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stalagmitic    
a. 石笋的,石笋状的

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  • Rapid self-healing, multiple recyclability and mechanically . . .
    plant oil-based epoxy resins enabled by incorporating covalent bondings Chaoqun Zhang, Xiao Wang, Dunsheng Liang, Henghui Deng, Zirun Lin, Pengju Feng* and Qingwen Wang* Prof C Zhang, X Wang, D Liang, H Deng, Q Wang Key Laboratory for Biobased Materials and Energy of Ministry of Education, College of Materials
  • 12 IX September 2024 https: doi. org 10. 22214 ijraset. 2024
    itself The study focuses on optimizing the tower by incorporating X and K bracings and using different steel sections, all analyzed through static analysis The findings reveal that modifying the bracings and steel sections leads to a notable 6% reduction in the tower's weight, achieved through improvements based on displacement values
  • Deep Learning with Adaptive Hyper-parameters for Low-Dose CT . . .
    anatomical structures using X-ray scanning and computational tomographic imaging techniques As excessive exposure to radiation from X-ray CT scanner may increase the risk of damage to issue, many techniques have been developed to use less radiation than conventional CT scan, including decreasing the number of projection views [1] and lowering
  • Scanned by CamScanner
    * Multi-wavelength approach, incorporating X-ray, UV, Optical and IR imaging and spectroscopy * Age-dating, evolution of stellar, dynamical, gas consumption and relativistic-magnetised AGN-jet plasma * Understanding physical processes behind galaxy evolution transformation
  • INCORPORATING SYMMETRY INTO DEEP DYNAMICS MODELS FOR IMPROVED . . .
    equipped with an action on 2-dimensional space X= R2 and 3-dimensional spacetime X^ = R3 Let V = Rdbe a G-representation Denote the set of all V-fields on X^ as F^ V = fw: X^ !V : wsmoothg:Define F V similarly to be V-fields on X Then Ghas an induced action on F^ V by (gw)(x;t) = g(w(g 1x;g 1t)) and on F V analogously
  • Improving Simultaneous Translation by Incorporating Pseudo . . .
    (x,y⇤)2D p wait-k(y ⇤ | x; ) This way, the model learns to implicitly anticipate at testing time, though not always correct (e g , in Fig 2,afterseeingx1 x2=“-˝Ñ”(China’s),out-put y1=“there”) The decoder generates the target sentence ˆy with k words behind source sentence x: yˆ t=argmax yt p wait- k(y | x <t+,yˆ ; wait-k)





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