Metalearning attracted considerable interest in the machine learning community in the last years. Yet, some disagreement remains on what does or what does not constitute a metalearning problem and in which contexts the term is used in. This survey aims at giving an all-encompassing overview of the research directions pursued under the umbrella of metalearning, reconciling different definitions given in scientific literature, listing the choices involved when designing a metalearning system and identifying some of the future research challenges in this domain.

Early footsteps in the Americas Despite a plethora of archaeological research over the past century, the timing of human migration into the Americas is still far from resolved. In a study of exposed outcrops of Lake Otero in White Sands National Park in New Mexico, Bennett et al . reveal numerous human footprints dating to about 23,000 to 21,000 years ago. These finds indicate the presence of humans in North America for approximately two millennia during the Last Glacial Maximum south of the migratory barrier created by the ice sheets to the north. This timing coincided with a Northern Hemispheric abrupt warming event, Dansgaard-Oeschger event 2, which drew down lake levels and allowed humans and megafauna to walk on newly exposed surfaces, creating tracks that became preserved in the geologic record. —AMS

Contemporaneous sloth and human footprints from the terminal Pleistocene at White Sands National Monument suggest stalking.

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The Laetoli site (Tanzania) contains the oldest known hominin footprints, and their interpretation remains open to debate, despite over 35 years of research. The two hominin trackways present are parallel to one another, one of which is a composite formed by at least two individuals walking in single file. Most researchers have focused on the single, clearly discernible G1 trackway while the G2/3 trackway has been largely dismissed due to its composite nature. Here we report the use of a new technique that allows us to decouple the G2 and G3 tracks for the first time. In so doing we are able to quantify the mean footprint topology of the G3 trackway and render it useable for subsequent data analyses. By restoring the effectively ‘lost’ G3 track, we have doubled the available data on some of the rarest traces directly associated with our Pliocene ancestors.

Human tracks at White Sands National Park record more than one and a half kilometres of an out-andback journey and form the longest Late Pleistocene-age double human trackway in the world. An adolescent or small adult female made two trips separated by at least several hours, carrying a young child in at least one direction. Despite giant ground sloth and Columbian Mammoth transecting them between the outbound and return journeys, the human tracks show no changes indicative of predator/prey awareness. In contrast, the giant ground sloth tracks show behaviour consistent with human predator awareness, while mammoth tracks show no such apparent concern. The human footprints are morphologically variable and exhibit left-right asymmetry, which might be due to child carrying. We explore this morphological variability using methods based on the analysis of objective track outlines, which add to the analytical toolkit available for use at other human footprint sites. The sheer number of tracks and their remarkable morphological variability have implications for the reliability of inferences made using much smaller samples as are more common at typical footprint sites. One conclusion is that the number of footprints required to make reliable biometric inferences is larger than often assumed. Highlights We describe a long prehistoric human trackway (1.5 km) of Late Pleistocene age at White Sands National Park (New Mexico, USA).  The double trackway consists of two parallel journeys. The outward journey is crosscut by the tracks of giant ground sloth and Columbian Mammoth. The return journey crosscuts these animal tracks.  Morphological variability is explored using track outlines.  This variability suggests that minimum sample sizes for biometric inferences are larger than commonly assumed.

Abstract-We propose a new method for characterizing the dynamics of complex networks with its application to the link prediction problem. Our approach is based on the discovery of network subgraphs (in this study: triads of nodes) and measuring their transitions during network evolution. We define the Triad Transition Matrix (TTM) containing the probabilities of transitions between triads found in the network, then we show how it can help to discover and quantify the dynamic patterns of network evolution. We also propose the application of TTM to link prediction with an algorithm (called TTM-predictor) which shows good performance, especially for sparse networks analyzed in short time scales. The future applications and research directions of our approach are also proposed and discussed. Link prediction, network evolution, triad transitions

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