Dynamic modification of cut marks by trampling: temporal assessment through the use of mixed-effect regressions and deep learning methods
Tóm tắt
Từ khóa
Tài liệu tham khảo
Amadasi A, Camici A, Sironi L, Profumo A, Merli D, Mazzarelli D, Porta D, Duday H, Cattaneo C (2015) The effects of acid and alkaline solutions on cut marks and on the structure of bone: an experimental study on porcine ribs. Legal Med 17:503–508
Behrensmeyer AK, Gordon KD, Yanagi GT (1986) Trampling as a cause of bone surface damage and pseudo-cutmarks. Nature 319:768–771
Brownlee J (2017) Deep learning with Python. Machine Learning Mastery Publ, Sidney
Brownlee J (2018) Better Deep Learning. Machine Learning Mastery Publ, Sidney
Buduma, N. 2017. Fundamentals of deep learning. O’Reilley Media
Byeon, W., Domínguez-Rodrigo, M., Arampatzis, G., Baquedano, E., Yravedra, J., Maté-González, M.A., Koumoutsakos, P., 2019. Automated identification and deep classification of cut marks on bones and its paleoanthropological implications. J Comput Sci. https://doi.org/10.1016/j.jocs.2019.02.005
Chollet, F., 2017. Deep Learning with Python. Manning Publications Company
Cifuentes-Alcobendas, G., Domínguez-Rodrigo, M. Deep learning and taphonomy: high accuracy in the classification of cut marks made on fleshed and defleshed bones using convolutional neural networks. Sci Rep 9, 18933 (2019) https://doi.org/10.1038/s41598-019-55439-6
Courtenay L, Yravedra J, Huguet R, Ollé A, Maté MA, González-Aguilera D (2019) New taphonomic advances in 3D digitala microscopy: a morpohological characterization of trampling marks. Quat Int (in press)
Domínguez-Rodrigo M, de Juana S, Galán AB, Rodríguez M (2009) A new protocol to differentiate trampling marks from butchery cut marks. J Archaeol Sci 36:2643–2654
Domínguez-Rodrigo M, Saladié P, Cáceres I, Huguet R, Yravedra J, Rodríguez-Hidalgo A, Martín P, Pineda A, Marín J, Gené C, Aramendi J, Cobo-Sánchez L (2017) Use and abuse of cut mark analyses: the Rorschach effect. J Archaeol Sci 86:14–23
Domínguez-Rodrigo, M., (2018). Succesful classification of experimental bone surface modifications (BSM) through machine learning algorithms: a solution to the controversial use of BSM in paleoanthropology? Archaeological and anthropological sciences, http://doi.org/10.1007/s12520-018-0684-9
Driscoll K, Alcaína J, Egüez N, Mngado X, Fullola JM, Tejero JM (2016) Trampled under foot: a quartz and chert human trampling experiment at the Cova del Parco rock shelter, Spain. Quat Int 424:130–142
Eren MI, Durant AJ, Neudorf C, Haslam M, Shipton C, Bora J, Korisettar R, Petraglia M (2010) Experimental examination of animal trampling effects on artifact movement in dry and water saturated substrates: a test case from South India. J Archaeol Sci 37(12):3010–3021
Fiorillo AR (1984) An experimental study of trampling: implications for the fossil record. In: Bonnichsen R, Sorg MH (eds) Bone Modification. University of Maine Press, Maine, pp 61–72
Gaudzinski-Windheuser S, Kindler L, Rabinovich R, Goren-Inbar N (2010) Testing heterogeneity in faunal assemblages from archaeological sites. Tumbling and trampling experiments at the early-middle Pleistocene site of Gesher Benot Ya’aqov (Israel). J Archaeol Sci 37:3170–3190
Goodfellow I, Bengio Y, Courville A (2015) Deep learning. MIT Press, Cambridge
Hardt, M., Recht, B., Singer, Y., 2015. Train faster, generalize better: stability of stochastic gradient descent. arXiv [cs.LG]
Harris JA, Marean CW, Ogle K, Thompson J (2017) The trajectory of bone surface modification studies in paleoanthropology and a new Bayesian solution to the identification controversy. J Hum Evol 110:69–81
Hijazi, S., Kumar, R., Rowen, C., 2015. Using Convolutional Neural Networks for Image Recognition
Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313:504–507
Khan S, Rahmani H, Shah SAA, Bennamoun M (2018) A guide to convolutional neural networks for computer vision. Morgan & Claypool Publishers
Kim, D.H., MacKinnon, T., 2018. Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks. Clin Radiol https://doi.org/https://doi.org/10.1016/j.crad.2017.11.015
Kim, P., 2017. Convolutional Neural Network. MATLAB Deep Learning. https://doi.org/10.1007/978-1-4842-2845-6_6
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in neural information processing systems 25. Curran Associates, Inc., pp 1097–1105
Lu L, Zheng Y, Carneiro G, Yang L (2017) Deep learning and convolutional neural networks for medical image computing: precision medicine. Springer, High Performance and Large-Scale Datasets
Oliver JS (1984) Analogues and site context: bone damages from shield trap cave (24CB91), Carbon County, Montana, USA. In: Bonnichsen R, Sorg MH (eds) Bone Modification. University of Maine Press, Maine, pp 73–98
Olsen SL, Shipman P (1988) Surface modification on bone: trampling versus butchery. J Archaeol Sci 15:535–553
Patterson, J., Gibson, A. 2017. Deep learning. A practitioner’s approach. O’Reilley Media
Pineda A, Cáceres I, Saladié P, Huguet R, Morales JI, Rosas A, Vallverdú J (2019) Tumbling effects on bone surface modifications (BSM): an experimental application on archaeological deposits from the Barranc de la Boella site (Tarragona, Spain). J Archaeol Sci 102:35–47
Pineda A, Saladié P, Vergès JM, Huguet R, Cáceres I, Vallverdú J (2014) Trampling versus cut marks on chemically altered surfaces: an experimental approach and archaeological application at the Barranc de la Boella site (la Canonja, Tarragona, Spain). J Archaeol Sci 50:84–93
Rabinovich, R., Gaudzinski-Windheuser, S., Kindler, L., Goren-Inbar, N., 2012. The Acheulian Site of Gesher Benot Ya‘aqov Volume III: Mammalian Taphonomy. The Assemblages of Layers V-5 and V-6, Vertebrate Paleobiology and Paleoanthropology. Springer, New York
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929–1958