dataesr/scientific-paragraphs-categorization huggingface dataset:
An estimated captured flux of 2. 1 × 109 n/cm2/s gained since the sample position was 49. 4 cm away. A control computer is connected to the DUT through a USB-to-JTAG connection to run the tests. Authors modified a version of OpenOCD software  [21]  to implement the GCapture instruction and the readback operation  [22]  to communicate with the FPGA. These modifications allow to read back the whole FPGA's configuration memory and even frame by frame. By performing a GCapture operation before the readback procedure, reading the actual state of the FFs in the design is The FPGA was irradiated with four incident angles. These are depicted in Fig. 2 : Normal front and back incidences, grazing angles in two directions (East-West (EW) and North-South (NS)), 45 • , and 135 •. The following stages were used for each test: 1 The target design is firstly loaded.
This bitstream will be later referred to as "golden" since it contains the information that has not been corrupted by radiation. This design was a large shift counter using 126,800 FFs, which is 63% of the total FFs of the FPGA. No more than this amount of FFs could be used in the design, since the Xilinx Vivado tool  [23]  could not synthesize and implement it otherwise. In the HDL code itself, 50% of these FFs were initialized to 1 ′ s and the rest, to 0 ′ s. 2 To initialize each byte of the BRAM with a 0 × 55 pattern. This was done by manually, again by modifying the HDL code itself of the design. 3 To irradiate the device at fluences ranging from 0. 87 × 1011 to 6. 30 × 1011 n/cm2 in rounds of 1, 2, and 5 min. 4 To perform a readback of the whole CRAM and save it in the control computer, in a binary file.
Comparing the results of thermal neutrons and fast neutrons, it is observed that fast neutrons had a higher sensitivity to thermal neurons in multiplicities of 1 to 4. In events greater than 3, more various sizes happened in thermal ones. Shapes of observed multiple events, including large-scale (128-bit and 384-bit) ones, were also discussed, revealing that they are more prone to affect cells in the same column. This is consistent with the addressing organization of the target Artix-7 FPGA, whose 32-bit words are organized in columns. Finally, a modelling tool called MUSCA-SEP3 was used to predict the device's sensitivity. The obtained experimental results matched with predicted ones in a very accurate way. The Spanish "Ministerio de Ciencia e Innovación" (MICIN) partially funded this work under grants TIN2017-87237 and PID2020-112916GB-I00; as well as the French national program "Programme d'Investissements d'Avenir, IRT Nanoelec'" under grant ANR-10-AIRT-05.
While other individual factors like gender and socio-economic position may certainly influence transmission October 6, 2023 4/34  [32] , age is generally considered as the most important, justifying this first choice for stratification. In this work, we present a Bayesian workflow for a compartmental transmission model to analyze the transmission of respiratory viruses that includes these four essential features. To this aim, we assess the statistical accuracy and computational efficiency of several versions of the model, including three sampling distributions (Poisson, quasi-Poisson, and negative binomial) and three methods for implementing a time-varying transmission rate (Brownian motion, B-splines, and approximate Gaussian processes). For these assessments, we use both simulated data and real-world data from SARS-CoV-2 in Geneva, Switzerland.
Since our study period was less than a year, we could assume that there is no waning of immunity and that the October 6, 2023 5/34 total population size is constant. Feature 1: Adjustment for incomplete case ascertainment using seroprevalence data From a given set of parameter values and initial conditions, the SEIR model generates the total number of newly recovered individuals in the population by unit of time (i. e. the true incidence by our definition, see Figure  1 ) as follows: dS dt = -βρ(t)cS 1 N dE dt = βρ(t)cS 1 N -τ E dI dt = τ E -γI (1) dR dt = γI S E I R 𝛽𝜌 𝑡 𝑐 𝑆 𝐼 𝑁 𝜏 𝐸 𝛾 𝐼 Modelled: Incidence H(t) = R(t+1) -R(t) Modelled: ascertained incidence 𝐴(𝑡) = π H(t) H(t) = R(t + 1) -R(t). Only a fraction of this incidence will be ascertained as a laboratory-confirmed cases by testing positive (A(t)).
Bayesian inference We consider the models in a Bayesian framework, with the objective of estimating β, ρ(t), π t , and where relevant, θ, from two data sources: weekly counts of laboratory-confirmed cases of SARS-CoV-2 infection (this would also apply to any other respiratory virus) and one or more seroprevalence estimates. When relevant, these data need to be stratified by age group in the same way. We use weakly informative prior distributions for these parameters (Supplementary methods). The different versions of the model are implemented in Stan, a platform for Bayesian inference  [8] ,  [43]. Stan allows for coding a large variety of model features, relying on a few principles to optimize computational efficiency. For a detailed description of how to implement and scale-up ODE-based models, see Grinztajn et al.
Software implementation We use R version 4. 2. 1  [51]. We published a R package called HETTMO that contains all functions needed to perform the analysis and run the models. The package is based on Stan (version 2. 21. 7)  [43]  and the cmdstanr package (version 0. 5. 3)  [52]. HETTMO is available on GitHub at https://github. com/JudithBouman2412/HETTMO.
The exact implementation of two of these features, the sampling distribution and time-varying transmission, are the result of a benchmark and comparison of several methods using real and simulated data. We then apply this approach to real data on SARS-CoV-2 in the canton of Geneva in 2020. We also release the workflow as an out-of-the-box R package, where all model variations are available (https://github. com/JudithBouman2412/HETTMO). Our work provides important methodological insights into fitting compartmental transmission models to surveillance data. First, we find that the variability in laboratory-confirmed case counts for SARS-CoV-2 was best described with a quasi-Poisson distribution, which is more commonly used in ecology to describe overdispersed data  [16] ,  [17]. The correct choice of this sampling distribution is critical for both inference and short-term forecasting of epidemic dynamics and can depend on the epidemiological situation.
A prior predictive check is shown in Supplementary Figure  S5. Second, it is possible that the relative performance of the three presented methods differs for distinct datasets, for instance for data collected during a longer time-period, different epidemic dynamics or a different infectious disease. The publication of our workflow in the HETTMO R package allows users to apply all presented methods and evaluate which one is the most suitable for their data. Third, for the approximate Gaussian process model, performance depends heavily on the choice of the hyperparameters (S3 Fig). Additionally, when applying the B-spline based method from this package, one should be aware that the performance of this method depends on the choice of the knots (Figure  2 ). A sufficient number of knots can be identified by subsequently increasing their number until the estimate of the transmission rate does not change any more.
100% for the cutoff suggested by the manufacturer [50]. We aggregate all data according to the age groups used in the first serosurvey (0-19 years old, 20-64 years old and 65 and older). As the second serosurvey uses a different grouping, we reallocated the results in age group 18-24 to age groups 0-19 and 20-59 assuming a uniform distribution of age. were performed during this time period: the first one from April 6th until May 10th [13], and the second from November 23th until December 23st [49]. The results are summarized in Table S8. Both surveys use the EuroImmune IgG test (Euroimmun; Lübeck, Germany #EI 2606-9601 G), which has a sensitivity of 93% and a specificity of October 6, 2023 11/34 provides an overview of all parameter values chosen to create the simulated data. We generate one simulated dataset and apply all model versions to these data.
We evaluate predictive performance by computing the root mean squared error between the estimated and true value of ρ(t), and evaluate computational performance by comparing the number of effective samples per second. In a second step, we select the best performing model version from step 1, add the stratification by age, and validate again on stratified simulated data. We modify several parameters to simulate a stratified dataset with three age classes (S6 Table ). The simulated data are available in the HETTMO R package. Data from the canton of Geneva in 2020 Finally, we apply the best performing model version to weekly counts of laboratory-confirmed cases of SARS-CoV-2 infection in the canton of Geneva in 2020 (data from the Federal Office of Public Health). During this time period, we could reasonably assume that there was no waning of immunity and that vaccination did not yet influence transmission dynamics.
Data rank is important in ICA because ICA produces the same number of ICs as the data rank number, not the apparent number of scalp electrodes. When ICA is forced to produce more ICs than the data rank, the process fails and yields ghost ICs, at least in the case of implementation in EEGLAB  (Delorme and Makeig, 2004). The ghost IC is akin to ICA's "bug," which we have examined to determine its properties in the time, frequency, and spatial domains. Whether a pair of channels are linearly dependent or not is a qualitative question that can be answered with yes or no. However, the mathematical meaning of the rank has more granularity, as shown as follows. First, we applied the eigenvalue decomposition to the channel-by-channel covariance matrix of the input EEG data matrix. Next, we obtained the condition number c by dividing the largest eigenvalue λ max by the smallest eigenvalue λ min  (Strang, 2006).
In other words, one can rereference unipolarly referenced EEG data offline from the original reference location A to the new reference location B, then from B to C, and from C to A, which brings back the original data without any change, regardless of the history of prior re-referencing processes. However, once we re-referenced to the average potential, for example, we would need to re-reference back to a unipolar reference with any chosen electrode for the next re-referencing. Repeating average-referencing after rejecting the initial reference electrode invalidates the data and should be avoided. Based on these facts, there is no ambiguity about how to rereference the data properly. Unfortunately, the current implementation of the re-referencing process in the past and current versions of EEGLAB (ver.
2022. 0 or prior) does not follow this correct practice. Additionally, there are several implementation issues in the related solutions, which further complicate the problem: 1. EEGLAB does not account for the initial reference electrode. Therefore, EEGLAB reduces data rank by re-referencing. This violates the first and second properties described previously  (Hu et al. , 2019). 2. Although EEGLAB's implementation of the ICA (pop_runica) includes an effective rank deficiency checker, the hard-coded detection of λ min <10 -7 at the bottom (suggested by SH) does not operate to make the input data effectively rank-full by reducing input data dimensions; instead, it forces ICA into effectively rank-deficient decomposition.
The goal of the current study The study goals included the following: 1) to determine the effective λ min threshold; 2) to confirm the signatures of forced rankdeficient decomposition, namely ghost ICs, using empirical EEG data; and 3) to confirm solutions for this problem. For these goals, we split the study into two parts. The first part was a simulation study using auditory signals to determine the smallest λ min that causes the ghost IC issue. We parametrically manipulated the mixing weights to systematically vary λ min from 10 -1 to 10 -12 logarithmically to determine where ICA starts to fail. In other words, the aim of the first part was to perform a torture test for ICA to determine its tolerance to the progressively smaller λ min. The second part was an investigation of how effective rank deficiency arises from the two EEG preprocessing stages, namely, incorrect re-referencing and electrode interpolation, using empirical EEG data and EEGLAB.
Audio signal mixing simulation As an homage to the original ICA demonstration performed in the late 1990s by four researchers from the Salk Institute (Terry Sejnowski, Te-Won Lee, Scott Makeig, and Tzyy-Ping Jung), we recorded the following four sentences as independent sound recordings: "Independent component analysis is a new data tool that is sensitive to higher-order structure in multidimensional data" (JL). "ICA is also a potentially important tool for data mining, factor analysis, clustering, and data compression" (SC). "Neural networks that perform information maximization may emulate processes that drive evolution and brain organization" (MM). "Infomax ICA is proving successful at modeling complex biological time series and imaging data" (TPJ). These sound demonstrations are available at https://github.
Empirical EEG data demonstrations We designed a demonstration of the two cases of effective rank deficiency caused by 1) incorrect re-referencing and 2) non-linear electrode interpolation using EEGLAB's tutorial dataset (128 Hz sampling, 32 channels, 238. 3 s), which was obtained from a previously published study  (Makeig et al. , 1999). To briefly describe the data, a visual attention experiment was performed, where stimuli appeared in any of five squares arrayed horizontally above a central fixation cross. In each experimental block, one (target) box was a color that was different from the rest. Whenever a square appeared in the target box, the subject was asked to respond quickly with a right thumb button press. If the stimulus was a circular disk, the subject was asked to ignore it. These data were constructed by concatenating 3-s epochs from one subject, each containing a target square in the attended location ("square" events, left-hemifield locations 1 or 2 only), followed by a button response ("rt" events).
The data were stored in continuous data format to illustrate the process of epoch extraction from continuous data. There were 80 trials included in the analysis, regardless of the conditions. For the case of incorrect average referencing, we compared two re-referencing approaches: one with the addition of the initial reference channel (i. e. , a channel with zero-filled data) and the other approach without (current default method). We calculated λ min for each case to determine how much margin they had before causing effective rank deficiency. The additional ICA was performed using the Infomax algorithm implemented in EEGLAB's runica() function with default parameters ("extended," 1). The subsequent equivalent dipole fitting was applied to ICs using Fieldtrip library  (Oostenveld et al. , 2011)  and the electric forward model based on the Montreal Neurological Institute (MNI) template  (Evans et al.
These results from the case of undercomplete ICA replicated the predictions made by the classic study on the nature of ICA  (Hyvärinen et al. , 2001 ). We will discuss the recommended solution for the case of EEG preprocessing in which data are known to be rank-deficient. The source audio signals, their linear mixings, and the decomposed signals obtained are available at https://github. com/MakotoMiyakoshi/ Effective-Rank-Deficiency/. Empirical EEG data demonstrations: Ghost ICs are ICA's bug For the case of re-referencing, the correct method, i. e. , including the initial reference when re-referencing and then discarding the initial reference channel, resulted in a successful rank-full decomposition. Meanwhile, the incorrect method, i. e. , without including the initial reference ICA, failed to decompose the signals.
Discussion What is the bug, after all? The main problem we were motivated to address in this study is that even if the input data are confirmed to be rank-full (e. g. , using MATLAB's rank () function), under the condition where the minimum eigenvalue λ min is smaller than 10 -7 , the effective rank is no longer full from the viewpoint of ICA, which causes rank deficiency. Such decomposition produces ghost ICs, which forces the ICA to produce a number of components that is larger than the effective rank; hence, the "bug". We investigated the two major causes of this issue in the scenario of standard EEG preprocessing, namely an incorrect average reference calculation and the non-linear spherical spline interpolation for electrode interpolation. To avoid this issue, we propose two solutions: 1) apply the correct average referencing, and 2) calculate the effective data rank that is used for PCA dimension reduction in applying ICA.
The third goal of the study: To confirm the solution The effective rank deficiency caused by EEGLAB's rereferencing is a human error as there is no theoretical reason for data rank to become deficient during this operation. Although we wait for EEGLAB's update and correction, we provide a modified function "reref. m" that can be used to overwrite the relevant code as a temporary patch to address the issue; the patch code can be downloaded at https://github. com/MakotoMiyakoshi/Effective-Rank-Deficiency. On the other hand, to address the effective rank deficiency introduced by non-linear electrode interpolation, the best solution is to explicitly input the effective rank for ICA, i. e. , the number of eigenvalues larger than 10 -6. Using the MATLAB command line, it can be calculated as follows: dataRank sum eig cov double EEG.
e. , data length is too short for the number of electrodes). The EEGLAB developers have been suggesting a heuristic criterion, indicating that the input data length should be greater than (number of electrodes) 2 x 20  (Makeig and Onton, 2011) , given a 256 Hz sampling rate or lower. If the input data length is shorter than this criterion, the use of PCA is recommended to avoid overlearning. Unfortunately, the current implementation of the data rank checker in EEGLAB is suboptimal because it is unable to reduce the data rank using PCA to allow effective rank-full decomposition. We propose that the number of λ larger than 10 -6 should be routinely calculated to enable rank-guided ICA for improvement.
Thereafter, however, the status of the initial reference electrode when re-referencing became ambiguous. Whether the initial reference should be included or not is not discussed in a later theoretical study  (Bertrand et al. , 1985). Similarly, whether n in p. 35  (Dien, 1998), N in Eqs 7. 8-7. 10 (Nunez and Srinivasan, 2006) , and m in Eq. 3  (Dong et al. , 2017)  include the initial reference electrode or not remains ambiguous. We contacted these authors and confirmed that they included the initial reference when re-referenced data were calculated (Dezhong Yao, Joseph Dien, Ramesh Srinivasan, and Paul Nunez, personal communications). We summarized the problem that relates to the confusion about average referencing in Figure  2. There are actually 2 × 2 ways to calculate average-referenced data, depending on whether the initial reference is included in calculating the average and whether it is recovered after subtraction.
Future study A potential future study on the issue of effective rank-deficient ICA includes an investigation of the validity of other ICs when a ghost IC appears. Because of the multivariate nature of ICA, obtaining one invalid IC could undermine the quality of all other ICs. However, as demonstrated in this study, a ghost IC may be paired with a certain normal IC as a pseudo-subspace, and the impact on other ICs may be limited. We attempted to address this in our study but encountered an issue with the reproducibility of the ICA. Using the same tutorial data, we confirmed that using the options "extended," 1, "lrate," 1e-5, and "maxsteps," 2000 allowed reproducible results in IC scalp topographies and their variancesorted orders across 10 runs. However, we decided not to include this test in the current report for practical purposes; when we find a ghost IC in our results, the whole batch of datasets processed using the same code should be re-processed anyway, for the sake of perfection.
Acknowledgments The authors express gratitude to  Tzyy-Ping Jung  for helping them make the cocktail party problem by recording his voice and to  Dezhong Yao ,  Joseph Dien ,  Ramesh Srinivasan , and  Paul Nunez  for insightful discussion and permission to cite personal communications with MM. They also thank  Jason Palmer  and  Scott Makeig  for helpful discussion, and  Andreas Widmann  for raising the issue of including vs. excluding the implicit reference in average re-referencing and its effect on data rank via discussion with MM in 2016.
Funding MM and HK were supported by NSF 2011716 CRCNS US-Japan Research Proposal: A computational neuroscience approach to skill acquisition and transfer from visuo-haptic VR to the real-world. MM was supported by NINDS 5R01NS047293-16 'EEGLAB: Software for Analysis of Human Brain Dynamics'. HK and MM were supported by The Swartz Foundation (Old Field, New York). SH was supported by a grant of the German Research Foundation (DFG HO 5054/8-1). JL joined this project as a high school student via UC San Diego's REHS and Pioneer Research programs.
The fascia, epimysium, and aponeurosis are then manually cropped if necessary (Figure  1 ). Finally, we used these new images with a semi-automatic interface developed in Matlab®R2017b (Mathworks) to quantify the perimysium and endomysium based on a previous study  (Jalal et al. 2020). The surface fractions (Φs) of the perimysium and endomysium are then calculated as follows: 𝛷𝑠 𝐶𝑜𝑙𝑙𝑎𝑔𝑒𝑛 = 𝛷𝑠 𝑃𝑒𝑟𝑖𝑚𝑦𝑠𝑖𝑢𝑚 + 𝛷𝑠 𝐸𝑛𝑑𝑜𝑚𝑦𝑠𝑖𝑢𝑚 = (𝑆 𝑃𝑒𝑟𝑖𝑚𝑦𝑠𝑖𝑢𝑚 + 𝑆 𝐸𝑛𝑑𝑜𝑚𝑦𝑠𝑖𝑢𝑚 ) 𝑆 𝐶𝑆𝐴 ⁄ , ( 1 ) where SCSA is the surface area of the slice.
PARTICLENET is compared to the previous state-of-the-art cc discriminant "DEEPAK15"  [33, 90] , yielding an improvement by a factor of 4 to 7 in the rejection of other jet flavors. Three working points are defined on the cc discriminant distribution with approximately 58%, 40%, and 16% efficiencies for identifying a cc pair. The corresponding misidentification rates of light quark and gluon jets (b b jets) are 2 (9)%, 0. 7 (5)%, and 0. 08 (1)%. These working points are used to separate events into three mutually exclusive categories with different cc purity to improve the sensitivity of the analysis. The cc identification efficiency in data is measured using a sample of events containing a gluon splitting to cc. To increase the similarity to H → cc decay, a dedicated boosted decision tree (BDT) classifier is developed to enrich jets where a large fraction of momentum is carried by the quark pair from gluon splitting rather than by additionally radiated gluons  [91].
, 2020) : 𝐺𝐴𝐼 𝑖 = 𝐺𝐴𝐼 𝑚𝑎𝑥 * (1 + 𝑡 𝑒 -𝑡 𝑖 𝑡 𝑒 -𝑡 𝑚 ) * ( 𝑡 𝑖 𝑡 𝑒 ) 𝑡 𝑒 𝑡 𝑒 -𝑡 𝑚 (2) where GAImax is the maximum GAI, tm is the cumulated GDD between sowing/transplanting and the time to reach GAImax, te is the time at the end of canopy growth. The parameter and coefficient values in equations (1, 2) to fit the simulated data to the observed data could be obtained by the iterative "trial and error" procedure by the user or automatically by Excel (Solver add-in programme) using the Generalised Reduced Gradient (GRG) non-linear algorithm as the solving method. The daily canopy cover (CCi; 0-1) is obtained by: 𝐶𝐶 𝑖 = 1 -𝑒 (-𝑘 * 𝐺𝐴𝐼 𝑖 * 𝑐𝑓) (3) where k is the light extinction coefficient (0. 72) and cf is the clumping factor  (Garofalo and Rinaldi, 2013) , as follows: 𝑐𝑓 𝑖 = 0. 75 + (0. 25 * (1 -𝑒 (-0.