arXiv subjects

Yan Zhao

Publications and source records attributed to Yan Zhao.

At least 55 records · Page 3Linked to original sources

arXiv2025

Determining Linker Ratios of Mixed Metal-Organic Frameworks via Magnetic Susceptibility Measurements

Partial replacement of the organic linkers of metal-organic frameworks (MOFs) often optimizes their functionalities, however, accurate characterization of their molar ratios in many cases is challenging. This work presents a method of determining such linker ratios via measurements of the magnetic susceptibility of small quantities of powdered samples. The main presumption is taking the diamagnetic and paramagnetic contributions to the molar magnetic susceptibility of the two parent MOFs to be additive. To verify this, four examples are provided with commonly used MOFs to represent the cases when both parent MOFs are either paramagnetic or diamagnetic but with different linkers with the following systems: [MIL-101(Cr)-SO$_3$H]$_{(1-δ)}$[MIL-101(Cr)-NO$_2$]$_δ$, [EuMOF]$_{(1-δ)}$[EuPDCA]$_δ$, [UiO-66-COOH]$_{(1-δ)}$[UiO-66]$_δ$ and [MIL-101(Cr) F Free]$_{(1-δ)}$[MIL-53(Al)]$_δ$, where 1-$δ$ : $δ$ are the ratios to be determined. Depending on whether the systems were strictly paramagnetic, strictly diamagnetic or mixed, the experimental error of $δ$ ranged between 0.00002 and 0.012, respectively. We expect the presented method to be widely employed since samples only need to be in powdered form and because there is a lack of characterization tools in the area of MOF linker ratios. The presented method is also applicable to resolving the ratios of mixed ordinary paramagnetic systems as well as other types of non-magnetic composite materials such as tapes, zeolites and thin films.

cond-mat.mtrl-sci↗

arXiv2025

Progressive Inertial Poser: Progressive Real-Time Kinematic Chain Estimation for 3D Full-Body Pose from Three IMU Sensors

The motion capture system that supports full-body virtual representation is of key significance for virtual reality. Compared to vision-based systems, full-body pose estimation from sparse tracking signals is not limited by environmental conditions or recording range. However, previous works either face the challenge of wearing additional sensors on the pelvis and lower-body or rely on external visual sensors to obtain global positions of key joints. To improve the practicality of the technology for virtual reality applications, we estimate full-body poses using only inertial data obtained from three Inertial Measurement Unit (IMU) sensors worn on the head and wrists, thereby reducing the complexity of the hardware system. In this work, we propose a method called Progressive Inertial Poser (ProgIP) for human pose estimation, which combines neural network estimation with a human dynamics model, considers the hierarchical structure of the kinematic chain, and employs a multi-stage progressive network estimation with increased depth to reconstruct full-body motion in real time. The encoder combines Transformer Encoder and bidirectional LSTM (TE-biLSTM) to flexibly capture the temporal dependencies of the inertial sequence, while the decoder based on multi-layer perceptrons (MLPs) transforms high-dimensional features and accurately projects them onto Skinned Multi-Person Linear (SMPL) model parameters. Quantitative and qualitative experimental results on multiple public datasets show that our method outperforms state-of-the-art methods with the same inputs, and is comparable to recent works using six IMU sensors.

cs.CV↗

arXiv2025

Conditional Lagrangian Wasserstein Flow for Time Series Imputation

Time series imputation is important for numerous real-world applications. To overcome the limitations of diffusion model-based imputation methods, e.g., slow convergence in inference, we propose a novel method for time series imputation in this work, called Conditional Lagrangian Wasserstein Flow (CLWF). Following the principle of least action in Lagrangian mechanics, we learn the velocity by minimizing the corresponding kinetic energy. Moreover, to enhance the model's performance, we estimate the gradient of a task-specific potential function using a time-dependent denoising autoencoder and integrate it into the base estimator to reduce the sampling variance. Finally, the proposed method demonstrates competitive performance compared to other state-of-the-art imputation approaches.

cs.LG↗

arXiv2025

Colossal Dielectric Response and Electric Polarization in Lithium Nitrate

Lithium nitrate LiNO$_3$ is identified to possess a dielectric constant $ε$' larger than 6x10$^6$ at 1 kHz in powder samples above the critical temperature $T$$_W$ = 306 K. For single crystalline samples, $ε$' can be sustained to remain above 10$^5$ and the dissipation factor below 10 in the temperature region of 280-340 K after a simple 'activation' process. Moreover, pyroelectric current measurements show LiNO$_3$ to be ferroelectric with an electric polarization of $P$ = 1,200 $μ$C/cm$^2$. Both $ε$' and $P$ are amongst one of the highest in all known materials. We propose a model suggesting the mechanism underlying the colossal magnitudes of $ε$' and $P$ to stem from a gearing-ungearing process of the planar NO$_3$$^-$ at the macroscopic level. Our results potentially push the boundaries of ceramic capacitors.

cond-mat.mtrl-sci↗

arXiv2025

Efficient Multivariate Time Series Forecasting via Calibrated Language Models with Privileged Knowledge Distillation

Multivariate time series forecasting (MTSF) endeavors to predict future observations given historical data, playing a crucial role in time series data management systems. With advancements in large language models (LLMs), recent studies employ textual prompt tuning to infuse the knowledge of LLMs into MTSF. However, the deployment of LLMs often suffers from low efficiency during the inference phase. To address this problem, we introduce TimeKD, an efficient MTSF framework that leverages the calibrated language models and privileged knowledge distillation. TimeKD aims to generate high-quality future representations from the proposed cross-modality teacher model and cultivate an effective student model. The cross-modality teacher model adopts calibrated language models (CLMs) with ground truth prompts, motivated by the paradigm of Learning Under Privileged Information (LUPI). In addition, we design a subtractive cross attention (SCA) mechanism to refine these representations. To cultivate an effective student model, we propose an innovative privileged knowledge distillation (PKD) mechanism including correlation and feature distillation. PKD enables the student to replicate the teacher's behavior while minimizing their output discrepancy. Extensive experiments on real data offer insight into the effectiveness, efficiency, and scalability of the proposed TimeKD.

cs.LG↗

arXiv2025

Engineering Graphene Nanoribbons via Periodically Embedding Oxygen Atoms

Heteroatom doping is an important method for engineering graphene nanoribbons (GNRs) because of its ability to modify electronic properties by introducing extra electrons or vacancies. However, precisely integrating oxygen atoms into the lattice of GNRs is unexplored, and the resulting electronic properties remain elusive. Here, we achieve the precise embedding of oxygen atoms into the lattice of GNRs via in situ formation of pyrans, synthesizing two types of oxygen-doped GNRs (O-doped chevron-GNR and O-doped chiral (2,1)-GNR). Using scanning tunneling microscopy, non-contact atomic force microscopy, and density functional theory calculations, the atomic structures and electronic properties of O-doped GNRs are determined, demonstrating that both GNRs are direct bandgap semiconductors with different sensitivities to oxygen dopants. Oxygen dopants have a minor impact on the bandgap of chevron-GNR but a significant effect on the bandgap of chiral (2,1)-GNR, which is attributed to the difference in density of states near the Fermi level between substituted intrinsic carbon atoms and their pristine counterparts. Compared with the pristine chiral (2,1)-GNR, the band structure of O-doped chiral (2,1)-GNR exhibits unexpected band edges transition, which is ascribed to sp2-hybridized oxygen atoms which introduces additional electrons to the conduction band of chiral (2,1)-GNR, leading to the upward shift of Fermi surface.

cond-mat.mtrl-sci↗

arXiv2025

DATA-WA: Demand-based Adaptive Task Assignment with Dynamic Worker Availability Windows

With the rapid advancement of mobile networks and the widespread use of mobile devices, spatial crowdsourcing, which involves assigning location-based tasks to mobile workers, has gained significant attention. However, most existing research focuses on task assignment at the current moment, overlooking the fluctuating demand and supply between tasks and workers over time. To address this issue, we introduce an adaptive task assignment problem, which aims to maximize the number of assigned tasks by dynamically adjusting task assignments in response to changing demand and supply. We develop a spatial crowdsourcing framework, namely demand-based adaptive task assignment with dynamic worker availability windows, which consists of two components including task demand prediction and task assignment. In the first component, we construct a graph adjacency matrix representing the demand dependency relationships in different regions and employ a multivariate time series learning approach to predict future task demands. In the task assignment component, we adjust tasks to workers based on these predictions, worker availability windows, and the current task assignments, where each worker has an availability window that indicates the time periods they are available for task assignments. To reduce the search space of task assignments and be efficient, we propose a worker dependency separation approach based on graph partition and a task value function with reinforcement learning. Experiments on real data demonstrate that our proposals are both effective and efficient.

cs.LG↗

arXiv2025

Sharp eigenvalue estimates and related rigidity theorems

In this paper, sharp bounds for the first nonzero eigenvalues of different type have been obtained. Moreover, when those bounds are achieved, related rigidities can be characterized. More precisely, first, by applying the Bishop-type volume comparison proven in [10,13] and the Escobar-type eigenvalue comparisons for the first nonzero Steklov eigenvalue of the Laplacian proven in [26], for manifolds with radial sectional curvature upper bound, under suitable preconditions, we can show that the first nonzero Wentzell eigenvalue of the geodesic ball on these manifolds can be bounded from above by that of the geodesic ball with the same radius in the model space (i.e., spherically symmetric manifolds) determined by the curvature bound. Besides, this upper bound for the first nonzero Wentzell eigenvalue can be achieved if and only if these two geodesic balls are isometric with each other. This conclusion can be seen as an extension of eigenvalue comparisons in [9,26]. Second, we prove a general Reilly formula for the drifting Laplacian, and then use the formula to give a sharp lower bound for the first nonzero Steklov eigenvalue of the drifting Laplacian on compact smooth metric measure spaces with boundary and convex potential function. Besides, this lower bound can be achieved only for the Euclidean ball of the prescribed radius.

math.DG↗

arXiv2025

Preventing the Popular Item Embedding Based Attack in Federated Recommendations

Privacy concerns have led to the rise of federated recommender systems (FRS), which can create personalized models across distributed clients. However, FRS is vulnerable to poisoning attacks, where malicious users manipulate gradients to promote their target items intentionally. Existing attacks against FRS have limitations, as they depend on specific models and prior knowledge, restricting their real-world applicability. In our exploration of practical FRS vulnerabilities, we devise a model-agnostic and prior-knowledge-free attack, named PIECK (Popular Item Embedding based Attack). The core module of PIECK is popular item mining, which leverages embedding changes during FRS training to effectively identify the popular items. Built upon the core module, PIECK branches into two diverse solutions: The PIECKIPE solution employs an item popularity enhancement module, which aligns the embeddings of targeted items with the mined popular items to increase item exposure. The PIECKUEA further enhances the robustness of the attack by using a user embedding approximation module, which approximates private user embeddings using mined popular items. Upon identifying PIECK, we evaluate existing federated defense methods and find them ineffective against PIECK, as poisonous gradients inevitably overwhelm the cold target items. We then propose a novel defense method by introducing two regularization terms during user training, which constrain item popularity enhancement and user embedding approximation while preserving FRS performance. We evaluate PIECK and its defense across two base models, three real datasets, four top-tier attacks, and six general defense methods, affirming the efficacy of both PIECK and its defense.

cs.CR↗

arXiv2025

Magnetism based on nitrate-nitrate interactions: The cases of LiNO$_3$, K$_{0.5}$Rb$_{0.5}$NO$_3$, Ca(NO$_3$)$_2$ and C(NH$_2$)$_3$NO$_3$

Long-range magnetic ordering of the orbital motion of oxygen atoms within NO$_3$$^-$ cations is identified from experimental measurements of the magnetic susceptibility $χ$($T$) in LiNO$_3$, Ca(NO$_3$)$_2$, K$_{0.5}$Rb$_{0.5}$NO$_3$ and C(NH$_2$)$_3$NO$_3$ at their respective order-disorder, solid-solid phase transitions $T$$_N$. The observed sharp changes in $χ$($T$) and accompanying hysteretic behavior indicate the phase transitions to be first order. A model employing the law of conservation of angular momentum is used to explain why the librations between neighboring NO$_3$$^-$ become geared below $T$$_N$. Since the periodic motions involve concerted motion of net charges, the associated magnetic moments of the NO$_3$$^-$ ions indirectly establish an antiferromagnetic structure below $T$$_N$. Our findings identify a previously unidentified type of molecular interaction which may be exploited to further increase the enthalpy of the widely-popular hydrated salts employed as energy storage devices.

cond-mat.mtrl-sci↗

arXiv2024

L3TC: Leveraging RWKV for Learned Lossless Low-Complexity Text Compression

Learning-based probabilistic models can be combined with an entropy coder for data compression. However, due to the high complexity of learning-based models, their practical application as text compressors has been largely overlooked. To address this issue, our work focuses on a low-complexity design while maintaining compression performance. We introduce a novel Learned Lossless Low-complexity Text Compression method (L3TC). Specifically, we conduct extensive experiments demonstrating that RWKV models achieve the fastest decoding speed with a moderate compression ratio, making it the most suitable backbone for our method. Second, we propose an outlier-aware tokenizer that uses a limited vocabulary to cover frequent tokens while allowing outliers to bypass the prediction and encoding. Third, we propose a novel high-rank reparameterization strategy that enhances the learning capability during training without increasing complexity during inference. Experimental results validate that our method achieves 48% bit saving compared to gzip compressor. Besides, L3TC offers compression performance comparable to other learned compressors, with a 50x reduction in model parameters. More importantly, L3TC is the fastest among all learned compressors, providing real-time decoding speeds up to megabytes per second. Our code is available at https://github.com/alipay/L3TC-leveraging-rwkv-for-learned-lossless-low-complexity-text-compression.git.

cs.CL↗

arXiv2024

MILLION: A General Multi-Objective Framework with Controllable Risk for Portfolio Management

Portfolio management is an important yet challenging task in AI for FinTech, which aims to allocate investors' budgets among different assets to balance the risk and return of an investment. In this study, we propose a general Multi-objectIve framework with controLLable rIsk for pOrtfolio maNagement (MILLION), which consists of two main phases, i.e., return-related maximization and risk control. Specifically, in the return-related maximization phase, we introduce two auxiliary objectives, i.e., return rate prediction, and return rate ranking, combined with portfolio optimization to remit the overfitting problem and improve the generalization of the trained model to future markets. Subsequently, in the risk control phase, we propose two methods, i.e., portfolio interpolation and portfolio improvement, to achieve fine-grained risk control and fast risk adaption to a user-specified risk level. For the portfolio interpolation method, we theoretically prove that the risk can be perfectly controlled if the to-be-set risk level is in a proper interval. In addition, we also show that the return rate of the adjusted portfolio after portfolio interpolation is no less than that of the min-variance optimization, as long as the model in the reward maximization phase is effective. Furthermore, the portfolio improvement method can achieve greater return rates while keeping the same risk level compared to portfolio interpolation. Extensive experiments are conducted on three real-world datasets. The results demonstrate the effectiveness and efficiency of the proposed framework.

q-fin.PM↗

arXiv2024

Separate Anything You Describe

Language-queried audio source separation (LASS) is a new paradigm for computational auditory scene analysis (CASA). LASS aims to separate a target sound from an audio mixture given a natural language query, which provides a natural and scalable interface for digital audio applications. Recent works on LASS, despite attaining promising separation performance on specific sources (e.g., musical instruments, limited classes of audio events), are unable to separate audio concepts in the open domain. In this work, we introduce AudioSep, a foundation model for open-domain audio source separation with natural language queries. We train AudioSep on large-scale multimodal datasets and extensively evaluate its capabilities on numerous tasks including audio event separation, musical instrument separation, and speech enhancement. AudioSep demonstrates strong separation performance and impressive zero-shot generalization ability using audio captions or text labels as queries, substantially outperforming previous audio-queried and language-queried sound separation models. For reproducibility of this work, we will release the source code, evaluation benchmark and pre-trained model at: https://github.com/Audio-AGI/AudioSep.

eess.AS↗

arXiv2024

Efficient Data-aware Distance Comparison Operations for High-Dimensional Approximate Nearest Neighbor Search

High-dimensional approximate $K$ nearest neighbor search (AKNN) is a fundamental task for various applications, including information retrieval. Most existing algorithms for AKNN can be decomposed into two main components, i.e., candidate generation and distance comparison operations (DCOs). While different methods have unique ways of generating candidates, they all share the same DCO process. In this study, we focus on accelerating the process of DCOs that dominates the time cost in most existing AKNN algorithms. To achieve this, we propose an Data-Aware Distance Estimation approach, called DADE, which approximates the exact distance in a lower-dimensional space. We theoretically prove that the distance estimation in DADE is unbiased in terms of data distribution. Furthermore, we propose an optimized estimation based on the unbiased distance estimation formulation. In addition, we propose a hypothesis testing approach to adaptively determine the number of dimensions needed to estimate the exact distance with sufficient confidence. We integrate DADE into widely-used AKNN search algorithms, e.g., IVF and HNSW, and conduct extensive experiments to demonstrate the superiority.

cs.DB↗

arXiv2024

Less is More: Efficient Time Series Dataset Condensation via Two-fold Modal Matching--Extended Version

The expanding instrumentation of processes throughout society with sensors yields a proliferation of time series data that may in turn enable important applications, e.g., related to transportation infrastructures or power grids. Machine-learning based methods are increasingly being used to extract value from such data. We provide means of reducing the resulting considerable computational and data storage costs. We achieve this by providing means of condensing large time series datasets such that models trained on the condensed data achieve performance comparable to those trained on the original, large data. Specifically, we propose a time series dataset condensation framework, TimeDC, that employs two-fold modal matching, encompassing frequency matching and training trajectory matching. Thus, TimeDC performs time series feature extraction and decomposition-driven frequency matching to preserve complex temporal dependencies in the reduced time series. Further, TimeDC employs curriculum training trajectory matching to ensure effective and generalized time series dataset condensation. To avoid memory overflow and to reduce the cost of dataset condensation, the framework includes an expert buffer storing pre-computed expert trajectories. Extensive experiments on real data offer insight into the effectiveness and efficiency of the proposed solutions.

cs.DB↗

arXiv2024

A Novel Quantum-Classical Hybrid Algorithm for Determining Eigenstate Energies in Quantum Systems

Developing efficient quantum computing algorithms is essential for tackling computationally challenging problems across various fields. This paper presents a novel quantum algorithm, XZ24, for efficiently computing the eigen-energy spectra of arbitrary quantum systems. Given a Hamiltonian $\hat{H}$ and an initial reference state $|ψ_{\text{ref}} \rangle$, the algorithm extracts information about $\langle ψ_{\text{ref}} | \cos(\hat{H} t) | ψ_{\text{ref}} \rangle$ from an auxiliary qubit's state. By applying a Fourier transform, the algorithm resolves the energies of eigenstates of the Hamiltonian with significant overlap with the reference wavefunction. We provide a theoretical analysis and numerical simulations, showing XZ24's superior efficiency and accuracy compared to existing algorithms. XZ24 has three key advantages: 1. It removes the need for eigenstate preparation, requiring only a reference state with non-negligible overlap, improving upon methods like the Variational Quantum Eigensolver. 2. It reduces measurement overhead, measuring only one auxiliary qubit. For a system of size $L$ with precision $ε$, the sampling complexity scales as $O(L \cdot ε^{-1})$. When relative precision $ε$ is sufficient, the complexity scales as $O(ε^{-1})$, making measurements independent of system size. 3. It enables simultaneous computation of multiple eigen-energies, depending on the reference state. We anticipate that XZ24 will advance quantum system simulations and enhance applications in quantum computing.

quant-ph↗

arXiv2024

Optimal boundary gradient estimates for the insulated conductivity problem

In this paper we study the boundary gradient estimate of the solution to the insulated conductivity problem with the Neumann boundary data when a convex insulating inclusion approaches the boundary of the matrix domain. The gradient of solutions may blow up as the distance between the inclusion and the boundary, denoted as $\varepsilon$, approaches to zero. The blow up rate was previously known to be sharp in dimension $n=2$ (see Ammari et al.\cite{AKLLL}). However, the sharp rates in dimensions $n\geq3$ are still unknown. In this paper, we solve this problem by establishing upper and lower bounds on the gradient and prove that the optimal blow up rates of the gradient are always of order $ε^{-1/2}$ for general strictly convex inclusions in dimensions $n\geq3$. Several new difficulties are overcome and the impact of the boundary data on the gradient is specified. This result highlights a significant difference in blow-up rates compared to the interior estimates in recent works (\cites{LY,Weinkove,DLY,DLY2,LZ}), where the optimal rate is $ε^{-1/2+β(n)}$, with $β(n)\in(0,1/2)$ varying with dimension $n$. Furthermore, we demonstrate that the gradient does not blow up for the corresponding Dirichlet boundary problem.

math.AP↗

arXiv2024

Heterogeneity-Aware Coordination for Federated Learning via Stitching Pre-trained blocks

Federated learning (FL) coordinates multiple devices to collaboratively train a shared model while preserving data privacy. However, large memory footprint and high energy consumption during the training process excludes the low-end devices from contributing to the global model with their own data, which severely deteriorates the model performance in real-world scenarios. In this paper, we propose FedStitch, a hierarchical coordination framework for heterogeneous federated learning with pre-trained blocks. Unlike the traditional approaches that train the global model from scratch, for a new task, FedStitch composes the global model via stitching pre-trained blocks. Specifically, each participating client selects the most suitable block based on their local data from the candidate pool composed of blocks from pre-trained models. The server then aggregates the optimal block for stitching. This process iterates until a new stitched network is generated. Except for the new training paradigm, FedStitch consists of the following three core components: 1) an RL-weighted aggregator, 2) a search space optimizer deployed on the server side, and 3) a local energy optimizer deployed on each participating client. The RL-weighted aggregator helps to select the right block in the non-IID scenario, while the search space optimizer continuously reduces the size of the candidate block pool during stitching. Meanwhile, the local energy optimizer is designed to minimize energy consumption of each client while guaranteeing the overall training progress. The results demonstrate that compared to existing approaches, FedStitch improves the model accuracy up to 20.93%. At the same time, it achieves up to 8.12% speedup, reduces the memory footprint up to 79.5%, and achieves 89.41% energy saving at most during the learning procedure.

cs.LG↗