dataesr/scientific-paragraphs-categorization huggingface dataset:
Benchmark Datasets We used four public ProteomeXchange datasets: PXD008355, PXD023047, PXD021518, and PXD023361 (Table  1 ). RAW data from each dataset was converted to MGF using the ThermoRawFileParser (version: 1. 2. 3) tool  (17)  with default parameters. Among them, PXD008355, PXD023047, and PXD021518 are from Arabidopsis thaliana (mouse-ear cress), and PXD023361 is from Saccharomyces cerevisiae (baker's yeast). The datasets have been acquired using three different instrument models: Q Exactive, Q Exactive HF, and Q Exactive HF-X. The description of the samples, instrument configuration, sample processing steps, and analytical method can be read in the original publications: PXD008355 (18), PXD023047  (19) , PXD021518  (20) , and PXD023361 (21).
Results Figure  2  shows the number of PSMs (FDR=1%) identified with MS-GF+ (datasets PXD023047, PXD021528, PXD008355, and PXD023361) for spectrum clustering using MaRaCluster and spectra-cluster followed by consensus spectrum generation using the MOST, AVERAGE, BIN, and BEST procedures. Among the four public proteomics datasets, whether using spectrum clustering results from MaRaCluster or spectra-cluster, the identification rate for the MOST method is lower compared to the other methods, while the BIN and BEST methods achieve a higher spectrum identification rate (Figure  2 ). While the number of identified spectra only differs by a small amount between the various consensus spectrum generation procedures, when analyzing big public proteomics databases (billions of spectra) (29) these differences can be translated into millions of spectrum identifications.
As expected, for clusters of one spectrum, no differences have been seen between different consensus methods, but minor differences are observed between clustering algorithms. For other small clusters containing three or fewer spectra, consensus spectra derived from the spectra-cluster results, in combinations with all the consensus spectrum generation methods, provide higher mean RawScores than consensus spectra derived from MaRaCluster results. In contrast, for larger clusters, MaRaCluster consensus spectra lead to higher mean RawScores. For both spectra-cluster and MaRaCluster, the mean RawScore increases with increasing cluster size. The BEST and BIN algorithms are stable for both clustering algorithms and all datasets (Supplementary Note 1), and the scores of these two algorithms are generally higher than MOST and AVERAGE.
In combination with MaRaCluster, the AVERAGE algorithm shows instability and the score of the AVERAGE algorithm is generally lower than the other three algorithms. In addition to peptide identification, we explored how using consensus spectra instead of the original spectra affects phospho-peptide identification and phosphorylation site localization. We analyzed the number of phosphorylation sites identified in dataset PXD008355 after clustering with both tools (MaRaCluster and spectra-cluster) and the four different consensus spectrum generation methods (MOST, AVERAGE, BIN, and BEST). We have evaluated two metrics, (i) the number of phosphorylated PSMs identified and (ii) the phosphorylation sites identified. While the majority of phosphorylated PSMs are aggregated among all methods, around ~1% are different and we also observed differences in terms of phosphorylation sites.
Conclusions Representative spectra from clusters have typically been generated using four different algorithms: spectrum averaging, spectrum binning, the most similar spectrum, and the bestidentified spectrum. Most tools and resources, including SpectraST (9), MassIVE  (11, 30)  spectral libraries, or spectra-cluster and PRIDE Cluster (2) use one of these methods. However, to our knowledge, no systematic analysis has been performed to compare multiple algorithms to generate consensus spectra. We implemented a Python framework to benchmark existing algorithms to generate representative spectra from clustering results from two different popular clustering tools-MaRaCluster and spectra-cluster. The BEST and BIN methods were found to be the most reliable methods for consensus spectrum generation, including for datasets with post-translational modifications such as phosphorylation.
Analysis of phosphorylation sites identification of dataset PXD008355, after clustering with MaRaCluster and spectra-cluster, and generation of the consensus spectra using two different methods (BEST, BIN). We quantified the number of total phosphorylated PSMs and phosphorylation sites for each combination of clustering method and consensus generation method. In addition, we added the number of identical and different phosphosites between the BEST and BIN methods for each clustering algorithm.
For datasets PXD023047 and PXD021518 two modifications were allowed (NumMods=2): fixed carbamidomethyl cysteine modification, and variable methionine oxidation; while for datasets PXD008355 and PXD023361 Phosphorylation was also considered as variable modification. that were used include Matplotlib (version 3. 1. 2) (23), Numba (version 0. 47. 0) (24), NumPy (version 1. 17. 3) (25), Pandas (version 0. 25. 3), pyOpenMS (version 2. 4. 0) (26), Pyteomics (version 4. 1. 2) (27), and spectrum_utils (version 0. 3. 3) (28). (22)rithm. The number of peptide identifications and peptide-spectrum matches can be found in the Supplementary Notes. In addition, the description of each dataset can be found in the original publication and PRIDE Archive(22).
Introduction Post-quantum cryptography. The most widely used public-key cryptosystems today are based on RSA and ECC, but they are breakable in polynomial time using a quantum computer. While it is currently unknown whether building a scalable quantum computer is feasible or not, the goal of post-quantum cryptography is to design alternatives to RSA and ECC with resistance against quantum attacks. Initiated in 2016, the NIST post-quantum standardization has now entered its final round, with the selection of the Kyber algorithm [BDK + 18, ABD + 21] for general encryption. The security of Kyber is based on the hardness of the module learning-with-errors (M-LWE) problem, which is conjectured to remain hard even with a full-scale quantum computer. Side-channel attacks and the masking countermeasure.
Finally, we provide a detailed description of the masking of the full IND-CCA decryption of the Kyber scheme at any order. We also describe the practical results of a C implementation of the full high-order masking of Kyber and Saber. The source code is public and can be found at https://github. com/fragerar/HOTableConv/tree/main/Masked_KEMs Finally, we have also performed a t-test evaluation using the ChipWhisperer platform for power traces. Follow-up works. Very recently,  [DBV22]  described a variant of our hybrid method for the ciphertext comparison in Kyber, in which for the first part of the ciphertext, the results of the comparison over uncompressed coefficients are converted from arithmetic to Boolean masking. While the asymptotic complexity remains the same, for a bitsliced implementation, the authors obtain a 25% speed-up factor compared to our hybrid method.
As previously, Equation (3) can be securely computed by a sequence of high-order multiplication SecMult, using as input the arithmetic shares y (j) i modulo q. The shares of b are only recombined at the end, so that an adversary with at most t = n -1 probes does not learn more than the bit b. Since the complexity of a single SecMult is O(n 2 ), the complexity for κ coefficients is O(κn 2 log q), and the total complexity is therefore O(ℓκn + κn 2 log q). We provide in Appendix D. 7 a pseudocode description of the corresponding PolyZeroTes-tExpo algorithm. The proof of the following theorem is straightforward and is therefore omitted. Theorem 5 ((n -1)-NIo of PolyZeroTestExpo). The PolyZeroTestExpo algorithm is (n -1)-NIo.
Operation count and concrete running time We provide in Table  9  a comparison of the operation count for the ciphertext comparison in Kyber, first using the approach from [BGR + 21] without Compress, and then our hybrid approach described in the previous section, with either the PolyZeroTestExpo or the PolyZeroTestMult methods. We see that the hybrid approach is significantly faster, especially for high security orders. We have also performed a C implementation that confirms these results, see Table  10  below. The three parameter sets share the common parameters N = 256, q = 3329 and η 2 = 2, while the security level is defined by setting the module rank k = 2, 3, 4, and the parameters η 1 , d t , d u and d v (see Table  6 ). In the following, we first recall the definition of the Kyber scheme.
We recall the pseudo-code from [BDK + 18] below. For simplicity we omit the NTT transform for fast polynomial multiplication. The NTT is indeed a linear operation, so it is easily masked with arithmetic masking modulo q. Algorithm 3 Kyber. CPA. KeyGen() 1: ρ, σ ← {0, 1} 256 2: A ← R k×k q := Sam(ρ) 3: s, e ← χ k η1 × χ k η1 := Sam(σ) 4: t := Compress q,dt (As + e) 5: return pk := (t, ρ), sk := s Algorithm 4 Kyber. CPA. Dec(sk, c = (u, v)) 1: u := Decompress q,du (u) 2: v := Decompress q,dv (v) 3: return Compress q,1 (vs T u) Algorithm 5 Kyber. CPA. Enc(pk, m) 1: r ← {0, 1} 256 2: t := Decompress q,dt (t) 3: A ← R k×k q := Sam(ρ) 4: r, e 1 , e 2 ← χ k η1 × χ k η2 × χ η2 := Sam(r) 5: u := Compress q,du (A T r + e 1 ) 6: v := Compress q,dv (t T r + e 2 + ⌊q/2⌉ • m) 7: return c := (u, v) The Kyber CCA-secure KEM.
The Kyber scheme provides a CCA-secure key encapsulation mechanism, based on the Fujisaki-Okamoto transform  [FO99]. We recall the pseudo-code from [BDK + 18] below. It requires two different hash functions H and G. The main principle of the Fujisaki-Okamoto transform is to check the validity of a ciphertext by performing a re-encryption with the same randomness (see the variable r ′ at Line 3 of Algorithm 7 below), and a comparison with the original ciphertext. Note that the Kyber. Decaps algorithm does not output ⊥ for invalid ciphertexts, as originally in the FO transform. Instead, it outputs a pseudo-random value from the hash of a secret seed z and the ciphertext c. This variant of the FO transform was proven secure in  [HHK17]. However, the variant remains secure even if the adversary is given the result of the ciphertext comparison, under the condition that the IND-CPA scheme is γ-spread, which essentially means that ciphertexts have sufficiently large entropy (see  [HHK17] ), which is the case in Kyber.
Implementation of Kyber and Saber We have implemented in C a high-order version of the algorithm Kyber. Decaps, following the description of Section 6. 2. For comparison, we have also performed a high-order implementation of the Saber algorithm. Due to lack of space, we do not provide a description of the high-order masking of Saber; however, it is quite similar to Kyber. For both schemes, we have targeted the parameter set corresponding to NIST security category 3 (parameter Kyber768 for Kyber, see Table  6 ). We have run our implementation on a laptop and an embedded component. We provide the source code of the laptop implementation at: https://github. com/fragerar/HOTableConv/tree/main/Masked_KEMs For the embedded component, we have used a 100 MHz ARM Cortex-M3 architecture with 48k of RAM, which also includes a hardware accelerator for secure 32-bit random generation.
D. 2 Polynomial comparison modulo 2 k via arithmetic to Boolean conversion We are given as input a set of ℓ • n shares (x (j) ) i for 1 ≤ j ≤ ℓ and 1 ≤ i ≤ n, corresponding to ℓ coefficients: x (j) = x (j) 1 + • • • + x (j) n (mod q) and we must output a single bit b such that b = 1 if x (j) = 0 for all 1 ≤ j ≤ ℓ, and b = 0 otherwise. For this we simply perform an arithmetic to Boolean conversion of each coefficient x (j) separately and then apply the previous PolyZeroTestBool algorithm. The complexity of each Boolean to arithmetic conversion is O(n 2 • k) for k = ⌈log 2 q⌉. Therefore the total complexity is O(ℓ • n 2 • k). We provide the pseudocode of the corresponding algorithm PolyZeroTestAB below.
The proposed system, called Root-GAST-Net, is based on modified versions of GAST-Net and RootNet networks. The efficiency of the proposed Root-GAST-Net system is demonstrated through quantitative and qualitative evaluations on two benchmark datasets, Human3. 6M and MuPoTS-3D. On all evaluated metrics, our experimental results on the MuPoTS-3D dataset outperform the current state-of-the-art by a significant margin, and can run in real-time at 15 fps on the Nvidia GeForce GTX 1080.
State-of-the-art approaches that use monocular data  [15] [16] [17] [18] [19] [20] [21] [22]  usually decouple the problem into two main phases: 2D pose estimation for joint detection and localization in the image space, and then lifting of the 2D pixel coordinates to 3D keypointposition predictions in the camera space. In our research, we followed the same strategy and focused on the second phase, i. e. , the 3D pose reconstruction from a sequence of 2D keypoints. Two-dimensional (2D) pose estimation is a popular vision problem that has been studied in many works, e. g. ,  [23] [24] [25] [26] [27] [28]  and has been greatly improved especially using the deep learning paradigm. Indeed, 3D pose estimation approaches show promising results on single-person datasets, such as Human3. 6M [29]  and HumanEva-I  [30].
This shows the gap between scientific literature and real-world requirements. The purpose of this study was to present a framework that could accomplish more accurate and robust 3D multi-person pose estimations from a monocular video, from these circumstances and industrial constraints. Thus, we propose an integrated top-down approach that combines GAST-Net for reconstructing 3D root-relative keypoints from 2D keypoints and RootNet for estimating root depth from human bounding boxes. It generates an appropriate 3D multi-skeleton estimation result from a monocular video while maintaining low computational costs and short execution times. Basically, the system is the result of a series of improvements that boost accuracy by more than 8. 8 percentage points on 3D-PCKabs on the MuPoTS-3D  [34]  dataset, when compared to the approaches in the literature  [31] [32] [33] 35, 36].
• Outperforming existing 3D multi-person absolute pose estimation methods in a MuPoTS-3D dataset by more than 8. 8 percentage points on 3D-PCK abs and by more than 12. 6 percentage points on AP root 25. The paper is organized as follows. Section 2 illustrates the review of conventional literature on 3D pose estimation based on different levels: the input type (video), the number of instances (multi-person), and the approach following the 3D root-relative pose estimation (two-stage approach). Section 3 demonstrates the proposed framework methodology. Section 4 explains the implementation details, the results and discussion. Section 5 provides a conclusion of the work.
Two-Stage Pose Estimation Several works  [22, [37] [38] [39] [40] [41] [42] [43]  apply deep neural networks on 3D pose estimation tasks to learn the direct mapping between RGB images and their corresponding 3D poses in one stage. However, this needs labeled data for supervised training, usually impractical out of MoCap labs. Unsupervised learning algorithms require sophisticated architectures with high computation costs, which are impractical too in realistic applications. To this end, Martinez et al. [44]  introduced a two-stage prediction approach. They first predicted the 2D pose from the image and then lifted 2D joint coordinates to the 3D space via a fully connected residual network. Fang et al. [45]  introduced a model to encode the mapping function of the human pose from 2D to 3D by explicitly encoding the human body configuration with pose grammar.
The graph attention spatiotemporal convolutional network (GAST-Net)  [57]  also combines common convolutional networks to integrate the spatiotemporal information. GAST-Net comprises two types of graph attention blocks: a local spatial attention network (to model the hierarchical and symmetrical structures of the human skeleton) and a global spatial attention network (to extract global semantic information and better encode the human body's spatial characteristics). Cai et al. [62]  developed an undirected graph to model the spatial-temporal connections between distinct joints for 3D single-person pose estimation from video data. In Ref. [32] , the authors utilized a graphical neural network (GNN) to efficiently aggregate the features corresponding to the different types of articulation, where each type was represented by a graph node.
The global spatial attention network adaptively extracts global semantic information to better encode the spatial characteristics of the human body. Depth estimator (RootNet): Moon et al. [31]  proposed a top-down system to estimate 3D multi-person poses from a single RGB image, consisting of human detection by the DetectNet model, absolute 3D human root localization by the RootNet model, and rootrelative 3D single-person pose estimation by the PoseNet model. Both models adopt ResNet-50 pre-trained on the ImageNet dataset as a backbone to extract the global data. We are particularly interested in the RootNet model, which generates two outputs: the 2D image coordinates of the root's keypoint (x, y) estimated using soft-argmax on the root-heatmap (the central point of the individual), and the root depth absolute determined using a scalar value k, computed using focal lengths divided by the per-pixel distance factors and the human area ratio between the real-world and the image.
GAST-Net was applied (as much as the number of people in the frame). GAST-Net was chosen since it provides the best compromise between the number of frames required to process and the estimation precision. In fact, the methods with the best accuracies on monocular videos from Human3. 6M (the largest database of 3D human pose estimation) are: temporal convolution  [39]  trained in semi-supervision learning, the Attention 3D Human Pose  [77] , which identifies significant frames and tensor outputs from each layer using the attention mechanism, the RIE paper  [43] , which improves the accuracy by relative information encoding that yields positional and temporal-enhanced representations, and Anatomy3D  [78] , which estimates the 3D skeleton by predicting bone orientation and length. These methods reached the MPJPEs (defined in Section 4) of 44.
On the MuPoTS-3D dataset, the system adopting the GR method outperformed previous methods by more than 12. 1 percentage points on AP root 25 , contributing to more than 6. 7 percentage points on 3D-PCK abs. However, we observed that the root-relative keypoints were less good by 25. 8 percentage points on PCK, which sparked the idea to upgrade the GAST-Net. While the original GAST-Net was trained on single-person databases  [29] , we chose to retrain our model on both a single-person video database (MPII-3DHP  [81] ) and a multi-person video database (MuCo-Temp  [56] ) with the required processing, following  [56] , to produce direct absolute keypoint coordinates. The TCN-based approaches evaluated on MuPoTS-3D were trained on the MPII-3DHP database, containing videos of a single person recorded in a green-screen studio and/or on the MuCo-3DHP database, composed of MPII-3DHP frames, containing multiple positions copied into a single frame.
Experimentation and Results Discussion This section deals with the experimental details and results of the proposed system. Results are discussed and evaluated using MPJPE, MRPE, 3D-PCK, AP root 25 , 3D-PCK abs metrics and response times. The proposed Root-GAST-Net system and its three variants (GR, GA, GAR), 3D pose absolute methodologies, were compared to the existing methods grouped in papers_With_Code link of 3D multi-person pose estimations (absolutes) on the MuPoTS-3D page ( https://paperswithcode. com/sota/3d-multi-person-pose-estimationabsolute-on, accessed on 1 April 2022). The compared methods are 3D MPPE PoseNet  [31] , HDNet  [32] , SMAP  [33] , HMOR  [69] , GnTCN  [35] , and TDBU_Net  [36]. The goal of evaluating the three methodologies was to measure the impact of each adjustment.
Datasets and Evaluation Metrics Human3. 6M is the most popular and biggest dataset/benchmark for 3D human pose estimation  [29]. It contains 3. 6 million single-person indoor video frames and the corresponding poses of 11 professional actors (6 males, 5 females) captured by the MoCap system from 4 camera viewpoints. Camera extrinsic (rotation and translation with respect to world coordinates) and intrinsic parameters (focal length and principal point) are also available. This could be used to evaluate the single-person-centric pose estimate  [39, 41, 43, 57, [77] [78] [79] [80]  as well as the camera-centered coordinate prediction  [31] [32] [33] 35, 36, 69]. Only subjects 9 and 11 were used for testing, as in prior studies. For evaluation, we computed the mean per joint position error metric (MPJPE), which is the mean Euclidean error averaged over all joints and all poses, calculated after aligning the human root of the estimated and ground truth 3D poses, calculated on relative poses, as shown in the formula below: MPJPE = 1 T 1 N T ∑ t=1 N ∑ i=1 J (t) i -J i * (t) 2 , (1) where T denotes the total number of test samples and N denotes the number of joints.
J and J * denote the predicted joint and the ground truth joint, respectively. Another evaluation metric used in this database, proposed in  [31] , is the mean root position error (MRPE), which is the average error of the absolute root joint (the hip) localization, as follows: MRPE = 1 T T ∑ t=1 (R (t) -R * (t) ) 2 , (2) where R and R * denote the predicted root joint and the ground truth root joint respectively. MuCo-3DHP and MuPoTS-3D MuCo-3DHP and MuPoTS-3D are two datasets proposed by Mehta et al. [34]  for 3D multi-person pose estimation evaluation. MuCo-3DHP is the training dataset that merges randomly sampled 3D poses from a single-person 3D human pose dataset MPI-INF-3DHP  [81]  to form realistic multi-person scenes. MuPoTS-3D is a dataset used for testing 3D multi-person estimation.
Moreover, we used 3D-PCK abs , which is PCK without the root alignment used to evaluate the absolute poses. MuCo-Temp This dataset was proposed by  [56]. It is generated in the same way as MuCo-3DHP. Both use images composited from the MPI-INF-3DHP dataset. The difference is that MuCo-Temp consists of videos instead of frames. So we can use it for temporal network training.
Implementation Details We adopted Yolo-v3 architecture  [75] , which is based on the Darknet-53 model as a backbone and is pre-trained on the COCO dataset  [82]. The input resolution is 608 × 608. The cropped image of the bounding box was transformed to 384 × 288 to be used as input for the 2D pose estimator. The transformation applied was an affine transformation that preserves collinearity, parallelism, and the ratio of distances between the points, as in  [23]. A unique ID was assigned to each person using the tracking method  [83]  based on the Hungarian optimization algorithm. Then, we used the small architecture of HrNet (HRNet-w32) pre-trained on the COCO dataset  [82] , implemented in PyTorch. The output was 17 heatmaps (resolution: 96 × 72). Cropping was resized to 256 × 256 to be processed by RootNet for depth root prediction Z root abs.
A unique ID was affected for each person using the tracking method based on the Hungarian optimization algorithm. The 27 consecutive 2D coordinates were collected for each person, to be given to GAST-NET. All networks, except GAST-NET, were optimized to TensorRT ( https://developer. nvidia. com/tensorrt, accessed on 1 April 2022), a Nvidia library allowing to optimize computations on the GPU in order to reach lower computation times. This library also offers lower precision arithmetic but in our experiments, we kept models in the FP32 precision. For GAST-NET ABS training, we used the Adam optimizer with a learning rate of 1 × 10 -3 and a batch size of 32. We trained the model for 80 epochs on MPII-3DHP  [81]  and MuCo-Temp  [56]  datasets. Computations were performed at the supercomputer facilities at Mésocentre Clermont Auvergne University for one week.