41,944 research outputs found
Unpacking constructs: a network approach for studying war exposure, daily stressors and post-traumatic stress disorder
Conflict affected populations are exposed to stressful events during and after war, and it is well established that both take a substantial toll on individuals' mental health. Exactly how exposure to events during and after war affect mental health is a topic of considerable debate. Various hypotheses have been put forward on the relation between stressful war exposure (SWE), daily stressors (DS) and the development of post-traumatic stress disorder (PTSD). This paper seeks to contribute to this debate by critically reflecting upon conventional modeling approaches and by advancing an alternative model to studying interrelationships between SWE, DS, and PTSD variables. The network model is proposed as an innovative and comprehensive modeling approach in the field of mental health in the context of war. It involves a conceptualization and representation of variables and relationships that better approach reality, hence improving methodological rigor. It also promises utility in programming and delivering mental health support for war-affected populations
Occlusion resistant learning of intuitive physics from videos
To reach human performance on complex tasks, a key ability for artificial
systems is to understand physical interactions between objects, and predict
future outcomes of a situation. This ability, often referred to as intuitive
physics, has recently received attention and several methods were proposed to
learn these physical rules from video sequences. Yet, most of these methods are
restricted to the case where no, or only limited, occlusions occur. In this
work we propose a probabilistic formulation of learning intuitive physics in 3D
scenes with significant inter-object occlusions. In our formulation, object
positions are modeled as latent variables enabling the reconstruction of the
scene. We then propose a series of approximations that make this problem
tractable. Object proposals are linked across frames using a combination of a
recurrent interaction network, modeling the physics in object space, and a
compositional renderer, modeling the way in which objects project onto pixel
space. We demonstrate significant improvements over state-of-the-art in the
intuitive physics benchmark of IntPhys. We apply our method to a second dataset
with increasing levels of occlusions, showing it realistically predicts
segmentation masks up to 30 frames in the future. Finally, we also show results
on predicting motion of objects in real videos
A Developmental Organization for Robot Behavior
This paper focuses on exploring how learning and development can be structured in synthetic (robot) systems. We present a developmental assembler for constructing reusable and temporally extended actions in a sequence. The discussion adopts the traditions
of dynamic pattern theory in which behavior
is an artifact of coupled dynamical systems
with a number of controllable degrees of freedom. In our model, the events that delineate
control decisions are derived from the pattern
of (dis)equilibria on a working subset of sensorimotor policies. We show how this architecture can be used to accomplish sequential
knowledge gathering and representation tasks
and provide examples of the kind of developmental milestones that this approach has
already produced in our lab
Kinetic Monte Carlo and Cellular Particle Dynamics Simulations of Multicellular Systems
Computer modeling of multicellular systems has been a valuable tool for
interpreting and guiding in vitro experiments relevant to embryonic
morphogenesis, tumor growth, angiogenesis and, lately, structure formation
following the printing of cell aggregates as bioink particles. Computer
simulations based on Metropolis Monte Carlo (MMC) algorithms were successful in
explaining and predicting the resulting stationary structures (corresponding to
the lowest adhesion energy state). Here we present two alternatives to the MMC
approach for modeling cellular motion and self-assembly: (1) a kinetic Monte
Carlo (KMC), and (2) a cellular particle dynamics (CPD) method. Unlike MMC,
both KMC and CPD methods are capable of simulating the dynamics of the cellular
system in real time. In the KMC approach a transition rate is associated with
possible rearrangements of the cellular system, and the corresponding time
evolution is expressed in terms of these rates. In the CPD approach cells are
modeled as interacting cellular particles (CPs) and the time evolution of the
multicellular system is determined by integrating the equations of motion of
all CPs. The KMC and CPD methods are tested and compared by simulating two
experimentally well known phenomena: (1) cell-sorting within an aggregate
formed by two types of cells with different adhesivities, and (2) fusion of two
spherical aggregates of living cells.Comment: 11 pages, 7 figures; submitted to Phys Rev
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