dataesr/scientific-paragraphs-categorization huggingface dataset:
5 and 4. 2 min, which biological activities on breast cancer cells were not reported. It is important to note here that these corresponded to signals that were not detected in the crude extract. It could be due to a concentration phenomenon inherent to the fractionation step. For other features, due to the difficulty to predict the molecular formula or a number of matches in databases too important, a good annotation could not be obtained. Further purification work is then currently in progress towards these other features that were not identified. In fact, even if we cannot ascertain that all the features highlighted by the script have an activity on MCF-7 cells, these first results leading to the annotation of two other ergosterol derivatives, with one of them presenting proliferation inhibition properties, together with chaetoglobosin derivatives already reported to inhibit breast cancer cells proliferation, tends to prove our method is quite powerful.
Virtual trainning While the application of the workflow to a real case proved to be successful in the detection of the main active compound, further evaluation of the FiBiCo script was performed with virtual modifications of the data matrix to verify its ability to detect interesting features and investigate its limits. A virtual feature was then added on the initial MMS5 P. chrysogenum data matrix with different peak area values according to proliferation inhibition for each sample. A total of 16 matrices were constructed with one additional feature, which peak area values in the 19 samples, were attributed according to linear, exponential, logarithmic, monotonic, non-monotonic or random relationships to biological activity. To avoid perfect fit and be closer to real conditions, random variations of 30 and 50% were applied on each peak area value defined for this additional feature in the different matrices.
After uploading the matrices on the R FiBiCo script, the results obtained were analysed and reported on a graphical representation (Figure  7  and Supplementary information S2. 9). For each assay, the position of the additional virtual feature in the "Super list" was picked together with the information on which of the biochemometrics models allowed the selection of this feature. Data results obtained with a random variation of 30% performed in triplicate for all relationships (Fig. 7 ) revealed that R FiBiCo script was able to highlight the additional feature for all meaningful relationships except for the random model, for which no hit was expected. The selection of the additional feature was very good when the relationship was linear or logarithmic, i. e. detected by three or four statistical models (Spearman, F-PCA, PLS and PLS-DA) and with the best global score in the super list (ranking 1 st ).
Conclusion Currently, the central challenge in NP research is the discovery of new bioactive compounds. With the recrudescence of "omics" studies in this domain, several strategies based on NMR and MS analyses, are currently emerging to highlight and identify bioactive compound from natural complex mixtures at a very early stage. In this study, a complete workflow is proposed, which combines explorative SPE and chemometrics tools using LC-MS data with the integration of four statistical models (Spearman, F-PCA, PLS and PLS-DA) to highlight potential bioactive compounds. Additionally, a script encoded with R software (named FiBiCo) was here developed to automatically highlight the most interesting features by including the complementary results of all statistical analyses, and made available for the scientific community (Supplementary information S1).
This complete workflow applied to a bioactive P. chrysogenum MMS5 crude extract, where ergosterol was the main active component, successfully highlighted this compound as responsible for the proliferation inhibition of MCF-7 breast cancer cells. This allowed to prove the efficiency of the developed R FiBiCo script. This is even more interesting as ergosterol was very poorly ionised with electrospray ionization showing that minor compounds can be highlighted, which is essential in bioactive natural product research. The performance of the FiBiCo script was evaluated with virtual modifications of the data matrix, proving its ability to detect different types of relationships between activities and chemical compounds amounts. The complementarity of the statistical models proved to be very important, especially if a low false positive rate is preferred.
Moreover, the performance of the script in picking only one feature (among 796), with low values in a small range and with a high variation (30-50%), proved its efficiency to "find the needle in the haystack". The workflow was also evaluated by comparing the results obtained to those given by another recently described automatic deconvolution strategy using NMR data. The pharmocophoric fingerprint revealed also ergosterol as the bioactive compound, strengthening the validity of the workflow described and developed in the present study. Perspective of this work will be to apply the methodology to other bioactive extracts, in the context of drug discovery screening programs. It effectively responds to one of the many issues encountered in this field, by rationalizing subsequent purification work to directly focus on compounds responsible for the activity.
The task-based approach has emerged as a viable way to effectively use modern heterogeneous computing nodes. It allows the development of parallel applications with an abstraction of the hardware by delegating task distribution and load balancing to a dynamic scheduler. In this organization, the scheduler is the most critical component that solves the DAG scheduling problem in order to select the right processing unit for the computation of each task. In this work, we extend our Heteroprio scheduler that was originally created to execute the fast multipole method on multi-GPUs nodes. We improve Heteroprio by taking into account data locality during task distribution. The main principle is to use different task-lists for the different memory nodes and to investigate how locality affinity between the tasks and the different memory nodes can be evaluated without looking at the tasks' dependencies.
, 2016; Beaumont, Eyraud-Dubois & Kumar, 2017 , 2018; Agullo et al. , 2016a) , which revealed its advantages and gave a positive theoretical insight on the performance. However, the original Heteroprio scheduler does not take into account data locality. The distribution of the tasks-the choice of the processing unit that will compute a given task-is done without considering the distribution of the data. Therefore, depending on the applications and the test cases, Heteroprio can not only lead to huge data movement between CPUs and GPUs but also between GPUs, which dramatically penalizes the execution. The current work proposed different mechanisms to consider data locality in order to reduce the data transfers and the makespan. The contributions of this paper are as follows: We summarize the main ideas of the Heteroprio scheduler and explain how it can be implemented in a simple and efficient manner; We propose new mechanisms to include data locality in the Heteroprio scheduler's decision model; We define different formulas to express the locality affinity for a given task relative to the different memory nodes.
priority]. push_back(t). Meanwhile, the pop method can iterate over the lists and when it finds one non-empty list, it pops a task from it. Furthermore, in the case of heterogeneous computing, a pop must return a task compatible with the worker that performs the request. Managing data locality was already a challenge before the use of heterogeneous computing because of NUMA hardware and a simple scheduling strategy has been proposed to improve data locality on the NUMA nodes  (Al-Omairy et al. , 2015). Past work has introduced distance-aware work-stealing scheduling heuristics within the OmpSs runtime, targeting dense linear algebra applications on homogeneous x86 hardware. While the method provides a significant speedup, it does not take into account the different data accesses (read or write) or look at the cache levels to find data replication.
Last recently used In this strategy, we consider that the memory node related to the worker that pushes the task has the best locality; a newly ready task t released by worker w is pushed into G(t. bucket_id, w. memory_node). Indeed, t and the last task executed by w have at least one data dependency in common, and this data is already on the memory node if it has not been evicted. The main advantage of this technique is its simplicity and low overhead. However, it is obviously far from accurate. For example, it does not evaluate the amount of data that is already available on the memory node compared to the total amount of data that t will use. Moving cost estimation seems natural to consider that the best memory node is the one that will allow moving the data in the shortest time. StarPU provides the function starpu_task_expected _data_transfer_time_for that predicts this transfer duration by looking where the pieces of data are and the possible transfer paths between the memory nodes.
Data locality affinity formulas StarPU's prediction has two potential drawbacks: The first is that it treats all data dependencies similarly without making a distinction if the dependencies are read or write, and the second is that the memory transfer predictions are difficult to achieve since they are based on models that can be inaccurate and influenced by the on-going execution. Therefore, we propose different formulas to estimate the locality of a task and we obtain either a locality score for each memory node (the higher the better), or a moving cost (the lower the better). This information is used to decide where to put the newly ready tasks in the grid. In our next formulas, we use the following notations D t;m ¼ t:data \ m:data; (1) D t;:m ¼ t:data \ :m:data; (2) D READ t;m ¼ t:data \ m:data \ READ; (3) D WRITE t;m ¼ t:data \ m:data \ WRITE; (4) READ \ WRITE ¼ [: (5) Here, D t,m is the set of data used by task t and that exist on memory node m, whereas D t,¬m represents the set of data used by t that is not on m.
D READ t,m and D WRITE t,m are the sets of data used by t that exist on m and that are accessed in read mode and write mode, respectively. We define the sum of all the pieces of data hosted (LS_SDH) with the score given by LS SDHðm; tÞ ¼ X d2D t;m d:size: (6) The core idea of LS_SDH is to consider that the memory node that already hosts the largest amount of data (in volume) needed by t is the one where t has to be executed. If all the tasks use different/independent pieces of data and each of them is used once, then we except that both MC_StarPU and LS_SDH(m,t) return meaningful scores. However, there are other aspects to consider. For example, if there is a piece of data duplicated on every node it should be ignored. Moreover, we can also consider that a piece of data used in read is less critical than the ones used in write for multiple reasons.
A piece of data used in read might be used by several tasks (in read) at the same time, and thus the transfer cost only impacts the first task to be executed on the memory node. In addition, a piece of data in write is expected to be used in read later on, which means that moving a piece of data that will be accessed in write on a memory node, partially guarantees that this data will be re-used soon. Finally, writing on a set of data invalidates all copies on other memory nodes. Thus, we define three different formulas based on these principles, where we attribute more weight to the write accesses to reduce the importance of the read accesses. The LS_SDH 2 is the score given by summing the amount of data already on a node, but the difference with LS_SDH is that each data in write is counted in a quadratic manner LS SDH 2 ðm; tÞ ¼ X d2D READ t;m d:size 0 @ 1 A þ X d2D WRITE t;m d:size 2 0 @ 1 A : (7) Alternatively, we propose the LS_SDHB score where we sum the amount of data on a node but we balance the data in write with a coefficient h.
PERFORMANCE STUDY Configuration The following software configuration was used: GNU compiler 6. 2, CUDA Tookit 9. 0, Intel MKL 2019 and StarPU 1. We set the environment variables STARPU_CUDA_PIPELINE=4, STARPU_PREFETCH=1 and STARPU_DISABLE_PINNING=0. From Eq. (  12 ), we defined a = 0. 5, and as a result the closest memory node to any GPU was always the CPU. StarPU supports multi-streaming capability of modern GPUs by running multiple CPU threads to compute on the same GPU. This is controlled by STARPU_NWORKER_PER_CUDA Table  2  Priority distance examples between buckets/priorities indexes of i and j.
Speedup We provide the speedup obtained with our method against Heteroprio in Figs. 6A and 6B for QrMumps, Figs. 7A and 7B for SpLDLT,  For all configurations, the LaRU and MC_StarPU formulas did not significantly improve the execution, furthermore, they were slower than Heteroprio in some cases. For LaRU, this means that having one piece of data already on the memory node and neglecting the others is not efficient. Meanwhile, for MC_StarPU, it means that putting a task on the memory node for which it is the cheapest in terms of data transfer is not the best choice. This is not surprising, since this kind of decision would make sense if we have only task to compute. However, we clearly see that in the present study, when we had to deal with a graph of tasks, where the data were used concurrently and could be re-used by other tasks, this was not accurate.
Nevertheless, this result could also have been affected from inaccurate predictions made by StarPU. Comparing the different DLAF, it can be seen that both LS_SDH 2 and LS_SDHB significantly improved the three applications. LC_SMWB was competitive for QrMumps and StarPU-Stencil but not for SpLDLT, and LS_SDH was competitive for StarPU-Stencil but not for QrMumps and it had poor performance for SpLDLT. The main difference between LS_SDH 2 /LS_SDHB and LC_SMWB/LS_SDH is that the second ones are not giving an important load to the pieces of data used in write, and LS_SDH does not even make a distinction between read and write. It seems that taking into account write is important for QrMumps and SpLDLT but not for StarPU-stencil. On the two linear algebra applications, the tasks transform the blocks of the matrix, and many of the blocks are written several times before being read multiple times.
Transfer The total amount of memory transfer obtained with our method and Heteroprio are provided in Figs. 6C and 6D for QrMumps, Figs. 7C and 7D for SpLDLT,  For QrMumps, all approaches used in this study reduced the total memory transfer. However, a decrease of the memory transfer does not necessary mean having better performance. For example, for the K40 configuration, and with either one or two GPUs, MC_StarPU drastically reduced the amount of data transfer compared to Heteroprio, see Fig. 6C , but it had a negative speedup, see Fig. 6A. It means that, even if in all LAHeteroprio-based executions the workers iterated similarly on G, the placement of the tasks on the grid can be quite efficient in terms of transfer, but it penalized the whole execution. In the case of SpLDLT, the memory transfer did not decrease compared to Heteroprio when MC_StarPU, LaRU, or LS_SDH were used.
This further supports our idea that the data in write should count more than the data in read. Moreover, LC_SMWB balances the data in write but only with a factor 2 at most; even if it reduced the memory transfer compared to Heteroprio, the reduction was not as large compared with LS_SDH 2 /LS_SDHB. Finally, when we used SpLDLT the amount of memory transfer and the execution time were reduced. Looking at the results of StarPU-Stencil, the memory transfer reduction was not as strong as for QrMumps. In addition, there is a correlation between the transfer reduction and the resulting speedup, such that the lowest amount of transfer were obtained with LS_SDH, LS_SMWB and LS_SDHB for most of the configurations. Again, the automatic mode is efficient and even when one of the DLAF is not competitive, for instance LC_SMWB in the case of QrMumps/SpLDLT or LC_SDH 2 for StarPU-Stencil, the automatic system is robust enough to make correct decisions and remains competitive.
BMD We provide the BMD values for the different DLAF in Figs. 6E and 6F for QrMumps, Figs. 7E and 7F for SpLDLT,  For QrMumps, the BMD values were low for all formulas except LS_SDH and LaRU. These measures proof that LS_SDH is sensitive to the data changes that happen in the time that takes a pushed task to be popped. Furthermore, this is due to its formula as it considers the data in read or write to be the same. On the other hand, MC_StarPU was stable with a small BMD value. However, this is surprising, because the high value for LS_SDH illustrates the volatility of the data, and thus MC_StarPU should also be sensitive to the changes that happened between push/pop. For SpLDLT and StarPU-Stencil, we observed a clear relation between the BMD values and the speedup. The formulas that did not provide a speedup are the ones with the highest BMD values.
CONCLUSION We have improved our Heteroprio scheduler with a new mechanism that considers data locality. The new system divides the task buckets into as many lists as there are memory nodes. We have created different formulas to evaluate the locality of a task regarding a memory node, and we found that formulas that omit many parameters (as the use of the StarPU prediction functions) provide a low performance; this is probably due to the neglect of the type of accesses of the tasks on the data. Nevertheless, we have shown that locality evaluation is more sensitive to write accesses and this has been validated with the results of the BMD metric. Concerning the pop strategy, it is necessary to set the locality coefficient to the largest value for the GPUs, to ensure that workers focus on locality before priorities.
It is possible to use our new scheduler, without introducing additional information or modification, using our automatic DLAF selection system, which is close to the best executions in most cases. Finally, our new scheduler improves the performance of QrMumps, SpLDLT and StarPU-Stencil by 30%, 80% and 30%, respectively. It also reduces the data transfer more than 50%. In terms of perspective, the scheduler could still be improved on different aspects. It could be beneficial to change the distance between the memory nodes at runtime, which means changing the victims of the work stealing and even having workers of the same memory node that steal the tasks on other memory nodes. In addition, the original priorities of the scheduler are set per architecture, and the new locality heuristic is set per memory node, but a finer approach could be interesting even if it has a challenging tuning and setup.
ACKNOWLEDGEMENTS The experiments presented in this paper were carried out using the PlaFRIM experimental testbed, supported by  Inria ,  CNRS  (LABRI and IMB),  Université de Bordeaux, Bordeaux INP  and  Conseil Régional d'Aquitaine  (see https://www. plafrim. fr/). We would like to thank  Alfredo Buttari  for his support on QrMumps, and  Florent Lopez  for his support on SpLDLT.
Firstly, at the time of the USO proposal in 2015, nearly half of rural premises enjoyed broadband access speeds of less than 10 Mbps  (Ofcom, 2015, p 18 ). An infrastructure digital divide between urban and rural areas could and still can be observed. Secondly, users with a broadband connection below 10 Mbps are associated with lower rates of data consumption  (Ofcom, 2015, p 23). This would appear to suggest that they are not participating in the online digital economy as extensively as they could be. Thirdly, Ofcom (2015)  argues that faster speeds are associated with better consumer experience. With a 10 Mbps connection, a family of four could simultaneously stream a high definition video (6 Mbps), watch catch-up TV (2 Mbps), undertake a video call and browse the Internet (1. 5 Mbps) and engage in basic web browsing (0.
Methodology This paper draws on data from the replies made to the broadband universal service obligation consultation. This consultation was announced by Ofcom in April 2016, and closed in mid-June 2016  (Ofcom, 2016a). While a campaign was run by a lobbying organisation to encourage contributions, resulting in more than 90,000 email replies (Ofcom, 2016b), 110 substantive replies were submitted. These vary in length, from just a few words to 25 pages. In total, the submissions ran to more than 450 pages. NVIVO 11, a qualitative software programme, was used to facilitate data analysis across the sample. This software has been used in a wide variety of contexts, 8 and can include documents as well as videos and audio. Echoing  Venkatraman (2013)  and  Welsh (2002)  among others, NVIVO was used to ensure analytical consistency and provide an audit of how the analysis was undertaken.
Other replies more generally emphasize the growing importance of upload speeds for applications like video conferencing via Skype (cf. Name withheld 3, 2016, p 1), file sharing (cf. Rural Services Network, 2016, p 1) or cloud services (cf. New Economy Greater Manchester, 2016, p 1; Reed, 2016, p 1). In contrast, UK Broadband (2016) notes that they: "do not think that any minimum specifications in terms of upload, latency, jitter or contention are necessary or appropriate, as these are not currently required under broadband advertising rules"  (UK Broadband, 2016, p 2). This last reply raises further issues with respect to the quality of data transmission and additional metrics like latency (that is, the round-trip delay) or jitter. Several replies emphasize the relevance of additional metrics beyond speed and recognize their (potential) impact on broadband consumer experience.
Inclusion Criteria Search terms were directly taken from Cortese et al. (1)  with the exception of the need for a control arm, which is detailed in Supplementary Material  (65). The requirements included: • studies have to assess NFB efficacy; • subjects must have received a diagnosis of ADHD based on DSM-IV (66), DSM-5 (3), ICD-10 (67) criteria, or by a qualified psychiatrist; • studies have to be written in English, German, Spanish, or French; • studies have to include at least eight subjects in each group; • patients must be younger than 25 years old; • the publication has to disclose sufficient details about the data to compute required metrics for the ensuing analysis. The studies satisfying all these criteria were included in the SAOB. In order to replicate and update Cortese et al. 's (1) metaanalysis, we applied the original inclusion criteria of their metaanalysis to our search (the main difference being the presence of a control group).
Meta-Analysis The goal of a meta-analysis is to aggregate results from different clinical investigations and offer a consolidated body of evidence. To achieve this, it is necessary to assume some level of homogeneity in the design of the studies: inclusion criteria, and the presence and type of control (active, semi-active, or nonactive). Because studies occasionally use slight variations of a clinical scale and because of the clinical heterogeneity of patients and control, the scores are standardized before being pooled into a Summary Effect (SE). The between-Effect Size (ES) is one such standardized metric, which we have implemented in this paper [see Supplementary Material  (65) ]. The meta-analysis was performed with a Python package developed for this work. The package offers a transparent approach for the choice of parameters in an effort to ease replicability.