dataesr/scientific-paragraphs-categorization huggingface dataset:
Frequency Hopping Except for the real world attack in Section 4, frequency hopping is disabled and the carrier is fixed at 2. 4 GHz. As in Screaming Channels, we assume that a motivated attacker could build a receiver able to follow the hopping sequence, listen to all channels simultaneously with a wider band or multiple receivers, or listen at a fixed frequency and wait for the carrier to jump to that channel. The last case might be particularly reasonable for the advertising packets, which are sent over only three fixed channels. In addition, under certain conditions the attacker can reduce the number of channels used for hopping. A BLE master can ask the peripheral to blacklist up to 35 data channels out of 37. This is a standard feature of the BLE standard, used to reduce interference by adapting to the environment (adaptive frequency hopping).
Combining the Profiles of Different Bytes In a software implementation of AES-128 such as the tinyAES one, there are 16 bytes that leak at 16 different points in time. In our attacks, we profile each of them independently. For a byte i, given a leak variable p⊕k, each of the 256 possible values of each profile model Yi (y i ) is estimated with N/256 measurements, taken at different points in time. Assuming that the profile model Yi (y i ) is the same for each byte i, we can build a single profile using 16 measurements from each trace, instead of 1. Each of the 256 possible values of the single profile model Y (y) is estimated with 16•N/256 measurements, taken at different points in time. This brings a gain in accuracy equivalent to collecting 16 times more traces. The hypothesis that the profile of each byte is the same is reasonable in our case, since the instructions executed for each byte are always the same.
2. 3. Following the BLE [SIG16] specifications, we craft a Set Host Channel Classification Command to blacklist all data channels but 0,1,16,34. On the Linux host of the attacker, we issue this command to the BLE dongle that acts as master using the hcitool program, as shown in Listing 1. Once set, the new channel map will be valid for the following connections as well. Extracting Encryption Traces. We start triggering encryptions in the beacon by reading the unlock characteristic. To have an idea of the leak trace to expect, we first extract hardware block 5. Indeed, we have seen that it appears harder to exploit due to its lower power consumption. However, using the hardware implementation would tie the code of the application to a particular hardware peripheral, making it less generic and portable.
Conclusions Screaming channels are an interesting leak vector, with peculiar characteristics compared to other forms of side channels. The fact that analog leaks propagate over an intended digital radio channel is at the same time a tremendous advantage and a new problem. On the one hand, the leaks profit from dedicated transmission hardware, making side-channel attacks possible at large distance, as highlighted in the seminal Screaming Channels paper. On the other hand, the analog leaks have to coexist with the intended data sent as digitally modulated discrete packets, making reception more complex. Sometimes, such problems can be addressed at the system level, for example, minimizing frequency hopping by blacklisting most channels. While the amount of correlation of screaming channels is comparable to conventional leaks, the leak model is nonlinearly distorted.
Journal of Geophysical Research: Planets MEM specifically models the sporadic component of the meteoroid environment, that is, those meteoroids not affiliated with a meteor shower. For particles <0. 5 cm, less than 10% of the net fraction of impactors hitting Earth are from showers  (Moorhead et al. , 2017). Thus, showers are considered by the authors of MEM to be a minor contributor to spacecraft risk and are not included. As an additional check on the importance of showers for Bennu impacts, we examined the International Astronomical Union database of meteor streams, specifically those from  Jenniskens et al. (2016) , and calculated the minimum distance between them and Bennu's orbit. We found a few streams that may be close enough to Bennu to represent an increase in the impact flux (i.
Model Runs An ephemeris for Bennu was obtained from the JPL Horizons On-Line Ephemeris System (https://ssd. jpl. nasa. gov/horizons. cgi); we downloaded state vectors for the asteroid at 1-day intervals between 10 January 2019 and 22 March 2020. We thus consider one orbital period of the asteroid, starting with its 2019 perihelion passage. Technically, this period excludes a portion of the period during which particle ejection events were observed, but the orbital elements of Bennu are effectively static for our purposes. MEM generates a mass-limited flux for a given target; the code also divides the total flux into bins by the angle and speed with which they encounter the target. However, the amount of material generated by a crater-forming impact event is generally held to be a function of the impactor's KE (e.
The energy densities of the former are an order of magnitude lower than those of the high-speed millimeter-sized impacts discussed here, but the latter are many orders of magnitude higher (i. e. , one would have to go to ~1,000 km s -1 for an impact to equal the energy density of a nuclear bomb). This tells us that numerical hydrocodes and crater scaling laws can handle high-speed impacts. Numerical impact experiments with the code iSALE  (Collins et al. , 2004; Wünnemann et al. , 2006) , where millimeter-sized impactors hit a surface at 40 km s -1 , show that such impacts produce substantial ejecta (J. Melosh, personal communication). The shock wave, while very strong near the impact point, weakens as it spreads out and generates ejecta just like a slower impact. If energy density were indeed an ejecta inhibiter, we would expect nuclear explosions to produce little ejecta, contrary to observations  (Melosh, 1989).
Multi-nucleotide variants (MNVs), defined as two or more nearby variants existing on the same haplotype in an individual, are a clinically and biologically important class of genetic variation. However, existing tools typically do not accurately classify MNVs, and understanding of their mutational origins remains limited. Here, we systematically survey MNVs in 125,748 whole exomes and 15,708 whole genomes from the Genome Aggregation Database (gnomAD). We identify 1,792,248 MNVs across the genome with constituent variants falling within 2 bp distance of one another, including 18,756 variants with a novel combined effect on protein sequence. Finally, we estimate the relative impact of known mutational mechanisms -CpG deamination, replication error by polymerase zeta, and polymerase slippage at repeat junctions -on the generation of MNVs.
M ulti-nucleotide variants (MNVs) are defined as clusters of two or more nearby variants existing on the same haplotype in an individual  1, 2  (Fig. 1a ). When variants in an MNV are found within the same codon, the overall impact may differ from the functional consequences of the individual variants  3. For instance, the two variants depicted in Fig. 1b  are each predicted individually to have missense consequences, but in combination result in a nonsense variant. Such cases, which would be missed by virtually all existing tools for clinical variant annotation, can result both in missed diagnoses and false positive pathogenic candidates in analyses of families affected by genetic diseases  1, 2. MNV identification tools  [4] [5] [6] [7] [8]  have been applied to databases of human genetic variation at varying scales, including 1000 Genomes  9  Phase 3 (2504 individuals with high coverage exome and low coverage genome-sequencing data), and the Exome Aggregation Consortium  1  (60,706 individuals with high coverage exome data).
For this project, we generated read-based phasing results for variants in the Genome Aggregation Database (gnomAD) v2. 1 callset using GATK HaplotypeCaller  21  , yielding 125,748 human whole exomes and 15,708 genomes with local phase information; the properties of this callset are described in detail in an accompanying paper  22. To assess phasing accuracy, we used 5785 family trios with exome-sequencing data and 635 family trios with whole-genome sequencing data that largely overlapped with the gnomAD 2. 1 release data. We calculated the phasing sensitivity, defined as the fraction of heterozygous variant pairs that have read-based phase information assigned for both variants, and found that it was 87. 9% for adjacent heterozygous variant pairs, reflecting the stringent haplotype-calling criteria of GATK  21  (Supplementary Tables  1 2 3 ).
Discussion We analyzed 125,748 human exomes and 15,708 genomes and identified 1,792,248 MNVs across genome with constituent variants falling within 2 bp distance, including 31,575 that exist within a codon. We have shown that MNVs represent an important class of genetic variation, and that they have a significant impact on the functional interpretation of genomic data, both at the population and individual level. Although we did not encounter an individual in which an MNV is the likely cause of a rare disease after sequencing 6072 individuals from rare disease families, we expect that applying our pipeline to larger numbers of disease samples will identify previously missed diagnoses, as has been observed in another study of developmental delay cases  2. The large number and high quality of variant calls in the gnomAD database provided increased power for statistical analysis of the three major mutational mechanisms (combinations of independent SNVs; replication errors by pol-zeta; and polymerase slippage at repeat junctions) responsible for the generation of MNVs, and importantly allowed us to estimate the relative contribution of each of these processes.
broadinstitute. org). Although some fraction of MNVs is missing from this list due to incomplete phasing sensitivity and read coverage, the database provides the most comprehensive set of estimates of MNV allele frequencies to date, valuable for further analysis of mutational mechanisms as well as the interpretation of MNVs in rare disease and cancer genomics  52, 53. Finally, despite the large sample size of our MNV data set, the fraction of MNVs that we have observed out of all the possible MNV configurations is still very far from saturating the space of possible MNVs, with only ~0. 005% of all possible adjacent MNVs observed in our data (Supplementary Figs. 10, 11 ). Increasing the number of sequenced individuals  54  in both disease and nondisease cohorts will permit the discovery and determination of the phenotypic impact of an increasingly comprehensive catalogue of variation.
Methods Ethics. We have complied with all relevant ethical regulations. This study was overseen by the Broad Institute's Office of Research Subject Protection and the Partners Human Research Committee, and was given a determination of Not Human Subjects Research. Informed consent was obtained from all participants. MNV calling. 125,748 human exomes and 15,708 genomes from gnomAD 2. 1 callset were used for the analyses (Supplementary Tables  5, 6 ). We used hail (https://github. com/hail-is/hail), an open source, cloud-based scalable analysis tool for large genomic data. For MNV discovery, we exhaustively looked for variants that appear in the same individual, in cis, and within 2 bp distance for the exome data set and 10 bp distance for the genome data set, using the hail window_by_locus function (i.
e. , we computationally checked every pair of genotypes within a certain window size, for every individual, to see whether the individual carries a pair(s) of mutation in the same haplotype. See Supplementary Methods for further detail. Also, we did not expand the window size >10 bp for MNV discovery, as phasing sensitivity significantly drops when the distance between variants is >10 bp, as shown in Supplementary Fig. 1d ). For trio-based analyses, we expanded the range to 100 bp to obtain a more macroscopic view. Although we performed MNV calling in sex chromosomes for the coding region, we restricted our analysis to autosomes, in order to control for differences in zygosity. MNV calling in rare disease samples was performed in a similar fashion as in the gnomAD exome data set. In total, 6072 rare disease whole-exome sequences were curated at the Broad Center for Mendelian Genomics (CMG)  55  and went through the MNV calling pipeline with the window size of 2 bp distance.
220 in total per person). The full list of such MNVs are available as a separate file at: https://gnomad. broadinstitute. org/downloads. Frame-restoring indel analysis was performed in a similar fashion. We used the gnomAD exome data set to call and filter the insertion/deletion pairs using the same filtering criteria (except for the fact that we did not restrict our analysis to cases where the frameshift effect would be rescued in all individuals), and focused on the canonical transcripts for the functional impact evaluation. Defining one-step MNVs and MNVs in repetitive contexts. A one-step MNV was defined as a MNV for which the allele count of both SNVs that make up the MNV is the same and close to the allele count of the MNV itself. We also compared the allele count of constituent SNVs (AC1 and AC2) with the allele count of the corresponding MNV (AC_mnv), and observed that the majority of one-step MNVs we discovered have AC_mnv divided by AC1 >0.
The full data for all the 78 patterns are shown in Supplementary Data 3. Further details are explained in the Supplementary Methods (section "Models and assumptions for estimation of the global MNV rate per substitution pattern"). Functional enrichment. Thirteen functional annotations were collected from Finucane et al. 41  as a bed file (which originates from database, such as ENCODE, Roadmap  61  and UCSC genome browser  62. ) For the methylation data, we collected the genome methylation level from ENCODE, and calculated the fraction of methylated CpG out of all the CpGs in the region, and ordered by the fraction (Supplementary Table  4 ). MNV density calculation was performed under the null hypothesis that the number of MNV of type WX→YZ we observe in an arbitrary genomic interval is proportional to the number of WX in the interval.
Acknowledgements We would like to thank the many individuals whose sequence data are aggregated in gnomAD for their contributions to research, and for making this work possible. The results published here are in part based upon data: (1) generated by The Cancer Genome Atlas managed by the  NCI  and  NHGRI  (accession: phs000178. v10. p8). Information about TCGA can be found at http://cancergenome. nih. gov,(2) generated by the  Genotype-Tissue Expression Project (GTEx)  managed by the  NIH Common Fund  and  NHGRI  (accession: phs000424. v7. p2), (3) generated by the  Exome Sequencing Project , managed by  NHLBI , (4) generated by the  Alzheimer's Disease Sequencing Project (ADSP) , managed by the  NIA  and  NHGRI  (accession: phs000572. v7. p4). We would like to thank the  Hail team  for developing tools essential for the large-scale computation in this work.
We would like to thank the analysis team of the  Broad's Rare Disease Group  for their manual inspection of MNVs in rare disease cohorts. This work was funded by  NIDDK   U54 DK105566 ,  NIGMS R01 GM104371 , and  NHGRI UM1 HG008900-01. Q. W. was supported by the  Nakajima Foundation Scholarship. K. J. K. was supported by  NIGMS   F32 GM115208. A. O. D. L. was supported by  NICHD   K12 HD052896.
Secondly, CNNs are weight sharing, which enable CNNs share learned features from a single sample via back-propagation with other samples. It efficiently decreases the necessary required input sample size and quantity. [10]. In practices, large amount of data is required to train the CNNs from scratch. Due to limited calculation power and lack of labeled big data set for relevant problems, the concept of transfer learning has been introduced. When applying transfer learning, a network is trained on a new dataset of samples using the stored weights for some or all layers from a pretrained network as an initialization, rather than initializing weights randomly. The selection of the pre-trained model in transfer learning is influenced by two factors : problem size and similarity  [11]. The selection of the pre-trained model is made on the basis of the associated problem which is relevant to the target problem.
If the size of the target data set is smaller and similar to the source training data set then the chance of over fitting is high. Similarly, if the size of the target data is larger and similar to the source data sets then the chance of over fitting is low and it requires only the fine tuning of the pre-trained model  [12]. In this paper, we applied transfer learning method on a CNNs architecture based pre-trained model, to analyze and classify neutron scattering image from SANS experiment. This model named Inception-v3 is pre-trained on ImageNet database. With fine-tuning, this model is first retrained and validated by simulated scattering images, then verified on real SANS experimental data collected at the Institut Laue-Langevin (ILL).
II. METHODS A. Data simulation To generate two-dimensional Small-Angle Neutron Scattering (SANS) images we use the data reduction and simulation code GRASP  [13]. GRASP is a MatlabTM script application designed for the graphical inspection, analysis and reduction of position sensitive detector(PSD) data produced by the 3 SANS instruments of the ILL. GRASP is using a mixed analitical and Monte-Carlo approach for the neutron event generetion and can include the real intrument resolution function, the measured backgrounds from a variety of different samples and the influence of the sample environment. By using GRASP, we could generate simulated SANS 2D detector images with variable sets of parameters. We could select different materials' structures and also freely change the instrument configuration.
III. EXPERIMENTAL RESULTS The small angle neutron scattering instrument D22 is used to investigate a wide range of samples, to identify their atomic structure and composition. Hence, we classify the samples with a 2-D scattering image at an early stage and then, we tried to predict its structure. We have started to consider four different geometrical structures: sphere, core-shell sphere, cylinder and ellipsoid, which procuce a quite similar scattering patterns. All the simulations are performed on a Linux Server (Ubuntu 16. 04. 5) with two NVIDIA Tesla P100 GPUs.
A. Experiment I: sphere vs. flux line bragg peak Spheres and flux-line bragg peak produce quite different images on the detector. Unfortunately, in real experiments, the instrument parameters are set according to a "guess" of the structure type and sample parameters, which sometimes are far away from the real structure. Hence,we first try to recognize the scattering image at an early stage without any priori knowledge. 1) Dataset: By using GRASP, we have generated 2000 sample images are generated respectively for the sphere and flux line bragg peak model. The corresponding dimensions of the geometric bodies, i. e. , radius for sphere, rocking angle for flux line bragg peak etc. are generated in a certain range randomly and independently. Fig. 3  shows some examples in this database, and a square beam stop is applied in the center of detector.
As shown in Fig. 3 , because of data collection time is not long enough or the parameters setting of the instrument are not always perfect, to obtain a high quality scattering images is not easy for further analysis. 2) Result: We begin the training trail with the pre-trained model Inception-v3. The initial weights are taken from the pretrained weights based on ImageNet. Only the weights of the  B. Experiment II: sphere vs. core-shell sphere vs. cylinder vs. ellipsoid The four models: sphere, core-shell sphere, cylinder and ellipsoid geometrically are similar to each other under some particular parameters in the structure. For example, if the model cylinder with length 0, it becomes a sphere model. Recognizing these four structures only from scattering image is a challenge task. 1) Dataset: 10000 simulated scattering images are generated by GRASP,for each individual sample structure.
2000 simulated scattering images with beam stream arrival with certain bias from the center are added respectively to each class. Fig. 8  shows two scattering samples in this kind of scattering data. Table. I shows the average precision, sensitivity (recall), and F1-Score of the model with fine-tuning and augmented database. The average accuracy of this model is 79. 8%, which is better than the performance of the model without finetuning. Except class unknown, the other four classes have proximity F1-score (between 75% and 80%). This unknown class is consisted of scattering images generated from simulated program, without or less recording useful scattering information by improper instrument parameters setting. For cylinder, the precision is high 90%, which is the ratio of correctly predicted positive observations to the total predicted positive observations, meanwhile the sensitivity is low 65%, which is the ratio of correctly predicted positive observations   Then, this classifier is tested on 9 silica sphere scattering image, 9 cylinder scattering image, 2 ellipsoid scattering image and 1 core-shell sphere scattering image from real experiment.
Also it performs better on scattering images with neutron beam arrival at the center of detector which contain scattering information in all directions rather than with center bias scattering images for which part of the information is missing. The training trail of deep CNNs is done on simulated database. And the trained classifier is tested on several raw scattering images from real experiments. With this small among of scattering images from real experiment, we couldn't get solid conclusion about the performance of our classifier. However, the result indicates the tendency that our classifier is able to correctly recognize real scattering images without any training samples from real scattering data, it could work equally on simulated and real images.
For example, in SNESIM  (Strebelle et al. , 2002) , the statistics of patterns on a pre-defined geometry are stored in a tree shape database that is built by scanning the whole TI before starting the simulation. Then, the simulation proceeds pixel by pixel, a value is drawn randomly according to probabilities conditioned by the surrounding patterns and computed from the data base. As a consequence, the method is memory consuming and limited to the simulation of categorical variables. In IMPALA  (Straubhaar et al. , 2011 (Straubhaar et al. , , 2013)) , the limitation due to the memory is alleviated by using a list shape database, and non-stationary TIs can also be handled with the use of auxiliary variables  (Chugunova and Hu (2008) ). In other algorithms, such as FILTERSIM  (Zhang et al. , 2006)  or CCSIM  (Tahmasebi et al.
, 2012) , the simulation grid is filled by directly pasting patches, i. e. several pixels at a time. FILTERSIM allows the simulation of continuous variables, whereas CCISM is based on cross-correlation between patches. The main drawbacks of patch-based methods is their difficulty to honour conditioning data. One of the most flexible MPS algorithms is the Direct Sampling  (Mariethoz et al. , 2010). It is a pixel-based method, where the simulation of one pixel consists in randomly searching for a pattern in the TI that is similar to the pattern centered on the considered pixel in the simulation grid, and then copying the value of the variable from the TI. Hence, it has the advantage of making database creation unnecessary, no probability is computed, and patterns of varying geometry are handled.