BACKGROUND Patients with Parkinson's disease(PD)exhibit symptoms such as antecollis(AC)and camptocormia(CC).The pathology of these two conditions is unclear.Additionally,standard treatment methods have not been es...BACKGROUND Patients with Parkinson's disease(PD)exhibit symptoms such as antecollis(AC)and camptocormia(CC).The pathology of these two conditions is unclear.Additionally,standard treatment methods have not been established.The article reports the case of a 65-year-old female patient with AC and CC who was treated with central and peripheral interventions to alleviate symptoms.CASE SUMMARY We present the case of a 65-year-old female PD patient with AC and CC.The course of the disease was 5 years.She was treated with rehabilitation strategies such as sensory tricks and trunk strength training.During the inpatient period,we compared and analyzed the patient's gait,rehabilitation assessment scale score,and angles of her abnormal trunk posture in the first week,the third week,and the fifth week.The patient's stride length increased,indicating that the patient's walking ability was improved.The Unified Parkinson's Disease Scale Part Three score and CC severity score decreased.Furthermore,the score of the other scale increased.In addition,the patient showed significant improvements in AC,upper CC,and lower CC angles.CONCLUSION This case study suggested that sensory tricks and trunk strength training are beneficial and safe for patients with AC and CC.展开更多
In the need of some real applications, such as text categorization and image classification, the multi-label learning gradually becomes a hot research point in recent years. Much attention has been paid to the researc...In the need of some real applications, such as text categorization and image classification, the multi-label learning gradually becomes a hot research point in recent years. Much attention has been paid to the research of multi-label classification algorithms. Considering the fact that the high dimensionality of the multi-label datasets may cause the curse of dimensionality and wil hamper the classification process, a dimensionality reduction algorithm, named multi-label kernel discriminant analysis (MLKDA), is proposed to reduce the dimensionality of multi-label datasets. MLKDA, with the kernel trick, processes the multi-label integrally and realizes the nonlinear dimensionality reduction with the idea similar with linear discriminant analysis (LDA). In the classification process of multi-label data, the extreme learning machine (ELM) is an efficient algorithm in the premise of good accuracy. MLKDA, combined with ELM, shows a good performance in multi-label learning experiments with several datasets. The experiments on both static data and data stream show that MLKDA outperforms multi-label dimensionality reduction via dependence maximization (MDDM) and multi-label linear discriminant analysis (MLDA) in cases of balanced datasets and stronger correlation between tags, and ELM is also a good choice for multi-label classification.展开更多
A novel method based on the improved Laplacian eigenmap algorithm for fault pattern classification is proposed. Via modifying the Laplacian eigenmap algorithm to replace Euclidean distance with kernel-based geometric ...A novel method based on the improved Laplacian eigenmap algorithm for fault pattern classification is proposed. Via modifying the Laplacian eigenmap algorithm to replace Euclidean distance with kernel-based geometric distance in the neighbor graph construction, the method can preserve the consistency of local neighbor information and effectively extract the low-dimensional manifold features embedded in the high-dimensional nonlinear data sets. A nonlinear dimensionality reduction algorithm based on the improved Laplacian eigenmap is to directly learn high-dimensional fault signals and extract the intrinsic manifold features from them. The method greatly preserves the global geometry structure information embedded in the signals, and obviously improves the classification performance of fault pattern recognition. The experimental results on both simulation and engineering indicate the feasibility and effectiveness of the new method.展开更多
Kernel independent component analysis(KICA) is a newly emerging nonlinear process monitoring method,which can extract mutually independent latent variables called independent components(ICs) from process variables. Ho...Kernel independent component analysis(KICA) is a newly emerging nonlinear process monitoring method,which can extract mutually independent latent variables called independent components(ICs) from process variables. However, when more than one IC have Gaussian distribution, it cannot extract the IC feature effectively and thus its monitoring performance will be degraded drastically. To solve such a problem, a kernel time structure independent component analysis(KTSICA) method is proposed for monitoring nonlinear process in this paper. The original process data are mapped into a feature space nonlinearly and then the whitened data are calculated in the feature space by the kernel trick. Subsequently, a time structure independent component analysis algorithm, which has no requirement for the distribution of ICs, is proposed to extract the IC feature.Finally, two monitoring statistics are built to detect process faults. When some fault is detected, a nonlinear fault identification method is developed to identify fault variables based on sensitivity analysis. The proposed monitoring method is applied in the Tennessee Eastman benchmark process. Applications demonstrate the superiority of KTSICA over KICA.展开更多
There is a steep increase in data encoded as symmetric positive definite(SPD)matrix in the past decade.The set of SPD matrices forms a Riemannian manifold that constitutes a half convex cone in the vector space of mat...There is a steep increase in data encoded as symmetric positive definite(SPD)matrix in the past decade.The set of SPD matrices forms a Riemannian manifold that constitutes a half convex cone in the vector space of matrices,which we sometimes call SPD manifold.One of the fundamental problems in the application of SPD manifold is to find the nearest neighbor of a queried SPD matrix.Hashing is a popular method that can be used for the nearest neighbor search.However,hashing cannot be directly applied to SPD manifold due to its non-Euclidean intrinsic geometry.Inspired by the idea of kernel trick,a new hashing scheme for SPD manifold by random projection and quantization in expanded data space is proposed in this paper.Experimental results in large scale nearduplicate image detection show the effectiveness and efficiency of the proposed method.展开更多
目的探讨时间分辨对比剂动态显像技术(TRICKS)对于脑动静脉畸形的诊断价值。方法收集笔者所在医院25例脑动脉畸形(AVM)患者资料,使用GE公司超导Signa HD 1.5T MR扫描仪,应用椭圆中心TRICKS,对病灶区进行连续不间断扫描,利用多平面重建...目的探讨时间分辨对比剂动态显像技术(TRICKS)对于脑动静脉畸形的诊断价值。方法收集笔者所在医院25例脑动脉畸形(AVM)患者资料,使用GE公司超导Signa HD 1.5T MR扫描仪,应用椭圆中心TRICKS,对病灶区进行连续不间断扫描,利用多平面重建及最大强度投影技术对原始图像进行血管重建。结果 TRICKS清楚显示大小不等的畸形血管巢及供血动脉和引流静脉。畸形血管巢位于颞叶7例,顶叶6例,额叶5例、枕叶6例和小脑半球1例。发现供血动脉37支,其中单支供血动脉15例,多支供血动脉10例;引流静脉33支,单只引流10例,多支引流15例。结论 TRICKS显示AVM的供血动脉、畸形血管巢和引流静脉,为临床治疗方案的制定提供可靠的依据,值得在临床上推广使用。展开更多
The thermodynamics and the phase diagram of random field Ising model (RFIM) on Bethe lattice are studied by using a replica trick. This lattice is placed in an external magnetic field (B). A Gaussian distribution ...The thermodynamics and the phase diagram of random field Ising model (RFIM) on Bethe lattice are studied by using a replica trick. This lattice is placed in an external magnetic field (B). A Gaussian distribution of random field (hi) with zero mean and variance (hi2 = H2RF is considered. The free-energy (F), the magnetization (M) and the order parameter (q) are investigated for several values of coordination number (z). The phase diagram shows several interesting behaviours and presents tricritical point at critical temperature Tc = J/k and when HRF = 0 for finite z. The free-energy (F) values increase as T increases for different intensities of random field (HRE) and finite z. The internal energy (U) has a similar behaviour to that obtained from the Monte Carlo simulations. The ground state of magnetization decreases as the intensity of random field HRF increases, The ferromagnetic (FM) paramagnetic (PM) phase boundary is clearly observed only when z →∞. While FM PM-spin glass (SG) phase boundaries are present for finite z. The magnetic susceptibility (X) shows a sharp cusp at Tc in a small random field for finite z and rounded different peaks on increasing HRF.展开更多
Many different types of polarized eukaryotic cells have been shown to segregate synthesis for some protein subpopulations to cytoplasmic domains distant from their nucleus.For neurons,these distances can be tens-to-th...Many different types of polarized eukaryotic cells have been shown to segregate synthesis for some protein subpopulations to cytoplasmic domains distant from their nucleus.For neurons,these distances can be tens-to-thousands fold more than the diameter of the cell body.展开更多
When I was young I tried many jobs. But the easiestway I found of making money was as 'medicine man.' Iset myself up as① Dr Waugh-Hoo, the famous Indianmedicine man, and travelled around from town to town sel...When I was young I tried many jobs. But the easiestway I found of making money was as 'medicine man.' Iset myself up as① Dr Waugh-Hoo, the famous Indianmedicine man, and travelled around from town to town sellingcures. One day I found myself in a town on the East coast展开更多
Some results indicate that quantum information based on quantum physics is more powerful than classical one. In this paper, we propose new tricks based on quantum physics. Our tricks are methods inspired by the strate...Some results indicate that quantum information based on quantum physics is more powerful than classical one. In this paper, we propose new tricks based on quantum physics. Our tricks are methods inspired by the strategies of quantum game theory. In these tricks, magicians have the ability of quantum physics, but spectators have only classical one. We propose quantum tricks such that, by manipulating quantum coins and quantum cards, magicians guess spectators’ values.展开更多
On the 14th Massy International Circus Festival,“Rope Tricks”by Fujian Acrobatic Troupe won the highest award—France Republic President Award.As the main tutor and direc-
Speed reading can seem like an almost superhuman feat-but is it really possible to read quickly and retain the information?Many of us would love to be able to read faster,yet still take everything in.There are methods...Speed reading can seem like an almost superhuman feat-but is it really possible to read quickly and retain the information?Many of us would love to be able to read faster,yet still take everything in.There are methods dating back decades that people have tried in the hope of being able to digest a lengthy book in well under an hour.展开更多
文摘BACKGROUND Patients with Parkinson's disease(PD)exhibit symptoms such as antecollis(AC)and camptocormia(CC).The pathology of these two conditions is unclear.Additionally,standard treatment methods have not been established.The article reports the case of a 65-year-old female patient with AC and CC who was treated with central and peripheral interventions to alleviate symptoms.CASE SUMMARY We present the case of a 65-year-old female PD patient with AC and CC.The course of the disease was 5 years.She was treated with rehabilitation strategies such as sensory tricks and trunk strength training.During the inpatient period,we compared and analyzed the patient's gait,rehabilitation assessment scale score,and angles of her abnormal trunk posture in the first week,the third week,and the fifth week.The patient's stride length increased,indicating that the patient's walking ability was improved.The Unified Parkinson's Disease Scale Part Three score and CC severity score decreased.Furthermore,the score of the other scale increased.In addition,the patient showed significant improvements in AC,upper CC,and lower CC angles.CONCLUSION This case study suggested that sensory tricks and trunk strength training are beneficial and safe for patients with AC and CC.
基金supported by the National Natural Science Foundation of China(5110505261173163)the Liaoning Provincial Natural Science Foundation of China(201102037)
文摘In the need of some real applications, such as text categorization and image classification, the multi-label learning gradually becomes a hot research point in recent years. Much attention has been paid to the research of multi-label classification algorithms. Considering the fact that the high dimensionality of the multi-label datasets may cause the curse of dimensionality and wil hamper the classification process, a dimensionality reduction algorithm, named multi-label kernel discriminant analysis (MLKDA), is proposed to reduce the dimensionality of multi-label datasets. MLKDA, with the kernel trick, processes the multi-label integrally and realizes the nonlinear dimensionality reduction with the idea similar with linear discriminant analysis (LDA). In the classification process of multi-label data, the extreme learning machine (ELM) is an efficient algorithm in the premise of good accuracy. MLKDA, combined with ELM, shows a good performance in multi-label learning experiments with several datasets. The experiments on both static data and data stream show that MLKDA outperforms multi-label dimensionality reduction via dependence maximization (MDDM) and multi-label linear discriminant analysis (MLDA) in cases of balanced datasets and stronger correlation between tags, and ELM is also a good choice for multi-label classification.
基金National Hi-tech Research Development Program of China(863 Program,No.2007AA04Z421)National Natural Science Foundation of China(No.50475078,No.50775035)
文摘A novel method based on the improved Laplacian eigenmap algorithm for fault pattern classification is proposed. Via modifying the Laplacian eigenmap algorithm to replace Euclidean distance with kernel-based geometric distance in the neighbor graph construction, the method can preserve the consistency of local neighbor information and effectively extract the low-dimensional manifold features embedded in the high-dimensional nonlinear data sets. A nonlinear dimensionality reduction algorithm based on the improved Laplacian eigenmap is to directly learn high-dimensional fault signals and extract the intrinsic manifold features from them. The method greatly preserves the global geometry structure information embedded in the signals, and obviously improves the classification performance of fault pattern recognition. The experimental results on both simulation and engineering indicate the feasibility and effectiveness of the new method.
基金Supported by the National Natural Science Foundation of China(61273160)the Natural Science Foundation of Shandong Province of China(ZR2011FM014)+1 种基金the Doctoral Fund of Shandong Province(BS2012ZZ011)the Postgraduate Innovation Funds of China University of Petroleum(CX2013060)
文摘Kernel independent component analysis(KICA) is a newly emerging nonlinear process monitoring method,which can extract mutually independent latent variables called independent components(ICs) from process variables. However, when more than one IC have Gaussian distribution, it cannot extract the IC feature effectively and thus its monitoring performance will be degraded drastically. To solve such a problem, a kernel time structure independent component analysis(KTSICA) method is proposed for monitoring nonlinear process in this paper. The original process data are mapped into a feature space nonlinearly and then the whitened data are calculated in the feature space by the kernel trick. Subsequently, a time structure independent component analysis algorithm, which has no requirement for the distribution of ICs, is proposed to extract the IC feature.Finally, two monitoring statistics are built to detect process faults. When some fault is detected, a nonlinear fault identification method is developed to identify fault variables based on sensitivity analysis. The proposed monitoring method is applied in the Tennessee Eastman benchmark process. Applications demonstrate the superiority of KTSICA over KICA.
文摘There is a steep increase in data encoded as symmetric positive definite(SPD)matrix in the past decade.The set of SPD matrices forms a Riemannian manifold that constitutes a half convex cone in the vector space of matrices,which we sometimes call SPD manifold.One of the fundamental problems in the application of SPD manifold is to find the nearest neighbor of a queried SPD matrix.Hashing is a popular method that can be used for the nearest neighbor search.However,hashing cannot be directly applied to SPD manifold due to its non-Euclidean intrinsic geometry.Inspired by the idea of kernel trick,a new hashing scheme for SPD manifold by random projection and quantization in expanded data space is proposed in this paper.Experimental results in large scale nearduplicate image detection show the effectiveness and efficiency of the proposed method.
文摘The thermodynamics and the phase diagram of random field Ising model (RFIM) on Bethe lattice are studied by using a replica trick. This lattice is placed in an external magnetic field (B). A Gaussian distribution of random field (hi) with zero mean and variance (hi2 = H2RF is considered. The free-energy (F), the magnetization (M) and the order parameter (q) are investigated for several values of coordination number (z). The phase diagram shows several interesting behaviours and presents tricritical point at critical temperature Tc = J/k and when HRF = 0 for finite z. The free-energy (F) values increase as T increases for different intensities of random field (HRE) and finite z. The internal energy (U) has a similar behaviour to that obtained from the Monte Carlo simulations. The ground state of magnetization decreases as the intensity of random field HRF increases, The ferromagnetic (FM) paramagnetic (PM) phase boundary is clearly observed only when z →∞. While FM PM-spin glass (SG) phase boundaries are present for finite z. The magnetic susceptibility (X) shows a sharp cusp at Tc in a small random field for finite z and rounded different peaks on increasing HRF.
基金supported by funds from National Institutes of Health(P01-NS055976 and R01-NS041596)National Science Foundation(MCB-1020970)+1 种基金Dr.Miriam and Sheldon G.Adelson Medical Research Foundationthe Department of Defense/US Army Medical Research and Development(OR120042)
文摘Many different types of polarized eukaryotic cells have been shown to segregate synthesis for some protein subpopulations to cytoplasmic domains distant from their nucleus.For neurons,these distances can be tens-to-thousands fold more than the diameter of the cell body.
文摘When I was young I tried many jobs. But the easiestway I found of making money was as 'medicine man.' Iset myself up as① Dr Waugh-Hoo, the famous Indianmedicine man, and travelled around from town to town sellingcures. One day I found myself in a town on the East coast
文摘Some results indicate that quantum information based on quantum physics is more powerful than classical one. In this paper, we propose new tricks based on quantum physics. Our tricks are methods inspired by the strategies of quantum game theory. In these tricks, magicians have the ability of quantum physics, but spectators have only classical one. We propose quantum tricks such that, by manipulating quantum coins and quantum cards, magicians guess spectators’ values.
文摘On the 14th Massy International Circus Festival,“Rope Tricks”by Fujian Acrobatic Troupe won the highest award—France Republic President Award.As the main tutor and direc-
文摘Speed reading can seem like an almost superhuman feat-but is it really possible to read quickly and retain the information?Many of us would love to be able to read faster,yet still take everything in.There are methods dating back decades that people have tried in the hope of being able to digest a lengthy book in well under an hour.