dataesr/scientific-paragraphs-categorization huggingface dataset:
box coordinates, and converted into a. star particle stack in Relion2  (49). Micrograph contrast transfer function (CTF) correction and two-dimensional (2D) classification were performed in cryoSPARC v2 after particle reextraction using the Relion2-generated. star file as metadata input  (50). A total of 3,810 particles were classified for the Bcs macrocomplex carrying full-length BcsE (control) and 6,242 particles for the complex purified from cells expressing the BcsE 217Ϫ523 truncated variant. Protein identification by mass spectrometry. Coomassie-stained gel bands were excised and subjected to in-gel enzymatic digestion. Briefly, the bands were extensively washed with acetonitrile and 100 mM NH 4 HCO 3 , dried, and treated with 10 mM DTT at 56°C for 30 min. After DTT removal, cysteine carbamidomethylation was performed at room temperature for 30 min by the addition of 55 mM iodoacetamide.
d. ], 2-cm length) using a loading buffer containing 2% acetonitrile and 0. 05% TFA in water and a flow rate of 5 l/min. A second Acclaim Pepmap 100 C 18 column (2 m, 100 Å, 75-m i. d. , 50-cm length) was then used as an analytical column, and bound peptides were eluted from the reverse phase using a 5% to 35% solvent B gradient for 40 min at a flow rate of 300 nl/min (solvent A, 0. 1% formic acid in water; solvent B, 0. 1% formic acid in 100% acetonitrile). nanoLC-MS/MS experiments were conducted using data-dependent acquisition by selecting the 20 most intense precursors for collision-induced dissociation (CID) fragmentation with the Q1 quadrupole set to low resolution for increased sensitivity. Raw data were processed using proprietary MS data converter software (Sciex), and protein identification was performed using the Mascot search engine (Matrix Science) against the E.
coli taxon in the Swiss-Prot database and with carbamidomethylation of cysteines set as fixed modification. Oxidation of methionines was set as variable modifications. Peptide and fragment tolerances were set at 25 ppm and 0. 05 Da, respectively. Only peptides with a Mascot score higher than the identity threshold  (30)  at less than 1% of false-positive discovery rate were considered. Size exclusion chromatography coupled with static multiangle light scattering. For size exclusion chromatography coupled with static multiangle light scattering (SEC-MALS), purified proteins or protein complexes were subjected to gel filtration using a Superose 6 Increase 10/300 GL column (GE Healthcare) for the BcsRQ-BcsE FL complex or a Superdex 200 Increase 10/300 GL column for all other samples. The columns were preequilibrated with the respective gel filtration buffers.
For the BcsE 217Ϫ523 construct, the protein was analyzed in its apo form, as well as following incubation with 2-fold excess c-di-GMP. The high-performance liquid chromatography (HPLC) system (Shimadzu) was coupled to a 3-angle light-scattering detector (miniDAWN TREOS) and a refractive index detector (Optilab rEX) (Wyatt technology). For each experiment, the system was preequilibrated overnight with gel filtration buffer and at the desired flow rate. Data were collected every second at a flow rate of 0. 5 ml/min and were analyzed using the ASTRA software to obtain the molar mass and polydispersity of the sample across the protein elution peaks. For detector normalization and data quality control, purified delipidated bovine serum albumin (BSA; Sigma) was used prior to each experiment. Protein complex reconstitution using purified proteins.
All ITC data were analyzed by integrating the injection heat effects, normalized to the amount of ligand and protein present, and curve fitting based on a single-site binding model using the Origin software package for Microcal. For all titrations, titrations of the ligand into buffer were performed to account for heat dilution effects, and the latter were subtracted during the ligand binding analysis. The apparent dissociation constants (K d ) and stoichiometries of interaction (N) were derived from the data by using standard procedures, and the graphs were replotted using GraphPad Prism software. Calcofluor-binding cellulose secretion assay. To test for the functional effects of the C-terminal BcsQ deletion (BcsQ ∆C10 ), chemically competent cells were prepared from an E. coli 1094 ∆bcsQ deletion strain  (6).
Protein and RNA structure prediction tools. Protein conserved domain detection and tertiary structure prediction were carried out using the NCBI BLASTP suite (https://blast. ncbi. nlm. nih. gov/Blast. cgi), PFAM database (https://pfam. xfam. org), Phyre2 (http://www. sbg. bio. ic. ac. uk), and Robetta servers (http://new. robetta. org) (22, 23, 41, 51). A search for structural homologues in the Protein Data Bank was carried out using the DALI server (http://ekhidna2. biocenter. helsinki. fi/dali/) (25). Potential intrinsic terminators in mRNA were predicted using the ARNold (http://rssf. i2bc. paris-saclay. fr/toolbox/arnold/) and iTERM-PseKNC (http://lin-group. cn/server/iTerm-PseKNC/) tools  (38, 39). Data availability. Crystallographic structure factors and coordinates have been deposited in the RCSB Protein Data Bank (https://www.
ACKNOWLEDGMENTS We thank all current and former members of the  Structural Biology of Biofilms group  and especially  Agnès Thomas-Collignon ,  Marius Poulain ,  Gaurav Shajepal , and  Lucia Torres-Sanchez  for technical assistance and/or work peripheral to the project,  Stéphane Roche  for X-ray crystallography advice and insightful discussions, the  I2BC Virology department  for provided equipment and laboratory space,  Yoshiharu Yamaichi ,  Virginia Vicky Lioy ,  Jean-Marc Ghigo , and  Holger Sondermann  for providing expression vectors and bacterial strains, and  Rémi Fronzes ,  Holger Sondermann , and  Axel Innis  for critical reading of the manuscript prior to submission. P. V. K. conceived the project; P. V. K. , S. Z. , W. A. , and M. C. designed, performed, and optimized the experimental procedures; P.
V. K. , S. Z. , and W. A. analyzed the data; and P. V. K. wrote the paper with feedback from all authors. This project was supported by the  Institute for Integrative Biology  of the Cell (I2BC), the  European Institute of Chemistry and Biology (IECB) , the  Centre National de la Recherche Scientifique (CNRS) , an  ATIP-Avenir starting grant  (to P. V. K. ), and a starting grant from the  European Research Council (ERC StG BioMatrix  to P. V. K. ). The work has also benefited from the  I2BC proteomics platform SICaPS , supported by  IBiSA ,  Ile de France Region ,  Plan Cancer ,  CNRS  and  Paris-Sud University , as well as from the  I2BC platforms PIM and Cryo-EM , which are supported by the  French Infrastructure for Integrated Structural Biology (FRISBI ;  ANR-10-INBS-05 ).
Interestingly, the same difference was also observed for two other data sets that reported inelastic cross sections on 52 Cr  [7, 19]  (see figure  3 ). This nucleus was measured by Voss et al. using the same setup as for nickel  [7]  while Mihailescu et al. used the same facility (GELINA neutron source), a similar experimental setup and a 200 m flight path as compared with the present work. The data of Mihailesc et al. , taken from the EXFOR database, reveal a similar difference of around 20% when compared with the data from Ref. [7]  for 52 Cr. The presence of this discrepancy in all these measurements suggests a systematic error in normalization of the data reported by F. Voss et al. [7]. Our results are compared also with theoretical calculations performed with the talys 1. 9 code using the default input parameters.
To generate a model that efficiently locates the PoIs, the evaluator has to configure a function, called loss, and appropriate performance metrics (e. g. accuracy) in order to evaluate properly the performance of a model. In machine learning, a loss function computes the error generated by the model. During the training process, the evaluator aims to minimize the loss function in order to get the best model possible. The most used loss function in the literature is the cross-entropy  [GBC16]. In  [MDP19b] , from the "Learning to Rank" approach, is linked with the mutual information between a label and an input. Finally, we confirm the relevance of our loss by applying it to the main public datasets and we compare the results with the classical cross-entropy and the recently introduced cross-entropy ratio [ZZN + 20].
All these experiments can be reproduced through the following GitHub repository: https://github. com/gabzai/Ranking-Loss-SCA. Paper Organization. The paper is organized as follows. Section 2 explains the link between the profiled side-channel attacks and the machine learning approach. It also introduces the learning to rank approach applied to SCA. Section 3 proposes a new loss, called Ranking Loss  (RkL) , that generates a model converging towards the optimal distinguisher. In Section 4, we theoretically link the ranking loss with the mutual information. Finally, Section 5 presents an experimental validation of ranking loss on public datasets showing its efficiency.
Consequently, this error can be quantified for a given number of profiling traces. • Optimization error -it characterizes the error made by the optimization algorithm (e. g. Stochastic Gradient Descent [RM51, KW52, BCN18], RMSprop, Momentum  [Qia99] , Adam  [KB15] ,. ) to converge towards the best solution of the categorical cross-entropy. These errors caused by the categorical cross-entropy could impact the training process. Section 5 emphasizes the impact of these error terms on different datasets. In addition, the more secure the system, the larger the inequality between the empirical PI and the MI. As mentioned in [DFS15, BHM + 19], a higher MI implies a more powerful maximum likelihood attack where the secret key k * can be extracted more efficiently. In other words, the probability of the success rate is linked with the MI.
g. Discounted Cumulative Gain  [JK02] , Normalized Discounted Cumulative Gain [JK00], Expected Reciprocal Rank  [CMZG09] , Mean Average Precision, etc. ) to compute the loss. However, in our context, d i = d i , therefore the difference between the scores of each pair (d i , d j ) and (d i , d j ) gives us enough information on the position of d i related to d j and d j. Consequently, the addition of IR metrics is not relevant. Moreover, IR metrics can be either discontinuous or flat, so gradient descent appears to be problematic (i. e. gradient equals to 0 or not defined) unless some appropriate approximation is used. b Here, the output score denotes the value before the softmax function. This choice is made to impact the training process accordingly to the relative order of the key hypotheses' relevance instead of the normalized probability distribution.
Settings The experiments are implemented in Python using the Keras library [C + 15] and are run on a workstation equipped with 16GB RAM and a NVIDIA GTX1080Ti with 11GB memory. All of the following architectures and hyperparameters c are based on the best state-of-the-art results  [ZBHV19]. Table  1  summarizes the choices made by Zaid et al. No more investigations are performed on these hyperparameters. We define N t GE as the number of traces that are needed to reach a constant guessing entropy of 1. For a good estimation of N t GE , the attack traces are randomly shuffled and 100 N t GE are computed to give the average value denoted N t GE. In the next sections, the N a value needed to compute the ranking loss, is set to 1. Hence, the ranking loss tends to maximize the success rate when only 1 attack trace is considered (see Definition 2).
Comparison with the Cross-Entropy Ratio In the following, we compare the categorical cross-entropy and the cross-entropy ratio (CER) loss functions with the ranking loss on different publicly available datasets. Introduced in [ZZN + 20], the cross-entropy ratio is defined as: L CER (F θ , T r ) = L CCE (F θ , T ) L CCE (F θ , T r ) , where T r identifies the profiling set T with shuffled labels while keeping the traces unchanged. Remark 4. In [ZZN + 20], Zhang et al. construct the cross-entropy ratio loss as specialized for imbalanced labels (e. g. when a particular leakage model is considered). The ranking loss proposed in this paper is generic, no leakage model is chosen (i. e. the identity function is used). A future work could study the suitability of the ranking loss on imbalanced labels.
Presentation of the Datasets We use three different datasets for our experiments. All the datasets correspond to implementations of Advanced Encryption Standard (AES)  [DR02]. The datasets offer a wide range of use cases: high-SNR unprotected implementation on a smart card, low-SNR unprotected implementation on a FPGA, low-SNR protected implementation with first-order masking  [SPQ05]  and random-delay effect. • Chipwhisperer d is an unprotected implementation of AES-128 (8-bit XMEGA Target). Due to the lack of countermeasures, we can recover the secret directly. In this experiment, we attack the first round S-box operation. We identify each trace with the sensitive variable Y (i) (k * ) = Sbox[P (i) 0 ⊕ k * ] where P (i) 0 is the first byte of the i-th plaintext. The measured SNR equals 7.
• ASCAD f is introduced in [BPS + 19] and is the first open database that has been specified to serve as a common basis for further works on the application of deep learning techniques in the side-channel context. The target platform is an 8-bit AVR microcontroller (ATmega8515) where a masked AES-128 with different levels of random delay (i. e. 0, 50, 100) is implemented. The leakage model is the first round S-box operation such that Y (i) (k * ) = Sbox[P (i) 3 ⊕ k * ]. As explained in [BPS + 19], the third byte is exploited. The measured SNR equals 0. 007 (see Appendix D Figure  7 ). This SNR computation was performed on the unmasked sbox output which is targeted by the network. To generate our network, we divide the dataset of ASCAD into three subsets: 45, 000 traces for the training set, 5, 000 for the validation set and 10, 000 for the attack phase.
Evaluation of the Ranking Loss on Public Datasets From a practical perspective, the generation of suitable architectures is known as a difficult task. Hence, two kinds of models are considered. In Section 5. 4. 1, models that exploit a partial set of PoI in the leakage traces are evaluated. In Section 5. 4. 2, models that exploit all the relevant information in the leakage traces are considered. This subsection evaluates the efficiency of the ranking loss compared to the categorical cross-entropy and the cross-entropy ratio in both cases through various scenario, notably in presence of high noise, masking and desynchronization.
A Partial Exploitation of the Leakages For simplicity, we evaluate this case study with the ChipWhisperer dataset. The model implemented is a CNN architecture with one convolutional block of 2 filters of size 1 and one fully-connected layer with 2 nodes. When considering unprotected implementation with low noise, all the models trained with different losses provide the same N t GE value (see Table  2  for small σ values). In  [ZBHV19] ,  Zaid et al. propose to visualize the weights corresponding to the flatten layer in order to evaluate the capacity of the network to extract the relevant features. Through this visualization, an evaluator is able to retrieve the points of interest selected by a network. However, due to the effect of the convolutional block, the number of weighted samples is divided by the value of the pooling stride  [ZBHV19].
In Section 4. 3, we have shown that the ranking loss prevents the approximation error compared to the categorical cross-entropy. Hence, when the ranking loss is used, the related performance should be, at least, as good as a model trained with the categorical cross-entropy. If the PoIs amplitude is low compared to the noise, the performance gap between a model trained with the categorical cross-entropy, the cross-entropy ratio and the ranking loss could increase. To illustrate this phenomenon, we add Gaussian noise N ∼ B 0, σ 2 such that σ defines the standard deviation of the noise. Table  2  shows the evolution of the N t GE value depending on the added noise on the Chipwhisperer dataset. When the additional noise level is low (i. e. σ ≤ 10 -2 ), the feature detection is effective regardless of the loss function and the performance gap is low (i.
But its overall error rate stays a lower bound of the error rate generated by a model trained with a cross-entropy loss function (see Section 4. 3) with the assumption that the optimization errors are similar. The evaluation of the approximation error was also made on the AES_HD and the ASCAD datasets but the architectures proposed in  [ZBHV19]  already give the same best solution for all the losses. For these datasets, we can assume that the approximation error is negligible. Hence, we assume that all losses exploit the entire set of relevant information. The next section evaluates the benefits of the ranking loss against the categorical cross-entropy and the cross-entropy ratio when all leakages are detected. Remark 6. In this experiment, we noticed that when the noise level is high, the best value of α used by the ranking loss decreases.
A Total Exploitation of the Leakages As previously mentioned, given the architecture provided in  [ZBHV19] , the entire set of leakages is detected on the AES_HD and the ASCAD datasets. Hence, we can assume that the approximation error does not impact the overall performance of the model regardless of the loss function. When all the losses converge towards the same best solution, a comparison method consists in the evaluation of the number of profiling traces that are needed to reach this performance. From an evaluator point of view, it is more interesting to converge faster towards the best solution because it is difficult to estimate a priori the number of profiling traces needed to reach the best performance. To highlight the benefits of each loss, we decompose this experimental study into an Estimation Error Gap (EEG) and a performance gap evaluations.
We introduce the EEG that characterizes the difference between the number of profiling traces N p , when different losses are used, for a given N t GE. We note EEG(L i , L j ) the EEG value between models trained with the loss functions L i and L j. For each dataset, we report the performance results given by 10 models converging towards a constant guessing entropy of 1 and display the evolution of the average N t GE values for different level of N p. When the number of profiling traces is low (i. e. ≤ 30, 000), some models do not retrieve the sensitive information and the resulted N t GE value cannot be estimated. For allowing an equal comparison between the losses, we only consider the models for which the N t GE value can be computed for all the learning metrics.
2 such that the ranking loss is, at least, as efficient as a model trained with a cross-entropy loss function. Remark 8. The value α of the ranking loss needs to be adapted depending on the number of profiling traces. For example, when N p is low, the risk of overfitting is a major issue. One solution, to limit the overfitting effect, is to fix a higher learning rate  [HHS17, ST17]. Hence, following Equation 12 and Equation 13, α can be monitored as the learning rate, in order to optimize the training process. For the AES_HD dataset, increasing α to 10 generates a more powerful model than α equal to 1 (see Appendix A Figure  4c ) when the number of profiling traces equals 20, 000.
ASCAD. In contrast with the previous datasets, ASCAD is a protected implementation with 1 st -order masking and random-delay countermeasures. Figure  2b , Figure  3a  and Figure  3b  provide a comparison between models trained with the different losses for synchronized and desynchronized traces. In Figure  2b , when the model is trained with the ranking loss, only 15, 000 profiling traces are needed to perform a successful attack while 18, 500 (resp. 20, 000) are needed to reach the same performance when the categorical cross-entropy (resp. cross-entropy ratio) loss is used for the training process. Consequently, if the evaluator chooses the categorical cross-entropy (resp. cross-entropy ratio) as loss function, he needs to increase its training set by 3, 500 (resp. 5, 000) profiling traces on average.
Thus, when N t GE = 1, 700, EEG(L RkL , L CCE ) = 3, 500. Furthermore, when no desynchronization occurs, the model converges faster towards the average best solution (i. e. N t GE ≈ 260) when the ranking loss is used (i. e. 35, 000) compared to the categorical crossentropy or the cross-entropy ratio losses (i. e. about 45, 000). The resulting EEG value equals 10, 000. This estimation error gap is up to 6, 000 profiling traces when desynchronization occur (see Figure  3a  and Figure  3b ) g. Hence for the ASCAD dataset, EEG is not increased with the desynchronization effect. Indeed, in comparison with synchronized traces, this countermeasure only impacts the exploitation of the relevant information. Finding suitable CNN architectures reduce the desynchronization effect [CDP17, ZBHV19] while preserving the same performance as a model trained with synchronized traces.
Through Table  4 , we confirm the observations on the AES_HD dataset. In our experiment, for a small number of profiling traces N p (i. e. ≤ 25, 000), a model trained with the ranking loss is, on average, more efficient than one trained with the categorical cross-entropy or the cross-entropy ratio. For synchronized and desynchronized traces, an evaluator with a limited number of profiling traces shall use the ranking loss. When the entire set of leakages is detected by the network, a model trained with the ranking loss converges faster towards the best solution compared to the categorical crossentropy and the cross-entropy ratio losses. From a theoretical perspective, we can assume that the estimation error is reduced when the ranking loss is considered. As discussed in Section 2. 3, the estimation error defines the gap between the empirical estimation of the PI (resp.
the empirical estimation of the MI), computed with the categorical cross-entropy (resp. ranking loss), and the real value of the PI (resp. MI). When the number of profiling traces N p is large enough, we validate that the impact of the estimation error can be negligible. However, in practice, the number of profiling traces is limited. For that purpose, the ranking loss function seems to be more appropriate with the assumption that an attacker has not an infinite number of traces in the profiling phase  [PHG19]. Hence, the ranking loss is a solid alternative to the cross-entropy losses for side-channel attacks. Remark 9. As previously mentioned, depending on the number of classes, the training time can be impacted when the ranking loss is considered. In comparison with the cross-entropy loss function, the training time is increased by 60s, when N p = 15, 000, and by up to 253s when 45, 000 profiling traces are used for generating a network on the ASCAD dataset when no desynchronization occurs.
A Impact of α We select a wide range of α values in order to efficiently evaluate the impact of this parameter on the training process. Figure  4  illustrates the impact of α on the loss function depending on the dataset used for training the model. The architectures are the same as in Section 5. 4. For the Chipwhisperer dataset, if α is small (e. g. less than 10), the sigmoid approximates the indicator function less accurately  [BZBN19]. Consequently, the loss function optimizes the model that is far from the original ranking loss. However, this property is not necessarily verified in practice. Indeed, depending on the score value, the impact of α could be scaled. When α is too large, the gradient tends to vanish (see Equation  13 ) and the resulted training process provides poor performance.