Robust spatial coherence 5 μ\,\mum from a room-temperature atom chip

We study spatial coherence near a classical environment by loading a Bose-Einstein condensate into a magnetic lattice potential and observing diffraction. Even very close to a surface (5$\,\mu$m), and even when the surface is at room temperature, spatial coherence persists for a relatively long time ($\ge$500$\,$ms). In addition, the observed spatial coherence extends over several lattice sites, a significantly greater distance than the atom-surface separation. This opens the door for atomic circuits, and may help elucidate the interplay between spatial dephasing, inter-atomic interactions, and external noise.Comment: 15 pages, 14 figures, revised for final publication. This manuscript includes in-depth analysis of the data presented in arXiv:1502.0160

Tactical fixed job scheduling with spread-time constraints

We address the tactical fixed job scheduling problem with spread-time constraints. In such a problem, there are a fixed number of classes of machines and a fixed number of groups of jobs. Jobs of the same group can only be processed by machines of a given set of classes. All jobs have their fixed start and end times. Each machine is associated with a cost according to its machine class. Machines have spread-time constraints, with which each machine is only available for L consecutive time units from the start time of the earliest job assigned to it. The objective is to minimize the total cost of the machines used to process all the jobs. For this strongly NP-hard problem, we develop a branch-and-price algorithm, which solves instances with up to 300 jobs, as compared with CPLEX, which cannot solve instances of 100 jobs. We further investigate the influence of machine flexibility by computational experiments. Our results show that limited machine flexibility is sufficient in most situations

Suppression and enhancement of decoherence in an atomic Josephson junction

We examine the role of interactions for a Bose gas trapped in a double-well potential ("Bose-Josephson junction") when external noise is applied and the system is initially delocalized with equal probability amplitudes in both sites. The noise may have two kinds of effects: loss of atoms from the trap, and random shifts in the relative phase or number difference between the two wells. The effects of phase noise are mitigated by atom-atom interactions and tunneling, such that the dephasing rate may be reduced to half its single-atom value. Decoherence due to number noise (which induces fluctuations in the relative atom number between the wells) is considerably enhanced by the interactions. A similar scenario is predicted for the case of atom loss, even if the loss rates from the two sites are equal. In fact, interactions convert the increased uncertainty in atom number (difference) into (relative) phase diffusion and reduce the coherence across the junction. We examine the parameters relevant for these effects using a simple model of the trapping potential based on an atom chip device. These results provide a framework for mapping the dynamical range of barriers engineered for specific applications and sets the stage for more complex circuits ("atomtronics")

Solitons in One-Dimensional Bose Einstein Condensate with Higher-Order Interactions

National Natural Science Foundation of China [11791240178, 11674338, 11547024]We model a one-dimensional Bose-Einstein condensate with the one-dimensional Gross-Pitaevskii equation (1D GPE) incorporating higher-order interaction effects. Based on the F-expansion method, we analytically solve the 1D GPE, identifying the typical soliton solution under certain experimental settings within the general wave-like solution set, and demonstrating the applicability of the theoretical treatment that is employed

A priori Estimates for Deep Residual Network in Continuous-time Reinforcement Learning

Deep reinforcement learning excels in numerous large-scale practical applications. However, existing performance analyses ignores the unique characteristics of continuous-time control problems, is unable to directly estimate the generalization error of the Bellman optimal loss and require a boundedness assumption. Our work focuses on continuous-time control problems and proposes a method that is applicable to all such problems where the transition function satisfies semi-group and Lipschitz properties. Under this method, we can directly analyze the \emph{a priori} generalization error of the Bellman optimal loss. The core of this method lies in two transformations of the loss function. To complete the transformation, we propose a decomposition method for the maximum operator. Additionally, this analysis method does not require a boundedness assumption. Finally, we obtain an \emph{a priori} generalization error without the curse of dimensionality

Evaluation of Trace Alignment Quality and its Application in Medical Process Mining

Trace alignment algorithms have been used in process mining for discovering the consensus treatment procedures and process deviations. Different alignment algorithms, however, may produce very different results. No widely-adopted method exists for evaluating the results of trace alignment. Existing reference-free evaluation methods cannot adequately and comprehensively assess the alignment quality. We analyzed and compared the existing evaluation methods, identifying their limitations, and introduced improvements in two reference-free evaluation methods. Our approach assesses the alignment result globally instead of locally, and therefore helps the algorithm to optimize overall alignment quality. We also introduced a novel metric to measure the alignment complexity, which can be used as a constraint on alignment algorithm optimization. We tested our evaluation methods on a trauma resuscitation dataset and provided the medical explanation of the activities and patterns identified as deviations using our proposed evaluation methods.Comment: 10 pages, 6 figures and 5 table