arXiv subjects

Jianping Yin

Publications and source records attributed to Jianping Yin.

14 recordsLinked to original sources

arXiv2023

MS-UNet-v2: Adaptive Denoising Method and Training Strategy for Medical Image Segmentation with Small Training Data

Models based on U-like structures have improved the performance of medical image segmentation. However, the single-layer decoder structure of U-Net is too "thin" to exploit enough information, resulting in large semantic differences between the encoder and decoder parts. Things get worse if the number of training sets of data is not sufficiently large, which is common in medical image processing tasks where annotated data are more difficult to obtain than other tasks. Based on this observation, we propose a novel U-Net model named MS-UNet for the medical image segmentation task in this study. Instead of the single-layer U-Net decoder structure used in Swin-UNet and TransUnet, we specifically design a multi-scale nested decoder based on the Swin Transformer for U-Net. The proposed multi-scale nested decoder structure allows the feature mapping between the decoder and encoder to be semantically closer, thus enabling the network to learn more detailed features. In addition, we propose a novel edge loss and a plug-and-play fine-tuning Denoising module, which not only effectively improves the segmentation performance of MS-UNet, but could also be applied to other models individually. Experimental results show that MS-UNet could effectively improve the network performance with more efficient feature learning capability and exhibit more advanced performance, especially in the extreme case with a small amount of training data, and the proposed Edge loss and Denoising module could significantly enhance the segmentation performance of MS-UNet.

eess.IV↗

arXiv2021

Video Abnormal Event Detection by Learning to Complete Visual Cloze Tests

Although deep neural networks (DNNs) enable great progress in video abnormal event detection (VAD), existing solutions typically suffer from two issues: (1) The localization of video events cannot be both precious and comprehensive. (2) The semantics and temporal context are under-explored. To tackle those issues, we are motivated by the prevalent cloze test in education and propose a novel approach named Visual Cloze Completion (VCC), which conducts VAD by learning to complete "visual cloze tests" (VCTs). Specifically, VCC first localizes each video event and encloses it into a spatio-temporal cube (STC). To achieve both precise and comprehensive localization, appearance and motion are used as complementary cues to mark the object region associated with each event. For each marked region, a normalized patch sequence is extracted from current and adjacent frames and stacked into a STC. With each patch and the patch sequence of a STC compared to a visual "word" and "sentence" respectively, we deliberately erase a certain "word" (patch) to yield a VCT. Then, the VCT is completed by training DNNs to infer the erased patch and its optical flow via video semantics. Meanwhile, VCC fully exploits temporal context by alternatively erasing each patch in temporal context and creating multiple VCTs. Furthermore, we propose localization-level, event-level, model-level and decision-level solutions to enhance VCC, which can further exploit VCC's potential and produce significant performance improvement gain. Extensive experiments demonstrate that VCC achieves state-of-the-art VAD performance. Our codes and results are open at https://github.com/yuguangnudt/VEC_VAD/tree/VCC.

cs.CV↗

arXiv2021

Multi-view Deep One-class Classification: A Systematic Exploration

One-class classification (OCC), which models one single positive class and distinguishes it from the negative class, has been a long-standing topic with pivotal application to realms like anomaly detection. As modern society often deals with massive high-dimensional complex data spawned by multiple sources, it is natural to consider OCC from the perspective of multi-view deep learning. However, it has not been discussed by the literature and remains an unexplored topic. Motivated by this blank, this paper makes four-fold contributions: First, to our best knowledge, this is the first work that formally identifies and formulates the multi-view deep OCC problem. Second, we take recent advances in relevant areas into account and systematically devise eleven different baseline solutions for multi-view deep OCC, which lays the foundation for research on multi-view deep OCC. Third, to remedy the problem that limited benchmark datasets are available for multi-view deep OCC, we extensively collect existing public data and process them into more than 30 new multi-view benchmark datasets via multiple means, so as to provide a publicly available evaluation platform for multi-view deep OCC. Finally, by comprehensively evaluating the devised solutions on benchmark datasets, we conduct a thorough analysis on the effectiveness of the designed baselines, and hopefully provide other researchers with beneficial guidance and insight to multi-view deep OCC. Our data and codes are opened at https://github.com/liujiyuan13/MvDOCC-datasets and https://github.com/liujiyuan13/MvDOCC-code respectively to facilitate future research.

cs.CV↗

arXiv2020

Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Events

As a vital topic in media content interpretation, video anomaly detection (VAD) has made fruitful progress via deep neural network (DNN). However, existing methods usually follow a reconstruction or frame prediction routine. They suffer from two gaps: (1) They cannot localize video activities in a both precise and comprehensive manner. (2) They lack sufficient abilities to utilize high-level semantics and temporal context information. Inspired by frequently-used cloze test in language study, we propose a brand-new VAD solution named Video Event Completion (VEC) to bridge gaps above: First, we propose a novel pipeline to achieve both precise and comprehensive enclosure of video activities. Appearance and motion are exploited as mutually complimentary cues to localize regions of interest (RoIs). A normalized spatio-temporal cube (STC) is built from each RoI as a video event, which lays the foundation of VEC and serves as a basic processing unit. Second, we encourage DNN to capture high-level semantics by solving a visual cloze test. To build such a visual cloze test, a certain patch of STC is erased to yield an incomplete event (IE). The DNN learns to restore the original video event from the IE by inferring the missing patch. Third, to incorporate richer motion dynamics, another DNN is trained to infer erased patches' optical flow. Finally, two ensemble strategies using different types of IE and modalities are proposed to boost VAD performance, so as to fully exploit the temporal context and modality information for VAD. VEC can consistently outperform state-of-the-art methods by a notable margin (typically 1.5%-5% AUROC) on commonly-used VAD benchmarks. Our codes and results can be verified at github.com/yuguangnudt/VEC_VAD.

cs.CV↗

arXiv2020

Unsupervised Heterogeneous Coupling Learning for Categorical Representation

Complex categorical data is often hierarchically coupled with heterogeneous relationships between attributes and attribute values and the couplings between objects. Such value-to-object couplings are heterogeneous with complementary and inconsistent interactions and distributions. Limited research exists on unlabeled categorical data representations, ignores the heterogeneous and hierarchical couplings, underestimates data characteristics and complexities, and overuses redundant information, etc. The deep representation learning of unlabeled categorical data is challenging, overseeing such value-to-object couplings, complementarity and inconsistency, and requiring large data, disentanglement, and high computational power. This work introduces a shallow but powerful UNsupervised heTerogeneous couplIng lEarning (UNTIE) approach for representing coupled categorical data by untying the interactions between couplings and revealing heterogeneous distributions embedded in each type of couplings. UNTIE is efficiently optimized w.r.t. a kernel k-means objective function for unsupervised representation learning of heterogeneous and hierarchical value-to-object couplings. Theoretical analysis shows that UNTIE can represent categorical data with maximal separability while effectively represent heterogeneous couplings and disclose their roles in categorical data. The UNTIE-learned representations make significant performance improvement against the state-of-the-art categorical representations and deep representation models on 25 categorical data sets with diversified characteristics.

cs.LG↗

arXiv2019

Understand Dynamic Regret with Switching Cost for Online Decision Making

As a metric to measure the performance of an online method, dynamic regret with switching cost has drawn much attention for online decision making problems. Although the sublinear regret has been provided in many previous researches, we still have little knowledge about the relation between the dynamic regret and the switching cost. In the paper, we investigate the relation for two classic online settings: Online Algorithms (OA) and Online Convex Optimization (OCO). We provide a new theoretical analysis framework, which shows an interesting observation, that is, the relation between the switching cost and the dynamic regret is different for settings of OA and OCO. Specifically, the switching cost has significant impact on the dynamic regret in the setting of OA. But, it does not have an impact on the dynamic regret in the setting of OCO. Furthermore, we provide a lower bound of regret for the setting of OCO, which is same with the lower bound in the case of no switching cost. It shows that the switching cost does not change the difficulty of online decision making problems in the setting of OCO.

cs.LG↗

arXiv2019

Simultaneous Clustering and Optimization for Evolving Datasets

Simultaneous clustering and optimization (SCO) has recently drawn much attention due to its wide range of practical applications. Many methods have been previously proposed to solve this problem and obtain the optimal model. However, when a dataset evolves over time, those existing methods have to update the model frequently to guarantee accuracy; such updating is computationally infeasible. In this paper, we propose a new formulation of SCO to handle evolving datasets. Specifically, we propose a new variant of the alternating direction method of multipliers (ADMM) to solve this problem efficiently. The guarantee of model accuracy is analyzed theoretically for two specific tasks: ridge regression and convex clustering. Extensive empirical studies confirm the effectiveness of our method.

cs.LG↗

arXiv2019

Three-dimensional modeling magneto-optical trapping of MgF molecule with multilevel rate equations

We present a theoretical study of magneto-optical trapping (MOT) force exerted on magnesium monofluoride (MgF) with 3D rate equations, in which we have considered the complex vibrational and rotational levels and the effects of small internal splittings and degeneracies,including fine and hyperfine structures and the magnetic quantum numbers. We investigate the feasibility of MOT for MgF with a very small excited state g-factor ($g_{e} = -0.0002$) and a large radiative decay reate ($Γ = 2π\times22$MHz) for the electronic transition of X$^2Σ^{+}$ to A$^2Π_{1/2}$ states. We also optimize the MOT with reference to the three-, four- and more-frequency component models with various polarization configurations and detunings. By applying the dual frequency arrangement to more than one hyperfine level, we suggest a configuration of the 3+1 frequency components for achieving the MOT of MgF.

physics.atom-ph↗

arXiv2019

Dynamic Online Gradient Descent with Improved Query Complexity: A Theoretical Revisit

We provide a new theoretical analysis framework to investigate online gradient descent in the dynamic environment. Comparing with the previous work, the new framework recovers the state-of-the-art dynamic regret, but does not require extra gradient queries for every iteration. Specifically, when functions are $α$ strongly convex and $β$ smooth, to achieve the state-of-the-art dynamic regret, the previous work requires $O(κ)$ with $κ= \fracβα$ queries of gradients at every iteration. But, our framework shows that the query complexity can be improved to be $O(1)$, which does not depend on $κ$. The improvement is significant for ill-conditioned problems because that their objective function usually has a large $κ$.

cs.LG↗

arXiv2018

Theoretical investigation of the HgF radical towards laser cooling and eEDM measurement

In order to realize more sensitive eEDM measurement, it would be worthwhile to find some new laser-cooled molecules with larger internal effective electric field (E$_{eff}$), higher electric polarizability and longer lifetime of the eEDM measurement state. Here we explore the merits of mercuric monofluoride ($^{202}Hg^{19}F$, X$^ 2 Σ_{1/2}$) for its potential of laser cooling and eEDM measurement. We theoretically investigated the electronic, rovibrational and hyperfine structures and verified the highly diagonal Franck-Condon factors (FCFs) of the main transitions by the Rydberg-Klein-Rees inversion (RKR) method and the Morse approximation. Hyperfine manifolds of the X$^ 2 Σ_{1/2} (ν=0$) rotational states were examined with the effective Hamiltonian approach and a feasible sideband modulation scheme was proposed. In order to enhance optical cycling, the microwave remixing method was employed to address all the necessary levels. The Zeeman effect and the hyperfine structure magnetic g factors of the X$^ 2 Σ_{1/2} (ν=0$,$\mathit{ N } $=1) state were studied subsequently. Finally, its statistical sensitivities for the eEDM measurement were estimated respectively to be about $9\times 10^{-31} e\bullet cm $ (the laser cooled transverse beam experiment), $2\times 10^{-31} e\bullet cm$ (the fountain experiment) and $1\times 10^{-32}$ e\bullet cm (experiment with trapped cold molecules), indicating that $^{202}Hg^{19}F$ might be another promising eEDM candidate when compared with the most recent ThO result of $d_{ e } = (4.3 \pm 3.1_{ stat } \pm 2.6_{ syst })\times 10^{-30} e\bullet cm$ (Nature, 562, 355 (2018)). In addition, the possibility of direct Stark decelerating of the HgF radical was also discussed.

physics.atom-ph↗

arXiv2018

Triangle Lasso for Simultaneous Clustering and Optimization in Graph Datasets

Recently, network lasso has drawn many attentions due to its remarkable performance on simultaneous clustering and optimization. However, it usually suffers from the imperfect data (noise, missing values etc), and yields sub-optimal solutions. The reason is that it finds the similar instances according to their features directly, which is usually impacted by the imperfect data, and thus returns sub-optimal results. In this paper, we propose triangle lasso to avoid its disadvantage. Triangle lasso finds the similar instances according to their neighbours. If two instances have many common neighbours, they tend to become similar. Although some instances are profiled by the imperfect data, it is still able to find the similar counterparts. Furthermore, we develop an efficient algorithm based on Alternating Direction Method of Multipliers (ADMM) to obtain a moderately accurate solution. In addition, we present a dual method to obtain the accurate solution with the low additional time consumption. We demonstrate through extensive numerical experiments that triangle lasso is robust to the imperfect data. It usually yields a better performance than the state-of-the-art method when performing data analysis tasks in practical scenarios.

cs.LG↗

arXiv2018

Optical stimulated slowing of polar heavy-atom molecules with a constant beat phase

Polar heavy-atom molecules have been well recognized as promising candidates for precision measurements and tests of fundamental physics. A much slower molecular beam to increase the interaction time should lead to a more sensitive measurement. Here we theoretically demonstrate the possibility of the stimulated longitudinal slowing of heavy-atom molecules by the coherent optical bichromatic force with a constant beat phase. Taking the YbF meolecule as an example, we show that a rapid and short-distance deceleration of heavy molecules by a phase-compensation method is feasible with moderate conditions. A molecular beam of YbF with a forward velocity of 120 m/s can be decelerated below 10 m/s within a distance of 3.5 cm and with a laser irradiance for each traveling wave of 107.2 W/cm$^2$. We also give a simple approach to estimate the performance of the BCF on some other heavy molecules, which is helpful for making a rapid evaluation on the feasibility of the stimulated slowing experiment. Our proposed slowing method could be a promising approach to break through the space constraint or the limited capture efficiency of molecules loadable into a MOT in traditional deceleration schemes, opening the possibility for a significant improvement of the precision measurement sensitivity.

physics.atom-ph↗

arXiv2013

Multiple Kernel Learning in the Primal for Multi-modal Alzheimer's Disease Classification

To achieve effective and efficient detection of Alzheimer's disease (AD), many machine learning methods have been introduced into this realm. However, the general case of limited training samples, as well as different feature representations typically makes this problem challenging. In this work, we propose a novel multiple kernel learning framework to combine multi-modal features for AD classification, which is scalable and easy to implement. Contrary to the usual way of solving the problem in the dual space, we look at the optimization from a new perspective. By conducting Fourier transform on the Gaussian kernel, we explicitly compute the mapping function, which leads to a more straightforward solution of the problem in the primal space. Furthermore, we impose the mixed $L_{21}$ norm constraint on the kernel weights, known as the group lasso regularization, to enforce group sparsity among different feature modalities. This actually acts as a role of feature modality selection, while at the same time exploiting complementary information among different kernels. Therefore it is able to extract the most discriminative features for classification. Experiments on the ADNI data set demonstrate the effectiveness of the proposed method.

cs.LG↗

arXiv2008

Optical Stark decelerator for cw molecular beam with a quasi-cw semi-Gaussian laser beam

We propose a promising scheme to decelerate a cw filtering out molecular beam using a red-detuned quasi-cw semi-Gaussian laser beam. By using Monte-Carlo simulation method, we demonstrate this optical Stark deceleration scheme, and study its decelerated dynamic process for a cw ND3 molecular beam as well as the dependence of the deceleration effect on the border width of the semi-Gaussian beam. Our study shows that the proposed scheme can be used to efficiently slow a cw ND3 molecular beam, and obtain a relative average kinetic-energy loss of 9.33% by using a single semi-Gaussian beam with a well depth of 7.3 mK.

physics.atom-ph↗