Results 161 to 170 of about 570,845 (343)
Scalable Platform Enabling Reservoir Computing With Nanoporous Oxide Memristors for Image Recognition and Time Series Prediction
Advanced Intelligent Systems, EarlyView.The approach of physical in materia computing incorporates parallel computing within the medium itself. A scalable and energy‐efficient, oxide‐based computational platform is realized in form of a nanoporous network of volatile niobium oxide memristors sandwiched between top and bottom metallic electrodes, and then tested for prediction and ...Joshua Donald, Ben A. Johnson, Amir Mehrnejat, Alex Gabbitas, Arthur G. T. Coveney, Alexander G. Balanov, Sergey Savel’ev, Pavel Borisov +7 morewiley +1 more sourceUltrahigh-energy gamma-ray emission associated with black hole-jet systems. [PDF]
Natl Sci RevCao Z, Aharonian F, Bai YX, Bao YW, Bastieri D, Bi XJ, Bi YJ, Bian WY, Bukevich AV, Cai C, Cao WY, Cao Z, Chang J, Chang JF, Chen A, Chen ES, Chen G, Chen HX, Chen L, Chen L, Chen MJ, Chen ML, Chen QH, Chen S, Chen SH, Chen SZ, Chen TL, Chen XB, Chen X, Chen Y, Cheng N, Cheng YD, Chu MC, Cui MY, Cui SW, Cui XH, Cui YD, Dai BZ, Dai HL, Dai Z, Luobu D, Diao YX, Dong XQ, Duan KK, Fan JH, Fan YZ, Fang J, Fang JH, Fang K, Feng CF, Feng H, Feng L, Feng S, Feng XT, Feng Y, Feng YL, Gabici S, Gao B, Gao CD, Gao Q, Gao W, Gao WK, Ge M, Ge TT, Geng L, Giacinti G, Gong G, Gou Q, Gu MH, Guo FL, Guo J, Guo XL, Guo YQ, Guo YY, Han YA, Hannuksela OA, Hasan M, He HH, He HN, He JY, He X, He Y, Hernández-Cadena S, Hou BW, Hou C, Hou X, Hu HB, Hu SC, Huang C, Huang DH, Huang J, Huang TQ, Huang WJ, Huang XT, Huang XY, Huang Y, Huang YY, Ji XL, Jia HY, Jia K, Jiang HB, Jiang K, Jiang XW, Jiang ZJ, Jin M, Kaci S, Kang MM, Karpikov I, Khangulyan D, Kuleshov D, Kurinov K, Li BB, Li C, Li C, Li D, Li F, Li H, Li H, Li J, Li J, Li K, Li L, Li RL, Li SD, Li TY, Li WL, Li XR, Li X, Li Y, Li Y, Li Z, Li Z, Liang EW, Liang YF, Lin SJ, Liu B, Liu C, Liu D, Liu DB, Liu H, Liu HD, Liu J, Liu JL, Liu JR, Liu MY, Liu RY, Liu SM, Liu W, Liu X, Liu Y, Liu Y, Liu YN, Lou YQ, Luo Q, Luo Y, Lv HK, Ma BQ, Ma LL, Ma XH, Mao JR, Min Z, Mitthumsiri W, Mou GB, Mu HJ, Neronov A, Ng KCY, Ni MY, Nie L, Ou LJ, Pattarakijwanich P, Pei ZY, Qi JC, Qi MY, Qin JJ, Raza A, Ren CY, Ruffolo D, Sáiz A, Semikoz D, Shao L, Shchegolev O, Shen YZ, Sheng XD, Shi Z, Shu FW, Song HC, Stenkin YV, Stepanov V, Su Y, Sun D, Sun H, Sun Q, Sun X, Sun Z, Tabasam NH, Takata J, Tam PHT, Tan HB, Tang Q, Tang R, Tang Z, Tian W, Tong C, Wan LH, Wang C, Wang G, Wang H, Wang J, Wang K, Wang K, Wang L, Wang L, Wang LY, Wang R, Wang W, Wang X, Wang XJ, Wang XY, Wang Y, Wang YD, Wang ZH, Wang ZX, Wang Z, Wei DM, Wei JJ, Wei YJ, Wen T, Weng SS, Wu CY, Wu HR, Wu QW, Wu S, Wu XF, Wu YS, Xi SQ, Xia J, Xia JJ, Xiang GM, Xiao DX, Xiao G, Xin YL, Xing Y, Xiong DR, Xiong Z, Xu DL, Xu RF, Xu RX, Xu WL, Xue L, Yan DH, Yan JZ, Yan T, Yang CW, Yang CY, Yang FF, Yang LL, Yang MJ, Yang RZ, Yang WX, Yang Z, Yao ZG, Ye XA, Yin LQ, Yin N, You XH, You ZY, Yu YH, Yuan Q, Yue H, Zeng HD, Zeng TX, Zeng W, Zeng X, Zha M, Zhang BB, Zhang BT, Zhang C, Zhang F, Zhang HF, Zhang HM, Zhang HY, Zhang JL, Zhang L, Zhang PF, Zhang PP, Zhang R, Zhang SR, Zhang SS, Zhang W, Zhang X, Zhang XP, Zhang Y, Zhang Y, Zhang ZP, Zhao J, Zhao L, Zhao LZ, Zhao SP, Zhao XH, Zhao Z, Zheng F, Zhong WJ, Zhou B, Zhou H, Zhou JN, Zhou M, Zhou P, Zhou R, Zhou XX, Zhou XX, Zhu BY, Zhu CG, Zhu FR, Zhu H, Zhu KJ, Zou YC, Zuo X. +316 moreeuropepmc +1 more sourceRewiring Droplet Interface Synapses
Advanced Intelligent Systems, EarlyView.Droplet interface synapses are used to create a neuromorphic network. Voltage pulses draw the droplets into contact and enable rewiring of the network through electrowetting. Results show that the heterogeneous droplet compositions and pore‐forming molecules may be used to tune the neuromorphic properties of the device using trapped charge as a form of Sarita Shrestha, Eric Freemanwiley +1 more sourceData‐Driven Review and Machine Learning Prediction of Diamond Vacancy Center Synthesis
Advanced Intelligent Systems, EarlyView.A machine learning framework is applied to photoluminescence spectra to extract linewidths and uncover how NV, SiV, GeV, and SnV centers evolve with growth and processing conditions. Unified normalization and k‐fold validation reveal cross‐method trends and enable rapid prediction of defect size and fabrication parameters, offering a data‐driven route ...Zhi Jiang, Marco Peres, Carlo Bradac, Gil Gonçalves +3 morewiley +1 more sourceImaging moiré flat bands and Wigner molecular crystals in twisted bilayer MoTe<sub>2</sub>. [PDF]
Natl Sci RevLiu Y, Gu Y, Bao T, Mao N, Jiang S, Liu L, Guan D, Li Y, Zheng H, Liu C, Watanabe K, Taniguchi T, Duan W, Jia J, Liu X, Li C, Zhang Y, Li T, Wang S. +18 moreeuropepmc +1 more sourceA Large and Precise All-Sky Photometric Standard Star Dataset Across More Than 200 Passbands. [PDF]
Sci DataXiao K, Huang Y, Yuan H, Huang B, Fan D, Beers TC, Li Z, Han H, Zhang Q, Wang T, Ma M, Wang Y, Xu S, Yang L, Liu J. +14 moreeuropepmc +1 more source