Academic literature on the topic 'Trap avoidance'
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Journal articles on the topic "Trap avoidance"
Oliver, Stanley. "Trap avoidance techniques." Manufacturing Engineer 69, no. 1 (1990): 34. http://dx.doi.org/10.1049/me:19900024.
Full textYoung, Julie, Jeffrey Schultz, Buck Jolley, Nekol Basili, and John Draper. "Social Learning of Avoidance Behaviors: Trap Aversion in Captive Coyotes." Animal Behavior and Cognition 9, no. 3 (August 1, 2022): 336–48. http://dx.doi.org/10.26451/abc.09.03.06.2022.
Full textLaurance, WF. "Abundance estimates of small mammals in Australian tropical rainforest: a comparison of four trapping methods." Wildlife Research 19, no. 6 (1992): 651. http://dx.doi.org/10.1071/wr9920651.
Full textRödel, 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, no. 1 (2014): 141–44. http://dx.doi.org/10.1163/15685381-00002932.
Full textFoot, G., S. P. Rice, and J. Millett. "Red trap colour of the carnivorous plant Drosera rotundifolia does not serve a prey attraction or camouflage function." Biology Letters 10, no. 4 (April 2014): 20131024. http://dx.doi.org/10.1098/rsbl.2013.1024.
Full textGracia, Luis, Antonio Sala, and Fabricio Garelli. "A path conditioning method with trap avoidance." Robotics and Autonomous Systems 60, no. 6 (June 2012): 862–73. http://dx.doi.org/10.1016/j.robot.2012.01.009.
Full textHarding, G. C., W. P. Vass, B. T. Hargrave, and 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, no. 5 (May 1, 1986): 952–67. http://dx.doi.org/10.1139/f86-118.
Full textPham, Duc, Thach-Thao Duong, and Abdul Sattar. "Trap Avoidance in Local Search Using Pseudo-Conflict Learning." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 542–48. http://dx.doi.org/10.1609/aaai.v26i1.8149.
Full textSmith, Michael Timothy, and Evelyn T. Bruner. "Pitfalls and Traps in Neuropathology." AJSP: Reviews and Reports 25, no. 2 (March 2020): 83–86. http://dx.doi.org/10.1097/pcr.0000000000000365.
Full textZhao, Yibo, Li-Ying Hao, and Zhi-Jie Wu. "Obstacle Avoidance Control of Unmanned Aerial Vehicle with Motor Loss-of-Effectiveness Fault Based on Improved Artificial Potential Field." Sustainability 15, no. 3 (January 28, 2023): 2368. http://dx.doi.org/10.3390/su15032368.
Full textDissertations / Theses on the topic "Trap avoidance"
Godeme, Jean-Jacques. "Ρhase retrieval with nοn-Euclidean Bregman based geοmetry." Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMC214.
Full textIn 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.
Full textThis 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
Books on the topic "Trap avoidance"
Preston, Thomas. Pandora's trap: Presidential decision making and blame avoidance in Vietnam and Iraq. Lanham, Md: Rowman & Littlefield Publishers, 2014.
Find full textHarding, 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.
Find full textPandora's trap: Presidential decision making and blame avoidance in Vietnam and Iraq. Lanham: Rowman & Littlefield Publishers, 2011.
Find full textPreston, Thomas. Pandora's Trap: Presidential Decision Making and Blame Avoidance in Vietnam and Iraq. Rowman & Littlefield Publishers, Incorporated, 2011.
Find full textAuty, Richard M., and Haydn I. Furlonge. The Rent Curse. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198828860.001.0001.
Full textBook chapters on the topic "Trap avoidance"
Miles, Chris, Sushil J. Louis, and Rich Drewes. "Trap Avoidance in Strategic Computer Game Playing with Case Injected Genetic Algorithms." In 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.
Full textKumar, Anup, Sandeep Rai, and Rajesh Boghey. "A Novel Approach for SQL Injection Avoidance Using Two-Level Restricted Application Prevention (TRAP) Technique." In Advances in Intelligent Systems and Computing, 227–38. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5113-0_17.
Full textPreston, Thomas. "Chapter 05. Opening Pandora’s Box: Blame Avoidance during the Iraq War." In Pandora's Trap, 149–92. Rowman & Littlefield Publishers, 2011. http://dx.doi.org/10.5771/9781442212152-149.
Full textPreston, Thomas. "Chapter 03. The Politics of Blame Avoidance: Presidential Strategies for Surviving the Washington “Blame Game”." In Pandora's Trap, 69–104. Rowman & Littlefield Publishers, 2011. http://dx.doi.org/10.5771/9781442212152-69.
Full textPreston, Thomas. "Chapter 04. Opening Pandora’s Box: Blame Avoidance, 9/11, and the Push for War with Iraq." In Pandora's Trap, 105–48. Rowman & Littlefield Publishers, 2011. http://dx.doi.org/10.5771/9781442212152-105.
Full textCarter, Bryan D., William G. Kronenberger, and Eric L. Scott. "Session 7: Assertiveness and Relationships." In 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.
Full text"Terry Hogan : The Avoidance Trap: Developing a Case Formulation." In 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.
Full textPezzini, Mario. "Citizens’ Rising Expectations." In Trapped in the Middle?, 117–38. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198852773.003.0006.
Full textKouzak, Valeska, and Erika Reimann. "The Psychoanalytic Crisis: The Place of Ego in a Contemporary World." In 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.
Full textBajo, Claudia Sanchez. "Is the Debt Trap Avoidable?" In Co-Operatives in a Post-Growth Era. Zed Books, 2014. http://dx.doi.org/10.5040/9781350219380.ch-014.
Full textConference papers on the topic "Trap avoidance"
Collin, Jean, Chuck Marks, Jack Dingee, and Jonathan Tatman. "Demonstration of Helium Measurement Capability to Support Repair of Irradiated Components." In AM-EPRI 2024, 135–46. ASM International, 2024. http://dx.doi.org/10.31399/asm.cp.am-epri-2024p0135.
Full textKrainak, Michael A., and Frederic M. Davidson. "Two-wave Mixing Gain in BSO with Applied Alternating Electric Fields." In Nonlinear Optical Properties of Materials. Washington, D.C.: Optica Publishing Group, 1988. http://dx.doi.org/10.1364/nlopm.1988.mf11.
Full textThatte, Nitish, Nandagopal Srinivasan, and Hartmut Geyer. "Real-Time Reactive Trip Avoidance for Powered Transfemoral Prostheses." In Robotics: Science and Systems 2019. Robotics: Science and Systems Foundation, 2019. http://dx.doi.org/10.15607/rss.2019.xv.034.
Full textTakahashi, Sho, Masahiro Yagi, and Toru Hagiwara. "Data Accumulation System of Obstacle Avoidance Behavior on Bicycle Trip for Transportation Engineering." In 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE). IEEE, 2019. http://dx.doi.org/10.1109/gcce46687.2019.9014633.
Full textYagi, Masahiro, Sho Takahashi, and Toru Hagiwara. "An Evaluation Method of Obstacle Avoidance Behavior on Bicycle Trip Using Rider's Gesture." In 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE). IEEE, 2019. http://dx.doi.org/10.1109/gcce46687.2019.9015353.
Full textMiyake, Tamon, Yo Kobayashi, Masakatsu G. Fujie, and Shigeki Sugano. "Timing of intermittent torque control with wire-driven gait training robot lifting toe trajectory for trip avoidance." In 2017 International Conference on Rehabilitation Robotics (ICORR). IEEE, 2017. http://dx.doi.org/10.1109/icorr.2017.8009267.
Full textDollar, R. Austin, and Ardalan Vahidi. "Predictively Coordinated Vehicle Acceleration and Lane Selection Using Mixed Integer Programming." In ASME 2018 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/dscc2018-9177.
Full textSmith, Kenneth, and Anthony Fahme. "Back Side-Cooled Combustor Liner for Lean-Premixed Combustion." In 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.
Full textReynolds, John. "The Vital Role of the Corrosion/Materials Engineer in the Life Cycle Management of Pressure Equipment." In ASME 2011 Pressure Vessels and Piping Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/pvp2011-57012.
Full textLi, Boxiao, Hemant Phale, Yanfen Zhang, Timothy Tokar, and Xian-Huan Wen. "Caveats and Pitfalls of Production Forecast Uncertainty Analysis Using Design of Experiments." In SPE Reservoir Simulation Conference. SPE, 2021. http://dx.doi.org/10.2118/203919-ms.
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