dataesr/scientific-paragraphs-categorization huggingface dataset:
We set T = 150, meaning that speech utterances of 2. 4 s were extracted from the raw dataset and pre-processed with the STFT. In summary, each speech sequence is a 150 × 257 STFT spectrogram. This data preprocessing resulted in a set of N tr = 13, 272 training sequences (about 9 hours of speech signal) and N val = 2, 143 validation sequences (about 1. 5 hour). For testing, we used the STFT spectrogram of each complete test sequence (with beginning and ending silence portions removed), which can be of variable length, most often larger than 2. 4 s. As discussed in  (Leglaive et al. , 2020)  and briefly mentioned in Section 10. 1, the complex-valued STFT coefficients are modeled with a zero-mean circular complex Gaussian distribution, see (10. 2), whereas z t is modeled as usual with a real-valued Gaussian distribution.
Results We first present the loss curves (i. e. , VLB up to a constant term) obtained on the training data and the validation data in Fig. 13  the different models, but a thorough examination of these aspects is beyond the scope of this paper. The values of the three metrics described in the previous section averaged over the above-described test dataset at the end of model training are reported in Table  13. 1. From this table, we can draw the following comments: • First, all tested DVAE models lead to "correct" signal reconstruction, with an RMSE that represents only a few percents (generally lower than 5%) of the maximum waveform amplitude. The quality of the reconstructed speech signals, as measured by PESQ, goes from fair to good. STOI scores, generally higher than 0. 90 show their good intelligibility.
This is because the z 1:T sequence is assumed to encode high-level characteristics of the data x 1:T that generally evolve smoothly over time (at least for some of these characteristics). This is not ensured by the i. i. d. standard Gaussian prior distribution of z t used in STORN. • The performance of DSAE is quite disappointing, especially compared to the performance of DKF. Indeed, as DKF, DSAE is also an SSM-like model. Actually, DSAE can be seen as an improved version of DKF, with an additional sequence-level variable v and infinite-order temporal dependency of z t (as opposed to first-order for DKF). Again, this poor performance could come from the structure of the inference model. In particular, for DSAE, the inference model of z t depends on x 1:T , whereas the exact posterior distribution depends on z 1:t-1 and x t:T.
The paper only mentions that the arithmetic encoding of z 1:T requires a number of iterations. Moreover, the DSAE model is an SSMlike model, i. e. x t is generated from z t "alone," not considering the potential of using x t-1 (or its quantized version) for predicting and encoding more efficiently x t. In contrast, in  (Yang et al. , 2020) , the authors propose different schemes for encoding a data sequence x 1:T through the inference and quantization of the corresponding sequence of latent vectors z 1:T with different options for recurrent connections. One of them, called Feedback Recurrent AutoEncoder (FRAE), has recurrent connections at both the encoder and the decoder, and a feedback connection from decoder to encoder. The recurrent connections are reminiscent of the predictive coding principle that is classical in the source coding theory  (Gersho and Gray, 2012) : FRAE can be interpreted as a non-linear predictive coding scheme, where the encoder forms a latent code which encodes only the residual information that is missing when reconstructing a data vector from the deterministic internal state, which depends on past data vectors.
More detailed information about the pdfs and implementation issues are found in annexes from the ArXiv version of the paper. The authors use different variants for different datasets according to the nature of the data (basically, video clips or speech signals). We only report here the model implemented for speech signals. The dynamical model p θz (z t |z 1:t-1 ) is a Gaussian distribution whose parameters are provided by an LSTM network. The observation model p θx (x t |z t , v) is a Gaussian distribution whose parameters are provided by a feed-forward DNN. With the simplified generic RNN formalism used for LSTM.
More detailed information about the pdfs and implementation issues are found in annexes from the ArXiv version of the paper. The authors use different variants for different datasets according to the nature of the data (basically, video clips or speech signals). We only report here the model implemented for speech signals. The dynamical model p θz (z t |z 1:t-1 ) is a Gaussian distribution whose parameters are provided by an LSTM network. The observation model p θx (x t |z t , v) is a Gaussian distribution whose parameters are provided by a feed-forward DNN. With the simplified generic RNN formalism used for LSTM z t-1 ). (11. 1).
Performance of the tested DVAE models in our speech analysisresynthesis experiment. The RMSE, PESQ and STOI scores are averaged over the test subset of the WSJ0 dataset. DVAE RMSE PESQ STOI VAE 0. 0510 2. 05 0. 86 DKF 0. 0344 3. 30 0. 94 STORN 0. 0338 3. 05 0. 93 VRNN 0. 0267 3. 60 0. 96 SRNN 0. 0248 3. 64 0. 97 RVAE-Causal 0. 0499 2. 27 0. 89 RVAE-NonCausal 0. 0479 2. 37 0. 89 DSAE 0. 0469 2. 32 0. 90 Table 13. 1:. 1. Those curves suggest that the training procedure has successfully converged for all the implemented DVAE models. We observe certain differences in the convergence behavior of.
Methods We analytically derive a recurrent network of spiking neurons that close to optimally tracks a continuously varying input online. We start with two assumptions: 1) every spike is decoded linearly and 2) the network aims to perform optimal stimulus representation (i. e. reduces the meansquared error between the input and the estimate). We construct a cost function that consists of three terms: 1) the mean-squared error between the stimulus and the estimate, 2) a linear cost that punishes high firing rates and 3) a quadratic cost that promotes distributed firing. Every spike that is fired in the network reduces this cost function. The code for simulating such a network can be found at GitHub: github. com/fleurzeldenrust/Efficient-coding-in-aspiking-predictive-coding-network.
ACKNOWLEDGEMENTS FZ acknowledges support from the  Netherlands Organisation for Scientific Research (Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO)   Veni grant  ( 863. 150. 25 ) and the  Radboud University  (Christine Mohrmann Foundation), SD acknowledges support from  Neuropole Region Île de France (NERF)  and  ERC   consolidator grant  "predispike" and BG acknowledges partial support from  INSERM ,  CNRS , and from IDEX  ANR-10-IDEX-0001-02 PSL * as well as from  HSE Basic Research Program  and the  Russian Academic Excellence Project  " 5-100 ".
CellRegMap does not require any discretization of cells into cell types, nor is it required to annotate or define specific cell states a priori. Instead, the model leverages a multidimensional cell state manifold estimated from single-cell transcriptome profiles to define cellular contexts in a continuous and unbiased manner. CellRegMap then allows to test for and characterize interaction effects between genetic variants and cellular context on gene expression traits (Fig. 1 ). We validate CellRegMap using simulated data, and apply the model to two recent single-cell genetics studies  1, 3  , where we demonstrate increased power to detect interactions, and identify regulatory modules of eQTL that are active in the same cellular contexts. Finally, we demonstrate the relevance of cell-context interactions to fine-map colocalization events with human disease variants.
Model validation using simulated data Initially, we considered simulated data to validate the calibration of CellRegMap and to assess statistical power of the model. We simulated single-cell expression profiles by sampling from the generative model of CellRegMap (Methods), using a cell context covariance  derived from the leading 20 principal components of a real scRNA-seq dataset (from ref. 1  ; see below). We confirmed statistical calibration of CellRegMap, both when considering expression phenotypes generated from a null without simulated genetic effect (Supplementary Fig. 2. 1), or when simulating from a persistent effect model without GxC interactions (Fig. 2a, b , Supplementary Fig. 2. 1; Methods). We also compared CellRegMap to a reduced model that does not include the relatedness component (which is similar to the existing StructLMM 20 model), thereby confirming that the relatedness component is required to retain calibrated pvalues when multiple cells are assayed from each individual (Fig.
Application to a continuous trajectory of iPS cells differentiating towards definitive endoderm Next, we applied our model to a recent single-cell RNA-seq dataset of differentiating induced pluripotent stem cells (iPSCs) that spans 125 genetically diverse individuals  1. Briefly, a total of ~30,000 cells were captured at four time points of iPSC differentiation (day0: iPSCs, day1, day2 and day3 of differentiation towards definitive endoderm; Fig. 3b ), using the SMART-Seq2 21 protocol. As expected, cell differentiation is the dominant cellular context in this study, and hence this dataset is an ideal test case to assess the ability of CellRegMap to identify continuous changes of allelic effects across a cellular trajectory. We used MOFA  15  to infer latent factors that explain variation in cellular contexts in the data, which captured both differences in major cell types across the differentiation trajectory, but also more subtle cell states.
For example, the first factor (MOFA 1) primarily explained the differentiation axis, with cells transitioning between a pluripotent state and the definitive endoderm fate. Higher order factors captured other cellular contexts, including cell cycle phase (MOFA 3 and 6), respiration (MOFA 4) and others (Supplementary Fig. 3. 1; Methods). We applied CellRegMap to test for GxC effects at 4,470 eQTL variant / gene pairs that were previously identified in the primary analysis of the dataset using a conventional eQTL mapping workflow that does not account for GxC interactions  1. We compared CellRegMap when only using the first MOFA factor to define the cell context covariance, which is similar to the approach taken in the primary analysis  1  , to a model that leverages the information contained in the leading 10 MOFA factors.
Application to iPSC-derived dopaminergic neurons Next, we applied CellRegMap to a single-cell dataset of 215 iPS cell lines that were assayed at three stages of differentiation towards dopaminergic neurons 3 (11, 30 and 52 days of differentiation) using the 10X Genomics technology (3' kit  22  ), as well as a stress condition at the most differentiated time point. These data feature prominent discrete cell states rather than continuous changes, thus providing a complementary use case. To assess whether CellRegMap can identify GxC effects associated with finer grained neuronal subtypes, we considered 147,801 cells that were annotated as dopaminergic neurons in the primary analysis of this dataset (based on marker genes 3 ). This selection included cells collected at three of the four time points and conditions: young neurons (at day 30 of iPSC differentiation), mature neurons (day 52) and mature neurons followed by rotenone treatment (day 52 ROT).
CellRegMap retains calibrated test statistics (Fig. 2a, b  and Supplementary Fig. 2. 1) and enjoys power benefits compared to conventional fixed-effect interaction tests (Fig. 2c -e and Supplementary Fig. 2. 2). To illustrate the model, we applied CellRegMap to a single-cell dataset of iPS cells from 125 individuals across differentiation towards a definitive endoderm fate  1. The main source of variation in this dataset is a continuous differentiation signal, which manifests in dynamic eQTL across differentiation. Notably, we also identify eQTL associated with other dimensions of transcriptome variation, including factors associated with cell cycle phase or respiration (Fig. 3 ). As a second-use case, we applied CellRegMap to scRNA-seq data from iPSCs differentiating toward dopaminergic neurons 3.
A second limitation of the model is that it currently requires appropriate processing steps to provide cell-level or pseudo-cell expression estimates that can be treated as Gaussian distributed traits. Although our results indicate that this approximation is acceptable in practice and retains statistical calibration (Supplementary Fig. 2. 2), explicit modelling of count data could provide additional power benefits, in particular in the regime of lowly expressed genes. Finally, as datasets grow in size, future developments on the scalability may be warranted. While CellRegMap scales linearly in the number of cells already, the computations required to account for the relatedness component could be prohibitive when analysing very large datasets from thousands of individuals. Datasets of this magnitude could become available through large data-integration efforts, for example via federated analysis envisioned in the single-cell eQTLGen consortium  27.
AmpliconArchitect  (Deshpande et al. , 2019) ,  Circle-Map (Prada-Luengo et al. , 2019) , Circle_finder  (Kumar et al. , 2017) , and ECCsplorer 2 are tools based on Illumina short reads. Moreover, except for ECCsplorer, all software packages require a reference genome, thus limiting the analyses to model species (Figure  1 ). Here, we developed a new tool called ecc_finder dedicated to the detection of eccDNA from both Illumina and Nanopore 2 https://github. com/crimBubble/ECCsplorer eccDNA sequencing data. We demonstrate its suitability and sensitivity when applied on eccDNA data sets originating from small (Arabidopsis thaliana, 120 Mb) and very large genomes (wheat Triticum aestivum, 17 Gb) for detecting eccDNAs.
Description of the ecc_finder Algorithm and Validation Metrics The complete ecc_finder source code and documentation are available on GitHub at https://github. com/njaupan/ecc_finder. ecc_finder is written in Python3 and relies on two mapping tools: minimap2  (Li, 2018)  for ONT long reads and BWA  (Li and Durbin, 2009)  for Illumina short reads. It also bundles TideHunter  (Gao et al. , 2019)  for discovering tandem repeat patterns and generating high-quality consensus sequences and Genrich 3  for peak calling. ecc_finder mainly uses the PAF format generated by paftools. js to realize format conversion and alignment filtering.
Short-Read Pipeline Algorithm Overview Ecc_finder uses a standard method based on discordant pairs and split reads at the junction to detect reads originating from circular DNA in short-read sequencing data (Supplementary Figure  2 ). ecc_finder uses BWA-MEM as the default mapping software for short reads because it is more accurate at the basic level alignment, but users can still choose minimap2 with the short-read parameter "sr" to speed up the alignment for large genomes. By grouping by chromosome and read ID, the read alignments are sorted and merged to remove the overlapping reads between a pair, and ecc_finder extracts the read pair information and read pair direction. Properly mapped read pairs with inward-facing tags ("->, <-") or single mapped reads ("-> / <-") are discarded, while discordant read pairs with outward-facing tags ("<-, ->") are kept.
Plant Material and Growth Conditions Seeds from Arabidopsis thaliana WT ecotype Columbia-0 were surface sterilized and sown on 1/2 MS medium [1% sucrose, 0. 5% Phytagel (Sigma), pH 5. 8], stratified for 2 days at 4°C, and grown in a controlled chamber (Percival, United States) at 21°C under long-day conditions (16-h light). Leaf material from 12 individuals was harvested after 2 weeks. For heat shock, the in vitro plates were exposed at 6°C for 12 h and 37°C for 24 h and material was extracted after a 24 h recovery at 21°C. Swiss winter wheat (Triticum aestivum cv. Arina) seeds originate from the Agroscope GenBank. Wheat seeds were presoaked in sterilized water overnight and sterilized by a 10 min 50°C heat shock. Seedlings were germinated and grown under controlled conditions in a Sanyo MLR-350 growth chamber under long-day conditions 16 h (light) at 20°C (day) and 18°C (night) for 4 days.
FUNDING MM is supported by a grant from the French National Agency for Research (ANR-13-JSV6-0002 "ExtraChrom"). This study is set within the framework of the "Laboratoire d'Excellence (LABEX)" TULIP (ANR-10-LABX-41) and of the "Ecole Universitaire de Recherche (EUR)" TULIP-GS (ANR-18-EURE-0019.
The Android operating system (OS) is often updated, where each new version may involve API deprecation. Usages of deprecated APIs in Android apps need to be updated to ensure the apps' compatibility with the old and new versions of the Android OS. In this work, we propose AndroEvolve, an automated tool to update usages of deprecated Android APIs, that addresses the limitations of the state-of-the-art tool, CocciEvolve. AndroEvolve utilizes data flow analysis to solve the problem of out-of-method-boundary variables, and variable denormalization to remove the temporary variables introduced by CocciEvolve. We evaluated the accuracy of AndroEvolve using a dataset of 360 target files and 20 deprecated Android APIs, where AndroEvolve is able to produce 319 correct updates, compared to CocciEvolve which only produces 249 correct updates.
I. INTRODUCTION Android is one of the most widely used operating systems (OS) in the recent years due to the popularity of smartphones. It is frequently updated to add new features and fix bugs. Following each OS update, changes and modifications in the Android APIs are common, which often results in deprecation of the older version of an API and makes the deprecated APIs unusable for the newer version of the OS. This problem, named Android fragmentation  [1] ,  [2] , is a common occurrence. Due to the above issue, updating usages of deprecated Android APIs should be prioritized. Several studies have promoted automated approaches to updating deprecated Android API usages. A4  [3]  automatically assists Android API migrations by learning API migration patterns from the differences between the deprecated and updated code examples.
App-Evolve  [4]  uses both before-and after-update code examples to automatically learn the update. However, a replication study by Thung et al. [5]  found several weaknesses in AppEvolve, where the target file must be structured the same way as the code examples. Following Thung et al. 's findings, Haryono et al. [6]  presented CocciEvolve, an automated deprecated Android API usage update tool built on top of Coccinelle4J  [7]. Cocci-Evolve offers several improvements from AppEvolve: (1) a readable update script expressed in the Semantic Patch Language (SmPL)  [8] ;  (2)  only requiring a single after-update example; and (3) the ability to update multiple invocations of a deprecated API within a single file. Code example used in CocciEvolve must be in the form of an if statement, containing the updated API in the then statement and the old API in the else statement, or vice versa.
An example of such a code example is given in Fig. 1 , where the deprecated API getCurrentMinute is shown at line 5 and the updated API getMinute at line 3. Fig. 1. An example of after-update code for getCurrentMinute() API While CocciEvolve significantly improves on AppEvolve, it has several shortcomings. CocciEvolve is only able to resolve (i. e. , locate and identify the definition for) the value of a variable within the same method, meaning that if an expression used in the updated API invocation is defined outside of the currently processed method, CocciEvolve will not be able to resolve its value. Furthermore, during the update process, CocciEvolve introduces temporary variables that refer to other existing variables in the target file. These temporary variables are not deleted in the update result, making the update result less readable.
In this paper, we present AndroEvolve, an automated Android deprecated API usage update tool that addresses the limitations of CocciEvolve. This paper is a tool demo paper that supplements our full paper  [9]. AndroEvolve addresses the limitations through two features: data flow analysis and variable denormalization. Data flow analysis is used to resolve the values used as the updated API invocation arguments, including values of all out-of-method variables. Using the data flow analysis, AndroEvolve automatically locates the definitions of those variables, and uses their values to replace the variables used in the API invocation arguments. Variable name denormalization refers to the process that reverts the normalization done by CocciEvolve that introduces the temporary variables in the update result.
This process reverts the temporary variables back to their original variable names within the code, removing all unnecessary changes and improving the overall readability of the update result. We evaluated AndroEvolve on a dataset of 360 target files containing 20 deprecated Android APIs. Based on this evaluation, AndroEvolve produces 316 correct updates while CocciEvolve only produces 249 correct updates, showing that AndroEvolve outperforms CocciEvolve by 26. 90%. We also measure the readability of the update results of both tools using manual and automatic evaluation methods. For the manual evaluation, we asked the opinions of two experienced Android engineers. For the automated evaluation, we use a popular readability scoring tool  [10]. We found that AndroEvolve's update results are 83% and 50% more readable compared to CocciEvolve's according to the manual and automated readability evaluation, respectively.
A. AndroEvolve Architecture The overall architecture of AndroEvolve is shown in Fig. 2. The workflow of AndroEvolve is comprised of update-script creation followed by update-script application. Update-script creation takes as input the API update mapping and an after-update example of the API. The API update mapping consists of the API signatures of the deprecated API and its corresponding updated API. This mapping is used to identify the deprecated and the updated API from the code example and target code. Next, data flow analysis is employed to resolve the values of out-of-method variables in the update example that are used as the arguments of the updated API invocation but are not used in the deprecated API invocation. Variable normalization is then applied to the part of the code containing the deprecated and updated API invocations, introducing temporary variables to name all complex subexpressions to facilitate the update.
The goal is to minimize the syntactic differences between the update example and the target code to be updated. The update script, expressed using the Semantic Patch Language (SmPL), is created from the normalized update example. Update-script application takes as input the target code to be updated, the update script from the update-script creation process, and the API update mapping. The updatescript application is also comprised of several steps. First, variable normalization is applied into the target code. Then, the update script is applied to the normalized target code using Coccinelle4J  [7] , resulting in the updated code. Following the update script application, we copy method and class definitions that are being used in the updated API arguments to the updated code. Finally, variable denormalization is applied to this updated code, replacing the temporary variables with their original expressions.