The Magnetic Properties of 1111-type Diluted Magnetic Semiconductor (La1−x_{1-x}Bax_{x})(Zn1−x_{1-x}Mnx_{x})AsO in the Low Doping Regime

We investigated the magnetic properties of (La$_{1-x}$Ba$_{x}$)(Zn$_{1-x}$Mn$_{x}$)AsO with $x$ varying from 0.005 to 0.05 at an external magnetic field of 1000 Oe. For doping levels of $x$ $\leq$ 0.01, the system remains paramagnetic down to the lowest measurable temperature of 2 K. Only when the doping level increases to $x$ = 0.02 does the ferromagnetic ordering appear. Our analysis indicates that antiferromagnetic exchange interactions dominate for $x$ $\leq$ 0.01, as shown by the negative Weiss temperature fitted from the magnetization data. The Weiss temperature becomes positive, i.e., ferromagnetic coupling starts to dominate, for $x$ $\geq$ 0.02. The Mn-Mn spin interaction parameter $\mid$$2J/k_B$$\mid$ is estimated to be in the order of 10 K for both $x$ $\leq$ 0.01 (antiferromagnetic ordered state) and $x$ $\geq$ 0.02 (ferromagnetic ordered state). Our results unequivocally demonstrate the competition between ferromagnetic and antiferromagnetic exchange interactions in carrier-mediated ferromagnetic systems.Comment: 9 pages, 3 figure

YOLOCS: Object Detection based on Dense Channel Compression for Feature Spatial Solidification

In this study, we examine the associations between channel features and convolutional kernels during the processes of feature purification and gradient backpropagation, with a focus on the forward and backward propagation within the network. Consequently, we propose a method called Dense Channel Compression for Feature Spatial Solidification. Drawing upon the central concept of this method, we introduce two innovative modules for backbone and head networks: the Dense Channel Compression for Feature Spatial Solidification Structure (DCFS) and the Asymmetric Multi-Level Compression Decoupled Head (ADH). When integrated into the YOLOv5 model, these two modules demonstrate exceptional performance, resulting in a modified model referred to as YOLOCS. Evaluated on the MSCOCO dataset, the large, medium, and small YOLOCS models yield AP of 50.1%, 47.6%, and 42.5%, respectively. Maintaining inference speeds remarkably similar to those of the YOLOv5 model, the large, medium, and small YOLOCS models surpass the YOLOv5 model's AP by 1.1%, 2.3%, and 5.2%, respectively

Kuaipedia: a Large-scale Multi-modal Short-video Encyclopedia

Online encyclopedias, such as Wikipedia, have been well-developed and researched in the last two decades. One can find any attributes or other information of a wiki item on a wiki page edited by a community of volunteers. However, the traditional text, images and tables can hardly express some aspects of an wiki item. For example, when we talk about ``Shiba Inu'', one may care more about ``How to feed it'' or ``How to train it not to protect its food''. Currently, short-video platforms have become a hallmark in the online world. Whether you're on TikTok, Instagram, Kuaishou, or YouTube Shorts, short-video apps have changed how we consume and create content today. Except for producing short videos for entertainment, we can find more and more authors sharing insightful knowledge widely across all walks of life. These short videos, which we call knowledge videos, can easily express any aspects (e.g. hair or how-to-feed) consumers want to know about an item (e.g. Shiba Inu), and they can be systematically analyzed and organized like an online encyclopedia. In this paper, we propose Kuaipedia, a large-scale multi-modal encyclopedia consisting of items, aspects, and short videos lined to them, which was extracted from billions of videos of Kuaishou (Kwai), a well-known short-video platform in China. We first collected items from multiple sources and mined user-centered aspects from millions of users' queries to build an item-aspect tree. Then we propose a new task called ``multi-modal item-aspect linking'' as an expansion of ``entity linking'' to link short videos into item-aspect pairs and build the whole short-video encyclopedia. Intrinsic evaluations show that our encyclopedia is of large scale and highly accurate. We also conduct sufficient extrinsic experiments to show how Kuaipedia can help fundamental applications such as entity typing and entity linking

Family function, anxiety and depression in adults with disabilities: a network analysis

BackgroundThe prevalence of family dysfunction, anxiety and depression is high in people with disabilities due to long-term activity constraints and social difficulties. Recently, although studies have attempted to provide guidance for family therapy by focusing on the relationship between family function and negative emotions, the specific effects of improved family function during family therapy on alleviation of anxiety and depressive symptoms have been obscured. Thus, this study attempted to elucidate the impact of specific family functioning on specific symptoms of anxiety and depression through network analysis.MethodsFamily APGAR Index Questionnaire (APGAR), Generalized Anxiety Scale (GAD-7), and Patient Health Questionnaire Depression Scale (PHQ-9) were used to survey 897 adults with disabilities in Sichuan Province. Meanwhile, network analysis for studying the relationship between anxiety, depression and family functioning among the disabled via R software.ResultsThe network analysis showed that (1) Nodes PHQ4 (“Energy”), APGAR3 (“Growth”), GAD1 (“Nervousness”) and GAD4 (“Relaxing Trouble”) were central nodes in the network model; (2) Bridge nodes linking family function, anxiety and depressive symptoms in the sample were PHQ9 (“Suicide ideation”), PHQ6 (“Worthlessness”), GAD1 (“Nervousness”) and GAD5 (“Restlessness”); (3) The node APGAR5 (“Resolve”) directly connects the bridge symptoms PHQ9 (“Suicide ideation”) and PHQ8 (“Motor”).ConclusionThis study suggests that therapists could target the resolve of family members during family therapy to reduce suicidal ideation and enhance the level of activity of people with disabilities, thereby improving the network of anxiety and depression symptoms and alleviating negative emotions of people with disabilities

The CircHAS2/RPL23/MMP9 Axis Facilitates Brain Tumor Metastasis

Background: Circular RNAs (circRNAs) regulate tumor development by interacting with microRNAs. However, limited research has been conducted on the roles of circRNAs in gliomas. Therefore, we sought to demonstrate the function and molecular mechanism of circHAS2 in gliomas. Methods: CircHAS2, hsa-miR-508-3p, RPL23, and MMP9 mRNA levels were assessed with qRT-PCR. RPL23 and MMP9 protein levels were determined with western blotting and immunohistochemical staining. Glioma cell migration and invasion were assessed with Transwell assays. The interaction between hsa-miR-508-3p and circHAS2 or RPL23 was predicted with RNAhybrid and miRanda, and confirmed through luciferase reporter assays. The effects of circHAS2 on glioma cells were demonstrated in a nude mouse orthotopic xenograft glioma model. Results: We computationally analyzed the differentially expressed circRNAs in glioma tissues by using the GEO database. The screening indicated that circHAS2 was located primarily in the cytoplasm. Functionally, silencing of circHAS2 inhibited glioma migration and invasion. Mechanically, hsa-miR-508-3p was identified as a downstream target of circHAS2. CircHAS2 was found to regulate RPL23 and influence MMP9 via hsa-miR-508-3p, thereby promoting glioma migration and invasion. Moreover, inhibition of circHAS2 impeded the progression of U87 glioma cells in vivo. Conclusion: CircHAS2 regulates RPL23 and subsequent MMP9 expression by sponging hsa-miR508-3p in glioma cells

A Unified Model for Video Understanding and Knowledge Embedding with Heterogeneous Knowledge Graph Dataset

Video understanding is an important task in short video business platforms and it has a wide application in video recommendation and classification. Most of the existing video understanding works only focus on the information that appeared within the video content, including the video frames, audio and text. However, introducing common sense knowledge from the external Knowledge Graph (KG) dataset is essential for video understanding when referring to the content which is less relevant to the video. Owing to the lack of video knowledge graph dataset, the work which integrates video understanding and KG is rare. In this paper, we propose a heterogeneous dataset that contains the multi-modal video entity and fruitful common sense relations. This dataset also provides multiple novel video inference tasks like the Video-Relation-Tag (VRT) and Video-Relation-Video (VRV) tasks. Furthermore, based on this dataset, we propose an end-to-end model that jointly optimizes the video understanding objective with knowledge graph embedding, which can not only better inject factual knowledge into video understanding but also generate effective multi-modal entity embedding for KG. Comprehensive experiments indicate that combining video understanding embedding with factual knowledge benefits the content-based video retrieval performance. Moreover, it also helps the model generate better knowledge graph embedding which outperforms traditional KGE-based methods on VRT and VRV tasks with at least 42.36% and 17.73% improvement in HITS@10

DESI Legacy Imaging Surveys Data Release 9: Cosmological Constraints from Galaxy Clustering and Weak Lensing using the Minimal Bias Model

We present a tentative constraint on cosmological parameters $\Omega_m$ and $\sigma_8$ from a joint analysis of galaxy clustering and galaxy-galaxy lensing from DESI Legacy Imaging Surveys Data Release 9 (DR9), covering approximately 10000 square degrees and spanning the redshift range of 0.1 to 0.9. To study the dependence of cosmological parameters on lens redshift, we divide lens galaxies into seven approximately volume-limited samples, each with an equal width in photometric redshift. To retrieve the intrinsic projected correlation function $w_{\rm p}(r_{\rm p})$ from the lens samples, we employ a novel method to account for redshift uncertainties. Additionally, we measured the galaxy-galaxy lensing signal $\Delta\Sigma(r_{\rm p})$ for each lens sample, using source galaxies selected from the shear catalog by applying our \texttt{Fourier\_Quad} pipeline to DR9 images. We model these observables within the flat $\Lambda$CDM framework, employing the minimal bias model. To ensure the reliability of the minimal bias model, we apply conservative scale cuts: $r_{\rm p} > 8$ and $12 ~h^{-1}{\rm Mpc}$, for $w_{\rm p}(r_{\rm p})$ and $\Delta\Sigma(r_{\rm p})$, respectively. Our findings suggest a mild tendency that $S_8 \equiv \sigma_8 \sqrt{\Omega_m/0.3}$ increases with lens redshift, although this trend is only marginally significant. When we combine low redshift samples, the value of $S_8$ is determined to be $0.84 \pm 0.02$, consistent with the Planck results but significantly higher than the 3$\times$ 2pt analysis by 2-5$\sigma$. Despite the fact that further refinements in measurements and modeling could improve the accuracy of our results, the consistency with standard values demonstrates the potential of our method for more precise and accurate cosmology in the future.Comment: slightly different with the published versio