dataesr/scientific-paragraphs-categorization huggingface dataset:
As explained in Section 4. 1, we make the assumption that the images from Fashion must have a high mean score. We also assume that although the actual scores for Nasnet-based NIMA on the Fashion dataset are not correct, the ranking of the images is. Then, to create artificial scores for the Fashion images, we shifted the average value of the histogram of scores given by Nasnet-based NIMA to a higher value. More specifically, if the current mean score for the dataset on Nasnet-based NIMA is , the ground truth score for each image of score s is computed as s = s - +  where  =  high + 1 is the new mean score. We considered the value  high = 6 because it corresponds to high aesthetics value according to many previous work. 4, 28 Using this method, we ensured that the fashion dataset had a mean score of , and therefore, a majority of images from Fashion had a score higher than  high.
NIMA models Results indicate that the mean score of ground truth scores (5. 1) is significantly lower than the mean score of tested professional photographs (ranging from 5. 52 to 5. 84). This difference, although very small on a scale of 10 grades, was expected and statistically significant. From the t-test analysis, we can infer a clustering of datasets depending on the p-values of the t-test. However, these clusters are quite different for the different models. They are represented on Figure  5a ,b, and given below with clusters between parentheses in ascending order of aesthetic scores: -For NasNet-based NIMA model: (AVA), (AVA), (Cars), (War; Fashion; Architecture; Sport) and (National Geographic). -For Inception-based NIMA model: (AVA), (AVA), (War; Fashion; Cars; Sport), (Architecture) and (National Geographic).
Ranking network model As the distributions are not normal, we did not use a t-test, but a Wilcoxon rank-sum test. We observe that the range of scores is greater than for NIMA; the dynamic of scores represents 54. 4% of the whole scoring scale, whereas NIMA scores represent only 22. 3% of the scoring scale in average. This observation would suggest that the ranking model is more selective for the professional categories. As previously, Figure  5c  represents the clustering inferred from statistical analysis. The clusters for the Ranking Network are: (War), (AVA; Architecture), (Cars; Fashion; AVA) and (National Geographic; Sport). We notice that in both cases, AVA and National Geographic are separated from the others. The mean scores of datasets Cars and Architecture do not vary much between the models, so we can argue that their scores are more reliable than the score of the four other datasets.
Does fine-tuning Nasnet-based NIMA model improve the overall prediction capabilities? Figure  4  presents predicted scores when the Nasnet-based NIMA model is fine-tuned following the procedure described in Section 4. 2. The blue histograms represent the score distribution of AVA and our Fashion dataset on the original networks, and the red histograms those after fine-tuning the model. As expected, we notice that the scores of Fashion have increased after fine-tuning while AVA scores slightly decreased. The fine-tuned model is then able to better discriminate Fashion photography from competitive photography. It may suggest that models trained over AVA are specialized for competitive photography. However, a simple fine-tuning process allows to make the model more generic and more relevant. The results of the statistical tests for the fine-tuning experiment are not represented here, but a representation of the clusters can be found in Figure  5a.
Comparison with weighted CNN As the proposed fine-tuning process and the weighted CNN  16  have the same goal (reducing the bias caused by the imbalance of AVA), we compare the performance of both networks. The model and weights of Jin et al. 16  are available on their website. 29 We can then compare our Nasnet-based NIMA model fine-tuned with Fashion and the original weighted CNN model. Besides, to assess the relevance of their proposed novel loss function, we trained another model-namely the Ranking Network 9 -on AVA using their loss function. Figure  4  presents our datasets outputs on Nasnet-based NIMA fine-tuned (in orange) and Figure  3  presents the results for the original weighted CNN model (in red) and Figure  6  presents the results for the Ranking Network retrained using the weighted loss function.
We can verify this using a correlation metric. Therefore, we compute the Pearson correlation metric-as well as the mean-square error (MSE) to measure the differences per image-with AVA for different models: our Nasnet-based NIMA fine-tuned with the Fashion dataset, the original Nasnet-based NIMA and the weighted CNN proposed by Jin et al. These values are reported on Table  2. First, we notice that the best model according to both metrics is the original Nasnet-based NIMA. Our fine-tuning process slightly degrades the performances of NIMA on AVA. However, as explained previously, it also significantly improves the performances on the Fashion dataset. The fine-tuning process is thus quite effective and allows for better results on Fashion, and good results on AVA. In terms of correlation, the weighted CNN is worse than NIMA and the fine-tuned NIMA.
Discussion Using the fine-tuning method, we managed to increase the score of the Fashion database without modifying too much the scores from other categories. If we assume that the Fashion dataset is mainly composed of high aesthetic quality images, we have effectively improved the model accuracy and coverage. However, we can discuss the relevance of our assumption. The score threshold used as high aesthetic quality is used in previous work as a distinction between professional and amateur photographs. The professional photographs we used in our experiments were chosen because of their relevance. Indeed, these photographs were taken by professional photographers, furthermore they were published, which proves that they are acknowledged to be efficient. This shows that our assumptions on the scores (of images from War and Fashion) are reasonable.
CONCLUSION In this article, we have presented a study based on the models NIMA and the Ranking Network. We wanted to understand how these models behave for other kinds of photography than their own training dataset. As the dataset AVA is composed of competitive photographs, we have chosen six datasets of professional photographs in order to test whether or not the models generalize well for these photographs. We have observed that NIMA and the Ranking Network have different behaviors. NIMA gives scores with rather small deviation around the mean, whereas the Ranking Network scores are much more spread on the rating scale. We have also noticed that, for NIMA, there is a strong discrepancy between the scores of AVA and professional photographs. This is alleviated by the fine-tuning process, but raises other issues, such as the correct way to fine-tune and train the networks.
Despite the fact that there is a significant difference of scores for the datasets, meaning that NIMA models actually recognize photographs from professional sources as more beautiful than images from AVA, this study raises a number of issues. First, we do not observe, but we were expected to observe, a strong discrepancy between the scores of AVA and professional photographs. Second, all aesthetic scores of professional photographs are in a very limited range, approximately from 5 to 6, whereas AVA scores span from 3 to 8. These observations reflect how far we are from accurately predicting the aesthetics of an image. However, we have demonstrated that fine-tuning existing models with professional photography can reduce the specialization of existing models to competitive photography. This work shows the importance of the training dataset, especially when dealing with such a difficult notion as aesthetics.
There are several future improvements of this work. First, to improve the training datasets of aesthetics assessment networks, we could define and provide the community with a new annotated image dataset of professional photographs. Then, by looking more in details into a network, we could find how a network discriminates between different kinds of photographs, and eventually better understand and model human aesthetics feeling. This could lead to the design of novel personalized aesthetics prediction architectures that are based on new generic techniques used for example in BIQA (as presented in Section 2). Finally, as our work does not take into account the standard deviation of scores, a more thorough study about means and standard deviations would also be interesting, because this could help characterize the categories of professional photography.
Clustering the datasets according to the p-value of the statistical tests. The arrows point to the mean aesthetic score given by the corresponding model. AVA corresponds to the average estimated score of 26,122 images of AVA on the model and AVA corresponds to the ground truth mean. (a) NasNet. Red arrows show the modifications of the mean scores brought by the fine-tuning: Sport and National Geographic are not modified whereas War and AVA are in the same cluster. (b) Inception. (c) Ranking network. F I G U R E 6 Histograms of scores for different categories on the retrained ranking network. (a) AVA, (b) architecture, (c) fashion, (d) national geographic, (e) cars, (f) war, (g) sport F I G U R E 7 Histograms of scores for different categories on the weighted CNN proposed by Jin et al. 16 (a) Architecture, (b) fashion, (c) national geographic, (d) cars, (e) war, (f) sport TA B L E 2 Mean square error and Pearson correlation metric for four different modelsMSE.
F I G U R E 1 1 Different images from the AVA dataset (competitive photographs) with scores from the third-party implementation (on the left)-quoted from the original article 8 (on the right). (a) 5. 81-6. 24, (b) 5. 34-5. 24, (c) 4. 76-4. 77, (d) 5. 70-5. 51.
F I G U R E 2 2 Different sample images from professional datasets. We show here the worst (on left) and the best (on right) of each category according to NIMA. (a) Fashion, (b) architecture, (c) cars, (d) sport, (e) national geographic, (f) war also devised a new training database called AADB. The implementation was provided by the authors themselves on their GitHub. 26 More details can be found in the original paper.
5 5 Clustering the datasets according to the p-value of the statistical tests. The arrows point to the mean aesthetic score given by the corresponding model. AVA corresponds to the average estimated score of 26,122 images of AVA on the model and AVA corresponds to the ground truth mean. (a) NasNet. Red arrows show the modifications of the mean scores brought by the fine-tuning: Sport and National Geographic are not modified whereas War and AVA are in the same cluster. (b) Inception. (c) Ranking network. F I G U R E 6 Histograms of scores for different categories on the retrained ranking network. (a) AVA, (b) architecture, (c) fashion, (d) national geographic, (e) cars, (f) war, (g) sport F I G U R E 7 Histograms of scores for different categories on the weighted CNN proposed by Jin et al. 16 (a) Architecture, (b) fashion, (c) national geographic, (d) cars, (e) war, (f) sport TA B L E 2 Mean square error and Pearson correlation metric for four different modelsMSE.
NeuroTorp protocol and data analysis Fifty µL of TBS-BSA, or TBS-BSA containing toxin sample were applied to the sample pad. Once the whole was entirely migrated, 50 µL of the toxin tracer Biotin-α-BgTX (80 nM) was dispensed onto the sample pad. Following complete migration of the toxin tracer, 100 µL of neutravidin-NGP, extemporarily diluted 10-times with EIA-buffer (100 mM potassium phosphate, 150 mM NaCl, 0. 1% BSA, 0. 01% NaN3, 0. 5% Tween 20, pH7. 4), was added to the sample pad. To improve the contrast 100 µL EIA-buffer was added to the sample pad. The results were evaluated by naked-eye after 15 min. Since NeuroTorp is a competitive inhibition test, the presence of a nicotinic toxin will displace the toxin-tracer in a concentration dependent manner: At 100% inhibition, the test control-line is colorless.
Acknowledgements The authors acknowledge the funding support from the  LABEX LERMIT  ( DETECTNEUROTOX  project,  CDE 2017-001173 -RD 91  to RA), the  NRBC-E Program  ( MULTITOX  project, Fiche  N° H35  to RA) and the  INTERREG Atlantic Area  ( ALERTOX-NET EAPA_317/2016  project to LMB and DS). The  GDR PHYCOTOX  is acknowledged for financial support to RA.
The samples selected for the study was of convenience: participants were recruited until each of the seven age groups included a minimum of 20 participants (see Tab. 1). Participants were selected to match, as best as possible, the age groups in gender, handedness and education. Data from 5 participants were excluded from the analysis, due to either below-chance performance (correct trial percentage < 50 % in no distractor condition, see Fig. 1 : n = 2) or technical issues (n = 3). A total of 186 subjects (86 % right-handed, 9%left-handed, 5 % ambidextrous; 63 % female; 21 to 86 years old) were included in the analysis (see Tab. 1 for details by age ranges). All participants gave written informed consent. This study was conducted according to the Helsinki Declaration, Convention of the Council of Europe on Human Rights and Biomedicine, and the experimental paradigm was approved by the French ethics committee Comité de Protection des Personnes.
Measure parameters We used a custom MATLAB program to preprocess behavioral data. The shortest RT for a correct response (RT lower limit) was calculated for every age range (150 ms in the 21 to 60year-olds and to 200 ms in the 61 to 86-year-olds, see Fig. S2. 1, Supplemental Material). For each participant, the longest RT for a correct response (RT upper limit) was calculated from all RT > 0 ms using the Tukey method of leveraging the interquartile range. Based on the lower and upper RT limits, responses can be divided into three categories: (i) responses before the RT lower limit were considered as a part of the false alarm response type; (ii) responses between the lower and the upper RT limits were considered as correct responses; (iii) responses after the RT upper limits were considered as late responses.
Statistical analysis To estimate physical tendencies linked to the behavioral measures at the single trial level, we used Generalized Linear Mixed Models (GLMM) in a frequentist approach; models were adapted to the data distribution (i. e. , Gaussian for RT related measures, binomial for response types). In order to test the similarity between groups, Bayesian statistics were used. Contrastingly to Frequentist statistics, Bayesian analyses allow to assess the credibility of both the alternative and null hypotheses.
Sample characteristics To confirm that our sample population was similarly distributed across age ranges in block order, gender, handedness and socio-economic statuses (SES), we performed Bayesian contingency table tests. We performed a Bayesian ANOVA on the education level with AGE as between-subject factor to investigate potential differences in education across age ranges. Bayesian statistics were performed using JASP® software (JASP Team, 2021, Version 0. 14. 1). We reported Bayes Factor (BF10) as a measure of evidence in favor of the null hypothesis (BF10 0. 33 to 1, 0. 1 to 0. 33, 0. 01 to 0. 1 and lower than 0. 01: weak, positive, strong and decisive evidence respectively) and in favor of the alternative hypothesis (value of 1 to 3, 3 to 10, 10 to 100 and more than 100: weak, positive, strong and decisive evidence respectively;  Lee & Wagenmakers, 2013).
Materials and Methods Additionally, we analyzed participants' performance by computing psychometric functions on the temporal order judgement (TOJ) data in order to measure participants' point of subjective simultaneity (PSS) and the sensitivity  (Sternberg & Knoll, 1973). To do this, we used the 'quickpsy' package  (Linares & López-Moliner, 2016. ). We plotted the probability of 'right first' responses as a function of SOA. Negative SOAs corresponded to 'left-right' order of appearance and positive SOAs to 'right-left' order of appearance. We fitted a cumulative normal distribution function to all participants' data to obtain the mean (corresponding to the PSS) and the standard deviation of the function (SD or slope, corresponding to participants' discrimination sensitivity between left-right and right-left stimulus presentation order).
These two measures were then compared separately between groups. Three participants (one HC and two patients with SZ) were excluded from these analyses because their data could not be fitted to a cumulative normal distribution function (i. e. , they had flat psychometric curves). In case of PSS, data were normally distributed and we performed frequentist as well as multivariate Bayesian analyses on the same data. A normal distribution of data was not respected for the dataset of the sensitivity measure; thus, we normalized this dataset for each group before applying the ANOVA following the same procedure as described in the main manuscript. Further, we used a beta regression to fit the raw data in the multivariate Bayesian analysis. A one-way ANOVA conducted on the SD of the psychometric functions revealed a significant effect of between-factor 'group' (F2,68 = 5.
Apparatus A custom-written Python program was run by Psychopy2 (Peirce, Gray, Simpson, MacAskill, Downloaded from jov. arvojournals. org on 05/04/2022 Höchenberger, Soso, Kastman, & Lindeløv, 2019) on a PC computer. Stimuli were displayed on a 22 inch CRT monitor (HP, p1230, refresh rate = 110 Hz, resolution = 1152 × 864). Supported by a head-and chinrest, observers were seated at 57 cm distance from the screen in a dimly lit room.
STAR+METHODS KEY RESOURCES To measure pupal volume, pupae (24-30-hours APF) were collected using a wet brush. Following which, they were washed with PBS to dislodge attached food and glue. Each pupa was then transferred to a single well of a transparent 24-well plate and imaged using Zeiss Sv11 dissection scope fitted with a CCD camera (Jenoptik ProgRes C10). Sex determination was done post-eclosion and only females were taken for analysis. Quantitative real-time PCR Total RNA was extracted from homogenized L3 larvae using Qiagen RNeasy Mini Kit (cat #74104), from which cDNA was subsequently prepared using SuperScript III RT-PCR kit (Thermo Fisher, cat#12574026). Purity of RNA and DNA were checked by measuring absorbance using a NanoDrop 2000 (Thermo Fisher scientific). qRT-PCR was performed using SYBR green (Roche FastStart Essential, cat#06402712001) on a Roche LightCycler 96 instrument.
Data analysis was done using LightCycler 4. 1 software (Roche). Standard curves were generated for each primer pair. Primers were adapted from a recent publication. 56 All expression levels were normalized to the housekeeping gene rp49. The fold changes in expression were calculated using relative quantification function. Expression values are represented as fold changes as compared to the control. Dilp2-HF ELISA Nunc MaxiSorp Flat-Bottom plates (Thermo Fisher Scientific) were coated with anti-FLAG M2 antibody (Sigma, cat#F1804) diluted in BupH Carbonate-Bicarbonate Buffer (Thermo Fisher Scientific, cat#28382) overnight at 4 C. The plates were washed 2x in wash buffer -PBS + 0. 2% Tween20 (Sigma, cat#P7949) and blocked in 2% filter-sterilized BSA in PBS overnight at 4 C. 1mL of hemolymph was collected by bleeding 5-8 heterozygous gDilp2-HF larvae on a clean cavity slide.
Confocal laser scanning microscopy Three imaging setups were used in this paper. First, Olympus FV1000 laser scanning confocal microscope on Olympus BX61 inverted stand and driven by FV10-ASW 1. 7 software. Brain and ring gland whole mounts were imaged with Olympus UApochromat 40x 1. 3NA oil immersion objective. Corpora cardiaca cells were imaged with Olympus UApochromat 60x 1. 35 NA oil immersion objective. Second, Andor spinning disc confocal microscope on Olympus IX81 inverted stand using Andor iXON 897 EMCCD camera with Yokogawa CSUX1 scanhead, running on Andor software. Some fat bodies were imaged with Olympus U Plan SApo 60x/ 1. 35NA oil immersion objective. Third, Olympus FV3000 laser scanning confocal microscope on Olympus IX83 inverted stand and driven by FV31S-SW acquisition software.
ACKNOWLEDGMENTS This article is dedicated to the memory of our esteemed colleague and mentor  Prof. /Dr. Suzanne Eaton. We are grateful to  Dr. Natalie Dye  for her supervision and leadership. We thank  Dr. Anne Grapin-Botton ,  Dr. Jonathan Rodenfels , and the  Eaton and Leopold lab members  for insightful discussions and comments on the manuscript. Stocks obtained from the  Bloomington Drosophila Stock Center  ( NIH P40OD018537 ) were used in this study. We would also like to thank the  Prof. Linda Partridge  and  Dr. Jason Tennessen  for providing us with fly lines,  Prof. Marc Tatar  and  Dr. Stephanie Post  for sharing reagents, and  Dr. Hugo Stocker  for gifting us anti-Imp-L2 antibodies. The scientific illustrations were done by  Dr. Bertsy Goic. This work was supported by  Deutsche Forschungsgemeinschaft  ( DFG/FOR2682 ),  Max-Planck-Gesellschaft ,  Institut Curie , and INSERM.
For pupariation timing, number of pupae including white pre-pupae were manually counted at fixed intervals as indicated. Counting was stopped when there was no increase for 3-4 consecutive time points. The same dataset have been used in Figure  3A , and to depict fed controls in Figures  5B  and 5C. For all other starvation experiments, starved and fed control larvae were washed out of the 1% agar or food with PBS, rinsed and used in respective assays.
In this paper, we present an innovative MRI-based method for Alzheimer's Disease (AD) detection and mild cognitive impairment (MCI) prognostic, using lifespan trajectories of brain structures. After a full screening of the most discriminant structures between AD and normal aging based on MRI volumetric analysis of 3032 subjects, we propose a novel Hippocampal-Amygdalo-Ventricular Alzheimer score (HAVAs) based on normative lifespan models and AD lifespan models. During a validation on three external datasets on 1039 subjects, our approach showed very accurate detection (AUC ≥ 94%) of patients with AD compared to control subjects and accurate discrimination (AUC=78%) between progressive MCI and stable MCI (during a 3 years follow-up). Compared to normative modelling, classical machine learning methods and recent state-of-the-art deep learning methods, our method demonstrated better classification performance.
As explained in  [7] , normative lifespan modelling is similar to growth charts used in pediatric medicine to detect abnormal child development in terms of height or weight related to the age's subject. Indeed, such charts can be used to detect outliers considered as pathological. For AD detection, volume or thickness of key structures as a function of age is usually used. The main advantages of normative modelling are to robustly capture the heterogeneity of normal anatomy and to provide an easily interpretable distance between an individual and the normative range. Normative modelling is the approach used in most of the available software for quantitative brain analysis (in open access such as volBrain  [10]  or for commercial use as in Neuroquant ®  [11] , Qscore ®  [12]  or Qreport ®  [13] ).