Non-equilibrium dynamics of a Bose-Einstein condensate in an optical lattice

The dynamical evolution of a Bose-Einstein condensate trapped in a one-dimensional lattice potential is investigated theoretically in the framework of the Bose-Hubbard model. The emphasis is set on the far-from-equilibrium evolution in a case where the gas is strongly interacting. This is realized by an appropriate choice of the parameters in the Hamiltonian, and by starting with an initial state, where one lattice well contains a Bose-Einstein condensate while all other wells are empty. Oscillations of the condensate as well as non-condensate fractions of the gas between the different sites of the lattice are found to be damped as a consequence of the collisional interactions between the atoms. Functional integral techniques involving self-consistently determined mean fields as well as two-point correlation functions are used to derive the two-particle-irreducible (2PI) effective action. The action is expanded in inverse powers of the number of field components N, and the dynamic equations are derived from it to next-to-leading order in this expansion. This approach reaches considerably beyond the Hartree-Fock-Bogoliubov mean-field theory, and its results are compared to the exact quantum dynamics obtained by A.M. Rey et al., Phys. Rev. A 69, 033610 (2004) for small atom numbers.Comment: 9 pages RevTeX, 3 figure

Measuring the Accuracy of Object Detectors and Trackers

The accuracy of object detectors and trackers is most commonly evaluated by the Intersection over Union (IoU) criterion. To date, most approaches are restricted to axis-aligned or oriented boxes and, as a consequence, many datasets are only labeled with boxes. Nevertheless, axis-aligned or oriented boxes cannot accurately capture an object's shape. To address this, a number of densely segmented datasets has started to emerge in both the object detection and the object tracking communities. However, evaluating the accuracy of object detectors and trackers that are restricted to boxes on densely segmented data is not straightforward. To close this gap, we introduce the relative Intersection over Union (rIoU) accuracy measure. The measure normalizes the IoU with the optimal box for the segmentation to generate an accuracy measure that ranges between 0 and 1 and allows a more precise measurement of accuracies. Furthermore, it enables an efficient and easy way to understand scenes and the strengths and weaknesses of an object detection or tracking approach. We display how the new measure can be efficiently calculated and present an easy-to-use evaluation framework. The framework is tested on the DAVIS and the VOT2016 segmentations and has been made available to the community.Comment: 10 pages, 7 Figure

Planarian Fragments Behave As Whole Animals

Behavioral responses of freshwater planarians have been studied for over a century. In recent decades, behavior has been used as a readout to study planarian development and regeneration, wound healing, molecular evolution, neurotoxicology, and learning and memory. The planarian nervous system is among the simplest of the bilaterally symmetric animals, with an anterior brain attached to two ventral nerve cords interconnected by multiple commissures. We found that, in response to mechanical and near-UV stimulation, head stimulation produces turning, tail stimulation produces contraction, and trunk stimulation produces midbody elongation in the planarian Dugesia japonica. When cut into two or three pieces, the anterior end of each headless piece switched its behavior to turning instead of elongation; i.e., it responded as though it were the head. In addition, posterior ends of the head and midbody pieces sometimes produced contraction instead of elongation. Thus, each severed piece acts like an intact animal, with each midbody region having nearly complete behavioral capabilities. These observations show that each midbody region reads the global state of the organism and adapts its response to incoming signals from the remaining tissue. Selective lateral incisions showed that the changes in behavior are not due to nonselective pain responses and that the ventral nerve cords and cross-connectives are responsible for coordinating local behaviors. Our findings highlight a fast functional reorganization of the planarian nervous system that complements the slower repairs provided by regeneration. This reorganization provides needed behavioral responses for survival as regeneration proceeds

Supervised learning with quantum enhanced feature spaces

Machine learning and quantum computing are two technologies each with the potential for altering how computation is performed to address previously untenable problems. Kernel methods for machine learning are ubiquitous for pattern recognition, with support vector machines (SVMs) being the most well-known method for classification problems. However, there are limitations to the successful solution to such problems when the feature space becomes large, and the kernel functions become computationally expensive to estimate. A core element to computational speed-ups afforded by quantum algorithms is the exploitation of an exponentially large quantum state space through controllable entanglement and interference. Here, we propose and experimentally implement two novel methods on a superconducting processor. Both methods represent the feature space of a classification problem by a quantum state, taking advantage of the large dimensionality of quantum Hilbert space to obtain an enhanced solution. One method, the quantum variational classifier builds on [1,2] and operates through using a variational quantum circuit to classify a training set in direct analogy to conventional SVMs. In the second, a quantum kernel estimator, we estimate the kernel function and optimize the classifier directly. The two methods present a new class of tools for exploring the applications of noisy intermediate scale quantum computers [3] to machine learning.Comment: Fixed typos, added figures and discussion about quantum error mitigatio

Quantum Chi-Squared and Goodness of Fit Testing

The density matrix in quantum mechanics parameterizes the statistical properties of the system under observation, just like a classical probability distribution does for classical systems. The expectation value of observables cannot be measured directly, it can only be approximated by applying classical statistical methods to the frequencies by which certain measurement outcomes (clicks) are obtained. In this paper, we make a detailed study of the statistical fluctuations obtained during an experiment in which a hypothesis is tested, i.e. the hypothesis that a certain setup produces a given quantum state. Although the classical and quantum problem are very much related to each other, the quantum problem is much richer due to the additional optimization over the measurement basis. Just as in the case of classical hypothesis testing, the confidence in quantum hypothesis testing scales exponentially in the number of copies. In this paper, we will argue 1) that the physically relevant data of quantum experiments is only contained in the frequencies of the measurement outcomes, and that the statistical fluctuations of the experiment are essential, so that the correct formulation of the conclusions of a quantum experiment should be given in terms of hypothesis tests, 2) that the (classical) $\chi^2$ test for distinguishing two quantum states gives rise to the quantum $\chi^2$ divergence when optimized over the measurement basis, 3) present a max-min characterization for the optimal measurement basis for quantum goodness of fit testing, find the quantum measurement which leads both to the maximal Pitman and Bahadur efficiency, and determine the associated divergence rates.Comment: 22 Pages, with a new section on parameter estimatio

Head Removal Enhances Planarian Electrotaxis

Certain animal species utilize electric fields for communication, hunting and spatial orientation. Freshwater planarians move toward the cathode in a static electric field (cathodic electrotaxis). This planarian behavior was first described by Raymond Pearl more than a century ago. However, planarian electrotaxis has received little attention since, and the underlying mechanisms and evolutionary significance remain unknown. To close this knowledge gap, we developed an apparatus and scoring metrics for automated quantitative and mechanistic studies of planarian behavior upon exposure to a static electric field. Using this automated setup, we characterized electrotaxis in the planarian Dugesia japonica and found that this species responds to voltage instead of current, in contrast to results from previous studies using other planarian species. Surprisingly, we found differences in electrotaxis ability between small (shorter) and large (longer) planarians. To determine the cause of these differences, we took advantage of the regenerative abilities of planarians and compared electrotaxis in head, tail and trunk fragments of various lengths. We found that tail and trunk fragments electrotaxed, whereas head fragments did not, regardless of size. Based on these data, we hypothesized that signals from the head may interfere with electrotaxis when the head area/body area reached a critical threshold. In support of this hypothesis, we found that (1) smaller intact planarians that cannot electrotax have a relatively larger head-to-body-ratio than large planarians that can electrotax, and (2) the electrotaxis behavior of cut head fragments was negatively correlated with the head-to-body ratio of the fragments. Moreover, we could restore cathodic electrotaxis in head fragments via decapitation, directly demonstrating inhibition of electrotaxis by the head

Identification of Neural Circuits by Imaging Coherent Electrical Activity with FRET-Based Dyes

AbstractWe show that neurons that underlie rhythmic patterns of electrical output may be identified by optical imaging and frequency-domain analysis. Our contrast agent is a two-component dye system in which changes in membrane potential modulate the relative emission between a pair of fluorophores. We demonstrate our methods with the circuit responsible for fictive swimming in the isolated leech nerve cord. The output of a motor neuron provides a reference signal for the phase-sensitive detection of changes in fluorescence from individual neurons in a ganglion. We identify known and possibly novel neurons that participate in the swim rhythm and determine their phases within a cycle. A variant of this approach is used to identify the postsynaptic followers of intracellularly stimulated neurons

CRP 463: Active Transportation Plan for the City of Paso Robles

As a part of CRP 463 led by Dr. William Riggs, this report provided an analysis of the bicycle, pedestrian and transit travel in the City of Paso Robles and produced a draft Active Transportation Plan for the City

Impact of Community-Based Larviciding on the Prevalence of Malaria Infection in Dar es Salaam, Tanzania.

The use of larval source management is not prioritized by contemporary malaria control programs in sub-Saharan Africa despite historical success. Larviciding, in particular, could be effective in urban areas where transmission is focal and accessibility to Anopheles breeding habitats is generally easier than in rural settings. The objective of this study is to assess the effectiveness of a community-based microbial larviciding intervention to reduce the prevalence of malaria infection in Dar es Salaam, United Republic of Tanzania. Larviciding was implemented in 3 out of 15 targeted wards of Dar es Salaam in 2006 after two years of baseline data collection. This intervention was subsequently scaled up to 9 wards a year later, and to all 15 targeted wards in 2008. Continuous randomized cluster sampling of malaria prevalence and socio-demographic characteristics was carried out during 6 survey rounds (2004-2008), which included both cross-sectional and longitudinal data (N = 64,537). Bayesian random effects logistic regression models were used to quantify the effect of the intervention on malaria prevalence at the individual level. Effect size estimates suggest a significant protective effect of the larviciding intervention. After adjustment for confounders, the odds of individuals living in areas treated with larviciding being infected with malaria were 21% lower (Odds Ratio = 0.79; 95% Credible Intervals: 0.66-0.93) than those who lived in areas not treated. The larviciding intervention was most effective during dry seasons and had synergistic effects with other protective measures such as use of insecticide-treated bed nets and house proofing (i.e., complete ceiling or window screens). A large-scale community-based larviciding intervention significantly reduced the prevalence of malaria infection in urban Dar es Salaam