arXiv subjects

Ziwen Wang

Publications and source records attributed to Ziwen Wang.

At least 73 records · Page 4Linked to original sources

arXiv2024

All-Optical Manipulation of Band Gap Dynamics via Electron-Phonon Coupling

The electron-phonon coupling (EPC) is a ubiquitous interaction in condensed systems and plays a vital role in shaping the electronic properties of materials. Yet, achieving coherent manipulation of electron-phonon coupling has posed a considerable challenge. Here, employing time-resolved high-harmonic generation (tr-HHG) spectroscopy, we demonstrate the coherent manipulation of bandgap dynamics in a BaF2 crystal by precisely controlling the EPC using ultrashort light pulses. The tr-HHG spectrum perturbed by a triply degenerate phonon mode T2g, exhibits simultaneously a remarkable two-dimensional (2D) sensitivity, namely intensity domain in addition to the previously reported energy domain. The dynamic compression and enhancement of the harmonics in the intensity domain showed a π/2 phase shift compared to the manifestation of shifts of the harmonics in the energy domain, an astounding example of a physical phenomenon being observed simultaneously in two different perspectives. To complement our experimental observations, we employed a quantum model that incorporates the EPC, successfully reproducing the results. In addition, we demonstrated complete control over the EPC strength and initial phase of the coherent phonon oscillations by varying the incident electric field polarization over crystal orientation. Our findings lay a foundation for future investigations aiming to harness and exploit the remarkable potential of EPC in solid-state systems.

cond-mat.mtrl-sci↗

arXiv2024

State Space Model for New-Generation Network Alternative to Transformers: A Survey

In the post-deep learning era, the Transformer architecture has demonstrated its powerful performance across pre-trained big models and various downstream tasks. However, the enormous computational demands of this architecture have deterred many researchers. To further reduce the complexity of attention models, numerous efforts have been made to design more efficient methods. Among them, the State Space Model (SSM), as a possible replacement for the self-attention based Transformer model, has drawn more and more attention in recent years. In this paper, we give the first comprehensive review of these works and also provide experimental comparisons and analysis to better demonstrate the features and advantages of SSM. Specifically, we first give a detailed description of principles to help the readers quickly capture the key ideas of SSM. After that, we dive into the reviews of existing SSMs and their various applications, including natural language processing, computer vision, graph, multi-modal and multi-media, point cloud/event stream, time series data, and other domains. In addition, we give statistical comparisons and analysis of these models and hope it helps the readers to understand the effectiveness of different structures on various tasks. Then, we propose possible research points in this direction to better promote the development of the theoretical model and application of SSM. More related works will be continuously updated on the following GitHub: https://github.com/Event-AHU/Mamba_State_Space_Model_Paper_List.

cs.LG↗

arXiv2024

High Harmonic Spectroscopy Probes Lattice Dynamics

The probing of coherent lattice vibrations in solids has been conventionally carried out using time-resolved transient spectroscopy where only the relative oscillation amplitude can be obtained. Using time-resolved X-ray techniques, absolute electron-phonon coupling strength could be extracted. However, the complexity of such an experiment renders it impossible to be carried out in conventional laboratories. Here we demonstrate that the electron-phonon, anharmonic phonon-phonon coupling, and their relaxation dynamics can be probed in real-time using high-harmonic spectroscopy. Our technique is background-free and has extreme sensitivity directly in the energy domain. In combination with the optical deformation potential calculated from density functional perturbation theory and the absolute energy modulation depth, our measurement reveals the maximum displacement of neighboring oxygen atoms in α-quartz crystal to tens of picometers in real space. By employing a straightforward and robust time-windowed Gabor analysis for the phonon-modulated high-harmonic spectrum, we successfully observe channel-resolved four-phonon scattering processes in such highly nonlinear interactions. Our work opens a new realm for accurate measurement of coherent phonons and their scattering dynamics, which allows for potential benchmarking ab-initio calculations in solids

cond-mat.mes-hall↗

arXiv2024

Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival Data

Survival analysis serves as a fundamental component in numerous healthcare applications, where the determination of the time to specific events (such as the onset of a certain disease or death) for patients is crucial for clinical decision-making. Scoring systems are widely used for swift and efficient risk prediction. However, existing methods for constructing survival scores presume that data originates from a single source, posing privacy challenges in collaborations with multiple data owners. We propose a novel framework for building federated scoring systems for multi-site survival outcomes, ensuring both privacy and communication efficiency. We applied our approach to sites with heterogeneous survival data originating from emergency departments in Singapore and the United States. Additionally, we independently developed local scores at each site. In testing datasets from each participant site, our proposed federated scoring system consistently outperformed all local models, evidenced by higher integrated area under the receiver operating characteristic curve (iAUC) values, with a maximum improvement of 11.6%. Additionally, the federated score's time-dependent AUC(t) values showed advantages over local scores, exhibiting narrower confidence intervals (CIs) across most time points. The model developed through our proposed method exhibits effective performance on each local site, signifying noteworthy implications for healthcare research. Sites participating in our proposed federated scoring model training gained benefits by acquiring survival models with enhanced prediction accuracy and efficiency. This study demonstrates the effectiveness of our privacy-preserving federated survival score generation framework and its applicability to real-world heterogeneous survival data.

cs.AI↗

arXiv2024

Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission

Survival analysis is essential for studying time-to-event outcomes and providing a dynamic understanding of the probability of an event occurring over time. Various survival analysis techniques, from traditional statistical models to state-of-the-art machine learning algorithms, support healthcare intervention and policy decisions. However, there remains ongoing discussion about their comparative performance. We conducted a comparative study of several survival analysis methods, including Cox proportional hazards (CoxPH), stepwise CoxPH, elastic net penalized Cox model, Random Survival Forests (RSF), Gradient Boosting machine (GBM) learning, AutoScore-Survival, DeepSurv, time-dependent Cox model based on neural network (CoxTime), and DeepHit survival neural network. We applied the concordance index (C-index) for model goodness-of-fit, and integral Brier scores (IBS) for calibration, and considered the model interpretability. As a case study, we performed a retrospective analysis of patients admitted through the emergency department of a tertiary hospital from 2017 to 2019, predicting 90-day all-cause mortality based on patient demographics, clinicopathological features, and historical data. The results of the C-index indicate that deep learning achieved comparable performance, with DeepSurv producing the best discrimination (DeepSurv: 0.893; CoxTime: 0.892; DeepHit: 0.891). The calibration of DeepSurv (IBS: 0.041) performed the best, followed by RSF (IBS: 0.042) and GBM (IBS: 0.0421), all using the full variables. Moreover, AutoScore-Survival, using a minimal variable subset, is easy to interpret, and can achieve good discrimination and calibration (C-index: 0.867; IBS: 0.044). While all models were satisfactory, DeepSurv exhibited the best discrimination and calibration. In addition, AutoScore-Survival offers a more parsimonious model and excellent interpretability.

cs.LG↗

arXiv2023

Alterferroicity with seesaw-type magnetoelectricity

Primary ferroicities like ferroelectricity and ferromagnetism are essential physical properties of matter. Multiferroics, with coexisting multiple ferroic orders in a single phase, provide a convenient route to magnetoelectricity. Even so, the general trade-off between magnetism and polarity remains inevitable, which prevents practicable magnetoelectric cross control in the multiferroic framework. Here an alternative strategy, i.e. the so-called alterferroicity, is proposed to circumvent the magnetoelectric exclusiveness, which exhibits multiple but non-coexisting ferroic orders. The natural exclusion between magnetism and polarity, as an insurmountable weakness of multiferroicity, becomes a distinct advantage in alterferroicity, making it an inborn rich ore for intrinsic strong magnetoelectricity. The general design rules for alterferroic materials rely on the competition between the instabilities of phononic and electronic structures in covalent systems. Based on primary density functional theory calculations, Ti-based trichalcogenides are predicted to be alterferroic candidates, which exhibit unique seesaw type magnetoelectricity. This alterferroicity, as an emerging branch of ferroic family, re-shapes the framework of magnetoelectricity, going beyond the established scenario based on multiferroicity.

cond-mat.mtrl-sci↗

arXiv2023

Large in-plane negative piezoelectricity and giant nonlinear optical susceptibility in elementary ferroelectric monolayers

Negative piezoelectrics contract in the direction of applied electric field, which are opposite to normal piezoelectrics and rare in dielectric materials. The raising of low dimensional ferroelectrics, with unconventional mechanisms of polarity, opens a fertile branch for candidates with prominent negative piezoelectricity. Here, the distorted $α$-Bi monolayer, a newly-identified elementary ferroelectric with puckered black phosphorous-like structure [J. Guo, {\it et al}. Nature \textbf{617}, 67 (2023)], is computationally studied, which manifests a large negative in-plane piezoelectricity (with $d_{33}\sim-26$ pC/N). Its negative piezoelectricity originates from its unique buckling ferroelectric mechanism, namely the inter-column sliding. Consequently, a moderate tensile strain can significantly reduce its ferroelectric switching energy barrier, while the compressive strain can significantly enhance its prominent nonlinear optical response. The physical mechanism of in-plane negative piezoelectricity also applies to other elementary ferroeletric monolayers.

cond-mat.mtrl-sci↗

arXiv2023

First-principles demonstration of Roman surface topological multiferroicity

The concept of topology has been widely applied to condensed matter, going beyond the band crossover in reciprocal spaces. A recent breakthrough suggested unconventional topological physics in a quadruple perovskite TbMn$_3$Cr$_4$O$_{12}$, whose magnetism-induced polarization manifests a unique Roman surface topology [Nat. Commun. \textbf{13}, 2373 (2022)]. However, the available experimental evidence based on tiny polarizations of polycrystalline samples is far from sufficient. Here, this topological multiferroicity is demonstrated by using density functional theory calculations, which ideally confirms the Roman surface trajectory of magnetism-induced polarization. In addition, an alternative material in this category is proposed to systematically enhance the performance, by promoting its magnetism-induced polarization to an easily detectable level.

cond-mat.mtrl-sci↗

arXiv2023

Electrical tuning of robust layered antiferromagnetism in MXene monolayer

A-type antiferromagnetism, with an in-plane ferromagnetic order and the interlayer antiferromagnetic coupling, owns inborn advantages for electrical manipulations but is naturally rare in real materials except in those artificial antiferromagnetic heterostructures. Here, a robust layered antiferromagnetism with a high Néel temperature is predicted in a MXene Cr$_2$CCl$_2$ monolayer, which provides an ideal platform as a magnetoelectric field effect transistor. Based on first-principles calculations, we demonstrate that an electric field can induce the band splitting between spin-up and spin-down channels. Although no net magnetization is generated, the inversion symmetry between the lower Cr layer and the upper Cr layer is broken via electronic cloud distortions. Moreover, this electric field can be replaced by a proximate ferroelectric layer for nonvolatility. The magneto-optic Kerr effect can be used to detect this magnetoelectricity, even if it is a collinear antiferromagnet with zero magnetization.

cond-mat.mtrl-sci↗

arXiv2023

Randomly Projected Convex Clustering Model: Motivation, Realization, and Cluster Recovery Guarantees

In this paper, we propose a randomly projected convex clustering model for clustering a collection of $n$ high dimensional data points in $\mathbb{R}^d$ with $K$ hidden clusters. Compared to the convex clustering model for clustering original data with dimension $d$, we prove that, under some mild conditions, the perfect recovery of the cluster membership assignments of the convex clustering model, if exists, can be preserved by the randomly projected convex clustering model with embedding dimension $m = O(ε^{-2}\log(n))$, where $0 < ε< 1$ is some given parameter. We further prove that the embedding dimension can be improved to be $O(ε^{-2}\log(K))$, which is independent of the number of data points. Extensive numerical experiment results will be presented in this paper to demonstrate the robustness and superior performance of the randomly projected convex clustering model. The numerical results presented in this paper also demonstrate that the randomly projected convex clustering model can outperform the randomly projected K-means model in practice.

cs.LG↗

arXiv2021

Ferroelectricity in strained Hf$_2$CF$_2$ monolayer

Low dimensional ferroelectrics are highly desired for applications and full of exotic physics. Here a functionalized MXene Hf$_2$CF$_2$ monolayer is theoretically studied, which manifests a nonpolar to polar transition upon moderate biaxial compressive strain. Accompanying this structural transition, a metal-semiconductor transition occurs. The in-plane shift of unilateral fluorine layer leads to a polarization pointing out-of-plane. Such ferroelectricity is unconventional, similar to the recently-proposed interlayer-sliding ferroelectricity but not identical. Due to its specific hexapetalous potential energy profile, the possible ferroelectric switching paths and domain walls are nontrivial, which are mediated via the metallic paraelectric state. In this sense, the metallic walls can be manipulated by reshaping the ferroelectric domains.

cond-mat.mtrl-sci↗

arXiv2020

MixModule: Mixed CNN Kernel Module for Medical Image Segmentation

Convolutional neural networks (CNNs) have been successfully applied to medical image classification, segmentation, and related tasks. Among the many CNNs architectures, U-Net and its improved versions based are widely used and achieve state-of-the-art performance these years. These improved architectures focus on structural improvements and the size of the convolution kernel is generally fixed. In this paper, we propose a module that combines the benefits of multiple kernel sizes and we apply the proposed module to U-Net and its variants. We test our module on three segmentation benchmark datasets and experimental results show significant improvement.

cs.CV↗

arXiv2019

GetNet: Get Target Area for Image Pairing

Image pairing is an important research task in the field of computer vision. And finding image pairs containing objects of the same category is the basis of many tasks such as tracking and person re-identification, etc., and it is also the focus of our research. Existing traditional methods and deep learning-based methods have some degree of defects in speed or accuracy. In this paper, we made improvements on the Siamese network and proposed GetNet. The proposed method GetNet combines STN and Siamese network to get the target area first and then perform subsequent processing. Experiments show that our method achieves competitive results in speed and accuracy.

cs.CV↗

arXiv2019

Cross-Platform Modeling of Users' Behavior on Social Media

With the booming development and popularity of mobile applications, different verticals accumulate abundant data of user information and social behavior, which are spontaneous, genuine and diversified. However, each platform describes user's portraits in only certain aspect, resulting in difficult combination of those internet footprints together. In our research, we proposed a modeling approach to analyze user's online behavior across different social media platforms. Structured and unstructured data of same users shared by NetEase Music and Sina Weibo have been collected for cross-platform analysis of correlations between music preference and other users' characteristics. Based on music tags of genre and mood, genre cluster of five groups and mood cluster of four groups have been formed by computing their collected song lists with K-means method. Moreover, with the help of user data of Weibo, correlations between music preference (i.e. genre, mood) and Big Five personalities (BFPs) and basic information (e.g. gender, resident region, tags) have been comprehensively studied, building up full-scale user portraits with finer grain. Our findings indicate that people's music preference could be linked with their real social activities. For instance, people living in mountainous areas generally prefer folk music, while those in urban areas like pop music more. Interestingly, dog lovers could love sad music more than cat lovers. Moreover, our proposed cross-platform modeling approach could be adapted to other verticals, providing an online automatic way for profiling users in a more precise and comprehensive way.

cs.SI↗

arXiv2019

Automatic Conditional Generation of Personalized Social Media Short Texts

Automatic text generation has received much attention owing to rapid development of deep neural networks. In general, text generation systems based on statistical language model will not consider anthropomorphic characteristics, which results in machine-like generated texts. To fill the gap, we propose a conditional language generation model with Big Five Personality (BFP) feature vectors as input context, which writes human-like short texts. The short text generator consists of a layer of long short memory network (LSTM), where a BFP feature vector is concatenated as one part of input for each cell. To enable supervised training generation model, a text classification model based convolution neural network (CNN) has been used to prepare BFP-tagged Chinese micro-blog corpora. Validated by a BFP linguistic computational model, our generated Chinese short texts exhibit discriminative personality styles, which are also syntactically correct and semantically smooth with appropriate emoticons. With combination of natural language generation with psychological linguistics, our proposed BFP-dependent text generation model can be widely used for individualization in machine translation, image caption, dialogue generation and so on.

cs.CL↗

arXiv2019

Modeling of User Portrait Through Social Media

Nowadays, massive useful data of user information and social behavior have been accumulated on the Internet, providing a possibility of profiling user's personality traits online. In this paper, we propose a psychological modeling method based on computational linguistic features to profile Big Five personality traits of users on Sina Weibo (a Twitter-like microblogging service in China) and their correlations with user's social behaviors. To the best of our knowledge, this is the first research on investigating the potential relationship between profile information, social-network behaviors and personality traits of users on Sina Weibo. Our results demonstrate an effective modeling approach to understanding demographic and psychological portraits of users on social media without customer disruption, which is useful for commercial incorporations to provide better personalized products and services.

cs.SI↗

arXiv2017

Oxygen migration during resistance switching and failure of hafnium oxide memristors

While the recent establishment of the role of thermophoresis/diffusion-driven oxygen migration during resistance switching in metal oxide memristors provided critical insights required for memristor modeling, extended investigations of the role of oxygen migration during ageing and failure remain to be detailed. Such detailing will enable failure-tolerant design, which can lead to enhanced performance of memristor-based next-generation storage-class memory. Here we directly observed lateral oxygen migration using in-situ synchrotron x-ray absorption spectromicroscopy of HfOx memristors during initial resistance switching, wear over millions of switching cycles, and eventual failure, through which we determined potential physical causes of failure. Using this information, we reengineered devices to mitigate three failure mechanisms, and demonstrated an improvement in endurance of about three orders of magnitude.

cond-mat.mtrl-sci↗

arXiv2017

Quantum-trajectory analysis for charge transfer in solid materials induced by strong laser fields

We investigate the dependence of charge transfer on the intensity of driving laser field when SiO2 crystal is irradiated by an 800 nm laser. It is surprising that the direction of charge transfer undergoes a sudden reversal when the driving laser intensity exceeds critical values with different carrier envelope phases. By applying quantum-trajectory analysis, we find that the Bloch oscillation plays an important role in charge transfer in solid. Also, we study the interaction of strong laser with gallium nitride (GaN) that is widely used in optoelectronics. A pump-probe scheme is applied to control the quantum trajectories of the electrons in the conduction band. The signal of charge transfer is controlled successfully by means of theoretically proposed approach.

physics.optics↗