The prediction of molecular properties is a fundamental task in the field of drug ***,graph neural networks(GNNs)have been gaining prominence in this *** a molecule tends to have multiple correlated properties,there i...
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The prediction of molecular properties is a fundamental task in the field of drug ***,graph neural networks(GNNs)have been gaining prominence in this *** a molecule tends to have multiple correlated properties,there is a great need to develop the multi-task learning ability of ***,limited by expensive and time-consuming human annotations,collecting complete labels for each task is *** a result,most existing benchmarks involve many missing labels in training data,and the performance of GNNs is impaired due to the lack of sufficient supervision *** overcome this obstacle,we propose to improve multi-task molecular property prediction by missing label ***,a bipartite graph is first introduced to model the molecule-task co-occurrence ***,the imputation of missing labels is transformed into predicting missing edges on this bipartite *** predict the missing edges,a graph neural network is devised,which can learn the complex molecule-task co-occurrence *** that,we select reliable pseudo labels according to the uncertainty of the prediction *** with enough and reliable supervision information,our approach achieves state-of-the-art performance on a variety of real-world datasets.
Data augmentation is widely recognized as an effective means of bolstering model ***,when applied to monocular 3D object detection,non-geometric image augmentation neglects the critical link between the image and phys...
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Data augmentation is widely recognized as an effective means of bolstering model ***,when applied to monocular 3D object detection,non-geometric image augmentation neglects the critical link between the image and physical space,resulting in the semantic collapse of the extended *** address this issue,we propose two geometric-level data augmentation operators named Geometric-Copy-Paste(Geo-CP)and Geometric-Crop-Shrink(Geo-CS).Both operators introduce geometric consistency based on the principle of perspective projection,complementing the options available for data augmentation in monocular ***,Geo-CP replicates local patches by reordering object depths to mitigate perspective occlusion conflicts,and Geo-CS re-crops local patches for simultaneous scaling of distance and scale to unify appearance and *** operations ameliorate the problem of class imbalance in the monocular paradigm by increasing the quantity and distribution of geometrically consistent *** demonstrate that our geometric-level augmentation operators effectively improve robustness and performance in the KITTI and Waymo monocular 3D detection benchmarks.
intelligent indoor robotics is expected to rapidly gain importance in crucial areas of our modern society such as at-home health care and factories. Yet, existing mobile robots are limited in their ability to perceive...
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intelligent indoor robotics is expected to rapidly gain importance in crucial areas of our modern society such as at-home health care and factories. Yet, existing mobile robots are limited in their ability to perceive and respond to dynamically evolving complex indoor environments because of their inherently limited sensing and computing resources that are, moreover, traded off against their cruise time and payload. To address these formidable challenges, here we propose intelligent indoor metasurface robotics(I2MR),where all sensing and computing are relegated to a centralized robotic brain endowed with microwave perception; and I2MR's limbs(motorized vehicles, airborne drones, etc.) merely execute the wirelessly received instructions from the brain. The key aspect of our concept is the centralized use of a computation-enabled programmable metasurface that can flexibly mold microwave propagation in the indoor wireless environment, including a sensing and localization modality based on configurational diversity and a communication modality to establish a preferential high-capacity wireless link between the I2MR's brain and limbs. The metasurface-enhanced microwave perception is capable of realizing low-latency and high-resolution three-dimensional imaging of humans, even around corners and behind thick concrete walls, which is the basis for action decisions of the I2MR's brain. I2MR is thus endowed with real-time and full-context awareness of its operating indoor environment. We implement, experimentally, a proof-of-principle demonstration at ~2.4 GHz, in which I2MR provides health-care assistance to a human inhabitant. The presented strategy opens a new avenue for the conception of smart and wirelessly networked indoor robotics.
Non-volatile memories(NVMs)provide lower latency and higher bandwidth than block ***,NVMs are byte-addressable and provide persistence that can be used as memory-level storage devices(non-volatile main memory,NVMM).Th...
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Non-volatile memories(NVMs)provide lower latency and higher bandwidth than block ***,NVMs are byte-addressable and provide persistence that can be used as memory-level storage devices(non-volatile main memory,NVMM).These features change storage hierarchy and allow CPU to access persistent data using load/store ***,we can directly build a file system on ***,traditional file systems are designed based on slow block *** use a deep and complex software stack to optimize file system *** design results in software overhead being the dominant factor affecting NVMM file ***,scalability,crash consistency,data protection,and cross-media storage should be reconsidered in NVMM file *** survey existing work on optimizing NVMM file ***,we analyze the problems when directly using traditional file systems on NVMM,including heavy software overhead,limited scalability,inappropriate consistency guarantee techniques,***,we summarize the technique of 30 typical NVMM file systems and analyze their advantages and ***,we provide a few suggestions for designing a high-performance NVMM file system based on real hardware Optane DC persistent memory ***,we suggest applying various techniques to reduce software overheads,improving the scalability of virtual file system(VFS),adopting highly-concurrent data structures(e.g.,lock and index),using memory protection keys(MPK)for data protection,and carefully designing data placement/migration for cross-media file system.
Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities,such as overfishing and deforestation,and the effects of such damage on ecological stability are ***...
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Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities,such as overfishing and deforestation,and the effects of such damage on ecological stability are *** recent advances in experimental and theoretical studies on regime shifts and tipping points,theoretical tools for understanding the extinction chain,which is the sequence of species extinctions resulting from overexploitation,are still lacking,especially for large-scale nonlinear networked *** this study,we developed a mathematical tool to predict regime shifts and extinction chains in ecosystems under multiple exploitation situations and verified it in 26 real-world mutualistic networks of various sizes and *** discovered five phases during the exploitation process:safe,partial extinction,bistable,tristable,and collapse,which enabled the optimal design of restoration strategies for degraded or collapsed *** validated our approach using a 20-year dataset from an eelgrass restoration ***,we also found a specific region in the diagram spanning exploitation rates and competition intensities,where exploiting more species helps increase *** computational tool provides insights into harvesting,fishing,exploitation,or deforestation plans while conserving or restoring the biodiversity of mutualistic ecosystems.
Sharing the hardware platform between diverse information systems to establish full cooperation among different functionalities has attracted substantial ***,broadband multifunctional integrated systems with large ope...
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Sharing the hardware platform between diverse information systems to establish full cooperation among different functionalities has attracted substantial ***,broadband multifunctional integrated systems with large operating frequency ranges are challenging due to the bandwidth and computing speed restrictions of electronic ***,we report an analog parallel processor(APP)based on the silicon photonic platform that directly discretizes and parallelizes the broadband signal in the analog *** APP first discretizes the signal with the optical frequency comb and then adopts optical dynamic phase interference to reassign the analog signal into 2N parallel *** photonic analog parallelism,data rate and data volume in each sequence are simultaneously compressed,which mitigates the requirement on each parallel computing ***,the fusion of the outputs from each computing core is equivalent to directly processing broadband *** the proof-of-concept experiment,two-channel analog parallel processing of broadband radar signals and high-speed communication signals is implemented on the single photonic integrated *** bandwidth of broadband radar signal is 6 GHz and the range resolution of 2.69 cm is *** wireless communication rate of 8 Gbit/s is also *** the bandwidth and speed limitations of the single-computing core along with further exploring the multichannel potential of this architecture,we anticipate that the proposed APP will accelerate the development of powerful optoelectronic processors as critical support for applications such as satellite networks and intelligent driving.
作者:
Du, AnJia, JieChen, JianWang, XingweiHuang, MinNortheastern University
School of Computer Science and Engineering The Engineering Research Center of Security Technology of Complex Network System Shenyang110819 China Northeastern University
Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Northeastern University
College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China
Mobile edge computing (MEC) integrated with Network Functions Virtualization (NFV) helps run a wide range of services implemented by Virtual Network Functions (VNFs) deployed at MEC networks. This emerging paradigm of...
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Dual labeling of an RNA can provide Förster resonance energy transfer(FRET)sensors for studying RNA folding,miRNA maturation,and RNA-protein ***,we report the development of a highly efficient strategy for direct...
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Dual labeling of an RNA can provide Förster resonance energy transfer(FRET)sensors for studying RNA folding,miRNA maturation,and RNA-protein ***,we report the development of a highly efficient strategy for direct dual-terminal labeling of any RNA of *** explored new Michael cycloaddition for facile labeling of 5′-terminal RNA with improved *** chemical tetrazinylation of RNA at the 3′-terminus was achieved with the highly efficient and catalysis-free tetrazine-cycloalkyne *** single-terminal labeling methods were combined for dual-terminal labeling of an RNA including short hairpin RNA,pre-miRNA,riboswitch,and noncoding ***,these dual-labeled RNA-based FRET sensors were used to monitor RNA-ligand interactions in vitro and in live *** is anticipated that these universal RNA labeling strategies will be useful to study RNA structures and functions.
Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse informa-tion of images,which may be captured under different times,angles,or *** several surveys have reviewed ...
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Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse informa-tion of images,which may be captured under different times,angles,or *** several surveys have reviewed the development of medical image registration,they have not systematically summarized the existing med-ical image registration *** this end,a comprehensive review of these methods is provided from traditional and deep-learning-based perspectives,aiming to help audiences quickly understand the development of medical image *** particular,we review recent advances in retinal image registration,which has not attracted much *** addition,current challenges in retinal image registration are discussed and insights and prospects for future research provided.
Graph convolutional networks(GCNs)have received significant attention from various research fields due to the excellent performance in learning graph *** GCN performs well compared with other methods,it still faces **...
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Graph convolutional networks(GCNs)have received significant attention from various research fields due to the excellent performance in learning graph *** GCN performs well compared with other methods,it still faces *** a GCN model for large-scale graphs in a conventional way requires high computation and storage ***,motivated by an urgent need in terms of efficiency and scalability in training GCN,sampling methods have been proposed and achieved a significant *** this paper,we categorize sampling methods based on the sampling mechanisms and provide a comprehensive survey of sampling methods for efficient training of *** highlight the characteristics and differences of sampling methods,we present a detailed comparison within each category and further give an overall comparative analysis for the sampling methods in all ***,we discuss some challenges and future research directions of the sampling methods.
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