dataesr/scientific-paragraphs-categorization huggingface dataset:
If dual(q) exists, the pattern q is outputted, dual(q) is flagged as already outputted (redundant pattern) and the process recursively continues. Otherwise, the algorithm backtracks. In this way, q and dual(q) are never outputted together. Computing the balance for non mirror databases is straightforward since for each node/pattern we have access to its dual support. For mirror databases, we need however to know common(q) which is stored for each node. The proof of the completeness and correctness of PREFIXSPANNAIVE for extracting all balanced patterns without redundancy is direct: first, PREFIXSPAN extracts all frequent patterns, thus any pattern s and its dual dual(s) are nodes of the pattern tree and none can be missed; second, as we visit in the tree traversal both a pattern s and its dual dual(s) (if frequent), we ensure no redundant patterns.
Example. PREFIXSPANNAIVE requires a signed intereaction database. Let us consider that of Table IV with = 1/4 (i. e. , frequent items are items that only appear once). PRE-FIXSPANNAIVE starts with an empty sequence s = hi (dual(hi) = hi) and the entire database. First, it searches for frequent items: e. g. , both a + and a are frequent. Thus, PREFIXSPANNAIVE flags that ha i is the dual of ha + i, and calls P ref ixSpanN aive(ha + i, S |ha + i , ). Then, PREFIXS-PANNAIVE searches for frequent items in S |ha + i : e. g. , c is frequent. So, it will search in the children of ha i if there is c + , but at this step, the node of ha i has not been explored yet. Thus, when ha i is explored, PREFIXSPANNAIVE extracts frequent items in S |ha i : e. g. , c + is frequent. So it will search among the children of dual(ha i) if dual(c + ) exists: ha c + i is a frequent sequential pattern that could be outputted.
D. Mining Balanced Patterns with BALANCESPAN The problem of PREFIXSPANNAIVE is that it generates both a pattern and its dual as different nodes in the pattern tree. Furthermore, it also generates nodes for patterns that are frequent but whose dual is not frequent. Consequently, and this is shown in the experiments (Section VII), an important amount of nodes are useless. To solve that problem, and to be sure only nodes corresponding to balanced patterns are generated (and only them, i. e. correct and complete), we propose the BALANCESPAN approach. The general idea is the following: instead of considering each item a 2 I s as an extension on sequence s leading to a new projected database S |s a and consequently a new node in the pattern tree, we consider simultaneously an item and its dual, hence two projected databases S |s a and S |dual(s) dual(a) are related to a single node (this is done for both kinds of extensions i and s ).
Thus, it ensures that no redundant patterns are generated, since both a sequence s and dual(s) are generated at the same node, and it allows to compute balance(s) (or balance(dual(s)) directly if and only if both s and dual(s) are frequent. It follows that BALANCESPAN produces a correct and complete collection of frequent balanced patterns. Example. BALANCESPAN also requires a signed interaction database, but contrary to PREFIXSPANNAIVE, it proceeds to a double projection at a time. Let us still consider the toy dataset of Table  IV with  = 1/4. Starting with the empty sequence and the entire dataset, the first step consists of finding frequent items: e. g. , a + is frequent. Contrary to PREFIXSPANNAIVE, BALANCESPAN directly generates the dual of ha + i to proceed to the double projection.
Thus, it checks if a is frequent and then projects on both ha + i and ha i at a time: i. e. , it creates a new node in the pattern tree that is related to the couple (ha + i, ha i). The next step calls BalanceSpan on this new node. So it searches for frequent items in the projected database S |ha + i : e. g. , c. Thus it checks if dual(c ) is frequent in S |ha i : the node containing the couple (ha + c i, ha c + i) is created and explored in the next step.
B. Datasets StarCraft II replays are files that store any action performed by all players during a game, and are easily accessible on the Web 1. We retained 91, 503 games with a total of 3. 19 years of game time. The average length of a game is about 20 minutes. A game is selected if it involves a high level players (in the highest leagues and playing at least 200 actions per minute), since casual (by opposition to professional) players are not able to follow specific strategies. We divided the 91, 503 replays into six different sequence datasets, one for every match up. Buildings are one of the key elements of a strategy, since they enable different kinds of units production: from  each replay, we derive a sequence where the items represent the buildings the players chose to produce in real time, and itemsets denote time windows of 30 seconds.
C. Run-time analysis and memory usage We implemented our algorithms over the original C++ version of PREFIXSPAN  [11] , and experimented on a 1. 8 GHz Intel Core i5 with 8 GB machine. Note that we released both the source-code and the datasets used in the following experiments  [15]   2. We discuss running times of the proposed algorithms. Firstly, we consider the non-mirror databases given by transformed interaction databases T and signed databases S (since signed databases correspond to the general case whereas transformed databases are specific to non-mirror databases). For different minimum frequency thresholds , we present the runtime of PREFIXSPAN on S as a rough baseline (since it does not compute the balance of a pattern), and the runtimes of the three other algorithms on their respective data representation.
It follows that our general algorithm BALANCESPAN is the only one able to be executed with lowest (Figure  1 (d) , (e) and (f)). We report the same results for the general case with mirror datasets (i. e. , S only for PvP, TvT and ZvZ) in Figure  1  (a), (b) and (c). BALANCESPAN clearly outperforms PREFIXSPANNAIVE, its only competitor (remembering that PREFIXSPAN is given as baseline since it does not compute the balances, and EMERGSPAN does not apply for mirror databases). Indeed, even if sometimes PREFIXSPANNAIVE seems to have the same run time as BALANCESPAN with high value of , it cannot reach lowest frequency thresholds. Note that on the figures, missing points correspond to unterminated runs when available memory is insufficient. Figure  3  shows the distribution of the length of the extracted patterns for the PvT and ZvZ datasets.
D. Exploration of the extracted patterns It is interesting to visualize the distribution of both the support and the balance of the patterns. Figure  4  gives such distribution for dataset ZvZ that enables very fast computations with low (less than 5 seconds for = 0. 001). There, both a pattern and its dual are presented, which allows interestingly to observe that the equation y = 0. 5 (where y is the vertical axis) gives almost a symmetry axis. Indeed, both a pattern and its dual do not necessarily have the same support. One can notice that empirically, there are high chances for a pattern with high frequency to have a fair balance around 0. 5. This behavior applies for the other dataset and is what we could expect given the definition of the balance measure. It is possible to query the set of extracted patterns in various ways.
Indeed, the pattern mining task is related to the Knowledge Discovery in Databases (KDD) process that aims at extracting knowledge from data  [8]. Data mining approaches and more precisely pattern mining approaches are a step of the KDD process that results in patterns from transformed data  [9]. Our work is a pattern mining approach to study strategy balance that is applied to RTS games. Thus, one gets a set of patterns that are a generalization of the local strategies within the data. BALANCESPAN still requires an analysis once the patterns are outputted. Exploring a large collection of patterns can be done in many ways. First, as illustrated hereafter, the expert can filter the collection with specific constraints such as a minimum number of itemsets, or specific items using regular expressions, etc.
In all cases, the pattern language must be clearly defined and efficient algorithms proposed to compute the measure of interest, our main contribution. We now provide a basic example of exploration by querying the pattern set. Out of the 43, 610 patterns for ZvZ with = 0. 001, we can keep the patterns involving two players (containing both + and ), which returns 40, 674 patterns. Then we restricted the set of patterns to those involving two specific items (RoachWarren and Spire) to get only 290 patterns. hSpawP ool + , SpawnP ool , SpiCrawler + , RoachW arren + , Spire i denotes for example games where one of the players go to air units and the second to ground units, two known openings, with balance 0. 47 and support 68. The pattern mining task can be adapted in various ways, 1) Discovering strategy openings: Openings are the most well-known strategies and executed during the first five to ten minutes of a StarCraft II game.
It is expected that openings are balanced to make the game enjoyable for the casual player, competitive for the professional, but also interesting to watch for the spectators  [2]. We build our sequence databases with a set of items composed of tuples (building, sign, i th window), with fixed windows of 30 seconds by default, i. e. , the i th window contains the items performed between the ((i 1) ⇥ 30) th second and the ((i ⇥ 30) 1) th second. We expect that openings are found as the more frequent patterns and being also balanced: Figure  5  shows the complete set of patterns for the ZvZ dataset which differs with Figure  4  by its skewness. We explored the resulting patterns with a game expert. When considering another dataset (PvZ), we obtain only 591 patterns with = 0. 05. Top frequent patterns represent all well-known strategies: s = h{(Nexus, +, 5 )} {(Gateway, +, 6 ) (P hoton Cannon, + 6 )}i represents a popular Protoss strategy, no matter the strategy of the opponent is.
We split dataset TvZ into two parts: the first one, called S bef ore , contains replays that happened strictly before 10 th May 2012 (17, 171 replays), and the second one, called S af ter , contains the replays that happened strictly after this change (6, 698 replays). The mining of the dataset S bef ore (respectively S af ter ) with a low = 0. 05 returns 8, 138 patterns (resp. 7, 735). According to the experts, the bunker should be built during the sixth window of time (between 2'30" and 3' of the game). There are 20 (resp. 12) patterns that involve the item (Bunker, c, 6) with c 2 {+, }. With S bef ore (resp. S af ter ), the average value of the balance is 0. 58 (resp. 0. 51) with a standard deviation equals to 0. 5 (resp. 1. 6). This is clear that since the patch was released, this strategy has become balanced.
Moreover, we can remark that this strategy is no longer used by players: in fact the number of extracted patterns related to this strategy decreases by 40% whereas the number of extracted patterns only decreases by 5% from S bef ore to S af ter. Thus, BALANCESPAN enables to see the impact of the release of patch, by analyzing the period before and after this key date. 3) On the diversity in mirror match-ups: It is more delicate to speak about balance when both players belong to the same faction (mirror match-ups): both players have access to the same strategies (same building, hence a symmetrical game). Let us observe for example the dataset PvP with a new vocabulary: items are tuples (building, sign, i th window, j building ) where the last element records how many of this building were already made at the moment of the action (cumulative).
VIII. RELATED WORK Discovering patterns that highly distinguish a dataset from others (e. g. , win labeled objects versus lose labeled objects) is an important task in machine learning and data mining  [13]. One of the main reason is that such patterns enable the building of comprehensible classifiers  [18]. In the general settings, we are given a set of objects of different classes that take descriptions, generally from a partially ordered set (itemsets, graphs, intervals, etc. )  [19]. The goal is to find good description generalizations that mostly appear in one class of objects and not in the others. In different fields of AI and applied mathematics, such descriptions have different names  [13]  like version spaces  [20] , contrast sets  [21]  and subgroups discovery  [22]  in machine learning, emerging patterns  [12]  in data-mining; or no counter-example hypothesis in formal concept analysis  [19].
Other kinds of approaches are also used for several prediction tasks. Predicting the opponent's production was studied with answer set programming  [25] , while learning transition probabilities within build orders was achieved with hidden Markov models  [26]. Weber et al. described any past game by a vector of building and upgrade timings: such features allow an accurate strategy prediction  [27]. This comes with a limit: game logs are a priori labeled with a strategy using rules based on "manual" expert analysis. The same applies for opening prediction  [28]. To discover strategies in large volumes of replays, avoiding to manually label game logs, knowledge discovery in database (KDD) methods are required, and especially pattern mining techniques. This was highlighted in the open problems category Domain knowledge of the recent survey mentioned before  [6] : "Is it possible to devise techniques that can automatically mine existing collections [.
IX. CONCLUSION We tackled the problem of mining frequent sequential patterns in real time strategy games whose balance measures provide meaningful insights on the strategies played and their ability of being in equilibrium or not. For that matter, we revisited the well known notions of discriminant pattern mining to provide efficient algorithms for the elicitation of balance hypotheses from the data. From that, we presented several algorithms that enable (partially or not) dealing with interaction databases, and we showed that only BALANCESPAN enables to deal with all datasets efficiently. We empirically validated that the balance measure is able to distinguish balanced and imbalanced strategies. We believe that our approach can become a basic tool for balance designers when analyzing a subset of historical data of a game in beta phase, or even after its release, through an exploratory process (KDD and interactive mining).
The first step of PREFIXSPAN consists of finding frequent item from D: hai (|sup(hai, D)| = 4), hbi (|sup(hbi, D)| = 4), hci (|sup(hci, D)| = 4), hdi (|sup(hdi, D)| = 3), hei (|sup(hei, D)| = 3) and hf i (|sup(hf i, D)| = 3). For each of these previous frequent sequential patterns s of size 1, PREFIXSPAN projects D into a projected database with prefix s. Thus, the hai-projected database D |hai is composed of 4 sequences: h{abc}{ac}d{cf }i, h{ d}c{bc}{ae}i, h{ b}{df }cbi and h{ f }cbci. Then, PREFIXSPAN searches for frequent sequential patterns of size 2: haai.
The plasmid for bacterial expression of the mutant Y-FAST W94F was generated by site directed mutagenesis from pAG87. Y-FAST, Y-FAST W94F , MiniSOG, EGFP and mVenus were expressed and purified following previously described protocols with small modifications  29. Briefly, after elution from the Ni-NTA beads with a PBS solution supplemented with 0. 5 M Imidazole, the purified protein fractions (as confirmed by SDS-PAGE) were buffer changed to PBS buffer (sodium phosphate 50 mM, NaCl 150 mM, pH 7. 4, I = 280 mM) in PD-10 desalting columns (GE Healthcare) to remove excess imidazole. Instrumentation and Irradiation Methods. Steady state UV-Vis absorption spectra were recorded using a Cary 300 UV-Vis spectrometer (Agilent Technologies), equipped with a Versa20 Peltier-based temperature-controlled cuvette chamber (Quantum Northwest) and fluorescence data were recorded using a LPS 220 spectrofluorometer (PTI, Monmouth Junction, NJ), equipped with a TLC50TM Legacy/PTI Peltier-based temperature-controlled cuvette chamber (Quantum Northwest).
LED intensity changes during the course of the experiments were measured using a small Si photodiode (S1336-44BQ) placed directly underneath the cuvette chamber window and the fluorescence data normalized for measured power changes over time. O 2 was removed by gaseous exchange of air by N 2 in a sealed cuvette for over 30 min while the solution was under constant stirring. The needle blowing N 2 was kept just above (and not inside) the liquid to avoid protein denaturation. All experiments were performed with 2 mL solutions in 1 cm 2 quartz cuvettes with an irradiation window corresponding to 60% of the solution volume. To avoid any effect from diffusion stirring was kept constant throughout irradiation in all experiments. Affinity constants were determined by spectrofluorometric titration using a Spark 10 M plate reader (Tecan) (Ex 480 ± 10 nm, Em 560 ± 10 nm, 25 °C) using a 96 well-plate with 0.
Acknowledgements This work was supported by the  Agence National de la Recherche  ( ANR-14-CE09-0002-01 ),  PSL Research University  ( ANR-10-IDEX-0001-02 PSL  for the projects IMRESOV and SuperLine),  France BioImaging  ( ANR-10-INBS-04 ), the  Equipex   Morphoscope 2  ( ANR-11-EQPX-0029 ) and the  FR 3624 TGE FT-ICR platform.
The libraries were multiplexed, pooled and 2 Â 76 base pair (bp) reads were generated on two lanes with the Illumina GAIIX. Sequencing libraries of the Ethiopian T. evansi strains MU09 and MU10 and the T. b. gambiense type 2 strain ABBA were prepared with Illumina's Nextera XT sample preparation kit and 2 Â 300 bp reads were generated with the Illumina MiSeq (ABBA) or 2 Â 100 bp reads with the HiSeq 1500 (MU09 and MU10). Quality scored sequence reads have been submitted to the NCBI Sequence Read Archive (SRA) with accession numbers SRP100981 and SRP100990.
2012 ) using default parameters. Human Serum Resistance Genes, Diagnostic VSG's, and F1-ATP Synthase Subunit Gamma For all genomes, we have investigated the presence and sequence of the trypanosomes' human resistance genes, coding for the T. b. rhodesiense specific Serum Resistance Associated (SRA) protein  (Xong et al. 1998 ), the T. b. gambiense specific glycoprotein (TgsGP;  Uzureau et al. 2013 ) and haptoglobinhemoglobin (TbHpHb) receptor  (Vanhollebeke et al. 2008). FASTA sequences of the reference genes (Genbank accession number Z37159. 2, FN555988, AF317914, and AY007705. 1, AY007706. 1 and AJ870487, respectively) were downloaded and the reads from the 56 sequenced genomes were mapped against these references sequences. Bowtie2 was used in local mode to cover both the beginning and the end of the genes, except for the VSG's that required the full read sequence alignment given their repetitive nature.
Acknowledgments Funding was received from the  Research Foundation Flanders (FWO , grants  1501413N  and  1101614N ) and the  European DG Health and Food Safety (SANTE). We thank the  Center of Medical Genetics at the University of Antwerp  for hosting the  NGS facility. The computational resources used for this work were provided by the  VSC (Flemish Supercomputer Center) at the University of Antwerp. We thank  Dr Mark Sistrom ,  Dr Joshua Richardson , and  Dr Adalgisa Caccone  ( Yale University ) for providing genome reads from 19 T. b. brucei, 13 T. b. rhodesiense, 3 T. b gambiense and 4 T. evansi strains. We thank  Dr Dave Barry  for critical reading of the manuscript. The T. equiperdum strain TeAp-N/D1 was kindly donated by  Dr Maria Isabel Gonzatti  ( Universidad Sim on Bol ıvar, Venezuela ).
Introduction In 2003 the Hubble Space Telescope (HST) performed a 1 Megasecond observation with its Advanced Camera for Surveys (ACS) in a tiny 11 arcmin 2 region located within the Chandra Deep Field South: the Hubble Ultra Deep Field (HUDF,  Beckwith et al. 2006). The HUDF immediately became the deepest observation of the sky. This initial observation was augmented a few years later with far ultraviolet images from ACS/SBC  (Voyer et al. 2009 ) and with deep near ultraviolet  (Teplitz et al. 2013 ) and near infrared imaging  (Oesch et al. 2010; Bouwens et al. 2011; Ellis et al. 2013; Koekemoer et al. 2013 ) using the Wide Field Camera 3 (WFC3). These datasets have been assembled into the eXtreme Deep Field (XDF) by  Illingworth et al. (2013). With an achieved sensitivity ranging from 29.
The udf-10 ultra deep field In addition to the mosaic, we also performed deeper observations of a single 1 ×1 field, called UDF-10. The field location 1  was selected to be in the deepest part of the XDF/HUDF09/HUDF12 area and to overlap as much as possible with the deep ALMA pointing from the ASPECS pilot program  (Walter et al. 2016). A different PA of 0 • was deliberately chosen to better control the systematics. Specifically, when this field is combined with the overlapping mosaic fields (at a PA of -42 • ), the instrumental slice and channel orientation with respect to the sky is different. This helps to break the symmetry and minimize the small systematics that are left by the data reduction process. Care was taken to have a bright star within the slow-guiding field in order to obtain the best possible spatial resolution, even when the field transits near zenith.
Self calibration Although the standard pipeline is efficient at removing most of the instrumental signatures, one can still see a low-level footprint of the instrumental slices and channels. This arises from a mix of detector instabilities and imperfect flatfielding, which are difficult to correct for with standard calibration exposures. We therefore use a self-calibration procedure 2 , similar in spirit to the one used for the HDFS  (Bacon et al. 2015)  but enhanced to produce a better correction. It is also similar to the CubeFIX flat-fielding correction method, part of the CubExtractor package developed by Cantalupo (in prep. ) and used, for instance, in  Borisova et al. (2016)  (see therein for a short description) but it works directly on the pixtable. Compared to the HDFS version, the major changes in the new procedure are to perform polychromatic correction and to use a more efficient method to reject outliers.
2016)  and CubExtractor (Cantalupo, in prep. ), a three-dimensional automatic extraction software based on connecting-labeling-component algorithm  (used, e. g. , in Borisova et al. 2016 and Fumagalli et al. 2016). Each of these methods has its own pros and cons: some achieved high sensitivity but at the expense of low purity, others are optimized to provide reliable results, that is high purity, but with lower sensitivity. Given the depth and the field of view of the UDF observations, we expect to find thousands of emission line galaxies which, considering the MUSE spatial resolution, will include a significant fraction of blended sources. The total size of the datacube (3. 3 billion voxels for the mosaic) is not negligible either. In order to handle these methodological and computational challenges, we have begun to develop a new automated method, called ORIGIN.
Application to the UDF The ORIGIN algorithm is implemented as a Python package and was successfully run on the UDF fields using the parameters defined in the previous section. The full computation takes ≈1 and ≈6 hours of computing time on our 32 multi-core linux workstation for udf-10 and mosaic datacubes, respectively. The program reported the detection of 355 (udf-10) and 1923 (mosaic) candidate sources. After removing the 49 (udf-10) and 672 (mosaic) false detections 12  identified after visual inspection, we are left with 306 and 1251 potentially real detections, corresponding to 86% and 65% purity, respectively, for the udf-10 and mosaic fields. As shown in paper II  (Inami et al. 2017) , not all detections will eventually turn into a redshift. Generally, the detected sources without redshift have a S/N that is too low to identify the emission and/or absorption lines, but the vast majority at least have an HST counterpart, validating their detection status.