Long-Term Rhythmic Video Soundtracker

We consider the problem of generating musical soundtracks in sync with rhythmic visual cues. Most existing works rely on pre-defined music representations, leading to the incompetence of generative flexibility and complexity. Other methods directly generating video-conditioned waveforms suffer from limited scenarios, short lengths, and unstable generation quality. To this end, we present Long-Term Rhythmic Video Soundtracker (LORIS), a novel framework to synthesize long-term conditional waveforms. Specifically, our framework consists of a latent conditional diffusion probabilistic model to perform waveform synthesis. Furthermore, a series of context-aware conditioning encoders are proposed to take temporal information into consideration for a long-term generation. Notably, we extend our model's applicability from dances to multiple sports scenarios such as floor exercise and figure skating. To perform comprehensive evaluations, we establish a benchmark for rhythmic video soundtracks including the pre-processed dataset, improved evaluation metrics, and robust generative baselines. Extensive experiments show that our model generates long-term soundtracks with state-of-the-art musical quality and rhythmic correspondence. Codes are available at \url{https://github.com/OpenGVLab/LORIS}.Comment: ICML202

Hierarchical Semantic Community Detection in Information Networks: A Complete Information Graph Approach

In order to detect the hierarchical semantic community which is helpful to discover the true organization of information network,we propose a complete information graph approach. In this method, we first use complete information graphs including semantic edges and link edges to represent information networks. Then we define semantic modularity as an objective function, a measure that can express not only the tightness of links, but also the consistency of content. Next, we improve Lovain\u27s algorithm and propose simLV algorithm to detect communities on the complete information graph. This recursive algorithm itself can discover semantic communities of different sizes in the process of execution. Experiment results show the hierarchical community detected by the simLV algorithm performs better than the Louvain in measuring the consistency of semantic content for our approach takes into account the content attributes of nodes, which are neglected by many other methods. It can detect more meaningful community structures with consistent content and tight structure in information networks such as social networks, citation networks, web networks, etc., which is helpful to the application of information dissemination analysis, topic detection, public opinion detection, etc

Infection-generated electric field in gut epithelium drives bidirectional migration of macrophages.

Many bacterial pathogens hijack macrophages to egress from the port of entry to the lymphatic drainage and/or bloodstream, causing dissemination of life-threatening infections. However, the underlying mechanisms are not well understood. Here, we report that Salmonella infection generates directional electric fields (EFs) in the follicle-associated epithelium of mouse cecum. In vitro application of an EF, mimicking the infection-generated electric field (IGEF), induces directional migration of primary mouse macrophages to the anode, which is reversed to the cathode upon Salmonella infection. This infection-dependent directional switch is independent of the Salmonella pathogenicity island 1 (SPI-1) type III secretion system. The switch is accompanied by a reduction of sialic acids on glycosylated surface components during phagocytosis of bacteria, which is absent in macrophages challenged by microspheres. Moreover, enzymatic cleavage of terminally exposed sialic acids reduces macrophage surface negativity and severely impairs directional migration of macrophages in response to an EF. Based on these findings, we propose that macrophages are attracted to the site of infection by a combination of chemotaxis and galvanotaxis; after phagocytosis of bacteria, surface electrical properties of the macrophage change, and galvanotaxis directs the cells away from the site of infection

Trainable Fixed-Point Quantization for Deep Learning Acceleration on FPGAs

Quantization is a crucial technique for deploying deep learning models on resource-constrained devices, such as embedded FPGAs. Prior efforts mostly focus on quantizing matrix multiplications, leaving other layers like BatchNorm or shortcuts in floating-point form, even though fixed-point arithmetic is more efficient on FPGAs. A common practice is to fine-tune a pre-trained model to fixed-point for FPGA deployment, but potentially degrading accuracy. This work presents QFX, a novel trainable fixed-point quantization approach that automatically learns the binary-point position during model training. Additionally, we introduce a multiplier-free quantization strategy within QFX to minimize DSP usage. QFX is implemented as a PyTorch-based library that efficiently emulates fixed-point arithmetic, supported by FPGA HLS, in a differentiable manner during backpropagation. With minimal effort, models trained with QFX can readily be deployed through HLS, producing the same numerical results as their software counterparts. Our evaluation shows that compared to post-training quantization, QFX can quantize models trained with element-wise layers quantized to fewer bits and achieve higher accuracy on both CIFAR-10 and ImageNet datasets. We further demonstrate the efficacy of multiplier-free quantization using a state-of-the-art binarized neural network accelerator designed for an embedded FPGA (AMD Xilinx Ultra96 v2). We plan to release QFX in open-source format

Moderating effects of perceived social support on self-efficacy and psychological well-being of Chinese nurses: a cross-sectional study

IntroductionNurses experience significant physical and psychological stress that negatively influences their psychological well-being. The objective of this study was to explore the association between self-efficacy and psychological well-being among Chinese nurses and to assess the moderating effects of perceived social support (PSS).MethodsIn 2020, a hospital-based cross-sectional study using a multistage random sampling approach was performed in five regions of Liaoning, China. Of the 1,200 surveyed nurses, 1,010 completed questionnaires that evaluated the demographic information, 14-item Hospital Anxiety and Depression Scale, General Self-Efficacy Scale, and Multidimensional Scale of Perceived Social Support. To examine the factors associated with mental health parameters, hierarchical multiple regression analysis was performed. The interactions were visualized using a simple slope analysis.ResultsThe mean depression and anxiety scores for Chinese nurses were 8.74 ± 3.50 and 6.18 ± 3.26, respectively. The association between self-efficacy and depression differed between the low perceived social support (PSS) group (1 SD below the mean, β = −0.169, p < 0.01) and high PSS group (1 SD above the mean, β = −0.077, p < 0.01). Similarly, the association between self-efficacy and anxiety differed between the low PSS group (1 SD below the mean, β = −0.155, p < 0.01) and high PSS group (1 SD above the mean, β = −0.044, p < 0.01).ConclusionWe found that Chinese nurses experienced high levels of anxiety and depression. Furthermore, PSS moderates the relationship between self-efficacy and psychological well-being. Therefore, interventions targeting self-efficacy and PSS should be implemented to improve the psychological well-being of nurses

Synchronization modulation increases transepithelial potentials in MDCK monolayers through Na/K pumps

Peer reviewedPublisher PD