dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, we edited Magento by the book, we replaced the Mysql database with a Nosql model of database, and we pushed it to the community. And if Magento today is being used by all large accounts, it is because three years ago, we pushed ahead the state of the art of Magento" (Firm 2, manager 2). Both have an inter-connected involvement with some open source knowing communities, but the extent of the support provided to the projects is different. Therefore, in the three cases, it is impossible for formal organizations to work in full inter-connection with all communities they rely on. We confirm that the term "full inter-connection" is to be understood as a very high level of coordination and exchanges between actors in a knowing community.
"People start by using LibreOffice on their PC, and perhaps see some bits of Linux or Gimp, and they don't even realize that they're working with open source tools. And little by little, it spreads. They start understanding the value of it. It takes time. (…) It is an approach that goes towards innovation and that is extremely complicated to implement. I understand that those who do not have this in their genes find it extremely complicated. (…) If you're a Tech, you would like to contribute to Drupal, how do you proceed? The community codes, how you go, how you contribute, it has to be learned, to be transmitted, it is not enough to read a book on the subject" (Firm 2, manager 2). To conclude on our second study, the interviews first revealed that it is very difficult for formal structures in the IT industry to get interconnected with all their communities they work with when they develop and maintain their products.
All paths are significant, which tends to show that formalized involvement partially mediates the relationship between the use of open source and inter-connected involvement on the one hand and resultoriented involvement on the other hand. Considering "formalized involvement" as a mediator, the direct links "use of open source" to "result-oriented involvement" and to "inter-connected involvement" are still significant. "If the rank order of the constructs' relevance, when explaining a dependent construct in the structural model, differs when comparing the size of the path coefficients and the f 2 effect sizes, the r-covenant. org/version/1/4/code-of-conduct/). The Linux code of conduct is also based on it and now serves as reference for all communities (https://www. kern el. org/doc/html/latest/process/code-of-conduct.
This result echoes with the positive relationship of the use of open source with the creation of "social ties" with communities that was empirically identified by  Sims et Seidel (2016) , and the learning path to involvement in communities  (Nagle, 2018; Shaikh & Levina, 2019). We quantitatively demonstrate that this learning path is associated with an internal use of open source rules, methods and components. At last, the effect of the use of open source with the "formalized involvement" is nearly twice higher than the two other relations. The relation with the "inter-connected involvement" is the lowest one. As the software industry is mature, following existing graduations of involvement  (Ciesielska & Westenholz, 2016; Grand et al. , 2004) , we would have expected "inter-connected involvement" to be more represented.
On the other hand, knowing communities can reduce dependence on certain formal structures by multiplying the sources of knowledge and improving sharing practices that allow the integration of external knowledge, thus supporting innovation. Future research can further explore these relational practices that can be considered as boundary practices. This research has also several practical and managerial contributions, the more so as open source software is emerging in all industrial sectors thanks to their digitalization. This research could provide formal newcomers with specific guidance in interacting with knowing communities. First, open source is not only the use of components, but it rather means the use of open source methods and tools as well. This consideration leads to more global reflections concerning the functioning of open source and the innovation results that can be expected.
Second, this research highlights the role of experience for interacting with knowing communities, thus emphasizing a gradual knowledge acquisition. Third, there are three ways of interacting, innovating with communities and contributing to the development of the software. One of these is formalization, to which organizations are already accustomed through open innovation. Formalization seems a good boundary practice, based on specific boundary objects, to develop inter-connection with open-source communities. It appears as a pre-requisite to more advanced forms of involvement. This work has limitations, some of which offer avenues for future research. First, inter-connected formal structures cannot be interconnected with all the communities they depend on. Further research could aim at assessing the way formal structures spread the three community involvement boundary practices  (Formalized, Resultoriented and Inter-connected)  between all the components they use.
Acknowledgements Comments from participants at the June 2021 KCO symposium in Toulon and from participants at the May 2019 AIM conference in Nantes are gratefully acknowledged. We would like to express our gratitude to the co-organizers of the survey, and specifically to  Stefane Fermigier , cochairman of the CNLL, the French national council of free/open source companies, for his useful critiques on our research on formal structures using open source and involving in open source communities. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Training data in computational science are usually limited, requiring predictive models to have predictive capabilities beyond the scope of training samples. Domain adaptation (DA) is a field associated with machine learning that aligns the data distributions of the source (training dataset) and target (test dataset) domains by learning a more generalized feature space. In this article, we propose an optimal transportbased algorithm named MROT to overcome this DA challenge and improve the generalization capacity of predictive models. Our study shows that our MROT framework provides a potential solution for basic chemical regression prediction tasks, including chemical property prediction and materials adsorption selection. This work contributes a new idea to achieve the proper transformation from simulated data to experimental data, which play a crucial role in exploring new drugs or materials.
But most prior methods are unsuitable for regression tasks and fail to prove their effectiveness in solving chemical problems. Joint distribution optimal transportation (JDOT)  30  strives to minimize the OT loss between the joint source distribution and an estimated target distribution for UDA but neglects the label information in the source domain to constrain the OT plan. While a few studies had been introduced to either learn a better metric  31, 32  or reduce the bias brought by mini-batches,  18, 33  none of them succeed in realizing those two objectives simultaneously. DA in chemical applications DA in chemistry has raised a growing interest in the past few years. 5] [36] [37]  Jin et al. 38  improve the IRM  25  with predictive regret to generalize to new scaffolds or protein families, while other methods  39  merge multiple materials datasets by introducing an additional state variable to indicate the fidelity of each dataset.
Problem statement Throughout the article, we consider the chemical regression tasks g : X /Y4R, where x ˛X can be any small molecules, crystals, or proteins and y ˛Y represents a physical or chemical property. Typically, one assumes the existence of two distinct joint probability distributions P s ðx s ; y s Þ and P t ðx t ; y t Þ defined over X 3Y and related to the source and target domains, respectively. We denote them as P s and P t for sake of simplicity. Let E be the domain set, and E t and E s represent the source and target domain sets, respectively. The DA strategies can be divided into two families as semi-DA  45  and UDA,  46, 47  which depend on the presence of few labels in the target domain set E t. ðx e i ; y e i Þ and D e denote an input-label pair and a dataset drawn from the data distribution of some domain e ˛Es WE t , respectively.
The goal of DA is to seek a regressor gÃ that minimizes the worst-domain loss on E t : g Ã = argmin g ˛G L E À E t ; g Á ; L E ðE; gÞbmax e ˛E E Â [ À g À x e i ÁÁ ; y e i ÁÃ ; (Equation  1 ) where G : X /R is the hypothesis space and [ is the loss function. Furthermore, the problem is simplified in our setting, where we only consider a single source domain, s, and a single target domain, t, instead of two domain sets. Practically, we aim to minimize the error of a dataset in the target domain D t as L D ðD t ;gÞ, where L D corresponds to the total loss of g on a given dataset D. Similar to previous studies,  25, 38, [48] [49] [50]  g is assumed to be decomposed into h+f, where f : X /R d is the feature extractor that maps the input into the feature space H and h : R d /R is the predictor.
Molecular property prediction datasets In the unsupervised task, to demonstrate that the MROT model can generalize beyond its training domain to make predictions, we split datasets based on the objective property instead of molecular constituents, which accords with a semantic shift. We consider a low-to-high environment rather than the high-to-low environment because molecules with high values of desired properties are traditionally what scientists or pharmacists search for in drug or materials design. 51, 52 The train and test sets contain molecules with the lowest 80% and the 10% highest properties, respectively. Then, we use the remaining 10% data points for validation. The following six regression datasets from quantum chemistry and physical chemistry were used. 53 is a subset of GDB-13, which records the computed atomization energies of stable and synthetically accessible organic molecules.
d QM8 54 contains computer-generated quantum mechanical properties, including electronic spectra and excitedstate energy of small molecules. d QM9 10 is a comprehensive dataset that provides geometric, energetic, electronic, and thermodynamic properties for a subset of GDB-17. d ESOL  55  is a small dataset documenting the solubility of compounds. d FreeSolv  56  provides experimental and calculated hydration-free energy of small molecules in water. The calculated values are derived from alchemical freeenergy calculations using molecular dynamics simulations. d Lipophilicity 57 (Lipo) is selected from ChEMBL, which is an important property that affects molecular membrane permeability and solubility. The data are obtained via octanol/water distribution coefficient experiments. d QM7.
Material adsorption prediction datasets The algorithm for semi-DA is evaluated on the materials adsorption dataset. To meet the setting of semi-DA, we randomly select a ratio (25% or 50%) of Experimental metal-organic frameworks (Exp-MOF) with labeled target samples for training and use the rest as the testing set. Each method is conducted three times due to the small size of Exp-MOF, and we report the mean performance. It consists of the simulated materials data and the experimental materials data described as follows. d Computation-ready experimental metal-organic frameworks (CoRE-MOF)  58.
MROT surpasses all baselines with significant margins. Particularly, MROT exceeds JDOT, illustrating its validity to discover better decision boundaries and overcome the drawbacks of mini-batch training. Besides, both DANN and CDAN achieve lower errors than ERM, which means that learning invariant representations can benefit UDA to some extent on the regression molecular presentation learning tasks. Remarkably, the improvement brought by MROT over other approaches in QM8 and QM9 is higher than that in ESOL, FreeSolv, and Lipo, which have less than 5,000 samples. This is because large datasets have more minibatches, so the bias of the local data distribution in mini-batches with respect to the global data distribution is much greater. The dynamic loss empowers MROT to resist this bias. Thus, the advantage of MROT is enhanced when the data size increases.
Semi-DA problem Table  4  the root-mean-squared error (RMSE), the Pearson correlation (R p ), and the Spearman correlation (R s ) in Table  4  with 25% and 50% labeled target data. The superiority of MROT over baseline methods is supported by its lowest mean absolute error (MAE) and highest R p and R s. It is worth noting that although MLDG performs poorly in UDA, it beats adversarial methods in semi-DA, demonstrating the efficacy of metalearning when the size of the target dataset is small.
MROT performs mini-batch operations on datasets in the calculation process to overcome the computational load brought by large data distribution, jumps out of the local data distribution of small batch training during metric learning, and considers the global data distribution of multiple domains. A dynamic hierarchical triplet loss is introduced to overcome the shortage of conventional metrics and mitigate the bias by computing minibatches to help achieve more distinguishable decision boundaries and catch the global data distributions. We convincingly show that it can reach state-of-the-art performance on challenging unsupervised and semi-supervised tasks. Notably, we do not examine the predictive performance of our model on some classic prediction tasks, such as the lipophilicity prediction problem.
This is because our MORT is mainly designed for regression tasks. Our motivation is founded on that most existing mechanisms to tackle DA merely consider the classification setting, where the label space is discrete. Nevertheless, many real-world chemical applications are regression tasks, such as molecular property prediction,  62  binding affinity prediction,  14, 67  molecular dynamics simulations,  68  and protein-protein docking. 69 Our work targets this category of problems and is mainly distinct from previous methods in its efficient utilization of continuous label space. Moreover, we do not choose to split the datasets by time and argue that the time split is not a proper split standard. Indeed, some competitions, such as Critical Assessment of Structure Prediction (CASP),  70  adopt the time split to separate proteins, but this is because competitions usually take place every year or every 2 years.
Researchers have to follow this paradigm to make a fair comparison with prior competition winners. But for proteins, a more widely accepted split option is to separate the proteins by sequence identity. People usually forbid the case in which the train and test sets have proteins with sequence identity higher than a pre-defined ratio (e. g. , 50%). It can also be discovered that molecular datasets seldom provide time information, which shows that time split may not be necessary for data scientists. However, there are more challenging split methodologies and more complex tasks. Our settings, which adapt models from distributions of low chemical properties to high chemical properties and from simulated data to experimental data, are only an example to showcase the superiority of our method. With the light we shed, future studies will notice this essential issue and apply our technique to more professional and specific tasks in biology, chemistry, and medicine.
Data and code availability The code and data are available at GitHub (https://github. com/smiles724/ BROT). Original data have been deposited to Zenodo. 61, 71 thod Unsupervised and semi-DA In UDA, the ground truth of property distributions in the target domain P t ðy t Þ is inaccessible, and labels are only in the source domain. It is usually correlated with a semantic shift. The priority in UDA is the metric to measure the distance between x s and x t on the ground space Z = X. In most applications, the metric in the feature space, d H , is readily available, and the Euclidean distance is a popular option. 29 Consequently, the distance between two molecules d f Z : X 3X/R + is defined as d f Z x s i ; x t j = d H f À x s i Á ; f x t j : (Equation 2) Semi-DA is a more realistic setting, where learners can access a small amount of labeled data but no unlabeled data from the target domain.
Algorithm 1. The workflow of MROTRequire: A source dataset D s with N s samples and a target dataset D t with N t samples. A pre-defined number of all clusters K. Ensure: Model parameters q g. M ðtÞ ) update M ðt À 1Þ , construct a hierarchical tree U ðtÞ by Equation8, calculate the regression loss L reg , calculate the loss L m by Equation.
ACKNOWLEDGMENTS We thank  Doctor Jiyu Cui  at  Zhejiang University  for providing valuable material datasets. This work was supported by  National Key R&D Program of China  (no. 2022ZD0115100 ),  National Natural Science Foundation of China Project  (no. U21A20427 ), and  Project  (no. WU2022A009 ) from the  Center of Synthetic Biology and Integrated Bioengineering of Westlake University.
61p-MOF61contains samples that can be synthesized and acquired throughout rigorous and high-priced experiments. But due to the expensive costs, its data size is much smaller, with only 113 crystals. CoRE-MOF and Exp-MOF share the same target label: the adsorption capability of materials to carbon dioxide. We aspire to adapt the predictive model to forecast that property from CoRE-MOF to Exp-MOF. 60ns over 10,000 crystal samples and a wide variety of 79 atom classes. It is obtained from the Cambridge Structural Database 59 and a Web of Science60search and is derived through semi-automated reconstruction of disordered structures using a topologybased crystal generator. CoRE-MOF offers 12 chemical properties, such as carbon dioxide adsorption. Nonetheless, most of its materials are unable to be synthesized.
Algorithm 1. The workflow of MROTRequire: A source dataset D s with N s samples and a target dataset D t with N t samples. A pre-defined number of all clusters K. Ensure: Model parameters q g. M ðtÞ ) update M ðt À 1Þ , construct a hierarchical tree U ðtÞ by Equation8, calculate the regression loss L reg , calculate the loss L m by Equation M ð0Þ ) K-means ðB s ð0Þ ; B t ð0Þ Þ for t = 1;. ; % AEN s b À 1 do 8 initialize centroids, fS ðtÞ i g K i = 1 ) cluster B s ðtÞ and B t ðtÞ ,.
B. GRAPH MODEL Using a modeling framework from  [31]  and  [35] , a graph model of 2PM data was generated (Fig. 1 B ). In this framework, vascular segmentation was performed using a fully-convolutional neural network based on densely connected layers  [31]. The graphing/skeletonization step was achieved by generating a 3D grid-graph model followed by a geometric graph contraction and refinement algorithms. Training the segmentation model was done using the Theano framework  [36]. As for 3D modeling and geometric contraction processes  [35] , they are based on the VascGraph Python package  [35]. Using this framework, a file containing vessel network geometry and topology (nodes and edges), with their corresponding inlet and outlet vessels is generated. The corresponding vessel radii are assigned as features to the nodes of the vascular network.
C. PARTICLE TRAJECTORY GENERATION The graph model that we obtain from the vascular data has nodes that are arbitrarily spaced and are located at the centerline of each vessel. However, we need MBs to be flowing at specific velocities and at different radial positions in the vascular network. We also need to measure the impact of MB concentration, therefore it must be kept constant throughout the simulation. Before generating a steady state flow simulation where MB concentration is always kept constant, MB trajectories were computed using the graph model source nodes, target nodes and vessels radii with the shortestpathtree MATLAB (MathWorks) function. For each trajectory, the path between a source node (i. e. , has no parent node) and an extremity node (i. e. , has no target node) was selected (Fig.
We then used the pdfrnd MATLAB (MathWorks) function to select a random trajectory given the calculated probability obtained using equation (4). To populate the first set of MBs in the steady-state flow, a first population of particles was generated at random positions in their respective trajectory to match a desired concentration. Then, a new microbubble was added to the flow each time a microbubble reached the end of its trajectory. After this process, we were left with sets of constant number of microbubbles positions (X i , Y i , Z i ) at each time frame.
E. SCATTERERS' ARRANGEMENT The dataset that we had access to spanned only about 0. 5 × 0. 5 × 0. 65 mm in space, which corresponds to about 0. 1625 mm 3. It provides a realistically dense vascular network, but it does not span the entire field of view. Since the PSF varies based on its position in the field of view, we needed MB scatterers in most of the field of view to assess the performance of the system. We therefore decided to distribute unique MB trajectories of a vascular network throughout the field of view by translating their positions in a rigid manner in all 3 dimensions of space. MB trajectories were randomly distributed in a 10 × 10 × 10 pattern in the x, y, and z dimensions to span 5 × 5 × 6. 5 mm (Fig. 3 ). This arrangement also allowed to span a usable range of MB concentrations.
Bandwidths and transmit frequencies are depicted in TABLE  1. Pitch and element width were set to 0. 3 and 0. 275 mm, respectively. The transmit pulse was a 0-degree plane wave, and the effective framerate was equal to the PRF of 1 kHz. The pulse length was chosen to be 4 cycles to enhance contrast. Since 2D imaging is widely used in ULM, we incorporated the study of a linear array. Probe parameters matched a L22-14 array with 128 elements, central frequency of 15. 6 MHz, pitch of 0. 1 mm and element width of 0. 08 mm (Vermon, France). To simulate the ultrasound images, an in-house GPU implementation of the SIMUS simulation software was performed as described in  [42]  and  [43]. Microbubbles were modeled as linear point scatterers. White gaussian noise was added to the acoustic response to obtain a signal-to-noise ratio (SNR) of 15 dB in 2D and 10 dB in 3D to mimic typical SNR we find in our in-vivo data.
H. MB SIMULATOR METRICS To validate MB behavior, 5000 MB trajectories were simulated to replicate similar MB count as in  [7]. The two dependencies from in-vivo data are 1) the MB count as a function of vessel diameter and 2) MB velocity as a function of vessel diameter. To assess the first dependency, the occurrence of MBs flowing in specific vessel diameters was calculated. Natural logarithm (ln) was applied on both dependencies to generate a linear fit as in  [7]. As for the second dependency, MB velocity was simply plotted against its corresponding vessel diameter.
This also implies that the network size in wavelengths is about 5λ at 15 MHz and about 1λ at 3 MHz. At 3 MHz, the resolution limit is about λ/10 or 50 µm, which is the size of the largest vessel in the network. Therefore, at that frequency, only one vessel can be reconstructed. However, even with the small dataset we had access to, we still wanted to test the limits of the ULM processing with this validation framework. The proposed simulation framework provides access to both quantitative metrics and a visual assessment of the vascular reconstruction compared to the reference network. This provides insight on aspects that can be harder to capture with metrics only. Future work with limited access to large datasets could mitigate the issue by artificially dilating small networks isotropically to span vessel diameters that are representative of a whole organ's distribution.