dataesr/scientific-paragraphs-categorization huggingface dataset:
Health coaches/subscription services Another approach to utilizing m-health technologies uses health coaches via subscription services to improve outcomes; mySugr is an example. mySugr, an app-based program, offers two basic elements. The first element is a program that is free to download and enables users to log and track their glucose data, meal, exercise, and other activities. With the second element, users purchase a subscription that gives them access to health coaches and health care professionals. A free blood glucose meter and unlimited test strips are included in the service bundle. Another company, Livongo, focuses on helping employers reallocate health care dollar resources by tracking employee health data (including glucose, blood pressure, and weight) and developing support systems.
OurPath digital weight-loss program OurPath program is a digital lifestyle change program developed to support weight management and improve glycemic control in individuals with T2D. The 3-month program provides structured education, health tracking devices, and continuous support from health care providers and coaches. The program is designed to help users learn at their own pace, delivering information, training, and coaching while taking into account each individual's learning style, information/education needs, and level of support needed. In addition, the digital program links health tracking devices, which allow users to monitor their weight loss, physical activity, sleep, and other factors in real time. This continuous feedback reinforces the positive changes made, providing further motivation, which can lead to more positive outcomes and additional reinforcement.
It has recently been applied to diabetes. Development of the BCW was based on findings from a systematic search of electronic databases and consultations with behavior change experts to identify frameworks of behavior change interventions. The frameworks were evaluated according to their comprehensiveness, coherence, and having a clear link to an overarching model of behavior. The BCW utilizes the three key components of behavior change science: (1) a methodology for designing, delivering, and evaluating interventions; (2) a comprehensive theory of behavior; and (3) a replicable way of understanding what is inside complex behavior change interventions.
I present the first public releases (v3. 4 and v3. 5) of the usine code for cosmic-ray propagation in the Galaxy (https://lpsc. in2p3. fr/usine). It contains several semi-analytical propagation models previously used in the literature (leaky-box model, 2zone 1D and 2D diffusion models) for the calculation of nuclei (Z = 1 -30), anti-protons, and anti-deuterons. For minimisations, the geometry, transport, and source parameters of all models can be enabled as free parameters, whereas nuisance parameters are enabled on solar modulation levels, cross sections (inelastic and production), and systematics of the CR data. With a single ASCII initialisation file to configure runs, its many displays, and the speed associated to semi-analytical approaches, usine should be a useful tool for beginners, but also for experts to perform statistical analyses of high-precision cosmic-ray data.
Solving the diffusion equation came the public code GALPROP  [57] , relying on finite difference numerical scheme  [13] ; GAL-PROP rapidly became the new reference in the field. This approach allows a more realistic description of the Galaxy: there is a unique system of algebraic equations per coordinate system, which applies to any spatial and energy dependence of the Galaxy parameters (transport, gas, and sources). However, the difficulty lies in choosing an optimal solver to ensure a reasonable speed and numerical stability of the solution. More recently, two other public codes based on optimised or different solvers (also based on finite differences) were released, namely DRAGON  [21, 22]  and PICARD  [34]. These next generation codes have been specifically designed to tackle difficult questions, such as the impact of 3D spatial structures in the sources and gas, anisotropic diffusion, etc.
The main benefit of these models is that they remain much faster than any numerical model (and by definition less sensitive to numerical instabilities). They may also provide insight into the main dependences on the ingredients through their solution. Historically, this approach was the first developed. The two-zone diffusion model (thin disc and thick diffusive halo) in 1D  [32]  or 2D  [65]  remain useful and provide a complementary approach to analyse recent data on nuclei and anti-nuclei  [19, 27, 52]   1. The usine package implements several semi-analytical models. The present version has a simple text-user interface, and running or fitting CR data with usine depends on a single initialisation ASCII file (no code recompilation needed). It also provides many pop-up and comparison plots, making it a useful tool for both beginners and experts in the field.
The code and its documentation are distributed through gitlab (see below). The paper is organised as follows: Sect 2 recalls the master equation for Galactic CR propagation. The code is described in Sect. 3 and the input ingredients in Sect. 4. All usine runs rely on an ASCII initialisation file, which is described in Sect. 5, and run examples are provided in Sect. 6. I conclude and briefly comment on the undergoing developments for the next release in Sect. 7. This article describes usine v3. 4, used in  [1] , but it also highlights in Appendix A the improved v3. 5, used for AMS-02 data analyses in  [7, 15, 29].
Description of the code The code usine is written in C++ (with classes). It relies on the root cern 4 library and wrappers for displays and minimisation routine. The code's structure is standard, with headers in include/*. h, sources in src/*. cc, compiled libraries in lib/, objects in obj/, and one executable in bin/. The other directories present in the package are: • FunctionParser/ third-party library 5 to handle formulae for many usine parameters (diffusion coefficient, source spectrum, etc. ); • inputs/ necessary ASCII files to initialise and run usine (see Sect. 4); • tests/ reference test files to check that the installation is successful and usine ready to run, based on both unit and integration tests; • doc/ usine documentation (sphinx 6 with readthedocs 7 theme). The usine package and its documentation are available at https://lpsc.
in2p3. fr/usine. The compilation relies on cmake 8 (files CmakeLists. txt and FindROOT. cmake), see the documentation for details. During the development, both static (cppcheck 9 ) and dynamic (valgrind 10 ) code analyses were performed to track and fix code errors, memory leaks, etc. The present version of the usine code was recently used to hint at the presence of a break in the diffusion coefficient from recent AMS-02 B/C data  [27]. It was also used to rank the most important cross sections involved in Li, Be, B, C, and N fluxes for CR studies  [28]. These two studies can be easily repeated and extended with the usine package as delivered, as illustrated in Sect. 6. About previous unreleased versions. The first usine version was developed last century. With a growing interest for semianalytical models, especially in the context of dark matter indirect detection, a public release was planned in the early 00's, but was delayed for all these years 11 : the first unreleased usine ver-4 http://root.
cern. ch 5 http://warp. povusers. org/FunctionParser/fparser. html 6 www. sphinx-doc. org 7 https://readthedocs. org 8 https://cmake. org 9 http://cppcheck. sourceforge. net/ 10 http://valgrind. org/ 11 The best advice I could provide to young researchers is not to wait for a perfect, nice, and tidy code version before making it public, because it is a never-ending process in which boredom often wins over completion. More importantly, as published science and story telling is never about delay or failure, I recommend reading Kahneman  [33]  -Nobel Memorial Prize in Economic Sciences (2002) for his work on the psychology of judgement and decisionmaking, as well as behavioural economics. To quote him on one of the many biases (on decision making) human beings are prone to: We focus on our goal, anchor on our plan, and neglect relevant base rates, exposing ourselves to the planning fallacy.
Rather than use the model value at the data mean energy, a more accurate calculation is to compare the data to model i = E i+1 E i model(E)dE (E i+1 -E i ). I assume that we have a power-law in the bin, J = a × E b , so that model i ≈ J i+1 × E i+1 -J i × E i (b + 1) × E i+1 -E i , with b = ln(J i+1 /J i )/ ln(E i+1 /E i ). This assumption can be refined by adding intermediate points (on which the power-law approximation is also assumed) : this is controlled by the parameter fNExtraInBinRange (see Table  5 )  15. • Covariance: if a covariance matrix is provided (see Sect. 4. 3) for a given (t, e, q) dataset of N E energies, the corresponding term in Eq. (  4 ) is replaced with: χ 2 cov = α n E ,n E i, j=0 (data i -model i )(C α ) -1 i j (data j -model j ), (5) with C α i j the covariance matrix provided by the user for the systematics α (several systematics with a different covariance may exist for a given set of data).
Enumerators and keywords The code contains many enumerators (enum key), most of them being relevant for developers only. The ones relevant for users are gathered in Table  1. They are used for parameter values in the initialisation file (Sect. 5) and/or as arguments of the executable. /bin/usine (Sect. 6). The complete list of enumerators is defined in include/TUEnum. h. † Boundary condition at low or high energy end.
In more recent data, at least statistical and systematic uncertainties are provided separately (format of data gathered in CRDB). Recent experiments (e. g. , AMS02) go further and provide also systematics broken down in various contributions. However, this may not be sufficient for the model analysis of high-precision data, which may require the error correlations between different energies for all types of errors in the instrument, as encoded in the covariance matrix. The other option to deal with systematics uncertainties is via nuisance parameters (see Sect. 5. 5), and it is not clear yet which is the best way to account for them.
Acknowledgements I am indebted to my colleagues for their encouragements and contributions on previous unreleased usine versions:  F. Barao ,  L. Derome ,  F. Donato ,  A. Putze ,  P. Salati , and  R. Taillet. Thanks a lot to the  CRAC team  for their feedback and suggestions on this release:  G. Bélanger ,  M. Boudaud ,  S. Caroff ,  Y. Génolini ,  J. Lavalle ,  V. Poireau ,  V. Poulin ,  S. Rosier-Lees ,  P. I. Silva Batista ,  P. Serpico , and  M. Vecchi. Special thanks to  Y. Génolini  and  M. Boudaud  for helping find out many bugs, for providing the initialisation files obtained in [29], and for the new anti-proton cross sections used in [7]. I thank the three anonymous referees for their comments and questions that helped to provide a more complete description of the code. Many thanks to  C.
To demonstrate our method we used four different datasets all obtained from the SocioPatterns project  [35]. These open datasets record the time evolving physical proxy interactions of a large number of people in different settings like in conference, high school, hospital, or primary school. The data comes as a long sequence of network snapshots recording simultaneous interactions between any participants in every 20 seconds. While for demonstration most of the result in the paper are shown only for the conference and primary school settings, a detailed data description together with the analysis for the other networks are presented in the Supplementary Information (SI).
This suggests that while structural correlations can be captured by the embedding, local temporal correlations might be better predictors than weight-structural correlations. Interestingly, the link shuffling method, which conserves only local timeline dynamics on links, performs the best among the RRMs, sometimes even better than the original dataset. Consequently, indeed local temporal event dynamics is the most important feature of the temporal network, while removing structural correlations the system becomes homogeneous and easier to predict (for a supporting analysis see SI). Although these general conclusions seem to be consistent over the several analysed datasets, results computed in different settings may have some fluctuations. As explained in the SI, this can be partially explained by the variance of the epidemic size distribution reflecting the fluctuation of epidemic size started from different times and events.
Data As a general conclusion we showed that the embedding successfully captures a combination of temporal and structural features of the network. On the other hand, the fact that temporal and structural features can be entangled has an impact on embedding performance but not on what the embedding is able to learn about them. For instance, the model can under-perform in a community-rich network where nodes of the same community have totally uncorrelated activities, while can provide precise predictions in shuffled datasets if structural and temporal correlations code redundant information.
Entropy Computation To measure the entropy over the distributions of the euclidean distances between pairs of event coordinates in each of the temporal network embedding, we build 10 embeddings for 50 different dimensions (from 2 to 100 at step 2), setting α = 0. 5. The hyper parameters nb and s were set to 10 and 10 respectively. For each embedding, we then divide our dataset in 10 consecutive samples of 1000 consecutive events each, both to avoid to compute all the pairwise distances (which would be very costly computationally) and at the same time have a representative set of events. We compute the euclidean distance of each pair of events in the samples for each of the 10 different embeddings. We then bin the distances into k = 10 bins ranging from the global maximum and the global minimum values over all the possible dimension and realisations of the embedding for the same network, and measure the entropy over these sets of distances as H = k p k log p k , (4) where p k represents the probability associated with the k th bin.
Spreading Process The simplest epidemic models are based on the assumption that the population can be divided into compartments, each representing a phase of the disease  [43, 44, 45, 46, 47]. The one we used for our analysis, the Susceptible-Infected model, which foresees that once a healthy node (in state S) is exposed to the infection, it will become infected (state I) with a given rate β and will never return to the original healthy state. In our specific case, the SI model has been implemented in such a way that β = 1, which defines a a deterministic process from the spreading point of view. Taking each event as the starting of the epidemic, we simulate the epidemic spreading on the temporal network and we assign the final epidemic size to each seed event. We build a dataset containing embedding coordinates of each event, the associated epidemic size that will be the target to be predicted, the square of each coordinate and the euclidean distance from each event in the network and the first event in time, that will be used as regressors.
Entropy measures We performed the same entropy analysis on the hospital and high school networks what was presented for other datasets in the main text. Similarly, we measured the entropy of the variance of the relative distance between embedded events as the function of the number of embedding dimensions. Qualitatively we found the same results (see Figure  10 ) as reported in the main text, that the entropy measure becomes invariant of the number of dimensions after an optimal value and involving further dimensions would not increase significantly the quality of the prediction but would only introduce redundancies in the representation.
Epidemic size distribution of original networks and randomized models While in the main text we only discussed the correlations between the simulated and predicted epidemic sizes, we never looked at how simulated epidemic sizes are varying over several realisations. Here we look at these distributions measured for simulated epidemic processes seeded from every event in the empirical networks. As shown in Figure  11  for each dataset, we find that these distributions vary in different ways for different networks. While they are concentrated for the (a) conference and (c) high school datasets, they are more homogeneously distributed for the (b) hospital and (d) primary school data. To further understand the effects of the different RRMs on the epidemic outcome, for each models we generated five different randomised network realisation and simulated the spreading process starting from each event in the networks to obtain the distribution of final epidemic sizes.
Datasets analysed in this paper are defined as sequences of snapshots aggregating temporal interactions in consecutive time windows of size ∆t. In these systems we compute wco for adjacent links as the number of co-occurrence of corresponding events within single snapshots. This definition may slightly underestimate the real co-occurrence (if δt ≤ ∆t), but provides the best plausible solution due to the un-ambiguity of timings of events within a single snapshot.
INTRODUCTION K-Nearest-Neighbor (KNN) graphs  1  play a fundamental role in many big data applications, including search  [5, 6] , recommendation  [8, 29, 32]  and classification  [39]. A KNN graph is a directed graph of entities (e. g. , users, documents etc. ), in which each entity (or node) is connected to its k most similar counterparts or neighbors, according to a given similarity metric. In many applications, this similarity metric is computed from a second set of entities (termed items) associated with each node in a bipartite graph (often extended with weights, such as ratings or frequencies). For instance, in a movie rating database, nodes are users, and each user is associated with the movies (items) she has already rated  [22]. Being able to compute a KNN graph efficiently is crucial in situations that are constrained, either in terms of time or resources.
This is the case of real time  2  web applications, such as news recommenders, that must regularly recompute their suggestions in short intervals on fresh data to remain relevant. This is also the case of privacy-preserving personal assistants executing learning tasks on personal devices with limited resources  [1]. Computing an exact KNN graph rapidly becomes intractable on large datasets. Fortunately, many applications only require a good approximation of the KNN graph  [25, 27]. Recent KNN construction algorithms  [8, 19]  have therefore sought to reduce the number of similarity computations by exploiting a greedy strategy. These techniques, among the most efficient to date, seem, however, to have reached their limits. In this paper, rather than reducing an algorithm's complexity (e. g.
We use these SHFs to rapidly construct KNN graphs, in an overall approach we have dubbed GoldFinger. In this paper, we make the following contributions:  (1)  we introduce GoldFinger, a generic and efficient approach to accelerate any KNN graph algorithm relying on Jaccard's index, one of the most common metrics used to compute KNN graphs, which can be tuned to trade space and time for accuracy;  (2)  we propose a formal analysis of GoldFinger's estimation mechanism;  (3)  we formally analyze the privacy protection granted as a side effect by GoldFinger in terms of k-anonymity and ℓ-diversity; (4) we extensively evaluate our approach on a range of state-of-the-art KNN graph algorithms such as Locality Sensitive Hashing (LSH), on six representative datasets, and we show that GoldFinger is able to deliver speedups of up to 78.
We then define the quality of G KNN as quality( G KNN ) = avg_sim( G KNN ) avg_sim(G KNN ). ( 3 ) 3 In other words, argtop k generalizes the concept of argument of the maximum (usually noted argmax) to the k top values of a function over a finite discrete set. A quality close to 1 indicates that the approximate neighborhoods have a quality close to that of ideal neighborhoods, and can replace them with little loss in most applications. With the above notations, we can summarize our problem as follows: for a given dataset (U , I, (P u ) u ∈U ) and item-based similarity f sim , we wish to compute an approximate G KNN in the shortest time with the highest overall quality.
Intuition A large portion of a KNN graph's construction time often comes from computing individual similarity values (up to 90% of the total construction time  [9] ). This is because computing explicit similarity values on even medium-sized profiles can be relatively expensive. Figure  1  shows on its y-axis the time required to compute Jaccard's index J (P 1 , P 2 ) = |P 1 ∩P 2 | |P 1 ∪P 2 | between two user profiles of the same size, when this size varies (x-axis). The cost of computing a single index is relatively high even for medium-size profiles: 2. 7 ms for two random profiles of 80 items, a typical profile size of the datasets we have considered. Earlier KNN graph construction approaches have therefore sought to limit the number of similarity computations  [8, 19]. They typically adopt a greedy strategy, starting from a random graph, and progressively converging to a better KNN approximation.
For instance, one of the datasets we consider, AmazonMovies, has 171,356 items. With 1024-bit SHFs (the typical size we use), GoldFinger provides 2 166 -anonymity, i. e. each compacted profile is indistinguishable from at least 2 166 ≈ 9. 35 × 10 49 possible profiles. 2. 5. 1 k-anonymity. Definition 2. 2. Consider an obfuscation mechanism obf : X → Y that maps a clear-text input x ∈ X to an obfuscated value in Y. obf () is k-anonymous for x ∈ X, if the observed obfuscated value obf (x) is indistinguishable from that of at least k -1 other explicit input values. Formally, obf () is k-anonymous for x ∈ X iff obf -1 (obf (x)) ≥ k. ( 9.
ℓ-diversity. Although k-anonymity provides a measure of the difficulty to recover the complete profile P u of a user u, it does not cover cases in which an attacker would seek to guess some partial information about u. This type of question is better captured by a second metric, ℓ-diversity  [34]. The ℓ-diversity model ensures that, for a given SHF (B u , c u ), the actual profile P u it was created from is indistinguishable from ℓ -1 other profiles {P i } i ∈ 1. ℓ-1 , and that these profiles form a well-represented set. The difference with k-anonymity lies in this notion of wellrepresentedness. In our case it means that we cannot infer any taste from possible profiles: for example in a movie dataset, if all the possible profiles of a given SHF include science-fiction movies, you can infer that the user enjoys science fiction.
W. l. o. g. , we can choose our order so that i x 0 ∈ P u for all x. Consider now the profiles Q j = ∪ x :B u [x ]=1 {i x j } for j ∈ 1. m b -1. By construction (i) P u Q j for j ≥ 1, (ii) the {Q j } j ∈ 1. m b -1 are pair-wise disjoint, and (iii) they are all indistinguishable from P u once mapped onto their SHF. □ Applying Theorem 2. 5 to the dataset AmazonMovies, using 1024 bit long SHFs, we ensure 167-diversity. It is important to note that SHFs were not designed to provide privacy but faster similarity computation. The above two privacy properties are therefore obtained "for free", as a side effect of our approach. They do not preclude, however, the use of additional strengthening privacy-protection mechanisms-such as the insertion of random noise to the SHF  [2]  to obtain differential privacy guaranties  [20] -albeit typically at the cost of reduced performances.