Did you call me?:5-month-old infants own name guides their attention

An infant's own name is a unique social cue. Infants are sensitive to their own name by 4 months of age, but whether they use their names as a social cue is unknown. Electroencephalogram (EEG) was measured as infants heard their own name or stranger's names and while looking at novel objects. Event related brain potentials (ERPs) in response to names revealed that infants differentiate their own name from stranger names from the first phoneme. The amplitude of the ERPs to objects indicated that infants attended more to objects after hearing their own names compared to another name. Thus, by 5 months of age infants not only detect their name, but also use it as a social cue to guide their attention to events and objects in the world

Building an AS-topology model that captures route diversity

An understanding of the topological structure of the Internet is needed for quite a number of networking tasks, e.g., making decisions about peering relationships, choice of upstream providers, inter-domain traffic engineering. One essential component of these tasks is the ability to predict routes in the Internet. However, the Internet is composed of a large number of independent autonomous systems (ASes) resulting in complex interactions, and until now no model of the Internet has succeeded in producing predictions of acceptable accuracy. We demonstrate that there are two limitations of prior models: (i) they have all assumed that an Autonomous System (AS) is an atomic structure - it is not, and (ii) models have tended to oversimplify the relationships between ASes. Our approach uses multiple quasi-routers to capture route diversity within the ASes, and is deliberately agnostic regarding the types of relationships between ASes. The resulting model ensures that its routing is consistent with the observed routes. Exploiting a large number of observation points, we show that our model provides accurate predictions for unobserved routes, a first step towards developing structural models of the Internet that enable real applications

Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments.

peer reviewedMolecular dynamics (MD) simulations allow insights into complex processes, but accurate MD simulations require costly quantum-mechanical calculations. For larger systems, efficient but less reliable empirical force fields are used. Machine-learned force fields (MLFFs) offer similar accuracy as ab initio methods at orders-of-magnitude speedup, but struggle to model long-range interactions in large molecules. This work proposes a general approach to constructing accurate MLFFs for large-scale molecular simulations (GEMS) by training on “bottom-up” and “top-down” molecular fragments, from which the relevant interactions can be learned. GEMS allows nanosecond-scale MD simulations of >25,000 atoms at essentially ab initio quality, correctly predicts dynamical oscillations between different helical motifs in polyalanine, and yields good agreement with terahertz vibrational spectroscopy for large-scale protein-water fluctuations in solvated crambin. Our analyses indicate that simulations at ab initio accuracy might be necessary to understand dynamic biomolecular processes.9. Industry, innovation and infrastructur

Realistic BGP Traffic for Test Labs

This paper examines the possibility of generating realistic routing tables of arbitrary size along with realistic BGP updates of arbitrary frequencies via an automated tool deployable in a small-scale test lab. Such a tool provides the necessary foundations to study such questions as: the limits of BGP scalability, the reasons behind routing instability, and the extent to which routing instability influences the forwarding performance of a router.We find that the answer is affirmative. In this paper we identify important characteristics/metrics of routing tables and updates which provide the foundation of the proposed BGP workload model. Based on the insights of an extensive characterization of BGP traffic according to such metrics as prefix length distributions, fanout, amount of nesting of routing table prefixes, AS path length, number and times between BGP update bursts and number and times between BGP session resets, etc., we introduce our prototype tool, rtg. rtg realizes the workload model and is capable of generating realistic BGP traffic. Through its flexibility and parameterization rtg enables us to study the sensibilities of test systems in a repeatable and consistent manner while still providing the possibility of capturing the different characteristics from different vantage points in the network.Olaf Maennel and Anja Feldman