dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Media capture. We use survey data provided by Reporters Without Borders. To our knowledge, this is the first dataset that allows the different forms of media capture to be identified at a cross-country level. Expert surveys were conducted in 180 countries every year between 2012 and 2018, with the exception of 2014. Selected experts answered a wide range of questions related to the freedom of the press in their country. Answers range from 0 to 10, where low values stand for poorer conditions. The wording of the questions can be found in Table  1 , where we report all summary statistics. We focus on the questions that best represent the two types of capture. For internal capture, we consider the share of media owners with interests in other economic sectors reported by respondents. Answers also range from 0 to 10 for this question and one unit corresponds to 10%.
9  However, even in a repeated setup, multiple individual payments would be dominated by a centralized control of the media. Thus, we expect in-kind benefits and external payments to target journalists about to release sensitive information, which in our model corresponds to external capture. Finally, we consider the Press Freedom Country Score, which is the global score computed by Reporters Without Borders. This score, which we rescale to range from 0 to 10, aggregates the different dimensions encompassed in the survey. We focus on the 169 countries that are included in both the Transparency International dataset (for which we document an effect of the shock) and the Reporters Without Borders dataset for more than two consecutive years. Panama Papers. We use the list of the offshore entities mentioned in the leaked documents to measure country exposure to the Panama Papers.
This could be done because of the accumulation of data about volatile chemicals (then under threat of regulatory restrictions): data about people's inhalation of chemicals, data about biological metabolisation of these chemicals and data about the quantity of chemicals eliminated by the human body and exhaled. These data originated, notably, from the use of costly inhalation chambers. Once databases were elaborated, models started to be elaborated and calibrated in more reliable ways, for more chemicals, allowing to envisage the possibility to model together the chemical and the human body. In this field, the main challenge has always been the capacity to calibrate the model with realistic and varied biological data, to counterbalance the drive to make predictions based on more quickly produced, but less representative average values.
Computational results The algorithms presented in this paper were coded in C programming language and run over Dell PowerEdge C6420 cluster nodes with Intel Xeon Gold 6152 CPUs at 2. 10GHz with 64Gb RAM each. All the Mixed Integer Linear Programming problems are solved using the callable library of CPLEX  [31]. When generating the gradient and Benders cuts or building heuristic solutions from incumbents, we use the user cuts and callbacks technology of the callable library of CPLEX 12. 6.
With regard to mean squared error (MSE) and relative efficiency values for all estimating approaches included in the study, it is concluded from the data provided that RSS estimates outperform their SRS counterparts. Comparatively speaking, the RTAD estimates (RTADEs) are more effective. Finally, for purposes of illustration, we analyze one real dataset. The study is set up as follows: Section 2 contains the method to obtain the ML estimates (MLEs) of the parameters of the IPCD. The MPS estimates (MPSEs) are provided in Section 3. In Section 4, other estimates are derived using the LS and WLS approaches. We employ the AD, RTAD, and CM strategies to get alternative estimates in Section 5. In Section 6, to demonstrate the superiority of the RSS over the SRS estimates a simulation analysis is carried out.
Numerical evaluation Here, a simulation investigation is supplemented using the MathCAD software, version 14. 0, to examine how effectively the estimation procedures perform via the SRS and RSS. The IPCD was used to generate 10,000 random samples for various values of c 1 and c 2 , with a starting initial point c 0 1 = c 0 2 = 1. The simulation is performed assuming that the RSS ranking structure is perfect, which entails that there are no ranking errors. According to  Wolfe (2012, pp. 1-32) , set sizes greater than five would surely lead to too many ranking errors. To avoid the ranking error, the set sizes are selected as b = 3, 4, and 5, whereas the cycle counts are s = 5 and 10. The RSS design must have the same sample size as the SRS, which has a size of m Δ = bs, i. e m Δ = 15, 20, 25, 30, 40 and 50.
Application In this section, the applicability of the suggested RSS estimates is evaluated using a well-known real dataset. This dataset represents the accelerated life test of 59 conductors, where failure times are measured in hours and there are no censored observations. These data were investigated by  Lawless (2003). The data are: 6. 545, 9. 289, 7. 543, 6. 956, 6. 492, 5. 459, 8. 120, 4. 706, 8. 687, 2. 997, 8. 591, 6. 129, 11. 038, 5. 381, 6. 958, 4. 288, 6. 522, 4. 137, 7. 459, 7. 495, 6. 573, 6. 538, 5. 589, 6. 087, 5. 807, 6. 725, 8. 532, 9. 663, 6. 369, 7. 024, 8. 336, 9. 218, 7. 945, 6. 869, 6. 352, 4. 700, 6. 948, 9. 254, 5. 009, 7. 489, 7. 398, 6. 033, 10. 092, 7. 496, 4. 531, 7. 974, 8. 799, 7. 683, 7. 224, 7. 365, 6. 923, 5. 640, 5. 434, 7. 937, 6. 515, 6. 476, 6. 071, 10. 491, 5.
923. The IPCD distribution is fitted to these data (see  Sapkota, 2022). Inferring from these data, we assume an SRS of size 30, whereas the RSS is assumed to have a small set size of b = 3 with a cycle count of s = 10. It is significant that the SRS and RSS techniques are compared using the same number of measurement units. Using the preceding techniques above and a perfect ranking except the MPS method, we cannot use this method to estimate the parameters because this dataset has more equal values, then ω l1 (c 1 , c 2 ) = G ( v (l1:m Δ ) ⃒ ⃒ c 1 , c 2 ) -G ( v (l1-1:m Δ ) ⃒ ⃒ c 1 , c 2 ) , ω • l2 (c 1 , c 2 ) = G ( v (l2) ⃒ ⃒ c 1 , c 2 ) - G ( v (l2) ⃒ ⃒ c 1 , c 2 ) are equal to zero at most observations in both RSS and SRS. We determine the estimates of the parameters in each method of sampling.
To compare the various estimates from this dataset, we apply the prior criteria measures, including the CM test statistic (CM-TS), AD test statistic (AD-TS), Kolmogorov-Smirnov (KS) test statistic (KS-TS), and p-value. Therefore, Tables  4  and 5  just include the ML, LS, WLS, CM, AD, and RTAD estimates. Based on this dataset, the initial PDF shapes are reported utilizing the non-parametric kernel density estimation approach in Fig. 4. From Fig. 4 , as can be seen, the obtained PDF has an asymmetrical form. Using the quantile-quantile (QQ) plot, the normality condition is examined (see Fig. 4 ). The box plot may also be used to identify the outliers, as seen in Fig. 4. The probability-probability (PP) plots, estimated survival function (ESF), estimated CDF (ECDF), and estimated PDF (EPDF) for the IPCD are shown in Fig.
It is important to have in mind that, while the two may be related, there is not a direct relationship between them: it may very well be that a paper seems to include all the necessary information for reproduction and that reproduction attempts fail (for instance, because the original study had too many degrees of freedom and led to a method that only works on a single dataset, see Section 4). Various guidelines have been proposed to improve research reproducibility. Such guidelines may be general  [10]  or devoted to specific fields including brain imaging  [22, 23, 24, 25]  and ML for healthcare and life sciences  [26, 27]. Moreover many other papers, even though not strictly providing guidelines, provide very valuable pieces of advice for making research more trustworthy and in particular more reproducible (e.
In particular, one must distinguish between research data (acquired as part of a research protocol), which can often be made public or semi-public 1  provided that adequate measures have been taken at data collection (e. g. adequate participant consent), and routine clinical data (acquired as part of the routine clinical care of the patients), which sharing can be much more complicated. It is important that data is easily findable and that it is shared on a server which has a long-term maintenance. General purpose data repositories such as Zenodo 2  provide a good solution. Another important aspect is to adhere to community standards for data organization, so that it can easily be reused by researchers. For brain imaging, the community standard is BIDS (Brain Imaging Data Structure)  [38]  3.
The ClinicaDL software platform provides a framework for easing exact reproducibility of deep learning for neuroimaging  [5]  9. Although it is targeted at brain imaging, many of its components and concepts are applicable to medical imaging in general. Also in the field of brain imaging, NiLearn 10 facilitates the reproducibility of statistics and ML. One can also cite Pymia 11 which provides data handling and validation functionalities for deep learning in medical imaging  [45]. Finally, it may seem obvious but, even when the code is shared, the underlying theory of the method, all its components and implementation details need to be present in the paper  [14]. It is in principle possible to retrain models identically if the above elements are provided. It nevertheless remains a good practice to share trained models, in order to allow other researchers to check that retraining indeed led to the same results but also to save computational resources.
However, models can be attacked to recover training data  [27, 46]. This is not a problem when the training data is public. When it is privacy-sensitive, methods to preserve privacy exist  [27, 47]. In medical imaging, preprocessing and feature extraction are often critical steps that will subsequently influence the ML results. It is thus necessary to also provide code for such parts. Several software initiatives including BIDSApps  [48]   12 and Clinica  [49]  13 provide ready-to-use tools for preprocessing and feature extraction for various brain imaging modalities. Applicable to many medical imaging modalities, the ITK  [50, 51]  14 framework provides a wide range of processing tools. It can ease the work of researchers who do not want to spend time on preprocessing and feature extraction pipelines and focus on the ML part of their work.
Statistical reproducibility What is it? Statistical reproducibility aims at reproducing findings under statistically equivalent conditions  15. The specific definition may vary but the following choices are often considered reasonable. The implementation of the method (the code) remains the same. Random components are left random. Regarding the data, the general idea is that the sample would be drawn from the same population. One could for instance use subsamples of the original data or another subsample of a larger source population. An interesting case is to study different data splits. A less restrictive view of statistical reproducibility would be to use another dataset whose characteristics are similar to those of the original dataset (for instance similar age, sex, scanner distributions).
Furthermore, it is very interesting to attempt replication on a different dataset with statistically equivalent characteristics: for instance another subsample which has not be used in the original study (but comes from the same larger dataset) or a different dataset but with similar characteristics (e. g. same MRI scanners, similar age, similar disease stage. ). Unsuccessful replication may be an indication of overfitting of the dataset of the original study through excessive experimentation with different architectures or hyper-parameters which ended up with a method that would work only on this very specific dataset. This is referred to as the researcher degrees of freedom  [6, 22]. This concept extends beyond the field of ML. It actually comes from experimental sciences where different statistical procedures are tried until a statistically significant result is found, a bad practice known as p-hacking  [58].
Conceptual reproducibility What is it? Conceptual reproducibility can be seen as the ultimate goal: the one which lead to the consolidation of scientific knowledge. The general idea is to be able to validate the findings under conceptually similar conditions 17. Conceptually similar means that the method, the data and the experiments are compatible with the claims of the original study but they are not identical. We will come back to the notion of claims of a study, and their relationships to generalizability and limitations, later in this section. What does it require? Again, we may distinguish between factors that make it possible to attempt replication and those that will make it more likely to be successful. In theory, nothing but the original paper should be strictly necessary. Nevertheless, this assumes that the original paper has adhered to the scientific gold-standard of providing all details necessary for replication: not only a description of the methods which makes reimplementation possible but a detailed description of the datasets and experimental procedure.
In summary, none of the elements of exact reproducibility are required, all of them are welcome. There are several characteristics of an original study that make it less likely for it to be replicated. Low sample size not only means that it is less likely to find a true effect if it exists but also increases the odds that a positive finding is false  [9]. This is not only true in ML but in experimental sciences in general. Ideally, the sample size should be justified by a previous power analysis  [24]. Causes for failure of statistical reproducibility also apply here. In particular, too many researcher degrees of freedom increase the likelihood of having built a method that is overly specific to a dataset. Another problem is that the datasets used in medical imaging ML papers are very often not representative of what would be found in the clinic  [30].
Indeed, they often come from research datasets where the inclusion criteria are specific, the medical imaging protocols are harmonized and the data quality is controlled. Thus, it is necessary to have more studies including clinical routine data (e. g. [60, 61] ). Finally, it is very important to have in mind that most scientific findings will not universally replicate but that the replication will only succeed under specific conditions. This is why it is critical that scientific papers precisely define their claims and their limitations. For instance, a claim could be that a given algorithm can segment brain tumors with a Dice of 0. 9 ± 0. 02 when the MR images are acquired at 3 Tesla and have only minimal artifacts. The same paper would mention as limitations that it is unclear how the algorithm would perform at 1.
5 Tesla or with data of lower quality. One can see that stating clear claims and limitations will allow defining the scope of conceptual replication studies. Studies outside that scope would aim at studying generalizability beyond original claims. Why is it useful? As mentioned above, conceptual reproducibility is the ultimate goal, the one which, through accumulation of evidence, builds consensus about new scientific knowledge. Its utility in general is thus obvious. More specifically, it provides different benefits. In particular, in the field of ML for medical imaging, it allows studying the generalizability of a method. It is thus a step towards its applicability to the clinic. To that aim, the use of multiple datasets is of paramount importance. This will not only allow ruling out that a method is overly specific to a given dataset.
About the different types of reproducibility We have presented different types of reproducibility. Our taxonomy is not original nor aims at being universal. The boundaries between types are partly fuzzy. For instance, to which degree replication with a different but similar dataset should be considered statistical or conceptual reproducibility? We do not believe such questions to be of great importance. Rather, it is fruitful, following Gundersen and Kjensmo  [14]  and Peng  [76] , to consider reproducibility as a spectrum. In particular, one can consider that the first three types provide increasing support for a finding: conceptual provides more support than statistical which in turns provides more support than exact. The amount of components necessary to perform them is in the reverse order: exact requires more than statistical which requires more than conceptual.
Thus aiming at reproducibility is also in the researchers' self interest. There are also considerable benefits for the scientific community as a whole. As mentioned before, reproducible research is often associated to open code, open data and available trained models. This allows re-searchers not only to use them to perform replication studies but also to use these research artifacts for completely different purposes such as building new methods or conducting analysis on pooled datasets. In the specific case of ML for medical imaging, it also allows assessing independently the influence of preprocessing, feature extraction and ML method. This is particularly important when claims of superiority of a new ML method are made but the original paper uses overly specific preprocessing steps. Of course, at the end of the path, the goal itself brings many benefits.
One size does not fit all We hope the reader is now convinced of the benefits of aiming towards reproducible research. Does it mean that reproducibility requirements should be the same for all studies? We strongly believe the opposite. To take an extreme example, requiring all studies to be exactly reproducible with minimal efforts (like with running a single command) would be an awful idea. We believe, on the contrary, that reproducibility efforts should vary according to many factors including the type of study and the context in which it is performed. One would probably not have the same level of requirement for a methodological paper and for an extensive medical application with strong claims about clinical applicability. For the former, one may be satisfied with an experiment on a single or a few datasets.
For the later, one would expect the study to include multiple datasets with varying characteristics and a comprehensive assessment of generalizability under different factors such as imaging devices and acquisition parameters. Also, there are some cases where sharing the code is not desired (e. g. because an industrial application is foreseen) or where the code will not adhere to best development practices because it is just a prototype to test a new methodology. Nevertheless, sharing weakly documented code is always better than no sharing at all. Similarly, there are cases where data sharing is difficult or even impossible due to regulatory constraints. As mentioned above, reproducibility is a spectrum. Where a given study should lie in this spectrum should depend on the type of study and the constraints the researchers face.
Acknowledgments The authors are grateful to  Gaël Varoquaux  for pointing them towards useful references. This work was supported by the  French government  under management of  Agence Nationale de la Recherche  as part of the " Investissements d'avenir" program , reference  ANR-19-P3IA-0001  ( PRAIRIE 3IA Institute ) and  ANR-10-IAIHU-06  ( Agence Nationale de la Recherche -10-IA   Institut Hospitalo-Universitaire -6 ). ETS acknowledges funding from the  4TU Precision Medicine program  supported by  High Tech for a Sustainable Future , a framework commissioned by the four  Universities of Technology of the Netherlands.
For this aim, we apply the data-driven optimization approaches and consider the corresponding Monte Carlo solution procedure. The optimized PID trading algorithm we propose is also studied in the Fourier analysis framework. This equivalent frequency domain representation involves a new concept in the financial engineering, namely, the "stock market energy" concept. For the evaluation, we implement the proposed PID optimal trading algorithm and develop a Python based prototype software. We finally apply the corresponding prototype software to a dataset from the Binance BTC / USDT stock market. The experimental result illustrates the implementability of the proposed optimal PID trading scheme and also shows the effectiveness of the proposed CE methods in the modern AT.
Regression Involved Gains Optimization In this section we use a conventional regression based optimization framework applied to the historical stock data. Our aim is to combine the least square optimization with the generic Forward Testing (FT). Note that the FT technique is a common methodology of the modern AT (see e. g. ,  [23] ,  [26] ). It includes the so called In-Sample and Out-of-Sample data subsets of the initially given historical data set. The In-Sample data set is part of historical data on which the optimization is performed. The subset of historical data that has been reserved for a possible validation of the optimized trading algorithm is known as an Out-of-Sample data set. We now choose a number M 1 ∈ N as a cardinal number of the In-Sample set and consider the corresponding investment volumes: v j (t + 1), j = 1,.
, M 1 -1. Let M 2 denotes a cardinal number of the Out-of-Sample sat such that the complete historical data set under consideration has  M := M 1 + M 2 Evidently, (13) constitutes a specific nonlinear regression determined on the In-Sample data set. Note that problem (13) involves the previously obtained statistical characterisation  (12). This statistical result for the PID gains constitutes a natural restriction in the minimization problem  (13). The main problem (  13 ) can be solved by some known numerical optimization methods (see e. g. ,  [3] ,  [21] ,  [29]  and references therein). It finally leads to the optimal PID gains selection: K opt (t + 1) := {K opt P (t + 1), K opt D (t + 1), K opt I (t + 1)}. The Out-of-Sample data set can next be used for the validation procedure of optimal solution K(t + 1) obtained using the In-Sample set.
Attendees We had 482 people registered for our event through the ISMB/ECCB 2021 website [1]. On the platform, 233 attendees connected at some time during the event, 40% of them joined a discussion table, and approximately a maximum of 95 people connected simultaneously in Airmeet. The demographics of attendees can be found in Figure  1. We found out that people tend not to attend all talks but some of them. This could be the consequence of the virtual symposium, as people prefer to attend specific talks and not the entire program or people doing other activities in parallel, as a consequence of not being in a physical venue. Additionally, the free-to-attend nature of the meeting makes it easy for people to register without a strong incentive to maintain the attendance commitment. We should delineate strategies to encourage people to attend more talks.
Keynote speakers For this edition, we welcomed three keynote speakers: Prof. Wolfgang Huber from EMBL, whose topic was "Doing great (in) computational biology. "; Prof. Nicola Mulder from the University of Cape Town, whose keynote was "Building capacity and resources to enable genomic medicine in Africa. "; and Prof. Yana Bromberg from Rutgers University, who spoke about "Decoding the DNA blueprints of molecular functionality. " Prof. Wolfgang Huber: Doing great (in) computational biology Prof. Huber's talk focussed on Open science and why it is an essential part of great computational biology projects. He explained how transparent and accessible research has better reproducibility, transparency, credibility, and usefulness. It also supports research in developing countries and cities. He also talked about steps taken at EMBL to make tools reusable, peer-reviewable, transparent, and access to FAIR data (findable, accessible, interoperable, and reusable).