Zhuang Li, Fei Wang
We propose a minimal Yukawa deflection scenario of AMSB from the Kahler potential through the Higgs-messenger mixing. Salient features of this scenario are discussed and realistic MSSM spectrum can be obtained. Such a scenario, which are very predictive, can solve the tachyonic slepton problem with less messenger species. Numerical results indicate that the LOSPs predicted by this scenario can not be good DM candidates. So it is desirable to extend this scenario with a Peccei-Quinn sector to solve the strong CP problem and at the same time provide new DM candidates. We propose a way to obtain a light axino mass in SUSY KSVZ axion model with Yukawa deflected anomaly mediation SUSY breaking mechanism. The axino can possibly be the LSP and act as a good DM candidate.
Fei Wang, Shujin Lin, Hanhui Li, Hefeng Wu, Junkun Jiang, Ruomei Wang, Xiaonan Luo
Traditional sketch segmentation methods mainly rely on handcrafted features and complicate models, and their performance is far from satisfactory due to the abstract representation of sketches. Recent success of Deep Neural Networks (DNNs) in related tasks suggests DNNs could be a practical solution for this problem, yet the suitable datasets for learning and evaluating DNNs are limited. To this end, we introduce SketchSeg, a large dataset consisting of 10,000 pixel-wisely labeled sketches.Besides, due to the lack of colors and textures in sketches, conventional DNNs learned on natural images are not optimal for tackling our problem.Therefore, we further propose the Multi-column Point-CNN (MCPNet), which (1) directly takes sampled points as its input to reduce computational costs, and (2) adopts multiple columns with different filter sizes to better capture the structures of sketches. Extensive experiments validate that the MCPNet is superior to conventional DNNs like FCN. The SketchSeg dataset is publicly available on https://drive.google.com/open?id=1OpCBvkInhxvfAHuVs-spDEppb8iXFC3C.
Anders Forsgren, Fei Wang
Pivoting methods are of vital importance for linear programming, the simplex method being the by far most well-known. In this paper, a primal-dual pair of linear programs in canonical form is considered. We show that there exists a sequence of pivots, whose length is bounded by the minimum dimension of the constraint matrix, such that the pivot creates a nonsingular submatrix of the constraint matrix which increases by one row and one column at each iteration. Solving a pair of linear equations for each of these submatrices generates a sequence of optimal solutions of a primal-dual pair of linear programs of increasing dimensions, originating at the origin. The optimal solutions to the original primal-dual pair of linear programs are obtained in the final step. It is only an existence result, we have not been able to generate any rules based on properties of the problem to generate the sequence. The result is obtained by a decomposition of the final basis matrix.
Xin Wang, Xilei Wu, Huina Meng, Yuhan Fan, Jingang Shi, Han Ding, Fei Wang
Social distancing is an efficient public health practice during the COVID-19 pandemic. However, people would violate the social distancing practice unconsciously when they conduct some social activities such as handshaking, hugging, kissing on the face or forehead, etc. In this paper, we present SoDA, a social distancing practice violation alert system based on smartwatches, for preventing COVID-19 virus transmission. SoDA utilizes recordings of accelerometers and gyroscopes to recognize activities that may violate social distancing practice with simple yet effective Vision Transformer models. Extensive experiments over 10 volunteers and 1800+ samples demonstrate that SoDA achieves social activity recognition with the accuracy of 94.7%, 1.8% negative alert, and 2.2% missing alert.
Fei Wang
Mar 17, 2022·quant-ph·PDF We derive an equivalent traveling wave form description for Dirac field. In the non-relativistic limit, such form can reduce to inverse-Galilean transformed Schrodinger-type equation. We find that, the resulting two-component Schrodinger-type equation from the reduction of traveling wave form description of Dirac field is different to the naive Galilean transformed Schrodinger equation. Taking into account the interactions of the system to electromagnetic field by adding proper forms of covariant derivative, the traveling wave form description for Pauli equation can be similarly obtained in the non-relativistic limit. Such descriptions allow one to choose arbitrary convenient reference frame for quantum system involving spins. Using Bargmann-Wigner formalism for field with arbitrary spin $s\geq 1/2$, which satisfy Dirac-type equations in all its indices, the traveling wave description for such a field can be similarly obtained from the traveling wave form description of Dirac field, for example, for the spin-3/2 Rarita-Schwinger field and spin-2 gravitational field.
Fei Wang, Ling Zhou, Lu Tang, Peter X. -K. Song
Simultaneous inference after model selection is of critical importance to address scientific hypotheses involving a set of parameters. In this paper, we consider high-dimensional linear regression model in which a regularization procedure such as LASSO is applied to yield a sparse model. To establish a simultaneous post-model selection inference, we propose a method of contraction and expansion (MOCE) along the line of debiasing estimation that enables us to balance the bias-and-variance trade-off so that the super-sparsity assumption may be relaxed. We establish key theoretical results for the proposed MOCE procedure from which the expanded model can be selected with theoretical guarantees and simultaneous confidence regions can be constructed by the joint asymptotic normal distribution. In comparison with existing methods, our proposed method exhibits stable and reliable coverage at a nominal significance level with substantially less computational burden, and thus it is trustworthy for its application in solving real-world problems.
Fei Wang, Wenyu Wang, Jin Min Yang
We recalculate the two-loop beta functions for three gauge couplings taking into account all low energy threshold corrections in split supersymmetry (split-SUSY) which assumes a very high scalar mass scale M_S. We find that, in split-SUSY with gaugino mass unification assumption and a large M_S, the gauge coupling unification requires a lower bound on gaugino mass. Combined with the constraints from the dark matter relic density and direct detection limits, we find that split-SUSY is very restricted and for dark matter mass below 1 TeV the allowed parameter space can be fully covered by XENON-1T(2017).
Fei Wang, Greg Reid, Henry Wolkowicz
Recent breakthroughs have been made in the use of semidefinite programming and its application to real polynomial solving. For example, the real radical of a zero dimensional ideal, can be determined by such approaches as shown by Lasserre and collaborators. Some progress has been made on the determination of the real radical in positive dimension by Ma, Wang and Zhi. Such work involves the determination of maximal rank semidefinite moment matrices. Existing methods are computationally expensive and have poorer accuracy on larger examples. This paper is motivated by problems in the numerical computation of the real radical ideal in the general positive case. In this paper we give a method to compute the generators of the real radical for any given degree $d$. We combine the use of moment matrices and techniques from SDP optimization: facial reduction first developed by Borwein and Wolkowicz. In use of the semidefinite moment matrices to compute the real radical, the maximum rank property is very key, and with facial reduction, it can be guaranteed with very high accuracy. Our algorithm can be used to test the real radical membership of a given polynomial. In a special situation, we can determine the real radical ideal in the positive dimensional case.
Fei Wang, Shi-Di Huang, Ke-Qing Xia
The effects of insulating lids on the convection beneath were investigated experimentally using rectangular convection cells in the flux Rayleigh number range $2.3\times10^{9}\leq Ra_F \leq 1.8\times10^{11}$ and cylindrical cells in the range $1.4\times10^{10}\leq Ra_F \leq 1.2\times10^{12}$ with the Prandtl number Pr fixed at 4.3. It is found that the presence of the insulating lids leads to reduction of the global heat transfer efficiency as expected, which primarily depends on the insulating area but is insensitive to the detailed insulating patterns. At the leading order level, the magnitude of temperature fluctuation in the bulk fluid is, again, found to be insensitive to the insulating pattern and mainly depends on the insulating area; while the temperature probability density function (PDF) in the bulk is essentially invariant with respect to both insulating area and the spatial pattern of the lids. The flow dynamics, on the other hand, is sensitive to both the covering area and the spatial distribution of the lids. At fixed $Ra_F$, the flow strength is found to increase with increasing insulating area so as to transfer the same amount of heat through a smaller cooling area. Moreover, for a constant insulating area, a symmetric insulating pattern results in a symmetric flow pattern, i.e. double-roll structure; whereas asymmetric insulating pattern leads to asymmetric flow, i.e. single-roll structure. It is further found that the symmetry breaking of the insulating pattern leads to a stronger flow that enhances the horizontal velocity more than the vertical one.
Csaba Balazs, Zhaofeng Kang, Tianjun Li, Fei Wang, Jin Min Yang
We propose a realistic flipped SU(5) model derived from a five-dimensional orbifold SO(10) model. The Standard Model (SM) fermion masses and mixings are explained by combining the traditional Froggatt-Nielsen mechanism with the five-dimensional wave function profiles of the SM fermions. Employing tree-level spontaneous R-symmetry breaking in the hidden sector and extra(ordinary) gauge mediation, we obtain realistic supersymmetry breaking soft mass terms with non-vanishing gaugino masses. Including the messenger fields at the intermediate scale and Kaluza-Klein states at the compactification scale, we study gauge coupling unification. We show that the SO(10) unified gauge coupling is very strong and the unification scale can be much higher than the compactification scale. We briefly discuss proton decay as well.
Fei Wang, Qi Liu, Enhong Chen, Chuanren Liu, Zhenya Huang, Jinze Wu, Shijin Wang
Cognitive diagnosis models have been widely used in different areas, especially intelligent education, to measure users' proficiency levels on knowledge concepts, based on which users can get personalized instructions. As the measurement is not always reliable due to the weak links of the models and data, the uncertainty of measurement also offers important information for decisions. However, the research on the uncertainty estimation lags behind that on advanced model structures for cognitive diagnosis. Existing approaches have limited efficiency and leave an academic blank for sophisticated models which have interaction function parameters (e.g., deep learning-based models). To address these problems, we propose a unified uncertainty estimation approach for a wide range of cognitive diagnosis models. Specifically, based on the idea of estimating the posterior distributions of cognitive diagnosis model parameters, we first provide a unified objective function for mini-batch based optimization that can be more efficiently applied to a wide range of models and large datasets. Then, we modify the reparameterization approach in order to adapt to parameters defined on different domains. Furthermore, we decompose the uncertainty of diagnostic parameters into data aspect and model aspect, which better explains the source of uncertainty. Extensive experiments demonstrate that our method is effective and can provide useful insights into the uncertainty of cognitive diagnosis.
Fei Wang, Wenyu Wang, Jin Min Yang
In split-supersymmetry (split-SUSY), gluino is a metastable particle and thus can freeze out in the early universe. The late decay of such a long-life gluino into the lightest supersymmetric particle (LSP) may provide much of the cosmic dark matter content. In this work, assuming the LSP is gravitino produced from the late decay of the metastable gluino, we examine the WMAP dark matter constraints on the gluino mass. We find that to provide the full abundance of dark matter, the gluino must be heavier than about 14 TeV and thus not accessible at the CERN Large Hadron Collider (LHC).
Fei Wang, Yuewen Zheng, Qin Li, Jingyi Wu, Pengfei Li, Luxia Zhang
Objective: This study introduces ChatSchema, an effective method for extracting and structuring information from unstructured data in medical paper reports using a combination of Large Multimodal Models (LMMs) and Optical Character Recognition (OCR) based on the schema. By integrating predefined schema, we intend to enable LMMs to directly extract and standardize information according to the schema specifications, facilitating further data entry. Method: Our approach involves a two-stage process, including classification and extraction for categorizing report scenarios and structuring information. We established and annotated a dataset to verify the effectiveness of ChatSchema, and evaluated key extraction using precision, recall, F1-score, and accuracy metrics. Based on key extraction, we further assessed value extraction. We conducted ablation studies on two LMMs to illustrate the improvement of structured information extraction with different input modals and methods. Result: We analyzed 100 medical reports from Peking University First Hospital and established a ground truth dataset with 2,945 key-value pairs. We evaluated ChatSchema using GPT-4o and Gemini 1.5 Pro and found a higher overall performance of GPT-4o. The results are as follows: For the result of key extraction, key-precision was 98.6%, key-recall was 98.5%, key-F1-score was 98.6%. For the result of value extraction based on correct key extraction, the overall accuracy was 97.2%, precision was 95.8%, recall was 95.8%, and F1-score was 95.8%. An ablation study demonstrated that ChatSchema achieved significantly higher overall accuracy and overall F1-score of key-value extraction, compared to the Baseline, with increases of 26.9% overall accuracy and 27.4% overall F1-score, respectively.
Avin Seneviratne, Peter L. Walters, Fei Wang
Nov 27, 2024·quant-ph·PDF In this work, we developed an efficient quantum algorithm for the simulation of non-Markovian quantum dynamics, based on the Feynman path integral formulation. The algorithm scales polynomially with the number of native gates and the number of qubits, and has no classical overhead. It demonstrates the quantum advantage by overcoming the exponential cost on classical computers. In addition, the algorithm is efficient regardless of whether the temporal entanglement due to non-Markovianity is low or high, making it a unified framework for simulating non-Markovian dynamics in open quantum system.
Mariia Ivanchenkoa, Peter L. Walters, Fei Wang
Nov 30, 2024·quant-ph·PDF In this work, we developed a rigorous procedure for mapping the exact non-Markovian propagator to the generalized Lindblad form. It allows us to extract the negative decay rate that is the indicator of the non-Markovian effect. As a consequence, we can investigate the influence of the non-Markovian bath on the system's properties such as coherence and equilibrium state distribution. The understanding of the non-Markovian contribution to the dynamical process points to the possibility of leveraging non-Markovianity for quantum control.
Fei Wang, Kun Li, Yiqi Nie, Zhangling Duan, Peng Zou, Zhiliang Wu, Yuwei Wang, Yanyan Wei
In this paper, we present our solution to the Cross-View Isolated Sign Language Recognition (CV-ISLR) challenge held at WWW 2025. CV-ISLR addresses a critical issue in traditional Isolated Sign Language Recognition (ISLR), where existing datasets predominantly capture sign language videos from a frontal perspective, while real-world camera angles often vary. To accurately recognize sign language from different viewpoints, models must be capable of understanding gestures from multiple angles, making cross-view recognition challenging. To address this, we explore the advantages of ensemble learning, which enhances model robustness and generalization across diverse views. Our approach, built on a multi-dimensional Video Swin Transformer model, leverages this ensemble strategy to achieve competitive performance. Finally, our solution ranked 3rd in both the RGB-based ISLR and RGB-D-based ISLR tracks, demonstrating the effectiveness in handling the challenges of cross-view recognition. The code is available at: https://github.com/Jiafei127/CV_ISLR_WWW2025.
Xiao Kang Du, Guo-Li Liu, Fei Wang, Wenyu Wang, Jin Min Yang, Yang Zhang
We propose to generate a realistic soft SUSY breaking spectrum for Next-to-Minimal Supersymmetric Standard Model (NMSSM) with a generalized deflected mirage mediation scenario, in which additional Yukawa and gauge mediation contributions are included to deflect the renormalization group equation(RGE) trajectory. Based on the Wilsonian effective action obtained by integrating out the messengers, the NMSSM soft SUSY breaking spectrum can be given analytically at the messenger scale. We find that additional contributions to $m_S^2$ can possibly ameliorate the stringent constraints from the electroweak symmetry breaking (EWSB) and 125 GeV Higgs mass. Constraints from dark matter and fine-tuning are also discussed. The Barbieri-Giudice fine-tuning measure and electroweak fine-tuning measure in our scenario can be as low as ${\cal O}(1)$, which possibly indicates that our scenario is natural.
Fei Wang, James Y. Huang, Tianyi Yan, Wenxuan Zhou, Muhao Chen
Natural language understanding (NLU) models often suffer from unintended dataset biases. Among bias mitigation methods, ensemble-based debiasing methods, especially product-of-experts (PoE), have stood out for their impressive empirical success. However, previous ensemble-based debiasing methods typically apply debiasing on top-level logits without directly addressing biased attention patterns. Attention serves as the main media of feature interaction and aggregation in PLMs and plays a crucial role in providing robust prediction. In this paper, we propose REsidual Attention Debiasing (READ), an end-to-end debiasing method that mitigates unintended biases from attention. Experiments on three NLU tasks show that READ significantly improves the performance of BERT-based models on OOD data with shortcuts removed, including +12.9% accuracy on HANS, +11.0% accuracy on FEVER-Symmetric, and +2.7% F1 on PAWS. Detailed analyses demonstrate the crucial role of unbiased attention in robust NLU models and that READ effectively mitigates biases in attention. Code is available at https://github.com/luka-group/READ.
Fei Wang, Kaiqiang Song, Hongming Zhang, Lifeng Jin, Sangwoo Cho, Wenlin Yao, Xiaoyang Wang, Muhao Chen, Dong Yu
Abstractive summarization models typically learn to capture the salient information from scratch implicitly. Recent literature adds extractive summaries as guidance for abstractive summarization models to provide hints of salient content and achieves better performance. However, extractive summaries as guidance could be over strict, leading to information loss or noisy signals. Furthermore, it cannot easily adapt to documents with various abstractiveness. As the number and allocation of salience content pieces vary, it is hard to find a fixed threshold deciding which content should be included in the guidance. In this paper, we propose a novel summarization approach with a flexible and reliable salience guidance, namely SEASON (SaliencE Allocation as Guidance for Abstractive SummarizatiON). SEASON utilizes the allocation of salience expectation to guide abstractive summarization and adapts well to articles in different abstractiveness. Automatic and human evaluations on two benchmark datasets show that the proposed method is effective and reliable. Empirical results on more than one million news articles demonstrate a natural fifteen-fifty salience split for news article sentences, providing a useful insight for composing news articles.
Fei Wang, Zhewei Xu, Pedro Szekely, Muhao Chen
Controlled table-to-text generation seeks to generate natural language descriptions for highlighted subparts of a table. Previous SOTA systems still employ a sequence-to-sequence generation method, which merely captures the table as a linear structure and is brittle when table layouts change. We seek to go beyond this paradigm by (1) effectively expressing the relations of content pieces in the table, and (2) making our model robust to content-invariant structural transformations. Accordingly, we propose an equivariance learning framework, which encodes tables with a structure-aware self-attention mechanism. This prunes the full self-attention structure into an order-invariant graph attention that captures the connected graph structure of cells belonging to the same row or column, and it differentiates between relevant cells and irrelevant cells from the structural perspective. Our framework also modifies the positional encoding mechanism to preserve the relative position of tokens in the same cell but enforce position invariance among different cells. Our technology is free to be plugged into existing table-to-text generation models, and has improved T5-based models to offer better performance on ToTTo and HiTab. Moreover, on a harder version of ToTTo, we preserve promising performance, while previous SOTA systems, even with transformation-based data augmentation, have seen significant performance drops. Our code is available at https://github.com/luka-group/Lattice.