[{"ec_funded":1,"article_number":"8006529","author":[{"id":"EC09FA6A-02D0-11E9-8223-86B7C91467DD","last_name":"Skórski","first_name":"Maciej","full_name":"Skórski, Maciej"}],"date_updated":"2021-01-12T08:07:53Z","date_created":"2019-06-06T12:53:09Z","year":"2017","publication_status":"published","publisher":"IEEE","department":[{"_id":"KrPi"}],"month":"08","publication_identifier":{"isbn":["9781509040964"]},"conference":{"start_date":"2017-06-25","location":"Aachen, Germany","end_date":"2017-06-30","name":"ISIT: International Symposium on Information Theory"},"doi":"10.1109/isit.2017.8006529","language":[{"iso":"eng"}],"external_id":{"arxiv":["1702.01666"]},"oa":1,"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1702.01666"}],"quality_controlled":"1","project":[{"_id":"258AA5B2-B435-11E9-9278-68D0E5697425","grant_number":"682815","name":"Teaching Old Crypto New Tricks","call_identifier":"H2020"}],"abstract":[{"lang":"eng","text":"This paper studies the complexity of estimating Rényi divergences of discrete distributions: p observed from samples and the baseline distribution q known a priori. Extending the results of Acharya et al. (SODA'15) on estimating Rényi entropy, we present improved estimation techniques together with upper and lower bounds on the sample complexity. We show that, contrarily to estimating Rényi entropy where a sublinear (in the alphabet size) number of samples suffices, the sample complexity is heavily dependent on events occurring unlikely in q, and is unbounded in general (no matter what an estimation technique is used). For any divergence of integer order bigger than 1, we provide upper and lower bounds on the number of samples dependent on probabilities of p and q (the lower bounds hold for non-integer orders as well). We conclude that the worst-case sample complexity is polynomial in the alphabet size if and only if the probabilities of q are non-negligible. This gives theoretical insights into heuristics used in the applied literature to handle numerical instability, which occurs for small probabilities of q. Our result shows that they should be handled with care not only because of numerical issues, but also because of a blow up in the sample complexity."}],"type":"conference","oa_version":"Preprint","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","_id":"6526","status":"public","title":"On the complexity of estimating Rènyi divergences","day":"09","scopus_import":1,"date_published":"2017-08-09T00:00:00Z","publication":"2017 IEEE International Symposium on Information Theory (ISIT)","citation":{"mla":"Skórski, Maciej. “On the Complexity of Estimating Rènyi Divergences.” 2017 IEEE International Symposium on Information Theory (ISIT), 8006529, IEEE, 2017, doi:10.1109/isit.2017.8006529.","short":"M. Skórski, in:, 2017 IEEE International Symposium on Information Theory (ISIT), IEEE, 2017.","chicago":"Skórski, Maciej. “On the Complexity of Estimating Rènyi Divergences.” In 2017 IEEE International Symposium on Information Theory (ISIT). IEEE, 2017. https://doi.org/10.1109/isit.2017.8006529.","ama":"Skórski M. On the complexity of estimating Rènyi divergences. In: 2017 IEEE International Symposium on Information Theory (ISIT). IEEE; 2017. doi:10.1109/isit.2017.8006529","ista":"Skórski M. 2017. On the complexity of estimating Rènyi divergences. 2017 IEEE International Symposium on Information Theory (ISIT). ISIT: International Symposium on Information Theory, 8006529.","ieee":"M. Skórski, “On the complexity of estimating Rènyi divergences,” in 2017 IEEE International Symposium on Information Theory (ISIT), Aachen, Germany, 2017.","apa":"Skórski, M. (2017). On the complexity of estimating Rènyi divergences. In 2017 IEEE International Symposium on Information Theory (ISIT). Aachen, Germany: IEEE. https://doi.org/10.1109/isit.2017.8006529"}},{"date_updated":"2021-01-12T08:07:55Z","date_created":"2018-12-11T11:47:44Z","volume":6,"author":[{"last_name":"Renault","first_name":"Thibaud","full_name":"Renault, Thibaud"},{"last_name":"Abraham","first_name":"Anthony","full_name":"Abraham, Anthony"},{"last_name":"Bergmiller","first_name":"Tobias","orcid":"0000-0001-5396-4346","id":"2C471CFA-F248-11E8-B48F-1D18A9856A87","full_name":"Bergmiller, Tobias"},{"first_name":"Guillaume","last_name":"Paradis","full_name":"Paradis, Guillaume"},{"full_name":"Rainville, Simon","first_name":"Simon","last_name":"Rainville"},{"last_name":"Charpentier","first_name":"Emmanuelle","full_name":"Charpentier, Emmanuelle"},{"full_name":"Guet, Calin C","first_name":"Calin C","last_name":"Guet","id":"47F8433E-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-6220-2052"},{"full_name":"Tu, Yuhai","first_name":"Yuhai","last_name":"Tu"},{"full_name":"Namba, Keiichi","first_name":"Keiichi","last_name":"Namba"},{"last_name":"Keener","first_name":"James","full_name":"Keener, James"},{"full_name":"Minamino, Tohru","first_name":"Tohru","last_name":"Minamino"},{"full_name":"Erhardt, Marc","first_name":"Marc","last_name":"Erhardt"}],"publication_status":"published","publisher":"eLife Sciences Publications","department":[{"_id":"CaGu"}],"year":"2017","license":"https://creativecommons.org/licenses/by/4.0/","file_date_updated":"2020-07-14T12:47:33Z","publist_id":"7082","article_number":"e23136","language":[{"iso":"eng"}],"doi":"10.7554/eLife.23136","quality_controlled":"1","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"oa":1,"month":"03","publication_identifier":{"issn":["2050084X"]},"file":[{"file_id":"4716","relation":"main_file","date_created":"2018-12-12T10:08:53Z","date_updated":"2020-07-14T12:47:33Z","checksum":"39e1c3e82ddac83a30422fa72fa1a383","file_name":"IST-2017-904-v1+1_elife-23136-v2.pdf","access_level":"open_access","creator":"system","content_type":"application/pdf","file_size":5520359},{"file_id":"4717","relation":"main_file","date_updated":"2020-07-14T12:47:33Z","date_created":"2018-12-12T10:08:54Z","checksum":"a6d542253028f52e00aa29739ddffe8f","file_name":"IST-2017-904-v1+2_elife-23136-figures-v2.pdf","access_level":"open_access","creator":"system","file_size":11242920,"content_type":"application/pdf"}],"oa_version":"Published Version","pubrep_id":"904","status":"public","ddc":["579"],"title":"Bacterial flagella grow through an injection diffusion mechanism","intvolume":" 6","_id":"655","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","abstract":[{"text":"The bacterial flagellum is a self-assembling nanomachine. The external flagellar filament, several times longer than a bacterial cell body, is made of a few tens of thousands subunits of a single protein: flagellin. A fundamental problem concerns the molecular mechanism of how the flagellum grows outside the cell, where no discernible energy source is available. Here, we monitored the dynamic assembly of individual flagella using in situ labelling and real-time immunostaining of elongating flagellar filaments. We report that the rate of flagellum growth, initially ~1,700 amino acids per second, decreases with length and that the previously proposed chain mechanism does not contribute to the filament elongation dynamics. Inhibition of the proton motive force-dependent export apparatus revealed a major contribution of substrate injection in driving filament elongation. The combination of experimental and mathematical evidence demonstrates that a simple, injection-diffusion mechanism controls bacterial flagella growth outside the cell.","lang":"eng"}],"type":"journal_article","date_published":"2017-03-06T00:00:00Z","publication":"eLife","citation":{"apa":"Renault, T., Abraham, A., Bergmiller, T., Paradis, G., Rainville, S., Charpentier, E., … Erhardt, M. (2017). Bacterial flagella grow through an injection diffusion mechanism. ELife. eLife Sciences Publications. https://doi.org/10.7554/eLife.23136","ieee":"T. Renault et al., “Bacterial flagella grow through an injection diffusion mechanism,” eLife, vol. 6. eLife Sciences Publications, 2017.","ista":"Renault T, Abraham A, Bergmiller T, Paradis G, Rainville S, Charpentier E, Guet CC, Tu Y, Namba K, Keener J, Minamino T, Erhardt M. 2017. Bacterial flagella grow through an injection diffusion mechanism. eLife. 6, e23136.","ama":"Renault T, Abraham A, Bergmiller T, et al. Bacterial flagella grow through an injection diffusion mechanism. eLife. 2017;6. doi:10.7554/eLife.23136","chicago":"Renault, Thibaud, Anthony Abraham, Tobias Bergmiller, Guillaume Paradis, Simon Rainville, Emmanuelle Charpentier, Calin C Guet, et al. “Bacterial Flagella Grow through an Injection Diffusion Mechanism.” ELife. eLife Sciences Publications, 2017. https://doi.org/10.7554/eLife.23136.","short":"T. Renault, A. Abraham, T. Bergmiller, G. Paradis, S. Rainville, E. Charpentier, C.C. Guet, Y. Tu, K. Namba, J. Keener, T. Minamino, M. Erhardt, ELife 6 (2017).","mla":"Renault, Thibaud, et al. “Bacterial Flagella Grow through an Injection Diffusion Mechanism.” ELife, vol. 6, e23136, eLife Sciences Publications, 2017, doi:10.7554/eLife.23136."},"day":"06","has_accepted_license":"1","scopus_import":1},{"scopus_import":1,"day":"21","citation":{"ieee":"B. Möller et al., “Auxin response cell autonomously controls ground tissue initiation in the early arabidopsis embryo,” PNAS, vol. 114, no. 12. National Academy of Sciences, pp. E2533–E2539, 2017.","apa":"Möller, B., Ten Hove, C., Xiang, D., Williams, N., López, L., Yoshida, S., … Weijers, D. (2017). Auxin response cell autonomously controls ground tissue initiation in the early arabidopsis embryo. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1616493114","ista":"Möller B, Ten Hove C, Xiang D, Williams N, López L, Yoshida S, Smit M, Datla R, Weijers D. 2017. Auxin response cell autonomously controls ground tissue initiation in the early arabidopsis embryo. PNAS. 114(12), E2533–E2539.","ama":"Möller B, Ten Hove C, Xiang D, et al. Auxin response cell autonomously controls ground tissue initiation in the early arabidopsis embryo. PNAS. 2017;114(12):E2533-E2539. doi:10.1073/pnas.1616493114","chicago":"Möller, Barbara, Colette Ten Hove, Daoquan Xiang, Nerys Williams, Lorena López, Saiko Yoshida, Margot Smit, Raju Datla, and Dolf Weijers. “Auxin Response Cell Autonomously Controls Ground Tissue Initiation in the Early Arabidopsis Embryo.” PNAS. National Academy of Sciences, 2017. https://doi.org/10.1073/pnas.1616493114.","short":"B. Möller, C. Ten Hove, D. Xiang, N. Williams, L. López, S. Yoshida, M. Smit, R. Datla, D. Weijers, PNAS 114 (2017) E2533–E2539.","mla":"Möller, Barbara, et al. “Auxin Response Cell Autonomously Controls Ground Tissue Initiation in the Early Arabidopsis Embryo.” PNAS, vol. 114, no. 12, National Academy of Sciences, 2017, pp. E2533–39, doi:10.1073/pnas.1616493114."},"publication":"PNAS","page":"E2533 - E2539","date_published":"2017-03-21T00:00:00Z","type":"journal_article","issue":"12","abstract":[{"lang":"eng","text":"Plant organs are typically organized into three main tissue layers. The middle ground tissue layer comprises the majority of the plant body and serves a wide range of functions, including photosynthesis, selective nutrient uptake and storage, and gravity sensing. Ground tissue patterning and maintenance in Arabidopsis are controlled by a well-established gene network revolving around the key regulator SHORT-ROOT (SHR). In contrast, it is completely unknown how ground tissue identity is first specified from totipotent precursor cells in the embryo. The plant signaling molecule auxin, acting through AUXIN RESPONSE FACTOR (ARF) transcription factors, is critical for embryo patterning. The auxin effector ARF5/MONOPTEROS (MP) acts both cell-autonomously and noncell-autonomously to control embryonic vascular tissue formation and root initiation, respectively. Here we show that auxin response and ARF activity cell-autonomously control the asymmetric division of the first ground tissue cells. By identifying embryonic target genes, we show that MP transcriptionally initiates the ground tissue lineage and acts upstream of the regulatory network that controls ground tissue patterning and maintenance. Strikingly, whereas the SHR network depends on MP, this MP function is, at least in part, SHR independent. Our study therefore identifies auxin response as a regulator of ground tissue specification in the embryonic root, and reveals that ground tissue initiation and maintenance use different regulators and mechanisms. Moreover, our data provide a framework for the simultaneous formation of multiple cell types by the same transcriptional regulator."}],"_id":"657","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","intvolume":" 114","title":"Auxin response cell autonomously controls ground tissue initiation in the early arabidopsis embryo","status":"public","oa_version":"Submitted Version","publication_identifier":{"issn":["00278424"]},"month":"03","oa":1,"main_file_link":[{"open_access":"1","url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5373392/"}],"external_id":{"pmid":["28265057"]},"quality_controlled":"1","doi":"10.1073/pnas.1616493114","language":[{"iso":"eng"}],"publist_id":"7076","pmid":1,"year":"2017","department":[{"_id":"JiFr"}],"publisher":"National Academy of Sciences","publication_status":"published","author":[{"full_name":"Möller, Barbara","first_name":"Barbara","last_name":"Möller"},{"first_name":"Colette","last_name":"Ten Hove","full_name":"Ten Hove, Colette"},{"full_name":"Xiang, Daoquan","first_name":"Daoquan","last_name":"Xiang"},{"last_name":"Williams","first_name":"Nerys","full_name":"Williams, Nerys"},{"full_name":"López, Lorena","first_name":"Lorena","last_name":"López"},{"id":"2E46069C-F248-11E8-B48F-1D18A9856A87","first_name":"Saiko","last_name":"Yoshida","full_name":"Yoshida, Saiko"},{"full_name":"Smit, Margot","first_name":"Margot","last_name":"Smit"},{"first_name":"Raju","last_name":"Datla","full_name":"Datla, Raju"},{"first_name":"Dolf","last_name":"Weijers","full_name":"Weijers, Dolf"}],"volume":114,"date_updated":"2021-01-12T08:08:02Z","date_created":"2018-12-11T11:47:45Z"},{"citation":{"chicago":"Novarino, Gaia. “Modeling Alzheimer’s Disease in Mice with Human Neurons.” Science Translational Medicine. American Association for the Advancement of Science, 2017. https://doi.org/10.1126/scitranslmed.aam9867.","mla":"Novarino, Gaia. “Modeling Alzheimer’s Disease in Mice with Human Neurons.” Science Translational Medicine, vol. 9, no. 381, eaam9867, American Association for the Advancement of Science, 2017, doi:10.1126/scitranslmed.aam9867.","short":"G. Novarino, Science Translational Medicine 9 (2017).","ista":"Novarino G. 2017. Modeling Alzheimer’s disease in mice with human neurons. Science Translational Medicine. 9(381), eaam9867.","apa":"Novarino, G. (2017). Modeling Alzheimer’s disease in mice with human neurons. Science Translational Medicine. American Association for the Advancement of Science. https://doi.org/10.1126/scitranslmed.aam9867","ieee":"G. Novarino, “Modeling Alzheimer’s disease in mice with human neurons,” Science Translational Medicine, vol. 9, no. 381. American Association for the Advancement of Science, 2017.","ama":"Novarino G. Modeling Alzheimer’s disease in mice with human neurons. Science Translational Medicine. 2017;9(381). doi:10.1126/scitranslmed.aam9867"},"publication":"Science Translational Medicine","quality_controlled":"1","date_published":"2017-03-15T00:00:00Z","doi":"10.1126/scitranslmed.aam9867","language":[{"iso":"eng"}],"scopus_import":1,"publication_identifier":{"issn":["19466234"]},"day":"15","month":"03","user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","_id":"656","year":"2017","department":[{"_id":"GaNo"}],"publisher":"American Association for the Advancement of Science","intvolume":" 9","status":"public","title":"Modeling Alzheimer's disease in mice with human neurons","publication_status":"published","author":[{"full_name":"Novarino, Gaia","last_name":"Novarino","first_name":"Gaia","orcid":"0000-0002-7673-7178","id":"3E57A680-F248-11E8-B48F-1D18A9856A87"}],"oa_version":"None","volume":9,"date_created":"2018-12-11T11:47:45Z","date_updated":"2021-01-12T08:07:59Z","type":"journal_article","article_number":"eaam9867","publist_id":"7079","issue":"381","abstract":[{"lang":"eng","text":"Human neurons transplanted into a mouse model for Alzheimer’s disease show human-specific vulnerability to β-amyloid plaques and may help to identify new therapeutic targets."}]},{"status":"public","title":"Self organized behavior generation for musculoskeletal robots","ddc":["006"],"intvolume":" 11","user_id":"2EBD1598-F248-11E8-B48F-1D18A9856A87","_id":"658","oa_version":"Published Version","file":[{"checksum":"b1bc43f96d1df3313c03032c2a46388d","date_created":"2018-12-12T10:18:49Z","date_updated":"2020-07-14T12:47:33Z","file_id":"5371","relation":"main_file","creator":"system","file_size":8439566,"content_type":"application/pdf","access_level":"open_access","file_name":"IST-2017-903-v1+1_fnbot-11-00008.pdf"}],"pubrep_id":"903","type":"journal_article","abstract":[{"lang":"eng","text":"With the accelerated development of robot technologies, control becomes one of the central themes of research. In traditional approaches, the controller, by its internal functionality, finds appropriate actions on the basis of specific objectives for the task at hand. While very successful in many applications, self-organized control schemes seem to be favored in large complex systems with unknown dynamics or which are difficult to model. Reasons are the expected scalability, robustness, and resilience of self-organizing systems. The paper presents a self-learning neurocontroller based on extrinsic differential plasticity introduced recently, applying it to an anthropomorphic musculoskeletal robot arm with attached objects of unknown physical dynamics. The central finding of the paper is the following effect: by the mere feedback through the internal dynamics of the object, the robot is learning to relate each of the objects with a very specific sensorimotor pattern. Specifically, an attached pendulum pilots the arm into a circular motion, a half-filled bottle produces axis oriented shaking behavior, a wheel is getting rotated, and wiping patterns emerge automatically in a table-plus-brush setting. By these object-specific dynamical patterns, the robot may be said to recognize the object's identity, or in other words, it discovers dynamical affordances of objects. Furthermore, when including hand coordinates obtained from a camera, a dedicated hand-eye coordination self-organizes spontaneously. These phenomena are discussed from a specific dynamical system perspective. Central is the dedicated working regime at the border to instability with its potentially infinite reservoir of (limit cycle) attractors "waiting" to be excited. Besides converging toward one of these attractors, variate behavior is also arising from a self-induced attractor morphing driven by the learning rule. We claim that experimental investigations with this anthropomorphic, self-learning robot not only generate interesting and potentially useful behaviors, but may also help to better understand what subjective human muscle feelings are, how they can be rooted in sensorimotor patterns, and how these concepts may feed back on robotics."}],"issue":"MAR","publication":"Frontiers in Neurorobotics","citation":{"ama":"Der R, Martius GS. Self organized behavior generation for musculoskeletal robots. Frontiers in Neurorobotics. 2017;11(MAR). doi:10.3389/fnbot.2017.00008","ieee":"R. Der and G. S. Martius, “Self organized behavior generation for musculoskeletal robots,” Frontiers in Neurorobotics, vol. 11, no. MAR. Frontiers Research Foundation, 2017.","apa":"Der, R., & Martius, G. S. (2017). Self organized behavior generation for musculoskeletal robots. Frontiers in Neurorobotics. Frontiers Research Foundation. https://doi.org/10.3389/fnbot.2017.00008","ista":"Der R, Martius GS. 2017. Self organized behavior generation for musculoskeletal robots. Frontiers in Neurorobotics. 11(MAR), 00008.","short":"R. Der, G.S. Martius, Frontiers in Neurorobotics 11 (2017).","mla":"Der, Ralf, and Georg S. Martius. “Self Organized Behavior Generation for Musculoskeletal Robots.” Frontiers in Neurorobotics, vol. 11, no. MAR, 00008, Frontiers Research Foundation, 2017, doi:10.3389/fnbot.2017.00008.","chicago":"Der, Ralf, and Georg S Martius. “Self Organized Behavior Generation for Musculoskeletal Robots.” Frontiers in Neurorobotics. Frontiers Research Foundation, 2017. https://doi.org/10.3389/fnbot.2017.00008."},"date_published":"2017-03-16T00:00:00Z","scopus_import":1,"day":"16","article_processing_charge":"Yes","has_accepted_license":"1","publication_status":"published","department":[{"_id":"ChLa"},{"_id":"GaTk"}],"publisher":"Frontiers Research Foundation","year":"2017","date_created":"2018-12-11T11:47:45Z","date_updated":"2021-01-12T08:08:04Z","volume":11,"author":[{"full_name":"Der, Ralf","first_name":"Ralf","last_name":"Der"},{"id":"3A276B68-F248-11E8-B48F-1D18A9856A87","first_name":"Georg S","last_name":"Martius","full_name":"Martius, Georg S"}],"article_number":"00008","file_date_updated":"2020-07-14T12:47:33Z","publist_id":"7078","ec_funded":1,"quality_controlled":"1","project":[{"grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7"}],"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"oa":1,"language":[{"iso":"eng"}],"doi":"10.3389/fnbot.2017.00008","month":"03","publication_identifier":{"issn":["16625218"]}}]