On the dynamics of two photons interacting with a two-qubit coherent feedback network}

The purpose of this paper is to study the dynamics of a quantum coherent feedback network composed of two two-level systems (qubits) driven by two counter-propagating photons, one in each input channel. The coherent feedback network enhances the nonlinear photon-photon interaction inside the feedback loop. By means of quantum stochastic calculus and the input-output framework, the analytic form of the steady-state output two-photon state is derived. Based on the analytic form, the applications on the Hong-Ou-Mandel (HOM) interferometer and marginally stable single-photon devices using this coherent feedback structure have been demonstrated. The difference between continuous-mode and single-mode few-photon states is demonstrated.Comment: 15 pages, 4 figures; accepted by Automatica; comments are welcome

Background Subtraction for Night Videos

Motion analysis is important in video surveillance systems and background subtraction is useful for moving object detection in such systems. However, most of the existing background subtraction methods do not work well for surveillance systems in the evening because objects are usually dark and reflected light is usually strong. To resolve these issues, we propose a framework that utilizes a Weber contrast descriptor, a texture feature extractor, and a light detection unit, to extract the features of foreground objects. We propose a local pattern enhancement method. For the light detection unit, our method utilizes the finding that lighted areas in the evening usually have a low saturation in hue-saturation-value and hue-saturation-lightness color spaces. Finally, we update the background model and the foreground objects in the framework. This approach is able to improve foreground object detection in night videos, which do not need a large data set for pre-training

NeuralMarker: A Framework for Learning General Marker Correspondence

We tackle the problem of estimating correspondences from a general marker, such as a movie poster, to an image that captures such a marker. Conventionally, this problem is addressed by fitting a homography model based on sparse feature matching. However, they are only able to handle plane-like markers and the sparse features do not sufficiently utilize appearance information. In this paper, we propose a novel framework NeuralMarker, training a neural network estimating dense marker correspondences under various challenging conditions, such as marker deformation, harsh lighting, etc. Besides, we also propose a novel marker correspondence evaluation method circumstancing annotations on real marker-image pairs and create a new benchmark. We show that NeuralMarker significantly outperforms previous methods and enables new interesting applications, including Augmented Reality (AR) and video editing.Comment: Accepted by ToG (SIGGRAPH Asia 2022). Project Page: https://drinkingcoder.github.io/publication/neuralmarker

RD-VIO: Robust Visual-Inertial Odometry for Mobile Augmented Reality in Dynamic Environments

It is typically challenging for visual or visual-inertial odometry systems to handle the problems of dynamic scenes and pure rotation. In this work, we design a novel visual-inertial odometry (VIO) system called RD-VIO to handle both of these two problems. Firstly, we propose an IMU-PARSAC algorithm which can robustly detect and match keypoints in a two-stage process. In the first state, landmarks are matched with new keypoints using visual and IMU measurements. We collect statistical information from the matching and then guide the intra-keypoint matching in the second stage. Secondly, to handle the problem of pure rotation, we detect the motion type and adapt the deferred-triangulation technique during the data-association process. We make the pure-rotational frames into the special subframes. When solving the visual-inertial bundle adjustment, they provide additional constraints to the pure-rotational motion. We evaluate the proposed VIO system on public datasets. Experiments show the proposed RD-VIO has obvious advantages over other methods in dynamic environments

Geometric Scaling of the Current-Phase Relation of Niobium Nano-Bridge Junctions

The nano-bridge junction (NBJ) is a type of Josephson junction that is advantageous for the miniaturization of superconducting circuits. However, the current-phase relation (CPR) of the NBJ usually deviates from a sinusoidal function which has been explained by a simplified model with correlation only to its effective length. Here, we investigated both measured and calculated CPRs of niobium NBJs of a cuboidal shape with a three-dimensional bank structure. From a sine-wave to a saw-tooth-like form, we showed that deviated CPRs of NBJs can be described quantitatively by its skewness {\Delta}{\theta}. Furthermore, the measured dependency of {\Delta}{\theta} on the critical current {I_0} from 108 NBJs turned out to be consistent with the calculated ones derived from the change in geometric dimensions. It suggested that the CPRs of NBJs can be tuned by their geometric dimensions. In addition, the calculated scaling behavior of {\Delta}{\theta} versus {I_0} in three-dimensional space was provided for the future design of superconducting circuits of a high integration level by using niobium NBJs.Comment: 20 pages, 10 figure

Substantial transition to clean household energy mix in rural China

The household energy mix has significant impacts on human health and climate, as it contributes greatly to many health- and climate-relevant air pollutants. Compared to the well-established urban energy statistical system, the rural household energy statistical system is incomplete and is often associated with high biases. Via a nationwide investigation, this study revealed high contributions to energy supply from coal and biomass fuels in the rural household energy sector, while electricity comprised ∼20%. Stacking (the use of multiple sources of energy) is significant, and the average number of energy types was 2.8 per household. Compared to 2012, the consumption of biomass and coals in 2017 decreased by 45% and 12%, respectively, while the gas consumption amount increased by 204%. Increased gas and decreased coal consumptions were mainly in cooking, while decreased biomass was in both cooking (41%) and heating (59%). The time-sharing fraction of electricity and gases (E&G) for daily cooking grew, reaching 69% in 2017, but for space heating, traditional solid fuels were still dominant, with the national average shared fraction of E&G being only 20%. The non-uniform spatial distribution and the non-linear increase in the fraction of E&G indicated challenges to achieving universal access to modern cooking energy by 2030, particularly in less-developed rural and mountainous areas. In some non-typical heating zones, the increased share of E&G for heating was significant and largely driven by income growth, but in typical heating zones, the time-sharing fraction was <5% and was not significantly increased, except in areas with policy intervention. The intervention policy not only led to dramatic increases in the clean energy fraction for heating but also accelerated the clean cooking transition. Higher income, higher education, younger age, less energy/stove stacking and smaller family size positively impacted the clean energy transition