arXiv subjects

Yan Gu

Publications and source records attributed to Yan Gu.

At least 37 records · Page 2Linked to original sources

arXiv2025

Parallel Joinable B-Trees in the Fork-Join I/O Model

Balanced search trees are widely used in computer science to efficiently maintain dynamic ordered data. To support efficient set operations (e.g., union, intersection, difference) using trees, the join-based framework is widely studied. This framework has received particular attention in the parallel setting, and has been shown to be effective in enabling simple and theoretically efficient set operations on trees. Despite the widespread adoption of parallel join-based trees, a major drawback of previous work on such data structures is the inefficiency of their input/output (I/O) access patterns. Some recent work (e.g., C-trees and PaC-trees) focused on more I/O-friendly implementations of these algorithms. Surprisingly, however, there have been no results on bounding the I/O-costs for these algorithms. It remains open whether these algorithms can provide tight, provable guarantees in I/O-costs on trees. This paper studies efficient parallel algorithms for set operations based on search tree algorithms using a join-based framework, with a special focus on achieving I/O efficiency in these algorithms. To better capture the I/O-efficiency in these algorithms in parallel, we introduce a new computational model, Fork-Join I/O Model, to measure the I/O costs in fork-join parallelism. This model measures the total block transfers (I/O work) and their critical path (I/O span). Under this model, we propose our new solution based on B-trees. Our parallel algorithm computes the union, intersection, and difference of two B-trees with $O(m \log_B(n/m))$ I/O work and $O(\log_B m \cdot \log_2 \log_B n + \log_B n)$ I/O span, where $n$ and $m \leq n$ are the sizes of the two trees, and $B$ is the block size.

cs.DS↗

arXiv2025

Relaxation of A Thermally Bathed Harmonic Oscillator: A Study Based on the Group-theoretical Formalism

Quantum dynamics of a damped harmonic oscillator has been extensively studied since the sixties of the last century. Here, with a distinct tool termed the ``group-theoretical characteristic function" (GCF), we investigate analytically how a harmonic oscillator immersed in a thermal environment would relax to its equilibrium state. We assume that the oscillator is at a pure state initially and its evolution is governed by a well-known quantum-optical master equation. By taking advantage of the GCF, the master equation can be transformed into a first-order linear partial differential equation that allows us to write down its solution explicitly. Based on the solution, it is found that, in clear contrast with the monotonic relaxation process of its classical counterpart, the quantum oscillator may demonstrate some intriguing nonmonotonic relaxation characteristics. In particular, when the initial state is a Gaussian state (i.e., a squeezed coherent state), it is found that there is a critical value of the environmental temperature, below which the entropy will first increase to reach its maximum value, then turn down and converge to its equilibrium value from above. For the temperature higher than the critical value, the entropy will converge to its equilibrium value from below monotonically. However, when the initial state is a Fock state, it is found that there is a new phase additional to the previous case, where the time curve of entropy features two extreme points. Namely, the entropy will increase to reach its maximum first, then turn down to reach its minimum, from where it begins to increase and converges to the equilibrium value eventually. Other related issues are discussed as well.

quant-ph↗

arXiv2025

A Benders and column generation method to the integrated airline schedule and aircraft recovery with gate reassignment

Disruptions are inevitable during airline operations, and disruptions cost airlines and the traveling public. Disruption recovery decisions are often made in a sequential process that includes flight rescheduling, aircraft rerouting, crew reassignment, passenger re-accommodation, and gate reassignment for airline operations. Such a sequential recovery approach alleviates the complexity of the whole recovery process but often causes further disruptions to airport gate assignments and high recovery costs for airlines and passengers. This paper presents a novel airline disruption recovery approach by integrating the schedule and aircraft recovery with gate reassignment. We propose a Benders and column generation (BCG) method to solve the integrated problem. By exploiting the favorable structure arising from the Benders decomposition framework, we provide two acceleration techniques for Benders subproblems, a separation technique and an effective infeasibility certificate, to enhance the efficiency of the BCG method. The proposed model is tested on real-world data. In our experiments, all test instances are solved by the BCG method under a five-minute threshold with optimality gaps within 5%. Compared with the sequential recovery approach, the integrated recovery approach significantly saves recovery costs and avoids infeasible gate reassignments caused by the sequential recovery approach.

math.OC↗

arXiv2025

Model-agnostic Meta-learning for Adaptive Gait Phase and Terrain Geometry Estimation with Wearable Soft Sensors

This letter presents a model-agnostic meta-learning (MAML) based framework for simultaneous and accurate estimation of human gait phase and terrain geometry using a small set of fabric-based wearable soft sensors, with efficient adaptation to unseen subjects and strong generalization across different subjects and terrains. Compared to rigid alternatives such as inertial measurement units, fabric-based soft sensors improve comfort but introduce nonlinearities due to hysteresis, placement error, and fabric deformation. Moreover, inter-subject and inter-terrain variability, coupled with limited calibration data in real-world deployments, further complicate accurate estimation. To address these challenges, the proposed framework integrates MAML into a deep learning architecture to learn a generalizable model initialization that captures subject- and terrain-invariant structure. This initialization enables efficient adaptation (i.e., adaptation with only a small amount of calibration data and a few fine-tuning steps) to new users, while maintaining strong generalization (i.e., high estimation accuracy across subjects and terrains). Experiments on nine participants walking at various speeds over five terrain conditions demonstrate that the proposed framework outperforms baseline approaches in estimating gait phase, locomotion mode, and incline angle, with superior accuracy, adaptation efficiency, and generalization.

cs.RO↗

arXiv2025

GWQ: Gradient-Aware Weight Quantization for Large Language Models

Large language models (LLMs) show impressive performance in solving complex language tasks. However, its large number of parameters presents significant challenges for the deployment. So, compressing LLMs to low bits can enable to deploy on resource-constrained devices. To address this problem, we propose gradient-aware weight quantization (GWQ), the first quantization approach for low-bit weight quantization that leverages gradients to localize outliers, requiring only a minimal amount of calibration data for outlier detection. GWQ retains the top 1\% outliers preferentially at FP16 precision, while the remaining non-outlier weights are stored in a low-bit. We widely evaluate GWQ on different task include language modeling, grounding detection, massive multitask language understanding and vision-language question and answering. Results show that models quantified by GWQ performs better than other quantization method. During quantization process, GWQ only need one calibration set to realize effective quant. Also, GWQ achieves 1.2x inference speedup in comparison to the original model and effectively reduces the inference memory.

cs.LG↗

arXiv2025

New Algorithms for Incremental Minimum Spanning Trees and Temporal Graph Applications

Processing graphs with temporal information (the temporal graphs) has become increasingly important in the real world. In this paper, we study efficient solutions to temporal graph applications using new algorithms for Incremental Minimum Spanning Trees (MST). The first contribution of this work is to formally discuss how a broad set of setting-problem combinations of temporal graph processing can be solved using incremental MST, along with their theoretical guarantees. Despite the importance of the problem, we observe a gap between theory and practice for efficient incremental MST algorithms. While many classic data structures, such as the link-cut tree, provide strong bounds for incremental MST, their performance is limited in practice. Meanwhile, existing practical solutions used in applications do not have any non-trivial theoretical guarantees. Our second and main contribution includes new algorithms for incremental MST that are efficient both in theory and in practice. Our new data structure, the AM-tree, achieves the same theoretical bound as the link-cut tree for temporal graph processing and shows strong performance in practice. In our experiments, the AM-tree has competitive or better performance than existing practical solutions due to theoretical guarantees, and can be significantly faster than the link-cut tree (7.8-11x in updates and 7.7-13.7x in queries).

cs.DS↗

arXiv2025

Parallel Contraction Hierarchies Can Be Efficient and Scalable

Contraction Hierarchies (CH) (Geisberger et al., 2008) is one of the most widely used algorithms for shortest-path queries on road networks. Compared to Dijkstra's algorithm, CH enables orders of magnitude faster query performance through a preprocessing phase, which iteratively categorizes vertices into hierarchies and adds shortcuts. However, constructing a CH is an expensive task. Existing solutions, including parallel ones, may suffer from long construction time. Especially, in our experiments, we observe that existing parallel solutions demonstrate unsatisfactory scalability, and have performance close to sequential algorithms. We present SPoCH (Scalable Parallelization of Contraction Hierarchies), an efficient and scalable CH construction algorithm in parallel. To address the challenges in previous work, our improvements focus on both redesigning the algorithm and leveraging parallel data structures. We compare SPoCH with the state-of-the-art sequential and parallel implementations on 16 graphs of various types. Our experiments show that SPoCH achieves speedups of 11 to 68 times over the best sequential baseline and 3.8 to 41 times over the best parallel baseline in CH construction, while maintaining competitive query performance and CH graph size. We have released our code and all datasets used in this paper.

cs.DS↗

arXiv2025

MBE-ARI: A Multimodal Dataset Mapping Bi-directional Engagement in Animal-Robot Interaction

Animal-robot interaction (ARI) remains an unexplored challenge in robotics, as robots struggle to interpret the complex, multimodal communication cues of animals, such as body language, movement, and vocalizations. Unlike human-robot interaction, which benefits from established datasets and frameworks, animal-robot interaction lacks the foundational resources needed to facilitate meaningful bidirectional communication. To bridge this gap, we present the MBE-ARI (Multimodal Bidirectional Engagement in Animal-Robot Interaction), a novel multimodal dataset that captures detailed interactions between a legged robot and cows. The dataset includes synchronized RGB-D streams from multiple viewpoints, annotated with body pose and activity labels across interaction phases, offering an unprecedented level of detail for ARI research. Additionally, we introduce a full-body pose estimation model tailored for quadruped animals, capable of tracking 39 keypoints with a mean average precision (mAP) of 92.7%, outperforming existing benchmarks in animal pose estimation. The MBE-ARI dataset and our pose estimation framework lay a robust foundation for advancing research in animal-robot interaction, providing essential tools for developing perception, reasoning, and interaction frameworks needed for effective collaboration between robots and animals. The dataset and resources are publicly available at https://github.com/RISELabPurdue/MBE-ARI/, inviting further exploration and development in this critical area.

cs.CV↗

arXiv2025

Parallel $k$-Core Decomposition: Theory and Practice

This paper proposes efficient solutions for $k$-core decomposition with high parallelism. The problem of $k$-core decomposition is fundamental in graph analysis and has applications across various domains. However, existing algorithms face significant challenges in achieving work-efficiency in theory and/or high parallelism in practice, and suffer from various performance bottlenecks. We present a simple, work-efficient parallel framework for $k$-core decomposition that is easy to implement and adaptable to various strategies for improving work-efficiency. We introduce two techniques to enhance parallelism: a sampling scheme to reduce contention on high-degree vertices, and vertical granularity control (VGC) to mitigate scheduling overhead for low-degree vertices. Furthermore, we design a hierarchical bucket structure to optimize performance for graphs with high coreness values. We evaluate our algorithm on a diverse set of real-world and synthetic graphs. Compared to state-of-the-art parallel algorithms, including ParK, PKC, and Julienne, our approach demonstrates superior performance on 23 out of 25 graphs when tested on a 96-core machine. Our algorithm shows speedups of up to 315$\times$ over ParK, 33.4$\times$ over PKC, and 52.5$\times$ over Julienne.

cs.DS↗

arXiv2025

Leveraging Perturbation Robustness to Enhance Out-of-Distribution Detection

Out-of-distribution (OOD) detection is the task of identifying inputs that deviate from the training data distribution. This capability is essential for safely deploying deep computer vision models in open-world environments. In this work, we propose a post-hoc method, Perturbation-Rectified OOD detection (PRO), based on the insight that prediction confidence for OOD inputs is more susceptible to reduction under perturbation than in-distribution (IND) inputs. Based on the observation, we propose an adversarial score function that searches for the local minimum scores near the original inputs by applying gradient descent. This procedure enhances the separability between IND and OOD samples. Importantly, the approach improves OOD detection performance without complex modifications to the underlying model architectures. We conduct extensive experiments using the OpenOOD benchmark~\cite{yang2022openood}. Our approach further pushes the limit of softmax-based OOD detection and is the leading post-hoc method for small-scale models. On a CIFAR-10 model with adversarial training, PRO effectively detects near-OOD inputs, achieving a reduction of more than 10\% on FPR@95 compared to state-of-the-art methods.

cs.CV↗

arXiv2025

A novel boundary integrated neural networks for in plane fracture mechanics analysis of elastic and piezoelectric materials

In this study, we propose a novel approach, termed boundary integrated neural networks (BINNs), for analyzing in-plane crack problems within the framework of linear elastic fracture mechanics. The proposed approach integrates artificial neural networks (ANNs) with classical boundary integral equations (BIEs), enabling an efficient and accurate evaluation of partial differential equations (PDEs) associated with fracture mechanics. Additionally, novel special ANN-based crack-tip elements, the Special crack-tip Neural Networks(SPNNs) are developed to improve the modeling of displacement and stress fields in regions near crack tips. These specialized elements integrate the asymptotic characteristics of fracture mechanics into the neural network framework, ensuring enhanced accuracy in capturing the intricate singularities and steep gradients near the crack tips. Compared to conventional simulation tools in fracture mechanics, the present method offers several distinct advantages. First, by embedding higher-order fracture mechanics principles into the neural networks, the method achieves a more accurate and reliable representation of the near-tip fields, even when using relatively large crack-tip elements. Second, the SPNNs, which incorporates information about varying near-tip singularity orders, improve the method's versatility in solving problems involving complex and diverse crack-tips geometries. Moreover, the method demonstrates excellent computational efficiency due to the dimensionality reduction achieved by employing BIEs. Numerical experiments confirm that the proposed framework serves as a reliable, robust, and accurate tool for addressing fracture mechanics problems, offering substantial advantages over conventional numerical approaches.

cs.CE↗

arXiv2025

Parallel $k$d-tree with Batch Updates

The $k$d-tree is one of the most widely used data structures to manage multi-dimensional data. Due to the ever-growing data volume, it is imperative to consider parallelism in $k$d-trees. However, we observed challenges in existing parallel kd-tree implementations, for both constructions and updates. The goal of this paper is to develop efficient in-memory $k$d-trees by supporting high parallelism and cache-efficiency. We propose the Pkd-tree (Parallel $k$d-tree), a parallel $k$d-tree that is efficient both in theory and in practice. The Pkd-tree supports parallel tree construction, batch update (insertion and deletion), and various queries including k-nearest neighbor search, range query, and range count. We proved that our algorithms have strong theoretical bounds in work (sequential time complexity), span (parallelism), and cache complexity. Our key techniques include 1) an efficient construction algorithm that optimizes work, span, and cache complexity simultaneously, and 2) reconstruction-based update algorithms that guarantee the tree to be weight-balanced. With the new algorithmic insights and careful engineering effort, we achieved a highly optimized implementation of the Pkd-tree. We tested Pkd-tree with various synthetic and real-world datasets, including both uniform and highly skewed data. We compare the Pkd-tree with state-of-the-art parallel $k$d-tree implementations. In all tests, with better or competitive query performance, Pkd-tree is much faster in construction and updates consistently than all baselines. We released our code.

cs.DS↗

arXiv2025

Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review

Cloud computing has revolutionized the provisioning of computing resources, offering scalable, flexible, and on-demand services to meet the diverse requirements of modern applications. At the heart of efficient cloud operations are job scheduling and resource management, which are critical for optimizing system performance and ensuring timely and cost-effective service delivery. However, the dynamic and heterogeneous nature of cloud environments presents significant challenges for these tasks, as workloads and resource availability can fluctuate unpredictably. Traditional approaches, including heuristic and meta-heuristic algorithms, often struggle to adapt to these real-time changes due to their reliance on static models or predefined rules. Deep Reinforcement Learning (DRL) has emerged as a promising solution to these challenges by enabling systems to learn and adapt policies based on continuous observations of the environment, facilitating intelligent and responsive decision-making. This survey provides a comprehensive review of DRL-based algorithms for job scheduling and resource management in cloud computing, analyzing their methodologies, performance metrics, and practical applications. We also highlight emerging trends and future research directions, offering valuable insights into leveraging DRL to advance both job scheduling and resource management in cloud computing.

cs.DC↗

arXiv2024

Findings of the WMT 2024 Shared Task on Discourse-Level Literary Translation

Following last year, we have continued to host the WMT translation shared task this year, the second edition of the Discourse-Level Literary Translation. We focus on three language directions: Chinese-English, Chinese-German, and Chinese-Russian, with the latter two ones newly added. This year, we totally received 10 submissions from 5 academia and industry teams. We employ both automatic and human evaluations to measure the performance of the submitted systems. The official ranking of the systems is based on the overall human judgments. We release data, system outputs, and leaderboard at https://www2.statmt.org/wmt24/literary-translation-task.html.

cs.CL↗

arXiv2024

Parallel Cluster-BFS and Applications to Shortest Paths

Breadth-first Search (BFS) is one of the most important graph processing subroutines, especially for computing the unweighted distance. Many applications may require running BFS from multiple sources. Sequentially, when running BFS on a cluster of nearby vertices, a known optimization is using bit-parallelism. Given a subset of vertices with size $k$ and the distance between any pair of them is no more than $d$, BFS can be applied to all of them in total work $O(dm(k/w+1))$, where $w$ is the length of a word in bits and $m$ is the number of edges. We will refer to this approach as cluster-BFS (C-BFS). Such an approach has been studied and shown effective both in theory and in practice in the sequential setting. However, it remains unknown how this can be combined with thread-level parallelism. In this paper, we focus on designing efficient parallel C-BFS based on BFS to answer unweighted distance queries. Our solution combines the strengths of bit-level parallelism and thread-level parallelism, and achieves significant speedup over the plain sequential solution. We also apply our algorithm to real-world applications. In particular, we identified another application (landmark-labeling for the approximate distance oracle) that can take advantage of parallel C-BFS. Under the same memory budget, our new solution improves accuracy and/or time on all the 18 tested graphs.

cs.DS↗

arXiv2024

Adaptive Ankle Torque Control for Bipedal Humanoid Walking on Surfaces with Unknown Horizontal and Vertical Motion

Achieving stable bipedal walking on surfaces with unknown motion remains a challenging control problem due to the hybrid, time-varying, partially unknown dynamics of the robot and the difficulty of accurate state and surface motion estimation. Surface motion imposes uncertainty on both system parameters and non-homogeneous disturbance in the walking robot dynamics. In this paper, we design an adaptive ankle torque controller to simultaneously address these two uncertainties and propose a step-length planner to minimize the required control torque. Typically, an adaptive controller is used for a continuous system. To apply adaptive control on a hybrid system such as a walking robot, an intermediate command profile is introduced to ensure a continuous error system. Simulations on a planar bipedal robot, along with comparisons against a baseline controller, demonstrate that the proposed approach effectively ensures stable walking and accurate tracking under unknown, time-varying disturbances.

cs.RO↗

arXiv2024

Time-Varying Foot-Placement Control for Underactuated Humanoid Walking on Swaying Rigid Surfaces

Locomotion on dynamic rigid surface (i.e., rigid surface accelerating in an inertial frame) presents complex challenges for controller design, which are essential for deploying humanoid robots in dynamic real-world environments such as moving trains, ships, and airplanes. This paper introduces a real-time, provably stabilizing control approach for underactuated humanoid walking on periodically swaying rigid surface. The first key contribution is the analytical extension of the classical angular momentum-based linear inverted pendulum model from static to swaying grounds. This extension results in a time-varying, nonhomogeneous robot model, which is fundamentally different from the existing pendulum models. We synthesize a discrete footstep control law for the model and derive a new set of sufficient stability conditions that verify the controller's stabilizing effect. Another key contribution is the development of a hierarchical control framework that incorporates the proposed footstep control law as its higher-layer planner to ensure the stability of underactuated walking. The closed-loop stability of the complete hybrid, full-order robot dynamics under this control framework is provably analyzed based on nonlinear control theory. Finally, experiments conducted on a Digit humanoid robot, both in simulations and with hardware, demonstrate the framework's effectiveness in addressing underactuated bipedal locomotion on swaying ground, even in the presence of uncertain surface motions and unknown external pushes.

cs.RO↗

arXiv2024

An Adaptive Framework for Manipulator Skill Reproduction in Dynamic Environments

Robot skill learning and execution in uncertain and dynamic environments is a challenging task. This paper proposes an adaptive framework that combines Learning from Demonstration (LfD), environment state prediction, and high-level decision making. Proactive adaptation prevents the need for reactive adaptation, which lags behind changes in the environment rather than anticipating them. We propose a novel LfD representation, Elastic-Laplacian Trajectory Editing (ELTE), which continuously adapts the trajectory shape to predictions of future states. Then, a high-level reactive system using an Unscented Kalman Filter (UKF) and Hidden Markov Model (HMM) prevents unsafe execution in the current state of the dynamic environment based on a discrete set of decisions. We first validate our LfD representation in simulation, then experimentally assess the entire framework using a legged mobile manipulator in 36 real-world scenarios. We show the effectiveness of the proposed framework under different dynamic changes in the environment. Our results show that the proposed framework produces robust and stable adaptive behaviors.

cs.RO↗