Path probability distribution of stochastic motion of non dissipative systems: a classical analog of Feynman factor of path integral

We investigate, by numerical simulation, the path probability of non dissipative mechanical systems undergoing stochastic motion. The aim is to search for the relationship between this probability and the usual mechanical action. The model of simulation is a one-dimensional particle subject to conservative force and Gaussian random displacement. The probability that a sample path between two fixed points is taken is computed from the number of particles moving along this path, an output of the simulation, devided by the total number of particles arriving at the final point. It is found that the path probability decays exponentially with increasing action of the sample paths. The decay rate increases with decreasing randomness. This result supports the existence of a classical analog of the Feynman factor in the path integral formulation of quantum mechanics for Hamiltonian systems.Comment: 19 pages, 6 figures, 1 table. It is a new text based on arXiv:1202.0924 (to be withdrawn) with a completely different presentation. Accepted by Chaos, Solitons & Fractals for publication 201

Memristive operation mode of a site-controlled quantum dot floating gate transistor

The authors gratefully acknowledge financial support from the European Union (FPVII (2007-2013) under Grant Agreement No. 318287 Landauer) as well as the state of Bavaria.We have realized a floating gate transistor based on a GaAs/AlGaAs heterostructure with site-controlled InAs quantum dots. By short-circuiting the source contact with the lateral gates and performing closed voltage sweep cycles, we observe a memristive operation mode with pinched hysteresis loops and two clearly distinguishable conductive states. The conductance depends on the quantum dot charge which can be altered in a controllable manner by the voltage value and time interval spent in the charging region. The quantum dot memristor has the potential to realize artificial synapses in a state-of-the-art opto-electronic semiconductor platform by charge localization and Coulomb coupling.Publisher PDFPeer reviewe

Induced magnetization in La0.7_{0.7}Sr0.3_{0.3}MnO3_3/BiFeO3_3 superlattices

Using polarized neutron reflectometry (PNR), we observe an induced magnetization of 75$\pm$ 25 kA/m at 10 K in a La$_{0.7}$Sr$_{0.3}$MnO$_3$ (LSMO)/BiFeO$_3$ superlattice extending from the interface through several atomic layers of the BiFeO$_3$ (BFO). The induced magnetization in BFO is explained by density functional theory, where the size of bandgap of BFO plays an important role. Considering a classical exchange field between the LSMO and BFO layers, we further show that magnetization is expected to extend throughout the BFO, which provides a theoretical explanation for the results of the neutron scattering experiment.Comment: 5 pages, 4 figures, with Supplemental Materials. To appear in Physical Review Letter

Nanobubbles at hydrophilic particle–water interfaces

The puzzling persistence of nanobubbles breaks Laplace’s law for bubbles, which is of great interest for promising applications in surface processing, H2 and CO2 storage, water treatment, and drug delivery. So far, nanobubbles are mostly reported on the hydrophobic planar substrates with atom flatness. It remains a challenge to quantify nanobubbles on rough and irregular surfaces due to the lack of characterization technique that can detect both the nanobubble morphology and chemical composition inside individual nanobubble-like objects. Here, by using synchrotron-based scanning transmission soft X-ray microscopy (STXM) with nanometer resolution, we discern nanoscopic gas bubbles > 25 nm with direct in-situ proof of O2 inside the nanobubbles at a hydrophilic particle-water interface under ambient conditions. We find a stable cloud of O2 nanobubbles at the diatomite particle-water interface hours after oxygen aeration and temperature variation. The in situ technique may be useful for many surface nanobubble related studies such as material preparation and property manipulation, phase equilibrium, nucleation kinetics and their relationships with chemical composition within the confined nanoscale space. The oxygen nanobubble clouds may be important in modifying particle-water interfaces and offering breakthrough technologies for oxygen delivery in sediment and/or deep water environment

Oxygen isotope effect on the in-plane penetration depth in underdoped La_{2-x}Sr_{x}CuO_{4} single crystals

We report measurements of the oxygen isotope effect (OIE) on the in-plane penetration depth \lambda_{ab}(0) in underdoped La_{2-x}Sr_{x}CuO_{4} single crystals. A highly sensitive magnetic torque sensor with a resolution of \Delta \tau ~ 10^{-12} Nm was used for the magnetic measurements on microcrystals with a mass of ~ 10 microg. The OIE on \lambda_{ab}^{-2}(0) is found to be -10(2)% for x = 0.080 and -8(1)% for x = 0.086. It arises mainly from the oxygen mass dependence of the in-plane effective mass m_{ab}*. The present results suggest that lattice vibrations are important for the occurrence of high temperature superconductivity.Comment: 4 pages, 3 figures, submitted to PR

ATIC and PAMELA Results on Cosmic e^\pm Excesses and Neutrino Masses

Recently the ATIC and PAMELA collaborations released their results which show the abundant e^\pm excess in cosmic rays well above the background, but not for the \bar{p}. Their data if interpreted as the dark matter particles' annihilation imply that the new physics with the dark matter is closely related to the lepton sector. In this paper we study the possible connection of the new physics responsible for the cosmic e^\pm excesses to the neutrino mass generation. We consider a class of models and do the detailed numerical calculations. We find that these models can natually account for the ATIC and PAMELA e^\pm and \bar{p} data and at the same time generate the small neutrino masses.Comment: 7 pages, 5 figures. Published version with minor corrections and more reference

Calibration and Stokes Imaging with Full Embedded Element Primary Beam Model for the Murchison Widefield Array

15 pages, 11 figures. Accepted for publication in PASA. © Astronomical Society of Australia 2017The Murchison Widefield Array (MWA), located in Western Australia, is one of the low-frequency precursors of the international Square Kilometre Array (SKA) project. In addition to pursuing its own ambitious science program, it is also a testbed for wide range of future SKA activities ranging from hardware, software to data analysis. The key science programs for the MWA and SKA require very high dynamic ranges, which challenges calibration and imaging systems. Correct calibration of the instrument and accurate measurements of source flux densities and polarisations require precise characterisation of the telescope's primary beam. Recent results from the MWA GaLactic Extragalactic All-sky MWA (GLEAM) survey show that the previously implemented Average Embedded Element (AEE) model still leaves residual polarisations errors of up to 10-20 % in Stokes Q. We present a new simulation-based Full Embedded Element (FEE) model which is the most rigorous realisation yet of the MWA's primary beam model. It enables efficient calculation of the MWA beam response in arbitrary directions without necessity of spatial interpolation. In the new model, every dipole in the MWA tile (4 x 4 bow-tie dipoles) is simulated separately, taking into account all mutual coupling, ground screen and soil effects, and therefore accounts for the different properties of the individual dipoles within a tile. We have applied the FEE beam model to GLEAM observations at 200 - 231 MHz and used false Stokes parameter leakage as a metric to compare the models. We have determined that the FEE model reduced the magnitude and declination-dependent behaviour of false polarisation in Stokes Q and V while retaining low levels of false polarisation in Stokes U.Peer reviewedFinal Accepted Versio

Learning Generalized Segmentation for Foggy-scenes by Bi-directional Wavelet Guidance

Learning scene semantics that can be well generalized to foggy conditions is important for safety-crucial applications such as autonomous driving. Existing methods need both annotated clear images and foggy images to train a curriculum domain adaptation model. Unfortunately, these methods can only generalize to the target foggy domain that has seen in the training stage, but the foggy domains vary a lot in both urban-scene styles and fog styles. In this paper, we propose to learn scene segmentation well generalized to foggy-scenes under the domain generalization setting, which does not involve any foggy images in the training stage and can generalize to any arbitrary unseen foggy scenes. We argue that an ideal segmentation model that can be well generalized to foggy-scenes need to simultaneously enhance the content, de-correlate the urban-scene style and de-correlate the fog style. As the content (e.g., scene semantic) rests more in low-frequency features while the style of urban-scene and fog rests more in high-frequency features, we propose a novel bi-directional wavelet guidance (BWG) mechanism to realize the above three objectives in a divide-and-conquer manner. With the aid of Haar wavelet transformation, the low frequency component is concentrated on the content enhancement self-attention, while the high frequency component is shifted to the style and fog self-attention for de-correlation purpose. It is integrated into existing mask-level Transformer segmentation pipelines in a learnable fashion. Large-scale experiments are conducted on four foggy-scene segmentation datasets under a variety of interesting settings. The proposed method significantly outperforms existing directly-supervised, curriculum domain adaptation and domain generalization segmentation methods. Source code is available at https://github.com/BiQiWHU/BWG

Learning Content-Enhanced Mask Transformer for Domain Generalized Urban-Scene Segmentation

Domain-generalized urban-scene semantic segmentation (USSS) aims to learn generalized semantic predictions across diverse urban-scene styles. Unlike generic domain gap challenges, USSS is unique in that the semantic categories are often similar in different urban scenes, while the styles can vary significantly due to changes in urban landscapes, weather conditions, lighting, and other factors. Existing approaches typically rely on convolutional neural networks (CNNs) to learn the content of urban scenes.In this paper, we propose a Content-enhanced Mask TransFormer (CMFormer) for domain-generalized USSS. The main idea is to enhance the focus of the fundamental component, the mask attention mechanism, in Transformer segmentation models on content information. We have observed through empirical analysis that a mask representation effectively captures pixel segments, albeit with reduced robustness to style variations. Conversely, its lower-resolution counterpart exhibits greater ability to accommodate style variations, while being less proficient in representing pixel segments. To harness the synergistic attributes of these two approaches, we introduce a novel content-enhanced mask attention mechanism. It learns mask queries from both the image feature and its down-sampled counterpart, aiming to simultaneously encapsulate the content and address stylistic variations. These features are fused into a Transformer decoder and integrated into a multi-resolution content-enhanced mask attention learning scheme.Extensive experiments conducted on various domain-generalized urban-scene segmentation datasets demonstrate that the proposed CMFormer significantly outperforms existing CNN-based methods by up to 14.0% mIoU and the contemporary HGFormer by up to 1.7% mIoU. The source code is publicly available at https://github.com/BiQiWHU/CMFormer