dataesr/scientific-paragraphs-categorization huggingface dataset:
Additionally, the uniform privacy metric setting require the users to always sample a new attribute, increasing the privacy leakage. In a more realistic scenario, the nonuniform privacy metric setting minimizes the RID-ACC (see Fig. 11  of Appendix C of  [6] ) as already shown in Fig. 1. Furthermore, the FK-RI model allows the attacker to use the whole background knowledge D  to match the inferred profiles. For instance, the attacker's RID-ACC metric decreased by almost half when considering the PK-RI model (cf. Fig. 10  of Appendix C of  [6] ) since there are fewer attributes as background information to use for the matching algorithm R (see Section 3. 2. 4). Lastly, we used the same dataset for private data collection and as (partial) background knowledge. A different set of experiments could mix demographic attributes and (synthetic) application usage in each survey, limiting the number of demographic attributes per user to constitute a profile.
Uncovering the Sampled Attribute of the RS+FD Solution (→ SMP) Classifier. We use the state-of-the-art XGBoost  [10]    (a) Re-identification risk of the GRR  [27, 28]  protocol. Figure  2 : Attacker's re-identification accuracy (RID-ACC) on the Adult dataset for top- re-identification on using the SMP solution, the full knowledge FK-RI model with uniform -LDP privacy metric across users, and by varying the LDP protocol and the number of surveys (i. e. , data collections). Omitted results for the -SS protocol  [49, 56]  is due to similarity to plot (a). 𝑛 𝑝𝑘 = [0. 1𝑛, 0. 3𝑛, 0. 5𝑛]. Finally, for the HM setting, we combined both intervals, i. e. , (,   ) = [(1, 0. 1), (3, 0. 3), (5, 0. 5)]. Results. Fig. 3  illustrates the attacker's AIF-ACC metric on the ACSEmployement dataset with the three attack models (i.
, RS+FD[GRR], RS+FD[SUE-z], RS+FD[OUE-z], RS+FD[SUE-r] and RS+FD[OUE-r] ), varying , the number of synthetic profiles  and the number of compromised profiles. Additional results (Adult and Nursery datasets  [18] ) are presented in Appendix D of  [6]. Analysis. From Fig. 3 , one can notice that the proposed attack models, namely, NK, PK and HM present significant 2-20 fold increments in the attacker's AIF-ACC over the Baseline model. Surprisingly, even under an NK model in which the attacker has access only to the estimated frequencies satisfying -LDP, generating  = [1, 3, 5] synthetic profiles to train a classifier provides higher attacker's AIF-ACC than having compromised   = 0. 5 profiles in the PK model. On the other hand, increasing the number of synthetic profiles  that the attacker generates in the NK model has less impact than increasing the number of compromised profiles   that the attacker has access to in the PK model.
This high accuracy with RS+FD[UE-z] protocols is because there is only one parameter to perturb each bit when generating fake data, i. e. , Pr[0 → 1] =  (cf. Section 2. 2. 4). When using different UE protocols, the randomization parameters  and  (cf. Section 2. 2. 4) also influence the attacker's AIF-ACC, which led RS+FD[SUE] protocols to have lower attacker's AIF-ACC when  is small, but higher attacker's AIF-ACC in low privacy regimes. Lastly, we remark that due to the original formulation of RS+FD in  [4]  to generate fake data uniformly at random, a classifier was able to learn the sampled attribute from the users, as the distribution of the attributes was not always uniform with the ACSEmployement dataset. Nevertheless, when the attributes follow uniform-like distribution, none of the three attack models NK, PK or HM achieves a meaningful increment over the Baseline model (cf.
Results. Fig. 4  illustrates the attacker's RID-ACC metric on the Adult dataset for top- re-identification using the FK-RI model and the RS+FD[GRR] protocol and by varying the uniform -LDP privacy metric and the number of surveys. Analysis. From Fig. 4 , one can note that the re-identification rates with RS+FD has drastically decreased in comparison with the results of the SMP solution in Fig. 2. Re-identification attacks on the RS+FD solution are not trivial, as the attacker has no guarantee that the predicted attribute is correct. Indeed, from Fig. 14  in Appendix D of  [6] , the attacker's AIF-ACC on the Adult dataset with the RS+FD[GRR] protocol is upper bounded in 40%, which leads to chained errors when profiling a target user in multiple collections. For instance, the attacker's RID-ACC for the top-1 group is nearly equal the random Baseline model.
General Experimental Setup. We use the ACSEmployement dataset described in Section 4. 1. Prior distribution. To simulate "Correct" prior distributions f = [ f 1 , f 2 ,. , f  ] to be used to generate realistic fake data with RS+RFD, we perturb the real frequency of each attribute  ∈ [] with the standard Laplace mechanism  [20] [21] [22]  in centralized DP satisfying  = 0. 1/ (i. e. , split  = 0. 1 by  attributes). In addition, to simulate an "Incorrect" scenario in which prior distributions are wrongly specified, we use Dirichlet distributions with parameter 1. Methods evaluated. We consider for evaluation three protocols within the RS+RFD solution from Section 5. 1, namely, RS+RFD[GRR], RS+RFD[SUE-r] and RS+RFD[OUE-r].
Frequency Estimation of Multiple Attributes. We compare the results of our RS+RFD protocols with their respective version within the RS+FD  [4]  solution, i. e. , RS+FD[GRR], RS+FD[SUE-r] and RS+FD[OUE-r] (cf. Section 2. 3. 2). Evaluation metrics. To compare with  [4] , we vary  in the interval  = [ln (2), ln (3),. , ln  (7) ] and we measure the quality of the estimated frequencies with the averaged mean squared error metric: 𝑀𝑆𝐸 𝑎𝑣𝑔 = 1 𝑑 𝑗 ∈ [𝑑 ] 1 |𝐴 𝑗 | 𝑣 ∈𝐴 𝑗 (𝑓 (𝑣) -f (𝑣)) 2. Results. Fig. 5  illustrates for all methods the   metric (-axis) according to the privacy parameter  (x-axis) for both "Correct" and "Incorrect" priors. Additional empirical and analytical results with the Adult dataset are provided in Appendix E of  [6]. Analysis. For the "Correct" prior, one can observe that the   metric of our proposed RS+RFD protocols consistently and significantly outperform the utility of their respective version within the RS+FD solution.
5. 2. 3 Uncovering the Sampled Attribute of the RS+RFD Solution (→ SMP). This section follows similar parameters (dataset,  range and attacker's AIF-ACC metric) used in the experiments of Section 4. 3. Results. Fig. 6  illustrates the attacker's AIF-ACC metric on the ACSEmployement dataset with three attack models (i. e. , NK, PK and hybrid) and our three protocols (i. e. , RS+RFD[GRR], RS+RFD[SUE-r] and RS+RFD[OUE-r] with "Correct" priors), varying , the number of synthetic profiles  and the number of compromised profiles. Further results with "Incorrect" priors are in Appendix E of  [6]. Analysis. We highlight that the non-stability in the plots of Fig. 6  is due to different sources of randomness:  = 0. 1-DP for "Correct" prior distributions f, -LDP randomization, fake data generation and the XGBoost algorithm.
Finally, we proposed a refinement to the RS+FD solution, called RS+RFD that improves both utility and privacy. That is, in our experiments, RS+RFD minimized the estimation error in comparison with the RS+FD solution, as well as almost fully mitigated the inference of the sampled attribute attack. Though we identified and investigated two privacy threats for LDP protocols for multidimensional data in single and multiple data collections, these are not unique and we believe that our work opens new avenues of research in this direction. For future work, we suggest and aim to formalize the re-identification risks considering different LDP and -privacy  [2, 8, 50]  protocols, the number of collections, the number of attributes and the "uniqueness" of users in a given dataset. Such a formalization will allow to design other countermeasure solutions beyond RS+FD  [4]  and our RS+RFD.
ACKNOWLEDGMENTS The authors deeply thank the anonymous reviewers for their insightful suggestions. This work was partially supported by the  ERC  project  HYPATIA  with grant agreement Nº  835294  and by the  EIPHI-BFC Graduate School  (contract " ANR-17-EURE-0002 "). Sébastien Gambs  is supported by the  Canada  Research Chair program as well as a  Discovery Grant  from  NSERC. All computations were performed on the " Mésocentre de Calcul de Franche-Comté ".
Measure of DNA content by flow cytometry 1 ml of cells were resuspended in 70% Ethanol and kept at 4˚C. Cells were centrifuged for 3 min at 7,000 RPM and washed once in PBS, resuspended in PBS with RNase A (0. 5 mg/ml) for 2 hours at 50˚C. Cells were centrifugated and resuspended in PBS with iodure propidium (50 μg/ml) for one hour at room temperature. Cells were diluted 10-fold in PBS and cell aggregates were dissociated by sonication. DNA content analysis was performed using LSRII flow cytometer (BD Biosciences) and analyzed with the FlowJo software.
Numerical simulations are indispensable for understanding and predicting the power deposition in coils of superconducting magnets. In order to assess performance limitations and quench margins, an advanced simulation chain has been developed at CERN for studying collimation losses  [33, 34]. The simulation chain couples the particle tracking code SIXTRACK  [35] [36] [37] , updated to treat tracking of ions with different rigidities, with the Monte Carlo code FLUKA  [38] [39] [40]. FLUKA is widely used for energy deposition studies in accelerator environments, in particular the LHC  [41]. Various BLM response studies were done in the past to validate the simulation setup for proton operation  [41] [42] [43] [44]. The physics processes are more involved for heavy ions, giving rise to a variety of ion fragments, which can leak to superconducting magnets.
III. SIMULATION TOOLS We use a two-step modeling approach for simulating collimation losses and the resulting energy deposition in the machine. The simulation setup is based on the tracking code SIXTRACK  [35, 36, 50]  and the particle-matter interaction code FLUKA  [38] [39] [40]. A coupling of the two codes has been developed previously at CERN  [33] , offering an advanced tool for studying multiturn collimation losses. Tracking studies for crystal-assisted collimation in the LHC are discussed in detail in a separate paper  [51]. In the present paper, we investigate the resulting power deposition in machine equipment, which is computed in a second step using a stand-alone FLUKA model. The simulation chain is employed in this paper to compare the relative performance of the standard collimation system with the crystal-assisted one.
The fragment mass spectra are qualitatively different for hadronic and electromagnetic collisions, which can also affect the collimation efficiency. The relative difference of the mean free paths is hence not the only criterion for assessing the relative importance of the two processes for 208 Pb 82þ collimation. FLUKA integrates the DPMJET-III  [53, 54]  (dual parton model and JETs) event generator for hadronic nucleus-nucleus collisions above 5 GeV=nucleon. EMD is simulated by means of the native nuclear collision generator in FLUKA, called PEANUT, which can handle photo-nuclear interactions  [52]. For both types of collisions, hadronic and EMD, the subsequent nuclear deexcitation, including evaporation, as well as fission, is treated by the same FLUKA module. Benchmarks against experimental data showed that the models can accurately reproduce fragmentation yields following the impact of 400 ZGeV 208 Pb beams on different fixed targets  [52].
D. Model for energy deposition simulations In order to quantify the particle-induced energy deposition in the machine, a detailed geometry model of the accelerator is required, including key components such as beam-intercepting devices and magnets. Figure  6  illustrates the FLUKA geometry model of the primary collimators used in this paper. Considering the sophisticated geometry of the LHC machine, a common repository of accelerator components was used to carry out the energy deposition studies. The FLUKA element database (FEDB)  [57]  is a collection of FLUKA models of accelerator components such as magnets, collimators, and beam loss monitors. The model of the accelerator line was assembled by means of the LINEBUILDER tool  [57] , which reproduces the lattice sequence as defined by MAD-X [58-60]; it places different pieces of accelerator equipment from the FEDB into a master geometry model.
No experimental data exists, which quantifies the reduction of projectile dissociation of channeled ions in this energy regime. In order to assess the impact of EMD on the simulation benchmark, we studied the two extreme cases, where EMD is either fully suppressed in channeling or retains the same probability as in the amorphous regime. The results indicate that dissociation of channeled ions yields only a small contribution to the fragment population lost in cold magnets. Since channeled ions receive an angular kick, the dissociation products are less likely to escape the collimation system. The benchmark results for the cold regions are therefore largely independent of the model assumption concerning EMD in channeling. We will present a detailed assessment of fragment production and leakage in the next section, where this aspect will also be discussed.
Quality assessment of raw diffusion data Prior to analysing the dMRI data, a rigorous quality analysis was conducted using  ExploreDTI (v4. 8. 5; Leemans et al. , 2009)  which helped (1) detect potential mistakes which may have occurred when acquiring the data; and (2) identify and eliminate artefacts. Once the dMRI scans were acquired, raw folders were inspected to detect if the correct number of volumes had been acquired. This was the first step to ensure that the data had been acquired correctly. Following this, the quality of the data was inspected with ExploreDTI through three major steps. First, "looping" through each of the raw diffusion weighted images was utilised to detect any major movements throughout each participant scans. Following this, a colour coded FA map of the raw diffusion weighted images was checked to ensure that the direction of the colours were correct (i.
We conducted rigorous visual quality check inspections of the data. Firstly, we visualised the reconstructed tracts derived from TractSeg in individual space to check that all 72 tracts were not delineated in regions of lesioned tissue. Following this, similar to recent applications of this toolbox using FBA, for each individual participant, we warped the tracts from subject space into a common population template space to be able to compare metrics along each tract more robustly  (Attyé et al. , 2021; Genc et al. , 2020). Importantly, we performed a QA check of all tracts for each individual patient within the template space to ensure that all tracts were anatomically representative. All tracts were generated using the default TractSeg pipeline for each individual participant at each time point using their individual white matter FODs which were then warped into population template space (https://github.
Individualised profiling statistical analysis To develop individualised profiles, our analysis was two-fold. This is presented as a proof-of-concept. First, we used the TractLearn toolbox (Attyé et al. , 2021) (https://github. com/GeodAIsics/TractLearn-Who leBrain) to identify damaged white matter tracts from the 72 tracts delineated via TractSeg  (Wasserthal et al. , 2018 (Wasserthal et al. , , 2019. For a given dMRI metric, TractLearn detects abnormal voxels for each tract in every patient by using a manifold learning approach to determine which voxels deviate from normative healthy control measures  (Attyé et al. , 2021). A Z-score is applied to all voxels in each tract, which indicates a difference in the dMRI metric between a patient and the control group. Bonferroni correction for multiple comparisons (given by the size of the tract in number of voxels) is then applied.
4 logarithm of the minimum angle of resolution (logMAR), and clinical corneal edema. The main exclusion criteria were any ocular disease (ocular surface disease, inflammatory or infectious diseases, intraocular pressure >21 mmHg or diagnosed glaucoma, retinal disease) aside from Fuchs' dystrophy and cataract. Patients with macular edema (CMT >300 µm) without any other retinal abnormality were not excluded. Other exclusion criteria were age <21 years, pregnancy, or an inability to understand the study and give informed consent. Patients' preoperative data were collected: age; gender; symptoms including photophobia or morning fog; diabetic status; distant BCVA (logMAR scale); keratometry; refractive spherical equivalent, intraocular pressure (IOP); optical coherence tomography (OCT) corneal thickness mapping (RT-Vue TM , Optovue, Fremont, USA); OCT foveal thickness; and macular status (Spectralis-OCT TM , Sanotek, Heidelberg, Germany).
Population Of the patients considered, 103 eyes met the inclusion criteria. Of these patients, 7 were excluded from the analysis because of ocular comorbidities during the 1-year follow-up period (2 patients with increasing cataract who had cataract surgery during the follow-up period, 4 patients with long-term ocular hypertension requiring additional medication, one with herpes simplex virus keratitis), and 13 due to missing data. Therefore, a total of 83 eyes were analyzed. The patients' preoperative features are detailed in Table  1. Analysis of correlations between the preoperative data revealed significant relationships between preoperative distant BCVA and mean CCT (p < 0. 0001), and between preoperative distant BCVA and mean CMT (p = 0. 01). No other significant correlations were found.
Image data were collected for at least 500 preanaphase cells per strain. Kip2-3xsfGFP fluorescence along microtubules was extracted by line scanning analysis and aligned using the peak of the Spc42-mCherry signal at the minus-end. The average distribution of GFP fluorescence along  cytoplasmic microtubules was then computed as a function of microtubule length as reported previously  (Chen et al. , 2019b; Fig. 3, B-D). In contrast to wild-type cells  (Fig. 3 B; Chen et al. , 2019b) , where it peaks sharply at the plus-end of microtubules, the Kip2-3xsfGFP signal in bik1Δ cells increased along the entire microtubule without forming much of a distinct peak at microtubule ends. This distribution suggests that in these cells, Kip2 is also recruited along microtubule shafts and not only at the minus-end of microtubules and does not make a pause at the plus-ends of microtubules, two key features of its behavior in wild-type cells  (Chen et al.
Confocal microscopy A Nipkow spinning disk (Carl Zeiss) equipped with an incubator for temperature was employed. Time-lapse movies were acquired using a back-illuminated EM-CCD camera Evolve 512 (Photometrics, Inc) mounted on the spinning disk microscope with a motorized piezo stage (ASI  MS-2000)  and 100 × 1. 46 NA alpha Plan Apochromat oil immersion objective, driven by Metamorphbased software VisiVIEW (Visitron Systems). 17 Z-section images separated by 0. 24 µm increments were captured with the exposure time of 30 ms each; the whole stack took 1. 07 s. For imaging aMT dynamics, 80 continuous repetitions were taken. For imaging strains with both GFP and mCherry signals, the GFP channel was always set to 30 ms exposure time and the mCherry channel to 50 ms exposure time. Images in figures represent sum fluorescence intensities across Z-projections.
Image and data analysis Preanaphase cells were collected based on the shape of cells and the size of spindles. For analyzing the profiles of GFP fusion proteins along aMTs, the sum intensity projection of the images was used. A 5-pixel (666. 7 nm) width line was used to scan aMTs from plus-ends toward SPBs both in the GFP and the mCherry channels using Fiji  (Schindelin et al. , 2012) , and exported to CSV files. These profiles were then aggregated for further analysis using MATLAB (R2018a; Mathworks), and peak detection for the GFP and mCherry signals was performed, respectively. Profile length was defined as the peak-to-peak distance, and the profiles were then binned into length bins as detailed in the figure legends. For the purpose of demonstration and for calculating the speeds of fluorescent speckles, kymographs were generated and analyzed using Fiji.
, 2014). The tracking tool does make mistakes when microtubules depolymerize with a very high rate, or when microtubules pivot quickly with large angles, or when the intensity of the plus-end marker is low. Therefore, all tracked trajectories were inspected by eye to find and to correct those very rare mistakes. All of the time series tracking results were analyzed with custom functions written in Matlab (MathWorks). The distance between the b-and m-SPBs is the spindle length. Cells with spindles longer than 2 µm were excluded. The distance between a microtubule plus-end and the corresponding SPB represents the length of the microtubule. Only microtubules longer than 5 pixels (666. 7 nm) were considered detectable due to the limit of the microscope resolution. Using this criterion, the maximum length (one for each microtubule) and lifetime of each microtubule within the recorded time window (85.
Kip2-MD homology model The homology model of the Kip2-MD was generated in PHYRE2  (Kelley et al. , 2015)  using the crystal structure of the motor domain of CENP-E (PDB: 1T5C). In the Kip2-MD, the three positively charged patches that represent potential tubulin/ microtubule-binding interfaces were identified by a combination of a sequence alignment of the Kip2-MD with other motor domains (human CENP-E, human Kif2C, and human Kif5a) and a structural alignment of the Kip2-MD with the structure of a kinesin-13 motor domain bound to tubulin-DARPin1 (PDB: 5MIO).
Kip2 motility assay in vitro MTs stabilized with GMPCPP were polymerized with 20 µM tubulin (with 5% of tubulin-atto488 and 5% tubulin-biotin) with 0. 5 mM GMPCPP in BRB80 at 37°C for 1 h. Then the GMPCPPstabilized MTs were attached into the flow chamber functionalized with neutravidin and passivated with pluronic-F-127 as described above. Then 1 nM of MBP-Kip2-(1-560)WT-RFP and MBP-Kip2-(1-560)P1-RFP in BRB20 buffer (20 mM PIPES, 1 mM MgCl2, 1 mM EGTA, pH 6. 9) supplemented with 100 mM KCl, 20 mM glucose, 20 μg/ml glucose oxidase, 8 μg/ml catalase, 0. 1 mg/ml BSA, 1 mM DTT, 1 mM GTP, and 1 mM ATP, was flowed in the flow chamber. Time-lapse recording every 5 s for 10 min at 488 and 565 nm was performed using Metamorph software. The analysis of Kip2 motility and the generation of kymographs were done using ImageJ software.
The family Alaudidae, larks, comprises 93-100 species (depending on taxonomy) that are widely distributed across Africa and Eurasia, with single species extending their ranges to North and northernmost South America and Australia. A decade-old molecular phylogeny, comprising ~80% of the species, revealed multiple cases of parallel evolution and large variation in rates of morphological evolution, which had misled taxonomists into creating many non-monophyletic genera. Here, we reconstruct the phylogeny of the larks, using a dataset covering one mitochondrial and 16 nuclear loci and comprising all except one of the currently recognised species as well as several recently proposed new species (in total 133 taxa; not all loci available for all species). We provide additional support using genome-wide markers to infer a genus-level phylogeny based on near-complete generic sampling (in total 51 samples of 44 taxa across 40 species).