Particle Production of Vector Fields: Scale Invariance is Attractive

In a model of an Abelian vector boson with a Maxwell kinetic term and non-negative mass-squared it is demonstrated that, under fairly general conditions during inflation, a scale-invariant spectrum of perturbations for the components of a vector field, massive or not, whose kinetic function (and mass) is modulated by the inflaton field is an attractor solution. If the field is massless, or if it remains light until the end of inflation, this attractor solution also generates anisotropic stress, which can render inflation weakly anisotropic. The above two characteristics of the attractor solution can source (independently or combined together) significant statistical anisotropy in the curvature perturbation, which may well be observable in the near future

Centerline Depletion in Direct-Chill Cast Aluminum Alloys: The Avalanche Effect and Its Consequence for Turbulent Jet Casting

Avalanche dynamics of sedimenting grains in direct-chill casting of aluminum ingots is investigated as a primary driving force for centerline segregation. An analytical model predicting the importance of avalanche events as a function of casting parameters is proposed and validated with prior art results. New experimental results investigating the transient and steady-state centerline segregation of DC casting with a turbulent jet are reported

Investigations of the design and performance of high-speed spring-loaded cam systems

This investigation of the design and performance of high-speed spring loaded cam systems attempts to relate the design technique to a more realistic model of the system. The testing of the validity of experimental and analytical results is also a requirement that has received particular attention. Where appropriate, experimental, analytical and design techniques are examined in a more general way. The starting point of this work is the author's study of pushrod operated valve gear in an automobile engine. Investigations have been continued on a variety of engines, all having the basic common factor of a controlling valve spring. The results are therefore particularly relevant to this class of system and appropriate to the types of motion controls required to operate automobile valves efficiently

High-resolution coproecology: Using coprolites to reconstruct the habits and habitats of New Zealand’s extinct upland Moa (Megalapteryx didinus)

Knowledge about the diet and ecology of extinct herbivores has important implications for understanding the evolution of plant defence structures, establishing the influences of herbivory on past plant community structure and composition, and identifying pollination and seed dispersal syndromes. The flightless ratite moa (Aves: Dinornithiformes) were New Zealand's largest herbivores prior to their extinction soon after initial human settlement. Here we contribute to the knowledge of moa diet and ecology by reporting the results of a multidisciplinary study of 35 coprolites from a subalpine cave (Euphrates Cave) on the South Island of New Zealand. Ancient DNA analysis and radiocarbon dating revealed the coprolites were deposited by the extinct upland moa (Megalapteryx didinus), and span from at least 6,368±31 until 694±30 ¹⁴C years BP; the approximate time of their extinction. Using pollen, plant macrofossil, and ancient DNA analyses, we identified at least 67 plant taxa from the coprolites, including the first evidence that moa fed on the nectar-rich flowers of New Zealand flax (Phormium) and tree fuchsia (Fuchsia excorticata). The plant assemblage from the coprolites reflects a highly-generalist feeding ecology for upland moa, including browsing and grazing across the full range of locally available habitats (spanning southern beech (Nothofagus) forest to tussock (Chionochloa) grassland). Intact seeds in the coprolites indicate that upland moa may have been important dispersal agents for several plant taxa. Plant taxa with putative anti-browse adaptations were also identified in the coprolites. Clusters of coprolites (based on pollen assemblages, moa haplotypes, and radiocarbon dates), probably reflect specimens deposited at the same time by individual birds, and reveal the necessity of suitably large sample sizes in coprolite studies to overcome potential biases in diet interpretation

Exploratory Analysis of Highly Heterogeneous Document Collections

We present an effective multifaceted system for exploratory analysis of highly heterogeneous document collections. Our system is based on intelligently tagging individual documents in a purely automated fashion and exploiting these tags in a powerful faceted browsing framework. Tagging strategies employed include both unsupervised and supervised approaches based on machine learning and natural language processing. As one of our key tagging strategies, we introduce the KERA algorithm (Keyword Extraction for Reports and Articles). KERA extracts topic-representative terms from individual documents in a purely unsupervised fashion and is revealed to be significantly more effective than state-of-the-art methods. Finally, we evaluate our system in its ability to help users locate documents pertaining to military critical technologies buried deep in a large heterogeneous sea of information.Comment: 9 pages; KDD 2013: 19th ACM SIGKDD Conference on Knowledge Discovery and Data Minin

Semi-Supervised Eigenbasis Novelty Detection

Recent discoveries in high-time-resolution radio astronomy data have focused attention on a new class of events. Fast transients are rare pulses of radio frequency energy lasting from microseconds to seconds that might be produced by a variety of exotic astrophysical phenomena. For example, X-ray bursts, neutron stars, and active galactic nuclei are all possible sources of short-duration, transient radio signals. It is difficult to anticipate where such signals might appear, and they are most commonly discovered through analysis of high-time- resolution data that had been collected for other purposes. Transients are often faint and difficult to detect, so improved detection algorithms can directly benefit the science yield of all such commensal monitoring. A new detection algorithm learns a low-dimensional linear manifold for describing the normal data. High reconstruction error indicates a novel signal that does not match the patterns of normal data. One unsupervised portion of the manifold model adapts its representation in response to recent data. A second supervised portion of the model is made of a basis trained in advance using labeled examples of RFI; this prevents false positives due to these events. For a linear model, an orthonormalization operation is used to combine these bases prior to the anomaly detection decision. Another novel aspect of the approach lies in combining basis vectors learned in an unsupervised, online fashion from the data stream with supervised basis vectors learned in advance from known examples of false alarms. Adaptive, data-driven detection is achieved that is also informed by existing domain knowledge about signals that may be statistically anomalous, but are not interesting and should therefore be ignored. The method was evaluated using data from the Parkes Multibeam Survey. This data set was originally collected to search for pulsars, which are astronomical sources that emit radio pulses at regular periods. However, several non-pulsar anomalies have recently been discovered in this dataset, making it a compelling test case. By explicitly filtering known false alarm patterns, the approach yields significantly better performance than current transient detection methods