arXiv subjects

Gang Wang

Publications and source records attributed to Gang Wang.

At least 361 records · Page 20Linked to original sources

arXiv2020

Distributed Consensus of Nonlinear Multi-Agent Systems With Mismatched Uncertainties and Unknown High-Frequency Gains (Extended Version)

This brief addresses the distributed consensus problem of nonlinear multi-agent systems under a general directed communication topology. Each agent is governed by higher-order dynamics with mismatched uncertainties, multiple completely unknown high-frequency gains, and external disturbances. The main contribution of this brief is to present a new distributed consensus algorithm, enabling the control input of each agent to require minimal information from its neighboring agents, that is, only their output information. To this end, a dynamic system is explicitly constructed for each agent to generate a reference output. Theoretical and simulation verifications of the proposed algorithm are rigorously studied to ensure that asymptotic consensus can be achieved and that all closed-loop signals remain bounded.

eess.SY↗

arXiv2020

Gauss-Newton Unrolled Neural Networks and Data-driven Priors for Regularized PSSE with Robustness

Distributed renewable generation, elastic loads, and purposeful manipulation of meter readings challenge the monitoring and control of today's power systems (PS). In this context, to maintain a comprehensive view of the system in real time, fast and robust state estimation (SE) methods are urgently needed. Conventional PSSE solvers typically entail minimizing a nonlinear and nonconvex least-squares by e.g., the workhorse Gauss-Newton method. Those iterative solvers however, are sensitive to initialization and may get stuck in local minima. To overcome these hurdles and inspired by recent image denoising techniques, this paper advocates a learnable regularization term for PSSE that uses a deep neural network (DNN) prior. For the resultant regularized PSSE problem, a "Gauss-Newton-like" alternating minimization solver is first developed. To accommodate real-time monitoring, a novel end-to-end DNN is constructed by unrolling the proposed alternating minimization solver. Interestingly, the power network topology can be easily incorporated into the DNN by designing a graph neural network (GNN) based prior. To further endow the physics-based DNN with robustness against bad data, an adversarial DNN training method is discussed. Numerical tests using real load data on the IEEE $118$-bus benchmark system showcase the improved estimation and robustness performance of the proposed scheme compared with several state-of-the-art alternatives.

eess.SY↗

arXiv2020

Reinforcement Learning for Caching with Space-Time Popularity Dynamics

With the tremendous growth of data traffic over wired and wireless networks along with the increasing number of rich-media applications, caching is envisioned to play a critical role in next-generation networks. To intelligently prefetch and store contents, a cache node should be able to learn what and when to cache. Considering the geographical and temporal content popularity dynamics, the limited available storage at cache nodes, as well as the interactive in uence of caching decisions in networked caching settings, developing effective caching policies is practically challenging. In response to these challenges, this chapter presents a versatile reinforcement learning based approach for near-optimal caching policy design, in both single-node and network caching settings under dynamic space-time popularities. The herein presented policies are complemented using a set of numerical tests, which showcase the merits of the presented approach relative to several standard caching policies.

cs.IT↗

arXiv2020

Confinement of long-lived interlayer excitons in WS$_2$/WSe$_2$ heterostructures

Interlayer excitons in layered materials constitute a novel platform to study many-body phenomena arising from long-range interactions between quantum particles. The ability to localise individual interlayer excitons in potential energy traps is a key step towards simulating Hubbard physics in artificial lattices. Here, we demonstrate spatial localisation of long-lived interlayer excitons in a strongly confining trap array using a WS$_{2}$/WSe$_{2}$ heterostructure on a nanopatterned substrate. We detect long-lived interlayer excitons with lifetime approaching 0.2 ms and show that their confinement results in a reduced lifetime in the microsecond range and stronger emission rate with sustained optical selection rules. The combination of a permanent dipole moment, spatial confinement and long lifetime places interlayer excitons in a regime that satisfies one of the requirements for observing long-range dynamics in an optically resolvable trap lattice.

cond-mat.mes-hall↗

arXiv2020

Adaptive Distributed Laser Charging for Efficient Wireless Power Transfer

Distributed laser charging (DLC) is a wireless power transfer technology for mobile electronics. Similar to traditional wireless charging systems, the DLC system can only provide constant power to charge a battery. However, Li-ion battery needs dynamic input current and voltage, thus power, in order to optimize battery charging performance. Therefore, neither power transmission efficiency nor battery charging performance can be optimized by the DLC system. We at first propose an adaptive DLC (ADLC) system to optimize wireless power transfer efficiency and battery charging performance. Then, we analyze ADLC's power conversion to depict the adaptation mechanism. Finally, we evaluate the ADLC's power conversion performance by simulation, which illustrates its efficiency improvement by saving at least 60.4% of energy, comparing with the fixed-power charging system.

eess.SY↗

arXiv2020

MobiGyges: A mobile hidden volume for preventing data loss, improving storage utilization, and avoiding device reboot

Sensitive data protection is essential for mobile users. Plausibly Deniable Encryption (PDE) systems provide an effective manner to protect sensitive data by hiding them on the device. However, existing PDE systems can lose data due to overriding the hidden volume, waste physical storage because of the reserved area used for avoiding data loss, and require device reboot when using the hidden volume. This paper presents MobiGyges, a hidden volume-based mobile PDE system, to fill the gap. MobiGyges addresses the problem of data loss by restricting each storage block used only by one volume, and it improves storage utilization by eliminating the reserved area. MobiGyges can also avoid device reboot by mounting the hidden volume dynamically on-demand with the Dynamic Mounting service. Moreover, we identify two novel PDE oriented attacks, the capacity comparison attack and the fill-to-full attack. MobiGyges can defend them by jointly leveraging the Shrunk U-disk method and multi-level deniability. We implement the MobiGyges proof-of-concept system on a real mobile phone Google Nexus 6P with LineageOS 13. Experimental results show that MobiGyges prevents data loss, avoids device reboot, improves storage utilization by over 30% with acceptable performance overhead compared with current works.

cs.CR↗

arXiv2020

Wireless Power Transmitter Deployment for Balancing Fairness and Charging Service Quality

Wireless Energy Transfer (WET) has recently emerged as an appealing solution for power supplying mobile / Internet of Things (IoT) devices. As an enabling WET technology, Resonant Beam Charging (RBC) is well-documented for its long-range, high-power, and safe "WiFi-like" mobile power supply. To provide high-quality wireless charging services for multi-user in a given region, we formulate a deployment problem of multiple RBC transmitters for balancing the charging fairness and quality of charging service. Based on the RBC transmitter's coverage model and receiver's charging / discharging model, a Genetic Algorithm (GA)-based and a Particle Swarm Optimization (PSO)-based scheme are put forth to resolve the above issue. Moreover, we present a scheduling method to evaluate the performance of the proposed algorithms. Numerical results corroborate that the optimized deployment schemes outperform uniform and random deployment in 10%-20% charging efficiency improvement.

cs.ET↗

arXiv2020

Channel-Dependent Scheduling in Wireless Energy Transfer for Mobile Devices

Resonant Beam Charging (RBC) is the Wireless Power Transfer (WPT) technology, which can provide high-power, long-distance, mobile, and safe wireless charging for Internet of Things (IoT) devices. Supporting multiple IoT devices charging simultaneously is a significant feature of the RBC system. To optimize the multi-user charging performance, the transmitting power should be scheduled for charging all IoT devices simultaneously. In order to keep all IoT devices working as long as possible for fairness, we propose the First Access First Charge (FAFC) scheduling algorithm. Then, we formulate the scheduling parameters quantitatively for algorithm implementation. Finally, we analyze the performance of FAFC scheduling algorithm considering the impacts of the receiver number, the transmitting power and the charging time. Based on the analysis, we summarize the methods of improving the WPT performance for multiple IoT devices, which include limiting the receiver number, increasing the transmitting power, prolonging the charging time and improving the single-user's charging efficiency. The FAFC scheduling algorithm design and analysis provide a fair WPT solution for the multi-user RBC system.

cs.NI↗

arXiv2020

"Butterfly Effect" in Shear-Banding Mediated Plasticity of Metallic Glasses

Metallic glasses response to the mechanical stress in a complex and inhomogeneous manner with plastic strain highly localized into nanoscale shear bands. Contrary to the well-defined deformation mechanism in crystalline solids, understanding the mechanical response mechanism and its intrinsic correlation with the macroscopical plasticity in metallic glasses remains long-standing issues. Through a combination of experimental and theoretical analysis, we showed that the shear banding process in metallic glasses exhibits complex chaotic dynamics, which manifests as the existence of a torus destroyed phase diagram, a positive Lyapunov exponent and a fractional Lyapunov dimension. We also demonstrated that the experimentally observed large plasticity fluctuation of metallic glasses tested at the same conditions can be interpreted from the chaotic shear-band dynamics, which could leads to an uncertainty on the appearance of the critical condition for runaway shear banding. Physically, the chaotic shear-band dynamics arises from the interplay between structural disordering and temperature rise within the shear band. By tuning the deformation parameters, the chaotic dynamics can be transformed to a periodic orbit state corresponding to a smaller plasticity fluctuation in metallic glasses. Our results suggest that the plastic flow of metallic glasses is a complex dynamic process, which is highly sensitive to initial conditions and reminiscent of the "butterfly effect" as observed in many complex dynamic systems.

cond-mat.mtrl-sci↗

arXiv2020

Chiral Magnetic Effects in Nuclear Collisions

The interplay of quantum anomalies with strong magnetic field and vorticity in chiral systems could lead to novel transport phenomena, such as the chiral magnetic effect (CME), the chiral magnetic wave (CMW) and the chiral vortical effect (CVE). In high-energy nuclear collisions, these chiral effects may survive the expansion of a quark-gluon plasma fireball and be detected in experiments. The experimental searches for the CME, the CMW and the CVE, have aroused extensive interest over the past couple of decades. The main goal of this article is to review latest experimental progress in search for these novel chiral transport phenomena at Relativistic Heavy Ion Collider at BNL and the Large Hadron Collider at CERN. Future programs to help reduce uncertainties and facilitate the interpretation of the data are also discussed.

nucl-ex↗

arXiv2020

Investigating eccentricities of the binary black hole signals from the LIGO-Virgo catalog GWTC-1

In the first Gravitational-Wave Transient Catalogue of LIGO and Virgo, all events are announced having zero eccentricity. In the present paper, we investigate the performance of SEOBNRE which is a spin-aligned eccentric waveform model in time-domain. By comparing with all the eccentric waveforms in SXS library, we find that the SEOBNRE coincides perfectly with numerical relativity data. Employing the SEOBNRE, we re-estimate the eccentricities of all black hole merger events. We find that most of these events allow a possibility for existence of initial eccentricities at 10 Hz band, but are totally circularized at the observed frequency ($ \gtrsim 20$ Hz). The upcoming update of LIGO and the next generation detector like as Einstein Telescope, will observe the gravitational waves starting at 10 Hz or even lower. If the eccentricity exists at the lower frequency, it may significantly support the dynamical formation mechanism taking place in globular clusters.

gr-qc↗

arXiv2020

Multidimensional topological strings by curved potentials: Simultaneous realization of mobility edge and topological protection

By considering a cigar-shaped trapping potential elongated in a proper curvilinear coordinate, we discover a new form of wave localization which arises from the interplay of geometry and topological protection. The potential is modulated in its shape such that local curvature introduces a trapping potential. The curvature varies along the trap curvilinear axis encodes a topological Harper modulation. The varying geometry maps our system in a one-dimensional Andre-Aubry-Harper grating. We show that a mobility edge exists and topologically protected states arises. These states are extremely robust with respect to disorder in shape of the string. The results may be relevant for localization phenomena in Bose-Einstein condensates, optical fibers and waveguides, and new laser devices, but also for fundamental studies on string theory. Taking into account that the one-dimensional modulation mimic the existence of a additional dimensions, our system is the first example of physically realizable five-dimensional string.

physics.optics↗

arXiv2020

Security Certification in Payment Card Industry: Testbeds, Measurements, and Recommendations

The massive payment card industry (PCI) involves various entities such as merchants, issuer banks, acquirer banks, and card brands. Ensuring security for all entities that process payment card information is a challenging task. The PCI Security Standards Council requires all entities to be compliant with the PCI Data Security Standard (DSS), which specifies a series of security requirements. However, little is known regarding how well PCI DSS is enforced in practice. In this paper, we take a measurement approach to systematically evaluate the PCI DSS certification process for e-commerce websites. We develop an e-commerce web application testbed, BuggyCart, which can flexibly add or remove 35 PCI DSS related vulnerabilities. Then we use the testbed to examine the capability and limitations of PCI scanners and the rigor of the certification process. We find that there is an alarming gap between the security standard and its real-world enforcement. None of the 6 PCI scanners we tested are fully compliant with the PCI scanning guidelines, issuing certificates to merchants that still have major vulnerabilities. To further examine the compliance status of real-world e-commerce websites, we build a new lightweight scanning tool named PciCheckerLite and scan 1,203 e-commerce websites across various business sectors. The results confirm that 86% of the websites have at least one PCI DSS violation that should have disqualified them as non-compliant. Our in-depth accuracy analysis also shows that PciCheckerLite's output is more precise than w3af. We reached out to the PCI Security Council to share our research results to improve the enforcement in practice.

cs.CR↗

arXiv2020

Finite-Sample Analysis of Decentralized Temporal-Difference Learning with Linear Function Approximation

Motivated by the emerging use of multi-agent reinforcement learning (MARL) in engineering applications such as networked robotics, swarming drones, and sensor networks, we investigate the policy evaluation problem in a fully decentralized setting, using temporal-difference (TD) learning with linear function approximation to handle large state spaces in practice. The goal of a group of agents is to collaboratively learn the value function of a given policy from locally private rewards observed in a shared environment, through exchanging local estimates with neighbors. Despite their simplicity and widespread use, our theoretical understanding of such decentralized TD learning algorithms remains limited. Existing results were obtained based on i.i.d. data samples, or by imposing an `additional' projection step to control the `gradient' bias incurred by the Markovian observations. In this paper, we provide a finite-sample analysis of the fully decentralized TD(0) learning under both i.i.d. as well as Markovian samples, and prove that all local estimates converge linearly to a small neighborhood of the optimum. The resultant error bounds are the first of its type---in the sense that they hold under the most practical assumptions ---which is made possible by means of a novel multi-step Lyapunov analysis.

cs.LG↗

arXiv2020

Scene Text Recognition With Finer Grid Rectification

Scene Text Recognition is a challenging problem because of irregular styles and various distortions. This paper proposed an end-to-end trainable model consists of a finer rectification module and a bidirectional attentional recognition network(Firbarn). The rectification module adopts finer grid to rectify the distorted input image and the bidirectional decoder contains only one decoding layer instead of two separated one. Firbarn can be trained in a weak supervised way, only requiring the scene text images and the corresponding word labels. With the flexible rectification and the novel bidirectional decoder, the results of extensive evaluation on the standard benchmarks show Firbarn outperforms previous works, especially on irregular datasets.

cs.CV↗

arXiv2020

Miscibility gap and possible intrinsic Griffiths phase in Sr(Fe1-xMnx)2As2 crystals grown by transitional metal arsenide flux

The crystal structure, magnetic, electronic, and thermal properties of Mn-doped SrFe2As2 crystals have been systematically investigated. A miscibility gap is found in the system from x = 0.4362(4) to x = 0.9612(9). For x < 0.2055(2), the single crystals holding tetragonal structure (space group I4/mmm (no. 139)) have a continuously enlarged lattice parameter c, followed by a phase separation with crystals holding both tetragonal and trigonal structures up to x = 0.4362(4). Beyond the miscibility gap, the crystals with x > 0.9612(9) hold the trigonal structure (space group P-3m1 (no. 164)). Moreover, the spin density wave ordering for x = 0 to x = 0.0973(1) is suppressed, followed by an abnormal and broadened increase of the ordering temperature for x = 0.0973(1) to x = 0.2055(2). Eliminating the possibility of real-space phase separation of Mn and Fe based on the results of X-ray diffraction, this novel phenomenon can be attributed to a possible intrinsic Griffiths phase. No any superconducting signals are observed down to 2 K in the whole composition range with 0 < x < 1. A phase diagram with multi-critical points of Mn-doped SrFe2As2 system is established accordingly.

cond-mat.supr-con↗

arXiv2020

Security Vetting Process of Smart-home Assistant Applications: A First Look and Case Studies

The popularity of smart-home assistant systems such as Amazon Alexa and Google Home leads to a booming third-party application market (over 70,000 applications across the two stores). While existing works have revealed security issues in these systems, it is not well understood how to help application developers to enforce security requirements. In this paper, we perform a preliminary case study to examine the security vetting mechanisms adopted by Amazon Alexa and Google Home app stores. With a focus on the authentication mechanisms between Alexa/Google cloud and third-party application servers (i.e. endpoints), we show the current security vetting is insufficient as developer mistakes can not be effectively detected and notified. A weak authentication would allow attackers to spoof the cloud to insert/retrieve data into/from the application endpoints. We validate the attack through ethical proof-of-concept experiments. To confirm vulnerable applications have indeed passed the security vetting and entered the markets, we develop a heuristic-based searching method. We find 219 real-world Alexa endpoints that carry the vulnerability, many of which are related to critical applications that control smart home devices and electronic cars. We have notified Amazon and Google about our findings and offered our suggestions to mitigate the issue.

cs.CR↗

arXiv2020

Transform-limited photons from a coherent tin-vacancy spin in diamond

Solid-state quantum emitters that couple coherent optical transitions to long-lived spin qubits are essential for quantum networks. Here we report on the spin and optical properties of individual tin-vacancy (SnV) centers in diamond nanostructures. Through cryogenic magneto-optical and spin spectroscopy, we verify the inversion-symmetric electronic structure of the SnV, identify spin-conserving and spin-flipping transitions, characterize transition linewidths, measure electron spin lifetimes and evaluate the spin dephasing time. We find that the optical transitions are consistent with the radiative lifetime limit even in nanofabricated structures. The spin lifetime is phononlimited with an exponential temperature scaling leading to $T_1$ $>$ 10 ms, and the coherence time, $T_2$ reaches the nuclear spin-bath limit upon cooling to 2.9 K. These spin properties exceed those of other inversion-symmetric color centers for which similar values require millikelvin temperatures. With a combination of coherent optical transitions and long spin coherence without dilution refrigeration, the SnV is a promising candidate for feasable and scalable quantum networking applications.

quant-ph↗