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Yue Chen

Publications and source records attributed to Yue Chen.

At least 127 records · Page 7Linked to original sources

Thermal transport of amorphous hafnia across the glass transition

Heat transport in glasses across a wide range of temperature is vital for applications in gate dielectrics and heat insulator. However, it remains poorly understood due to the challenges of modeling vibrational anharmonicity below glass transition temperature and capturing configurational dynamics across the transition. Interestingly, recent calculations predicted that amorphous hafnia (a-HfO$_2$) exhibits an unusual drop in thermal conductivity ($κ$) with temperature, contrasting with the typical rise or saturation observed in glasses upon heating. Using molecular dynamics simulations with a machine-learning-based neuroevolution potential, we compute the vibrational properties and $κ$ of a-HfO$_2$ from 50~K to 2000~K. At low temperatures, we employ the Wigner transport equation to incorporate both anharmonicity and Bose-Einstein statistics of atomic vibration in the calculation of $κ$. At above 1200~K, atomic diffusion breaks down the Lorentzian-shaped quasiparticle picture and makes the lattice-dynamics treatment invalid. We thus use molecular dynamics with the Green-Kubo method to capture convective heat transport in a-HfO$_2$ near the glass transition at around 1500~K. Additionally, by extending the Wigner transport equation to supercooled liquid states, we find the crucial role of low-frequency modes in facilitating heat convection. The computed $κ$ of a-HfO$_2$, based on both Green-Kubo and Wigner transport theories, reveals a continuous increase with temperature up to 2000~K.

cond-mat.mtrl-sci↗

Anisotropic galvanomagnetic effects in single-crystal Fe(001) films elucidated by a phenomenological theory

Utilizing the phenomenological theory based on crystal symmetry operation, we have established the complete angular dependencies of the galvanomagnetic effects, encompassing both anisotropic magnetoresistance (AMR) and the planar Hall effect (PHE), for the ferromagnetic films with C4v symmetry. These dependencies were experimentally confirmed via comprehensive angular-mapping of AMR and PHE in single-crystal Fe(001) films at room temperature. We demonstrated that the intrinsic magnetization-induced effects are independent of the field strength by carefully separating the field-induced and magnetization-induced galvanomagnetic effects. Our theoretical and experimental findings highlight the absence of in-plane four-fold angular dependence in PHE, a feature prohibited by the Onsager relation in systems with C4 symmetry. This study affirms that the universal angular dependencies of AMR and PHE in single crystals can be accurately predicted by the conventional phenomenological theory.

cond-mat.mtrl-sci↗

Robust Microgrid Dispatch with Real-Time Energy Sharing and Endogenous Uncertainty

With the rising adoption of distributed energy resources (DERs), microgrid dispatch is facing new challenges: DER owners are independent stakeholders seeking to maximize their individual profits rather than being controlled centrally; and the dispatch of renewable generators may affect the microgrid's exposure to uncertainty. To address these challenges, this paper proposes a two-stage robust microgrid dispatch model with real-time energy sharing and endogenous uncertainty. In the day-ahead stage, the connection/disconnection of renewable generators is optimized, which influences the size and dimension of the uncertainty set. As a result, the uncertainty set is endogenously given. In addition, non-anticipative operational bounds for energy storage (ES) are derived to enable the online operation of ES in real-time. In the real-time stage, DER owners (consumers and prosumers) share energy with each other via a proposed energy sharing mechanism, which forms a generalized Nash game. To solve the robust microgrid dispatch model, we develop an equivalent optimization model to compute the real-time energy sharing equilibrium. Based on this, a projection-based column-and-constraint generation (C&CG) method is proposed to handle the endogenous uncertainty. Numerical experiments show the effectiveness and advantages of the proposed model and method.

math.OC↗

On the Sum Rate and User Fairness of STAR-RIS Aided Communications

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided communication system is investigated. A robust joint beamforming design problem under the imperfect channel state information (CSI) is formulated to maximize the weighted sum of the Jain's fairness index and the normalized system sum rate. To solve this non-convex problem, an alternating optimization (AO) algorithm is proposed, which leverages the S-Procedure, successive convex approximation (SCA), and semidefinite relaxation (SDR). Simulation results demonstrate that with proposed algorithm: 1) various trade-offs between sum rate and user fairness can be achieved; 2) a larger trade-off region can be achieved by adopting STAR-RIS compared to conventional RIS; and 3) the performance degradation caused by imperfect CSI is less than 7% with our proposed robust beamforming approach.

eess.SP↗

A capacity renting framework for shared energy storage considering peer-to-peer energy trading of prosumers with privacy protection

Shared energy storage systems (ESS) present a promising solution to the temporal imbalance between energy generation from renewable distributed generators (DGs) and the power demands of prosumers. However, as DG penetration rates rise, spatial energy imbalances become increasingly significant, necessitating the integration of peer-to-peer (P2P) energy trading within the shared ESS framework. Two key challenges emerge in this context: the absence of effective mechanisms and the greater difficulty for privacy protection due to increased data communication. This research proposes a capacity renting framework for shared ESS considering P2P energy trading of prosumers. In the proposed framework, prosumers can participate in P2P energy trading and rent capacities from shared ESS. A generalized Nash game is formulated to model the trading process and the competitive interactions among prosumers, and the variational equilibrium of the game is proved to be equivalent to the optimal solution of a quadratic programming (QP) problem. To address the privacy protection concern, the problem is solved using the alternating direction method of multipliers (ADMM) with the Paillier cryptosystem. Finally, numerical simulations demonstrate the impact of P2P energy trading on the shared ESS framework and validate the effectiveness of the proposed privacy-preserving algorithm.

eess.SY↗

Online Low-Carbon Workload, Energy, and Temperature Management of Distributed Data Centers

Data centers have become one of the major energy consumers, making their low-carbon operations critical to achieving global carbon neutrality. Although distributed data centers have the potential to reduce costs and emissions through cooperation, they are facing challenges due to uncertainties. This paper proposes an online approach to co-optimize the workload, energy, and temperature strategies across distributed data centers, targeting minimal total cost, controlled carbon emissions, and adherence to operational constraints. Lyapunov optimization technique is adopted to derive a parametric real-time strategy that accommodates uncertainties in workload demands, ambient temperature, electricity prices, and carbon intensities, without requiring prior knowledge of their distributions. A theoretical upper bound for the optimality gap is derived, based on which a linear programming problem is proposed to optimize the strategy parameters, enhancing performance while ensuring operational constraints. Case studies and method comparison validate the proposed method's effectiveness in reducing costs and carbon emissions.

eess.SY↗

Brain-to-Text Benchmark '24: Lessons Learned

Speech brain-computer interfaces aim to decipher what a person is trying to say from neural activity alone, restoring communication to people with paralysis who have lost the ability to speak intelligibly. The Brain-to-Text Benchmark '24 and associated competition was created to foster the advancement of decoding algorithms that convert neural activity to text. Here, we summarize the lessons learned from the competition ending on June 1, 2024 (the top 4 entrants also presented their experiences in a recorded webinar). The largest improvements in accuracy were achieved using an ensembling approach, where the output of multiple independent decoders was merged using a fine-tuned large language model (an approach used by all 3 top entrants). Performance gains were also found by improving how the baseline recurrent neural network (RNN) model was trained, including by optimizing learning rate scheduling and by using a diphone training objective. Improving upon the model architecture itself proved more difficult, however, with attempts to use deep state space models or transformers not yet appearing to offer a benefit over the RNN baseline. The benchmark will remain open indefinitely to support further work towards increasing the accuracy of brain-to-text algorithms.

cs.CL↗

Lattice distortion leads to glassy thermal transport in crystalline Cs$_3$Bi$_2$I$_6$Cl$_3$

The glassy thermal conductivities observed in crystalline inorganic perovskites such as Cs$_3$Bi$_2$I$_6$Cl$_3$ is perplexing and lacking theoretical explanations. Here, we first experimentally measure such its thermal transport behavior from 20~K to 300~K, after synthesizing Cs$_3$Bi$_2$I$_6$Cl$_3$ single crystals. Using path-integral molecular dynamics simulations driven by machine learning potentials, we reveal that Cs$_3$Bi$_2$I$_6$Cl$_3$ has large lattice distortions at low temperatures, which may be related to the large atomic size mismatch. Employing the Wigner formulation of thermal transport, we reproduce the experimental thermal conductivities based on lattice-distorted structures. This study thus provides a framework for predicting and understanding glassy thermal transport in materials with strong lattice disorder.

cond-mat.mtrl-sci↗

A Differentially Private Quantum Neural Network for Probabilistic Optimal Power Flow

The stochastic nature of renewable energy and load demand requires efficient and accurate solutions for probabilistic optimal power flow (OPF). Quantum neural networks (QNNs), which combine quantum computing and machine learning, offer computational advantages in approximating OPF by effectively handling high-dimensional data. However, adversaries with access to non-private OPF solutions can potentially infer sensitive load demand patterns, raising significant privacy concerns. To address this issue, we propose a privacy-preserving QNN model for probabilistic OPF approximation. By incorporating Gaussian noise into the training process, the learning algorithm achieves ($\varepsilon, δ$)-differential privacy with theoretical guarantees. Moreover, we develop a strongly entangled quantum state to enhance the nonlinearity expressiveness of the QNN. Experimental results demonstrate that the proposed method successfully prevents privacy leakage without compromising the statistical properties of probabilistic OPF. Moreover, compared to classical private neural networks, the QNN reduces the number of parameters by 90% while achieving significantly higher accuracy and greater stability.

eess.SY↗

Joint Beamforming for STAR-RIS in Near-Field Communications

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided near-field multiple-input multiple-output (MIMO) communication framework is proposed. A weighted sum rate maximization problem for the joint optimization of the active beamforming at the base station (BS) and the transmission/reflection-coefficients (TRCs) at the STAR-RIS is formulated. The resulting non-convex problem is solved by the developed block coordinate descent (BCD)-based algorithm. Numerical results illustrate that the near-field beamforming for the STAR-RIS aided MIMO communications significantly improve the achieved weighted sum rate.

eess.SP↗

STAR-RIS Aided Integrated Sensing, Computing, and Communication for Internet of Robotic Things

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided integrated sensing, computing, and communication (ISCC) Internet of Robotic Things (IoRT) framework is proposed. Specifically, the full-duplex (FD) base station (BS) simultaneously receives the offloading signals from decision robots (DRs) and carries out target robot (TR) sensing. A computation rate maximization problem is formulated to optimize the sensing and receive beamformers at the BS and the STAR-RIS coefficients under the BS power constraint, the sensing signal-to-noise ratio constraint, and STAR-RIS coefficients constraints. The alternating optimization (AO) method is adopted to solve the proposed optimization problem. With fixed STAR-RIS coefficients, the sub-problem with respect to sensing and receiving beamformer at the BS is tackled with the weighted minimum mean-square error method. Given beamformers at the BS, the sub-problem with respect to STAR-RIS coefficients is tacked with the penalty method and successive convex approximation method. The overall algorithm is guaranteed to converge to at least a stationary point of the computation rate maximization problem. Our simulation results validate that the proposed STAR-RIS aided ISCC IoRT system can enhance the sum computation rate compared with the benchmark schemes.

eess.SP↗

STAR-RIS in Cognitive Radio Networks

The development of sixth-generation (6G) communication technologies is confronted with the significant challenge of spectrum resource shortage. To alleviate this issue, we propose a novel simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided multiple-input multiple-output (MIMO) cognitive radio (CR) system. Specifically, the underlying secondary network in the proposed CR system reuses the same frequency resources occupied by the primary network with the help of the STAR-RIS. The secondary network sum rate maximization problem is first formulated for the STAR-RIS aided MIMO CR system. The adoption of STAR-RIS necessitates an intricate beamforming design for the considered system due to its large number of coupled coefficients. The block coordinate descent method is employed to address the formulated optimization problem. In each iteration, the beamformers at the secondary base station (SBS) are optimized by solving a quadratically constrained quadratic program (QCQP) problem. Concurrently, the STAR-RIS passive beamforming problem is resolved using tailored algorithms designed for the two phase-shift models: 1) For the independent phase-shift model, a successive convex approximation-based algorithm is proposed. 2) For the coupled phase-shift model, a penalty dual decomposition-based algorithm is conceived, in which the phase shifts and amplitudes of the STAR-RIS elements are optimized using closed-form solutions. Simulation results show that: 1) The proposed STAR-RIS aided CR communication framework can significantly enhance the sum rate of the secondary system. 2) The coupled phase-shift model results in limited performance degradation compared to the independent phase-shift model.

eess.SP↗

Real-time Hosting Capacity Assessment for Electric Vehicles: A Sequential Forecast-then-Optimize Method

Hosting capacity (HC) assessment for electric vehicles (EVs) is crucial for EV secure integration and reliable power system operation. Existing methods primarily focus on a long-term perspective (e.g., system planning), and consider the EV charging demands as scalar values, which introduces inaccuracies in real-time operations due to the inherently stochastic nature of EVs. In this regard, this paper proposes a real-time HC assessment method for EVs through a three-step process, involving real-time probabilistic forecasting, risk analysis and probabilistic optimization. Specifically, we conduct real-time probabilistic forecasting to capture the stochastic nature of EV charging demands across multiple charging stations by performing deterministic forecasting and fitting the distribution of forecasting errors. The deterministic forecasting is conducted using an adaptive spatio-temporal graph convolutional network (ASTGCN). ASTGCN leverages adaptive spatial feature extraction, attention-based temporal feature extraction, and second-order graph representation to improve the forecasting performance. Subsequently, based on the probabilistic forecasting of EV charging demands, we conduct real-time risk analysis and operational boundary identification by utilizing probabilistic power flow calculations to assess potential violations of secure operation constraints. Furthermore, we present the formulation of real-time HC of EVs considering expected satisfaction of stochastic EV charging demands, and propose an optimization model for real-time HC assessment of EVs. Numerical experiments on a real-world dataset demonstrate that the proposed ASTGCN model outperforms state-of-the-art forecasting models by achieving the lowest root mean square error of 0.0442, and the real-time HC is improved by 64% compared to long-term HC assessment.

math.OC↗

Self-testing quantum randomness expansion on an integrated photonic chip

The power of quantum random number generation is more than just the ability to create truly random numbers$\unicode{x2013}$it can also enable self-testing, which allows the user to verify the implementation integrity of certain critical quantum components with minimal assumptions. In this work, we develop and implement a self-testing quantum random number generator (QRNG) chipset capable of generating 15.33 Mbits of certifiable randomness in each run (an expansion rate of $5.11\times 10^{-4}$ at a repetition rate of 10 Mhz). The chip design is based on a highly loss-and-noise tolerant measurement-device-independent protocol, where random coherent states encoded using quadrature phase shift keying are used to self-test the quantum homodyne detection unit: well-known to be challenging to characterise in practice. Importantly, this proposal opens up the possibility to implement miniaturised self-testing QRNG devices at production scale using standard silicon photonics foundry platforms.

quant-ph↗

ElectionSim: Massive Population Election Simulation Powered by Large Language Model Driven Agents

The massive population election simulation aims to model the preferences of specific groups in particular election scenarios. It has garnered significant attention for its potential to forecast real-world social trends. Traditional agent-based modeling (ABM) methods are constrained by their ability to incorporate complex individual background information and provide interactive prediction results. In this paper, we introduce ElectionSim, an innovative election simulation framework based on large language models, designed to support accurate voter simulations and customized distributions, together with an interactive platform to dialogue with simulated voters. We present a million-level voter pool sampled from social media platforms to support accurate individual simulation. We also introduce PPE, a poll-based presidential election benchmark to assess the performance of our framework under the U.S. presidential election scenario. Through extensive experiments and analyses, we demonstrate the effectiveness and robustness of our framework in U.S. presidential election simulations.

cs.CL↗

Revisit Many-body Interaction Heat Current and Thermal Conductivity Calculation in Moment Tensor Potential/LAMMPS Interface

The definition of heat current operator for systems for non-pairwise additive interactions and its impact on related lattice thermal conductivity ($κ_{L}$) via molecular dynamics simulation (MD) are ambiguous and controversial when migrating from conventional empirical potential models to machine learning potential (MLP) models. Empirical model descriptions are often limited to three- to four-body interaction while a sophisticated representation of the many-body physics could be resembled in MLPs. Herein, we study and compare the significance of many-body interaction to the heat current computation in one of the most popular MLP models, the Moment Tensor Potential (MTP). Non-equilibrium MD simulations and equilibrium MD simulations among four different materials, $PbTe$, amorphous $Sc_{0.2}Sb_{2}Te_{3}$, graphene, and $BAs$, were performed. We found inconsistency between the simulation thermostat and its implemented heat current operator in our non-equilibrium MD results which violate law of energy conservation and suggest a need for revision. We revisit the virial stress tensor expression within the calculator and identified the lack of a generalised many-body heat current description in it. We uncover the influence of the modified heat current formula that could alter the $κ_{L}$ results 29% to 64% using the equilibrium MD computational approach. Our work demonstrates the importance of a many-body description during thermal analysis in MD simulations when MLPs are in concern. This work sheds light on a better understanding of the relationship between interatomic interaction and its heat transport mechanism.

cond-mat.mtrl-sci↗

Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods

With extensive pre-trained knowledge and high-level general capabilities, large language models (LLMs) emerge as a promising avenue to augment reinforcement learning (RL) in aspects such as multi-task learning, sample efficiency, and high-level task planning. In this survey, we provide a comprehensive review of the existing literature in LLM-enhanced RL and summarize its characteristics compared to conventional RL methods, aiming to clarify the research scope and directions for future studies. Utilizing the classical agent-environment interaction paradigm, we propose a structured taxonomy to systematically categorize LLMs' functionalities in RL, including four roles: information processor, reward designer, decision-maker, and generator. For each role, we summarize the methodologies, analyze the specific RL challenges that are mitigated, and provide insights into future directions. Lastly, a comparative analysis of each role, potential applications, prospective opportunities, and challenges of the LLM-enhanced RL are discussed. By proposing this taxonomy, we aim to provide a framework for researchers to effectively leverage LLMs in the RL field, potentially accelerating RL applications in complex applications such as robotics, autonomous driving, and energy systems.

cs.LG↗

Exact Potts/Tutte Polynomials for Hammock Chain Graphs

We present exact calculations of the $q$-state Potts model partition functions and the equivalent Tutte polynomials for chain graphs comprised of $m$ repeated hammock subgraphs $H_{e_1,...,e_r}$ connected with line graphs of length $e_g$ edges, such that the chains have open or cyclic boundary conditions (BC). Here, $H_{e_1,...,e_r}$ is a hammock (series-parallel) subgraph with $r$ separate paths along ``ropes'' with respective lengths $e_1, ..., e_r$ edges, connecting the two end vertices. We denote the resultant chain graph as $G_{\{e_1,...,e_r\},e_g,m;BC}$. We discuss special cases, including chromatic, flow, and reliability polynomials. In the case of cyclic boundary conditions, the zeros of the Potts partition function in the complex $q$ function accumulate, in the limit $m \to \infty$, onto curves forming a locus ${\cal B}$, and we study this locus.

cond-mat.stat-mech↗