Datasets:

mawadalla
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scientific-figures-captions-context

image_filename string	context sequence	label string	caption string
dataset/figures/1508.02466_fig1.eps	[ "ique was employed. For these analysis the samples were diluted in alcohol. Magnetic measurements was carried out using a commercial Superconducting Quantum Interference Device (SQUID). Crystal structure and morphologyX-ray powder diffraction of the nanostructures and bulk are shown in the Fig. DRXTODOS; the result...	DRXTODOS	Powder diffractograms for the Sm_0.6Sr_0.4MnO_3 nanoparticles, nanotubes and bulk samples.DRXTODOS
dataset/figures/1508.02466_fig2.eps	[ "th Fe-K$$ ($$ = 1.936087 ) for bulk sample. Data were collected in the 15$^o$ < 2$$ < 85$^o$ range in a Bragg-Brentano geometry, with a step size of 0.02$^o$ and a counting time of 0.1 s per step. To confirm the formation of the nanotubes, high resolution transmission electron microscopy (HRTEM) technique was empl...	TEM	Transmission Electron Microscospy for (a) nanotube and (b) high resolution mode used to verify the features of particle that constitutes the wall of the nanotube. TEM
dataset/figures/1508.02466_fig3.eps	[ "s of particle that constitutes the wall of the nanotube. TEMMagnetocaloric potential evaluationDue to the different morphology of the prepared samples, as detailed before, it is highly expected a different magnetic behavior for those samples; and indeed it was found, as shown on figure MxTtodos-(a), that presents ...	MxTtodos	(a) Temperature dependence of field-cooled (FC) and zero field-cooled (ZFC) magnetization for nanoparticle, nanotube and bulk of Sm_0.6Sr_0.4MnO_3 manganite. Nanotube magnetization was multiplied by a factor 10 for better visualization. Bottom-(b): magnetization as a function of external magnetic field, at 4 K. Bottom-(c): magnetization as a function of H/T presenting an evidence of superparamagnetic behavior for nanotubes and nanoparticle. MxTtodos
dataset/figures/1508.02466_fig4.eps	[ "$; and indeed this occurs, as can be seen in figure MxTtodos-(c). Other works indeed agree with this assumptionPhysRevB.45.9778. hwidth=6cmfig4.epsMagnetization as a function of temperature for several values of external magnetic field for (a) bulk, (b) nanoparticle and (c) nanotube. MxTvsHhwidth=5.8cmfig5.epsEntr...	MxTvsH	Magnetization as a function of temperature for several values of external magnetic field for (a) bulk, (b) nanoparticle and (c) nanotube.MxTvsH
dataset/figures/1508.02466_fig5.eps	[ "d for (a) bulk, (b) nanoparticle and (c) nanotube. MxTvsHhwidth=5.8cmfig5.epsEntropy change as a function of temperature for some values of external magnetic field for (a) bulk, (b) nanoparticle and (c) nanotube. The error bars were only presented for 5 T curve for clarity purpose. emchwidth=5.6cmfig6.epsArrott pl...	emc	Entropy change as a function of temperature for some values of external magnetic field for (a) bulk, (b) nanoparticle and (c) nanotube. The error bars were only presented for 5 T curve for clarity purpose.emc
dataset/figures/1508.02466_fig6.eps	[ "nge as a function of temperature for some values of external magnetic field for (a) bulk, (b) nanoparticle and (c) nanotube. The error bars were only presented for 5 T curve for clarity purpose. emchwidth=5.6cmfig6.epsArrott plots for different temperatures for all samples analyzed. arrotplotMagnetization as a fun...	arrotplot	Arrott plots for different temperatures for all samples analyzed.arrotplot
dataset/figures/2106.04847_Case_Introduction.png	[ "summarization wang2013domain, pasunuru2018multi, document clustering hulth2006study, information retrieval kim2013applying. !twidth=0.48Case_Introduction.pdfAn example of an input document and its expected keyphrases. Blue and red denote present and absent keyphrases, respectively. fig1:case-studyKeyphrases of a d...	fig1:case-study	An example of an input document and its expected keyphrases. Blue and red denote present and absent keyphrases, respectively.
dataset/figures/2106.04847_visualization_analysis.png	[ "compare the distance between the PKE representation and AKG representation in different settings. In detail, we randomly sample 2000 pairs of PKE representation vector and AKG representation vector on different positions from test data and compute euclidean metric in each pair. As shown in Figure fig:visualization...	fig:visualization_analysis	Distance between PKE representation and AKG representation on different settings.
dataset/figures/2106.04847_BWC_Loss.png	[ "n. Also, the blue points are denser than the yellow points, which means the PKE and AKG representation with SRL is more diverse than the one without SRL on different samples. BWC Analysiswidth=0.4BWC_Loss.pngBWC's influence on total training loss (sequence labeling + text generation). fig:bwc_lossLoss Compare: Fro...	fig:bwc_loss	BWC's influence on total training loss (sequence labeling + text generation).
dataset/figures/2106.04847_BWC_Error.png	[ "iginal supervised signal from a global view. It guides the model to learn how many to predict and how to allocate present and absent keyphrases, while original loss only teaches what to predict in each position. width=0.4BWC_Error.pngBag-of-words Error comparison between vanilla and BWC. fig:bow_diffBag-of-words E...	fig:bow_diff	Bag-of-words Error comparison between vanilla and BWC.
dataset/figures/2106.04847_case-study.png	[ "X I X X I''. We concatenate all the tokenized absent keyphrases into one sequence using a special delimiter “ ; ”. An example of absent keyphrase sequence will like ``peer to peer ; content delivery ; t \\#\\#f \\#\\#rc ; ran \\#\\#su \\#\\#b\". Case Study!twidth=0.4case-study.pdfCase study. fig1:case-study-append...	fig1:case-study-appendix	Case study.
dataset/figures/2009.11347_imgs_FSmethods.png	[ "these features employed in a multilayer perceptron (MLP) as the traffic classifier discussing costs/benefits. Finally, Section sec:conclusions contains some concluding remarks. Feature Selection through Mutual Information!ht\t\twidth=imgs/FSmethods.png\tFeature Selection methods. fig:fsMethods\tFeature Selection m...	fig:fsMethods	Feature Selection methods.
dataset/figures/2009.11347_._imgs_metrics.png	[ "d: 46\\width=0.8./imgs/metrics.png SVM metrics during backward feature elimination with the mRMR and MIFS rankings. fig:svmMeanFig. fig:svmMean shows the accuracy, precision and recall metrics calculated during the elimination process from 23 to 5 features, using the mRMR and MIFS rankings. All the performance ind...	fig:svmMean	SVM metrics during backward feature elimination with the mRMR and MIFS rankings.
dataset/figures/2009.11347_._imgs_errorAE.png	[ "set (46639 samples). The AE training will be performed using the learning and validation sets, whereas the testing set (which is never fed to the network during training) will be used for the final performance tests. ht width=0.8./imgs/errorAE.png Autoencoder reconstruction error. fig:AElossesThe AE is trained...	fig:AElosses	Autoencoder reconstruction error.
dataset/figures/2009.11347_._imgs_avg-DTs.png	[ "ws the AE effect which mixes the distribution of the samples and change the range of values, creating only sporadic small clusters and less outliers than the RRw-Optimized MTA-KDD'19 dataset. It is worth noting that, in the AE MTA-KDD'19 dataset, all the features are now relevant, as shown in Fig. fig:featimp.", ...	fig:featimp	Average of Feature Importance with four Decision Trees.
dataset/figures/2009.11347_._imgs_dnnMLP2.png	[ "is learning) and its Validation Learning Curve (VLC, showing the loss evolution during the validation phase at the end of each epoch, i.e., how well the model is generalizing). !ht\t\twidth=./imgs/dnnMLP2.png \tTLC and VLC on the Optimized (a) and RRw-Optimized (b) MTA-KDD'19 datasets. fig:dnnRPwThe left side (a)...	fig:dnnRPw	TLC and VLC on the Optimized (a) and RRw-Optimized (b) MTA-KDD'19 datasets.
dataset/figures/2009.11347_._imgs_AElearning.png	[ "A-KDD'19 dataset. Here the TLC and VLC evolve similarly and the neural network loss stabilizes earlier at an acceptable level. Thus, as expected, re-weighting the dataset helped the classifier. !ht width=./imgs/AElearning.png TLC and VLC on the AE-Generated MTA-KDD'19 dataset. fig:AEtraintestOn the other...	fig:AEtraintest	TLC and VLC on the AE-Generated MTA-KDD'19 dataset.
dataset/figures/2004.08759_Fig_5.eps	[ "in). For instance, Jang et al. studied the impact of currency crises on the MST structure of stock markets Jang-Lee-Chang-2011-PA. Motivated by this, we investigate the evolution of the MSA structure of the Chinese stock market before, during and after two stock market turmoils. As shown in Fig.~Fig:Crash, the two...	Fig:Crash	The closing price of Shanghai Stock Exchange Composite Stock Price (SSEC) Index from 04 January 2000 to 29 December 2017. The two gray regions are on behalf of two crashes of the Chinese stock market which from 16 October 2007 to 04 November 2008 and 12 June 2015 to 28 January 2016 respectively.
dataset/figures/2004.08759_Fig_9.eps	[ "rol group, for each year, we randomly select the yearly return time series of non-root sectors to form a control sample $r_t^s$ ($t=2000, , 2017$) and calculate the correlation coefficients between these randomly selected sectors and the Shanghai Composite index. \t\twidth=9cmFig_9.eps\tComparison of the correlati...	Fig:ret:root	Comparison of the correlation coefficients between the returns of different types of sectors (source, sink and non-root) and the SSEC index.
dataset/figures/1301.2048_sep_vs_mass.ps	[ "). These three groups correspond to three different detection methods: radialvelocities (RVs), illumination effects, and timing (using either the stellarpulsation or the eclipses as a clock). Figure detectionmethods compares the sensitivities of these different detection methods. The EXOTIME project is primarily o...	detectionmethods	Substellar companions around subdwarf B stars (black dots) and comparison of the detection methods mentioned in the text (RV, illumination and timing). For most of these systems the inclination is not known and we report the minimum mass. Horizontal lines show 1 Jupiter mass, 13 Jupiter masses (dividing the exoplanet and brown dwarf regimes) and 75 Jupiter masses (dividing the substellar and stellar regimes). The big dots are the two candidates presented in this paper.-5mm
dataset/figures/1301.2048_phasefolding-0444-f1.eps	[ "wobble of the stararound the common barycenter. Another effect resulting in asinusoidal O--C component is the beating of close unresolvedfrequencies. All these possible components are thoroughlyinvestigated in 2011PhDT........85L. In order to finally obtain the O--C diagrams shown inFigs.~hs0444f1 and hs0702f1, we...	hs0444f1	Phase-folded O--C diagrams for V1636\,Ori based on the independent frequencies f_1(top) and f_2(bottom). Evolutionary and beating signals are already subtracted. The data are folded with an orbital period as given in the plots. Data points are duplicated on the phase-axis for plotting purposes.-5mm
dataset/figures/1301.2048_phasefolding-0702-f1.eps	[ "stararound the common barycenter. Another effect resulting in asinusoidal O--C component is the beating of close unresolvedfrequencies. All these possible components are thoroughlyinvestigated in 2011PhDT........85L. In order to finally obtain the O--C diagrams shown inFigs.~hs0444f1 and hs0702f1, we i) subtracted...	hs0702f1	Phase-folded O--C diagrams for DW\,Lyn based on the independent frequencies f_1(top) and f_2(bottom). Evolutionary and beating signals are already subtracted. The data are folded with an orbital period as given in the plots. Data points are duplicated on the phase-axis for plotting purposes.-5mm
dataset/figures/1301.2048_inclinations-paper.eps	[ "on mass and orbitalseparation. Assuming a canonical sdB mass of $0.47 M_$ and weightingover the independent frequencies $f_1$ and $f_2$, we find aminimum mass $m i$ of $31.41 7.25$ Jupiter masses for thiscandidate, V1636\\,Ori\\,b, which would place it to the brown dwarf regime(see Fig.~inclinations for the effect...	inclinations	True companion masses as a function of the unknown inclinations for V1636\,Ori\,b (HS\,0444+0458\,b) and DW\,Lyn\,b (HS\,0702+6043\,b). Horizontal dashed lines separate the stellar, brown dwarf and exoplanet regimes, respectively. The cases of zero inclination (face on) and 90 degrees inclination (edge on) are indicated in the figure. Assuming a random distribution of orbital inclinations, the arrows at the top show the probabilities P (i < ) = 1 - that the orbital inclination i is lower than a certain value , e.60 degrees has a probability of 50\%. The brown dwarf candidate V1636\,Ori\,b would be stellar for inclinations below 23.12 degrees. The possibility for this being the case is 8.03\%. Accordingly, the exoplanet candidate DW\,Lyn\,b would be a brown dwarf for inclinations below 25.42 degrees (possibility 9.68\%) and a star for inclinations below 4 degrees (possibility 0.24\%).
dataset/figures/1612.08949_figure1.png	[ "simple case with just two agents, $N=2$, and the three possiblecombinations: two mimetics, one mimetic and one contrarian, and twocontrarians. We choose parameters such that for both agents $d$ isslightly higher than $u_i$, for example $d=0.01$ and $u_1=u_2=0$. Theresults are exhibited on Fig.~fig:twoagents (black...	fig:twoagents	Case of two agents: (a) Two mimetics, the final value of n is n=1; (b) one mimetic and one contrarian, n quickly converges to n=0.5; (c) two contrarians, the system exhibits oscillations. Black squares correspond to PD and open red circles to MC dynamics. In all cases, d=0.01 and u_1=u_2=0.
dataset/figures/1612.08949_figure2.png	[ "illations. Black squares correspond to PD and open red circles to MC dynamics. In all cases, $d=0.01$ and $u_1=u_2=0$. fig:twoagents Let us now consider the case with an intermediate number of agents, $N=100$; henceforth we use the approximation given by Eq.(4). In Fig.~100agents, it can be verif...	100agents	Fraction of adopters as a function of time for N=100 agents and different values of the fraction of contrarians f: (a) f=0.2, no oscillations; (b) f=0.5, small amplitude oscillations; and (c) f=0.9, large amplitude sustained oscillations. Black squares correspond to PD and open red circles to MC dynamics. In all cases, d=0.4 and u_0 = 0.5.
dataset/figures/1612.08949_figure3.png	[ ", $d=0.4$ and $u_0 = 0.5$. 100agents\tFor an even larger number of agents, $N=10^7$, oscillations alwaysdisappear in the long term. Even for a high proportion of contrarians,$f = 0.9$, and with parallel dynamics, oscillations decay after ashort transient, as can be seen in Fig.~muchos. For a lowerfraction...	muchos	Fraction of adopters as a function of time for N=10^7 agents and different values of the fraction of contrarians f: (a) f=0.2, no oscillations; (b) f=0.5, very short lived oscillations; and (c) f=0.9, transient oscillations. Black squares correspond to PD and open red circles to MC dynamics. The other parameters in all cases are d=0.4 and u_0 = 0.5.
dataset/figures/1612.08949_figure4.png	[ "implies that $30\\the agents have an idiosyncrasy below $d$, i.e. $30\\early adopters. As we are interested in possible oscillations wefocus on a high concentration of contrarians, $f=0.9$, and threesystem sizes, $N=2$, $N=100$, and $N=10^7$. The results are shownin Fig.~lowd where a clear feature can be seen: the...	lowd	Fraction of adopters as a function of time for a large fraction of contrarians (f=0.9), but a low value of advertising (d=-0.2). Results for different system sizes: (a) N=2, (b) N=100, and (c) N=10^7. Black squares correspond to PD and open red circles to MC dynamics. The qualitative behavior is the same as in Fig.~100agents and muchos with the same value of f, but a bigger value of d.
dataset/figures/1612.08949_figure5.png	[ "ar, we have chosen$u_0=0.5$. If a narrower distribution of the idiosyncratic resistanceto adopt is considered, stable oscillations may appear for relativelyhigh values of the advertising. In the next section we show, as anexample, that such is the case for $u_0 = 0.25$ and $ P(u)=2$(see Fig.fig:extremes-b). Moreov...	fig:extremes	Extreme pay-offs as a function of the number of adoppters,n, for the uniform distribution with d=0.4, u_0 = 0.5. lines are extreme pay-offs, blue lines for mimetics and red lines for contrarians. In general they are given by Eq.~eq:MaxMinPayoffs, in this case by Eq.~eq:MaxMinPayoffs2.
dataset/figures/1612.08949_figure5b.png	[ "ar, we have chosen$u_0=0.5$. If a narrower distribution of the idiosyncratic resistanceto adopt is considered, stable oscillations may appear for relativelyhigh values of the advertising. In the next section we show, as anexample, that such is the case for $u_0 = 0.25$ and $ P(u)=2$(see Fig.fig:extremes-b). Moreov...	fig:extremes-b	Extreme pay-offs as a function of the number of adopters, n, for the uniform distribution with d=0.6, u_0 = 0.25. lines are extreme pay-offs, blue lines for mimetics and red lines for contrarians. In general they are given by Eq.~eq:MaxMinPayoffs. Inset: Numerical results of the fraction of adopters as a function of time in the parallel update dynamics for N=10^7(d=0.6, u_0 = 0.25, and f=0.7).
dataset/figures/1612.08949_figure6-new.png	[ "is with its variance given by$ = 2 \\, \\, 3.$Following the practice of the previous section, we present first thenumerical results. Numerical ResultsWe have performed different simulations with the logistic distributiongiven by Eq.~eq:logis. The results are presented inFigs.~umbral, logd04, logd03, and logres. W...	umbral	Thresholds value of the fraction of contrarians above which oscillations appear. The curves correspond to different values of the width of the logistic distribution , as indicated. We have represented just positive values of d as there are no oscillations for negative values. The curves go through a minimum that is lower the narrower the distribution. Notice that the oscillations disappear when the advertising is slightly higher than d=1
dataset/figures/1612.08949_figure7.png	[ "h its variance given by$ = 2 \\, \\, 3.$Following the practice of the previous section, we present first thenumerical results. Numerical ResultsWe have performed different simulations with the logistic distributiongiven by Eq.~eq:logis. The results are presented inFigs.~umbral, logd04, logd03, and logres. Whenthe...	logd04	Temporal behavior of the fraction of adopters for the logistic distribution with = 0.25, d=0.4, and different values of the fraction of contrarians f: (a) f=0.2, (b) f=0.5, and (c) f=0.9. Results are for a large number of agents, N=10^7. Open red circles correspond to the MC simulations and black squares to PD simulations. In the PD case it is possible to see the oscillations in the number of adopters for a high concentration of contrarians. We have considered much longer times than those represented in the figure and the oscillations are stable.
dataset/figures/1612.08949_figure8.png	[ "ance given by$ = 2 \\, \\, 3.$Following the practice of the previous section, we present first thenumerical results. Numerical ResultsWe have performed different simulations with the logistic distributiongiven by Eq.~eq:logis. The results are presented inFigs.~umbral, logd04, logd03, and logres. Whenthe simulatio...	logd03	Temporal behavior for the logistic distribution with = 0.25, f=0.9 and two different values of the parameter d(the normalized effective marketing): (a) d=-0.2 and (b) d=0.1. Oscillations in the number of adopters are obtained if d > 0.
dataset/figures/1612.08949_figure9-new.png	[ "$ = 2 \\, \\, 3.$Following the practice of the previous section, we present first thenumerical results. Numerical ResultsWe have performed different simulations with the logistic distributiongiven by Eq.~eq:logis. The results are presented inFigs.~umbral, logd04, logd03, and logres. Whenthe simulation is performe...	logres	This figure summarizes the numerical results for the logistic distribution of idiosyncrasies with = 0.25. All four panels exhibit the fraction of adopters as a function of the fraction of contrarians for four different values of d: (a) d=0.1, (b) d=0.4, (c) d=0.7, and (d) d=1.0. As expected, the number of adopters decreases when the number of contrarians increases. The red curves (dashed) correspond to Monte Carlo simulations and the black ones to a parallel dynamics. An oscillatory behavior is obtained only for parallel dynamics and the black lines correspond to the average value of the oscillations, while the shadowed areas indicates the amplitude of the oscillations. Both dynamics exhibit identical results for low and intermediate values of f, but there exists a critical value of f when the parallel dynamics exhibits period two oscillations. When increasing d the region of oscillations increases up to d=0.7 and then decreases for d=1.0. When d < 0 there are no oscillations and both dynamics produce the same results.
dataset/figures/1612.08949_figure11.png	[ "ond to the fixed points and arestable solutions provided that $ y_1' d y_1d n < 1$. However, solutions with $\| y_1'\| \|d y_1d n\| >1$ areunstable, and we are then obliged to consider a second iteration,i.e., $y_1(y_1(n))$. The solutions for this second iteration arerepresented on Fig. logana02: if more than one in...	logana02	Fixed points of y_1(y_1(n)) with y_2(n)(gray dot-dashed line). We have plot just the case with d=0.4. It is possible to observe that for f=0.2 and f=0.5 there is just one intersection, that corresponds to the stable solutions previously obtained. For f=0.9 there are three intersections. The middle one corresponds to the fixed point of y_1(n) while the other two represent the extremes of the oscillations. These extreme values are approximately 0.12 and 0.9 and correspond to the extreme value of the oscillations in the PD simulations, see Fig. logd04(c).
dataset/figures/1612.08949_figure12.png	[ "Iglesias12. Here we include thepossibility of coming back from previous decisions, thus, individualswill abandon innovation if the pay-off is negative. No doubts ordelays are allowed in this version of the model. The results of thepresent and the previous~GoncalvesLagunaIglesias12 models arecompared and discussed ...	compare	Comparison between the results of ref.~GoncalvesLagunaIglesias12(without repentants) and the present ones with repentants: final number of adopters for two values of d(d=0.4, squares, and d=-0.2, circles). Filled symbols correspond to no repentants and open ones with repentants (present contribution). Pairs of curves display similar behavior with always lower values of adoption for the case with repentants.
dataset/figures/2303.05972_images_dbn.png	[ "_t=0^T-1 p(X^t+1 \| X^t). eq:markov_1!t\twidth=0.6images/dbn.pdf\tExample of the structure of a first-order Markovian DBN with two time slices $t_0$ and $t_1$. To calculate the future values in $t_1$, we would only need to know the current values of our variables in $t_0$. fig:dbn_figAn example of the structure of ...	fig:dbn_fig	Example of the structure of a first-order Markovian DBN with two time slices t_0 and t_1. To calculate the future values in t_1, we would only need to know the current values of our variables in t_0.
dataset/figures/2303.05972_images_hybrid_ex.png	[ "expected severity of the symptoms in that patient. With this method, we can see if a patient is expected to end up suffering from critical COVID-19 and when approximately will this situation occur. To illustrate this whole process, a schematic representation of this framework can be seen in Fig. (fig:hybrid_fig). ...	fig:hybrid_fig	Schematic representation of the classifier-DBN framework. After obtaining a state vector S_0 from a patient, we can use it to forecast the next t state vectors with the DBN model and check if they are critical with our static classifier.
dataset/figures/2303.05972_images_nn_arch.png	[ "layer used a single neuron with a sigmoid activation function for binary classification. A result greater than 0.5 is equated to predicting a critical status for a patient, and a result lesser or equal to 0.5 predicts a non-critical scenario. A representation of this structure can be seen in Fig. (fig:nn_arch). !t...	fig:nn_arch	Structure of the neural network model used in the experiments.
dataset/figures/2303.05972_images_inst_hist.png	[ "owever, it will be used to train the classifier models. From the remaining patients with more than a single instance, the majority of them have either two or three rows of recorded values. To illustrate this, we show a histogram with the distribution of the number of instances per patient in Fig. (fig:hist). !t\tw...	fig:hist	Histogram with the number of instances per patient greater than 1 in the dataset. Inside the last bracket we have grouped all the patients with 10 or more instances. A higher number of instances indicates a longer stay in the hospital and as such a more severe case of COVID-19, which is far less common than a mild case.
dataset/figures/2303.05972_images_nn_time.png	[ "o be performed once. !t\twidth=images/nn_time.pdf\tClassification results of the neural network model as we feed it state vectors further ahead in time with the DBN model. The classification performance of the neural network improves monotonically by combining it with the DBN forecastings. fig:subplotsGiven that t...	fig:subplots	Classification results of the neural network model as we feed it state vectors further ahead in time with the DBN model. The classification performance of the neural network improves monotonically by combining it with the DBN forecastings.
dataset/figures/2303.05972_images_dbn_crop.png	[ "DBN model. The initial and maximum oxygen saturation variables from the last instant (in red) affect the calculation of the next maximum oxygen saturation value. Other variables like body temperature, systolic and diastolic blood pressures and heart rate also influence this value in the forecast. fig:dbn_cropIn ad...	fig:dbn_crop	Subset of relevant variables to the forecasting of maximum oxygen saturation (light blue) in the DBN model. The initial and maximum oxygen saturation variables from the last instant (in red) affect the calculation of the next maximum oxygen saturation value. Other variables like body temperature, systolic and diastolic blood pressures and heart rate also influence this value in the forecast.
dataset/figures/quant-ph0405030_figpuri.eps	[ "with a singlepair in a Werner state but with fidelity $F^ $. Then, they can taketwo successful pairs and repeat the same procedure to obtain a higherfidelity. By proceeding in this way they can reach a fidelity as close toone as they wish, but at the expenses of wasting many pairs. In Fig. figupuri we have plotted...	figupuri	New fidelity in terms of the old fidelity for the purification protocol. Successive applications lead to a fidelity as close to one as one wishes.
dataset/figures/quant-ph0405030_figpuriimperf.eps	[ "ring thepurification protocol (Controlled-NOT, measurements, etc.) are perfect. Inreality there will be imperfections in all these operations. One can takethem into account by using some explicit models Briegel98 or bystudying the worst case scenario Gi98. The result is schematized inFig. Figpuriimperf. Now, there...	Figpuriimperf	Same as in the previous figure, but with imperfections.
dataset/figures/quant-ph0405030_pzfigion1.eps	[ "ion motion in only one dimension. Hence, the Hamiltonian describing the free motion of the ion in the trap isH_0T=p^22M+12M ^2x^2. Htp1Here $x$ and $p$ are the position and momentum operatorsrespectively, $M$ is the ion mass, $ $ is the oscillation frequency (Fig.~pzfigion1. We can rewrite this Hamiltonian in the ...	pzfigion1	Energy levels of an ion trap. Left: internal level structure with % \|g \|r a metastable transition, and \|g \|e a strong dissipative transition coupled by Rabi frequencies _1 and _2, respectively. Right: quantized energy levels in the harmonic trapping potential
dataset/figures/quant-ph0405030_pzfigion2.eps	[ "imilarly, for $\| _L_1-( _rg+ )\| $, onlytransitions increasing the quantum number $n$ by one ($k=+1$) contribute, sothat $H_1$ can be approximated by the anti-Jaynes-CummingsHamiltonianH_AJC_ = a^ a-12 _1 _z+12 _1( _ _+a^ +h.c.). kmone(see Fig.~pzfigion2) For the above approximations to be valid werequire that th...	pzfigion2	Coupling to the atom + trap levels according to the Hamiltonians (%h0), (kone and (kmone, respectively, in lowest order Lamb-Dicke expansion.
dataset/figures/quant-ph0405030_pzfigion3.eps	[ "omplete set of quantum gatesbetween any set of (not necessarily neighboring) ions; (ii) decoherence iscomparatively small, and (iii) the final readout can be performed withessentially unit efficiency Steane97,Monroe95,King98,Roos00,Nagerl99,Turchette98,Sackett00,Kielpinski01,Rowe01. hbppzfigion3width=8.0cmpzfigion...	pzfigion3	Ion trap quantum computer (schematic).
dataset/figures/quant-ph0405030_pzfigion4.eps	[ "(k /2)\|r_q _j\|0 , \\\\\|r _j\|0 & & (k /2)\|r_q_j\|0 -ie^-i (k /2)\|g _j\|1 , where $\|0 $ ($\|1 $) denotes a state of the CM mode with no(one) phonon. Let us now show how a two-bit gate can be performed using this interaction. We consider the following three--step process (see Fig.~pzfigion4): (i) A $ $ laser pulse wi...	pzfigion4	The two-qubit quantum gate. a) First step according to (%bigone): the qubit of the first atom is swapped to the photonic data bus with a -pulse on the lower motional sideband, b) Second step: the state \|g,1 acquires a minus sign due to a 2% -rotation via the auxiliary atomic level \|r_1 on the lower motional sideband.
dataset/figures/quant-ph0405030_pzfigion5.eps	[ "ng an internal-state dependent two-body interactionbetween the ions Cirac00,Calarco01. This proposal has the advantagebeing conceptually simpler (e.g. there is no zero temperature requirement),and obviously scalable. The model assumes that ions are stored in an array of microtraps (Fig.~pzfigion5). Similar to the ...	pzfigion5	Ions stored in an array of microtraps. By addressing two adjacent ions with an external field the ion wave packet is displaced conditional to its internal state.
dataset/figures/quant-ph0405030_pzfigion6.eps	[ "nian). It is this term which is responsible forentangling the atoms, giving rise to a conditional phase shift, which can besimply interpreted as arising from the energy shifts due to the Coulombinteractions of atoms accumulated on different trajectories according totheir internal states (Fig.~pzfigion6), =-e^24 _...	pzfigion6	Trajectories of the qubits as a function of time. Depending on the internal state different phases are accumulated.
dataset/figures/quant-ph0405030_pzfigcqed1.eps	[ "tored in internal state of atoms. The task isto transmit the qubit according to transfer( \|0 _1+ \|1 _1) \|0_2 \|0 _1 ( \|0 _2+ \|1_2)from the first to the second atom. Below we study a model of an opticalinterconnect based on storing atoms in high--Q optical cavities (see Fig.~pzfigcqed1). By applying laser beams, on...	pzfigcqed1	Transmission of a qubit from an atom at the first node to an atom at the second node according to (transfer) and (%transfer1).
dataset/figures/quant-ph0405030_pzfigcqed2.eps	[ "\|g _2+c_e\|e _2) \|0_1\|0 _2\|vac ,where $c_g,e$ are complex numbers. In (transfer1), $\|0 _i$and $\|$vac$ $ represent the vacuum state of the cavity modes and thefree electromagnetic modes connecting the cavities. Transmission will occurby photon exchange via these modes. hbppzfigcqed2width=8.0cmpzfigcqed2.epsTransmiss...	pzfigcqed2	Transmission of a qubit between two atoms as a cascaded quantum system
dataset/figures/quant-ph0405030_pzfigatom1.eps	[ "tices thesecollisional interactions can be controlled via laser parameters Jaksch99. Furthermore, these nonlinear atom-atom interactions can be largeJaksch99, even for interactions between individual pairs of atoms,thus providing the necessary ingredients to implement quantum logic. hbppzfigatom1width=8.0cmpzfigat...	pzfigatom1	We collide a first atom in the internal state \|a with a second atom in state \|b. In the collision the wave function accumulates a phase according to (transf
dataset/figures/quant-ph0405030_pzfigatom2.eps	[ "o sloshingmotion is excited. In this case, (transf) still holds with $ =^a+ ^b+ ^ab$, where in addition to (trivial) singleparticle kinetic phases $ ^a$ and $ ^b$ arising from moving thepotentials, we have a collisional phase shift ^ab=_- ^ dt E(t)/ . phicolhbppzfigatom2width=8.0cmpzfigatom2.epsBy moving an optica...	pzfigatom2	By moving an optical lattice in a state-dependent way neighboring atoms collide and acquire a phase shift.
dataset/figures/quant-ph0405030_pzfigatom3.eps	[ "ve permanent dipole moments $ _ze_z=3/2\\,nqea_0e_z$. In alkali atoms the $s$ and$p$-states are shifted relative to the higher angular momentum states due totheir quantum defects, and the Stark maps of the $m=0$ and $m=1$ manifoldsare correspondingly modified Gallagher94. hbppzfigatom3width=8.0cmpzfigatom3.epsAto...	pzfigatom3	Atomic level scheme of the two-qubit gate. The laser excites the atoms in state \|1 to Rydberg states in an electric field. The Rydberg states interact via a dipole-dipole interaction.
dataset/figures/quant-ph0405030_pzfigatom4.eps	[ "atoms a dipole-dipole interaction $u(R)= r\| r\|V_dip(Re_z)\|r \|r $ with $u(R)=-9n(n-1)^2(a_0/R)^3(e^2/8 _0a_0) n^4$. In alkali atoms we have to replace $n$ by the effective quantum number $ $ Gallagher94. We will use this large energy shift to entangleatoms. hbppzfigatom4width=8.0cmpzfigatom4.epsLaser excitation s...	pzfigatom4	Laser excitation sequence of the dipole-dipole gate with Rydberg atoms. Qubits are stored in two internal atomic ground states denoted by % \|0_j and \|1_j.
dataset/figures/quant-ph0405030_d63reviewp1-bw.eps	[ "ional light propagation model. The result confirms that we have the same kind of collective enhancement inthe signal-to-noise ratio. $ $I-level configurationIn the first level configuration, we assume that the atoms have two groundstates $\| 1 ,$ $\| 2 $ and one excitedstate $\| 3 $ (see Fig.~ d63reviewp1). All the a...	d63reviewp1	(1a) An atomic ensemble in a weak coupling cavity. (1b) The %I-level configuration.
dataset/figures/quant-ph0405030_d63reviewp2-bw.eps	[ "tum signal. $ $II-level configurationIn the second level configuration, each atom still has three levels $\|1 ,$ $\| 2 $ and $\| 3 $. Thedifference is now that the classical driving laser is coupling to thetransition $\| 1 \| 3 $, andthe quantum signal to the transition $\| 3 \| 2 $\\ (see Fig.~ d63reviewp2). This level...	d63reviewp2	The II-level configuration.
dataset/figures/quant-ph0405030_d63reviewp3-bw.eps	[ "ng coherent light-atomcoupling. To show this more directly, we consider another light-atominteraction configuration with four levels, and in this level scheme, wesolve directly the interaction of light with free-space atomic ensembles byassuming a one-dimensional light propagation model. tbpd63reviewp3width=6.0cmd...	d63reviewp3	The four-level configuration.
dataset/figures/quant-ph0405030_d63reviewp4-bw.eps	[ "to entangleatomic ensembles with significant improvements in the communicationefficiency thanks to the collective enhancement of the signal-to-noise ratiofor many-atom ensembles. The system is a sample of atoms prepared in the ground state $\|1 $ with the $ $II-level configuration (see Fig.~ d63reviewp4). It has be...	d63reviewp4	(4a) The relevant level structure of the atoms in the ensemble with 1, the ground state, 2, the metastable state for storing a qubit, and 3, the excited state. The transition 13 is coupled by the classical laser with the Rabi frequency %, and the forward scattering Stokes light comes from the transition %32. For convenience, we assume off-resonant coupling with a large detuning . (4b) Schematic setup for generating entanglement between the two atomic ensembles L and R. The two ensembles are pencil shaped and illuminated by the synchronized classical laser pulses. The forward-scattering Stokes pulses are collected after the filters (polarization and frequency selective) and interfered at a 50\%-50\% beam splitter BS after the transmission channels, with the outputs detected respectively by two single-photon detectors D1 and D2. If there is a click in D1 or D2, the process is finished and we successfully generate entanglement between the ensembles L and R. Otherwise, we first apply a repumping pulse to the transition 23 on the ensembles L and R to set the state of the ensembles back to the ground state 0_a^L0_a^R, then the same classical laser pulses as the first round are applied to the transition 13 and we detect again the forward-scattering Stokes pulses after the beam splitter. This process is repeated until finally we have a click in the D1 or D2 detector.
dataset/figures/quant-ph0405030_d63reviewp5-bw.eps	[ "ining the purification efficiency. Entanglement connection through swappingAfter the successful generation of the entanglement within the attenuationlength, we want to extend the quantum communication distance. This is donethrough entanglement swapping with the configuration shown in Fig.~ d63reviewp5. Suppose tha...	d63reviewp5	(5a) Illustrative setup for the entanglement swapping. We have two pairs of ensembles L, I_1 and I_2, R distributed at three sites L, I and R. Each of the ensemble-pairs L, I_1 and I_2, R is prepared in an EME state in the form of Eq. (3). The excitations in the collective modes of the ensembles I_1 and I_2 are transferred simultaneously to the optical excitations by the repumping pulses applied to the atomic transition 23, and the stimulated optical excitations, after a 50\%-50\% beam splitter, are detected by the single-photon detectors D1 and D2. If either D1 or D2 clicks, the protocol is successful and an EME state in the form of Eq. (3) is established between the ensembles L and R with a doubled communication distance. Otherwise, the process fails, and we need to repeat the previous entanglement generation and swapping until finally we have a click in D1 or D2, that is, until the protocol finally succeeds. (5b) The two intermediated ensembles I_1 and I_2 can also be replaced by one ensemble but with two metastable states I_1 and I_2 to store the two different collective modes. The 50\%-50\% beam splitter operation can be simply realized by a /2 pulse on the two metastable states before the collective atomic excitations are transferred to the optical excitations.
dataset/figures/quant-ph0405030_d63reviewp6-bw.eps	[ "(21), which is entangled in the Fock basis,is useful for these tasks since in the Fock basis it is experimentally hardto do certain single-bit operations. In the following we will show how theEME\\ states can be used to realize all these protocols with simpleexperimental configurations. tbpd63reviewp6width=8.0cmd6...	d63reviewp6	(6a) Schematic setup for the realization of quantum cryptography and Bell inequality detection. Two pairs of ensembles L_1, R_1 and L%_2, R_2(or two pairs of metastable states as shown by Fig.~ (d63reviewp2b)) have been prepared in the EME states. The collective atomic excitations on each side are transferred to the optical excitations, which, respectively after a relative phase shift _L or %_R and a 50\%-50\% beam splitter, are detected by the single-photon detectors D_1^L,D_2^L and D_1^R,D_2^R. We look at the four possible coincidences of D_1^R,D_2^R with % D_1^L,D_2^L, which are functions of the phase difference %_L-_R. Depending on the choice of %_L and _R, this setup can realize both the quantum cryptography and the Bell inequality detection. (6b) Schematic setup for probabilistic quantum teleportation of the atomic ``polarization'' state. Similarly, two pairs of ensembles L_1, R_1 and L_2, R%_2 are prepared in the EME states. We want to teleport an atomic ``polarization'' state d_0S_I_1^+d_1S_I_2^0_a0_a_I_1I_2 with unknown coefficients d_0,d_1 from the left to the right side, where S_I_1^,S_I_2^ denote the collective atomic operators for the two ensembles I_1 and I_2(or two metastable states in the same ensemble). The collective atomic excitations in the ensembles I_1, L_1 and I_2, L_2 are transferred to the optical excitations, which, after a 50\%-50\% beam splitter, are detected by the single-photon detectors D_1^I,D_1^L and D_2^I,D_2^L. If there are a click in D_1^Ior % D_1^L and a click in D_2^Ior D_2^I, the protocol is successful. A -phase rotation is then performed on the collective mode of the ensemble R_2 conditional on that the two clicks appear in the detectors D_1^I,D_2^L or D_2^I,D_1^L. The collective excitation in the ensembles R_1 and R_2, if appearing, would be found in the same ``polarization'' state d_0S_R_1^+d_1S_R_2^0_a0_a_R_1R_2.
dataset/figures/quant-ph0405030_d63reviewp7-bw.eps	[ "reversal, the pulse willbe nearly completely absorbed by the second cavity since the input processis exactly the time reversal of the output process. Besides the applicationas a pulse shape modulator, the quantum light memory setup can also be usedas a pulse shape splitter illustrated by Fig.~ d63reviewp7. Conside...	d63reviewp7	Schematic setup to illustrate pulse shape splitter.
dataset/figures/quant-ph0405030_d63reviewp8-bw.eps	[ "ortation. The following of thissection is mainly devoted to a review of the scheme proposed in Ref. Duan002, and we will also briefly remark at the end of this section thepossibilities of using atomic ensembles for realization of other continuousvariable quantum information protocols. tbpd63reviewp8width=8.0cmd63r...	d63reviewp8	Schematic setup for Bell measurements. A linearly polarized strong laser pulse (decomposed into two circular polarization modes a_1,a_2) propagates successively through the two atomic samples. The two polarization modes a_1+ia_2/2 and a_1-ia_2/2 are then split by a polarizing beam splitter (PBS), and finally the difference of the two photon currents (integrated over the pulse duration T) is measured.
dataset/figures/2010.02473_figures_framework.png	[ "t both source and target in-domain monolingual data. The whole framework starts with pre-trained out-of-domain bidirectional NMT models, and then these models are adopted to perform round-trip translation on monolingual data to obtain initial bidirectional DR models. Next, as illustrated in Figure fig:iter-DRBT-ov...	fig:iter-DRBT-overview	The training process of the iterative domain-repaired back-translation (iter-DRBT) framework at epoch k, where x and y represent the source and target sentences respectively, x and y denote the translation generated by NMT models. The whole framework consists of translation repair and round-trip translation procedures, which are used to generate corresponding training data for NMT and DR models respectively.
dataset/figures/2010.02473_figures_dr-model.png	[ "e NMT models. Domain-Repair Model subsec-training-apet width=0.48figures/dr-model.pdfThe dual-source transformer architecture of the Domain-Repair model ($DR_ (y, x) y$). For simplicity, we omit some architecture details such as layer normalization and residual connection. fig:dr-modelSince the DR model takes th...	fig:dr-model	The dual-source transformer architecture of the Domain-Repair model (DR_(y, x) y). For simplicity, we omit some architecture details such as layer normalization and residual connection.
dataset/figures/2010.02473_figures_iter-dr.png	[ "t-training with one more iteration, demonstrating the effectiveness of our method in the semi-supervised scenario. Effect of Joint Trainingt width=0.47figures/iter-dr.pdfBLEU scores(\\We further investigate the effect of joint training with more iterations. Specifically, we conduct experiments on adapting from th...	fig:iter-bt-ape-bt	BLEU scores(\%) at different iterations of joint training. The model at '0'-th iteration is the unadapted model.
dataset/figures/2010.02473_figures_dr-word-acc-law2med.png	[ "nt of Lexical Translation. t b0.48 width=figures/dr-word-acc-law2med.pdf b0.48 width=figures/dr-word-acc-wmt2med.pdfF-measures of the word translation on medical development set bucketed by the frequency of words occurring in the out-Of-domain training data. fig:ape-wttables/ape-examples.texWe then asses...	fig:ape-wt	F-measures of the word translation on medical development set bucketed by the frequency of words occurring in the out-Of-domain training data.
dataset/figures/2007.08224_system_architecture	[ "X users should specify bibliography style 'splncs04'. Architecture and Main Featuressec:architectureHwidth=0.85system_architectureOrganization of the SAILenv architecture. fig:system_architectureSAILenv is organized following a client-server architecture that naturally implements the idea of having a virtual scene...	fig:system_architecture	Organization of the SAILenv architecture.
dataset/figures/2007.08224_fig_code2.png	[ "e executed (we provide builds for the most common operative systems). Once the scene is running, the Unity server listens for connections on port 8085 (by default). The Python code needed to create a valid client and get data from the virtual environment is minimal, as shown in the snippet of Fig.~fig:code. !ht ...	fig:code	Code that runs the Python client and get data to process.
dataset/figures/2007.08224_fig_temp_comparison.png	[ "n the case of LiteFlowNet, we considered a PyTorch implementationhttps://github.com/sniklaus/pytorch-liteflownet which leverages GPU-based computations (CUDA). For each compared method, we measured the time needed to produce the flow at six different resolutions, reported in the x-axis of Fig.~fig:flow_comparison....	fig:flow_comparison	Average time (seconds) needed to compute the optical flow associated to a frame sampled from the SAILenv scenes. We compare the SAILenv performances with an OpenCV-based implementation of the Farneback algorithm and the neural model LiteFlowNetflownetlite.
dataset/figures/cond-mat0006128_ figure1.eps	[ "ilicon single crystals reflecting from($311$) lattice planes were used as monochromator and analyser. Theinstrumental resolution in the scattering plane has been determined atthe position of the superlattice reflection a few degrees below thetransition temperature, examples are shown infigure~fig:resolution. It re...	fig:resolution	Longitudinal (a) and transverse (b) scattering profiles of the (511)/2-superlattice reflection a few degrees below the critical temperature. The solid lines represent the best fits to the data using a Lorentzian-squared profile. fig:resolution
dataset/figures/cond-mat0006128_ figure2.eps	[ "a width of$2$\\,mm. The surface of the sample was arranged parallel to the beamprofile, the experimental procedure for the alignment of themicro-slit is given in Rue97a. By vertical translation of thesample the scattering volume can be moved to well-defined positions inthe sample see figure~fig:shirane. Due to the...	fig:shirane	Schematic drawing of the sample. The left hand side shows the original sample investigated in [Rue97], the right hand side shows the two samples obtained after cutting a 560\, thick platelet from the top of the original sample. The capital letters A-E define the nomenclature for this paper. Region A corresponds to the surface of the original block. Region B and C correspond to the two surfaces of the platelet, region D denotes the surface of the residual block and region E labels the bulk of this block. The lighter rectangles (not to scale) indicate locations of the incident beam with respect to the sample surface. fig:shirane
dataset/figures/cond-mat0006128_ figure3.eps	[ "original sample. The depth dependence of the integrated intensity, the mosaicity andthe variation of the lattice parameter has been measured at the($511$) main reflection around the phase transition temperature of$ 100$\\,K for the different surfaces B--D and in the bulk~E ofsample~I. In figure~fig:int_D the gain ...	fig:int_D	Depth dependence of the integrated intensity of the (511)-reflection in region~D for different temperatures around the critical temperature with respect to the corresponding bulk value I_bulk. The increase of the integrated intensity in the surface near region is well described by an exponential relation with a 1/e-length of =26(1)\,. fig:int_D
dataset/figures/cond-mat0006128_ figure4.eps	[ "kin/I_0=7.0 10^-5 220 I_dyn/I_0$. Thus the diffraction mechanism in the bulk of thissample is close to the expectations for a perfect crystal, which hasbeen shown before by means of $$-ray diffraction experimentsSch86. Close to the surface the mosaic spread of the sampleincreases Fig.~fig:char, and therefore the s...	fig:char	Schematic view of the depth dependence of integrated intensity and the widths (HWHM) of longitudinal and transverse scans at the (511) reflection position. The surface of the residual block (D) and the old surface of the plate (B) exhibit the same features, whereas the new surface of the plate (C) shows no effects in the crystallographic quantities. The intrinsic mosaicity of the plate (picture in the lower left corner) could not be measured due to the bending of the plate. The decay of the crystallographic parameters at the different surfaces is well described by exponential functions with the same 1/e-length 25.5(15)\,. fig:char
dataset/figures/cond-mat0006128_ figure5.eps	[ "B_3$, $_B$ is theBragg-angle) follow the same exponential depth dependence as theintegrated intensity shown in figure~fig:int_D, the results areplotted fig.~fig:char. The identical characterisation of the crystallographic properties wascarried out for the platelet (regions B and C). Figure~fig:int_BC shows the dat...	fig:int_BC	Depth dependence of the integrated intensity and the HWHM of the longitudinal scans at the (511)-reflection position in the plate. The width of the longitudinal scans is proportional to the lattice parameter variations: d/d=12_B_3. The left hand side corresponds to region~B, an exponential increase ((I-I_bulk)(-z)) of both quantities is clearly visible. The 1/e-length results to =25(1)\,. On the right hand side (region~C) no changes at all can be observed. fig:int_BC
dataset/figures/cond-mat0006128_ figure6.ps	[ "s-section of the beam, e.g.~$5050$$^2$, and measuring the shift of the position of a mainreflection depending on the position in real space on the plate, whichwas oriented perpendicular to the beam. Using this technique, areal-space picture of the plate can be reconstructed from the dataFig.~fig:scheibe. The bendi...	fig:scheibe	Illustration of the bent plate in real-space. The bending radius is about 14\,m. The upper part corresponds to region~C, the lower part represents region~B. fig:scheibe
dataset/figures/cond-mat0006128_ figure7.eps	[ "with a photon energy of $20$\\,keV on a triple-axis diffractometer atthe HASYLAB beamline D4 Als86. The absorption length at thisenergy was determined to $^-1 55$\\,, i.e.~the relevantcontribution to the Bragg peaks results from the surface near regionof some ten microns thickness. In figure~fig:d4 the scatteringp...	fig:d4	Rocking curves of the (200) bragg reflection at the different surfaces of the -samples, measured at room temperature with 20\,keV x-rays at beamline D4. The Rocking curves in region~B and D are much broader than those of region~C and of the edge region of the residual block. The inset shows the same data in a logarithmic scale. I= represent the values of the integrated intensities of the respective scans. fig:d4
dataset/figures/cond-mat0006128_ figure8.eps	[ "e worse Verneuil samples, which leads to anadditional decrease of the phase transition temperature. In analogy to the crystallographic characterisations described above,the depth dependence of the critical temperature has been investigatedin detail in sample~I. The results are plotted infigure~fig:tc. Both the tra...	fig:tc	Depth dependence of the critical temperatures in sample~I after the cut (opaque circles), compared to the values for the original sample (black squares). fig:tc

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Dataset Card for Scientific Figures, Captions, and Context

A novel vision-language dataset of scientific figures taken directly from research papers. We scraped approximately ~150k papers, with about ~690k figures total. We extracted each figure's caption and label from the paper. In addition, we searched through each paper to find references of each figure and included the surrounding text as 'context' for this figure.

All figures were taken from arXiv research papers.

Figure 5: Comparisons between our multifidelity learning paradigm and single low-fidelity (all GPT-3.5) annotation on four domain-specific tasks given the same total 1000 annotation budget. Note that the samples for all GPT-3.5 are drawn based on the uncertainty score.

Figure 3: Problem representation visualization by T- SNE. Our model with A&D improves the problem rep- resentation learning, which groups analogical problems close and separates non-analogical problems.

Usage

The merged.json file is a mapping between the figure's filename as stored in the repository and its caption, label, and context. To use, you must extract the parts located under dataset/figures/ and keep the raw images in the same directory so that they match the image_filename fields. The images are named in the format <paper id>-<figure name> where paper id is the id given by arXiv and figure name is the name of the figure as given in the raw format of each paper.

Contributors

Yousef Gomaa (@yousefg-codes) and Mohamed Awadalla (@mawadalla)

Dataset Summary

This dataset includes ~690,000 figures from ~150,000 scientific papers taken from arXiv papers. Each object in the json file is a single research paper with a list of figures each with their caption and surrounding context.

Category	Count
Figure	690883
Paper	152504

Data Instances

An example of an object in the merged.json file:

{
  [
    {
      'image_filename': 'dataset/figures/example.png' (or .eps or .pdf or other type),
      'label': 'fig_example',
      'caption': 'an example caption for this figure',
      'context': ['example context where this figure was referenced', 'up to 600 characters']
    },
  ...
  ]
}

Dataset Creation

We utilized the bulk access of arXiv's papers.

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Citation Information

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