Analyzing the capabilities of crowdsourcing services for text summarization Abstract This paper presents a detailed analysis of the use of crowdsourcing services for the Text Summarization task in the context of the tourist domain. In particular, our aim is to retrieve relevant information about a place or an object pictured in an image in order to provide a short summary which will be of great help for a tourist. For tackling this task, we proposed a broad set of experiments using crowdsourcing services that could be useful as a reference for others who want to rely also on crowdsourcing. From the analysis carried out through our experimental setup and the results obtained, we can conclude that although crowdsourcing services were not good to simply gather gold-standard summaries (i.e., from the results obtained for experiments 1, 2 and 4), the encouraging results obtained in the third and sixth experiments motivate us to strongly believe that they can be successfully employed for finding some patterns of behaviour humans have when generating summaries, and for validating and checking other tasks. Furthermore, this analysis serves as a guideline for the types of experiments that might or might not work when using crowdsourcing in the context of text summarization. Natural Language Processing (NLP) tasks often require annotated data or goldstandards to carry out large experiments, as well as to evaluate the task's performance itself. However, to obtain this type of data is normally very costly and time-consuming. Some of these tasks can be done automatically if a gold-standard is available, for instance, to evaluate if a question answering system is able to provide the correct answer for a factual question; but others, in which a gold-standard is difficult to obtain, such as reading comprehension, machine translation, information retrieval or text summarization, are much more difficult to evaluate.. Crowdsourcing services, such as Amazon's Mechanical Turk (AMT) 1 or Crowdflower 2 have recently appeared as services where users (also known as requesters) can upload tasks (or HITs 3 ) which are performed by anonymous people, also called workers. For each task performed, a small amount of money is given as a reward depending on the price assigned to the task. Crowdsourcing services are very appropriate for carrying out those tasks that are simple for humans, but very difficult for computers and would require a lot of time to be completed. However, the quality of the output was repeatedly brought into question (e.g. Gillick and Liu (2010) ). Although these services include some quality measures for ensuring the quality of the results, and one can refuse to pay if the task is not properly done, workers can still cheat and game the system. At least, two basic issues have to be taken into consideration in an attempt to guarantee the results' quality. On the one hand, we need to ensure that workers are suitable for the task, and on the other hand, we have to check that workers do not give random answers while performing it. AMT and Crowdflower themselves, provide a qualification facility to assist quality control. Requesters can attach various qualification requirements to their task in order to force workers to meet these requirements before they are allowed to work. It is also possible to obtain the overall credibility of a worker by measuring the percentage of accepted tasks he/she has completed since joining AMT.. AMT has been used in a number of NLP tasks, such as relevance judgement (Alonso et al. 2008) , image region annotation (Sorokin and Forsyth 2008) , extracting facets from documents (Dakka and Ipeirotis 2008) , or paraphrasing verbs for noun compound interpretation (Nakov 2008) . Crowdflower has also been employed for tasks such as named entity annotation (Finin et al. 2010) or corpus creation (Negri and Mehdad 2010) . However, to the best of our knowledge, none of these services has been analyzed in detail, regarding their reliability on text summarization tasks.. Therefore, the main objective of this paper is to analyze to what extent crowdsourcing services are useful for text summarization, in particular, for the task of writing human summaries that may be use as gold-standard or model summaries for evaluation. To this end, we aim to assess the reliability of AMT through CrowdFlower workers for identifying relevant information that can be used as part of a summary. If crowdsourcing proves reliable and renders model summaries of sufficient quality, it could be used to replace the very costly process of generating human (non AMT workers) written model summaries. We set up different experiments addressed to retrieve information (e.g., sentences or nuggets) worth including in a model summary. We focus on the tourist domain, and our aim is to obtain summaries that convey useful and interesting information about a specific place or object (e.g., ''river Thames'', ''Eiffel Tower'') that is shown in an image. In the first experiment we show workers an image together with the name of the object it refers to, and ten related documents retrieved from the Web. The workers are asked to select the 10 most relevant sentences to build a summary that provides useful information about the place in the image for a potential tourist. In the second experiment, we repeat the same task, but include trap sentences in the documents, in order to account for the reliability of the summaries provided in the former experiment, and to analyze whether the workers tend to cheat or not. Asking workers to select sentences may lead to some agreement problems in the information selected. However, the task of summary generation can be broadly defined, if we ask for the kind of information users are interested in about a specific place shown in an image. In light of this, we set up the experiment 3, where workers are required to write ten questions about which information they would like to know about a place. Further on, we analyze those questions, and use the most frequent ones, to ask the workers to select the sentences in the documents that contain such information (experiment 4). In the fifth experiment, we repeat the second experiment with different quality control mechanisms to ensure the commitment and skills of the workers, and evaluate their effectiveness. Finally, the last experiment (experiment 6) aims at validating the summaries generated in experiment 5 by other AMT annotators. By doing so, we can analyze whether the refinement of the task and the addition of quality control mechanisms and acceptance requirements leads to better summaries.. Results show that crowdsourcing services may be useful when simple tasks are defined. However, despite this fact, we cannot rely on the results directly, since they need to be checked afterwards in order to account for their quality. In this sense, one limitation encountered is the time spent for validating the results, sometimes taking longer than expected. Although from our experience, some of the experiments did not work as we expected, this analysis serves as a guideline of what might and might not work, when using crowdsourcing for text summarization.. The paper is structured as follows: the related work on different tasks performed with crowdsourcing services, as well as its benefits and limitations for NLP are introduced in Sect. 2 The description of the corpus and the crowdsourcing used and the suggested experiments is provided in Sect. 3 Furthermore, an individual section is devoted to each of the proposed experiments, where a description of the task as well as a detailed analysis of the results are provided. This comprises Sects. 4-9. Finally, a general conclusion and some orientations for future work are presented in Sect. 10 As an appendix, we provide the definition of a complete HIT in Sect. 11. Based on the results obtained, our experimental set up for using crowdsourcing services to directly build gold-standards for text summarization was not successful. ROUGE results were not as expected, since the difference between model versus model summaries and AMT summaries versus model ones is quite significant. One possible explanation is that building model summaries by nonexpert people is really challenging, being necessary to give specific instructions to tackle it or not being so ambitious in the proposed task. However, after having a look at the summaries generated, our intuition is that most workers might not care about the task, and were more focused on the money, so they tried to complete the task as quickly as possible, as evidenced by the fact that most of the generated summaries were incomplete or included a lot of redundant or irrelevant information. Both issues will be analyzed in depth in the remaining experiments.. The results of this experiment were not satisfactory either. Even if the instructions for this task were clear and detailed, the annotators did not follow them at all, as evidenced by the fact that 8 out of the 16 annotators involved did select trap sentences. The problem, thus, does not seem to be the difficulty of the task, but the little attention paid by the workers when performing it. The annotators showed a random behaviour, and only the best annotator achieved results that were comparable to the Wikipedia results according to the ROUGE tool. In light of the results obtained in this and the previous experiment, we think that it may not be feasible to use crowdsourcing services for directly building model summaries for automatic summarization. Therefore, in the following experiments, we will focus on narrowing and facilitating the task, in order to ask for more concrete information helpful for generating summaries.. The results obtained in this experiment are much better and accurate than in the previous experiments. One reason for this is the nature of the task, which was easier than having to produce directly extractive summaries. Regarding the questions generated, it is worth noting that half (50 %) of the questions were classified within the visiting, location, foundation-year, surrounding and features attributes. This means that people do share ideas as to what types of information are required about a place, and the set of top five attributes captures these information types. These results are of great value when deciding what to include in the image description or summary. Therefore, we can conclude that this type of experiments are more appropriate for crowdsourcing services. In the next experiment, we verify whether this information can be used to guide and support the annotators in the summarization task. In this experiment, we used the questions generated by the annotators in the previous experiment in order to account for the type of information a summary should contain. Therefore, the objective of this experiment is to ask the annotators to select the sentences in the documents that best answer a set of questions regarding different aspects of a place. These answers can be then put together to build up a model summary. With this experiment, we want to analyze whether there is a relationship between the information that users may be interested in by means of a set of proposed questions they have formulated in the previous experiment, and the information stated in the source documents and model summaries (its presence or its absence). Consequently, through this experiment we can study: i) whether such questions are representative of the most important information; ii) to what extent the answer to these questions appear in the documents and summaries; and finally, iii) if they can be generalized for any tourist place/object in an image.. Based on the qualitative and quantitative evaluation results, contrary to what we might have expected, the AMT annotators were showing again a random behaviour, that led to very poor results. Even though the instructions given to the annotators were clear, the task was bounded and well-defined, and the answer to most questions was easy to find in the documents, the results obtained confirmed that in most of the cases, the annotators were not paying any interest in doing the task properly. The little time spent to finish each annotation task (39 s on average) demonstrates that, in this case, crowdsourcing services were not appropriate for this task, which required high dedication and commitment of annotators. One of the reason why this could be happening is the definition of the task itself. As we previously stated, workers may be more comfortable with tasks that are more specific and easy to perform. However, we also think that quality control mechanisms are crucial in the proper development of the task. Therefore, in the following experiment, we will design and implement different quality control mechanisms that allow us to select committed and qualified workers.. The results of this experiment seem to indicate that the use of control mechanisms may be of help when obtaining automatic summaries from crowdsourcing services, since they allow to quickly detect malicious user behavior. However, this control policy does not seem to guarantee the quality of the annotations, and still the results are not satisfactory. First, we have found a good number of annotators that have obviously cheated. As a consequence, more than 86 % of the summaries had to be rejected. Second, the remaining annotators seem to have paid little attention to the task, as evidenced by the fact that the resulting summaries are still far from the human-made model ones. These findings seem to confirm our intuition that the annotators are not taking the task seriously. The task seems to be excessively time-consuming and to require too much effort, so that the annotators are unwilling to perform it. 9 Experiment 6: Validating the informative summaries about tourist places using quality control mechanisms. In this experiment, we assess the quality of the summaries generated in the previous experiment (Sect. 8) For this, we design another HIT, where the annotators rate the summaries that have been produced by other annotators. The annotators do not know where the summaries come from.. This experiment validated the generated summaries created by the annotators. In light of the results obtained, we can conclude that these type of tasks, where the annotators have to evaluate the work of others, are useful to discard noisy results. In addition, we confirm that it is necessary to design quality control mechanisms as well, in order to distinguish the annotators who perform the task correctly from the ones who do not pay any attention to it.. The quantitative results obtained with ROUGE showed that the performance of the summaries in which the annotators had a 100 % of agreement increased with respect to the results of the whole set of summaries. This result indicates that the annotators read the summaries and rated them according to their opinion about how good they were, thus showing, in this case, more commitment to the task than in the previous experiments. This paper carried out a in-depth analysis of crowdsourcing services in the context of the text summarization task, by employing Crowdflower through Amazon's Mechanical Turk workers. Specifically, we focused on the identification of relevant information and the generation of multi-document summaries for providing useful details about places or objects shown in images, according to the information in which the users are interested. Through the set of proposed experiments, our aim was to determine the appropriateness of non-expert human collection of model summaries. Such experiments included different tasks, addressing the generation of extractive summaries from a collection of documents first, and focusing later on the kind of information a human is interested in, and therefore a good summary should contain. Moreover, we conducted additional experiments, where we specifically established quality control mechanisms, and acceptance requirements in order to avoid malicious workers performing the tasks. The results obtained for experiments 1, 2 and 4 were not satisfactory, leading us to further analyze what could be happening. To this end, several reasons have been found to explain why this may occur. On the one hand, some annotators did not pay any attention to the task itself, performing it randomly, or even cheating. This was confirmed when ''trap'' sentences were included in the documents, and the annotators were still selecting trap sentences from the model summaries, even if being aware of this fact. On the other hand, we found that some tasks are more complex than others. This means that not all annotators are equally capable of performing the task successfully, and since they are not experts, the quality of the results may be affected. However, it is important to stress the fact that, when a less time-consuming task is requested, the quality of the results improves. That is the case of the experiment 3, where, instead of asking annotators to select the top 10 relevant sentences for describing a place shown in an image (which requires to read the entire documents related to such image), we asked them to come up with 10 questions concerning the information they wanted to know about a place. From this experiment, we obtained some specific information that users were interested in, being of great value for developing text summarization systems. From experiments 5 and 6, we can also conclude that the quality control mechanisms introduced may be of help in some cases, although they are not a guarantee that the annotators will be committed to the task, as we noticed in experiment 5, where the results were not very good either. In light of our experience, the design of a task specifically addressed to check what other annotators did may be successful, and it will allow to discard low quality results. We have demonstrated this in the experiment 6. Finally, the results seem to indicate that the observed low reliability may be due to motivational factors, and this aspect should be studied in future work. In previous work payment was studied as one of the motivational factor for controlling the results quality. Aker et al. (2012) showed that high payments lead to better results whereas in Mason and Watts (2010) and Feng et al. (2009) it is discussed that an increase in payment attracks more spammers and as consequence it leads to low quality results. In our summary generation experiments we varied the payments in small steps from low to high however, in overall we have not manage to obtain any useful results from the MTurk workers. As discussed above the only useful results were obtained through the experiment 3. This experiment 3 differred from the other in a way that it was far easy to complete and did not require any time consuming reading task. Thus, we think that the major factor in obtaining high quality results is the level of difficulty of a task. If a task is easy and fast to perform then we think that this will positively influence the results quality. On ther other hand if the task requires a lot of time to complete and is difficult to perform this causes that the workers loose motivation on the task which then reflects the results negatively.. One disadvantage found when using crowdsourcing services is that, in all cases, it is not possible to rely directly on the workers' annotations. A process of validation is needed afterwards to ensure the proper quality of the results. To some extent this contradicts the basic idea behind using crowdsourcing services which is to provide a framework to carry out tasks that are difficult for computers but not for humans, in a rapid and cheap way. However, the reality shows that the results provided are far from ideal, and therefore, we have to spend time checking their quality, which itself can require substantial resources.. Finally, although crowdsourcing services were not good to simply gather goldstandard summaries, the encouraging results obtained in the third and sixth experiments motivate us to strongly believe that they can be successfully employed for finding some patterns of behavior humans have when generating summaries (e.g., what type of information they usually include), and for validating and checking other tasks. From our analysis, we have provided some guidelines such as types of experiments that might or might not work when using crowdsourcing in the context of text summarization.. In the future, we would like to explore some issues in more detail. In the shortterm, we will focus on analyzing the patterns found, as well as discovering additional ones, with the final purpose of improving the quality of the summaries produced by automatic systems. In the medium and long-term, we would like to design a validation HIT associated to each normal HIT, in order to study whether this strategy improves the results obtained with crowdsourcing services, and decreases the time humans have to spend checking manually the results provided by the annotators.. Appendix: HIT for Experiment 5 (Generating Informative Summaries about Tourist Places using Quality Control Mechanisms). In this appendix, we provide the complete HIT for the experiment regarding the selection of five pieces of information or sentences that best answer different questions a person would like to know about a specific place. Together with the instructions we provide an example of how to perform the task, and we also warned the annotators of the acceptance requirements for the task to be considered correctly done.. Information Selection: Instructions You will be given a name of a geo-graphical place, a picture of it and 10 documents related to the place. Imagine you are a tourist and have got 5 questions about the place for which you would like to know the answers (i.e., what information you would ask for). Please select your answers from the given documents. The documents contain in each line a sentence. Your answer selections can contain the entire sentence or a part of it.. Finally, you will be given two questions. Please answer them. The answers might not come from the given documents. In this section, we describe previous literature concerning the use of crowdsourcing services for NLP. Therefore, we first explain the general use of such services. Then, we describe different mechanisms that have been proposed to ensure the quality of the results obtained. Finally, we focus on different approaches that have used AMT or Crowdflower 4 for the specific task of text summarization, and we highlight how our work differs from them. Six different experiments are set up and performed following a logical order, the output of some of them being used for the other experiments. The first experiment aims at asking workers to produce a model summary that satisfies the information needs of potential tourists, by selecting the top 10 sentences of the documents, most relevant to the image shown. The second one asks the same task, but the documents include some ''trap'' sentences, so that we can analyze to what extent workers are intentionally cheating when carrying out the task. The third and the fourth experiments focus more on the type of information that humans would like to know about a place. In this way, the goal of the third experiment is to ask workers to provide 10 questions about the information they would like to obtain about the place whereas the fourth asks them to select those sentences in the documents that answer a set of questions. These questions are the most frequent ones provided by workers in the third experiment. The aforementioned experiments are completed with the last two experiments by implementing different quality control policies aiming at selecting qualified and involved workers, and validating the summaries generated by other workers.. In the following sections, we explain each experiment in detail. The set of images together with the names of the corresponding places, and the 10 Web documents describing each place were presented to 5 AMT annotators through Crowdflower platform, and they were asked to select the 10 sentences that best describe the place shown in each image. They were clearly advised that the end was to build a brief summary which presents interesting and useful information about the place for tourism purposes (e.g., opening hours, location, a brief historical details, price, etc.), and that we are not interested in the description of the image itself, i.e., what you can see on it. Moreover, they were warned not to include redundant sentences. All annotators were required to have a 95 % trust rating. In addition, users were asked to write a short text providing their overall impression on the adequacy of the documents to the images. We offered 0.05 US dollars for each summary.. As in the previous experiment, the images together with the name of the places, and the 10 Web documents describing them were presented to 16 annotators. However, in this experiment the 10 documents contain a variable number of trap sentences. An example of trap sentence is ''The image shows the place X, but please note I am just a sentence generated by the authors and thus I am not relevant''. They were asked to select 10 sentences from the documents, in the same conditions as experiment 1. They were advised of the existence of trap sentences that should not select. Otherwise, their work would be rejected. We offered 0.035 US dollars per each summary.. In this experiment, we showed the annotators an image pertaining to a particular place (e.g., the Eiffel Tower in Paris). We also presented the annotator with the name of the place (Eiffel Tower) and its scene or object type (tower). The annotators were asked to take the role of a tourist and provide ten questions for which they would like to know the answers when they see the place shown in the image. We showed images picturing 200 different places around the world together with their names. These places were manually selected from Wikipedia. Each image was shown to five different annotators. In total we collected 7,644 questions for 187 different places. The questions came from 184 different annotators. The expected number of questions was 10,000 (200 9 5 annotators 9 10 questions). However, there are some places for which we only have questions from two or three annotators. We paid 0.05 US dollars for each image.. Based on the results from experiment 3 we selected as the input for this experiment 10 questions representing the most frequent 10 types of questions from the entire set of questions. Table 6 shows such questions. This set of questions, 45 images together with the name of the object they represent, and the 10 documents describing each image were presented to 22 annotators, who were requested to select, from each set of documents, the 10 sentences which they think best answer each of the questions. As in experiment 2, the 10 documents contain some trap sentences. Also, they were told to avoid redundancy in the summaries, so in the case they found several sentences matching the same question, they could only select one. We collected a total of 312 annotations, which means an average of 6.93 annotations per image. We offered 0.04 US dollars per image.. As in experiments 1 and 2, the images together with the name of the places and the 10 web documents describing them were presented to workers. Motivated by the encouraging results of experiment 3, they were asked to think of 5 questions about the place for which they would like to know the answers and to select from the web documents the answers for each of the questions. An answer was allowed to be a full sentence or just part of a sentence. 8 The answers are used as a summary for the place. We offered 0.15 US dollars per correct annotation, and obtained 241 annotations.. In order to ensure that workers were suitable for the task, we implemented several quality control mechanisms. First, only annotators from English speaking countries 9 and with a 100 % trust rating were allowed. We allowed workers from the U.S.A and India. Second, a recruitment task was added which consisted in answering the following objective and single-answer questions, which eliminates the uncertainty usually present in subjective tasks and allows us to discard those workers that are deliberately cheating: 1) What is the name of the place for which you have entered the 5 pieces of information as answers?, and 2) In which country is the place located?.. It is worth mentioning that the answer to the first question is given as part of the HIT, while the answer to the second may be easily found in the documents that the annotators were required to read. Workers who did not answer both questions correctly would not be considered for the main task. Third, as in the experiments 2 and 4, the annotators were advised of the presence of trap sentences. Finally, workers were told that they would not be paid if they did not perform the task correctly (i.e., if they did include trap sentences or redundant information as answers).. Moreover, to ensure that workers clearly understand what they are asked for, we included an example of possible summary for the place Ararat. In Sect. 11 the complete definition of this task is provided as an appendix.. In this task, we evaluate the 37 summaries that annotators generated in the previous experiment. The HIT was designed as follows: the name of a geo-graphical place, its picture, and a description (summary) were shown to the annotators, and they were asked to rate this description within a range 1 to 5. The explanation for the ratings was also provided. A score of 1 indicated an inappropriate description, whereas the value 5 was an indication of a good description. Finally, we evaluated the 37 summaries that annotators generated in the previous experiment. Each summary consisted of five sentences, and was evaluated by 4 different annotators. Since we consider that this task was easier to perform than the previous one, we paid 0.05 US dollars for each description rating.. In addition, the same quality control mechanisms as in the fifth experiment were taken into account (only annotators from English speaking countries with a 100 % trust rating were allowed). At the end of each task, we also included two objective questions, in order to verify that the annotators were paying attention to the task. Specifically, these questions were: (i) What is the place name for which you have read its description?, and (ii) In which country is the given place?. As we have mentioned, the purpose of these questions was to try to discover neglected behaviour of the annotators, since the answer to the first question was already given in the HIT itself, and the answer to the second one was very easy to find in the summaries.

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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O B I O O O B O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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Fast Detection of Robust Features by Reducing the Number of Box Filtering in SURF Speeded up robust features (SURF) can detect scale-and rotation-invariant features at high speed by relying on integral images for image convolutions. However, since the number of image convolutions greatly increases in proportion to the image size, another method for reducing the time for detecting features is required. In this letter, we propose a method, called ordinal convolution, of reducing the number of image convolutions for fast feature detection in SURF and compare it with a previous method based on sparse sampling. key words: ordinal convolution, fast feature detection, SURF In the literature, the problem of detecting robust distinctive features over images captured in different viewing conditions has been studied extensively [4] , [5] . The state-ofthe-art methods have been successfully used in applications where the real-time constraint is less significant. However, they are too computationally expensive to be applied to realtime applications.. Bay et al. [2] enabled to rapidly detect robust features by employing integral image [6] . However, their method, i.e. SURF, requires three convolutions with different shaped box filters for each point and thus its speed greatly drops as the image size increases.. Although having a different framework from SURF, Agrawal et al. [1] proposed a method, called CenSurE, that also uses integral images for convolution and reduces the whole number of convolutions by first computing an approximated circular Laplacian which is computed by a convolution with a box filter and then computing the Harris measure only at the points where the computed approximated circular Laplacian is larger than a threshold. Afterward, Ebrahimi and Mayol-Cuevas [3] made the CenSurE much faster by further reducing the number of convolutions using the concept of sparse sampling. They called the method SUSurE.. In this letter, we are interested in making SURF faster and propose an ordinal convolution method for reducing the number of convolutions in SURF. Then, we compare it with one of applying SUSurE to SURF. In this letter, we proposed an ordinal convolution method of reducing the number of image convolutions for fast feature detection in SURF and compared it with applying SUSurE to SURF. The proposed ordinal convolution method could greatly reduced the processing time (about 2-3 times) while maintaining the repeatability of detected features (within 5%) and thus outperformed the method of applying SUSurE to SURF (though SUSurE did not fit SURF perfectly).. The proposed ordinal convolution method can be applied to other Hessian-based feature detectors. The related analysis remains a near future work.

O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I I O B O O O O O O O O O O O O O B I B I I B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B I O O O O O O O O B O O O O O O O O O O B O O O O O O B I O O O O O O O O O B I I O B O O O O O O O B O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O B I O O O O O O O O O O O O O O O O O


Algorithms for detecting and analysing autocatalytic sets Background: Autocatalytic sets are considered to be fundamental to the origin of life. Prior theoretical and computational work on the existence and properties of these sets has relied on a fast algorithm for detecting self-sustaining autocatalytic sets in chemical reaction systems. Here, we introduce and apply a modified version and several extensions of the basic algorithm: (i) a modification aimed at reducing the number of calls to the computationally most expensive part of the algorithm, (ii) the application of a previously introduced extension of the basic algorithm to sample the smallest possible autocatalytic sets within a reaction network, and the application of a statistical test which provides a probable lower bound on the number of such smallest sets, (iii) the introduction and application of another extension of the basic algorithm to detect autocatalytic sets in a reaction system where molecules can also inhibit (as well as catalyse) reactions, (iv) a further, more abstract, extension of the theory behind searching for autocatalytic sets. Results: (i) The modified algorithm outperforms the original one in the number of calls to the computationally most expensive procedure, which, in some cases also leads to a significant improvement in overall running time, (ii) our statistical test provides strong support for the existence of very large numbers (even millions) of minimal autocatalytic sets in a well-studied polymer model, where these minimal sets share about half of their reactions on average, (iii) "uninhibited" autocatalytic sets can be found in reaction systems that allow inhibition, but their number and sizes depend on the level of inhibition relative to the level of catalysis. Conclusions: (i) Improvements in the overall running time when searching for autocatalytic sets can potentially be obtained by using a modified version of the algorithm, (ii) the existence of large numbers of minimal autocatalytic sets can have important consequences for the possible evolvability of autocatalytic sets, (iii) inhibition can be efficiently dealt with as long as the total number of inhibitors is small. The modified RAF algorithm, based on the notion of pseudo-RAFs, clearly improves on the number of calls to the closure computation procedure, which is computationally the most expensive part of the algorithm. Depending on the structure of catalysis in a reaction network, this can also lead to a significant improvement in the overall running time of the algorithm. However, in a purely "random" system (as in the binary polymer model), the additional overhead of keeping track of the number of reactions that produce a given molecule seems to cancel out the gain obtained from a reduced number of closure computations, at least for polymers of the sizes studied here. But, in general, the modified algorithm is never worse -and in some cases it is faster -than the original version.. Our statistical test on the expected number of irrRAFs within a reaction network provides strong support for theexistence of very large numbers of irrRAFs. In instances of the binary polymer model, even for n = 8 with maxRAF sets of (on average) 375 reactions, is it highly likely (99% confidence) that at least hundreds of millions of irrRAFs exist. The overlap statistics show that these irrRAFs share, on average, about half of their reactions with each other, ranging from 25% to 80% overlap. In other words, there is always at least 20% (and up to 75%) difference between two arbitrary irrRAFs within the same reaction network. This large number and the relatively high variability could have important (and positive) consequences for the possible evolvability of autocatalytic sets [15, 21] .. Even though the general problem of finding RAF sets in systems that also allow inhibition (as well as catalysis) is NP-complete, we have shown that this problem becomes tractable when the total number of inhibitors is small (in absolute value). We have implemented and applied an extension of the basic RAF algorithm to search for such "uninhibited" RAF sets (u-RAFs), and have shown that their number and sizes depend largely on the level of inhibition (i.e., the probability that an inhibitor actually inhibits any given reaction) relative to the level of catalysis. So, at least in certain limited cases, inhibition can be dealt with efficiently. A full investigation of u-RAFs will be deferred to future work, however. The concept of autocatalytic sets was introduced by Kauffman [1] [2] [3] to study the idea of life as a functionally closed and self-sustaining chemical reaction system. This concept is closely related to other such models and ideas [4] [5] [6] [7] and is believed to have played a crucial role in the origin of life. It was later formalized mathematically in the form of RAF theory [8] [9] [10] .. To briefly review RAF theory, we first define a chemical reaction system (CRS) as a tuple (X, R, C) consisting of a set of molecule types X, a set of (possible or allowed) *Correspondence: wim@WorldWideWanderings.net 1 SmartAnalytiX.com, Lausanne, Switzerland Full list of author information is available at the end of the article chemical reactions R, and a catalysis set C indicating which molecule types can catalyse which reactions. Next, a food set F ⊂ X is defined as a subset of molecule types that are assumed to be freely available from the environment (i.e., they do not necessarily have to be produced by any of the reactions in R). Thus F is a subset of X, and we will denote a CRS with associated food set F as a quadruple Q = (X, R, C, F).. An autocatalytic set (or RAF set) for Q = (X, R, C, F) is a subset R ⊆ R of reactions which is:. 1. reflexively autocatalytic (RA): each reaction r ∈ R is catalysed by at least one molecule that is either present in F or can be formed from F by using a series of reactions only from R itself.

B O O O O B I O B I O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


An accurate and efficient yield optimization method for analog circuits based on computing budget allocation and memetic search technique Monte-Carlo (MC) simulation is still the most commonly used technique for yield estimation of analog integrated circuits, because of its generality and accuracy. However, although some speed acceleration methods for MC simulation have been proposed, their efficiency is not high enough for MC-based yield optimization (determines optimal device sizes and optimizes yield at the same time), which requires repeated yield calculations. In this paper, a new sampling-based yield optimization approach is presented, called the Memetic Ordinal Optimization (OO)-based Hybrid Evolutionary Constrained Optimization (MOHECO) algorithm, which significantly enhances the efficiency for yield optimization while maintaining the high accuracy and generality of MC simulation. By proposing a two-stage estimation flow and introducing the OO technology in the first stage, sufficient samples are allocated to promising solutions, and repeated MC simulations of non-critical solutions are avoided. By the proposed memetic search operators, the convergence speed of the algorithm can considerably be enhanced. With the same accuracy, the resulting MOHECO algorithm can achieve yield optimization by approximately 7 times less computational effort compared to a state-of-the-art MC-based algorithm integrating the acceptance sampling (AS) plus the Latin-hypercube sampling (LHS) techniques. Experiments and comparisons in 0.35 m μ and 90nm CMOS technologies show that MOHECO presents important advantages in terms of accuracy and efficiency. Industrial analog integrated circuit design not only calls for fully optimized nominal design solutions, but also requires high robustness and yield in the light of varying supply voltage and temperature conditions, as well as inter-die and intra-die process variations [1, 2] . The flow and available methods of yield optimization are summarized in Fig. 1 . The yield analysis can be classified into statistical methods and non-statistical methods. Non-statistical methods include corner-based methods [2] and performance-specific worst-case design (PSWCD) methods [3] . Such methods are efficient due to the limited number of simulations. But their drawback is the difficulty to know in advance the worst-case corner points, and it may result in serious design overkill [1] . Moreover, simple and fast sensitivity analysis in worst-case design methods may harm the accuracy in nanometer technologies. Statistical methods include Monte-Carlo (MC)-based methods and response-surface-based (RSB) methods [1, 4] . The latter category often has to face dilemmas considering the balance between the accuracy and the complexity of the model, as well as the accuracy and the number of samples. Hence, nonstatistical and RSB methods can be used for yield optimization but have significant accuracy problems. MC-based methods have the advantages of generality and high accuracy [5] , so they are still the most reliable and commonly used technique for yield estimation till now. Nevertheless, a large number of simulations are needed for MC analysis, therefore limiting its use within an iterative yield optimization loop, as in Fig. 1 . Although some speed enhancement techniques for MC simulation have been proposed [6, 7] , the efficiency gain is not high enough to make MC-based yield optimization practical. Therefore, in this paper we address yield optimization by proposing a different (but complementary) approach: we dramatically increase the efficiency of yield optimization by optimally allocating the computing budget to candidate solutions and by enhancing the convergence speed of the search strategy by means of a memetic algorithm in combination with the state-of-the-art sampling techniques. • avoid over-design;. • be general enough to be applied to any analog circuit in any technology and any distribution of the process parameters; • simultaneously handle inter-die and intra-die variations in nanometer technologies; • provide high-accuracy results comparable to MC; • consume far less computational effort compared with state-ofthe-art MC-based methods.. The remainder of the paper is organized as follows. Section 2 introduces the basic components and the general framework of MOHECO. Section 3 tests MOHECO by practical examples. The concluding remarks are presented in Section 4. In this paper, the MOHECO algorithm has been proposed for yield optimization of analog integrated circuits, considering both the inter-die and intra-die process variations. The method is general and has no risk of over-design. MOHECO can provide high-accuracy results with far less computational costs (about 1/7) even when compared with the state-of-the-art MC-based methods. The reason is that: (1) it uses a two-stage yield optimization process and OO in the first stage, which determines the simulation effort for each candidate solution "intelligently"; (2) a memetic algorithm combing the DE and the NM simplex algorithm is used and triggered by an adaptive rule enhance the convergence speed of the search engine considerably; (3) the state-of-the-art AS and DOE techniques and the selection-based constraint-handling technique also contribute positively to MOHECO. Therefore, MOHECO is an efficient good candidate for analog circuit yield optimization, especially for new nanometer technologies with large variability.

O O O O B I O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O


Mobile application to provide personalized sightseeing tours Tourist recommendation systems have been growing over the last few years, mainly because of the use of mobile devices to obtain user context. This work discusses some of the most relevant systems on the field and presents PSiS Mobile, which is a mobile recommendation and planning application designed to support a tourist during his vacations. It provides recommendations about points of interest to visit based on tourist preferences and on user and sight context. Also, it suggests a visit planning which can be dynamically adapted based on current user and sight context. This tool works also like a journey dairy since it records the tourist moves and tasks to help him remember how the trip was like. To conclude, some field experiences will be presented. When a tourist goes to a new location (country, city or region), he would certainly like to have a user-friendly tool to help planning his staying according to his objectives, preferences, knowledge, budget and available time. The tasks of planning where to go and what to do, in the limited amount of time available, are common problems encountered by tourists when visiting a city for the first time. In effect, cities are very large information spaces and in order to navigate through these spaces, visitors have available numerous guide books and maps that provide large amounts of information. Haubl and Dellaert (2004) state that this can be a bless and a curse. Although the amount of information allows tourists to select more appropriately the points of interest to visit, it can also turn the process so complex that the tourist might not be able to assimilate adequately all the given information. A recommendation system helps the tourist narrow the universe of choice, giving results according to his preferences. Also, it processes much more information than the tourist could possibly do.. Mobile devices are very useful to use on a tourism scenario, due to its pocket size and computational capabilities. It can be used to support a tourist on planning his stay, show point of interest's detailed information or recommend nearby points of interest to visit.. However, mobile devices have limited capabilities when compared to a traditional computer, which must be considered because of possible technical, ergonomic or economic implications on the application development. Although in recent years mobile technology has evolved significantly it has yet low performance, mainly on battery life time, which is the biggest obstacle to the growth of mobile performance.. To provide an effective support to the tourist on a trip, a set of factors must be considered including actual available technology, such as connectivity, localization and user interface. Mobile device's wireless capabilities can be slower and with higher latency compared with wired data connections. Also, the use of wireless communications increases a lot the device power consumption. To develop a client-server application for a mobile environment, all of these problems and limitations must be considered to provide an effective and reliable application. This paper is organized in the following topics. Section 2 presents some of the most important and recent works in the area. Section 3 describes PSiS (Personalized Sightseeing Planning System) architecture and functionalities. In Section 4 PSiS Mobile is compared with the systems presented in Section 2. Some lessons learnt and system results are presented in Section 5. Finally, in Section 6, conclusions are exposed, as well as, the future work. The main purpose of this work was to develop an application to a mobile device, which extends PSiS project, to support a tourist on his staying at a city by recommending and planning his visits based on his preferences and current context.. PSiS Mobile provides dynamic adaptation of the tourist route according to context changes beyond the time restriction. Also, since time restrictions cannot be welcomed by the users we use an architectonic feature, which indicates that the building can be closed but it is worth visiting its architectonic part.. Since mobile devices consume a lot of battery power the application adapts itself according to mobile device context. Like e-Tourism, our system uses different visit durations for each person's stereotype however, PSiS Mobile updates that durations according to the real time that the users spend while visiting them. Finally, the application generates automatically some feedback that is useful in order to create statistical reports about what the tourists saw and for how much time.. One big problem in this type of device is the small screen, however tablets are trying to overcome this problem but they are uncomfortable to use in a tourism context, and they also consume a lot of battery power. Nowadays battery power is the biggest problem for these systems, because the batteries take a lot of time to charge.. In the future, this application will be adapted to an audio guide, since it is uncomfortable to be always looking to the mobile device screen. Also, we are trying to integrate PSiS Mobile with an inertial navigation system adapted to the person's body, to also offer indoor guides.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Time-order error and scalar variance in a computational model of human timing: simulations and predictions Background: This work introduces a computational model of human temporal discrimination mechanism -the Clock-Counter Timing Network. It is an artificial neural network implementation of a timing mechanism based on the informational architecture of the popular Scalar Timing Model. Methods: The model has been simulated in a virtual environment enabling computational experiments which imitate a temporal discrimination task -the two-alternative forced choice task. The influence of key parameters of the model (including the internal pacemaker speed and the variability of memory translation) on the network accuracy and the time-order error phenomenon has been evaluated.The results of simulations reveal how activities of different modules contribute to the overall performance of the model. While the number of significant effects is quite large, the article focuses on the relevant observations concerning the influence of the pacemaker speed and the scalar source of variance on the measured indicators of network performance.The results of performed experiments demonstrate consequences of the fundamental assumptions of the clock-counter model for the results in a temporal discrimination task. The results can be compared and verified in empirical experiments with human participants, especially when the modes of activity of the internal timing mechanism are changed because of some external conditions, or are impaired due to some kind of a neural degradation process. The results of performed experiments demonstrate consequences of the fundamental assumptions of the clockcounter model for the results in a temporal discrimination task. We showed that the CCTN is able to model internal timing processes including the scalar variance property. We examined the behavior of a number of neural networks during the temporal discrimination task. The outcome of this study is a set of predictions which can be verified straightforwardly in empirical research. The timing literature proves that a lot of effort is devoted to test how changes in modes of activity of the timing mechanism can change behaviors of humans in the timing task (Meck 2005; Meck and Benson 2002; Wiener et al. 2010 ). This influence is inter alia tested in experiments that concern Parkinson's Disease (Artieda et al. 1992; Hellström et al. 1997; Koch et al. 2008; Merchant et al. 2008; Malapani et al. 1998; Malapani et al. 2002; Rammsayer and Classen 1997; Smith et al. 2007 ), mental disorders Sévigny et al. 2003) , the influence of drug application (Lustig and Meck 2005; Meck 1983; 1996; Rammsayer 1999) , and presentation of stimuli in different modalities (Melgire et al. 2005; Penney et al. 2000; Ulrich et al. 2006) . Some of the these works suggest that anatomical correlates of the internal clock are present in basal ganglia and other brain structures connected to this important part of the dopaminergic system (Coull et al. 2008; Coull et al. 2010; Macar et al. 1999; Meck 2006; Perbal et al. 2005) .. Such research emphasizes the need for a model which is able to integrate experimental data, to explain obtained results, and finally, to predict patterns of responses in situations that have not been tested empirically. The CCTN model and the simulation results presented in this work demonstrate how the clock speed and other timing mechanism manipulation may influence accuracy and timeorder error in the temporal discrimination task. Since it is possible to manipulate the Pacemaker speed (and it is also possible to find patients with impaired Pacemaker speed), the predictions of our model concerning this property of the timing mechanism can be fully verified in empirical experiments. What is more, we investigated a range of parameters of one of the possible sources of the scalar variance proposed in (Gibbon et al. 1984 ) -the source that is responsible for the memory transfer from the Accumulator module. There is evidence suggesting that memory storage may be unsettled in people with Parkinson's Disease (Malapani et al. 1998; Malapani et al. 2002) . One of the several interesting predictions stemming from our research is that the slowdown of the memory transfer, when it is accompanied by the increase in the relative variability, may reduce the overall accuracy and the magnitude of the time-order error in the temporal discrimination task; this can be verified in PD patients.. Apart from quantitative and qualitative analyses of changes of performance indicators, data fitting analyses have been performed. We are well aware that the proposed model has many free parameters, however it was still important to verify whether it had the potential to reproduce human behavior. The results of the BJ participant (Allan 1977) were used as an example of human characteristics. As the CCTN demonstrated a similar pattern and for some parameters closely resembled experimental data (Figure 12 ), this is an indication that the model has the potential to be a good explanatory platform for human timing mechanisms. Interestingly, specific analyses revealed that the mean MSE dropped with the increase of the relative scalar variance produced by the SV module (though the differences in MSE were small, see Figure 13 ), which further emphasizes the importance of the scalar property in timing.. Each of the 270 gray lines shown in Figure 12 shows an average across 32 runs with the same set of parameter values. Since the BJ participant took the experiment only once, a direct comparison between human and simulation data is limited. It is still worth noting that the trend of changes in TOE is consistent for all averaged runs, thus the model can and does simulate empirical reality.. While the CCTN is able to explain more effects than just the influence of stimuli duration on the TOE, our experiments were designed to avoid effects other than those related to stimuli duration. Modeling across-trials effects could be achieved easily, but it would complicate the behavior of the model, while the goal of this work was to isolate and study stimuli duration effects exclusively.. Developing a complete model of human timing is a difficult task; this work demonstrated how to represent a clock-counter timing model in a connectionist architecture. Simulation results prove that the model is a suitable tool to analyze the influence of the scalar sources of variability on temporal judgements, which makes it a descendant of the Scalar Timing Model. Simulations concerning the time-order error phenomenon demonstrated that our model is not only able to manifest it, but also that the manifestation of the TOE may resemble the behavior of a human. This justified investigating interactions between the scalar variance and the TOE -the two hardwired properties of human timing.. There are some analogies between the CCTN and the Sensation Weighting Model (Hellström 2003) : in both models, the magnitude of the first or the second stimulus is "strengthened" depending on the context. Our further work will concern the analysis of the recently discovered Type B TOE phenomenon (Dyjas and Ulrich 2014; Ulrich and Vorberg 2009) , the exploration of the ISI effect, and the presentation of the results in terms of Just Noticeable Differences instead of raw frequencies of answers. Another issue is the employment of more sophisticated methods of analysis of temporal behaviors, such as adaptive psychophysical procedures -i.e., one of the versions of the up-down procedure (Kaernbach 1991; Leek 2001) . To gain more specific knowledge about the stimuli comparison process, simulations will be performed in order to establish determinants of discrimination thresholds or psychometric functions (Wichmann and Hill 2001) related to perception of durations.. Since there are many free parameters in the CCTN, we are going to perform extensive fitting of the proposed model to human data. We would also like to transform the model into the representation consisting only of the integrate-and-fire neurons. Such a representation may cause some of the inherent properties of timing to emerge spontaneously (Buhusi and Oprisan 2013) , and it would be a good compromise between the classical clockcounter models and the neural models that have been gaining more and more attention recently. This would meet the need for a connectionist model of timing, fitted to both behavioral and neurobiological data, but still allowing one to comprehend the activity of the networkthus preserving the explanatory power of classical models of timing and at the same time maintaining biological adequacy. This will open a path for further exploration of the patterns of temporal judgements in a wide range of experimental situations. As the number of significant effects is quite large, the following discussion focuses on the relevant observations concerning the influence of the pacemaker speed and the scalar source of variance on the measured indicators of network performance. These parameters are of the highest importance because the internal clock speed and the memory transfer process are of particular interest in research on human timing. They are also highly influential on the response pattern in the presented version of the 2-AFC task. Other factors are more difficult to be directly operationalized in empirical experiments with human participants without additional low-level empirical research. Nevertheless, complex interactions between the two main factors and the rest of the parameters provide predictions concerning accuracy of the answers of networks and concerning the TOE in the temporal discrimination task. The results obtained from the simulations show the relationship between the human timing mechanism and the exact response patterns in a specific experimental situation. The easiest way to verify predictions of the CCTN without resorting to neuropharmacological manipulations or neuroimaging techniques would be to perform exhaustive timing experiments using a rich set of stimuli and ISIs, and to observe patterns of changes in performance across different types of trials. Such experiments are the next step of our investigation. Timing is one of the fundamental cognitive abilities among humans and animals. Temporal information is used by living organisms to perform many crucial tasks such as movement, planning and communication. Therefore it is of great importance to gain knowledge on how human timing mechanisms work, what are the biological (and especially neural) bases of these mechanisms, and what are their limitations.. Psychology, psychophysics and neuroscience of human timing have provided many theoretical approaches. One of the prominent approaches are clock-counter models (Eisler 1981; Gibbon 1992; Gibbon et al. 1984; Ulrich et al. 2006; Wearden 1999 Wearden , 2003 Wearden and Doherty 1995) . This class of models revolves around the concept of the internal clock emitting impulses and the counter storing these pulses whenever a stimulus is presented. One of the most popular models of this class is the Scalar Timing Model. Another type of model that has been recently proposed is the state-dependent network model (Buonomano et al. 2009; Karmarkar and Buonomano 2007) . This model relies on the idea of a network of simple computational elements, where internal time is encoded as a changing state of neurons during and after exposition of stimuli. Yet another prominent group of models is constituted by psychophysical quantitative models (Church 1999; Getty 1975 Getty , 1976 Killeen and Weiss 1987; Rammsayer and Ulrich 2001) . These models are usually represented as sets of equations describing dependencies between physical properties of a stimulus and an internal, subjective representation of time. These psychophysical equations are sometimes closely related to the other groups of models, and may be seen as their specification. Apart from these groups of models, there exist more complex interdisciplinary approaches, combining neurological, psychological and computational knowledge (Church 2003; Matell and Meck 2004; Meck 2005) . More information on different classes of time perception models is provided in (Buhusi and Meck 2005; Grondin 2001; Ivry and Schlerf 2008; Zakay et al. 1999) . Models that are outside of the classification outlined above are described in (Shi et al. 2013; Staddon and Higga 1999; Yamazaki and Tanaka 2005) . Overall, these are good theoretical frameworks: they provide explanations to experimental data, some of them are equipped with tools allowing to perform advanced simulations, and some of them integrate knowledge and data from different scientific disciplines. Nevertheless, a unified, commonly accepted theory of human timing is yet to be proposed.. Apart from the development of better and better explanations of human timing processes, much time and resources have also been devoted to explore human (and animal) timing phenomena. There is a great body of research concerning interval timing, ranging from behavioral experiments conducted on animal and human subjects (Gibbon 1977; Grondin 2005; Wearden et al. 2007) to neuroimaging studies and research on patients with mental or neurodegenerative diseases (Grondin 2010; Hairston and Nagarajan 2007; Malapani et al. 1998; Riesen and Schnider 2001; Sévigny et al. 2003; Smith et al. 2007 ). Among experimental findings, several phenomena have been frequently reported and analyzed. One of them is the scalar property of animal and human timing -a characteristic often perceived as an equivalent of Weber's law in the domain of timing (Eisler et al. 2008; Wearden and Lejeune 2008; Wearden et al. 1997; Komosinski 2012) . Depending on the type of an experiment, the scalar property may denote a constant coefficient of variation of a subject's timing judgments/measurements when perceiving stimuli of different durations, or a superposition of distributions of estimations of different time intervals, expressed on the same, relative timescale (Church 2002) . This property became the core assumption of one of the most popular timing models -the Scalar Timing Model (Gibbon et al. 1984; Wearden 1999 Wearden , 2003 or STM, which is a part of the Scalar Expectancy Theory -SET. The STM and the SET are popular (Perbal et al. 2005; Wearden et al. 2007) despite the fact that the scalar property is not observed in some experiments (Komosinski 2012; Lewis and Miall 2009; Wearden and Lejeune 2008) .. Another frequently reported and robust phenomenon is the time-order error -TOE (Allan 1977; Hairston and Nagarajan 2007; Hellström 1985; Hellström and Rammsayer 2004; Jamieson and Petrusic 1975) . The TOE is reported when duration (but also loudness, pitch, weight, etc.) of two successively presented stimuli is compared; this procedure is known as the twoalternative forced choice task (2-AFC). In the domain of human timing it is called the temporal discrimination task.. The TOE is a systematic overestimation (a positive TOE) or underestimation (a negative TOE) of the first stimulus relative to the second one. While the negative TOE is generally more common, many factors influence the magnitude and even the polarity of the TOE. For example, it is often reported that when the intensity of stimuli is low (in a context of timing research they have short durations), the TOE is closer to zero, and it even becomes positive (Allan 1977; Hellström 2003) . Another important factor influencing the TOE is an interstimulus interval (ISI); it was reported that longer ISIs cause a decrease of the magnitude of the TOE (Jamieson and Petrusic 1975) . Many kinds of explanations have been proposed (Eisler et al. 2008; Hellström 1985) , but there is no single explanation that would cover every property of the TOE. What is quite certain is that this is a perceptionrelated phenomenon, not the decision-making one. As much as the TOE is a matter to consider by theoreticians of human timing, it is also a methodological issue. The order of presentation of temporal stimuli may distort the response pattern of participants, increasing or decreasing correct response rate by tens of percent (Jamieson and Petrusic 1975; Schab and Crowder 1988) .. Responding to the need for a unified model of human timing, we have implemented an informational architecture of a commonly known clock-counter model, the STM, in a connectionist environment of an artificial neural network (Komosinski and Kups 2009, 2011) . We call this implementation the Clock-Counter Timing Network. In order to research the responses of the CCTN we developed a software platform which is able to conduct an artificial behavioral experiment: the temporal discrimination task. The CCTN consists of a number of modules, of which some are adopted directly from the STM. Furthermore, by including a few additional assumptions, the CCTN is able to manifest the TOE. A preliminary computational experiment proved that the CCTN can mimic the behavior of a human (the participant "BJ" from the study of (Allan 1977) ). This was possible even though those simulations did not demonstrate the scalar property which, according to various studies, does not always hold; such experiments allowed minimizing the influence of additional sources of variability in the otherwise complex system. After establishing that the CCTN is able to successfully manifest the TOE, it is natural to ask whether the source of scalar variability in the network affects the TOE and the network's ability to mimic human behaviors. Answering this question may reveal how the two frequently reported phenomena interact. What is more, including the assumption of the scalar property in the CCTN makes this architecture more similar to the STM, which is the original theoretical foundation of the CCTN.. Results reported in this paper demonstrate that the computational representation of the STM is capable of explaining the robustness of the TOE, and it is also useful in predicting performance in the temporal discrimination task under different modes of activity. These capabilities make the CCTN a valuable contribution in the quest for explaining human timing mechanisms.

B I O B I O O O O O O B O O O O O O O O O O O O B I O O O B O O O O O B I O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B O


Adaptive data rate control for throughput improvement and energy efficiency in low rate WPAN Energy Efficiency is one of the key issues in Low Rate Wireless Personal Area Networks (LR-WPANs). So far, many research papers have been published on the subject of energy efficiency. However, the throughput in LR-WPAN hasn't been extensively investigated. Therefore, in this paper, we focus on improving the throughput and energy efficiency. LR-WPAN is designed for short range wireless communication based on low power consumption and low cost. In this type of network, the communication capacity may be limited by the low HW specifications such as the small memory size (e.g. network interface buffer) and low processing capacity. In this study, we focus on the former limitation. In this situation, congestion occurs at the Cluster Head (CH) or coordinator owing to its small memory size, which may lead to packet dropping and a reduction of the overall performance. Therefore, we approached this problem from the viewpoint of improving the throughput. Network Devices should consider the congestion situation at the sensor nodes and adopt adaptive data rate control to avoid this situation. The improvement in the throughput and energy efficiency afforded by the adaptive data rate control scheme that is proposed in this paper is confirmed by the network simulation results. Basically, LR-WPAN is a wireless communication networking technology for specific applications such as those requiring lowcost and low power. That is, for networks whose main purpose is to process the relevant data that is collected by the network devices and reported to the sink. Accordingly, LR-WPAN is widely used in fields that require a relatively low data rate and low quality of service. In this type of network, the sensor devices usually operate as an independent RF transceiver without any external power supply. Therefore, they require electric cells or other form of independent power supply. LR-WPAN has the advantages of easy installation and low cost OA&M (Operation, Administration and Management), so it should have good durability.. For this reason, when we design an LR-WPAN, the most important factor is to operate it with energy efficiency. Nowadays, many wireless communication engineers are studying LR-WPAN from the viewpoint of the energy efficiency.. To solve the problem of excessive energy consumption, two methods can be used.. The first method is to adapt the duty cycle according to the network environment. However, the synchronization problem must be resolved prior to the application of a variable duty cycle. It this problem is not solved, it may lead to a decrease in the network throughput. The second method, the solution is to limit the transmission power at the RF transceiver. However, this scheme requires multi-hop network protocols. And, it may have an implicit weakness that may result in lower throughput. Therefore, we always need to associate the energy efficiency with the throughput when we consider the energy consumption.. As mentioned above, the sensor devices that constitute the LR-WPAN have low-cost HW requirements. Consequently, the memory size that is needed by a specific processor and protocol is limited by the specific circumstances. Hence, congestion may be induced by the limited capability or insufficient resources on the receiver side.. When the receiver side is unable to accept or receive packets from the network devices owing to the limited memory size at the network interface of the receiver, the network device that is sending the data adaptively regulates its data rate by receiving feedback messages from the receiver. By means of this mechanism, we can not only prevent unnecessary transmissions causing congestion and dropping due to the limited memory, but also obtain an improvement in the throughput and energy consumption. The aim of this study is to control the data rate at the transmitter by receiving feedback information from the receiver. The receiver always monitors its memory of the network interface, which is referred to as a network interface buffer (Queue). Extending the network lifetime by minimizing the energy consumption is one of the most active research fields in LR-WPAN.. In this paper, we propose an adaptive data rate mechanism.. This algorithm first considers the network interface buffer capacity of the receiver (CH or coordinator) and the receiver monitors the network interface buffer state to determine whether it exceeds the threshold or not.. If the number of packets in the network interface buffer exceeds the threshold, the coordinator broadcasts a new beacon message with a modified payload field to inform the network devices to control their data rate. Through this mechanism, we can improve the performance in terms of both the throughput and energy efficiency. These two factors together allow the network lifetime to be extended.. Through the performance evaluation, we confirmed the validity of the proposed mechanism using the NS-2 simulator and demonstrated its superiority over the current scheme.. We hope that the proposed data rate control mechanism will contribute to the performance enhancement of WPAN.

O B I O O B O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O B O O O O O O O O O O O O O B O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O B I O O O O O O O O O B O B O O O O O B I O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O B O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O B O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B O O O O O B O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B O O O


A Pattern-Theoretic Characterization of Biological Growth Abstract-Mathematical and statistical modeling of biological growth is an important problem in medical diagnostics. Here, we seek tools to analyze changes in anatomical parts using images collected over time. We introduce a structured model, called Growth by Random Iterated Diffeomorphisms (GRID), that treats a cumulative growth deformation as a composition of several elementary deformations. Each elementary deformation applies to a small region by capturing deformation local to that region and is characterized by a seed and a radial deformation pattern around that seed. These GRID variables-seed locations and radial deformation patterns-are estimated from observed images in two steps: 1) estimate a cumulative deformation over an observation interval; 2) estimate GRID variables using maximum-likelihood criterion from this estimated cumulative deformation. We demonstrate this framework using an MRI image data of a rat's brain growth. For future statistical analysis, we propose a time-varying Poisson process for the seed placements and a random drawing from a predetermined catalog of deformations for the radial deformation patterns.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O