The Atapuerca sites and the Ibeas hominids

The Atapuerca railway Trench and Ibeas sites near Burgos, Spain, are cave fillings that include a series of deposits ranging from below the Matuyama/Bruhnes reversal up to the end of Middle Pleistocene. The lowest fossil-bearing bed in the Trench contains an assemblage of large and small Mammals including Mimomys savini, Pitymys gregaloides, Pliomys episcopalis, Crocuta crocuta, Dama sp. and Megacerini; the uppermost assemblage includes Canis lupus, Lynx spelaea, Panthera (Leo) fossilis, Felis sylvestris, Equus caballus steinheimensis, E.c. germanicus, Pitymys subtenaneus, Microtus arvalis agrestis, Pliomys lenki, and also Panthera toscana, Dicerorhinus bemitoechus, Bison schoetensacki, which are equally present in the lowest level. The biostratigraphic correlation and dates of the sites are briefly discussed, as are the paleoclimatic interpretation of the Trench sequences. Stone artifacts are found in several layers; the earliest occurrences correspond to the upper beds containing Mimomys savini. A set of preserved human occupation floors has been excavated in the top fossil-bearing beds. The stone-tool assemblages of the upper levels are of upper-medial Acheulean to Charentian tradition. The rich bone breccia SH, in the Cueva Mayor-Cueva del Silo, Ibeas de Juarros, is a derived deposit, due to a mud flow that dispersed and carried the skeletons of many carnivores and humans. The taxa represented are: Vrsus deningeri (largely dominant), Panthera (Leo) fossilis, Vulpes vulpes, Homo sapiens var. Several traits of both mandibular and cranial remains are summarized. Preliminary attempts at dating suggest that the Ibeas fossil man is older than the Last Interglacial, or oxygen-isotope stage 5

Accidental Pinhole and Pinspeck Cameras

We identify and study two types of “accidental” images that can be formed in scenes. The first is an accidental pinhole camera image. The second class of accidental images are “inverse” pinhole camera images, formed by subtracting an image with a small occluder present from a reference image without the occluder. Both types of accidental cameras happen in a variety of different situations. For example, an indoor scene illuminated by natural light, a street with a person walking under the shadow of a building, etc. The images produced by accidental cameras are often mistaken for shadows or interreflections. However, accidental images can reveal information about the scene outside the image, the lighting conditions, or the aperture by which light enters the scene.National Science Foundation (U.S.) (CAREER Award 0747120)United States. Office of Naval Research. Multidisciplinary University Research Initiative (N000141010933)National Science Foundation (U.S.) (CGV 1111415)National Science Foundation (U.S.) (CGV 0964004

Early Pleistocene enamel proteome from Dmanisi resolves Stephanorhinus phylogeny

The sequencing of ancient DNA has enabled the reconstruction of speciation, migration and admixture events for extinct taxa. However, the irreversible post-mortem degradation2 of ancient DNA has so far limited its recovery—outside permafrost areas—to specimens that are not older than approximately 0.5 million years (Myr). By contrast, tandem mass spectrometry has enabled the sequencing of approximately 1.5-Myr-old collagen type I, and suggested the presence of protein residues in fossils of the Cretaceous period—although with limited phylogenetic use. In the absence of molecular evidence, the speciation of several extinct species of the Early and Middle Pleistocene epoch remains contentious. Here we address the phylogenetic relationships of the Eurasian Rhinocerotidae of the Pleistocene epoch, using the proteome of dental enamel from a Stephanorhinus tooth that is approximately 1.77-Myr old, recovered from the archaeological site of Dmanisi (South Caucasus, Georgia). Molecular phylogenetic analyses place this Stephanorhinus as a sister group to the clade formed by the woolly rhinoceros (Coelodonta antiquitatis) and Merck’s rhinoceros (Stephanorhinus kirchbergensis). We show that Coelodonta evolved from an early Stephanorhinus lineage, and that this latter genus includes at least two distinct evolutionary lines. The genus Stephanorhinus is therefore currently paraphyletic, and its systematic revision is needed. We demonstrate that sequencing the proteome of Early Pleistocene dental enamel overcomes the limitations of phylogenetic inference based on ancient collagen or DNA. Our approach also provides additional information about the sex and taxonomic assignment of other specimens from Dmanisi. Our findings reveal that proteomic investigation of ancient dental enamel—which is the hardest tissue in vertebrates, and is highly abundant in the fossil record—can push the reconstruction of molecular evolution further back into the Early Pleistocene epoch, beyond the currently known limits of ancient DNA preservation

Complete Primate Skeleton from the Middle Eocene of Messel in Germany: Morphology and Paleobiology

The best European locality for complete Eocene mammal skeletons is Grube Messel, near Darmstadt, Germany. Although the site was surrounded by a para-tropical rain forest in the Eocene, primates are remarkably rare there, and only eight fragmentary specimens were known until now. Messel has now yielded a full primate skeleton. The specimen has an unusual history: it was privately collected and sold in two parts, with only the lesser part previously known. The second part, which has just come to light, shows the skeleton to be the most complete primate known in the fossil record.We describe the morphology and investigate the paleobiology of the skeleton. The specimen is described as Darwinius masillae n.gen. n.sp. belonging to the Cercamoniinae. Because the skeleton is lightly crushed and bones cannot be handled individually, imaging studies are of particular importance. Skull radiography shows a host of teeth developing within the juvenile face. Investigation of growth and proportion suggest that the individual was a weaned and independent-feeding female that died in her first year of life, and might have attained a body weight of 650-900 g had she lived to adulthood. She was an agile, nail-bearing, generalized arboreal quadruped living above the floor of the Messel rain forest.Darwinius masillae represents the most complete fossil primate ever found, including both skeleton, soft body outline and contents of the digestive tract. Study of all these features allows a fairly complete reconstruction of life history, locomotion, and diet. Any future study of Eocene-Oligocene primates should benefit from information preserved in the Darwinius holotype. Of particular importance to phylogenetic studies, the absence of a toilet claw and a toothcomb demonstrates that Darwinius masillae is not simply a fossil lemur, but part of a larger group of primates, Adapoidea, representative of the early haplorhine diversification

C.H.: Optimizing one-shot recognition with micro-set learning

For object category recognition to scale beyond a small number of classes, it is important that algorithms be able to learn from a small amount of labeled data per additional class. One-shot recognition aims to apply the knowledge gained from a set of categories with plentiful data to categories for which only a single exemplar is available for each. As with earlier efforts motivated by transfer learning, we seek an internal representation for the domain that generalizes across classes. However, in contrast to existing work, we formulate the problem in a fundamentally new manner by optimizing the internal representation for the one-shot task using the notion of micro-sets. A micro-set is a sample of data that contains only a single instance of each category, sampled from the pool of available data, which serves as a mechanism to force the learned representation to explicitly address the variability and noise inherent in the one-shot recognition task. We optimize our learned domain features so that they minimize an expected loss over micro-sets drawn from the training set and show that these features generalize effectively to previously unseen categories. We detail a discriminative approach for optimizing one-shot recognition using micro-sets and present experiments on the Animals with Attributes and Caltech-101 datasets that demonstrate the benefits of our formulation. 1