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Bin Zhou

Publications and source records attributed to Bin Zhou.

At least 91 records · Page 5Linked to original sources
arXiv2024

Automated calculation of Jet fragmentation at NLO in QCD

We present FMNLO, a framework to combine general-purpose Monte Carlo generators and fragmentation functions (FFs). It is based on a hybrid scheme of phase-space slicing method and local subtraction method, and accurate to next-to-leading order (NLO) in QCD. The new framework has been interfaced to MG5 aMC@NLO and made publicly available in this work. We demonstrate its unique ability by giving theoretical predictions of various fragmentation measurements at the LHC, followed by comparison with the data. With the help of interpolation techniques, FMNLO allows for fast calculation of fragmentation processes for a large number of different FFs, which makes it a promising tool for future fits of FFs. As an example, we perform a NLO fit of parton fragmentation functions to unidentified charged hadrons using measurements at the LHC. We find the ATLAS data from inclusive dijet production show a strong constraining power. Notable disparities are found between our gluon FF and that of BKK, DSS and NNFF, indicating the necessities of additional constraints and data for gluon fragmentation function.

hep-ph↗
arXiv2024

High-Efficiency Resonant Beam Charging and Communication

With the development of Internet of Things (IoT), demands of power and data for IoT devices increase drastically. In order to resolve the supply-demand contradiction, simultaneous wireless information and power transfer (SWIPT) has been envisioned as an enabling technology by providing high-power energy transfer and high-rate data delivering concurrently. In this paper, we introduce a high-efficiency resonant beam (RB) charging and communication scheme. The scheme utilizes the semiconductor materials as gain medium, which has a better energy absorption capacity compared with the traditional solid-state one. Moreover, the telescope internal modulator (TIM) are adopted in the scheme which can concentrate beams to match the gain size, reducing the transmission loss. To evaluate the scheme SWIPT performance, we establish an analytical model and study the influence factors of its beam transmission, energy conversion, output power, and spectral efficiency. Numerical results shows that the proposed RB system can realize 16 W electric power output with 11 % end-to-end conversion efficiency, and support 18 bit/s/Hz spectral efficiency for communication.

cs.ET↗
arXiv2023

Periodically driven four-dimensional topological insulator with tunable second Chern number

In recent years, Floquet engineering has attracted considerable attention as a promising approach for tuning topological phase transitions. In this work, we investigate the effects of high-frequency time-periodic driving in a four-dimensional (4D) topological insulator, focusing on topological phase transitions at the off-resonant quasienergy gap. The 4D topological insulator hosts gapless three-dimensional boundary states characterized by the second Chern number $C_{2}$. We demonstrate that the second Chern number of 4D topological insulators can be modulated by tuning the amplitude of time-periodic driving. This includes transitions from a topological phase with $C_{2}=\pm3$ to another topological phase with $C_{2}=\pm1$, or to a topological phase with an even second Chern number $C_{2}=\pm2$ which is absent in the 4D static system. Finally, the approximation theory in the high-frequency limit further confirms the numerical conclusions.

cond-mat.mes-hall↗
arXiv2023

MixTEA: Semi-supervised Entity Alignment with Mixture Teaching

Semi-supervised entity alignment (EA) is a practical and challenging task because of the lack of adequate labeled mappings as training data. Most works address this problem by generating pseudo mappings for unlabeled entities. However, they either suffer from the erroneous (noisy) pseudo mappings or largely ignore the uncertainty of pseudo mappings. In this paper, we propose a novel semi-supervised EA method, termed as MixTEA, which guides the model learning with an end-to-end mixture teaching of manually labeled mappings and probabilistic pseudo mappings. We firstly train a student model using few labeled mappings as standard. More importantly, in pseudo mapping learning, we propose a bi-directional voting (BDV) strategy that fuses the alignment decisions in different directions to estimate the uncertainty via the joint matching confidence score. Meanwhile, we also design a matching diversity-based rectification (MDR) module to adjust the pseudo mapping learning, thus reducing the negative influence of noisy mappings. Extensive results on benchmark datasets as well as further analyses demonstrate the superiority and the effectiveness of our proposed method.

cs.LG↗
arXiv2023

The nature and nurture of network evolution

Although the origin of the fat-tail characteristic of the degree distribution in complex networks has been extensively researched, the underlying cause of the degree distribution characteristic across the complete range of degrees remains obscure. Here, we propose an evolution model that incorporates only two factors: the node's weight, reflecting its innate attractiveness (nature), and the node's degree, reflecting the external influences (nurture). The proposed model provides a good fit for degree distributions and degree ratio distributions of numerous real-world networks and reproduces their evolution processes. Our results indicate that the nurture factor plays a dominant role in the evolution of social networks. In contrast, the nature factor plays a dominant role in the evolution of non-social networks, suggesting that whether nodes are people determines the dominant factor influencing the evolution of real-world networks.

physics.soc-ph↗
arXiv2023

Scale-Free Networks beyond Power-Law Degree Distribution

Complex networks across various fields are often considered to be scale free -- a statistical property usually solely characterized by a power-law distribution of the nodes' degree $k$. However, this characterization is incomplete. In real-world networks, the distribution of the degree-degree distance $η$, a simple link-based metric of network connectivity similar to $k$, appears to exhibit a stronger power-law distribution than $k$. While offering an alternative characterization of scale-freeness, the discovery of $η$ raises a fundamental question: do the power laws of $k$ and $η$ represent the same scale-freeness? To address this question, here we investigate the exact asymptotic {relationship} between the distributions of $k$ and $η$, proving that every network with a power-law distribution of $k$ also has a power-law distribution of $η$, but \emph{not} vice versa. This prompts us to introduce two network models as counterexamples that have a power-law distribution of $η$ but not $k$, constructed using the preferential attachment and fitness mechanisms, respectively. Both models show promising accuracy by fitting only one model parameter each when modeling real-world networks. Our findings suggest that $η$ is a more suitable indicator of scale-freeness and can provide a deeper understanding of the universality and underlying mechanisms of scale-free networks.

physics.soc-ph↗
arXiv2023

Cooperative Dispatch of Microgrids Community Using Risk-Sensitive Reinforcement Learning with Monotonously Improved Performance

The integration of individual microgrids (MGs) into Microgrid Clusters (MGCs) significantly improves the reliability and flexibility of energy supply, through resource sharing and ensuring backup during outages. The dispatch of MGCs is the key challenge to be tackled to ensure their secure and economic operation. Currently, there is a lack of optimization method that can achieve a trade-off among top-priority requirements of MGCs' dispatch, including fast computation speed, optimality, multiple objectives, and risk mitigation against uncertainty. In this paper, a novel Multi-Objective, Risk-Sensitive, and Online Trust Region Policy Optimization (RS-TRPO) Algorithm is proposed to tackle this problem. First, a dispatch paradigm for autonomous MGs in the MGC is proposed, enabling them sequentially implement their self-dispatch to mitigate potential conflicts. This dispatch paradigm is then formulated as a Markov Game model, which is finally solved by the RS-TRPO algorithm. This online algorithm enables MGs to spontaneously search for the Pareto Frontier considering multiple objectives and risk mitigation. The outstanding computational performance of this algorithm is demonstrated in comparison with mathematical programming methods and heuristic algorithms in a modified IEEE 30-Bus Test System integrated with four autonomous MGs.

eess.SY↗
arXiv2023

Mapping tidal flat topography using time-series Sentinel-2 images and ICESat-2 data: A case study in Cixi City

Tidal flat topography provides crucial insights for understanding tidal flats and their dynamic evolution. However, the wide-ranging and rapidly changing nature of tidal flats, which are periodically submerged in shallow water, pose challenges for many current monitoring methods in terms of both efficiency and precision. In this study, we considered the dynamic process of tidal flat submergence and utilized time-series Sentinel-2 images on Google Earth Engine (GEE) to calculate the tidal flat exposure frequency. This information was used to determine the spatial extent of the tidal flats, and subsequently, by employing ICESat-2 data, we established a 1D-linear regression model based on elevation and frequency values, which realizes the inversion of the tidal flat elevation within Cixi City. The study shows the following: (1) the tidal flat exposure frequency and ICESat-2 elevation data exhibit a strong positive correlation (R2=0.85); (2) the tidal flat area within Cixi City is 115.81 km2, and the overall accuracy is 95.36%; and (3) the elevation range of the tidal flats in the study area is between -0.42 and 2.73 m, and the mean absolute error (MAE) is 0.24 m. Additionally, we consider that the temporal resolution of remote sensing imagery plays a crucial role in determining the accuracy of the elevation inversion, and we found that higher tidal flats exhibit better inversion accuracy than lower tidal flats.

physics.ao-ph↗
arXiv2023

Leveraging Optical Communication Fiber and AI for Distributed Water Pipe Leak Detection

Detecting leaks in water networks is a costly challenge. This article introduces a practical solution: the integration of optical network with water networks for efficient leak detection. Our approach uses a fiber-optic cable to measure vibrations, enabling accurate leak identification and localization by an intelligent algorithm. We also propose a method to access leak severity for prioritized repairs. Our solution detects even small leaks with flow rates as low as 0.027 L/s. It offers a cost-effective way to improve leak detection, enhance water management, and increase operational efficiency.

eess.SY↗
arXiv2023

Sobolev inequalities and regularity of the linearized complex Monge-Ampere and Hessian equations

Let $u$ be a smooth, strictly $k$-plurisubharmonic function on a bounded domain $Ω\in\mathbb C^n$ with $2\leq k\leq n$. The purpose of this paper is to study the regularity of solution to the linearized complex Monge-Ampère and Hessian equations when the complex $k$-Hessian $H_k[u]$ of $u$ is bounded from above and below. We first establish some estimates of Green's functions associated to the linearized equations. Then we prove a class of new Sobolev inequalities. With these inequalities, we use Moser's iteration to investigate the a priori estimates of Hessian equations and their linearized equations, as well as the Kähler scalar curvature equation. In particular, we obtain the Harnack inequality for the linearized complex Monge-Ampère and Hessian equations under an extra integrability condition on the coefficients. The approach works in both real and complex case.

math.AP↗
arXiv2023

Chiral edge state coupling theory of transport in quantum anomalous Hall insulators

Theoretically, the quantum anomalous Hall effect is characterized by a quantized Hall conductance. However, many experiments only reported the quantization of the Hall resistance, which is accompanied by a non-vanishing longitudinal resistance, resulting in a non-quantized Hall conductance. Meanwhile, the non-vanishing longitudinal resistance features a universal exponential decay with the increase in magnetic field. Such a discrepancy obviously challenges the understanding of the quantum anomalous Hall effect. To this end, we propose that the coupling of chiral edge states, which has not been properly evaluated in the previous theories, hinders the quantization of the Hall conductance, while it maintains the quantization of the Hall resistance. The coupling between the chiral edges states along the opposite boundaries can be assisted by magnetic domains or defects inside the sample bulk, which has been already identified in recent experiments. We demonstrate that the chiral edge state coupling theory works rather well to explain the experimental results in both magnetic topological insulator and moiré superlattice systems.

cond-mat.mes-hall↗
arXiv2023

Quasicrystalline second-order topological semimetals

Three-dimensional higher-order topological semimetals in crystalline systems exhibit higher-order Fermi arcs on one-dimensional hinges, challenging the conventional bulk-boundary correspondence. However, the existence of higher-order Fermi arc states in aperiodic quasicrystalline systems remains uncertain. In this work, we present the emergence of three-dimensional quasicrystalline second-order topological semimetal phases by vertically stacking two-dimensional quasicrystalline second-order topological insulators. These quasicrystalline topological semimetal phases are protected by rotational symmetries forbidden in crystals, and are characterized by topological hinge Fermi arcs connecting fourfold degenerate Dirac-like points in the spectrum. Our findings reveal an intriguing class of higher-order topological phases in quasicrystalline systems, shedding light on their unique properties.

cond-mat.mes-hall↗
arXiv2023

Relative Algebro-Geometric stabilities of Toric Manifolds

In this paper we study the relative Chow and $K$-stability of toric manifolds in the toric sense. First, we give a criterion for relative $K$-stability and instability of toric Fano manifolds in the toric sense. The reduction of relative Chow stability on toric manifolds will be investigated using the Hibert-Mumford criterion in two ways. One is to consider the maximal torus action and its weight polytope. We obtain a reduction by the strategy of Ono [Ono13], which fits into the relative GIT stability detected by Székelyhidi. The other way relies on $\mathbb{C}^*$-actions and Chow weights associated to toric degenerations following Donaldson and Ross-Thomas [D02, RT07]. As applications of our main theorem, we partially determine the relative $K$-stability of toric Fano threefolds and present counter-examples which are relatively $K$-stable in the toric sense but which are asymptotically relatively Chow unstable. In the end, we explain the erroneous parts of the published version of this article (corresponding to Sections 1-5), which provides some inconclusive results for relative $K$-stability in Table 6.

math.DG↗
arXiv2023

Applications of Reinforcement Learning in Deregulated Power Market: A Comprehensive Review

The increasing penetration of renewable generations, along with the deregulation and marketization of power industry, promotes the transformation of power market operation paradigms. The optimal bidding strategy and dispatching methodology under these new paradigms are prioritized concerns for both market participants and power system operators, with obstacles of uncertain characteristics, computational efficiency, as well as requirements of hyperopic decision-making. To tackle these problems, the Reinforcement Learning (RL), as an emerging machine learning technique with advantages compared with conventional optimization tools, is playing an increasingly significant role in both academia and industry. This paper presents a comprehensive review of RL applications in deregulated power market operation including bidding and dispatching strategy optimization, based on more than 150 carefully selected literatures. For each application, apart from a paradigmatic summary of generalized methodology, in-depth discussions of applicability and obstacles while deploying RL techniques are also provided. Finally, some RL techniques that have great potentiality to be deployed in bidding and dispatching problems are recommended and discussed.

cs.LG↗
arXiv2023

How to Use Reinforcement Learning to Facilitate Future Electricity Market Design? Part 1: A Paradigmatic Theory

In face of the pressing need of decarbonization in the power sector, the re-design of electricity market is necessary as a Marco-level approach to accommodate the high penetration of renewable generations, and to achieve power system operation security, economic efficiency, and environmental friendliness. However, existing market design methodologies suffer from the lack of coordination among energy spot market (ESM), ancillary service market (ASM) and financial market (FM), i.e., the "joint market", and the lack of reliable simulation-based verification. To tackle these deficiencies, this two-part paper develops a paradigmatic theory and detailed methods of the joint market design using reinforcement-learning (RL)-based simulation. In Part 1, the theory and framework of this novel market design philosophy are proposed. First, the controversial market design options while designing the joint market are summarized as the targeted research questions. Second, the Markov game model is developed to describe the bidding game in the joint market, incorporating the market design options to be determined. Third, a framework of deploying multiple types of RL algorithms to simulate the market model is developed. Finally, several market operation performance indicators are proposed to validate the market design based on the simulation results.

cs.AI↗
arXiv2023

How to Use Reinforcement Learning to Facilitate Future Electricity Market Design? Part 2: Method and Applications

This two-part paper develops a paradigmatic theory and detailed methods of the joint electricity market design using reinforcement-learning (RL)-based simulation. In Part 2, this theory is further demonstrated by elaborating detailed methods of designing an electricity spot market (ESM), together with a reserved capacity product (RC) in the ancillary service market (ASM) and a virtual bidding (VB) product in the financial market (FM). Following the theory proposed in Part 1, firstly, market design options in the joint market are specified. Then, the Markov game model is developed, in which we show how to incorporate market design options and uncertain risks in model formulation. A multi-agent policy proximal optimization (MAPPO) algorithm is elaborated, as a practical implementation of the generalized market simulation method developed in Part 1. Finally, the case study demonstrates how to pick the best market design options by using some of the market operation performance indicators proposed in Part 1, based on the simulation results generated by implementing the MAPPO algorithm. The impacts of different market design options on market participants' bidding strategy preference are also discussed.

cs.GT↗
arXiv2023

Fast Event-based Double Integral for Real-time Robotics

Motion deblurring is a critical ill-posed problem that is important in many vision-based robotics applications. The recently proposed event-based double integral (EDI) provides a theoretical framework for solving the deblurring problem with the event camera and generating clear images at high frame-rate. However, the original EDI is mainly designed for offline computation and does not support real-time requirement in many robotics applications. In this paper, we propose the fast EDI, an efficient implementation of EDI that can achieve real-time online computation on single-core CPU devices, which is common for physical robotic platforms used in practice. In experiments, our method can handle event rates at as high as 13 million event per second in a wide variety of challenging lighting conditions. We demonstrate the benefit on multiple downstream real-time applications, including localization, visual tag detection, and feature matching.

cs.RO↗
arXiv2023

Improving Knowledge Graph Entity Alignment with Graph Augmentation

Entity alignment (EA) which links equivalent entities across different knowledge graphs (KGs) plays a crucial role in knowledge fusion. In recent years, graph neural networks (GNNs) have been successfully applied in many embedding-based EA methods. However, existing GNN-based methods either suffer from the structural heterogeneity issue that especially appears in the real KG distributions or ignore the heterogeneous representation learning for unseen (unlabeled) entities, which would lead the model to overfit on few alignment seeds (i.e., training data) and thus cause unsatisfactory alignment performance. To enhance the EA ability, we propose GAEA, a novel EA approach based on graph augmentation. In this model, we design a simple Entity-Relation (ER) Encoder to generate latent representations for entities via jointly modeling comprehensive structural information and rich relation semantics. Moreover, we use graph augmentation to create two graph views for margin-based alignment learning and contrastive entity representation learning, thus mitigating structural heterogeneity and further improving the model's alignment performance. Extensive experiments conducted on benchmark datasets demonstrate the effectiveness of our method.

cs.CL↗