dataesr/scientific-paragraphs-categorization huggingface dataset:
3. For each use case below, download the dataset and explain if and why the dataset could be processed directly with SurfCut, without any workflow modification, using the checks defined above. We assume that the output of the workflow (a 2D projection) is what we need. (a) Use case 1: 3D light sheet microscopy images of a Tribolium epithelium  (Vorkel et al. , 2020). Dataset: 7 https://zenodo. org/record/3981193#. Xzo8pTU6-60, take "Strausberg_Tribolium_LAGFP_tailpole_runC0opticsprefused301310. tif". This dataset was used as an example to showcase another projection tool. 12  (b) Use case 2: 3D confocal and spinning disk microscopy images of Drosophila epithelia (notum and wing disc,  Valon and Staneva, 2020). Dataset: 7 https://zenodo. org/record/4114074#. X5AJAe06-60. Take the image named "notum2_GFP.
tif". (c) Use case 3: 3D light sheet microscopy images of single cells  (Driscoll et al. , 2019). Dataset: 7 https://cloud. biohpc. swmed. edu/index. php/s/Z9j62w2FCareyJY/download), in the folder called "testData". Each image is associated with a text file describing the imaging conditions (AcqInfo. txt). There are three examples of MV3 melanoma cells ("krasMV3") and one example of conditionally immortalized hematopoietic precursors to dendritic cells ("lamDendritic"). Associated GitHub repository: 7 https://github. com/ DanuserLab/u-shape3D and research article: 7 https://www. ncbi. nlm. nih. gov/ pmc/articles/PMC7238333/. (d) Use case 4: light sheet image of a gastric cancer spheroid  (Rocha et al. , 2020). Dataset: 7 https://zenodo. org/record/4244952#. X6L_ZIj7SHs. v Solution to Exercise 3 1.
However, these are time-lapse data (SurfCut can process only one time-point at a time), and the tissue is very curvy. The data could be processed by first extracting each individual time points and then analysing the images in a batch after having defined the proper parameters in the calibrate mode. However, SurfCut is not recommended in this case due to the high curvature of the tissue. Use case 2: 3D confocal and spinning disk microscopy images of Drosophila epithelia (notum and wing disc), image named "notum2_GFP. tif" We have a 3D stack, in. tif format. We know the pixel size from the description of the Zenodo upload, and the E-Cadherin marker delimits well a relatively thin epidermal layer, which is only slightly curved. SurfCut is appropriate here, since the tissue is not too curvy. Moreover, SurfCut can help remove noise above and below the epidermis and hence render a sharper projection of the cell contours.
After adding the pixel size specified in the description of the dataset to the metadata of the image, we can process the image with SurfCut using the following parameters: gaussian blur of radius 3; threshold of 50; top = 6; bottom = 11. 5 Use case 3: 3D light sheet microscopy images of single cells. We have 3D stacks,. tif format, and we know the pixel size from the AcqInfo. txt file. However, these are 3D closed objects with quite some relief. Here SurfCut is not appropriate to project the 3D stacks, it would deform the cells too much. 5 Use case 4: 3D light sheet images of a gastric cancer spheroid. We have a 3D stack in. tif format. However, the stack contains the first bright and blurry slice that needs to be removed first, the proper voxel size needs to be set based on the information found in the description of the dataset, and the z resolution is rather low (5 micron) compared to the x and y (1 micron).
6. 4). Each component should ideally match with a block of code containing one or several built-in or custom functions, some loops and conditional statements etc. To further understand the order of execution of the workflow, identify also the different input and output variables. SurfCut contains several defects often found in real codes, and especially in ImageJ macros. 17 We will see, for instance, in the exercise below that, in SurfCut, some components are spread over several blocks of code and intermingled with other components. We will see also that some components in SurfCut are made of several built-in functions that are not wrapped in one bigger function. Of course, a modular code with clearly separated blocks of code and one function per component is easier to read and understand, but SurfCut is representative of ImageJ macros.
This lack of structure comes from two elements: (i) macro authors are seldom software developers and hence lack good code writing practices (commenting, wrapping components into functions. ) and (ii) most macro authors rely on the macro recorder to find the proper functions to use. The macro recorder prints the macro commands corresponding to the steps done manually by the user through the graphical user interface of ImageJ/Fiji. Whereas some components correspond to a single ImageJ macro builtin function (e. g. a Gaussian blur), other require several functions (e. g. Edge detect). The modular structure with components is lost when using the macro recorder. In addition to these defects, SurfCut contains many repetitions of code lines. This is due 6 to the presence of two types of workflows in one code, the calibrate and the batch workflows, and the lack of optimization in the code to reuse functionalities of one workflow in the other rather than copy functionalities.
As explained in the introduction, we took into account all these defects and carried out a complete refactoring of the code (as described in Step 5 and 6 of this book chapter), fixed bugs, and created new functions to reach a new version of SurfCut, called SurfCut2. 18  We also made a simpler version of the macro, called SurfCut2-Lite. We propose two alternatives to the exercises below, corresponding to two levels of difficulty. For the beginner level, use the SurfCut2-Lite code 19 and do the exercises 4. 1, 6, 7 and 8 (skip exercise 4. 2 and 5, which are already implemented in the code of SurfCut2-Lite). For the advanced level, use the code of the original SurfCut macro and follow all the steps and exercises proposed.
Step 5. Code Refactoring In Step 4, we identified the basic components of the workflow in the code and extracted a minimal version of the code. To simplify the later replacement of a component in the code, we propose an optional step: refactoring the code. This step aims at reorganizing the code in order to improve its design and re-usability without changing its input or behavior. The refactored code will be constituted of several user-defined functions, each corresponding to one component of the workflow. The replacement of a component is then equivalent to replacing a function. Here, we also suggest to split one of the components into two, as a part of the refactoring process. Indeed, while some of the workflow components described in the publication text (and identified in Step 1 and Step 2) correspond to single ImageJ builtin functions, Component 4, "Edge detection", and Component 5, "Masking", with implementation inspired by the algorithm used in the software MorphoGraphX, correspond to many lines of code directly coming from the macro recorder.
SurfCutWorkflowFunc. ijm Code available in the GitHub repository of this book. 22  2. The code for opening an image and getting the variable names can be found in lines 32-38 of the original SurfCut macro. Variables related to the radius of the Gaussian blur filter, threshold for the segmentation, and top and bottom depths (in micron) for masking can be found in lines 42-45 and 149-150. These variables are used for the definition of Cut1 and Cut2 (the actual values of the Z-axis shifts, depending on the thickness of the stack slice steps). The values of Cut1 and Cut2 depend on the stack slice thickness, which, in the macro, is extracted from the metadata of the image (line 96). For simplicity, we can here define it directly in the code.
6 In the solution below, the parameters identified above are called at the beginning of the macro. The functions defined in the answer to Question 1 (above) are successively called, giving a clear overview and easy reading of the workflow. Now we have a well organized and flexible workflow. It is time to inspect it in detail and determine if the different components are best adapted to our needs. As an example, we will now examine the Component 5a "Layer mask creation" created during the refactoring of the SurfCut macro code (line 76-107); this component is included in the SurfCut2-Lite code (line 158-197). In the current implementation, we use a sequential Z-axis shift of the mask to make a layer-mask. But in principle, as discussed in  (Erguvan et al. , 2019) , a 3D erosion, although more computationally demanding, would be more suitable for samples with high curvature.
In Step 4 we went through the code in the macro and identified the ~50 lines of codes that compose the backbone of the workflow. We found that the components were much more interwoven in the code than in the corresponding text description. We also found that some of the workflow components (as described in the available macro description) correspond to roughly a single ImageJ built-in function, while others are custom multi-line implementations of processes within the macro. In Step 5, we cleaned the code by removing all the batch-loops, user interactions and accessory code lines, and separated all of the identified components into userdefined functions. While being optional, the clear separation of components in code blocks provided a much more readable and re-usable version of the macro, that could be further modified without the risk of breaking the whole workflow.
Acknowledgements We would like to thank  Kota Miura  for inviting us to write this book chapter and reviewing it,  Mafalda Sousa  for reviewing the chapter and providing data for the Exercise number 3, all the trainees of the NEUBIAS training school 15 who worked on the deconstruction of the SurfCut macro and gave us the idea to write this chapter, our co-authors on the SurfCut publication  Özer Erguvan  and  Olivier Hamant , without whom this work would not exist, and  Robert Haase ,  Léo Valon ,  Ralitza Staneva , and  Meghan Driscoll  for publicly sharing datasets that could be taken as example in the Exercise number 3.
3a) 1 2 +1 -1 dy U(x, y, z) = 2 3 e x = 2 3 (cos θ ex + sin θ ez) (2. 3b) To integrate (2. 1) with boundary conditions (2. 3), we use the parallelised pseudospectral C++ code ChannelFlow  [26] , which employs a Fourier-Chebychev spatial discretisation. The velocity field can be decomposed into the stationary laminar parabolic base flow U base = (1 -y 2 )e x and the deviation u ≡ U -U base which satisfies the same equations and boundary conditions as U but with zero flux instead of (2. 3b). A Green's function method is used to impose the flux in each direction. More specifically, for each periodic direction, one computes and uses the pressure gradient such that the resulting flow field will have the desired bulk velocity, e. g. [27, 28]. Throughout our study, we present the deviation u = (u, v, w) so as to highlight the difference with the dominant laminar flow U base and the motion of flow features with respect to the bulk velocity.
From fits with τ d = exp (exp(a d Re + b d )) and τs = exp (exp(as Re + bs)) in the decay and split regimes, we find Rec 980 as an improved estimate of the crossing Reynolds number for this flow configuration. (Previous fits to the Monte Carlo data gave a crossing Reynolds number of 965. ) We recall a few details from the Monte Carlo computations in  [6]. The initial fields for the simulations are taken from snapshots of long-lasting bands simulated at Re ∈ [900 -1050]. The Reynolds number is then changed to the desired value. Decay and splitting times from the start of the simulation are recorded. From these, the mean times and associated error bars are obtained The AMS initial fields are created from long-lasting bands, as in the Monte Carlo method, except that each initial field is simulated for an additional relaxation time of 500 before commencing the AMS algorithm.
Wang and Yeh  [25]  solved a mixed-integer linear programming model using a modified particle swarm algorithm. Others considered the uncertainty in the problem parameters. For example, Wang and Liang  [26]  formulated the problem as a possibilistic linear programming model. Chakrabortty et al. [27]  considered also imprecise demand, operating costs, and capacity parameters in their model and used a modified variant of a possibilistic environment-based particle swarm optimization approach to solve it. Baykasoglu and Gocken  [11] ; Wang and Liang  [28]  formulated the problem as a multi-objective fuzzy model. Zhu et al. [29]  modeled the problem as interval-based programming considering uncertainty and solving it using Lingo software. Moreover, Jamalnia et al. [30]  evaluated the performance of the different strategies of the APP.
Basic data After identifying the required parameters, the complete factory data sets were collected for three successive years, known here as yearly Plan-I, yearly Plan-II, and yearly Plan-III. These three plans were dedicated to producing a total of twenty-nine products. The complete data set for Plan-I is presented in appendix A. The learning curves expressing the evolution of the required effort in mandays are also provided. Regarding the prediction of these learning curves: For the same factory, the management committee was motivated to know the most appropriate learning curve that can be used to estimate the workforce productivity evolution over time. Responding to this incentive, an investigation study was per-formed. That uses a real production dataset of 42 months, with an average monthly production rate of about 17,000 units.
The dataset used refers to 110 manufacturing processes and the different styles of final products. After conducting the non-linear regression analysis using ten formulas for mono-variable learning curves, the quality of each learning curve used to fit the data was evaluated. Five criteria were adopted for this quality evaluation: regression coefficient R 2 , the stability of the learning model to fit the data, sum of squares of the errors (SSE), the variation of R 2 , and variation of SSE. To prioritize these learning curves relying on the previous five criteria, the analytical hierarchy procedure (AHP) was adopted with the goal of finding the most appropriate learning curve for the current case study and the alternatives are the ten mono-variable learning curves. The analysis shows that the learning curve of Wright  [15]  is the most relevant curve to be used.
8. According to this dataset, considering an average learning rate for the whole workshop rather than specific values for each product induces an underestimation of the cost: 0. 3 Million (MU), or 0. 15% of the total cost. We can retain from this investigation that the impact of the learning effect is significant on the cost of industrial operation. In that view, it makes sense to use an aver-age value, easier to handle, instead of accurate measurements performed for every kind of operation or product family. Consequently, Firms should estimate the corresponding parameters of their products, and for simplicity of computation, they can use an average value instead of the detailed ones. On the other side, using values from literature for the learning rate is misleading especially if the applications are different.
Conclusion The current paper presents a mathematical model for the problem of Aggregate Production Planning (APP). This model considers a wide range of cost sources: machines, manpower, inventory, subcontracting. Moreover, it considers the organizational learning concept to forecast the workforce productivity rates in terms of workload (''man-days", where a man-day is the number of labours required to produce a number of 1,000 units of a given product or part during one day"). Moreover, the different working constraints or restrictions were considered: demand, storing, manpower capacity, overtime hours of permanent or part-time workforce, etc. then the model was solved using ILOG-CPLEX software after it was coded with OPL language. A real case study was used to validate the model. The data were taken from a factory for electric motors.
Property right game This paper uses data from a computer laboratory experiment. Subjects play 15 periods of a social dilemma with a partner. In each period, subjects play a property rights allocation game, similar to the baseline treatment in  Ahn et al. (2016). In each period, each subject is endowed with 10 effort tokens. All 10 effort tokens need to be allocated among three activities. 1. Production: Which yields wealth tokens with diminishing returns. We will denote this as p i for the number of tokens subject i allocates to wealth production. 2. Theft: Which is the theft of wealth tokens from subject i's partner-subject j, denoted as t i. 3. Protection: Which protects theft of wealth tokens from other subjects, denoted as r i. Table  1  shows the wealth production schedule. Production has increasing returns until an allocation of three, then decreasing returns thereafter.
A C K N O W L E D G M E N T S Funding was provided by the Hypo Bank of Tirol through the University of Innsbruck. The use of human subjects was approved by the Board for Ethical Practices in Science at the University of Innsbruck. We would like to thank Elisabeth Gsottbauer, Loukas Balafoutas, and Kai Konrad for helpful comments and Sarah Stuefer for research assistance. We also thank two anonymous referees for their helpful comments.
We benefited from comments and suggestions received at the 2019  Harvard Center  for Risk Analysis workshop on Risk Assessment, Economic Evaluation, and Decisions, as well as the 2021 SBCA and EAERE conferences. James K. Hammitt  acknowledges funding from the  U. S. National Science Foundation  (award number  1824492 ) and the  French National Research Agency (ANR)  under the  Investments for the Future program (Investissements d'Avenir , grant  ANR-17-EURE-0010 ). Daniel Herrera-Araujo  acknowledges financial support by the  French National Research Agency (ANR)  through the  VHEALTH  project, award number  ANR-20-CE36-0010-01. The views expressed in this paper are those of the authors and do not represent official positions of the  European Chemicals Agency.
Conclusions We end this paper with a brief illustration based on actual data. We use a fire insurance claim data set, labeled "beaonre" within the R package CASdatasets. This dataset includes 1823 observations of fire insurance claims from the year 1997. We transform this dataset of claim costs into a dataset of reimbursements, from which we can compute VaR, TCE, and TCE (m). To compare VaR or TCE with a high-order TCE indicator, we need to adjust the latter quantity in terms of scale. For instance, we may want to solve VaR q (X T ) = TCE (m) c (X T ) 1 m. While exact solutions to this equation do not exist, we can still deduce a relation between VaR and TCE (m). We show in Figure  13  the link between VaR and the high-order TCE (computed with m = 2) in the case of fire data. This figure is consistent with the theoretical results shown, for instance, in Figure  6.
Our second contribution is to propose a novel solution, the Hybrid mechanism, that integrates elements of decentralization into a centralized market to facilitate agents' learning. Specifically, in a decentralized phase of the Hybrid mechanism, an agent can hold multiple offers, thereby know for sure the availability of these matches, and thus learn more efficiently. The advantages of this new mechanism are shown in a theoretical model and in simulations based on the German data set. Our empirical investigation takes advantage of a unique centralized matching procedure with features of a decentralized market. It is called DoSV, Dialogorientiertes Serviceverfahren (literally, "dialogue-oriented service procedure"), and is used for admissions to over-demanded university programs in Germany. The procedure is based on the program-proposing Gale-Shapley (GS) algorithm, but differs from the standard implementation in that students can defer their commitment to rank-order lists (ROLs) of programs.
Moreover, we find that the student can do worse under the DoSV than under the DA, because an early offer from an ex-ante low quality university may still sub-optimally increase its acceptance probability. The comparison of the three mechanisms is further quantified in a set of simulations that use the same German data set. Relative to the DA, the Hybrid mechanism makes 72. 2 percent of the students better off, and only 20. 8 percent of them worse off. Further, the Hybrid also improves upon the DoSV by making 41. 7 percent of the students better off and 37. 1 percent worse off. Other related literature. Violations of the assumption of known-and-fixed preferences have received little attention in the market-design literature. As an exception, Narita (2018) finds that students reveal contradictory preferences in the main round and the subsequent round of school choice in New York City.
EarlyOffer i,k is a dummy variable that equals one if i receives a potential early offer from program k during Phase 1 (i. e. , up to August 18) rather than in Phase 2. A potential early offer is defined as either an offer that was actually received by the student in Phase 1 or-if the student cancelled her application to the program before Phase 2-an offer that the student would have received in Phase 1. 16  The row vector X i,k includes other student-programspecific controls. The coefficient of interest, δ, is thus identified by the within-student variation in the timing of offer arrival, conditional on programs' observed heterogeneity and unobserved average quality. 15 As the DoSV is based on the program-proposing GS algorithm, it is not strategy-proof for students. A student can misreport her preferences in an ROL by "reversal" (i.
The marginal effect of the first early offer is calculated in a similar manner. b The marginal effect of a non-first early offer measured in distance is calculated as the reduction in distance from 126 km that is needed to equalize the effect on utility of switching EarlyOffer from one to zero. A similar calculation is performed for the marginal effect of the very first offer. Another way to evaluate the magnitude of the early-offer effect is to use distance as a "numeraire" (Panel C of Table  3 ). At the sample mean of distance (126 km, as shown in column 3 of Table  1 ), an early offer that is not the first offer gives the program a boost in utility equivalent to reducing the distance by 61 km, based on the results in column 5. 22  The first early offer has an even larger effect, amounting to a reduction of 79 km.
EarlyOffer is a dummy variable, equal to one if the program became feasible to the student during Phase 1 and zero if it became feasible in Phase 2. FirstEarlyOffer is a dummy variable, equal to one if the program is the first to have become feasible to the student during Phase 1. *: p ă0. 1; **: p ă0. 05: ***: p ă0. 01. a For the marginal effect of a non-first early offer on top-ranking probability, we measure the difference between the following two predictions on top-ranking behavior: While keeping all other variables at their original values, we let (i) EarlyOffer " 1 and FirstEarlyOffer " 0, and (ii) EarlyOffer " FirstEarlyOffer " 0. The baseline probability is the average of the second prediction across students, while the reported marginal effect is the average of the difference between the two predictions across students.