dataesr/scientific-paragraphs-categorization huggingface dataset:
Xenacoelomorpha Homeobox Gene Content from an Evolutionary Perspective One interesting observation is that more homeobox genes were identified in N. vectensis, now 134 including the putative ONECUT and CERS homeodomain proteins (although not from all the classes) than we found in Xenacoelomorpha. For example, more than twice as many genes were found in the NKL subclass in N. vectensis than in the acoel species, which is also many more in comparison to D. melanogaster and C. elegans. This may be due to a rapid expansion of some homeobox gene families in cnidarians similar to the expansion of other important gene families such as wnt  (Kusserow et al. 2005). We also observe small expansions in our data set, for example in the Lmx or Cers gene families of N. westbladi (supplementary table S1, Supplementary Material online).
The evolutionary reasons for expansions or losses of homeobox genes in specific animal groups are currently not well understood. Some homeodomain proteins are lacking from the currently analyzed Xenacoelomorpha. It is tempting to speculate whether these genes were never present, have been lost or are simply not detected. For example, among the ANTP HOXL families we currently find only a single antHox member in Xenacoelomorpha that is most similar to Hox1. This is in contrast to the situation in N. vectensis where several additional antHox homologs exist  (Chourrout et al. 2006; Ryan et al. 2006). The family relationship of cnidarian antHox genes is debated but their existence is used to imply a ProtoHox cluster with more than one anterior Hox gene  (Chourrout et al. 2006). Our data would then support a scenario where the Hox3 homolog was lost in Xenacoelomorpha  (Moreno and Martinez 2010).
Acknowledgments We would like to thank members of the  Sprecher and Bruggmann laboratories , colleagues at the marine biology station in Roscoff and the  Sven Lov en Center in Kristineberg  for constructive and fruitful discussions. Moreover,  J. Cannon  and  B. Vellutini  shared previous data sets with us. A. B. would like to thank  Walid Gharib  and  Romain Studer  for fruitful discussions regarding phylogenetic analyses. This work was supported by the  Swiss National Science Foundation  ( 51RT-0_145733 ) and the  University of Fribourg  to S. G. S.
This paper shows how leading Internet enterprises manage problem-solving processes occurring on their interfaces through the use of empathy. The data of the developer supports forums of Facebook and Google Maps reveal a particularly low problem solving rate (less than 15% of problems solves over a period of six months). To explain this phenomenon a generic construct for business models is proposed on the basis of empirical examination of the problem-solving process followed in those forums, rendering compatible the notion of empathy with well-known value adding activities.
, 2011; Simon, 1989, and others)  taking place within business ecosystems  (Adner and Kapoor, 2010; Van De Vrande et al. , 2010; Tee and Gawer, 2009, and others) , one would expect a high performance on problem-solving in the developer support forums of these leaders (i. e. that many problems reported in these forums would be solved). Yet, the data of the developer support forums of Facebook and of Google Maps examined in this paper show a particularly low problem-solving rate.
For instance, think about the Google Car: almost everyone has heard about it, but no one can actually buy it. In such a case, we talk about a value proposition from a research project, a prototype or a concept car which actually captures a value, while the realization remains potential insofar it hasn't been turned into a product or a service. • Value Creation refers to the actual creation of the technologies the enterprise proposes to a market. Still, not all the technologies are sold. For instance, think about the enterprises manufacturing terminals for the Minitel, the French data communication network preceding the Web, when the Internet started spreading in France during the late 90's.
Research Methodology The following paragraph outlines the setting of the developer support forums of Google Maps and Facebook, which have identical procedures. As pointed out by von  Krogh et al. (2012) , the first step of phenomenological research is the distinction of the phenomenon from against existing practices. To this end, we use evidence provided by the data of the two forums. The analysis of data coming from online settings has become a common practice, especially in field of software development, where the traces of online collaboration are available to harness  (Chrysos, 2016; Conaldi and Lomi, 2013; Nan and Kumar, 2013). Then, we proceed to an empirical exploration to "detail the flow of value adding activities"  (Ritter and Lettl, 2017)  in the case of Google Maps forum. This exploration will suggest a construct  (Rietveld and Eggers, 2018; Zott et al.
e. those appearing twice or more in the forum and thus have been merged. It is a common practice to use the rate of problem resolution within a specific period of time, to qualify the performance of a problem-solving activity  (Thomke et al. , 1998; Öllinger et al. , 2015). Thus, the performance of problem-solving is eventually measured in terms of solutions provided. Accepting for the moment that the problem-solving framework is sufficient to analyze our data, we measure problem-solving performance to test the relevance of this framework  (von Krogh et al. , 2012)  for the for the two different developer support forums, Google Maps and Facebook. Examining whether or not the problems ("defections") reported in the forums by the users are eventually solved, for the period examined. To extract the evidence of from the data we calculate the following indicator: Problem-solving performance = Problems solved Problems reported In the case of the Facebook developer forum, 1821 problems were reported from external developers during the period from 1/1/2010 to 31/5/2010 (not counting the 198 issues reported that were merged with previous discussions).
Empirical exploration of the observed process To propose more relevant concepts for the study of the phenomenon, provided that the problem-solving rate has been found to be particularly low in the previous step, we operate a qualitative analysis of three types of data from the Google Maps developer forum, focusing our attention into the data samples. To this end, additional insight from internal Google documentation on the management of developer support forums were taken into account. In fact, reading the guide "Issue Tracking: Why & How" written by a Google manager and addressed to her colleagues one sees that employees themselves attempt to produce some criteria and best practices on how to manage thirdparty developers. As typically mentioned in its introduction, "the area of developer support is quite new, and there isn't much written about how to do it -what works, what doesn't".
Here, we present a novel toolbox for spike sorting in vitro and in vivo, validated on ground truth recordings. Our sorting algorithm is based on a combination of density-based clustering and template matching. To validate our method, we performed experiments where a large-scale extracellular recording was performed while one of the neurons was recorded with a patch electrode. We showed that the performance of our algorithm was always close to an optimal classifier, both in vitro and in vivo. We demonstrate that our sorting algorithm could process recordings from up to thousands of electrodes with similar accuracy. To handle data from thousands of electrodes, we developed a tool automating the step that is usually left to manual curation. Our method is a fast and accurate solution for spike sorting for up to thousands of electrodes, and we provide it as a freely available software that can be run on multiple platforms and several computers in parallel.
Performance on ground truth data To test our algorithm, we performed dual recordings (Figure  2A, B ) using both a multielectrode array to record many cells (see schematic on Figure  2A ), and simultaneous loose patch to record the spikes of one of the cell (Figure  2B ). For this cell we know what should be the output of the spike sorting. In vitro, we recorded 18 neurons from 14 retinas with a 252 electrode MEA where the spacing between electrodes was 30 m (see Materials and methods,  (Spampinato et al. , 2018) ). We also generated datasets where we removed the signals of some electrodes, such that the density of the remaining electrodes was either 42 or 60 m (by removing half or 3 quarters of the electrodes, respectively). We then ran the spike sorting algorithm only on the extracellular data, and estimated the error rate (as the mean of false positives and false negatives, see Materials and methods) for the cell recorded in loose patch, where we know where the spikes occurred.
For 37 neurons with large waveform sizes (above) the average error of the classifier is 2. 7% and the one for our algorithm is 4. 8% (see Figure  2E ). For 22 neurons with lower spike size (below), the average error of the classifier is 11. 1% and the one for our algorithm is 15. 2%. This suggests that our algorithm reached an almost optimal performance on this in vitro data. We also used similar ground truth datasets recorded in vivo in rat cortex using dense silicon probes with either 32 or 128 recording sites  (Neto et al. , 2016). With the same approach as for in vitro data, we also found that our algorithm achieved near optimal performance (Figure  2F , right; r ¼ 0:92, p<10 À5 ), although there were only two recordings where the spike size of the patchrecorded neuron was large enough to be sorted with a good accuracy.
It also scaled up linearly with the number of electrodes (Figure  3B ), and with the number of templates (Figure  3C ). It was therefore possible to run it on long recordings with more than 4000 electrodes, and the runtime could be be divided by simply having more CPUs available. To test the accuracy of our algorithm on 4225 electrodes, we generated hybrid ground truth datasets where artificial spikes were added to real recordings performed on mouse retina in vitro (see Materials and methods). We ran the spike-sorting algorithm on different datasets, picked some templates and used them to create new artificial templates that we added at random places to the real recordings (see Materials and methods). This process, as shown in Figure  3D  allowed us to generate 'hybrid' datasets were we know the activity of a number of artificially injected neurons.
We Comparison between the error rates produced by the algorithm on different ground truth datasets and the error rates of nonlinear classifiers (Best Ellipsoidal Error Rate) trained to detect the spikes for in vitro data  (Spampinato et al. , 2018). (E) Comparison of average performance for all neurons detected by the Optimal Classifier with an error less than 10% (n = 37). F. Same as D. but for in vivo data  (Neto et al. , 2016)    then ran our sorting algorithm on these datasets and measured if the algorithm was able to find at which times the artificial spikes were added. We counted a false-negative error when an artificial spike was missed and a false-positive error when the algorithm detected a spike when there was not any (see Materials and methods). Summing these two types of errors, the total error rate remained below 5% for all the spikes whose size was significantly above spike detection threshold (normalized amplitude corresponds to the spike size divided by the spike threshold).
Error rates were similar for recordings with 4255 electrodes in vitro, 128 electrodes in vivo or 252 electrodes in vitro. Performance did not depend on the firing rate of the injected templates (Figure  3E ) and only weakly on the normalized amplitude of the templates (Figure  3F ), as long as it was above the spike threshold. The accuracy of the algorithm is therefore invariant to the size of the recordings. A crucial issue when recording thousands of densely packed electrodes is that more and more spikes overlap with each other. If an algorithm misses overlapping spikes, then the estimation of the amplitude of correlations between cells will be biased. To test if our method was able to solve the problem of overlapping spikes and thus estimate correlations properly, we generated hybrid datasets where we injected templates with a controlled amount of overlapping spikes (see Materials and methods).
In one example, where we artificially split synthetic spike trains (Figure  4A ; see Materials and methods), we could clearly isolate a cluster of pairs lying near this diagonal, corresponding to the pairs that needed to be merged (Figure  4A  right panels). We have designed a GUI such the user can automatically select this cluster and merge all the pairs at once. Thanks to this, with a single manipulation by the user, all the pairs are merged. We then tested this method on our in vitro ground truth data. In these recordings, the cell recorded with loose patch might be split by the algorithm between different spike trains. We can determine the units that correspond to the patch-recorded cell. For one particular neuron taken from our database, we can visualize all the units that need to be merged together (blue points in Figure  4B ), and that should not be merged with units corresponding to other cells (green pairs in Figure  4B ).
We found that the error rate obtained with our automated method was close to this best error rate (Figure  4C ). We have therefore automated the process of merging spike trains while keeping a low error rate. The performance did not vary much with the values of the two parameters (similarity threshold and dip threshold), and we used the same parameters for all the different datasets. This shows that the sorting can be fully automated while limiting the error rate to a small value. We thus have a solution to fully automate the sorting, including all the decisions that need to be taken during the manual curation step. However, because we used cross-correlograms in order to help automate the merging process, it is worth noticing that one can no longer use cross-correlograms as a validation metric.
Discussion We have shown that our method, based on density-based clustering and template matching, allows sorting spikes from large-scale extracellular recordings both in vitro and in vivo. We tested the performance of our algorithm on 'ground truth' datasets, where one neuron is recorded both with extracellular recordings and with a patch electrode. We showed that our performance was close to an optimal nonlinear classifier, trained using the true spike trains. Our algorithm has also been tested on purely synthetic datasets  (Hagen et al. , 2015)  and similar results were obtained (data not shown). Note that tests were performed by different groups on our algorithm and show its high performance on various datasets (see http://spikesortingtest. com/ and http://phy. cortexlab. net/data/sortingComparison/).
Our algorithm is entirely parallelized and could therefore handle long datasets recorded with thousands of electrodes. Our code has already been used by other groups  (Denman et al. , 2017; Mena et al. , 2017; Chung et al. , 2017; Wilson et al. , 2017)  and is available as a complete multi-platform, open source software for download (http://spyking-circus. rtfd. org) with a complete documentation. Note that all the parameters mentioned in the description of the algorithm can be modified easily to work with different kinds of data. We have made all the ground truth data available for download (see Source Code section in Materials and methods), so that improvements in our algorithm as well as alternative solutions could be benchmarked easily in the future. Classical approaches to the spike sorting problem involve extracting some features from each detected spike  (Hubel, 1957; Meister et al.
These three issues preclude the use of this approach for large-scale recordings with dense arrays of electrodes. In comparison, here we have parallelized the clustering step efficiently, using a template matching approach, so that we only needed to infer the centroid of each cluster and not their precise borders. The template matching approach also allowed us to deconvolve overlapping spikes in a fast, efficient and automated manner. Some template matching approaches have been previously tested, mostly on in vitro data  (Marre et al. , 2012; Pillow et al. , 2013; Franke et al. , 2015b)  but were not validated on ground truth datasets like the ones we acquired here. Also, they only had one waveform for each template, which did not allow any variation in the shape of the spike, while we have designed our template matching method to take into account not only variation in the amplitude of the spike waveform, but also in shape.
In vivo recordings with 128 electrodes We use the freely available datasets provided by  (Neto et al. , 2016). Those are 32 or 128 dense silicon probes recordings (20 m spacing) at 30 kHz performed in rat visual cortex, combined with juxta-cellular recordings. The dataset gave us a total of 13 neurons for Figure  2. C with recordings between 4 and 10 min each. Similarly to the in vitro case, the detection thresholds for the juxta-cellular spikes were manually adjusted based on the data provided by  Neto et al. (2016)  and on spiketriggered waveforms. For the validation with 'hybrid' dataset, shown in Figure  3 , we used the longest dataset recorded with 128 electrodes.
Spike detection Once signals have been filtered, we computed a spike threshold k for every electrode k: k ¼ lMAD s k t ð Þ ð Þ, where MAD is the Median Absolute Deviation, and l is a free parameter. For all the datasets shown in this paper, we set l ¼ 6. We detected the putative spike times t i as times where there was at least one electrode k where s k t i ð Þ was below the threshold À k and a local minimum of the voltage vects k t ð Þ.
Template estimation At the end of the clustering phase, pooling the clusters obtained from every electrode, we obtained a list of clusters. Each cluster m is defined by a list of spike times t m 1;::;l f g. During this phase we limited the number of spike times per template to a maximal value of 500 to avoid memory saturation, because we had to keep in memory these 500 snippets. We computed the first component from the raw data as the point-wise median of all the waveforms belonging to the cluster: w m t ð Þ ¼ med l s t m l þ t À Á. Note that w m is only different from zero on the electrodes close to its peak (see Figure  1C ). This information is used internally by the algorithm to save templates as sparse structures. We set to 0 all the electrodes k where kw k m t ð Þk< k , where k is the detection threshold on electrode k.
Simulated ground truth tests In order to assess the performance of the algorithm, we injected new templates in real datasets (see Figure  3D ). To do so, we ran the algorithm on a given dataset and obtain a list of putative templates w j2 1;. N f g. Then, we randomly selected some of those templates w j and shuffled the list of their electrodes before injecting them elsewhere in the datasets at controlled firing rates  (Harris et al. , 2000; Rossant et al. , 2016; Kadir et al. , 2014; Segev et al. , 2004; Marre et al. , 2012; Chung et al. , 2017). This enabled us to properly quantify the performance of the algorithm. In order not to bias the clustering, when a template w j was selected and shuffled as a new template w k centered on a newelectrode k, we ensured that the injected template was not too similar to one that would already be in the data: 8h 2 1;.
Performance estimation Estimation of false positives and false negatives To quantify the performance of the algorithm we matched the spikes recovered by the algorithm to the real ground-truth spikes (either synthetic or obtained with juxta-cellular recordings). A spike was considered to be a match if it had a corresponding spike in the ground truth at less than 2 ms. Spikes in the ground-truth datasets that had no matches in the spike sorting results in a 2 ms window were labeled as 'false negatives', while those that are not present while the algorithm detected a spike were 'false positives'. The false-negative rate was defined as the number of false negatives divided by the number of spikes in the ground truth recording. The false-positive rate was defined as the number of false positives divided by the number of spikes in the spike train extracted by the algorithm.
Implementation and source code SpyKING CIRCUS is a pure Python package, based on the python wrapper for the Message Passing Interface (MPI) library  (Dalcin et al. , 2011)  to allow parallelization over distributed computers, and is available with its full documentation at http://spyking-circus. rtfd. org. Results can easily be exported to the kwik or phy format  (Rossant and Harris, 2013). All the datasets used in this manuscript are available on-line, for testing and comparison with other algorithms  (Spampinato et al. , 2018 ).
Errors bars show standard error over eight templates (F) Error rate as function of the normalized amplitude of injected templates, in various datasets. Errors bars show standard error over nine different templates (G) Injected and recovered cross-correlation value between pairs of neurons for five templates injected at 10 Hz, with a normalized amplitude of 2 (see Materials and methods). DOI: https://doi. org/10. 7554/eLife. 34518. 005.
Acknowledgements We would like to thank  Charlotte Deleuze  for her help with the in vitro juxtacellular recordings, and  Steve Baccus  and  Sami El Boustani  for insightful discussions. We also would like to thank  Kenneth Harris ,  Cyrille Rossant  and  Nick Steimetz  for feedbacks and the help with the interface to the phy software. This work was supported by  ANR-14-CE13-0003  to  PY, ANR TRAJECTORY ,  ANR OPTIMA , the  French State program Investissements d'Avenir  managed by the  Agence Nationale de la Recherche  [ LIFESENSES :  ANR-10-LABX-65 ], an  EC grant  from the  Human Brain Project  ( FP7-720270 ), and  NIH  grant  U01NS090501  to OM,  ERC Starting Grant  ( 309776 ) to JD and Foundation Figthing Blindness to SP.
2017 ). Therefore, detection schemes should also identify the cyber components that have been attacked; a non-negligible challenge in large water networks. Third, all networked devices, including SCADA systems, represent potential targets. This means that the information provided by SCADA systems may not be fully reliable. As the field of intrusion detection continues to grow, so too does the need of an objective comparison of attack detection algorithms for water distribution systems. The BATtle of the Attack Detection ALgorithms (BATADAL) was oragnized for this purpose. Participants were provided with datasets containing (simulated) SCADA data for a water distribution system victim of cyber attacks, and were tasked with the design of an attack detection mechanism. The design goals of a detection algorithm were to: (1) disclose the presence of an ongoing attack in the minimum time possible, (2) avoid issuing false alarms, and (3) identify which components of the system have been compromised (optional).
PROBLEM DESCRIPTION The operators of C-Town water distribution system have observed anomalous behaviors in some hydraulic components, e. g. , tank overflows, reduction in pump speed, anomalous activation/deactivation of pumps. They suspect that the anomalies are attributable to cyberphysical attacks that interfered with the system operations and tampered with the readings recorded by the SCADA system. The aim of the participants was to develop an attack detection mechanism that detects the presence of attacks-in the shortest amount of timefrom the available hourly SCADA data. In particular, attack detection algorithms must classify the system state as either 'safe' or 'under attack'. A summary description of C-Town is provided below, along with the development data and evaluation criteria.