arXiv subjects

Jing Zhang

Publications and source records attributed to Jing Zhang.

At least 325 records · Page 18Linked to original sources

arXiv2025

Observation of high partial-wave Feshbach resonances in $^{39}$K Bose-Einstein condensates

We report the new observation of several high partial-wave (HPW) magnetic Feshbach resonances (FRs) in $^{39}$K atoms of the hyperfine substate $\left|F=1,m_{F}=-1\right\rangle$. These resonances locate at the region between two broad $s$-wave FRs from 32.6 G to 162.8 G, in which Bose-Einstein condensates (BECs) can be produced with tunable positive scattering length obtained by magnetic FRs. These HPW FRs are induced by the dipolar spin-spin interaction with s-wave in the open channel and HPW in the closed channel. Therefore, these HPW FRs have distinct characteristics in temperature dependence and loss line shape from that induced by spin-exchange interaction with HPWs in both open and closed channels. Among these resonances, one $d$-wave and two $g$-wave FRs are confirmed by the multichannel quantum-defect theory (MQDT) calculation. The HPW FRs have significant applications in many-body physics dominated by HPW pairing.

cond-mat.quant-gas↗

arXiv2025

DGSolver: Diffusion Generalist Solver with Universal Posterior Sampling for Image Restoration

Diffusion models have achieved remarkable progress in universal image restoration. While existing methods speed up inference by reducing sampling steps, substantial step intervals often introduce cumulative errors. Moreover, they struggle to balance the commonality of degradation representations and restoration quality. To address these challenges, we introduce \textbf{DGSolver}, a diffusion generalist solver with universal posterior sampling. We first derive the exact ordinary differential equations for generalist diffusion models and tailor high-order solvers with a queue-based accelerated sampling strategy to improve both accuracy and efficiency. We then integrate universal posterior sampling to better approximate manifold-constrained gradients, yielding a more accurate noise estimation and correcting errors in inverse inference. Extensive experiments show that DGSolver outperforms state-of-the-art methods in restoration accuracy, stability, and scalability, both qualitatively and quantitatively. Code and models will be available at https://github.com/MiliLab/DGSolver.

cs.CV↗

arXiv2025

Inter-Diffusion Generation Model of Speakers and Listeners for Effective Communication

Full-body gestures play a pivotal role in natural interactions and are crucial for achieving effective communication. Nevertheless, most existing studies primarily focus on the gesture generation of speakers, overlooking the vital role of listeners in the interaction process and failing to fully explore the dynamic interaction between them. This paper innovatively proposes an Inter-Diffusion Generation Model of Speakers and Listeners for Effective Communication. For the first time, we integrate the full-body gestures of listeners into the generation framework. By devising a novel inter-diffusion mechanism, this model can accurately capture the complex interaction patterns between speakers and listeners during communication. In the model construction process, based on the advanced diffusion model architecture, we innovatively introduce interaction conditions and the GAN model to increase the denoising step size. As a result, when generating gesture sequences, the model can not only dynamically generate based on the speaker's speech information but also respond in realtime to the listener's feedback, enabling synergistic interaction between the two. Abundant experimental results demonstrate that compared with the current state-of-the-art gesture generation methods, the model we proposed has achieved remarkable improvements in the naturalness, coherence, and speech-gesture synchronization of the generated gestures. In the subjective evaluation experiments, users highly praised the generated interaction scenarios, believing that they are closer to real life human communication situations. Objective index evaluations also show that our model outperforms the baseline methods in multiple key indicators, providing more powerful support for effective communication.

cs.GR↗

arXiv2025

Multi-level Governance, Smart Meter Adoption, and Utilities' Energy Efficiency Savings in the U.S

Smart grids enable the alignment of energy supply and demand, enhance energy efficiency, and raise consumer awareness of energy conservation. Smart meter, a vital technological component of smart grids, enables bidirectional communication between consumers and utility companies. This paper employs the two-stage least squares panel data model to examine the effects of federal funding and state legislative actions on smart meter adoption and the resulting energy efficiency savings in the residential sector of the United States. Additionally, we use machine learning techniques to select the subset of control variables that are influential for smart meter adoption and energy efficiency savings. The findings suggest that both federal financial assistance and state legislative actions have positive effects on smart meter adoption, and the interaction of federal funding and state policy interventions have a significantly greater effect on adoption as compared to the sum of the individual policy instrument alone. Furthermore, there exists a positive association between the adoption rate and energy efficiency savings. This study presents empirical evidence that underscores the significance of multi-level governance as an effective means for policy integration regarding smart meter adoptions, and it discusses the policy implications for grid modernization and environmental policies.

econ.GN↗

arXiv2025

Quantum Acoustics with Superconducting Qubits in the Multimode Transition-Coupling Regime

Hybrid mechanical-superconducting systems for quantum information processing have attracted significant attention due to their potential applications. In such systems, the weak coupling regime, dominated by dissipation, has been extensively studied. The strong coupling regime, where coherent energy exchange exceeds losses, has also been widely explored. However, the transition-coupling regime, which lies between the above two and exhibits rich, unique physics, remains underexplored. In this study, we fabricate a tunable coupling device to investigate the coupling of a superconducting transmon qubit to a seven-mode surface acoustic wave resonator (SAWR), with a particular focus on the transition-coupling regime. Through a series of phonon oscillation experiments and studies in the dispersive regime, we systematically characterize the performance of the SAWR. We then explore the complex dynamics of energy exchange between the qubit and the mechanical modes, highlighting the interplay between dissipation and coherence. Finally, we propose a protocol for qubit readout and fast reset with a multimode mechanical cavity using one mode for readout and another mode for reset. We have demonstrated in simulation that the qubit achieves both fast reset and high coherence performance when the qubit is coupled to the reset mode in the transition-coupling regime.

quant-ph↗

arXiv2025

LREA: Low-Rank Efficient Attention on Modeling Long-Term User Behaviors for CTR Prediction

With the rapid growth of user historical behavior data, user interest modeling has become a prominent aspect in Click-Through Rate (CTR) prediction, focusing on learning user intent representations. However, this complexity poses computational challenges, requiring a balance between model performance and acceptable response times for online services. Traditional methods often utilize filtering techniques. These techniques can lead to the loss of significant information by prioritizing top K items based on item attributes or employing low-precision attention mechanisms. In this study, we introduce LREA, a novel attention mechanism that overcomes the limitations of existing approaches while ensuring computational efficiency. LREA leverages low-rank matrix decomposition to optimize runtime performance and incorporates a specially designed loss function to maintain attention capabilities while preserving information integrity. During the inference phase, matrix absorption and pre-storage strategies are employed to effectively meet runtime constraints. The results of extensive offline and online experiments demonstrate that our method outperforms state-of-the-art approaches.

cs.IR↗

arXiv2025

Probability Density Geodesics in Image Diffusion Latent Space

Diffusion models indirectly estimate the probability density over a data space, which can be used to study its structure. In this work, we show that geodesics can be computed in diffusion latent space, where the norm induced by the spatially-varying inner product is inversely proportional to the probability density. In this formulation, a path that traverses a high density (that is, probable) region of image latent space is shorter than the equivalent path through a low density region. We present algorithms for solving the associated initial and boundary value problems and show how to compute the probability density along the path and the geodesic distance between two points. Using these techniques, we analyze how closely video clips approximate geodesics in a pre-trained image diffusion space. Finally, we demonstrate how these techniques can be applied to training-free image sequence interpolation and extrapolation, given a pre-trained image diffusion model.

cs.CV↗

arXiv2025

Impact of Radio Frequency Power on Columnar and Filamentary Modes in Atmospheric Pressure Very Low Frequency Plasma within Pores

The impact of radio frequency (RF) power on columnar and filamentary modes of very low frequency (VLF) plasma within pores is investigated in this work. The 12.5 kHz VLF discharge under various RF powers (13.56 MHz) was analyzed using optical photography and current-voltage measurements. Two-dimensional electron densities were derived using optical emission spectroscopy combined with collisional radiation modeling methods. It is found that RF power and very low frequency voltage (VVLF) significantly influence the plasma and its discharge modes within the 200 μm pore. Under low VVLF conditions, the plasma is more intense within the pore, and the discharge mode is columnar discharge. With increasing RF power, the reciprocal motion of electrons counteracts the local enhancement effect of columnar discharge, the discharge transforms into RF discharge, the pore is completely wrapped by the sheath, and the plasma inside is gradually quenched. Under high VVLF conditions, the electron density within the pore is low and the discharge mode is filamentary discharge. RF introduction reduces plasma intensity within the pores firstly. As RF power increases, more ion trapping in the pore increases the field strength distortion and enhances the plasma intensity inside the pore, this enhancement effects becomes more obvious with increasing RF power. In addition, the above effects were observed for all pore widths from 100 um to 1000 um. These findings provide key insights for controlling plasma in pores and offer new methodologies for plasma technology applications.

physics.plasm-ph↗

arXiv2025

Simultaneously detecting spatiotemporal changes with penalized Poisson regression models

In the realm of large-scale spatiotemporal data, abrupt changes are commonly occurring across both spatial and temporal domains. This study aims to address the concurrent challenges of detecting change points and identifying spatial clusters within spatiotemporal count data. We introduce an innovative method based on the Poisson regression model, employing doubly fused penalization to unveil the underlying spatiotemporal change patterns. To efficiently estimate the model, we present an iterative shrinkage and threshold based algorithm to minimize the doubly penalized likelihood function. We establish the statistical consistency properties of the proposed estimator, confirming its reliability and accuracy. Furthermore, we conduct extensive numerical experiments to validate our theoretical findings, thereby highlighting the superior performance of our method when compared to existing competitive approaches.

stat.ME↗

arXiv2025

Quadratic Interest Network for Multimodal Click-Through Rate Prediction

Multimodal click-through rate (CTR) prediction is a key technique in industrial recommender systems. It leverages heterogeneous modalities such as text, images, and behavioral logs to capture high-order feature interactions between users and items, thereby enhancing the system's understanding of user interests and its ability to predict click behavior. The primary challenge in this field lies in effectively utilizing the rich semantic information from multiple modalities while satisfying the low-latency requirements of online inference in real-world applications. To foster progress in this area, the Multimodal CTR Prediction Challenge Track of the WWW 2025 EReL@MIR Workshop formulates the problem into two tasks: (1) Task 1 of Multimodal Item Embedding: this task aims to explore multimodal information extraction and item representation learning methods that enhance recommendation tasks; and (2) Task 2 of Multimodal CTR Prediction: this task aims to explore what multimodal recommendation model can effectively leverage multimodal embedding features and achieve better performance. In this paper, we propose a novel model for Task 2, named Quadratic Interest Network (QIN) for Multimodal CTR Prediction. Specifically, QIN employs adaptive sparse target attention to extract multimodal user behavior features, and leverages Quadratic Neural Networks to capture high-order feature interactions. As a result, QIN achieved an AUC of 0.9798 on the leaderboard and ranked second in the competition. The model code, training logs, hyperparameter configurations, and checkpoints are available at https://github.com/salmon1802/QIN.

cs.IR↗

arXiv2025

CityWalker: Learning Embodied Urban Navigation from Web-Scale Videos

Navigating dynamic urban environments presents significant challenges for embodied agents, requiring advanced spatial reasoning and adherence to common-sense norms. Despite progress, existing visual navigation methods struggle in map-free or off-street settings, limiting the deployment of autonomous agents like last-mile delivery robots. To overcome these obstacles, we propose a scalable, data-driven approach for human-like urban navigation by training agents on thousands of hours of in-the-wild city walking and driving videos sourced from the web. We introduce a simple and scalable data processing pipeline that extracts action supervision from these videos, enabling large-scale imitation learning without costly annotations. Our model learns sophisticated navigation policies to handle diverse challenges and critical scenarios. Experimental results show that training on large-scale, diverse datasets significantly enhances navigation performance, surpassing current methods. This work shows the potential of using abundant online video data to develop robust navigation policies for embodied agents in dynamic urban settings. Project homepage is at https://ai4ce.github.io/CityWalker/.

cs.CV↗

arXiv2025

Partial-Wetting Phenomena in Active Matter

Abundant interfacial phenomena in nature, such as water droplets on lotus leaves and water transport in plant vessels, originate from partial-wetting phenomena, which can be well described by Young's equation. It remains an intriguing question whether similar behaviors exist in active matter. In this letter, we present a clear demonstration of the partial-wetting phenomenon in a ternary laning system, which is a typical active system. A phase diagram is constructed in which the relative drift velocities of different components govern the transitions among drying, partial wetting, and complete wetting states. The mechanical balance on the contact lines of the partial-wetting phase described by Young's equation is verified. A theoretical picture is proposed to explain the analogy of partial wetting in the laning system to that in the equilibrium system.

cond-mat.soft↗

arXiv2025

Robust Quantum Control using Reinforcement Learning from Demonstration

Quantum control requires high-precision and robust control pulses to ensure optimal system performance. However, control sequences generated with a system model may suffer from model bias, leading to low fidelity. While model-free reinforcement learning (RL) methods have been developed to avoid such biases, training an RL agent from scratch can be time-consuming, often taking hours to gather enough samples for convergence. This challenge has hindered the broad application of RL techniques to larger and more complex quantum control issues, limiting their adaptability. In this work, we use Reinforcement Learning from Demonstration (RLfD) to leverage the control sequences generated with system models and further optimize them with RL to avoid model bias. By avoiding learning from scratch and starting with reasonable control pulse shapes, this approach can increase sample efficiency by reducing the number of samples, which can significantly reduce the training time. Thus, this method can effectively handle pulse shapes that are discretized into more than 1000 pieces without compromising final fidelity. We have simulated the preparation of several high-fidelity non-classical states using the RLfD method. We also find that the training process is more stable when using RLfD. In addition, this method is suitable for fast gate calibration using reinforcement learning.

quant-ph↗

arXiv2025

Joint Channel Estimation and Signal Detection for MIMO-OFDM: A Novel Data-Aided Approach with Reduced Computational Overhead

The acquisition of channel state information (CSI) is essential in MIMO-OFDM communication systems. Data-aided enhanced receivers, by incorporating domain knowledge, effectively mitigate performance degradation caused by imperfect CSI, particularly in dynamic wireless environments. However, existing methodologies face notable challenges: they either refine channel estimates within MIMO subsystems separately, which proves ineffective due to deviations from assumptions regarding the time-varying nature of channels, or fully exploit the time-frequency characteristics but incur significantly high computational overhead due to dimensional concatenation. To address these issues, this study introduces a novel data-aided method aimed at reducing complexity, particularly suited for fast-fading scenarios in fifth-generation (5G) and beyond networks. We derive a general form of a data-aided linear minimum mean-square error (LMMSE)-based algorithm, optimized for iterative joint channel estimation and signal detection. Additionally, we propose a computationally efficient alternative to this algorithm, which achieves comparable performance with significantly reduced complexity. Empirical evaluations reveal that our proposed algorithms outperform several state-of-the-art approaches across various MIMO-OFDM configurations, pilot sequence lengths, and in the presence of time variability. Comparative analysis with basis expansion model-based iterative receivers highlights the superiority of our algorithms in achieving an effective trade-off between accuracy and computational complexity.

cs.IT↗

arXiv2025

Density-Functional Theory and Triply-Periodic Minimal Surfaces

Several authors have suggested that the surfaces of vanishing potential generated by the electrostatic fields from a distribution of point charges resemble triply periodic minimal surfaces (TPMS) corresponding to the positions of the point charges. We provide a theoretical basis for this phenomenological comparison by starting with the Boltzmann equation to show that the surface corresponding to zero charge density is a minimal surface. We then use density-functional calculations for elemental materials that differ electronically and structurally, Na, Cu, and Al, to show that surfaces of vanishing charge density converge to the corresponding TPMS.

cond-mat.mtrl-sci↗

arXiv2025

HeterRec: Heterogeneous Information Transformer for Scalable Sequential Recommendation

Transformer-based sequential recommendation (TSR) models have shown superior performance in recommendation systems, where the quality of item representations plays a crucial role. Classical representation methods integrate item features using concatenation or neural networks to generate homogeneous representation sequences. While straightforward, these methods overlook the heterogeneity of item features, limiting the transformer's ability to capture fine-grained patterns and restricting scalability. Recent studies have attempted to integrate user-side heterogeneous features into item representation sequences, but item-side heterogeneous features, which are vital for performance, remain excluded. To address these challenges, we propose a Heterogeneous Information Transformer model for Sequential Recommendation (HeterRec), which incorporates Heterogeneous Token Flatten Layer (HTFL) and Hierarchical Causal Transformer Layer (HCT). Our HTFL is a novel item tokenization method that converts items into a heterogeneous token set and organizes these tokens into heterogeneous sequences, effectively enhancing performance gains when scaling up the model. Moreover, HCT introduces token-level and item-level causal transformers to extract fine-grained patterns from the heterogeneous sequences. Additionally, we design a Listwise Multi-step Prediction (LMP) Loss function to further improve performance. Extensive experiments on both offline and online datasets show that the HeterRec model achieves superior performance.

cs.IR↗

arXiv2025

CSMF: Cascaded Selective Mask Fine-Tuning for Multi-Objective Embedding-Based Retrieval

Multi-objective embedding-based retrieval (EBR) has become increasingly critical due to the growing complexity of user behaviors and commercial objectives. While traditional approaches often suffer from data sparsity and limited information sharing between objectives, recent methods utilizing a shared network alongside dedicated sub-networks for each objective partially address these limitations. However, such methods significantly increase the model parameters, leading to an increased retrieval latency and a limited ability to model causal relationships between objectives. To address these challenges, we propose the Cascaded Selective Mask Fine-Tuning (CSMF), a novel method that enhances both retrieval efficiency and serving performance for multi-objective EBR. The CSMF framework selectively masks model parameters to free up independent learning space for each objective, leveraging the cascading relationships between objectives during the sequential fine-tuning. Without increasing network parameters or online retrieval overhead, CSMF computes a linearly weighted fusion score for multiple objective probabilities while supporting flexible adjustment of each objective's weight across various recommendation scenarios. Experimental results on real-world datasets demonstrate the superior performance of CSMF, and online experiments validate its significant practical value.

cs.IR↗

arXiv2025

FairACE: Achieving Degree Fairness in Graph Neural Networks via Contrastive and Adversarial Group-Balanced Training

Fairness has been a significant challenge in graph neural networks (GNNs) since degree biases often result in un-equal prediction performance among nodes with varying degrees. Existing GNN models focus on prediction accuracy, frequently overlooking fairness across different degree groups. To addressthis issue, we propose a novel GNN framework, namely Fairness- Aware Asymmetric Contrastive Ensemble (FairACE), which inte-grates asymmetric contrastive learning with adversarial training to improve degree fairness. FairACE captures one-hop local neighborhood information and two-hop monophily similarity to create fairer node representations and employs a degree fairness regulator to balance performance between high-degree and low-degree nodes. During model training, a novel group-balanced fairness loss is proposed to minimize classification disparities across degree groups. In addition, we also propose a novel fairness metric, the Accuracy Distribution Gap (ADG), which can quantitatively assess and ensure equitable performance across different degree-based node groups. Experimental results on both synthetic and real-world datasets demonstrate that FairACE significantly improves degree fairness metrics while maintaining competitive accuracy in comparison to the state-of-the-art GNN models.

cs.LG↗