dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Table  2  compares the results of our approach (CNN) to other approaches presented above as baselines on dataset FB15k-237. CNN was run with timeouts that are compatible with user interaction (0. 01s, 0. 1s, 1s), and description depth 10, which ensures that most if not all relevant graph features of the test entity are captured. The output logs of CNN predictions and explanations is available from the companion page. Except for ConvE that outperforms other approaches on FB15k-237, CNN outperforms all other approaches as soon as 0. 01s for the fine-grain measures (MRR, Table  3. Results on JF17k and Mondial for baseline Freq, two latent-based approaches (mTransH, RAE), a rule-based approach (AMIE+), and our approach (CNN, timeout=0. 1s). Results marked with * are from  [20].
JF17k Mondial Approach MRR Hits@1 Hits@3 Hits@10 MRR Hits@1 Hits@3 Hits@10 Freq Hits@1, Hits@3), and as soon as 1s for measure Hits@10. CNN-1s reaches MRR=0. 286, halfway between the two other best approaches, R-GCN (-3. 8%) and ConvE (+3. 9%). It can also be observed that CNN outperforms the simple Freq baseline on all measures and for all timeouts, which implies that it learns something useful beyond global statistics. Note that this is not the case of other approaches except ConvE, especially for fine-grain measures, MRR and Hits@1. Apart from CNN and ConvE, none improves Hits@1 over Freq. Those positive results are confirmed on the two other datasets, as far as results are available (see Table  3 ). CNN results are significantly higher than Freq and AMIE+ in both datasets and for all measures, by an MRR margin of 17.
[13]istics of datasets. Bordes et al[2]for link prediction evaluation. It is a set of triples derived from the Freebase KG. FB15k-237 is more challenging than FB15k because relations that are almost equivalent to another relation or to the inverse of another relation have been removed, and because (head, tail) entity pairs that exist in the train dataset have been removed from the validation and test datasets to avoid potential trivial inferences. The dataset also comes with textual mentions but we ignore them as we focus on knowledge graphs. The two other datasets are used to complement and confirm results. JF17k is another dataset extracted from Freebase, introduced by[10]and available at http://github. com/lijp12/SIR. Although it was designed to go beyond binary relations, we here only consider binary relations, letting n-ary relations to future work.
We introduce Mondial as a subset of the Mondial database[13], which contains facts about world geography. We simplified it to the task of link prediction by removing labelling edges and edges containing dates and numbers, and by unreifying n-ary relations. It is available from the companion page. Dataset entities relations train edges valid. edges test edges FB15k-237 15,541 237 272,115 17,535 20,466 JF17k 28,645 322 171,559 - 66,615 Mondial 2,473 20 7,979 778 970 by.
Profiling Attacks and their Evaluation This section presents the framework we will consider when attacking a device through a profiling attack. Once presented, we will set the goal of an evaluator. The considered scenario is made of the following steps: • Profiling acquisition: a dataset of N p profiling traces is acquired on the prototype device. It will be seen as a realization of the random variable S p {(x 1 , z 1 ),. , (x Np , z Np )} ∼ Pr[X, Z] Np , where all the x i (resp. all the z i ) are i. i. d. realizations of X (resp. Z). • Profiling phase: based on S p , a model is built that returns a set of scores for each hypothetical value of Z, that can be assimilated to a pmf (possibly after normalization). F : X → P(Z). • Attack acquisition: a dataset of N a attack traces is acquired on the target device.
Hence the choice of a binomial law for U i when emulating non-informative components. Every possible combination of the (d + 1)-sharing has been generated and replicated a given number of times (denoted by q) before adding the noise, in order to have an exhaustive dataset. Therefore, it contains q × 2 (d+1)n simulated traces. Once the data were generated, we trained a MLP with one hidden layer made of r = 1, 000 neurons. The training loss is naturally the NLL loss. 12  The training lasts T = 200 epochs 13 , with a Stochastic Gradient Descent and a learning rate of 10 -3. Our simulations comprise three main campaigns: Experiment 1 (masking only): in this experiment, we set D = d + 1 in order to avoid to consider irrelevant input features. The simulations are done over n = 4 bits, d ∈ {0, 1, 2, 3} and σ ∈ {0.
01, 0. 1, 0. 2, 0. 4, 0. 8, 1. 6, 3. 2}. We also generate enough data so that the training 11 A masking scheme of order d consists in a d + 1-sharing of the sensitive target variable. 12 Beware that in Pytorch and Tensorflow, the NLL loss is computed with natural logarithms, whereas one must consider the logarithm in base 2. 13 One epoch refers to the number of iterations needed to process the whole dataset through the SGD algorithm. set is "exhaustive", i. e. the number of replicas is q = 2, 000. With such generated dataset, we expect to make the estimation error (19) negligible. The gap between the MI and the PI should therefore only be composed of the optimization error (18) and the approximation error (20). Experiment 2 (masking, with uninformative components): in a second experiment, we have D = 40, including the uninformative components.
4, 0. 8, 1. 6, 3. 2} here. From those experiments, the Perceived Information PI (Z; X; θ SGD ) is estimated thanks to a hold-out dataset of 1/5-th of the size of the training set size. For the sake of comparison, we estimate the MI between the target sensitive 4-bit variable and its simulated leakage model with a Monte Carlo sampling of the leakage pmf Pr[X|Z].
Application on Experimental Data So far, we have seen that deep neural networks could reach the informational security bounds of a leakage in simulated experiments, thereby giving useful estimations for the developer. This success did not rely on any prior knowledge on the leakage, but was achieved thanks to a simple MLP with one hidden layer. To confirm these observations, we propose to complete the investigations by considering experimental leakage traces. Subsection 6. 1 presents the acquisition of the dataset used for the experiments, Subsection 6. 2 presents the methodology of our experiments, and Subsection 6. 3 discusses their results. Besides, details on the used DNN architectures for these experiments can be found in Appendix D.
Presentation of the Dataset The leakage traces represent the power consumption of a XMEGA128D4 chip supported on a Chip Whisperer Lite board  [OC14]. The program ran on the chip aims at simulating the leakage of several shares that may be processed by a protected implementation of a cryptographic primitive. The firmware is directly written in assembly code and consists in loading each byte of an input plaintext array to a register, setting it to zero and then storing it back to the input array. Some details of the code are given in Appendix B. 500, 000 traces of 2, 500 time samples each have been acquired, along with the corresponding bytes array denoted by plain[i], i ∈ 0, 15. The complete acquisition has been done within 15 hours. To reproduce conditions similar to the simulations, we only target the n = 4 most significant bits of the target variable.
Application on Public Datasets So far, we have considered our experimental investigations through the view of the Leakage Assessment Problem (i. e. Problem 2). However, we remind that the final task an evaluator is given to achieve is the SCA Optimization (i. e. Problem 1), namely to find N a. It is recalled that Corollary 1 argued that by solving the Leakage Assessment Problem, one could get an accurate estimation N a , θ of the quantity f (β) MI(Z;X) , known to be a lowerbound of the optimal solution of the SCA Optimization Problem, namely N a. One could wonder whether this inequality still holds for any model, maybe sub-optimal, i. e. when estimating the minimal number of queries N a (θ) to the target device for such a model with the quantity N a,θ. A formal proof would be a promising further work, though beyond the scope of this paper.
Nevertheless we propose here to empirically verify this hypothesis by training a CNN on two public datasets and by implementing the key enumeration in order to evaluate the smallest N a such that SR(N a ) ≥ β, as defined in the SCA Optimization Problem. In the following, we will restrict to β = 0. 9. To this end, we considered three public datasets. The first one is the Random Delay Counter-Measure Dataset (AES-RD) released by Coron and Kizhvatov  [CK09]. 17 The target smart-card is an 8-bit Atmel AVR micro-controller, protected by a Random Delay counter-measure, which has an effect on the misalignment of the traces, making some attacks like Gaussian Templates much harder but possibly having no effect on deep learning based attacks  [CDP17]. The targeted variable is the output of the first S-Box.
50, 000 traces of D = 3, 500 time samples each are given in this dataset. We use N p = 40, 000 traces for profiling and the remaining N v = 10, 000 for validation. The second one is the ASCAD dataset [PSB + 18]. 18 The target platform is an 8-bit ATMEGA8515 running a masked AES-128 implementation and measurements are made using electromagnetic emanation. The targeted variable is the output of the third S-Box. The dataset provides 60, 000 traces of D = 700 time samples each, where N p = 50, 000 traces are used for profiling and N v = 10, 000 for validation. The third one is the AES-HD dataset, gathering traces measured on an unprotected AES-128 on FPGA. The dataset contains 100, 000 traces of 1, 250 time samples each. 19 80, 000 traces have been used for profiling and the remaining 20, 000 have been used for the key recovery phase.
For each training, a VGG-like CNN architecture has been used. Specific details about the parameters used can be found in Appendix D. The training have been run on 200 epochs on the AES-RD, 50 epochs on the AES-HD, and stopped after 30 epochs on the ASCAD since the model started over-fitting. After each epoch, an estimation of N a (θ SGD ) (i. e. for the current model given by the Adam optimizer) is computed thanks to a key enumeration, according to the procedure detailed in Appendix E. The results are given in Figure  3. On each graph, N a,θ is denoted in green, whereas the enumeration key estimation N a (θ SGD ) is denoted by the orange curve. 20 n Figure  3  (left), we can first remark that the first epochs of the profiling of the AES-RD dataset show a chaotic behavior. This is explained by the fact that the NLL loss is initially close to n = 8 bits, or in other words, the PI is close to zero, leading to unstable estimations of N a (θ SGD ).
BN has been shown to make the loss function smoother, making the optimization easier and faster  [STIM18]. Finally, a dropout layer δ p [SHK + 14, GBC16] has been added on the input of the softmax classifier. Dropout is known to prevent DNNs from overfitting (i. e. to prevent the estimation error to explode), which is useful when one lacks data. The dropout parameter has been set to p = 0. 1 i. e. each neuron of the hidden layer is randomly set to 0 with probability p each time an output F (x, θ) is computed during the optimization. All together, we can sum up the architecture of our MLP as follows: F (x, θ) = s • λ θ2 • µ • δ p • σ • λ θ1 • µ(x) (23) For the experiment on the ASCAD dataset, we have considered the same architecture as proposed in [CDP17, PSB + 18], with the same notations: s • [λ • σ] n1 • δ G • [δ • σ • µ • γ] n3 , ( 24 ) where γ denotes a convolutional layer, σ denotes an activation function i.
The same architecture has been used in the experiments on the AES-HD dataset. For experiments on the AES-RD dataset, the same VGG-like architecture as the one presented in [KPH + 19] has been used. More specifically, n 3 is set to 9 so that there is enough pooling layers to get feature maps on the last convolutional layer whose width equals one. Besides, n 1 = 0, i. e. there is no intermediate dense layer, except softmax.
The EDR is defined in this paper as the region where electrons decouple from magnetic field lines. The purpose of this paper is to use data from MMS to quantify the acceleration by E N of magnetosheath electrons across the magnetopause current sheet and compare it to simulations by  Shay et al. (2016)  and  Bessho et al. (2016). While the initial energy gain of the crescent electrons is governed by their acceleration by E N , they are further accelerated by the much weaker reconnection electric field (E M ), so that subsequent returns to the E N region result in higher-energy crescent distributions as shown by  Shay et al. (2016)  and  Bessho et al. (2016). We use data from a unique dayside asymmetric reconnection event with guide field of about 0. 4 observed by MMS on 29 December 2016 to quantify this total acceleration.
This feedback was used to inform modifications to the intervention to maximise engagement and minimise any burden posed by the intervention. At the end of data collection, patient and charity representatives will be involved in the discussion and interpretation of the trial and process evaluation data and in planning dissemination with stakeholders. recruitment Participants (n=2500) will be recruited across England and Wales from sites in Southampton, Oxford and Bangor, via general practitioner (GP) surgery mail-outs. Practices will be informed about the study via clinical research networks and practices who opt-in can proceed to participate. Practice staff will search GP databases for eligible patients (see table  1.
inclusion/exclusion criteria). Lists of potentially eligible patients will be screened by GPs prior to mail-out. Anonymous data (age, cancer type, postcode and diagnosis date) will be collected via GP record searches on all invited patients to allow exploration of the generalisability of our sample. Patients will be mailed a letter of invitation, an information sheet, instructions on how to start the study and a reply slip collecting reasons for non-participation. Opportunistic recruitment by clinicians in participating surgeries will also be used to recruit patients who fit the inclusion criteria. Posters advertising the study may also be presented in waiting areas. The expected dates of recruitment run from October 2017 until July 2019.
Eligibility criteria See table 1. We will be using a cut-off of 85 on the primary outcome measure for QoL (where 100 is the best possible score), the EORTC QLQ-c30  35  so that those scoring above 85 (who have high quality of life) will be excluded. This score was calculated using existing data examining the change in QoL scores for cancer survivors, where the lowest scoring two-thirds scored 87. 1 or lower, with a median of 80. 9 36 (higher scores denoting higher QoL). Participants will be excluded if they have had sarcoma or lymphoma of the breast as these have a different course, prognosis and treatment, as agreed by the clinical members of the research team. Those living in the same household as another study participant will also be excluded to avoid potential contamination across study groups.
randomisation and blinding After online screening, patients will complete online consent and baseline measures. LifeGuide software will then randomise each participant to one of the three study groups using computer-generated random numbers. Once randomised, participants will be informed of their allocation and, if in one of the intervention arms, given access to Renewed. Randomisation will be stratified by cancer type: breast/prostate/colon and by EORTC score (high/low QoL; 64 or less/65-85 taking 65 as the lower 25% CI from previous study data  36  ). Blinding of randomisation is ensured by the use of automated computer software. Blinding of patients to the intervention is not possible. Participants will be informed online as to which group they have been allocated to immediately and will also be sent notification via email.
renewed intervention Participants randomised to the intervention groups will have access to their usual medical care and have access to the Renewed intervention. The Renewed intervention was created using LifeGuide software ( www. lifeguideonline. org). 38 39 During the development of Renewed a synthesis of the growing evidence base was conducted that relates to digital interventions directed at improving QoL in cancer survivors. 40 Consistent with best practice in digital behaviour change intervention development,  41 42  iterative qualitative research was used to elicit user (patient and health professional) views and experiences of the intervention modules throughout development, including barriers and facilitators to engagement with altering diet and exercise. 43 he content of Renewed has been refined based on these findings.
usual care Participants allocated to usual care will receive a link to the National Health Service's (NHS) LiveWell website where they can access advice about living a healthier lifestyle. Other than this, participants in the usual care group will continue to use their existing medical support as they usually would. At the end of the study, participants will be asked what other interventions, if any, they have used while taking part in the study. data collection Participants will receive three automated notifications to complete follow-up measures at 6 and 12 months, one being the initial invitation to ask them to complete the online questionnaires and reminders at 1 week and 2 weeks later if they are not complete. Participants will receive a £10 gift voucher via email when they complete the 6 month follow-up questionnaires online.
The Patient Enablement Instrument (PEI) was modified so that it asks about confidence and understanding relating to health as a result of using the website as opposed to receiving support from a doctor (PEI  54 55  ). Data regarding website and/or app usage (eg, pages accessed, time spent on each page) will be downloaded using LifeGuide software. A medical record notes review will be conducted after 12 month follow-up to extract NHS and Personal Social Service use. This will enable collection of data pertaining to clinical status including cancer recurrence. Online questions will be used to collect patients' personal costs. The EQ-5D-5L  56 57  which measures medical QoL (QALY) will be collected at baseline, 6 months and 12 months. We will apply the UK tariff to translate the EQ-5D-5L to utility scores.
statistical analysis SPSS, Stata and Excel software will be used to evaluate outcomes. All participant data will be analysed, including those who have withdrawn, unless participant/s specifically request that their data be removed from the data set. All participants will be analysed on an intention to treat basis, that is, as randomised with any missing data imputed using a chained equations multiple imputation model. Complete cases analysis will be undertaken as a sensitivity analysis. Linear regression models will be used for the analysis of continuous variables, controlling for baseline values, stratification variables, practice as a cluster variable, and also controlling for potential confounding variables as appropriate, both for the overall trial sample and for each clinical subgroup (breast, prostate, colon).
Open access After at least 1-2 months of participation, support providers, GPs and any other primary care staff significantly involved in trial procedures or intervention delivery (n=20, or until saturation) will be invited to take part in this additional study. Focus groups, interviews or phone interviews will be held, depending on availability or else electronic (written) feedback will be requested. In both process studies interviews will be recorded and fully transcribed. Transcriptions will be anonymised. To ensure that we remain open to, and grounded in, interviewee perspectives we will carry out inductive thematic analysis of all textual data,  58  triangulated where appropriate with other trial data (eg, web usage), and with discussion among team members (including our PPI representatives) to elaborate our interpretations.
58 ndling of adverse events It is very unlikely that there will be any adverse events during screening and questionnaire completion, given that screening online consists of answering questions; however, participants are repeatedly told that they can contact the research team who can signpost them to other support services as appropriate. Participants have the right to withdraw from the study at any time. If a participant withdraws having completed questionnaires, their data will be retained to evaluate potential differences and reasons for attrition, unless they ask us not to use their data. Should a participant be ineligible for the study for any reason, they are presented with a page of alternative support services. Any serious adverse event (SAE) occurring to a research participant will be reported to the Ethics Committee where, in the opinion of the Chief Investigator, the event was related to administration of any of the research procedures, and was an unexpected occurrence.
Acknowledgements The trial team would like to thank  Julie Hooper ,  Megan Liddiard  and  Karen Middleton  for their assistance with the recruitment of GP surgeries. Eat for Health was largely developed by  Tara Cheetham-Blake  in collaboration with  Joanna Slodkowska-Barabasz , also of the  University of Southampton. We would also like to thank  Kirsten Smith ,  Mary Steele  and  Jin Zhang  of the  University of Southampton  for their work on programming Renewed Online. We would like to thank all of our PPIs who helped in discussions surrounding the study procedures and helped us to develop our intervention on the project:  Tamsin Burford ,  Geoff Sharman ,  Kevin Summers ,  Roger Bacon  and other members of the  PPI  team who wish to remain anonymous.
The detection and identification of species of fungi in the environment using molecular methods heavily depends on reliable reference sequence databases. However, these databases are largely incomplete in terms of taxon coverage, and a significant effort is required from herbaria and living fungal collections for the mass-barcoding of well-identified and well-curated fungal specimens or strains. Here, a PacBio amplicon sequencing approach is applied to recent lichen herbarium specimens for the sequencing of the fungal ITS barcode, allowing a higher throughput sample processing than Sanger sequencing, which often required the use of cloning. Out of 96 multiplexed samples, a full-length ITS sequence of the target lichenised fungal species was recovered for 85 specimens. In addition, sequences obtained for co-amplified fungi gave an interesting insight into the diversity of endolichenic fungi.
Introduction Across the world, herbaria and fungal culture collections host large numbers of specimens and strains with the main goal being to document, preserve and classify the many species within the fungal kingdom. Among characters generally used to identify fungi, DNA sequences have been particularly useful for the numerous fungal species with plastic or convergent morphologies. Of the DNA markers traditionally used for fungal identification, the internal transcribed spacer region (ITS) was chosen as the primary barcode because it allowed species-level identification for the broadest range of fungi  (Schoch et al. 2012). Following the formal acceptance of this fungal barcode, substantial effort was put into improving the curation of ITS sequences in several databases, including RefSeq  (Schoch et al.