Li Yao

Henan University

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Xiaojie Zhao

Beijing Normal University

Xia Wu

Beijing Institute of Technology

Zhiying Long

Beijing Normal University

Kewei Chen

Banner Alzheimer’s Institute

Jiacai Zhang

Beijing Normal University

Xiaotong Wen

Renmin University of China

Xiaojuan Guo

Beijing Normal University

Lele Xu

Technology and Engineering Center for Space Utilization

Eric M. Reiman

Translational Genomics Research Institute

Rui Li

University of Chinese Academy of Sciences

Cooperative Institutions

Beijing Normal University

135

Chinese Academy of Sciences

Capital Medical University

Chinese Institute for Brain Research

Peking University

University of Chinese Academy of Sciences

Sichuan University

Chinese Academy of Medical Sciences & Peking Union Medical College

Shanghai Jiao Tong University

Huazhong University of Science and Technology

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The newsvendor problem with pricing: Extensions

International Journal of Management Science and Engineering Management (2006)

Li Yao Frank Chen Houmin Yan

Abstract Abstract The newsvendor problem with pricing decision provides an important vehicle for examining how operational problems interacts with marketing issues to influence decision-making at the firm level. This paper considers the newsvendor problem with pricing and its extensions. We try to answer two questions: (1) how to model the price-dependent stochastic demand and (2) how to derive the combined pricing and inventory solutions. We identify a class of demand functions which can result in well behaved profit functions. A demand function can be divided into two parts: the mean demand and random demand. If the part of mean demand has increasing price elasticity (IPE) and the part of random demand has generalized strict increasing failure rate (GSIFR), then the expected profit of the newsvendor is unimodal or quasi-concave. This appears generalizing the existent models in the literature. Keywords: inventory and pricingnewsvendor problem with pricingstochastic inventory model

Newsvendor model

On demand

10.1080/17509653.2006.10670993

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Citations (99)

Alterations of Directional Connectivity among Resting-State Networks in Alzheimer Disease

American Journal of Neuroradiology (2012)

R. Li Xia Wu Kewei Chen Adam Fleisher Eric M. Reiman

BACKGROUND AND PURPOSE:

AD has been documented as a kind of disconnection syndrome by functional neuroimaging studies. The primary focus of this study was to examine, with the use of resting-state fMRI, whether AD would impact connectivity among RSNs.

MATERIALS AND METHODS:

Fourteen patients with AD and 16 NC were recruited and scanned by using resting-state fMRI. Group independent-component analysis and the BN learning approach were used, respectively, to separate the RSNs and construct the network-to-network connectivity patterns for each group. The convergence index for the special network DMN was measured.

RESULTS:

Three of the 4 connections were significantly lower in AD compared with NC. Although numerically the AD group had more connections, none was statistically different from that in the NC group except for 1 increased connection from the DMN to the DAN. The convergence index for the DMN node was lower in AD than in NC.

CONCLUSIONS:

Connections among cognitive networks in AD were more vulnerable to impairment than sensory networks. The DMN decreased its integration function for other RSNs but may also play a role in compensating for the disrupted connections in AD.

Disconnection

10.3174/ajnr.a3197

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Citations (48)

Analysis Motion Imagination EEG Signal in Spatiotemporal-energy domain

Zhengyan Sheng Xiao Yao Ruixuan Chen Wenyu Diao Weiliang Li

Brain is the most advanced part of the nervous system. As an emerging neighborhood exploring the brain, the brain-computer interface (BCI) may completely influence people's current communication and lifestyle in the future. We propose an architecture for feature extraction and classification model in brain-computer interface (BCI). Three features of EEG signal are extracted and combined in an attempt to describe the EEG signal more comprehensively including Wavelet packet decomposition, Information entropy and Co-space pattern (CSP). We construct the C-LSTM model combining the Convolutional Neural Networks (CNN) and the Long Short-Term Memory (LSTM) to perform the classification. Comparison was made with traditional approaches, and results show the method has a better performance for classification of motor imagery.

Interface (matter)

SIGNAL (programming language)

10.1145/3374587.3374639

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Citations (2)

Investigation of effective connectivity in the motor cortex of fMRI data using Granger causality model

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (2007)

Xingchun Wu Ni Tang Kai Yin Xia Wu Xiaotong Wen

Effective connectivity of brain regions based on brain data (e.g. EEG, fMRI, etc.) is a focused research at present. Many researchers tried to investigate it using different methods. Granger causality model (GCM) is presently used to investigate effective connectivity of brain regions more and more. It can explore causal relationship between time series, meaning that if a time-series y causes x, then knowledge of y should help predict future values of x. In present work, time invariant GCM was applied to fMRI data considering slow changing of blood oxygenation level dependent (BOLD). The time invariant GCM often requires determining model order, estimating model parameters and significance test. In particular, we extended significance test method to make results more reasonable. The fMRI data were acquired from finger movement experiment of two right-handed subjects. We obtained the activation maps of two subjects using SPM'2 software firstly. Then we chose left SMA and left SMC as regions of interest (ROIs) with different radiuses, and calculated causality from left SMA to left SMC using the mean time courses of the two ROIs. The results from both subjects showed that left SMA influenced on left SMC. Hence GCM was suggested to be an effective approach in investigation of effective connectivity based on fMRI data.

Causal model

Causality

10.1117/12.708369

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Automated detection of white matter abnormality and its application to Alzheimer's disease

Xiaojie Zhao Xianjun Wu Li Yao

Diffusion tensor imaging (DTI) technique, which can be used to research the white matter of human brain noninvasively, provides more valuable information in the study of white matter abnormality, especially using fractional anisotropy (FA) images calculated from DTI data. Although there are many different DTI data analysis software, most of them are inconvenient to use and qualitative via visual inspection. To provide assistance to physicians in improving the facilitation and sensitivity of the white matter abnormality analysis, we developed an automated FA analysis method of an individual in comparison with a group of normal controls, which was based on the principle of voxel based analysis (VBA), and contained preprocessing, database, and statistical analysis procedures. Only using FA and B _o images, the final results about the white matter structure difference would be displayed automatically and interactively. Some data generated during the processing were stored to corresponding path and could be checked when necessary. We applied this methods to Alzheimer's disease (AD). The final result supplied the brain regions where FA values of AD were reduced. DTI data from fifteen AD and sixteen elderly healthy subjects was used to analyse and the brain regions which affected by AD were consonant in the main with the previous study, such as corpus callosum, cingulate regions, and so on. This method could be valuable tool to help physicians in making their clinical decisions.

Abnormality

10.1109/iccme.2012.6275656

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A spatiotemporal approach to N170 detection with application to brain-computer interfaces

Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics (2008)

Yaqin Xu Kai Yin Jiacai Zhang Li Yao

Over the past decade, many laboratories have begun to explore brain-computer interface (BCI) technology as a radically new communication option. BCI can help users send messages and commands to the external world without using their brain's normal output channels or muscles. The central element in each BCI system is to find a reliable method to detect the specific feature patterns extracted from the raw brain signals, and then translate it into usable control signals. In this paper, we introduce our approach to detect N170 component of the event-related brain potential (ERP) based on its spatiotemporal patterns in single-trial EEG signals. Common spatial pattern (CSP) method and machine-learning technique support vector machine (SVM) are adopted for N170 feature extraction and translation, i.e. they convert electrophysiological input from the user into on-off signal to control external devices. Our results indicate that the CSP can effectively extract discriminatory information, and SVM has an efficient performance for N170 classification. Comparing to several other methods, high performances of our framework show that the temporal and spatial features of N170 are very stable and it is promising for new type BCI applications.

USable

Interface (matter)

Component (thermodynamics)

Feature (linguistics)

10.1109/icsmc.2008.4811392

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Citations (3)

Correlation analysis between momentary phases of ongoing EEG oscillations and erp amplitudes to identify the optimal brain state for stimulus presentation

Liang Zhang Jiacai Zhang Li Yao

Users of BCI systems are always encouraged to develop and maintain the most discriminable brain signal features that the BCI employs to decipher their intent. Instead of training users to develop new skills or strategies allwoing the users best control their brain signals by rewarding better performance with appropiate feedback, this paper exploited to augment driven neuroelectic responses to externally stimuli by presenting the visual stimulus at the time when brain fall in the state of effective processing the stimulus. Our work present electrophysiological evidence showing that the amplitudes of ERP(event related potential) component P300 indicating the brain recognitive state is actually modulated periodically. Firstly, we separated stimulus evoked response from ongoing spontaneous EEG with differentially variable component analysis (dVCA) method, and then analyzed correlation between amplitudes of P300 and the phase of ongoing EEG oscillations with circular-linear correlation. The ERP component P300 evoked by stimulus may reflect the human cognitive processing, such as visual information recognition, and the ongoing EEG oscillations relate to subsecond fluctuations of neuronal excitability. So, correlation analysis of ERP amplitudes and Ongoing EEG oscillations phases reveal the brain states that conducive for stimulus presentation. Our analysis demonstrated that amplitude of P300 induced with visual stimulus actually fluctuated over time along with the phase of spontaneous oscillations in the alpha (6.6 Hz) frequency band just about 396 milliseconds before stimulus onset. This fluctuation suggests that our perceptional and cognitive system in fact exerts its facilitative effect on P300 in a periodic fashion. Thus, we can record augmented P300 by presenting stilulus during the optimal state conducive for visual stimulus processing. Our study may help to improve reactive BCI where brain activity arise in reaction to external stimulation.

Stimulus (psychology)

10.1109/iccme.2012.6275697

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Citations (1)

Classification of Cognitive Level of Patients with Leukoaraiosis on the Basis of Linear and Non-Linear Functional Connectivity

Frontiers in Neurology (2017)

Ranran Li Youzhi Lai Yumei Zhang Li Yao Xia Wu

Leukoaraiosis (LA) describes diffuse white matter abnormalities apparent in computed tomography (CT) or magnetic resonance (MR) brain scans. Patients with LA generally show varying degrees of cognitive impairment, which can be classified as cognitively normal (CN), mild cognitive impairment (MCI), and dementia. However, a consistent relationship between the degree of LA and the level of cognitive impairment has not yet been established. We used functional magnetic resonance imaging (fMRI) to explore possible neuroimaging biomarkers for classification of cognitive level in LA. Functional connectivity (FC) between brain regions was calculated using Pearson's correlation coefficient (PCC), maximal information coefficient (MIC), and extended maximal information coefficient (eMIC). Next, FCs with high discriminative power for different cognitive levels in LA were used as features for classification based on support vector machine. CN and MCI were classified with accuracies of 75.0, 61.9, and 91.1% based on features from PCC, MIC, and eMIC, respectively. MCI and dementia were classified with accuracies of 80.1, 86.2, and 87.4% based on features from PCC, MIC, and eMIC, respectively. CN and dementia were classified with accuracies of 80.1, 89.9, and 94.4% based on features from PCC, MIC, and eMIC, respectively. Our results suggest that features extracted from fMRI were efficient for classification of cognitive impairment level in LA, especially, when features were based on a non-linear method (eMIC).

Leukoaraiosis

Discriminative model

10.3389/fneur.2017.00002

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Citations (12)

WeiboFinder: A Topic-Based Chinese Word Finding and Learning System

Lecture notes in computer science (2017)

Wenhao Chen Yi Cai Kin Keung Lai Li Yao Jun Zhang

Microblogging

10.1007/978-3-319-66733-1_4

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Citations (4)

A self assembled real time fMRI system

IEEE/ICME International Conference on Complex Medical Engineering (2010)

Yi Li Baoquan Xie Li Yao Xiaojie Zhao

Real time functional magnetic resonance imaging (rtfMRI) allows capturing and analyzing the image of brain activity instantly by measuring the blood oxygen level-dependent (BOLD) signal. Based on rtfMRI, it could provide on-line feedback of human subjects to learn self-regulation of physiological processes, which had shown significant and potential applications in many conducted researches. With the recent advances in computational power and algorithms, constructing rtfMRI system with high efficiency was available which had not been limited by time-consuming data analysis. In this study, we developed the rtfMRI system utilizing custom-made computer and integrated commercial on-line analysis software: Turbo-BrainVoyager as the critical part of the software system. We chose motor imagery task with real time feedback by instructing subjects to regulate his BOLD activity of supplement motor area (SMA) as confirmatory experiment. This online feedback results and the post processed activation results showed that the subject had a good ability of self-control of SMA. It demonstrated that the rtfMRI system could operate effectively.

10.1109/iccme.2010.5558866

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Citations (2)