then, the least absolute shrinkage and selection operator (Lasso) was used to select features. Four experimental groups were generated according to combination strategies: Features in infarct lesions (IL), features in whole brain (WB), direct combination of them (IW) and Lasso selection again after direct combination (IWS), which were used to evaluate the predictive performance. The results of ten-fold cross-validation showed that IWS achieved the best AUC of 0.904, which improved by 13.5% compared with IL (0.769), by 18.7% compared with WB (0.717) and 4.2% compared with IW (0.862). In conclusion, combining infarct lesions and whole brain features from multiple sequences can further improve the accuracy of AIS onset time

Adaptive Feature Medical Segmentation Network: an adaptable deep learning paradigm for high-performance 3D brain lesion segmentation in medical imaging

IntroductionIn neurological diagnostics, accurate detection and segmentation of brain lesions is crucial. Identifying these lesions is challenging due to its complex morphology, especially when using traditional methods. Conventional methods are either computationally demanding with a marginal impact/enhancement or sacrifice fine details for computational efficiency. Therefore, balancing performance and precision in compute-intensive medical imaging remains a hot research topic.MethodsWe introduce a novel encoder-decoder network architecture named the Adaptive Feature Medical Segmentation Network (AFMS-Net) with two encoder variants: the Single Adaptive Encoder Block (SAEB) and the Dual Adaptive Encoder Block (DAEB). A squeeze-and-excite mechanism is employed in SAEB to identify significant data while disregarding peripheral details. This approach is best suited for scenarios requiring quick and efficient segmentation, with an emphasis on identifying key lesion areas. In contrast, the DAEB utilizes an advanced channel spatial attention strategy for fine-grained delineation and multiple-class classifications. Additionally, both architectures incorporate a Segmentation Path (SegPath) module between the encoder and decoder, refining segmentation, enhancing feature extraction, and improving model performance and stability.ResultsAFMS-Net demonstrates exceptional performance across several notable datasets, including BRATs 2021, ATLAS 2021, and ISLES 2022. Its design aims to construct a lightweight architecture capable of handling complex segmentation challenges with high precision.DiscussionThe proposed AFMS-Net addresses the critical balance issue between performance and computational efficiency in the segmentation of brain lesions. By introducing two tailored encoder variants, the network adapts to varying requirements of speed and feature. This approach not only advances the state-of-the-art in lesion segmentation but also provides a scalable framework for future research in medical image processing

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

The Impact of Parental Self-esteem, Parental Rearing Behavior on the Adolescent Attachment to Parents

In this study the relationship of parental self-esteem, parental rearing and adolescent adult attachment was investigated. A total 448 senior high school students completed EMBU(Egna Minnen av Barndoms Uppfostran, or ―Own memories of parental rearing‖, Perris  et al., 1980), the Experiences in Close Relationships Scale (ECR; Brennan, Clark, &Shaver, 1998), and their parents completed  The Rosenberg Self-Esteem Scale (SES; Rosenberg, 1965). The results suggested that parental global self-esteem has no effect on the adolescent attachment to parents. Parental positive rearing behaviors have been significantly associated with avoidance to parents. Furthermore, the negative rearing behaviors, such as  paternal  denying and rejecting,maternal punitiveness, maternal overinvolved and overprotective behavior, can predict the adolescent avoidance and anxiety to parents