[{"main_file_link":[{"open_access":"0","url":"http://edok01.tib.uni-hannover.de/edoks/e01dh09/609861891.pdf"}],"month":"08","page":"1 - 72","title":"Biosynthesis of Galactofuranose Containing Glycans and Their Relevance for the Pathogenic Fungus Aspergillus fumigatus","_id":"3400","year":"2009","publication_status":"published","citation":{"ista":"Schmalhorst PS. 2009. Biosynthesis of Galactofuranose Containing Glycans and Their Relevance for the Pathogenic Fungus Aspergillus fumigatus. Gottfried Wilhelm Leibniz Universität Hannover.","short":"P.S. Schmalhorst, Biosynthesis of Galactofuranose Containing Glycans and Their Relevance for the Pathogenic Fungus Aspergillus Fumigatus, Gottfried Wilhelm Leibniz Universität Hannover, 2009.","mla":"Schmalhorst, Philipp S. <i>Biosynthesis of Galactofuranose Containing Glycans and Their Relevance for the Pathogenic Fungus Aspergillus Fumigatus</i>. Gottfried Wilhelm Leibniz Universität Hannover, 2009, pp. 1–72.","apa":"Schmalhorst, P. S. (2009). <i>Biosynthesis of Galactofuranose Containing Glycans and Their Relevance for the Pathogenic Fungus Aspergillus fumigatus</i>. Gottfried Wilhelm Leibniz Universität Hannover.","ieee":"P. S. Schmalhorst, “Biosynthesis of Galactofuranose Containing Glycans and Their Relevance for the Pathogenic Fungus Aspergillus fumigatus,” Gottfried Wilhelm Leibniz Universität Hannover, 2009.","ama":"Schmalhorst PS. Biosynthesis of Galactofuranose Containing Glycans and Their Relevance for the Pathogenic Fungus Aspergillus fumigatus. 2009:1-72.","chicago":"Schmalhorst, Philipp S. “Biosynthesis of Galactofuranose Containing Glycans and Their Relevance for the Pathogenic Fungus Aspergillus Fumigatus.” Gottfried Wilhelm Leibniz Universität Hannover, 2009."},"date_updated":"2021-01-12T07:43:13Z","date_created":"2018-12-11T12:03:07Z","author":[{"first_name":"Philipp S","id":"309D50DA-F248-11E8-B48F-1D18A9856A87","full_name":"Philipp Schmalhorst","orcid":"0000-0002-5795-0133","last_name":"Schmalhorst"}],"publisher":"Gottfried Wilhelm Leibniz Universität Hannover","quality_controlled":0,"date_published":"2009-08-13T00:00:00Z","abstract":[{"text":"Invasive fungal infections pose a serious threat to immunocompromised people. Most of these infections are caused by either Candida or Aspergillus species, with A. fumigatus being the predominant causative agent of Invasive Aspergillosis. Affected people comprise mainly haematopoietic stem cell or solid organ transplant patients who receive either high-dose corticosteroids or immunosuppressants. These risk factors predispose to the development of Invasive\nAspergillosis which is lethal in 20 to 80 % of the cases, largely due to insufficient efficacy of current antifungal therapy. Thus one major aim in current mycological research is the identification of new drug targets.\nThe polysaccharide-based fungal cell wall is both essential to fungi and absent from human cells which makes it appear an attractive new target. Notably, many components of the A. fumigatus cell wall, including the polysaccharide galactomannan, glycoproteins, and glycolipids, contain the unusual sugar galactofuranose (Galf). In contrast to the other cell wall monosaccharides, Galf does not occur on human cells but is known as component of cell surface molecules of many pathogenic bacteria and protozoa, such as Mycobacterium tuberculosis or Leishmania major. These molecules are often essential for virulence or viability of these organisms which suggested a possible role of Galf in the pathogenicity of A. fumigatus.\nTo address the importance of Galf in A. fumigatus, the key biosynthesis gene glfA, encoding UDPgalactopyranose mutase (UGM), was deleted. In different experimental approaches it was demonstrated that the absence of the glfA gene led to a complete loss of Galf-containing glycans.\nAnalysis of the DeltaglfA phenotype revealed growth and sporulation defects, reduced thermotolerance and an increased susceptibility to antifungal drugs. Electron Microscopy indicated a cell wall defect as a likely cause for the observed impairments. Furthermore, the virulence of the DeltaglfA mutant was found to be severely attenuated in a murine model of Invasive Aspergillosis.\nThe second focus of this study was laid on further elucidation of the galactofuranosylation pathway in A. fumigatus. In eukaryotes, a UDP-Galf transporter is likely required to transport UDP-Galf from the\ncytosol into the organelles of the secretory pathway, but no such activity had been described. Sixteen candidate genes were identified in the A. fumigatus genome of which one, glfB, was found in close proximity to the glfA gene. In vitro transport assays revealed specificity of GlfB for UDP-Galf suggesting that glfB encoded indeed a UDP-Galf transporter. The influence of glfB on\ngalactofuranosylation was determined by a DeltaglfB deletion mutant, which closely recapitulated the DeltaglfA phenotype and was likewise found to be completely devoid of Galf. It could be concluded that all galactofuranosylation processes in A. fumigatus occur in the secretory pathway, including the biosynthesis of the cell wall polysaccharide galactomannan whose subcellular origin was previously disputed.\n\nThus in the course of this study the first UDP-Galf specific nucleotide sugar transporter was identified and its requirement for galactofuranosylation in A. fumigatus demonstrated. Moreover, it was shown that blocking the galactofuranosylation pathway impaired virulence of A. fumigatus which suggests the UDP-Galf biosynthesis enzyme UGM as a target for new antifungal drugs.","lang":"eng"}],"publist_id":"3058","extern":1,"day":"13","type":"dissertation","status":"public"},{"_id":"3408","title":"Periodic forces trigger a complex mechanical response in ubiquitin","publication":"Journal of Molecular Biology","volume":390,"doi":"10.1016/j.jmb.2009.04.071","year":"2009","publication_status":"published","citation":{"ista":"Szymczak P, Janovjak HL. 2009. Periodic forces trigger a complex mechanical response in ubiquitin. Journal of Molecular Biology. 390(3), 443–456.","short":"P. Szymczak, H.L. Janovjak, Journal of Molecular Biology 390 (2009) 443–456.","mla":"Szymczak, Piotr, and Harald L. Janovjak. “Periodic Forces Trigger a Complex Mechanical Response in Ubiquitin.” <i>Journal of Molecular Biology</i>, vol. 390, no. 3, Elsevier, 2009, pp. 443–56, doi:<a href=\"https://doi.org/10.1016/j.jmb.2009.04.071\">10.1016/j.jmb.2009.04.071</a>.","apa":"Szymczak, P., &#38; Janovjak, H. L. (2009). Periodic forces trigger a complex mechanical response in ubiquitin. <i>Journal of Molecular Biology</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.jmb.2009.04.071\">https://doi.org/10.1016/j.jmb.2009.04.071</a>","ama":"Szymczak P, Janovjak HL. Periodic forces trigger a complex mechanical response in ubiquitin. <i>Journal of Molecular Biology</i>. 2009;390(3):443-456. doi:<a href=\"https://doi.org/10.1016/j.jmb.2009.04.071\">10.1016/j.jmb.2009.04.071</a>","ieee":"P. Szymczak and H. L. Janovjak, “Periodic forces trigger a complex mechanical response in ubiquitin,” <i>Journal of Molecular Biology</i>, vol. 390, no. 3. Elsevier, pp. 443–456, 2009.","chicago":"Szymczak, Piotr, and Harald L Janovjak. “Periodic Forces Trigger a Complex Mechanical Response in Ubiquitin.” <i>Journal of Molecular Biology</i>. Elsevier, 2009. <a href=\"https://doi.org/10.1016/j.jmb.2009.04.071\">https://doi.org/10.1016/j.jmb.2009.04.071</a>."},"month":"07","page":"443 - 456","abstract":[{"text":"Mechanical forces govern physiological processes in all living organisms. Many cellular forces, for example, those generated in cyclic conformational changes of biological machines, have repetitive components. In apparent contrast, little is known about how dynamic protein structures respond to periodic mechanical information. Ubiquitin is a small protein found in all eukaryotes. We developed molecular dynamics simulations to unfold single and multimeric ubiquitins with periodic forces. By using a coarse-grained representation, we were able to model forces with periods about 2 orders of magnitude longer than the protein's relaxation time. We found that even a moderate periodic force weakened the protein and shifted its unfolding pathways in a frequency- and amplitude-dependent manner. A complex dynamic response with secondary structure refolding and an increasing importance of local interactions was revealed. Importantly, repetitive forces with broadly distributed frequencies elicited very similar molecular responses compared to fixed-frequency forces. When testing the influence of pulling geometry on ubiquitin's mechanical stability, it was found that the linkage involved in the mechanical degradation of cellular proteins renders the protein remarkably insensitive to periodic forces. We also devised a complementary kinetic energy landscape model that traces these observations and explains periodic-force, single-molecule measurements. In turn, this analytical model is capable of predicting dynamic protein responses. These results provide new insights into ubiquitin mechanics and a potential mechanical role during protein degradation, as well as first frameworks for dynamic protein stability and the modeling of repetitive mechanical processes.","lang":"eng"}],"publist_id":"2994","extern":1,"day":"17","status":"public","type":"journal_article","date_updated":"2021-01-12T07:43:16Z","date_created":"2018-12-11T12:03:10Z","intvolume":"       390","issue":"3","publisher":"Elsevier","author":[{"first_name":"Piotr","full_name":"Szymczak, Piotr","last_name":"Szymczak"},{"orcid":"0000-0002-8023-9315","last_name":"Janovjak","id":"33BA6C30-F248-11E8-B48F-1D18A9856A87","first_name":"Harald L","full_name":"Harald Janovjak"}],"quality_controlled":0,"date_published":"2009-07-17T00:00:00Z"},{"month":"12","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","page":"728 - 729","language":[{"iso":"eng"}],"doi":"10.1016/j.molcel.2009.11.027","publication_status":"published","year":"2009","publication":"Molecular Cell","_id":"3428","article_processing_charge":"No","volume":36,"title":"Hydroxyurea triggers cellular responses that actively cause bacterial cell death","citation":{"mla":"Bollenbach, Mark Tobias, and Roy Kishony. “Hydroxyurea Triggers Cellular Responses That Actively Cause Bacterial Cell Death.” <i>Molecular Cell</i>, vol. 36, no. 5, Cell Press, 2009, pp. 728–29, doi:<a href=\"https://doi.org/10.1016/j.molcel.2009.11.027\">10.1016/j.molcel.2009.11.027</a>.","apa":"Bollenbach, M. T., &#38; Kishony, R. (2009). Hydroxyurea triggers cellular responses that actively cause bacterial cell death. <i>Molecular Cell</i>. Cell Press. <a href=\"https://doi.org/10.1016/j.molcel.2009.11.027\">https://doi.org/10.1016/j.molcel.2009.11.027</a>","ista":"Bollenbach MT, Kishony R. 2009. Hydroxyurea triggers cellular responses that actively cause bacterial cell death. Molecular Cell. 36(5), 728–729.","short":"M.T. Bollenbach, R. Kishony, Molecular Cell 36 (2009) 728–729.","ieee":"M. T. Bollenbach and R. Kishony, “Hydroxyurea triggers cellular responses that actively cause bacterial cell death,” <i>Molecular Cell</i>, vol. 36, no. 5. Cell Press, pp. 728–729, 2009.","ama":"Bollenbach MT, Kishony R. Hydroxyurea triggers cellular responses that actively cause bacterial cell death. <i>Molecular Cell</i>. 2009;36(5):728-729. doi:<a href=\"https://doi.org/10.1016/j.molcel.2009.11.027\">10.1016/j.molcel.2009.11.027</a>","chicago":"Bollenbach, Mark Tobias, and Roy Kishony. “Hydroxyurea Triggers Cellular Responses That Actively Cause Bacterial Cell Death.” <i>Molecular Cell</i>. Cell Press, 2009. <a href=\"https://doi.org/10.1016/j.molcel.2009.11.027\">https://doi.org/10.1016/j.molcel.2009.11.027</a>."},"issue":"5","date_updated":"2021-01-12T07:43:24Z","date_created":"2018-12-11T12:03:17Z","intvolume":"        36","date_published":"2009-12-11T00:00:00Z","publisher":"Cell Press","author":[{"id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","full_name":"Bollenbach, Mark Tobias","first_name":"Mark Tobias","orcid":"0000-0003-4398-476X","last_name":"Bollenbach"},{"last_name":"Kishony","first_name":"Roy","full_name":"Kishony, Roy"}],"oa_version":"None","publist_id":"2972","abstract":[{"text":"In this issue of Molecular Cell, Davies et al. (2009) work out a sequence of active cellular events that lead to the death of Escherichia coli in the presence of the drug hydroxyurea.","lang":"eng"}],"status":"public","type":"journal_article","extern":"1","day":"11"},{"alternative_title":["LNCS"],"extern":1,"day":"15","status":"public","type":"conference","abstract":[{"lang":"eng","text":"We give polynomial-time algorithms for computing the values of Markov decision processes (MDPs) with limsup and liminf objectives. A real-valued reward is assigned to each state, and the value of an infinite path in the MDP is the limsup (resp. liminf) of all rewards along the path. The value of an MDP is the maximal expected value of an infinite path that can be achieved by resolving the decisions of the MDP. Using our result on MDPs, we show that turn-based stochastic games with limsup and liminf objectives can be solved in NP ∩ coNP. "}],"publist_id":"2884","quality_controlled":0,"publisher":"Springer","author":[{"last_name":"Chatterjee","orcid":"0000-0002-4561-241X","full_name":"Krishnendu Chatterjee","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","first_name":"Krishnendu"},{"orcid":"0000−0002−2985−7724","last_name":"Henzinger","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","full_name":"Thomas Henzinger","first_name":"Thomas A"}],"date_published":"2009-12-15T00:00:00Z","date_updated":"2021-01-12T07:43:54Z","intvolume":"      5489","date_created":"2018-12-11T12:03:40Z","oa":1,"conference":{"name":"ILC: Infinity in Logic and Computation"},"citation":{"short":"K. Chatterjee, T.A. Henzinger, in:, Springer, 2009, pp. 32–45.","ista":"Chatterjee K, Henzinger TA. 2009. Probabilistic systems with limsup and liminf objectives. ILC: Infinity in Logic and Computation, LNCS, vol. 5489, 32–45.","apa":"Chatterjee, K., &#38; Henzinger, T. A. (2009). Probabilistic systems with limsup and liminf objectives (Vol. 5489, pp. 32–45). Presented at the ILC: Infinity in Logic and Computation, Springer. <a href=\"https://doi.org/10.1007/978-3-642-03092-5_4\">https://doi.org/10.1007/978-3-642-03092-5_4</a>","mla":"Chatterjee, Krishnendu, and Thomas A. Henzinger. <i>Probabilistic Systems with Limsup and Liminf Objectives</i>. Vol. 5489, Springer, 2009, pp. 32–45, doi:<a href=\"https://doi.org/10.1007/978-3-642-03092-5_4\">10.1007/978-3-642-03092-5_4</a>.","ieee":"K. Chatterjee and T. A. Henzinger, “Probabilistic systems with limsup and liminf objectives,” presented at the ILC: Infinity in Logic and Computation, 2009, vol. 5489, pp. 32–45.","ama":"Chatterjee K, Henzinger TA. Probabilistic systems with limsup and liminf objectives. In: Vol 5489. Springer; 2009:32-45. doi:<a href=\"https://doi.org/10.1007/978-3-642-03092-5_4\">10.1007/978-3-642-03092-5_4</a>","chicago":"Chatterjee, Krishnendu, and Thomas A Henzinger. “Probabilistic Systems with Limsup and Liminf Objectives,” 5489:32–45. Springer, 2009. <a href=\"https://doi.org/10.1007/978-3-642-03092-5_4\">https://doi.org/10.1007/978-3-642-03092-5_4</a>."},"volume":5489,"_id":"3503","title":"Probabilistic systems with limsup and liminf objectives","doi":"10.1007/978-3-642-03092-5_4","year":"2009","publication_status":"published","page":"32 - 45","main_file_link":[{"open_access":"1","url":"http://arxiv.org/abs/0809.1465"}],"month":"12"},{"day":"26","extern":1,"type":"journal_article","status":"public","abstract":[{"lang":"eng","text":"Neurons possess elaborate dendritic arbors which receive and integrate excitatory synaptic signals. Individual dendritic subbranches exhibit local membrane potential supralinearities, termed dendritic spikes, which control transfer of local synaptic input to the soma. Here, we show that dendritic spikes in CA1 pyramidal cells are strongly regulated by specific types of prior input. While input in the linear range is without effect, supralinear input inhibits subsequent spikes, causing them to attenuate and ultimately fail due to dendritic Na+ channel inactivation. This mechanism acts locally within the boundaries of the input branch. If an input is sufficiently strong to trigger axonal action potentials, their back-propagation into the dendritic tree causes a widespread global reduction in dendritic excitability which is prominent after firing patterns occurring in vivo. Together, these mechanisms control the capability of individual dendritic branches to trigger somatic action potential output. They are invoked at frequencies encountered during learning, and impose limits on the storage and retrieval rates of information encoded as branch excitability."}],"publist_id":"2838","quality_controlled":0,"publisher":"Elsevier","author":[{"full_name":"Remy,Stefan","first_name":"Stefan","last_name":"Remy"},{"id":"3FA14672-F248-11E8-B48F-1D18A9856A87","first_name":"Jozsef L","full_name":"Jozsef Csicsvari","orcid":"0000-0002-5193-4036","last_name":"Csicsvari"},{"first_name":"Heinz","full_name":"Beck,Heinz","last_name":"Beck"}],"date_published":"2009-03-26T00:00:00Z","date_updated":"2021-01-12T07:44:13Z","intvolume":"        61","date_created":"2018-12-11T12:03:54Z","issue":"6","citation":{"apa":"Remy, S., Csicsvari, J. L., &#38; Beck, H. (2009). Activity-dependent control of neuronal output by local and global dendritic spike attenuation. <i>Neuron</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.neuron.2009.01.032\">https://doi.org/10.1016/j.neuron.2009.01.032</a>","mla":"Remy, Stefan, et al. “Activity-Dependent Control of Neuronal Output by Local and Global Dendritic Spike Attenuation.” <i>Neuron</i>, vol. 61, no. 6, Elsevier, 2009, pp. 906–16, doi:<a href=\"https://doi.org/10.1016/j.neuron.2009.01.032\">10.1016/j.neuron.2009.01.032</a>.","short":"S. Remy, J.L. Csicsvari, H. Beck, Neuron 61 (2009) 906–916.","ista":"Remy S, Csicsvari JL, Beck H. 2009. Activity-dependent control of neuronal output by local and global dendritic spike attenuation. Neuron. 61(6), 906–916.","chicago":"Remy, Stefan, Jozsef L Csicsvari, and Heinz Beck. “Activity-Dependent Control of Neuronal Output by Local and Global Dendritic Spike Attenuation.” <i>Neuron</i>. Elsevier, 2009. <a href=\"https://doi.org/10.1016/j.neuron.2009.01.032\">https://doi.org/10.1016/j.neuron.2009.01.032</a>.","ieee":"S. Remy, J. L. Csicsvari, and H. Beck, “Activity-dependent control of neuronal output by local and global dendritic spike attenuation,” <i>Neuron</i>, vol. 61, no. 6. Elsevier, pp. 906–916, 2009.","ama":"Remy S, Csicsvari JL, Beck H. Activity-dependent control of neuronal output by local and global dendritic spike attenuation. <i>Neuron</i>. 2009;61(6):906-916. doi:<a href=\"https://doi.org/10.1016/j.neuron.2009.01.032\">10.1016/j.neuron.2009.01.032</a>"},"title":"Activity-dependent control of neuronal output by local and global dendritic spike attenuation","_id":"3547","publication":"Neuron","volume":61,"doi":"10.1016/j.neuron.2009.01.032","publication_status":"published","year":"2009","page":"906 - 916","month":"03"},{"alternative_title":["Mathematics and Visualization"],"day":"22","extern":1,"type":"book_chapter","status":"public","abstract":[{"text":"The medial axis of a geometric shape captures its connectivity. In spite of its inherent instability, it has found applications in a number of areas that deal with shapes. In this survey paper, we focus on results that shed light on this instability and use the new insights to generate simplified and stable modifications of the medial axis.","lang":"eng"}],"publist_id":"2807","quality_controlled":0,"author":[{"last_name":"Attali","first_name":"Dominique","full_name":"Attali, Dominique"},{"last_name":"Boissonnat","first_name":"Jean","full_name":"Boissonnat, Jean-Daniel"},{"id":"3FB178DA-F248-11E8-B48F-1D18A9856A87","first_name":"Herbert","full_name":"Herbert Edelsbrunner","orcid":"0000-0002-9823-6833","last_name":"Edelsbrunner"}],"publisher":"Springer","date_published":"2009-06-22T00:00:00Z","date_updated":"2021-01-12T07:44:25Z","date_created":"2018-12-11T12:04:03Z","citation":{"ieee":"D. Attali, J. Boissonnat, and H. Edelsbrunner, “Stability and computation of medial axes: a state-of-the-art report,” in <i>Mathematical Foundations of Scientific Visualization, Computer Graphics, and Massive Data Exploration</i>, Springer, 2009, pp. 109–125.","ama":"Attali D, Boissonnat J, Edelsbrunner H. Stability and computation of medial axes: a state-of-the-art report. In: <i>Mathematical Foundations of Scientific Visualization, Computer Graphics, and Massive Data Exploration</i>. Springer; 2009:109-125. doi:<a href=\"https://doi.org/10.1007/b106657_6\">10.1007/b106657_6</a>","chicago":"Attali, Dominique, Jean Boissonnat, and Herbert Edelsbrunner. “Stability and Computation of Medial Axes: A State-of-the-Art Report.” In <i>Mathematical Foundations of Scientific Visualization, Computer Graphics, and Massive Data Exploration</i>, 109–25. Springer, 2009. <a href=\"https://doi.org/10.1007/b106657_6\">https://doi.org/10.1007/b106657_6</a>.","apa":"Attali, D., Boissonnat, J., &#38; Edelsbrunner, H. (2009). Stability and computation of medial axes: a state-of-the-art report. In <i>Mathematical Foundations of Scientific Visualization, Computer Graphics, and Massive Data Exploration</i> (pp. 109–125). Springer. <a href=\"https://doi.org/10.1007/b106657_6\">https://doi.org/10.1007/b106657_6</a>","mla":"Attali, Dominique, et al. “Stability and Computation of Medial Axes: A State-of-the-Art Report.” <i>Mathematical Foundations of Scientific Visualization, Computer Graphics, and Massive Data Exploration</i>, Springer, 2009, pp. 109–25, doi:<a href=\"https://doi.org/10.1007/b106657_6\">10.1007/b106657_6</a>.","ista":"Attali D, Boissonnat J, Edelsbrunner H. 2009.Stability and computation of medial axes: a state-of-the-art report. In: Mathematical Foundations of Scientific Visualization, Computer Graphics, and Massive Data Exploration. Mathematics and Visualization, , 109–125.","short":"D. Attali, J. Boissonnat, H. Edelsbrunner, in:, Mathematical Foundations of Scientific Visualization, Computer Graphics, and Massive Data Exploration, Springer, 2009, pp. 109–125."},"_id":"3578","title":"Stability and computation of medial axes: a state-of-the-art report","publication":"Mathematical Foundations of Scientific Visualization, Computer Graphics, and Massive Data Exploration","doi":"10.1007/b106657_6","publication_status":"published","year":"2009","page":"109 - 125","main_file_link":[{"open_access":"0","url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.103.9122"}],"month":"06"},{"page":"187 - 195","department":[{"_id":"NiBa"}],"month":"11","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"ama":"Barton NH. Why sex and recombination? . In: <i>Cold Spring Harbor Symposia on Quantitative Biology</i>. Vol 74. Cold Spring Harbor Laboratory Press; 2009:187-195. doi:<a href=\"https://doi.org/10.1101/sqb.2009.74.030\">10.1101/sqb.2009.74.030</a>","ieee":"N. H. Barton, “Why sex and recombination? ,” in <i>Cold Spring Harbor Symposia on Quantitative Biology</i>, vol. 74, Cold Spring Harbor Laboratory Press, 2009, pp. 187–195.","chicago":"Barton, Nicholas H. “Why Sex and Recombination? .” In <i>Cold Spring Harbor Symposia on Quantitative Biology</i>, 74:187–95. Cold Spring Harbor Laboratory Press, 2009. <a href=\"https://doi.org/10.1101/sqb.2009.74.030\">https://doi.org/10.1101/sqb.2009.74.030</a>.","apa":"Barton, N. H. (2009). Why sex and recombination? . In <i>Cold Spring Harbor Symposia on Quantitative Biology</i> (Vol. 74, pp. 187–195). Cold Spring Harbor Laboratory Press. <a href=\"https://doi.org/10.1101/sqb.2009.74.030\">https://doi.org/10.1101/sqb.2009.74.030</a>","mla":"Barton, Nicholas H. “Why Sex and Recombination? .” <i>Cold Spring Harbor Symposia on Quantitative Biology</i>, vol. 74, Cold Spring Harbor Laboratory Press, 2009, pp. 187–95, doi:<a href=\"https://doi.org/10.1101/sqb.2009.74.030\">10.1101/sqb.2009.74.030</a>.","ista":"Barton NH. 2009.Why sex and recombination? . In: Cold Spring Harbor Symposia on Quantitative Biology. vol. 74, 187–195.","short":"N.H. Barton, in:, Cold Spring Harbor Symposia on Quantitative Biology, Cold Spring Harbor Laboratory Press, 2009, pp. 187–195."},"acknowledgement":"Royal Society and the Engineering and Physical Sciences for support (GR/ T11753/01)","doi":"10.1101/sqb.2009.74.030","language":[{"iso":"eng"}],"publication_status":"published","year":"2009","publication":"Cold Spring Harbor Symposia on Quantitative Biology","_id":"3675","title":"Why sex and recombination? ","volume":74,"date_published":"2009-11-10T00:00:00Z","author":[{"orcid":"0000-0002-8548-5240","last_name":"Barton","first_name":"Nicholas H","full_name":"Barton, Nicholas H","id":"4880FE40-F248-11E8-B48F-1D18A9856A87"}],"publisher":"Cold Spring Harbor Laboratory Press","quality_controlled":"1","date_updated":"2021-01-12T07:45:04Z","intvolume":"        74","date_created":"2018-12-11T12:04:33Z","status":"public","scopus_import":1,"type":"book_chapter","day":"10","oa_version":"None","publist_id":"2708","abstract":[{"text":"Sex and recombination have long been seen as adaptations that facilitate natural selection by generating favorable variations. If recombination is to aid selection, there must be negative linkage disequilibria—favorable alleles must be found together less often than expected by chance. These negative linkage disequilibria can be generated directly by selection, but this must involve negative epistasis of just the right strength, which is not expected, from either experiment or theory. Random drift provides a more general source of negative associations: Favorable mutations almost always arise on different genomes, and negative associations tend to persist, precisely because they shield variation from selection.\r\n\r\nWe can understand how recombination aids adaptation by determining the maximum possible rate of adaptation. With unlinked loci, this rate increases only logarithmically with the influx of favorable mutations. With a linear genome, a scaling argument shows that in a large population, the rate of adaptive substitution depends only on the expected rate in the absence of interference, divided by the total rate of recombination. A two-locus approximation predicts an upper bound on the rate of substitution, proportional to recombination rate.\r\n\r\nIf associations between linked loci do impede adaptation, there can be substantial selection for modifiers that increase recombination. Whether this can account for the maintenance of high rates of sex and recombination depends on the extent of selection. It is clear that the rate of species-wide substitutions is typically far too low to generate appreciable selection for recombination. However, local sweeps within a subdivided population may be effective.","lang":"eng"}]},{"quality_controlled":0,"publisher":"IEEE","author":[{"last_name":"Dhillon","full_name":"Dhillon, Paramveer S","first_name":"Paramveer"},{"last_name":"Nowozin","full_name":"Nowozin, Sebastian","first_name":"Sebastian"},{"id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","first_name":"Christoph","full_name":"Christoph Lampert","orcid":"0000-0001-8622-7887","last_name":"Lampert"}],"date_published":"2009-01-01T00:00:00Z","date_created":"2018-12-11T12:04:38Z","date_updated":"2021-01-12T07:48:59Z","issue":"174","day":"01","extern":1,"status":"public","type":"conference","abstract":[{"lang":"eng","text":"An important cue to high level scene understanding is to analyze the objects in the scene and their behavior and interactions. In this paper, we study the problem of classification of activities in videos, as this is an integral component of any scene understanding system, and present a novel approach for recognizing human action categories in videos by combining information from appearance and motion of human body parts. Our approach is based on tracking human body parts by using mixture particle filters and then clustering the particles using local non - parametric clustering, hence associating a local set of particles to each cluster mode. The trajectory of these cluster modes provides the &quot;motion&quot; information and the &quot;appearance&quot; information is provided by the statistical information about the relative motion of these local set of particles over a number of frames. Later we use a &quot;Bag of Words&quot; model to build one histogram per video sequence from the set of these robust appearance and motion descriptors. These histograms provide us characteristic information which helps us to discriminate among various human actions which ultimately helps us in better understanding of the complete scene. We tested our approach on the standard KTH and Weizmann human action dataseis and the results were comparable to the state of the art methods. Additionally our approach is able to distinguish between activities that involve the motion of complete body from those in which only certain body parts move. In other words, our method discriminates well between activities with &quot;global body motion&quot; like running, jogging etc. and &quot;local motion&quot; like waving, boxing etc."}],"publist_id":"2675","page":"22 - 29","month":"01","conference":{"name":"CVPR: Computer Vision and Pattern Recognition"},"citation":{"mla":"Dhillon, Paramveer, et al. <i>Combining Appearance and Motion for Human Action Classification in Videos</i>. no. 174, IEEE, 2009, pp. 22–29, doi:<a href=\"https://doi.org/10.1109/CVPRW.2009.5204237\">10.1109/CVPRW.2009.5204237</a>.","apa":"Dhillon, P., Nowozin, S., &#38; Lampert, C. (2009). Combining appearance and motion for human action classification in videos (pp. 22–29). Presented at the CVPR: Computer Vision and Pattern Recognition, IEEE. <a href=\"https://doi.org/10.1109/CVPRW.2009.5204237\">https://doi.org/10.1109/CVPRW.2009.5204237</a>","ista":"Dhillon P, Nowozin S, Lampert C. 2009. Combining appearance and motion for human action classification in videos. CVPR: Computer Vision and Pattern Recognition, 22–29.","short":"P. Dhillon, S. Nowozin, C. Lampert, in:, IEEE, 2009, pp. 22–29.","ieee":"P. Dhillon, S. Nowozin, and C. Lampert, “Combining appearance and motion for human action classification in videos,” presented at the CVPR: Computer Vision and Pattern Recognition, 2009, no. 174, pp. 22–29.","ama":"Dhillon P, Nowozin S, Lampert C. Combining appearance and motion for human action classification in videos. In: IEEE; 2009:22-29. doi:<a href=\"https://doi.org/10.1109/CVPRW.2009.5204237\">10.1109/CVPRW.2009.5204237</a>","chicago":"Dhillon, Paramveer, Sebastian Nowozin, and Christoph Lampert. “Combining Appearance and Motion for Human Action Classification in Videos,” 22–29. IEEE, 2009. <a href=\"https://doi.org/10.1109/CVPRW.2009.5204237\">https://doi.org/10.1109/CVPRW.2009.5204237</a>."},"title":"Combining appearance and motion for human action classification in videos","_id":"3690","year":"2009","publication_status":"published","doi":"10.1109/CVPRW.2009.5204237"},{"type":"journal_article","status":"public","day":"07","extern":1,"publist_id":"2663","abstract":[{"lang":"eng","text":"Discriminative techniques, such as conditional random fields (CRFs) or structure aware maximum-margin techniques (maximum margin Markov networks (M3N), structured output support vector machines (S-SVM)), are state-of-the-art in the prediction of structured data. However, to achieve good results these techniques require complete and reliable ground truth, which is not always available in realistic problems. Furthermore, training either CRFs or margin-based techniques is computationally costly, because the runtime of current training methods depends not only on the size of the training set but also on properties of the output space to which the training samples are assigned. We propose an alternative model for structured output prediction, Joint Kernel Support Estimation (JKSE), which is rather generative in nature as it relies on estimating the joint probability density of samples and labels in the training set. This makes it tolerant against incomplete or incorrect labels and also opens the possibility of learning in situations where more than one output label can be considered correct. At the same time, we avoid typical problems of generative models as we do not attempt to learn the full joint probability distribution, but we model only its support in a joint reproducing kernel Hilbert space. As a consequence, JKSE training is possible by an adaption of the classical one-class SVM procedure. The resulting optimization problem is convex and efficiently solvable even with tens of thousands of training examples. A particular advantage of JKSE is that the training speed depends only on the size of the training set, and not on the total size of the label space. No inference step during training is required (as M3N and S-SVM would) nor do we have calculate a partition function (as CRFs do). Experiments on realistic data show that, for suitable kernel functions, our method works efficiently and robustly in situations that discriminative techniques have problems with or that are computationally infeasible for them."}],"tmp":{"name":"Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc/4.0/legalcode","short":"CC BY-NC (4.0)","image":"/images/cc_by_nc.png"},"date_published":"2009-04-07T00:00:00Z","author":[{"id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","full_name":"Christoph Lampert","first_name":"Christoph","orcid":"0000-0001-8622-7887","last_name":"Lampert"},{"last_name":"Blaschko","first_name":"Matthew","full_name":"Blaschko,Matthew B"}],"publisher":"Springer","quality_controlled":0,"issue":"2-3","date_created":"2018-12-11T12:04:40Z","intvolume":"        77","date_updated":"2021-01-12T07:49:01Z","citation":{"ieee":"C. Lampert and M. Blaschko, “Structured prediction by joint kernel support estimation,” <i>Machine Learning</i>, vol. 77, no. 2–3. Springer, pp. 249–269, 2009.","ama":"Lampert C, Blaschko M. Structured prediction by joint kernel support estimation. <i>Machine Learning</i>. 2009;77(2-3):249-269. doi:<a href=\"https://doi.org/10.1007/s10994-009-5111-0\">10.1007/s10994-009-5111-0</a>","chicago":"Lampert, Christoph, and Matthew Blaschko. “Structured Prediction by Joint Kernel Support Estimation.” <i>Machine Learning</i>. Springer, 2009. <a href=\"https://doi.org/10.1007/s10994-009-5111-0\">https://doi.org/10.1007/s10994-009-5111-0</a>.","apa":"Lampert, C., &#38; Blaschko, M. (2009). Structured prediction by joint kernel support estimation. <i>Machine Learning</i>. Springer. <a href=\"https://doi.org/10.1007/s10994-009-5111-0\">https://doi.org/10.1007/s10994-009-5111-0</a>","mla":"Lampert, Christoph, and Matthew Blaschko. “Structured Prediction by Joint Kernel Support Estimation.” <i>Machine Learning</i>, vol. 77, no. 2–3, Springer, 2009, pp. 249–69, doi:<a href=\"https://doi.org/10.1007/s10994-009-5111-0\">10.1007/s10994-009-5111-0</a>.","ista":"Lampert C, Blaschko M. 2009. Structured prediction by joint kernel support estimation. Machine Learning. 77(2–3), 249–269.","short":"C. Lampert, M. Blaschko, Machine Learning 77 (2009) 249–269."},"year":"2009","publication_status":"published","doi":"10.1007/s10994-009-5111-0","_id":"3696","title":"Structured prediction by joint kernel support estimation","publication":"Machine Learning","volume":77,"license":"https://creativecommons.org/licenses/by-nc/4.0/","page":"249 - 269","month":"04"},{"abstract":[{"lang":"eng","text":"Kernel Canonical Correlation Analysis (KCCA) is a general technique for subspace learning that incorporates principal components analysis (PCA) and Fisher linear discriminant analysis (LDA) as special cases. By finding directions that maximize correlation, CCA learns representations tied more closely to underlying process generating the the data and can ignore high-variance noise directions. However, for data where acquisition in a given modality is expensive or otherwise limited, CCA may suffer from small sample effects. We propose to use semisupervised Laplacian regularization to utilize data that are present in only one modality. This approach is able to find highly correlated directions that also lie along the data manifold, resulting in a more robust estimate of correlated subspaces. Functional magnetic resonance imaging (fMRI) acquired data are naturally amenable to subspace techniques as data are well aligned. fMRI data of the human brain are a particularly interesting candidate. In this study we implemented various supervised and semi-supervised versions of CCA on human fMRI data, with regression to single and multivariate labels (corresponding to video content subjects viewed during the image acquisition). In each variate condition, the semi-supervised variants of CCA performed better than the supervised variants, including a supervised variant with Laplacian regularization. We additionally analyze the weights learned by the regression in order to infer brain regions that are important to different types of visual processing."}],"publist_id":"2661","extern":1,"day":"10","type":"conference_poster","status":"public","date_created":"2018-12-11T12:04:41Z","date_updated":"2019-04-26T07:22:33Z","author":[{"full_name":"Blaschko,Matthew B","first_name":"Matthew","last_name":"Blaschko"},{"id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","first_name":"Christoph","full_name":"Christoph Lampert","orcid":"0000-0001-8622-7887","last_name":"Lampert"},{"first_name":"Andreas","full_name":"Bartels, Andreas","last_name":"Bartels"}],"quality_controlled":0,"publisher":"Berlin Institute of Technology","date_published":"2009-07-10T00:00:00Z","title":"Semi-supervised analysis of human fMRI data","_id":"3699","publication":"BBCI: Berlin Brain-Computer Interface Workshop - Advances in Neurotechnology","publication_status":"published","year":"2009","citation":{"short":"M. Blaschko, C. Lampert, A. Bartels, Semi-Supervised Analysis of Human FMRI Data, Berlin Institute of Technology, 2009.","ista":"Blaschko M, Lampert C, Bartels A. 2009. Semi-supervised analysis of human fMRI data, Berlin Institute of Technology,p.","mla":"Blaschko, Matthew, et al. “Semi-Supervised Analysis of Human FMRI Data.” <i>BBCI: Berlin Brain-Computer Interface Workshop - Advances in Neurotechnology</i>, Berlin Institute of Technology, 2009.","apa":"Blaschko, M., Lampert, C., &#38; Bartels, A. (2009). <i>Semi-supervised analysis of human fMRI data</i>. <i>BBCI: Berlin Brain-Computer Interface Workshop - Advances in Neurotechnology</i>. Berlin Institute of Technology.","chicago":"Blaschko, Matthew, Christoph Lampert, and Andreas Bartels. <i>Semi-Supervised Analysis of Human FMRI Data</i>. <i>BBCI: Berlin Brain-Computer Interface Workshop - Advances in Neurotechnology</i>. Berlin Institute of Technology, 2009.","ama":"Blaschko M, Lampert C, Bartels A. <i>Semi-Supervised Analysis of Human FMRI Data</i>. Berlin Institute of Technology; 2009.","ieee":"M. Blaschko, C. Lampert, and A. Bartels, <i>Semi-supervised analysis of human fMRI data</i>. Berlin Institute of Technology, 2009."},"month":"07","main_file_link":[{"open_access":"0","url":"http://pubman.mpdl.mpg.de/pubman/faces/viewItemOverviewPage.jsp?itemId=escidoc:1789281"}]},{"status":"public","type":"conference","day":"10","extern":1,"alternative_title":["Proceedings of the BMVC"],"publist_id":"2655","abstract":[{"text":"Recent research has shown that the use of contextual cues significantly improves performance in sliding window type localization systems. In this work, we propose a method that incorporates both global and local context information through appropriately defined kernel functions. In particular, we make use of a weighted combination of kernels defined over local spatial regions, as well as a global context kernel. The relative importance of the context contributions is learned automatically, and the resulting discriminant function is of a form such that localization at test time can be solved efficiently using a branch and bound optimization scheme. By specifying context directly with a kernel learning approach, we achieve high localization accuracy with a simple and efficient representation. This is in contrast to other systems that incorporate context for which expensive inference needs to be done at test time. We show experimentally on the PASCAL VOC datasets that the inclusion of context can significantly improve localization performance, provided the relative contributions of context cues are learned appropriately.","lang":"eng"}],"tmp":{"image":"/images/cc_by.png","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)"},"date_published":"2009-09-10T00:00:00Z","quality_controlled":0,"publisher":"BMVA Press","author":[{"last_name":"Blaschko","first_name":"Matthew","full_name":"Blaschko,Matthew B"},{"orcid":"0000-0001-8622-7887","last_name":"Lampert","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","full_name":"Christoph Lampert","first_name":"Christoph"}],"date_created":"2018-12-11T12:04:42Z","date_updated":"2021-01-12T07:51:36Z","citation":{"chicago":"Blaschko, Matthew, and Christoph Lampert. “Object Localization with Global and Local Context Kernels,” 1–11. BMVA Press, 2009. <a href=\"https://doi.org/10.5244/C.23.63\">https://doi.org/10.5244/C.23.63</a>.","ama":"Blaschko M, Lampert C. Object localization with global and local context kernels. In: BMVA Press; 2009:1-11. doi:<a href=\"https://doi.org/10.5244/C.23.63\">10.5244/C.23.63</a>","ieee":"M. Blaschko and C. Lampert, “Object localization with global and local context kernels,” presented at the BMVC: British Machine Vision Conference, 2009, pp. 1–11.","ista":"Blaschko M, Lampert C. 2009. Object localization with global and local context kernels. BMVC: British Machine Vision Conference, Proceedings of the BMVC, , 1–11.","short":"M. Blaschko, C. Lampert, in:, BMVA Press, 2009, pp. 1–11.","apa":"Blaschko, M., &#38; Lampert, C. (2009). Object localization with global and local context kernels (pp. 1–11). Presented at the BMVC: British Machine Vision Conference, BMVA Press. <a href=\"https://doi.org/10.5244/C.23.63\">https://doi.org/10.5244/C.23.63</a>","mla":"Blaschko, Matthew, and Christoph Lampert. <i>Object Localization with Global and Local Context Kernels</i>. BMVA Press, 2009, pp. 1–11, doi:<a href=\"https://doi.org/10.5244/C.23.63\">10.5244/C.23.63</a>."},"acknowledgement":"The research leading to these results has received funding from the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007- 2013) / ERC grant agreement no. 228180. This work was funded in part by the EC project CLASS, IST 027978, and the PASCAL2 network of excellence. The first author is supported by the Royal Academy of Engineering through a Newton International Fellowship.","conference":{"name":"BMVC: British Machine Vision Conference"},"year":"2009","publication_status":"published","doi":"10.5244/C.23.63","title":"Object localization with global and local context kernels","_id":"3703","license":"https://creativecommons.org/licenses/by/4.0/","page":"1 - 11","month":"09","main_file_link":[{"url":"http://www.bmva.org/bmvc/2009/Papers/Paper228/Paper228.pdf","open_access":"0"}]},{"page":"951 - 958","month":"06","conference":{"name":"CVPR: Computer Vision and Pattern Recognition"},"acknowledgement":"This work was funded in part by the EC project CLASS, IST 027978, and the PASCAL2 network of excellence.","citation":{"apa":"Lampert, C., Nickisch, H., &#38; Harmeling, S. (2009). Learning to detect unseen object classes by between-class attribute transfer (pp. 951–958). Presented at the CVPR: Computer Vision and Pattern Recognition, IEEE. <a href=\"https://doi.org/10.1109/CVPR.2009.5206594\">https://doi.org/10.1109/CVPR.2009.5206594</a>","mla":"Lampert, Christoph, et al. <i>Learning to Detect Unseen Object Classes by Between-Class Attribute Transfer</i>. IEEE, 2009, pp. 951–58, doi:<a href=\"https://doi.org/10.1109/CVPR.2009.5206594\">10.1109/CVPR.2009.5206594</a>.","ista":"Lampert C, Nickisch H, Harmeling S. 2009. Learning to detect unseen object classes by between-class attribute transfer. CVPR: Computer Vision and Pattern Recognition, 951–958.","short":"C. Lampert, H. Nickisch, S. Harmeling, in:, IEEE, 2009, pp. 951–958.","ieee":"C. Lampert, H. Nickisch, and S. Harmeling, “Learning to detect unseen object classes by between-class attribute transfer,” presented at the CVPR: Computer Vision and Pattern Recognition, 2009, pp. 951–958.","ama":"Lampert C, Nickisch H, Harmeling S. Learning to detect unseen object classes by between-class attribute transfer. In: IEEE; 2009:951-958. doi:<a href=\"https://doi.org/10.1109/CVPR.2009.5206594\">10.1109/CVPR.2009.5206594</a>","chicago":"Lampert, Christoph, Hannes Nickisch, and Stefan Harmeling. “Learning to Detect Unseen Object Classes by Between-Class Attribute Transfer,” 951–58. IEEE, 2009. <a href=\"https://doi.org/10.1109/CVPR.2009.5206594\">https://doi.org/10.1109/CVPR.2009.5206594</a>."},"title":"Learning to detect unseen object classes by between-class attribute transfer","_id":"3704","publication_status":"published","year":"2009","doi":"10.1109/CVPR.2009.5206594","author":[{"full_name":"Christoph Lampert","first_name":"Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","last_name":"Lampert","orcid":"0000-0001-8622-7887"},{"last_name":"Nickisch","full_name":"Nickisch,Hannes","first_name":"Hannes"},{"first_name":"Stefan","full_name":"Harmeling,Stefan","last_name":"Harmeling"}],"publisher":"IEEE","quality_controlled":0,"date_published":"2009-06-20T00:00:00Z","date_created":"2018-12-11T12:04:43Z","date_updated":"2021-01-12T07:51:36Z","day":"20","extern":1,"type":"conference","status":"public","abstract":[{"lang":"eng","text":"We study the problem of object classification when training and test classes are disjoint, i.e. no training examples of the target classes are available. This setup has hardly been studied in computer vision research, but it is the rule rather than the exception, because the world contains tens of thousands of different object classes and for only a very few of them image, collections have been formed and annotated with suitable class labels. In this paper, we tackle the problem by introducing attribute-based classification. It performs object detection based on a human-specified high-level description of the target objects instead of training images. The description consists of arbitrary semantic attributes, like shape, color or even geographic information. Because such properties transcend the specific learning task at hand, they can be pre-learned, e.g. from image datasets unrelated to the current task. Afterwards, new classes can be detected based on their attribute representation, without the need for a new training phase. In order to evaluate our method and to facilitate research in this area, we have assembled a new large-scale dataset, ldquoAnimals with Attributesrdquo, of over 30,000 animal images that match the 50 classes in Osherson‘s classic table of how strongly humans associate 85 semantic attributes with animal classes. Our experiments show that by using an attribute layer it is indeed possible to build a learning object detection system that does not require any training images of the target classes."}],"publist_id":"2652"},{"month":"09","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","page":"112","publication_status":"published","year":"2009","doi":"10.1561/0600000027","language":[{"iso":"eng"}],"article_processing_charge":"No","_id":"3707","title":"Kernel Methods in Computer Vision","volume":4,"publication_identifier":{"isbn":["978-1-60198-268-1"],"eisbn":["978-1-60198-269-8"]},"citation":{"ieee":"C. Lampert, <i>Kernel Methods in Computer Vision</i>, vol. 4. now publishers, 2009.","ama":"Lampert C. <i>Kernel Methods in Computer Vision</i>. Vol 4. now publishers; 2009. doi:<a href=\"https://doi.org/10.1561/0600000027\">10.1561/0600000027</a>","chicago":"Lampert, Christoph. <i>Kernel Methods in Computer Vision</i>. Vol. 4. now publishers, 2009. <a href=\"https://doi.org/10.1561/0600000027\">https://doi.org/10.1561/0600000027</a>.","mla":"Lampert, Christoph. <i>Kernel Methods in Computer Vision</i>. Vol. 4, now publishers, 2009, doi:<a href=\"https://doi.org/10.1561/0600000027\">10.1561/0600000027</a>.","apa":"Lampert, C. (2009). <i>Kernel Methods in Computer Vision</i> (Vol. 4). now publishers. <a href=\"https://doi.org/10.1561/0600000027\">https://doi.org/10.1561/0600000027</a>","short":"C. Lampert, Kernel Methods in Computer Vision, now publishers, 2009.","ista":"Lampert C. 2009. Kernel Methods in Computer Vision, now publishers, 112p."},"date_created":"2018-12-11T12:04:44Z","intvolume":"         4","date_updated":"2021-12-21T15:38:43Z","date_published":"2009-09-03T00:00:00Z","author":[{"first_name":"Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","full_name":"Lampert, Christoph","orcid":"0000-0001-8622-7887","last_name":"Lampert"}],"quality_controlled":"1","publisher":"now publishers","publist_id":"2651","oa_version":"None","abstract":[{"lang":"eng","text":"Over the last years, kernel methods have established themselves as powerful tools for computer vision researchers as well as for practitioners. In this tutorial, we give an introduction to kernel methods in computer vision from a geometric perspective, introducing not only the ubiquitous support vector machines, but also less known techniques for regression, dimensionality reduction, outlier detection and clustering. Additionally, we give an outlook on very recent, non-classical techniques for the prediction of structure data, for the estimation of statistical dependency and for learning the kernel function itself. All methods are illustrated with examples of successful application from the recent computer vision research literature."}],"status":"public","type":"book","day":"03","extern":"1","alternative_title":["Foundations and Trends® in Computer Graphics and Vision"]},{"day":"20","extern":1,"status":"public","type":"conference","abstract":[{"lang":"eng","text":"Markov random field (MRF, CRF) models are popular in computer vision. However, in order to be computationally tractable they are limited to incorporate only local interactions and cannot model global properties, such as connectedness, which is a potentially useful high-level prior for object segmentation. In this work, we overcome this limitation by deriving a potential function that enforces the output labeling to be connected and that can naturally be used in the framework of recent MAP-MRF LP relaxations. Using techniques from polyhedral combinatorics, we show that a provably tight approximation to the MAP solution of the resulting MRF can still be found efficiently by solving a sequence of max-flow problems. The efficiency of the inference procedure also allows us to learn the parameters of a MRF with global connectivity potentials by means of a cutting plane algorithm. We experimentally evaluate our algorithm on both synthetic data and on the challenging segmentation task of the PASCAL VOC 2008 data set. We show that in both cases the addition of a connectedness prior significantly reduces the segmentation error."}],"publist_id":"2649","quality_controlled":0,"publisher":"IEEE","author":[{"full_name":"Nowozin, Sebastian","first_name":"Sebastian","last_name":"Nowozin"},{"orcid":"0000-0001-8622-7887","last_name":"Lampert","full_name":"Christoph Lampert","first_name":"Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87"}],"date_published":"2009-06-20T00:00:00Z","date_updated":"2021-01-12T07:51:38Z","date_created":"2018-12-11T12:04:44Z","acknowledgement":"Conference Information URL:\n\nhttp://www.cvpr2009.org/","conference":{"name":"CVPR: Computer Vision and Pattern Recognition"},"citation":{"apa":"Nowozin, S., &#38; Lampert, C. (2009). Global connectivity potentials for random field models (pp. 818–825). Presented at the CVPR: Computer Vision and Pattern Recognition, IEEE. <a href=\"https://doi.org/10.1109/CVPR.2009.5206567\">https://doi.org/10.1109/CVPR.2009.5206567</a>","mla":"Nowozin, Sebastian, and Christoph Lampert. <i>Global Connectivity Potentials for Random Field Models</i>. IEEE, 2009, pp. 818–25, doi:<a href=\"https://doi.org/10.1109/CVPR.2009.5206567\">10.1109/CVPR.2009.5206567</a>.","ista":"Nowozin S, Lampert C. 2009. Global connectivity potentials for random field models. CVPR: Computer Vision and Pattern Recognition, 818–825.","short":"S. Nowozin, C. Lampert, in:, IEEE, 2009, pp. 818–825.","chicago":"Nowozin, Sebastian, and Christoph Lampert. “Global Connectivity Potentials for Random Field Models,” 818–25. IEEE, 2009. <a href=\"https://doi.org/10.1109/CVPR.2009.5206567\">https://doi.org/10.1109/CVPR.2009.5206567</a>.","ieee":"S. Nowozin and C. Lampert, “Global connectivity potentials for random field models,” presented at the CVPR: Computer Vision and Pattern Recognition, 2009, pp. 818–825.","ama":"Nowozin S, Lampert C. Global connectivity potentials for random field models. In: IEEE; 2009:818-825. doi:<a href=\"https://doi.org/10.1109/CVPR.2009.5206567\">10.1109/CVPR.2009.5206567</a>"},"title":"Global connectivity potentials for random field models","_id":"3708","doi":"10.1109/CVPR.2009.5206567","year":"2009","publication_status":"published","page":"818 - 825","month":"06"},{"abstract":[{"text":"We study the task of detecting the occurrence of objects in large image collections or in videos, a problem that combines aspects of content based image retrieval and object localization. While most previous approaches are either limited to special kinds of queries, or do not scale to large image sets, we propose a new method, efficient subimage retrieval (ESR), which is at the same time very flexible and very efficient. Relying on a two-layered branch-and-bound setup, ESR performs object-based image retrieval in sets of 100,000 or more images within seconds. An extensive evaluation on several datasets shows that ESR is not only very fast, but it also achieves detection accuracies that are on par with or superior to previously published methods for object-based image retrieval.","lang":"eng"}],"publist_id":"2647","extern":1,"day":"29","status":"public","type":"conference","date_updated":"2021-01-12T07:51:38Z","date_created":"2018-12-11T12:04:44Z","quality_controlled":0,"publisher":"IEEE","author":[{"id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","first_name":"Christoph","full_name":"Christoph Lampert","last_name":"Lampert","orcid":"0000-0001-8622-7887"}],"date_published":"2009-09-29T00:00:00Z","title":"Detecting objects in large image collections and videos by efficient subimage retrieval","_id":"3709","doi":"10.1109/ICCV.2009.5459359","publication_status":"published","year":"2009","conference":{"name":"ICCV: International Conference on Computer Vision"},"acknowledgement":"Conference Information URL:\n\nhttp://www.iccv2009.org/","citation":{"apa":"Lampert, C. (2009). Detecting objects in large image collections and videos by efficient subimage retrieval (pp. 987–994). Presented at the ICCV: International Conference on Computer Vision, IEEE. <a href=\"https://doi.org/10.1109/ICCV.2009.5459359\">https://doi.org/10.1109/ICCV.2009.5459359</a>","mla":"Lampert, Christoph. <i>Detecting Objects in Large Image Collections and Videos by Efficient Subimage Retrieval</i>. IEEE, 2009, pp. 987–94, doi:<a href=\"https://doi.org/10.1109/ICCV.2009.5459359\">10.1109/ICCV.2009.5459359</a>.","short":"C. Lampert, in:, IEEE, 2009, pp. 987–994.","ista":"Lampert C. 2009. Detecting objects in large image collections and videos by efficient subimage retrieval. ICCV: International Conference on Computer Vision, 987–994.","chicago":"Lampert, Christoph. “Detecting Objects in Large Image Collections and Videos by Efficient Subimage Retrieval,” 987–94. IEEE, 2009. <a href=\"https://doi.org/10.1109/ICCV.2009.5459359\">https://doi.org/10.1109/ICCV.2009.5459359</a>.","ama":"Lampert C. Detecting objects in large image collections and videos by efficient subimage retrieval. In: IEEE; 2009:987-994. doi:<a href=\"https://doi.org/10.1109/ICCV.2009.5459359\">10.1109/ICCV.2009.5459359</a>","ieee":"C. Lampert, “Detecting objects in large image collections and videos by efficient subimage retrieval,” presented at the ICCV: International Conference on Computer Vision, 2009, pp. 987–994."},"month":"09","page":"987 - 994"},{"page":"2129 - 2142","main_file_link":[{"open_access":"0","url":"http://www2.computer.org/portal/web/csdl/doi/10.1109/TPAMI.2009.144"}],"month":"12","citation":{"apa":"Lampert, C., Blaschko, M., &#38; Hofmann, T. (2009). Efficient subwindow search: A branch and bound framework for object localization. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. IEEE. <a href=\"https://doi.org/10.1109/TPAMI.2009.144\">https://doi.org/10.1109/TPAMI.2009.144</a>","mla":"Lampert, Christoph, et al. “Efficient Subwindow Search: A Branch and Bound Framework for Object Localization.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 31, no. 12, IEEE, 2009, pp. 2129–42, doi:<a href=\"https://doi.org/10.1109/TPAMI.2009.144\">10.1109/TPAMI.2009.144</a>.","ista":"Lampert C, Blaschko M, Hofmann T. 2009. Efficient subwindow search: A branch and bound framework for object localization. IEEE Transactions on Pattern Analysis and Machine Intelligence. 31(12), 2129–2142.","short":"C. Lampert, M. Blaschko, T. Hofmann, IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (2009) 2129–2142.","chicago":"Lampert, Christoph, Matthew Blaschko, and Thomas Hofmann. “Efficient Subwindow Search: A Branch and Bound Framework for Object Localization.” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. IEEE, 2009. <a href=\"https://doi.org/10.1109/TPAMI.2009.144\">https://doi.org/10.1109/TPAMI.2009.144</a>.","ama":"Lampert C, Blaschko M, Hofmann T. Efficient subwindow search: A branch and bound framework for object localization. <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>. 2009;31(12):2129-2142. doi:<a href=\"https://doi.org/10.1109/TPAMI.2009.144\">10.1109/TPAMI.2009.144</a>","ieee":"C. Lampert, M. Blaschko, and T. Hofmann, “Efficient subwindow search: A branch and bound framework for object localization,” <i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, vol. 31, no. 12. IEEE, pp. 2129–2142, 2009."},"acknowledgement":"This work was funded in part by the EU projects CLASS, IST 027978, and PerAct, EST 504321. ","doi":"10.1109/TPAMI.2009.144","year":"2009","publication_status":"published","_id":"3710","volume":31,"publication":"IEEE Transactions on Pattern Analysis and Machine Intelligence","title":"Efficient subwindow search: A branch and bound framework for object localization","date_published":"2009-12-01T00:00:00Z","author":[{"orcid":"0000-0001-8622-7887","last_name":"Lampert","first_name":"Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","full_name":"Christoph Lampert"},{"first_name":"Matthew","full_name":"Blaschko,Matthew B","last_name":"Blaschko"},{"last_name":"Hofmann","first_name":"Thomas","full_name":"Hofmann,Thomas"}],"publisher":"IEEE","quality_controlled":0,"issue":"12","date_updated":"2021-01-12T07:51:39Z","intvolume":"        31","date_created":"2018-12-11T12:04:45Z","status":"public","type":"journal_article","extern":1,"day":"01","publist_id":"2648","abstract":[{"text":"Most successful object recognition systems rely on binary classification, deciding only if an object is present or not, but not providing information on the actual object location. To estimate the object‘s location, one can take a sliding window approach, but this strongly increases the computational cost because the classifier or similarity function has to be evaluated over a large set of candidate subwindows. In this paper, we propose a simple yet powerful branch and bound scheme that allows efficient maximization of a large class of quality functions over all possible subimages. It converges to a globally optimal solution typically in linear or even sublinear time, in contrast to the quadratic scaling of exhaustive or sliding window search. We show how our method is applicable to different object detection and image retrieval scenarios. The achieved speedup allows the use of classifiers for localization that formerly were considered too slow for this task, such as SVMs with a spatial pyramid kernel or nearest-neighbor classifiers based on the chi^2 distance. We demonstrate state-of-the-art localization performance of the resulting systems on the UIUC Cars data set, the PASCAL VOC 2006 data set, and in the PASCAL VOC 2007 competition.","lang":"eng"}]},{"main_file_link":[{"url":"http://www.nowozin.net/sebastian/papers/dhillon2008actionclassification.pdf","open_access":"0"}],"month":"08","page":"22 - 29","doi":"10.1109/CVPRW.2009.5204237","publication_status":"published","year":"2009","_id":"3711","title":"Combining appearance and motion for human action classification in videos","citation":{"ieee":"P. Dhillon, S. Nowozin, and C. Lampert, “Combining appearance and motion for human action classification in videos,” presented at the CVPR: Computer Vision and Pattern Recognition, 2009, pp. 22–29.","ama":"Dhillon P, Nowozin S, Lampert C. Combining appearance and motion for human action classification in videos. In: IEEE; 2009:22-29. doi:<a href=\"https://doi.org/10.1109/CVPRW.2009.5204237\">10.1109/CVPRW.2009.5204237</a>","chicago":"Dhillon, Paramveer, Sebastian Nowozin, and Christoph Lampert. “Combining Appearance and Motion for Human Action Classification in Videos,” 22–29. IEEE, 2009. <a href=\"https://doi.org/10.1109/CVPRW.2009.5204237\">https://doi.org/10.1109/CVPRW.2009.5204237</a>.","mla":"Dhillon, Paramveer, et al. <i>Combining Appearance and Motion for Human Action Classification in Videos</i>. IEEE, 2009, pp. 22–29, doi:<a href=\"https://doi.org/10.1109/CVPRW.2009.5204237\">10.1109/CVPRW.2009.5204237</a>.","apa":"Dhillon, P., Nowozin, S., &#38; Lampert, C. (2009). Combining appearance and motion for human action classification in videos (pp. 22–29). Presented at the CVPR: Computer Vision and Pattern Recognition, IEEE. <a href=\"https://doi.org/10.1109/CVPRW.2009.5204237\">https://doi.org/10.1109/CVPRW.2009.5204237</a>","short":"P. Dhillon, S. Nowozin, C. Lampert, in:, IEEE, 2009, pp. 22–29.","ista":"Dhillon P, Nowozin S, Lampert C. 2009. Combining appearance and motion for human action classification in videos. CVPR: Computer Vision and Pattern Recognition, 22–29."},"conference":{"name":"CVPR: Computer Vision and Pattern Recognition"},"date_updated":"2021-01-12T07:51:39Z","date_created":"2018-12-11T12:04:45Z","date_published":"2009-08-18T00:00:00Z","quality_controlled":0,"author":[{"full_name":"Dhillon, Paramveer S","first_name":"Paramveer","last_name":"Dhillon"},{"last_name":"Nowozin","first_name":"Sebastian","full_name":"Nowozin, Sebastian"},{"last_name":"Lampert","orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","first_name":"Christoph","full_name":"Christoph Lampert"}],"publisher":"IEEE","publist_id":"2645","abstract":[{"text":"An important cue to high level scene understanding is to analyze the objects in the scene and their behavior and interactions. In this paper, we study the problem of classification of activities in videos, as this is an integral component of any scene understanding system, and present a novel approach for recognizing human action categories in videos by combining information from appearance and motion of human body parts. Our approach is based on tracking human body parts by using mixture particle filters and then clustering the particles using local non - parametric clustering, hence associating a local set of particles to each cluster mode. The trajectory of these cluster modes provides the ldquomotionrdquo information and the ldquoappearancerdquo information is provided by the statistical information about the relative motion of these local set of particles over a number of frames. Later we use a ldquoBag of Wordsrdquo model to build one histogram per video sequence from the set of these robust appearance and motion descriptors. These histograms provide us characteristic information which helps us to discriminate among various human actions which ultimately helps us in better understanding of the complete scene. We tested our approach on the standard KTH and Weizmann human action datasets and the results were comparable to the state of the art methods. Additionally our approach is able to distinguish between activities that involve the motion of complete body from those in which only certain body parts move. In other words, our method discriminates well between activities with ldquoglobal body motionrdquo like running, jogging etc. and ldquolocal motionrdquo like waving, boxing etc.","lang":"eng"}],"type":"conference","status":"public","day":"18","extern":1},{"citation":{"short":"C. Lampert, J. Peters, in:, Springer, 2009, pp. 221–231.","ista":"Lampert C, Peters J. 2009. Active structured learning for high-speed object detection. DAGM: German Association For Pattern Recognition, LNCS, vol. 5748, 221–231.","mla":"Lampert, Christoph, and Jan Peters. <i>Active Structured Learning for High-Speed Object Detection</i>. Vol. 5748, Springer, 2009, pp. 221–31, doi:<a href=\"https://doi.org/10.1007/978-3-642-03798-6_23\">10.1007/978-3-642-03798-6_23</a>.","apa":"Lampert, C., &#38; Peters, J. (2009). Active structured learning for high-speed object detection (Vol. 5748, pp. 221–231). Presented at the DAGM: German Association For Pattern Recognition, Springer. <a href=\"https://doi.org/10.1007/978-3-642-03798-6_23\">https://doi.org/10.1007/978-3-642-03798-6_23</a>","chicago":"Lampert, Christoph, and Jan Peters. “Active Structured Learning for High-Speed Object Detection,” 5748:221–31. Springer, 2009. <a href=\"https://doi.org/10.1007/978-3-642-03798-6_23\">https://doi.org/10.1007/978-3-642-03798-6_23</a>.","ama":"Lampert C, Peters J. Active structured learning for high-speed object detection. In: Vol 5748. Springer; 2009:221-231. doi:<a href=\"https://doi.org/10.1007/978-3-642-03798-6_23\">10.1007/978-3-642-03798-6_23</a>","ieee":"C. Lampert and J. Peters, “Active structured learning for high-speed object detection,” presented at the DAGM: German Association For Pattern Recognition, 2009, vol. 5748, pp. 221–231."},"acknowledgement":"This work was funded in part by the EU project CLASS, IST 027978.\nConference Information URL: http://www.optecnet.de/veranstaltungen/2009/09/dagm-2009/","conference":{"name":"DAGM: German Association For Pattern Recognition"},"publication_status":"published","year":"2009","doi":"10.1007/978-3-642-03798-6_23","_id":"3715","volume":5748,"title":"Active structured learning for high-speed object detection","page":"221 - 231","month":"10","status":"public","type":"conference","extern":1,"day":"07","alternative_title":["LNCS"],"publist_id":"2642","abstract":[{"lang":"eng","text":"High-speed smooth and accurate visual tracking of objects in arbitrary, unstructured environments is essential for robotics and human motion analysis. However, building a system that can adapt to arbitrary objects and a wide range of lighting conditions is a challenging problem, especially if hard real-time constraints apply like in robotics scenarios. In this work, we introduce a method for learning a discriminative object tracking system based on the recent structured regression framework for object localization. Using a kernel function that allows fast evaluation on the GPU, the resulting system can process video streams at speed of 100 frames per second or more. Consecutive frames in high speed video sequences are typically very redundant, and for training an object detection system, it is sufficient to have training labels from only a subset of all images. We propose an active learning method that select training examples in a data-driven way, thereby minimizing the required number of training labeling. Experiments on realistic data show that the active learning is superior to previously used methods for dataset subsampling for this task."}],"date_published":"2009-10-07T00:00:00Z","quality_controlled":0,"publisher":"Springer","author":[{"orcid":"0000-0001-8622-7887","last_name":"Lampert","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","first_name":"Christoph","full_name":"Christoph Lampert"},{"last_name":"Peters","first_name":"Jan","full_name":"Peters, Jan"}],"intvolume":"      5748","date_created":"2018-12-11T12:04:46Z","date_updated":"2021-01-12T07:51:41Z"},{"publist_id":"2640","tmp":{"image":"/images/cc_by.png","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)"},"abstract":[{"text":"We introduce RTblob, an open-source real-time vision system for 3D object detection that achieves over 200 Hz tracking speed with only off-the-shelf hardware component. It allows fast and accurate tracking of colored objects in 3D without expensive and often custom-built hardware, instead making use of the PC graphics cards for the necessary image processing operations.","lang":"eng"}],"status":"public","type":"conference_poster","extern":1,"day":"27","date_updated":"2020-07-14T12:46:14Z","date_created":"2018-12-11T12:04:47Z","date_published":"2009-09-27T00:00:00Z","publisher":"IEEE","quality_controlled":0,"author":[{"full_name":"Christoph Lampert","first_name":"Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8622-7887","last_name":"Lampert"},{"full_name":"Peters, Jan","first_name":"Jan","last_name":"Peters"}],"year":"2009","publication_status":"published","publication":"ICCV: International Conference on Computer Vision","_id":"3717","title":"A high-speed object tracker from off-the-shelf components","citation":{"apa":"Lampert, C., &#38; Peters, J. (2009). <i>A high-speed object tracker from off-the-shelf components</i>. <i>ICCV: International Conference on Computer Vision</i>. IEEE.","mla":"Lampert, Christoph, and Jan Peters. “A High-Speed Object Tracker from off-the-Shelf Components.” <i>ICCV: International Conference on Computer Vision</i>, IEEE, 2009.","short":"C. Lampert, J. Peters, A High-Speed Object Tracker from off-the-Shelf Components, IEEE, 2009.","ista":"Lampert C, Peters J. 2009. A high-speed object tracker from off-the-shelf components, IEEE,p.","chicago":"Lampert, Christoph, and Jan Peters. <i>A High-Speed Object Tracker from off-the-Shelf Components</i>. <i>ICCV: International Conference on Computer Vision</i>. IEEE, 2009.","ama":"Lampert C, Peters J. <i>A High-Speed Object Tracker from off-the-Shelf Components</i>. IEEE; 2009.","ieee":"C. Lampert and J. Peters, <i>A high-speed object tracker from off-the-shelf components</i>. IEEE, 2009."},"acknowledgement":"IEEE Workshop URL:  http://humanoidscv.ime.cmc.osaka-u.ac.jp/","main_file_link":[{"url":"http://pubman.mpdl.mpg.de/pubman/faces/viewItemOverviewPage.jsp?itemId=escidoc:1789154","open_access":"0"}],"month":"09"},{"month":"01","main_file_link":[{"open_access":"1","url":"http://arxiv.org/abs/0912.5409v1"}],"year":"2009","publication_status":"published","publication":"ArXiv","_id":"3732","volume":"q-bio.NC","title":"Spin glass models for a network of real neurons","citation":{"ista":"Tkačik G, Schneidman E, Berry M, Bialek W. 2009. Spin glass models for a network of real neurons. ArXiv, q-NC, .","short":"G. Tkačik, E. Schneidman, M. Berry, W. Bialek, ArXiv q-NC (2009).","apa":"Tkačik, G., Schneidman, E., Berry, M., &#38; Bialek, W. (2009). Spin glass models for a network of real neurons. <i>ArXiv</i>. ArXiv.","mla":"Tkačik, Gašper, et al. “Spin Glass Models for a Network of Real Neurons.” <i>ArXiv</i>, vol. q-NC, ArXiv, 2009.","ieee":"G. Tkačik, E. Schneidman, M. Berry, and W. Bialek, “Spin glass models for a network of real neurons,” <i>ArXiv</i>, vol. q-NC. ArXiv, 2009.","ama":"Tkačik G, Schneidman E, Berry M, Bialek W. Spin glass models for a network of real neurons. <i>ArXiv</i>. 2009;q-NC.","chicago":"Tkačik, Gašper, Elad Schneidman, Michael Berry, and William Bialek. “Spin Glass Models for a Network of Real Neurons.” <i>ArXiv</i>. ArXiv, 2009."},"oa":1,"date_created":"2018-12-11T12:04:52Z","date_updated":"2021-01-12T07:51:48Z","date_published":"2009-01-01T00:00:00Z","publisher":"ArXiv","quality_controlled":0,"author":[{"orcid":"0000-0002-6699-1455","last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Gasper Tkacik","first_name":"Gasper"},{"last_name":"Schneidman","full_name":"Schneidman, Elad","first_name":"Elad"},{"first_name":"Michael","full_name":"Berry, Michael J","last_name":"Berry"},{"full_name":"Bialek, William S","first_name":"William","last_name":"Bialek"}],"publist_id":"2496","abstract":[{"text":"Ising models with pairwise interactions are the least structured, or maximum-entropy, probability distributions that exactly reproduce measured pairwise correlations between spins. Here we use this equivalence to construct Ising models that describe the correlated spiking activity of populations of 40 neurons in the salamander retina responding to natural movies. We show that pairwise interactions between neurons account for observed higher-order correlations, and that for groups of 10 or more neurons pairwise interactions can no longer be regarded as small perturbations in an independent system. We then construct network ensembles that generalize the network instances observed in the experiment, and study their thermodynamic behavior and coding capacity. Based on this construction, we can also create synthetic networks of 120 neurons, and find that with increasing size the networks operate closer to a critical point and start exhibiting collective behaviors reminiscent of spin glasses. We examine closely two such behaviors that could be relevant for neural code: tuning of the network to the critical point to maximize the ability to encode diverse stimuli, and using the metastable states of the Ising Hamiltonian as neural code words.","lang":"eng"}],"status":"public","type":"preprint","extern":1,"day":"01"}]