arXiv subjects

Fan Zhang

Publications and source records attributed to Fan Zhang.

At least 667 records · Page 37Linked to original sources

arXiv2022

How should the contact angle of a noncircular wetting boundary be described?

For over 200 years, wettability has made significant contributions to understanding the properties of objects, advancing technological progress. Theoretical model of the contact angle (CA) for evaluating wettability has constantly been modified to address relevant emerging issues. However, these existing models disregard the difference in the CA along the contact line and use a single-point CA to evaluate the entire contact line. From this perspective, there is no reasonable explanation for noncircular wetting. Here, we reveal that noncircular wetting boundaries result from property differences in the surfaces along the boundary, and utilize friction as a comprehensive factor reflecting local wettability. Average CA is proposed to evaluate the contact line instead of the single-point CA, making the Cassie method and Wenzel method obsolete, which will take an average property of the whole surface as a weight coefficient of the single-point CA, ignoring the subordination between physical properties and roughness in systematics.

cond-mat.soft↗

arXiv2022

Lattice distortion inducing exciton splitting and coherent quantum beating in CsPbI3 perovskite quantum dots

Anisotropic exchange-splitting in semiconductor quantum dots (QDs) results in bright-exciton fine-structure-splitting (FSS) important for quantum information processing. Direct measurement of FSS usually requires single/few QDs at liquid-helium temperatures, because of its sensitivity to QD size and shape, whereas measuring and controlling FSS at an ensemble-level seem to be impossible unless all the dots are made to be nearly the same. Here we report strong bright-exciton FSS up to 1.6 meV in solution-processed CsPbI3 perovskite QDs, manifested as quantum beats in ensemble-level transient absorption at liquid-nitrogen to room temperatures. The splitting is robust to QD size and shape heterogeneity, and increases with decreasing temperature, pointing towards a mechanism associated with orthorhombic distortion of perovskite lattice. Effective-mass-approximation calculations reveal an intrinsic "fine-structure gap" that agrees well with the observed FSS. This gap stems from an avoided crossing of bright-excitons confined in orthorhombically-distorted QDs that are bounded by the pseudocubic {100} family of planes.

cond-mat.mes-hall↗

arXiv2022

Tensor-Network Approaches to Counting Statistics for the Current in a Boundary-Driven Diffusive System

We apply tensor networks to counting statistics for the stochastic particle transport in an out-of-equilibrium diffusive system. This system is composed of a one-dimensional channel in contact with two particle reservoirs at the ends. Two tensor-network algorithms, namely, Density Matrix Renormalization Group (DMRG) and Time Evolving Block Decimation (TEBD), are respectively implemented. The cumulant generating function for the current is numerically calculated and then compared with the analytical solution. Excellent agreement is found, manifesting the validity of these approaches in such an application. Moreover, the fluctuation theorem for the current is shown to hold.

cond-mat.stat-mech↗

arXiv2022

COCONUT, a novel fast-converging MHD model for solar corona simulations: I. Benchmarking and optimization of polytropic solutions

We present a novel global 3-D coronal MHD model called COCONUT, polytropic in its first stage and based on a time-implicit backward Euler scheme. Our model boosts run-time performance in comparison with contemporary MHD-solvers based on explicit schemes, which is particularly important when later employed in an operational setting for space weather forecasting. It is data-driven in the sense that we use synoptic maps as inner boundary input for our potential field initialization as well as an inner boundary condition in the further MHD time evolution. The coronal model is developed as part of the EUropean Heliospheric FORecasting Information Asset (EUHFORIA) and will replace the currently employed, more simplistic, empirical Wang-Sheeley-Arge (WSA) model. At 21.5 Rs where the solar wind is already supersonic, it is coupled to EUHFORIA's heliospheric model. We validate and benchmark our coronal simulation results with the explicit-scheme Wind-Predict model and find good agreement for idealized limit cases as well as real magnetograms, while obtaining a computational time reduction of up to a factor 3 for simple idealized cases, and up to 35 for realistic configurations, and we demonstrate that the time gained increases with the spatial resolution of the input synoptic map. We also use observations to constrain the model and show that it recovers relevant features such as the position and shape of the streamers (by comparison with eclipse white-light images), the coronal holes (by comparison with EUV images) and the current sheet (by comparison with WSA model at 0.1 AU).

astro-ph.SR↗

arXiv2022

Enhancing VVC with Deep Learning based Multi-Frame Post-Processing

This paper describes a CNN-based multi-frame post-processing approach based on a perceptually-inspired Generative Adversarial Network architecture, CVEGAN. This method has been integrated with the Versatile Video Coding Test Model (VTM) 15.2 to enhance the visual quality of the final reconstructed content. The evaluation results on the CLIC 2022 validation sequences show consistent coding gains over the original VVC VTM at the same bitrates when assessed by PSNR. The integrated codec has been submitted to the Challenge on Learned Image Compression (CLIC) 2022 (video track), and the team name associated with this submission is BVI_VC.

eess.IV↗

arXiv2022

One Model, Multiple Modalities: A Sparsely Activated Approach for Text, Sound, Image, Video and Code

People perceive the world with multiple senses (e.g., through hearing sounds, reading words and seeing objects). However, most existing AI systems only process an individual modality. This paper presents an approach that excels at handling multiple modalities of information with a single model. In our "{SkillNet}" model, different parts of the parameters are specialized for processing different modalities. Unlike traditional dense models that always activate all the model parameters, our model sparsely activates parts of the parameters whose skills are relevant to the task. Such model design enables SkillNet to learn skills in a more interpretable way. We develop our model for five modalities including text, image, sound, video and code. Results show that, SkillNet performs comparably to five modality-specific fine-tuned models. Moreover, our model supports self-supervised pretraining with the same sparsely activated way, resulting in better initialized parameters for different modalities. We find that pretraining significantly improves the performance of SkillNet on five modalities, on par with or even better than baselines with modality-specific pretraining. On the task of Chinese text-to-image retrieval, our final system achieves higher accuracy than existing leading systems including Wukong{ViT-B} and Wenlan 2.0 while using less number of activated parameters.

cs.CL↗

arXiv2022

SkillNet-NLU: A Sparsely Activated Model for General-Purpose Natural Language Understanding

Prevailing deep models are single-purpose and overspecialize at individual tasks. However, when being extended to new tasks, they typically forget previously learned skills and learn from scratch. We address this issue by introducing SkillNet-NLU, a general-purpose model that stitches together existing skills to learn new tasks more effectively. The key feature of our approach is that it is sparsely activated guided by predefined skills. Different from traditional dense models that always activate all the model parameters, SkillNet-NLU only activates parts of the model parameters whose skills are relevant to the target task. When learning for a new task, our approach precisely activates required skills and also provides an option to add new skills. We evaluate on natural language understandings tasks and have the following findings. First, with only one model checkpoint, SkillNet-NLU performs better than task-specific fine-tuning and two multi-task learning baselines (i.e., dense model and Mixture-of-Experts model) on six tasks. Second, sparsely activated pre-training further improves the overall performance. Third, SkillNet-NLU significantly outperforms baseline systems when being extended to new tasks.

cs.CL↗

arXiv2022

Multi-Graph based Multi-Scenario Recommendation in Large-scale Online Video Services

Recently, industrial recommendation services have been boosted by the continual upgrade of deep learning methods. However, they still face de-biasing challenges such as exposure bias and cold-start problem, where circulations of machine learning training on human interaction history leads algorithms to repeatedly suggest exposed items while ignoring less-active ones. Additional problems exist in multi-scenario platforms, e.g. appropriate data fusion from subsidiary scenarios, which we observe could be alleviated through graph structured data integration via message passing. In this paper, we present a multi-graph structured multi-scenario recommendation solution, which encapsulates interaction data across scenarios with multi-graph and obtains representation via graph learning. Extensive offline and online experiments on real-world datasets are conducted where the proposed method demonstrates an increase of 0.63% and 0.71% in CTR and Video Views per capita on new users over deployed set of baselines and outperforms regular method in increasing the number of outer-scenario videos by 25% and video watches by 116%, validating its superiority in activating cold videos and enriching target recommendation.

cs.AI↗

arXiv2022

SkillNet-NLG: General-Purpose Natural Language Generation with a Sparsely Activated Approach

We present SkillNet-NLG, a sparsely activated approach that handles many natural language generation tasks with one model. Different from traditional dense models that always activate all the parameters, SkillNet-NLG selectively activates relevant parts of the parameters to accomplish a task, where the relevance is controlled by a set of predefined skills. The strength of such model design is that it provides an opportunity to precisely adapt relevant skills to learn new tasks effectively. We evaluate on Chinese natural language generation tasks. Results show that, with only one model file, SkillNet-NLG outperforms previous best performance methods on four of five tasks. SkillNet-NLG performs better than two multi-task learning baselines (a dense model and a Mixture-of-Expert model) and achieves comparable performance to task-specific models. Lastly, SkillNet-NLG surpasses baseline systems when being adapted to new tasks.

cs.CL↗

arXiv2022

Phonon Stability of Quantum Droplets in a dipolar Bose gases

Stabilized by quantum fluctuations, dipolar Bose-Einstein condensates can form self-bound liquidlike droplets in the mean-field unstable regime. However in the Bogoliubov theory, some phonon energies are imaginary in the long-wavelength limit, implying dynamical instability of this system. A similar instability appears in the Bogoliubov theory of a binary quantum droplet, and is removed due to higher-order quantum fluctuations as shown recently [1]. In this work, we study the phonon energy of a dipolar quantum droplet in the Beliaev formalism, and find that quantum fluctuations can enhance the phonon stability. We obtain the anisotropic sound velocity which can be tested in experiment.

cond-mat.quant-gas↗

arXiv2022

Magnetic phase transition induced ferroelectric polarization in BaFeF4 with room temperature weak ferromagnetism

BaMF4 (M=Fe, Co, Ni and Mn) family are typical multiferroic materials, having antiferromagnetism at around liquid nitrogen temperature. In this work, polycrystalline BaFeF4 has been prepared by solid state reaction. The slight deficiency of Fe leads to the coexistence of valence states of +2 and +3, facilitating the electrons to hop between the neighboring Fe2+ and Fe3+ ions through the middle F- ion, leading to the strong double exchange interaction with weak ferromagnetism above room temperature. A bifurcation at about 170 K between the zero-field-cooled and field-cooled temperature dependent magnetization curves indicates the onset of 2-dimensional antiferromagnetism, which is completed at about 125 K with the sudden drop of magnetization. Despite the fact of type-I multiferroic, its magnetoelectricity can be evidenced by the pyroelectric current, which shows a peak starting at about 170 K and finishing at about 125 K. The saturated ferroelectric polarization change of around 34 μC/m2 is observed, which is switchable by the reversed poling electric field and decreases to about 30 μC/m2 under a magnetic field of 90 kOe. This magnetoelectricity can be qualitatively reproduced by first-principles calculations. Our results represent substantial progress to search for high-temperature multiferroics in ferroelectric fluorides.

cond-mat.mtrl-sci↗

arXiv2022

Enhancing Non-mass Breast Ultrasound Cancer Classification With Knowledge Transfer

Much progress has been made in the deep neural network (DNN) based diagnosis of mass lesions breast ultrasound (BUS) images. However, the non-mass lesion is less investigated because of the limited data. Based on the insight that mass data is sufficient and shares the same knowledge structure with non-mass data of identifying the malignancy of a lesion based on the ultrasound image, we propose a novel transfer learning framework to enhance the generalizability of the DNN model for non-mass BUS with the help of mass BUS. Specifically, we train a shared DNN with combined non-mass and mass data. With the prior of different marginal distributions in input and output space, we employ two domain alignment strategies in the proposed transfer learning framework with the insight of capturing domain-specific distribution to address the issue of domain shift. Moreover, we propose a cross-domain semantic-preserve data generation module called CrossMix to recover the missing distribution between non-mass and mass data that is not presented in training data. Experimental results on an in-house dataset demonstrate that the DNN model trained with combined data by our framework achieves a 10% improvement in AUC on the malignancy prediction task of non-mass BUS compared to training directly on non-mass data.

eess.IV↗

arXiv2022

SAD: A Large-scale Dataset towards Airport Detection in Synthetic Aperture Radar Images

Airports have an important role in both military and civilian domains. The synthetic aperture radar (SAR) based airport detection has received increasing attention in recent years. However, due to the high cost of SAR imaging and annotation process, there is no publicly available SAR dataset for airport detection. As a result, deep learning methods have not been fully used in airport detection tasks. To provide a benchmark for airport detection research in SAR images, this paper introduces a large-scale SAR Airport Dataset (SAD). In order to adequately reflect the demands of real world applications, it contains 624 SAR images from Sentinel 1B and covers 104 airfield instances with different scales, orientations and shapes. The experiments of multiple deep learning approach on this dataset proves its effectiveness. It developing state-of-the-art airport area detection algorithms or other relevant tasks.

cs.CV↗

arXiv2022

Quasi-periodic oscillations of the X-ray burst from the magnetar SGR J1935+2154 and associated with the fast radio burst FRB 200428

The origin(s) and mechanism(s) of fast radio bursts (FRBs), which are short radio pulses from cosmological distances, have remained a major puzzle since their discovery. We report a strong Quasi-Periodic Oscillation(QPO) of 40 Hz in the X-ray burst from the magnetar SGR J1935+2154 and associated with FRB 200428, significantly detected with the Hard X-ray Modulation Telescope (Insight-HXMT) and also hinted by the Konus-Wind data. QPOs from magnetar bursts have only been rarely detected; our 3.4 sigma (p-value is 2.9e-4) detection of the QPO reported here reveals the strongest QPO signal observed from magnetars (except in some very rare giant flares), making this X-ray burst unique among magnetar bursts. The two X-ray spikes coinciding with the two FRB pulses are also among the peaks of the QPO. Our results suggest that at least some FRBs are related to strong oscillation processes of neutron stars. We also show that we may overestimate the significance of the QPO signal and underestimate the errors of QPO parameters if QPO exists only in a fraction of the time series of a X-ray burst which we use to calculate the Leahy-normalized periodogram.

astro-ph.HE↗

arXiv2022

STICC: A multivariate spatial clustering method for repeated geographic pattern discovery with consideration of spatial contiguity

Spatial clustering has been widely used for spatial data mining and knowledge discovery. An ideal multivariate spatial clustering should consider both spatial contiguity and aspatial attributes. Existing spatial clustering approaches may face challenges for discovering repeated geographic patterns with spatial contiguity maintained. In this paper, we propose a Spatial Toeplitz Inverse Covariance-Based Clustering (STICC) method that considers both attributes and spatial relationships of geographic objects for multivariate spatial clustering. A subregion is created for each geographic object serving as the basic unit when performing clustering. A Markov random field is then constructed to characterize the attribute dependencies of subregions. Using a spatial consistency strategy, nearby objects are encouraged to belong to the same cluster. To test the performance of the proposed STICC algorithm, we apply it in two use cases. The comparison results with several baseline methods show that the STICC outperforms others significantly in terms of adjusted rand index and macro-F1 score. Join count statistics is also calculated and shows that the spatial contiguity is well preserved by STICC. Such a spatial clustering method may benefit various applications in the fields of geography, remote sensing, transportation, and urban planning, etc.

cs.LG↗

arXiv2022

ST-MFNet: A Spatio-Temporal Multi-Flow Network for Frame Interpolation

Video frame interpolation (VFI) is currently a very active research topic, with applications spanning computer vision, post production and video encoding. VFI can be extremely challenging, particularly in sequences containing large motions, occlusions or dynamic textures, where existing approaches fail to offer perceptually robust interpolation performance. In this context, we present a novel deep learning based VFI method, ST-MFNet, based on a Spatio-Temporal Multi-Flow architecture. ST-MFNet employs a new multi-scale multi-flow predictor to estimate many-to-one intermediate flows, which are combined with conventional one-to-one optical flows to capture both large and complex motions. In order to enhance interpolation performance for various textures, a 3D CNN is also employed to model the content dynamics over an extended temporal window. Moreover, ST-MFNet has been trained within an ST-GAN framework, which was originally developed for texture synthesis, with the aim of further improving perceptual interpolation quality. Our approach has been comprehensively evaluated -- compared with fourteen state-of-the-art VFI algorithms -- clearly demonstrating that ST-MFNet consistently outperforms these benchmarks on varied and representative test datasets, with significant gains up to 1.09dB in PSNR for cases including large motions and dynamic textures. Project page: https://danielism97.github.io/ST-MFNet.

cs.CV↗

arXiv2022

Global Attitude Synchronization of Networked Rigid Bodies Under Directed Topologies

The global attitude synchronization problem is studied for networked rigid bodies under directed topologies. To avoid the asynchronous pitfall where only vector parts converge to some identical value but scalar parts do not, multiplicative quaternion errors are leveraged to develop attitude synchronization protocols for rigid bodies with the absolute measurements. It is shown that global synchronization of networked rigid bodies can be achieved if and only if the directed topology is quasi-strongly connected. Simultaneously, a novel double-energy-function analysis method, equipped with an ordering permutation technique about scalar parts and a coordinate transformation mechanism, is constructed for the quaternion behavior analysis of networked rigid bodies. In particular, global synchronization is achieved with our analysis method regardless of the highly nonlinear and strongly coupling problems resulting from multiplicative quaternion errors, which seriously hinder the traditional analysis of global synchronization for networked rigid bodies. Simulations for networked spacecraft are presented to show the global synchronization performances under different directed topologies.

eess.SY↗

arXiv2022

Correlation between nuclear temperatures and symmetry energy in sub-saturation

A study of the correlation between nuclear temperatures and symmetry energy is presented for heavy-ion collisions at intermediate energies via the isospin-dependent quantum molecular-dynamics model. It is found that different symmetry energy parameters will change the density and kinetic energy distribution of the hot nuclei. More importantly, nuclear temperatures that are based on kinetic energy properties can be used to study symmetry energy information.

nucl-th↗