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"text": "comparison of training time . for our experiments , double hashing is roughly 22 % slower than the baseline . our hybrid method is at least comparable to , or slightly better than the baseline .",
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"text": "empirical estimations of the false positive rate on experimental data ( example 3 ) when n varies , using the welch β s t-test ( blue ) and the bootstrap confidence interval test ( orange ) .",
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"text": "accuracy on mnist .",
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"content": [
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"index": null,
"text": "Can you provide a scientific caption for this image?\n",
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"text": null,
"type": "image"
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"content": [
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"index": null,
"text": ". two layer oblivious uncorrelated bm achievable rates under unequal antenna layering power distribution as a function of source power sp , fixed relay power rp and the collocation gain q = 10db .",
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"text": "What are the key features observed in this image?\n",
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"content": [
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"index": null,
"text": ". two layer oblivious uncorrelated bm achievable rates under unequal antenna layering power distribution as a function of source power sp , fixed relay power rp and the collocation gain q = 10db .",
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"text": "Can you provide a scientific explanation for this image?\n",
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"content": [
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"index": null,
"text": ". two layer oblivious uncorrelated bm achievable rates under unequal antenna layering power distribution as a function of source power sp , fixed relay power rp and the collocation gain q = 10db .",
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"text": "What is the significance of the findings shown in this image?\n",
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"content": [
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"index": null,
"text": ". two layer oblivious uncorrelated bm achievable rates under unequal antenna layering power distribution as a function of source power sp , fixed relay power rp and the collocation gain q = 10db .",
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"content": [
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"index": null,
"text": "Can you provide a scientific caption for this image?\n",
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{
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"text": null,
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"content": [
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"text": "ber for sfo of 5 hz and 10 hz .",
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"content": [
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"index": null,
"text": "What are the key features observed in this image?\n",
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"content": [
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"text": "ber for sfo of 5 hz and 10 hz .",
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"content": [
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"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
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"content": [
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"text": "ber for sfo of 5 hz and 10 hz .",
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"text": "What is the significance of the findings shown in this image?\n",
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"content": [
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"index": null,
"text": "ber for sfo of 5 hz and 10 hz .",
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"content": [
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"index": null,
"text": "Can you provide a scientific caption for this image?\n",
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"index": 0,
"text": null,
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"content": [
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"text": "average coverage of each search heuristic for execution-generated testing on multiple subsequent program variants .",
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"content": [
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"text": "What are the key features observed in this image?\n",
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"content": [
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"text": "average coverage of each search heuristic for execution-generated testing on multiple subsequent program variants .",
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"text": "Can you provide a scientific explanation for this image?\n",
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"content": [
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"text": "average coverage of each search heuristic for execution-generated testing on multiple subsequent program variants .",
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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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"content": [
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"index": null,
"text": "average coverage of each search heuristic for execution-generated testing on multiple subsequent program variants .",
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"content": [
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"index": null,
"text": "Can you provide a scientific caption for this image?\n",
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{
"index": 0,
"text": null,
"type": "image"
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"content": [
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"index": null,
"text": "mean ( thick lines ) and standard deviation around the mean ( dotted lines ) for different endpoint constraints . all results used the parameter configuration c = 5sβ , c2 = 0.sβ .",
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"content": [
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"index": null,
"text": "What are the key features observed in this image?\n",
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"content": [
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"index": null,
"text": "mean ( thick lines ) and standard deviation around the mean ( dotted lines ) for different endpoint constraints . all results used the parameter configuration c = 5sβ , c2 = 0.sβ .",
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"text": "Can you provide a scientific explanation for this image?\n",
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"content": [
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"index": null,
"text": "mean ( thick lines ) and standard deviation around the mean ( dotted lines ) for different endpoint constraints . all results used the parameter configuration c = 5sβ , c2 = 0.sβ .",
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"text": "What is the significance of the findings shown in this image?\n",
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"content": [
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"index": null,
"text": "mean ( thick lines ) and standard deviation around the mean ( dotted lines ) for different endpoint constraints . all results used the parameter configuration c = 5sβ , c2 = 0.sβ .",
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"content": [
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"index": null,
"text": "Can you provide a scientific caption for this image?\n",
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{
"index": 0,
"text": null,
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"content": [
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"text": "dcdb use case 1 : efficiency of heat removal β the graph shows that the heat removal efficiency of our coolmuc-3 system is around 0 % , independent of inlet water temperature .",
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"content": [
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"text": "What are the key features observed in this image?\n",
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"content": [
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"index": null,
"text": "dcdb use case 1 : efficiency of heat removal β the graph shows that the heat removal efficiency of our coolmuc-3 system is around 0 % , independent of inlet water temperature .",
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"content": [
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"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
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"content": [
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"index": null,
"text": "dcdb use case 1 : efficiency of heat removal β the graph shows that the heat removal efficiency of our coolmuc-3 system is around 0 % , independent of inlet water temperature .",
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"content": [
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"index": null,
"text": "What is the significance of the findings shown in this image?\n",
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"content": [
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"index": null,
"text": "dcdb use case 1 : efficiency of heat removal β the graph shows that the heat removal efficiency of our coolmuc-3 system is around 0 % , independent of inlet water temperature .",
"type": "text"
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"content": [
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"index": null,
"text": "Can you provide a scientific caption for this image?\n",
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{
"index": 0,
"text": null,
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"content": [
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"index": null,
"text": "inner and outer bounds on the rate-equivocation region for the gray-wyner model with equivocation constraint of figure 3a . case of dsbs ( p ) sources and symmetric rates on the private links , i.e. , r1 = r2 .",
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"content": [
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"index": null,
"text": "What are the key features observed in this image?\n",
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"content": [
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"index": null,
"text": "inner and outer bounds on the rate-equivocation region for the gray-wyner model with equivocation constraint of figure 3a . case of dsbs ( p ) sources and symmetric rates on the private links , i.e. , r1 = r2 .",
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"content": [
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"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
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"role": "user"
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{
"content": [
{
"index": null,
"text": "inner and outer bounds on the rate-equivocation region for the gray-wyner model with equivocation constraint of figure 3a . case of dsbs ( p ) sources and symmetric rates on the private links , i.e. , r1 = r2 .",
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"content": [
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"index": null,
"text": "What is the significance of the findings shown in this image?\n",
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"content": [
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"index": null,
"text": "inner and outer bounds on the rate-equivocation region for the gray-wyner model with equivocation constraint of figure 3a . case of dsbs ( p ) sources and symmetric rates on the private links , i.e. , r1 = r2 .",
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"content": [
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"index": null,
"text": "Can you provide a scientific caption for this image?\n",
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{
"index": 0,
"text": null,
"type": "image"
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"content": [
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"index": null,
"text": "average packet delay under different channel qualities .",
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"content": [
{
"index": null,
"text": "What are the key features observed in this image?\n",
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"content": [
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"index": null,
"text": "average packet delay under different channel qualities .",
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"content": [
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"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
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"content": [
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"index": null,
"text": "average packet delay under different channel qualities .",
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"content": [
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"index": null,
"text": "What is the significance of the findings shown in this image?\n",
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"content": [
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"index": null,
"text": "average packet delay under different channel qualities .",
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"content": [
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"index": null,
"text": "Can you provide a scientific caption for this image?\n",
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{
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"text": null,
"type": "image"
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"content": [
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"text": "feature selection using the average score over the 27 parameter regimes . the red dashed line represents a possible feature cut-off point . the green line represents the cut-off point which we selected when using the average rank , as can be seen in fig . 8a .",
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"content": [
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"index": null,
"text": "What are the key features observed in this image?\n",
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"content": [
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"index": null,
"text": "feature selection using the average score over the 27 parameter regimes . the red dashed line represents a possible feature cut-off point . the green line represents the cut-off point which we selected when using the average rank , as can be seen in fig . 8a .",
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"content": [
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"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
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"content": [
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"index": null,
"text": "feature selection using the average score over the 27 parameter regimes . the red dashed line represents a possible feature cut-off point . the green line represents the cut-off point which we selected when using the average rank , as can be seen in fig . 8a .",
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"content": [
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"index": null,
"text": "What is the significance of the findings shown in this image?\n",
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"content": [
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"index": null,
"text": "feature selection using the average score over the 27 parameter regimes . the red dashed line represents a possible feature cut-off point . the green line represents the cut-off point which we selected when using the average rank , as can be seen in fig . 8a .",
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"content": [
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"index": null,
"text": "Can you provide a scientific caption for this image?\n",
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{
"index": 0,
"text": null,
"type": "image"
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"role": "user"
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"content": [
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"index": null,
"text": "speed-up of the kernel loop in the matrix_pp subroutine versus number of mpi tasks . the problem size is ntri_p=46080 .",
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"content": [
{
"index": null,
"text": "What are the key features observed in this image?\n",
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"content": [
{
"index": null,
"text": "speed-up of the kernel loop in the matrix_pp subroutine versus number of mpi tasks . the problem size is ntri_p=46080 .",
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"content": [
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"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
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"content": [
{
"index": null,
"text": "speed-up of the kernel loop in the matrix_pp subroutine versus number of mpi tasks . the problem size is ntri_p=46080 .",
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"content": [
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"index": null,
"text": "What is the significance of the findings shown in this image?\n",
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"content": [
{
"index": null,
"text": "speed-up of the kernel loop in the matrix_pp subroutine versus number of mpi tasks . the problem size is ntri_p=46080 .",
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"content": [
{
"index": null,
"text": "Can you provide a scientific caption for this image?\n",
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{
"index": 0,
"text": null,
"type": "image"
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],
"role": "user"
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"content": [
{
"index": null,
"text": "the charge time with the practical charger for variouspr .",
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"role": "assistant"
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"content": [
{
"index": null,
"text": "What are the key features observed in this image?\n",
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"role": "user"
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{
"content": [
{
"index": null,
"text": "the charge time with the practical charger for variouspr .",
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"role": "assistant"
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"content": [
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"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
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"content": [
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"text": "the charge time with the practical charger for variouspr .",
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"content": [
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"index": null,
"text": "What is the significance of the findings shown in this image?\n",
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"content": [
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"index": null,
"text": "the charge time with the practical charger for variouspr .",
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"content": [
{
"index": null,
"text": "Can you provide a scientific caption for this image?\n",
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{
"index": 0,
"text": null,
"type": "image"
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"role": "user"
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"content": [
{
"index": null,
"text": "coded ber for ofdm with 1024 total tones , 150 pilot tones , and 0 null tones under a 10-tap rayleigh-fading channel in i.i.d-gm noise . here , jcisb- # corresponds to jcisb after # turbo iterations , and similar for jcis- # .",
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"content": [
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"index": null,
"text": "What are the key features observed in this image?\n",
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{
"content": [
{
"index": null,
"text": "coded ber for ofdm with 1024 total tones , 150 pilot tones , and 0 null tones under a 10-tap rayleigh-fading channel in i.i.d-gm noise . here , jcisb- # corresponds to jcisb after # turbo iterations , and similar for jcis- # .",
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"content": [
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"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
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{
"content": [
{
"index": null,
"text": "coded ber for ofdm with 1024 total tones , 150 pilot tones , and 0 null tones under a 10-tap rayleigh-fading channel in i.i.d-gm noise . here , jcisb- # corresponds to jcisb after # turbo iterations , and similar for jcis- # .",
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"content": [
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"index": null,
"text": "What is the significance of the findings shown in this image?\n",
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"content": [
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"index": null,
"text": "coded ber for ofdm with 1024 total tones , 150 pilot tones , and 0 null tones under a 10-tap rayleigh-fading channel in i.i.d-gm noise . here , jcisb- # corresponds to jcisb after # turbo iterations , and similar for jcis- # .",
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"content": [
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"index": null,
"text": "Can you provide a scientific caption for this image?\n",
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{
"index": 0,
"text": null,
"type": "image"
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],
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"content": [
{
"index": null,
"text": "long-term user rate distribution ( c = 1 gbps ) .",
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"content": [
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"index": null,
"text": "What are the key features observed in this image?\n",
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"content": [
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"index": null,
"text": "long-term user rate distribution ( c = 1 gbps ) .",
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"content": [
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"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
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"content": [
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"index": null,
"text": "long-term user rate distribution ( c = 1 gbps ) .",
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"content": [
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"index": null,
"text": "What is the significance of the findings shown in this image?\n",
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"content": [
{
"index": null,
"text": "long-term user rate distribution ( c = 1 gbps ) .",
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"content": [
{
"index": null,
"text": "Can you provide a scientific caption for this image?\n",
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{
"index": 0,
"text": null,
"type": "image"
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"content": [
{
"index": null,
"text": "roc curve for Ξ³ = 0 and Ξ³ = 1 on the middlebury dataset . we observe that roc of Ξ³ = 0 stays below the roc of Ξ³ = 1 all the time , which suggests better disparity prediction and confidence learning .",
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"content": [
{
"index": null,
"text": "What are the key features observed in this image?\n",
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"content": [
{
"index": null,
"text": "roc curve for Ξ³ = 0 and Ξ³ = 1 on the middlebury dataset . we observe that roc of Ξ³ = 0 stays below the roc of Ξ³ = 1 all the time , which suggests better disparity prediction and confidence learning .",
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"content": [
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"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
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"content": [
{
"index": null,
"text": "roc curve for Ξ³ = 0 and Ξ³ = 1 on the middlebury dataset . we observe that roc of Ξ³ = 0 stays below the roc of Ξ³ = 1 all the time , which suggests better disparity prediction and confidence learning .",
"type": "text"
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"content": [
{
"index": null,
"text": "What is the significance of the findings shown in this image?\n",
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"content": [
{
"index": null,
"text": "roc curve for Ξ³ = 0 and Ξ³ = 1 on the middlebury dataset . we observe that roc of Ξ³ = 0 stays below the roc of Ξ³ = 1 all the time , which suggests better disparity prediction and confidence learning .",
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"content": [
{
"index": null,
"text": "Can you provide a scientific caption for this image?\n",
"type": "text"
},
{
"index": 0,
"text": null,
"type": "image"
}
],
"role": "user"
},
{
"content": [
{
"index": null,
"text": "the cifar-100 test error of different normalization techniques , vs. the number of training step .",
"type": "text"
}
],
"role": "assistant"
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{
"content": [
{
"index": null,
"text": "What are the key features observed in this image?\n",
"type": "text"
}
],
"role": "user"
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{
"content": [
{
"index": null,
"text": "the cifar-100 test error of different normalization techniques , vs. the number of training step .",
"type": "text"
}
],
"role": "assistant"
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"content": [
{
"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
"type": "text"
}
],
"role": "user"
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{
"content": [
{
"index": null,
"text": "the cifar-100 test error of different normalization techniques , vs. the number of training step .",
"type": "text"
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"role": "assistant"
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{
"content": [
{
"index": null,
"text": "What is the significance of the findings shown in this image?\n",
"type": "text"
}
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"role": "user"
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{
"content": [
{
"index": null,
"text": "the cifar-100 test error of different normalization techniques , vs. the number of training step .",
"type": "text"
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{
"content": [
{
"index": null,
"text": "Can you provide a scientific caption for this image?\n",
"type": "text"
},
{
"index": 0,
"text": null,
"type": "image"
}
],
"role": "user"
},
{
"content": [
{
"index": null,
"text": "measured 3 ghz small-scale spatial fading distributions of the directional received signal voltage amplitude over a 35.31-cm ( about 8 wavelengths ) linear track in los conditions , and the corresponding ricean fitted curves with different k-factors . the angles in the legend denote the receiver antenna azimuth pointing angle during the small-scale measurements with 20β¦ denoting the angle pointing directly at the transmitter .",
"type": "text"
}
],
"role": "assistant"
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"content": [
{
"index": null,
"text": "What are the key features observed in this image?\n",
"type": "text"
}
],
"role": "user"
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{
"content": [
{
"index": null,
"text": "measured 3 ghz small-scale spatial fading distributions of the directional received signal voltage amplitude over a 35.31-cm ( about 8 wavelengths ) linear track in los conditions , and the corresponding ricean fitted curves with different k-factors . the angles in the legend denote the receiver antenna azimuth pointing angle during the small-scale measurements with 20β¦ denoting the angle pointing directly at the transmitter .",
"type": "text"
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"content": [
{
"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
"type": "text"
}
],
"role": "user"
},
{
"content": [
{
"index": null,
"text": "measured 3 ghz small-scale spatial fading distributions of the directional received signal voltage amplitude over a 35.31-cm ( about 8 wavelengths ) linear track in los conditions , and the corresponding ricean fitted curves with different k-factors . the angles in the legend denote the receiver antenna azimuth pointing angle during the small-scale measurements with 20β¦ denoting the angle pointing directly at the transmitter .",
"type": "text"
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"role": "assistant"
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"content": [
{
"index": null,
"text": "What is the significance of the findings shown in this image?\n",
"type": "text"
}
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"role": "user"
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{
"content": [
{
"index": null,
"text": "measured 3 ghz small-scale spatial fading distributions of the directional received signal voltage amplitude over a 35.31-cm ( about 8 wavelengths ) linear track in los conditions , and the corresponding ricean fitted curves with different k-factors . the angles in the legend denote the receiver antenna azimuth pointing angle during the small-scale measurements with 20β¦ denoting the angle pointing directly at the transmitter .",
"type": "text"
}
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{
"content": [
{
"index": null,
"text": "Can you provide a scientific caption for this image?\n",
"type": "text"
},
{
"index": 0,
"text": null,
"type": "image"
}
],
"role": "user"
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{
"content": [
{
"index": null,
"text": "the satisfaction probability of the sufficient rank-one condition in theorem 1. Οi = 0.1 , Ξ΅i = 0.1 , r1 = Β· Β· Β· = rk = r , , 000 simulation trials . the presumed channels are randomly generated at each trial , following a complex circular gaussian distribution with mean 0 and covariance i .",
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}
],
"role": "assistant"
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"content": [
{
"index": null,
"text": "What are the key features observed in this image?\n",
"type": "text"
}
],
"role": "user"
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{
"content": [
{
"index": null,
"text": "the satisfaction probability of the sufficient rank-one condition in theorem 1. Οi = 0.1 , Ξ΅i = 0.1 , r1 = Β· Β· Β· = rk = r , , 000 simulation trials . the presumed channels are randomly generated at each trial , following a complex circular gaussian distribution with mean 0 and covariance i .",
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"content": [
{
"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
"type": "text"
}
],
"role": "user"
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{
"content": [
{
"index": null,
"text": "the satisfaction probability of the sufficient rank-one condition in theorem 1. Οi = 0.1 , Ξ΅i = 0.1 , r1 = Β· Β· Β· = rk = r , , 000 simulation trials . the presumed channels are randomly generated at each trial , following a complex circular gaussian distribution with mean 0 and covariance i .",
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{
"content": [
{
"index": null,
"text": "What is the significance of the findings shown in this image?\n",
"type": "text"
}
],
"role": "user"
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{
"content": [
{
"index": null,
"text": "the satisfaction probability of the sufficient rank-one condition in theorem 1. Οi = 0.1 , Ξ΅i = 0.1 , r1 = Β· Β· Β· = rk = r , , 000 simulation trials . the presumed channels are randomly generated at each trial , following a complex circular gaussian distribution with mean 0 and covariance i .",
"type": "text"
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{
"content": [
{
"index": null,
"text": "Can you provide a scientific caption for this image?\n",
"type": "text"
},
{
"index": 0,
"text": null,
"type": "image"
}
],
"role": "user"
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{
"content": [
{
"index": null,
"text": "four species shown together as a logβ log ccdf .",
"type": "text"
}
],
"role": "assistant"
},
{
"content": [
{
"index": null,
"text": "What are the key features observed in this image?\n",
"type": "text"
}
],
"role": "user"
},
{
"content": [
{
"index": null,
"text": "four species shown together as a logβ log ccdf .",
"type": "text"
}
],
"role": "assistant"
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{
"content": [
{
"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
"type": "text"
}
],
"role": "user"
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{
"content": [
{
"index": null,
"text": "four species shown together as a logβ log ccdf .",
"type": "text"
}
],
"role": "assistant"
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{
"content": [
{
"index": null,
"text": "What is the significance of the findings shown in this image?\n",
"type": "text"
}
],
"role": "user"
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{
"content": [
{
"index": null,
"text": "four species shown together as a logβ log ccdf .",
"type": "text"
}
],
"role": "assistant"
}
] | |
[
{
"content": [
{
"index": null,
"text": "Can you provide a scientific caption for this image?\n",
"type": "text"
},
{
"index": 0,
"text": null,
"type": "image"
}
],
"role": "user"
},
{
"content": [
{
"index": null,
"text": "rpys-co graph for liu and jordan ( 1960 ) and period 1 ( 1915 β 1959 ) with ncr ( red line ) and 5-year-median deviation ( blue line ) .",
"type": "text"
}
],
"role": "assistant"
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{
"content": [
{
"index": null,
"text": "What are the key features observed in this image?\n",
"type": "text"
}
],
"role": "user"
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{
"content": [
{
"index": null,
"text": "rpys-co graph for liu and jordan ( 1960 ) and period 1 ( 1915 β 1959 ) with ncr ( red line ) and 5-year-median deviation ( blue line ) .",
"type": "text"
}
],
"role": "assistant"
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{
"content": [
{
"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
"type": "text"
}
],
"role": "user"
},
{
"content": [
{
"index": null,
"text": "rpys-co graph for liu and jordan ( 1960 ) and period 1 ( 1915 β 1959 ) with ncr ( red line ) and 5-year-median deviation ( blue line ) .",
"type": "text"
}
],
"role": "assistant"
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{
"content": [
{
"index": null,
"text": "What is the significance of the findings shown in this image?\n",
"type": "text"
}
],
"role": "user"
},
{
"content": [
{
"index": null,
"text": "rpys-co graph for liu and jordan ( 1960 ) and period 1 ( 1915 β 1959 ) with ncr ( red line ) and 5-year-median deviation ( blue line ) .",
"type": "text"
}
],
"role": "assistant"
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[
{
"content": [
{
"index": null,
"text": "Can you provide a scientific caption for this image?\n",
"type": "text"
},
{
"index": 0,
"text": null,
"type": "image"
}
],
"role": "user"
},
{
"content": [
{
"index": null,
"text": "npsr versus < ( -norm based approaches for sparse representation in the presence of double-exponential noise .",
"type": "text"
}
],
"role": "assistant"
},
{
"content": [
{
"index": null,
"text": "What are the key features observed in this image?\n",
"type": "text"
}
],
"role": "user"
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{
"content": [
{
"index": null,
"text": "npsr versus < ( -norm based approaches for sparse representation in the presence of double-exponential noise .",
"type": "text"
}
],
"role": "assistant"
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{
"content": [
{
"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
"type": "text"
}
],
"role": "user"
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{
"content": [
{
"index": null,
"text": "npsr versus < ( -norm based approaches for sparse representation in the presence of double-exponential noise .",
"type": "text"
}
],
"role": "assistant"
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{
"content": [
{
"index": null,
"text": "What is the significance of the findings shown in this image?\n",
"type": "text"
}
],
"role": "user"
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{
"content": [
{
"index": null,
"text": "npsr versus < ( -norm based approaches for sparse representation in the presence of double-exponential noise .",
"type": "text"
}
],
"role": "assistant"
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[
{
"content": [
{
"index": null,
"text": "Can you provide a scientific caption for this image?\n",
"type": "text"
},
{
"index": 0,
"text": null,
"type": "image"
}
],
"role": "user"
},
{
"content": [
{
"index": null,
"text": "computational complexity required by sphere decoder with s-e and proposed simplified ml decoder , in quasi-static rayleigh channel with -qam constellation .",
"type": "text"
}
],
"role": "assistant"
},
{
"content": [
{
"index": null,
"text": "What are the key features observed in this image?\n",
"type": "text"
}
],
"role": "user"
},
{
"content": [
{
"index": null,
"text": "computational complexity required by sphere decoder with s-e and proposed simplified ml decoder , in quasi-static rayleigh channel with -qam constellation .",
"type": "text"
}
],
"role": "assistant"
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{
"content": [
{
"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
"type": "text"
}
],
"role": "user"
},
{
"content": [
{
"index": null,
"text": "computational complexity required by sphere decoder with s-e and proposed simplified ml decoder , in quasi-static rayleigh channel with -qam constellation .",
"type": "text"
}
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"role": "assistant"
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{
"content": [
{
"index": null,
"text": "What is the significance of the findings shown in this image?\n",
"type": "text"
}
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"role": "user"
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{
"content": [
{
"index": null,
"text": "computational complexity required by sphere decoder with s-e and proposed simplified ml decoder , in quasi-static rayleigh channel with -qam constellation .",
"type": "text"
}
],
"role": "assistant"
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] | |
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{
"content": [
{
"index": null,
"text": "Can you provide a scientific caption for this image?\n",
"type": "text"
},
{
"index": 0,
"text": null,
"type": "image"
}
],
"role": "user"
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{
"content": [
{
"index": null,
"text": "comparison of the time to solve the optimization problem for pipeline s , sp , and spr for dataset v1_01 .",
"type": "text"
}
],
"role": "assistant"
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{
"content": [
{
"index": null,
"text": "What are the key features observed in this image?\n",
"type": "text"
}
],
"role": "user"
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{
"content": [
{
"index": null,
"text": "comparison of the time to solve the optimization problem for pipeline s , sp , and spr for dataset v1_01 .",
"type": "text"
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],
"role": "assistant"
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"content": [
{
"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
"type": "text"
}
],
"role": "user"
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{
"content": [
{
"index": null,
"text": "comparison of the time to solve the optimization problem for pipeline s , sp , and spr for dataset v1_01 .",
"type": "text"
}
],
"role": "assistant"
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{
"content": [
{
"index": null,
"text": "What is the significance of the findings shown in this image?\n",
"type": "text"
}
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"role": "user"
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{
"content": [
{
"index": null,
"text": "comparison of the time to solve the optimization problem for pipeline s , sp , and spr for dataset v1_01 .",
"type": "text"
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],
"role": "assistant"
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{
"content": [
{
"index": null,
"text": "Can you provide a scientific caption for this image?\n",
"type": "text"
},
{
"index": 0,
"text": null,
"type": "image"
}
],
"role": "user"
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{
"content": [
{
"index": null,
"text": "number of clusters formed vs. neighbourhood distance Ξ΅ .",
"type": "text"
}
],
"role": "assistant"
},
{
"content": [
{
"index": null,
"text": "What are the key features observed in this image?\n",
"type": "text"
}
],
"role": "user"
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{
"content": [
{
"index": null,
"text": "number of clusters formed vs. neighbourhood distance Ξ΅ .",
"type": "text"
}
],
"role": "assistant"
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"content": [
{
"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
"type": "text"
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],
"role": "user"
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{
"content": [
{
"index": null,
"text": "number of clusters formed vs. neighbourhood distance Ξ΅ .",
"type": "text"
}
],
"role": "assistant"
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{
"content": [
{
"index": null,
"text": "What is the significance of the findings shown in this image?\n",
"type": "text"
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],
"role": "user"
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{
"content": [
{
"index": null,
"text": "number of clusters formed vs. neighbourhood distance Ξ΅ .",
"type": "text"
}
],
"role": "assistant"
}
] | |
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{
"content": [
{
"index": null,
"text": "Can you provide a scientific caption for this image?\n",
"type": "text"
},
{
"index": 0,
"text": null,
"type": "image"
}
],
"role": "user"
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{
"content": [
{
"index": null,
"text": "learning curves for swimmer6 svg agents . the gnbased agent ( blue ) asymptotes earlier , and at a higher performance , than the model-free agent ( red ) . the lines represent median performance for 6 random seeds , with 25 and 75 % confidence intervals .",
"type": "text"
}
],
"role": "assistant"
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{
"content": [
{
"index": null,
"text": "What are the key features observed in this image?\n",
"type": "text"
}
],
"role": "user"
},
{
"content": [
{
"index": null,
"text": "learning curves for swimmer6 svg agents . the gnbased agent ( blue ) asymptotes earlier , and at a higher performance , than the model-free agent ( red ) . the lines represent median performance for 6 random seeds , with 25 and 75 % confidence intervals .",
"type": "text"
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"role": "assistant"
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"content": [
{
"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
"type": "text"
}
],
"role": "user"
},
{
"content": [
{
"index": null,
"text": "learning curves for swimmer6 svg agents . the gnbased agent ( blue ) asymptotes earlier , and at a higher performance , than the model-free agent ( red ) . the lines represent median performance for 6 random seeds , with 25 and 75 % confidence intervals .",
"type": "text"
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"role": "assistant"
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{
"content": [
{
"index": null,
"text": "What is the significance of the findings shown in this image?\n",
"type": "text"
}
],
"role": "user"
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{
"content": [
{
"index": null,
"text": "learning curves for swimmer6 svg agents . the gnbased agent ( blue ) asymptotes earlier , and at a higher performance , than the model-free agent ( red ) . the lines represent median performance for 6 random seeds , with 25 and 75 % confidence intervals .",
"type": "text"
}
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"role": "assistant"
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] | |
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{
"content": [
{
"index": null,
"text": "Can you provide a scientific caption for this image?\n",
"type": "text"
},
{
"index": 0,
"text": null,
"type": "image"
}
],
"role": "user"
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{
"content": [
{
"index": null,
"text": "impact of the filter number on classification accuracy .",
"type": "text"
}
],
"role": "assistant"
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{
"content": [
{
"index": null,
"text": "What are the key features observed in this image?\n",
"type": "text"
}
],
"role": "user"
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{
"content": [
{
"index": null,
"text": "impact of the filter number on classification accuracy .",
"type": "text"
}
],
"role": "assistant"
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"content": [
{
"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
"type": "text"
}
],
"role": "user"
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{
"content": [
{
"index": null,
"text": "impact of the filter number on classification accuracy .",
"type": "text"
}
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"role": "assistant"
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"content": [
{
"index": null,
"text": "What is the significance of the findings shown in this image?\n",
"type": "text"
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],
"role": "user"
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{
"content": [
{
"index": null,
"text": "impact of the filter number on classification accuracy .",
"type": "text"
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"role": "assistant"
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{
"content": [
{
"index": null,
"text": "Can you provide a scientific caption for this image?\n",
"type": "text"
},
{
"index": 0,
"text": null,
"type": "image"
}
],
"role": "user"
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{
"content": [
{
"index": null,
"text": "runtime of various load balancing algorithms in a loglog plot . a weak scaling experiment shows the evolution of the runtime as more and more processes are used .",
"type": "text"
}
],
"role": "assistant"
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{
"content": [
{
"index": null,
"text": "What are the key features observed in this image?\n",
"type": "text"
}
],
"role": "user"
},
{
"content": [
{
"index": null,
"text": "runtime of various load balancing algorithms in a loglog plot . a weak scaling experiment shows the evolution of the runtime as more and more processes are used .",
"type": "text"
}
],
"role": "assistant"
},
{
"content": [
{
"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
"type": "text"
}
],
"role": "user"
},
{
"content": [
{
"index": null,
"text": "runtime of various load balancing algorithms in a loglog plot . a weak scaling experiment shows the evolution of the runtime as more and more processes are used .",
"type": "text"
}
],
"role": "assistant"
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{
"content": [
{
"index": null,
"text": "What is the significance of the findings shown in this image?\n",
"type": "text"
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"role": "user"
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"content": [
{
"index": null,
"text": "runtime of various load balancing algorithms in a loglog plot . a weak scaling experiment shows the evolution of the runtime as more and more processes are used .",
"type": "text"
}
],
"role": "assistant"
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] | |
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{
"content": [
{
"index": null,
"text": "Can you provide a scientific caption for this image?\n",
"type": "text"
},
{
"index": 0,
"text": null,
"type": "image"
}
],
"role": "user"
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{
"content": [
{
"index": null,
"text": "term lengths & numbers & percentage distribution in the corpus .",
"type": "text"
}
],
"role": "assistant"
},
{
"content": [
{
"index": null,
"text": "What are the key features observed in this image?\n",
"type": "text"
}
],
"role": "user"
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{
"content": [
{
"index": null,
"text": "term lengths & numbers & percentage distribution in the corpus .",
"type": "text"
}
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"role": "assistant"
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"content": [
{
"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
"type": "text"
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"role": "user"
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"content": [
{
"index": null,
"text": "term lengths & numbers & percentage distribution in the corpus .",
"type": "text"
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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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"content": [
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"text": "term lengths & numbers & percentage distribution in the corpus .",
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"content": [
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"index": null,
"text": "Can you provide a scientific caption for this image?\n",
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"text": null,
"type": "image"
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"content": [
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"text": "the combined model normalized probability generated from the latest block b , where various numbers of the models in b have been infected ( attacked ) .",
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"content": [
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"index": null,
"text": "What are the key features observed in this image?\n",
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"content": [
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"index": null,
"text": "the combined model normalized probability generated from the latest block b , where various numbers of the models in b have been infected ( attacked ) .",
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"content": [
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"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
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"content": [
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"index": null,
"text": "the combined model normalized probability generated from the latest block b , where various numbers of the models in b have been infected ( attacked ) .",
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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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"content": [
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"index": null,
"text": "the combined model normalized probability generated from the latest block b , where various numbers of the models in b have been infected ( attacked ) .",
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"content": [
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"index": null,
"text": "Can you provide a scientific caption for this image?\n",
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{
"index": 0,
"text": null,
"type": "image"
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"content": [
{
"index": null,
"text": "the ratio of normal witnesses ( rnw ) changes with the ratio of abnormal full nodes ( rafn ) .",
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"content": [
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"index": null,
"text": "What are the key features observed in this image?\n",
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{
"content": [
{
"index": null,
"text": "the ratio of normal witnesses ( rnw ) changes with the ratio of abnormal full nodes ( rafn ) .",
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"content": [
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"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
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"content": [
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"index": null,
"text": "the ratio of normal witnesses ( rnw ) changes with the ratio of abnormal full nodes ( rafn ) .",
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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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"content": [
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"index": null,
"text": "the ratio of normal witnesses ( rnw ) changes with the ratio of abnormal full nodes ( rafn ) .",
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"content": [
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"index": null,
"text": "Can you provide a scientific caption for this image?\n",
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{
"index": 0,
"text": null,
"type": "image"
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"role": "user"
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"content": [
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"index": null,
"text": "sensitivity analzsis of the nls solution for the same case as in figure 28 , however for perfect knowledge of horizontal velocity components ( vx , vy ) . these may be obtained from the induced drag model , i. e. the external force . when the horizontal wind velocity is known , the vertical component maz be determined robustly from aerodynamic power measurements .",
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"content": [
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"text": "What are the key features observed in this image?\n",
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"content": [
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"index": null,
"text": "sensitivity analzsis of the nls solution for the same case as in figure 28 , however for perfect knowledge of horizontal velocity components ( vx , vy ) . these may be obtained from the induced drag model , i. e. the external force . when the horizontal wind velocity is known , the vertical component maz be determined robustly from aerodynamic power measurements .",
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"content": [
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"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
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"content": [
{
"index": null,
"text": "sensitivity analzsis of the nls solution for the same case as in figure 28 , however for perfect knowledge of horizontal velocity components ( vx , vy ) . these may be obtained from the induced drag model , i. e. the external force . when the horizontal wind velocity is known , the vertical component maz be determined robustly from aerodynamic power measurements .",
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"content": [
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"index": null,
"text": "What is the significance of the findings shown in this image?\n",
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"content": [
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"index": null,
"text": "sensitivity analzsis of the nls solution for the same case as in figure 28 , however for perfect knowledge of horizontal velocity components ( vx , vy ) . these may be obtained from the induced drag model , i. e. the external force . when the horizontal wind velocity is known , the vertical component maz be determined robustly from aerodynamic power measurements .",
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"content": [
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"index": null,
"text": "Can you provide a scientific caption for this image?\n",
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{
"index": 0,
"text": null,
"type": "image"
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"content": [
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"text": "ber for the case 2 scenario .",
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"content": [
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"index": null,
"text": "What are the key features observed in this image?\n",
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"content": [
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"text": "ber for the case 2 scenario .",
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"content": [
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"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
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"content": [
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"index": null,
"text": "ber for the case 2 scenario .",
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"content": [
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"index": null,
"text": "What is the significance of the findings shown in this image?\n",
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"content": [
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"index": null,
"text": "ber for the case 2 scenario .",
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"content": [
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"index": null,
"text": "Can you provide a scientific caption for this image?\n",
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"index": 0,
"text": null,
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"content": [
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"text": "bleu scores of two models on the testing dataset d , ds and dd . the three dotted curves represent the performance of the seq2seq model while the three solid curves represent the performance of the variational decipher .",
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"content": [
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"index": null,
"text": "What are the key features observed in this image?\n",
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"content": [
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"index": null,
"text": "bleu scores of two models on the testing dataset d , ds and dd . the three dotted curves represent the performance of the seq2seq model while the three solid curves represent the performance of the variational decipher .",
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"text": "Can you provide a scientific explanation for this image?\n",
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"content": [
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"index": null,
"text": "bleu scores of two models on the testing dataset d , ds and dd . the three dotted curves represent the performance of the seq2seq model while the three solid curves represent the performance of the variational decipher .",
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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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"content": [
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"index": null,
"text": "bleu scores of two models on the testing dataset d , ds and dd . the three dotted curves represent the performance of the seq2seq model while the three solid curves represent the performance of the variational decipher .",
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"content": [
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"index": null,
"text": "Can you provide a scientific caption for this image?\n",
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"index": 0,
"text": null,
"type": "image"
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"content": [
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"text": "empirical comparison of the convergence of typical user-wise stochastic gradient descent to our learnbpr algorithm with bootstrap sampling .",
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"content": [
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"index": null,
"text": "What are the key features observed in this image?\n",
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"content": [
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"index": null,
"text": "empirical comparison of the convergence of typical user-wise stochastic gradient descent to our learnbpr algorithm with bootstrap sampling .",
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"content": [
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"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
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"content": [
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"text": "empirical comparison of the convergence of typical user-wise stochastic gradient descent to our learnbpr algorithm with bootstrap sampling .",
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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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"content": [
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"index": null,
"text": "empirical comparison of the convergence of typical user-wise stochastic gradient descent to our learnbpr algorithm with bootstrap sampling .",
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"content": [
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"index": null,
"text": "Can you provide a scientific caption for this image?\n",
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{
"index": 0,
"text": null,
"type": "image"
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"content": [
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"text": "comparison of different versions of our methods with cvm-net i and cvm-net ii [ 18 ] on cvusa dataset [ 6 ] .",
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"content": [
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"text": "What are the key features observed in this image?\n",
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"content": [
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"index": null,
"text": "comparison of different versions of our methods with cvm-net i and cvm-net ii [ 18 ] on cvusa dataset [ 6 ] .",
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"content": [
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"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
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"content": [
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"index": null,
"text": "comparison of different versions of our methods with cvm-net i and cvm-net ii [ 18 ] on cvusa dataset [ 6 ] .",
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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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"content": [
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"text": "comparison of different versions of our methods with cvm-net i and cvm-net ii [ 18 ] on cvusa dataset [ 6 ] .",
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"content": [
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"index": null,
"text": "Can you provide a scientific caption for this image?\n",
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"index": 0,
"text": null,
"type": "image"
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"content": [
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"text": "response time .",
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"content": [
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"index": null,
"text": "What are the key features observed in this image?\n",
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"content": [
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"text": "response time .",
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"content": [
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"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
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"content": [
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"text": "response time .",
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"content": [
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"index": null,
"text": "What is the significance of the findings shown in this image?\n",
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"content": [
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"index": null,
"text": "response time .",
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"content": [
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"index": null,
"text": "Can you provide a scientific caption for this image?\n",
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{
"index": 0,
"text": null,
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"content": [
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"text": "roc and roc-auc results from different url transformer training approaches . the mixed objective approach , shown in green , outperforms a generic decodeto-label approach . neither fine-tuning approaches yielded impressive performance .",
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"content": [
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"index": null,
"text": "What are the key features observed in this image?\n",
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"content": [
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"index": null,
"text": "roc and roc-auc results from different url transformer training approaches . the mixed objective approach , shown in green , outperforms a generic decodeto-label approach . neither fine-tuning approaches yielded impressive performance .",
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"content": [
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"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
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"content": [
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"index": null,
"text": "roc and roc-auc results from different url transformer training approaches . the mixed objective approach , shown in green , outperforms a generic decodeto-label approach . neither fine-tuning approaches yielded impressive performance .",
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"content": [
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"index": null,
"text": "What is the significance of the findings shown in this image?\n",
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"content": [
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"index": null,
"text": "roc and roc-auc results from different url transformer training approaches . the mixed objective approach , shown in green , outperforms a generic decodeto-label approach . neither fine-tuning approaches yielded impressive performance .",
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"content": [
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"index": null,
"text": "Can you provide a scientific caption for this image?\n",
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{
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"text": null,
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"content": [
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"text": "performance comparison of m-mimo , irs and fd/hd af-based mimo relay .",
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"content": [
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"text": "What are the key features observed in this image?\n",
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"content": [
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"text": "performance comparison of m-mimo , irs and fd/hd af-based mimo relay .",
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"content": [
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"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
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"content": [
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"text": "performance comparison of m-mimo , irs and fd/hd af-based mimo relay .",
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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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"content": [
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"text": "performance comparison of m-mimo , irs and fd/hd af-based mimo relay .",
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"content": [
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"index": null,
"text": "Can you provide a scientific caption for this image?\n",
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{
"index": 0,
"text": null,
"type": "image"
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"content": [
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"text": "fitness function f1 : f ( x ) = x3 β 15x2 + 50 .",
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"content": [
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"index": null,
"text": "What are the key features observed in this image?\n",
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"content": [
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"index": null,
"text": "fitness function f1 : f ( x ) = x3 β 15x2 + 50 .",
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"content": [
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"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
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"content": [
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"index": null,
"text": "fitness function f1 : f ( x ) = x3 β 15x2 + 50 .",
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"content": [
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"index": null,
"text": "What is the significance of the findings shown in this image?\n",
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"content": [
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"text": "fitness function f1 : f ( x ) = x3 β 15x2 + 50 .",
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"content": [
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"index": null,
"text": "Can you provide a scientific caption for this image?\n",
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"content": [
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"content": [
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"text": "What are the key features observed in this image?\n",
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"content": [
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"text": "illustration of antenna array with pseudo-randomly oriented elements .",
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"content": [
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"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
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"content": [
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"text": "illustration of antenna array with pseudo-randomly oriented elements .",
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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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"content": [
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"index": null,
"text": "illustration of antenna array with pseudo-randomly oriented elements .",
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"content": [
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"index": null,
"text": "Can you provide a scientific caption for this image?\n",
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{
"index": 0,
"text": null,
"type": "image"
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"content": [
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"text": "a pushing sequence for pivoting the aluminum object in a parallel-jaw grasp . the pushing sequence involves discrete pusher switch-overs to push the object from different facets to eventually get to the desired pose .",
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"content": [
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"index": null,
"text": "What are the key features observed in this image?\n",
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"content": [
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"index": null,
"text": "a pushing sequence for pivoting the aluminum object in a parallel-jaw grasp . the pushing sequence involves discrete pusher switch-overs to push the object from different facets to eventually get to the desired pose .",
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"content": [
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"index": null,
"text": "Can you provide a scientific explanation for this image?\n",
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"content": [
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"index": null,
"text": "a pushing sequence for pivoting the aluminum object in a parallel-jaw grasp . the pushing sequence involves discrete pusher switch-overs to push the object from different facets to eventually get to the desired pose .",
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"content": [
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"index": null,
"text": "What is the significance of the findings shown in this image?\n",
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"content": [
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"index": null,
"text": "a pushing sequence for pivoting the aluminum object in a parallel-jaw grasp . the pushing sequence involves discrete pusher switch-overs to push the object from different facets to eventually get to the desired pose .",
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"content": [
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"index": null,
"text": "Can you provide a scientific caption for this image?\n",
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{
"index": 0,
"text": null,
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"text": "number of holes ( edits ) vs. running time . lines of code varies between 50 and 800 .",
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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": "number of holes ( edits ) vs. running time . lines of code varies between 50 and 800 .",
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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": "energy efficiency ( ee ) for different ul transmit power and three different settings : ( i ) without decoupled access ( da ) and without power adaptation ( pa ) , ( ii ) with da and without pa and ( iii ) with daand with pa .",
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"content": [
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"text": "energy efficiency ( ee ) for different ul transmit power and three different settings : ( i ) without decoupled access ( da ) and without power adaptation ( pa ) , ( ii ) with da and without pa and ( iii ) with daand with pa .",
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"text": "Can you provide a scientific explanation for this image?\n",
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"content": [
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"text": "energy efficiency ( ee ) for different ul transmit power and three different settings : ( i ) without decoupled access ( da ) and without power adaptation ( pa ) , ( ii ) with da and without pa and ( iii ) with daand with pa .",
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"text": "energy efficiency ( ee ) for different ul transmit power and three different settings : ( i ) without decoupled access ( da ) and without power adaptation ( pa ) , ( ii ) with da and without pa and ( iii ) with daand with pa .",
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"text": "throughput per stream otfs vs ofdm for etu-300 with 1x1 , 2x2 , x and 8x8 mimo .",
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"content": [
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"text": "annotated objects area : cumulative distribution of the percentage of image area occupied by the annotated objects of pascal , coco , and open images ; i.e . , percentage of instances whose area is below a certain value . as a baseline , we plot the function corresponding to boxes with uniformly distributed area and side length . we ignore here boxes marked as crowd in coco and marked as group in open images . train set for all datasets .",
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"content": [
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"text": "annotated objects area : cumulative distribution of the percentage of image area occupied by the annotated objects of pascal , coco , and open images ; i.e . , percentage of instances whose area is below a certain value . as a baseline , we plot the function corresponding to boxes with uniformly distributed area and side length . we ignore here boxes marked as crowd in coco and marked as group in open images . train set for all datasets .",
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"content": [
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"text": "annotated objects area : cumulative distribution of the percentage of image area occupied by the annotated objects of pascal , coco , and open images ; i.e . , percentage of instances whose area is below a certain value . as a baseline , we plot the function corresponding to boxes with uniformly distributed area and side length . we ignore here boxes marked as crowd in coco and marked as group in open images . train set for all datasets .",
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"content": [
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"text": "annotated objects area : cumulative distribution of the percentage of image area occupied by the annotated objects of pascal , coco , and open images ; i.e . , percentage of instances whose area is below a certain value . as a baseline , we plot the function corresponding to boxes with uniformly distributed area and side length . we ignore here boxes marked as crowd in coco and marked as group in open images . train set for all datasets .",
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