The Z2Z_2 Classification of Dimensional Reduced Hopf Insulators

The Hopf insulators are characterized by a topological invariant called Hopf index which classifies maps from three-sphere to two-sphere, instead of a Chern number or a Chern parity. In contrast to topological insulator, the Hopf insulator is not protected by any kind of symmetry. By dimensional reduction, we argue that there exists a new type of $\mathbb{Z}_2$ index for 2D Hamiltonian with vanishing Chern number. Specific model Hamiltonian with this nontrivial $\mathbb{Z}_2$ index is constructed. We also numerically calculate the topological protected edge modes of this dimensional reduced Hopf insulator and show that they are consistent with the $\mathbb{Z}_2$ classification.Comment: 6 pages, 3 figure

CamSwarm: Instantaneous Smartphone Camera Arrays for Collaborative Photography

Camera arrays (CamArrays) are widely used in commercial filming projects for achieving special visual effects such as bullet time effect, but are very expensive to set up. We propose CamSwarm, a low-cost and lightweight alternative to professional CamArrays for consumer applications. It allows the construction of a collaborative photography platform from multiple mobile devices anywhere and anytime, enabling new capturing and editing experiences that a single camera cannot provide. Our system allows easy team formation; uses real-time visualization and feedback to guide camera positioning; provides a mechanism for synchronized capturing; and finally allows the user to efficiently browse and edit the captured imagery. Our user study suggests that CamSwarm is easy to use; the provided real-time guidance is helpful; and the full system achieves high quality results promising for non-professional use. A demo video is provided at https://www.youtube.com/watch?v=LgkHcvcyTTM

PanoSwarm: Collaborative and Synchronized Multi-Device Panoramic Photography

Taking a picture has been traditionally a one-persons task. In this paper we present a novel system that allows multiple mobile devices to work collaboratively in a synchronized fashion to capture a panorama of a highly dynamic scene, creating an entirely new photography experience that encourages social interactions and teamwork. Our system contains two components: a client app that runs on all participating devices, and a server program that monitors and communicates with each device. In a capturing session, the server collects in realtime the viewfinder images of all devices and stitches them on-the-fly to create a panorama preview, which is then streamed to all devices as visual guidance. The system also allows one camera to be the host and to send direct visual instructions to others to guide camera adjustment. When ready, all devices take pictures at the same time for panorama stitching. Our preliminary study suggests that the proposed system can help users capture high quality panoramas with an enjoyable teamwork experience. A demo video of the system in action is provided at http://youtu.be/PwQ6k_ZEQSs

An Optimal LiDAR Configuration Approach for Self-Driving Cars

LiDARs plays an important role in self-driving cars and its configuration such as the location placement for each LiDAR can influence object detection performance. This paper aims to investigate an optimal configuration that maximizes the utility of on-hand LiDARs. First, a perception model of LiDAR is built based on its physical attributes. Then a generalized optimization model is developed to find the optimal configuration, including the pitch angle, roll angle, and position of LiDARs. In order to fix the optimization issue with off-the-shelf solvers, we proposed a lattice-based approach by segmenting the LiDAR's range of interest into finite subspaces, thus turning the optimal configuration into a nonlinear optimization problem. A cylinder-based method is also proposed to approximate the objective function, thereby making the nonlinear optimization problem solvable. A series of simulations are conducted to validate our proposed method. This proposed approach to optimal LiDAR configuration can provide a guideline to researchers to maximize the utility of LiDARs.Comment: Conferenc

On the Relationship Between Coronal Magnetic Decay Index and CME Speed

Numerical simulations suggest that kink and torus instabilities are two potential contributors to the initiation and prorogation of eruptive events. A magnetic parameter named decay index (i.e., the coronal magnetic gradient of the overlying fields above the eruptive flux ropes) could play an important role in controlling kinematics of eruptions. Previous studies have identified a threshold range of the decay index that distinguishes between eruptive and confined configurations. Here we advance the study by investigating if there is a clear correlation between the decay index and CME speed. 38 CMEs associated with filament eruptions and/or two-ribbon flares are selected using the Halpha data from the Global Halpha Network. The filaments and flare ribbons observed in Halpha associated with the CMEs help to locate the magnetic polarity inversion line, along which the decay index is calculated based on the potential field extrapolation using MDI magnetograms as boundary conditions. The speeds of CMEs are obtained from the LASCO C2 CME catalog available online. We find that the mean decay index increases with CME speed for those CMEs with a speed below 1000 km/s, and stays flat around 2.2 for the CMEs with higher speeds. In addition, we present a case study of a partial filament eruption, in which the decay indexes show different values above the erupted/non-erupted part.Comment: ApJ, accepte

Service Provisioning and Profit Maximization in Network-assisted Adaptive HTTP Streaming

Adaptive HTTP streaming with centralized consideration of multiple streams has gained increasing interest. It poses a special challenge that the interests of both content provider and network operator need to be deliberately balanced. More importantly, the adaptation strategy is required to be flexible enough to be ported to various systems that work under different network environments, QoE levels, and economic objectives. To address these challenges, we propose a Markov Decision Process (MDP) based network-assisted adaptation framework, wherein cost of buffering, significant playback variation, bandwidth management and income of playback are jointly investigated. We then demonstrate its promising service provisioning and maximal profit for a mobile network in which fair or differentiated service is required.Comment: ICIP 2015 submissio

Reinforcement Learning for Learning Rate Control

Stochastic gradient descent (SGD), which updates the model parameters by adding a local gradient times a learning rate at each step, is widely used in model training of machine learning algorithms such as neural networks. It is observed that the models trained by SGD are sensitive to learning rates and good learning rates are problem specific. We propose an algorithm to automatically learn learning rates using neural network based actor-critic methods from deep reinforcement learning (RL).In particular, we train a policy network called actor to decide the learning rate at each step during training, and a value network called critic to give feedback about quality of the decision (e.g., the goodness of the learning rate outputted by the actor) that the actor made. The introduction of auxiliary actor and critic networks helps the main network achieve better performance. Experiments on different datasets and network architectures show that our approach leads to better convergence of SGD than human-designed competitors.Comment: 7 pages, 9 figure

Predicting Depression Severity by Multi-Modal Feature Engineering and Fusion

We present our preliminary work to determine if patient's vocal acoustic, linguistic, and facial patterns could predict clinical ratings of depression severity, namely Patient Health Questionnaire depression scale (PHQ-8). We proposed a multi modal fusion model that combines three different modalities: audio, video , and text features. By training over AVEC 2017 data set, our proposed model outperforms each single modality prediction model, and surpasses the data set baseline with ice margin.Comment: Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18

Engineering entangled microwave photon states via multiphoton interactions between two cavity fields and a superconducting qubit

It has been shown that there are not only transverse but also longitudinal couplings between microwave fields and a superconducting qubit with broken inversion symmetry of the potential energy. Using multiphoton processes induced by longitudinal coupling fields and frequency matching conditions, we design a universal algorithm to produce arbitrary superpositions of two-mode photon states of microwave fields in two separated transmission line resonators, which are coupled to a superconducting qubit. Based on our algorithm, we analyze the generation of evenly-populated states and NOON states. Compared to other proposals with only single-photon process, we provide an efficient way to produce entangled microwave states when the interactions between superconducting qubits and microwave fields are in the ultrastrong regime

Field induced quantum spin liquid with spinon Fermi surfaces in the Kitaev model

Recent experimental evidence for a field-induced quantum spin liquid (QSL) in $\alpha$-RuCl$_3$ calls for an understanding for the ground state of honeycomb Kitaev model under a magnetic field. In this work we address the nature of an enigmatic gapless paramagnetic phase in the antiferromagnetic Kitave model, under an intermediate magnetic field perpendicular to the plane. Combining theoretical and numerical efforts, we identify this gapless phase as a $U(1)$ QSL with spinon Fermi surfaces. We also reveal the nature of continuous quantum phase transitions involving this $U(1)$ QSL, and obtain a phase diagram of the Kitaev model as a function of bond anisotropy and perpendicular magnetic field.Comment: 6+15 pages, 7 figures, 1 table, references update