arXiv subjects

Ruixin Yang

Publications and source records attributed to Ruixin Yang.

At least 19 recordsLinked to original sources

arXiv2026

Vacuum breakdown in a misaligned magnetized Kerr spacetime

Electron-positron ($e^{+}e^{-}$) pair creation by vacuum breakdown around compact objects is believed to power high-energy astrophysical transients like gamma-ray bursts (GRBs). In this work, we focus on vacuum breakdown around a Kerr black hole (BH) immersed in an asymptotically uniform magnetic field that is inclined with respect to the BH spin axis. The dyadoregion, the region where the induced electric field exceeds the critical value $E_{\text{c}}=m_{e}^{2}c^{3}/(e\hbar)$, is identified via the electromagnetic invariants. It is found that the dyadoregion consists of several lobes whose number, size, and orientation vary with the inclination. We also estimate the electromagnetic energy available for pair creation and derive a beaming factor that allows a conversion between the intrinsic dyadoregion energy and the observed isotropic energy. The thermodynamic properties of the resulting electron-positron-photon ($e^{+}e^{-}\gamma$) plasma are included, revealing an initial magnetic dominance. The evaluation of the minimum magnetic field required shows that misaligned magnetic fields generally favor pair creation more than aligned ones.

gr-qc↗

arXiv2026

Aerial IRS Deployment-Aided Secure Computation Offloading Against DISCO Jamming Attacks

With the rapid growth of Multi-access Edge Computing (MEC), secure and efficient computation offloading from user equipment (UEs) to edge access points (APs) is critical. However, DISCO intelligent reflective surface-based fully-passive jammers (DIRS-based FPJs) use random time-varying phase shifts to launch DISCO jamming attacks, disrupting offloading performance. This paper leverages an aerial intelligent reflective surface (AIRS) to enable secure computation offloading against DISCO jamming by jointly optimizing offloading ratios, AIRS phase shifts, and deployment. A two-timescale (2Ts) framework is proposed to address the optimization challenge caused by the distinct update frequencies of different strategies. Specifically, AIRS deployment is adjusted on a long timescale to boost antijamming capability due to the impracticality of frequent physical adjustment, while offloading ratios and phase shifts are optimized on a short timescale to adapt to DIRS-jammed dynamic channel conditions. We propose a dual-agent deep reinforcement learning (DRL)-based AIRS deployment-aided secure computation offloading (DDADSO) scheme to maximize the secure offloading utility under DISCO jamming. Simulation results verify that the proposed DDADSO scheme outperforms benchmark schemes, demonstrating the effectiveness of AIRS deployment in improving offloading performance against DISCO jamming attacks.

eess.SP↗

arXiv2026

Do Vision-Language Models Respect Contextual Integrity in Location Disclosure?

Vision-language models (VLMs) have demonstrated strong performance in image geolocation, a capability further sharpened by frontier multimodal large reasoning models (MLRMs). This poses a significant privacy risk, as these widely accessible models can be exploited to infer sensitive locations from casually shared photos, often at street-level precision, potentially surpassing the level of detail the sharer consented or intended to disclose. While recent work has proposed applying a blanket restriction on geolocation disclosure to combat this risk, these measures fail to distinguish valid geolocation uses from malicious behavior. Instead, VLMs should maintain contextual integrity by reasoning about elements within an image to determine the appropriate level of information disclosure, balancing privacy and utility. To evaluate how well models respect contextual integrity, we introduce VLM-GEOPRIVACY, a benchmark that challenges VLMs to interpret latent social norms and contextual cues in real-world images and determine the appropriate level of location disclosure. Our evaluation of 14 leading VLMs shows that, despite their ability to precisely geolocate images, the models are poorly aligned with human privacy expectations. They often over-disclose in sensitive contexts and are vulnerable to prompt-based attacks. Our results call for new design principles in multimodal systems to incorporate context-conditioned privacy reasoning.

cs.CR↗

arXiv2025

Effects of tidal charge on Blandford-Znajek process around braneworld black holes

The Blandford-Znajek (BZ) process is a pivotal mechanism to efficiently extract the energy from a rotating black hole (BH) via its plasma-filled magnetosphere in relativistic astrophysics. Within the framework of extended BZ monopole expansion, we have studied BZ process in the Randall-Sundrum braneworld BH spacetime and analyzed effects of the tidal charge on the energy and angular momentum extraction rates. It is found that the positive tidal charge reduces the BZ power of a braneworld BH, while the negative tidal charge enhances the power. Compared with a Kerr BH of the same mass and angular velocity, the BZ power exhibits a maximum reduction of approximately $15.2\%$ in positive cases, whereas in negative cases, it achieves a maximum enhancement of $66.5\%$ in power output. A similar qualitative trend is also observed for the relative angular momentum extraction rate, albeit with different magnitudes.

gr-qc↗

arXiv2024

Confidence Calibration and Rationalization for LLMs via Multi-Agent Deliberation

Uncertainty estimation is a significant issue for current large language models (LLMs) that are generally poorly calibrated and over-confident, especially with reinforcement learning from human feedback (RLHF). Unlike humans, whose decisions and confidences not only stem from intrinsic beliefs but can also be adjusted through daily observations, existing calibration methods for LLMs focus on estimating or eliciting individual confidence without taking full advantage of the "Collective Wisdom": the interaction among multiple LLMs that can collectively improve both accuracy and calibration. In this work, we propose Collaborative Calibration, a post-hoc training-free calibration strategy that leverages the collaborative and expressive capabilities of multiple tool-augmented LLM agents in a simulated group deliberation process. We demonstrate the effectiveness of Collaborative Calibration on generative QA tasks across various domains, showing its potential in harnessing the rationalization of collectively calibrated confidence assessments and improving the reliability of model predictions.

cs.CL↗

arXiv2024

Large-eddy simulation of turbulent spray flames: Effects of scalar correlation and enthalpy reduction in flamelet modeling

Numerical modeling of turbulent spray combustion provides a promising tool for advanced engine design. In spray flames, the droplet evaporation not only reduces the ambient gas temperature, but also influences flame structure by generating substantial local fluctuations of the mixture fraction $\widetilde{Z}$ and progress variable $\widetilde{c}$. These two scalars, conventionally assumed independent in flamelet models, exhibit significant correlations arising from the coupling among evaporation, turbulent mixing and chemical reactions. This study proposes a six-dimensional flamelet-generated manifolds (FGM) method, considering the evaporation-induced specific enthalpy reduction and scalar correlation. A novel joint presumed probability density function (PDF) method is derived using the copula theory, achieving rapid grid convergence and good feasibility. Large-eddy simulation (LES) is performed on the Sydney ethanol turbulent spray flames (EtF1, EtF4 and EtF7), which feature different ethanol mass flow rates and jet Reynolds numbers. Both gas and liquid phase statistics show good agreement with experimental data across the three flames. The incorporation of specific enthalpy reduction and scalar correlation in FGM modeling improves gas temperature predictions, along with enhanced liquid-phase prediction through refined gas-field resolution. The correlation coefficient of $\widetilde{Z}$ and $\widetilde{c}$ is found to be a competing result of local evaporation and combustion, since evaporation elevates $\widetilde{Z}$ and dilutes reaction products, whereas chemical reactions enhance $\widetilde{c}$ fluctuations.

physics.flu-dyn↗

arXiv2023

A comprehensive study on the accuracy and generalization of deep learning-generated chemical ODE integrators

The application of deep neural networks (DNNs) holds considerable promise as a substitute for the direct integration of chemical source terms in combustion simulations. However, challenges persist in ensuring high precision and generalisation across various different fuels and flow conditions. In this study, we propose and validate a consistent DNN approach for chemistry integration in a range of fuels and premixed flame configurations. This approach generates thermochemical base state from a set of low-dimensional laminar flames, followed by an effective perturbation strategy to enhance the coverage of the composition space for higher generalisation ability. A constraint criterion based on heat release rate is then employed to remove the nonphysical perturbed states for improved accuracy.Without specific tuning, three DNNs are consistently trained for three representative fuels, i.e., hydrogen, ethylene and Jet-A. Comprehensive validations are conducted using 1-D laminar flames and two typical turbulent premixed flames. The DNN model predictions on various physical characteristics, including laminar and turbulent flame speeds, dynamic flame structures influenced by turbulence-chemistry interactions, and conditional scalar profiles, all exhibit good agreement with the results obtained from direct integration. This demonstrates the exceptional accuracy and generalisation ability of the proposed DNN approach. Furthermore, when the DNN is used in the simulation, a significant speed-up for the chemistry integration is achieved, approximately 50 for the ethylene/air flame and 90 for the Jet-A/air flame.

physics.flu-dyn↗

arXiv2023

GPU-accelerated Large Eddy Simulation of turbulent stratified flames with machine learning chemistry

Stratified premixed combustion, known for its capability to expand flammability limits and reduce overall-lean combustion instability, has been widely adopted to comply with increasingly stringent environmental regulations. Numerous numerical simulations with different combustion models and mesh resolutions have been conducted on laboratory-scale flames to further understand the stratified premixed combustion. However, the trade-off between the high-fidelity and low computational cost for simulating laboratory-scale flames still remains, particularly for those combustion models involving direct coupling of chemistry and flow. In the present study, a GPU-based solver is employed to solve partial differential equations and calculate the thermal and transport properties, while an artificial neural network (ANN) is introduced to replace reaction rate calculation. Particular emphasis is placed on evaluating the proposed GPU-ANN approach through the large eddy simulation of the Cambridge stratified flame. The simulation results show good agreement for the flow and flame statistics between the GPU-ANN approach and the conventional CPU-based solver with direct integration (DI). The comparison suggests that the GPU-ANN approach can achieve the same level of accuracy as the conventional CPU-DI solver. In addition, the overall speed-up factor for the GPU-ANN approach is over two orders of magnitude. This study lays the potential groundwork for fully resolved laboratory-scale flame simulations based on detailed chemistry with much more affordable computational cost.

physics.flu-dyn↗

arXiv2023

Detailed simulation of LOX/GCH4 flame-vortex interaction in supercritical Taylor-Green flows with machine learning

Accurate and affordable simulation of supercritical reacting flow is of practical importance for developing advanced engine systems for liquid rockets, heavy-duty powertrains, and next-generation gas turbines. In this work, we present detailed numerical simulations of LOX/GCH4 flame-vortex interaction under supercritical conditions. The well-established benchmark configuration of three-dimensional Taylor-Green vortex (TGV) embedded with a diffusion flame is modified for real fluid simulations. Both ideal gas and Peng-Robinson (PR) cubic equation of states are studied to reveal the real fluid effects on the TGV evolution and flame-vortex interaction. The results show intensified flame stretching and quenching arising from the intrinsic large density gradients of real gases, as compared to that for the idea gases. Furthermore, to reduce the computational cost associated with real fluid thermophysical property calculations, a machine learning-based strategy utilising deep neural networks (DNNs) is developed and then assessed using the three-dimensional reactive TGV. Generally good prediction accuracy is achieved by the DNN, meanwhile providing a computational speed-up of 13 times over the convectional approach. The profound physics involved in flame-vortex interaction under supercritical conditions demonstrated by this study provides a benchmark for future related studies, and the machine learning modelling approach proposed is promising for practical high-fidelity simulation of supercritical combustion.

physics.flu-dyn↗

arXiv2023

Visual Analytics for Generative Transformer Models

While transformer-based models have achieved state-of-the-art results in a variety of classification and generation tasks, their black-box nature makes them challenging for interpretability. In this work, we present a novel visual analytical framework to support the analysis of transformer-based generative networks. In contrast to previous work, which has mainly focused on encoder-based models, our framework is one of the first dedicated to supporting the analysis of transformer-based encoder-decoder models and decoder-only models for generative and classification tasks. Hence, we offer an intuitive overview that allows the user to explore different facets of the model through interactive visualization. To demonstrate the feasibility and usefulness of our framework, we present three detailed case studies based on real-world NLP research problems.

cs.CL↗

arXiv2023

Training Socially Aligned Language Models on Simulated Social Interactions

Social alignment in AI systems aims to ensure that these models behave according to established societal values. However, unlike humans, who derive consensus on value judgments through social interaction, current language models (LMs) are trained to rigidly replicate their training corpus in isolation, leading to subpar generalization in unfamiliar scenarios and vulnerability to adversarial attacks. This work presents a novel training paradigm that permits LMs to learn from simulated social interactions. In comparison to existing methodologies, our approach is considerably more scalable and efficient, demonstrating superior performance in alignment benchmarks and human evaluations. This paradigm shift in the training of LMs brings us a step closer to developing AI systems that can robustly and accurately reflect societal norms and values.

cs.CL↗

arXiv2023

Robust Power Allocation for Integrated Visible Light Positioning and Communication Networks

Integrated visible light positioning and communication (VLPC), capable of combining advantages of visible light communications (VLC) and visible light positioning (VLP), is a promising key technology for the future Internet of Things. In VLPC networks, positioning and communications are inherently coupled, which has not been sufficiently explored in the literature. We propose a robust power allocation scheme for integrated VLPC Networks by exploiting the intrinsic relationship between positioning and communications. Specifically, we derive explicit relationships between random positioning errors, following both a Gaussian distribution and an arbitrary distribution, and channel state information errors. Then, we minimize the Cramer-Rao lower bound (CRLB) of positioning errors, subject to the rate outage constraint and the power constraints, which is a chance-constrained optimization problem and generally computationally intractable. To circumvent the nonconvex challenge, we conservatively transform the chance constraints to deterministic forms by using the Bernstein-type inequality and the conditional value-at-risk for the Gaussian and arbitrary distributed positioning errors, respectively, and then approximate them as convex semidefinite programs. Finally, simulation results verify the robustness and effectiveness of our proposed integrated VLPC design schemes.

cs.IT↗

arXiv2023

Robust Power Allocation for UAV-aided ISAC Systems with Uncertain Location Sensing Errors

Unmanned aerial vehicle (UAV) holds immense potential in integrated sensing and communication (ISAC) systems for the Internet of Things (IoT). In this paper, we propose a UAV-aided ISAC framework and investigate three robust power allocation schemes. First, we derive an explicit expression of the Cram\'er-Rao bound (CRB) based on time-of-arrival (ToA) estimation, which serves as the performance metric for location sensing. Then, we analyze the impact of the location sensing error (LSE) on communications, revealing the inherent coupling relationship between communication and sensing. Moreover, we formulate three robust communication and sensing power allocation problems by respectively characterizing the LSE as an ellipsoidal distributed model, a Gaussian distributed model, and an arbitrary distributed model. Notably, the optimization problems seek to minimize the CRB, subject to data rate and total power constraints. However, these problems are non-convex and intractable. To address the challenges related to the three aforementioned LSE models, we respectively propose to use the ${\cal{S}}$-Procedure and alternating optimization (${\cal{S}}$-AO) method, Bernstein-type inequality and successive convex approximation (BI-SCA) method, and conditional value-at-risk (CVaR) and AO (CVaR-AO) method to solve these problems. Finally, simulation results demonstrate the robustness of our proposed UAV-aided ISAC system against the LSE by comparing with the non-robust design, and evaluate the trade-off between communication and sensing in the ISAC system.

eess.SY↗

arXiv2022

Optimal Power Allocation for Integrated Visible Light Positioning and Communication System with a Single LED-Lamp

In this paper, we investigate an integrated visible light positioning and communication (VLPC) system with a single LED-lamp. First, by leveraging the fact that the VLC channel model is a function of the receiver's location, we propose a system model that estimates the channel state information (CSI) based on the positioning information without transmitting pilot sequences. Second, we derive the Cramer-Rao lower bound (CRLB) on the positioning error variance and a lower bound on the achievable rate with on-off keying modulation. Third, based on the derived performance metrics, we optimize the power allocation to minimize the CRLB, while satisfying the rate outage probability constraint. To tackle this non-convex optimization problem, we apply the worst-case distribution of the Conditional Value-at-Risk (CVaR) and the block coordinate descent (BCD) methods to obtain the feasible solutions. Finally, the effects of critical system parameters, such as outage probability, rate threshold, total power threshold, are revealed by numerical results.

cs.IT↗

arXiv2022

Covert Beamforming Design for Integrated Radar Sensing and Communication Systems

We propose covert beamforming design frameworks for integrated radar sensing and communication (IRSC) systems, where the radar can covertly communicate with legitimate users under the cover of the probing waveforms without being detected by the eavesdropper. Specifically, by jointly designing the target detection beamformer and communication beamformer, we aim to maximize the radar detection mutual information (MI) (or the communication rate) subject to the covert constraint, the communication rate constraint (or the radar detection MI constraint), and the total power constraint. For the perfect eavesdropper's channel state information (CSI) scenario, we transform the covert beamforming design problems into a series of convex subproblems, by exploiting semidefinite relaxation, which can be solved via the bisection search method. Considering the high complexity of iterative optimization, we further propose a single-iterative covert beamformer design scheme based on the zero-forcing criterion. For the imperfect eavesdropper's CSI scenario, we develop a relaxation and restriction method to tackle the robust covert beamforming design problems. Simulation results demonstrate the effectiveness of the proposed covert beamforming schemes for perfect and imperfect CSI scenarios.

cs.IT↗

arXiv2022

Robust Beamforming for Localization-Aided Millimeter Wave Communication Systems

In this letter, we investigate a robust beamforming problem for localization-aided millimeter wave (mmWave) communication systems. To handle this problem, we propose a novel restriction and relaxation (R&R) method. The proposed R&R method aims at minimizing the total transmit power while the positioning error follows a Gaussian distribution. Specifically, in the restriction phase of R&R, the probabilistic constraint is transformed into the deterministic form by using the Bernsteintype inequality. In the relaxation phase of R&R, the non-convex optimization problem is reformulated into a convex semidefinite program (SDP) by using semidefinite relaxation (SDR) and firstorder Taylor expansion methods. To the best of our knowledge, we first consider the impact of the distribution of the positioning error on the channel state information (CSI), which further influences the data rate. Numerical results present the trade-off of the beamforming between the communication and positioning.

cs.IT↗

arXiv2022

Spectral and Energy Efficiency of DCO-OFDM in Visible Light Communication Systems with Finite-Alphabet Inputs

The bound of the information transmission rate of direct current biased optical orthogonal frequency division multiplexing (DCO-OFDM) for visible light communication (VLC) with finite-alphabet inputs is yet unknown, where the corresponding spectral efficiency (SE) and energy efficiency (EE) stems out as the open research problems. In this paper, we derive the exact achievable rate of {the} DCO-OFDM system with finite-alphabet inputs for the first time. Furthermore, we investigate SE maximization problems of {the} DCO-OFDM system subject to both electrical and optical power constraints. By exploiting the relationship between the mutual information and the minimum mean-squared error, we propose a multi-level mercury-water-filling power allocation scheme to achieve the maximum SE. Moreover, the EE maximization problems of {the} DCO-OFDM system are studied, and the Dinkelbach-type power allocation scheme is developed for the maximum EE. Numerical results verify the effectiveness of the proposed theories and power allocation schemes.

cs.IT↗

arXiv2021

Optimal Discrete Constellation Inputs for Aggregated LiFi-WiFi Networks

In this paper, we investigate the performance of a practical aggregated LiFi-WiFi system with the discrete constellation inputs from a practical view. We derive the achievable rate expressions of the aggregated LiFi-WiFi system for the first time. Then, we study the rate maximization problem via optimizing the constellation distribution and power allocation jointly. Specifically, a multilevel mercy-filling power allocation scheme is proposed by exploiting the relationship between the mutual information and minimum mean-squared error (MMSE) of discrete inputs. Meanwhile, an inexact gradient descent method is proposed for obtaining the optimal probability distributions. To strike a balance between the computational complexity and the transmission performance, we further develop a framework that maximizes the lower bound of the achievable rate where the optimal power allocation can be obtained in closed forms and the constellation distributions problem can be solved efficiently by Frank-Wolfe method. Extensive numerical results show that the optimized strategies are able to provide significant gains over the state-of-the-art schemes in terms of the achievable rate.

cs.IT↗