Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science

As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning---pipeline design. We implement an open source Tree-based Pipeline Optimization Tool (TPOT) in Python and demonstrate its effectiveness on a series of simulated and real-world benchmark data sets. In particular, we show that TPOT can design machine learning pipelines that provide a significant improvement over a basic machine learning analysis while requiring little to no input nor prior knowledge from the user. We also address the tendency for TPOT to design overly complex pipelines by integrating Pareto optimization, which produces compact pipelines without sacrificing classification accuracy. As such, this work represents an important step toward fully automating machine learning pipeline design.Comment: 8 pages, 5 figures, preprint to appear in GECCO 2016, edits not yet made from reviewer comment

Perspectief van bloembollenteelt in de binnenduinrand

Wat is de toekomst van mijn bedrijf? Wat moet ik daar nu al aan doen? Met welke maatregelen kan ik dit bereiken? Wat kan ik straks doen? En: ben ik er dan, of moet ik nog meer doen? De antwoorden op deze vragen hebben te maken met veranderingen in de bedrijfsontwikkeling van bloembollenbedrijven. PPO Sector Bloembollen heeft in opdracht van LNV het toekomstperspectief voor de bloembollensector in de binnenduinrand geschetst. Het gaat om de 3 oude teeltgebieden op zand: Bollenstreek, Kennemerland en Noordelijk Zandgebied. Van de bedrijven in deze regio wordt gevraagd zich aan te passen aan maatschappelijke randvoorwaarden en eisen tot een duurzame bollenteelt. Daarnaast nemen de claims op ruimte vanuit andere dan agrarische bestemmingen toe, waardoor het areaal voor bollenteelt afneemt. Deze studie heeft zich toegespitst op de hoofdthema's emissie naar het milieu, ruimtelijke ordening, natuurontwikkeling en waterproblematiek. Naast deze hoofdthema¿s zijn ook de thema¿s energie, afval en recreatie in de beschouwing meegenomen

Antiferromagnetic Order of Strongly Interacting Fermions in a Trap: Real-Space Dynamical Mean-Field Analysis

We apply Dynamical Mean-Field Theory to strongly interacting fermions in an inhomogeneous environment. With the help of this Real-Space Dynamical Mean-Field Theory (R-DMFT) we investigate antiferromagnetic states of repulsively interacting fermions with spin 1/2 in a harmonic potential. Within R-DMFT, antiferromagnetic order is found to be stable in spatial regions with total particle density close to one, but persists also in parts of the system where the local density significantly deviates from half filling. In systems with spin imbalance, we find that antiferromagnetism is gradually suppressed and phase separation emerges beyond a critical value of the spin imbalance.Comment: 4 pages 5 figures, published versio

Op weg naar duurzame bloembollenteelt : evaluatie bedrijfssystemenonderzoek geïntegreerde bollenteelt

In dit rapport wordt zes jaar bedrijfssystemenonderzoek in de bloembollenteelt beschreven dat werd uitgevoerd op de proefbedrijven "De Noord" en "De Zuid". In het onderzoek naar de haalbaarheid van geïntegreerde teelten werd gezocht naar strategieën om op een veilige, duurzame en concurrerende wijze bloembollen te telen

Effect of the lattice alignment on Bloch oscillations of a Bose-Einstein condensate in a square optical lattice

We consider a Bose-Einstein condensate of ultracold atoms loaded into a square optical lattice and subject to a static force. For vanishing atom-atom interactions the atoms perform periodic Bloch oscillations for arbitrary direction of the force. We study the stability of these oscillations for non-vanishing interactions, which is shown to depend on an alignment of the force vector with respect to the lattice crystallographic axes. If the force is aligned along any of the axes, the mean field approach can be used to identify the stability conditions. On the contrary, for a misaligned force one has to employ the microscopic approach, which predicts periodic modulation of Bloch oscillations in the limit of a large forcing.Comment: 4 pages, 3 figure

High-level feature detection from video in TRECVid: a 5-year retrospective of achievements

Successful and effective content-based access to digital video requires fast, accurate and scalable methods to determine the video content automatically. A variety of contemporary approaches to this rely on text taken from speech within the video, or on matching one video frame against others using low-level characteristics like colour, texture, or shapes, or on determining and matching objects appearing within the video. Possibly the most important technique, however, is one which determines the presence or absence of a high-level or semantic feature, within a video clip or shot. By utilizing dozens, hundreds or even thousands of such semantic features we can support many kinds of content-based video navigation. Critically however, this depends on being able to determine whether each feature is or is not present in a video clip. The last 5 years have seen much progress in the development of techniques to determine the presence of semantic features within video. This progress can be tracked in the annual TRECVid benchmarking activity where dozens of research groups measure the effectiveness of their techniques on common data and using an open, metrics-based approach. In this chapter we summarise the work done on the TRECVid high-level feature task, showing the progress made year-on-year. This provides a fairly comprehensive statement on where the state-of-the-art is regarding this important task, not just for one research group or for one approach, but across the spectrum. We then use this past and on-going work as a basis for highlighting the trends that are emerging in this area, and the questions which remain to be addressed before we can achieve large-scale, fast and reliable high-level feature detection on video

Revisiting Proposal-based Object Detection

This paper revisits the pipeline for detecting objects in images with proposals. For any object detector, the obtained box proposals or queries need to be classified and regressed towards ground truth boxes. The common solution for the final predictions is to directly maximize the overlap between each proposal and the ground truth box, followed by a winner-takes-all ranking or non-maximum suppression. In this work, we propose a simple yet effective alternative. For proposal regression, we solve a simpler problem where we regress to the area of intersection between proposal and ground truth. In this way, each proposal only specifies which part contains the object, avoiding a blind inpainting problem where proposals need to be regressed beyond their visual scope. In turn, we replace the winner-takes-all strategy and obtain the final prediction by taking the union over the regressed intersections of a proposal group surrounding an object. Our revisited approach comes with minimal changes to the detection pipeline and can be plugged into any existing method. We show that our approach directly improves canonical object detection and instance segmentation architectures, highlighting the utility of intersection-based regression and grouping.Comment: 10 pages, 7 figure