Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA

Article / Letter to editorLeiden Inst Advanced Computer Science

Late Eocene to middle Miocene (33 to 13 million years ago) vegetation and climate development on the North American Atlantic Coastal Plain (IODP Expedition 313, Site M0027)

ArticleWe investigated the palynology of sediment cores from Site M0027 of IODP (Integrated Ocean Drilling Program) Expedition 313 on the New Jersey shallow shelf to examine vegetation and climate dynamics on the east coast of North America between 33 and 13 million years ago and to assess the impact of over-regional climate events on the region. Palynological results are complemented with pollen-based quantitative climate reconstructions. Our results indicate that the hinterland vegetation of the New Jersey shelf was characterized by oak–hickory forests in the lowlands and conifer-dominated vegetation in the highlands from the early Oligocene to the middle Miocene. The Oligocene witnessed several expansions of conifer forest, probably related to cooling events. The pollen-based climate data imply an increase in annual temperatures from ∼11.5 °C to more than 16 °C during the Oligocene. The Mi-1 cooling event at the onset of the Miocene is reflected by an expansion of conifers and mean annual temperature decrease of ∼4 °C, from ∼16 °C to ∼12 °C around 23 million years before present. Relatively low annual temperatures are also recorded for several samples during an interval around ∼20 million years before present, which may reflect the Mi-1a and the Mi-1aa cooling events. Generally, the Miocene ecosystem and climate conditions were very similar to those of the Oligocene. Miocene grasslands, as known from other areas in the USA during that time period, are not evident for the hinterland of the New Jersey shelf, possibly reflecting moisture from the proto-Gulf Stream. The palaeovegetation data reveal stable conditions during the mid-Miocene climatic optimum at ∼15 million years before present, with only a minor increase in deciduous–evergreen mixed forest taxa and a decrease in swamp forest taxa. Pollen-based annual temperature reconstructions show average annual temperatures of ∼14 °C during the mid-Miocene climatic optimum, ∼2 °C higher than today, but ∼1.5 °C lower than preceding and following phases of the Miocene. We conclude that vegetation and regional climate in the hinterland of the New Jersey shelf did not react as sensitively to Oligocene and Miocene climate changes as other regions in North America or Europe due to the moderating effects of the North Atlantic. An additional explanation for the relatively low regional temperatures reconstructed for the mid-Miocene climatic optimum could be an uplift of the Appalachian Mountains during the Miocene, which would also have influenced the catchment area of our pollen record.We thank the entire IODP Expedition 313 Scientific Party for input, and the IODP staff for support. We thank M. Drljepan, R. Zanatta, V. Menke, K. Reichel, and S. Namyslo for their assistance with preparing and processing the samples, and during photographing. Discussions with C. Bjerrum, J. Browning, T. Donders, L. Fang, M. Katz, Y. Milker, K. Miller, and P. Sugarman are gratefully acknowledged. Input from K. Dybkjær and anonymous reviewers was very much appreciated and contributed to a significant condensing of the manuscript. The German Science Foundation supported the research (DFG project KO 3944/3-1 to U. Kotthoff). Funding was also provided by NSERC Discovery Grants to F. M. G. McCarthy and to D. R. Greenwood respectively. NERC supported work by S. P. Hesselbo. This research used samples and/or data provided by the Integrated Ocean Drilling Program (IODP)

Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA

Algorithms and the Foundations of Software technolog

Pseudovector vs. pseudoscalar coupling in one-boson exchange NN potentials

We examine the effects of pseudoscalar and pseudovector coupling of the pi and eta mesons in one-boson exchange models of the NN interaction using two approaches: time-ordered perturbation theory unitarized with the relativistic Lippmann-Schwinger equation, and a reduced Bethe-Salpeter equation approach using the Thompson equation. Contact terms in the one-boson exchange amplitudes in time-ordered perturbation theory lead naturally to the introduction of s-channel nucleonic cutoffs for the interaction, which strongly suppresses the far off-shell behavior of the amplitudes in both approaches. Differences between the resulting NN predictions of the various models are found to be small, and particularly so when coupling constants of the other mesons are readjusted within reasonable limits.Comment: 24 pages, 4 figure

Shaping electron wave functions in a carbon nanotube with a parallel magnetic field

A magnetic field, through its vector potential, usually causes measurable changes in the electron wave function only in the direction transverse to the field. Here we demonstrate experimentally and theoretically that in carbon nanotube quantum dots, combining cylindrical topology and bipartite hexagonal lattice, a magnetic field along the nanotube axis impacts also the longitudinal profile of the electronic states. With the high (up to 17T) magnetic fields in our experiment the wave functions can be tuned all the way from "half-wave resonator" shape, with nodes at both ends, to "quarter-wave resonator" shape, with an antinode at one end. This in turn causes a distinct dependence of the conductance on the magnetic field. Our results demonstrate a new strategy for the control of wave functions using magnetic fields in quantum systems with nontrivial lattice and topology.Comment: 5 figure

Marine resource abundance drove pre-agricultural population increase in Stone Age Scandinavia

How climate and ecology affect key cultural transformations remains debated in the context of long-term socio-cultural development because of spatially and temporally disjunct climate and archaeological records. The introduction of agriculture triggered a major population increase across Europe. However, in Southern Scandinavia it was preceded by ~500 years of sustained population growth. Here we show that this growth was driven by long-term enhanced marine production conditioned by the Holocene Thermal Maximum, a time of elevated temperature, sea level and salinity across coastal waters. We identify two periods of increased marine production across trophic levels (P1 7600–7100 and P2 6400–5900 cal. yr BP) that coincide with markedly increased mollusc collection and accumulation of shell middens, indicating greater marine resource availability. Between ~7600–5900 BP, intense exploitation of a warmer, more productive marine environment by Mesolithic hunter-gatherers drove cultural development, including maritime technological innovation, and from ca. 6400–5900 BP, underpinned a ~four-fold human population growth

An extensive experimental evaluation of automated machine learning methods for recommending classification algorithms

This paper presents an experimental comparison among four automated machine learning (AutoML) methods for recommending the best classification algorithm for a given input dataset. Three of these methods are based on evolutionary algorithms (EAs), and the other is Auto-WEKA, a well-known AutoML method based on the combined algorithm selection and hyper-parameter optimisation (CASH) approach. The EA-based methods build classification algorithms from a single machine learning paradigm: either decision-tree induction, rule induction, or Bayesian network classification. Auto-WEKA combines algorithm selection and hyper-parameter optimisation to recommend classification algorithms from multiple paradigms. We performed controlled experiments where these four AutoML methods were given the same runtime limit for different values of this limit. In general, the difference in predictive accuracy of the three best AutoML methods was not statistically significant. However, the EA evolving decision-tree induction algorithms has the advantage of producing algorithms that generate interpretable classification models and that are more scalable to large datasets, by comparison with many algorithms from other learning paradigms that can be recommended by Auto-WEKA. We also observed that Auto-WEKA has shown meta-overfitting, a form of overfitting at the meta-learning level, rather than at the base-learning level