dataesr/scientific-paragraphs-categorization huggingface dataset:
(  Middle  the Wasserstein distance is that the average number of used amino acids of the UDM-256-LCLR model is actually lower than the one of the original data. Phylogenetic artifact can be reproduced in simulation study. The results presented above rely on assumptions about the correct topology  (Whelan et al. , 2016). As an alternative, we can experiment with simulations, for which we know the true phylogeny. Interestingly, the LBA artifact observed in the microsporidia data set could be reproduced in a simple simulation study. We used the 175 330 site distributions obtained from the HOGENOM database to simulate an alignment along a phylogeny exhibiting the currently accepted topology T2 where microsporidia branch within fungi (left phylogeny in Figure  5 ). Then, maximum-likelihood phylogenies were inferred with the Poisson model, and the LG model, as well as the CXX models, and the UDM models which account for across-site compositional heterogeneity.
This classic LBA attraction artifact is eliminated when accounting for across-site compositional heterogeneity. In order to provide robust and accurate models that account for across-site compositional heterogeneity, we developed a new method EDCluster to find empirical stationary distributions of amino acids with corresponding weights. ED-Cluster was used to provide universal stationary distributions estimated from curated databases, but also allows construction of EDM models with a large number of components directly from the data set at hand. The CAT model is employed to infer site distributions, that is, the expectations of the posterior distributions of the stationary distributions of amino acids per site. Subsequently we use a clustering algorithm to explore the structure of the hundred thousands of site distributions.
In order to provide sets of universal stationary distributions and weights available for general use we have applied our method, which implements these steps to subsets of databases spanning the whole tree of life. Analysis of the distributions of K eff for these universal stationary distributions indicate that a large number of stationary distributions is necessary to adequately model the diversity of site distributions present in empirical alignments. For example, a set of 16 stationary distributions of amino acids is by far not sufficient to describe the observed variety of site distributions (Figure  1 ). When the number of clusters is too low, we notice that many site distributions are assigned to overly general stationary distributions, because they do not fit in any particular stationary distribution, and not because they are general themselves (Figure  2 ).
For the analysis of the microsporidia data set, the performance of the UDM models was comparable to that of CXX models when using the same number of components. The UDM models outperformed the CXX models in analyses of the data sets including nematodes and platyhelminths. Assaying the ability of different EDM models to adequately recover across-site heterogeneity we found that UDM models outperform CXX models. In fact, the maximum number of components of the CXX models is currently limited to 60 due to the computational cost of the expectation maximization algorithm. In contrast, our method allows for mixture models with many more components. As a proof of concept, we show results for UDM models with 128 and 256 components. All presented analyses support that these component-rich UDM models outperform the C60 model.
The results presented above use sets of stationary distributions and weights estimated from a subset of the HOGENOM database. A parallel analysis of a subset of the HSSP database was performed and corresponding components were collected. However, the stationary distributions obtained from the HSSP database were mostly outperformed by the ones obtained from the HOGENOM database. Analysis of the taxonomic composition of the databases (see Materials and Methods) revealed that the taxonomic composition of the subset of the HOGENOM database is enriched for eukaryotes with an approximate value of 70 %, which is in agreement with the taxonomic compositions of the three analyzed data sets. For completeness, the stationary distributions obtained from the HSSP database as well as universal stationary distributions obtained from the union of both databases are also provided, and may exhibit better performance on data sets enriched for bacteria or with a balanced distribution of eukaryotes, archaea, and bacteria, respectively.
Overall, the computational requirements are much less than a complete analysis with CAT. Second, the site distributions can be analyzed using the provided script (Section S2). Finally, phylogenetic inference can be performed using an EDM model specific to the data set. We tested this procedure on the three discussed data sets and the LBA artifacts were removed in all three cases (see supplementary data). Before closing the discussion, we would like to examine the relation of EDM models with other available methods. For example, transition rate matrix recoding methods split the amino acids into separate groups representing different physicochemical properties (e. g. ,  Kosiol et al. , 2004; Susko et al. , 2007). Amino acids within the same group are frequently exchanged whereas there is hardly any exchange between amino acids of different groups.
g. ,  Simion et al. , 2017; Philippe et al. , 2019). Further, dissimilarities in compositional heterogeneity be detected by applying EDCluster to specific species groups. Also, the ideal number of EDM model components is still an open question. Statistical tests may not be the best guidance in developing appropriate methods because they favor component-rich EDM models. Albeit, parametric bootstrap analyses, and posterior predictive analyses with Bayesian methods can be used. For EDCluster, automatic clustering algorithms could be used. In conclusion, the presented UDM models constitute a valuable alternative to the widely used CXX models, and can be used for comparisons against the CAT or the CXX models. EDCluster allows estimation of stationary distributions that are specific to the data set at hand and suitable for use with non-uniform exchangeabilities.
by using a discretized Gamma distribution  (Yang, 1994a)  with parameter α. Then, the complete set of EDM model parameters is (T , w n , α). Excluding the phylogeny, EDM models with N components have N parameters, because n w n = 1. 0. HOGENOM and HSSP databases. Subsets of the HOGENOM  (Dufayard et al. , 2005)  and HSSP  (Schneider et al. , 1997)  databases consisting of 1005, and 1236 randomly selected alignments were obtained  (Quang et al. , 2008). For the HOGENOM database, the 1005 alignments contain 15 to 50 sequences and a total number of 175330 amino acid sites. For the HSSP database, the 1236 alignments contain 10 to 100 sequences and a total number of 260961 amino acid sites. Table  1  shows summary statistics of both databases. The summary statistics include the number of sequences and the number sites of the complete databases, and of the analyzed subsets.
Further, the percentage of analyzed sites falling into each domain of life provides a rough idea about the taxonomic composition of the analyzed data. First, the analyzed number of sites is slightly larger in the HSSP database compared to the HOGENOM database. Second, and more importantly, the proportion of eukaryotes and bacteria differs largely. The analyzed subset of the HOGENOM database contains a substantially higher proportion of eukaryotes compared to the HSSP database, which comprises a higher proportion of bacteria. Site distributions. For each alignment, a separate Bayesian analysis was conducted using the CAT model  (Lartillot et al. , 2004)  with Poisson exchangeabilities. Phylobayes  (Lartillot et al. , 2013)  was used for the Bayesian analyses. The phylogenies were fixed to the ones estimated by  Quang et al.
, 2011). A maximum number of 500 iterations and a tolerance of 5 × 10 -5 were used. The stationary distributions of the components of the UDM models are assigned to the obtained cluster centers. The weight of each component is set to the proportion of sites belonging to the respective cluster. During phylogenetic inference, the proposed mixture model weights can be employed without change, or estimated during maximization of the likelihood. In fact, all analyses presented in this manuscript use variable weights estimated during maximization of the likelihood. For details on the EDCluster script used for transforming and clustering the site distributions, please refer to Section S2. In total, we distinguish UDM models with seven different numbers of components, three different types of transformations, and three different databases (HOGENOM, HSSP, and their union).
EDCluster, and the obtained stationary distributions and weights are available at https://github. com/dschrempf/edcluster. Sections S3 and S4 present additional analyses of the stationary distributions and weights, and usage instructions for IQ-TREE  (Nguyen et al. , 2015) , Phylobayes, and RevBayes  (Höhna et al. , 2016) , respectively. Effective number of amino acids. Metaphorically speaking, entropy is a measure of disorder of a probability distribution. The entropy of a given site distribution π is defined as S(π) = - 1≤i≤20 π i log π i. (3) Here, we use the entropy to measure the diversity of a site distribution in the following way K eff (π) = e S(π) ∈ [1, 20]. (4) For readability, the explicit dependency on π is mostly omitted. We term K eff the effective number of amino acids, and stationary distributions with high (low) K eff general (constrained).
An effective number of amino acids of K eff = 1 corresponds to a highly constrained stationary distribution where a single amino acid has probability 1. 0, whereas all other amino acids have zero probability. The uniform stationary distribution with K eff = 20 is the most general. Analyses of data sets. The alignments of the microsporidia, nematode and platyhelminth data sets were obtained from  Brinkmann et al. (2005) , and from  Philippe et al. (2005) , respectively. The microsporidia data set contains 40 sequences with a length of 24294 sites. The percentage of gaps is 24. 1 % and the average effective number of amino acids is 2. 569. The nematode data set contains 37 sequences with a length of 35371 sites. The percentage of gaps is 28. 7 % and the average effective number of amino acids is 2.
116. The platyhelminth data set contains 32 sequences with a length of 35371 sites. The percentage of gaps is 30. 7 % and the average effective number of amino acids is 2. 069. The IQ-TREE software package was used for all analyses of the three data sets. Phylogenetic inference was performed using the WAG, and the LG  (Le et al. , 2008a)  substitution models, the C10 to C60 models (collectively called CXX models;  Quang et al. , 2008) , and the UDM models with 4, 8, 16, A. , 256 components. For all analyses, a discrete Gamma distribution with four bins was used to deal with across-site rate heterogeneity (+G4 model string). For the WAG and LG models, the stationary distribution of amino acids was set to the one observed in the respective alignment. We refrained from adding the stationary distribution of amino acids observed in the data as an additional component to the CXX and UDM models.
The weights of the mixture model components of the CXX and UDM models was inferred during maximization of the likelihood. Detailed instruction about how to perform phylogenetic inference with UDM models in IQ-TREE is given in Section S4. For each data set and model, three maximum likelihood analyses were conducted. First, a maximum likelihood analysis inferring the model parameters as well as the topology and the branch lengths of the phylogeny. Further, two analyses with fixed topologies (T1, and T2, see Figure  3 ) were conducted (-t option in IQ-TREE). The branch lengths were inferred without exception. The analyses of the separated microsporidia genes was performed with Phylobayes. Results for the LG model, the CAT model with Poisson exchangeabilities, and UDM models with four, up to 128 components also with Poisson exchangeabilities were obtained.
Subsequently, the effective number of amino acids K eff was calculated per site in the alignment. The obtained distribution of K eff values was compared to the one of the original data set using the Wasserstein distance as it is implemented in SciPy  (Jones et al. , 2001). Phylogenetic artifact can be reproduced in simulation study. The phylogeny used to simulate the alignment was chosen from an analysis of the microsporidia data set  (Brinkmann et al. , 2005).
Figure1: Distributions of effective number of amino acids of the stationary distributions used by universal distribution mixture models with different numbers of components. Violin plots of the effective number of amino acids of the stationary distributions obtained from the log center log ratio transformed site distributions (Godichon-Baggioni et al. , 2018)  of the HOGENOM database (Dufayard et al. , 2005)  are shown. On the far right, the distribution of the effective number of amino acids of the site distributions obtained with the CAT model (Lartillot et al. , 2004)  using Poisson exchangeabilities (Felsenstein, 1981)  is displayed. The width of the violin plots was normalized such that all areas are equal. Horizontal bars display the means of the distributions.
Figure4: Across-site compositional heterogeneity of classical substitution models and empirical distribution mixture models. Similarity between the across-site compositional heterogeneity of the microsporidia data set(Brinkmann et  al. , 2005), and simulated alignments for the maximum likelihood parameter estimates of the WAG (Whelan et al. , 2001) , LG(Le et al. , 2008a), CXX (Quang et al. , 2008) , and universal distribution mixture (UDM) models. Results of UDM models obtained from untransformed (None), center log ratio (CLR; Aitchison, 1982)  transformed, and log center log ratio (LCLR; Godichon-Baggioni et al. , 2018)  transformed site distributions are shown. Similarity is measured by the Wasserstein distance between the distributions of effective number of amino acids per site between empirical data and the sequences simulated using parametric bootstrap.
with the UDM model with 64 components obtained from clustering the LCLR transformed site distributions. elynx (Section S8) was used to simulate 25 000 sites using Poisson exchangeabilities. Each site was randomly assigned a stationary distribution sampled with replacement from the site distributions of the HOGENOM database which had been obtained by the Bayesian CAT analyses described above. The simulated alignment was analyzed with IQ-TREE using the Poisson model with the empirical distribution observed in the alignment (Poisson+F model string), and the UDM models with four up to 64 components obtained from clustering the LCLR transformed site distributions. Ultra fast bootstrap (Hoang et al. , 2018) with 1000 samples was used with all models (-bb 1000 option). The simulated alignment with 25 000 columns was used to randomly subsample short alignments of length 100, 200, 400, 800, and 1000 columns with replacement.
Background: The COVID-19 crisis and consequent confinement restrictions have caused significant psychosocial stress and reports of sleep complaints, which require early management, have increased during recent months. To help individuals concerned about their sleep, we developed a smartphone-based app called KANOPEE that allows users to interact with a virtual agent dedicated to autonomous screening and delivering digital behavioral interventions. Objective: Our objective was to assess the feasibility of this app, in terms of inclusion rate, follow-up rate, perceived trust and acceptance of the virtual agent, and effects of the intervention program, in the context of COVID-19 confinement in France. Methods: The virtual agent is an artificial intelligence program using decision tree architecture and interacting through natural body motion and natural voice.
To our knowledge, no study has focused on insomnia complaints, despite the evidence that digital behavioral therapies are effective to treat insomnia  [12] [13] [14]. Soon after confinement measures were effective in France on March 17, 2020, we launched the first social media campaign in affiliation with our hospital and university through major national radios and newspapers. This campaign focused on the risk of insomnia and the measures to evaluate and correct inappropriate sleep hygiene practices among people during the COVID-19 confinement. In addition to social media campaigns, we developed a free smartphone app to help individuals with sleep concerns in the context of the COVID-19 pandemic. Named KANOPEE, the program is based on our previous research on embodied conversational agents (ECAs), also called virtual agents, which may be defined as animated characters that can engage in face-to-face dialogue through verbal and nonverbal behaviors.
Description and Implementation of KANOPEE KANOPEE was implemented using the same architecture as our previous ECAs  [15] [16] [17] [18] [19] , with C# programming language in Unity 3D software (Unity Technologies). The virtual agent, named Louise, interacts through natural voice and body motion, as recorded by a professional artist by using motion capture technologies (Optitrack suit for 3D animations of Louise's body and GoPro for 3D animations of Louise's face and her voice). The interaction scenario is predefined, using decision trees to adapt to the user's answers. The app was made freely available on Google Play Store on April 22, 2020 (see Figure  1 ). After its launch, we organized a second media campaign presenting KANOPEE and showing how the app could help the French population to self-evaluate their sleep quality and provide practical solutions to manage insomnia.
Sociodemographics and Clinical Characteristics of Users For the purpose of this study, users were selected for the analysis if they met the following inclusion criteria: (1) aged 18 years old and above and (2) had downloaded KANOPEE app before May 5, 2020, such that they had access to the 1-week intervention before the end of confinement period in France (ie,  May 11, 2020). Their use of the app was recorded from April 22 until May 26. After getting approval by the University and Hospital scientific committees, we obtained authorizations to be registered on the University Hospital register for General Data Protection Regulation (GDPR) approval by the French authorities-Commission nationale de l'informatique et des libertés (CNIL). Informed consent was obtained from all users downloading the app according to the GDPR and CNIL regulations.
Study Limitations Nevertheless, this study has a few limitations. First, the very peculiar period of recruitment, during the COVID-19 confinement, makes our results preliminary. Future work needs to confirm, in a more "normal period of time," the fact that KANOPEE can help individuals improve their insomnia complaints and sleep hygiene. Second, the drop-out rate was quite high. We were unable to determine why users did not follow the program until the end, and further study is therefore needed for a precise examination of usage (eg, frequency of use and errors made) and qualitative interviews with app users to unveil the reasons why they decided to drop out of KANOPEE. Another limitation of our study is related to the fact that we did not note an improvement among the most severe users, which shows the limitations of nonhuman interventions.
Acknowledgments We thank  Yannick Levavasseur  and  Emeric Labbé  for developing the app. This project was supported by the grants  LABEX BRAIN  ( ANR-10-LABX-43 ), EQUIPEX  PHENOVIRT  ( ANR-10-EQPX-12-01 ), and funding from the  Region Nouvelle-Aquitaine  ( IS-OSA  project, Contract No. :  18000389 ).
The leak from the internal operations is not visible, preventing Differential Power Analysis (DPA). The memory transfer of the key leaks: it can be attacked with Simple Power Analysis (SPA), though key recovery was not possible with the current setup. • A significant step forward towards realistic key recovery attacks against software AES-128. In particular, we show that it is possible to: -Profile a device at short distance in convenient conditions (connection via cable), and attack another instance at 10 m and 15 m via radio in an office environment. -Detect the leak at 34 m and extract traces at 60 m in an office environment. -Attack a device at 0. 55 m in a challenging home environment with obstacles. -Attack the authentication method used in Google Eddystone beacons. Related Work. Electromagnetic side channels are particularly effective when they result from the modulation of a carrier signal that radiates from the device  [AARR02].
As discussed by the authors, the harmonics of the clock are usually one of the strongest signals emitted by the device, but also other intended communication signals might act as carrier for a leak. This is also mentioned in a TEMPEST document  [NSA82] , but the details are redacted. Radio transmitters, which intentionally emit strong intended carriers, are therefore a potential vector for information leaks. In Screaming Channels [CPM + 18], the unintended side-channel leaks generated by the digital part of a mixed-signal chip modulate the carrier generated in the analog part. This is relevant for modern connected devices, given the popularity of the mixed-signal architecture for many wireless protocols. A radio front-end could also be actively exploited by an attacker to exfiltrate data from a device.
Screaming Channels In [CPM + 18] we presented Screaming Channels, a side channel that may appear if the operation of a radio transmitter is impacted by a nearby digital block, letting the attacker retrieve sensitive information by simply observing the radio signal at a large distance from the target. We focused on mixed-signal chips, pointing out that the root cause of the problem is the coupling between digital blocks and the radio. Additionally, we developed proof-of-concept attacks on the tinyAES implementation (running at 10 m in an anechoic chamber), and on the mbedTLS implementation (running at 1 m in a normal environment). The main target was a Nordic Semiconductor nRF52832 (which is easy to program and offers simple access to low-level radio features), but we also observed signs of leaks on another mixed-signal chip (Qualcomm Atheros AR9271).
Note on terminology. The terms conventional and screaming-channel distinguish between classic attacks on unintended EM emissions, and attacks that exploit the modulation of an intended radio signal. We refer to the paper Screaming Channels [CPM + 18] in upper case, whereas we use lowercase for the general term screaming channel(s). An intended radio carrier (e. g. , at 2. 4 GHz) is both intentionally modulated to transmit data packets (e. g. , Bluetooth) and unintentionally modulated by a leak trace l y p,k (t) (e. g. , corresponding to an AES-128 encryption). The actual leak signal that brings sensitive information is the difference among leak traces that correspond to different values of the leak variable y, as explained in Section 1. 3.
It has an antenna that can be bypassed with a switch to connect to the radio output with a cable. Figure  1  shows these devices. We use the same nRF5  [Nora]  Software Development Kit (SDK) as Screaming Channels. For reception, we use the following off-the-shelf Software Defined Radios (SDRs): HackRF  [Gre17] , USRP N210  [Etta] , and USRP B210  [Ettb]. To collect the signal, we use off-the-shelf WiFi antennas (standard or directional [TP-]), a custom cable connection, or a custom loop probe based on  [emp]. When necessary we use low-noise amplifiers (ZEL-1724LN  [Mina] , ZKL-1R5  [Minb] , ZX60-272LN  [Minc] ). When not stated otherwise, we attack the tinyAES implementation of AES-128, with optimization level 0, we tune at 2. 528 GHz, and we sample at 5 MHz. The output power of the radio is set to 4 dBm.