dataesr/scientific-paragraphs-categorization huggingface dataset:
At validation and test time, the network makes a prediction by extracting the 224 × 224 center patch. 1) Fine-tuning: This first framework is based on the finetuning strategy. The concept of fine-tuning is to use a model pretrained on a large dataset, replace its last layers by new layers dedicated to the new task, and fine-tune the weights of the pretrained network by continuing the backpropagation. The main motivation is that the most generic features of a CNN are contained in the earlier layers and should be useful for solving many different tasks. However, later layers of a CNN become more and more specific to the task for which the CNN has been originally trained. In this work, we fine-tune the model proposed in  [1]  composed of five stacked convolutional layers (some are followed by local response normalization and max-pooling), followed by three fully-connected layers.
B. Experimental Results To learn and evaluate the various frameworks, the dataset presented in Section III and composed of 26,525 1-second segments extracted from 30 movies is distributed into a training set, a validation set and a test set. Approximately 60% of the data is assigned to the training set and 20% of the data is assigned to both the validation and test sets. More precisely, 16,065 1-second segments extracted from 15 movies are assigned to the training set, 5,310 segments from 8 movies to the validation set and finally, 5,150 segments from 7 movies to the test set. This distribution makes also sure that the genre of the movies in each set is as diverse as possible. Table  II  presents the results of using CNNs (fine-tuned and learned from scratch), SVR and transfer learning for the prediction of valence and arousal dimensions based on the MSE and the Pearson's r correlation coefficient.
The gain in terms of MSE is more important for valence. For arousal, the MSE value is close to the performance obtained by the transfer learning strategy. However, for both arousal and valence, the correlation is much lower than the performance obtained with transfer learning. Nevertheless, it is a promising result given that the performance of this model on the training set indicates that, despite the use of a validation set to stop the learning phase if the performance on the validation set increased for 5 consecutive measurements, the size of the dataset is not big enough to prevent overfitting. Indeed, previous work has shown that overfitting and training set size are closely related  [27]. For example, the performance of the fine-tuned model on the training set for the prediction of valence is much better (M SE = 0.
VI. CONCLUSION This work presents the performance of Convolutional Neural Networks for affective movie content analysis and introduces a new dataset composed of 30 movies continuously annotated along the induced valence and arousal axes split into 25,525 1-second length video segments. This new dataset is publicly available at: http://liris-accede. ec-lyon. fr/, and is complementary to the original LIRIS-ACCEDE dataset. We have found that the fine-tuned CNN framework is a promising solution for emotion prediction. However, the limited size of the training set (16,065 samples) prevents the pure CNNbased frameworks to obtain good performances in terms of correlation. Nevertheless, intermediate layers, originally trained to perform image recognition tasks, are generic enough to provide mid-level image representations that can greatly improve the prediction of affective scores in videos.
As long as very large datasets annotated along affective dimensions are not available, transfer learning is a convenient trade-off to improve the performance of affective movie content analysis frameworks. In future work, we plan to treat the prediction of valence and arousal as a 2D regression problem to take into account the correlation of valence and arousal. We also plan to investigate the use of audio cues that are known to be important to model the arousal in particular. Finally, all the frameworks investigated in this work are static frameworks that do not model the dynamic of the videos. We hope that using this temporal information may help to produce more accurate affective predictions for videos.
1995)  and the Astronomical Image Processing System (AIPS:  Greisen 2003)  packages respectively, following the standard reduction procedures. For the ATCA data, the standard flux calibrator 1934-638 was used in all instances for both absolute gain and bandpass calibration. For the VLA observations, three of the VLA standard flux calibrators (namely 3C286, 3C48 and 3C147) were used, with the actual source used for each observing block dependent on observability. For VLA datasets with more than one frequency channel per spectral window, these sources could also be used for bandpass calibration in most cases. However, in a couple of instances the primary calibrator appeared slightly resolved (specifically 3C147 for AE117 and AE125 at X-band) and so an alternate, unresolved bandpass calibrator was chosen from amongst the phase calibrators for that particular run.
Literature searches were performed, and SEDs populated, for all sources in the Main Sample and its extension. Data were taken from the major radio catalogues (e. g. Australia Telescope 20 GHz Survey (AT20G), Table  2. Breakdown of the ATCA and VLA observations used in this paper that are not previously published elsewhere (All: P. I. Edge). Note that several BCGs are repeated, so these numbers do not correspond to the total number of clusters. See Appendices for full list of sources. This data was used when compiling our SEDs and is presented here for the first time. Cohen et al. 2007 ). Additional fluxes found by searches around the radio-peak coordinates in the NASA/IPAC Extragalactic Database (NED), High Energy Astrophysics Science Archive Research Center (HEASARC) database and/or the National Radio Astronomy Archive (NRAO) were individually scrutinised to ensure matches.
This creates a total [OIII]-sample of 113 BCGs. Using the bhkmethod tool within the Image Reduction and Analysis Facility (IRAF) shows that [OIII] flux is correlated with both the core and non-core radio flux. Both return a probability of no correlation <0. 001 (the limit) with calculated Kendall's-τ coefficents of 0. 7533 and 0. 7272 respectively. We use the buckleyjames linear regression tool within IRAF to compute best-fits to the data. This algorithm can handle censors only in the dependent variable of a dataset. Neither of our variables is truly independent. We assign the [OIII] line-flux as our independent variable, since we have selected this sub-sample on the availability of  [OIII]  data and also it requires the smallest removal of data. Nine sources with [OIII] limits are removed.
Appears to be a small double in FIRST and marginally resolved at C-band. Extrapolate from the peak of the C-band measurement to get an upper limit for the core. Note that the given position is the C-band peak and may best represent a lobe rather than the true nucleus. A1045 Unresolved at C-band with a hint of a slight peak-to-integrated flux deficit at L-band although no clear extent. Spectrum continues to be steep down to WENSS. Appear to be seeing a non-core component dominating at lower frequencies. The core cannot be unambiguosly separated with available data hence fit for the non-core only and take a limit on core. RXJ1715. 1+0309 Spectrum is a steep power law and source is resolved at C-band showing that the fit gives a good measure of the non-core. Extrapolate from the peak at C-band to get an upper limit for the core.
See Hogan et al. 2015b for a more in-depth look at this source. A3638 This source is unresolved and has a steep spectrum (α≈0. 93) between C-band, X-band and 20 GHz  (Murphy et al. 2010)  but no other literature fluxes are available. However, there is a nearby bright source that is confused with the BCG at low resolutions and hence could explain why this source is apparently missing in some low frequency catalogues. From the high resolution data the source can be classified as a CSS. Fit the steep spectrum to obtain a measurement of the non-core and then extrapolate from the 20 GHz detection to get an upper-limit on the active core component. A3639 Steep spectrum source although there is a detection at 20 GHz in AT20g  (Murphy et al. 2010 ) that suggests the spectrum may flatten to higher frequencies, which is indicative of this system containing an active core.
There are insufficient data points to adequately fit using a direct split power model although the steep component can be fit with a single power-law for a non-core component and then interpolating between the 20 GHz and X-band data with conservative uncertainty allows for the core component to be estimated. AS851 Source has a flat spectrum and multi-epoch observations at C-band suggest that it varies, indicating that this is core dominated. Fit to get a measure of the core flux and then extrapolate from the SUMSS detection for an upper limit on the non-core. RXJ2014. 9-2430 A steep spectrum source that is potentially slightly extended to the West as seen with the ATCA-6km array at C-band although this is not apparent at X-band. Fit the SED to get a non-core flux and then extrapolate from the X-band detection to get an upper limit on the core.
RXJ2213. 1-2754 The SED for this source suggests significant variability at GHz frequencies. Additionally, it appears as if some low resolution observations may suffer from confusion. Fitting a power-law to the SED returns a flat index (α≈0. 5). Use this to obtain an estimate of the core component and then extrapolate from the NVSS detection (lower frequency observations appear to be confused and hence are excluded) to get an upper limit on the non-core. A3880 The BCG is associated with the radio-source PKS 2225-308, and shows extended single-sided structure at both C-and X-band. From lower resolution data at C-band (e. g. with Parkes, Wright, Otrupcek 1990) it appears as if the interferometric observations are still missing extended emission. Fitting a single power-law to the SED comprising the integrated fluxes returns a steep index (α≈0.
D0. 10 BCS -LEs Hercules A A well studied classic FRII, bright object with esily distinguishable core and lobes. There are a large number of literature data points of integrated emission available in NED from which to get a measure of the non-core. Extrapolate from a 14. 9 GHz VLA observation  (Dicken et al. 2008)  of the core region for an estimate of the core component. This cluster is also known as RXJ1651. 1+0459, and the radio source associated with the BCG is often called 3C 348. A2052 UGC 09799, associated with the radio source 3C 317. Extended source with clearly distinguishable core. The source is reasonably well studied (see e. g. Hogan et al. 2015b). Fit to the SED of integrated fluxes for a non-core measurement. Source has multi-frequency VLBA observations  (Venturi et al. 2004 ) that isolate the core.
5 GHz (all  Lin & Mohr 2009). This non-detection shows the spectrum does not substantially flatten. Use only the higher resolution maps to create an SED and fit this for a measure of the steep non-core component and then extrapolate back from limit at 22. 5 GHz for core limit. A923 B2 1003+26. The available archival observations show the source to be an unresolved CSS up to C-band. Higher resolution VLA-A image in VLA archive (see also  Fanti et al. 1986)  shows the presence of an active core component at ∼0. 5 mJy. There is also an X-band image in the NRAO database taken in VLA-D/A array. Although the automatic pipeline image suffers from phase issues, blindly taking the peak pixel of the recovered source does suggest a ∼0. 5 mJy source is present. Fit the integrated lower resolution SED for a measure of the non-core component and then extrapolate from the VLA-A detection at C-band with index α=0.
Fit for a measure of the non-core and extrapolate for an upper limit on the core. Z1370 4C +74. 13. The BCG is associated with an unresolved, ultra-steep spectrum radio source. There are X and U-band observations available in the VLA Image database although neither recovers the source and both limits are consistent with the extrapolated index. Fit the observations of the USS >0. 074 GHz to retrieve an estimate of teh non-core and then extrapolate from the limit imposed by the non-detection at U-band to put an upper-limit on the core component. Note that there are very low frequency (38 and 22 MHz) observations that suggest the USS index may flatten to lower frequencies. Z3959 SDSS J111551. 90+012955. Distant cluster (z≈0. 35), at FIRST resolution the BCG is consistent with being associated with a steep spectrum radio source.
The authors present the uv data and from the amplitudes it is clear that ∼50 mJy is recovered on even the longest baselines. It appears as if there may be a currently active component or perhaps that this source was very recently shut down. For a core estimate, fit the unresolved core components at X and U band with suitably large uncertainties. A278 B2 0154+32. BCG is associated with a head-tail source. Fit the integrated flux SED for a measure of the non-core. Archival VLA-B observations at L and C band recover structure but do not fully isolate core component. For an estimate of the core contribution at 10 GHz, extrapolate from the 'head' component on the optical BCG position in the highest resolution map in the VLA Image Archive (at L-band). The recovered estimate is consistent with the limits imposed by low resolution observations at wavelengths higher than L-band.
ACKNOWLEDGMENTS We thank the anonymous referee for useful comments and suggestions that have greatly improved this work. MTH acknowledges support from the  Science and Technologies Funding Council (STFC) , studentship number  ST/I505656/1. ACE acknowledges support from  STFC  grant  ST/I001573/1. ACF and HRR acknowledge support from  ERC  Advanced Grant Feedback. HRR also acknowledges support from a  COFUND Junior Research Fellowship  at the  Durham University Institute of Advanced Study. This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration. This research has made use of data, software and/or web tools obtained from  NASA's High Energy Astrophysics Science Archive Research Center (HEASARC) , a service of  Goddard Space Flight Center  and the  Smithsonian Astrophysical Observatory.
Behavioral observations Behavioral data were collected by one observer (LR) using focal animal sampling  [43]  distributed from 8. 00 AM to 4. 00 PM, 7 days per week, yielding a total of 454 hours of observation over the 3. 5 month period. We maximized our efforts by following each female 2 hours per week, with 6 blocks of 20-min continuous focal sampling equally distributed across days and females. Due to exceptionally bad weather conditions we were sometimes unable to access the island, with the biggest gap occurring in February 2014. We selected 153 focal observations for analyses: 46 for the month preceding the pregnancy (i. e. pre-conceptive period or PCP, mean per female = 9. 2, range = 3-14 focal), 57 for the first month of pregnancy (mean per female = 11. 4, range = 9-15 focal) and 50 for the second month (mean per female = 10.
Assessment of female face color and luminance Objective collection of face redness (chromatic) and luminance (achromatic) data followed the method previously described in Rigaill et al. [29]. Photos were usually taken in the morning between 8. 00 AM and 12. 00 PM, whenever possible in an open area of the island where the light conditions were optimal, rather than in the forest. We used a Canon EOS 350D camera with an 8 megapixel CMOS censor and an EF 28-135 mm f/3. 5-5. 6 IS USM lens. White balance was set manually using an X-Rite White Balance Card (GretagMacbeth ColorChecker) Table  1. Definition and distribution of female and male sexual behaviors, female estrus and copulation calls in relation to female reproductive status (i. e. pre-conceptive period PCP, first and second months of pregnancy).
Estrus calls Female utters loud and long calls when alone or in the proximity of a male 1809 804 3065 Copulation before daily data collection, and reset between individual photo captures if needed (e. g. when the time lag between each individual's photo was too long or when the light conditions changed drastically). We collected images in RAW format with the flash disabled and with the shutter speed and aperture size determined automatically by the camera. To standardize images of face color, we used a Gretag X-Rite Color Checker (24 colored squares of known and varying reflectance) placed on an easel 5 cm off the ground. An image of this chart was taken immediately after the female face image. We placed the chart at the exact same position (i. e. distance and orientation) from the camera as the subject with camera settings standardized such that the image of the female face and the corresponding image of the chart was captured using the same camera settings (shutter speed and aperture size)  [28, 29, 48].
This technique allowed images to be standardized such that color measurements were comparable across all images. We used a total of 88 photos for color analyses (PCP: N = 28, mean per female = 5. 6 ± SD 1. 9 photos; 1 st month: N = 35, mean per female = 7. 0 ± SD 0. 7 photos; 2 nd month: N = 25, mean per female = 5. 0 ± SD 1. 9 photos). We assessed color from the whole face excluding the eyes, nose, front and identity tattoos (Fig 1 ) following previously described methods using Colourworker software  [29, 49]. We converted our camera sensor measures to estimates of Japanese macaque retinal receptor stimulation using previously published methods  [29, 49, 50]. We calculated the red-green opponency channel (i. e. R/G ratio) as (LW-MW)/(LW+MW) to assess how female face varied chromatically at long-wavelengths, and we also analyzed the facial luminance as (MW+LW/2) to measure how light or dark the face was according to our model of Japanese macaque visual perception.
25 g) were shaken with 2. 5 mL of modified phosphate buffer (0. 1 M, pH 7. 0, 0. 1% BSA with 0. 05% tween-20) at room temperature for 24 hr, and then centrifuged (3,000 rpm, 10 min). The supernatant was decanted into clean tubes and the residual pellet was discarded. 50 μL of solubilized fecal samples were used for PdG and E1C EIA, using pregnanediol-3-glucuronide-BSA antibody and estrone-3-glucuronide-BSA antibody (Cosmo, FKA334E and FKA224E, Japan). The PdG and E1C concentrations for each sample were calculated by fitting the absorbance of the standard curve using a curve-fitting program (LS-PLATE manager 2004, Wako Pure Chemical Industries, Japan). The E1C assay had a sensitivity of 0. 54-7. 00 ng/g with an intra-assay coefficient of variation (CV) of 2. 15% and inter-assay CV of 4. 27%.
We constructed hurdle models for each behavior in which a binary (presence/absence) model was first fit before subsequently modeling the zero-truncated (positive values only) count portion of the data  [56]. The reason for focusing on the binary component of the data rests with the large number of zeroes observed: the number of 0 values observed during focal samples for all behaviors examined was 70%. Thus, much of the variance in behaviors could be encapsulated by this simple binary model. We used the package glmmADMB  [57, 58]  in R to fit these hurdle models in a series of GLMMs with binomial (for binary models) and truncated negative binomial (for count models) error structures. We also tested whether a zero-inflation parameter was necessary in the binary models to account for the large numbers of zeroes observed for all behavior types, but in all cases the simpler binomial model prevailed.
The relationship between female estrus calls and reproductive status was examined using the same method. We tested whether face color and luminance were associated with reproductive status via LMEs with either luminance or R/G ratio as the response variable. Inspection of the cumulative distribution functions (CDFs) of each revealed good fits to the lognormal distribution, so each was log-transformed prior to model fitting. Gaussian models were then fit using the package nlme in R  [59]. The relationships between female reproductive hormones and pregnancy status were similarly examined via LMEs, with E1C, PdG and the E1C/PdG ratio used as response variables. Again, inspection of the CDFs of each revealed good fits to the lognormal distribution, so each was log-transformed prior to model fitting and Gaussian models were then fit using nlme.
Acknowledgments We thank the  Cooperative Research Program of Kyoto University's Wildlife Research Center  for the research permit and the use of their field station. Thanks also to  Takafumi Suzumura ,  Akiko Takahashi  and  Julie Duboscq  for their help in the field, and to  Louise Ducroix  for her help with the preparation of images. The authors especially thank  Prof. Fred Bercovitch  and two anonymous reviewers for helpful comments on the manuscript.
Objective-To identify obstacles and propose methods to cope with this difficulty, and illustrate them through experiments on the i2b2/UTHealth 2014 dataset. Methods-We outline several solutions to this problem and examine their requirements in terms of adequacy for component-level and task-level evaluation and of changes to the task framework. We select the solution which requires the least modifications to the i2b2 evaluation framework and illustrate it with our system. This system identifies risk factor mentions with a CRF system complemented by hand-designed patterns, identifies and normalizes temporal expressions through a tailored version of the Heideltime tool, and determines temporal relations of each risk factor with a One Rule classifier. Results-Giving a fixed value to the temporal attribute in risk factor identification proved to be the simplest way to evaluate the risk factor detection component independently.
Conversely, glass box evaluation of a non-final component is simple if gold standard input is available for this component, if the component's output has the same form as the full system's output, and if the parts of the output representation that are to be contributed by subsequent components can be ignored by the evaluation program or can be set to the gold standard values. We are close to this situation for the risk factor component in the present task: its output has the same form as the final output (a set of risk factor objects) and it can be fed directly to the evaluation program (the 2014 i2b2/UTHealth evaluation program can be downloaded from https://github. com/kotfic/i2b2_evaluation_scripts/tree/v1. 2. 1). However, this evaluation program, despite its numerous and very useful features, has no option to ignore temporal attributes.
Acknowledgments This work was supported by the  French National Agency for Medicines and Health Products Safety  under grant  Vigi4MED  ( Vigilance dans les forums sur les Médicaments )  ANSM-2013-S-060  and by the  National Research Agency , grant  Accordys  (Content and  Knowledge Aggregation for Case-based Reasoning in the field of Fetal Dysmorphology )  ANR-12-CORD-0007-03.
ACKNOWLEDGEMENTS We are much indebted to  Prof. P. Bouchet  for allowing us to study pectinoidean material from the  Mozambique Channel , taken by several French cruises, and to  Prof D. G. Herbert  for the opportunity to study pectinoids mainly sampled by the  Natal Museum Dredging Programme. Thanks are also due to  Virginie Héros ,  MNHN. We thank  Ms E. Hoenson  for information on the type data of Cyclopecten incubans Barnard, 1964 and  Cyclopecten vidalensis Barnard ,  1964 , housed in the  Iziko Africa Museum at Cape Town. Moreover ,  Dr S. van Noort  and  Mr D. Larsen  should be acknowledged for preparing images of both species. Thanks are also due to the  Direction of Collections (MNHN) , for allowing access to the  SEM ,  Sylvain Pont  for taking SEM photographs, and  Manuel Caballer  for digital photographs.