dataesr/scientific-paragraphs-categorization huggingface dataset:
• Regarding InstTimer, the timer-instruction-based pattern may be limited when cache attacks are not based on such timer instructions, but adopt a less-accurate timing measurement strategy by using other mechanisms such as a kernel library or a counter in a loop in a different core. To cover this kind of attack, it is possible to check the physical address of the library function (provided by the software Detection Module) or the loop counter (found using instruction-based or address-based pattern matching) and look for access to these suspicious addresses instead of timer instruction. • Some attacks, such as rowhammer, only use cache eviction and do not need any time measurement. Thus, the timing-instruction-based approach is not effective and the memory-access-instruction-based approach should be investigated instead.
Area Footprint Table  3  provides the area footprint of the solution in 3 scenarios: InstTimer only, MemAccess only, and InstTimer + MemAccess. The area of the unprotected SoC is presented for comparison purposes. The full implementation (i. e. , InstTimer + MemAccess) basically increases the number of Flip-Flops used by 12%, and the number of LUTs by 5%. The main area overhead is due to the synchronization for the Detection Module connections and the Parallel Connection required by InstTimer for the instruction flow signals. Some room for improvement is possible by revisiting the synchronization strategy and detection algorithms. We can also note that the combination of two completely different detection logics requires only 58% Flip-Flops of the sum of the two. This is due to the fact that many parts of the detection logic can be shared, especially the bus connection and some configuration registers.
Discussion MATANA is a framework suited for designing and implementing specific runtime detection logics (both hardware and software) for different classes of attacks. Even if we only described experiments for two specific attacks in this paper, we are highly confident that some suited detection logics can be designed and integrated in MATANA for other classes of attacks. For instance, if we consider microarchitectural attacks such as Flush + Reload and Spectre, we can observe that they both exhibit short sequences of repeated instruction patterns and that they use hardware components such as the cache and the branch predictor in a particular way. As a consequence, there is a high probability that they are distinguishable from legitimate programs with patterns based on the instructions executed and hardware events even at low frequency.
MATANA can of course be used along with other defense mechanisms to improve detection efficiency. Existing attack detection techniques, such as those cited in Section 2, can be a source of insight of probed signals and detection logics. Other classical detection or mitigation mechanisms such as Control Flow Integrity and shadow stack can be implemented in MATANA's Detection Module, by using an isolated hardware memory zone to store the control flow graph or shadow return address and performing a check when encountering jump instructions. Finally, let us note that in addition to attack detection, MATANA can be used to analyze the information leakage of hardware components or to design more secure hardware components and more security-oriented HPCs. Several limitations of MATANA can be identified.
Conclusions In this paper, we proposed MATANA, a framework that allows the design and integration of a flexible runtime attack detector for different classes of attacks that exhibit specific footprints on the microarchitecture. The detection is based on the fine-grained observation and analysis of different microarchitectural signals, obtained by inserting probes in the target component. The flexibility of the detection is provided by the Detection Module composed of reconfigurable hardware and software, which allow for updating of detection logics at any moment if necessary. Different data transmission techniques have been proposed to preserve high information throughput and timing information, even with the presence of frequency discrepancies between reconfigurable hardware and the target component.
The security of systems over their lifetime can be enhanced by reconfigurable hardware equipped with attack detection logics based on analysis of microarchitectural signals. We believe this is particularly valuable today, when hardware lifetime is relatively long compared with the evolution speed of software attacks, and the fact that software attacks are able to target hardware vulnerabilities. Hardware component or system designers should be aware of this possibility and consider including this adaptable security into their design. Researchers can use MATANA's tools and methodology to design and evaluate new attack detection algorithms on hardware and software, understanding microarchitectural behavior, without being limited by information provided by existing hardware. As for future work, we first plan to enrich our detection campaigns by submitting other use cases of attacks to MATANA-such as Spectre/Meltdown for example.
We also think that it would be interesting to make MATANA more automatic and easy to use by the research community. For example, automatically selecting signals based on the hardware's source code, or generating patterns that analyze the attack's characteristic or the correlation between signals and secret based on some formal description of the attack, could provide useful and relevant enhancements. We also plan to investigate more complex detection logics to estimate their cost in term of area footprint and execution time. Indeed, even if the experiments we carried out so far tend to show that quite simple detection logics are sufficient to exhibit satisfying detection rates, we think it interesting to investigate the implementation of complex detection algorithms (such as Machine Learning algorithms) in order to evaluate to what extent it is possible to implement such algorithms in MATANA, both in hardware and software, to optimize the detection efficiency as well as the area footprint on the reconfigurable fabric.
2. Probe Connection. The probe connection step consists in choosing a proper clock domain crossing technique used for connecting each selected signal to the Detection Module, with consideration of information quantity, area overhead, and detection accuracy. This step is only required if the Detection Module is not running at the same frequency as the processor. This distinction is important since, in the case of a reconfigurable Detection Module, the frequency may be much smaller that the processor's core. We illustrate the detection method at low frequency in Section 5. 2. For more information regarding transmission techniques, the reader can refer to Section 4. 3. 3. Detection Logic Design. This step aims at designing the detection algorithm intended to be implemented into the Detection Module.
GaTewAY post-processing tool for time-dependent conformational analysis We have developed 2D-molecular graphs (labeled 2D-MolGraph) and associated algorithms in order to automatically analyze the conformational dynamics of molecules and their assemblies from molecular dynamics simulations. The 2D-MolGraph and the algorithms are described in the next section. Different applications are presented in order to illustrate the versatility and transferrability of the 2D-MolGraphs that can be applied to gas phase molecules, clusters, liquids, solids, and inhomogeneous interfaces between solids and liquids. Our developments are included in the GaTewAY (Graph Theroy for conformAtional dYnamics analysis) software described in ref. [44]  A key component in the versatility and transferrability of our 2D-MolGraphs is the atomic level of granularity that was chosen to define the vertices and edges of topological 2D-graphs.
This is described in section 2. 2. Given a MD trajectory, the GaTewAY software thus provides the list of conformations that have been explored based on the isomorphism test from  [45] , their residence time, and the "graph of transitions" that represents all transitions that have been observed between the identified conformations along the MD time-length. Examples of applications to gas phase molecules and to more complex inhomogeneous aqueous interfaces will be given in section 2. 3. GaTewAY can be applied to any kind of MD trajectory, be it ab initio MD or classical MD or coarse grain MD, or any type of mixing between these atomistic representations, including chemical reactivity. The algorithms are implemented in such a way that they can be easily extended to take into account any other types of interactions and parameters to analyze the conformational dynamics.
Fingerprinting based on bonding patterns and types of interactions To analyze the conformational dynamics of a molecular system either for one trajectory or multiple trajectories treated in parallel, our software GaTewAY is composed of two main stages: • Construction of the series of 2D-MolGraphs: this stage consists in constructing the series of 2D-MolGraphs from the list of the positions of the atoms given by the trajectory file(s). At each step (snapshot) I of the MD trajectory, the list of atoms and their Cartesian coordinates are indeed known. Depending on the chemical type, we have established a database in which each possible atom has a specific covalent radius and a maximum number of covalent bonds that it can form. For each type of bond/interaction, a set of threshold In the left panel, carbon atoms are represented in dark gray, nitrogen atoms in blue, oxygen atoms in red, and hydrogen atoms in light gray.
In the 2D-MolGraph (right) the conventions are the following: vertices are colored in dark gray, blue, red, light gray when they respectively correspond to a carbon atom, nitrogen atom, oxygen atom and hydrogen atom ; the edges are colored and represented in black lines when a covalent bond exists between two atoms (i. e. between two vertices), in red dashed lines when a hydrogen bond exists between a heavy atom and the H-bond acceptor (i. e. between the associated vertices), this is an arc as seen by the arrow going from donor to acceptor of H-bond. distance/angle values have been established. These values are in a database that can be easily modified by any user. We always choose the nearest atoms to fix the eventual bond overflow, especially when the user sets high cut-off values. For example, given an atom a that should form at most one covalent bond, and two atoms b and c that respect the condition to form a covalent bond with atom a, whenever the distance(a, b) is less than distance(a, c), the algorithm will choose atom b for establishing a covalent bond from atom a as atom b has the shortest distance with atom a.
Evaluation and validation The GaTewAY software can analyze any molecular dynamics trajectory, i. e. AIMD, QM-MM-MD, FF-MD, CG-MD. Because of the atomic granularity level chosen for the 2D-MolGraph definitions, GaTewAY is versatile and can be applied to trajectories of gas phase molecules and clusters, to solids, nano-particles, to any complex inhomogeneous molecular system as e. g. solid/water interfaces. In the following we illustrate applications of the 2D-MolGraphs and associated graph algorithms in the gas phase (analysis of the trajectory of an isolated protonated peptide Z -Ala 6 -COOH, C 26 H 39 N 7 O 8 ) and in the condensed phase for trajectories of hydrophobic surfaces in contact with liquid water (air/water, graphene/water, boron-nitrate BN/water). One advantage of topological 2D-graphs is that higher levels of granularity can be employed for the graphs.
Methods for generating the 3D-structures of conformers have been developed in the literature. See for instance refs. [52] [53] [54] [55] [56] [57] [58]  Some of these methods typically rely on databases of 3D-structures of sub-units that will be used to generate a reasonable conformation of a more complex molecule. Other methods rely on cristallographic databases for the initial construction. Nowadays topological databases based on 2D-graphs such as the ones presented in the first part of this paper become more in use. See for instance the topologybased database of ref. [18, 59]  The present development is part of this latter domain, i. e. to generate 3D-structures of molecular conformers from a database of 2D-graphs. In our case, the database is built on the 2D-MolGraphs that were presented in section 2 of this paper.
The GaTeWay software has been developed and includes the definitions of the 2D-MolGraphs presented in this paper as well as graph algorithms applied in this paper. As discussed in the text, this software is easily modified and can be expanded in order to include supplementary definitions for the edges of the 2D-MolGraphs. We also illustrated the possibility to go beyond the molecular level of granularity of the 2D-MolGraphs (with atoms being the vertices) and hence adopt a more coarse grained level of representation of a given molecule (with e. g. H-Bonded rings being the vertices of the graph). We have shown that this representation is highly promising to characterize the time-evolution of H-Bonded molecules such as peptides. This level of granularity has already been addressed in  [25] , it can be used for either predicting the 3D coarse-grained structure of molecules or for searching stable polymorphic substructures in a sequence of conformers (identified by the algorithms of ref.
Acknowledgements SB and MB acknowledge PhD (SB) and Post-Doctoral (MB) fundings from the  LABEX (Laboratoire d'Excellence/Excellence Laboratory scheme) CHARM 3 AT 'CHimie des ARchitectures Moléculaires Multifonctionnelles et des MATériaux'  from the  University Paris-Saclay. HPC resources from  GENCI-France  Grant  072484  ( CINES/IDRIS/TGCC ) are acknowledged for the molecular dynamics simulations. Dr Simone Pezzotti  is acknowledged for highly fruitful discussions regarding 2D-graphs at hydrophobic aqueous interfaces.
Objective: With the strengths of deep learning, computer-aided diagnosis (CAD) is a hot topic for researchers in medical image analysis. One of the main requirements for training a deep learning model is providing enough data for the network. However, in medical images, due to the difficulties of data collection and data privacy, finding an appropriate dataset (balanced, enough samples, etc. ) is quite a challenge. Although image synthesis could be beneficial to overcome this issue, synthesizing 3D images is a hard task. The main objective of this paper is to generate 3D T1 weighted MRI corresponding to FDG-PET. In this study, we propose a separable convolutionbased Elicit generative adversarial network (E-GAN). The proposed architecture can reconstruct 3D T1 weighted MRI from 2D high-level features and geometrical information retrieved from a Sobel filter.
Experimental results on the ADNI datasets for healthy subjects show that the proposed model improves the quality of images compared with the state of the art. In addition, the evaluation of E-GAN and the state of art methods gives a better result on the structural information (13. 73% improvement for PSNR and 22. 95% for SSIM compared to Pix2Pix GAN) and textural information (6. 9% improvements for homogeneity error in Haralick features compared to Pix2Pix GAN).
Introduction In some medical image applications like segmentation, classification, and detection, the lack of database and unbalanced data (biased on age, normal subject, or abnormal subject) can affect the accuracy of deep learning models. Some studies tried to overcome this issue with traditional data augmentation methods. The most common data augmentation method includes simple transformations of the dataset such as rotation, crop, scale, etc. Although these methods improve the training process in a deep network, the informative information which is provided for the network is not significant  [1]. High-quality synthetic data augmentation with generative models can enrich the dataset in terms of variability. There are two different tasks in medical image synthesis: image generation and imageto-image translation or cross-modality generation.
Wang et al. proposed a 3D auto-context-based locality adaptive multimodality generative adversarial networks model (LA-GAN)  [13]. The locality adaptive network in this study is an end-to-end trained deep network and the fusion weights are automatically and simultaneously learned with the generative model. The objective of  [13]  is to synthesize high-quality PET images from the low-dose images in order to reduce the radiation exposure. The locality adaptive fusion network is fed with different modalities and generates the fused images. Then, the 3D U-net generates FDG-PET from the fused images. The training dataset contains 20 subjects. After patching images the size of the dataset has been increased to 2500 samples. Even though data augmentation can help the network to prevent overfitting, training a deep architecture like U-net with a few samples does not allow the model to be generalized.
The discriminator D, which is a binary classifier, attempts to distinguish the real images from the fake ones generated by G while the generator G tries to fool D by generating realistic examples. The opposition between the two networks, D and G, can be formalized as a min-max game. The minimization and maximization parts used to train the generator and the discriminator that represented by the loss function as follows  [7] : min G max D L(D, G) = E x∼p data(x) [log(D(x))] + E y∼p z (z) [1 -log(D(G(z)))] GAN is used to generate new samples having the same distribution as the dataset using a random noise as the input. For an image-to-image translation task, conditional GAN (cGAN) is more suitable. cGAN takes an additional input y (labels, text, image,. ) sampled from an empirical distribution.
Experimental Results and Evaluation To assess the performance of E-GAN for multimodality image translation, we conducted experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The FDG-PET images and the pairwise T1 weighted MRI of 256 healthy subjects (129 female and 127 male) have been used for this study. In ADNI participants usually take some scans at different periods of time, we only used the subjects with less than one year delay between the scans. By adding this condition we could attenuate the differences caused by a long time delay between successive acquisitions. Both the MRI and PET images were first registered in the Montreal Neurological Institute (MNI) space with spatial normalization to have the same resolution, with a voxel size of 2 × 2 × 2 mm 3. Then an intensity normalization was performed on the PET images to divide each voxel intensity by the global average value.
Pix2Pix GAN also represented a good performance to generate the texture descriptor. In order to have a quantitative assessment of the texture Table  4 , represents a comparison of the four most common Haralick features from GLCM for the proposed architecture and the state of the art methods. The energy of the generated image with E-GAN is equal to the real image so the model can generate the localized changes of the image as same as the real image. As shown in Table  4 , the proposed model has less contrast error in the generated images compared to the other methods. Although the homogeneity is approximately the same between the different models, E-GAN has the minimum difference with the ground truth. E-GAN can generate images with the similar textures information to the real image. This is critical when the image-to-image translation will be done with the aim of diagnosing or generating abnormal images to balance a dataset.
Conclusions In this paper, we proposed a model to generate 3D T1 weighted MRI from the FDG-PET images. E-GAN is capable of generating 3D T1 weighted MRI by eliciting relevant 3D features with separable convolutions and a Sobel filter. Moreover, the idea of adding a Sobel filter for passing the geometrical information of PET images through the decoder helps the generator to avoid the saturation problem in the discriminator by generating more realistic samples in the first steps of the training. Experimental results on the ADNI dataset show that the proposed model can defeat the state of the art and generate images of a better quality. The evaluation of the proposed model has been done in three aspects: visual analysis, structural analysis, and tissue analysis. Although E-GAN demonstrated good performance compared to the state-of-the-art methods, the operations that have to be done in the generator in order to mix the features increase the training time of the network, which could be a drawback.
Data Availability Statement: Data used in preparation of this article were obtained on (10/11/2020) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni. loni. usc. edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at http://adni. loni. usc. edu/wp-content/uploads/how_to_apply/ADNI_.
Haruta et al. have chosen to express the semantics of sentences in a version of First Order Logic (FOL) that incorporates events and integers/degrees, allowing them to translate a wide range of constructions involving adjectives, comparatives, generalised quantifiers and numerals. Key to many of their analyses is the notion of degree. For example, they analyse Tom is taller than Mary following the A-not-A analysis (see Schwarzschild 2008 and references therein) as meaning that there is some degree such that Tom has, but Mary has not, this degree of tallness. Haruta et al. evaluate their system on a large number of NLI datasets, including a novel one they have designed to cover the phenomena that they have been particularly interested in, usually absent from existing datasets such as FraCas  (Cooper et al.
ACKNOWLEDGMENTS The workshop and seminar out of which this special section grew was logistically supported by the "Groupe de Recherche en Linguistique Informatique, Formelle et de Terrain" (GdR LIFT, https://gdr-lift. loria. fr/), which is a structure funded by the French national scientific research agency in a effort to foster discussion among three communities related to language: the communities of field linguistics, formal linguistics and computational linguistics. [ 43 ] Timothée Bernard  0000-0003-4172-6986 timothee. bernard@u-paris. fr  https://dx. doi. org/10. 15398 /jlm. v10i1. 325 This work is licensed under the Creative Commons Attribution 4. 0 Public License.  http://creativecommons. org/licenses/by/4. 0/ [ 47 ].
Recent computational models of the acquisition of spoken language via grounding in perception exploit associations between spoken and visual modalities and learn to represent speech and visual data in a joint vector space. A major unresolved issue from the point of ecological validity is the training data, typically consisting of images or videos paired with spoken descriptions of what is depicted. Such a setup guarantees an unrealistically strong correlation between speech and the visual data. In the real world the coupling between the linguistic and the visual modality is loose, and often confounded by correlations with non-semantic aspects of the speech signal. Here we address this shortcoming by using a dataset based on the children's cartoon Peppa Pig. We train a simple bi-modal architecture on the portion of the data consisting of dialog between characters, and evaluate on segments containing descriptive narrations.