arXiv subjects

Cong Zhang

Publications and source records attributed to Cong Zhang.

At least 73 records · Page 4Linked to original sources

arXiv2025

Covariant effective spacetimes of spherically symmetric electrovacuum with a cosmological constant

An algebraic framework was introduced in our previous works to address the covariance issue in spherically symmetric effective quantum gravity. This paper extends the framework to the electrovacuum case with a cosmological constant. After analyzing the notion of covariance in the classical theory, we propose an effective Hamiltonian for the electromagnetic field. The effective Hamiltonian together with the effective Hamiltonian constraint of gravity determines an effective dynamical model of gravity coupled to the electromagnetic field. The resulting model is covariant with respect to both the effective metric and the effective vector potential. By solving the equations of motion derived from the effective Hamiltonian constraint, we obtain several quantum-corrected solutions. Notably, some of these solutions reveal quantum gravity effects manifesting not only in spacetime metrics but also in the electromagnetic field. Finally, the covariance of coupling models with general matter fields is discussed.

gr-qc↗

arXiv2025

Dark Parton Shower Effects for Cosmic Ray Boosted Dark Matter

We investigate the dark parton shower effects in the direct detection of cosmic-ray boosted dark matter (CRDM), focusing on a dark photon-mediated model with fermionic dark matter-electron interactions. Utilizing a Monte Carlo framework to incorporate the Sudakov form factors and kinematic dipole recoil schemes, we simulate the CRDM energy spectrum evolution under the dark sector splitting. Our results reveal a significant energy-dependent modification of the CRDM flux. For a 1 keV dark matter (DM) mass and a coupling of $g_D=3$, the CRDM flux can be enhanced by a factor up to 1.12 in the $\mathcal{O}(10^{-2} \sim 1)$ MeV energy range for $2m_χ\lesssim m_{A^\prime} \lesssim 10^{-2}$ MeV, while it is suppressed by more than $50\%$ at energy around 100 MeV for $m_{A^\prime} \lesssim 10^{-3}$ MeV. We then translate these effects into the experimental sensitivities for PandaX-4T, Super-Kamiokande, and JUNO. At $m_{A^\prime} = 10^{-3}$ MeV and $g_D=3$, the bounds on the kinetic mixing parameter $ε^2$ are relaxed by factors of 1.02, 1.6 and 1.4, respectively. Finally, we demonstrate that the parameter space considered is consistent with those astrophysical constraints on dark matter self-interactions from observations of the Bullet Cluster.

hep-ph↗

arXiv2025

AIM 2025 Challenge on High FPS Motion Deblurring: Methods and Results

This paper presents a comprehensive review of the AIM 2025 High FPS Non-Uniform Motion Deblurring Challenge, highlighting the proposed solutions and final results. The objective of this challenge is to identify effective networks capable of producing clearer and visually compelling images in diverse and challenging conditions, by learning representative visual cues for complex aggregations of motion types. A total of 68 participants registered for the competition, and 9 teams ultimately submitted valid entries. This paper thoroughly evaluates the state-of-the-art advances in high-FPS single image motion deblurring, showcasing the significant progress in the field, while leveraging samples of the novel dataset, MIORe, that introduces challenging examples of movement patterns.

cs.CV↗

arXiv2025

Dust shell in effective loop quantum black hole model

In this work, the dynamics of a dust shell in an effective theory of spherically symmetric gravity containing quantum corrections from loop quantum gravity is investigated. To provide a consistent framework for including the dust, we go beyond the standard formulation of the effective theory by introducing an action that includes not only the effective Hamiltonian constraint, but also the diffeomorphism constraint, along with appropriate gauge-fixing and boundary terms. By adding the dust shell action and substituting vacuum solutions for the interior and exterior regions, we derive a reduced action in which only the shell radius $\mathfrak{x}$ and the exterior black hole mass appear as dynamical variables. Varying the reduced action yields the evolution equation for $\mathfrak{x}$, which is then solved numerically to explore the dynamical properties of the dust shell and the continuity properties of the metric. Finally, our approach is compared with previous studies to highlight key differences and improvements.

gr-qc↗

arXiv2025

Towards Integrated Energy-Communication-Transportation Hub: A Base-Station-Centric Design in 5G and Beyond

The rise of 5G communication has transformed the telecom industry for critical applications. With the widespread deployment of 5G base stations comes a significant concern about energy consumption. Key industrial players have recently shown strong interest in incorporating energy storage systems to store excess energy during off-peak hours, reducing costs and participating in demand response. The fast development of batteries opens up new possibilities, such as the transportation area. An effective method is needed to maximize base station battery utilization and reduce operating costs. In this trend towards next-generation smart and integrated energy-communication-transportation (ECT) infrastructure, base stations are believed to play a key role as service hubs. By exploring the overlap between base station distribution and electric vehicle charging infrastructure, we demonstrate the feasibility of efficiently charging EVs using base station batteries and renewable power plants at the Hub. Our model considers various factors, including base station traffic conditions, weather, and EV charging behavior. This paper introduces an incentive mechanism for setting charging prices and employs a deep reinforcement learning-based method for battery scheduling. Experimental results demonstrate the effectiveness of our proposed ECT-Hub in optimizing surplus energy utilization and reducing operating costs, particularly through revenue-generating EV charging.

cs.NI↗

arXiv2025

The Interpretability Analysis of the Model Can Bring Improvements to the Text-to-SQL Task

To elevate the foundational capabilities and generalization prowess of the text-to-SQL model in real-world applications, we integrate model interpretability analysis with execution-guided strategy for semantic parsing of WHERE clauses in SQL queries. Furthermore, we augment this approach with filtering adjustments, logical correlation refinements, and model fusion, culminating in the design of the CESQL model that facilitates conditional enhancement. Our model excels on the WikiSQL dataset, which is emblematic of single-table database query tasks, markedly boosting the accuracy of prediction outcomes. When predicting conditional values in WHERE clauses, we have not only minimized our dependence on data within the condition columns of tables but also circumvented the impact of manually labeled training data. Our hope is that this endeavor to enhance accuracy in processing basic database queries will offer fresh perspectives for research into handling complex queries and scenarios featuring irregular data in real-world database environments.

cs.AI↗

arXiv2025

Lifelong Learner: Discovering Versatile Neural Solvers for Vehicle Routing Problems

Deep learning has been extensively explored to solve vehicle routing problems (VRPs), which yields a range of data-driven neural solvers with promising outcomes. However, most neural solvers are trained to tackle VRP instances in a relatively monotonous context, e.g., simplifying VRPs by using Euclidean distance between nodes and adhering to a single problem size, which harms their off-the-shelf application in different scenarios. To enhance their versatility, this paper presents a novel lifelong learning framework that incrementally trains a neural solver to manage VRPs in distinct contexts. Specifically, we propose a lifelong learner (LL), exploiting a Transformer network as the backbone, to solve a series of VRPs. The inter-context self-attention mechanism is proposed within LL to transfer the knowledge obtained from solving preceding VRPs into the succeeding ones. On top of that, we develop a dynamic context scheduler (DCS), employing the cross-context experience replay to further facilitate LL looking back on the attained policies of solving preceding VRPs. Extensive results on synthetic and benchmark instances (problem sizes up to 18k) show that our LL is capable of discovering effective policies for tackling generic VRPs in varying contexts, which outperforms other neural solvers and achieves the best performance for most VRPs.

cs.LG↗

arXiv2025

DiffAD: A Unified Diffusion Modeling Approach for Autonomous Driving

End-to-end autonomous driving (E2E-AD) has rapidly emerged as a promising approach toward achieving full autonomy. However, existing E2E-AD systems typically adopt a traditional multi-task framework, addressing perception, prediction, and planning tasks through separate task-specific heads. Despite being trained in a fully differentiable manner, they still encounter issues with task coordination, and the system complexity remains high. In this work, we introduce DiffAD, a novel diffusion probabilistic model that redefines autonomous driving as a conditional image generation task. By rasterizing heterogeneous targets onto a unified bird's-eye view (BEV) and modeling their latent distribution, DiffAD unifies various driving objectives and jointly optimizes all driving tasks in a single framework, significantly reducing system complexity and harmonizing task coordination. The reverse process iteratively refines the generated BEV image, resulting in more robust and realistic driving behaviors. Closed-loop evaluations in Carla demonstrate the superiority of the proposed method, achieving a new state-of-the-art Success Rate and Driving Score.

cs.RO↗

arXiv2025

Adaptive Tool Use in Large Language Models with Meta-Cognition Trigger

Large language models (LLMs) have shown remarkable emergent capabilities, transforming the execution of functional tasks by leveraging external tools for complex problems that require specialized processing or up-to-date data. While existing research expands LLMs access to diverse tools (e.g., program interpreters, search engines, calculators), the necessity of using these tools is often overlooked, leading to indiscriminate tool invocation. This naive approach raises two key issues: increased latency due to unnecessary tool calls, and potential errors resulting from faulty interactions with external tools. In this paper, we introduce meta-cognition as a proxy for LLMs self-assessment of their capabilities, reflecting the model's awareness of its own limitations. Based on this, we propose MeCo, an adaptive decision-making strategy for external tool use. MeCo quantifies metacognitive scores by capturing high-level cognitive signals in the representation space, guiding when to invoke tools. Notably, MeCo is fine-tuning-free and incurs minimal cost. Experiments across multiple backbone models and benchmarks show that MeCo reliably detects LLMs' internal cognitive signals and significantly improves tool-use decision-making.

cs.CL↗

arXiv2025

Can Effective $4-$Quark Operators Describe Signals of a Supersymmetric Diquark Model at the LHC?

The Standard Model Effective Field Theory (SMEFT) is constrained by current LHC data. Supposedly extensions of the Standard Model (SM) involving heavy particles can be constrained by matching onto the SMEFT. However, the reliability of these indirect constraints compared to those derived directly from the UV model remains an open question. In this paper, we investigate whether $4-$quark operators can accurately capture the effects of an $R-$parity-violating (RPV) supersymmetric model on the production of pairs of top quarks, for parameters that satisfy all known constraints and lead to measurable effects. We assume that the sbottom is the lightest supersymmetric particle and focus on its interaction with a light quark and a top quark; the sbottom thus acts like a specific diquark. The $4-$quark operators arise by integrating out the sbottom at tree level. We analyze measurements of inclusive top pair production by the CMS and ATLAS collaborations. We find that the $4-$quark operators can accurately describe the RPV model's effects only for very heavy sbottom squarks, where the effects are well below the sensitivity of LHC experiments for all values of the RPV coupling that satisfy unitarity constraints. Therefore present or near-future bounds on this RPV model can not be derived from SMEFT analyses.

hep-ph↗

arXiv2025

Three-Stage Composite Outlier Identification of Wind Power Data: Integrating Physical Rules with Regression Learning and Mathematical Morphology

Existing studies on identifying outliers in wind speed-power datasets are often challenged by the complicated and irregular distributions of outliers, especially those being densely stacked yet staying close to normal data. This could degrade their identification reliability and robustness in practice. To address this defect, this paper develops a three-stage composite outlier identification method by systematically integrating three complementary techniques, i.e., physical rule-based preprocessing, regression learning-enabled detection, and mathematical morphology-based refinement. Firstly, the raw wind speed-power data are preprocessed via a set of simple yet efficient physical rules to filter out some outliers obviously going against the physical operating laws of practical wind turbines. Secondly, a robust wind speed-power regression learning model is built upon the random sample consensus algorithm. This model is able to reliably detect most outliers with the help of an adaptive threshold automatically set by the interquartile range method. Thirdly, by representing the wind speed-power data distribution with a two-dimensional image, mathematical morphology operations are applied to perform refined outlier identification from a data distribution perspective. This technique can identify outliers that are not effectively detected in the first two stages, including those densely stacked ones near normal data points. By integrating the above three techniques, the whole method is capable of identifying various types of outliers in a reliable and adaptive manner. Numerical test results with wind power datasets acquired from distinct wind turbines in practice and from simulation environments extensively demonstrate the superiority of the proposed method as well as its potential in enhancing wind power prediction.

eess.SP↗

arXiv2025

Multi-Party Private Set Operations from Predicative Zero-Sharing

Typical protocols in the multi-party private set operations (MPSO) setting enable m > 2 parties to perform certain secure computation on the intersection or union of their private sets, realizing a very limited range of MPSO functionalities. Most works in this field focus on just one or two specific functionalities, resulting in a large variety of isolated schemes and a lack of a unified framework in MPSO research. In this work, we present an MPSO framework, which allows m parties, each holding a set, to securely compute any set formulas (arbitrary compositions of a finite number of binary set operations, including intersection, union and difference) on their private sets. Our framework is highly versatile and can be instantiated to accommodate a broad spectrum of MPSO functionalities. To the best of our knowledge, this is the first framework to achieve such a level of flexibility and generality in MPSO, without relying on generic secure multi-party computation (MPC) techniques. Our framework exhibits favorable theoretical and practical performance. The computation and communication complexity scale linearly with the set size n, and it achieves optimal complexity that is on par with the naive solution for widely used functionalities, such as multi-party private set intersection (MPSI), MPSI with cardinality output (MPSI-card), and MPSI with cardinality and sum (MPSI-card-sum), in the standard semi-honest model. Furthermore, the instantiations of our framework mainly from symmetric-key techniques yield efficient protocols for MPSI, MPSI-card, MPSI-card-sum, and multi-party private set union (MPSU), with online performance surpassing or matching the state of the art.

cs.CR↗

arXiv2025

VET: A Visual-Electronic Tactile System for Immersive Human-Machine Interaction

In the pursuit of deeper immersion in human-machine interaction, achieving higher-dimensional tactile input and output on a single interface has become a key research focus. This study introduces the Visual-Electronic Tactile (VET) System, which builds upon vision-based tactile sensors (VBTS) and integrates electrical stimulation feedback to enable bidirectional tactile communication. We propose and implement a system framework that seamlessly integrates an electrical stimulation film with VBTS using a screen-printing preparation process, eliminating interference from traditional methods. While VBTS captures multi-dimensional input through visuotactile signals, electrical stimulation feedback directly stimulates neural pathways, preventing interference with visuotactile information. The potential of the VET system is demonstrated through experiments on finger electrical stimulation sensitivity zones, as well as applications in interactive gaming and robotic arm teleoperation. This system paves the way for new advancements in bidirectional tactile interaction and its broader applications.

cs.RO↗

arXiv2025

RNN-BSDE method for high-dimensional fractional backward stochastic differential equations with Wick-Itô integrals

Fractional Brownian motions(fBMs) are not semimartingales so the classical theory of Itô integral can't apply to fBMs. Wick integration as one of the applications of Malliavin calculus to stochastic analysis is a fine definition for fBMs. We consider the fractional forward backward stochastic differential equations(fFBSDEs) driven by a fBM that have the Hurst parameter in (1/2,1) where $\int_{0}^{t} f_s \, dB_s^H$ is in the sense of a Wick integral, and relate our fFBSDEs to the system of partial differential equations by using an analogue of the Itô formula for Wick integrals. And we develop a deep learning algorithm referred to as the RNN-BSDE method based on recurrent neural networks which is exactly designed for solving high-dimensional fractional BSDEs and their corresponding partial differential equations.

math.PR↗

arXiv2025

Scalar fields around a loop quantum gravity black hole in de Sitter spacetime: Quasinormal modes, late-time tails and strong cosmic censorship

Loop quantum gravity, as one branch of quantum gravity, holds the potential to explore the fundamental nature of black holes. Recently, according to the quantum Oppenheimer-Snyder model in loop quantum cosmology, a novel loop quantum corrected black hole in de Sitter spacetime has been discovered. Here, we first investigate the corresponding quasinormal modes and late-time behavior of massless neutral scalar field perturbations based on such a quantum-modified black hole in de Sitter spacetime. The frequency and time domain analysis of the lowest-lying quasinormal modes is derived by Prony method, Matrix method as well as WKB approximation. The influences of loop quantum correction, the black hole mass ratio, and the cosmological constant on the quasinormal frequencies are studied in detail. The late-time behaviors of quantum-modified black holes possess an exponential decay, which is mainly determined not only by the multipole number but also by the cosmological constant. The impact of loop quantum correction on the late-time tail is negligible, but it has a significant impact on damping oscillation. To explore spacetime singularities, we examine the validity of strong cosmic censorship for a near-extremal quantum-modified black hole in de Sitter spacetime. As a result, it is found that the strong cosmic censorship is destroyed as the black hole approaches the near-extremal limit, but the violation becomes weaker as the cosmological constant and the loop quantum correction increase.

gr-qc↗

arXiv2025

Quantum representation of reduced twisted geometry in loop quantum gravity

In this article, the quantum representation of the algebra among reduced twisted geometries (with respect to the Gauss constraint) is constructed in the gauge invariant Hilbert space of loop quantum gravity. It is shown that the reduced twisted geometric variables not only describe the spatial discrete geometry more clearly, but also form a simple Poisson algebra which is analogous to that in quantum mechanics. By regularizing the reduced twisted geometric variables properly, the fundamental algebra of reduced twisted geometry is established, with the gauge invariant Hilbert space in loop quantum gravity as the corresponding quantum representation space. This quantum representation also leads to fundamental operators associated with reduced twisted geometry. Based on these fundamental operators, a new type of extrinsic curvature operator is constructed in loop quantum gravity.

gr-qc↗

arXiv2025

EACO-RAG: Towards Distributed Tiered LLM Deployment using Edge-Assisted and Collaborative RAG with Adaptive Knowledge Update

Large language models (LLMs) have demonstrated impressive capabilities in language tasks, but they require high computing power and rely on static knowledge. To overcome these limitations, Retrieval-Augmented Generation (RAG) incorporates up-to-date external information into LLMs without extensive fine-tuning. Meanwhile, small language models (SLMs) deployed on edge devices offer efficiency and low latency but often struggle with complex reasoning tasks. Unfortunately, current RAG approaches are predominantly based on centralized databases and have not been adapted to address the distinct constraints associated with deploying SLMs in edge environments. To bridge this gap, we propose Edge-Assisted and Collaborative RAG (EACO-RAG), a lightweight framework that leverages distributed edge nodes for adaptive knowledge updates and retrieval. EACO-RAG also employs a hierarchical collaborative gating mechanism to dynamically select among local, edge-assisted, and cloud-based strategies, with a carefully designed algorithm based on Safe Online Bayesian Optimization to maximize the potential performance enhancements. Experimental results demonstrate that EACO-RAG matches the accuracy of cloud-based knowledge graph RAG systems while reducing total costs by up to 84.6% under relaxed delay constraints and by 65.3% under stricter delay requirements. This work represents our initial effort toward achieving a distributed and scalable tiered LLM deployments, with EACO-RAG serving as a promising first step in unlocking the full potential of hybrid edge-cloud intelligence.

cs.DC↗

arXiv2025

WATCH: A Workflow to Assess Treatment Effect Heterogeneity in Drug Development for Clinical Trial Sponsors

This paper proposes a Workflow for Assessing Treatment effeCt Heterogeneity (WATCH) in clinical drug development targeted at clinical trial sponsors. WATCH is designed to address the challenges of investigating treatment effect heterogeneity (TEH) in randomized clinical trials, where sample size and multiplicity limit the reliability of findings. The proposed workflow includes four steps: Analysis Planning, Initial Data Analysis and Analysis Dataset Creation, TEH Exploration, and Multidisciplinary Assessment. The workflow offers a general overview of how treatment effects vary by baseline covariates in the observed data, and guides interpretation of the observed findings based on external evidence and best scientific understanding. The workflow is exploratory and not inferential/confirmatory in nature, but should be pre-planned before data-base lock and analysis start. It is focused on providing a general overview rather than a single specific finding or subgroup with differential effect.

stat.AP↗