Observation of a Spinning Top in a Bose-Einstein Condensate

Boundaries strongly affect the behavior of quantized vortices in Bose-Einstein condensates, a phenomenon particularly evident in elongated cigar-shaped traps where vortices tend to orient along a short direction to minimize energy. Remarkably, contributions to the angular momentum of these vortices are tightly confined to the region surrounding the core, in stark contrast to untrapped condensates where all atoms contribute $\hbar$. We develop a theoretical model and use this, in combination with numerical simulations, to show that such localized vortices precess in an analogous manner to that of a classical spinning top. We experimentally verify this spinning-top behavior with our real-time imaging technique that allows for the tracking of position and orientation of vortices as they dynamically evolve. Finally, we perform an in-depth numerical investigation of our real-time expansion and imaging method, with the aim of guiding future experimental implementation, as well as outlining directions for its improvement.Comment: 10 pages, 7 figure

An Individual's Connection to Nature Can Affect Perceived Restorativeness of Natural Environments : Some Observations about Biophilia

This study investigates the relationship between the level to which a person feels connected to Nature and that person's ability to perceive the restorative value of a natural environment. We assume that perceived restorativeness may depend on an individual's connection to Nature and this relationship may also vary with the biophilic quality of the environment, i.e., the functional and aesthetic value of the natural environment which presumably gave an evolutionary advantage to our species. To this end, the level of connection to Nature and the perceived restorativeness of the environment were assessed in individuals visiting three parks characterized by their high level of "naturalness" and high or low biophilic quality. The results show that the perceived level of restorativeness is associated with the sense of connection to Nature, as well as the biophilic quality of the environment: individuals with different degrees of connection to Nature seek settings with different degrees of restorativeness and biophilic quality. This means that perceived restorativeness can also depend on an individual's "inclination" towards Nature

A simulation comparison of imputation methods for quantitative data in the presence of multiple data patterns

An extensive investigation via simulation is carried out with the aim of comparing three nonparametric, single imputation methods in the presence of multiple data patterns. The ultimate goal is to provide useful hints for users needing to quickly pick the most effective impu- tation method among the following: Forward Imputation (ForImp), considered in the two variants of ForImp with the principal compo- nent analysis (PCA), which alternates the use of PCA and the Nearest- Neighbour Imputation (NNI) method in a forward, sequential pro- cedure, and ForImp with the Mahalanobis distance, which involves the use of the Mahalanobis distance when performing NNI; the itera- tive PCA technique, which imputes missing values simultaneously via PCA; the missForest method, which is based on random forests and is developed for mixed-type data. The performance of these methods is compared under several data patterns characterized by different levels of kurtosis or skewness and correlation structures

Neural Biclustering in Gene Expression Analysis

Clustering in high dimensional spaces is a very difficult task. Dealing with DNA microarrays is even more difficult because gene subsets are coregulated and coexpressed only under specific conditions. Biclusterng addresses the problem of finding such submanifolds by exploiting both gene and condition (tissue) clustering. The paper proposes a self-organizing neural network, GH EXIN, which builds a hierarchical tree by adapting its architecture to data. It is integrated in a framework in which gene and tissue clustering are alternated and controlled by the quality of the bicluster. Examples of the approach and a biological validation of results are also given

Optically loaded Strontium lattice clock with a single multi-wavelength reference cavity

We report on the realization of a new compact strontium optical clock using a 2-D magneto-optical-trap (2D-MOT) as cold atomic source and a multi-wavelength cavity as the frequency stabilization system. All needed optical frequencies are stabilized to a zero-thermal expansion high-finesse optical resonator and can be operated without frequency adjustments for weeks. We present the complete characterization of the apparatus. Optical control of the atomic source allows us to perform low-noise clock operation without atomic signal normalization. Long- and short-term stability tests of the clock have been performed for the 88 Sr bosonic isotope by means of interleaved clock operation. Finally, we present the first preliminary accuracy budget of the system

A survey on data integration for multi-omics sample clustering

Due to the current high availability of omics, data-driven biology has greatly expanded, and several papers have reviewed state-of-the-art technologies. Nowadays, two main types of investigation are available for a multi-omics dataset: extraction of relevant features for a meaningful biological interpretation and clustering of the samples. In the latter case, a few reviews refer to some outdated or no longer available methods, whereas others lack the description of relevant clustering metrics to compare the main approaches. This work provides a general overview of the major techniques in this area, divided into four groups: graph, dimensionality reduction, statistical and neural-based. Besides, eight tools have been tested both on a synthetic and a real biological dataset. An extensive performance comparison has been provided using four clustering evaluation scores: Peak Signal-to-Noise Ratio (PSNR), Davies-Bouldin(DB) index, Silhouette value and the harmonic mean of cluster purity and efficiency. The best results were obtained by using the dimensionality reduction, either explicitly or implicitly, as in the neural architecture

Bound state dynamics in the long-range spin- ½ XXZ model

Experimental platforms based on trapped ions, cold molecules, and Rydberg atoms have made possible the investigation of highly nonlocal spin-1/2 Hamiltonians with long-range couplings. Here, we study the effects of such nonlocal couplings in the long-range spin-1/2 XXZ Heisenberg Hamiltonian. We calculate explicitly the two-spin energy spectrum, which describes all possible energetic configurations of two spins pointing in a specific direction embedded in a background of spins with opposite orientation. For fast decay of the spin-spin couplings, we find that the two-spin energy spectrum is characterized by well-defined discrete values, corresponding to bound states, separated by a set of continuum states describing the scattering region. In the deep long-range regime instead, the bound states disappear as they get incorporated by the scattering region. The presence of two-spin bound states results to be crucial to determine both two- and many-spin dynamics. On one hand, radically different two-spin spreadings can be observed by tuning the decay of the spin couplings. On the other hand, two-spin bound states enable the dynamical stabilization of effective antiferromagnetic states in the presence of ferromagnetic couplings. Finally, we propose a novel scheme based on a trapped-ion quantum simulator to experimentally realize the long-range XXZ model and to study its out-of-equilibrium properties

Towards Uncovering Feature Extraction from Temporal Signals in Deep CNN: The ECG Case Study

Despite all the progress made in biomedical field, the Electrocardiogram (ECG) is still one of the most commonly used signal in medical examinations. Over the years, the problem of ECG classification has been approached in many different ways, most of which rely on the extraction of features from the signal in the form of temporal or morphological characteristics. Although feature engineering can led to adequately good results, it mostly relies on human ability and experience in selecting the correct feature set. In the last decade, a growing class of techniques based on Convolutional Neural Network (CNN) has been proposed in opposition to feature engineering. The efficiency and accuracy of CNN-based approaches is indisputable, however their ability in extracting and using temporal features from raw signal is poorly understood. The main objective of this work was to uncover the differences and the relationships between CNN feature maps and human-curated temporal features, towards a deeper understanding of neural-based approaches for ECG. In fact, the proposed study succeeded in finding a similarity between the output stage of the first layers of a deep 1D-CNN with several temporal features, demonstrating that not only that the engineered features effectively works in ECG classification tasks, but also that CNN can improve those features by elaborating them towards an higher level of abstraction

Therapeutic Contract in Adolescents Using Hybrid Closed Loop System

not require