dataesr/scientific-paragraphs-categorization huggingface dataset:
For the overall selected number of iterations, the module activates detection after every 10 or 100 encryptions in the case of AES cryptosystem, depending on what granularity has been specified through SamplingGranularity (lines 4-6). Once activated, the detection module collects the data from hardware events (line 7) and feeds them as features to selected binary classifiers (line 8) in order to perform individual voting. Based on these individual votes, the module generates a report after taking a majority vote on the results (line 9). Detection is then deactivated (line 10) and if the report is True then an attack is reported (lines 11 -12). Otherwise, the victim's process continues to execute uninterrupted.
A. SYSTEM MODEL We demonstrate the efficiency of the WHISPER tool on Intel's core i7 -4770 CPU running on Linux Ubuntu 16. 04. 1 with Kernel 4. 13. 0 -37 at 3. 40-GHz. Our threat model consists of access-driven CSCAs, which produce information leakage over the entire cache hierarchy (L1, L2 & LLC) of Intel x86 architecture. We take same-core and cross- / * No-attack detected ! * / 13 return 0 core CSCAs into account for the purpose of detection. We consider that the Operating System (OS) is not compromised in our system model (i. e. , it is part of the trusted computing base). The PAPI (Performance Application Programming Interface) library is used to access hardware events using HPCs from the Intel Core i7 machine. It provides machine and operating system independent access to HPCs. Any of over 100 preset events can be counted through either a simple high level programming interface or a more complete low level interface of PAPI.
D. CASE STUDY-III: DETECTING FLUSH+FLUSH Our third and last case study provides experimental evaluation of the detection of two implementations of Flush+Flush attack targeting AES cryptosystem. Flush+Flush is a stealth and high-resolution attack that targets LLC except that it is stealthier than Flush+Reload and Prime+Probe attacks. Unlike other CSCAs, the stealth nature of Flush+Flush does not make any memory accesses. Thus, as a running process itself, it causes no cache misses and the number of cache hits are reduced to minimum due to constant cache flushing. Authors in  [4]  claimed to detect their own attack using hardware events in Linux P erf _event_open interface. They documented that the attack (spy process) is non-detectable using 24 hardware events available with Linux syscall interface.
The later evaluation scenario inherently covers the trivial case. However, do provide results for both scenarios in Section VI-A. Attacks like Spectre  [21]  and Meltdown  [22]  exploit out-of-order and speculative execution techniques that are available in almost all modern processors to maximize the utilization of execution units in CPU cores. Unlike Flush+Reload and Flush+Flush, these attacks are independent of the cryptosys-tem and can retrieve data from the victim's address space without having access privilege. This section briefly elaborates the scalability of the WHISPER tool by detecting both Spectre and Meltdown attacks. We demonstrate the scalability under two scenarios. In the first scenario, no modification is performed to WHISPER, i. e. , no new features (hardware or software events) are added and retraining of the ML models is performed.
Although the performance of WHISPER is evaluated using the same evaluation metrics, in these cases, sampling is adjusted to a fixed interval of 100 ms rather than a percentage of attack completion. This is because both these attacks are independent of cryptosystems. Therefore, they do not have a single victim process. Moreover, the overhead mainly constitutes the sampling cost of HPCs and not of any victim's slowdown. With a rather fixed and coarse-grain sampling rate, the overhead for the detection module is much less than in previous cases. As shown in Table  17 , detection accuracy of all individual ML models under NL and AL conditions remains very high and the Ensemble consequently also provides very high accuracy. In the FL condition, however, the detection accuracy suffers and the tool generates a large number of False Positives.
Our experiments and analysis of Spectre attack code reveals that it has the much less number of total instructions to execute and, among those instructions, a large proportion of instructions comprise of branch instructions. Interestingly enough, the attack process generates the highest number of branch mispredictions. Our results show that, if total branch instructions and the total branch mispredictions generated by a process are collected as features, the ML models can detect Spectre attack with very high accuracy, as shown in Table  20  under all load conditions. In the case of a Meltdown attack, similar observations can be made by observing the number of page faults generated by the attacker. Meltdown generates segmentation faults or invalid page faults while bypassing the memory isolation through exceptions.
Even though this factorized picture does not always lead to full theories and leaves out details that are necessary for the self-consistency of single models (e. g. the mass generation for mediator particles), it is a starting point to prepare a set of distinct but complementary collider searches for Dark Matter, as it leads to benchmarks that are easily comparable across channels. 1. 3 Choices of benchmarks considered in this report and parameter scans Contact interaction operators have been outlined as basis set of theoretical building blocks representing possible types of interactions between SM and DM particles in  [Goo+10]. The approach followed by LHC searches (see e. g. Refs. [CMS15b; ATL15d] for recent jet+/ E T Run-1 searches with the 8 TeV dataset) so far has been to simulate only a prioritized set of the possible operators with distinct kinematics for the interpretation of the constraints obtained, and provide results that may be reinterpreted in terms of the other operators.
However, with the full Run 2 dataset, such models may be within reach. [GeV] Another case where the width can impact the kinematics is when m φ,a is slightly larger than 2m χ. Here, the width determines the relative contribution between on-shell and off-shell mediators. An example is given in Fig. 2. 26. As the minimal width choice pursued in this document is the most conservative one, this effect can be neglected in order to reduce the number of benchmark points to be generated. [GeV]  The points for the parameter scan chosen for this model are listed in Table  2. 5, chosen to be harmonized with those for other analyses employing the same scalar model as benchmark. Based on the sensitivity considerations above, DM masses are only simulated up to 500 GeV (but the 5 TeV mediator point is retained) leading to a total of 24 benchmark points.
5: Feynman diagrams for q q → h 1 h 1 + Z process contributing to mono-Z signature, adapted from  [Bel+15c]. Based on the various LHC search channels, DM phenomenology issues and theoretical considerations, numerous works have proposed benchmark scenarios for the IDM, see e. g. [Gus+12; GHS13b] while a FeynRules implementation (including MadGraph, CalcHEP and micrOMEGAs model files) was provided in  [Gus+12]. An updated analysis of the parameter space has recently been performed in Ref. [Bel+15c]. The authors suggested to study mono-X signatures that are relevant to model-independent collider DM searches, and evaluated their rates presented below. They have implemented and crosschecked the IDM model into CalcHEP and micrOMEGAs, with an implementation publicly available on the HEPMDB database, including loop-induced HHG and γγH models.
Acknowledgements The authors would like to thank  Daniel Whiteson  for helping in the review of this document. This research was supported by the  Munich Institute for Astro-and Particle Physics (MIAPP)  of the DFG cluster of excellence " Origin and Structure of the Universe ". The authors would like to express a special thanks to the  Mainz Institute for Theoretical Physics (MITP)  for its hospitality and support. P. Pani  wishes to thank the support of the  Computing Infrastructure of Nikhef.
Background: Renal cancer is one of the 10 most common cancers in human beings. The laparoscopic partial nephrectomy (LPN) is an effective way to treat renal cancer. Localization and delineation of the renal tumor from pre-operative CT Angiography (CTA) is an important step for LPN surgery planning. Recently, with the development of the technique of deep learning, deep neural networks can be trained to provide accurate pixel-wise renal tumor segmentation in CTA images. However, constructing the training dataset with a large amount of pixel-wise annotations is a time-consuming task for the radiologists. Therefore, weakly-supervised approaches attract more interest in research.
The main contributions of this paper are as follows: (1) To the best of our knowledge, we proposed a weakly-supervised CNN for renal tumor segmentation for the first time. The remaining paper is organized as follows: Materials section describes the datasets used in this paper. In Methods section the method is introduced in detail. Experimental results are summarized in Results section. We give extra discussion in Discussion section, a conclusion in Conclusion section and abbreviations section. The last section is the declarations of this paper.
Materials The pre-operative CT images of 200 patients who underwent an LPN surgery were included in this study. The CT images were generated on a Siemens dualsource 64-slice CT scanner. The contrast media was injected during the CT image acquisition. The study was already approved by the institutional review board of Nanjing Medical University. Two scan phases including arterial and excretion phases were performed for data acquisition. In this paper, CT images acquired in arterial phase were used for training and testing. The arterial scan was triggered by the bolus tracking technique after 100 ml of contrast injection (Ultravist 370, Schering) in the antecubital vein at a velocity of 5 ml/s. Bolus tracking used for timing and scanning was started automatically 6 s after contrast enhancement reached 250HU in a region of interest (ROI) placed in the descending aorta.
Thus, the kidneys in the images were firstly segmented by the multi-atlas-based method  [29]  to define the ROIs of kidneys as shown in Fig. 2(b). The multi-atlas-based method just produce initial segmentation of kidneys, two radiologists checked the contours of kidneys and corrected them if necessary. The contours of tumors were drawn manually by one radiologist with 7-years' experience and checked by another radiologist with 15-years' experience in the cross-sectional slices. However, the pixel-wise masks were only used for bounding boxes generation and testing dataset evaluation. Among 200-patient images, 120 patients were selected to build the training dataset and the other 80 patients were used as the testing dataset.
In addition, the reference labels of renal tumors in the training dataset were only used to generate bounding boxes and not used for CNN training, and the reference labels in the testing dataset were used for quantitative evaluation. The bounding boxes with different margins are defined according to the ground truth and used as weak annotations for CNN training. We set d to be 0, 5 and 10 pixels (Fig. 4(a)-(c )) in our study to simulate the manual weak annotations by radiologists. If the bounding boxes with margin d are beyond the range of images, it will be limited in the region of images. As shown in Fig. 4 , the comparison of bounding boxes with different margin values is given.
Group training and fusion mask generation Once we have generated pseudo masks of renal tumors, these masks are fed into CNN as weak labels for parameter learning. Most of weakly supervised segmentation methods used iterative training  [5, 7]  to optimize the accuracy of the weak labels from coarse to fine. However, the preliminary results showed that this iterative strategy is hard to improve the accuracy of pseudo masks due to the difficulties of the renal tumor segmentation mentioned before. To overcome this problem, we proposed a new CNN training strategy instead of iterative training method. In the group training stage, we have input images {X 1 …X M } and pseudo masks {I 1 …I M }. The input training dataset is divided into K subsets {S 1 …S K }. For each subset S k , a CNN f(X; θ k ), X ∈ S k with parameter θ k is trained.
In total, we can get K CNNs trained in this stage. After that, for each image X m , we can get K predictions fP 1 m …P K m g of renal tumors by these CNN models. We denote that P k m ¼ f ðX m ; θ k g. Pseudo code of group training is shown in Algorithm 1. One thing worth to be mentioned is that one image in the training dataset is used to train only one CNN model in this stage. Once K CNN models are trained successfully, all the images in the training dataset will be used to test each CNN model and obtain K results for prediction. Thus, the proposed group training strategy can ameliorate the overfitting of the model. In order to alleviate the under-segmentation in the K predictions, a mask image is generated by fusing these predictions. The fusion mask is defined as follows: FM m ¼ ConvCRFs PM m ∪P 1 m ∪…∪P K m À Á ð5Þ where FM indicates the fusion masks, and PM indicates pseudo masks generated in Pseudo masks generation section.
Training with VWCE loss After Section Pseudo masks generation and Group training and fusion mask generation, the fusion masks of training dataset are generated for the final CNN model training in this stage. Only the final CNN model will be used for testing dataset evaluation. In this stage, we train the CNN on the whole training dataset with the fusion masks. In addition, a new voxel-wise weighted cross-entropy (VWCE) loss function is designed to constrain the CNN training procedure. traditional cross-entropy loss is defined as follows: L CE ¼ - 1 M X m∈M X c∈C FM m;c log f X m;c ; θ À Á ð7Þ where FM are fusion masks defined in Eq. (  5 ), f(X; θ) are the outputs of CNN, M represents the number of samples and C represents the number of classes. In Eq. (  7 ), pixels belonging to different classes have equal weight.
In the case of unbalanced datasets,  [32]  proposed weighted cross-entropy loss defined as follows: L WCE ¼ - 1 M X m∈M X c∈C w c FM m;c log f X m;c ; θ À Á ð8Þ where, w c represents the weight of class c. Considering the weak annotations used in the training procedure, the voxel-wise weight map generated in the previous stage represents the probability of the predicted class given in the fusion mask. Thus, the voxel-wise weights obtained in Eq. (  6 ) are introduced into Eq. (  8 ) which is defined as follows: L VWCE ¼ - 1 M X m∈M v m X c∈C w c FM m;c log f X m;c ; θ À Á ð9Þ Finally, we conduct the final CNN model training with VWCE loss function on fusion masks. Our evaluations are all conducted on CNN trained in this stage.
Parameter settings The input are ROIs of kidneys and bounding boxes without any other annotations. Considering that UNet  [32]  has been widely used for medical image segmentation, we adopted UNet to be the CNN models in stage2 and stage3 in our experiments. The network parameters are updated by means of the back-propagation algorithm using the Adam optimizer. The initial learning rate was set to be 0. 001 and decreased by decayed learning rate ¼ learning rateÃdecay rate global step decay steps. In each epoch of training, it takes 3600 iterations to traverse all the training images with the batch size of 4. The class weights of cross-entropy w c in Eqs. (  8 ) and (  9 ) were set to 1. 0 and 0. 2 for renal tumor and background respectively. In stage2, we set the number of subset K to 3 for the training dataset of 120 CT images.
Existing methods We mainly compared with two weakly-supervised methods, i. e. , SDI  [5]  and constrained-CNN  [21]. The SDI method used 2D UNet to generate weak labels from bounding box by recursive training and carry out final segmentation. The weakly-supervised information used in the constrained-CNN method includes scribbles and the volume of target tissue. In this paper, the scribbles annotations used in constrained-CNN were generated by employing binary erosion on ground truth for every slice. Furthermore, the volumetric threshold of renal tumor was used in the loss function of Constrained-CNN. It was set to [0. 9 V, 1. 1 V], where V represents the volume of renal tumor in ground truth. As the architecture of UNet was used in  [5, 21] , as well as our proposed method, the UNet was trained by all the training dataset with the pixel-wise labels to generate a fully-supervised UNet model for extensive comparison.
The comparison of different weak labels and training losses As shown in Table  1 , DSCs between the different masks and the ground truth of the training dataset are displayed. The DSCs of bounding boxes are 0. 666, 0. 466 and 0. 341 respectively when the margins of bounding box were set to 0, 5 10 pixels. The DSCs pseudo masks generated by ConvCRFs can reach 0. 862, 0. 801 and 0. 679. However, the DSCs of fusion masks generated after group training has even higher DSC than pseudo masks. Obviously, the rectangular bounding boxes were improved significantly by the Stage 1 and Stage 2. Furthermore, the improvements of the weak labels contribute to the training of the final CNN model.
Evaluation of segmentation results of renal tumors in the testing dataset with different parameters The DSC, Hausdorff distance (HD)  [33]  and average surface distance (ASD) were adopted to evaluate the segmentation results of our proposed method. The segmentation results of renal tumors in the testing dataset were obtained with different settings of parameters, i. e. number of groups, loss function and margin of bounding box. The comparison of DSCs in the testing dataset is displayed in Table  2. k = 0 means that the procedure of stage2 not used. In this situation, the pseudo masks generated by ConvCRFs were used as weak labels directly for the final CNN model training in the stage3. The loss functions used during the final model training is marked in the parentheses. MC represents the connected component analysis in the postprocessing step.
While in large tumor, the shape and texture of the tumor are complicated, which leads to the difficulties of the segmentation. Although this problem exists in all three methods, our proposed method shows the most significant improvement compared with the other two methods. Finally, one limitation of this study is the lack of validation of the final CNN model with external datasets. The training and testing datasets in this paper are from the same hospital. Additional validation of the final CNN model with multi-center or multivendor images will be performed in the future. Due to the differences in image acquisition protocols or the other factors, the CNN model trained in this paper may not be able to achieve a similar performance on the other datasets. However, the parameters in our model can be optimized by fine-tuning with the external datasets to improve the accuracy.
Funding This study was funded by a grant from the  National Key Research and Development Program of China  ( 2017YFC0107900 ),  National Natural Science Foundation  ( 31571001 ,  61828101 ),  Key Research and Development Project of Jiangsu Province  ( BE2018749 ) and the  Southeast University-Nanjing Medical University Cooperative Research Project  ( 2242019K3DN08 ). These funds provided financial support for the research work of our article but had no role in the study.
The International Molecular Exchange (IMEx) Consortium provides scientists with a single body of experimentally verified protein interactions curated in rich contextual detail to an internationally agreed standard. In this update to the work of the IMEx Consortium, we discuss how this initiative has been working in practice, how it has ensured database sustainability, and how it is meeting emerging annotation challenges through the introduction of new interactor types and data formats. Additionally, we provide examples of how IMEx data are being used by biomedical researchers and integrated in other bioinformatic tools and resources.
Various techniques are used to identify PPIs, which often detect different subsets of the interactions potentially occurring within the same targeted interaction space  5, 6. Methodological issues may thus partly explain the frequent lack of significant overlap between large-scale PPI datasets. Therefore, in order to correctly interpret PPI data, it is important to understand the context in which the data was collected. This includes not only the experimental technique and the type of relationship it will detect (such as direct binding between two partners identified by X-ray crystallography, affinity purification of multiple proteins complexes or colocalization of two proteins in the same cellular compartment) but also the experimental conditions and modifications made to the participating molecules such as affinity tags or sequence mutations.
All these metadata will impact the composition of the interactome generated. Therefore, this metadata need to be recorded in a computationally accessible manner, enabling researchers to make informed decisions as to the quality of data they are working with. The International Molecular Exchange (IMEx) Consortium  7  was formed in 2005 with the goal to provide users with a dataset enhanced with controlled vocabulary (CV) terms to enable scoring, filtering and sophisticated searching of the information. Here, we review the advances made in curation practices and data formats since the IMEx Consortium was first described in 2012  7. We illustrate additional ways in which the data can be scored and filtered; and describe use cases where researchers have moved beyond simple high-coverage, gene-centric networks to use the additional level of detail provided by IMEx Consortium data in both analysis and visualisation.
The IMEx consortium The IMEx Consortium is open to any group or resource interested in curating physical molecular interactions, current members include IntAct 8 , MINT  8  , DIP  9  , UniProt 10 , MatrixDB  11  , InnateDB  12  , HPIDB  13  , UCL Functional Gene Annotation team and IID  14. The consortium comprises the majority of the existing database resources who have agreed to collaborate on the curation of published, experimentally derived interaction data. The IMEx Consortium members have agreed on a set of curation rules and map interaction data to a limited set of defined molecule identifiers to provide the user with a single and consistent dataset, with each interaction being assigned a unique and persistent identifier. While the IMEx Consortium is a global effort with contributing members from Europe, North America and Australasia, its common rigorous curation rules and standards have allowed IMEx to be selected as one of the Core Data Resources of the European Life Sciences Infrastructure for Biological Information (ELIXIR)  15  , which are considered essential for the long-term preservation of biological data.
All IMEx data are made publicly available in the HUPO-PSI standard formats, making them Findable, Accessible, Interoperable and Reusable (FAIR)  19. The initial data distribution model consisted of interaction databases retaining their data locally and making their IMEx dataset available through the Proteomics Standard Initiative Common QUery InterfaCe (PSICQUIC)  20, 21  (see also section "TOOLS TO VISUALIZE AND QUERY IMEx DATA" below). However, even at the IMEx website, each resource's records were listed separately and users had to cluster the results of their search to merge different evidences for the same interacting pair of molecules. Although a tool to enable this was supplied at the site, it was restricted to operating on <5000 records. As database infrastructure funding has become more difficult to obtain, members of the IMEx Consortium agreed 3 years ago to centralise their IMEx-compliant data-storage and curation efforts in the IntAct database maintained at the EMBL-European Bioinformatics Institute (EBI).