Vortex-lattice melting in a one-dimensional optical lattice

We investigate quantum fluctuations of a vortex lattice in a one-dimensional optical lattice. Our method gives full access to all the modes of the vortex lattice and we discuss in particular the Bloch bands of the Tkachenko modes. Because of the small number of particles in the pancake Bose-Einstein condensates at every site of the optical lattice, finite-size effects become very important. Therefore, the fluctuations in the vortex positions are inhomogeneous and the melting of the lattice occurs from the outside inwards. Tunneling between neighbouring pancakes substantially reduces the inhomogeneity as well as the size of the fluctuations.Comment: 4 pages, 4 figure

Ultracold Superstrings in atomic boson-fermion mixtures

We propose a setup with ultracold atomic gases that can be used to make a nonrelativistic superstring in four spacetime dimensions. In particular, we consider for the creation of the superstring a fermionic atomic gas that is trapped in the core of a vortex in a Bose-Einstein condensate. We explain the required tuning of experimental parameters to achieve supersymmetry between the fermionic atoms and the bosonic modes describing the oscillations in the vortex position. Furthermore, we discuss the experimental consequences of supersymmetry.Comment: 4 pages, 4 figures; published versio

No Spare Parts: Sharing Part Detectors for Image Categorization

This work aims for image categorization using a representation of distinctive parts. Different from existing part-based work, we argue that parts are naturally shared between image categories and should be modeled as such. We motivate our approach with a quantitative and qualitative analysis by backtracking where selected parts come from. Our analysis shows that in addition to the category parts defining the class, the parts coming from the background context and parts from other image categories improve categorization performance. Part selection should not be done separately for each category, but instead be shared and optimized over all categories. To incorporate part sharing between categories, we present an algorithm based on AdaBoost to jointly optimize part sharing and selection, as well as fusion with the global image representation. We achieve results competitive to the state-of-the-art on object, scene, and action categories, further improving over deep convolutional neural networks

Objects2action: Classifying and localizing actions without any video example

The goal of this paper is to recognize actions in video without the need for examples. Different from traditional zero-shot approaches we do not demand the design and specification of attribute classifiers and class-to-attribute mappings to allow for transfer from seen classes to unseen classes. Our key contribution is objects2action, a semantic word embedding that is spanned by a skip-gram model of thousands of object categories. Action labels are assigned to an object encoding of unseen video based on a convex combination of action and object affinities. Our semantic embedding has three main characteristics to accommodate for the specifics of actions. First, we propose a mechanism to exploit multiple-word descriptions of actions and objects. Second, we incorporate the automated selection of the most responsive objects per action. And finally, we demonstrate how to extend our zero-shot approach to the spatio-temporal localization of actions in video. Experiments on four action datasets demonstrate the potential of our approach

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

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