dataesr/scientific-paragraphs-categorization huggingface dataset:
e. 12 17 11 40, 40 13 40 39 132, 5, and 5,12,13,6 , 7, 17,40, 15 , 8,11,39,9 ). Since regularity is greater than (itemset ' ' does not regularly occur in the database), ' ' should be removed out of consideration (based on the downward closure property of regularity  [3] ). Next, item ' ' is merged with item ' ' and is intersected with 1,8,65,0 , 3,4,30,0 , 4,5,5,0 , 5, 1,12, 0 , 8,4,  35   Since regularity is smaller than and is greater than , it can be concluded that itemset ' ' and its supersets are potentially HUIRs. Then, an entry for itemset ' ' is created in 2 along with its information. Also, due to utility being not smaller than , MHUIRA identifies ' ' as an HUIR. The merging and intersection process continues for itemsets ' ', ' ', and ' '. If at the end 2 contains only ' ', then MHUIRA stops considering item ' ' and all of its supersets, and changes its consideration to items ' ', ' ', ' ', ' ' and ' ' in the same manner as ' '.
Experimental Evaluation and Complexity Analysis In this section, we report our experimental studies to investigate the performance of MHUIRA with the new modified utility-list (NUL). To the best of our ability, we pushed the effort to consider the regularity constraint together with the utility of the itemsets (as in  [5] ). Then, we only made a comparison between MHUIRA with UL (called HURI-UL in  [5] ) and MHUIRA with NUL. Moreover, we also made a modification of HUI-Miner  [7]  that scans the database twice, called HUI-Miner-reg, for mining high utility itemsets with regular occurrence in order to show the improvement of our proposed singlepass algorithm compared to the two-pass algorithm of HUI-Miner. In addition, time and space complexity analyses of our proposed method are provided. This can be the baseline for future approaches.
Experimental Setup For the experiments in this paper, four datasets downloaded from  [36]  were used (as detailed in Table  4 ). The regularity and utility thresholds were set and varied from 1 30% and 0. 001 22%, respectively. The setting of the thresholds was based on the density of the data in each dataset. However, it was similar to previous approaches (  [5, 6, 7, 30, 31, 32] ). MHUIRA with UL and NUL and HUI-Miner-reg were implemented in and run on Xeon® 2. 4 GHz with 64 GB of memory. Three kinds of experiments were conducted to observe runtime, memory consumption, and number of itemsets that are discovered. Experiments on runtime and memory usage were done based on two settings: (i) a fixed on highest value of regularity threshold and a varied on utility threshold; and (ii) a fixed on lowest value of utility threshold and a varied on regularity threshold.
Runtime The runtimes of MHUIRA with UL and NUL under a variation of regularity and utility thresholds are depicted in Figures  2  and 3. In both figures, the runtime of MHUIRA with UL and NUL and HUI-Miner-reg increases as the regularity threshold increases (also for the decrease of the utility threshold). The reason is that with a higher regularity threshold (lower utility threshold), items/itemsets have more chance to meet the threshold. Then, the three algorithms have to spend more time to consider items/itemsets with a high regularity (low utility). In most cases, MHUIRA with UL and NUL are faster than HUI-Miner-reg since they can take advantage from avoiding repeatedly scanning the database. Moreover, MHUIRA with NUL is faster than MHUIRA with UL, since it can take advantage from NUL, which can help to avoid repeated calculation of utility.
Complexity Analysis Lemma 1. complexity of MHUIRA is 2 where is the number of items in set and is the number of transactions in database. Proof. As described in Algorithm 1, MHUIRA scans all transactions once, where each transaction may contain at most items. Then, the time complexity of scanning the database is. For mining all HUIRs, each item/itemset is merged with other items/itemsets having the same prefix. Then, the total number of itemsets to be regarded is 2 n. Also, each item/itemset may contain at most entries of. Thus, the number of intersections to calculate the utility value of itemsets (the main computation of MHUIRA) is 2. Lastly, with the 2 main steps of MHUIRA, total computation of MHUIRA is equal to 2. Lemma 2. Space complexity of MHUIRA is of NUL's entries, where is the number of items in set and is the number of transactions in database.
Conclusion In this paper, we have proposed to add a regularity constraint to high utility itemset mining. Then, the problem of mining high utility itemsets with regular occurrence (MHUIR) was introduced. This can give information about "regular purchases by customers of high-profit products". To find such itemsets, an efficient single-pass algorithm named MHUIRA and a new modified utility-list structure (called NUL, used for maintaining utility and occurrence information) were presented. Experiments were conducted on both real and synthetic datasets and complexity analyses were also given to indicate the efficiency and effectiveness of MHUIRA and NUL. In the future, we will extend MHUIR by focusing on: (i) difficulties on assigning appropriate thresholds; (ii) redundancy of HUIRs; and (iii) finding HUIRs in different kinds of databases, respectively.
, , … , be a set of items. Each item ∈ has it own external utility, denoted as. The external utility of item can be profit, cost, and other user-defined factors. A set ⊆ is called -itemset if contains items. A transactional database , , … , contains transactions in which each transaction ∈ is a 2-tuple containing: (i) a unique transaction identifier p (usually called TID for short); and (ii) an itemset ⊆ where each ∈ associates with internal utility, i. e. quantity of occurrence of in transaction , denoted as ,. If ⊆ , it can be said that occurs in transaction or transaction contains , denoted as. Since can occur several times in , then the set of ordered TIDs of transactions containing can be expressed as , … , where and 1.
Definition 6. 6 Let ≻ be the order of items in set of items. The remaining utility of in the transaction ordered by ≻ is the summation of the utility values of Example 3. For the external utility values and the transactional database in Tables 1 and 2, the tight over-estimated utility of itemset ' ' can be 189 , , , , 189 5 16 10 5 225. all items in that were ordered after , defined as , ∑ ∈ , ≻ ,. Definition 7. The remaining utility of in a database is the summation of all remaining utility values of in all transactions that contain , defined as ∑ ∈ , ∈ ,. Definition 8. The tight over-estimated utility of in database is the summation between the utility and the remaining utility of in database , defined as.
Social learning -the transmission of behaviors through observation or interaction with conspecifics -can be viewed as a decision-making process driven by interactions among individuals. Animal group structures change over time and interactions among individuals occur in particular orders that may be repeated following specific patterns, change in their nature, or disappear completely. Here we used a stochastic actororiented model built using the RSiena package in R to estimate individual behaviors and their changes through time, by analyzing the dynamic of the interaction network of the fruit fly Drosophila melanogaster during social learning experiments. In particular, we re-analyzed an experimental dataset where uninformed flies, left free to interact with informed ones, acquired and later used information about oviposition site choice obtained by social interactions.
, 2014). In particular, a temporal network approach may help to clarify how the dynamics of animal interactions modify network topology and relates to information flow  (Charbonneau et al. , 2013)  and learning  (Skyrms, 2009). Some of these methods come from human social science and have principally been developed to predict behaviors based on social structure  (Steglich et al. , 2006; Mercken et al. , 2010; Snijders et al. , 2010b; Schaefer et al. , 2011). In this context, the use of the dynamic actor-oriented model developed in the R package RSiena  (Ripley et al. , 2013a)  provides powerful estimations of individual behaviors and their changes through time. These methods, developed in the RSiena package  (Ripley et al. , 2013a) , allow users to perform a wide range of data analysis on the same platform used for dynamic modeling operations.
, 2013). Indeed at large timescales, it was observed that information flow within the colony was slower than expected, whereas at smaller timescales it was faster, suggesting that the network facilitated local rather than global information transmission. In this work, we performed a social network analysis using the RSiena package to evaluate the dynamic of social interactions during social learning experiments, using the gregarious species Drosophila melanogaster, which has already been demonstrated to rely on social learning regarding oviposition site preferences  (Sarin and Dukas, 2009; Battesti et al. , 2012). Schneider et al. (2012)  have demonstrated the existence of non-random interaction networks in wild-type individuals in this species, and more recently, experiments performed by  Battesti et al.
Video Analysis The social transmission phases were recorded using a camera placed vertically above the arena. Using the Ctrax software  (Branson et al. , 2009) , the movements of each individual were automatically followed and its coordinates in the arena recorded for each frame of the video, at a rate of 10 frames per second. Using these coordinates as our raw data, we constructed interaction matrices for each experiment using an automated code we specifically developed in R (code available under request). To this end, we defined an interaction between two individuals based on spatial and temporal constraints: proximity between two flies had to (1) be smaller than 1. 1 average body lengths and (2) last for more than five frames of the video (i. e. , 0. 5 s). These thresholds were derived from several preliminary assumptions and observations.
Dynamic Analysis A stochastic agent-based model was run using R (version 3. 1. 3) (R Core Team, 2015) and the RSiena package (version 1. 1-232)  (Ripley et al. , 2013a)  after testing for the different time-scales we had previously defined (i. e. , 5, 10, and 15 min). Indeed, changes between two consecutive networks can be too small to rise above the significance threshold, or too large for the model to consider the networks as consecutive stages of the same process. In our case, this lead to an impossibility for the models to converge on our data based on the 5 (few individuals are connected in the matrix) and 15-min (all individuals are connected) intervals. All modeling was thus performed on the 10-min intervals, for which convergence was always successful and satisfying; all t-statistics for convergence were inferior to 0.
However, this does not necessarily imply that past states are irrelevant; they can intervene through the influence they have on the current state itself. In our case, this will be reflected by the fact that past interactions will affect an individual's current behavior, as it is likely to have changed its state along the way (i. e. , acquired information) or have a different knowledge of its social environment as it contacts other informed and uninformed flies. Several methods of estimation have been implemented into Siena since its development: the Method of Moments  (Snijders, 2001) , the Maximum Likelihood method  (Snijders et al. , 2010a)  and the Bayesian method  (Koskinen and Snijders, 2007). While the two latter sets of methods usually yield smaller standard errors for estimates, their use is strongly encouraged in situations with small network datasets and/or very complex models.
RSiena allowed us to analyze the dynamics of interaction networks during social transmission experiments and to identify the flies' involvement in the process of information transfer. The implementation of additional effects taking into account social processes observable in more complex animal societies would make this tool even more useful to biologists studying other species by allowing them to construct complex models to explain the dynamics of their observed interaction networks. For instance, RSiena's actor-oriented models might be used to estimate the dynamic formation of triadic subgroups (i. e. , transTrip effect in the RSiena manual) in species experiencing triadic coalitions among group members (e. g. , primates  Kappeler, 2012; Corvus corax Loretto et al. , 2012). Moreover, estimation in RSiena is based on the analysis of unweighted networks, meaning that the data used as input for this program reflects the interactions between individuals in the group, but not their frequency.
A more rational and controllable route would be to apply a building-block approach of linking SMMs together using suitably-designed linker groups that will result in weak inter-SMM interactions, while ensuring that molecular oligomers rather than polymers are obtained. Our group has therefore initiated a program to link covalently two or more SMMs to give non-polymeric, magneticallysupramolecular 'aggregates-of-SMMs' exhibiting very weak inter-SMM exchange interactions. The objective is to obtain materials exhibiting exchange-biased QTM and, in some cases, allow detailed study of quantum superposition states and related phenomena. For these reasons we are targeting small oligomers at this stage, in order to better detect the signatures of these nebulous and fragile effects. In the present work, we report the synthesis and properties of four oligomeric aggregates of Mn 3 units; two [Mn 3 ] 2 dimers and two [Mn 3 ] 4 tetramers, all containing dioximate inter-Mn 3 linker groups.
Raw data frames were read by the program SAINT and integrated using 3D proling algorithms. The resulting data were reduced to produce hkl reections, their intensities and estimated standard deviations. The data were corrected for Lorentz and polarization effects, and numerical absorption corrections were applied based on indexed and measured faces. The structures were solved and rened in SHELXTL6. 1, 75 using full-matrix least-squares renement cycles. The non-H atoms were rened with anisotropic thermal parameters and all of the H atoms were calculated in idealized positions and rened as riding on their parent atoms. Unit cell data and structural renement details for the four compounds are listed in Table  1. More details are provided in ESI. † 76.
Physical and spectroscopic measurements Infrared spectra were recorded in the solid state (KBr pellets) on a Nicolet Nexus 670 FTIR spectrometer in the 400-4000 cm À1 range. Elemental analyses (C, H, and N) were performed by the in-house facilities of the University of Florida, Chemistry Department. Variable-temperature direct current (dc) and alternating current (ac) magnetic susceptibility data were collected at the University of Florida using a Quantum Design MPMS-XL SQUID magnetometer equipped with a 7 T magnet and operating in the 1. 8-300 K range. Samples were embedded in solid eicosane to prevent torquing. Magnetization vs. eld and temperature data were t using the program MAGNET. 77a Pascal's constants were used to estimate the diamagnetic correction, which was subtracted from the experimental susceptibility to give the molar paramagnetic susceptibility (c M ).
The J vs. J 0 error surfaces for the ts, obtained with the program GRID, 77b showed them to be unique (Fig. S5  and S6 †). 76 The obtained J and J 0 reveal 3 to have an S ¼ 3/2 ground state with an S ¼ 5/2 rst excited state at 49. 7 cm À1. 4 has an S ¼ 2 ground state and an S ¼ 3 rst excited state at 29. 6 cm À1. The presence of both F and AF interactions is as expected from the structures of 3 and 4. The bis-acetate-bridged edges correspond to the AF J interactions, while the bis-oximate-bridged edge corresponds to the F J 0 interaction. This is consistent with previous studies of (i) [Mn 3 O(O 2 CR) 6 L 3 ] 0,+ complexes (2Mn III Mn II and 3Mn III ),  73  where all couplings are AF, and (ii) mono-oximate bridged 2, where all couplings are F. 80  In 3 and 4, two oximates now bridge the J 0 (Mn  III  2 ) edge and the coupling is again F, consistent with the non-zero oximate Mn-O-N-Mn torsion angles in 3 (av 17.
1 cm À1 above the ground states of 5 and 6, respectively, and the ground states are thus well-isolated. The ground states of the Mn 3 units in 5 and 6 were conrmed by ts of magnetization (M) data collected in the 0. 1-7 T and 1. 8-10 K ranges. The data, plotted as M/Nm B vs. H/T in Fig. S9 †  (N is Avogadro's number and m B is the Bohr magneton) were t, as described for 2, 80 using the program MAGNET 77a by diagonalization of the spin Hamiltonian matrix assuming only the ground state is populated, incorporating axial anisotropy (DS ˆz2 ) and Zeeman terms, and employing a full powder average. 103, 104 e assumed each Mn 3 to be acting independently, i. e. , that any Fig. 7  2-D contour plot of the error surface for the J vs. J 0 fit of the dc c M T vs. T data for complex 5. The darkest-brown region has the lowest error, where the two fits (asterisks) of comparable quality are located, and the blue region has the highest error.
This contrasts the case for the axial molecular zfs parameter, D, which results from an addition of the single-ion anisotropies. The effect of the two triangular topologies is therefore likely to be quite similar to a disorder that disproportionately affects the transverse anisotropy parameter, E. This type of disorder is well known in other Mn III containing clusters,  105  including the Mn 12 -acetate SMM,  106, 107  where it has been shown to signicantly impact the perpendicular spectrum without affecting the parallel components signicantly. These inuences can be taken into consideration when simulating spectra using a program such as EasySpin. 108 We note that the temperature dependent studies (Fig. 9 ) do not provide any strong indications that there are lowlying states with S ¼ 5, 4, etc.
Acknowledgements This work was supported by the  USA National Science Foundation  (GC:  CHE-0910472 ,  DMR-1213030 ; SH:  DMR-1309463 ) and by the  Air Force  (SH: AOARD grant # 134031 ). WW acknowledges grants  FP7-ICT-2013-10-610449   MoQuaS  and  ANR-13-BS10-0001-03  MolQuSpin. T. N. N thanks the  Vietnam Education Foundation  for a fellowship. Work performed at the NHMFL is supported by the  USA National Science Foundation  ( DMR-1157490 ) and the  State of Florida.
The Database of Protein Disorder (DisProt, URL: www. disprot. org) has been significantly updated and upgraded since its last major renewal in 2007. The current release holds information on more than 800 entries of IDPs/IDRs, i. e. intrinsically disordered proteins or regions that exist and function without a well-defined three-dimensional structure. We have re-curated previous entries to purge DisProt from conflicting cases, and also upgraded the functional classification scheme to reflect continuous advance in the field in the past 10 years or so. We define IDPs as proteins that are disordered along their entire sequence, i. e. entirely lack structural elements, and IDRs as regions that are at least five consecutive residues without well-defined structure. We base our assessment of disorder strictly on experimental evidence, such as X-ray crystallography and nuclear magnetic resonance (primary techniques) and a broad range of other experimental approaches (secondary techniques).
INTRODUCTION Our traditional view of protein structure and function is deeply rooted in the structure-function paradigm which stated that the polypeptide chain of proteins needs to fold into a stable three-dimensional (3D) structure, which is a prerequisite of the functioning of the protein. The extreme explanatory power and success of this model is attested by more than hundred thousand high-resolution structures in the Protein Data Bank (PDB) (1) and many Nobel Prizes awarded for describing structures central to understanding important cell-biological phenomena. It has been suggested almost 20 years ago, however, that many proteins or regions of proteins in various proteomes lack such stable 3D structure, and are rather intrinsically disordered under native, physiological-like conditions (thus named IDPs/IDRs, respectively)  (2) (3) (4).
The current best structural description of IDPs/IDRs is by structural ensembles, which can be solved by a combination of experimental and computational approaches and are collected into a dedicated structural database, PED  (8). Studies of the structure-function relationship of disordered proteins have shown that in certain cases their function arises directly from the disordered state (entropic chains), whereas in many other cases their function emanates from molecular recognition accompanied by induced folding to specific binding partners, such as another protein, RNA or DNA molecule  (9, 10). In these functions, the sensitivity to regulated remodeling of the disordered structural ensemble is an excellent substrate for protein regulation, as exemplified by frequent post-translational modifications  (11)  and special modes of allosteric regulation  (12)  involving IDPs/IDRs.
Due to the prevalence and importance of structural disorder, several dedicated databases covering various aspects of IDPs/IDRs have appeared in the past decade. DisProt is the primary repository of disorder-related data on sequenceand functional annotations, focusing on disordered proteins or regions with experimental verification  (13, 14). Several other databases are based on predictions of disorder, such as D 2 P 2 , which contains disorder protein predictions by a variety of predictors on 1765 complete proteomes (15), Mo-biDB, which features three levels of annotations, manually curated, indirect and predicted for all UniProt sequences (over 80 million)  (16) , and IDEAL, which contains manual annotations of interaction regions undergoing induced folding, sites of post-translational modifications and assignments of structural domains  (17).
In addition, as already mentioned, PED is the database that gathers structural information on IDPs/IDRs, in the form of structural ensembles  (8). The interaction of IDPs/IDRs with their target(s) is most often mediated by short continuous stretches of amino acids such as Molecular Recognition Elements/Features (MoREs/MoRFs) (18) and short/eukaryotic linear motifs (SLiMs/ELMs), which have been collected in the ELM database  (19). Less frequently, partner interactions of IDPs/IDRs may also be mediated by intrinsically disordered domains (IDDs), i. e. longer regions that conform to the definition of domains as functional, evolutionary and structural units  (20). Although probably still underappreciated, some of these IDDs may be found in the Pfam database of protein families which includes their annotations and underlying multiple sequence alignments  (21).
These data enable probing sequence-specific structural information in IDPs/IDRs. A particular strength of NMR is that it can be increasingly applied under truly in vivo conditions, in live cells  (25). Therefore, these two experimental approaches, X-ray crystallography and multidimensional NMR, are considered as the 'primary techniques' providing evidence for structural disorder on a per residue basis in DisProt. It should not miss our attention, though, that due to the expenses of isotopic labeling in NMR and the high rate of failure in protein crystallization, it would be unreasonable to only rely on these two approaches to document protein disorder. Therefore, beyond X-ray crystallography and NMR, a plethora of alternative biochemical and biophysical approaches (termed 'secondary techniques') provide orthogonal information on protein disorder in DisProt  (4, 22).