dataesr/scientific-paragraphs-categorization huggingface dataset:
Mean temperature matrix Generally, the above equations (  3 ), (  6 ) and (  8 ) can be written in a 3x3 matrix form: The matrix can be expressed as [A] [T] = [B] The mean temperature vector can be solved by matrix inversion: [T] = [A] -1 [B] A computer program was developed by MATLAB ® , following the 5 main procedures shown in Fig. 8. This procedure includes the following steps: 1. In the Input Process, all physical properties and design parameters of the simplified solar updraft device in Ut, mass flow rate, air physical properties were attained from equations in Table  3. 3. In the Solving Process, all the required variables were subsequently put into a 3x3 matrix and the associated equations were solved by using an iterative process until the solution converged in the MATLAB program. 4.
Experimental setup and data The size of the airflow channel is 1. 8 m height × 0. 5 m width × 0. 12 m air cavity thickness (i. e. the gap between the glass panel and the wall is 0. 12 m), which is decided based on the best flexibility to be field tested on dairy farms. Four sensors were fitted in the airflow channel to measure airflow temperature and velocity. Experimental data were collected on different days under various solar radiation conditions. Fig. 2B  shows the typical air temperature rise (6. 4 ⁰C) and airflow velocity (0. 46 m/s) in the airflow channel on a particular day with solar radiation of 397 W/m 2 at the time of the measurement. More data measured under a wide range of solar radiation (from 100 W/m 2 to 600 W/m 2 ) is summarised and presented in Figures  3  and 4. It can be clearly seen that when the solar radiation is low (i.
Model validation Research in the field reveals that different dimension of a Trombe wall has a great impact on the solar updraft (i. e. temperature rise, airflow velocity and mass flowrate). Numerical modelling is an effective and efficient way to find more insights, which can provide guidance for design and future applications. Ansys Fluent is a commercially available option for CFD modelling. We also tried to develop a mathematical model based on more widely available MATLB and expected it to be more efficient as it needs less computing resources. To validate the accuracy of the two models, they were validated by comparing with the data from the experiment as shown in Fig. 3  and 4. The RSMD (root mean square deviation) is considered as an evaluation indicator to analyze the error between the simulation and experiment.
The RSMD is calculated using the following expression: ] "" = e ∑--˜ ™M -˜C š/˜ ™M › ‹ × 100% As shown in Fig. 3  and 4 , it can be seen that the numerical results from both models have the same trend with the experimental values as the intensity of sunlight changes. The values of RSMD of air temperature difference and air mass flow rate from the MATLAB model are 5. 03% and 4. 48% respectively, while the values of those from the Fluent model are 8. 56% and 2. 52% respectively. It could be concluded that the numerical results have an acceptable agreement with the experimental data. Therefore, both models are reliable and can be confidently applied for further study of the performance of solar updraft devices. (3) the accuracy is also slightly higher; and also (4) it is more flexible, and all contents can be modified to suit specific needs.
Applications of the Models With the reliable models developed, we can investigate 1) how device dimensions will affect the solar updraft performances using the MATLAB model as a fast-analytical tool, 2) how the air moves and distributes in the airflow channel using the visualizability of the Fluent model. This is becauseΔT is positively correlated to the buoyancy of air and thus airflow velocity. The above observation can provide guidance for the design and applications of the device. For example, if high mass flowrate is the only target of an application, thicker and higher dimensions are preferable subject to available space and construction costs. If high airflow velocity and/or high air temperature is also needed for a particular application, there will be a compromise to decide on the optimum gap.
Conclusion In this work, we have conducted both experimental measurements and numerical modelling to investigate whether a solar updraft device can generate enough airflow under low solar conditions, and then developed a fast-analytical mathematical model to effectively and efficiently find design rules for further application. We found that: (1) The experimental results prove that the air mass flowrate generated by solar updraft in a 0. 9 m 2 solar updraft device can be as high as 75. 6 kg/hr when the solar radiation is only 120 W/m 2. (2) Comparison of two numerical models shows that it is feasible to develop a fast-analytical tool based on widely available MATLAB with low demand of computing resources and to provide guidance on how a future device should be designed for particular applications.
Acknowledgement We gratefully acknowledge the support by the  European Commission H2020 Marie S Curie Research and Innovation Staff Exchange (RISE)  award (Grant No. 871998 ),  Department for Business, Energy and Industrial Strategy DAC  and  GGR Programme  ( Sch202036 ), and  National Key Research and Development Plan (Key Special Project of Inter-governmental National Scientific and Technological Innovation Cooperation , Grant No. 2019YFE0197500 ).
This work describes the ManTraNet network for image forgery detection. ManTraNet is an end-to-end convolutional neural network composed of two sub-networks, one to extract features linked to traces of manipulation, and another to detect local anomalies between the features. It is trained on pristine and forged images from several datasets. We briefly analyze the results provided by ManTraNet, so as to highlight its qualities and limitations. Overall, ManTraNet yields state-of-the-art results on benchmark datasets with images similar to the one it sees in training, but is unreliable on wild images, due to its opacity and the difficulty distinguishing true detections from false positives.
Introduction Image forensics has become an important field of study over the past few years, sparked by the ubiquity of images on the internet and the proliferation of fake news in social media. Originally, image forgeries were mainly detected by manual methods targeting specific traces left by the image signal processing pipeline (ISP) such as demosaicing artifacts  [3, 10, 13, 14, 20, 23, 27, 33, 35, 4] , JPEG compression  [1, 8, 18, 25, 26, 31, 32, 30] , or noise inconsistencies  [11, 15, 28, 29 ]. 1 https://doi. org/10. 5201/ipol. 2022. 431 2 https://github. com/RonyAbecidan/ManTraNet-pytorch With the advent of deep learning, convolutional neural networks (CNN) were recently introduced to image forensics. They can be trained on pristine images so as to detect whether two patches could come from the same image or might have been processed differently, indicating a forgery  [12, 17].
Some networks are introduced to improve the analysis of a specific point, such as demosaicing artifacts  [2, 6]. ManTraNet  [39] , which we study here, pioneers a third category of methods, that are trained directly on forged images to localize their forged regions. The ability of methods such as ManTraNet to provide exceptional results on benchmark datasets has already been demonstrated. However, in such datasets, neural networks can be trained on images and forgeries similar to those of the evaluation set. One can wonder whether such performances hold in the wild, where images are diverse and largely differ from the controlled environment of training datasets. Furthermore, the interpretability of the results can be questioned; as the reason behind the detection is not immediately clear, such results can often be seen as opaque.
After a brief description of ManTraNet, we will analyze its results on various images to question the performances in uncontrolled scenarios. While highlighting the interpretability issue of ManTraNet, we will also conduct short experiments to try to understand what triggers detections. We use the pytorch re-implementation from https://github. com/RonyAbecidan/ManTraNet-pytorch, which is equivalent and uses the same weights and network structure as the original authors'  [39]  repository, available at https://github. com/ISICV/ManTraNet. Note that even in the official implementation, the network architecture is slightly different than described in the paper, this discrepancy has already been acknowledged by the authors. The structure described here corresponds to the one that was actually implemented.
Training Here we describe the training of the network in the original paper. We focus on analyzing the results of the method and do not recreate the training ourselves, as details for training are not provided in the paper nor in the implementation. The first sub-network is trained using the Dresden Image Database  [16]  of pristine images. Of all the images, 80% are used for training, 10% for validation and the remaining 10% for testing. Images are divided into 256×256 patches, and homogeneous patches with intensity standard deviation below 32 255 are rejected. In total, 1. 25M patches are kept. Samples are generated by uniformly sampling a random patch and manipulation, applying the manipulation to the image, and cropping a random 128 × 128 region of the output. The network is fed with the cropped manipulated region, and trained to identify which manipulation was used.
Details on the module for auxiliary training are not provided in the original paper. The second sub-network is trained on four synthetic datasets:  [37] ,  [38] , a dataset synthesized by using OpenCV  [9]  inpainting on the Dresden images  [16] , and another dataset obtained by locally applying random manipulations on the same images. Training is done on patches of size 256 × 256. Both networks are trained with a batch size of 64, 1000 batches per epoch, using the Adam  [19]  optimizer with an initial learning rate of 10 -4 without decay. The learning rate is halved if the validation loss fails to improve for 20 epochs. The epoch with the best validation loss is kept for the final model.
Response to Authentic Images We first test the detections of the method on authentic images. The Korus database  [21, 22]  contains 220 tampered images of size 1920 × 1080 from 4 different cameras, and their associated pristine images. We run ManTraNet on the 220 pristine images, and check the proportion of images that contain false positive connected regions over a given size where the output is above a given confidence threshold. Results can be seen in Table  1. These observations on pristine images show the difficulty of identifying forgeries with ManTraNet; even at a threshold of 0. 9, more than half of the pristine images contain a detection over 256 pixels (corresponding to a 16×16 square region). In other words, even if ManTraNet detects a small region with very high confidence, it is impossible to consider this detection reliable, as many (authentic) regions will be detected with the same confidence.
Detection of Forgeries In Figure  3 , we show the response of ManTraNet to forged images from the Korus Dataset  [21, 22]. First of all, we note that the ManTraNet strongly responds to many forgeries, which can help in their detection. That being said, the response of ManTraNet to those forgeries is not unlike its response to pristine images as those from Figure  2. Given only the output from ManTraNet, it is thus difficult to distinguish true forgeries from false positives, even to a trained eye. To complicate the matter, the tested images correspond to controlled forgeries from a dataset. In real cases, images often undergo much more post-processing and editing, which can make their detection different or more difficult than on benchmark datasets, where forgeries are closer in nature to those on which the method has been trained.
Interpretability The Trace  [5]  database introduces invisible forgery traces in the form of pipeline inconsistencies. Each raw image is processed with two different camera pipelines, then both obtained images are merged according to a forgery mask. The content of the image stays unchanged, only the pipeline is altered. The database is divided into several datasets with the same images and masks but different changes in the pipeline: the region inside the forgery mask can have a different demosaicing pattern (cfa grid dataset), a different demosaicing altogether (cfa alg), a different JPEG grid (jpeg grid dataset), a completely different JPEG compression (jpeg quality), different noise levels on the raw image (noise) or a combination of those changes (hybrid). The forgery masks are selected randomly from a semantic segmentation of the image 4.
Because the only change between the authentic and forged images is the processing inside a mask, detections cannot be made for purely semantic reasons. The Forged image ManTraNet response Ground Truth Figure  3 : ManTraNet response to forged images from the Korus Dataset  [21, 22]. In many cases, ManTraNet is sensitive to the forgeries. However, it also responds to other, authentic regions in the image. In the last image, the forgery is an internal copy-move. Note that ManTraNet also responds to the original (authentic) region, albeit not as strongly. Overall, even the true positives are difficult to distinguish from false positives from Figure  2. structure of this database enables us to see whether ManTraNet is sensitive to the different changes in processing. We report the scores in Table  2.
As can be seen, ManTraNet is entirely insensitive to shifts in the traces of the image, such as shifts in the mosaic (CFA Grid) or in the JPEG grid. It is somehow sensitive to inconsistencies of the noise level, demosaicing algorithm and JPEG compression quality, but this sensitivity remains low compared to the standard deviation of the scores. Overall, the grounds for detection by ManTraNet remain obscure, as only a small part of the reasons can be explained by the analysis with the Trace dataset. We note, though, that this lack of interpretability is not fatal. Indeed, the first part of the network is trained to identify manipulations among a large palette of potential changes. The output of the auxiliary network, if it were made public, might enable a much better interpretability of the results, as it would be possible to know at least why the network responds positively to some regions.
The MCC takes values between -1 and 1, with 1 representing a perfect detection, -1 its complementary. As a baseline, a random classifier or input-independent method is expected to yield a score of 0. The globally-best threshold is selected over each dataset, then the MCC is computed separately for each image and averaged. We also provide the standard deviation of the score over the dataset. As can be seen, ManTraNet is entirely insensitive to shifts in the traces of the image, such as shifts in the mosaic (CFA Grid) or in the JPEG grid. It is somehow sensitive to inconsistencies of the noise level, demosaicing algorithm and JPEG compression quality, but this sensitivity remains low compared to the standard deviation of the scores. Forgery ManTraNet output Forgery ManTraNet output.
Conclusion In this work, we briefly described the ManTraNet forgery detection network, and analyzed its results in different cases. While ManTraNet is able to respond to many forgeries in datasets, it fails to do so in controlled environments. Furthermore, it is highly sensitive to even pristine regions, and lacks a method for automatic detection -the network only outputs a confidence heatmap. This creates difficulties in distinguishing true forgeries from false positives in the output of the method, even to expert eyes. Finally, the reasons for which a strong response is output by the end-to-end network remain obscure. Access to the auxiliary network in the middle of the method, used in training to different kinds of forgeries, could provide insight into the reasons for detection, and ultimately more interpretability at least to experts.
2017 , Brownlees & Engle 2017). In this context, X = X 1 and Y = Y 1 represent the marginal losses on the returns of a company and of the entire market, and the marginal expected shortfall (MES) is E(X | Y > u), where u is large to reflect a substantial market downturn. Our results apply both to the expectile-based form of the extreme marginal expected shortfall, where u is taken to be a high expectile of Y , and to its quantile-based counterpart. We discuss the construction of confidence intervals for extreme expectile-based risk measures and compare them with intervals obtained by assuming independent observations. Our methods and data have been incorporated into the R package ExtremeRisks, freely available on CRAN. The online Supplementary Material contains technical results, further discussion and additional numerical work.
, 700} for each simulated dataset and obtain various 95% confidence intervals based on (7). The big-and small-block sequences are chosen as r n = log 2 (n) and l n = C log n , where C is selected such that l n is greater than or equal to a lag after which the largest of the sample autocorrelations of the data from the time series (X t ) and (X 2 t ) are smaller than 0. 1. Checking whether the confidence intervals contain the true expectile allows us to compute the empirical coverage probability. The top row of Figure  1  shows that our confidence intervals have much better coverages than those that assume the data to be independent, and the coverages for intervals based on least asymmetrically weighted squares outperform those for the quantile-based estimators over a wider range of the intermediate sequence k.
Financial returns of individual banks We now analyse the financial returns of Goldman Sachs and Morgan Stanley in the context of systemic risk. We consider the daily negative log-returns (X t ) on their equity prices from  These estimates slightly fluctuate initially, then stabilise around a common value, and finally drift away due to the inclusion of data from the centre of the distribution; taking k = 150 seems reasonable. With the same setup, the estimates obtained with the composite extrapolating least asymmetrically weighted squares and composite extrapolating quantilebased estimators of QMES X,αn , and the various confidence intervals at level τ n = τ n (α n ) are reported in first three panels of Figure  3. The estimates and confidence intervals are more stable for Goldman Sachs than for Morgan Stanley and for the least asymmetric squares method than for the quantile-based method.
According to these results, taking k = 150 seems reasonable, and we then find τ n (α n ) ≈ 0. 9997239 for the Goldman Sachs data and 0. 9996626 for the Morgan Stanley data, which we compare to the Weissman estimate QMES X,αn. All estimates are reported in Table  2  and the least asymmetrically weighted squares estimates are shown in the rightmost panels of the bottom two rows of Figure  2 ; they lead to conclusions similar to those in Section 7. 1. For k = 150 they give similar results on the Goldman Sachs data, and somewhat shorter confidence intervals on the Morgan Stanley data. Like on the stock market index data, the confidence intervals constructed with the method of  Drees (2003)  are more volatile than ours. In an application either to stock market index data or to an individual bank, it would be wise to inspect the analogue of Figure  2 , in order to identify stable regions for the estimates and confidence intervals.
Acknowledgements  G. Stupfler  was previously based at the  University of Nottingham , and the support of the  Nottingham PEF Fund , of the  French National Research Agency  (grant  ANR-19-CE40-0013 ) and of the  AXA Research Fund  is gratefully acknowledged. S. A. Padoan is supported by the  Bocconi Institute for Data Science and Analytics. A. C. Davison  is supported by the  Swiss National Science Foundation.
NGS analysis Software of the Illumina NextSeq500 platform was used to assign raw read sequences to libraries based on the indexes and to trim the NGS adapters. For subsequent analysis, a 7-step bioinformatics pipeline mixing existing programs and custommade scripts was built (Figure  2 , https://gitbio. ens-lyon. fr/rdp/ crispr_proto_maize). In the first step the paired-end reads were assembled with PEAR software  (Zhang et al. , 2014, version 0. 9. 10). The assembled reads were quality checked using fastQC (http:// www. bioinformatics. babraham. ac. uk/projects/fastqc, version 0. 11. 7) and trimmed using fastq-mcf (https://github. com/ ExpressionAnalysis/ea-utils, version 1. 04. 676). To avoid problems in downstream sequence alignments, sequences containing one or several undetermined nucleotides (N) after this step (on average, 0.
02% of the output sequences) were eliminated from further analysis despite their overall acceptable quality. For the third step of the pipeline a specifically developed Python program was used to trim the 5-nt tags at both extremities of the sequence and to concatenate their sequences to the sequence name. This program then identified identical sequences, counted their number of occurrences and adjusted this count if not only the sequences but also the tags were identical, i. e. the sequences were PCR duplicates, corresponding in fact to a single initial editing event. At the end of this step the obtained file (in FASTA format) contained one sequence for each group of identical sequences and their counts. The next step consisted in the pairwise alignment of the representatives of the different groups of reads with the reference sequence using the Needleman & Wunsch algorithm, as implemented in the 'needle' program from EMBOSS  (Needleman and Wunsch, 1970, Rice et al.
This tag is not to be confused with the index added during the second amplification with the full Illumina adapter, which allowed multiplexing of libraries in a single flow cell. The raw sequence data obtained were deposited at EBI under the accession number PRJEB56234. To analyze the type and frequency of CRISPR/Cas9mediated mutations, a 7-step bioinformatics pipeline combining existing programs and custom-made scripts was developed (Figure  2A ) and made available (https://gitbio. enslyon. fr/rdp/crispr_proto_maize). The overlapping paired-end mode was chosen to enhance sequence quality and allow the processing of slightly larger PCR products compared to single read mode. After classical pairing with PEAR  (Zhang et al. , 2014) , quality control with FASTQC  (Andrews, 2010)  and trimming of remaining adapter sequences with Fastq-mcf  (Aronesty, 2013) , the tags at the 5' and 3' ends of the sequences were removed with a Python script and added to the sequence name.
In the next step, unique sequences were counted and extracted for alignment with the non-mutated reference sequence. After testing several alternatives such as BLAST, Bowtie or Smith & Waterman, pairwise alignments with the reference sequence were performed with the Needleman & Wunsch algorithm using a custom score matrix and suitable gap opening and extension penalties, since it (i) allowed systematic alignment over the entire length of the reference sequence and (ii) satisfactorily handled even important size differences between the mutated and the reference sequence. The next step allowed to quantify by a Python script the different types of mutations (deletion, insertion, mismatch) for each position of the reference sequence, excluding the primer regions and distinguishing the 20 nt CRISPR/Cas9 range corresponding to the sgRNA binding site from the rest of the amplified sequence.