dataesr/scientific-paragraphs-categorization huggingface dataset:
Again, the depth resolution depends on the coherence length of the light source and is decoupled from the lateral resolution. The velocity resolution depends on the acquisition time and the scan angle. The range of velocities that can be measured is wide, varying from several micrometres per second to tens of millimetre per second. The OCT apparatus used in this study is a commercial spectral domain OCT (SD-OCT) Telesto II from Thorlabs. The light source has a central wavelength of 1310 nm and a bandwidth of 270 nm, giving a resolution in both the axial and transverse directions of 3 µm. Here, OCT together with Doppler acquisition is employed for 3D measurements of thread topology and velocity fields. More details on the OCT acquisition and data processing can be found in Appendix A.
8) where γ is the interfacial tension and κ is the curvature of the interface κ = ∇ • ∇α |∇α|. (2. 9) The numerical simulations were performed with the finite-volume-based open-source code OpenFOAM  (Weller et al. 1998). The PISO (pressure-implicit with splitting of operators) scheme  (Rusche 2003 ) is applied for solving the momentum balance equation (2. 3) in conjunction with the continuity equation (2. 4). More detailed information about the implementation of the above equations in the OpenFOAM framework can be found elsewhere  (Deshpande et al. 2012; Nekouei & Vanapalli 2017). A first order implicit Euler scheme was used for transient terms, and the time step was controlled by setting the maximum Courant number to 0. 3. A second order Total Variation Diminishing (TVD) scheme with van Leer limiter was used for the spatial discretization.
Conclusions In this paper, we present a comprehensive comparison of experimental and numerical results on 3D thread formation under the influence of EIT with a miscible fluid pair of colloidal dispersion and its own solvent as a model system. With a high Péclet number, this system is a weakly diffusive i. e. , the timescale for diffusion of the nanofibrils from the core flow into the sheath flows being substantially larger than the residence time of the two fluids in the channel. Therefore, the system exhibits a near-immiscible behaviour with a de facto dynamic interface where EIT is acting. Three-dimensional versions of the finite volume based open-source code OpenFOAM (OpenCFD 2017) with volume of fluid (VOF) method have been used for the numerical simulations and experimental measurements were carried out with Optical Coherence Tomography (OCT).
Optical simulation of nanostructures FDTD simulations were performed using a commercially available package from Optiwave to simulate the optical absorption of the samples. The OptiFDTD software is based on the FDTD algorithm proposed by Yee in 1966 and was applied in our previous work  [47, 48] , which introduced a modeling technique with second-order central differences to solve the Maxwell equations by applying a finite difference approach. The FDTD algorithm can directly calculate the value of E (the electric field intensity) and H (the magnetic field intensity) at different points of the computational domain. The excitation field is a Gaussianmodulated continuous wave with the center wavelength of 550 nm.
Temporal variations of surface masses, such as the hydrosphere and atmosphere of the Earth, load the surfaces of planetary bodies causing temporal variations in deformation. Surface shear forces and gravitational fields also drive ongoing planetary deformation. Characterizing the spatiotemporal patterns of planetary deformation can constrain allowable models for the interior structure of a planetary body as well as for the distribution of surface and body forces. Pertinent applications include hydrology, glaciology, geodynamics, atmospheric science, and climatology. To address the diversity of emerging applications, we introduce a software suite called LoadDef that provides a collection of modular functions for modeling planetary deformation within a self-consistent, Python-based computational framework.
Key features of LoadDef include computation of real-valued potential, load, and shear Love numbers for self-gravitating and spherically symmetric planetary models; computation of Love-number partial derivatives with respect to planetary density and elastic structure; computation of displacement, gravity, tilt, and strain load Green's functions; and computation of three-component surface displacements induced by surface mass loading. At a most basic level, only a planetary-structure model and a mass-load model must be supplied as input to LoadDef to utilize all the main features of the software. The end-to-end forward-modeling capabilities for mass-loading applications lay the foundation for sensitivity studies and geodetic tomography. LoadDef results have been validated with Global Navigation Satellite System observations and verified against independent software and published results.
Plain Language Summary The Earth and other planets are continually stretched and compressed due to changes in gravity and surface pressure. The amount of distortion that a planet experiences depends on the nature of the force as well as on the interior structure of the planet. We have developed a software package, called LoadDef, that models how the shapes of planets become altered when acted upon by various forces, including gravity and the weights of surface fluids. Examples of surface fluids include the atmosphere, fresh-water reservoirs, and the oceans. As the ocean tides ebb and flow, for instance, the pressure on the ocean floor changes. Due to the changes in pressure, the surface of the Earth compresses downward and decompresses upward with time, much like the push of a finger could indent a foam ball.
Examples include estimating the depletion of groundwater reservoirs during long-term drought (e. g. ,  Argus et al. , 2017; Borsa et al. , 2014)  and tracking rainfall accumulation as hurricanes make landfall  (Milliner et al. , 2018). Motivated by the multitude of emerging applications that use geodetic data to infer planetary structure and surface-mass distribution, we have developed a software suite called LoadDef that models the response of self-gravitating, SNREI planetary bodies to gravitational fields, surface mass loading, and surface shear forcing. In particular, LoadDef generates four main products: (1) potential, load, and shear Love numbers; (2) partial derivatives of potential, load, and shear Love numbers with respect to density and elastic parameters; (3) vertical and horizontal displacement, gravity, tilt, and strain load Green's functions (LGFs); and (4) three-dimensional surface displacements caused by surface mass loading.
One of the unique features of LoadDef with respect to independent software packages is its ability to compute the full forward problem for elastic planetary deformation caused by surface mass loading within a single computational framework. At a minimum, only a model for planetary structure and a model for surface-mass distribution must be provided to compute surface displacements at a particular geographic location. Independent software packages, such as SPOTL  (Agnew, 1997 (Agnew, , 2013) )  and REAR  (Melini et al. , 2015) , compute load-induced surface displacements, albeit from externally provided load-deformation coefficients and Green's functions. Furthermore, additional software packages, such as ELLN  (Chen et al. , 2017; Pan et al. , 2015) , generate elastic LLNs from spherically symmetric planetary models, but do not convolve the Love numbers with mass-load models to compute the surface displacements.
With the forward 10. 1029/2018EA000462 problem self-contained within a single computational platform, LoadDef may be seamlessly adapted to perform a range of application-specific inverse problems, either to constrain the internal structure of an astronomical object or to estimate the spatial distribution of a surface mass load. To our knowledge, Load-Def is also the first publicly available software that includes the functionality to assess structural sensitivities by computing partial derivatives of potential, load, and shear Love numbers with respect to the elastic moduli and density as a function of depth. LoadDef takes advantage of existing Python libraries and includes Message Passing Interface (MPI)-based parallel-processing capabilities. Modules are designed to be versatile for a range of applications and input models.
Methods and Applications Details of the theoretical principles and methods underlying LoadDef may be found in the literature (e. g. ,  Agnew, 2015; Alterman et al. , 1959; Farrell, 1972; Longman, 1962 Longman, , 1963;; Love, 1909; Saito, 1978; Takeuchi & Saito, 1972). Here we review key concepts and applications, describing each of LoadDef's four principal modules in turn: (1) Love numbers, (2) partial derivatives of  Love numbers, (3)  LGFs, and (4) load-induced surface displacements. Additional information on the theory and methods may be found in the supporting information, as well as in the LoadDef User Manual provided with the source code.
The equations of motion are initialized within the planetary body using either the analytical solutions for the spheroidal deformation of a homogeneous solid sphere (e. g. ,  Takeuchi & Saito, 1972)  or power-series expansions of the equations of motion (e. g. ,  Smylie, 2013). For lower spherical-harmonic degrees, integrations begin deep within the core; while for higher spherical-harmonic degrees, integrations begin closer to the surface. LoadDef automatically determines the integration starting radius based on spherical-harmonic degree and a user-controlled sensitivity cutoff value. Solution vectors are integrated to the surface using built-in Python functions for solving ordinary differential equations. The default of LoadDef is to use a built-in Runge-Kutta solver with adaptive step sizing; users may control the solution tolerances.
Displacement LGFs are integrated across each grid cell, and then multiplied by the constant load height and load density associated with each cell. To derive the total response at the chosen surface location, individual response contributions from all grid cells are summed together; in-phase and quadrature components are summed separately. A land-sea mask may optionally be applied to nullify grid cells over land areas or, alternatively, over the oceans. LoadDef includes a default land-sea mask for the Earth based on ETOPO1  (Amante & Eakins, 2009) , with additional refinement around the Antarctic coastline from the Antarctic Digital Database (http://www. add. scar. org/). As with the Love-number and Green's-function libraries, the convolution algorithm is parallelized with MPI. Each load model for a particular station may be treated independently and therefore evaluated on a separate processor.
Verification and Validation We verified the modeling results of LoadDef against independent software packages and published modeling results from the literature. Figure  S5  shows a comparison between LLNs computed by  Guo et al. (2004)  10. 1029/2018EA000462 and those computed by LoadDef for the same Earth model: the oceanless variant of PREM described in section 2. 1 and Table  S1. On average, the Love numbers agree to within one part in 1,000, with maximum deviations better than one part in 250. Similar comparisons with LLNs from  Farrell (1972) ,  Wang et al. (2012), and Chen et al. (2017)  are also provided in the supporting information. Since LoadDef is not limited to modeling the deformation of the Earth, we further verified Love-number results using a recent model for the internal structure of Mars  (Khan et al.
We use LGFs derived from the GBA Earth model by  Farrell (1972)  with respect to the CE reference frame and convolve the LGFs with the M 2 harmonic from the EOT11a ocean-tide model  (Savcenko & Bosch, 2012). The EOT11a ocean-tide model and the Green's functions for the GBA Earth model are distributed with the most recent SPOTL software release. Figure  4  shows the forward modeled OTL displacements and the vector differences between the two sets of predictions. The predicted surface displacements computed by LoadDef and SPOTL are nearly identical, indicating good consistency and precision between the softwares. Globally averaged residual amplitudes are 0. 020 mm in the horizontal components and 0. 065 mm in the vertical component. The largest discrepancies between the two softwares are seen in regions with large-amplitude tides and complex coastal geometries, reflecting differences in load-model interpolation and land-sea mask representation around intricate coastlines.
Another source of minor discrepancies between the results is the assumed density of sea water. SPOTL uses a global grid of spatially variable sea-water densities, whereas LoadDef assumes a globally uniform value. For the benchmark comparison, we adopted a sea-water density of 1,026 kg/m 3 in LoadDef, which is roughly the average sea-water density used by SPOTL. The sea-water densities in SPOTL reflect densities at the sea surface, though studies suggest that sea-water densities at the ocean floor are more appropriate for OTL (e. g. ,  Ray, 2013). On average globally, sea-water densities typically vary by less than 1%. Comparison with empirical data, such as GNSS observations, could further elucidate the absolute performance of each software. We defer such a detailed comparison to a future study, but note that many settings in LoadDef are customizable, including the land-sea mask.
Advantages and Limitations LoadDef has both advantages and limitations that make the software suitable for some applications, but not for others. One of the key advantages of LoadDef is the ability to perform end-to-end modeling of a planetary body's elastic response to surface mass loading. At a minimum, only a planetary-structure model and a mass-load model are required as input to compute load-induced surface displacements. Having the forward model for surface mass loading entirely self-contained within a unified computational framework lays the foundation for many secondary applications, including inversions for planetary structure, inversions for Earth and Space Science 10. 1029/2018EA000462 surface-mass distribution, and investigations into structural sensitivities. In addition, LoadDef computes potential and shear Love numbers, which may be used to model a planetary body's elastic response to gravitational body forces and surface shear tractions, as well as derivatives of the Love numbers, which may be used to analyze Love-number sensitivities to the body's rigidity structure and internal mass distribution.
LoadDef is open-source and written in Python, with parallel-processing capabilities built-in to reduce computation time. The software is compatible with both Python2. X and Python3. X. LoadDef provides an independent deformation-modeling software to the community, which may be used for benchmarking against other software as well as for a variety of specialized research applications. One of the main limitations of LoadDef is that structural models must conform to the SNREI assumption. The assumption allows LoadDef to compute real-valued Love numbers and LGFs from the equations of motion for spheroidal deformation. However, the assumption is not appropriate where anelastic effects are important, such as for Earth deformation on time scales longer than approximately 1 year. In this case, software packages designed to study long-period loading requiring more complex rheologies, such as glacial isostatic adjustment (e.
g. ,  Spada, 2008; Spada et al. , 2011) , may be more suitable. Furthermore, numerical instabilities may arise with fine-scale structural variations near the surface. For shallow subsurface and very local-scale loading applications, software specializing in stability at high spherical-harmonic degrees (e. g. ,  Chen et al. , 2017 ) may be preferred. Given the range of emerging mass-loading applications, from geodetic tomography using OTL (e. g. ,  Ito & Simons, 2011)  to tracking water resources during periods of drought (e. g. ,  Argus et al. , 2017; Borsa et al. , 2014) , LoadDef is designed to be versatile and adaptable. Mass-load models are provided to the software in a generalized format. When convolved with a set of LGFs to compute the load-induced surface displacements, the mass-load models are interpolated onto the station-centered template grids on-the-fly (see section 2.
Motivated by the diversity of current and possible future applications, both for the Earth as well as for other planetary bodies, we have developed a generalized Python-based toolkit to model the elastic-deformation response of spherically symmetric bodies to gravitational fields, surface mass loading, and surface shear forcing. The primary features of LoadDef and their interrelationships are summarized in Figure  5. A key functionality of LoadDef is the ability to compute load-induced surface displacements from.
The Iowa Gambling Task (IGT) is one of the most common paradigms used to assess decision-making and executive functioning in neurological and psychiatric disorders. Several reinforcement-learning (RL) models were recently proposed to refine the qualitative and quantitative inferences that can be made about these processes based on IGT data. Yet, these models do not account for the complex exploratory patterns which characterize participants' behavior in the task. Using a dataset of more than 500 subjects, we demonstrate the existence of sequential exploration in the IGT and we describe a new computational architecture disentangling exploitation, random exploration and sequential exploration in this large population of participants. The new Value plus Sequential Exploration (VSE) architecture provided a better fit than previous models.
Parameter recovery, model recovery and simulation analyses confirmed the superiority of the VSE scheme. Furthermore, using the VSE model, we confirmed the existence of a significant reduction in directed exploration across lifespan in the IGT, as previously reported with other paradigms. Finally, we provide a user-friendly toolbox enabling researchers to easily and flexibly fit computational models on the IGT data, hence promoting reanalysis of the numerous datasets acquired in various populations of patients and contributing to the development of computational psychiatry.
The classical analysis strategy for IGT data results in a crude estimate of decision-making deficits. Based on the relative preferences for "advantageous" decks (typically offering small gains but even smaller losses) over "disadvantageous" decks (typically offering big gains but even bigger losses), this approach does not leverage the full potential of the IGT. From a computational viewpoint, the IGT is indeed a highly complex task engaging value-based learning, risky decision-making, working memory and-as we shall see-different types of exploration. A series of computational models have been developed to better isolate these components, thereby offering clinicians and clinical neuroscientists more precise analytical tools to assess the cognitive profile of their patients. So far, computational neuroscientists interested in the IGT have mainly focused their efforts on the value-based learning and decision-making components of the task.
Unlike other forms of directed exploration (such as uncertainty-dependent exploration), the exploration weights of the VSE model are totally independent of gains and losses. Different parameter combinations are thus able to reproduce the full range of possible SE indexes, from 0 to 100% frequency (see Methods for more details). In order to demonstrate the superiority of the VSE model over the alternatives mentioned above (EV, PVL, PVL-delta, VPP, ORL), we reanalyzed a multi-study dataset of 504 participants who passed the 100 trials version of the IGT  [13]  (Fig  1A ). State-of-the-art model comparison, simulation, as well as model and parameter recovery analyses were performed  [14]. Second, in order to evaluate the cognitive validity to our model and illustrate its heuristic value, we focused on the data corresponding to the study of Wood and colleagues testing IQ-matched groups of old and young adults  [15].
Indeed, it was recently shown that directed exploration diminishes across lifespan  [16, 17] , so that the exploration bonus of older participants should be smaller than that of young participants. Third, we provide an open-source, user-friendly Matlab toolbox which has been developed to obtain the current results and which shall enable researchers who are not experts in computational models to re-analyze IGT data using both our new model and previous ones (https://github. com/romainligneul/igt-toolbox).
Presence of sequential exploration in the IGT First, we evaluated whether sequential exploration (SE) occurred in the Iowa Gambling Task. To this end, we computed the "SE index" probing situations in which participants selected each of the four different decks over four successive trials using 25 independent consecutive quadruplets: e. g. 1-4, 5-8, etc. In the 504 subjects dataset, we observed such pattern 1400 times (11. 1%) while only 1182 occurrences would be expected under random exploration (i. e. 9. 38%, binomial test: p<10 -10 ). Note that this test is highly conservative, as reward-maximization strategies bias choices towards the most valuable decks. Accordingly, a permutation approach in which trials were shuffled in time for each subject independently (total number of permutations: 5000) showed that the actual chance level was at 6.
Model comparison The comparison of the VSE architecture with the 5 alternative models (described in Methods) was performed using the 504 subjects dataset. First, a fixed-effect analysis comparing summed Bayesian Information Criterion (BIC), Akaike Information Criterion (AIC) and Free Energy (F) metrics over the whole cohort demonstrated decisive evidence in favor of VSE. In order to compare Free Energy (F) with the other metrics, it was transformed to -2 � F for this analysis  [18]. The difference between the VSE model and other models was everywhere superior to 512 (Fig 2A ; the least difference being observed with the VPP model based on the Free Energy estimator). Note that a difference superior to 100 is generally considered as decisive evidence indicating that choosing the second-best fitting model would incur unacceptable information loss   [19].
Simulation: Model and parameter recovery In order to make sure that the advantage of the VSE model reflected a better ability to predict choice data, both qualitatively and quantitatively, we used the 504 sets of parameters associated with each model to simulate 504 in silico agents playing the IGT. For each deck, feedbacks were drawn randomly from their corresponding empirical distributions, hence keeping reward contingencies similar across actual and simulated datasets. Then, we applied the exact same fitting procedure to this simulated dataset. First, we evaluated to which extent each model was able to reproduce participants' choices using both first-pass and second-pass (i. e simulated) predictions (chance level: 25%, Fig  2C ). Again, the VSE model was the most performant model. The first-pass (i.
Aging In their study (included in the 504 participants dataset analyzed above), Woods and colleagues reported that old and young adults performed equally well on the Iowa Gambling Task but resorted to different strategies. More precisely, old adults appeared to forget more rapidly about outcomes than healthy participants but compensated this forgetting by a better ability to translate what they learned into consistent choice patterns. Thus, we used this subset of the data to evaluate how well the VSE model could capture heterogeneities in IGT strategies and to validate our modeling approach (Fig  4A ). In particular, based on the existing literature, we hypothesized that the exploration bonus should be lower in old as compared to young participants. First, we confirmed that old participants indeed forgot more rapidly than young participants according to the VSE model, as indicated by a lower decay parameter (young: 0.