LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment

The video-language (VL) pretraining has achieved remarkable improvement in multiple downstream tasks. However, the current VL pretraining framework is hard to extend to multiple modalities (N modalities, N>=3) beyond vision and language. We thus propose LanguageBind, taking the language as the bind across different modalities because the language modality is well-explored and contains rich semantics. Specifically, we freeze the language encoder acquired by VL pretraining, then train encoders for other modalities with contrastive learning. As a result, all modalities are mapped to a shared feature space, implementing multi-modal semantic alignment. While LanguageBind ensures that we can extend VL modalities to N modalities, we also need a high-quality dataset with alignment data pairs centered on language. We thus propose VIDAL-10M with Video, Infrared, Depth, Audio and their corresponding Language, naming as VIDAL-10M. In our VIDAL-10M, all videos are from short video platforms with complete semantics rather than truncated segments from long videos, and all the video, depth, infrared, and audio modalities are aligned to their textual descriptions. LanguageBind has achieved superior performance on a wide range of 15 benchmarks covering video, audio, depth, and infrared. Moreover, multiple experiments have provided evidence for the effectiveness of LanguageBind in achieving indirect alignment and complementarity among diverse modalities. Code address: https://github.com/PKU-YuanGroup/LanguageBindComment: Accepted by ICLR 202

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Recurrence of low-risk vaginal embryonal rhabdomyosarcoma: A case report and literature review

Abstract Background Low-risk vaginal embryonal rhabdomyosarcoma (ERMS) requires no radiotherapy (RT) for local control. Case summary A 32-month-old girl presented with an exophytic vaginal botryoid mass, which was confirmed of be ERMS. She was given two courses of vincristine, topotecan, and cyclophosphamide (VAC) as neoadjuvant therapy, after which she underwent a hysteroscopy and conservative resection of the vaginal lesion with a negative margin. She was diagnosed with low-risk ERMS (stage I, subgroup A and Group I) and was discharged after another four courses of VAS. However, twenty-eight months after the last treatment, she presented with a giant mass protruding through the vaginal introitus, which was confirmed to be a recurrence of ERMS. Despite multiple rounds of therapy, the patient died 39 months after her diagnosis, at 5 years of age. Conclusion When making the decision to eliminate RT for low-risk vaginal ERMS patients, the risk of local recurrence should be considered and emphasized.</jats:p

Enjoyment or Indulgence? Social Media Service Usage, Social Gratification, Self-Control Failure and Emotional Health

Social networking site smartphone applications have been widely used among Chinese young adults. However, less is known about their effects on emotional health and the mechanisms through which they function. This study analyzes the relationship between college students’ smartphone social networking service use patterns, social gratification, social media self-control failure, and emotional health. Data was collected from 360 college students in China via application log tracking and a self-administered questionnaire. Structural equation modeling results showed that, after controlling for demographic variables, the use of video social networking site smartphone applications was associated with decreased social gratification, and ultimately, adverse emotional health. Using social networking site smartphone applications late at night exhibited worse emotional health via more social media self-control failure. The implications for designing and using social media applications are discussed.</jats:p

Enjoyment or Indulgence? Social Media Service Usage, Social Gratification, Self-Control Failure and Emotional Health

Social networking site smartphone applications have been widely used among Chinese young adults. However, less is known about their effects on emotional health and the mechanisms through which they function. This study analyzes the relationship between college students’ smartphone social networking service use patterns, social gratification, social media self-control failure, and emotional health. Data was collected from 360 college students in China via application log tracking and a self-administered questionnaire. Structural equation modeling results showed that, after controlling for demographic variables, the use of video social networking site smartphone applications was associated with decreased social gratification, and ultimately, adverse emotional health. Using social networking site smartphone applications late at night exhibited worse emotional health via more social media self-control failure. The implications for designing and using social media applications are discussed

A Graph Neural Network for superpixel image classification

Abstract The classification of superpixel images by graph neural networks has gradually become a research hotspot. It is a crucial issue to embed super-pixel images from lowdimensional to high-dimensional so as to turn complex image information into graph signals. This paper proposes a method for image classification using a graph neural network (GNN) model. We convert the input image into a region adjacency graph (RAG) composed of superpixels as nodes, and use residual and concat structure to extract deep features. Finally, the loss function that increases the distance between classes and compactness within classes is used as supervision. Experiments have been tested with different numbers of superpixels on multiple datasets, and the results show that our method has a great performance in superpixel images classification.</jats:p

Spectrum decomposition in Gaussian scale space for uneven illumination image binarization

Although most images in industrial applications have fewer targets and simple image backgrounds, binarization is still a challenging task, and the corresponding results are usually unsatisfactory because of uneven illumination interference. In order to efficiently threshold images with nonuniform illumination, this paper proposes an efficient global binarization algorithm that estimates the inhomogeneous background surface of the original image constructed from the first k leading principal components in the Gaussian scale space (GSS). Then, we use the difference operator to extract the distinct foreground of the original image in which the interference of uneven illumination is effectively eliminated. Finally, the image can be effortlessly binarized by an existing global thresholding algorithm such as the Otsu method. In order to qualitatively and quantitatively verify the segmentation performance of the presented scheme, experiments were performed on a dataset collected from a nonuniform illumination environment. Compared with classical binarization methods, in some metrics, the experimental results demonstrate the effectiveness of the introduced algorithm in providing promising binarization outcomes and low computational costs.</jats:p