Rosen-Zener interferometry with Ultracold Atoms

We propose a time-domain "interferometer" based on ultracold Bose atoms loaded on a double well potential. By the adiabatic Rosen-Zener process, the barrier between two wells is ramped down slowly, held for a while, then ramped back. Starting with a coherent state of double well system, the final occupations on one well show interesting interference fringes in the time-domain. The fringe pattern is sensitive to the initial state, the interatomic interaction, and the external forces such as gravity which can change the shape of the double well. In this sense, this interferometric scheme has the potentials for precision measurements with ultracold atoms. The underlying mechanism is revealed and possible applications are discussed.Comment: 4 pages, 5 figure

Symbolic representation of iterated maps

This paper presents a general and systematic discussion of various symbolic representations of iterated maps through subshifts. We give a unified model for all continuous maps on a metric space, by representing a map through a general subshift over usually an uncountable alphabet. It is shown that at most the second order representation is enough for a continuous map. In particular, it is shown that the dynamics of one-dimensional continuous maps to a great extent can be transformed to the study of subshift structure of a general symbolic dynamics system. By introducing distillations, partial representations of some general continuous maps are obtained. Finally, partitions and representations of a class of discontinuous maps, piecewise continuous maps are discussed, and as examples, a representation of the Gauss map via a full shift over a countable alphabet and representations of interval exchange transformations as subshifts of infinite type are given

The Study of Intelligent Vehicle Navigation Path Based on Behavior Coordination of Particle Swarm

In the behavior dynamics model, behavior competition leads to the shock problem of the intelligent vehicle navigation path, because of the simultaneous occurrence of the time-variant target behavior and obstacle avoidance behavior. Considering the safety and real-time of intelligent vehicle, the particle swarm optimization (PSO) algorithm is proposed to solve these problems for the optimization of weight coefficients of the heading angle and the path velocity. Firstly, according to the behavior dynamics model, the fitness function is defined concerning the intelligent vehicle driving characteristics, the distance between intelligent vehicle and obstacle, and distance of intelligent vehicle and target. Secondly, behavior coordination parameters that minimize the fitness function are obtained by particle swarm optimization algorithms. Finally, the simulation results show that the optimization method and its fitness function can improve the perturbations of the vehicle planning path and real-time and reliability

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Microstructure, mechanical properties and machinability of particulate reinforced Al matrix composites: a comparative study between SiC particles and high-entropy alloy particles

In this study, 2024Al matrix composites reinforced by SiC particles (SiC-2024Al) and nanocrystalline high-entropy alloy particles (HEA-2024Al) fabricated by powder metallurgy were systematically compared for the first time. There is a significant difference in microstructure and mechanical properties as well as machinability between two kinds of composites. In term of microstructure, when the volume fraction of reinforcements was 10%, both SiC-2024Al and HEA-2024Al composites showed a homogeneous particle distribution in the matrix. With the increase of reinforcement content, HEA-2024Al composites presented denser microstructure than that of SiC-2024Al composites. The composites with 10, 20 and 30 vol.% HEA reinforcements all showed better plasticity than that of the SiC-2024Al composites with same volume fraction of reinforcements, which was related with better particle distribution and interface bonding. However, the strength showed the opposite tendency in the two kinds of composites. Selecting 10SiC-2024Al and 10HEA-2024Al composites as examples to explore the difference in the yield strength of two kinds of composites, it is ascribed to the dislocation punched zones around interface between the Al matrix and reinforcements, which was analyzed in detail by a combination of calculation, nanoindentation tests and finite element analysis. Additionally, HEA-2024Al composites showed better machinability than those of SiC-2024Al composites. This work provides insight into the application of particulate reinforced Al matrix composites

Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA

Visual Question Answering (VQA) models are prone to learn the shortcut solution formed by dataset biases rather than the intended solution. To evaluate the VQA models' reasoning ability beyond shortcut learning, the VQA-CP v2 dataset introduces a distribution shift between the training and test set given a question type. In this way, the model cannot use the training set shortcut (from question type to answer) to perform well on the test set. However, VQA-CP v2 only considers one type of shortcut and thus still cannot guarantee that the model relies on the intended solution rather than a solution specific to this shortcut. To overcome this limitation, we propose a new dataset that considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets. In addition, we overcome the three troubling practices in the use of VQA-CP v2, e.g., selecting models using OOD test sets, and further standardize OOD evaluation procedure. Our benchmark provides a more rigorous and comprehensive testbed for shortcut learning in VQA. We benchmark recent methods and find that methods specifically designed for particular shortcuts fail to simultaneously generalize to our varying OOD test sets. We also systematically study the varying shortcuts and provide several valuable findings, which may promote the exploration of shortcut learning in VQA.Comment: Fingdings of EMNLP-202