Synthesis, Structural, Optical and Mechanical Characterization of SrB\u3csub\u3e2\u3c/sub\u3eO\u3csub\u3e4\u3c/sub\u3e Nanorods

Single crystalline strontium borate (SrB2O4) nanorods were synthesized for the first time via a facile sol–gel route at low temperature. The SrB2O4 nanorods have a good crystalline nature with the growth direction along the [511] orientation and they are transparent from the ultraviolet to the visible regimes. Nanoscale three-point bending tests were performed directly on individual nanorods to probe their mechanical properties using an atomic force microscope. The elastic modulus of SrB2O4 nanorods was measured to be 158.2 ± 2.8 GPa, exhibiting a significant increase compared with other borate nanostructures and bulk borates

Analysis on Current Situation and Present Development of Intelligent Transport System Traffic

This article introduces the constitution of intelligent transport system and the development of intelligent transport system in home and abroad. At the same time it analyzes the current situation development and suggestion of intelligent transport system in national

Mars: Modeling Context & State Representations with Contrastive Learning for End-to-End Task-Oriented Dialog

Traditional end-to-end task-oriented dialog systems first convert dialog context into belief state and action state before generating the system response. The system response performance is significantly affected by the quality of the belief state and action state. We first explore what dialog context representation is beneficial to improving the quality of the belief state and action state, which further enhances the generated response quality. To tackle our exploration, we propose Mars, an end-to-end task-oriented dialog system with two contrastive learning strategies to model the relationship between dialog context and belief/action state representations. Empirical results show dialog context representations, which are more different from semantic state representations, are more conducive to multi-turn task-oriented dialog. Moreover, our proposed Mars achieves state-of-the-art performance on the MultiWOZ 2.0, CamRest676, and CrossWOZ.Comment: Findings of ACL202

A Relay System for Semantic Image Transmission based on Shared Feature Extraction and Hyperprior Entropy Compression

Nowadays, the need for high-quality image reconstruction and restoration is more and more urgent. However, most image transmission systems may suffer from image quality degradation or transmission interruption in the face of interference such as channel noise and link fading. To solve this problem, a relay communication network for semantic image transmission based on shared feature extraction and hyperprior entropy compression (HEC) is proposed, where the shared feature extraction technology based on Pearson correlation is proposed to eliminate partial shared feature of extracted semantic latent feature. In addition, the HEC technology is used to resist the effect of channel noise and link fading and carried out respectively at the source node and the relay node. Experimental results demonstrate that compared with other recent research methods, the proposed system has lower transmission overhead and higher semantic image transmission performance. Particularly, under the same conditions, the multi-scale structural similarity (MS-SSIM) of this system is superior to the comparison method by approximately 0.2

SegCLIP: Patch Aggregation with Learnable Centers for Open-Vocabulary Semantic Segmentation

Recently, the contrastive language-image pre-training, e.g., CLIP, has demonstrated promising results on various downstream tasks. The pre-trained model can capture enriched visual concepts for images by learning from a large scale of text-image data. However, transferring the learned visual knowledge to open-vocabulary semantic segmentation is still under-explored. In this paper, we propose a CLIP-based model named SegCLIP for the topic of open-vocabulary segmentation in an annotation-free manner. The SegCLIP achieves segmentation based on ViT and the main idea is to gather patches with learnable centers to semantic regions through training on text-image pairs. The gathering operation can dynamically capture the semantic groups, which can be used to generate the final segmentation results. We further propose a reconstruction loss on masked patches and a superpixel-based KL loss with pseudo-labels to enhance the visual representation. Experimental results show that our model achieves comparable or superior segmentation accuracy on the PASCAL VOC 2012 (+1.4% mIoU), PASCAL Context (+2.4% mIoU), and COCO (+5.6% mIoU) compared with baselines. We release the code at https://github.com/ArrowLuo/SegCLIP

Transcriptomic responses in mouse brain exposed to chronic excess of the neurotransmitter glutamate

Background: Increases during aging in extracellular levels of glutamate (Glu), the major excitatory neurotransmitter in the brain, may be linked to chronic neurodegenerative diseases. Little is known about the molecular responses of neurons to chronic, moderate increases in Glu levels. Genome-wide gene expression in brain hippocampus was examined in a unique transgenic (Tg) mouse model that exhibits moderate Glu hyperactivity throughout the lifespan, the neuronal Glutamate dehydrogenase (Glud1) mouse, and littermate 9 month-old wild type mice. Results: Integrated bioinformatic analyses on transcriptomic data were used to identify bio-functions, pathways and gene networks underlying neuronal responses to increased Glu synaptic release. Bio-functions and pathways up-regulated in Tg mice were those associated with oxidative stress, cell injury, inflammation, nervous system development, neuronal growth, and synaptic transmission. Increased gene expression in these functions and pathways indicated apparent compensatory responses offering protection against stress, promoting growth of neuronal processes (neurites) and re-establishment of synapses. The transcription of a key gene in the neurite growth network, the kinase Ptk2b, was significantly up-regulated in Tg mice as was the activated (phosphorylated) form of the protein. In addition to genes related to neurite growth and synaptic development, those associated with neuronal vesicle trafficking in the Huntington's disease signalling pathway, were also up-regulated. Conclusions: This is the first study attempting to define neuronal gene expression patterns in response to chronic, endogenous Glu hyperactivity at brain synapses. The patterns observed were characterized by a combination of responses to stress and stimulation of nerve growth, intracellular transport and recovery

Transcriptomic responses in mouse brain exposed to chronic excess of the neurotransmitter glutamate

<p>Abstract</p> <p>Background</p> <p>Increases during aging in extracellular levels of glutamate (Glu), the major excitatory neurotransmitter in the brain, may be linked to chronic neurodegenerative diseases. Little is known about the molecular responses of neurons to chronic, moderate increases in Glu levels. Genome-wide gene expression in brain hippocampus was examined in a unique transgenic (Tg) mouse model that exhibits moderate Glu hyperactivity throughout the lifespan, the neuronal <it>Glutamate dehydrogenase </it>(<it>Glud1</it>) mouse, and littermate 9 month-old wild type mice.</p> <p>Results</p> <p>Integrated bioinformatic analyses on transcriptomic data were used to identify bio-functions, pathways and gene networks underlying neuronal responses to increased Glu synaptic release. Bio-functions and pathways up-regulated in Tg mice were those associated with oxidative stress, cell injury, inflammation, nervous system development, neuronal growth, and synaptic transmission. Increased gene expression in these functions and pathways indicated apparent compensatory responses offering protection against stress, promoting growth of neuronal processes (neurites) and re-establishment of synapses. The transcription of a key gene in the neurite growth network, the kinase <it>Ptk2b</it>, was significantly up-regulated in Tg mice as was the activated (phosphorylated) form of the protein. In addition to genes related to neurite growth and synaptic development, those associated with neuronal vesicle trafficking in the Huntington's disease signalling pathway, were also up-regulated.</p> <p>Conclusions</p> <p>This is the first study attempting to define neuronal gene expression patterns in response to chronic, endogenous Glu hyperactivity at brain synapses. The patterns observed were characterized by a combination of responses to stress and stimulation of nerve growth, intracellular transport and recovery.</p

Deep Generative Modeling on Limited Data with Regularization by Nontransferable Pre-trained Models

Deep generative models (DGMs) are data-eager because learning a complex model on limited data suffers from a large variance and easily overfits. Inspired by the classical perspective of the bias-variance tradeoff, we propose regularized deep generative model (Reg-DGM), which leverages a nontransferable pre-trained model to reduce the variance of generative modeling with limited data. Formally, Reg-DGM optimizes a weighted sum of a certain divergence and the expectation of an energy function, where the divergence is between the data and the model distributions, and the energy function is defined by the pre-trained model w.r.t. the model distribution. We analyze a simple yet representative Gaussian-fitting case to demonstrate how the weighting hyperparameter trades off the bias and the variance. Theoretically, we characterize the existence and the uniqueness of the global minimum of Reg-DGM in a non-parametric setting and prove its convergence with neural networks trained by gradient-based methods. Empirically, with various pre-trained feature extractors and a data-dependent energy function, Reg-DGM consistently improves the generation performance of strong DGMs with limited data and achieves competitive results to the state-of-the-art methods