Literatura académica sobre el tema "Trap avoidance"
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Artículos de revistas sobre el tema "Trap avoidance"
Oliver, Stanley. "Trap avoidance techniques". Manufacturing Engineer 69, n.º 1 (1990): 34. http://dx.doi.org/10.1049/me:19900024.
Texto completoYoung, Julie, Jeffrey Schultz, Buck Jolley, Nekol Basili y John Draper. "Social Learning of Avoidance Behaviors: Trap Aversion in Captive Coyotes". Animal Behavior and Cognition 9, n.º 3 (1 de agosto de 2022): 336–48. http://dx.doi.org/10.26451/abc.09.03.06.2022.
Texto completoLaurance, WF. "Abundance estimates of small mammals in Australian tropical rainforest: a comparison of four trapping methods". Wildlife Research 19, n.º 6 (1992): 651. http://dx.doi.org/10.1071/wr9920651.
Texto completoRödel, Mark-Oliver, Sebastian Demtröder, Claire Fuchs, Diana Petrich, Friedrich Pfisterer, Andreas Richter, Clemens Stolpe et al. "Does intraspecific and intersexual attraction or avoidance influence newt abundance estimates based on fish funnel trap records?" Amphibia-Reptilia 35, n.º 1 (2014): 141–44. http://dx.doi.org/10.1163/15685381-00002932.
Texto completoFoot, G., S. P. Rice y J. Millett. "Red trap colour of the carnivorous plant Drosera rotundifolia does not serve a prey attraction or camouflage function". Biology Letters 10, n.º 4 (abril de 2014): 20131024. http://dx.doi.org/10.1098/rsbl.2013.1024.
Texto completoGracia, Luis, Antonio Sala y Fabricio Garelli. "A path conditioning method with trap avoidance". Robotics and Autonomous Systems 60, n.º 6 (junio de 2012): 862–73. http://dx.doi.org/10.1016/j.robot.2012.01.009.
Texto completoHarding, G. C., W. P. Vass, B. T. Hargrave y S. Pearre Jr. "Diel Vertical Movements and Feeding Activity of Zooplankton in St. Georges Bay, N.S., Using Net Tows and a Newly Developed Passive Trap". Canadian Journal of Fisheries and Aquatic Sciences 43, n.º 5 (1 de mayo de 1986): 952–67. http://dx.doi.org/10.1139/f86-118.
Texto completoPham, Duc, Thach-Thao Duong y Abdul Sattar. "Trap Avoidance in Local Search Using Pseudo-Conflict Learning". Proceedings of the AAAI Conference on Artificial Intelligence 26, n.º 1 (20 de septiembre de 2021): 542–48. http://dx.doi.org/10.1609/aaai.v26i1.8149.
Texto completoSmith, Michael Timothy y Evelyn T. Bruner. "Pitfalls and Traps in Neuropathology". AJSP: Reviews and Reports 25, n.º 2 (marzo de 2020): 83–86. http://dx.doi.org/10.1097/pcr.0000000000000365.
Texto completoZhao, Yibo, Li-Ying Hao y Zhi-Jie Wu. "Obstacle Avoidance Control of Unmanned Aerial Vehicle with Motor Loss-of-Effectiveness Fault Based on Improved Artificial Potential Field". Sustainability 15, n.º 3 (28 de enero de 2023): 2368. http://dx.doi.org/10.3390/su15032368.
Texto completoTesis sobre el tema "Trap avoidance"
Godeme, Jean-Jacques. "Ρhase retrieval with nοn-Euclidean Bregman based geοmetry". Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMC214.
Texto completoIn this work, we investigate the phase retrieval problem of real-valued signals in finite dimension, a challenge encountered across various scientific and engineering disciplines. It explores two complementary approaches: retrieval with and without regularization. In both settings, our work is focused on relaxing the Lipschitz-smoothness assumption generally required by first-order splitting algorithms, and which is not valid for phase retrieval cast as a minimization problem. The key idea here is to replace the Euclidean geometry by a non-Euclidean Bregman divergence associated to an appropriate kernel. We use a Bregman gradient/mirror descent algorithm with this divergence to solve thephase retrieval problem without regularization, and we show exact (up to a global sign) recovery both in a deterministic setting and with high probability for a sufficient number of random measurements (Gaussian and Coded Diffraction Patterns). Furthermore, we establish the robustness of this approachagainst small additive noise. Shifting to regularized phase retrieval, we first develop and analyze an Inertial Bregman Proximal Gradient algorithm for minimizing the sum of two functions in finite dimension, one of which is convex and possibly nonsmooth and the second is relatively smooth in the Bregman geometry. We provide both global and local convergence guarantees for this algorithm. Finally, we study noiseless and stable recovery of low complexity regularized phase retrieval. For this, weformulate the problem as the minimization of an objective functional involving a nonconvex smooth data fidelity term and a convex regularizer promoting solutions conforming to some notion of low-complexity related to their nonsmoothness points. We establish conditions for exact and stable recovery and provide sample complexity bounds for random measurements to ensure that these conditions hold. These sample bounds depend on the low complexity of the signals to be recovered. Our new results allow to go far beyond the case of sparse phase retrieval
Barakat, Anas. "Contributions to non-convex stochastic optimization and reinforcement learning". Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT030.
Texto completoThis thesis is focused on the convergence analysis of some popular stochastic approximation methods in use in the machine learning community with applications to optimization and reinforcement learning.The first part of the thesis is devoted to a popular algorithm in deep learning called ADAM used for training neural networks. This variant of stochastic gradient descent is more generally useful for finding a local minimizer of a function. Assuming that the objective function is differentiable and non-convex, we establish the convergence of the iterates in the long run to the set of critical points under a stability condition in the constant stepsize regime. Then, we introduce a novel decreasing stepsize version of ADAM. Under mild assumptions, it is shown that the iterates are almost surely bounded and converge almost surely to critical points of the objective function. Finally, we analyze the fluctuations of the algorithm by means of a conditional central limit theorem.In the second part of the thesis, in the vanishing stepsizes regime, we generalize our convergence and fluctuations results to a stochastic optimization procedure unifying several variants of the stochastic gradient descent such as, among others, the stochastic heavy ball method, the Stochastic Nesterov Accelerated Gradient algorithm, and the widely used ADAM algorithm. We conclude this second part by an avoidance of traps result establishing the non-convergence of the general algorithm to undesired critical points, such as local maxima or saddle points. Here, the main ingredient is a new avoidance of traps result for non-autonomous settings, which is of independent interest.Finally, the last part of this thesis which is independent from the two previous parts, is concerned with the analysis of a stochastic approximation algorithm for reinforcement learning. In this last part, we propose an analysis of an online target-based actor-critic algorithm with linear function approximation in the discounted reward setting. Our algorithm uses three different timescales: one for the actor and two for the critic. Instead of using the standard single timescale temporal difference (TD) learning algorithm as a critic, we use a two timescales target-based version of TD learning closely inspired from practical actor-critic algorithms implementing target networks. First, we establish asymptotic convergence results for both the critic and the actor under Markovian sampling. Then, we provide a finite-time analysis showing the impact of incorporating a target network into actor-critic methods
Libros sobre el tema "Trap avoidance"
Preston, Thomas. Pandora's trap: Presidential decision making and blame avoidance in Vietnam and Iraq. Lanham, Md: Rowman & Littlefield Publishers, 2014.
Buscar texto completoHarding, Roger D. Abundance and length, trap avoidance, and short-term spatial movement of cutthroat trout at McKinney Lake, southeast Alaska, 1996. Anchorage: Alaska Dept. of Fish and Game, Division of Sport Fish, 1999.
Buscar texto completoPandora's trap: Presidential decision making and blame avoidance in Vietnam and Iraq. Lanham: Rowman & Littlefield Publishers, 2011.
Buscar texto completoPreston, Thomas. Pandora's Trap: Presidential Decision Making and Blame Avoidance in Vietnam and Iraq. Rowman & Littlefield Publishers, Incorporated, 2011.
Buscar texto completoAuty, Richard M. y Haydn I. Furlonge. The Rent Curse. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198828860.001.0001.
Texto completoCapítulos de libros sobre el tema "Trap avoidance"
Miles, Chris, Sushil J. Louis y Rich Drewes. "Trap Avoidance in Strategic Computer Game Playing with Case Injected Genetic Algorithms". En Genetic and Evolutionary Computation – GECCO 2004, 1365–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24854-5_130.
Texto completoKumar, Anup, Sandeep Rai y Rajesh Boghey. "A Novel Approach for SQL Injection Avoidance Using Two-Level Restricted Application Prevention (TRAP) Technique". En Advances in Intelligent Systems and Computing, 227–38. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5113-0_17.
Texto completoPreston, Thomas. "Chapter 05. Opening Pandora’s Box: Blame Avoidance during the Iraq War". En Pandora's Trap, 149–92. Rowman & Littlefield Publishers, 2011. http://dx.doi.org/10.5771/9781442212152-149.
Texto completoPreston, Thomas. "Chapter 03. The Politics of Blame Avoidance: Presidential Strategies for Surviving the Washington “Blame Game”". En Pandora's Trap, 69–104. Rowman & Littlefield Publishers, 2011. http://dx.doi.org/10.5771/9781442212152-69.
Texto completoPreston, Thomas. "Chapter 04. Opening Pandora’s Box: Blame Avoidance, 9/11, and the Push for War with Iraq". En Pandora's Trap, 105–48. Rowman & Littlefield Publishers, 2011. http://dx.doi.org/10.5771/9781442212152-105.
Texto completoCarter, Bryan D., William G. Kronenberger y Eric L. Scott. "Session 7: Assertiveness and Relationships". En Children's Health and Illness Recovery Program (CHIRP), 73–84. Oxford University Press, 2020. http://dx.doi.org/10.1093/med-psych/9780190070472.003.0008.
Texto completo"Terry Hogan : The Avoidance Trap: Developing a Case Formulation". En Child and Adolescent Psychopathology: A Casebook, 43–55. 2455 Teller Road, Thousand Oaks California 91320: SAGE Publications, Inc, 2018. http://dx.doi.org/10.4135/9781506349336.n1.
Texto completoPezzini, Mario. "Citizens’ Rising Expectations". En Trapped in the Middle?, 117–38. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198852773.003.0006.
Texto completoKouzak, Valeska y Erika Reimann. "The Psychoanalytic Crisis: The Place of Ego in a Contemporary World". En The Wounds of Our Mother Psychoanalysis - New Models for a Psychoanalysis in Crisis [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.107249.
Texto completoBajo, Claudia Sanchez. "Is the Debt Trap Avoidable?" En Co-Operatives in a Post-Growth Era. Zed Books, 2014. http://dx.doi.org/10.5040/9781350219380.ch-014.
Texto completoActas de conferencias sobre el tema "Trap avoidance"
Collin, Jean, Chuck Marks, Jack Dingee y Jonathan Tatman. "Demonstration of Helium Measurement Capability to Support Repair of Irradiated Components". En AM-EPRI 2024, 135–46. ASM International, 2024. http://dx.doi.org/10.31399/asm.cp.am-epri-2024p0135.
Texto completoKrainak, Michael A. y Frederic M. Davidson. "Two-wave Mixing Gain in BSO with Applied Alternating Electric Fields". En Nonlinear Optical Properties of Materials. Washington, D.C.: Optica Publishing Group, 1988. http://dx.doi.org/10.1364/nlopm.1988.mf11.
Texto completoThatte, Nitish, Nandagopal Srinivasan y Hartmut Geyer. "Real-Time Reactive Trip Avoidance for Powered Transfemoral Prostheses". En Robotics: Science and Systems 2019. Robotics: Science and Systems Foundation, 2019. http://dx.doi.org/10.15607/rss.2019.xv.034.
Texto completoTakahashi, Sho, Masahiro Yagi y Toru Hagiwara. "Data Accumulation System of Obstacle Avoidance Behavior on Bicycle Trip for Transportation Engineering". En 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE). IEEE, 2019. http://dx.doi.org/10.1109/gcce46687.2019.9014633.
Texto completoYagi, Masahiro, Sho Takahashi y Toru Hagiwara. "An Evaluation Method of Obstacle Avoidance Behavior on Bicycle Trip Using Rider's Gesture". En 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE). IEEE, 2019. http://dx.doi.org/10.1109/gcce46687.2019.9015353.
Texto completoMiyake, Tamon, Yo Kobayashi, Masakatsu G. Fujie y Shigeki Sugano. "Timing of intermittent torque control with wire-driven gait training robot lifting toe trajectory for trip avoidance". En 2017 International Conference on Rehabilitation Robotics (ICORR). IEEE, 2017. http://dx.doi.org/10.1109/icorr.2017.8009267.
Texto completoDollar, R. Austin y Ardalan Vahidi. "Predictively Coordinated Vehicle Acceleration and Lane Selection Using Mixed Integer Programming". En ASME 2018 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/dscc2018-9177.
Texto completoSmith, Kenneth y Anthony Fahme. "Back Side-Cooled Combustor Liner for Lean-Premixed Combustion". En ASME 1999 International Gas Turbine and Aeroengine Congress and Exhibition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/99-gt-239.
Texto completoReynolds, John. "The Vital Role of the Corrosion/Materials Engineer in the Life Cycle Management of Pressure Equipment". En ASME 2011 Pressure Vessels and Piping Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/pvp2011-57012.
Texto completoLi, Boxiao, Hemant Phale, Yanfen Zhang, Timothy Tokar y Xian-Huan Wen. "Caveats and Pitfalls of Production Forecast Uncertainty Analysis Using Design of Experiments". En SPE Reservoir Simulation Conference. SPE, 2021. http://dx.doi.org/10.2118/203919-ms.
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