arXiv subjects

Xiaohu Wu

Publications and source records attributed to Xiaohu Wu.

At least 37 records · Page 2Linked to original sources

arXiv2022

Spontaneous emission modulation in biaxial hyperbolic van der Waals material

As a natural van der Waals crystal, α-MoO3 has excellent in-plane hyperbolic properties and essential nanophotonics applications. However, its actively tunable properties are generally neglected. In this work, we achieved active modulation of spontaneous emission from a single-layer flat plate using the rotation method for the first time. Numerical results and theoretical analysis show that α-MoO3 exhibits good tunability when rotated in the y-z or x-y plane. A modulation factor of more than three orders of magnitude can be obtained at 634 cm-1. However, when the rotation is in the x-z plane, the spontaneous emission of the material exhibits strong angle independence. The theoretical formulation and the physical mechanism analysis explain the above phenomenon well. In addition, for the semi-infinite α-MoO3 flat structure, we give the distribution of the modulation factor of spontaneous emission with wavenumber and rotation angle. Finally, we extended the calculation results from semi-infinite media to finite thickness films. We obtained the general evolution law of the peak angle of the modulation factor with thickness, increasing the modulation factor to about 2000. We believe that the results of this paper can guide the active modulation of spontaneous emission based on anisotropic materials.

physics.optics↗

arXiv2022

Tuning Topological Transitions in Twisted Thermophotovoltaic Systems

Twisted bilayer two-dimensional electronic systems give rise to many exotic phenomena and unveil a new frontier for the study of quantum materials. In photonics, twisted two-dimensional systems coupled via near-field interactions offer a platform to study localization and lasing. Here, we propose that twisting can be an unprecedented tool to tune the performance of near-field thermophotovoltaic systems. Remarkably, through twisting-induced photonic topological transitions, we achieve significant tuning of the thermophotovoltaic energy efficiency and power. The underlying mechanism is related to the change of the photonic iso-frequency contours from elliptical to hyperbolic geometries in a setup where the hexagonal-boron-nitride metasurface serves as the heat source and the indium antimonide $p$-$n$ junction serves as the cell. We find a notably high energy efficiency, nearly 53\% of the Carnot efficiency, can be achieved in our thermophotovoltaic system, while the output power can reach to $1.1\times10^4$~W/m$^2$ without requiring a large temperature difference between the source and the cell. Our results indicate the promising future of twisted near-field thermophotovoltaics and paves the way towards tunable, high-performance thermophotovoltaics and infrared detection.

cond-mat.mes-hall↗

arXiv2022

MarS-FL: Enabling Competitors to Collaborate in Federated Learning

Federated learning (FL) is rapidly gaining popularity and enables multiple data owners ({\em a.k.a.} FL participants) to collaboratively train machine learning models in a privacy-preserving way. A key unaddressed scenario is that these FL participants are in a competitive market, where market shares represent their competitiveness. Although they are interested to enhance the performance of their respective models through FL, market leaders (who are often data owners who can contribute significantly to building high performance FL models) want to avoid losing their market shares by enhancing their competitors' models. Currently, there is no modeling tool to analyze such scenarios and support informed decision-making. In this paper, we bridge this gap by proposing the \underline{mar}ket \underline{s}hare-based decision support framework for participation in \underline{FL} (MarS-FL). We introduce {\em two notions of $δ$-stable market} and {\em friendliness} to measure the viability of FL and the market acceptability of FL. The FL participants' behaviours can then be predicted using game theoretic tools (i.e., their optimal strategies concerning participation in FL). If the market $δ$-stability is achievable, the final model performance improvement of each FL-PT shall be bounded, which relates to the market conditions of FL applications. We provide tight bounds and quantify the friendliness, $κ$, of given market conditions to FL. Experimental results show the viability of FL in a wide range of market conditions. Our results are useful for identifying the market conditions under which collaborative FL model training is viable among competitors, and the requirements that have to be imposed while applying FL under these conditions.

cs.LG↗

arXiv2021

Chirality of plasmonic metasurfaces with rectangular holes

Chiral response is of tremendous importance to many fields, such as analytical chemistry, polarization manipulation and biological sensing. Here, a chiral metasurface based on rectangular holes is systematically investigated. The results show that the chirality is closely related to the size and the orientation of resonance unit. It is found that the period of the structure is always smaller than the wavelength at which chirality appears, which will provide a good basis for the design of chiral structures. More importantly, the CD is highly sensitive to the orientation of resonance unit. By adjusting the rotation angle, it is not only possible to invert the CD, but also to change the symmetry of the structure to realize the regulation of chirality. The chirality can be significantly enhanced in the proposed structure, and the maximum of circular dichroism (CD) can reach 0.76. To better understand the physical mechanism, the distributions of electric field for LCP and RCP waves are also discussed as well. This work will not only deepen the understanding of chiral metasurfaces, but also provide guidance for the design of similar chiral structures.

physics.optics↗

arXiv2021

Coupling between hyperbolic phonon polaritons excited in two ultrathin hBN sheets

In this work, the hyperbolic phonon polaritons (HPPs) in ultrathin hBN sheets are numerically studied. The dispersion relation and distribution of electric field are calculated to confirm the excitation of HPPs. Besides, the coupling effect between HPPs of two ultrathin hBN sheets are investigated. When the distance between two hBN sheets are smaller than the propagation length of the HPPs in the air, the HPPs can be strongly coupled. Therefore, the photon tunneling probability can be greatly enhanced. The split of the HPPs is similar to that of the surface waves, and such phenomenon is well explained in this work. We believe that this work will deepen our understanding of the HPPs in ultrathin hyperbolic materials. In addition, the knowledge about the HPPs will help us understand the near-field radiative heat transfer between hyperbolic materials.

physics.optics↗

arXiv2021

Rotation-induced significant modulation of near-field radiative heat transfer between hyperbolic nanoparticles

Modulation of near-field radiative heat transfer (NFRHT) with rotated anisotropic hBN and MoO3 nanoparticles (NPs) are investigated. The spectral heat power, total heat power and electric field energy density are calculated at different particle orientations. Numerical results show that the modulation factor of the MoO3 NPs could be up to ~12000 with particle radius of 40 nm at a gap distance of 200 nm, which is ~ 10-fold larger than the state of the art. Excitation of localized hyperbolic phonon polaritons (LHPPs) in different particle orientations allow for the excellent modulation. The modulation factor of the hBN NPs is 5.9 due to the insensitivity of the LHPPs in the type II hyperbolic band to the particle orientations. This work not only provides a new method to modulate NFRHT in particle systems, but also paves the way to explore radiative heat transfer characteristics of anisotropic media.

physics.optics↗

arXiv2021

Near-complete violation of Kirchhoff s law in thermal radiation in ultrathin magnetic Weyl semimetal films

The ability to break Kirchhoff s law is of fundamental importance in thermal radiation. Various nonreciprocal emitters have been proposed to break the balance between absorption and emission. However, the thicknesses of the nonreciprocal materials are usually larger than 1/10 times of the wavelength. Besides, the previous proposed nonreciprocal emitters are complex, thus they can hardly be fabricated in experiment to verify the Kirchhoff s law for nonreciprocal materials. In this paper, we investigate the nonreciprocal thermal radiation of the magnetic Weyl semimetal (MWSM) film atop of the metal substrate. It is found that the strong nonreciprocal radiation at the wavelength of 9.15 μm can be achieved when the thickness of the MWSM film is 100 nm. The enhanced nonreciprocity is attributed to the Fabry-Perot resonances. The results indicate that the MWSM film is the promising candidate to engineer the ultrathin and simple nonreciprocal thermal emitters. What is perhaps most intriguing here is that the proposed structure can be more easily fabricated in experiment to verify the Kirchhoff s law for nonreciprocal materials.

cond-mat.mes-hall↗

arXiv2021

Nonreciprocal thermal radiation based on Fibonacci quasi-periodic structures

To violate Kirchhoff s law is very important in the areas of thermal radiation. However, due to the weak nonreciprocity in natural materials, it is necessary to engineer novel structures to break the balance between emission and absorption. In this work, we introduce magneto-optical material into Fibonacci photonic crystals. Assisted by the nonreciprocity of the magneto-optical material and the excitation of Tamm plasmon polaritons, strong nonreciprocal thermal radiation can be realized. The difference between absorption and emission at wavelength of 16 μm can reach 0.9 at the incident angle of 60o. The distributions of the magnetic field are also calculated to verify the underlying physical origin. By engineering the parameters of the structure, it is found that strong nonreciprocal thermal radiation can be achieved at shorter wavelength and smaller incident angle. The results indicate that the Fibonacci magnetophotonic crystals are the promising candidate to engineer the nonreciprocal emission for various requirements.

physics.optics↗

arXiv2021

Dual-band nonreciprocal thermal radiation by coupling optical Tamm states in magnetophotonic multilayers

Kirchhoff s law is one of the most fundamental law in thermal radiation. The violation of traditional Kirchhoff s law provides opportunities for higher energy conversion efficiency. Various micro-structures have been proposed to realize single-band nonreciprocal thermal emitters. However, dual-band nonreciprocal thermal emitters remain barely investigated. In this paper, we introduce magneto-optical material into a cascading one-dimensional (1-D) magnetophotonic crystal (MPC) heterostructure composed of two 1-D MPCs and a metal layer. Assisted by the nonreciprocity of the magneto-optical material and the coupling effect of two optical Tamm states (OTSs), a dual-band nonreciprocal lithography-free thermal emitter is achieved. The emitter exhibits strong dual-band nonreciprocal radiation at the wavelengths of 15.337 um and 15.731 um when the external magnetic field is 3 T and the angle of incidence is 56 degree. Besides, the magnetic field distribution is also calculated to confirm that the dual-band nonreciprocal radiation originates from the coupling effect between two OTSs. Our work may pave the way for constructing dual-band and multi-band nonreciprocal thermal emitters.

physics.optics↗

arXiv2021

Thermal conductivity of micro/nano-porous polymers: Prediction models and applications

Micro/nano porous polymeric material is considered a unique industrial material due to its extremely low thermal conductivity, low density, and high surface area. Therefore, it is necessary to establish an accurate thermal conductivity prediction model suiting their applicable conditions and provide a theoretical basis for expanding of their applications. In this work, the development of the calculation model of equivalent thermal conductivity of micro/nano porous polymeric materials in recent years was summarized. Firstly, it reviews the process of establishing the overall equivalent thermal conductivity calculation model for micro/nano porous polymers. Then, the predicted calculation models of thermal conductivity are introduced according to the conductive thermal conductivity models and the radiative thermal conductivity models separately. In addition, the thermal conduction part is divided into the gaseous thermal conductivity model, solid thermal conductivity model and gas-solid coupling model. Finally, it is concluded that, compared with other porous materials, there are few studies on heat transfer of micro/nano porous polymers, especially on the special heat transfer mechanisms such as scale effects at the micro/nanoscale. In particular, the following aspects of porous polymers still need to be further studied: micro scaled thermal radiation, heat transfer characteristics of special morphologies at the nanoscale, heat transfer mechanism and impact factors of micro/nano porous polymers. Such studies would provide a more accurate prediction of thermal conductivity and a wider application in energy conversion and storage systems.

physics.comp-ph↗

arXiv2021

Towards Cost-Optimal Policies for DAGs to Utilize IaaS Clouds with Online Learning

Premier cloud service providers (CSPs) offer two types of purchase options, namely on-demand and spot instances, with time-varying features in availability and price. Users like startups have to operate on a limited budget and similarly others hope to reduce their costs. While interacting with a CSP, central to their concerns is the process of cost-effectively utilizing different purchase options possibly in addition to self-owned instances. A job in data-intensive applications is typically represented by a directed acyclic graph which can further be transformed into a chain of tasks. The key to achieving cost efficiency is determining the allocation of a specific deadline to each task, as well as the allocation of different types of instances to the task. In this paper, we propose a framework that determines the optimal allocation of deadlines to tasks. The framework also features an optimal policy to determine the allocation of spot and on-demand instances in a predefined time window, and a near-optimal policy for allocating self-owned instances. The policies are designed to be parametric to support the usage of online learning to infer the optimal values against the dynamics of cloud markets. Finally, several intuitive heuristics are used as baselines to validate the cost improvement brought by the proposed solutions. We show that the cost improvement over the state-of-the-art is up to 24.87% when spot and on-demand instances are considered and up to 59.05% when self-owned instances are considered.

cs.PF↗

arXiv2020

Delay and Price Differentiation in Cloud Computing: A Service Model, Supporting Architectures, and Performance

Many cloud service providers (CSPs) provide on-demand service at a price with a small delay. We propose a QoS-differentiated model where multiple SLAs deliver both on-demand service for latency-critical users and delayed services for delay-tolerant users at lower prices. Two architectures are considered to fulfill SLAs. The first is based on priority queues. The second simply separates servers into multiple modules, each for one SLA. As an ecosystem, we show that the proposed framework is dominant-strategy incentive compatible. Although the first architecture appears more prevalent in the literature, we prove the superiority of the second architecture, under which we further leverage queueing theory to determine the optimal SLA delays and prices. Finally, the viability of the proposed framework is validated through numerical comparison with the on-demand service and it exhibits a revenue improvement in excess of 200%. Our results can help CSPs design optimal delay-differentiated services and choose appropriate serving architectures.

cs.PF↗

arXiv2020

Distributed Energy Trading and Scheduling among Microgrids via Multiagent Reinforcement Learning

The development of renewable energy generation empowers microgrids to generate electricity to supply itself and to trade the surplus on energy markets. To minimize the overall cost, a microgrid must determine how to schedule its energy resources and electrical loads and how to trade with others. The control decisions are influenced by various factors, such as energy storage, renewable energy yield, electrical load, and competition from other microgrids. Making the optimal control decision is challenging, due to the complexity of the interconnected microgrids, the uncertainty of renewable energy generation and consumption, and the interplay among microgrids. The previous works mainly adopted the modeling-based approaches for deriving the control decision, yet they relied on the precise information of future system dynamics, which can be hard to obtain in a complex environment. This work provides a new perspective of obtaining the optimal control policy for distributed energy trading and scheduling by directly interacting with the environment, and proposes a multiagent deep reinforcement learning approach for learning the optimal control policy. Each microgrid is modeled as an agent, and different agents learn collaboratively for maximizing their rewards. The agent of each microgrid can make the local scheduling decision without knowing others' information, which can well maintain the autonomy of each microgrid. We evaluate the performances of our proposed method using real-world datasets. The experimental results show that our method can significantly reduce the cost of the microgrids compared with the baseline methods.

eess.SY↗

arXiv2019

A Framework for Allocating Server Time to Spot and On-demand Services in Cloud Computing

Cloud computing delivers value to users by facilitating their access to computing capacity in periods when their need arises. An approach is to provide both on-demand and spot services on shared servers. The former allows users to access servers on demand at a fixed price and users occupy different periods of servers. The latter allows users to bid for the remaining unoccupied periods via dynamic pricing; however, without appropriate design, such periods may be arbitrarily small since on-demand users arrive randomly. This is also the current service model adopted by Amazon Elastic Cloud Compute. In this paper, we provide the first integral framework for sharing the time of servers between on-demand and spot services while optimally pricing spot instances. It guarantees that on-demand users can get served quickly while spot users can stably utilize servers for a properly long period once accepted, which is a key feature to make both on-demand and spot services accessible. Simulation results show that, by complementing the on-demand market with a spot market, a cloud provider can improve revenue by up to 464.7%. The framework is designed under assumptions which are met in real environments. It is a new tool that cloud operators can use to quantify the advantage of a hybrid spot and on-demand service, eventually making the case for operating such service model in their own infrastructures.

cs.PF↗

arXiv2019

Towards Designing Cost-Optimal Policies to Utilize IaaS Clouds under Online Learning

Many businesses possess a small infrastructure that they can use for their computing tasks, but also often buy extra computing resources from clouds. Cloud vendors such as Amazon EC2 offer two types of purchase options: on-demand and spot instances. As tenants have limited budgets to satisfy their computing needs, it is crucial for them to determine how to purchase different options and utilize them (in addition to possible self-owned instances) in a cost-effective manner while respecting their response-time targets. In this paper, we propose a framework to design policies to allocate self-owned, on-demand and spot instances to arriving jobs. In particular, we propose a near-optimal policy to determine the number of self-owned instance and an optimal policy to determine the number of on-demand instances to buy and the number of spot instances to bid for at each time unit. Our policies rely on a small number of parameters and we use an online learning technique to infer their optimal values. Through numerical simulations, we show the effectiveness of our proposed policies, in particular that they achieve a cost reduction of up to 64.51% when spot and on-demand instances are considered and of up to 43.74% when self-owned instances are considered, compared to previously proposed or intuitive policies.

cs.DC↗

arXiv2018

Performance Analysis of QoS-Differentiated Pricing in Cloud Computing: An Analytical Approach

A fundamental goal in the design of IaaS service is to enable both user-friendly and cost-effective service access, while attaining high resource efficiency for revenue maximization. QoS differentiation is an important lens to achieve this design goal. In this paper, we propose the first analytical QoS-differentiated resource management and pricing architecture in the cloud computing context; here, a cloud service provider (CSP) offers a portfolio of SLAs. In order to maximize the CSP's revenue, we address two technical questions: (1) how to set the SLA prices so as to direct users to the SLAs best fitting their needs, and, (2) determining how many servers should be assigned to each SLA, and which users and how many of their jobs are admitted to be served. We propose optimal schemes to jointly determine SLA-based prices and perform capacity planning in polynomial time. Our pricing model retains high usability at the customer's end. Compared with standard usage-based pricing schemes, numerical results show that the proposed scheme can improve the revenue by up to a five-fold increase.

cs.DC↗

arXiv2018

Algorithms for Scheduling Malleable Cloud Tasks

Due to the ubiquity of batch data processing in cloud computing, the related problem of scheduling malleable batch tasks and its extensions have received significant attention recently. In this paper, we consider a fundamental model where a set of n tasks is to be processed on C identical machines and each task is specified by a value, a workload, a deadline and a parallelism bound. Within the parallelism bound, the number of machines assigned to a task can vary over time without affecting its workload. For this model, we obtain two core results: a sufficient and necessary condition such that a set of tasks can be finished by their deadlines on C machines, and an algorithm to produce such a schedule. These core results provide a conceptual tool and an optimal scheduling algorithm that enable proposing new algorithmic analysis and design and improving existing algorithms under various objectives.

cs.DC↗

arXiv2017

Realizing polarization conversion and unidirectional transmission by using a uniaxial crystal plate

We show that polarization states of electromagnetic waves can be manipulated easily using a single thin uniaxial crystal plate. By performing a rotational transformation of the coordinates and controlling the thickness of the plate, we can achieve a complete polarization conversion between TE wave and TM wave in a spectral band. We show that the off-diagonal element of the permittivity is the key for polarization conversion. Our analysis can explain clearly the results found in experiments with metamaterials. Finally, we propose a simple device to realize unidirectional transmission based on polarization conversion and excitation of surface plasmon polaritons.

physics.optics↗