dataesr/scientific-paragraphs-categorization huggingface dataset:
This could be in the form of a GitHub issue if there is a repository for the code or an email (see Figure  3 ). If there is indeed an error in code, and it is not due to differences in software version (e. g. , differences in R and package versions) or due to inherent stochasticity (e. g. , simulations or MCMC sampling), then the authors should be given a chance to contact the journal themselves to highlight and correct their mistakes. For instance, as per American Naturalist's stance  (Bolnick, 2022)  authors who contact the journal to correct code or data errors will not be penalized and corrections are encouraged (when warranted). However, in cases where updated results would alter the narrative of a published paper, corrections may be more difficult to address without newer methods of documenting changes.
| CON CLUDING REMARK S In this brief overview, we have provided a basic set of guidelines for peer code review, recommendations for producing reviewable code, and considerations for how it should be adopted at every level of research throughout the publication process. The principles and advice listed here should form a baseline for code review that should be improved upon. We hope that this encourages coders at all levels to try and promote more reproducible, transparent, and open coding practices. In addition, we hope that this provides a primer to start a code reviewing club of your own.
It includes an absolute distance meter (ADM) to determine the distance d between the two parts of the system. To determine the time of flight of the acoustic waves, we measure the difference of times of arrival (DTOA) between two electrical signals, a reference signal and a measurement one. To this end, a digital signal of the acoustic wave to be transmitted is first built numerically using Matlab software and sent either by Wi-Fi or an Ethernet cable to the signal generators (RedPitaya boards, STEMlab 125-14) of parts A and B. Next, for each acoustic path, the generated electrical signal modulates the intensity of a distributed feedback laser diode at 1550 nm (Koheron LD101) by driving its current. This laser ensures two things: it generates the reference signal for the DTOA measurement thanks to an internal monitor photodetector, and it transmits the acoustic wave to the distant acoustic transmitter.
The reference and measurement signals are captured by acquisition boards (the aforementioned RedPitaya boards) and sent to the computer for signal processing using Matlab software. In the acoustic thermometer, each acoustic path can be used independently. To prevent crosstalk, the measurements on both paths are not performed simultaneously, but rather within an interval of 2. 0 s, during which time the airflow is assumed to remain constant. While the time of flight is less than 0. 1 s, the interval of over 2. 0 s stems from the time required for data processing. In parallel, the distance d required for speed of sound calculation is measured by a commercial ADM (Leica Disto d210). Ultimately, the ADM developed in-house, part of the multilateration system presented in  [23]  and of uncertainty of 2.
In this study, we followed the procedures proposed by  Parker et al. (2020)  and  Aczel et al. (2021). The project comprised the following five phases: 1. Recruitment: We recruited independent groups of researchers to analyze the data and review others' data analyses. 2. Team analysis: We gave researchers access to the speech corpus and let them analyze the data as they saw fit. 3. Review: We asked reviewers to generate peerreview ratings of the analyses based on methods (not results). 4. Meta-analysis: We evaluated variability among the different analyses and how different predictors affected the outcomes. 5. Write-up: We collaboratively produced the final manuscript. We initially estimated that this process, from the time of an in-principle acceptance of the Stage 1 registered report to the end of Phase 5, would take 9 months.
Phase 1: recruitment of analysts and initial survey An online landing page provided a general description of the project, including a short prerecorded slide show that summarizes the data set and research question (https:// many-speech-analyses. github. io). The project was advertised via social media using mailing lists for linguistic and psychological societies and via word of mouth. Socialmedia advertising was accompanied by a short recruitment form. The target population comprised active speechscience researchers with a graduate/doctoral degree (or currently studying for a graduate/doctoral degree) in relevant disciplines. All individuals interested in participating were asked to complete a questionnaire detailing their familiarity with numerous analytic approaches common in the speech sciences.
Researchers could choose to work independently or in small teams. For the sake of simplicity, we refer to a single researcher and teams both as analysis teams. 3 Recruitment for this project commenced after having received in-principle acceptance. As outlined above, our primary aim is to assess the variability of the reported effects rather than the metaanalytic estimate of the investigated effect per se. To estimate the degree of uncertainty around effect variability as driven by number of teams, we ran a series of sample-size simulations with values of variability extracted from  Silberzahn et al. (2018). The code is available at https://many-speech-analyses. github. io/many_ analyses/scripts/r/simulations/simulations. 4 Variability among teams was operationalized as the standard deviation of the teams' reported effects from  Silberzahn et al.
Phase 2: primary data analyses The analysis teams registered for participation, and each of the analysts individually answered a demographic and expertise questionnaire. A PDF version of this and all other questionnaires are available at https://osf. io/ h6z8w. The questionnaire collected information on the analysts' current position and self-estimated breadth and level of statistical expertise and acoustic-analysis skills. We then requested that they answer the following research question: "Do speakers acoustically modify utterances to signal atypical word combinations?" To do so, they were given the data generated by the experiment described in The Data Set section above. Data included the audio recordings with corresponding timealigned transcriptions in the form of Praat TextGrid files.
These files can be found at https://osf. io/5agn9. Once their analysis was complete, they answered a structured questionnaire that provided information about their analysis technique, an explanation of their analytic choices, their quantitative results, and a statement describing their conclusions. They also uploaded their analysis files (including the additionally derived data and text files that were used to extract and preprocess the acoustic data), their analysis code (if applicable), and a detailed journal-ready analysis section.
Phase 3: peer review of analyses The analyses from each team were evaluated by four different teams who functioned as peer reviewers. Each peer reviewer was randomly assigned to analyses from at least four analysis teams. Reviewers evaluated the methods of each of their assigned analyses one at a time in a sequence determined by the initiating authors. The sequences were systematically assigned so that, if possible, each analysis was allocated to each position in the sequence for at least one reviewer. The process for a single reviewer was as follows. First, the reviewer received a description of the methods of a single analysis. This included the narrative methods and results sections, the analysis team's answers to the questionnaire regarding their methods, including the analysis code and data set.
2. 1; R Core Team, 2020). Model refitting also constituted a way of validating the reported analyses, a step recommended by  Aczel et al. (2021). Details about the refitting procedure can be found at https://many-speech-analyses. github. io/many_ analyses/scripts/r/04_refit_workflow. Relative to the registered protocol, we made minor changes to the refitting procedure, specifically file and variable naming conventions and the use of treatment contrasts instead of sum coding. All models converged ( R  was approximately 1). Of the models with divergent transitions (n = 10), the number of divergences ranged from 1 to 156 (156 represents 3. 9% of the total number of samples), which the authors deemed not to be problematic. The coefficients of the critical predictors (i. e. , critical according to the analysis teams' self-reported inferential criteria) obtained from the standardized models were used as the standardized effect size (η i ) of each reported model.
The model did not incur any divergent transitions (R  was not greater than 1), and the estimated sample sizes were sufficient. The code used to run the model can be found at https:// many-speech-analyses. github. io/many_analyses/scripts/ r/06_meta-analysis_prereg. The posterior distribution of the population-level intercept α allowed us to estimate the range of probable values of the standardized effect size η . The posterior distribution further allowed us to investigate the effect of a set of analytic and researcher-related predictors, as detailed in the next section. Crucially, the posterior distribution of the group-level standard deviation σ α t (i. e. , the standard deviation of the group-level effect of team) allowed us to quantify the degree of variation between the teams' analyses on a standardized scale.
For each pair of analyses ( , X Y ; across and within teams), the SDI was obtained using the following formula: SDI = + ∩ 2| | | | | | X Y X Y where | | X Y ∩ is the number of variables common to both models in the pair, and | | | | X Y + is the sum of the number of variables that occur in each model. For example, if two pairs of models differ in either only one predictor (e. g. , DV ~ typicality vs. DV ~ typicality + trial) or in two predictors (e. g. , DV ~ typicality vs. DV ~ typicality + trial + speech rate), the latter model pair would exhibit a larger SDI than the former. To generate a unique SDI for each analysis team, we calculated the average of all pairwise SDIs for all pairs of analyses using the beta. pair() function in the betapart R package  (Baselga et al. , 2020). The major measurement dimension of each analysis was categorized according to the following possible groups: duration, intensity, f 0 , other spectral properties (e.
Model specification. The model was fitted as a measurement-error model, with the predictors detailed in the preceding paragraphs. The outcome variables of the model were the standardized effect sizes and related standard deviation. A normal distribution was used the likelihood function of α t i [ ]. The mean of α t i [ ] was modeled on the basis of the overall intercept β and on the coefficients of each predictor. The numeric predictors were centered and scaled and the categorical predictors were sum-coded. We used a normal distribution with M = 0 and SD = 1 as the prior for the intercept and the predictors. The model was run with the same settings as with the meta-analytic model. The code used to run the model can be found at https://many-speech-analyses. github. io/many_analy ses/scripts/r/06_meta-analysis_prereg.
Data management. All relevant data, code, and materials have been publicly archived on OSF (https://osf. io/3bmcp). Archived data include the original data set distributed to all analysts, any edited versions of the data analyzed by individual teams, and the data we analyzed with our meta-analyses, which include the standardized effect sizes, the statistics describing variation in model structure among analysis teams, and the anonymized answers to our questionnaires of analysts. Similarly, we archived both the analysis code used for each individual analysis and the code from our meta-analyses. We also archived copies of our survey instruments from analysts and peer reviewers. Further documents concerning the collaborative editing of the registered report can be found at https://drive. google.
Results This section is divided into three parts. We first provide a statistical description of team composition, nature of acoustic analyses and statistical approaches, and peer-review ratings. Second, we report the results of the meta-analytic model, focusing on between-team and between-model variability. Finally, we present the analysis of the effect of analytic and researcher-related predictors on the meta-analytic effect. The research compendium of the study, containing all the code and data presented here, can be found at https://osf. io/3bmcp. An interactive web application that allows the interested reader to explore the data set is available at https://many-speechanalyses. github. io/shiny.
Reasons for exclusion were use of generalized additive models (four teams), which do not lend themselves easily to the meta-analytic methods used in this study; use of machine-learning techniques (three teams); use of typicality as the outcome variable/ response (three teams); or use of other methods that returned statistics that could not be included in the metaanalytic model. Note that due to the unforeseen variability across teams, the latter exclusion criteria were not preregistered and were applied after having seen all analytic strategies. In what follows, we describe the characteristics of those teams whose analyses were included in the metaanalytic model. A complete summary of all the analyses from the 46 submitting teams is available at https:// many-speech-analyses. github. io/many_analyses/RR_ manuscript/supplementary_materials.
pdf. The included analyses were provided by 33 teams, comprising 120 analysts, with a median of 3. 0 individuals per team. Upon sign-up, we collected background information from each analyst through the intake form, which was administered during Phase 1 before the data were released to the teams. Analysts had a median of 5. 4 years of experience after completing their PhD, ranging from -3. 8 years, that is, PhD students (or less experienced) to 12. 4 years, suggesting that, on average, analysts were experienced researchers. The analysts' prior belief in the effect under investigation, on a scale from 0 to 100, ranged from 46. 4 to 92. 0 with a median of 70. 0. We take this to suggest that, overall, analysts had a rather high positive prior belief in the investigated relationship between acoustics and word-combination typicality.
Statistical analysis. The large decision space related to how the acoustic signal was measured is further expanded by the choices in the statistical analysis, including the chosen inferential framework, the type of model, and the model specification, including choice of predictors, interactions, and group-level effects. The mean of the number of different predictors included in teams' models was 2 (defined as variables or columns in the data table ). This means that, in addition to the critical predictor (typicality of the adjective-noun combinations), models had on average one additional predictor (range = 1-5). Possible information that was used as predictors included the information structure of the sentence, trial number, semantic dimensions of the referent, part of speech, and speaker gender.
Sixty-two percent specified random intercepts only, and 29% specified both random intercepts and random slopes to account for the nonindependence. On average, teams that specified random effects included 2. 5 random terms in their models. Based on statistical framework, type of model, distribution family, fixed terms, and not including random effects, there were a total of 52 different model specifications. When considering both acoustic and statistical analyses, we have found a total of 119 different analytic pipelines. In other words, each individual analysis submitted was unique. A sankey diagram illustrating the relationship between choices related to outcome, temporal window, and operationalization can be found at https:// many-speech-analyses. github. io/many_analyses/RR_ manuscript/supplementary_materials.
contributing to  IdEx Université Paris Cité  Grant  ANR-18-IDEX-0001 ;  G. Moroz  and  S. Carraturo  received funding from the  Basic Research Program  at the  National Research University Higher School of Economics ; I. Urrestarazu-Porta received funding from  French National Research Agency  Grant  ANR-20-CE27-0007  and  Spanish Ministry of Science and Innovation  Grant  PID2020-118445GB-I00 ;  C. Kaland ,  M. Grice ,  F. Cangemi ,  M. Lialiou ,  M. Spaniol , and  S. Wehrle  received funding from  German Research Foundation  Grant  281511265-SFB 1252 ; and  N. R. Benway  was supported in part through computational resources provided by  Syracuse University  Grants  NSF   ACI-1341006  and  NSF   ACI-1541396.
INTEGRATING LQ-CMA-ES INTO SOFOMORE Integrating lq-CMA-ES into the Sofomore framework is straightforward, as we mainly replace each single-objective CMA-ES in COMO-CMA-ES with the lq-CMA-ES as single-objective optimization algorithm. Using the single-objective lq-CMA-ES of  [11]  within the Sofomore framework  [21]  results in the surrogate-assisted multiobjective COMO-lq-CMA-ES algorithm. Algorithm 1 shows its pseudocode using an ask-and-tell interface. In the following, we describe its components in more detail. Basic structure. In the Sofomore framework,  single-objective optimization algorithms optimize the multiobjective problem concertedly in the sense that their  incumbents aim at approximating the optimal -distribution of the hypervolume indicator  [2]. Henceforth, single-objective optimization algorithms will also be called kernels (they represent in our case also a unimodal probability density over the input space whose modal and mean value is the kernel's incumbent).
Each of the six resulting NMR solutions (three sample preparations providing two aliquots per sample) were analyzed in triplicate. The qNMR data was acquired with a 600 MHz spectrometer equipped with a dual broadband probe. The sample temperature was controlled at 25 °C during the experiment. An equilibration delay of 5 min was applied to ensure the sample was equilibrated to the set temperature each time. In preliminary experiments the longest spin-lattice relaxation time (T 1 ) for the signals subject to quantification was measured in order to establish a relaxation delay setting of 10 times the longest T 1. The specific parameters used for the qNMR experiment were spectral width of 100 ppm, audio filter band width of 33 kHz, 90°pulse width of 11. 4 μs, 4 s FID acquisition time, 13 C decoupling during the acquisition delay to simplify the resulting spectrum simple, 60 s relaxation delay.
Methodology The code and numerical results presented in this section can be found in https://github. com/ othmaneM/RL_adap_stepsize. Here, we compare four algorithms. The two first ones are two different versions of RL. In the first version, the learning rate γ k (z) is taken such that γ k = η/n k (z) with η > 0 selected to provide the best convergence results and n k (z) the number of visits to the state z. In the second version, the step size follows the piece-wise constant policy (PC) described at the end of Section E. 2. 2. The third algorithm is SAGA where the step size is derived from PC policy. Finally, we use the PASS algorithm presented in the previous sections with a predefined learning rate following the PC policy. We consider three numerical examples to compare the convergence speed of these algorithms: drift estimation, optimal placement of limit orders and the optimal liquidation of shares.
Drift estimation Formulation of the problem. We observe a process (S n ) n≥0 which satisfies S n+1 = S n + f n+1 + W n , (22) with W n a centred noise with finite variance. We want to estimate the quantities f i with i ∈ {1, • • • , n max }. Using (  22 ) and E[W t ] = 0, we get E S i+1 -S i -f i+1 = 0, ∀i ∈ {0, • • • , n max -1}. Thus, we can estimate f i using stochastic iterative algorithms. The pseudo-code of our implementation of PASS for this problem can be found in the Appendix B under the name Implementation 1. Numerical results. Figure  2  shows the variation of the L 2 -error when the number of iterations increases. We can see that the algorithm PASS outperforms standard stochastic approximation algorithms. Moreover, other algorithms behave as expected: the standard RL decreases very slowly (but we know it will drive the asymptotic error to zero), the constant learning rate and SAGA provides better results than RL, while PASS seems to have captured the best of the two worlds for this application: very fast acceleration at the beginning and the asymptotic error goes to zero.
• τ is the first time when a market order is sent. • X = (Q Bef ore , Q Af ter , P ) is the state of the order book. • F (u) is the price of the transaction (i. e. F (u) = p + ψ when the agents crosses the spread and F (u) = p otherwise). We show in Section 2 that the Q-function is solution of  (6) , see details in https://github. com/ othmaneM/RL_adap_stepsize. Thus, we can use Algorithms RL, SAGA and PASS to estimate it. The pseudo-code of our implementation of PASS is available as Implementation 2 in Appendix B. | Same Opp Q Bef ore q Q Af ter Q Opp P (t).
Application 1: Fault Diagnosis To validate and demonstrate the effectiveness of the proposed method, a fault diagnosis application previously presented by Lin et al. [50]  is used. The fusion results of the proposed method are compared to those of the Dempster-Shafer method, our previous work  [86] , and other similar existing methods, including the Lin  [50] ,  Wang [54] , and IDCR  [73]  methods. In rotating machinery, the faults can be categorized into four types: F 1 = "Imbalance", F 2 = "Shaft crack", F 3 = "Misalignment", and F 4 = "Bearing loose". Therefore, the frame of discernment is Ω = {F 1 , F 2 , F 3 , F 4 }. To monitor the system status and determine the fault type, five different sensors were used. Fault features were extracted from the data provided by the various sensors to calculate the BPAs of the five sensors.
About computing There are 4 different computing paradigms and related programming models that could bring benefits to civil engineering, geophysics, and related fields. Some of the paradigms/models are well-established, while others are newly emerging. The 4 paradigms are: Control Flow (MultiCores as Intel products and ManyCores as NVidia products), Data Flow (Fixed ASIC-based as Google TPU and flexible FPGAbased as it initially was the case with Maxeler DFE products and recently with many other products of other vendors), Diffusion Flow (IoT, Internet of Things, and WSNs, Wireless Sensor Networks), and Energy Flow (BioMolecular and QuantumMechanical products). For a detailed elaboration on the above-mentioned computing paradigms, see  [2, 3, 7-9, 15, 21-23, 28, 32-34, 37]. These paradigms could be compared, hardware-wise, by: (a) Speed, (b) Power, (c) Size, and (d) Precision.
Classes of problems needing synergy of computing and management Examples that follow cover simulations of Big Data problems needing Data Mining in specific or Artificial Intelligence in general, and are related to complex problems in Civil Engineering, Geo Sciences, or related fields, namely: (1) NBCS-Nature-Based Construction Sciences. (2) GSNC-Genomics Supporting NBCS. (3) EQPA-EarthQuake (EQ) Information Systems for Prediction and Emergency Alarming. (4) NCMS-Creation of new civil engineering materials and structures (CO2 and EQ sensible). The algorithms used in these four domains could be of the following types: (1) Statistical and stochastic processes that mimic nature (life processes of plants and animals). (2) Genomic algorithms of the type NW (Niederman-Wuensch) or SN (Smith-Waterman) or similar.