Observation of B+ → χc0K+

journal articl

研究速報 : 補強箱型はりの横衝突崩壊挙動の弾／粘塑性解析

departmental bulletin pape

Spatial attention and quantization-based contrastive learning framework for mmWave massive MIMO beam training

Deep learning (DL)-based beam training schemes have been exploited to improve spectral efficiency with fast optimal beam selection for millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. To achieve high prediction accuracy, these DL models rely on training with a tremendous amount of labeled environmental measurements, such as mmWave channel state information (CSI). However, demanding a large volume of ground truth labels for beam training is inefficient and infeasible due to the high labeling cost and the requirement for expertise in practical mmWave massive MIMO systems. Meanwhile, a complex environment incurs critical performance degradation in the continuous output of beam training. In this paper, we propose a novel contrastive learning framework, named self-enhanced quantized phase-based transformer network (SE-QPTNet), for reliable beam training with only a small fraction of the labeled CSI dataset. We first develop a quantized phase-based transformer network (QPTNet) with a hierarchical structure to explore the essential features from frequency and spatial views and quantize the environmental components with a latent beam codebook to achieve robust representation. Next, we design the SE-QPTNet including self-enhanced pre-training and supervised beam training. SE-QPTNet pre-trains by the contrastive information of the target user and others with the unlabeled CSI, and then, it is utilized as the initialization to fine-tune with a reduced volume of labeled CSI. Finally, the experimental results show that the proposed framework improves beam prediction accuracy and data rates with 5% labeled data compared to existing solutions. Our proposed framework further enhances flexibility and breaks the limitation of the quantity of label information for practical beam training.journal articl

Region‐based fully convolutional networks with deformable convolution and attention fusion for steel surface defect detection in industrial Internet of Things

Abstract Next‐generation 6G networks will fully drive the development of the industrial Internet of Things. Steel surface defect detection as an important application in industrial Internet of Things has recently received increasing attention from the military industry, the aviation industry and other fields, which is closely related to the quality of industrial production products. However, many typical convolutional neural networks‐based methods are insensitive to the problem of unclear boundaries. In this article, the authors develop a region‐based fully convolutional networks with deformable convolution and attention fusion to adaptively learn salient features for steel surface defect detection. Specifically, deformable convolution is applied into selectively replace the standard convolution in the backbone of the region‐based fully convolutional networks, which performs significantly in scenarios with unclear defect boundaries. Moreover, convolutional block attention module is utilised in region proposal network to further enhance detection accuracy. The proposed architecture is demonstrated on two popular steel defect detection benchmarks, including NEU‐DET and GC10‐DET, which can effectively present the performance of steel surface defect detection by abundant experiments. The mean average precision on two datasets reaches 80.9% and 66.2%. The average precision of defect crazing, inclusion, patches, pitted‐surface, rolled‐in scale and scratches on NEU‐DET is 58.2%, 82.3%, 95.7%, 85.6%, 75.9%, and 87.9% respectively

Tuning PN size, optical properties and PN circulating form.

<p><b>A</b>) FPLC chromatograms of size-variable PN’s (constant IR-783 fluorochrome, variable PEG’s) are shown. Diameters are given in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0095406#pone-0095406-t001" target="_blank">Table 1</a>. <b>B,C</b>) Tuning PN size with different fluorochromes. The 5 kDa PEG yielded the magenta coded chromatograms with diameters of 4.3 nm regardless of the fluorochrome used. They are PN(783)4.3 (<b>2A</b>), PN(545)4.3 (<b>2B</b>), and PN(497)4.3 (<b>2C</b>). The 30 kDa PEG yielded the blue coded PN’s of 10 nm. Therefore PN dimensions are determined by PEG and independent of the fluorochrome selected. (<b>D,E,F</b>) FPLC chromatograms of PN’s are shown before (pre) and from the sera of injected mice. PN’s were PN(783)10.0 (<b>2D</b>), PN(783) 6.1 (<b>2E</b>), and PN(783)4.3 (<b>2F</b>). After injection, PN’s circulate at their PEG-determined and variable pre-injection sizes. (<b>G</b>) Since PN’s circulate at PEG-determined, pre-injection sizes, they cross capillaries at those sizes. ICG binds albumin and can cross capillaries in two forms. “FL” is the fluorescein fluorochrome.</p

Media 1: Multi-photon microscopy with a low-cost and highly efficient Cr:LiCAF laser

Originally published in Optics Express on 08 December 2008 (oe-16-25-20848

Multimodal imaging of EPR tumor targeting and elimination of PN(783)10.0.

<p><b>A</b>) SPECT/CT images of two mice bearing two HT-29 tumors as a function of time after injection. At 2 h post injection, agent is in the blood and interstitium. By 24 h post-injection tumors are becoming apparent as agent is being cleared. At 48 h, labeling is highly tumor selective. <b>B</b>) Surface fluorescence imaging of two additional mice bearing the same tumor. By surface fluorescence, as with SPECT, labeling is highly tumor selective at 48 h. <b>C</b>) Organ biodistribution was obtained by dissection and ¹¹¹In counting at 24 h and 48 h post-injection. Data are means and standard deviations, n = 5. <b>D</b>) A whole animal radioactivity elimination curve. Data are means and standard deviations, with extremely small standard deviations, n = 5.</p

Variable PN pharmacokinetics analyzed by the two compartment pharmacokinetic model in normal mice.

<p><b>A</b>) Two compartment pharmacokinetic model showing three microscopic rate constants. Serum fluorescence for PN(783)10.0 <b>B</b>) and PN(783)4.3 <b>C</b>) after injection are shown. Data were fit to the two compartment model with data provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0095406#pone-0095406-t001" target="_blank">Table 1</a>. <b>D)</b> Time courses for blood and interstitial fluorescence of PN(783)10.0 using microkinetic constants from <b>B</b>).</p

Media 5: Multi-photon microscopy with a low-cost and highly efficient Cr:LiCAF laser

Originally published in Optics Express on 08 December 2008 (oe-16-25-20848

Summary of PEG-like Nanoprobes (PN’s).

<p>*M.W. Obs. = molecular weights were determined from mass spectrometry results.</p><p>**Volumes are expressed as diameters in nm (to enable comparison with nanomaterials) and as the equivalent volumes of proteins (to enable comparison with proteins).</p><p>***Values are means±1 S.D.</p