dataesr/scientific-paragraphs-categorization huggingface dataset:
The literature search was constructed around search terms for obesity (obesity, overweight, weight, metabolic syndrome, adiposity), mindfulness (mindfulness, acceptance, meditation, awareness), disordered eating (binge eating, impulsive eating, disinhibition, uncontrolled eating, disordered eating, calorie intake), and exercise (exercise, physical activity, sport, energy expenditure) in full texts words. The search strategy was adapted for each database as necessary. Potential additional studies were searched through the reference lists of included trials. The selection process for studies included in this review is shown in Fig  1.
Data extraction First, titles and abstracts were screened to identify potentially eligible studies. Second, full texts of all potentially relevant articles were investigated. Two authors independently screened the articles to identify studies that met inclusion criteria, and conflicts of opinion were discussed with a third author until consensus was reached. Using a standardized data extraction form, two independent investigators extracted and tabulated all data with any disagreements resolved by discussion among the investigators, or, if required, by a third party. When necessary, the primary authors of the trials were contacted for additional information. Data extracted from the studies and study characteristics are available in Table  1. Risk of bias was independently assessed by two authors using the Cochrane risk of bias assessment tool  [52].
Statistical analysis Data for the primary outcome variable, change in BMI, were expressed as a mean difference (MD) because BMI was measured using identical units (kg/m 2 ) across studies. Data for secondary outcomes, impulsive eating, binge eating, and physical activity levels, were expressed as Cohen's d, because different measurement tools were used to assess each variable. Data from original articles were transformed as MD and Cohen's d by using Cochrane guidelines in each case (e. g. , transforming standard errors into standard deviation, calculating standard deviations of original MD if not provided)  [53]. MD and Cohen's d were analyzed using random effects because of small sample sizes in the included studies. We contacted the authors to obtain relevant missing data, if feasible. The magnitude of between-study heterogeneity after correcting for statistical artifacts evaluated by the I 2 statistic with levels below 40%, between 30% and 60%, between 50% and 90%, and greater than 75% equating to low, moderate, substantial, and high levels of heterogeneity, respectively  [53].
RKK do not provide the computational details of their optimization procedure, but there are currently a number of off-the-shelf algorithms available that implement the idea of maximizing intra-cluster similarities and minimizing inter-cluster similarities. These algorithms are able to cluster even moderately large data sets in a split second. This holds true, for instance, for the Partitioning Around Medoids (PAM) clustering algorithm as implemented in R, Mathematica, MATLAB, various libraries for Python, and other software packages. While PAM or similar algorithms do not guarantee that the optimal clustering will be found-such a guarantee could only be had by going through all possible sortings of the 330 chips, which, as said, is unrealistic-these algorithms are generally believed to often yield good approximations to the global maximum (which does not have exactly the same definition for the different algorithms).
Whether the use of the 330 Munsell chips from the WCS biased the clustering results is certainly a legitimate concern, for both the current study as well as RKK's, and the same might be said for the assumption of CIELAB space (for various specific concerns about the stimuli or CIELAB space see, for instance,  [17, 49, 50] ). Even granting that the foregoing are critical methodological concerns, however, the meaningfulness of the results of RKK's implementation of Jameson and D'Andrade's notion of most informative partitioning and its application to the 330 Munsell chips in CIELAB space cannot be ignored. After all, the model does have strong empirical backing from the results obtained for categorizations of up to six categories. It was therefore critical to explore the limits of the model with respect to its ability to explain the data concerning color categorization going beyond six categories.
Acknowledgments We are greatly indebted to  Terry Regier , who provided extensive and very valuable comments on two previous versions of this paper. For further comments, we are grateful to  Alejandro Parraga ,  Steven Verheyen , and an anonymous referee. Henrik Singmann gave useful advice on the hydroPSO optimization procedure, for which we thank him. The first author's research for this project was supported by a fellowship from the  Centre for the Study of the Senses, Institute of Philosophy, School of Advanced Study, University of London , in the context of  AHRC grant  " Rethinking the Senses " ( AH/L007053/1 ). This support is also gratefully acknowledged.
Auditory Stimuli Six natural Thai monosyllabic words were selected for the experiment: /ba1/, /pa1/, /p h a1/, /ba:1/, /pa:1/, /p h a:1/. These words varied in vowel length, with short (/ba1/, /pa1/ and /p h a1/; 261 ms on average) and long vowels (/ba:1/, /pa:1/ and /p h a:1/; 567 ms on average), in Voice Onset Time (VOT; /ba1/ and /ba:1/, VOT = -140 ms vs. /pa1/ and /pa:1/, VOT = 5 ms), and in aspiration (/pa1/ and /pa:1/, VOT = 5 ms vs. /p h a1/ and /p h a:1/, VOT = 77 ms). Stimuli were recorded by a female Thai-French bilingual, ensuring that all words were produced naturally. For each word, five versions were digitally recorded in order to reproduce natural speech variability. Fundamental frequency was similar for each word (F0: 175 Hz) and sound pressure level was normalized across all words to a mean level of 70 dB using Praat software  (Boersma and Weenink, 2011).
Experimental Tasks Children were tested individually in a quiet experimental room while they sat in a comfortable chair at about 1 m from a computer screen. Auditory stimuli were presented through HiFi headphones (Sennheiser, HD590). Visual and auditory stimuli presentation, as well as the collection of behavioral data, were controlled by the ''Presentation'' software (NeuroBehavioral Systems, Version 11. 0). Children performed six concatenated tasks (see Figure  1 ).
EEG Data Acquisition The Electroencephalogram (EEG) was continuously recorded at a sampling rate of 512 Hz with a band-pass filter of 0-102. 4 Hz by using a Biosemi amplifier system (Amsterdam, BioSemi Active 2) with 32 active Ag-Cl electrodes (Biosemi Pintype) located at standard positions according to the International 10/20 System  (Jasper, 1958). EEG recordings were referenced on-line to a common electrode (CMS) included in the headcap (next to Cz). Two additional electrodes were placed on the left and right mastoids and data were re-referenced off-line to the average activity of the left and right mastoids, filtered with a bandpass filter from 0. 1-40 Hz (slope of 12 dB/oct). The electrooculogram (EOG) was recorded from flat-type active electrodes placed 1 cm to the left and right of the external canthi, and from an electrode beneath the right eye.
Electrode impedance was kept below 5 kΩ. EEG data were analyzed using the Brain Vision Analyzer software (Version 1. 05. 0005; Brain Products, Gmbh). Independent component analysis (ICA) and inverse ICA were used to identify and remove components associated with vertical and horizontal ocular movements. Finally, baseline correction, DC-detrend and removal of artifacts above a gradient criterion of 10 µV/ms or a max-min criteria of 100 µV over the entire epoch were applied resulting in an average of 12% of rejected trials. For each child, ERPs were time-locked to word onset, and segmented (including a 200 ms baseline) into 1200 ms epochs in the phonological categorization tasks and into 1700 ms epochs in the other tasks (i. e. , word learning phase 1, matching and semantic tasks). To increase the signal to noise ratio, all responses were considered to compute the individual averages.
Statistical Analysis Analysis of Variance (ANOVAs) were computed using the Statistica software (Version 12. 0, StatSoft Inc. , Tulsa). For errors (ERRs) and RTs in each task, ANOVAs included Group (MUS vs. NM) as a between-subject factor. Additional within-subject factors were Task (voicing vs. vowel length vs. aspiration) in the phonological categorization task and Condition (match vs. mismatch or related vs. unrelated, respectively) in the matching and semantic tasks. ERPs in the phonological categorization task were analyzed by computing N100 maximum amplitudes in the 90-160 ms latency band. During the word learning phase 1, as well as in the matching and semantic tasks, ERPs were analyzed by computing the mean amplitudes of the N200 and N400 components in specific latency bands defined from visual inspection of the traces and from previous results in the literature ANOVAs included Group (MUS vs.
DISCUSSION This series of experiments revealed three main findings. First, ERPs recorded in the word learning phase showed that the temporal dynamics of novel word learning, as reflected by significant modulations of N200 and FN400 amplitudes after only a few minutes of picture-word associative learning, was faster in children with music training than in control children. Second, while all children were able to learn the meaning of new words, music training was associated with more efficient learning of picture-word associations as reflected by both behavioral and electrophysiological data in the matching and semantic tasks. Finally, a fronto-parietal network was involved in word learning with a shift of the distribution of the N200 and N400 components from frontal regions in the learning phase to parietal regions during the test phase (matching and semantic tasks).
ACKNOWLEDGMENTS We would like to thank the children who participated in the experiment and their parents,  Eric Castet ,  Agustin Lage ,  Pedro A. Rojas-Lopez  and  Pedro Valdès-Sosa  for very useful statistical advices, and  Stefan Elmer  for helpful suggestions for data analyses. JC was supported by a post-doctoral fellowship from the  ''Fondation de France '' and ED by a  doctoral fellowship  from the  ''Brain and Language Research Institute'' (BLRI). The BLRI partly supported this research ( ANR-11-LABX-0036  ( BLRI ) and  ANR-11-IDEX-0001-02  (A * MIDEX)).
, 2012; Seifert et al. , 2015). For instance,  Pijpers et al. (2006)  implied that exploratory behavior reflects poor performance. More recent studies have combined the analysis of exploratory actions with spatiotemporal performance outcomes, revealing that exploratory actions can be related to an improvement in performance through practice  (Seifert et al. , 2015). Thus, this review draws together studies that have reported on performance and discrete limb actions in climbing tasks to evaluate how, in combination, these analyses can explain successful and efficient climbing. The review is structured into three parts. First, we examine the existing state of the art on how spatial and temporal outcomes are used to quantify skilled climbing. Next, data pertaining to activity states are considered with respect to their functionality for the individual.
SEARCH METHODOLOGY Medline and SPORTDiscus databases were searched for published primary sources. Keywords related to climbing (rock climbing, ice climbing, mountain climbing, boulder climbing, artificial climbing, top-rope climbing, lead-rope climbing, mixed climbing, indoor climbing, outdoor climbing, route climbing, slope climbing) were pooled (via Boolean operation "OR") and combined (via Boolean operation "AND") with keywords related to skilled behavior  (skill, transfer, perform, ability, expert, novice, beginner, intermediate, advanced, elite, dynamic, force, kinematics, kinetics, perception, action, cognition, behavior, center of mass, trajectory, movement, movement pattern, recall, gaze, vision, coordination, motor, feet, hand, foot, grasp, reach, pattern, intervention, pedagogy, feedback, constraint, coach, learn, practice, applied, train, fluency, fluidity, smoothness, jerk, activity state, classification, intention, exploration, strategy)  and also pooled via Boolean operation "OR.
" Results were limited to human participants, written in the English language, and, Medline and SPORTDiscuss databases searched from their earliest available record up to November 2016. Google Scholar was then used to scrutinize the related articles and referencing studies. Reference lists of all eligible studies were then manually inspected. Articles were restricted to those written in the English language. Restrictions were also made on the participant sample, study design and outcomes measures. Specifically, for inclusion, studies were required to report sample characteristics so that ability level could be estimated as either beginner, intermediate, advanced, elite or upper elite  (Draper et al. , 2011a). Study designs were limited to experimental or technical reports that involved climbing a surface graded for difficulty  (Draper et al.
, 2011a). Furthermore, studies where the task goal did not, implicitly or otherwise, require getting to the end of the route were excluded. For example, if the task required participants to adopt a static posture or perform isolated reach and grasp actions, it was excluded since such task constraints do not impose a route finding problem. Outcomes were restricted to at least one measure to quantify spatial, temporal patterns of the COM or limbs, or, activity state during actual climbing. Appraisal of article quality was evaluated in terms of potential contribution to understanding how activity states influence performance efficiency along spatial-temporal measures. Eligible experiments were then identified to a standardized form which was then used to extract relevant study data (see Table  1 ).
RESULTS Using the search methodology, the Medline database yielded 1,099 titles and abstracts. These were screened yielding 35 relevant articles, which were identified and their full texts retrieved. Relevant studies were then screened using the standardized inclusion criteria and 13 eligible studies identified. Using the same search methodology, the SPORTDiscsuss database was searched. This analysis yielded 2,201 results from which titles and abstracts were screened, and 59 relevant articles identified. After duplicate removal, full texts were retrieved and eligibility was assessed using the standardized inclusion criteria, identifying 15 studies this way. The related articles, citing articles and reference lists of 7,400 eligible studies were searched using Google Scholar. 94 relevant studies were subsequently identified for eligibility screening.
Data reduction and statistics Four conditions arose from the combination of expertise (novice vs advanced) and the optimisation (actual vs optimal). For all trials, performance scores (G T H ) were obtained. In the meanwhile, components that relate to performance by means of controlling global body and segments rotations were identified. On one hand, the global body rotation included the rotation due to the release state (θ RS ) and the additional rotation due to the change in moment of inertia (θ ΔI ). On the other hand, the segments rotations relative to the global body are defined by complementary rotations of legs (θ TRlegs ), arms (θ TRarms ) and trunk (θ TRtrunk ) thanks to segmental angular momentum transfers. These components were calculated using angular velocity of the body at release (ω 0 = σ 0 /I 0 ), the moment of inertia time history (I(t)), and the segments angular velocity time histories (ω i ) such as: y RS ¼ o 0 ðt f À t 0 Þ ð1aÞ y DI ¼ Z t f t 0 s 0 IðtÞ dt À y RS ð1bÞ y TRi ¼ Z t f t 0 o i dt À y RS À y DI ð1cÞ Pearson correlation matrices were chosen to estimate a linear relationship between performance and rotation components in each group.
Method Participants A total of 59, right-handed, native French-speaking participants were included in the study: nineteen patients with a diagnosed AD (13 women; mean age ± standard deviation = 81. 2 ± 4. 9 years), 20 community-dwelling healthy older participants (16 women; mean age = 78. 3 ± 5. 7 years) and 20 healthy young participants (12 women; mean age = 23. 7 ± 3. 6 years). Young participants were recruited by advertisements spread on a French internet database of volunteers willing to participate in psychology or neuroscience research. Older participants were community dwelling and were recruited by advertisements and notices distributed through senior citizen organizations in the Paris areas. The patients with AD were recruited from a local memory center and were at the early to mild stage of the disease  (MMSE between 19 and 24; Feldman & Woodward, 2005).
Stimuli Stimuli consisted of 120 static greyscale photographs of 40 individuals (20 men/20 women) which were selected from a database of digitized portraits of young adult faces (see  Conty, N'Diaye, Tijus, & George, 2007; Vuilleumier, George, Lister, Armony, & Driver, 2005). All faces had neutral expression and involved individuals unknown to our participants. The age of each individual ranged from 20 to 60 years and our stimuli selection included approximately 1/3 of young-looking faces, 1/3 of middle-aged-looking faces and 1/3 of old-looking faces. Head direction was always oriented straight toward the observer. Each individual was presented in three different views: one with the eyes directed straight toward the observer (Direct Gaze condition), one with the eyes averted by 30° toward the right side from the observer position (Averted Gaze condition) and one with closed eyes.
Acknowledgements The authors would like to thank  Jérôme Blin, M. D. ,  Claire Gautherot  and  Oriane Breysse , neuropsychologists, and all the staff of the private geriatric hospital "Les Magnolias", as well as  Severine Rose , psychologist, and all the staff of the day care centre for people living with dementia "Mémoire Plus" for their kind and helpful assistance.
Subjects Sixteen AD patients and an equal number of aMCI subjects and aged healthy subjects (NE) were enrolled and performed an fMRI study and a neuropsychological assessment. Table  1  summarizes the demographic data of the three groups. Age, education level, and gender was not significantly different (ANOVA). Probable AD was diagnosed according to NIA-AA criteria  (McKhann et al. , 2011). aMCI subjects were diagnosed according to Petersen's criteria  (Petersen et al. , 1999 ,.
Participants were trained outside the scanner for 15 min before data acquisition. During the training session, subjects were instructed to keep their gaze on the fixation point for the entire duration of the experiment, and to execute grasping movements about once every 2 s. If the training task (that was conducted with different parallel versions), was not properly executed by the subjects, they were not included in the study. We used an MR-compatible visual system to present the stimuli (VisuaStim Digital system, Resonance Technology Inc. ). The use of E-Prime software (E-Prime 2. 0 Psychology Software tool, http://www. pstnet. com) ensured exact timing of prompts during MR acquisition. The performance was visually checked by the examiner during the execution of the task. The examiner maintained accuracy by reporting all the tasks correctly performed.
MRI Acquisition Functional images were collected by a gradient echo-planar (EPI) T2 * sequence (TR = 3,000 ms; TE = 50 ms; flip angle = 90 • ; voxel size = 2,8125 × 2,8125 × 4 mm; matrix size = 448 × 448; number of slices = 38; thickness = 4 mm) using BOLD (blood oxygenation level dependent) contrast. Each fMRI session included two runs of 122 dynamics. MRI data was acquired on a 1. 5 Tesla Siemens Magnetom Avanto at Santa Maria Nascente Institute IRCCS, Don Carlo Gnocchi Foundation. A conventional T2-weighted scan (TR = 2,920 ms; TE = 108 ms; voxel size = 0. 75 × 0. 75 × 5. 2 mm; matrix size = 320 × 320; slice thickness = 4 mm; number of slices = 25) was performed in every subject in order to exclude brain abnormalities. A 3D T1-weighted FFE scan (TR = 1,900 ms; TE = 3. 37 ms; voxel size = 1 × 1 × 1 mm; matrix size = 192 × 256; slice thickness = 1 mm; number of slices = 176) was acquired to be used as an anatomical reference for fMRI analysis and to compute hippocampal volumes on every subject.
Hippocampal Segmentation Bilateral hippocampi were segmented using a dedicated software (FSL-  FIRST Patenaude et al. , 2011). Brain tissue volume, normalized for subject head size, was estimated with SIENAX  (Smith et al. , 2002) , part of FSL  (Smith et al. , 2004 ). An ANCOVA analysis was performed to compare hippocampal volumes between groups. The Scaling Factor, obtained using SIENAX, was included as a covariate in the analysis. Results were considered as statistically significant if surviving p < 0. 05 with Bonferroni correction for multiple comparisons. fMRI Analysis fMRI data was analyzed in agreement with the General Linear Model running on MATLAB 7. 6 (MathWork, Natick, MA) and SPM12 (Wellcome Dept. Cogn. Neurol. , London; http://www. fil. ion. ucl. ac. uk/spm). Images were first corrected for motion, then they were realigned and movement parameters were estimated.
The hypothesis of a posterior-anterior decay in Alzheimer-type degeneration agrees with recent data obtained in MCI using EEG and MRI recording  (Moretti, 2016). In this investigation, alpha3/alpha2 frequency power ratio, considered as a predictor of conversion into AD  (Moretti et al. , 2011)  as well as cortical thickness at MRI were computed. Three MCI groups were obtained considering increasing tertile values of alpha3/alpha2 ratio. From this morphological MRI and EEG parameter data the author inferred that MN are impaired in prodromal AD. Conversely, we directly measured the activation of human MN with a specific fMRI task, thus giving a functional support to the hypothesis for the MCI and the AD group.
Study protocol Clinical data for the patients were obtained from their medical records. We recorded clinical characteristics before the initiation of treatment, the initial dose and   (23). As usual, the dose was adjusted according to weight and serum IGF-I levels. For safety reasons, if IGF-I levels reach an SDS of more than 2 for chronological age and pubertal stage according to our reference range  (24) , the dose should be decreased by 10%. All but one of the patients treated for at least one year continued treatment for at least 2. 4 years, until they reached near-adult height (HV ≤ 3 cm/year). Patients underwent a physical examination at baseline and at each six-monthly visit. Height, weight and pubertal status were assessed. All patients were followed in both Endocrinology (every 6 months) and Psychiatry (every 1-6 months) Units.
Statistical analysis Results are expressed as means ± s. d. or medians (25-75th percentiles) unless otherwise specified. Nonparametricpaired Wilcoxon tests were used for statistical analysis. Bonferroni correction was applied to adjust the significance threshold for multiple comparisons. All tests were two-tailed, and values of P < 0. 02 were considered statistically significant. All statistical analyses were carried out with SAS software, version 9. 12 (SAS Institute Inc. , Cary, NC, USA).