question:You will be presented with the full text of a science research paper. Please write an abstract for this paper. Your response should include the abstract and no additional text.Paper text:in @xmath1 the astronomer and mathematician s. newcomb made a remarkable observation with respect to logarithmic books @xcite .he noticed that the first pages were more worn out than the last .this led him to the conclusion that the significant digits of various physical datasets are not distributed with equal probability but the smaller significant digits are favored . in @xmath2 f. benfordcontinued this study and he derived the law of the anomalous numbers @xcite .the general significant digit law @xcite for all @xmath3 , @xmath4 and @xmath5 , for @xmath6 is @xmath7 ] ] where @xmath8 is the @xmath9 leftmost digit .for example , the probability to find a number whose first leftmost digit is @xmath10 , second digit is @xmath11 and third is @xmath12 is @xmath13 .for the first significant digit can be written as @xmath14 this law has been tested against various datasets ranging from statistics @xcite to geophysical sciences @xcite and from financial data @xcite to multiple choice exams @xcite .studies were also performed in physical data like complex atomic spectra @xcite , full width of hadrons @xcite and half life times for alpha and @xmath15 decays @xcite .an interesting property of this law is that it is invariant under the choice of units of the dataset ( scale invariance ) @xcite .for example , if the dataset contains lengths , the probability of the first significant digits is invariant in the case that the units are chosen to be meters , feet or miles .still , benford s law is not fully understood mathematically .a great step was done with the extension of scale to base invariance ( the dependance of the base in which numbers are written ) by theodore hill @xcite . combining these features and realising that all the datasets that follow benford s law are a mixture from different distributions, he made the most complete explanation of the law .another approach in the explanation of the logarithmic law was examined by jeff boyle @xcite using the fourier series method .a simple example of benford s law is performed on numerical sequences .it is already proven that the fibonacci and lucas numbers obey the benford s law @xcite .the three additional numerical sequences considered in this paper are : * jacobsthal numbers ( @xmath16 ) , defined as * * @xmath17 * * @xmath18 * * @xmath19 * jacobsthal - lucas numbers ( @xmath20 ) , defined as * * @xmath21 * * @xmath22 * * @xmath23 * and bernoulli numbers ( @xmath24 ) , defined by the contour interval * * @xmath25 * * @xmath26 a sample of the first @xmath27 numbers of these sequences is used to extract the probabilities of the first significant digit to take the values @xmath28 and the second and third significant digits to be @xmath29 .the results can be seen in figure [ fig : numerical ] .full circles represent the result from the analysis of the jacobsthal and jacobsthal - lucas numbers and the empty circles indicate the probabilities calculated from benford s formula ( equation [ formula_all_digits ] ) .it is clear that all three sequences follow benford s law for the first ( black ) , second ( red ) and third ( blue ) significant digit . in the following sections we examine the distances of stars and galaxies and compare the probabilities of occurrence of the first , second and third significant digit with benford s logarithmic law .if the location of the galaxies in our universe and the stars in our galaxy are caused by uncorrelated random processes , benford s law might not be followed because each digit would be equiprobable to appear . to our knowledgethis is the first paper that attempts to correlate cosmological observables with benford s law .cosmological data with accurate measurements of celestial objects are available since the 1970s .we examine if the frequencies of occurrence of the first digits of the distances of galaxies and stars follow benford s law .we use the measured distances of the galaxies from references @xcite .the distances considered on this dataset are based on measurements from type ii supernova and all the units are chosen to be @xmath30 . the type - ii supernova ( snii ) radio standard candleis based on the maximum absolute radio magnitude reached by these explosions , which is @xmath31 .the total number of galaxies selected is 702 with distances reaching @xmath32 ( see figure [ fig : dataset ] left ) .the results can be seen in figure [ fig : galaxies ] where with open circles we notate benford s law predictions and the measurements with the circle . unfortunately due to lack of statistics the second andthe third significant digit can not be analyzed .the trend of the distribution tends to follow benford s prediction reasonably well .[ fig : dataset ] the information for the distances of the stars is taken from the hyg database @xcite . in thislist @xmath33 stars are included , with distances reaching up to @xmath34 .the full dataset used for the extraction of the result can be seen in figure [ fig : dataset ] .the result after analysing this dataset can be seen in figure [ fig : stars ] .the first ( black full circles ) and especially the second ( red full circles ) and the third ( blue full circles ) significant digits follow well the probabilities predicted by benford s law ( empty circles ) .the benford law of significant digits was applied for the first time to astronomical measurements .it is shown that the stellar distances in the hyg database follow this law quite well for the first , second and third significant digits .also , the probabilities of the first significant digit of galactic distances using the type ii supernova photosphere method is in good agreement with the benford distribution ; however , the errors are sufficiently large so that additional digits can not be analyzed .we note , however , that the plots in figure [ fig : dataset ] indicate that selection effects due to the magnitude limits of both samples may be responsible for this behaviour and so it is not firmly established .therefore it is necessary to repeat this study using different galactic distance measures and larger catalogs of both galaxies and stars to see if the benford law is still followed when larger distances are probed. such larger samples of galaxies would also allow the examination of second and perhaps third significant digits .we would like to thank i.p .karananas for the lengthy discussions on this subject .we would like also to thank emeritus professor anastasios filippas , the editor of joaa and the reviewer for the valuable comments and suggestions .the present work was co - funded by the european union ( european social fund esf ) and greek national funds through the operational program `` education and lifelong learning '' of the national strategic reference framework ( nsrf ) 2007 - 1013 .aristeia-1893-atlas micromegas .bartel n. , _ angular diameter determinations of radio supernovae and the distance scale _supernovae as distance indicators ; proceedings of the workshop , cambridge , ma , september 27 , 28 , 1984 ( a86 - 38101 17 - 90 ) .berlin and new york , springer - verlag , 107 - 122 ( 1985 ) hoeflich , p. , _ model calculations for scattering dominated atmospheres and the use of supernovae as distance indicators _ , nuclear astrophysics ; proceedings of the workshop , tegernsee , federal republic of germany , apr .21 - 24 , 1987 ( a89 - 10202 01 - 90 ) .berlin and new york , springer - verlag , 307 - 315 ( 1987 ) bartel n. , _ determinations of distances to radio sources with vlbi _ , the impact of vlbi on astrophysics and geophysics ; proceedings of the 129th iau symposium , cambridge , ma , may 10 - 15 , 1987 ( a89 - 13726 03 - 90 ) .dordrecht , kluwer academic publishers , 175 - 184 ( 1988 ) chilukuri , m. et al . , _ type - ii supernova photospheres and the distance to supernova 1987a _ , atmospheric diagnostics of stellar evolution . chemical peculiarity , mass loss , and explosion .proceedings of the 108th .colloquium of the international astronomical union , held at the university of tokyo , japan , september 1 - 4 , 1987 .lecture notes in physics , volume 305 , editor , k. nomoto ; publisher , springer - verlag , berlin , new york , 1988 .isbn # 3 - 540 - 19478 - 9 .lc # qb806 .i18 1987 , p. 295 , ( 1987 ) schmidt , b. p. , _ expanding photospheres of type ii supernovae and the extragalactic distance scale _ , american astronomical society , 181st aas meeting , # 107.04d ; bulletin of the american astronomical society , * vol 24 * , p.1292 ( 1992 ) schmidt - kaler , t. , _ the distance to the large magellanic cloud from observations of sn1987a _ , variable stars and galaxies , in honour of m. w. feast on his retirement , asp conference series , * vol 30 * , b. warner , ed . , p. 195( 1992 ) brian p. schmidt et al ., _ type ii supernovae , expanding photospheres , and the extragalactic distance scale _ , thesis ( ph.d . ) - harvard university , 1993 .source : dissertation abstracts international , volume : 54 - 11 , section : b , page : 5717 ( 1993 ) romaniello m. et al . ,_ hubble space telescope observations of the large magellanic cloud field around sn 1987a : distance detetrmination with red clump and tup of the red giant branch stars _ the astrophysical journal , * vol 530 * , 738 - 743 ( 2000 ) mitchell r. c. et al . , _ detailed spectroscopic analysis of sn 1987a : the distance to the large magellanic cloud using the spectral - fitting expanding atmosphere method _ , the astrophysical journal , * vol 574 * , 293 - 305 ( 2002 ) dessart l. et al . , _ using quantitative spectroscopic analysis to determine the properties and distances of type ii plateau supernovae : sn 2005cs and sn 2006bp _ , the astrophysical journal , * vol 675 * , 644 - 669 ( 2008 ) crotts a. p. s. , et al ., _ the circumstellar envelope of sn 1987a . i. the shape of the double - lobed nebula and its rings and the distance to the large maggelanic cloud _ , the astrophysical journal , * vol 438 * , 724 - 734 ( 1995 )

question:You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.Context: As can be seen from Table 3, our large model is on par with the existing state-of-the-art (Zaremba et al. 2014), despite having approximately 60% fewer parameters. Our small model significantly outperforms other NLMs of sim- ilar size, even though it is penalized by the fact that the dataset already has OOV words replaced with < unk > (other models are purely word-level models). While lower perplex- ities have been reported with model ensembles (Mikolov and Zweig 2012), we do not include them here as they are not comparable to the current work.Question: Does large model always shows better performance than small model?

question:You will be shown a paragraph from a biomedical research paper, followed by a question about the paper. Please extract text from the paragraph to answer the question. Do not include any text in your repsonse other than the answer.Paragraph: For example, in [34] SCA algorithm was applied to improve the ANFIS model to forecast oil consumption in three countries, namely, Canada, Germany, and Japan. In the same context, in [35] , The MVO algorithm was used to enhance the ANFIS model to forecast oil consumption in two countries. In addition, in [36] the PSO was used with ANFIS to predict biochar yield. However, individual SI algorithms may stock at local optima. Therefore, one solution is to apply hybrid SI algorithms to avoid this problem. In [37] , a hybrid of two SI algorithms, namely GA and SSA, was presented to improve the ANFIS model. The proposed new model called GA-SSA-ANFIS was applied to forecast crude oil prices for long-term time series data. However, the previously mentioned methods suffer from some limitations that can affect the performance of the forecasting output such as slow convergence and the ability to balance between exploration and exploitation phases can influence the quality of the final output. This motivated us to propose an alternative forecasting method dependent on the hybridization concept. This concept avoids the limitations of traditional SI techniques by combining the strengths of different techniques, and this produces new SI techniques that are better than traditional ones. In the current study, we propose an improved ANFIS model based on a modified flower pollination algorithm (FPA) using the salp swarm algorithm (SSA). The FPA is an optimization algorithm proposed by Yang [38] , which was inspired by the flow pollination process of the flowering plants. The FPA was employed in various optimization applications, for example to estimate solar PV parameter [39, 40] , solving sudoku puzzles [41] , feature selection [42] , antenna design [43] , and other applications [44] [45] [46] [47] . Moreover, SSA is also an optimization algorithm proposed by Mirjalili et al. [48] inspired by the behavior of salp chains. In recent years, the SSA was utilized to solve different optimization problems, such as feature selection [49, 50] , data classification [51] , image segmentation [52] , and others [53, 54] . The proposed method called FPASSA is a hybrid of FPA and SSA, in which the SSA is applied as a local search method for FPA. The proposed FPASSA starts by receiving the historical COVID-19 dataset. Then a set of solutions is generated where each of them represents the value for the parameters of the ANFIS model. Then the quality of each solution is calculated using the fitness value, and the solution that has the best fitness value is chosen to represent the best solution. Then the probability of each solution is computed. Then the current solution will be updated, either using global or local strategy in FPA. However, in the case of local strategy, the operators of SSA or FPA will be used according to the probability of the fitness value for each solution. The process of updating the solutions is repeated until reaching the stop condition, and the best parameter configurations are used to forecast the number of confirmed cases of COVID-19. The main contribution points of the current study are as follows:1. We propose an efficient forecasting model to forecast the confirmed cases of the COVID-19 in China for the upcoming ten days based on previously confirmed cases. An improved ANFIS model is proposed using a modified FPA algorithm, using SSA. We compare the proposed model with the original ANFIS and existing modified ANFIS models, such as PSO, GA, ABC, and FPA. The rest of this study is organized as follows. The preliminaries of ANFIS, FPA, and SSA are described in Section 2. Section 3 presents the proposed FPASSA, and Section 4 presents the experimental setup and results. We conclude this study in Section 5. The principles of the ANFIS are given in this section. The ANFIS model links the fuzzy logic and neural networks [22] . It generates a mapping between the input and output by applying IF-THEN rules (it is also called Takagi-Sugeno inference model). Figure 1 illustrates the ANFIS model where, y and x define the inputs to Layer 1 whereas, O 1i is its output of node i that is computed as follows:where µ denotes the generalized Gaussian membership functions. A i and B i define the membership values of µ. α i and ρ i denote the premise parameters set. The output of Layer 2 (it is also known as the firing strength of a rule) is calculated as follows:Meanwhile, the output of Layer 3 (it is also known as the normalized firing strength) is calculated as follows:The output of Layer 4 (it is also known as an adaptive node) is calculated as follows:where r i , q i , and p i define the consequent parameters of the node i. Layer 5 contains only one node; its output is computed as: Flower Pollination Algorithm is an optimization method proposed by Yang [38] . It simulates the transfer of flowers' pollen by pollinators in nature. This algorithm utilizes the two types of pollination (i.e., self-pollination and cross-pollination). In self-pollination, the pollination occurs with no pollinators, whereas, in cross-pollination, the pollens are moved between different plants. In more detail, the self-pollination can be represented as a local pollination while the cross-pollination can be called global pollination. The global pollination or cross-pollination can be mathematically formed as follows:where x t i defines the pollen i at iteration t. L denotes the pollination's strength or the step size. F * is the target position or best solution. In some cases, insects can fly with different distance steps for a long space; therefore, Levy fly distribution is applied to simulate this movement. where λ = 1.5. Γ(λ) denotes the gamma function. This distribution is available for large steps s > 0. The self-pollination or local pollination can be mathematically formed as follows:where x t i and x k i represent pollens from different flower in the same plant. in the range [0,1] The process of pollination can be done using cross-pollination or self-pollination. Therefore, the random variable p, in the range [0, 1], is used to determine this process. SSA is an optimization technique introduced by [48] . It simulates the Salps' behavior in nature. This behavior is called salp chain. The mathematical model of SSA begins by splinting its population into a leader group and followers group. The leader is the front salp, whereas, the followers are the other salps. The search space is determined in n-dimensions with n variables. Equation (10) works to update the salps' positions. where x 1 j denotes the leader's position in j-th dimension. F j is the target position. ub j and lb j represent the max and min bounds, respectively. c 2 and c 3 denote random numbers in [0, 1]. c 1 is an important parameter; it balances between the exploration and exploitation phases. It is computed as follows:where the current loop number is t and the max loop' number is t max . Then, the followers' position is updated as follows:where x i j defines the i-th position of the follower in j-th dimension. i > 1. This section explains the proposed FPASSA-ANFIS method. It is a time series method for forecasting the confirmed cases of the COVID-19, as given in Figure 2 . The FPASSA-ANFIS utilizes the improved FPA to train the ANFIS model by optimizing its parameters. The FPASSA-ANFIS contains five layers as the classic ANFIS model. Layer 1 contains the input variables (the historical COVID-19 confirmed cases). Whereas Layer 5 produces the forecasted values. In the learning phase, the FPASSA is used to select the best weights between Layer 4 and Layer 5. The FPASSA-ANFIS starts by formatting the input data in a time series form. In our case, the autocorrelation function (ACF) was considered. ACF is one of the methods applied to find patterns in the data; it presents information about the correlation between points separated by various time lags. Therefore, in this paper, the variables with ACF greater than 0.2 are considered i.e., 5-lags. Besides, the training data contains 75% of the dataset, whereas the testing data contains 25% of them. The number of clusters is defined by the fuzzy c-mean (FCM) method to construct the ANFIS model. The parameters of the ANFIS model are prepared by the FPASSA algorithm. In the training phase, the calculation error (as in Equation (13)) between the real data and the predicted data is used to evaluate the parameters' quality. where T is the real data, and P is the predicted data. N s is the sample length. The smaller values of the objective function indicate good ANFIS's parameter. On the other hand, the updating phase of the followers' positions in the SSA algorithm is applied to improve the global pollination phase in the FPA algorithm. In this improvement, there is a random variable (r) used to switch between both phases. If r > 0.5, then the operators of the SSA is used; otherwise, the operators of the FPA are used. In general, The FPASSA starts by constructing the population (X); afterward, the objective function is calculated for each solution. The solution with the lowest error value is saved to the next iteration. This sequence is repeated until meeting the stop condition, which in this paper, is the maximum number of iterations. Then the best solution is passed to train the parameters of the ANFIS model. After finishing the training phase, the testing phase is started with the best solution to compute the final output. The performance of the proposed method is evaluated by comparing the real data with the predicted data using the performance measures. Finally, the FPASSA produces a foretasted value for confirmed cases of COVID-19 in China in the next day. The steps of the proposed FPASSA are presented in Algorithm 1. Input: Historical COVID-19 dataset, size of population N, total number of iterations t max . Divide the data into training and testing sets. Using Fuzzy c-mean method to determine the number of membership functions. Constructing the ANFIS network. Set the initial value for N solutions (X). Return the best solution that represents the best configuration for ANFIS. Apply the testing set to the best ANFIS model. Forecasting the COVID-19 for the next ten days. This section presents the description of the used dataset, the performance measures, the parameter setting for all methods, the experiment results, and discussions. The main dataset of this study is COVID-19 dataset. It was collected from the WHO website (https: //www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/). It contains the daily confirmed cases in China from 21 January 2020 to 18 February 2020, as shown in Table 1 . We used 75% from the dataset to train the model while the rest is used to test it. Moreover, we evaluated the performance of the proposed method using two datasets of weekly influenza confirmed cases. The first one is called DS1; it was collected from the Centers for Disease Control and Prevention (CDC) (https://www.cdc.gov/flu/weekly/). It starts from week number 40 in 2015 and continues until week number 6 in 2020. Whereas, the second one is called DS2. It was collected from the WHO website (https://www.who.int/influenza). It contains the data of weekly influenza confirmed cases in China from week number 1 in 2016 to week number 8 in 2020. The quality of the proposed method is evaluated using a set of performance metrics as follows:• Root Mean Square Error (RMSE):where Yp and Y are the predicted and original values, respectively. • Mean Absolute Error (MAE):• Mean Absolute Percentage Error (MAPE):• Root Mean Squared Relative Error (RMSRE):N s represents the sample size of the data. • Coefficient of Determination (R 2 ):where Y represents the average of Y. The lowest value of RMSE, MAE, MAPE, and RMSRE refers to the best method. The higher value of R 2 indicates better correlation for the method. This paper aims to assess the ability of the FPASSA to forecast the COVID-19 by comparing its performance with other methods, namely the ANFIS and the trained ANFIS models using PSO, GA, ABC, FPA, and FPASSA. The parameters' setting for these models is listed in Table 2 . The common parameters, such as population size, are set to 25 and 100 iterations are applied. Besides, each algorithm is performed for 30 independent runs to fair comparisons. The selected parameters are chosen because they produced good behavior in previous experiments, such as [34, 35, 55, 56] . Table 2 . Parameters' setting. Parameters SettingMax. epochs = 100, Error goal = 0, Initial step = 0.01, Decrease rate = 0.9, Increase rate = 1. In this section, the performance of the proposed FPASSA to predict the DS1 and DS2 is discussed. It can be concluded from Table 3 that the performance of FPASSA outperformed the compared methods in all measures, whereas the FPA is ranked second. The results of DS2 indicate that the FPASSA is ranked first in terms of RMSE, MAPE, R 2 , and the CPU time. Whereas, the PSO is ranked second, followed by the FPA, GA, then ABC. These results denote that the proposed method can optimize the parameters of the ANFIS model effectively and produce good results in terms of the performance measures. Comparison results between the proposed FPASSA and other models to forecast COVID-19 are given in Table 4 . It can be concluded that the FPASSA outperforms other models. For example, by analyzing the results of RMSE, MAE, MAPE, RMSRE, and CPU time(s) it can be observed that the FPASSA achieves the smallest value among the comparison algorithms, and this indicates the high quality of the FPASSA. Meanwhile, the FPA allocates the second rank, which provides better results than the rest of the methods. Moreover, the value of R 2 refers to the high correlation between the prediction obtained by the proposed FPASSA method and the original COVID-19, which has nearly 0.97. This can also be noticed from Figure 3 , which depicts the training of the algorithms using the historical data of the COVID-19 as well as their forecasting values for ten days. Table 5 depicts the forecasting value for the confirmed cases of the COVID-19 in China from 19/2/2020 to 28/2/2020. From these results, it can be noticed that the outbreak will reach its highest level on the day 28/2/2020. The average percentage of the increase over the forecasted period is 10%, the highest percentage is 12% on 28/2/2020, and the lowest percentage is 8.7% on 19/2/2020. From the previous results, it can be concluded that the proposed FPASSA-ANFIS has a high ability to forecast the COVID-19 dataset. These results avoid the limitations of traditional ANFIS because of the combination with the modified FPA method. Moreover, the operators of SSA are combined with the local strategy of FPA to enhance their exploitation ability. However, the time computational of the proposed FPASSA method still requires more improvements. This paper proposed a modified version for the flower pollination algorithm (FPA) using the salp swarm algorithm (SSA). This modified version, called FPASSA, is applied to improve the performance of the ANFIS through determining the optimal value for its parameters. The developed FPASSA-ANFIS model is applied as a forecasting technique for a novel coronavirus, called COVID-19, that was discovered in Wuhan, China at the end of last year and January of the current year.Question: For what SCA algorithm was applied to improve the ANFIS model ?

question:You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.Main Abstract:Actionness was introduced to quantify the likelihood of containing a generic action instance at a specific location. Accurate and efficient estimation of actionness is important in video analysis and may benefit other relevant tasks such as action recognition and action detection. This paper presents a new deep architecture for actionness estimation, called hybrid fully convolutional network (H-FCN), which is composed of appearance FCN (A-FCN) and motion FCN (M-FCN). These two FCNs leverage the strong capacity of deep models to estimate actionness maps from the perspectives of static appearance and dynamic motion, respectively. In addition, the fully convolutional nature of H-FCN allows it to efficiently process videos with arbitrary sizes. Experiments are conducted on the challenging datasets of Stanford40, UCF Sports, and JHMDB to verify the effectiveness of H-FCN on actionness estimation, which demonstrate that our method achieves superior performance to previous ones. Moreover, we apply the estimated actionness maps on action proposal generation and action detection. Our actionness maps advance the current state-of-the-art performance of these tasks substantially.Cited Abstract(s):[1]: We present a generic objectness measure, quantifying how likely it is for an image window to contain an object of any class. We explicitly train it to distinguish objects with a well-defined boundary in space, such as cows and telephones, from amorphous background elements, such as grass and road. The measure combines in a Bayesian framework several image cues measuring characteristics of objects, such as appearing different from their surroundings and having a closed boundary. These include an innovative cue to measure the closed boundary characteristic. In experiments on the challenging PASCAL VOC 07 dataset, we show this new cue to outperform a state-of-the-art saliency measure, and the combined objectness measure to perform better than any cue alone. We also compare to interest point operators, a HOG detector, and three recent works aiming at automatic object segmentation. Finally, we present two applications of objectness. In the first, we sample a small numberof windows according to their objectness probability and give an algorithm to employ them as location priors for modern class-specific object detectors. As we show experimentally, this greatly reduces the number of windows evaluated by the expensive class-specific model. In the second application, we use objectness as a complementary score in addition to the class-specific model, which leads to fewer false positives. As shown in several recent papers, objectness can act as a valuable focus of attention mechanism in many other applications operating on image windows, including weakly supervised learning of object categories, unsupervised pixelwise segmentation, and object tracking in video. Computing objectness is very efficient and takes only about 4 sec. per image.[2]: Super pixel and objectness algorithms are broadly used as a pre-processing step to generate support regions and to speed-up further computations. Recently, many algorithms have been extended to video in order to exploit the temporal consistency between frames. However, most methods are computationally too expensive for real-time applications. We introduce an online, real-time video super pixel algorithm based on the recently proposed SEEDS super pixels. A new capability is incorporated which delivers multiple diverse samples (hypotheses) of super pixels in the same image or video sequence. The multiple samples are shown to provide a strong cue to efficiently measure the objectness of image windows, and we introduce the novel concept of objectness in temporal windows. Experiments show that the video super pixels achieve comparable performance to state-of-the-art offline methods while running at 30 fps on a single 2.8 GHz i7 CPU. State-of-the-art performance on objectness is also demonstrated, yet orders of magnitude faster and extended to temporal windows in video.[3]: [4]: Supervoxel segmentation has strong potential to be incorporated into early video analysis as superpixel segmentation has in image analysis. However, there are many plausible supervoxel methods and little understanding as to when and where each is most appropriate. Indeed, we are not aware of a single comparative study on supervoxel segmentation. To that end, we study five supervoxel algorithms in the context of what we consider to be a good supervoxel: namely, spatiotemporal uniformity, object region boundary detection, region compression and parsimony. For the evaluation we propose a comprehensive suite of 3D volumetric quality metrics to measure these desirable supervoxel characteristics. We use three benchmark video data sets with a variety of content-types and varying amounts of human annotations. Our findings have led us to conclusive evidence that the hierarchical graph-based and segmentation by weighted aggregation methods perform best and almost equally-well on nearly all the metrics and are the methods of choice given our proposed assumptions.[5]: This paper addresses the problem of generating possible object locations for use in object recognition. We introduce selective search which combines the strength of both an exhaustive search and segmentation. Like segmentation, we use the image structure to guide our sampling process. Like exhaustive search, we aim to capture all possible object locations. Instead of a single technique to generate possible object locations, we diversify our search and use a variety of complementary image partitionings to deal with as many image conditions as possible. Our selective search results in a small set of data-driven, class-independent, high quality locations, yielding 99 recall and a Mean Average Best Overlap of 0.879 at 10,097 locations. The reduced number of locations compared to an exhaustive search enables the use of stronger machine learning techniques and stronger appearance models for object recognition. In this paper we show that our selective search enables the use of the powerful Bag-of-Words model for recognition. The selective search software is made publicly available (Software: http: disi.unitn.it uijlings SelectiveSearch.html ).[6]: In this paper we target at generating generic action proposals in unconstrained videos. Each action proposal corresponds to a temporal series of spatial bounding boxes, i.e., a spatio-temporal video tube, which has a good potential to locate one human action. Assuming each action is performed by a human with meaningful motion, both appearance and motion cues are utilized to measure the actionness of the video tubes. After picking those spatiotemporal paths of high actionness scores, our action proposal generation is formulated as a maximum set coverage problem, where greedy search is performed to select a set of action proposals that can maximize the overall actionness score. Compared with existing action proposal approaches, our action proposals do not rely on video segmentation and can be generated in nearly real-time. Experimental results on two challenging datasets, MSRII and UCF 101, validate the superior performance of our action proposals as well as competitive results on action detection and search.[7]: This paper considers the problem of action localization, where the objective is to determine when and where certain actions appear. We introduce a sampling strategy to produce 2D+t sequences of bounding boxes, called tubelets. Compared to state-of-the-art alternatives, this drastically reduces the number of hypotheses that are likely to include the action of interest. Our method is inspired by a recent technique introduced in the context of image localization. Beyond considering this technique for the first time for videos, we revisit this strategy for 2D+t sequences obtained from super-voxels. Our sampling strategy advantageously exploits a criterion that reflects how action related motion deviates from background motion. We demonstrate the interest of our approach by extensive experiments on two public datasets: UCF Sports and MSR-II. Our approach significantly outperforms the state-of-the-art on both datasets, while restricting the search of actions to a fraction of possible bounding box sequences.[8]:

question:Given an array of animal names and an erroneous piece of code, correct the code and transform all the entities within the array into uppercase letters. Consider the array animal_arr = ['Elephant', 'Lion', 'Giraffe']The given faulty code is:```def uppercase_animals(animal_arr): for animal in animal_arr: animal = animal.upper() return animal_arrprint(uppercase_animals(['Elephant', 'Lion', 'Giraffe']))```This code returns the original array without any transformations because it's trying to modify the 'animal' variable in-place, which Python does not allow. Your task is to fix this code or write a new one that will return the array with all animal names in uppercase. We encourage avoiding built-in python functions for such transformations to increase the difficulty. Also, the program should display an error message if the array contains anything other than strings. For example, if the array is ['Elephant', 'Lion', 100], return "Invalid input! The array can only contain strings."

question:Write an advanced SQL command that does the following:1. Extract the "email" and "phone_number" fields from a table specifically designated as "teachers".2. Filter out any teachers who are new and have joined within the last year.3. Sort the resulting list in descending order by the date they joined the institution, i.e., the field "join_date".4. Provide an additional query to calculate the total count of the remaining teachers.5. Restrict the list to the first 50 records.Assume that the "teachers" table also contains a "join_date" field which contains the date when each teacher joined the institution.