168 research outputs found

    Pruning Deep Neural Networks from a Sparsity Perspective

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    In recent years, deep network pruning has attracted significant attention in order to enable the rapid deployment of AI into small devices with computation and memory constraints. Pruning is often achieved by dropping redundant weights, neurons, or layers of a deep network while attempting to retain a comparable test performance. Many deep pruning algorithms have been proposed with impressive empirical success. However, existing approaches lack a quantifiable measure to estimate the compressibility of a sub-network during each pruning iteration and thus may under-prune or over-prune the model. In this work, we propose PQ Index (PQI) to measure the potential compressibility of deep neural networks and use this to develop a Sparsity-informed Adaptive Pruning (SAP) algorithm. Our extensive experiments corroborate the hypothesis that for a generic pruning procedure, PQI decreases first when a large model is being effectively regularized and then increases when its compressibility reaches a limit that appears to correspond to the beginning of underfitting. Subsequently, PQI decreases again when the model collapse and significant deterioration in the performance of the model start to occur. Additionally, our experiments demonstrate that the proposed adaptive pruning algorithm with proper choice of hyper-parameters is superior to the iterative pruning algorithms such as the lottery ticket-based pruning methods, in terms of both compression efficiency and robustness.Comment: ICLR 202

    A New Species of the Genus Trimeresurus from Southwest China (Squamata: Viperidae)

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    Species from the Trimeresurus popeiorum complex (Subgenus: Popeia) is a very complex group. T. popeiorum is the only Popeia species known from China. During the past two years, five adult Popeia specimens (4 males, 1 female) were collected from Yingjiang County, Southern Yunnan, China. Molecular, morphological and ecological data show distinct differences from known species, herein we describe these specimens as a new species Trimeresurus yingjiangensis sp. nov Chen, Ding, Shi and Zhang, 2018. Morphologically, the new species distinct from other Popeia species by a combination of following characters: (1) dorsal body olive drab,without cross bands on the scales; (2) a conspicuous bicolor ventrolateral stripe present on each side of males, first row of dorsal scales firebrick with a white ellipse dot on posterior upper part in male, these strips absent in females; (3) eyes firebrick in both gender; (4) suboculars separated from 3rd upper labial by one scale on each side; (5) ventrals 164–168 (n = 5); (6) MSR 21

    One-Dimensional Adapter to Rule Them All: Concepts, Diffusion Models and Erasing Applications

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    The prevalent use of commercial and open-source diffusion models (DMs) for text-to-image generation prompts risk mitigation to prevent undesired behaviors. Existing concept erasing methods in academia are all based on full parameter or specification-based fine-tuning, from which we observe the following issues: 1) Generation alternation towards erosion: Parameter drift during target elimination causes alternations and potential deformations across all generations, even eroding other concepts at varying degrees, which is more evident with multi-concept erased; 2) Transfer inability & deployment inefficiency: Previous model-specific erasure impedes the flexible combination of concepts and the training-free transfer towards other models, resulting in linear cost growth as the deployment scenarios increase. To achieve non-invasive, precise, customizable, and transferable elimination, we ground our erasing framework on one-dimensional adapters to erase multiple concepts from most DMs at once across versatile erasing applications. The concept-SemiPermeable structure is injected as a Membrane (SPM) into any DM to learn targeted erasing, and meantime the alteration and erosion phenomenon is effectively mitigated via a novel Latent Anchoring fine-tuning strategy. Once obtained, SPMs can be flexibly combined and plug-and-play for other DMs without specific re-tuning, enabling timely and efficient adaptation to diverse scenarios. During generation, our Facilitated Transport mechanism dynamically regulates the permeability of each SPM to respond to different input prompts, further minimizing the impact on other concepts. Quantitative and qualitative results across ~40 concepts, 7 DMs and 4 erasing applications have demonstrated the superior erasing of SPM. Our code and pre-tuned SPMs are available on the project page https://lyumengyao.github.io/projects/spm.Comment: CVPR 202

    Enhancing the Internet of Things with Knowledge-Driven Software-Defined Networking Technology : Future Perspectives

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    The Internet of Things (IoT) connects smart devices to enable various intelligent services. The deployment of IoT encounters several challenges, such as difficulties in controlling and managing IoT applications and networks, problems in programming existing IoT devices, long service provisioning time, underused resources, as well as complexity, isolation and scalability, among others. One fundamental concern is that current IoT networks lack flexibility and intelligence. A network-wide flexible control and management are missing in IoT networks. In addition, huge numbers of devices and large amounts of data are involved in IoT, but none of them have been tuned for supporting network management and control. In this paper, we argue that Software-defined Networking (SDN) together with the data generated by IoT applications can enhance the control and management of IoT in terms of flexibility and intelligence. We present a review for the evolution of SDN and IoT and analyze the benefits and challenges brought by the integration of SDN and IoT with the help of IoT data. We discuss the perspectives of knowledge-driven SDN for IoT through a new IoT architecture and illustrate how to realize Industry IoT by using the architecture. We also highlight the challenges and future research works toward realizing IoT with the knowledge-driven SDN.Peer reviewe

    Hydroxysafflor Yellow A Inhibits LPS-Induced NLRP3 Inflammasome Activation via Binding to Xanthine Oxidase in Mouse RAW264.7 Macrophages

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    Hydroxysafflor yellow A (HSYA) is an effective therapeutic agent for inflammatory diseases and autoimmune disorders; however, its regulatory effect on NLRP3 inflammasome activation in macrophages has not been investigated. In this study, we predicted the potential interaction between HSYA and xanthine oxidase (XO) via PharmMapper inverse docking and confirmed the binding inhibition via inhibitory test (IC50 = 40.04 μM). Computation docking illustrated that, in this HSYA-XO complex, HSYA was surrounded by Leu 648, Leu 712, His 875, Leu 873, Ser 876, Glu 879, Phe 649, and Asn 650 with a binding energy of −5.77 kcal/M and formed hydrogen bonds with the hydroxyl groups of HSYA at Glu 879, Asn 650, and His 875. We then found that HSYA significantly decreased the activity of XO in RAW264.7 macrophages and suppressed LPS-induced ROS generation. Moreover, we proved that HSYA markedly inhibited LPS-induced cleaved caspase-1 activation via suppressing the sensitization of NLRP3 inflammasome and prevented the mature IL-1β formation from pro-IL-1β form. These findings suggest that XO may be a potential target of HSYA via direct binding inhibition and the combination of HSYA-XO suppresses LPS-induced ROS generation, contributing to the depression of NLRP3 inflammasome and inhibition of IL-1β secretion in macrophages
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