Searcharxiv⌕ Search

arXiv subjects

Yong Wang

Publications and source records attributed to Yong Wang.

At least 271 records · Page 15Linked to original sources
arXiv2024

Twist, turn and encounter: the trajectories of small atmospheric particles unravelled

Every solid particle in the atmosphere, from ice crystals and pollen to dust, ash, and microplastics, is non-spherical. These particles play significant roles in Earth's climate system, influencing temperature, weather patterns, natural ecosystems, human health, and pollution levels. However, our understanding of these particles is largely based on the theories for extremely small particles and experiments conducted in liquid mediums. In this study, we used an innovative experimental setup and particle-resolved numerical simulations to investigate the behaviour of sub-millimetre ellipsoids of varying shapes in the air. Our results revealed complex decaying oscillation patterns involving numerous twists and turns in these particles, starkly contrasting their dynamics in liquid mediums. We found that the frequency and decay rate of these oscillations have a strong dependence on the particle shape. Interestingly, disk-shaped particles oscillated at nearly twice the frequency of rod-shaped particles, though their oscillations also decayed more rapidly. During oscillation, even subtly non-spherical particles can drift laterally up to ten times their volume-equivalent spherical diameter. This behaviour enables particles to sweep through four times more air both vertically and laterally compared to a volume-equivalent sphere, significantly increasing their encounter rate and aggregation possibility. Our findings provide an explanation for the long-range transport and naturally occurring aggregate formation of highly non-spherical particles such as snowflakes and volcanic ash.

physics.flu-dyn↗
arXiv2024

Large-Scale Metric Computation in Online Controlled Experiment Platform

Online controlled experiment (also called A/B test or experiment) is the most important tool for decision-making at a wide range of data-driven companies like Microsoft, Google, Meta, etc. Metric computation is the core procedure for reaching a conclusion during an experiment. With the growth of experiments and metrics in an experiment platform, computing metrics efficiently at scale becomes a non-trivial challenge. This work shows how metric computation in WeChat experiment platform can be done efficiently using bit-sliced index (BSI) arithmetic. This approach has been implemented in a real world system and the performance results are presented, showing that the BSI arithmetic approach is very suitable for large-scale metric computation scenarios.

cs.DC↗
arXiv2024

The spectral torsion for the Connes type operator

This paper aims to provide an explicit computation of the spectral torsion associated with the Connes type operator on even dimension compact manifolds.And we also extend the spectral torsion for the Connes type operator to compact manifolds with boundary.

math-ph↗
arXiv2024

The linear perturbation of the metric and the bimetric conformal invarints

In this paper, we give a method to construct bimetric conformal invarints by the linear metric perturbations and the conformal invarints. And we compute the metric perturbations of the Connes conformal invarints and the conformal Laplacian. As corollaries, some new bimetric conformal invarints on 4-dimensional Riemannian manifolds without boundary are obtained and we get the first order and second order variations of the Connes conformal invarints.

math.DG↗
arXiv2024

Causal Inference in Social Platforms Under Approximate Interference Networks

Estimating the total treatment effect (TTE) of a new feature in social platforms is crucial for understanding its impact on user behavior. However, the presence of network interference, which arises from user interactions, often complicates this estimation process. Experimenters typically face challenges in fully capturing the intricate structure of this interference, leading to less reliable estimates. To address this issue, we propose a novel approach that leverages surrogate networks and the pseudo inverse estimator. Our contributions can be summarized as follows: (1) We introduce the surrogate network framework, which simulates the practical situation where experimenters build an approximation of the true interference network using observable data. (2) We investigate the performance of the pseudo inverse estimator within this framework, revealing a bias-variance trade-off introduced by the surrogate network. We demonstrate a tighter asymptotic variance bound compared to previous studies and propose an enhanced variance estimator outperforming the original estimator. (3) We apply the pseudo inverse estimator to a real experiment involving over 50 million users, demonstrating its effectiveness in detecting network interference when combined with the difference-in-means estimator. Our research aims to bridge the gap between theoretical literature and practical implementation, providing a solution for estimating TTE in the presence of network interference and unknown interference structures.

stat.AP↗
arXiv2024

JobViz: Skill-driven Visual Exploration of Job Advertisements

Online job advertisements on various job portals or websites have become the most popular way for people to find potential career opportunities nowadays. However, the majority of these job sites are limited to offering fundamental filters such as job titles, keywords, and compensation ranges. This often poses a challenge for job seekers in efficiently identifying relevant job advertisements that align with their unique skill sets amidst a vast sea of listings. Thus, we propose well-coordinated visualizations to provide job seekers with three levels of details of job information: a skill-job overview visualizes skill sets, employment posts as well as relationships between them with a hierarchical visualization design; a post exploration view leverages an augmented radar-chart glyph to represent job posts and further facilitates users' swift comprehension of the pertinent skills necessitated by respective positions; a post detail view lists the specifics of selected job posts for profound analysis and comparison. By using a real-world recruitment advertisement dataset collected from 51Job, one of the largest job websites in China, we conducted two case studies and user interviews to evaluate JobViz. The results demonstrated the usefulness and effectiveness of our approach.

cs.HC↗
arXiv2024

Controllable and Fast Growth of High-Quality Atomically Thin and Atomically Flat Bi$_2$O$_2$Se Films

As a novel and promising 2D material, bismuth oxyselenide (Bi$_2$O$_2$Se) has demonstrated significant potential to overcome existing technical barriers in various electronic device applications, due to its unique physical properties like high symmetry, adjustable electronic structure, ultra-high electron mobility. However, the rapid growth of Bi$_2$O$_2$Se films down to a few atomic layers with precise control remains a significant challenge. In this work, the growth of two-dimensional (2D) Bi$_2$O$_2$Se thin films by the pulsed laser deposition (PLD) method is systematically investigated. By controlling temperature, oxygen pressure, laser energy density and laser emission frequency, we successfully prepare atomically thin and flat Bi$_2$O$_2$Se (001) thin films on the (001) surface of SrTiO3. Importantly, we provide a fundamental and unique perspective toward understanding the growth process of atomically thin and flat Bi$_2$O$_2$Se films, and the growth process can be primarily summarized into four steps: i) anisotropic non-spontaneous nucleation preferentially along the step roots; ii) monolayer Bi$_2$O$_2$Se nanosheets expanding across the surrounding area, and eventually covering the entire STO substrate step; iii) vertical growth of Bi$_2$O$_2$Se monolayer in a 2D Frank-van der Merwe (FM) epitaxial growth, and iv) with a layer-by-layer 2D FM growth mode, ultimately producing an atomically flat and epitaxially aligned thin film. Moreover, the combined results of the crystallinity quality, surface morphology and the chemical states manifest the successful PLD-growth of high-quality Bi$_2$O$_2$Se films in a controllable and fast mode.

cond-mat.mtrl-sci↗
arXiv2024

Low Mach number Limit of Steady Thermally Driven Fluid

In this paper, we establish the existence of strong solutions to the steady non-isentropic compressible Navier-Stokes system with Dirichlet boundary conditions in bounded domains where the fluid is driven by the wall temperature, and justify its low Mach number limit, i.e., $\v\to 0$, in $L^{\infty}$ sense with a rate of convergence. Notably, for the limiting system \eqref{fge} obtained in the low Mach number limit, the variation of the wall temperature is allowed to be independent of the Mach number. It is also worth pointing out that the velocity field $u_{1}$ acts like a ghost since it appears at $\v$-order in the expansion, but still affects the density and temperature at $O(1)$-order. In the proof, we design a new expansion, in which the density, velocity and temperature have different expansion forms with respect to $\v$, so that the density at higher orders is well-defined under the Boussinesq relations and the constraint of zero average. We also introduce a new $\v$-dependent functional space, allowing us to obtain some uniform estimates for high-order normal derivatives near the boundary.

math.AP↗
arXiv2024

Knowledge-driven AI-generated data for accurate and interpretable breast ultrasound diagnoses

Data-driven deep learning models have shown great capabilities to assist radiologists in breast ultrasound (US) diagnoses. However, their effectiveness is limited by the long-tail distribution of training data, which leads to inaccuracies in rare cases. In this study, we address a long-standing challenge of improving the diagnostic model performance on rare cases using long-tailed data. Specifically, we introduce a pipeline, TAILOR, that builds a knowledge-driven generative model to produce tailored synthetic data. The generative model, using 3,749 lesions as source data, can generate millions of breast-US images, especially for error-prone rare cases. The generated data can be further used to build a diagnostic model for accurate and interpretable diagnoses. In the prospective external evaluation, our diagnostic model outperforms the average performance of nine radiologists by 33.5% in specificity with the same sensitivity, improving their performance by providing predictions with an interpretable decision-making process. Moreover, on ductal carcinoma in situ (DCIS), our diagnostic model outperforms all radiologists by a large margin, with only 34 DCIS lesions in the source data. We believe that TAILOR can potentially be extended to various diseases and imaging modalities.

eess.IV↗
arXiv2024

The Rescaled Dirac operator fDh and the noncommutative residue for 6-dimensional manifolds

In this paper, we compute the noncommutative residue for the rescaled Dirac operator fDh on 6-dimensional compact manifolds without boundary. And we give the proof of the Kastler-Kalau-Walze type theorem for the rescaled Dirac operator fDh on 6-dimensional compact manifolds with boundary. We also give some important special cases which can be solved by our calculation methods.

math.DG↗
arXiv2024

GTPT: Group-based Token Pruning Transformer for Efficient Human Pose Estimation

In recent years, 2D human pose estimation has made significant progress on public benchmarks. However, many of these approaches face challenges of less applicability in the industrial community due to the large number of parametric quantities and computational overhead. Efficient human pose estimation remains a hurdle, especially for whole-body pose estimation with numerous keypoints. While most current methods for efficient human pose estimation primarily rely on CNNs, we propose the Group-based Token Pruning Transformer (GTPT) that fully harnesses the advantages of the Transformer. GTPT alleviates the computational burden by gradually introducing keypoints in a coarse-to-fine manner. It minimizes the computation overhead while ensuring high performance. Besides, GTPT groups keypoint tokens and prunes visual tokens to improve model performance while reducing redundancy. We propose the Multi-Head Group Attention (MHGA) between different groups to achieve global interaction with little computational overhead. We conducted experiments on COCO and COCO-WholeBody. Compared to other methods, the experimental results show that GTPT can achieve higher performance with less computation, especially in whole-body with numerous keypoints.

cs.CV↗
arXiv2024

3D E-textile for Exercise Physiology and Clinical Maternal Health Monitoring

Electronic textiles (E-textiles) offer great wearing comfort and unobtrusiveness, thus holding potential for next-generation health monitoring wearables. However, the practical implementation is hampered by challenges associated with poor signal quality, substantial motion artifacts, durability for long-term usage, and non-ideal user experience. Here, we report a cost-effective E-textile system that features 3D microfiber-based electrodes for greatly increasing the surface area. The soft and fluffy conductive microfibers disperse freely and securely adhere to the skin, achieving a low impedance at the electrode-skin interface even in the absence of gel. A superhydrophobic fluorinated self-assembled monolayer was deposited on the E-textile surface to render it waterproof while retaining the electrical conductivity. Equipped with a custom-designed motion-artifact canceling wireless data recording circuit, the E-textile system could be integrated into a variety of smart garments for exercise physiology and health monitoring applications. Real-time multimodal electrophysiological signal monitoring, including electrocardiogram (ECG) and electromyography (EMG), was successfully carried out during strenuous cycling and even underwater swimming activities. Furthermore, a multi-channel E-textile was developed and implemented in clinical patient studies for simultaneous real-time monitoring of maternal ECG and uterine EMG signals, incorporating spatial-temporal potential mapping capabilities.

physics.med-ph↗
arXiv2024

CAGRA: Highly Parallel Graph Construction and Approximate Nearest Neighbor Search for GPUs

Approximate Nearest Neighbor Search (ANNS) plays a critical role in various disciplines spanning data mining and artificial intelligence, from information retrieval and computer vision to natural language processing and recommender systems. Data volumes have soared in recent years and the computational cost of an exhaustive exact nearest neighbor search is often prohibitive, necessitating the adoption of approximate techniques. The balanced performance and recall of graph-based approaches have more recently garnered significant attention in ANNS algorithms, however, only a few studies have explored harnessing the power of GPUs and multi-core processors despite the widespread use of massively parallel and general-purpose computing. To bridge this gap, we introduce a novel parallel computing hardware-based proximity graph and search algorithm. By leveraging the high-performance capabilities of modern hardware, our approach achieves remarkable efficiency gains. In particular, our method surpasses existing CPU and GPU-based methods in constructing the proximity graph, demonstrating higher throughput in both large- and small-batch searches while maintaining compatible accuracy. In graph construction time, our method, CAGRA, is 2.2~27x faster than HNSW, which is one of the CPU SOTA implementations. In large-batch query throughput in the 90% to 95% recall range, our method is 33~77x faster than HNSW, and is 3.8~8.8x faster than the SOTA implementations for GPU. For a single query, our method is 3.4~53x faster than HNSW at 95% recall.

cs.DS↗
arXiv2024

Faithful geometric measures for genuine tripartite entanglement

We present a faithful geometric picture for genuine tripartite entanglement of discrete, continuous, and hybrid quantum systems. We first find that the triangle relation $\mathcal{E}^α_{i|jk}\leq \mathcal{E}^α_{j|ik}+\mathcal{E}^α_{k|ij}$ holds for all subadditive bipartite entanglement measure $\mathcal{E}$, all permutations under parties $i, j, k$, all $α\in [0, 1]$, and all pure tripartite states. It provides a geometric interpretation that bipartition entanglement, measured by $\mathcal{E}^α$, corresponds to the side of a triangle, of which the area with $α\in (0, 1)$ is nonzero if and only if the underlying state is genuinely entangled. Then, we rigorously prove the non-obtuse triangle area with $0<α\leq 1/2$ is a measure for genuine tripartite entanglement. Useful lower and upper bounds for these measures are obtained, and generalizations of our results are also presented. Finally, it is significantly strengthened for qubits that, given a set of subadditive and non-additive measures, some state is always found to violate the triangle relation for any $α>1$, and the triangle area is not a measure for any $α>1/2$. Hence, our results are expected to aid significant progress in studying both discrete and continuous multipartite entanglement.

quant-ph↗
arXiv2024

Efficient factored gradient descent algorithm for quantum state tomography

Reconstructing the state of quantum many-body systems is of fundamental importance in quantum information tasks, but extremely challenging due to the curse of dimensionality. In this work, we present an efficient quantum tomography protocol that combines the state-factored with eigenvalue mapping to address the rank-deficient issue and incorporates a momentum-accelerated gradient descent algorithm to speed up the optimization process. We implement extensive numerical experiments to demonstrate that our factored gradient descent algorithm efficiently mitigates the rank-deficient problem and admits orders of magnitude better tomography accuracy and faster convergence. We also find that our method can accomplish the full-state tomography of random 11-qubit mixed states within one minute.

quant-ph↗
arXiv2024

The general Kastler-Kalau-Walze type theorem for the J-twist DJ of the Dirac operator

In [21] and [22], we proved the Kastler-Kalau-Walze type theorem for the J-twist DJ of the Dirac operator on 3-dimensional, 4-dimensional and 6-dimensional almost product Riemannian spin manifold with boundary. In this paper, we generalize our previous conclusions and establish the proof of the general Kastler-Kalau-Walze type theorem for the J-twist DJ of the Dirac operator on even-dimensional almost product Riemannian spin manifold with boundary.

math.DG↗
arXiv2024

Orbit symmetry breaking in MXene implements enhanced soft bioelectronic implants

Bioelectronic implants with soft mechanics, biocompatibility, and excellent electrical performance enable biomedical implants to record electrophysiological signals and execute interventions within internal organs, promising to revolutionize the diagnosing, monitoring, and treatment of various pathological conditions. However, challenges remain in improving excessive impedance at the bioelectronic-tissue interface and thus the efficacy of electrophysiological signaling and intervention. Here, we devise orbit symmetry breaking in MXene (a low-cost scalability, biocompatible, and conductive 2D layered material, that we refer to as OBXene), that exhibits low bioelectronic-tissue impedance, originating from the out-of-plane charge transfer. Furthermore, the Schottky-induced piezoelectricity stemming from the asymmetric orbital configuration of OBXene facilitates interlayered charge transport in the device. In this study, we report an OBXene-based cardiac patch applied on the left ventricular epicardium of both rodent and porcine models to enable spatiotemporal epicardium mapping and pacing, while coupling the wireless and battery-free operation for long-term real-time recording and closed-loop stimulation.

physics.app-ph↗