dataesr/scientific-paragraphs-categorization huggingface dataset:
Results are presented in Fig. 8. The patterns found in the wave amplitudes provided by the spectral analysis clearly coincide with the wave patterns in the observed brightness temperature perturbations. The maximum wave amplitudes are up to 3. 9 K for IASI and 5. 2 K for AIRS. Considering only data points with wave amplitudes larger than 1 K, the amplitude-weighted mean horizontal wavelengths are 170 km for IASI and 130 km for AIRS. These results from the spectral analyses show that both the AIRS and IASI measurements are very similar. The remaining differences are most likely related to the fact that AIRS has better horizontal sampling than IASI, which allows for smaller horizontal wavelengths to be resolved in the spectral analysis.
We think that the larger variances of IASI are partly due to the better horizontal resolution of IASI (i. e. , its smaller footprint size), which makes the instrument more sensitive to large wave amplitudes from short-scale waves. We also analyzed this particular event for visibility effects, i. e. , changes in sensitivity due to wavelength changes along the line-of-sight depending on the scan angle. However, we found that this does not play a role in this case study. The combined AIRS and IASI data indicate that the island mountain waves are rather variable in time. In many situations we found strong wave events only in single AIRS or IASI overpasses, but not in subsequent satellite overpasses.
Spatial patterns of stratospheric gravity wave activity In this section we discuss the spatial distributions of the AIRS and IASI gravity wave variances in different seasons. Figures  13  and 14  show 5-year averages  (2008) (2009) (2010) (2011) (2012)  of monthly mean variances in January and July, respectively. The variances were calculated on a 1 • × 1 • horizontal grid. We scaled the AIRS data with the global factor c ai and the daytime data with the global factor c dn to compensate for some of the systematic differences discussed in Sect. 3. 4. The comparison of the scaled data shows good agreement in many cases. The remaining differences are mostly below ±0. 2 K 2. Larger differences are found only in a few locations with exceptionally strong gravity wave activity. In these cases the global correction of the scaling approach may not be suited for the specific local gravity wave spectra.
The observed wave patterns may not be as coherent as for AIRS, though, due to the coarser and more irregular sampling pattern of IASI. Another advantage of IASI is its broader swath that reduces data gaps between subsequent measurement tracks and improves global coverage. A statistical comparison based on 5-year time series (2008-2012) revealed that the IASI gravity wave variances are on average 45 % larger than for AIRS. There are several possible reasons for these differences: (i) IASI can be up to 2. 5 times more sensitive for vertical wavelengths from 10 to 30 km. Although the response in brightness temperature variances is quite low at these wavelengths (Fig. 5 ), this is a common wavelength range for mountain waves in the stratosphere. So this difference in sensitivity may be important.
Our approach takes spectrally correlated noise components into account, which is rather important as we average data from large numbers of channels. For both instruments, we found that the 4. 3 µm noise variances vary substantially with scene temperature, i. e. , by 2 to 3 orders of magnitude. The IASI noise at 4. 3 µm is about 2. 4 times larger than the AIRS noise. Noise poses a limiting factor for gravity wave observations in the cases with low amplitudes, in particular for convective waves. Previous studies on AIRS may not have taken the large variability of noise with scene temperature fully into account. For instance, in the study of  Gong et al. (2012) , it was assumed that noise does not vary with location and time. This may have been appropriate in their study because they analyzed 15 µm rather than 4.
While AIRS has been successfully used in many studies on gravity wave research, IASI data are applied here for the first time for that purpose. Our study indicates that gravity wave observations from different hyperspectral infrared nadir sounders such as AIRS and IASI can be directly related to each other if instrument-specific characteristics such as different noise levels as well as spatial sampling and resolution are carefully considered. This is promising concerning future work with IASI because of its long-term perspective with three MetOp satellites being launched in the time period of 2006 to 2018. The Cross-track Infrared Sounder (CrIS)  (Bloom, 2001; Smith Sr. et al. , 2009 ) onboard NASA's Suomi NPP satellite platform, launched in October 2011, offers another opportunity for gravity wave research based on combined nadir sounder observations.
Acknowledgements. AIRS data products are distributed by the  NASA Goddard Earth Sciences Data Information and Services Center (GES DISC ). IASI data products were obtained from the  British Atmospheric Data Centre (BADC). ERA-Interim data are provided by the  European Centre for Medium-Range Weather Forecasts (ECMWF). Support for this work was provided by the  NASA   program , The  Science of Terra and Aqua , contract  NNH11CD34C. C. Clerbaux  is grateful to the  Centre national d'études spatiales (CNES)  and to the  National Center for Atmospheric Research (NCAR)  for financial support. The service charges for this open access publication have been covered by a  Research Centre of the Helmholtz Association. Edited by:  S. Kirkwood.
Modern day biometric systems, such as those used by governments to issue biometric-based identity cards, maintain a deterministic link between the identity of the user and her biometric information. However, such a link brings in serious privacy concerns for the individual. Sensitive information about the individual can be retrieved from the database by using her biometric information. Individuals, for reasons of privacy therefore, may not want such a link to be maintained. Deleting the link, on the other hand, is not feasible because the information is used for purposes of identification or issuing of identity cards. In this work, we address this dilemma by hiding the biometrics information, and keeping the association between biometric information and identity probabilistic. We extend traditional Bloom filters to store the actual information and propose the SOBER data structure for this purpose.
Introduction Many nations are increasingly using biometric based systems for national identity cards for their citizens. Examples of these are the proposed project of Carte Nationale d'Identité Biométrique of the French government (see http://www. service-public. fr/actualites/002101. html) and the AADHAAR project undertaken by the Unique Identification Authority of India (see http://uidai. gov. in). These governments are building large biometric database systems to issue social security cards, health insurance cards etc. The main objective is to efficiently provide citizen services via accurate identity verification. For this purpose, these systems maintain a database of sensitive personal information, called the identity database, a biometric database containing individual's biometric information, and a deterministic link between the identity of the individual in the identity database and her biometric information in the biometric database.
This link, which is a primary-key/foreign-key relationship between the two databases, allows querying the identity database using the biometric information from the biometric database. The link helps to ensure that any improper access to the identity database via impersonation is detected and potentially prevented. Impersonation can happen at many stages of the biometric system operation. We are interested in two specific situations, the so called "First application for biometric card" scenario where the user is applying for the issue of a biometric based identity card, and the "Renewal without a document or ID card loss" scenario, where a user is applying for a replacement card. First application for biometric card: During the first application, the applicant goes through an eligibility determination step.
If the user is eligible and her biometric information does not exist in the biometric database, then the application is accepted. If the individual provides an identity that already exists in the identity database then it is a case of impersonation. Renewal without a document: During the renewal phase, the applicant has to go through the verification step in order to determine if he already exists in the system. Renewal can take place if and only if the relevant biometric information already exists in the biometric database. If the individual gives a different identity that is not associated with his biometric information it is a case of impersonation. The deterministic link between biometric information and identity help detect such impersonations. In almost all existing biometric information management systems that we have studied however, this link is public and not protected.
Consequently, it brings forth serious privacy concerns for the individuals. Biometric information is, surprisingly, easily available without consent. For example, fingerprints can be easily picked up from different surfaces. An attacker who is targeting a specific individual and has acquired by subterfuge that individual's biometric information and also has access to the identity database can then easily cause a privacy breach for the targeted individual. (Note that, by simply accessing the identify database, the attacker cannot launch such a targeted attack. ) Consequently, individuals who are concerned about their privacy, may want these deterministic links between the identity database and the biometric database removed. Unfortunately, since removing the link is not an option, we investigate in this work if a probabilistic link between biometric information and identity can maintain user privacy and at the same time preserve all the functionalities of the system.
One possible solution to this problem is to keep the information in the biometric database encrypted, so that the identity is linked to encrypted biometric data. If the attacker cannot correlate the illegally obtained biometric information with the encrypted information stored in the database, the attacker will not be able to breach privacy of the individual. Several works aim to provide biometric privacy employing variants of this theme  [11, 14, 17] , although they do not address the same problem as ours. In fact, these techniques cannot be used as native constructions to solve our problem. Under these setups, proper identification would work only if an individual could be associated with one and only one stored biometric information. Unfortunately, owing to the vagaries of biometric capture devices, a physical biometric pattern can have several captured versions -the so called multitude of traits issue.
In this paper, we present a novel scheme called ELITE (acronym for zEro Links Identity managemenT systEm) that allows a probabilistic link between biometric information and identity. We describe an initial construction called ELITE-1 that introduces the fundamental principles of the scheme. We then refine this to propose ELITE-2 -the second and main construction. ELITE-1 assumes that the biometric information stored in the database and the biometric information retrieved from the biometric capture device during the verification are the same. We propose a novel probabilistic data structure called "Stored Object Bloom Filter" (SOBER, for short) for storing identities, which is based on the traditional Bloom Filters. We then adapt the Greedy algorithm proposed by Azar et al. [2]  in the context of the balls in bins problem, to insert identities in the SOBER structure.
ELITE: zEro Links Identity managemenT systEm In real world biometric system, there is always a phase during which the system enrolls new individuals and issues their biometric ID cards or passports. During this phase, a deterministic link between the identity and the biometric information needs to be maintained. We assume that the enrollment phase is not compromised and that this link is deleted directly after the issuance. The ELITE scheme deals with issues related to storing the biometric information in the database after the biometric ID card has been issued -the storage phaseand verifying whether an individual is in the system or not -the search phase. We extend the classical Bloom filter data structure  [3]  to develop the ELITE solution. We call this data structure "Stored Object Bloom filtER (SOBER)".
It enables the separation of the identity from the biometric information by making the link between biometric information and identity probabilistic. The scheme also hides the biometric information so that it is impossible to recover stored information. ELITE-1 employs a multiple choice identity allocation algorithm, Greedy  [2]  that has been proposed in the context of the balls in bins problem. It allows the insertion of a number of identities in the SOBER data structure. In the following, we first introduce the Stored Object Bloom Filter data structure, present the Greedy algorithm, and then discuss the construction of the ELITE-1 scheme. We present an example to discuss how our system works and then analyze the privacy features of the scheme.
Preliminaries Stored Object Bloom Filter -SOBER Bloom filters  [3]  are probabilistic data structures that permit testing membership of an element in a group. Bloom filter as a structure does not allow the storage of the element, but only the membership verification of an element. The most important feature of a Bloom filter is that the time complexity for membership verification is constant i. e. O(1). SOBER is a key -value data structure that has similar construction as a normal Bloom filter with the additional feature that the cells can contain extra information. In the following, we first discuss the construction steps of SOBER, then describe how to search for an element using this probabilistic data structure. SOBER construction Briefly, a classical Bloom Filter works as follows. Let us consider a set of n elements S = {a 1 ,.
If for each j ∈ [[1, r]], A(h kj (a )) = 1 then we can conclude that a ∈ S (with a high probability), otherwise a ∈ S. Note that, in some membership verification cases, we can have a false positive for elements whose identities were not stored in the Bloom filter. However, the false positive rate can be arbitrarily reduced. The proposed Stored Object Bloom Filter data structure can be seen as a combination of the Bloom filter with Storage (BFS)  [4]  and the classical Bloom filter. In BFS data structure, each cell contains a set of values depending on the output of the hash functions while in the basic bloom filter a zero or one value is stored (for fast membership detection). Each cell in the SOBER data structure can be considered as a key, value cell, where key ∈ {0, 1} and the value is a set of elements.
This is illustrated on the right side in Figure  1. Greedy algorithm for inserting identities into SOBER The insertion of identities in the SOBER data structures can be mapped to the classical balls in bins prob-lem. In literature, there are many techniques that enable this kind of insertion, such as, the uniform insertion with a single choice insertion and multiple choice insertion. These schemes were constructed for different purposes but their main objective is to decrease the maximum load for every bin, that is the number of balls in any given bin. We are particularly interested in this article to the second type of insertion, namely, the multiple choice insertion. Moreover, we are interested in the special setting where the number of balls is larger than the number of bins but with a constant ratio.
Create an empty SOBER data structure (all cells initialized with value 0) with size equal to m and r independent hash functions h i : {0, 1} * → [[0, m]]. 2. For 1 ≤ i ≤ n, hash the biometric information b i in the set by applying the r hash functions and store a 1 in each location of SOBER corresponding to the output of the hash function. If the cell location is already a 1, leave it as 1. 3. Insert one identity value id i corresponding to b i uniformly at random in one of the r cell locations identified by outputs of the hash functions when applied to b i. 4. For 1 ≤ i ≤ n, insert l times the same identity id i associated with b i in l positions in SOBER following the Greedy(U, d), regardless of whether the cell position is 0 or 1. If id i is already in a cell of SOBER by virtue of step 3, do not duplicate id i in that cell but consider it one of the l insertions.
Thus, a small privacy degree p P reflects a high privacy level. It is clear that there is a trade off between the privacy level that the system offers, the reliability of fraud detection and the difficulty to mislead the system. Indeed, if the set of unique identities gets larger, the attacker gets a higher chance of cheating the system; on the other hand, if the set of unique identities gets larger, users get better privacy. The degree of privacy is based on how evenly we distribute the identities over all positions of the SOBER data structure. The best scenario will be a case where every position stores exactly l•n m identities. However, a random insertion of identities cannot guarantee this result. Thus, we have used the Greedy algorithm to decrease the maximum load of every position in order to be as close to the ideal situation as we can.
Moreover, decreasing the maximum load will increase the minimum load (i. e. the minimum number of identities in any cell). One may be led to believe that a deterministic identity insertion will be better in our scenario. However, it is not the case from a security perspective. This is because having a deterministic insertion algorithm will leak information about the strategy of identity insertion. Consequently, for any internal adversary the task of identity deletion will be straightforward. Essentially, we want to have a SOBER data structure where empty positions are very rare, almost non-existent. Empty positions refer to those positions in the SOBER data structure that do not contain any identity. We employ a classical problem known as the the occupancy problem  [8] , that gives us the exact probability of finding an empty position.
We will show that, using the Greedy algorithm, we can control the minimum and maximum load of every cell in SOBER data structure and consequently disperse the identities in a uniform manner throughout the entire data structure. Decreasing the false positive rate in SOBER: SOBER is a probabilistic data structure that involves some false positives. Let p f denote the probability of a false positive. We first determine the appropriate values to minimize the false positive rate. Let us consider a SOBER with a size equal to m associated with k hash functions. We have n entries. Each insertion in the SOBER will imply insertion of l + 1 same identity values uniformly at random. We consider hash functions as random functions. We can show that for k = m n ln(2), the probability of false positives is the minimum and is equal to p f = 2 -k.
Probability of an empty cell in the SOBER data structure: Let us assume that insertion of identities are made uniformly at random with a single choice, i. e. every identity has one random choice to get into a given cell. We have l • n identities and m cells. We denote by X n. l,m the number of empty cells after all insertions. We can show that the probability that all cells contain at least one identity is equal to: Pr(X n. l,m = 0) = n i=0 (-1) i m i (1 -i m ) n. l. This formula can be approximated  [9]  to: Pr(X n. l,m = 0) e -λ , where λ = m. e -n. l m. Minimum/Maximum load of any cell: The Greedy algorithm ensures with a high probability  [20]  a maximum load equal to n•l m + n•l•ln(m) m in the case where n • l > m • ln(m). While the maximum load defines the upper bound of the number of identities by cell, the minimum load is very important as well, since it controls the minimum size of the set I in the worst case.
The LSH approach consists of selecting k hash functions f H1 defined by randomly choosing k arrival position subsets H 1 , H 2 ,. ,H k. In order to compute the projection of a vector, we apply the k-hash functions; the output of each hash function is a binary vector with a size equal to h. Thus, using LSH, the Hamming distance similarity can be estimated  [16]  such that : hds(T 1 , T 2 ) ∼ = b∈B2 max b j ∈B1 (C(b, b j )) p h ) |B 1 |. k p h (1) where C(b, b j ) = l i=1 δ[f Hi (b) -f Hi (b j )] and δ is a Dirac symbol equal to 1 in case of equality and zero in the other case. We refer the reader to  [7]  to the experimental results on multiple well known biometrics databases. Summing up, since we do not require computing Hamming distance for an identification, the MCC representation and the multiple LSH solution can be integrated to the ELITE scheme in order to handle identification under the multitude of traits issue, while at the same time providing a probabilistic link between individuals and their biometric information.
4. Create a row in the look-up table L which contains the identity id i and the corresponding positions in the random selected SOBER where id i belongs to cells where hash functions outputted a 1's result for the biometric information B i. We reiterate these steps for all binary vectors B i. At the end we will output s filled out SOBERS which represents the new biometric database. Search phase The input of this phase is a scanned physical biometric information and an identity id. We want to verify whether the biometric information exists or not in the biometric database. The first step is to transform the scanned biometric information using the MCC representation. The output of the MCC representation is a binary vector set B. In order to perform the verification we follow these steps: (f H k (b i )), • • • , h r (f H k (b i )) and retrieve from {SOBER k [h 1 (f H k (b i,j ))], • • • , SOBER k [h r (f H k (b i,j ))]} the couple (or couples) existing in all the corresponding positions as well as all the corresponding identities.
Validation of CD-associated candidate proteins using selected reaction monitoring (SRM)-based targeted proteomics A targeted LC-SRM assay was developed to validate a subset of 13 candidate proteins discovered in the original 2D-DIGE nontargeted comprehensive survey. The subset of proteins was defined by choosing the ones containing at least two specific peptides for a protein or a group of proteins with identical function in phylogenetically close bacterial strains, and that at the same time, had already been identified in previous label-free shotgun experiments run on equivalent samples, preferentially without prefractionation. Details on sample preparation, the SRM-assay development, the list of transitions, chromatographic and acquisition conditions, data processing and statistical analysis of the quantitative datasets using MSstats,  23  are given in online supplementary method 7.
Discovery of protein signatures of CD-associated gut microbiota by 2D-DIGE The electrophoretic profile was well conserved across individual samples, and the internal standard (see online supplementary figure  S3  and magnified regions thereof in online supplementary figure  S4 ), making it possible to accurately compare spot volumes across the entire experiment. After image alignment and spot co-detection, 2007 protein spots were validated and simultaneously quantified in all 36 images derived from measuring three image channels for each of the 12 gels. There were no pronounced systematic differences between biological replicates (samples from a clinical group), and most protein spots (93%) were unchanged between patients and controls allowing reliable normalisation of the data (see online supplementary figure S5).