arXiv subjects

Nan Zhou

Publications and source records attributed to Nan Zhou.

At least 73 records · Page 4Linked to original sources

arXiv2021

A Semantic Segmentation Network for Urban-Scale Building Footprint Extraction Using RGB Satellite Imagery

Urban areas consume over two-thirds of the world's energy and account for more than 70 percent of global CO2 emissions. As stated in IPCC's Global Warming of 1.5C report, achieving carbon neutrality by 2050 requires a clear understanding of urban geometry. High-quality building footprint generation from satellite images can accelerate this predictive process and empower municipal decision-making at scale. However, previous Deep Learning-based approaches face consequential issues such as scale invariance and defective footprints, partly due to ever-present class-wise imbalance. Additionally, most approaches require supplemental data such as point cloud data, building height information, and multi-band imagery - which has limited availability and are tedious to produce. In this paper, we propose a modified DeeplabV3+ module with a Dilated Res-Net backbone to generate masks of building footprints from three-channel RGB satellite imagery only. Furthermore, we introduce an F-Beta measure in our objective function to help the model account for skewed class distributions and prevent false-positive footprints. In addition to F-Beta, we incorporate an exponentially weighted boundary loss and use a cross-dataset training strategy to further increase the quality of predictions. As a result, we achieve state-of-the-art performances across three public benchmarks and demonstrate that our RGB-only method produces higher quality visual results and is agnostic to the scale, resolution, and urban density of satellite imagery.

cs.CV↗

arXiv2021

Disorder-robust high-field superconducting phase of FeSe single crystals

When exposed to high magnetic fields, certain materials manifest an exotic superconducting (SC) phase that has attracted considerable attention. A proposed explanation for the origin of the high-field SC phase is the Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state. This state is characterized by inhomogeneous superconductivity, where the Cooper pairs have finite center-of-mass momenta. Recently, the high-field SC phase was observed in FeSe, and it was deemed to originate from the FFLO state. Here, we synthesize FeSe single crystals with different levels of disorder. The level of disorder is expressed by the ratio of the mean free path to the coherence length and ranges between 35 and 1.2. The upper critical field \textit{B}$_{\rm{c}2}$ was obtained by both resistivity and magnetic torque measurements over a wide range of temperatures, which went as low as $\sim$0.5 K, and magnetic fields, which went up to $\sim$38 T along the \textit{c} axis and in the \textit{ab} plane. In the high-field region parallel to the \textit{ab} plane, an unusual SC phase was confirmed in all the crystals, and the phase was found to be robust against disorder. This result suggests that the high-field SC phase in FeSe is not a conventional FFLO state.

cond-mat.supr-con↗

arXiv2021

Comparative study of superconducting and normal-state anisotropy in Fe$_{1+y}$Te$_{0.6}$Se$_{0.4}$ superconductors with controlled amounts of interstitial excess Fe

We report a systematic study of the superconducting (SC) and normal-state anisotropy of Fe$_{1+y}$Te$_{0.6}$Se$_{0.4}$ single crystals with controlled amounts of excess Fe ($y$ = 0, 0.07, and 0.14). The SC state anisotropy $γ_{H}$ was obtained by measuring the upper critical fields under high magnetic fields over 50 T for both $H\parallel ab$ and $H\parallel c$. On the other hand, the normal state anisotropy $γ_ρ$ was obtained by measuring the resistivity with current flowing in the $ab$ plane ($ρ_{ab}$) and along the $c$ axis ($ρ_c$). To precisely measure $ρ_{ab}$ and $ρ_c$ in the same part of a specimen avoiding the variation dependent on pieces or parts, we adopt a new method using a micro-fabricated bridge with an additional neck part along $c$ axis. The $γ_{H}$ decreases from a value dependent on the amount of excess Fe at $T_{\rm{c}}$ to a common value $\sim$ 1 at 2 K. The different $γ_{H}$ at $T_{\rm{c}}$ ($\sim$1.5 for $y$ = 0, and 2.5 for $y$ = 0.14) suggests that the anisotropy of effective mass $m_c^*/m_{ab}^*$ increases from $\sim$ 2.25 ($y$ = 0) to 6.25 ($y$ = 0.14) with the excess Fe. The almost isotropic $γ_{H}$ at low temperatures is due to the strong spin paramagnetic effect at $H\parallel ab$. By contrast, the $γ_ρ$ shows a much larger value of $\sim$ 17 ($y$ = 0) to $\sim$ 50 ($y$ = 0.14) at the temperature just above $T_{\rm{c}}$. Combined the results of $γ_{H}$ and $γ_ρ$ near $T_{\rm{c}}$, we found out that the discrepant anisotropies between the SC and normal states originates from a large anisotropy of scattering time $τ_{ab}$/$τ_c$ $\sim$ 7.8. The $τ_{ab}$/$τ_c$ is found to be independent of the excess Fe.

cond-mat.supr-con↗

arXiv2021

Electronic transport properties and hydrostatic pressure effect of FeSe$_{0.67}$Te$_{0.33}$ single crystals free of phase separation

FeSe$_{1-x}$Te$_{x}$ superconductors manifest some intriguing electronic properties depending on the value of $x$. In FeSe single crystal, the nematic phase and Dirac band structure have been observed, while topological surface superconductivity with the Majorana bound state was found in the crystal of $x \sim 0.55$. Therefore, the electronic properties of single crystals with $0 < x \leq 0.5$ are crucial for probing the evolution of those intriguing properties as well as their relations. However, this study is still left blank due to the lack of single crystals because of phase separation. Here, we report the synthesis, magnetization, electronic transport properties, and hydrostatic pressure effect of FeSe$_{0.67}$Te$_{0.33}$ single crystals free of phase separation. A structural (nematic) transition is visible at $T_{s} = 39$ K, below which the resistivity exhibits a Fermi-liquid behavior. Analysis of upper critical fields suggests that spin-paramagnetic effect should be taken into account for both $H \parallel c$ axis and $H \parallel ab$ plane. A crossover from the low-$H$ quadratic to the high-$H$ quasi-linear behavior is observed in the magnetoresistance, signifying the possible existence of Dirac-cone state. Besides, the strong temperature dependence of Hall coefficient, violation of (modified) Kohler's rule, and two-band model analysis indicate the multiband effects in FeSe$_{0.67}$Te$_{0.33}$ single crystals. Hydrostatic pressure measurements reveal that $T_{s}$ is quickly suppressed with pressure while $T_{c}$ is monotonically increased up to 2.31 GPa, indicating the competition between nematicity and superconductivity. No signature of magnetic order that has been detected in FeSe$_{1-x}$S$_{x}$ is observed. Our findings fill up the blank of the knowledge on the basic properties of FeSe$_{1-x}$Te$_{x}$ system with low-Te concentrations.

cond-mat.supr-con↗

arXiv2021

Hydrothermal synthesis and complete phase diagram of FeSe$_{1-x}$S$_{x}$ $(0 \leq x \leq 1)$ single crystals

We report the successful synthesis of FeSe$_{1-x}$S$_{x}$ single crystals with $x$ ranging from 0 to 1 via a hydrothermal method. A complete phase diagram of FeSe$_{1-x}$S$_{x}$ has been obtained based on resistivity and magnetization measurements. The nematicity is suppressed with increasing $x$, and a small superconducting dome appears within the nematic phase. Outside the nematic phase, the superconductivity is continuously suppressed and reaches a minimum $T_c$ at $x$ = 0.45; beyond this point, $T_c$ slowly increases until $x$ = 1. Intriguingly, an anomalous resistivity upturn with a characteristic temperature $T^*$ in the intermediate region of $0.31 \leq x \leq 0.71$ is observed. $T^{*}$ shows a dome-like behavior with a maximum value at $x$ = 0.45, which is opposite the evolution of $T_c$, indicating competition between $T^*$ and superconductivity. The origin of $T^*$ is discussed in detail. Furthermore, the normal state resistivity evolves from non-Fermi-liquid to Fermi-liquid behavior with S doping at low temperatures, accompanied by a reduction in electronic correlations. Our study addresses the lack of single crystals in the high-S doping region and provides a complete phase diagram, which will promote the study of relations among nematicity, superconductivity, and magnetism.

cond-mat.supr-con↗

arXiv2021

Event Occurrence Model for Power Distribution System Based on Data Eigenvalue Behavior Analytic

This paper describes a model for power distribution network, which is hten utilized for eigenvalue behavior distribution analysis. We utilized the compensation theory to model the event that occurs in the network and derived the relation between the system nodal voltage eigenvalues and the occurrence of events. The paper combines the power system model with the eigenvalue distribution analysis.

eess.SY↗

arXiv2020

Fully gapped superconductivity without sign reversal in the topological superconductor PbTaSe$_2$

We investigate the superconducting gap function of topological superconductor PbTaSe$_2$. Temperature, magnetic field, and three-dimensional (3D) field-angle dependences of the specific heat prove that the superconductivity of PbTaSe$_2$ is fully-gapped, with two isotropic $s$-wave gaps. The pair-breaking effect is probed by systematically increasing non-magnetic disorders through H$^+$-irradiations. The superconducting transition temperature, $T_{\rm{c}}$, is found to be robust against disorders, which suggests that the pairing should be sign-preserved rather than sign-reversed.

cond-mat.supr-con↗

arXiv2020

Combined Eco-Routing and Power-Train Control of Plug-In Hybrid Electric Vehicles in Transportation Networks

We study the problem of eco-routing for Plug-In Hybrid Electric Vehicles (PHEVs) to minimize the overall energy consumption cost. We propose an algorithm which can simultaneously calculate an energy-optimal route (eco-route) for a PHEV and an optimal power-train control strategy over this route. In order to show the effectiveness of our method in practice, we use a HERE Maps API to apply our algorithms based on traffic data in the city of Boston with more than 110,000 links. Moreover, we validate the performance of our eco-routing algorithm using speed profiles collected from a traffic simulator (SUMO) as input to a high-fidelity energy model to calculate energy consumption costs. Our results show significant energy savings (around 12%) for PHEVs with a near real-time execution time for the algorithm.

math.OC↗

arXiv2020

Information Spreading Dynamics on Adaptive Social Networks

There is currently growing interest in modeling the information diffusion on social networks across multi-disciplines. The majority of the corresponding research has focused on information diffusion independently, ignoring the network evolution in the diffusion process. Therefore, it is more reasonable to describe the real diffusion systems by the co-evolution between network topologies and information states. In this work, we propose a mechanism considering the coevolution between information states and network topology simultaneously, in which the information diffusion was executed as an SIS process and network topology evolved based on the adaptive assumption. The theoretical analyses based on the Markov approach were very consistent with simulation. Both simulation results and theoretical analyses indicated that the adaptive process, in which informed individuals would rewire the links between the informed neighbors to a random non-neighbor node, can enhance information diffusion (leading to much broader spreading). In addition, we obtained that two threshold values exist for the information diffusion on adaptive networks, i.e., if the information propagation probability is less than the first threshold, information cannot diffuse and dies out immediately; if the propagation probability is between the first and second threshold, information will spread to a finite range and die out gradually; and if the propagation probability is larger than the second threshold, information will diffuse to a certain size of population in the network. These results may shed some light on understanding the co-evolution between information diffusion and network topology.

physics.soc-ph↗

arXiv2019

Collision-Free Trajectory Design for 2D Persistent Monitoring Using Second-Order Agents

This paper considers a two-dimensional persistent monitoring problem by controlling movements of second-order agents to minimize some uncertainty metric associated with targets in a dynamic environment. In contrast to common sensing models depending only on the distance from a target, we introduce an active sensing model which considers the distance between an agent and a target, as well as the agent's velocity. We propose an objective function which can achieve a collision-free agent trajectory by penalizing all possible collisions. Applying structural properties of the optimal control derived from the Hamiltonian analysis, we limit agent trajectories to a simpler parametric form under a family of 2D curves depending on the problem setting, e.g. ellipses and Fourier trajectories. Our collision-free trajectories are optimized through an event-driven Infinitesimal Perturbation Analysis (IPA) and gradient descent method. Although the solution is generally locally optimal, this method is computationally efficient and offers an alternative to other traditional time-driven methods. Finally, simulation examples are provided to demonstrate our proposed results.

math.OC↗

arXiv2019

Density-Aware Convolutional Networks with Context Encoding for Airborne LiDAR Point Cloud Classification

To better address challenging issues of the irregularity and inhomogeneity inherently present in 3D point clouds, researchers have been shifting their focus from the design of hand-craft point feature towards the learning of 3D point signatures using deep neural networks for 3D point cloud classification. Recent proposed deep learning based point cloud classification methods either apply 2D CNN on projected feature images or apply 1D convolutional layers directly on raw point sets. These methods cannot adequately recognize fine-grained local structures caused by the uneven density distribution of the point cloud data. In this paper, to address this challenging issue, we introduced a density-aware convolution module which uses the point-wise density to re-weight the learnable weights of convolution kernels. The proposed convolution module is able to fully approximate the 3D continuous convolution on unevenly distributed 3D point sets. Based on this convolution module, we further developed a multi-scale fully convolutional neural network with downsampling and upsampling blocks to enable hierarchical point feature learning. In addition, to regularize the global semantic context, we implemented a context encoding module to predict a global context encoding and formulated a context encoding regularizer to enforce the predicted context encoding to be aligned with the ground truth one. The overall network can be trained in an end-to-end fashion with the raw 3D coordinates as well as the height above ground as inputs. Experiments on the International Society for Photogrammetry and Remote Sensing (ISPRS) 3D labeling benchmark demonstrated the superiority of the proposed method for point cloud classification. Our model achieved a new state-of-the-art performance with an average F1 score of 71.2% and improved the performance by a large margin on several categories.

cs.CV↗

arXiv2019

Random Directional Attack for Fooling Deep Neural Networks

Deep neural networks (DNNs) have been widely used in many fields such as images processing, speech recognition; however, they are vulnerable to adversarial examples, and this is a security issue worthy of attention. Because the training process of DNNs converge the loss by updating the weights along the gradient descent direction, many gradient-based methods attempt to destroy the DNN model by adding perturbations in the gradient direction. Unfortunately, as the model is nonlinear in most cases, the addition of perturbations in the gradient direction does not necessarily increase loss. Thus, we propose a random directed attack (RDA) for generating adversarial examples in this paper. Rather than limiting the gradient direction to generate an attack, RDA searches the attack direction based on hill climbing and uses multiple strategies to avoid local optima that cause attack failure. Compared with state-of-the-art gradient-based methods, the attack performance of RDA is very competitive. Moreover, RDA can attack without any internal knowledge of the model, and its performance under black-box attack is similar to that of the white-box attack in most cases, which is difficult to achieve using existing gradient-based attack methods.

cs.CR↗

arXiv2018

Design of Voltage Pulse Control Module for Free Space Measurement-Device-Independent Quantum Key Distribution

Measurement-Device-Independent Quantum Key Distribution (MDIQKD) protocol has been proved that it is unaffected by all hacking attacks, and ensures the security of information theory even when the performance of single-photon detectors is not ideal. Fiber channel has been used by the previous MDIQKD experimental device. However, the signal attenuation increases exponentially as the transmission distance increases. In order to overcome this, we regard free space as the channel of signal transmission, and the signal attenuation increases square as the transmission distance increases (regardless of the atmospheric scattering), which can effectively reduce the signal attenuation trend. In order to implement the free space MDIQKD experiments, a modulation module is needed to modulate the wide pulse chopping, decoy-state, normalization, phase encoding and time encoding. In this paper, we present the design of the Voltage Pulse Control Module for the free space MDIQKD.

eess.SP↗

arXiv2018

Optimal Threshold-Based Control Policies for Persistent Monitoring on Graphs

We consider the optimal multi-agent persistent monitoring problem defined by a team of cooperating agents visiting a set of nodes (targets) on a graph with the objective of minimizing a measure of overall node state uncertainty. The solution to this problem involves agent trajectories defined both by the sequence of nodes to be visited by each agent and the amount of time spent at each node. Since such optimal trajectories are generally intractable, we propose a class of distributed threshold-based parametric controllers through which agent transitions from one node to the next are controlled by threshold parameters on the node uncertainty states. The resulting behavior of the agent-target system can be described by a hybrid dynamic system. This enables the use of Infinitesimal Perturbation Analysis (IPA) to determine on line (locally) optimal threshold parameters through gradient descent methods and thus obtain optimal controllers within this family of threshold-based policies. We further show that in a single-agent case the IPA gradient is monotonic, which implies a simple structure whereby an agent visiting a node should reduce the uncertainty state to zero before moving to the next node. Simulation examples are included to illustrate our results and compare them to optimal solutions derived through dynamic programming when this is possible.

math.OC↗

arXiv2018

Algorithmic Collusion in Cournot Duopoly Market: Evidence from Experimental Economics

Algorithmic collusion is an emerging concept in current artificial intelligence age. Whether algorithmic collusion is a creditable threat remains as an argument. In this paper, we propose an algorithm which can extort its human rival to collude in a Cournot duopoly competing market. In experiments, we show that, the algorithm can successfully extorted its human rival and gets higher profit in long run, meanwhile the human rival will fully collude with the algorithm. As a result, the social welfare declines rapidly and stably. Both in theory and in experiment, our work confirms that, algorithmic collusion can be a creditable threat. In application, we hope, the frameworks, the algorithm design as well as the experiment environment illustrated in this work, can be an incubator or a test bed for researchers and policymakers to handle the emerging algorithmic collusion.

econ.EM↗

arXiv2017

The cointegral theory of weak multiplier Hopf algebras

In this paper, we introduce and study the notion of cointegrals in a weak multiplier Hopf algebras $(A, Δ)$. A cointegral is a non-zero element $h$ in the multiplier algebra $M(A)$ such that $ah=\v_t(a)h$ for any $a\in A$. When $A$ has a faithful set of cointegrals (now we call $A$ of {\it discrete type}), we give a sufficient and necessary condition for existence of integrals on $A$. Then we consider a special case, i.e., $A$ has a single faithful cointegral, and we obtain more better results, such as $A$ is Frobenius, quasi-Frobenius, et al. Moreover when an algebraic quantum groupoid $A$ has a faithful cointegral, then the dual $\widehat{A}$ must be weak Hopf algebra. In the end, we investigate when $A$ has a cointegral and study relation between compact and discrete type.

math.RA↗

arXiv2017

Decentralized Event-Driven Algorithms for Multi-Agent Persistent Monitoring

We address the issue of identifying conditions under which the centralized solution to the optimal multi-agent persistent monitoring problem can be recovered in a decentralized event-driven manner. In this problem, multiple agents interact with a finite number of targets and the objective is to control their movement in order to minimize an uncertainty metric associated with the targets. In a one-dimensional setting, it has been shown that the optimal solution can be reduced to a simpler parametric optimization problem and that the behavior of agents under optimal control is described by a hybrid system. This hybrid system can be analyzed using Infinitesimal Perturbation Analysis (IPA) to obtain a complete on-line solution through an event-driven centralized gradient-based algorithm. We show that the IPA gradient can be recovered in a distributed manner in which each agent optimizes its trajectory based on local information, except for one event requiring communication from a non-neighbor agent. Simulation examples are included to illustrate the effectiveness of this "almost decentralized" algorithm and its fully decentralized counterpart where the aforementioned non-local event is ignored.

math.OC↗

arXiv2017

Sine wave gating Silicon single-photon detectors for multiphoton entanglement experiments

Silicon single-photon detectors (SPDs) are the key devices for detecting single photons in the visible wavelength range. Here we present high detection efficiency silicon SPDs dedicated to the generation of multiphoton entanglement based on the technique of high-frequency sine wave gating. The silicon single-photon avalanche diodes (SPADs) components are acquired by disassembling 6 commercial single-photon counting modules (SPCMs). Using the new quenching electronics, the average detection efficiency of SPDs is increased from 68.6% to 73.1% at a wavelength of 785 nm. These sine wave gating SPDs are then applied in a four-photon entanglement experiment, and the four-fold coincidence count rate is increased by 30% without degrading its visibility compared with the original SPCMs.

quant-ph↗