The wet, the wild and the wondrous by Kuek, Sophia Ying Ting, Lim, Christel Wenfang, Josh, Raeburn Brandon, Toh, Yi Shien

“…What makes seemingly mundane grocery shopping venues and experiences so important to Singapore's social space? The Wet, the Wild and the Wondrous takes a granular look at the aspects of wet markets – architectural, sociological, economic – that make up its unique character. …”
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Fisher vector faces in the wild by Simonyan, K, Parkhi, O, Vedaldi, A, Zisserman, A

“…<br> This paper makes two contributions: first, and somewhat surprisingly, we show that Fisher vectors on densely sampled SIFT features, i.e. an off-the-shelf object recognition representation, are capable of achieving state-of-the-art face verification performance on the challenging “Labeled Faces in the Wild” benchmark; second, since Fisher vectors are very high dimensional, we show that a compact descriptor can be learnt from them using discriminative metric learning. …”

The intelligence of artifice: Oscar Wilde and AI by Tyson, C, Mendelssohn, M

“…The worship of artifice has long been a central thread in studies of modernity from aestheticism and decadence through postmodernism. Oscar Wilde has been a central figure in this conversation. …”

Towards robust monocular depth estimation in the wild by Zheng, Zhenkai

“…This research project aims to create a robust monocular depth estimation model that is capable of predicting accurate relative depth maps in the wild. The project will highlight the significance of the training dataset used during supervised model training by comparing models trained with our new mixed dataset in the wild with a common open-source dataset such as NYU. …”
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(Re)wilding London: fabric, politics, and aesthetics by Turnbull, J, Fry, T, Lorimer, J

Cryptography in the Wild: An Empirical Analysis of Vulnerabilities in Cryptographic Libraries by Blessing, Jenny, Specter, Michael A., Weitzner, Daniel J.

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Discriminative deep metric learning for face verification in the wild by Hu, Junlin, Lu, Jiwen, Tan, Yap Peng

“…This paper presents a new discriminative deep metric learning (DDML) method for face verification in the wild. Different from existing metric learning-based face verification methods which aim to learn a Mahalanobis distance metric to maximize the inter-class variations and minimize the intra-class variations, simultaneously, the proposed DDML trains a deep neural network which learns a set of hierarchical nonlinear transformations to project face pairs into the same feature subspace, under which the distance of each positive face pair is less than a smaller threshold and that of each negative pair is higher than a larger threshold, respectively, so that discriminative information can be exploited in the deep network. …”
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On hybridization between Theropithecus gelada and Papio anubis in the wild by Dunbar, RIM, Dunbar, P

“…Evidence is presented which suggests that <i>Theropithecus gelada</i> and <i>Papio anubis</i> may interbreed in the wild. The behavioural and ecological circumstances which give rise to this are discussed. …”

Neuroimaging meta regression for coordinate based meta analysis data with a spatial model by Yu, Y, Pintos Lobo, R, Riedel, MC, Bottenhorn, K, Laird, AR, Nichols, T

“…Coordinate-based meta-analysis combines evidence from a collection of neuroimaging studies to estimate brain activation. …”

Control-oriented meta-learning by Richards, Spencer M., Azizan, Navid, Slotine, Jean-Jacques, Pavone, Marco

“…Specifically, we meta-learn the adaptive controller with closed-loop tracking simulation as the base-learner and the average tracking error as the meta-objective. …”
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ContrastSense: domain-invariant contrastive learning for in-the-wild wearable sensing by Dai, Gaole, Xu, Huatao, Yoon, Hyungjun, Li, Mo, Tan, Rui, Lee, Sung-Ju

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Genetic, natal, and spatial drivers of social phenotypes in wild great tits by Satarkar, D, Sepil, I, Sheldon, BC

“…This requires the estimation of the genetic and environmental basis of these behaviours, which is challenging in uncontrolled wild populations.</li> <li>In this study, we partitioned genetic, developmental and spatial environmental variation in repeatable social network traits derived from foraging events. …”

Meta-Learning Frameworks for Imaging Applications / by Sharma, Ashok, 1977-, editor 319639, Sengar, Sandeep Singh, 1985-, editor 656390, Singh, Parveen, 1976-, editor 656391, IGI Global Scientific Publishing (Online service) 655412

“…Meta-learning, or learning to learn, has been gaining popularity in recent years to adapt to new tasks systematically and efficiently in machine learning. …”
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Meta-reinforcement learning in quantitative trading by Wong, Wei Jie

“…One technique used together with reinforcement learning to train an agent to adapt to new variations of tasks is deep meta-reinforcement learning (Meta-RL) (Wang, et al., 2017). …”
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Use of anthropogenic landscapes in a wild Columba livia (Rock Dove) population by Smith, WJ, Portugal, SJ, Jezierski, MT

“…The domestic pigeon’s wild form is the Rock Dove (<em>Columba livia</em>). …”

Beyond MELD Score: Association of Machine Learning-derived CT Body Composition with 90-Day Mortality Post Transjugular Intrahepatic Portosystemic Shunt Placement by Elhakim, Tarig, Mansur, Arian, Kondo, Jordan, Omar, Omar M. F., Ahmed, Khalid, Tabari, Azadeh, Brea, Allison, Ndakwah, Gabriel, Iqbal, Shams, Allegretti, Andrew S., Fintelmann, Florian J., Wehrenberg-Klee, Eric, Bridge, Christopher, Daye, Dania

“…Multivariable logistic regression showed that SMA (OR = 0.97, p < 0.01), SMI (OR = 0.94, p = 0.03), SFA (OR = 0.99, p = 0.01), and VFA (OR = 0.99, p = 0.02) remained significant predictors of 90-day mortality when adjusted for MELD score. ROC curve analysis demonstrated that including SMA, SFA, and VFA improves the predictive power of MELD score in predicting 90-day mortality after TIPS (AUC, 0.84; 95% CI: 0.77, 0.91; p = 0.02). …”
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Ultrasensitive LSPR meta-devices for biochemical sensing by Wu, Ruhua

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A review of reporting quality in case-control studies of pancreatic cancer by MacCarthy, A, MacCarthy, A, Dhiman, P, Dhiman, P, Kirtley, S, Kirtley, S, Logullo, P, Logullo, P, Copsey, B, Copsey, B, Collins, G, Collins, G

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Bird-Count: a multi-modality benchmark and system for bird population counting in the wild by Wang, Hongchang, Lu, Huaxiang, Guo, Huimin, Jian, Haifang, Gan, Chuang, Liu, Wu

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Meta-analysis of antecedents of information technology (IT) professionals turnover. by Kua, Shimin., Kong, Kua., Quek.

“…Information Technology (IT) Professionals, Meta-analysis, Antecedents of turnover, Relationship to turnover, Role factors, Career factors, Work factors, Job factors, Demographic factors, Organizational Commitment…”
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