dataesr/scientific-paragraphs-categorization huggingface dataset:
Converting pixel data to NIfTI image. DICOM volume loading and conversion to NIfTI format is performed using the pydicom and nibabel Python packages  23, 24. These packages were integrated in a Python class handling multi-dimensional volumes from lists of DICOM files enabling the building of pixel data arrays and managing data orientation to improve NIfTI encoding. Limitations remain for oblique orientations, frequently encountered in large animal models; special care should be provided when handling NIfTI conversions, and manual corrections might be necessary. Therefore, the orientation of the pixel data was manually validated using the ITK-snap software  25. The provided data were acquired on a clinical PET-MRI system without a stereotaxic frame; therefore, a unique subject position was established close to a patient position.
All images are shared in raw space; no registration to standard atlas space was applied. The oblique encoding of MRI sequences led to potential errors in conversions. The closest orientation was right-superior-anterior for the majority of subjects. Other subjects were oriented oblique closest to right-anterior-inferior. To provide uniform data, the identified right-anterior-inferior subject's data were manually oriented to right-superior-anterior. To match PRIME-DE orientation homogeneity criteria, the full dataset was then oriented to right-posterior-inferior. Additionally, DWI and diffusion tensor imaging require bval and bvec files to qualify the sequences. These files are obtained with the dicom2nifti Python package  26. Generating the metadata file. To collect the necessary metadata for each acquisition sequence, compensate missing tags and integrate subject-specific radioactivity parameters, three configuration files are necessary.
Sequence overview. The sequence overview configuration file provides a list of sequences to integrate in the BIDS formatting. It provides details on tags necessary for each sequence and replacement values if the tag is missing in the DICOM metadata. In this configuration file, the naming format for the sequence in the BIDS database is also defined along with instructions on where to find and store the corresponding data. Supplementary Table  1  provides a description of each column required in the sequence overview configuration file along with examples. PET doses. The PET-specific configuration file enables the definition of each subject's injected radioactivity parameters. The file sets values for BIDS-guideline required PET tags SpecificRadioactivity (that is, molar activity, the current term recommended by the guidelines  27  ), InjectedRadioactivity and InjectedMass.
This file is required only for PET data formatting. Configuration file description and examples are provided in Supplementary Table  2. DICOM to BIDS tag converter. This configuration file provides the DICOM tag name for each required BIDS tag compensating for variable naming conventions between the two formats. If no DICOM tag equivalent exists for a given BIDS requirement, the sequence overview or PET-doses replacement option is triggered. Description and examples of the configuration file are given in Supplementary Table  3.
Data quality assessment. To provide an indication of the quality of the database formatted to BIDS standards, we analyzed the distribution of three relevant metrics: SNR and CNR for MRI acquisitions, and median intensity for PET and MRI acquisitions. The metrics were measured on voxels with tissue signal (volume of interest, VOI) following Otsu thresholding on the volume with the highest intensity  28. Voxels below the given threshold were considered background for noise measurement. The metrics were calculated using the following formulas: SNR = μ VOI σ noise CNR = max VOI -min VOI σ noise Median intensity = median VOI Categorical violin plots were plotted for each metric. These plots show the distribution of the metric values across subjects. Strip plots were superimposed on the violin plots to provide additional information on time distributions.
The dynamic evolution of PNECR over time was represented on a logarithmic scale to uncover variations in tracer injection times. Lastly, a sample of the data was manually inspected to ensure that the results were accurate and to identify any issues that might have been missed. Based on these evaluations, we were able to provide an indication of the overall quality of the database for potential future users of the dataset, including the detection of potential outliers and the assessment of the homogeneity and consistency of the data. Execution protocol. Detailed protocol steps to execute the formatting script are provided on protocol. io 30. Reporting summary. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Code availability The code is hosted in a Public GitLab repository under the name cermep-bids-retro (https://gitlab. in2p3. fr/cermep/cermep-bids-retro). It is fully implemented in the Python programming language and is operating system independent. The Python environment requirements are provided in the environment. yml file. The code is licensed under MIT. LaB aNIMaL | www. nature. com/laban.
The PREMISE database of 20 Macaca fascicularis PET/MRI brain images available for research Lucie Chalet 1,2 , Justine Debatisse 3 , Oceane Wateau 4 , Timothe Boutelier 2 , Marlène Wiart 1 , Nicolas Costes 5 , Inés Mérida 5 , Jérôme Redouté 5 , Jean-Baptiste Langlois 5 , Sophie Lancelot 5,6 , Christelle Léon 1 , Tae-Hee Cho 1,6 , Laura Mechtouff 1,6 , Omer Faruk Eker 6,7 , Norbert Nighoghossian 1,6 , Emmanuelle Canet-Soulas 1 ✉ and Guillaume Becker. The data were formatted.
1,8 ✉ Non-human primate studies are unique in translational research, especially in neurosciences where neuroimaging approaches are the preferred methods used for cross-species comparative neurosciences. In this regard, neuroimaging database development and sharing are encouraged to increase the number of subjects available to the community, while limiting the number of animals used in research. Here we present a simultaneous positron emission tomography (PET)/magnetic resonance (MR) dataset of 20 Macaca fascicularis images structured according to the Brain Imaging Data Structure standards. This database contains multiple MR imaging sequences (anatomical, diffusion and perfusion imaging notably), as well as PET perfusion and inflamma- tion imaging using respectively [ 15 O]H 2 O and [ 11.
Observations were recorded until all localised smouldering reached extinction, which occurred within 60 h for all experiments, either due to extinction without intervention, or direct or indirect suppression (rainfall or firefighters with a water mist hose). The exception to this case is smouldering hotspot β 4 /τ 4 observed in CodeRed #04 (seen in the supplementary file, figs. A1 and 11) which was not detected due to its location, and proceeded to smoulder underneath the encapsulation beyond 48 h after the cessation of flaming. The location and progression of each hotspot over 48 h is depicted in Refs. [19] [20] [21]  and the supplementary material to this paper, and will be referred to by a Greek letter and subscript indicating the associated experiment (e. g. , α 4 is hotspot α from CodeRed #04).
3, 4 ) and a field of view able to guarantee the representativeness, showing petrophysical properties almost consistent with laboratory data (Fig. 2b ). Finally, VHR scan allows to achieve values even closer to laboratory data despite its small field of view (Fig. 2b ). We highlight that it is just a qualitative comparison between digital and laboratory measurements. Our digital results were, in fact, obtained adopting a very direct workflow to minimize the influence of different filtering, segmentation and simulation approaches. Moreover, laboratory data was measured on different plugs, and with diverse size, from those used for micro-CT analysis, even though drilled from the same tuff block. However, it can be useful to quickly discuss the threshold sensitivity  34  for the segmentation of the acquired scans.
6 dB, MSE (mean squared error) of 0. 0014 and SSIM (structure similarity index) of 0. 80 (Table  1 ). Consequently, the total porosity calculated from their segmentation (Fig. 4 ) is consistent with laboratory data and weakly sensitive to threshold variations, in contrast with their equivalent LR images (both LR scan and LR scan upsampled to HR size with cubic interpolation) which provide an overpredicted total porosity (Fig. 2 , Table  1 ). Conversely, all the employed ResNet-based networks seem inadequate to improve the quality of the images and to simplify their processing. Indeed, the application of these trained models to the validation images mainly results in too smoothed/blurred images, unable to resolve the small pores and the fine matrix (Fig. 3 ).
It is possibly due to the resulting lower number of training patches than their 2D counterpart (Table  1 ). In order to test the overall effectiveness of our best trained models, 2D U-Net and pix2pix, we applied them to a large unseen (i. e. , dominantly external to the training/validation dataset) LR image volume and estimated their transport properties using the described image analysis workflow. Applying the models on the central 640  3  px of the LR image we obtained a super-resolved 3D image of 2560 3 px with a substantial quality enhancement (Fig. 5 ), that allow to image a large field of view with high resolution. The estimated petrophysical properties are almost consistent with laboratory data and weakly sensitive to threshold variations (Fig. 2b ). Particularly, while the original LR image shows an apparent highly porous, permeable and heterogeneous microstructure, super-resolved images have porosity, permeability and structural uniformity coherent with laboratory data and macroscopic features (Figs.
607 ± 31 kg/m 326 ) by following empirical formula  40  : Multiscale X-ray imaging. X-ray microtomography investigations were performed on a cylindrical core with diameter of about 20 mm and a height of 40 mm from the same tuff block (Fig. 1c ). We examined the sample at multiple scales, progressively decreasing pixel sizes and fields of view, and increasing exposure time. We acquired three 3D images (of about 1000 3 px): a low resolution (LR) scan with a pixel size of about 16 μm, a high resolution (HR) scan with a pixel size of 4 μm and a very high resolution (VHR) scan with a pixel size of 1. 75 μm (Fig. 1c ). The scans were acquired at the micro-CT laboratory of the Istituto Nazionale di Geofisica e Vulcanologia-Osservatorio Vesuviano, equipped with a ZEISS Xradia Versa 410 micro-CT. X-ray imaging was performed in absorption mode acquiring 2D radiographs (projections) over a total angular scan of 360°, reconstructed with a filtered back-projection algorithm using the XRM Reconstructor software.
8 ). Finally, WDSR, starting from EDSR architecture, tests the use of wider features before the activation function with same parameters and computational budgets, and introduce weight normalization (rather than batch normalization or no normalization). Based on their width two different types are proposed: WDSR-a for wide 2×-4× channels, WDSR-b for even wider 6×-9× channels  46  (Fig. 8 ). These ResNet-based CNNs works directly on LR images and data are upsampled within the network through pixel shuffling (sub-pixel convolution). Further information for digital rocks are provided by Wang et al. 47. In contrast to CNNs, generative adversarial networks, GANs, are composed of two networks: a generator model learns to generate fake images, a discriminator model learns to classify images as real (training images) or fake (generated images).
Data preparation and training. We applied super-resolution methods to our LR scan (input data) and HR scan (ground truth data), which adequately captures transport properties (see "Multiscale imaging" section) with a more sustainable scaling factor (4× vs. 10×) and scan time (3. 2 vs. 14 s per projection) than VHR scan. Particularly, we used LR and HR crops opportunely registered and cropped. 3D registration was refined with Thermo Scientific PerGeos Software (based on Avizo Software; Thermo Fisher Scientific, Waltham, MA, USA -www. therm ofish er. com/ perge os) maximizing the normalized mutual information between the two scans  54  , thus resulting in a slight translation, rotation and scaling of LR scan with a final pixel size of 16 μm. A crop of 640 × 640 × 972 px (i. e. , the largest inscribable parallelepiped in our cylindrical reconstructed digital rock compatible with our networks) was extracted from the HR scan, and a corresponding volume of 160 × 160 × 243 px from the LR scan (Fig.
For 2D and 3D CNNs (U-Net and ResNet-based networks), grayscale values were scaled from 0 to 1 and a sigmoid activation layer was used at the end of the networks. They were trained for 100 epochs using mean squared error loss (which provided better results than mean absolute error loss) and Adam optimizer with an initial learning rate of 10 -3 and an exponential decay (decay rate of 0. 0625). In detail, for U-Net, batches of 32 and 1 images were used for 2D and 3D models respectively, shuffling the training data before each epoch; the effect of BatchNormalization and Dropout was also investigated without enhancements. For ResNet-based networks, results for 16 residual blocks using shuffled batches of 16 images, and for filter expansion factors of 4× and 6× for WDSR-a and WDSR-b respectively, are shown; however changing these parameters no significant improvements were achieved.
For pix2pix, we followed the suggestions proposed by its authors as it was developed as a generalpurpose solution to many different image-to-image translation tasks  50. Grayscale values were scaled from -1 to 1 and a tanh activation layer was used at the end of the generator. The model was trained for 100 epochs using a batch size of 1, randomly selecting the images. The number of epochs to train CNNs and GANs was selected in order to broadly stabilize the metrics for training and validation datasets. The trainings were performed using Tensorflow/Keras (Tensorflow 2. 5. 0, Python 3) on a NVIDIA GeForce RTX 3070 GPU. The results were then evaluated in term of image quality metrics and transport properties (see "Image analysis and flow simulations" section), and visual checks as well. Image quality (respect to HR images) was assessed estimating mean squared error (MSE), peak signal to noise ratio (PSNR) and structure similarity index (SSIM  55  ).
Finally, we also used unpaired data to train 2D and 3D CycleGANs. HR data was combined with a stack of 2D images from different FoVs, and a 3D subvolume, independently extracted from the central 640 3 px of the LR image and upsampled with cubic interpolation. These datasets were preprocessed similarly to (2D and 3D) pix2pix. We trained CycleGAN following the suggestions proposed by its authors, who developed a general solution for image-to-image translation tasks  53  , and randomly selecting the images. A relatively limited training of 25 epochs (455,625 and 5750 training steps in 2D and 3D) was possible in reasonable times (Table  1 ) due to the high computational costs, although a better performing GPU (NVIDIA GeForce RTX 3090 Ti) was employed for this network. The absence of paired images, did not allow the use of traditional image quality metrics to evaluate the results.
Image analysis and flow simulations. The obtained digital rocks were segmented and used to estimate transport properties. We defined a very simple and traditional workflow in order to evaluate the efficiency of our imaging of tuff microstructures without further complications due to filtering, segmentation and simulation approaches. Therefore, we fist segmented the images using Otsu's method  56  , which automatically estimate a grayscale threshold for a binary segmentation (i. e. , each voxel is labeled as pore or matrix), with scikit-image library in Python. The results were visually checked and the effect of threshold value on segmentation (i. e. , threshold sensitivity  34  ) is also discussed. The segmented images we then used to estimate porosity and perform permeability simulations.
Acknowledgements This research was performed in the ambit of the  INGV Progetto Dipartimentale LOVE-CF  (Linking surface Observables to sub-Volcanic plumbing-system: a multidisciplinary approach for Eruption forecasting at  Campi Flegrei caldera (Italy) ) financed by the  INGV , and of the  Horizon 2020  project  EXCITE (Electron and X-ray microscopy Community  for structural and chemical Imaging Techniques for Earth materials). The authors are grateful to  Dr. E. Di Clemente  and  P. Colantuono  for assistance during the hydraulic conductivity measurements, and to the two anonymous reviewers for their valuable comments and suggestions on our manuscript.
In that work, the authors managed to employ a power-waster circuit to generate fluctuations in the power supply of the circuit. Then, a sensor implemented in a different part of the reconfigurable fabric was used to retrieve the message. That work demonstrated that it was possible to implement power-based covert channels with transmission rates up to 8 Mbps. The Zynq Ultrascale+ SoCs allow to use the RPU and the APU independently. The real time cores generally would run a real-time operating system like RTOS  [Itu+15]  or simply run standalone applications. The application cores, on the other hand, are complex and their full potential can usually only be achieved through the use of a kernel like Linux. Our work employs this model, which is based on reasonable assumptions about the use cases of the Ultrascale+ technology.
The PL Power Domain will supply the reconfigurable fabric. The goal for this separation of power domains is to improve the energy footprint of the system by allowing to shut down complete areas of the SoC when these are not required. For the low and full power domains, the five main PLLs can be used to generate clocks. For the FPGA, only three of the PLLs (RPLL, IOPLL, DPLL) can be used to generate the four clocks available to the fabric (from the processing system, since it is also possible to use external clocks. ) Ultrascale+ SoCs allow to use the RPU and the APU independently. The cores in the RPU would normally run a real-time operating system like RTOS  [Itu+15]  or simply run standalone applications. The cores in the APU, on the other hand, are more complex and their full potential can best be drawn through the use of a kernel like Linux.
At low level, like in bare-metal applications, the control registers of the SoC can be edited through direct access operations. For example using the xil io library. However, to edit one of these control registers it is necessary to edit multiple security and configuration registers so that the frequency change is enacted. Furthermore, the application performing the operation must have the appropriate exception level. In the presence of a kernel this task can be simplified with the help of drivers which allow to request the modification of specific clocks. For example, the processor clocks (by using the cpufreq driver of Linux) or the FPGA clocks (by using the fclk drivers of Xilinx). This scenario is more favorable for attackers since the complexity of the kernel allows to hide malicious applications more easily.
Covert channels between the RPU and the FPGA The first class of covert channels we studied were those where an application running in the RPU (Cortex-R5F@533 MHz) acted as the transmitter, and a circuit implemented in the FPGA was the receiver. This scenario is illustrated in Fig. 5. Such attacks might be found in systems which use third party applications or accelerators. To perform the frequency modulation, the RPUbased application simply needed to access the CRL APB module and overwrite  In our experimentation, these tasks were performed using the xil io. h library. Under this attack model, the bandwidth will be limited by the delay for the application to modify the target oscillator (t ′ R5F toF P GA ). This delay is composed of the time required for a Cortex-R5F processor to modify a CRL APB register (t R5F ), plus the transition delay of the PLL (t P LL ), and the time for the RO-S to update its output (t RO-S ).
Covert channels between the APU and the FPGA The second type of covert channels under evaluation were those that originated from an application executed in the APU (Xilinx' Linux on Cortex-A53@1. 3GHz). As in the previous attack, our intended receiver was the RO-S in the FPGA. This scenario is depicted in Fig. 7. A regular Linux kernel, if configured properly, will feature the cpufreq driver which allows to modify the frequency of the underlying system. This might be leveraged to implement a covert channel between different cores controlled by the same operating system. In the case of the Xilinx' distribution of Linux, the kernel also features a set of APIs (/sys/devices) which allow to modify the frequency of the FPGA clocks. These two mechanisms use configuration files which can be managed from the application space.
Covert channels between the APU and the RPU The experiments in the previous Subsection assumed that the transmitter in the Linux-enabled Cortex-A53 would use the /sys/devices APIs to perform the frequency modulation of the FPGA clocks. However, this is not necessary if the application has access to the physical registers. To achieve this goal we can map the kernel memory through the devmem utility, then a register value can be read and written as any pointer from a C-language application. Revisiting the case where a transmitter in the Cortex-A53 intends to send data covertly to an application in the Cortex-R5F (illustrated in Fig. 9 ), we now expected the number of samples per window to be much lower. Thus we continued to use the encoding from Alg. 1. Again, the APU transmitter would perform the frequency modulation but now through direct access to the PLL dividers.
3. Formally, the difference in the register-register covert channel is that the Cortex-R5F application didn't have to retrieve the data from the FPGA (a difference of ∼ 90 ns). This is reflected in the delay model of Equation  7. Nonetheless, it remained difficult to estimate t A53 accurately. t ′ A53toR5F = t A53 + t P LL + t RO-S + t R5F (7) Then, from Fig. 10b , since the sampling period can be considered equivalent to t R5F and there are approximately five samples per bit transmitted, t ′ A53toR5F ≈ 5 × t R5F. The updated bandwidth is given in Equation  8. B ′ A53toR5F = 1 5t R5F = 1 2. 04 µs ≈ 490 Kbps (8) Finally, we revisited the case where the transmitter would be the application in the Cortex-R5F and the receiver would be an application in a Linux-enabled Cortex-A53. In this case both applications could operate without any additional delay.
Modeling virtual agents with behavior style is one factor for personalizing humanagent interaction. We propose an e cient yet e ective machine learning approach to synthesize gestures driven by prosodic features and text in the style of di erent speakers including those unseen during training. Our model performs zero-shot multimodal style transfer driven by multimodal data from the PATS database containing videos of various speakers. We view style as being pervasive; while speaking, it colors the communicative behaviors expressivity while speech content is carried by multimodal signals and text. This disentanglement scheme of content and style allows us to directly infer the style embedding even of a speaker whose data are not part of the training phase, without requiring any further training or fine-tuning.