arXiv subjects

Xu Wang

Publications and source records attributed to Xu Wang.

At least 235 records · Page 13Linked to original sources

arXiv2025

MeetMap: Real-Time Collaborative Dialogue Mapping with LLMs in Online Meetings

Video meeting platforms display conversations linearly through transcripts or summaries. However, ideas during a meeting do not emerge linearly. We leverage LLMs to create dialogue maps in real time to help people visually structure and connect ideas. Balancing the need to reduce the cognitive load on users during the conversation while giving them sufficient control when using AI, we explore two system variants that encompass different levels of AI assistance. In Human-Map, AI generates summaries of conversations as nodes, and users create dialogue maps with the nodes. In AI-Map, AI produces dialogue maps where users can make edits. We ran a within-subject experiment with ten pairs of users, comparing the two MeetMap variants and a baseline. Users preferred MeetMap over traditional methods for taking notes, which aligned better with their mental models of conversations. Users liked the ease of use for AI-Map due to the low effort demands and appreciated the hands-on opportunity in Human-Map for sense-making.

cs.HC↗

arXiv2025

TeachTune: Reviewing Pedagogical Agents Against Diverse Student Profiles with Simulated Students

Large language models (LLMs) can empower teachers to build pedagogical conversational agents (PCAs) customized for their students. As students have different prior knowledge and motivation levels, teachers must review the adaptivity of their PCAs to diverse students. Existing chatbot reviewing methods (e.g., direct chat and benchmarks) are either manually intensive for multiple iterations or limited to testing only single-turn interactions. We present TeachTune, where teachers can create simulated students and review PCAs by observing automated chats between PCAs and simulated students. Our technical pipeline instructs an LLM-based student to simulate prescribed knowledge levels and traits, helping teachers explore diverse conversation patterns. Our pipeline could produce simulated students whose behaviors correlate highly to their input knowledge and motivation levels within 5% and 10% accuracy gaps. Thirty science teachers designed PCAs in a between-subjects study, and using TeachTune resulted in a lower task load and higher student profile coverage over a baseline.

cs.HC↗

arXiv2025

Personalized Parsons Puzzles as Scaffolding Enhance Practice Engagement Over Just Showing LLM-Powered Solutions

As generative AI products could generate code and assist students with programming learning seamlessly, integrating AI into programming education contexts has driven much attention. However, one emerging concern is that students might get answers without learning from the LLM-generated content. In this work, we deployed the LLM-powered personalized Parsons puzzles as scaffolding to write-code practice in a Python learning classroom (PC condition) and conducted an 80-minute randomized between-subjects study. Both conditions received the same practice problems. The only difference was that when requesting help, the control condition showed students a complete solution (CC condition), simulating the most traditional LLM output. Results indicated that students who received personalized Parsons puzzles as scaffolding engaged in practicing significantly longer than those who received complete solutions when struggling.

cs.HC↗

arXiv2025

Get Rid of Isolation: A Continuous Multi-task Spatio-Temporal Learning Framework

Spatiotemporal learning has become a pivotal technique to enable urban intelligence. Traditional spatiotemporal models mostly focus on a specific task by assuming a same distribution between training and testing sets. However, given that urban systems are usually dynamic, multi-sourced with imbalanced data distributions, current specific task-specific models fail to generalize to new urban conditions and adapt to new domains without explicitly modeling interdependencies across various dimensions and types of urban data. To this end, we argue that there is an essential to propose a Continuous Multi-task Spatio-Temporal learning framework (CMuST) to empower collective urban intelligence, which reforms the urban spatiotemporal learning from single-domain to cooperatively multi-dimensional and multi-task learning. Specifically, CMuST proposes a new multi-dimensional spatiotemporal interaction network (MSTI) to allow cross-interactions between context and main observations as well as self-interactions within spatial and temporal aspects to be exposed, which is also the core for capturing task-level commonality and personalization. To ensure continuous task learning, a novel Rolling Adaptation training scheme (RoAda) is devised, which not only preserves task uniqueness by constructing data summarization-driven task prompts, but also harnesses correlated patterns among tasks by iterative model behavior modeling. We further establish a benchmark of three cities for multi-task spatiotemporal learning, and empirically demonstrate the superiority of CMuST via extensive evaluations on these datasets. The impressive improvements on both few-shot streaming data and new domain tasks against existing SOAT methods are achieved. Code is available at https://github.com/DILab-USTCSZ/CMuST.

cs.LG↗

arXiv2025

Location Privacy Threats and Protections in 6G Vehicular Networks: A Comprehensive Review

Location privacy is critical in vehicular networks, where drivers' trajectories and personal information can be exposed, allowing adversaries to launch data and physical attacks that threaten drivers' safety and personal security. This survey reviews comprehensively different localization techniques, including widely used ones like sensing infrastructure-based, optical vision-based, and cellular radio-based localization, and identifies inadequately addressed location privacy concerns. We classify Location Privacy Preserving Mechanisms (LPPMs) into user-side, server-side, and user-server-interface-based, and evaluate their effectiveness. Our analysis shows that the user-server-interface-based LPPMs have received insufficient attention in the literature, despite their paramount importance in vehicular networks. Further, we examine methods for balancing data utility and privacy protection for existing LPPMs in vehicular networks and highlight emerging challenges from future upper-layer location privacy attacks, wireless technologies, and network convergences. By providing insights into the relationship between localization techniques and location privacy, and evaluating the effectiveness of different LPPMs, this survey can help inform the development of future LPPMs in vehicular networks.

cs.CR↗

arXiv2024

Theoretical Investigation of (Zn, Co) co-Doped BaTiO3 for Advanced Energy and Photonic Applications

In light of recent advancements in energy technology, there is an urgent need for lead-free barium titanate (BTO) -based materials that exhibit remarkable ferroelectric and photoelectric properties. Notwithstanding the considerable experimental advances, a theoretical understanding from the electron and atomic perspectives remains elusive. This study employs the generalized gradient approximation plane wave pseudopotential technique to investigate the structural, electronic, ferroelectric, and optical properties of (Zn,Co) co-doped BaTiO3 (BZCT) based on density functional theory. The objective is to ascertain the extent of performance enhancement and the underlying mechanism of (Zn,Co) co-doping on barium titanate. Our findings reveal that incorporating (Zn,Co) into the BaTiO3 lattice significantly augments the tetragonality of the unit cell. Moreover, the ferroelectric properties are enhanced, with a spontaneous polarization stronger than that observed in pure BTO, exhibiting excellent ferroelectricity. The results of the Hubbard+U algorithm indicate that the band gap of BZCT is reduced. Concurrently, the enhanced ferroelectric polarization increases the built-in electric field of the material, facilitating the separation of photogenerated carriers and improving optical absorption. Consequently, the optical absorption ability and photorefractive ability are effectively enhanced. BZCT, with its high spontaneous polarization and outstanding optical properties, can be a promising candidate material in energy storage and photovoltaics.

cond-mat.mtrl-sci↗

arXiv2024

Linguistics-Vision Monotonic Consistent Network for Sign Language Production

Sign Language Production (SLP) aims to generate sign videos corresponding to spoken language sentences, where the conversion of sign Glosses to Poses (G2P) is the key step. Due to the cross-modal semantic gap and the lack of word-action correspondence labels for strong supervision alignment, the SLP suffers huge challenges in linguistics-vision consistency. In this work, we propose a Transformer-based Linguistics-Vision Monotonic Consistent Network (LVMCN) for SLP, which constrains fine-grained cross-modal monotonic alignment and coarse-grained multimodal semantic consistency in language-visual cues through Cross-modal Semantic Aligner (CSA) and Multimodal Semantic Comparator (MSC). In the CSA, we constrain the implicit alignment between corresponding gloss and pose sequences by computing the cosine similarity association matrix between cross-modal feature sequences (i.e., the order consistency of fine-grained sign glosses and actions). As for MSC, we construct multimodal triplets based on paired and unpaired samples in batch data. By pulling closer the corresponding text-visual pairs and pushing apart the non-corresponding text-visual pairs, we constrain the semantic co-occurrence degree between corresponding gloss and pose sequences (i.e., the semantic consistency of coarse-grained textual sentences and sign videos). Extensive experiments on the popular PHOENIX14T benchmark show that the LVMCN outperforms the state-of-the-art.

cs.CV↗

arXiv2024

Searching for Neutrinoless Double-Beta Decay of $^{136}$Xe with PandaX-4T

We report the search for neutrinoless double-beta decay of $^{136}$Xe from the PandaX-4T experiment with a 3.7-tonne natural xenon target. The data reconstruction and the background modeling are optimized in the MeV energy region. A blind analysis is performed with data from the commissioning run and the first science run. No significant excess of signal over the background is observed. A lower limit on the half-life of $^{136}$Xe neutrinoless double-beta decay is established to be $2.1 \times 10^{24}$~yr at the 90\% confidence level, with a $^{136}$Xe exposure of 44.6~kg$\cdot$year. Our result represents the most stringent constraint from a natural xenon detector to date.

nucl-ex↗

arXiv2024

SuperGS: Super-Resolution 3D Gaussian Splatting Enhanced by Variational Residual Features and Uncertainty-Augmented Learning

Recently, 3D Gaussian Splatting (3DGS) has exceled in novel view synthesis (NVS) with its real-time rendering capabilities and superior quality. However, it faces challenges for high-resolution novel view synthesis (HRNVS) due to the coarse nature of primitives derived from low-resolution input views. To address this issue, we propose Super-Resolution 3DGS (SuperGS), which is an expansion of 3DGS designed with a two-stage coarse-to-fine training framework. In this framework, we use a latent feature field to represent the low-resolution scene, serving as both the initialization and foundational information for super-resolution optimization. Additionally, we introduce variational residual features to enhance high-resolution details, using their variance as uncertainty estimates to guide the densification process and loss computation. Furthermore, the introduction of a multi-view joint learning approach helps mitigate ambiguities caused by multi-view inconsistencies in the pseudo labels. Extensive experiments demonstrate that SuperGS surpasses state-of-the-art HRNVS methods on both real-world and synthetic datasets using only low-resolution inputs. Code is available at https://github.com/SYXieee/SuperGS.

cs.CV↗

arXiv2024

PandaX-xT: a Multi-ten-tonne Liquid Xenon Observatory at the China Jinping Underground Laboratory

We propose a major upgrade to the existing PandaX-4T experiment in the China Jinping Underground Laboratory. The new experiment, PandaX-xT, will be a multi-ten-tonne liquid xenon, ultra-low background, and general-purpose observatory. The full-scaled PandaX-xT contains a 43-tonne liquid xenon active target. Such an experiment will significantly advance our fundamental understanding of particle physics and astrophysics. The sensitivity of dark matter direct detection will be improved by nearly two orders of magnitude compared to the current best limits, approaching the so-called "neutrino floor" for a dark matter mass above 10 GeV/$c^2$, providing a decisive test to the Weakly Interacting Massive Particle paradigm. By searching for the neutrinoless double beta decay of $^{136}$Xe isotope in the detector, the effective Majorana neutrino mass can be measured to a [10 -- 41] meV/$c^2$ sensitivity, providing a key test to the Dirac/Majorana nature of neutrino s. Astrophysical neutrinos and other ultra-rare interactions can also be measured and searched for with an unprecedented background level, opening up new windows of discovery. Depending on the findings, PandaX-xT will seek the next stage upgrade utilizing isotopic separation on natural xenon.

hep-ex↗

arXiv2024

Learn to Unlearn: Meta-Learning-Based Knowledge Graph Embedding Unlearning

Knowledge graph (KG) embedding methods map entities and relations into continuous vector spaces, improving performance in tasks like link prediction and question answering. With rising privacy concerns, machine unlearning (MU) has emerged as a critical AI technology, enabling models to eliminate the influence of specific data. Existing MU approaches often rely on data obfuscation and adjustments to training loss but lack generalization across unlearning tasks. This paper introduces MetaEU, a Meta-Learning-Based Knowledge Graph Embedding Unlearning framework. MetaEU leverages meta-learning to unlearn specific embeddings, mitigating their impact while preserving model performance on remaining data. Experiments on benchmark datasets demonstrate its effectiveness in KG embedding unlearning.

cs.AI↗

arXiv2024

MaintAGT:Sim2Real-Guided Multimodal Large Model for Intelligent Maintenance with Chain-of-Thought Reasoning

In recent years, large language models have made significant advancements in the field of natural language processing, yet there are still inadequacies in specific domain knowledge and applications. This paper Proposes MaintAGT, a professional large model for intelligent operations and maintenance, aimed at addressing this issue. The system comprises three key components: a signal-to-text model, a pure text model, and a multimodal model. Firstly, the signal-to-text model was designed to convert raw signal data into textual descriptions, bridging the gap between signal data and text-based analysis. Secondly, the pure text model was fine-tuned using the GLM4 model with specialized knowledge to enhance its understanding of domain-specific texts. Finally, these two models were integrated to develop a comprehensive multimodal model that effectively processes and analyzes both signal and textual data.The dataset used for training and evaluation was sourced from academic papers, textbooks, international standards, and vibration analyst training materials, undergoing meticulous preprocessing to ensure high-quality data. As a result, the model has demonstrated outstanding performance across multiple intelligent operations and maintenance tasks, providing a low-cost, high-quality method for constructing large-scale monitoring signal-text description-fault pattern datasets. Experimental results indicate that the model holds significant advantages in condition monitoring, signal processing, and fault diagnosis.In the constructed general test set, MaintAGT achieved an accuracy of 70%, surpassing all existing general large language models and reaching the level of an ISO Level III human vibration analyst.This advancement signifies a crucial step forward from traditional maintenance practices toward intelligent and AI-driven maintenance solutions.

eess.SP↗

arXiv2024

Cooperative engineering the multiple radio-frequency fields to reduce the X-junction barrier for ion trap chips

With the increasing number of ion qubits and improving performance of sophisticated quantum algorithms, more and more scalable complex ion trap electrodes have been developed and integrated. Nonlinear ion shuttling operations at the junction are more frequently used, such as in the areas of separation, merging, and exchanging. Several studies have been conducted to optimize the geometries of the radio-frequency (RF) electrodes to generate ideal trapping electric fields with a lower junction barrier and an even ion height of the RF saddle points. However, this iteration is time-consuming and commonly accompanied by complicated and sharp electrode geometry. Therefore, high-accuracy fabrication process and high electric breakdown voltage are essential. In the current work, an effective method was proposed to reduce the junction's pseudo-potential barrier and ion height variation by setting several individual RF electrodes and adjusting each RF voltage amplitude without changing the geometry of the electrode structure. The simulation results show that this method shows the same effect on engineering the trapping potential and reducing the potential barrier, but requires fewer parameters and optimization time. By combining this method with the geometrical shape-optimizing, the pseudo-potential barrier and the ion height variation near the junction can be further reduced. In addition, the geometry of the electrodes can be simplified to relax the fabrication precision and keep the ability to engineer the trapping electric field in real-time even after the fabrication of the electrodes, which provides a potential all-electric degree of freedom for the design and control of the two-dimensional ion crystals and investigation of their phase transition.

quant-ph↗

arXiv2024

Moving Forward: A Review of Autonomous Driving Software and Hardware Systems

With their potential to significantly reduce traffic accidents, enhance road safety, optimize traffic flow, and decrease congestion, autonomous driving systems are a major focus of research and development in recent years. Beyond these immediate benefits, they offer long-term advantages in promoting sustainable transportation by reducing emissions and fuel consumption. Achieving a high level of autonomy across diverse conditions requires a comprehensive understanding of the environment. This is accomplished by processing data from sensors such as cameras, radars, and LiDARs through a software stack that relies heavily on machine learning algorithms. These ML models demand significant computational resources and involve large-scale data movement, presenting challenges for hardware to execute them efficiently and at high speed. In this survey, we first outline and highlight the key components of self-driving systems, covering input sensors, commonly used datasets, simulation platforms, and the software architecture. We then explore the underlying hardware platforms that support the execution of these software systems. By presenting a comprehensive view of autonomous driving systems and their increasing demands, particularly for higher levels of autonomy, we analyze the performance and efficiency of scaled-up off-the-shelf GPU/CPU-based systems, emphasizing the challenges within the computational components. Through examples showcasing the diverse computational and memory requirements in the software stack, we demonstrate how more specialized hardware and processing closer to memory can enable more efficient execution with lower latency. Finally, based on current trends and future demands, we conclude by speculating what a future hardware platform for autonomous driving might look like.

cs.RO↗

arXiv2024

Time-integrated polarizations in GRB prompt phase via the Multi-window interpretation

The multi-window observations, including the light curve and the evolutions of the spectral peak energy ($E_p$), the polarization degree (PD) and the polarization angle (PA), are used to infer the model parameters to predict the time-integrated PD in gamma-ray burst (GRB) prompt phase. We select 23 GRBs co-detected by Fermi/GBM and polarization detectors (i.e., GAP, POLAR and AstroSat). In our multi-window fitting, the light curve, $E_p$ curve, PD curve and PA curve are interpreted simultaneously under the synchrotron radiation model in ordered magnetic fields (i.e., the aligned-fields case and the toroidal-fields case). For the bursts with abrupt PA rotations, the predicted time-integrated PD of the aligned-fields case roughly matches the corresponding observed best fit value, while it is higher for the toroidal-fields case. For the bursts without abrupt PA rotation(s), the predicted PDs of the aligned-fields case and the toroidal-fields case are comparable and could interpret the observational data equally well. For GRB 170206A, its observed time-resolved and time-integrated PDs are comparable and both smaller than our predicted upper limits in ordered magnetic fields. So mixed magnetic fields, i.e., the magnetic fields with both ordered and random components, should be reside in the radiation regions of this burst. Except 1 out of the total 23 bursts, the predicted time-integrated PDs, which are around $\sim44\%$ for the aligned-fields case and around $49\%$ for the toroidal-fields case, are consistent with the corresponding observed values. Therefore, consistent with the former study, the models with synchrotron radiation in ordered magnetic fields could interpret most of the current polrization data within $1σ$ error bar.

astro-ph.HE↗

arXiv2024

Origin of the twice ${90}^{\circ}$ rotations of the polarization angle in GRB 170114A and GRB 160821A

The observed twice abrupt ${90}^{\circ}$ rotations of the polarization angle (PA) in the prompt phase of gamma-ray bursts (GRBs) are difficult to be understandable within the current one-emitting-shell models. Here, we apply a model with multiple emitting shells to solve this new challenging problem. Two configurations of large-scale ordered magnetic fields in the shells are considered: toroidal and aligned. Together with the light curves and the spectral peak-energy evolutions, the twice ${90}^{\circ}$ PA rotations in GRB 170114A and GRB 160821A could be well interpreted with the multi-shell aligned magnetic fields configuration. Our numerical calculations also show that the multiple shells with the toroidal magnetic field configuration could not explain the observed twice ${90}^{\circ}$ PA rotations. An aligned magnetic field configuration in the GRB outflow usually indicate to prefer a magnetar central engine, while a toroidal field configuration is typically related to a central black hole. Therefore, the magnetar central engines for the two GRBs are favored.

astro-ph.HE↗

arXiv2024

A Powder Diffraction-AI Solution for Crystalline Structure

Determining the atomic-level structure of crystalline solids is critically important across a wide array of scientific disciplines. The challenges associated with obtaining samples suitable for single-crystal diffraction, coupled with the limitations inherent in classical structure determination methods that primarily utilize powder diffraction for most polycrystalline materials, underscore an urgent need to develop alternative approaches for elucidating the structures of commonly encountered crystalline compounds. In this work, we present an artificial intelligence-directed leapfrog model capable of accurately determining the structures of both organic and inorganic-organic hybrid crystalline solids through direct analysis of powder X-ray diffraction data. This model not only offers a comprehensive solution that effectively circumvents issues related to insoluble challenges in conventional structure solution methodologies but also demonstrates applicability to crystal structures across all conceivable space groups. Furthermore, it exhibits notable compatibility with routine powder diffraction data typically generated by standard instruments, featuring rapid data collection and normal resolution levels.

cond-mat.mtrl-sci↗

arXiv2024

Optimizing Neon-based Gas Mixtures for Two-stage Amplification Fast-timing Micromegas Detectors

Working gas components significantly impact the performance of gaseous detectors. A fast-timing Micromegas detector with two-stage amplification is prone to notable deterioration of uniformity when scaled up. This paper presents a simulation study based on Garfield++ that aims to enhance the performance of such detectors by exploring different gas mixtures. The properties of various gas compositions and their impact on detector performance including gain uniformity and time resolution were investigated in the simulation study. The gain uniformity and single-photon time resolution of the detector were evaluated in tests using a multi-channel PICOSEC Micromegas (MM) prototype with different gas mixtures. The experimental results are consistent with the findings of the simulation. Both simulation and experimental results indicate that a higher concentration of neon improves the detector's gain uniformity, while the impact of gas mixtures on time resolution should also be considered as a critical performance indicator. The study presented in this paper offers valuable insights for improving uniformity in large-area PICOSEC MM detectors and optimizing overall performance.

physics.ins-det↗