Collective oscillations of a Fermi gas in the unitarity limit: Temperature effects and the role of pair correlations

We present detailed measurements of the frequency and damping of three different collective modes in an ultracold trapped Fermi gas of $^6$Li atoms with resonantly tuned interactions. The measurements are carried out over a wide range of temperatures. We focus on the unitarity limit, where the scattering length is much greater than all other relevant length scales. The results are compared to theoretical calculations that take into account Pauli blocking and pair correlations in the normal state above the critical temperature for superfluidity. We show that these two effects nearly compensate each other and the behavior of the gas is close to the one of a classical gas.Comment: 8 pages, 5 figure

Dynamics of a strongly interacting Fermi gas: the radial quadrupole mode

We report on measurements of an elementary surface mode in an ultracold, strongly interacting Fermi gas of 6Li atoms. The radial quadrupole mode allows us to probe hydrodynamic behavior in the BEC-BCS crossover without being influenced by changes in the equation of state. We examine frequency and damping of this mode, along with its expansion dynamics. In the unitarity limit and on the BEC side of the resonance, the observed frequencies agree with standard hydrodynamic theory. However, on the BCS side of the crossover, a striking down shift of the oscillation frequency is observed in the hydrodynamic regime as a precursor to an abrupt transition to collisionless behavior; this indicates coupling of the oscillation to fermionic pairs.Comment: 11 pages, 11 figures v2: minor change

Dark cloud cores and gravitational decoupling from turbulent flows

We test the hypothesis that the starless cores may be gravitationally bound clouds supported largely by thermal pressure by comparing observed molecular line spectra to theoretical spectra produced by a simulation that includes hydrodynamics, radiative cooling, variable molecular abundance, and radiative transfer in a simple one-dimensional model. The results suggest that the starless cores can be divided into two categories: stable starless cores that are in approximate equilibrium and will not evolve to form protostars, and unstable pre-stellar cores that are proceeding toward gravitational collapse and the formation of protostars. The starless cores might be formed from the interstellar medium as objects at the lower end of the inertial cascade of interstellar turbulence. Additionally, we identify a thermal instability in the starless cores. Under par ticular conditions of density and mass, a core may be unstable to expansion if the density is just above the critical density for the collisional coupling of the gas and dust so that as the core expands the gas-dust coupling that cools the gas is reduced and the gas warms, further driving the expansion.Comment: Submitted to Ap

Distributed Anomaly Detection using Autoencoder Neural Networks in WSN for IoT

Wireless sensor networks (WSN) are fundamental to the Internet of Things (IoT) by bridging the gap between the physical and the cyber worlds. Anomaly detection is a critical task in this context as it is responsible for identifying various events of interests such as equipment faults and undiscovered phenomena. However, this task is challenging because of the elusive nature of anomalies and the volatility of the ambient environments. In a resource-scarce setting like WSN, this challenge is further elevated and weakens the suitability of many existing solutions. In this paper, for the first time, we introduce autoencoder neural networks into WSN to solve the anomaly detection problem. We design a two-part algorithm that resides on sensors and the IoT cloud respectively, such that (i) anomalies can be detected at sensors in a fully distributed manner without the need for communicating with any other sensors or the cloud, and (ii) the relatively more computation-intensive learning task can be handled by the cloud with a much lower (and configurable) frequency. In addition to the minimal communication overhead, the computational load on sensors is also very low (of polynomial complexity) and readily affordable by most COTS sensors. Using a real WSN indoor testbed and sensor data collected over 4 consecutive months, we demonstrate via experiments that our proposed autoencoder-based anomaly detection mechanism achieves high detection accuracy and low false alarm rate. It is also able to adapt to unforeseeable and new changes in a non-stationary environment, thanks to the unsupervised learning feature of our chosen autoencoder neural networks.Comment: 6 pages, 7 figures, IEEE ICC 201