arXiv subjects

Lin Xie

Publications and source records attributed to Lin Xie.

At least 19 recordsLinked to original sources

arXiv2026

Nonstationary Stochastic Timing Signatures in the Prompt Gamma-Ray Light Curve of GRB 170817A

We investigate time-dependent stochastic structure in the weak prompt gamma-ray emission of GRB 170817A using change-point and deep-kernel Gaussian-process (GP) models. The analysis is based on the 10-300 keV Fermi/GBM light curve with 0.10 s time resolution. Two change-point configurations identify similar covariance transitions at 0.269 and 0.237 s after the gravitational-wave merger, with 10-90% transition widths of 0.441 and 0.393 s, respectively. At the representative gate-defined boundary of t_tr = 0.27 s, all four fixed-split assignments yield positive evidence gains over the full-exposure stationary Matern-3/2 reference. The largest gain is Delta ln Z_split = 5.72 +/- 0.16 for the Matern-3/2 to Matern-3/2 assignment, while the gate-matched damped random walk (DRW) to Matern-3/2 assignment gives Delta ln Z_split = 5.50 +/- 0.16. The fixed-split comparison therefore supports segment-specific covariance evolution without requiring a change of covariance family. The deep-kernel models recover localized time-deformation features peaking at 1.65 and 1.75 s after the merger for DRW and Matern-3/2 base kernels, respectively. Their offsets from the adopted gamma-ray onset at 1.74 s are -0.09 and +0.01 s, both within the 0.10 s sampling resolution. The full-band timing features remain stable when the bin width is changed from 0.10 to 0.12 s, whereas energy subdivision produces larger shifts in gate locations than in warp peaks. Residual diagnostics show that the models reproduce the dominant temporal structure, although localized residual dependence remains. We interpret the gate-defined covariance transition and the localized time-deformation feature as model-dependent phenomenological timing diagnostics. Further simulation calibration and count-level modelling are needed to assess the statistical robustness and physical origin of the recovered nonstationary structure.

astro-ph.HE↗

arXiv2026

Lattice-renormalized geometric frustration drives fast ionic transport

Fast ionic transport is commonly understood from two complementary perspectives: soft lattices that facilitate ion migration, and frustrated ionic sublattices that host multiple nearly degenerate configurations. Here, we show that the host lattice and ionic geometric frustration are fundamentally coupled, and together shape a lattice-renormalized free-energy landscape that unifies these two perspectives. Starting from a coupled ion-lattice Hamiltonian and eliminating the adiabatic lattice response, we derive a configuration-dependent free-energy renormalization that takes a quadratic form of the ionic configurational forces weighted by the inverse stiffness of the host lattice. In a coarse-grained representation, this renormalization decomposes into local lattice relaxation and non-local interference between lattice-response fields. These two contributions reshape local ionic configurations and their correlations, thereby redistributing the configurational statistical weights underlying collective ionic transport. Atomistic simulations of cubic Li$_7$La$_3$Zr$_2$O$_{12}$ and AgCrSe$_2$ substantiate this physical picture, from microscopic configurational rearrangements to macroscopic diffusion dynamics. This framework therefore recasts fast ionic transport as a lattice-renormalized geometric frustration problem, in which lattice softness, ionic frustration, and collective diffusion all arise from the same underlying landscape.

cond-mat.mtrl-sci↗

arXiv2026

Tunable thermal conductivity through dual spin-phonon coupling in van der Waals ferromagnetic insulator Cr2Ge2Te6

The active manipulation of phonon transport remains a central challenge in phononics and spin caloritronics due to the charge-neutral nature of heat carriers. Spin-phonon coupling (SPC) offers a promising route for the dynamic control of heat carriers, yet its progress has been limited due to the lack of a unified framework and suitable material platforms. Here, we report on the magnetic field-tunable phonon transport behavior in the ferromagnetic insulator Cr2Ge2Te6. We observed two distinct anomalous regimes at both the high and low fields that were governed by isotropic magnon-phonon hybridization and an anisotropic magnon softening process, respectively. By integrating detailed transport behavior with Brillouin light scattering and ferromagnetic resonance, we uncovered the microscopic origins of these anomalous regimes and demonstrated that both the field magnitude and orientation could act as versatile tuning knobs to manipulate the thermal conductivity. Our findings provide experimental evidence of the SPC effect on phonon transport, demonstrating the dual impact of SPC within a unified system. This work will not only broaden the fundamental understanding of quasiparticle interactions but also establish a viable framework for dynamic phonon engineering. Furthermore, the characteristics of this system highlight the potential for achieving field-tunable phonon transport in similar platforms such as two-dimensional (2D) magnetic materials.

cond-mat.str-el↗

arXiv2026

Sub-angstrom many-body localization driven by phononic flat bands in real quantum materials

Defects, fluctuations, degenerate states and correlated interactions facilitate the emergence of exotic properties in condensed matter systems while also inducing atomic-scale local correlated structures that deviate from the average long-range order. Establishing the structure-property relationship from the perspective of these atomic-scale local correlated structures remains ambiguous and controversial due to the lack of direct methods for identifying such local correlated structures. In this work, based on the photoexcited ultrafast structural response, we propose a Bragg scattering phase breaking regime to identify sub-angstrom local correlated structures in quantum materials. With this regime, we unambiguously identify the many-body-interaction driven local correlated structures in the low temperature ground state of AgCrSe2, characterized by static off-center displacements of Ag atoms ranging from 0 to 0.5 angstrom. The competition between Ag-Ag Coulomb correlations and potential wells induced by CrSe2 layers, leading to phononic flat bands and driving the system into a many body localization (MBL) regime. As temperature rising, these static local correlated structures transform to a dynamic state where the thermal fluctuations overwhelm the multiple localized states. These distinctive local correlated structures constitute the first experimental observation of MBL with vortex-like topological characteristic in a real material system. Emergent vibrational modes arising from MBL have been confirmed and show excellent agreement with inelastic neutron scattering experiments. Our work not only offers a universal approach to characterize sub-angstrom local correlated structures across a wide range of quantum materials but also deepens our understanding of the fundamental mechanism behind exotic properties from the perspective of atomic-scale local correlated structures.

cond-mat.mtrl-sci↗

arXiv2025

A Clustering-Based Variable Ordering Framework for Relaxed Decision Diagrams for Maximum Weighted Independent Set Problem

Efficient exact algorithms for Discrete Optimization (DO) rely heavily on strong primal and dual bounds. Relaxed Decision Diagrams (DDs) provide a versatile mechanism for deriving such dual bounds by compactly over-approximating the solution space through node merging. However, the quality of these relaxed diagrams, i.e. the tightness of the resulting dual bounds, depends critically on the variable ordering and the merging decisions executed during compilation. While dynamic variable ordering heuristics effectively tighten bounds, they often incur computational overhead when evaluated globally across the entire variable set. To mitigate this trade-off, this work introduces a novel clustering-based framework for variable ordering. Instead of applying dynamic ordering heuristics to the full set of unfixed variables, we first partition variables into clusters. We then leverage this structural decomposition to guide the ordering process, significantly reducing the heuristic's search space. Within this framework, we investigate two distinct strategies: Cluster-to-Cluster, which processes clusters sequentially using problem-specific aggregate criteria (such as cumulative vertex weights in the Maximum Weighted Independent Set Problem (MWISP)), and Pick-and-Sort, which iteratively selects and sorts representative variables from each cluster to balance local diversity with heuristic guidance. Later on, developing some theoretical results on the growth of the size of DDs for MWISP we propose two different policies for setting the number of clusters within the proposed framework. We embed these strategies into a DD-based branch-and-bound algorithm and evaluate them on the MWISP. Across benchmark instances, the proposed methodology consistently reduces computational costs compared to standard dynamic variable ordering baseline.

cs.AI↗

arXiv2025

Multi-Action Self-Improvement for Neural Combinatorial Optimization

Self-improvement has emerged as a state-of-the-art paradigm in Neural Combinatorial Optimization (NCO), where models iteratively refine their policies by generating and imitating high-quality solutions. Despite strong empirical performance, existing methods face key limitations. Training is computationally expensive, as policy updates require sampling numerous candidate solutions per instance to extract a single expert trajectory. More fundamentally, these approaches fail to exploit the structure of combinatorial problems involving the coordination of multiple agents, such as vehicles in min-max routing or machines in scheduling. By supervising on single-action trajectories, they fail to exploit agent-permutation symmetries, where distinct sequences of actions yield identical solutions, hindering generalization and the ability to learn coordinated behavior. We address these challenges by extending self-improvement to operate over joint multi-agent actions. Our model architecture predicts complete agent-task assignments jointly at each decision step. To explicitly leverage symmetries, we employ a set-prediction loss, which supervises the policy on multiple expert assignments for any given state. This approach enhances sample efficiency and the model's ability to learn coordinated behavior. Furthermore, by generating multi-agent actions in parallel, it drastically accelerates the solution generation phase of the self-improvement loop. Empirically, we validate our method on several combinatorial problems, demonstrating consistent improvements in the quality of the final solution and a reduced generation latency compared to standard self-improvement.

cs.LG↗

arXiv2025

Solving the Pod Repositioning Problem with Deep Reinforced Adaptive Large Neighborhood Search

The Pod Repositioning Problem (PRP) in Robotic Mobile Fulfillment Systems (RMFS) involves selecting optimal storage locations for pods returning from pick stations. This work presents an improved solution method that integrates Adaptive Large Neighborhood Search (ALNS) with Deep Reinforcement Learning (DRL). A DRL agent dynamically selects destroy and repair operators and adjusts key parameters such as destruction degree and acceptance thresholds during the search. Specialized heuristics for both operators are designed to reflect PRP-specific characteristics, including pod usage frequency and movement costs. Computational results show that this DRL-guided ALNS outperforms traditional approaches such as cheapest-place, fixed-place, binary integer programming, and static heuristics. The method demonstrates strong solution quality and illustrating the benefit of learning-driven control within combinatorial optimization for warehouse systems.

cs.RO↗

arXiv2025

CPU-Based Layout Design for Picker-to-Parts Pallet Warehouses

Picker-to-parts pallet warehouses often face inefficiencies due to conventional layouts causing excessive travel distances and high labor requirements. This study introduces a novel layout design inspired by CPU architecture, partitioning warehouse space into specialized zones, namely Performance (P), Efficiency (E), and Shared (S). Discrete-event simulation is used to evaluate this design against traditional rectangular (random and ABC storage) and Flying-V layouts. Results demonstrate significant improvements in throughput time and reduced labor requirements, highlighting the potential for CPU-based layouts in optimizing warehouse operations.

cs.MA↗

arXiv2025

Learning to Solve the Min-Max Mixed-Shelves Picker-Routing Problem via Hierarchical and Parallel Decoding

The Mixed-Shelves Picker Routing Problem (MSPRP) is a fundamental challenge in warehouse logistics, where pickers must navigate a mixed-shelves environment to retrieve SKUs efficiently. Traditional heuristics and optimization-based approaches struggle with scalability, while recent machine learning methods often rely on sequential decision-making, leading to high solution latency and suboptimal agent coordination. In this work, we propose a novel hierarchical and parallel decoding approach for solving the min-max variant of the MSPRP via multi-agent reinforcement learning. While our approach generates a joint distribution over agent actions, allowing for fast decoding and effective picker coordination, our method introduces a sequential action selection to avoid conflicts in the multi-dimensional action space. Experiments show state-of-the-art performance in both solution quality and inference speed, particularly for large-scale and out-of-distribution instances. Our code is publicly available at http://github.com/LTluttmann/marl4msprp.

cs.MA↗

arXiv2024

PARCO: Parallel AutoRegressive Models for Multi-Agent Combinatorial Optimization

Combinatorial optimization problems involving multiple agents are notoriously challenging due to their NP-hard nature and the necessity for effective agent coordination. Despite advancements in learning-based methods, existing approaches often face critical limitations, including suboptimal agent coordination, poor generalization, and high computational latency. To address these issues, we propose PARCO (Parallel AutoRegressive Combinatorial Optimization), a general reinforcement learning framework designed to construct high-quality solutions for multi-agent combinatorial tasks efficiently. To this end, PARCO integrates three key novel components: (1) transformer-based communication layers to enable effective agent collaboration during parallel solution construction, (2) a multiple pointer mechanism for low-latency, parallel agent decision-making, and (3) priority-based conflict handlers to resolve decision conflicts via learned priorities. We evaluate PARCO in multi-agent vehicle routing and scheduling problems, where our approach outperforms state-of-the-art learning methods, demonstrating strong generalization ability and remarkable computational efficiency. We make our source code publicly available to foster future research: https://github.com/ai4co/parco.

cs.MA↗

arXiv2024

Identification and Estimation of the Bi-Directional MR with Some Invalid Instruments

We consider the challenging problem of estimating causal effects from purely observational data in the bi-directional Mendelian randomization (MR), where some invalid instruments, as well as unmeasured confounding, usually exist. To address this problem, most existing methods attempt to find proper valid instrumental variables (IVs) for the target causal effect by expert knowledge or by assuming that the causal model is a one-directional MR model. As such, in this paper, we first theoretically investigate the identification of the bi-directional MR from observational data. In particular, we provide necessary and sufficient conditions under which valid IV sets are correctly identified such that the bi-directional MR model is identifiable, including the causal directions of a pair of phenotypes (i.e., the treatment and outcome). Moreover, based on the identification theory, we develop a cluster fusion-like method to discover valid IV sets and estimate the causal effects of interest. We theoretically demonstrate the correctness of the proposed algorithm. Experimental results show the effectiveness of our method for estimating causal effects in bi-directional MR.

stat.ME↗

arXiv2024

Introducing Combi-Stations in Robotic Mobile Fulfilment Systems: A Queueing-Theory-Based Efficiency Analysis

In the era of digital commerce, the surge in online shopping and the expectation for rapid delivery have placed unprecedented demands on warehouse operations. The traditional method of order fulfilment, where human order pickers traverse large storage areas to pick items, has become a bottleneck, consuming valuable time and resources. Robotic Mobile Fulfilment Systems (RMFS) offer a solution by using robots to transport storage racks directly to human-operated picking stations, eliminating the need for pickers to travel. This paper introduces combi-stations, a novel type of station that enables both item picking and replenishment, as opposed to traditional separate stations. We analyse the efficiency of combi-stations using queueing theory and demonstrate their potential to streamline warehouse operations. Our results suggest that combi-stations can reduce the number of robots required for stability and significantly reduce order turnover time, indicating a promising direction for future warehouse automation.

cs.RO↗

arXiv2023

How to Integrate Digital Twin and Virtual Reality in Robotics Systems? Design and Implementation for Providing Robotics Maintenance Services in Data Centers

In the context of Industry 4.0, the physical and digital worlds are closely connected, and robots are widely used to achieve system automation. Digital twin solutions have contributed significantly to the growth of Industry 4.0. Combining various technologies is a trend that aims to improve system performance. For example, digital twinning can be combined with virtual reality in automated systems. This paper proposes a new concept to articulate this combination, which has mainly been implemented in engineering research projects. However, there are currently no guidelines, plans, or concepts to articulate this combination. The concept will be implemented in data centers, which are crucial for enabling virtual tasks in our daily lives. Due to the COVID-19 pandemic, there has been a surge in demand for services such as e-commerce and videoconferencing. Regular maintenance is necessary to ensure uninterrupted and reliable services. Manual maintenance strategies may not be sufficient to meet the current high demand, and innovative approaches are needed to address the problem. This paper presents a novel approach to data center maintenance: real-time monitoring by an autonomous robot. The robot is integrated with digital twins of assets and a virtual reality interface that allows human personnel to control it and respond to alarms. This methodology enables faster, more cost-effective, and higher quality data center maintenance. It has been validated in a real data centre and can be used for intelligent monitoring and management through joint data sources. The method has potential applications in other automated systems.

cs.RO↗

arXiv2023

An overview of optimization approaches for scheduling and rostering resources in public transportation

Public transport is vital for meeting people's mobility needs. Providers need to plan their services well to offer high quality and low cost. Optimized planning can benefit providers, customers, and municipalities. The planning process for public transport involves various decision problems, such as vehicle and crew planning. These problems are usually solved by providers. More and more studies suggest that integrated solution approaches for these problems are better than sequential and iterative ones. Integrated optimization of multiple planning phases allows more flexibility in planning, which can reduce operational costs and improve service quality. This paper reviews solution approaches for integrated optimization using operations research techniques for the vehicle scheduling, crew scheduling, and crew rostering problems. It also covers some relevant related approaches from other industries. The paper analyzes existing optimization approaches based on different aspects such as mathematical modeling, optimization objective and method, and data source and scope. Moreover, the paper examines the problem dimensions that are often required in practical applications. The paper identifies some directions for future research, such as focusing more on objectives other than cost-minimization like robustness, schedule regularity, or fairness.

math.OC↗

arXiv2023

RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark

Combinatorial optimization (CO) is fundamental to several real-world applications, from logistics and scheduling to hardware design and resource allocation. Deep reinforcement learning (RL) has recently shown significant benefits in solving CO problems, reducing reliance on domain expertise and improving computational efficiency. However, the absence of a unified benchmarking framework leads to inconsistent evaluations, limits reproducibility, and increases engineering overhead, raising barriers to adoption for new researchers. To address these challenges, we introduce RL4CO, a unified and extensive benchmark with in-depth library coverage of 27 CO problem environments and 23 state-of-the-art baselines. Built on efficient software libraries and best practices in implementation, RL4CO features modularized implementation and flexible configurations of diverse environments, policy architectures, RL algorithms, and utilities with extensive documentation. RL4CO helps researchers build on existing successes while exploring and developing their own designs, facilitating the entire research process by decoupling science from heavy engineering. We finally provide extensive benchmark studies to inspire new insights and future work. RL4CO has already attracted numerous researchers in the community and is open-sourced at https://github.com/ai4co/rl4co.

cs.LG↗

arXiv2021

Extend the FFmpeg Framework to Analyze Media Content

This paper introduces a new set of video analytics plugins developed for the FFmpeg framework. Multimedia applications that increasingly utilize the FFmpeg media features for its comprehensive media encoding, decoding, muxing, and demuxing capabilities can now additionally analyze the video content based on AI models. The plugins are thread optimized for best performance overcoming certain FFmpeg threading limitations. The plugins utilize the Intel OpenVINO Toolkit inference engine as the backend. The analytics workloads are accelerated on different platforms such as CPU, GPU, FPGA or specialized analytics accelerators. With our reference implementation, the feature of OpenVINO as inference backend has been pushed into FFmpeg mainstream repository. We plan to submit more patches later.

cs.MM↗

arXiv2021

Formulating and solving integrated order batching and routing in multi-depot AGV-assisted mixed-shelves warehouses

Different retail and e-commerce companies are facing the challenge of assembling large numbers of time-critical picking orders that include both small-line and multi-line orders. To reduce unproductive picker working time as in traditional picker-to-parts warehousing systems, different solutions are proposed in the literature and in practice. For example, in a mixed-shelves storage policy, items of the same stock keeping unit are spread over several shelves in a warehouse; or automated guided vehicles (AGVs) are used to transport the picked items from the storage area to packing stations instead of human pickers. This is the first paper to combine both solutions, creating what we call AGV-assisted mixed-shelves picking systems. We model the new integrated order batching and routing problem in such systems as an extended multi-depot vehicle routing problem with both three-index and two-commodity network flow formulations. Due to the complexity of the integrated problem, we develop a novel variable neighborhood search algorithm to solve the integrated problem more efficiently. We test our methods with different sizes of instances, and conclude that the mixed-shelves storage policy is more suitable than the usual storage policy in AGV-assisted mixed-shelves systems for orders with different sizes of order lines (saving up to 62% on driving distances for AGVs). Our variable neighborhood search algorithm provides optimal solutions within an acceptable computational time.

math.OC↗

arXiv2020

An Intelligent CNN-VAE Text Representation Technology Based on Text Semantics for Comprehensive Big Data

In the era of big data, a large number of text data generated by the Internet has given birth to a variety of text representation methods. In natural language processing (NLP), text representation transforms text into vectors that can be processed by computer without losing the original semantic information. However, these methods are difficult to effectively extract the semantic features among words and distinguish polysemy in language. Therefore, a text feature representation model based on convolutional neural network (CNN) and variational autoencoder (VAE) is proposed to extract the text features and apply the obtained text feature representation on the text classification tasks. CNN is used to extract the features of text vector to get the semantics among words and VAE is introduced to make the text feature space more consistent with Gaussian distribution. In addition, the output of the improved word2vec model is employed as the input of the proposed model to distinguish different meanings of the same word in different contexts. The experimental results show that the proposed model outperforms in k-nearest neighbor (KNN), random forest (RF) and support vector machine (SVM) classification algorithms.

cs.LG↗