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As part of the ethnoarchaeological study carried out by S. Burri and A. Durand on the way of life of current charcoal burners in Morocco, archaeometric analyses of samples from the floor of a hut in Naour were carried out in order to identify the presence of chemical concentrations. These analyses were performed in order to understand the chemical traces left by the various activities during the occupation of the hut and to provide data that could, in the future, serve as a framework for the interpretation of the archaeological record.The samples, taken from the floors in and around the hut, were analyzed by spot tests designed to identify the presence of phosphates, fats, and protein residues. Although it is not always possible to correlate certain concentrations with specific activities, in general, the analysis allowed for the traces of some domestic activities to be recognized, such as the preparation and consumption of food, both inside and outside the hut. International audience

Southeast Asia is home to rich human genetic and linguistic diversity, but the details of past population movements in the region are not well known. Here, we report genome-wide ancient DNA data from eighteen Southeast Asian individuals spanning from the Neolithic period through the Iron Age (4100–1700 years ago). Early farmers from Man Bac in Vietnam exhibit a mixture of East Asian (southern Chinese agriculturalist) and deeply diverged eastern Eurasian (hunter-gatherer) ancestry characteristic of Austroasiatic speakers, with similar ancestry as far south as Indonesia providing evidence for an expansive initial spread of Austroasiatic languages. By the Bronze Age, in a parallel pattern to Europe, sites in Vietnam and Myanmar show close connections to present-day majority groups, reflecting substantial additional influxes of migrants International audience

J. Kr., A.M. and C.P. are grateful for support from DFG grant number KR 4015/1-1, the Carl-Zeiss Foundation and the Baden Württemberg Foundation. S. P., G. R., Q. F., C. F., K. P., S.C. and J.Ke. acknowledge support from the Presidential Innovation Fund of the Max Planck Society. G.R. was supported by an NSERC fellowship. J.G.S. acknowledges use of the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by NSF grant number OCI-1053575. E.B. and O.B. were supported by RFBR grants 13-06-00670, 13-04-01711, 13-04-90420. B.M. was supported by grants OTKA 73430 and 103983. A.Saj. was supported by a Finnish Professor Pool (Paulo Foundation) Grant. The Lithuanian sampling was supported by the LITGEN project (VP1-3.1-ŠMM-07-K-01-013), funded by the European Social Fund under the Global Grant Measure. A.S. was supported by Spanish grants SAF2011-26983 and EM 2012/045. O.U. was supported by Ukrainian SFFS grant F53.4/071. S.A.T. was supported by NIH Pioneer Award 8DP1ES022577-04 and NSF HOMINID award BCS-0827436. K.T. was supported by an Indian CSIR Network Project (GENESIS: BSC0121). L.S. was supported by an Indian CSIR Bhatnagar Fellowship. R.V., M.M., J.P. and E.M. were supported by the European Union Regional Development Fund through the Centre of Excellence in Genomics to the Estonian Biocentre and University of Tartu and by an Estonian Basic Research grant SF0270177As08. M.M. was additionally supported by Estonian Science Foundation grant number 8973. J.G.S. and M.S. were supported by NIH grant GM40282. P.H.S. and E.E.E. were supported by NIH grants HG004120 and HG002385. D.R. and N.P. were supported by NSF HOMINID award BCS-1032255 and NIH grant GM100233. This project has been funded in part with federal funds from the National Cancer Institute, National Institutes of Health, under contract HHSN26120080001E. This Research was supported in part by the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research We sequenced the genomes of a ∼7,000-year-old farmer from Germany and eight ∼8,000-year-old hunter-gatherers from Luxembourg and Sweden. We analysed these and other ancient genomes with 2,345 contemporary humans to show that most present-day Europeans derive from at least three highly differentiated populations: west European hunter-gatherers, who contributed ancestry to all Europeans but not to Near Easterners; ancient north Eurasians related to Upper Palaeolithic Siberians, who contributed to both Europeans and Near Easterners; and early European farmers, who were mainly of Near Eastern origin but also harboured west European hunter-gatherer related ancestry. We model these populations' deep relationships and show that early European farmers had ∼44% ancestry from a 'basal Eurasian' population that split before the diversification of other non-African lineages.

Learning continuously during all model lifetime is fundamental to deploy machine learning solutions robust to drifts in the data distribution. Advances in Continual Learning (CL) with recurrent neural networks could pave the way to a large number of applications where incoming data is non stationary, like natural language processing and robotics. However, the existing body of work on the topic is still fragmented, with approaches which are application-specific and whose assessment is based on heterogeneous learning protocols and datasets. In this paper, we organize the literature on CL for sequential data processing by providing a categorization of the contributions and a review of the benchmarks. We propose two new benchmarks for CL with sequential data based on existing datasets, whose characteristics resemble real-world applications. We also provide a broad empirical evaluation of CL and Recurrent Neural Networks in class-incremental scenario, by testing their ability to mitigate forgetting with a number of different strategies which are not specific to sequential data processing. Our results highlight the key role played by the sequence length and the importance of a clear specification of the CL scenario. Comment: Published in Neural Networks

We propose a vision for directing research and education in the ICT field. Our Smart and Sustainable World vision targets at prosperity for the people and the planet through better awareness and control of both human-made and natural environment. The needs of the society, individuals, and industries are fulfilled with intelligent systems that sense their environment, make proactive decisions on actions advancing their goals, and perform the actions on the environment. We emphasize artificial intelligence, feedback loops, human acceptance and control, intelligent use of basic resources, performance parameters, mission-oriented interdisciplinary research, and a holistic systems view complementing the conventional analytical reductive view as a research paradigm especially for complex problems. To serve a broad audience, we explain these concepts and list the essential literature. We suggest planning research and education by specifying, in a step-wise manner, scenarios, performance criteria, system models, research problems and education content, resulting in common goals and a coherent project portfolio as well as education curricula. Research and education produce feedback to support evolutionary development and encourage creativity in research. Finally, we propose concrete actions for realizing this approach. Comment: 22 pages, 5 figures. This replacement has been edited based on the reviewers' comments. Table 1 and Figure 2 have been added, otherwise only minor changes to correct typos and improve clarity