dataesr/scientific-paragraphs-categorization huggingface dataset:
Contiguous memory allocation is the default. It will be used in early DSM investigations, where instruction or data blocks are stored in each local memory. In contiguous memory allocation, the shared memory is divided by sequential memory blocks, and each pattern in Figure  2  represents one memory block of M kB that will be assigned to a tile. For example, considering a shared memory of 64 kB, a 4x4 cluster has a memory block size of 4 kB (M=4), while an 8x8 cluster has a memory block size of 1 kB (M=1). While Ma et al. [8]  explore flavors of this unique DSM allocation, playing with interconnecting latencies, we further investigate other memory allocations. Depending on application behavior, contiguous memory allocation tends to degrade system performance due to the many requests coming from different cores simultaneously.
For example, Figure  7a  shows that the CSM model (Long Hops type) presents a speedup limitation for small cache memory sizes (i. e. , 8kB), which impacts on the performance of lightweight cores, such as Cortex-A7 and Cortex-A53. This scalability degradation is shown through the two highlighted moments in Figure  7a. Before the first moment, the scalability is almost proportional to the increase in the number of tiles, since the latency remains practically constant as shown in Figure  7b. Between the two moments, the latency begins to increase with each addition of a new tile, however, there is still a slight increase in speedup. It occurs because the time to process the local cache information is longer than the time to request a new instruction block. Finally, from the second moment, the tiles are no longer able to process the data sufficiently to overcome the latency time, causing the increase of tiles to worsen the scalability.
The reactive particle tracking method is implemented in the hydrogeological code DarcyTools  (Svensson and Ferry, 2014). DarcyTools is a finite volume code that embodies a wide range of techniques and methods needed in the hydrogeological evaluation of deep repositories. In fact, DarcyTools was developed with this type of applications in mind and can hence be described as the tailor made repository code. The construction of the computational grid is based on work performed by  Voutilainen et al. , 2012 Voutilainen et al. , , 2019a  using C-14-PMMA autoradiography and X-μCT. The recent ideas by  Iraola et al. (2017)  and  Svensson et al. (2018 Svensson et al. ( , 2019) )  for constructing 3D intergranular grids have been applied and further developed. Time Domain Random Walk (TDRW) has been used in the transport modelling where ideas presented by  Dentz et al.
In the first experiment C (t) was measured for four months and in the second experiment C (t) was measured for about three months. The numerical model is set up to simulate the experimental data and conditions as closely as possible. Another useful result from the experiments was the K d values obtained from crushed minerals and rocks. Our model is built on the concept that each tracer sorbs on one or several specific minerals. From the experiments mineral specific K d values were measured. It is expected that these are correlated to the adsorption time, T d , which is an important model parameter. The correlation will be evaluated.
If one out of six cell walls is in contact with a sorbing mineral the probability to get sorbed is 1/6. However, more cell walls can be in contact and we chose to put P s = 0. 2. • T s. The time to leave a cell by molecular diffusion is specified for T s (=(Δ/2) 2 /D mol ), where D mol is the molecular diffusion coefficient. It should be emphasized that the exact values chosen for P s and T s will influence T d , see discussion section. One may also note that P s and T s are both part of (3); strictly only three parameters are needed. However, we keep both P s and T s for easier physical interpretation. The particle tracking method is Time Domain Random Walk (TDRW)  (Russian et al. , 2016). The method is implemented for both advection and diffusion in the code used (DarcyTools,  Svensson and Ferry, 2014) , but here only diffusive transport is considered.
Biometric privacy preservation at a glance Conventional biometric systems involve separate enrolment and verification phases. During enrolment, biometric references B R are captured from a sensor before features and their representations (as templates or models) T R are 18 For instance: to protect the right to data protection, the right to privacy, and the freedom of information. extracted and then stored in a database (DB). During verification, a biometric probe B P is captured from the same sensor, giving features T P. Subsequently, the comparison subsystem compares probe T P with the reference T R that corresponds to the claimed identity ID R ; thereby giving a dis/similarity score S. The score is then compared to a threshold h, in order to determine an accept or reject decision. In contrast to the one-to-one comparison performed in verification scenarios, identification scenarios involve comparisons between the probe and the full set of references stored in the database.
Database controllers can also use such information to link the biometric information collected from the same subjects across different databases. This would enable the profiling and tracking of individuals. Privacy-preserving techniques have been devised in order to mitigate privacy concerns. These typically involve some form of encryption as part of appropriate security measures and safeguards, usually applied to protect the information stored in biometric databases. The quest to preserve privacy, among other factors, has led to the development of three general requirements (ISO/IEC JTC1 SC27 Security Techniques, 2011), namely: Unlinkability: Given only protected biometric information, it is not possible to say whether two protected biometric sample representations belong to the same subject.
This prevents cross-comparisons for databases of different applications and ensures the privacy of the subject. Renewability: If a protected biometric reference is leaked or lost, the reference data can be revoked and renewed from the same biometric trait without the need to re-enroll. Irreversibility: Recovering biometric data from leaked protected biometric information is impossible without knowing the secret used to protect the biometric information. Restoring of valid biometric features or samples is prevented. The techniques to meet these requirements generally refer to some form of biometric information protection. Since other elements of the biometric system may also require protection, the relevant standard concerning privacy preservation refers instead to Biometric Information Protection (BIP).
The ISO/IEC 24745 (ISO/IEC JTC1 SC27 Security Techniques, 2011) standard advocates the adoption of the BIP scheme depicted in Fig. 1  in order to fulfill the requirements of unlinkability, renewability, and irreversibility. In contrast to conventional systems, the feature extraction stage, used during enrolment, is followed by a Pseudonymous Identifier Encoder (PIE). The PIE generates Auxiliary Data (AD) and a Pseudonymous Identifier (PI) for the Reference (R). The corresponding data AD R and PI R are then stored in separate databases. PI R , in the form of a protected template or model, contains no information relating to the subject's ID. Furthermore, as it is protected, it cannot be used to restore the original biometric data. The subject's ID-related data is instead contained within AD R and is stored in a separate database.
Protection also means that it is possible to create a new PI R without requiring a subject to re-enroll; protected templates and models are renewable. Accordingly, since successive enrolments using the same biometric information will produce different PI R ; they are also unlinkable across different databases. While not always fully achievable, BIP should incur no degradation to recognition accuracy. In order to support real time verification, BIP should not significantly increase the computation time, or at least keep it manageable. With many such BIP schemes having been proposed in recent years, comparing approaches in a meaningful way has become critical to progress. The work in  Simoens et al. (2012)  proposes suitable techniques to benchmark different BIP solutions. The following identifies a number of general cryptographic techniques that may be applied to preserve privacy in biometric information.
STPC has been applied successfully in a number of privacy preservation studies involving various biometric characteristics. Most of this literature relates to biometric identification, where client feature vectors are compared to a database of biometric feature vectors stored on a remote server. In a similar way to the classical STPC setting, the database is stored on the server in plaintext format. Critically, though, the computation does not leak any information about the client feature vectors to the server, whereas the server database is hidden from the client. These solutions have been explored in face  (Erkin et al. , 2009; Sadeghi et al. , 2009; Osadchy et al. , 2010; Bringer et al. , 2014) , iris  (Blanton and Gasti, 2011; Bringer et al. , 2014) , and fingerprint  (Barni et al. , 2010; Bianchi et al.
3, 6. 4, and 7. 2. There are several cryptographic methods to search on encrypted data such as fully homomorphic encryption  (Gentry, 2009)  and oblivious RAMs (Pinkas and Reinman, 2010) but they are not yet practical. One of the practical methods to search on encrypted data is Searchable Symmetric Encryption (SSE). It was firstly proposed by  Song et al. (2000)  and later improved by  Curtmola et al. (2006). Based on the improved security model, many SSE schemes were proposed such as  Kamara et al. (2012)  and  Cash et al. (2013 Cash et al. ( , 2014)). An SSE scheme allows only a single user to encrypt documents and make them searchable without decrypting them. It enables an untrusted server, which is semi-honest, to search on a database consisting of encrypted documents without learning the client's secret keys.
Hardware-assisted security Secure computation between mutually non-trusting parties can also be achieved via hardware-assisted security by executing the code in a trusted execution environment (TEE). For instance, the Intel Software Guard Extension (Intel SGX)  (McKeen et al. , 2013)  is a widely available TEE implementation deployed by Intel in their recent CPUs that can also be used to implement secure two-party computation  (Koeberl et al. , 2015; Gupta et al. , 2016; Bahmani et al. , 2017). A user can upload their data and code into the TEE, called an enclave in SGX, attest to a remote party that the right code has been loaded, and the code can then be executed on the trusted SGX hardware of the untrusted party. This provides confidentiality and integrity to both the user's data and the computation.
GMMÀUBM comparison (supervectors) The work in  Pathak and Raj (2011, 2013)  and  Pathak et al. (2012)  reports the adaptation of a standard GMMÀUBM comparator to support privacy preservation using Paillier encryption. During enrolment, the UBM is adapted to the training data in the usual way (without encryption) to derive the biometric reference in the form of a speaker-specific GMM. This is then encrypted and stored in the enrolment database (on the server), while the associated secret key is retained by the end-user (on the client). Protocols are based on HE and secure two-party computation for both the enrolment and the verification phase in speaker recognition. Therefore, the log likelihood of a Gaussian N ðx; m; SÞ given observations x is formulated as: logN ðx j m; SÞ ¼ e x > f We x where e x is an extended vector obtained by concatenating 1 and x, and f W is the parameters of the Gaussian distribution in the form, f W ¼ À 1 2 S À1 S À1 m 0 w Ã 2 4 3 5 with w Ã ¼ À 1 2 m T S À1 mÀ 1 2 logjSj : ð3Þ By denoting x as all pairwise product terms e x i e x j ; the log-likelihood results as: x T W ; where W is the vectorised form of f W.
, 2016; 2017b; Nautsch et al. , 2018) : S cos X ; Y ð Þ¼ X T Y k X k k Y k ¼ X F f ¼1 x f k X k y f k Y k ; enc pk S cos X ; Y ð Þ ð Þ¼ Y F f ¼1 enc pk y f k Y k x f k X k ; ð4Þ where the protected reference Y enc pk cos is defined for length/normalised features as: Y enc pk cos ¼ enc pk y f À Á À Á F f ¼1 ¼ enc pk ðY Þ. Fig. 5  illustrates the deployable distributed architecture: a client C extracts the probe feature vector X and requests the encrypted reference feature vector enc pk ðY Þ from the database DB controller. Scores are calculated on the client and sent to the authentication server AS operator ; holding the key pair ðpk; skÞ. The AS operator outputs the decision D of whether the decrypted score S cos is greater or equal to a threshold h, or not. Ideally, DB controller is in the domain of an independent data controller, restricting access to operators.
PLDA comparisons of embeddings (i-vector/x-vector) In contrast to the cosine comparator, comparators of the PLDA family compute LLRs S PLDA. Therefore, logexpectations are compared, assuming reference and probe data originate from the same speaker, such that stacked embeddings correlate within class variance S within , whereas if they stem from different speakers, solely the total variability S total is modelled (i. e. , S within ¼ 0). For centred embeddings, PLDA scores are computed as (Garcia-Romero and Epsy-Wilson, 2011): S PLDA ðX ; Y Þ ¼ logN X Y " # 0 0 " # ; S total S within S within S total " # ! À logN X Y " # 0 0 " # ; S total 0 0 S total " # ! ; S PLDA ðX ; Y Þ ¼ X T Q X þ Y T Q Y þ X T P Y þ Y T P X þ const with Q ¼ S À1 total À S total ÀS within S À1 total S within À1 ; P ¼ S À1 total S within S total ÀS within S À1 total S within À1 : By employing the Frobenius inner product (denoting the operation x T A y ¼ vecðAÞ T vecðx y T Þ with the operator vec( ¢ ) stacking matrices into vectors), the PLDA score is expressed in terms of a dot product  (Cumani et al.
Binary approaches to speaker characterisation Many voice biometric applications require lightweight processing. For such scenarios, binarisation has been explored as a means to speed-up computation. In a speaker identification framework based on the GMMÀUBM paradigm, the work in  Billeb et al. (2014) , proposed a computationally efficient, two stage identification system which uses binarised voice biometric templates to pre-screen a large database before performing the final identification step using the original template. The number of full comparisons is reduced to a fraction of the original number, while the original speaker identification performance is preserved. Another method referred to as binary key speaker modelling was proposed as an efficient method to represent speaker-discriminative information in the form of single binary vectors.
Differentially private recognition systems In recognition systems, the query mechanism can be thought of as an algorithm learning the classification rule which is evaluated over the training data set. The output of an algorithm satisfying differential privacy is likely to be the same when the value of any single data set instance is modified, and therefore, no additional information can be obtained about any individual training instances with certainty by observing the output of the learning algorithm. In addition, the e-differential privacy model limits the information that an adversary can gain about a particular private value, by observing an entire function learned from a data set containing that value, even if s/he knows every other value in the database. Naturally, if we want to apply this method to the recognition problem (a.
Degree of biometric evidence in non-biometric characterisation tasks, quantifying the privacy adversary The aim of data Privacy by Design in characterisation systems is to yield features, discriminative for a nonbiometric task but not conveying any biometric information. With the goal of examining to which extent data of non-biometric applications is biometrically linkable, we propose to employ a biometric adversary, whose score outputs are measured regarding their evidential support towards making biometric decisions whether two feature representations are mated (proposition A) or non-mated (proposition B). By biometric evidence, we refer to LLR scores S ¼ log PrðE j AÞ PrðE j BÞ yielded by a biometric system. Ideally, the biometric adversary estimates LLRs directly by a subspace identity model, such as Gaussian PLDA (Garcia-Romero and Epsy-Wilson, 2011), see Section 5; its Gaussian assumption is for the latent subspace, not necessarily for the observed data.
By employing another biometric adversary, its scores are convertible to LLRs by ideal score calibration  (Br€ ummer and du Preez, 2009). Features of non-biometric algorithms (preserving Privacy by Design) cannot yield biometric evidence, i. e. , result in zero-evidence LLRs: S % 0, such that biometric adversaries are decepted. In systems not preserving privacy, decisions about A; B are inferable in terms of LLRs either supporting A or B; and, by examining multiple LLRs for a dataset, clusters of mated and non-mated features become inferable; being a data privacy exploit. Thus, the maximum LLR depicts the empirical upper bound to the degree of biometric evidence still present in a non-biometric system's features. For the sake of a unified reporting, we propose to employ the reference implementation of Gaussian PLDA (Garcia-Romero and Epsy-Wilson, 2011) directly on the assessed data to derive ideal LLRs.
The new era of artificial intelligence is seeing the emergence of diverse, new applications that exploit voice recordings. Many of these may not fall under the existing definition of sensitive data. Examples of such applications 28 and their potential impact upon privacy call for the existing definitions of privacy and protection to be revisited. funding from the European Union's H2020-INNOSUP-02-2016 (Innovation Associate Programme) under grant agreement no 739767; and by Omilia À Conversational Intelligence.
Acknowledgements This work is partially supported by: the  German Federal Ministry of Education and Research (BMBF)  and the  Hessen State Ministry for Higher Education, Research  and the Arts within the  German National Research Center for Applied Cybersecurity  (www. crisp-da. de);the  BioMobile II  project (no. 518/16-30 ); the  DFG  as part of project  E4  within the  CRC 1119 CROSSING  and project A. 1 within the  RTG 2050 Privacy and Trust for Mobile Users ; the  Horizon 2020 Victoria research project  (grant agreement  SEC-740754 ); research funds received from the  French Agence Nationale de la Recherche -pl2X-sim-(ANR) in connection with the bilateral VoicePersonae (with  JST CREST in Japan) ; RESPECT (with DFG in Germany) collaborative research projects; national funds in Portugal through Fundaçe ao para a Ciência e a Tecnologia (FCT) with reference  UID/CEC/50021/2013 ; the  SpeechXRays  project that has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no  653586 ; the  Privacy Preserving Secure Speech Recognition (PPSSR)  project which had received.
Early studies using control datasets demonstrated that Hi-C scaffolds large eukaryotic DNA regions  [10] [11] [12]. The Hi-C scaffolder GRAAL (Genome Re-Assembly Assessing Likelihood from 3D), a probabilistic tool that uses a Markov Chain Monte Carlo (MCMC) approach was able to generate the first chromosome-level assembly of an incomplete eukaryotic genome  [12]  by permuting DNA segments according to their contact frequencies until the most likely scaffold was reached (see also  [13] ). Since these proof of concept studies, the assemblies of many genomes of various sizes from eukaryotes [e. g. [14] [15] [16]  and prokaryotes  [17]  have been significantly improved using scaffolding approaches exploiting Hi-C data. Although GRAAL was effective on medium-sized or small (<100 Mb) eukaryotic genomes such as that of the fungus Trichoderma reesei  [18] , scalability limitations were encountered when tackling genomes whose complexity and size required significant computer calculation capacity.
Furthermore, as was also observed with other Hi-C-based scaffolders, the raw output of GRAAL includes a number of caveats that need to be corrected manually to obtain a finished genome assembly. To tackle these limitations, we developed instaGRAAL, an enhanced, open-source program optimized to reduce the computational load of chromosome scaffolding and that includes polishing steps to automatically complete the assembly process. The polishing, which aims to minimise assembly errors, can exploit available genetic linkage data. InstaGRAAL was applied to the 214 Mb and 500 Mb haploid genomes of the common brown algae Ectocarpus sp. and Desmarestia herbacea, respectively. The Ectocarpus genome is currently only published in draft form  [19]  and the Desmarestia genome was previously unpublished.
𝑃(𝑠) = { ( ⋅  - , ): ∈  :  The parameters are then iteratively updated directly from the real scaffolds once their size increases sufficiently  [12]. Each bin is tested in several positions relative to putative neighbouring fragments. The likelihood of each arrangement is assessed from the simulated or computed contact distribution, and the arrangement is either accepted or rejected  [12]. This analysis is carried out in cycles, with a cycle being completed when all the bins of the genome have been processed in this way. Any number of cycles can be run iteratively and the process is usually continued until the genome structure ceases to evolve, as measured by the evolution of the parameters of the model. The core functions of the program use Python libraries as well as the CUDA programming language, and therefore necessitate a NVIDIA graphics card with at least 1 Gb of memory.
The technical limitations of GRAAL were i) high memory usage when handling Hi-C data for large genomes (i. e. over 100 Mb), 2) difficulties when installing the software, and 3) the need to adjust multiple ad hoc parameters to adapt to differences in genome size, read coverage, Hi-C contact distribution, specific contact features, etc. InstaGRAAL (https://github. com/koszullab/instaGRAAL) addresses all these shortcomings. First, we rewrote the memory-critical parts of the program, such as permutation sampling and likelihood calculation, so that they are computed using sparse contact maps. We reduced the software's dependency footprint and added detailed documentation, deployment scripts and containers to ease its installation. Finally, we opened up multiple hard-coded parameters to give more control for end-users while improving the documentation on each of them, and selecting relevant default parameters that can be implemented for a wide range of applications (see options online, and Discussion).
Overall, these upgrades result in a program that is lighter in resources, more flexible, and more user-friendly. Other problems encountered with the original GRAAL program included 1) the presence of potential artefacts introduced by the permutation sampler, such as spurious permutations (e. g. local inversions) or incorrect junctions between bins; 2) difficulties with the correct integration of other types of data such as long reads and 3) difficulties with handling sequences that were either too short, highly repeated or with low coverage. We addressed these points by identifying and putting aside these problematic sequences during a filtering step. These sequences are subsequently reinserted into the final scaffolds, whenever possible (see Methods), with the help of linkage data when available.