arXiv subjects

Hong Xie

Publications and source records attributed to Hong Xie.

At least 55 records · Page 3Linked to original sources

arXiv2025

Advancing and Benchmarking Personalized Tool Invocation for LLMs

Tool invocation is a crucial mechanism for extending the capabilities of Large Language Models (LLMs) and has recently garnered significant attention. It enables LLMs to solve complex problems through tool calls while accessing up-to-date world knowledge. However, existing work primarily focuses on the fundamental ability of LLMs to invoke tools for problem-solving, without considering personalized constraints in tool invocation. In this work, we introduce the concept of Personalized Tool Invocation and define two key tasks: Tool Preference and Profile-dependent Query. Tool Preference addresses user preferences when selecting among functionally similar tools, while Profile-dependent Query considers cases where a user query lacks certain tool parameters, requiring the model to infer them from the user profile. To tackle these challenges, we propose PTool, a data synthesis framework designed for personalized tool invocation. Additionally, we construct \textbf{PTBench}, the first benchmark for evaluating personalized tool invocation. We then fine-tune various open-source models, demonstrating the effectiveness of our framework and providing valuable insights. Our benchmark is public at https://github.com/hyfshadow/PTBench.

cs.CL↗

arXiv2025

The Longest Duration SGRE Event in Solar Cycle 25

Solar Cycle (SC) 24 was the weakest in the space age, yet it produced many sustained gamma ray emission (SGRE) events from the Sun. Solar cycle (SC) 25, which is a bit stronger than SC 24 observed only a handful of SGRE events over the first five years. Here we report on the 2024 September 14 SGRE event, which has the longest duration (\~11.29 hrs) as of this writing. The associated type II radio burst is also of long duration (\~16 hr). Detailed analysis of the SGRE event reveals that the event is in good agreement with the linear relation of the SGRE duration with the ending frequency and duration of the type II burst. The kinematics of the associated coronal mass ejection (CME) shows that it is one of the fastest CMEs of SC 25, capable of driving a shock that accelerated >300 MeV protons to account for the observed SGRE. By comparing with an event with similar durations in SC 24, we find that it had a lower-speed CME but resulted in a larger-sized SGRE event. We speculate that the difference may be due to the change in the heliospheric state between the two cycles.

astro-ph.SR↗

arXiv2025

Third Harmonic Structure in an Interplanetary Type II Radio Burst and Other Energetic Phenomena During the 2024 September 14 Solar Eruption

We report on the observation of first, second, and third harmonic components during an interplanetary (IP) type II solar radio burst observed on 2024 September 14 by the radio instruments on board Wind, the Solar Terrestrial Relations Observatory (STEREO), and the Parker Solar Probe. The eruption resulted in an ultrafast coronal mass ejection (CME) that had a sky plane speed of \~2366 km per sec, and an X4.5 flare from NOAA active region 13825 (S15E56). Also observed were a large solar energetic particle (SEP) event and a sustained gamma ray emission (SGRE) event. The IP type II burst consists of multiple features. The first, second, and third harmonic bursts are smooth and diffuse with additional patchy bursts superposed only on the fundamental component. The existence of fundamental harmonic structure including the third harmonic can be readily explained by the coherent plasma emission mechanism and works against the possibility of synchrotron mechanism.

astro-ph.SR↗

arXiv2025

The SOHO LASCO CME Catalog -- Version 2

This paper provides an update on the coronal mass ejection (CME) catalog maintained at the CDAW Data Center, NASA Goddard Space Flight Center (https://cdaw.gsfc.nasa.gov/CME_list). This is version 2 (v2) of the Catalog that has been made as the default version as of May 1, 2024. The new features of the Catalog v2 are (i) online measurement tool, (ii) combination JavaScript movies from the STEREO and Solar Dynamics Observatory (SDO) missions, and (iii) insertion of newly identified CMEs for the period 1996 to 2004. The CME identification was revisited resulting in a set of $\sim$3000 new CMEs added to the Catalog. A vast majority of these CMEs are weak and narrow. The resulting statistical properties of CMEs are not significantly different from those reported using version 1.

astro-ph.SR↗

arXiv2025

Implications of the abundance of halo coronal mass ejections for the strength of solar cycle 25

We assess the relative strength of solar cycle (SC) 25 with respect to SCs 23 and 24 based on the abundance of halo coronal mass ejections (CMEs). We make use of the halo CME database (https://cdaw.gsfc.nasa.gov/CME_list/halo/halo.html) to compare the halo CME abundance during the first four years in each of SCs 23 to 25. The main result is that in several aspects such as the abundance, occurrence rate, source locations, and halo heights, halo CMEs are similar between SCs 24 and 25 but different from SC 23. This result follows from the fact that weaker cycles have low heliospheric total pressure, whose backreaction on CMEs allows them to expand more and hence enhancing the chance of becoming a halo. The solar cycle variation of halo CME properties is consistent with the precursor-based cycle prediction methods that indicate SC 25 is similar to or only slightly stronger than SC 24.

astro-ph.SR↗

arXiv2025

Solar Cycle Variation of Axial Orientations and Favorable Locations of Eruptive MFRs

Using multi-viewpoint observations from STEREO and SOHO during three solar cycles from 23 to 25, we study the magnetic flux rope (MFR) structures of coronal mass ejections (CMEs) near the Sun and magnetic clouds (MCs) at 1au. The study aims to investigate two phenomena: 1) the occurrence rate of CMEs near Hale sector boundaries (HBs) and 2) solar-cycle variation of MFR axial orientations in CMEs and MCs. Our preliminary results include: 1) the axes of MFRs in cycle 25 present a systematic northward orientation, which is the same as in cycle 23 but opposite to cycle 24; 2) the majority of the MFRs occurred near HBs (within 30 degrees) and some exceptional events occurred at non-HBs; 3) the axial fields in MCs present a similar north-south orientation, which changes from cycle to cycle. We discuss the implication of solar cycle variations of MFR axial orientations for space weather forecasts.

astro-ph.SR↗

arXiv2024

Tortho-Gaussian: Splatting True Digital Orthophoto Maps

True Digital Orthophoto Maps (TDOMs) are essential products for digital twins and Geographic Information Systems (GIS). Traditionally, TDOM generation involves a complex set of traditional photogrammetric process, which may deteriorate due to various challenges, including inaccurate Digital Surface Model (DSM), degenerated occlusion detections, and visual artifacts in weak texture regions and reflective surfaces, etc. To address these challenges, we introduce TOrtho-Gaussian, a novel method inspired by 3D Gaussian Splatting (3DGS) that generates TDOMs through orthogonal splatting of optimized anisotropic Gaussian kernel. More specifically, we first simplify the orthophoto generation by orthographically splatting the Gaussian kernels onto 2D image planes, formulating a geometrically elegant solution that avoids the need for explicit DSM and occlusion detection. Second, to produce TDOM of large-scale area, a divide-and-conquer strategy is adopted to optimize memory usage and time efficiency of training and rendering for 3DGS. Lastly, we design a fully anisotropic Gaussian kernel that adapts to the varying characteristics of different regions, particularly improving the rendering quality of reflective surfaces and slender structures. Extensive experimental evaluations demonstrate that our method outperforms existing commercial software in several aspects, including the accuracy of building boundaries, the visual quality of low-texture regions and building facades. These results underscore the potential of our approach for large-scale urban scene reconstruction, offering a robust alternative for enhancing TDOM quality and scalability.

cs.CV↗

arXiv2024

Commutation Relations in Adiabatic Elimination

The method of adiabatic elimination has been widely adopted in quantum optics in the past several decades. In the study of cavity-based light-matter interactions, the bad-cavity limit is often encountered, where the damping rate of the cavity is much larger than the interaction strength. The fast-damped cavity will quickly relax to a quasi-stationary state, and one can eliminate the cavity from the equation of motion by setting its time derivative to zero. Elimination of the cavity in the bad-cavity limit can reduce the dimensionality of the equations of motion of the system. However, we find that the adiabatic elimination procedure performed in the quantum Langevin equation leads to an incorrect commutation relation, which was rarely discussed in the former studies, as far as we know. Here, we show the incorrect commutation relation arises from the fact that the high frequency of the vacuum noise should be cut off to perform adiabatic elimination, but the noise with high frequency cutoff is still treated as white noise with infinite bandwidth and delta commutation relation. We also study the correlation function and show that the high frequency part of noise contributes very little when averaged over the bath. Therefore, the adiabatic elimination method can reduce the complexity of the calculations while maintaining physical reliability.

quant-ph↗

arXiv2024

Micro-Structures Graph-Based Point Cloud Registration for Balancing Efficiency and Accuracy

Point Cloud Registration (PCR) is a fundamental and significant issue in photogrammetry and remote sensing, aiming to seek the optimal rigid transformation between sets of points. Achieving efficient and precise PCR poses a considerable challenge. We propose a novel micro-structures graph-based global point cloud registration method. The overall method is comprised of two stages. 1) Coarse registration (CR): We develop a graph incorporating micro-structures, employing an efficient graph-based hierarchical strategy to remove outliers for obtaining the maximal consensus set. We propose a robust GNC-Welsch estimator for optimization derived from a robust estimator to the outlier process in the Lie algebra space, achieving fast and robust alignment. 2) Fine registration (FR): To refine local alignment further, we use the octree approach to adaptive search plane features in the micro-structures. By minimizing the distance from the point-to-plane, we can obtain a more precise local alignment, and the process will also be addressed effectively by being treated as a planar adjustment algorithm combined with Anderson accelerated optimization (PA-AA). After extensive experiments on real data, our proposed method performs well on the 3DMatch and ETH datasets compared to the most advanced methods, achieving higher accuracy metrics and reducing the time cost by at least one-third.

cs.CV↗

arXiv2024

Contextual Bandit with Herding Effects: Algorithms and Recommendation Applications

Contextual bandits serve as a fundamental algorithmic framework for optimizing recommendation decisions online. Though extensive attention has been paid to tailoring contextual bandits for recommendation applications, the "herding effects" in user feedback have been ignored. These herding effects bias user feedback toward historical ratings, breaking down the assumption of unbiased feedback inherent in contextual bandits. This paper develops a novel variant of the contextual bandit that is tailored to address the feedback bias caused by the herding effects. A user feedback model is formulated to capture this feedback bias. We design the TS-Conf (Thompson Sampling under Conformity) algorithm, which employs posterior sampling to balance the exploration and exploitation tradeoff. We prove an upper bound for the regret of the algorithm, revealing the impact of herding effects on learning speed. Extensive experiments on datasets demonstrate that TS-Conf outperforms four benchmark algorithms. Analysis reveals that TS-Conf effectively mitigates the negative impact of herding effects, resulting in faster learning and improved recommendation accuracy.

cs.LG↗

arXiv2024

Multi-agent Multi-armed Bandits with Stochastic Sharable Arm Capacities

Motivated by distributed selection problems, we formulate a new variant of multi-player multi-armed bandit (MAB) model, which captures stochastic arrival of requests to each arm, as well as the policy of allocating requests to players. The challenge is how to design a distributed learning algorithm such that players select arms according to the optimal arm pulling profile (an arm pulling profile prescribes the number of players at each arm) without communicating to each other. We first design a greedy algorithm, which locates one of the optimal arm pulling profiles with a polynomial computational complexity. We also design an iterative distributed algorithm for players to commit to an optimal arm pulling profile with a constant number of rounds in expectation. We apply the explore then commit (ETC) framework to address the online setting when model parameters are unknown. We design an exploration strategy for players to estimate the optimal arm pulling profile. Since such estimates can be different across different players, it is challenging for players to commit. We then design an iterative distributed algorithm, which guarantees that players can arrive at a consensus on the optimal arm pulling profile in only M rounds. We conduct experiments to validate our algorithm.

cs.AI↗

arXiv2024

Analytical and Empirical Study of Herding Effects in Recommendation Systems

Online rating systems are often used in numerous web or mobile applications, e.g., Amazon and TripAdvisor, to assess the ground-truth quality of products. Due to herding effects, the aggregation of historical ratings (or historical collective opinion) can significantly influence subsequent ratings, leading to misleading and erroneous assessments. We study how to manage product ratings via rating aggregation rules and shortlisted representative reviews, for the purpose of correcting the assessment error. We first develop a mathematical model to characterize important factors of herding effects in product ratings. We then identify sufficient conditions (via the stochastic approximation theory), under which the historical collective opinion converges to the ground-truth collective opinion of the whole user population. These conditions identify a class of rating aggregation rules and review selection mechanisms that can reveal the ground-truth product quality. We also quantify the speed of convergence (via the martingale theory), which reflects the efficiency of rating aggregation rules and review selection mechanisms. We prove that the herding effects slow down the speed of convergence while an accurate review selection mechanism can speed it up. We also study the speed of convergence numerically and reveal trade-offs in selecting rating aggregation rules and review selection mechanisms. To show the utility of our framework, we design a maximum likelihood algorithm to infer model parameters from ratings, and conduct experiments on rating datasets from Amazon and TripAdvisor. We show that proper recency aware rating aggregation rules can improve the speed of convergence in Amazon and TripAdvisor by 41% and 62% respectively.

cs.AI↗

arXiv2024

SfM on-the-fly: Get better 3D from What You Capture

In the last twenty years, Structure from Motion (SfM) has been a constant research hotspot in the fields of photogrammetry, computer vision, robotics etc., whereas real-time performance is just a recent topic of growing interest. This work builds upon the original on-the-fly SfM (Zhan et al., 2024) and presents an updated version with three new advancements to get better 3D from what you capture: (i) real-time image matching is further boosted by employing the Hierarchical Navigable Small World (HNSW) graphs, thus more true positive overlapping image candidates are faster identified; (ii) a self-adaptive weighting strategy is proposed for robust hierarchical local bundle adjustment to improve the SfM results; (iii) multiple agents are included for supporting collaborative SfM and seamlessly merge multiple 3D reconstructions into a complete 3D scene when commonly registered images appear. Various comprehensive experiments demonstrate that the proposed SfM method (named on-the-fly SfMv2) can generate more complete and robust 3D reconstructions in a high time-efficient way. Code is available at http://yifeiyu225.github.io/on-the-flySfMv2.github.io/.

cs.CV↗

arXiv2024

Foundations and Frontiers of Graph Learning Theory

Recent advancements in graph learning have revolutionized the way to understand and analyze data with complex structures. Notably, Graph Neural Networks (GNNs), i.e. neural network architectures designed for learning graph representations, have become a popular paradigm. With these models being usually characterized by intuition-driven design or highly intricate components, placing them within the theoretical analysis framework to distill the core concepts, helps understand the key principles that drive the functionality better and guide further development. Given this surge in interest, this article provides a comprehensive summary of the theoretical foundations and breakthroughs concerning the approximation and learning behaviors intrinsic to prevalent graph learning models. Encompassing discussions on fundamental aspects such as expressiveness power, generalization, optimization, and unique phenomena such as over-smoothing and over-squashing, this piece delves into the theoretical foundations and frontier driving the evolution of graph learning. In addition, this article also presents several challenges and further initiates discussions on possible solutions.

cs.LG↗

arXiv2024

Enhancing Terrestrial Net Primary Productivity Estimation with EXP-CASA: A Novel Light Use Efficiency Model Approach

The Light Use Efficiency model, epitomized by the CASA model, is extensively applied in the quantitative estimation of vegetation Net Primary Productivity. However, the classic CASA model is marked by significant complexity: the estimation of environmental stress parameters, in particular, necessitates multi-source observation data, adding to the complexity and uncertainty of the model's operation. Additionally, the saturation effect of the Normalized Difference Vegetation Index (NDVI), a key variable in the CASA model, weakened the accuracy of CASA's NPP predictions in densely vegetated areas. To address these limitations, this study introduces the Exponential-CASA (EXP-CASA) model. The EXP-CASA model effectively improves the CASA model by using novel functions for estimating the fraction of absorbed photosynthetically active radiation (FPAR) and environmental stress, by utilizing long-term observational data from FLUXNET and MODIS surface reflectance data. In a comparative analysis of NPP estimation accuracy among four different NPP products, EXP-CASA ($R^2 = 0.68, RMSE= 1.1gC\cdot m^{-2} \cdot d^{-1}$) outperforms others, followed by GLASS-NPP, and lastly MODIS-NPP and classic CASA. Additionally, this research assesses the EXP-CASA model's adaptability to various vegetation indices, evaluates the sensitivity and stability of its parameters over time, and compares its accuracy against other leading NPP estimation products. The findings reveal that the EXP-CASA model exhibits strong adaptability to diverse vegetation indices and stability of model parameters over time series. By introducing a novel estimation approach that optimizes model construction, the EXP-CASA model remarkably improves the accuracy of NPP estimations and paves the way for global-scale, consistent, and continuous assessment of vegetation NPP.

q-bio.QM↗

arXiv2024

Understanding and Patching Compositional Reasoning in LLMs

LLMs have marked a revolutonary shift, yet they falter when faced with compositional reasoning tasks. Our research embarks on a quest to uncover the root causes of compositional reasoning failures of LLMs, uncovering that most of them stem from the improperly generated or leveraged implicit reasoning results. Inspired by our empirical findings, we resort to Logit Lens and an intervention experiment to dissect the inner hidden states of LLMs. This deep dive reveals that implicit reasoning results indeed surface within middle layers and play a causative role in shaping the final explicit reasoning results. Our exploration further locates multi-head self-attention (MHSA) modules within these layers, which emerge as the linchpins in accurate generation and leveraing of implicit reasoning results. Grounded on the above findings, we develop CREME, a lightweight method to patch errors in compositional reasoning via editing the located MHSA modules. Our empirical evidence stands testament to CREME's effectiveness, paving the way for autonomously and continuously enhancing compositional reasoning capabilities in language models.

cs.CL↗

arXiv2024

Invariant Representation via Decoupling Style and Spurious Features from Images

This paper considers the out-of-distribution (OOD) generalization problem under the setting that both style distribution shift and spurious features exist and domain labels are missing. This setting frequently arises in real-world applications and is underlooked because previous approaches mainly handle either of these two factors. The critical challenge is decoupling style and spurious features in the absence of domain labels. To address this challenge, we first propose a structural causal model (SCM) for the image generation process, which captures both style distribution shift and spurious features. The proposed SCM enables us to design a new framework called IRSS, which can gradually separate style distribution and spurious features from images by introducing adversarial neural networks and multi-environment optimization, thus achieving OOD generalization. Moreover, it does not require additional supervision (e.g., domain labels) other than the images and their corresponding labels. Experiments on benchmark datasets demonstrate that IRSS outperforms traditional OOD methods and solves the problem of Invariant risk minimization (IRM) degradation, enabling the extraction of invariant features under distribution shift.

cs.CV↗

arXiv2024

Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users

We study the problem of federated contextual combinatorial cascading bandits, where $|\mathcal{U}|$ agents collaborate under the coordination of a central server to provide tailored recommendations to the $|\mathcal{U}|$ corresponding users. Existing works consider either a synchronous framework, necessitating full agent participation and global synchronization, or assume user homogeneity with identical behaviors. We overcome these limitations by considering (1) federated agents operating in an asynchronous communication paradigm, where no mandatory synchronization is required and all agents communicate independently with the server, (2) heterogeneous user behaviors, where users can be stratified into $J \le |\mathcal{U}|$ latent user clusters, each exhibiting distinct preferences. For this setting, we propose a UCB-type algorithm with delicate communication protocols. Through theoretical analysis, we give sub-linear regret bounds on par with those achieved in the synchronous framework, while incurring only logarithmic communication costs. Empirical evaluation on synthetic and real-world datasets validates our algorithm's superior performance in terms of regrets and communication costs.

cs.LG↗