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"text": "in the bert model , the hybrid weights favor dnas in all layers of the encoder ( u ≥ .5 ) ; unas gains more weight for closer-to-output layers .",
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"text": "in the bert model , the hybrid weights favor dnas in all layers of the encoder ( u ≥ .5 ) ; unas gains more weight for closer-to-output layers .",
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"text": "in the bert model , the hybrid weights favor dnas in all layers of the encoder ( u ≥ .5 ) ; unas gains more weight for closer-to-output layers .",
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"text": "comparison of load vs % pdr between esrpsdc , leach and pegasis .",
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"text": "histogram of densities of the logarithm of the mse for the different methods ( light blue corresponds to the approach of this paper ( algorithm 3 ) , orange - to the standard least squares regression , and green - to huber ’ s regression ) .",
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"text": "histogram of densities of the logarithm of the mse for the different methods ( light blue corresponds to the approach of this paper ( algorithm 3 ) , orange - to the standard least squares regression , and green - to huber ’ s regression ) .",
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"text": "histogram of densities of the logarithm of the mse for the different methods ( light blue corresponds to the approach of this paper ( algorithm 3 ) , orange - to the standard least squares regression , and green - to huber ’ s regression ) .",
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"text": "histogram of densities of the logarithm of the mse for the different methods ( light blue corresponds to the approach of this paper ( algorithm 3 ) , orange - to the standard least squares regression , and green - to huber ’ s regression ) .",
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"text": "quantum gilbert–elliott channel : pg = 0.0 is fixed ; pb varies from 0 to 1 ; u = exp ( −ıαh ) , where h is some fixed hermitian matrix and where α = 1 is fixed ; n = 10 .",
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"text": "quantum gilbert–elliott channel : pg = 0.0 is fixed ; pb varies from 0 to 1 ; u = exp ( −ıαh ) , where h is some fixed hermitian matrix and where α = 1 is fixed ; n = 10 .",
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"text": "quantum gilbert–elliott channel : pg = 0.0 is fixed ; pb varies from 0 to 1 ; u = exp ( −ıαh ) , where h is some fixed hermitian matrix and where α = 1 is fixed ; n = 10 .",
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"text": "self-coherence spectrum cxx ( ∆t = .0s ) and estimated threshold ( α=1 % ) for the first 34+ s in the simulation data .",
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"text": "self-coherence spectrum cxx ( ∆t = .0s ) and estimated threshold ( α=1 % ) for the first 34+ s in the simulation data .",
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"text": "Can you provide a scientific explanation for this image?\n",
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"text": "self-coherence spectrum cxx ( ∆t = .0s ) and estimated threshold ( α=1 % ) for the first 34+ s in the simulation data .",
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"text": "Can you provide a scientific caption for this image?\n",
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"text": "Can you provide a scientific explanation for this image?\n",
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"text": "What is the significance of the findings shown in this image?\n",
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"text": "What is the significance of the findings shown in this image?\n",
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"text": "Can you provide a scientific caption for this image?\n",
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"text": "relationships between identified bias errors and error in rems .",
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"text": "What are the key features observed in this image?\n",
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"text": "Can you provide a scientific explanation for this image?\n",
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"text": "relationships between identified bias errors and error in rems .",
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"text": "What is the significance of the findings shown in this image?\n",
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"text": "iterative decoding of double-diversity ldlc in dimensionn = 100 andn = 1000. point error rate is compared with respective pol .",
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"text": "Can you provide a scientific explanation for this image?\n",
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"text": "optimal jamming power coefficientφopt vs. se ; b under corollary 3 withde = [ 50m , 350m ] andrth = 5 bps/hz .",
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"text": "graph of the probabilities of the macro-states mmax and mhalf at each generation : the system s , a typical evolving agent population , was in the maximum macro-state mmax only after generation 178 and always after generation 482. it was also observed being in the sub-optimal macro-state mhalf only between generations 7 and 11 .",
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"text": "graph of the probabilities of the macro-states mmax and mhalf at each generation : the system s , a typical evolving agent population , was in the maximum macro-state mmax only after generation 178 and always after generation 482. it was also observed being in the sub-optimal macro-state mhalf only between generations 7 and 11 .",
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"text": "graph of the probabilities of the macro-states mmax and mhalf at each generation : the system s , a typical evolving agent population , was in the maximum macro-state mmax only after generation 178 and always after generation 482. it was also observed being in the sub-optimal macro-state mhalf only between generations 7 and 11 .",
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"content": [
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"content": [
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"content": [
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"text": "Can you provide a scientific explanation for this image?\n",
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"content": [
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"content": [
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"content": [
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"text": "training loss for tiny-imagenet with different optimizer and initial learning rate .",
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"content": [
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"text": "percentage of paths that go through the most common nodes ( assuming transaction sizes of 1000 millisatoshis ) .",
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"content": [
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"text": "Can you provide a scientific caption for this image?\n",
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"content": [
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"text": "ρ versusǫ for n = 15 , path-loss exponent , β = 3 and different network densitiesλ .",
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"content": [
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"text": "ρ versusǫ for n = 15 , path-loss exponent , β = 3 and different network densitiesλ .",
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"content": [
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"content": [
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"text": "ρ versusǫ for n = 15 , path-loss exponent , β = 3 and different network densitiesλ .",
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"content": [
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"text": "Can you provide a scientific caption for this image?\n",
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"content": [
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"text": "What is the significance of the findings shown in this image?\n",
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"text": "robustness of our our baseline+mmt+sg to k parameter ’ s changes ( k controls k-means number of clusters ) compared to the target-only framework mmt on duke-to-market .",
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"text": "robustness of our our baseline+mmt+sg to k parameter ’ s changes ( k controls k-means number of clusters ) compared to the target-only framework mmt on duke-to-market .",
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"text": "spaceex and hypro results on the thermostat benchmark with support function representation ( using an octagonal ( oct ) template for evaluation ) , when hypro separates clocks as well as discrete variables .",
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