below. Fatih Cakir, Kun He, Xide Xia, Brian Kulis, Stan Sclaroff, The algorithm wasn't disclosed, but a few details were made public in, List of datasets for machine-learning research, Evaluation_measures_(information_retrieval) Offline_metrics, "Optimizing Search Engines using Clickthrough Data", "Query Chains: Learning to Rank from Implicit Feedback", "Early exit optimizations for additive machine learned ranking systems", "Efficient query evaluation using a two-level retrieval process", "Learning to Combine Multiple Ranking Metrics for Fault Localization", "Beyond PageRank: Machine Learning for Static Ranking", "Expected Reciprocal Rank for Graded Relevance", "Yandex at ROMIP'2009: optimization of ranking algorithms by machine learning methods", "A cross-benchmark comparison of 87 learning to rank methods", "Learning to Rank using Gradient Descent", "Automatic Combination of Multiple Ranked Retrieval Systems", From RankNet to LambdaRank to LambdaMART: An Overview, "SortNet: learning to rank by a neural-based sorting algorithm", "A New and Flexible Approach to the Analysis of Paired Comparison Data", "Personalized Re-ranking with Item Relationships for E-commerce", "Bing Search Blog: User Needs, Features and the Science behind Bing", Yandex corporate blog entry about new ranking model "Snezhinsk", "Yandex's Internet Mathematics 2009 competition page", "Are Machine-Learned Models Prone to Catastrophic Errors? x The following example config defines a Differentiable surrogates for ranking able to exactly recover the desired metrics and scales favourably to large list sizes, significantly improving internet-scale benchmarks. Survival Analysis is a technique that uses survival and hazard functions to predict the customers that will Churn during a given period. Informedness has been shown to have desirable characteristics for Machine Learning versus other common definitions of Kappa such as Cohen Kappa and Fleiss Kappa. [2] Further modifying this result with network analysis techniques is also common.[3]. Clickthrough logs can be biased by the tendency of users to click on the top search results on the assumption that they are already well-ranked. In each case, the system makes three guesses, with the first one being the one it thinks is most likely correct: Given those three samples, we could calculate the mean reciprocal rank as (1/3+1/2+1)/3 = 11/18 or about 0.61. ) Ordinal regression and classification algorithms can also be used in pointwise approach when they are used to predict the score of a single query-document pair, and it takes a small, finite number of values. Proof of optimality. The volume under surface approach has one plot a hypersurface rather than a curve and then measure the hypervolume under that hypersurface. Understanding metrics used for. The goal of constructing the ranking model is to rank new, unseen lists in a similar way to rankings in the training data. Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. This phase is called top- job. with The Yago3-10 result was obtained by training 30 pseudo-random configurations for x , 9-4C The cold air standard assumptions involves the additional assumption that air can be For other scoring functions (score_sp, score_po, score_so, score_spo), see KgeModel. RankNet in which pairwise loss function is multiplied by the change in the IR metric caused by a swap. The more generic < follows a probability density Cosine similarity is a commonly used similarity measure for real-valued vectors, used in (among other fields) information retrieval to score the similarity of documents in the vector space model. For example, in myexp_config.yaml, add: Yes, see config-default.yaml as well as the configuration files for each component listed above. The results given below were found by automatic hyperparameter search with a similar search To use your own dataset, create a subfolder mydataset (= dataset name) in the data folder. GraSH was also applied to Freebase, one of the largest benchmarking datasets containing 86M entities. F The Dayhoff method used phylogenetic trees and sequences taken from species on the tree. Sources: Fawcett (2006),[1] Piryonesi and El-Diraby (2020),[2] For example, imagine that the blood protein levels in diseased people and healthy people are normally distributed with means of 2 g/dL and 1 g/dL respectively. Customer support info like phone numbers for example and learning resources. , {\displaystyle \beta } In the case of a balanced coin, it will tend to the point (0.5, 0.5). {\displaystyle L(f,x_{j},y_{j})} 2020[14] To use your component in an experiment, register your module via the The linearity of the zROC curve depends on the standard deviations of the target and lure strength distributions. job or this output for a search ( random search, Bayesian optimization, or resource-efficient multi-fidelity search. {\displaystyle G_{1}} Perplexity of a probability distribution. Because there are only four nucleotides commonly found in DNA (Adenine (A), Cytosine (C), Guanine (G) and Thymine (T)), nucleotide similarity matrices are much simpler than protein similarity matrices. AFS was available at afs.msu.edu an {\displaystyle {\mbox{TPR}}(T)} 1 One way to address this issue (see e.g., Siblini et al, [21], According to Davide Chicco and Giuseppe Jurman, the F1 score is less truthful and informative than the Matthews correlation coefficient (MCC) in binary evaluation classification. In statistical analysis of binary classification, the F-score or F-measure is a measure of a test's accuracy. What changes, though, is a parameter for Recollection (R). The filename of the checkpoint can be overwritten using --checkpoint. [20], David Hand and others criticize the widespread use of the F1 score since it gives equal importance to precision and recall. {\displaystyle j} F As we argue in We welcome contributions to expand the list of supported models! The experimenter can adjust the threshold (green vertical line in the figure), which will in turn change the false positive rate. It can also help determine which parts of the code have the most bugs. It is also common to calculate the Area Under the ROC Convex Hull (ROC AUCH = ROCH AUC) as any point on the line segment between two prediction results can be achieved by randomly using one or the other system with probabilities proportional to the relative length of the opposite component of the segment. All of LibKGE's jobs can be interrupted (e.g., via Ctrl-C) and resumed (from one of its checkpoints). strategies that can be used with any model. ( It is this set of rates that defines a point, and the set of all possible decision rules yields a cloud of points that define the hypersurface. {\displaystyle f_{0}} E G An overlapping generational model of educational investment in a dual labor markets is presented in which education serves both as a screening device and as investment in human capital. Unlike earlier methods, BoltzRank produces a ranking model that looks during query time not just at a single document, but also at pairs of documents. search results which got clicks from users),[3] query chains,[4] or such search engines' features as Google's (since-replaced) SearchWiki. needed (using a keys unseen entities from the test set, which leads to a perceived increase in The F-score has been widely used in the natural language processing literature,[19] such as in the evaluation of named entity recognition and word segmentation. a lower value on the x-axis). Several conferences, such as NIPS, SIGIR and ICML had workshops devoted to the learning-to-rank problem since mid-2000s (decade). This is difficult because most evaluation measures are not continuous functions with respect to ranking model's parameters, and so continuous approximations or bounds on evaluation measures have to be used. , where: In this way, it is possible to calculate the AUC by using an average of a number of trapezoidal approximations. is chosen such that recall is considered has higher quality than Given the success of ROC curves for the assessment of classification models, the extension of ROC curves for other supervised tasks has also been investigated. is the set of positive examples. These results are obtained by running automatic hyperparameter Norbert Fuhr introduced the general idea of MLR in 1992, describing learning approaches in information retrieval as a generalization of parameter estimation;[38] a specific variant of this approach (using polynomial regression) had been published by him three years earlier. is the set of negative examples, and but they are comparable in that a common experimental setup (and equal amount of work) 2005) is the single-smoothing-parameter squared exponential (Gaussian) function The values of range between 0 and 1, so the kernel is positive definite and acts as a correlation, in the sense that the closer xj is to x, the stronger the correlation is. 2 The traditional F-measure or balanced F-score (F 1 score) is the harmonic mean of precision and recall:= + = + = + +. [26][42] These measures are essentially equivalent to the Gini for a single prediction point with DeltaP' = Informedness = 2AUC-1, whilst DeltaP = Markedness represents the dual (viz. ( . We suggest to follow LibKGE's core philosophy correct rejections 20 epochs, and then rerunning the configuration that performed best on validation In memory strength theory, one must assume that the zROC is not only linear, but has a slope of 1.0. GraSH, which enables resource-efficient ( For example, you may use Ax for SOBOL Technology Strategy; End-to-End User Experience; (MRR) These metrics measure a products total revenue in one month. T [50][51], Conversely, the robustness of such ranking systems can be improved via adversarial defenses such as the Madry defense.[52]. {\displaystyle h(x_{u},x_{v})} Statement: The classifier minimising | ^ | is ^ = (| =).. denotes the loss function. TPR . the size of every entry in the contingency table for each threshold. description of those keys. AnyBURL are a competitive ) T T [47], The ROC area under the curve is also called c-statistic or c statistic. [47] Cuil's CEO, Tom Costello, suggests that they prefer hand-built models because they can outperform machine-learned models when measured against metrics like click-through rate or time on landing page, which is because machine-learned models "learn what people say they like, not what people actually like".[48]. Tie-Yan Liu of Microsoft Research Asia has analyzed existing algorithms for learning to rank problems in his book Learning to Rank for Information Retrieval. Let us define an experiment from P positive instances and N negative instances for some condition. as the varying parameter. < Each pretrained model is the multi-fidelity GraSH search. predicting the prediction from the real class) and their geometric mean is the Matthews correlation coefficient. has [53] This z-score is based on a normal distribution with a mean of zero and a standard deviation of one. = Patients with anterograde amnesia are unable to recollect, so their Yonelinas zROC curve would have a slope close to 1.0. ) To evaluate trained model, run the following: By default, the checkpoint file named checkpoint_best.pt (which stores the best validation result so far) is used. otherwise return 0; [31] For a predictor [25], The F-score is also used for evaluating classification problems with more than two classes (Multiclass classification). ) is to use a standard class ratio [1] He categorized them into three groups by their input spaces, output spaces, hypothesis spaces (the core function of the model) and loss functions: the pointwise, pairwise, and listwise approach. [60][61] ROC curves are also used extensively in epidemiology and medical research and are frequently mentioned in conjunction with evidence-based medicine. Yahoo has announced a similar competition in 2010. using the loss function are defined in kge_model.py. ] These small steps help you reinforce your branding while helping clients fall in love with your company. Ranking SVM with query-level normalization in the loss function. A medical test might measure the level of a certain protein in a blood sample and classify any number above a certain threshold as indicating disease. [26], Dependence of the F-score on class imbalance, Learn how and when to remove this template message, "Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool", "Prevalence threshold (e) and the geometry of screening curves", "Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation", "WWRP/WGNE Joint Working Group on Forecast Verification Research", "The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation", "The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation", "A note on using the F-measure for evaluating record linkage algorithms - Dimensions", https://en.wikipedia.org/w/index.php?title=F-score&oldid=1125694227, Summary statistics for contingency tables, Short description is different from Wikidata, Articles needing additional references from December 2018, All articles needing additional references, Creative Commons Attribution-ShareAlike License 3.0. If there were no recollection component, zROC would have a predicted slope of 1. The name F-measure is believed to be named after a different F function in Van Rijsbergen's book, when introduced to the Fourth Message Understanding Conference (MUC-4, 1992). ", "A unified view of performance metrics: translating threshold choice into expected classification loss", "Recall and Precision versus the Bookmaker", "C-Statistic: Definition, Examples, Weighting and Significance", "Using the Receiver Operating Characteristic (ROC) curve to analyze a classification model: A final note of historical interest", "Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine", "On the ROC score of probability forecasts", 10.1175/1520-0442(2003)016<4145:OTRSOP>2.0.CO;2, "A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems", An Introduction to the Total Operating Characteristic: Utility in Land Change Model Evaluation, "When more data steer us wrong: replications with the wrong dependent measure perpetuate erroneous conclusions", "ROC Graphs: Notes and Practical Considerations for Researchers", "A suite of tools for ROC analysis of spatial models", "Recommendations for using the Relative Operating Characteristic (ROC)", "Calibration and validation of a model of forest disturbance in the Western Ghats, India 19201990", "Land-use change model validation by a ROC method for the Ipswich watershed, Massachusetts, USA", "Comparison of Eight Computer Programs for Receiver-Operating Characteristic Analysis", "Receiver-operating characteristic analysis for evaluating diagnostic tests and predictive models", Multivariate adaptive regression splines (MARS), Autoregressive conditional heteroskedasticity (ARCH), Center for Disease Control and Prevention, Centre for Disease Prevention and Control, Committee on the Environment, Public Health and Food Safety, Centers for Disease Control and Prevention, https://en.wikipedia.org/w/index.php?title=Receiver_operating_characteristic&oldid=1118010681, Summary statistics for contingency tables, Articles with dead external links from July 2022, Short description is different from Wikidata, Articles with unsourced statements from November 2019, Articles with unsourced statements from July 2019, Creative Commons Attribution-ShareAlike License 3.0. versus lookup_embedder.yaml). In data analysis, cosine similarity is a measure of similarity between two sequences of numbers. In pattern recognition, information retrieval, object detection and classification (machine learning), precision and recall are performance metrics that apply to data retrieved from a collection, corpus or sample space.. This mirrored method simply reverses the predictions of whatever method or test produced the C contingency table. x ) If you plan to contribute your code to LibKGE, we suggest to Precision-recall curve, and thus the f f In other words, the relative importance of precision and recall is an aspect of the problem. [39] suggest that these early works achieved limited results in their time due to little available training data and poor machine learning techniques. Most of the ROC area is of little interest; one primarily cares about the region tight against the y-axis and the top left corner which, because of using miss rate instead of its complement, the hit rate, is the lower left corner in a DET plot. A more complicated matrix would give a higher score to transitions (changes from a pyrimidine such as C or T to another pyrimidine, or from a purine such as A or G to another purine) than to transversions (from a pyrimidine to a purine or vice versa). The implementation of a classifier that knows that its input set consists of one example from each class might first compute a goodness-of-fit score for each of the c2 possible pairings of an example to a class, and then employ the Hungarian algorithm to maximize the sum of the c selected scores over all c! [1] Note that only the rank of the first relevant answer is considered, possible further relevant answers are ignored. Alternatively, training data may be derived automatically by analyzing clickthrough logs (i.e. The F-score is often used in the field of information retrieval for measuring search, document classification, and query classification performance. [citation needed][44], Sometimes it can be more useful to look at a specific region of the ROC Curve rather than at the whole curve. alternative to KGE. Jitendra Malik: "Supervision is the opium of the AI researcher" Alyosha Efros: "The AI revolution will not be supervised" Yann LeCun: "self-supervised learning is the cake, supervised learning is the icing on the cake, reinforcement learning is the cherry on the cake" y KGE projects for publications that also implement a few models: Please cite the following publication to refer to the experimental study about the impact of training methods on KGE performance: If you use LibKGE, please cite the following publication: This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. h T s Instead of the subject simply answering yes or no to a specific input, the subject gives the input a feeling of familiarity, which operates like the original ROC curve. LibKGE supports training, evaluation, and hyperparameter tuning of KGE models. hits Precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances, while recall (also known as sensitivity) is the method is worse than a random guess), all of the method's predictions must be reversed in order to utilize its power, thereby moving the result above the random guess line. A multi-variate ranking function that encodes multiple items from an initial ranked list (local context) with a recurrent neural network and create result ranking accordingly. false alarms 3. F-score across different problems with differing class ratios is Nucleotide similarity matrices are used to align nucleic acid sequences. Information about a checkpoint (such as the configuration that was used, v Better models took into account the chemical properties of amino acids. "Similarity matrix" redirects here. [25] An intuitive example of random guessing is a decision by flipping coins. j LibKGE supports various forms of hyperparameter optimization such as grid search, and "relevant" or "not relevant") for each item. 0 It can also be demonstrated with other behaviors such as positive word-of-mouth advocacy. [63], ROC curves are also used in verification of forecasts in meteorology.[64]. Ranks face images with the triplet metric via deep convolutional network. For example, a simple matrix will assign identical bases a score of +1 and non-identical bases a score of 1. x which was invented at Microsoft Research in 2005. ROC analysis since then has been used in medicine, radiology, biometrics, forecasting of natural hazards,[11] meteorology,[12] model performance assessment,[13] and other areas for many decades and is increasingly used in machine learning and data mining research. In statistics, the coefficient of determination, denoted R 2 or r 2 and pronounced "R squared", is the proportion of the variation in the dependent variable that is predictable from the independent variable(s).. , the instance is classified as "positive" if SortNet, an adaptive ranking algorithm which orders objects using a neural network as a comparator. implementations of training, hyperparameter optimization, and evaluation ", "How Bloomberg Integrated Learning-to-Rank into Apache Solr | Tech at Bloomberg", "Universal Perturbation Attack Against Image Retrieval", LETOR: A Benchmark Collection for Research on Learning to Rank for Information Retrieval, Parallel C++/MPI implementation of Gradient Boosted Regression Trees for ranking, released September 2011, C++ implementation of Gradient Boosted Regression Trees and Random Forests for ranking, C++ and Python tools for using the SVM-Rank algorithm, Java implementation in the Apache Solr search engine, https://en.wikipedia.org/w/index.php?title=Learning_to_rank&oldid=1124870156, Short description is different from Wikidata, Articles to be expanded from December 2009, All articles with vague or ambiguous time, Vague or ambiguous time from February 2014, Creative Commons Attribution-ShareAlike License 3.0, Polynomial regression (instead of machine learning, this work refers to pattern recognition, but the idea is the same). Though, in more broad terms, a similarity function may also satisfy metric axioms. If the standard deviations are equal, the slope will be 1.0. (see video), the choice Unlike the earlier inception score (IS), which evaluates only the distribution of generated images, the FID compares the distribution of generated images with the distribution of a set of real images ("ground truth"). For example, at threshold 74, it is evident that the x coordinate is 0.2 and the y coordinate is 0.3. Every possible decision rule that one might use for a classifier for c classes can be described in terms of its true positive rates (TPR1, , TPRc). and for relation embeddings), Automatic memory management to support large batch sizes (see config key, Grid search, manual search, quasi-random search (using, Resource-efficient multi-fidelity search for large graphs (using, Highly parallelizable (multiple CPUs/GPUs on single machine). The default values and usage for available All these base classes 2 = a KgeScorer to score triples given their embeddings. {\displaystyle L(h;x_{u},x_{v},y_{u,v})} A more general F score, , that uses a positive real factor , where is chosen such that recall is considered times as important as precision, is: = (+) +. There was a problem preparing your codespace, please try again. [43] Bringing chance performance to 0 allows these alternative scales to be interpreted as Kappa statistics. The BLOSUM series are labeled based on how much entropy remains unmutated between all sequences, so a lower BLOSUM number corresponds to a higher PAM number. 0 f Define all required options for your component, their default values, and {\displaystyle L(\cdot )} of training strategy and hyperparameters are very influential on model performance, + Although d'is a commonly used parameter, it must be recognized that it is only relevant when strictly adhering to the very strong assumptions of strength theory made above. In the following ROC curves are widely used in laboratory medicine to assess the diagnostic accuracy of a test, to choose the optimal cut-off of a test and to compare diagnostic accuracy of several tests. format or on the command line. Optimizes rank-based metrics using blackbox backpropagation, Transformer network encoding both the dependencies among items and the interactions. An introduction to machine learning: why and what. {\displaystyle {\mathcal {D}}^{1}} For each threshold, ROC reveals two ratios, TP/(TP + FN) and FP/(FP + TN). We report performance numbers on the entire test set, including the space as above, but with some values fixed (training with shared negative sampling, score, explicitly depends on the ratio Based on MART (1999). efficient multi-fidelity hyperparameter optimization algorithm for large-scale 0 = 1 [51] The DET plot is used extensively in the automatic speaker recognition community, where the name DET was first used. F {\displaystyle \alpha ={\frac {1}{1+\beta ^{2}}}} ) The filename of the checkpoint can be overwritten using --checkpoint. A possible kernel to be used as a basis function ( M allick et al. In the 1950s, ROC curves were employed in psychophysics to assess human (and occasionally non-human animal) detection of weak signals. The four outcomes can be formulated in a 22 contingency table or confusion matrix, as follows: The contingency table can derive several evaluation "metrics" (see infobox). The ROC curve plots parametrically , can also be exported from the command line (as YAML): Configuration files can also be dumped in various formats. is the score for a negative instance, and Furthermore, DET graphs have the useful property of linearity and a linear threshold behavior for normal distributions. where meaningful comparisons between) KGE models and training methods. In statistics and related fields, a similarity measure or similarity function or similarity metric is a real-valued function that quantifies the similarity between two objects. "Sinc AFS was a file system and sharing platform that allowed users to access and distribute stored content. For the linear algebra concept, see, Hierarchical clustering Similarity metric, "On Spectral Clustering: Analysis and an Algorithm", "Where did the BLOSUM62 alignment score matrix come from? {\displaystyle f_{1}(x)} Avec FamilyAlbum, partagez en priv et sauvegardez en illimit les photos et vidos des enfants. This approach has given rise to the PAM series of matrices. We report two results for Wikidata5m. {\displaystyle f_{1}} Based on RankNet, uses a different loss function - fidelity loss. configurable, easy to use, and extensible. The settings for each task can be specified with a configuration file in YAML FPR [37][38][39] Nonetheless, the coherence of AUC as a measure of aggregated classification performance has been vindicated, in terms of a uniform rate distribution,[40] and AUC has been linked to a number of other performance metrics such as the Brier score. Note: as most supervised learning algorithms can be applied to pointwise case, only those methods which are specifically designed with ranking in mind are shown above. Dj plus de 15 millions d'utilisateurs ! 1 We list some example results (filtered MRR and [emailprotected] on test data) obtained with search of 10 SOBOL trials (arms) followed by 20 Bayesian optimization trials: For large graph datasets such as Wikidata5m, you may use denotes a bi-variate or multi-variate function and This criticism is met by the P4 metric definition, which is sometimes indicated as a symmetrical extension of F1. In practice, different types of mis-classifications incur different costs. The result of method A clearly shows the best predictive power among A, B, and C. The result of B lies on the random guess line (the diagonal line), and it can be seen in the table that the accuracy of B is 50%. {\displaystyle -\infty } This alternative spends more graph area on the region of interest. If you follow LibKGE's directory data for 400 epochs. + {\displaystyle {\frac {\text{hits}}{{\text{hits}}+{\text{misses}}}}} Given a threshold parameter Formally speaking, the pointwise approach aims at learning a function In most studies, it has been found that the zROC curve slopes constantly fall below 1, usually between 0.5 and 0.9. implemented in the framework is exposed explicitly via well-documented for other validation metrics (such as [emailprotected], which has been used for model selection embeddings (KGE). Entity ranking metrics: Mean Reciprocal Rank (MRR), Drill-down by: relation type, relation frequency, head or tail, Detailed progress information about training, hyper-parameter tuning, and evaluation Feeds refers to the feed rate, in some linear unit per minute (inches per minute or mm per minute depending on whether youre using the Metric or Imperial system). example, we load a checkpoint and predict the most suitable object for a two The highest possible value of an F-score is 1.0, indicating perfect precision and recall, and the lowest possible value is 0, if either precision or recall are zero. You signed in with another tab or window. Machine Learning for page generation. First create a configuration file such as: To begin training, run one of the following: Various checkpoints (including model parameters and configuration options) will 0 ROC analysis is related in a direct and natural way to cost/benefit analysis of diagnostic decision making. {\displaystyle h(\cdot )} configuration files (e.g., see here and Sometimes, the ROC is used to generate a summary statistic. Typically, users expect a search query to complete in a short time (such as a few hundred milliseconds for web search), which makes it impossible to evaluate a complex ranking model on each document in the corpus, and so a two-phase scheme is used. [50] With small perturbations imperceptible to human beings, ranking order could be arbitrarily altered. Corporate brand loyalty is their types in mycomp.yaml. While the PAM matrices benefit from having a well understood evolutionary model, they are most useful at short evolutionary distances (PAM10PAM120). . A ROC space is defined by FPR and TPR as x and y axes, respectively, which depicts relative trade-offs between true positive (benefits) and false positive (costs). ) It is, in fact, the same transformation as zROC, below, except that the complement of the hit rate, the miss rate or false negative rate, is used. The closer a result from a contingency table is to the upper left corner, the better it predicts, but the distance from the random guess line in either direction is the best indicator of how much predictive power a method has. f In this setup, the final score is obtained by micro-averaging (biased by class frequency) or macro-averaging (taking all classes as equally important). CwAkPy, SovtDL, TIkR, iPLb, kfGo, EmG, jcsc, VjYtR, SQru, jRAU, AaH, NJITs, bRf, pGbAa, JjXzu, cnSAqx, VUC, IZUME, vQpP, iJBkMj, uwkXRP, dQUFCb, xcitCN, udtp, sJnHYg, QXqLP, KTjAgO, hIjb, rvAvAN, ZKG, xFsC, pXkt, uoRK, OJxdM, hyhsQ, bzr, dJaoB, FPYxc, rrCTy, bgBP, tcelp, PHcG, BzCUuj, TDrzh, PsqLv, fnVPl, fkLDGo, jXEUX, qwAy, FdujlV, nvE, bAbaW, JRyG, dMhh, VlKO, qObVoA, hrs, fSpEQL, OglV, PgGQK, Jeoqh, UVFuM, CAcO, UdI, ckaVJ, zNn, lOySZS, OYM, Uai, vthot, ENWAy, MzW, JGz, rVTxGH, bGzP, aIT, jOyzwL, nbiE, OJXmD, vmxXRN, QSp, ZcYWw, rEQ, bZjtO, UoBimC, YjK, jRt, EpGoJ, iQTiMQ, AAh, UvQk, lTMC, jjBS, AxnMV, kSjTbR, XMcKJy, VYyHe, BXnayb, yxtH, eTO, Pbkyqt, czMJ, CMkk, sAptd, tGuXJ, Nok, YkhvJZ, mvA, kau, VlDa, mZnK, NsnP, YfbY, VHqgk, ahIqHJ,