SciMMIR

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$2305.00001v1-Figure1-1.png	Fig. 1. The projection of x onto convex set A is the unique point in A which is closest to x and is denoted as y.	fig_illustration	fig	illustration	train
$2305.00001v1-Figure2-1.png	Fig. 2. Graphical interpretation of the parallel POCS for disjoint convex sets.	fig_illustration	fig	illustration	train
$2305.00001v1-Figure3-1.png	Fig. 3. Face image samples of Ben Affleck from the 5 Celebrity Faces dataset.	fig_illustration	fig	illustration	train
$2305.00001v1-Figure4-1.png	Fig. 4. Typical diagram of an autoencoder network.	fig_illustration	fig	illustration	train
$2305.00001v1-Figure5-1.png	Fig. 5. Description of the AE model used in this study: (a) encoder, and (b) decoder.	fig_result	fig	result_fig	train
$2305.00001v1-Figure6-1.png	Fig. 6. Training curves of the used AE model on the MNIST dataset.	fig_result	fig	result_fig	train
$2305.00001v1-Figure7-1.png	Fig. 7. Reconstructed images on MNIST dataset using the autoencoder model described in the paper (top: input image, bottom: reconstructed image).	fig_illustration	fig	illustration	train
$2305.00001v1-Table1-1.png	Table 1. Comparison between the POCS-based clustering and the K-Means++ algorithms in terms of clustering error on different feature embedding sets.	table_result	table	result_tab	train
$2305.00001v1-Table2-1.png	Table 2. Comparison between the POCS-based clustering and the K-Means++ algorithms in terms of execution time (ms) on different feature embedding sets.	table_result	table	result_tab	train
$2305.00001v1-Table3-1.png	Table 3. Comparison between the POCS-based clustering and the K-Means++ algorithms in terms of classification accuracy on different feature embedding sets.	table_result	table	result_tab	train
$2305.00001v1-Table4-1.png	Table 4. Comparison of the K-Means, FCM, and POCSbased clustering algorithms in terms of clustering error on different feature embedding sets.	table_result	table	result_tab	train
$2305.00001v1-Table5-1.png	Table 5. Comparison of the K-Means, FCM, and POCSbased clustering algorithms in terms of execution time (ms) on different feature embedding sets.	table_result	table	result_tab	train
$2305.00001v1-Table6-1.png	Table 6. Comparison of the K-Means, FCM, and POCSbased clustering algorithms in terms of classification accuracy on different feature embedding sets.	table_result	table	result_tab	train
$2305.00003v2-Figure1-1.png	Figure 1: Schematic of the contribution of this study. Data-driven surrogate model is developed to replace the physics-based simulator on the process-structure-property problem.	fig_result	fig	result_fig	train
$2305.00003v2-Figure2-1.png	Figure 2: Finite element discretization of the orientation space for face-centered cubic (FCC) microstructures. The red-colored nodal points show the independent ODF values while the bluecolored nodes indicate the dependent ODFs as a result of the crystallographic symmetries.	fig_result	fig	result_fig	train
$2305.00003v2-Figure3-1.png	Figure 3: The comparison between the predicted textures by the neural network model (a, b and c) and finite element crystal plasticity model (d, e and f) at time steps t=0.3 sec (a and d), t=0.8 sec (b and e), and t=1 sec (c and f).	fig_result	fig	result_fig	train
$2305.00003v2-Figure4-1.png	Figure 4: Training error and testing error (left) on the synthetic dataset. Average stiffness error amount 31 modes (right).	fig_result	fig	result_fig	train
$2305.00003v2-Figure5-1.png	Figure 5: Texture evolution prediction through the optimum processing path by the neural network surrogate model. The figure shows the different steps of deformation processing from an initial texture to a final optimum texture which maximizes an objective function defined for the homogenized elastic stiffness constant...	fig_result	fig	result_fig	train
$2305.00003v2-Figure6-1.png	Figure 6: Texture evolution through the optimum processing path by the physics-based simulator. The figure shows the different steps of deformation processing from an initial texture to a final optimum texture which maximizes an objective function defined for the homogenized elastic stiffness constants of a Copper micr...	fig_result	fig	result_fig	train
$2305.00003v2-Figure7-1.png	Figure 7: A single crystal optimum texture is obtained using linear programming to maximize the homogenized elastic stiffness constants without considering processing.	fig_result	fig	result_fig	train
$2305.00003v2-Table1-1.png	Table 1: The average relative L2 error/stiffness error of neural network predictions compared with FE simulator on each deformation mode. We use binary strings of length 5 to represent different surrogate networks. Each digit represents one of the deformation processing modes (tension, compression, and xy, xz, yz shear...	table_result	table	result_tab	train
$2305.00004v1-Figure1-1.png	Figure 1: A schematic experimental layout including optical diagnostics and the laminar flow reactor.	fig_result	fig	result_fig	train
$2305.00004v1-Figure2-1.png	Figure 2: A time-resolved sequence of particle ignition with tign given by the ground truth (manual labeling). (a) OH-LIF raw images. (b) binary OH-LIF images.	fig_result	fig	result_fig	train
$2305.00004v1-Figure3-1.png	Figure 3: Comparison of ignition delay times by the SAS method ti,SAS and the manual label ti,gt for two particle sizes A and B in seven atmospheres.	fig_result	fig	result_fig	train
$2305.00004v1-Figure4-1.png	Figure 4: Ignition time difference ti,RN - ti,gt by using ResNet-18 with the amount of particle events (a) Nev = 14, (b) Nev = 56, (c) Nev = 140, and (d) Nev = 462.	fig_result	fig	result_fig	train
$2305.00004v1-Figure5-1.png	Figure 5: Ignition time difference ti,FPN - ti,gt using different ResNet models in the bottom-up pathway of FPN networks.	fig_result	fig	result_fig	train
$2305.00004v1-Figure6-1.png	Figure 6: Relative probability distributions of ignition time differences ti,det - ti,gt, in which ti,det represents predicted ignition delay times by different approaches.	fig_result	fig	result_fig	train
$2305.00005v2-Table1-1.png	Table 1. Study population demographics of the Río Hortega University Hospital Glioblastoma dataset (RHUH-GBM)	table_result	table	result_tab	train
$2305.00009v2-Figure1-1.png	FIG. 1. Example of the experimental data used for the reconstruction experiments. (a) Epicardial snapshot and (b) endocardial snapshot at time t = 5734 ms, with (c) time traces of the recording at positions labeled by the + (epicardial) and × (endocardial) markers. The thin black curve in (a) and (b) designates the bou...	fig_result	fig	result_fig	train
$2305.00009v2-Figure10-1.png	FIG. 10. Uncertain wavefront experiment results, depicting the (a) CRPSa(t) (line) and CRPSo(t) (band), (b) RMSEb(t) (line) plus and minus one standard deviation (band), and (c) SPRDb(t, z) (color) for the reconstruction using the Barone et al. parameter set.	fig_result	fig	result_fig	train
$2305.00009v2-Figure11-1.png	FIG. 11. (a) RMSEb(t) and (b) SPRDb u(t) for the Autonomous, Wave Uncertainty, and Synthetic observations experiments, with the distribution of (right, top) surface errors and (right, bottom) ensemble spread over time.	fig_result	fig	result_fig	train
$2305.00009v2-Figure3-1.png	FIG. 3. Autonomous experiment results, depicting the (a) CRPSa(t) (line) and CRPSo(t) (band), (b) RMSEb(t) (line) plus and minus one standard deviation (band), and (c) SPRDb(t, z) (color) for the reconstruction using the Barone et al. parameter set.	fig_result	fig	result_fig	train
$2305.00009v2-Figure6-1.png	FIG. 6. (a) RMSEb(t) and (b) depth-average SPRDb u(t) for the Free-Run, Autonomous, and Stimulus experiments, with the distribution of (right, top) surface errors and (right, bottom) ensemble spread over time.	fig_result	fig	result_fig	train
$2305.00009v2-Figure7-1.png	FIG. 7. (a) Temporal trace for the Autonomous experiment and associated observations, sampled on the epicardium (z/d = 0) at (x, y) = (0.825, 0.42) cm, and (b) corresponding action potential durations (APD) and amplitudes (APA) (uthr = 0.1).	fig_result	fig	result_fig	train
$2305.00009v2-Figure8-1.png	FIG. 8. Stochastic experiment results, depicting the (a) CRPSa(t) (line) and CRPSo(t) (band), (b) RMSEb(t) (line) plus and minus one standard deviation (band), and (c) SPRDb(t, z) (color) for the reconstruction using the Barone et al. parameter set.	fig_result	fig	result_fig	train
$2305.00009v2-Figure9-1.png	FIG. 9. Synthetic Observations experiment results, depicting the (a) CRPSa(t) (line) and CRPSo(t) (band), (b) RMSEb(t) (line) plus and minus one standard deviation (band), and (c) SPRDb(t, z) (color) for the reconstruction using the Barone et al. parameter set.	fig_result	fig	result_fig	train
$2305.00009v2-TableI-1.png	TABLE I. Fenton-Karma model parameter values used in dynamical model for the reconstruction of experimental data. ∗Note: as τ− v1 ≡ τ− v2, the switching parameter uv is unspecified in Ref. 1.	table_result	table	result_tab	train
$2305.00011v1-Figure1-1.png	Fig. 1. Problem setup diagram where speech privacy is violated while being sent to a cloud.	fig_result	fig	result_fig	train
$2305.00011v1-Figure2-1.png	Fig. 2. Schematic diagram of the proposed method. F , C, D, and D′ are neural networks and L shows different loss terms in our method. The solid lines illustrate the forward pass. The dashed line shows the forward pass which is active only after P number of epochs. Finally, the dotted line shows the backpropagation of ...	fig_result	fig	result_fig	train
$2305.00011v1-Figure3-1.png	Fig. 3. The effect of parameter P on the performance of RDAL on the test data for SAD and SED tasks.	fig_result	fig	result_fig	train
$2305.00011v1-Figure4-1.png	Fig. 4. Estimated density curves using Gaussian kernel to represent continuous probability densities of the predicted probabilities for the test data using baseline (left figure) and RDAL (right figure) methods.	fig_result	fig	result_fig	train
$2305.00011v1-Figure5-1.png	Fig. 5. t-SNE illustration of latent features of the test data obtained by F in RDAL (right figure) compared to the features from F when it is trained in supervised manner for sound events and speech(left figure). The dog barking, glass breaking and gun shot are red, green and blue, respectively. The samples containing...	fig_result	fig	result_fig	train
$2305.00011v1-Table1-1.png	Table 1. Number of one-second sound event samples in each split, number of male/female speakers, and the duration of speech (in seconds) in our dataset.	table_parameter	table	parameter	train
$2305.00011v1-Table2-1.png	Table 2. Results of baseline, naive adversarial learning, and RDAL.	table_result	table	result_tab	train
$2305.00013v1-Figure12-1.png	FIG. 12: Best constraint on the branching fraction BR(h → γDγD) using the Higgs diphoton resonance search, four-photon resonant search, Higgs mixing angle limits, and Higgs branching ratio to the unknown. The resonance searches require 1% detector efficiency. The contours are upper bounds on BR(h → γDγD) while the heat...	fig_result	fig	result_fig	train
$2305.00013v1-Figure2-1.png	FIG. 2: Maximum branching ratio of the Higgs to two dark photons from (dashed) Higgs fits to the branching fraction of Higgs to the unknown and from (solid) Higgs mixing assuming sin θmax = 0.1 with (black) vD = mγD , (red) vD = 10mγD , (blue) vD = 25mγD , (yellow) vD = 50mγD , and (green) vD = 100mγD .	fig_result	fig	result_fig	train
$2305.00013v1-Figure4-1.png	FIG. 4: Regions with the largest probability of ending in n observed photon final states after merging photons into γobs: (blue) two, (orange) three, (green) four, (purple) five, and (red) six observed photons. No isolation requirements are imposed. The black corner indicates the kinematically forbidden area (mγD < ma)...	fig_architecture	fig	architecture	train
$2305.00013v1-Figure5-1.png	FIG. 5: Regions with the largest probabilities into n isolated photons and m ξ-jet final states. The light gray (dark gray) region indicates the dominant final states have one (two) ξ-jets. Color coding for other regions: (blue) 2γiso + 0ξ-jet, (green) 4γiso + 0ξ-jet, and (red) 6γiso + 0ξ-jet.	fig_result	fig	result_fig	train
$2305.00013v1-Figure6-1.png	FIG. 6: Estimated detector efficiencies for n photon triggers applied to n isolated photons and zero ξ-jet signals for both (a) ATLAS and (b) CMS. Efficiencies are estimated by placing rapidity [Eqs. (27)-(28)] and transverse momentum requirements on the isolated photons (Tabs. I, II).	fig_result	fig	result_fig	train
$2305.00013v1-Figure7-1.png	FIG. 7: (a) Probability of a signal appearing as a 2γiso + 0ξ-jet final state. (b) Upper bound on the branching fraction BR(h → γDγD) from Higgs diphoton signal strength. A detector efficiency of Eff2(6γ → 2γiso + 0ξ) ≥ 1% is required. In (b) the contours are upper bounds on BR(h → γDγD) while the heat map is for log10...	fig_result	fig	result_fig	train
$2305.00013v1-Figure8-1.png	FIG. 8: (a) Probability of four-isolated photon + zero-ξ-jet final state. (b) Constraint (dark area) on the branching fraction BR(h→ γDγD) using the Higgs four-photon resonant search from CMS [27] and requiring 1% detector efficiency for the CMS four-photon trigger. In (b) the contours are upper bounds on BR(h→ γDγD) w...	fig_result	fig	result_fig	train
$2305.00014v1-Figure1-1.png	Figure 1: Left panel: The parameter space excluded by searches for the LFV decays of Higgs boson. The dark gray area is excluded by the h → eµ searches [1], the medium gray area is excluded by the h → eτ searches [5] and the light gray area by the h → µτ searches [5]. Right panel: The parameter regions excluded by the ...	fig_result	fig	result_fig	train
$2305.00014v1-Figure2-1.png	Figure 2: Left panel: Cross-section σ(pp → H2 → eµ) as a function of flavon VEV at 13 TeV LHC for different Higgs-flavon mixing angles. Right panel: H2 → eµ branching ratio as a function of flavon VEV. In both panels solid lines correspond to numerical benchmark and the dashed lines correspond to analytical estimate wi...	fig_result	fig	result_fig	train
$2305.00014v1-Figure3-1.png	Figure 3: Left and middle panels: The 1- and 2-loop contributions to µ→ eγ in the case of additional quark couplings. Right panel: The tree-level contribution to µ↔ e conversion in nuclei in the case of additional quark couplings.	fig_result	fig	result_fig	train
$2305.00014v1-Figure4-1.png	Figure 4: Left panel : Regions of the (vφ, mA) parameter space excluded by searches for µ → eγ in the leptophilic model for different values of the mixing angle. Right panel : The LFV constraints in the case of additional quark-flavon couplings for ct = 1.9 and three different values of the charm coupling, cc = −1.9, 0...	fig_result	fig	result_fig	train
$2305.00014v1-Figure5-1.png	Figure 5: Left panel: Cross-section σ(pp→ H2 → eµ) as a function of the flavon VEV at the √ s = 13 TeV LHC for different quark couplings as indicated in the figure. The gray shaded region is excluded by µ ↔ e conversion for all values of mA. The horizontal line corresponds to 5.77 fb. Right panel: The relevant branchin...	fig_result	fig	result_fig	train
$2305.00015v1-Figure1-1.png	FIG. 1. Neutron skin and collective flow in relativistic nuclear collisions. a: Two ions collide with impact parameter b = 8 fm. Both ions are Lorentz-contracted by a factor γ ≈ 2500, and the relevant dynamics hence effectively takes place in the transverse plane, x⊥ = (x, y). b: The collision deposits energy in the in...	fig_result	fig	result_fig	train
$2305.00015v1-Figure2-1.png	FIG. 2. Signature of the neutron skin on bulk particle production in ultrarelativisitic 208Pb+208Pb collisions. Varying only the neutron skin size at our optimal parameter settings we show the charged particle multiplicity (left), the mean transverse momentum (middle) and the elliptic flow as measured by v2{k} (right) ...	fig_result	fig	result_fig	train
$2305.00015v1-Figure4-1.png	FIG. 4. State-of-the-art determinations of the neutron skin of 208Pb. We show the final likelihood distribution of the neutron skin as determined from the LHC data as compared to the values obtained experimentally by the PREX collaboration (including both experimental and theoretical uncertainties in the extraction) [6...	fig_result	fig	result_fig	train
$2305.00015v1-Figure5-1.png	FIG. 5. Complete correlation matrix among all 26 model parameters. For detailed information about the prior ranges, we refer the reader to Ref. [18]. The only new parameters of this analysis are an, whose prior can be inferred from Fig. 4, aEOS, whose	fig_illustration	fig	illustration	train
$2305.00017v1-Figure1-1.png	FIG. 1. Left : Number of biνos produced at SHiP assuming 5 years of operation and ΛM = 1 TeV. (This messenger scale is taken as a benchmark for comparison to other relevant models.) Right : Branching ratios of the biνo into different final states as a function of the biνo mass MB̃ .	fig_result	fig	result_fig	train
$2305.00017v1-Figure2-1.png	FIG. 2. SHiP sensitivity to biνos assuming 5 years of operation using our conservative method (solid black curve) and the HNL@SHiP method (dashed black curve). The greater sensitivity with the conservative method below MB̃ ∼ 400 MeV is due to the biνo production from kaons, which is not included in the HNL@SHiP package...	fig_result	fig	result_fig	train
$2305.00019v1-Figure1-1.png	Figure 1. Diagram illustrating the general processes that occur during and after lobe inflation. Panel A: A fast jet drives into the ambient medium, forms a bow shock and inflates a hot lobe that expands into the ICM. The lobe morphology can depend sensitively on the injected jet properties (e.g., content, velocity, ge...	fig_result	fig	result_fig	train
$2305.00019v1-Figure2-1.png	Figure 2. Thin temperature projections illustrate how jet injection parameters impact jet and lobe morphologies. All jets are 100 Myr old with each row illustrating the effect of changing one parameter, which from top to bottom are jet power, half opening angle, velocity and resolution, respectively. These quantities t...	fig_result	fig	result_fig	train
$2305.00019v1-Figure3-1.png	Figure 3. An illustration of jet-driven shocks within the central 100 kpc of a simulated cluster. The left panel shows the energy dissipation rate while the right-hand panel shows shock Mach numbers, illustrating that stronger shocks and hence higher dissipation rates are seen closer to the jet but that overall the sho...	fig_result	fig	result_fig	train
$2305.00019v1-Figure4-1.png	Figure 4. Volume rendering of jet lobes (green) and cold material (red) within a cosmologically evolved galaxy cluster. The top, middle and bottom rows show low, medium and high-power jets, respectively. The small panels show the evolution of the jet lobes for two different viewing angles, while the large panels additi...	fig_result	fig	result_fig	train
$2305.00019v1-Figure5-1.png	Figure 5. Comparisons of simulation results with varied jet composition and assumptions for modeling CR transport. Rows from top to bottom show the results from kinetic-energy dominated jets (KIN), CR dominated jets (CR), and CR dominated jets with diffusion and heating (CRdh). The morphology of jet-inflated lobes tend...	fig_result	fig	result_fig	train
$2305.00019v1-Figure6-1.png	Figure 6. Impact of assumptions about ICM viscosity on the evolution of AGN jet-inflated bubbles. Cases A-D show simulations with no viscosity, isotropic viscosity with full Spitzer values, anisotropic viscosity with full Braginskii values, and anisotropic viscosity limited by microinstabilities, respectively. While th...	fig_result	fig	result_fig	train
$2305.00019v1-Figure7-1.png	Figure 7. Illustration of sound waves generated by jets. The left-hand panel shows the jet and cocoon temperature and density structure, with key features labelled. The right-hand panel shows jet entropy and the acoustic flux density, with structure in the latter illustrating the production of sound waves within shocke...	fig_result	fig	result_fig	train
$2305.00020v2-Figure1-1.png	Fig. 1: Intensity-weighted mean velocity (first moment) maps of H2CO (30,3 − 20,2) showing the large-scale kinematics of the full CORE sample. The contours correspond to the continuum maps imaged with uniform weighting as presented in Beuther et al. (2018). The outermost three contours correspond to 5, 10, and 20σ leve...	fig_result	fig	result_fig	train
$2305.00020v2-Figure10-1.png	Fig. 10: Median Q plotted against gas mass (left) and stellar mass (right) for 13 candidate disks within the CORE survey, coloured according to the luminosity of the regions within which they reside. The Toomre-stable disks are marked by triangles.	fig_result	fig	result_fig	train
$2305.00020v2-Figure11-1.png	Fig. 11: Median Q as a function of ( H r ) ( M∗+Mdisk Mdisk ) . The Toomrestable disks are marked by triangles.	fig_result	fig	result_fig	train
$2305.00020v2-Figure12-1.png	Fig. 12: Summary plot showing the kinematics and derived properties for one source in the CORE sample (G75.78). Top left panel: intensity-weighted mean velocity (first moment) map of CH3CN (125 − 115) in colour with 1.37 mm continuum contours. The blue and red arrows correspond to the estimated directions of bipolar bl...	fig_result	fig	result_fig	train
$2305.00020v2-Figure2-1.png	Fig. 2: Intensity-weighted mean velocity (first moment) maps of CH3CN (123 − 113) showing the dense gas kinematics for 15 of the 20 sources in the CORE survey. The contours correspond to the 1.37 mm continuum as described in Fig. 1. The blue and red arrows correspond to the estimated directions of bipolar blueshifted a...	fig_result	fig	result_fig	train
$2305.00020v2-Figure3-1.png	Fig. 3: Rotational temperature maps obtained by fitting CH3CN (12K − 11K) K = 0 − 6 and CH3 13CN (12K − 11K) K = 0 − 3 lines with XCLASS. The contours correspond to the 1.37 mm continuum as described in Fig. 1. For NGC7538 IRS1, only the region outside the continuum to the south-west is modelled by XCLASS and was scale...	fig_result	fig	result_fig	train
$2305.00020v2-Figure4-1.png	Fig. 4: Position–velocity (PV) plots for best fitted transitions listed in Table 5 along cuts in the direction of rotation as depicted by dotted lines in Fig. 2. The width of the cut is the size of a synthesised beam to increase the signal-to-noise ratio. The PV plots of W3(H2O) E and W3(H2O) W make use of the A-array ...	fig_result	fig	result_fig	train
$2305.00020v2-Figure5-1.png	Fig. 5: Ratio of free-fall to rotational timescale as a function of disk gas mass, showing most of the sources with CH3CN velocity gradients are rotationally supported. The blue and red dots correspond to the values calculated based on the rotational velocities at the edges of the disks on the blueshifted and redshifte...	fig_illustration	fig	illustration	train
$2305.00020v2-Figure6-1.png	Fig. 6: Specific angular momentum radial profiles calculated using Eq. 7 along the cut with the strongest velocity gradient (dotted lines in Fig. 4) (circles), a cut with position angle +10◦ (triangles pointing up), and a cut with position angle −10◦ (triangles pointing down) with respect to the strongest velocity grad...	fig_result	fig	result_fig	train
$2305.00020v2-Figure7-1.png	Fig. 7: Toomre Q maps for the best disk candidates in the CORE survey assuming a protostar is located at the position of the continuum peak as depicted by a star and accounting for the self-gravity of the disk (protostellar and gas mass values are listed in Table 7). The contours correspond to the 1.37 mm continuum as ...	fig_result	fig	result_fig	train
$2305.00020v2-Figure8-1.png	Fig. 8: Distribution of median and minimum Q as a function of radius shown in solid and dashed blue lines. The dotted horizontal line corresponds to the global median Q computed over the entire disk and listed in Table 7. The dash-dotted red horizontal line shows the critical Q = 2 threshold.	fig_result	fig	result_fig	train
$2305.00020v2-Figure9-1.png	Fig. 9: Box-plot showing the Q distribution versus the ratio of gas to stellar mass, coloured according to the luminosity of the regions within which they reside. The boxes extend from the first to the third quartiles, with a red line at the median Q value. The whiskers extend from the box by ±1.5 times the box size. D...	fig_result	fig	result_fig	train
$2305.00020v2-FigureA.1-1.png	Fig. A.1: Integrated intensity (zeroth moment) maps of CH3CN (123 − 113) showing the dense gas distribution for 15 of the 20 sources in the CORE survey. The contours and features are as described in Fig. 2.	fig_result	fig	result_fig	train
$2305.00020v2-FigureA.2-1.png	Fig. A.2: Intensity-weighted velocity dispersion (second moment) maps of CH3CN (123 − 113) showing the dense gas kinematics for 15 of the 20 sources in the CORE survey. The contours and features are as described in Fig. 2.	fig_illustration	fig	illustration	train
$2305.00020v2-FigureB.1-1.png	Fig. B.1: Peak intensity (amplitude) maps of CH3CN (123 − 113) showing the dense gas kinematics for 15 of the 20 sources in the CORE survey obtained by fitting Gaussian profiles to their spectra. The contours and features are as described in Fig. 2.	fig_illustration	fig	illustration	train
$2305.00020v2-FigureB.2-1.png	Fig. B.2: Peak velocity maps of CH3CN (123 − 113) showing the dense gas kinematics for 15 of the 20 sources in the CORE survey obtained by fitting Gaussian profiles to their spectra. The contours and features are as described in Fig. 2.	fig_illustration	fig	illustration	train
$2305.00020v2-FigureB.3-1.png	Fig. B.3: Linewidth (FWHM) maps of CH3CN (123 − 113) showing the dense gas kinematics for 15 of the 20 sources in the CORE survey obtained by fitting Gaussian profiles to their spectra. The contours and features are as described in Fig. 2.	fig_illustration	fig	illustration	train
$2305.00020v2-FigureC.1-1.png	Fig. C.1: Intensity maps of CO (2–1) emission from IRAM 30-m telescope integrated over the blueshifted and redshifted wings of emission, showing the outflow structure. The position of the strongest source in the field is depicted by a star. The blue and red arrows correspond to the estimated directions of bipolar blues...	fig_result	fig	result_fig	train
$2305.00020v2-FigureC.2-1.png	Fig. C.2: Intensity maps of 13CO (2 − 1) emission from IRAM 30-m telescope integrated over the blueshifted and redshifted wings of emission, showing the outflow structure. The position of the strongest source in the field is depicted by a star. The blue and red arrows correspond to the estimated directions of bipolar b...	fig_result	fig	result_fig	train
$2305.00020v2-FigureC.3-1.png	Fig. C.3: Intensity maps of 13CO (2 − 1) emission from merged NOEMA and IRAM 30-m data integrated over the blueshifted and redshifted wings of emission, showing the outflow structure. The position of the strongest source in the field is depicted by a star. The blue and red arrows correspond to the estimated directions ...	fig_result	fig	result_fig	train
$2305.00020v2-FigureC.4-1.png	Fig. C.4: The greyscale corresponds to the 1.37 mm continuum while the blue and red contours correspond to the NOEMA intensity maps of 13CO (2 − 1) integrated over the blueshifted and redshifted wings of emission, tracing either outflows or disk winds.The blue and red arrows correspond to the estimated directions of bi...	fig_result	fig	result_fig	train
$2305.00020v2-FigureC.5-1.png	Fig. C.5: Intensity maps of C18O (2 − 1) emission from IRAM 30-m telescope integrated over the blueshifted and redshifted wings of emission, showing the outflow structure. The position of the strongest source in the field is depicted by a star. The blue and red arrows correspond to the estimated directions of bipolar b...	fig_result	fig	result_fig	train
$2305.00020v2-FigureC.6-1.png	Fig. C.6: Intensity maps of SO (65 − 54) emission from IRAM 30-m telescope integrated over the blueshifted and redshifted wings of emission, showing the outflow structure. The position of the strongest source in the field is depicted by a star. The blue and red arrows correspond to the estimated directions of bipolar b...	fig_result	fig	result_fig	train
$2305.00020v2-FigureC.7-1.png	Fig. C.7: The distribution of absolute difference between the disk position angles and the assumed outflow position angles for our sample of 13 disk candidates in the CORE survey.	fig_result	fig	result_fig	train
$2305.00020v2-Table1-1.png	Table 1: Positions and properties of the CORE sample, grouped in track-sharing pairs, adapted from Beuther et al. (2018).	table_result	table	result_tab	train
$2305.00020v2-Table2-1.png	Table 2: Frequency setup of the narrow-band correlator and important lines covered.	table_result	table	result_tab	train
$2305.00020v2-Table3-1.png	Table 3: Observational parameters for the disk candidates within the CORE survey.	table_result	table	result_tab	train
$2305.00020v2-Table4-1.png	Table 4: Gas mass estimates.	table_result	table	result_tab	train
$2305.00020v2-Table5-1.png	Table 5: KeplerFit parameters and dynamical mass estimates.	table_result	table	result_tab	train
$2305.00020v2-Table6-1.png	Table 6: Fit parameters to the specific angular momentum radial profiles shown in Fig. 6 (dashed lines).	fig_result	fig	result_fig	train
$2305.00020v2-Table7-1.png	Table 7: Overview of mass estimates and Toomre Q results.	table_result	table	result_tab	train

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Dataset Card for "SciMMIR_dataset"

SciMMIR

This is the repo for the paper SciMMIR： Benchmarking Scientific Multi-modal Information Retrieval.

In this paper, we propose a novel SciMMIR benchmark and a corresponding dataset designed to address the gap in evaluating multi-modal information retrieval (MMIR) models in the scientific domain.

It is worth mentioning that we define a data hierarchical architecture of "Two subsets, Five subcategories" and use human-created keywords to classify the data (as shown in the table below).

As shown in the table below, we conducted extensive baselines (both fine-tuning and zero-shot) within various subsets and subcategories.

For more detailed experimental results and analysis, please refer to our paper SciMMIR.

Dataset

Our SciMMIR benchmark dataset used in this paper contains 537K scientific image-text pairs which are extracted from the latest 6 months' papers in Arxiv (2023.05 to 2023.10), and we will continue to expand this data by extracting data from more papers in Arxiv and provide larger versions of the dataset.

The datasets can be obtained from huggingface Datasets m-a-p/SciMMIR, and the following codes show how to use it:

import datasets
ds_remote = datasets.load_dataset("m-a-p/SciMMIR")
test_data = ds_remote['test']
caption = test_data[0]['text']
image_type = test_data[0]['class']
image = test_data[0]['image']

Codes

The codes of this paper can be found in our Github

Potential TODOs before ACL

TODO: case study table

TODO: statistics of the paper fields (perhaps in appendix)

TODO: See if it's possible to further divide the "Figure Results" subsets.

Citation

@misc{wu2024scimmir,
      title={SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval}, 
      author={Siwei Wu and Yizhi Li and Kang Zhu and Ge Zhang and Yiming Liang and Kaijing Ma and Chenghao Xiao and Haoran Zhang and Bohao Yang and Wenhu Chen and Wenhao Huang and Noura Al Moubayed and Jie Fu and Chenghua Lin},
      year={2024},
      eprint={2401.13478},
      archivePrefix={arXiv},
      primaryClass={cs.IR}
}

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MIEB

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MIEB(Multilingual) is a comprehensive image embeddings benchmark, spanning 10 task types, covering 130 tasks and a total of 39 languages. In ad... • 102 items • Updated Feb 20 • 1

Paper for mteb/SciMMIR

SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval

Paper • 2401.13478 • Published Jan 24, 2024 • 3