dataesr/scientific-paragraphs-categorization huggingface dataset:
IC is zero for a homogeneous firing over the M bins; it is equal to log 2 (M) when a single bin contains all the spikes and the animal spends an equal amount of time visiting each bin. To avoid potential bias, we normalized the IC by subtracting from it the mean IC of the 999 surrogate datasets (see above paragraph). Sparsity index. Following standard procedures  [3, 59] , we estimated sparsity by the ratio of L1 norm over L2 norm and defined as sparsity index s ¼ M À ð P M i¼1 l i Þ 2 P M i¼1 l 2 i ! = M À 1 ð Þ where M is the number of spatial bins and λ i the firing rate in bin i as above. The sparsity index s is 0 for a homogeneous firing map and 1 when a single bin contains all the spikes. Directional correlations. For each cell, we correlated the directional firing curves using the uncentered index: r ¼ P i2I l i li ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð P i2I l 2 i Þð P i2I l2 i Þ q λ i being the firing rate in the center, li the firing rate in the return paths, and I the set of directional bins commonly valid for the aggregated time spent in the maze center and its counterpart spent in return paths.
Supporting data See S2 Appendix for a description of the spike and behavior data files hosted on CRCNS (http://dx. doi. org/10. 6080/K0R49NQV) as well as some Matlab scripts necessary to work with them. [60] ). As the reward was never positioned at the end of the entry arm, we considered that learning of the reward position was manifest when the lower confidence bound exceeded 1/4 (black vertical line). C. Examples of performance during two individual sessions (monkey K, top; Monkey S, bottom). Each dot represents a correct (blue) or incorrect (red) response (i. e. , the monkey did or did not reach the rewarded arm) as a function of trial number (x axis) for the different entries (y axis: starting positions). On the top row, animal K started from entry 1 for the first 36 trials and usually performed incorrectly until the 30 th trial, after which the animal performed correctly.
Red indicates significantly positive differences (i. e. , center > periphery), and blue indicates negative differences (center < periphery). Overall, the activity in the center is both more consistent and more direction specific. Inset: Distribution of the correlations between directional tuning in the center and in the periphery. Significantly high (red) and low (blue) correlations are indicated. D-F. Difference in information per spike versus difference in sparsity when comparing cell activity readout in state space to activity readout in position space (D), direction (E), and point of gaze (F). Same graphical conventions as in C. Statistical significance was obtained with permutation tests (surrogate spike datasets). Underlying data can be found at http://dx. doi. org/10. 6080/K0R49NQV.
LABEX-CORTEX of the  University of Lyon  www. labex-cortex. com(grant number  ANR-11-LABEX-OO42 ). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Marie Curie Reintegration Grant  https://erc. europa. eu/(grant number  MIRG-CT-21939 ). Received by  SW. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Centre National de la Recherche Scientifique  www. cnrs. fr(grant number  PEPII ). Received by PB. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Agence Nationale pour la Recherche www. agence-nationale-recherche. fr(grant number  ANR-BLANC-2008-Brain-GPS ). Received by  JRD  and  SW.
Purpose or Objective The aim of the study (partially supported by a research grant from Accuray Inc. entitled "Data collection and analysis of Tomotherapy and CyberKnife breast clinical studies, breast physics studies and prostate study") is to assess the dosimetric benefit of intensity-modulated radiotherapy (IMRT) in postmastectomy patients with implant-based immediate breast reconstruction (IBR), candidates to locoregional radiotherapy with hypofractionation.
Material and Methods Data of the first 121 consecutive post-mastectomy locoregional patients treated with Helical Tomotherapy between May 2012 and May 2015 with a hypofractionated scheme (2. 67Gy/fr, 15 fractions) have been prospectively collected. At the time of surgery, all patients underwent IBR using either temporary tissue expander or permanent prosthesis. The impact of immediate breast reconstruction on the planning was analyzed. Treatment plans were scored in terms of coverage of the PTVs (chest wall and supraclavicular region) and sparing of organs at risk (heart, lungs and contralateral breast). The coverage of chest wall and supraclavicular region was evaluated according to the amount of volume receiving the 90% of the prescribed dose (V90%) while the sparing of each OAR was evaluated according to the number of satisfied constrains (Tab.
A number of searches at the LHC looking for low mass (2m µ -62 GeV) bosons in √ s = 8 TeV data have recently been published. We summarise the most pertinent ones, and look at how their limits affect a variety of supersymmetric and non-supersymmetric models which can give rise to such light bosons: the 2HDM (Types I and II), the NMSSM, and the nMSSM.
From the theoretical side, we will concentrate on the most popular BSM Higgs scenarios in which such a light object is realised, which is typically pseudoscalar in nature. From the experimental side, we will adopt published data obtained by the end of Run 1 of the LHC from either ATLAS and CMS, covering several signatures of such a pair of pseudoscalar Higgs states, including decays into pairs of muons, taus, and bottom quarks. It is natural to organise the discussion of the possible BSM scenarios behind such a decay phenomenology around the divide of BSMs with and without Supersymmetry (SUSY). In fact, among the possible BSM theories, SUSY remains one of the favourite ones. However, while its minimal realisation, the Minimal Supersymmetric Standard Model (MSSM), has been under close experimental scrutiny lately, through direct searches for both its sparticle and Higgs states, much less effort has gone into testing non-minimal SUSY scenarios.
18 [48] , and SuperIso-v3. 3 to check constraints from B physics. NMSSMTools includes both Higgs exclusion and signal strength constraints from experimental results, based on Lilith  [49]  database version 15. 09. Flavour constraints have also been implemented in NMSSMTools  [50] , and points are checked against these constraints. In order to use HiggsSignals with the output from NMSSMTools, we add a DMASS block to the SLHA file to represent theoretical uncertainties on the h 125 mass. This is set to 2 GeV for both h 1 and h 2. Additionally, HiggsSignals was modified to ensure that either h 1 or h 2 was correctly assigned to h 125 by increasing assignmentrange_massobs to 2. 0 in usefulbits_HS. f90.
Acknowledgements RCA and SM are supported in part through the  NExT Institute. The use of the CIS computer cluster at  NCBJ  is gratefully acknowledged. SM acknowledges partial support from the  STFC  Consolidated Grant  ST/J000396/1. The use of the DICE computer cluster at  UoB  is also gratefully acknowledged. Thanks to  Tim Stefaniak  for instructions on how to modify HiggsSignals. Thanks to  Ulrich Ellwanger  for discussions on Fig. 7.
Here we propose a method, implemented in a software called MaxTiC (Maximum Time Consistency), which takes as input a species tree and a series of (possibly inconsistent) time constraints between its internal nodes, weighted by confidence scores. MaxTiC outputs a ranked species tree compatible with a subset of constraints with maximum cumulated confidence score. We extensively tested the method on simulated datasets, under a wide range of conditions that we compare to measures on biological datasets. In most conditions the obtained ranked tree is very close to the real one, confirming the potential of dating the history of life with transfers by maximizing time consistency. MaxTiC is freely available, distributed along with a documentation and several examples: https://github. com/ssolo/ALE/tree/master/maxtic.
(2012) , which consists in finding the ranked tree that maximizes the likelihood according to a model of gene tree-species tree reconciliation taking lateral gene transfers into account. Due to the size of the space of dated trees and the time complexity of the gene tree-species tree reconciliation, this method is computationally demanding and hardly scales up to large datasets. This calls for methodological improvements. A fast alternative is to detect transfers on an unranked species tree, and combine all transfers to output a ranking. Several programs are available to detect transfers using phylogenetic incongruence between species trees and gene trees without the need of a ranked species tree  (Bansal et al. , 2012; Stolzer et al. , 2012; Szöllősi et al. , 2015; Badescu et al. , 2016; Jacox et al.
The constraints may be weighted with a confidence score, as it is the case in the output of several programs which sample in the space of solutions  (Szöllősi et al. , 2013a; Szöllősi et al. , 2015; Jacox et al. , 2016). The output ranked tree aims at maximizing the sum of the confidence scores of the compatible transfers, or equivalently minimizing the incompatible ones. We show that our problem is equivalent to a well-known difficult problem in computer science, the Feedback Arc Set, and we propose a method combining three heuristics inspired from the computer science literature on this problem. These are implemented in the MaxTiC tool. We provide a proof of principle that this method is able to efficiently date phylogenetic trees and test the limits of our method by generating a number of simulated datasets with SimPhy  (Mallo et al.
, 2016)  and detecting transfers with ALE_undated  (Szöllősi et al. , 2015). We use a wide range of transfer rates, population sizes (which affects the amount of gene tree species tree incongruence due to incomplete lineage sorting, which is not modeled by ALE_undated), and variations in the species tree. We show that under most conditions tested in our simulations, including some settings with features comparable to the ones observed in published fungi and cyanobacteria datasets, the ranked tree recovered by the method is very close to the true one, but is never exactly the same. Still, this is not due to the heuristic optimization but rather to false transfers inferred by ALE_undated. Indeed, inferred solutions are slightly better, according to the cost function, than the true ranking. The organization of the paper is the following: We first describe the protocol, including simulations, transfer detection and the conversion of each transfer into a time constraint.
II. Construction of the simulated datasets Simulation by SimPhy. We generated simulated datasets with SimPhy  (Mallo et al. , 2016) , a tool that was developed by an independent team with other purposes than to test our method. This has in particular the consequence of simulating processes that are not considered by our inference method, such as transfers with replacements or incomplete lineage sorting. However, SimPhy has been developed to validate evolutionary inference methods in general, it uses birth-death processes just as our inference method, and it assumes that there is no uncertainty in gene family clustering, which is a limit to independence  (Biller et al. , 2016). For all sets of parameters, we used SimPhy to generate a ranked species tree with 500 leaves. Along this species tree, we generated 100 to 5000 gene trees with a population size between 2 and 10 6 individuals per species, null rates of duplications and losses, and a rate of transfers from 10 -9 to 10 -5.
For example, sensitivity to errors in the species tree was only tested with one transfer rate-we chose the one giving a number of transfers close to what we measured on biological datasets. Duplications and losses rates are null, which simulates a filter of the families with universal single copy genes. Species extinction. We pruned each leaf of the species tree with a probability 0. 8, so that the final species tree has approximately 100 leaves. Gene trees were pruned accordingly by discarding leaves belonging to the removed species. This simulates a sampling of sequenced species, accounting for species extinction or species absence in the study  (Szöllősi et al. , 2013b).
Detection of transfers. Transfers were detected by ALEml_undated, a program from the ALE suite  (Szöllősi et al. , 2013a; Szöllősi et al. , 2015). ALEml_undated takes as input an unranked rooted species tree and an unrooted gene tree, and produces a sample of 100 reconciled gene trees (for each of the simulated gene families), sampled according to their likelihood under a model of duplication, loss, and transfers. Duplication, transfer and loss rates are estimated for each gene family independently with a maximum likelihood algorithm, and the 100 reconciled gene trees are sampled according to these ML rates. We ran ALEml_undated with the (undated) species tree and the gene trees (considered as unrooted) generated by SimPhy. From transfers to constraints. In order to reduce the noise from transfers found with a low probability, only transfers found in at least 5% of the reconciliations for one gene family were considered.
Availability. The implementation is available at https://github. com/ssolo/ALE/tree/master/ maxtic, along with a documentation, and example datasets. III. Finding a maximum consistent set of constraints with MaxTiC. Definition. We suppose we have as input a rooted, unranked, species tree S and a set of weighted constraints C, which are directed pairs X → Y of internal nodes of S. We call a constraint informative if its two nodes are not related by an ancestor/descendant relationship, and we suppose without loss of generality that C contains only informative constraints. Some constraints might be conflicting, for example like in Figure  2 : Y is found to be older than X, and Z is found to be older than T, but T is an ancestor of Y and X is an ancestor of Z. The two constraints Y → X and Z → T cannot be true at the same time in the context of the drawn species tree.
So the running time is bounded by O(n 2 m). Applying the mixing algorithm as a conquer step yields a recursive heuristic for the general problem, which consists in applying Algorithm 1 with parameters r, S, C, where r is the root of the species tree S and C is the set of time constraints. Implementation. In our software MaxTiC, we implemented in Python the heuristic recursive principle described above. In practice the running time is almost instantaneous for all the simulated datasets we tested. We also implemented two other heuristics: a greedy heuristic and a local search approach. The greedy heuristic consists in sorting the constraints in decreasing order of their weight, and examine them one by one in that order. Each constraint is kept in a consistent set if it is compatible with the partial order given by the species tree and not conflicting with other constraints already marked as kept, and discarded otherwise.
IV. Results Transfer rate and number of inferred transfers. We first tested the ability of the ALE method to infer a likely number of transfers, as well as the effect of inferring transfers in a phylogeny which is a small subtree of the one on which transfers have been simulated. On Figure  5 , we can see that up to a very high transfer rate, the number of inferred transfers follows a regular function of the transfer rate. Measures of transfer numbers on biological datasets were done for comparison purposes from the cyanobacteria and fungi dataset from  Szöllősi et al. (2015). They show that the range of the simulation parameters contains numbers of transfers per family comparable to published biological datasets. Figure  5 : Mean number of inferred transfers (number of transfers per family and per branch of the species tree), as a function of the transfer rate in the simulation (log 10 scale).
Each point is one simulation of a species tree and 1000 gene trees with its own transfer rate. The right boxplots show the distribution of the number of inferred transfers on gene families from two published biological datasets: 28 Fungi (red) and 40 cyanobacteria (green)  (Szöllősi et al. , 2015). For each gene family the number of inferred transfers per branch is computed. It shows that comparable numbers are found in simulated and biological datasets. Henceforth, we use the mean number of inferred transfers as the reference instead of the transfer rate, to relate our measures to numbers comparable with what is found in biological datasets, for which we do not know the transfer rate on the complete phylogeny containing extinct and unsampled species. Number of conflicting constraints. We then measured the fraction of constraints that has to be discarded to get a consistent set of constraints (Figure  6 ).
It has already been observed that current transfer detection methods usually infer an accurate number of transfers but they are less precise for the identification of donors and recipient  (Abby et al. , 2010). Figure  6 : Fraction of constraints that have to be removed in order to get a consistent set, as a function of the mean number of inferred transfers (log 10 scale). Red dots represent the fraction of constraints in conflict with the true (simulated) tree, while black dots represent the fraction of constraints in conflict with the reconstructed tree, minimizing the conflicts. Horizontal boxplots show the number of inferred transfers from two biological datasets: 28 Fungi (red) and 40 cyanobacteria (green)  (Szöllősi et al. , 2015).
The boxplots show the distribution of the number of inferred transfers on gene families from two published biological datasets: 28 Fungi (red) and 40 cyanobacteria (green)  (Szöllősi et al. , 2015). For each gene family the number of inferred transfers per branch is computed. not time compatible as the frequency of conflicting transfers increases also with population size. However on the bottom panel, we can see that nonetheless, and despite a decrease in the Kendall similarity with the true ranked tree, it is still possible to obtain a reasonable ranking of the species tree even in the presence of a high rate of false transfers that results from ILS or phylogenetic error. Sensitivity to errors in the species tree. Finally, the topology of the species tree is in general not known with high precision, so we tested the robustness of the method to errors in the species tree.
We chose a simulated dataset with a transfer rate of 10 -6 , because this is one of the rates that lead to a number of transfers per family close to what we measured in the two biological datasets. Then we compared the normalized Kendall similarity of 5 simulations with the true species tree (blue dots in Figure  10 ), with 5 simulations for 5 different conditions: re-rooting the species tree at a grand-child of the root (red dots), and respectively applying 5, 10, 15 and 20 random "nearest neighbor interchanges" (NNIs) to the species tree (green dots). We plot in Figure  10  the normalized Kendall similarity in function of the obtained Robinson-Foulds distance to the true tree (A certain number of random NNIs leads to a Robinson-Foulds distance of at most this number). The tendency of Figure  10  shows a good robustness to errors in the species tree, showing that even with quite distant species trees, the rank of true clades is well preserved.
Application to a small Cyanobacteria dataset. In order to test the ability of the program to find good solutions in biological conditions, and to test the dating method on a tree coming from biological data, we examined the Cyanobacteria transfers presented in  Szöllősi et al. (2015)  on a subtree restricted to 14 leaves out of the 40 in the original study. This restriction has the purpose to be able to enumerate all possible rankings (there are 69300 of them on this tree) and to test our heuristic against an exhaustive solution search. This Figure  9 : Minimum fraction of conflicting constraints, mean number of inferred transfers per family and normalized Kendall similarity of the true ranked tree and the obtained ranked tree, as functions of population size (log 10 scale), for a fixed transfer rate (10 -6 in the simulation).
Population size favors incomplete lineage sorting in SimPhy; as such, it is used here to measure the effect of non modeled processes and as a proxy for errors in phylogenetic reconstruction. Figure  10 : Normalized Kendall similarity of the true ranked tree and the obtained ranked tree, for different species trees in which errors have been introduced. The Kendall similarity is computed on the common clades, that is, on the fraction of true clades in the modified species tree. The three horizontal lines show three quantiles of the distribution of normalized Kendall similarity of randomly generated ranked trees. This shows that transfers give a robust dating information even in the presence of a highly erroneous species tree. example is described into details, with all associated data, in the MaxTiC repository https: //github.
com/ssolo/ALE/tree/master/maxtic. We found that 35% of the total constraint weight has to be discarded to get a consistent set of time constraints. This is similar to the worst conditions in our simulations, with the largest contribution of non-modeled processes in the gene trees. This is not surprising since it is known that with real sequences phylogenetic errors are not rare. This number corresponds to both the optimal value that was found using the exhaustive search, and the value given by the Maximum Consistency Heuristic, so it is a good sign that we can find decent solutions in instances coming from a biological dataset. A study of the full 40 leaf tree, along with comparisons with other domains of life, tests of the transfer dating methods with molecular clocks, can be found in a companion paper  (Davin et al.
V. Conclusion In this paper, we give a proof of principle of a method to get a ranked species tree with the information provided by gene transfers. We present a method and a software, called MaxTiC for Maximum Time Consistency, taking an unranked species tree as input, together with a set of possibly conflicting weighted time constraints, and outputting a ranked tree maximizing the total weight of a compatible subset of constraints. We validate our methodology on simulations produced by an independent software SimPhy, with characteristics that we compare to published biological datasets. The results confirm that we can date with transfers under a wide range of conditions including errors in gene trees and species trees. This additional source of information for dating can be a good alternative to fossils and the (relaxed) molecular clock since the fossil record is poor or difficult to interpret precisely in clades where transfers are abundant.
Acknowledgements The authors dedicate this paper to  Prof. Francis Leboeuf , the supervisor of  Feng Gao ,  Wei Ma  and  Xavier Ottavy , an efficient and energetic colleague, a credible friend. He devoted all his energies and enthusiasm to developing the relationship in scientific research between the laboratories of the five E ´coles Centrale in France and  Beihang University in China. Without his contributions, this work would not have been possible, and the authors would not have the opportunity to name together on this paper. His elegance, his kindness, his enthusiasm, his contagious optimism. and of course his brown Fedora hat will be remembered forever!This work was funded by the  Sino-French project   AXIOOM  (funding:  NSFC and ANR). The authors also wish to express their sincere gratitude to the supports from  NSFC  (Nos.