Skeletal stochastic differential equations for superprocesses

It is well understood that a supercritical superprocess is equal in law to a discrete Markov branching process whose genealogy is dressed in a Poissonian way with immigration which initiates subcritical superprocesses. The Markov branching process corresponds to the genealogical description of prolific individuals, that is, individuals who produce eternal genealogical lines of descent, and is often referred to as the skeleton or backbone of the original superprocess. The Poissonian dressing along the skeleton may be considered to be the remaining non-prolific genealogical mass in the superprocess. Such skeletal decompositions are equally well understood for continuous-state branching processes (CSBP).In a previous article [16] we developed an SDE approach to study the skeletal representation of CSBPs, which provided a common framework for the skeletal decompositions of supercritical and (sub)critical CSBPs. It also helped us to understand how the skeleton thins down onto one infinite line of descent when conditioning on survival until larger and larger times, and eventually forever.Here our main motivation is to show the robustness of the SDE approach by expanding it to the spatial setting of superprocesses. The current article only considers supercritical superprocesses, leaving the subcritical case open.

A RADIOCARBON DATING APPROACH TO THE DEPOSITION AND REMOVAL OF HUMAN BONE REMAINS IN MEGALITHIC MONUMENTS

ABSTRACTThe formation of commingled human bone assemblages is a key aspect for better understanding funerary rituals. The megalithic cemetery of Panoría (Spain) provides an excellent opportunity to explore bone assemblage formation thanks to the recent excavation of an undisturbed burial. For this purpose, we have approached the differential skeletal representation found between bone and teeth at the site through radiocarbon (14C) dating and Bayesian modeling. The comparison between the series of 14C dates on bone (n=12) and teeth (n=14) stress three main aspects: (1) the dates of teeth show a long period of funerary use before the deposition of the human bone remains; (2) both kinds of samples appear to be chronologically sequenced; the end of the teeth 14C series matches with the beginning of human bone deposition; and (3) bone remains span a short period, not more than a few decades, which probably represents the last episode of intense mortuary activity. These differences suggest that teeth could be the evidence of skeletal depositions subsequently removed from the tomb. The deposition and removal of bone remains emerge as key aspects in the formation of the bone assemblage.

Deep learning-based behavioral analysis reaches human accuracy and is capable of outperforming commercial solutions

To study brain function, preclinical research heavily relies on animal monitoring and the subsequent analyses of behavior. Commercial platforms have enabled semi high-throughput behavioral analyses by automating animal tracking, yet they poorly recognize ethologically relevant behaviors and lack the flexibility to be employed in variable testing environments. Critical advances based on deep-learning and machine vision over the last couple of years now enable markerless tracking of individual body parts of freely moving rodents with high precision. Here, we compare the performance of commercially available platforms (EthoVision XT14, Noldus; TSE Multi-Conditioning System, TSE Systems) to cross-verified human annotation. We provide a set of videos—carefully annotated by several human raters—of three widely used behavioral tests (open field test, elevated plus maze, forced swim test). Using these data, we then deployed the pose estimation software DeepLabCut to extract skeletal mouse representations. Using simple post-analyses, we were able to track animals based on their skeletal representation in a range of classic behavioral tests at similar or greater accuracy than commercial behavioral tracking systems. We then developed supervised machine learning classifiers that integrate the skeletal representation with the manual annotations. This new combined approach allows us to score ethologically relevant behaviors with similar accuracy to humans, the current gold standard, while outperforming commercial solutions. Finally, we show that the resulting machine learning approach eliminates variation both within and between human annotators. In summary, our approach helps to improve the quality and accuracy of behavioral data, while outperforming commercial systems at a fraction of the cost.

Skeletal stochastic differential equations for continuous-state branching processes

It is well understood that a supercritical continuous-state branching process (CSBP) is equal in law to a discrete continuous-time Galton–Watson process (the skeleton of prolific individuals) whose edges are dressed in a Poissonian way with immigration which initiates subcritical CSBPs (non-prolific mass). Equally well understood in the setting of CSBPs and superprocesses is the notion of a spine or immortal particle dressed in a Poissonian way with immigration which initiates copies of the original CSBP, which emerges when conditioning the process to survive eternally. In this article we revisit these notions for CSBPs and put them in a common framework using the well-established language of (coupled) stochastic differential equations (SDEs). In this way we are able to deal simultaneously with all types of CSBPs (supercritical, critical, and subcritical) as well as understanding how the skeletal representation becomes, in the sense of weak convergence, a spinal decomposition when conditioning on survival. We have two principal motivations. The first is to prepare the way to expand the SDE approach to the spatial setting of superprocesses, where recent results have increasingly sought the use of skeletal decompositions to transfer results from the branching particle setting to the setting of measure valued processes. The second is to provide a pathwise decomposition of CSBPs in the spirit of genealogical coding of CSBPs via Lévy excursions, albeit precisely where the aforesaid coding fails to work because the underlying CSBP is supercritical.

Broken Bones and Broken Stones: Exploring Fragmentation in Middle and Late Bronze Age Settlement Contexts in Ireland

This article examines the evidence for fragmentation practices on Middle–Late Bronze Age (c. 1600–700bc) settlement sites in Ireland by looking at two kinds of material: human remains, both burnt and non-burnt, and quern stones. It highlights evidence for the manipulation of non-burnt skulls through ‘de-facing’ and the potential retention of cranial and other fragments for ‘burial’ in settlements. It also explores the more difficult task of determining whether incomplete skeletal representation in cremated remains can be interpreted as deliberate fragmentation, and how the context of deposition must be considered. Human agency in relation to the fragmentation patterns of querns is also examined to understand whether the act of breaking these objects was intentional or unintended and if depositing them was symbolic or simply fortuitous. By discussing this evidence, I hope to contribute to the argument that the funerary and settlement spheres in later prehistoric Ireland were becoming increasingly intertwined.

FOREGROUND DETECTION ON DEPTH MAPS USING SKELETAL REPRESENTATION OF OBJECT SILHOUETTES

This article considers the problem of foreground detection on depth maps. The problem of finding objects of interest on images appears in many object detection, recognition and tracking applications as one of the first steps. However, this problem becomes too complicated for RGB images with multicolored or constantly changing background and in presence of occlusions. Depth maps provide valuable information about distance to the camera for each point of the scene, making it possible to explore object detection methods, based on depth features. We define foreground as a set of objects silhouettes, nearest to the camera relative to the local background. We propose a method of foreground detection on depth maps based on medial representation of objects silhouettes which does not require any machine learning procedures and is able to detect foreground in near real-time in complex scenes with occlusions, using a single depth map. Proposed method is implemented to depth maps, obtained from Kinect sensor.