dataesr/scientific-paragraphs-categorization huggingface dataset:
Our dataset To create the dataset, we developed a smartphone application which allows to take a picture of a landscape and meanwhile to record the geographic position of the user and the value of the various instruments that are available in the smartphone. More precisely, the data stored are the following: • latitude, longitude, altitude, • angle with north and magnetic north, • tilts X, Y and Z. Some of these parameters are already provided by some datasets, but the ones concerning the orientation of the camera are not available. All these elements are stored in a JSON file. Furthermore, for each acquired image, we created a ground truth and so we manually segmented the skyline. Hence, JSON file is accompanied by a binary file in which the pixels of the skyline are marked. For the tests, about twenty images are taken into account.
Results In order to assess the quality of our algorithm, we propose to test it on our dataset and to evaluate the average distance (in pixels) over all the images between the skyline we obtain and the ground-truth skyline. Of course this distance is relative to the image size 1024x768. The results are shown in figure  7. In order to check the relevance of each contribution of our algorithm, we have run several tests by removing each single step from the whole process and check the results for each. We recall that the main contributions of our algorithm are : color metric learning, score weighting and matching with DEM. In figure  7 , we clearly see the relevance of each individual step, since the average distance dramatically increases (from 3 pixels to 14 or even 23) when we remove one of them.
It is worth mentioning that our learning step has been run on the CH1 dataset described in  (Saurer et al. , 2016)  and not on our dataset. The CH1 dataset consists of images of mountains in which the skyline has been manually segmented (unfortunately, no geographic information are provided with this dataset, so that we can not extract the correct DEM to run our algorithm on these images). This shows that our new color metric has been learned once on one dataset and can be used on any other dataset without fine-tuning it to the new considered images. Furthermore, in figure  7 , we show the results obtained by our algorithm when the score map is binarized before the DEM matching. The binarization consists in only keeping the highest score in each column. We can see that this binarization does not help the matching step (and even slightly increases the distance).
Moreover, the tagged dataset built in this paper will benefit the entire community in this field. Future works will first consist in analyzing robustness to meteorologic conditions and then in detecting other notable elements in the image using image analysis but also the DEM. Let us remember that our final goal is to present to the user information concerning points of interest in the image using augmented reality. For this, we also intend to use databases containing monuments, roads, etc. There is still an immense potential in this field because of its utility in many areas like in tourism of course but also, for example, for hikers who want to visualize hiking trails, for persons interested in geology because it would be possible to show them the structure of the ground also and so on. Smartphones are today abundant in our daily life and augmented reality systems could be developed by mixing their instruments with image processing algorithms.
Their optical response to a probe depends on the details of their internal disorder, as well as on different parameters of the probe. Typically, an optical EAP has two stages  [4, 5]. The enrolment stage takes place only once, before the key is given to the user, and aims at its full characterization by the authority responsible for the distribution of the keys. To this end, the key is subject to a large number of random challenges (i. e. , it is interrogated by large number of probes with different parameters), and all of the challenge-response pairs (CRPs) are stored in a database together with the PIN. In the verification stage, the user inserts his key in a verification device and types in his secret PIN. If the PIN is correct, the verifier has to decide whether the key with the given PIN is authentic or not.
Assuming that the verifier has access to the database, the verification is achieved by interrogating the key with a moderate number of probes, whose parameters are chosen at random from the recorded challenges in the database, and by checking whether the corresponding responses agree with those in the database. The cloning resistance of optical PUKs is sufficient for preventing impersonation attacks in a tamper-resistant scenario, typically discussed and analysed in the literature  [3] [4] [5] 12] , where an adversary does not have access to the probes. In scenarios where an adversary may actually have access to the probes that are sent to the optical PUK, the nature of these probes (challenges) plays a significant role in the security of the authentication protocol. When the probe is classical light, the controlled parameters are classical quantities such as the incidence angle, the intensity, or the wavefront of the field  [4] [5] [6] 10].
Verification stage When a user gives the key for authentication, the verifier contacts the database over a secure authenticated classical channel to obtain the pertinent list of CRPs. The verification stage involves M 1 identical sessions, and proceeds as follows. 1. Set the phase mask of the SLM to Φ (s) opt , and position the SMF at the output to match the corresponding target mode s. 2. Prepare the probe in the coherent state |α P , chosen at random from a uniform distribution over A, and send it to the wavefront-shaping set-up. 3. Measure at random the real or the imaginary quadrature of the scattered field in the target mode by setting the LO phase to θ = 0 or π/2, respectively. Both quadratures are equally probable.
According to Eq. (  16 ), verification tests of error ε 1 × 10 -3 and confidence 99. 9% require M 2. 3 × 10 7 sessions, and the total verification time is expected to be a few seconds. Our EAP is the first one to rely on conjugate quantum continuous variables, and provides a practical way to secure authentication of optical PUKs without the need for photon counting. Assuming a tamper-resistant verification setup, we have shown that the protocol offers collision resistance and robustness against cloning. Moreover, it is worth emphasizing that, as long as the verification set-up is tamper resistant, a compromised database does not affect the security of the protocol. Indeed, even if an adversary has access to the list of CRPs to be used for the authentication of a key, the sequence of probe states as well as the sequence of the quadratures to be measured in M sessions, are not a priori known.
Literature Review A literature review was conducted to gain an understanding of current posttransplant clinical practices and to identify the key gaps in the available guidance related to the practical management of modifiable risk factors in posttransplantation care. MEDLINE and Google Scholar databases, and resources from international transplant societies were searched for kidney and liver transplantation guidelines using varying search terms including kidney transplant guidelines, liver transplant guidelines, kidney transplant recommendations, liver transplant recommendations. The original search was conducted between August 7,  2015, and September 10, 2015, and was restricted to English language articles; the guidelines included were published between 1999 and 2015. The search results were filtered according to relevance for kidney or liver transplantation and for guidance posttransplantation.
[10] [11] [12]  In liver transplantation, a retrospective study of 359 transplant recipients showed that low adherence to treatment during the first 6 to 18 months posttransplant led to a higher risk of graft loss. 35  Nonadherence predicted acute rejection (odds ratio [OR], 4. 95; 95% confidence interval [CI], 1. 6-14. 7) during a 5-year follow-up period. 65  Most importantly, nonadherence negatively impacted on graft and patient survival after liver transplantation. 66  A 2006 retrospective audit of the Scottish Liver Transplant Unit's database estimated nonadherence to be responsible for 1 in 10 deaths in liver transplant recipients. 67  A meta-analysis by Dew et al 62 demonstrated the rate of nonadherence to have been more common in kidney transplant patients compared to recipients of other solid organs, including liver, heart and lung, with a rate of 36 cases per 100 patients per year in the kidney group.
163  The introduction of mTORi, such as sirolimus, that combine both immunosuppressive and antiproliferative actions with potentially non-nephrotoxic properties, 174 coincided with the move toward CNI minimization strategies in kidney transplantation and in liver transplantation. As a result, CNI avoidance or conversion to mTORi regimens have been investigated in a number of large, prospective, multicenter, randomized clinical trials in kidney transplantation, with less study data available for liver transplantation.
Detection of DSAs DSA assessment should be done using solid-phase immunoassay technology. 274  The reactivity of DSA should be determined, and strength of reactivity expressed as mean fluorescence intensity (MFI). Quantification of antibody level is best achieved by titration. 274  Assessment of IgG subclasses is still not recommended. In 2013, the Transplantation Society proposed the following guidelines (Table  8 )  274  : In low-risk patients with stable kidney function, although there are no robust data to support systematic screening for DSAs, it is done in some centers at least once in the first 3 to 12 months after transplantation. After the first year posttransplant it is recommended that at least 1 serum sample is stored each year for patients in all risk categories, with evaluation of current serum in the case of significant change to an immunosuppressive regimen, suspected nonadherence, graft dysfunction, and before transfer to a remote center.
Prevalence and Impact of Preformed DSAs on Graft Function and Patient Survival Pretransplant DSA positivity with potential clinical significance has been tentatively defined as MFI ≥5000, but standardization is still needed. 276  The prevalence of preformed DSAs according to MFI criteria was investigated in 113 consecutive ABO-compatible liver transplants in a prospectively maintained transplant database. Preformed DSAs were found in 67%, 32%, 25%, 19%, 16% and 9% of liver transplant recipients at MFI cutoffs of 300, greater than 1000, greater than 2000, greater than 3000, greater than 5000 and greater than 10 000, respectively. 277  The MFI cutoff beyond which DSAs may be consistently deleterious to the liver graft is debatable. Recent data suggest that activation of complement is observed more frequently with DSA MFIs greater than 10 000.
Prevalence and Impact of De Novo DSAs on Graft Function and Patient Survival The incidence of de novo DSAs was reported in a retrospective study of 749 patients. 39  The incidence of de novo DSAs (MFI >5000) at 1 year was 8% and 0. 4% for anti-class II and anti-class I, respectively. 39  De novo DSAs had a negative impact on both graft and patient survival, reducing 5-year survival rates by 6% to 7%. Predictors of de novo DSA development included cyclosporine-based immunosuppression (versus tacrolimus) and low CNI trough levels. 39  In an update, presence of IgG3 antibodies, antibody-fixing complement (C1q) and de novo DSAs with MFI greater than 5000 were found to be associated with an increased risk of mortality. 285  Long-term data on the prevalence of de novo DSAs postliver transplant are scarce.
In a cross-sectional study of patients with and without histologically proven chronic rejection (n = 39 each), de novo DSAs were observed in 62% of patients with chronic rejection versus 38% without rejection (P = 0. 047). 286  The prevalence of de novo DSAs less than 1 year postliver transplant was significantly higher in patients with chronic rejection compared to those without rejection (44% vs 13%; P = 0. 004). 286  These data indicate that DSA monitoring postliver transplant may be beneficial, especially in patients in whom immunosuppression minimization is a consideration. DSAs should also be monitored in patients presenting with long-term graft dysfunction. Another area in which DSAs can impact on graft function is liver fibrosis posttransplant. Studies have suggested that, in liver transplant recipients with no obvious cause of fibrosis, or in patients with stable liver graft function, DSAs can promote graft fibrosis posttransplant and can accelerate fibrosis progression in patients with HCV recurrence.
Hypertension Analyses of patients from the CTS database suggest that control of systolic blood pressure (BP) may be associated with improved graft, patient, and CVD-free survival. 347 However, evidence from large-scale studies evaluating the effects of antihypertensive agents in kidney transplant recipients is limited. The Study on Evaluation of Candesartan Cilexetil after Renal Transplantation (SECRET), a randomized, double-blind, multicenter trial evaluating the effects of candesartan therapy on BP control and cardiovascular outcomes, was halted prematurely owing to low event rates. 348  However, it did show that candesartan provided improved BP control and decreased proteinuria in kidney transplant recipients compared with placebo. A recent randomized, double-blind, placebo-controlled trial in kidney transplant recipients with proteinuria showed no significant reduction in the doubling of serum creatinine, end-stage renal disease, or death, with ramipril therapy compared with placebo.
365, 373  However, data on the efficacy of the individual components of this combined technique have been negative or controversial: for example, methylprednisolone has no effect in renal transplantation, but may be beneficial in liver transplantation. 365  Irish et al  33  developed a validated score index based on a multivariate analysis of data from 24 337 deceased donor renal transplant recipients. Risk factors for DGF and outcomes were studied to predict DGF and long-term outcomes at the time of transplant. 33  Based on their findings, the group created a web-based DGF risk calculator, in which individual or population information can be inputted to obtain a DGF risk prediction. 33  This score index has now been adopted in a number of phase 2 trials to predict which high-risk patients would benefit from selective interventions.
The Strio-SOM and the Matri-SOM activity are mapped to compute state and action values respectively and used for action selection. This striatal model is extended to a multimodule based architecture to deal with multiple context paradigms. The biological plausibility imposes on the model limitations such as finite memory which is also incorporated into the theoretical model. Thus, the theoretical model sets a bound on the expected performance for a probabilistic context dependent task. We show that the neural model is very close to this bound for low values of stochasticity in the reward distribution.
Study Participants A total number of 300 participants were contacted via oral (in person, and via telephone) and e-mail communication and invited to participate in this study conducted in the Flemish region of Belgium. Purposive sampling (specifically the snowball method) was used to select those participants through family, friends, acquaintances, colleagues, and associations. Out of this number, 176 participants replied, agreed to participate, and were enrolled in the study. Age ≥50 years and not being vegetarian were the main inclusion criteria. Data were collected from 1 March till 1 April 2015. First the FFQ was collected in a two-week interval (FFQ1 then FFQ2) followed by the diary method. Ethical clearance was obtained from the Ethics Committee of Ghent University (number EC/2013/1124).
Data Management and Analysis Data from the FFQs were extracted using TeleForm designer software 10. 2 (Digital Vision, Hewlett Packard, Palo Alto, CA, USA). Errors or missing values were detected by the program and reviewed case by case. For the 3-day diary, Lucille software version 0. 1  [28]  was used for data entry and processing. The raw data were corrected for errors that occurred during completion and data-entering of the FFQ. In the case of absence of a response in one of the two FFQ's, the day with recorded items was used for the validity study.
For the 3-day diary, to account for non-uniform intake of some nutrients throughout the week, e. g. , alcohol, a weighting factor was assigned to compensate for the difference in dietary intake between weekdays and weekend days. To calculate the average intake per day, the weighting factor was multiplied by the amount consumed per foodstuff, i. e. , average daily intake = ((weekend intake × 2) + (average weekday intake × 5))/7. The nutritional composition of foods consumed (calcium, vitamin D, fiber, omega-3 fatty acids (protective association), and fat, alcohol, heme-iron and nitrate/nitrites (propagative association)) was obtained from the Belgian  [29]  or the Dutch  [30]  food composition table. In the absence of specific data in the latter databases, a heme-iron database was created from the values reported by Cross et al.
Data Analysis Estimates of dietary intake from the FFQs and 3-day diary, as well as the differences estimated by the two methods, were verified for normality. As both distributions deviated significantly from a normal distribution, in addition to the mean value, median and inter-quartile range (IQR) were used to describe the estimates. Measurement agreement for the reproducibility and validity was assessed using the Bland-Altman plots, whereby the difference of food and nutrient intake between methods was plotted against the average intake estimated by both methods  [33]. In case mean differences were associated with the estimated intake level, data were log-transformed prior to assessing measurement agreement. Although the use of correlation tests to assess measurement agreement has limitations  [33, 34] , Spearman correlation statistics are provided to enable comparison with previous studies.
Study Participant Characteristics A complete response for all measurements was obtained from 162 participants for both times the FFQs were administered. Due to incompleteness of the data in the diaries, we were unable to consider one male and five female participants in the validation analysis. Hence, a total of 156 (96. 8%) participants provided data for the validity analysis. The mean age of the participants was 57. 7 years (Standard Deviation = 7. 2 years), and the majority of the participants were female with a higher degree of education (Table  1 ). Mean energy intake as measured from the diary was 2127. 7 ± 1319. 3 kcal.
Compared to the 3-day diary, however, the estimated mean intake of various foods (i. e. , meat products and vegetables) and nutrients (i. e. , calcium, fat, and vitamin D), as measured by the FFQ, were substantially higher. Previous studies have reported that FFQs usually overestimate the food and nutrient values of calcium and vitamins compared to other dietary assessment methods  [15, 38, 39]. Except for vegetables, alcoholic drinks, processed red meat, and total processed meat intake, other food groups were overestimated by the FFQ compared to the diary. This overestimation might be mainly related to some food items in the FFQ that may not have been consumed during the three days of recall, and also the differences in terms of data collection, questionnaire structure, and time between the surveys.
However, there are some limitations that need to be considered when interpreting the results. First, the FFQ contained 109 food items, which imposed a considerable burden on the participants. The low number of questionnaires that had to be discarded due to insufficient data indicates satisfactory compliance of the participants. Although this food list was substantial, it is only slightly higher than the median number of 88 food items in FFQs  [14]. Second, total energy intake was not assessed by the FFQ and could not be used to adjust validity estimates. Third, both the FFQ and diary were self-reported. Although care was taken to provide clear instructions on how to fill out the form, some misreporting cannot be ruled out. It should also be noted that the results obtained and the conclusions drawn from this study are limited to the older age groups (≥50 years).
Using in situ Raman spectroscopy, Rao et al. [10]  measured the growth rates of nine individual tubes and found them proportional to their chiral angles, in agreement with the above model  [7]. However, recent Raman mapping on SWCNTs with five kinds of chiralities showed no length dependence on tube chiral angle  [11]. The discrepancy in experimental findings is likely due to the limited amount of data acquired in the above experiments  [10, 11]. In both cases, Raman spectroscopy is used to follow isolated SWCNTs grown on surfaces. As the resonance window of Raman scattering for a bare-isolated SWCNT is very narrow (~10 meV)  [12, 13] , for a given laser wavelength, the probability to satisfy the resonant condition for observing an individual SWCNT is less than 1%. Moreover, the relatively low spatial resolution of Raman spectroscopy, which is limited by diffraction to about half the wavelength of the excitation light, challenges researchers to measure tubes which are not very long  [11].
Calculations of interactions between SWCNTs and metal nanoparticles To study the interaction of carbon with nickel at the atomic level, we have developed a tight-binding model based on a description of the local electronic density of states of each atom at the fourth moment level. This provides an efficient tool to calculate the energies of systems containing a few hundreds of Ni and C atoms  [16, 17]. The energetic model is then implemented in ACCEPTED MANUSCRIPT 6 a Monte Carlo code using either a canonical or grand canonical algorithm with fixed volume, temperature, number of Ni atoms, and carbon chemical potential  [18]. was taken from the SWCNT across a hole (Fig. 1b ), with a measured diameter of 3. 6 nm.
Results and Discussion Nanobeam ED was performed on the suspended section of the SWCNT, the pattern of which is shown in Fig. 1c  (a magnification is presented in ESI Fig. S1a ). ED pattern of an SWCNT is consisted of many separated layer-lines parallel to each other. Based on the strategy of "intrinsic layer-line spacing"  [15] , the chirality indices of the SWCNT are assigned to (45, 1), with a chiral angle of 1. 1 o. Such an assignment is also confirmed by the simulated ED pattern of one (45, 1) SWCNT (ESI Fig. S1b ). Fig. 2  depicts TEM micrograph of a short SWCNT (Fig. 2a ) and its corresponding ED pattern (Fig. 2b ). The tube has a length of only 40 nm. Analyzing its ED pattern gives chiral indices of (25, 23), a tube with diameter of 3. 3 nm and chiral angle of 28. 6 o. Such an assignment is also verified by the simulated ED pattern of a (25, 23) tube (Supporting Information  In contrast with Raman spectroscopy which can only detect SWCNTs that are in resonance with the laser wavelength, characterizations adopted here not only permit precise determination of ACCEPTED MANUSCRIPT 8 SWCNT lengths by TEM, but facilitate unambiguous assignment of SWCNT chirality using nanobeam ED.