dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion The proposed S2S system can accelerate DNN operation in VHR images and evaluates the efficiency using building segmentation. The I/O test evaluates the limitation of the ROM disk using frequent random I/Os, and the RAM disk used in the S2S system processes over 2000 mb/s, which is 20 times faster than the RAM disk in the original system. The distributed GPU setting of the S2S system enables multi-GPU training from 12 to 5 h, and the accuracies become similar. Our results prove that the proposed system accelerates the prediction time by a factor of 11 (from 450 s to 38 s) in the INRIA dataset and by a factor of 9. 4 (from 780 s to 83 s) in the MITAIRS dataset using half the data precision. The pressure test demonstrates that the proposed S2S system can be maintained for high-intensity long-term tasks, reducing operation time from 18.
Disk I/O Test In this section, we discuss the assessment of the read and writing ability of the ROM and RAM disk. We select four evaluation factors: sequence writing/reading and random writing/reading. Sequence I/O refers to large-piece file transferring (e. g. , a VHR image over 200 mb). Random I/O refers to the transfer of a small-piece file (e. g. , a series of label files whose sizes are less than 20 kb). Alternatively, during the prediction operation, the south bridge (PCIE ports and ROM disk ports) is heavily occupied, which causes the scramble system bus during prediction, particularly when using multiple GPUs. Therefore, we test two scenarios: (1) test of the ROM/RAM disks without other operation. (2) test of the ROM/RAM disks when the GPUs are fully operated. As operational and frequent data I/O are transferring, system contention is inevitable because the GPUs also require importing the predicted image data and exporting the labeled map to save on the disk.
) The speeds of common disks (e. g. , SSDs and hard disk drives [HDD]) and RAM disks were evaluated under different sequence I/O and random I/O settings. (3) The efficiency of the proposed S2S system architecture was tested with and without the designed components and parallel processing on datasets from the Institut National de Recherche en Informatique et en Automatique (INRIA) and the Massachusetts Institute of Technology Aerial Imagery for Roof Segmentation (MITAIRS). (.
Label maps have storage costs of 151 and 483 KB, respectively. We allocated 32 GB for RAM disk because the total size of the INRIA and MITAIRS datasets is around 24 GB, and 8 GB is sufficient for saving label maps. Items Memory cost U-Net No Optimization 6. 1 GB U-Net Float 32 4. 7 GB U-Net Float 16 3. 5 GB U-Net Int 8 2. 4 GB Per Image in RAM (INRIA average) 136 MB Per Label Map in RAM (INRIA average) 128 MB Per Image in RAM (MIT average) 636 MB Per Label Map in RAM (MIT average) 592 MB Per Image in RAM Disk (INRIA average) 43 MB Per Label Map in RAM Disk (INRIA average) 151 KB Per Image in RAM Disk (MIT average) 119 MB Per Label Map in RAM Disk (MIT average) 483 KB the INRIA dataset using different number of GPUs were evaluated, as were the speeds using float-32, float-16, and Int-8 data formats with eight GPUs.
SPHINCS+ [BHK  + 19]. This paper focuses on CRYSTALS-Dilithium (also referred as Dilithium in the sequel) signature algorithm, that NIST recommends for most of the use cases [AASA + 22]. Dilithium is a signature scheme based on the Fiat-Shamir heuristic and its security relies on the hardness of the Module Learning with Errors and Module Short Integer Solutions problems. Given a public matrix A of elements in the ring R q = Z q [X]/(X n + 1) for integers n and q, the signing algorithm samples a vector y and computes w = A y. The algorithm extracts high-order and low-order bits out of each coefficient of w using the Decompose function that outputs the HighBits and LowBits vectors denoted respectively w 1 and w 0. Similarly to Schnorr signature  [Sch91] , a challenge c is derived from the message being signed and w 1 by using some hash function (modelized as a random oracle in the security analysis) and the signing algorithm computes z as y + c s 1 (where s 1 is part of the signer's secret key) and returns the pair (z, c) as the signature (if it satisfies some security properties).
Dilithium Dilithium is a post-quantum digital signature proposed by the "Cryptographic Suite for Algebraic Lattices" (CRYSTALS) team. In 2022, NIST selected it as the new primary standard for quantum-safe digital signatures. It is based on the Fiat-Shamir with aborts principle  [Lyu09] , and its security can be reduced to the hardness of solving two lattices problems over module lattices, module learning with errors (M-LWE) [LDK + 22], and SelfTargetMSIS  [KLS18]. When writing this paper, the latest specification version is 3. 1. It is designed with four main ideas in mind: simple to implement securely, conservative with parameters, minimizing the size of the public key and the size of the signature, and finally, easy to vary security. The essential functions of Dilithium scheme consist of KeyGen to generate the key, Sign to produce the signature of a message, and Verify to verify the signature.
Experimental Results This section demonstrates the practical feasibility of the theoretical attack when no specific countermeasure is implemented. We aim to demonstrate the vulnerability of the w 0 value in Dilithium. We simplify the attack as much as possible while still covering a wide range of study cases. Although we present extensively the practical results only for the first coefficient of the first coordinate of w 0 , it is important to note that the methodology and results can be extended to all the other indices of w 0 with minimal changes, as the implementation manipulates each coefficient independently in the same way and performs the same constant-time operation. We provide at the end of this section arguments in favor of this hypothesis. All side-channel analyses were coded in Python and will soon be available as notebooks.
Setup For our attack, we use the Post-quantum cryptographic library PQClean  [KSSW] , which offers "clean" standalone C implementations  [KSSW]  of most post-quantum schemes included in the NIST round 3 and 4 submissions  [nis]. Focusing mainly on the embedded system domain, the ChipWhisperer-Lite 32-bit, with an STM32f3 micro-controller, is often used in related academic research. We chose it as a test bench to deploy the Dilithium-2 signature scheme. We compiled Dilithium-2 using the gcc-arm cross-compiler arm-none-eabi-gcc 10. 2. 1 (with the options -O3). To go to the nitty-gritty, we use the ChipWhisperer (CW) "cw" library  [OC14]  for trace acquisition, and we use an open-source side-channel python library to process the side-channel information and perform the desired analysis. We also use the LSM and the rounding subroutines implemented in the "numpy" library [HMvdW + 20].
Reverse engineering The ChipWhisperer Lite has limited space capacity and can only handle a maximum of 24 400 samples, making it challenging to identify essential features in traces or capture longer operation sequences such as an entire Dilithium signature. To overcome this, we compute incomplete signatures by stopping when we obtain the value of the first w 0. Hence, using the PQClean library default implementation, an extract of the executed code can be viewed in Figure  3 , where the trigger signal is only active throughout the whole four poly_Decompose calls. In Figure  4 , we zoomed on a single Decompose for an acquisition performed directly on the ChipWhisperer. We highlight the four decompositions through the four shades of gray, where each coefficient of the polynomial (w) i is decomposed.
Leakage model and w 0 leakage detection Since we are now able to identify the Decompose consumption profile, we will now focus on the leakage inferred by w 0 and particularly when its value is equal to 0. To achieve this, we perform a reverse analysis of the intermediate value w 0 for a relevant leakage model according to our setup and explain in detail how we constructed the dataset used to perform the corresponding analyses. For this purpose, we built a profiling data set with 700 000 traces collected from the ChipWhisperer. Selection function. By default, the traces collected from the ChipWhisperer are easy to process as they do not require any synchronization. We know how the Decompose function works, and we can identify the inputs corresponding to the fixed outputs, so we choose the identity of (w 0 ) i,j as a selection function to determine the intermediate values which will be targeted by our attack.
Distinguisher. As a second step of our analysis, we apply one of the many statistical tools widely used when building profiled attacks. In an unprotected setting, all these tests are expected to give the same results  [MOS09]. Here, we choose to test the ANOVA distinguisher because it works with data partitioning, which is very similar to the templates partitioning mechanism  [YJ21]. Based on the previous reverse analysis and the code review of the poly_Decompose function implementation presented in Figure  1 , we identified a relevant leakage model. Leakage model. We know that for all 0 ≤ i < k and 0 ≤ j < n, -γ 2 < (w 0 ) i,j ≤ γ 2. In our study case, for Dilithium-2, γ 2 = (q-1)/88 = 95 232, which is 00 01 74 00 in hexadecimal, so if (w 0 ) i,j ≥ 0 it results that (w 0 ) i,j is bounded by this value.
Building the profiling data set. We must consider having as many occurrences as possible for each class of the leakage models. For our leakage model, each byte value has a significant representation when building the data set with random messages. To do so, we used the C reference implementation. We coded a script that performs Dilithium signatures for a given security parameter until there is a significant number of representatives for each class. In order to run the attack, we arbitrarily decided to collect 2 700 messages per class, to guarantee a minimal number of representative in each class. We then complete with random messages to have a data set of 700 000 messages. In the end, this number of traces might be oversized regarding the targeted implementation, but our objective is only to demonstrate the feasibility of the end-to-end attack.
Comparison of the minimization procedures As said in Subsection 3. 3, we assess the performance of our estimator π by computing the Kullback-Leibler divergence between π and π the benchmark class proportions. Figure  2  displays the evolution of the Kullback-Leibler divergence along the iterations of the two minimization procedures. For the descent-ascent procedure, the iterations displayed correspond to the iterations of the outer loop. This comparison has been realized when the source data set is Stanford1A segmented into 10 classes. The target data set is Stanford3A where we try to estimate the proportions of the 10 cell sub-populations. Both algorithms have been implemented in Python. From our numerical experiments, we have found that the sequence of estimates π produced by the second algorithm described in 3.
The Stanford data set is chosen as the segmented source data. The second data set is a series of cytometry measurements done in the same laboratory on a biological sample that comes from an other patient identified as patient 3. This second data set will be the target data set. The manual gating classification, and thus the class proportions are available for both samples. Nevertheless, we only use the classification available for the source (i. e. Stanford1A) data set in order to estimate the class proportions in the target (i. e. Stanford3A) data set. The manual gating classification for the target data set is only used as a benchmark to evaluate our method. First, to assess the relevance of our method, we present in Figure  6  the evolution of the regularized Wasserstein distance as a function of the weights associated to each class in the source distribution.
In the Stanford1A data set the CD4 cells constitute 45. 1 % of the cells and the CD8 cells 54. 9 % of the cells, whereas in the Stanford3A data set the CD4 cells constitute 73. 9 % (estimated at 73. 3 % by CytOpT) of the cells and the CD8 cells 26. 1 % (estimated at 26. 7 % by CytOpT). The results presented in Figure  7  and Figure  8  represent the use of the soft-clustering and classification methods described in Section 3. 2, and they outline the importance of re-weighting the source data. Without re-weighting, the classification obtained by optimal transport is really unlike the manual gating Figure  7 : Optimal transport plan Pε between the source and target distribution. A green line between X s i and X t j indicates that the optimal transport plan Pε moves some mass from X s i to X t j.
In this example, we solely used the information from one reference classification from manual gating of one data set (namely Stanford1A) to estimate the class proportions in each of the 61 unsegmented data sets targeted. In more than 90% of the cases the absolute difference between the estimated proportion and the manual gating gold-standard proportion is below 5%. And in more than 99% of the cases the absolute error is no more than 10%. Due to the stochastic nature of our algorithm, a new call to CytOpT would lead to a slightly different estimate π compare to a former estimation. However, the results displayed in Figure  9  and Figure  12  are representative of the general quality of the estimation produced by CytOpT in such settings. Finally, note that when applying our method to estimate class proportions on real flow cytometry data, a simple pre-processing is required.
But ultimately, the classification is performed with tools such as tclust B. Dost et al. [2010] , or Quadratic Discriminant Analysis T. Hastie et al. [2009]  that do not belong to the field of optimal transport. On the contrary, CytOpT only requires one pre-gated data set and the estimator of the class proportions is fully based on regularized optimal transport. In this comparison, OptimalFlow had a learning database composed of the nine Stanford cytometry data sets, and it was set to produce one prototype from the database (Note that OptimalFlow thus leverages the information from multiple source data sets, not just one). For the rest of the method, we applied OptimalFlow with the default settings proposed in the vignette 1. Figure  9  displays a Bland-Altman plot for each of the compared method.
This means that in theses cases CytOpT performs significantly better than OptimalFlow. We account for theses results by the spatial shift between the source and the target. This spatial shifting is displayed on Figure  11. To end this comparison, we present in Figure  12  a comparison of the class proportions estimation between CytOpT and OptimalFlow in two cases. The first case is the worst estimation for Optimalflow, namely, when the target data set is Miami3C. In this case, OptimalFlow overestimates very much the proportion of CD4 effector and the proportion of CD4 activated. The second case corresponds to the worst estimation for CytOpT, namely when the target data set is Nhlbi3B. If OptimalFlow performs slightly better in this case, CytOpT still offers an acceptable and competitive estimation.
Application to the OptimalFlow data A second evaluation of our method was performed on the cytometry data used in  Barrio et al. [2019]. This second cytometry panel is available on GitHub 2  , and we will refer to this panel as the OptimalFlow data sets. First, in order to run a sensible comparison between CytOpT, OptimalFlow and FlowMeans By plotting the difference against the mean of the results of two different methods, the Bland-Altman plot allows to assess the agreement between an automated method and the manual benchmark. we selected nine cell populations that were present in the first 21 data sets. As FlowMeans is an unsupervised method, it was directly applied to the 21 data sets. For OptimalFlow, it was set to use the 3 first data sets of the database, to build one template, and then to apply the classification step.
Sample Characterization After arc-melting and RSPS treatment, the intermediate and final products were analyzed by X-ray diffraction (X'Pert Pro from Malvern Panalytical in the UK, Cu-Kα radiation 1. 5406 Å with an accelerated detector PW3050/60 at 45 kV and 30 mA settings). The X-ray data were refined using the Rietveld method with the FullProf software  [61]. The chemical homogeneity and composition of the samples were checked using an energy dispersive analyzer of X-ray (EDX) S4800 mounted on an S2600N scanning electron microscope (SEM), both from Hitachi, Japan. Prior to the experiments, the samples were mirror-polished using absolute ethanol as a solvent to avoid surface oxidation. A pellet of diameter 8 mm and thickness 1. 456 mm (specimen 1) with a Ca 14 Si 19 content of weight percentage ∼89% was selected for measurements of the electrical resistivity ρ and Seebeck coefficient  [63]  in the temperature range 325 to 650 K.
Animal screening and afoxolaner administration Each animal was clinically examined and weighed, and all animals and their environments (enclosures) were assessed for mite infestations. All animals were treated with afoxolaner (NexGard ® ), administered orally, at a dose as close as possible to 2. 5 mg/kg based on the animal's weight, using 68 mg [intended for medium to large dogs (10-25 kg)] or 11. 3 mg [intended for small dogs (2-4 kg)] per tablet. To avoid stressful handling, gravid females and neonates were fed with dead mice containing the afoxolaner dose in their mouths(Fig. 2 ). To assess the parasitic load of mites, descriptive statistics was calculated using Quantitative Parasitology software, version 3. 0  [32]. Prevalence, mean abundance (i. e. number of mites per total number of hosts) and mean intensity (i.
PCR-purified products were sequenced using the Taq Dye Doxy Terminator Cycle Sequencing Kit (v. 2; Applied Biosystems, Thermo Fisher Scientific) in an automated sequencer (model ABI-PRISM 377; Applied Biosystems, Thermo Fisher Scientific). Sequences were analyzed by Geneious version 11. 1. 4 software and compared with those available in Genbank database by the Basic Local Alignment Search Tool (BLAST)  [42].
natricis through morphological diagnostic features. These included female features, such as a large dorsal anterior shield (podonotal) with 10 pairs of setae (Fig. 5a ), minute pygidial shield without setae (Fig. 5b ), with two pairs of minute mesonotal scutellae (Fig. 5c ), a sternal shield (Fig. 5d ) with a width/length ratio of 2. 5 and an anal shield with three setae (Fig. 5e ). In males, the holoventral shield was absent, and the sternogenital region had two pairs of setae. Male femur III and IV were without modified ventral setae, and the femur III ventral spur was absent (Fig. 5f  ). Mite species was also confirmed by a molecular method through BLASTn analysis, with an identity of 100% (accession number: OP752168) with sequence MT163329 of O. natricis from Mexico, and 99. 8% (accession number: OP752167) with sequence FJ911853 of O.
Acknowledgements The authors thank  Riccardo Paolo Lia  ( Department of Veterinary Medicine, University of Bari, Italy ) for optical microscopy images used in figures and  Mariaelisa Carbonara  ( Department of Veterinary Medicine, University of Bari, Italy ) for assisting with laboratory activities. Authors also thank  Angelo Polizzi  ( Caltagirone, Sicily, Italy ) for his field assistance in site 2. The present article is published with a waiver offered by Parasites and Vectors to JAM-R as the winner of the  Odile Bain Memorial Prize  ( OBMP) 2022.
Microdevices Microdevices were prepared as previously described. 12 Briefly, devices were fabricated using polydimethylsiloxane and custom- made molds, coated with 10 mg/mL fibronectin from bovine plasma (Sigma-Aldrich, St Louis, Mo) for 1 hour. Migration was recorded using a Zeiss Axio Observer Z1 (Hamamatsu digital camera C11440; Carl Zeiss, Oberkochen, Germany) microscope, with a time lapse of 1 or 2 minutes using a 103 (numerical aperture 0. 45) dry objective. The image analysis was done using Im-ageJ (National Institutes of Health, Bethesda, Md). Briefly, kymographs for each channel were generated using a semiautomatic macro, and single-cell trajectories were manually isolated. Trajectories were then analyzed with a custom-made MATLAB (MathWorks, Natick, Mass) code to determine the cell speed.
Statistics All data analysis was performed with GraphPad Prism 9 for MacOS (GraphPad Software, Boston, Mass). Statistical differences were considered when P <. 05, P <. 01, P <. 001, and P <. 0001. Data obtained from migration experiments were evaluated for normal distribution using the D'Agostino-Pearson test, and comparisons between conditions were performed using Mann-Whitney/unpaired t-test (for 2 conditions) or 2-way ANOVA test (for >2 conditions), depending on the normality result. The passage through microconstrictions follows a binomial distribution and P values were calculated using the chi-square method for each experiment and pair of samples compared.
This work was supported by the state funding from the Agence Nationale de la Recherche under ''Investissements d'avenir'' program (ANR-10-IAHU-01), and the French National Institute of Health and Medical Research (INSERM). T. G. was supported by the International PhD program of the Imagine Institute and the Fondation Bettencourt Schueller. M. B. was supported by Emergence Canceropole Ile-de-France (Syntec), ATEurope, and Institut Pierre-Gilles de Gennes High Risk/High Gain. M. L. was supported by the Imagine Institute (WP05T012). F. E. S. received funding and supports from The Agence Nationale de la Recherche (ANR-18-CE15-0017), the ARC Foundation (PJA 20191209614), La Ligue Contre le Cancer (RS19/75-79, RS20/ 75-30). G. M. received funding and support from La Ligue Contre le Cancer (RS21/75-3) and the ARC foundation (PJA 20181207755).
On the corresponding ultrasound brightness-mode (B-mode) image the tendon was segmented manually to mask all background signal. The axial displacement data was medianaggregated over the axial dimension which yields a two-dimensional time-lateral position representation of wave propagation. Five wave propagations were acquired in rapid succession and again medianaggregated to increase the signal to noise ratio. For lack of a suitable model, no diffraction-induced amplitude correction was employed. The two-dimensional discrete Fourier transform then provided the temporalspatial frequency spectrum of the impulse. Fig. 1A  -1C provides a visual depiction of the applied procedure.
This work also led to the production of a ground truth tractogram with less broken or looping streamlines and of an example of processed data, all available on the Tractometer website. This enhanced scoring system and new data should continue helping researchers develop and evaluate the next generation of tractography techniques. Tractography allows for the in-vivo non-invasive recovery of white-matter fiber trajectories in the brain. A good tractography algorithm builds a tractogram (set of streamlines) representing the ground truth (GT) of the brain anatomy. But such a GT still does not exist for in vivo data 1,2. To alleviate this limitation and enable the evaluation of tractography algorithms' quality, one typically relies on phantoms: simulated diffusion-weighted images (DWI) associated with GT tractograms 1.
This means that some low scores could be attributed to bad bundle segmentation during the scoring procedure, and not to problems in the input tractogram. The data of the ISMRM 2015 Tractography Challenge 4 is the most widely used phantom for tractography validation 1. It is nearly the only tractography dataset with human brain geometries offering a GT. The article, published in 2017, has been cited approximately 1,000 times (as of January 2023). It has provided important insights into the challenges of tractography, particularly regarding the strong presence of false positives and the poor overlap of true positives. Now, the development of algorithms for tractography often includes a tractography validation step using this phantom.