Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust $H_{\infty}$ Adaptive Fuzzy Tracking Control for MIMO Nonlinear Stochastic Poisson Jump Diffusion Systems

Recently, stochastic Poisson jump diffusion system has attracted much attention in stochastic control. Poisson jump process has been used to model the random discontinuous jump behavior of the intrinsic discontinuous perturbation in stochastic system. Wiener process also called diffusion process represents the continuous random fluctuation to the system. In this paper, a robust adaptive control is introduced for multi-input multi-output (MIMO) nonlinear stochastic Poisson jump diffusion system with continuous and discontinuous random fluctuations to achieve the <inline-formula> <tex-math notation="LaTeX">${{ {H}}}_{{\infty }}$ </tex-math></inline-formula> tracking performance with a prescribed disturbance attenuation level. The system structure is of a strict-feedback form. Based on the backstepping design technique and <inline-formula> <tex-math notation="LaTeX">${{{H}}}_{{\infty }}$ </tex-math></inline-formula> control theory, a robust adaptive control law is constructed for MIMO nonlinear stochastic Poisson jump diffusion system to achieve the <inline-formula> <tex-math notation="LaTeX">${{{H}}}_{{\infty }}$ </tex-math></inline-formula> tracking performance with a prescribed attenuation level of external disturbance, despite of fuzzy approximation error and the effect of continuous and discontinuous random fluctuations. The proposed <inline-formula> <tex-math notation="LaTeX">${{ {H}}}_{{\infty }}$ </tex-math></inline-formula> adaptive control law combines both merits of <inline-formula> <tex-math notation="LaTeX">${{ {H}}}_{{\infty }}$ </tex-math></inline-formula> tracking control and adaptive control scheme to sufficiently solve the robust <inline-formula> <tex-math notation="LaTeX">${{ {H}}}_{{\infty }}$ </tex-math></inline-formula> adaptive tracking control problem for MIMO stochastic nonlinear systems with continuous and discontinuous random fluctuations. In addition, the uniformly positive definite assumption of control coefficient matrix is relaxed for the proposed MIMO adaptive control as well. A stochastic quadrotor trajectory tracking control simulation is provided to show the effectiveness of the proposed <inline-formula> <tex-math notation="LaTeX">${{ {H}}}_{{\infty }}$ </tex-math></inline-formula> robust adaptive control law.
Pattern Classification for Gastrointestinal Stromal Tumors by Integration of Radiomics and Deep Convolutional Features

Predicting malignant potential is one of the most critical components of a computer-aided diagnosis system for gastrointestinal stromal tumors (GISTs). These tumors have been studied only on the basis of subjective computed tomography findings. Among various methodologies, radiomics, and deep learning algorithms, specifically convolutional neural networks (CNNs), have recently been confirmed to achieve significant success by outperforming the state-of-the-art performance in medical image pattern classification and have rapidly become leading methodologies in this field. However, the existing methods generally use radiomics or deep convolutional features independently for pattern classification, which tend to take into account only global or local features, respectively. In this paper, we introduce and evaluate a hybrid structure that includes different features selected with radiomics model and CNNs and integrates these features to deal with GISTs classification. The Radiomics model and CNNs are constructed for global radiomics and local convolutional feature selection, respectively. Subsequently, we utilize distinct radiomics and deep convolutional features to perform pattern classification for GISTs. Specifically, we propose a new pooling strategy to assemble the deep convolutional features of 54 three-dimensional patches from the same case and integrate these features with the radiomics features for independent case, followed by random forest classifier. Our method can be extensively evaluated using multiple clinical datasets. The classification performance (area under the curve (AUC): 0.882; 95% confidence interval (CI): 0.816–0.947) consistently outperforms those of independent radiomics (AUC: 0.807; 95% CI: 0.724–0.892) and CNNs (AUC: 0.826; 95% CI: 0.795–0.856) approaches.
Theoretical Study of Oscillator Neurons in Recurrent Neural Networks

Neurons in a network can be both active or inactive. Given a subset of neurons in a network, is it possible for the subset of neurons to evolve to form an active oscillator by applying some external periodic stimulus? Furthermore, can these oscillator neurons be observable, that is, is it a stable oscillator? This paper explores such possibility, finding that an important property: any subset of neurons can be intermittently co-activated to form a stable oscillator by applying some external periodic input without any condition. Thus, the existing of intermittently active oscillator neurons is an essential property possessed by the networks. Moreover, this paper shows that, under some conditions, a subset of neurons can be fully co-activated to form a stable oscillator. Such neurons are called selectable oscillator neurons. Necessary and sufficient conditions are established for a subset of neurons to be selectable oscillator neurons in linear threshold recurrent neuron networks. It is proved that a subset of neurons forms selectable oscillator neurons if and only if the real part of each eigenvalue of the associated synaptic connection weight submatrix of the network is not larger than one. This simple condition makes the concept of selectable oscillator neurons tractable. The selectable oscillator neurons can be regarded as memories stored in the synaptic connections of networks, which enables to find a new perspective of memories in neural networks, different from the equilibrium-type attractors.
Indoor Motion Detection Using Wi-Fi Channel State Information in Flat Floor Environments Versus in Staircase Environments

Recently, Wi-Fi channel state information (CSI) motion detection systems have been widely researched for applications in human health care and security in flat floor environments. However, these systems disregard the indoor context, which is often complex and consists of unique features, such as staircases. Motion detection on a staircase is also meaningful and important for various applications, such as fall detection and intruder detection. In this paper, we present the difference in CSI motion detection in flat floor and staircase environments through analysing the radio propagation model and experiments in real settings. For comparison in the two environments, an indoor CSI motion detection system is proposed with several novel methods including correlation-based fusion, moving variance segmentation (MVS), Doppler spread spectrum to improve the system performance, and a correlation check to reduce the implementation cost. Compared with existing systems, our system is validated to have a better performance in both flat floor and staircase environments, and further utilized to verify the superior CSI motion detection performance in staircase environments versus flat floor environments.
Compact Intrinsically Switchable FBAR Filters Utilizing Ferroelectric BST

Intrinsically switchable thin film bulk acoustic resonator (FBAR) filters based on ferroelectric barium strontium titanate (BST) are presented. A 1.5-stage <inline-formula> <tex-math notation="LaTeX">$\pi$ </tex-math></inline-formula>-network intrinsically switchable FBAR filter unit cell with a 3-dB fractional bandwidth of 3% at 2 GHz is systematically designed, simulated, and fabricated. The minimum insertion loss (IL) for the filter unit cell is 2.25 dB, representing the lowest IL reported for BST bulk acoustic wave filters to date, which is mainly due to its high <inline-formula> <tex-math notation="LaTeX">$Q \times K_{t}^{2}$ </tex-math></inline-formula> BST resonators. Two 1.5-stage filter unit cells are connected in series to form a 2.5-stage filter, providing more than 25 dB of out-of-band rejection and OFF-state isolation between the input and the output ports. The measured input third-order intercept point (IIP<sub>3</sub>) of the 2.5-stage filter is 47 dBm. Furthermore, the footprint of the fabricated filters is notably small, due to the high permittivity of BST.
Hierarchical Decision and Control for Continuous Multitarget Problem: Policy Evaluation With Action Delay

This paper proposes a hierarchical decision-making and control algorithm for the shepherd game, the seventh mission in the International Aerial Robotics Competition (IARC). In this game, the agent (a multirotor aerial robot) is required to contact targets (ground vehicles) sequentially and drive them to a certain boundary to earn score. During the game of 10 min, the agent should be fully autonomous without any human interference. Regarding the lower-level controller and dynamics of the agent, each action takes a duration of time to accomplish. Denoted as an action delay, in this paper, this action duration is nonconstant and is related to the final reward. Therefore, the challenging point is making the agent “aware of time” when applying a certain action. We solve this problem by two approaches: deep Q-networks and lookup table. The action delay predictor in the decision-level is fitted by a lower-level controller. Through simulations by the example of the shepherd game, the effectiveness and efficiency of this approach are validated. This paper helps our team winning the first prize in IARC 2017, and keeps the best record of this mission since it was released in 2013.
High-Order Temporal Correlation Model Learning for Time-Series Prediction

Time-series prediction has become a prominent challenge, especially when the data are described as sequences of multiway arrays. Because noise and redundancy may exist in the tensor representation of a time series, we focus on solving the problem of high-order time-series prediction under a tensor decomposition framework and develop two novel multilinear models: 1) the multilinear orthogonal autoregressive (MOAR) model and 2) the multilinear constrained autoregressive (MCAR) model. The MOAR model is designed to preserve as much information as possible from the original tensorial data under orthogonal constraints. The MCAR model is an enhanced version that is developed by replacing orthogonal constraints with an inverse decomposition error term. For both models, we project the original tensor into subspaces spanned by basis matrices to facilitate the discovery of the intrinsic temporal structure embedded in the original tensor. To build connections among consecutive slices of the tensor, we generalize a traditional autoregressive model to tensor form to better preserve the temporal smoothness. Experiments conducted on four publicly available datasets demonstrate that our proposed methods converge within a small number of iterations during the training stage and achieve promising results compared with state-of-the-art methods.
Dependent Types for Class-based Mutable Objects

We present an imperative object-oriented language featuring a dependent type system designed to support class-based programming and inheritance. Programmers implement classes in the usual imperative style, and may take advantage of a richer dependent type system to express class invariants and restrictions on how objects are allowed to change and be used as arguments to methods. By way of example, we implement insertion and deletion for binary search trees in an imperative style, and come up with types that ensure the binary search tree invariant. This is the first dependently-typed language with mutable objects that we know of to bring classes and index refinements into play, enabling types (classes) to be refined by indices drawn from some constraint domain. We give a declarative type system that supports objects whose types may change, despite being sound. We also give an algorithmic type system that provides a precise account of quantifier instantiation in a bidirectional style, and from which it is straightforward to read off an implementation. Moreover, all the examples in the paper have been run, compiled and executed in a fully functional prototype that includes a plugin for the Eclipse IDE. 2012 ACM Subject Classification Software and its engineering → Semantics
Visualizing a Thinker's Life

This paper presents a visualization framework that aids readers in understanding and analyzing the contents of medium-sized text collections that are typical for the opus of a single or few authors. We contribute several document-based visualization techniques to facilitate the exploration of the work of the German author Bazon Brock by depicting various aspects of its texts, such as the TextGenetics that shows the structure of the collection along with its chronology. The ConceptCircuit augments the TextGenetics with entities – persons and locations that were crucial to his work. All visualizations are sensitive to a wildcard-based phrase search that allows complex requests towards the author's work. Further development, as well as expert reviews and discussions with the author Bazon Brock, focused on the assessment and comparison of visualizations based on automatic topic extraction against ones that are based on expert knowledge.
Sleep-Related Eating Disorder (SRED): Paradoxical Effect of Clonazepam.

ABSTRACT A 9-year-old boy with primary myoclonus dystonia with comorbid severe attention deficit hyperactive disorder (ADHD) also had sleep onset and maintenance insomnia. Polysomnography showed features of non-rapid eye movement parasomnia. Daily bedtime clonidine helped sleep and hyperactivity. ADHD symptoms improved with 40 mg daily lisdexamphetamine mesylate. As the myoclonus and dystonia symptoms progressed, clonazepam was initiated at a dose of 0.5 mg daily at bedtime. It was anticipated that clonazepam would also improve parasomnia. Concomitant with the initiation of clonazepam, he developed a sleep-related eating disorder (SRED) continuing almost throughout the night. The symptoms went away upon stopping clonazepam. This is the first description of clonazepam producing SRED though it is the treatment of choice for this disorder.
Detecting Alzheimer's Disease on Small Dataset: A Knowledge Transfer Perspective

Computer-aided diagnosis (CAD) is an attractive topic in Alzheimer's disease (AD) research. Many algorithms are based on a relatively large training dataset. However, small hospitals are usually unable to collect sufficient training samples for robust classification. Although data sharing is expanding in scientific research, it is unclear whether a model based on one dataset is well suited for other data sources. Using a small dataset from a local hospital and a large shared dataset from the AD neuroimaging initiative, we conducted a heterogeneity analysis and found that different functional magnetic resonance imaging data sources show different sample distributions in feature space. In addition, we proposed an effective knowledge transfer method to diminish the disparity among different datasets and improve the classification accuracy on datasets with insufficient training samples. The accuracy increased by approximately 20% compared with that of a model based only on the original small dataset. The results demonstrated that the proposed approach is a novel and effective method for CAD in hospitals with only small training datasets. It solved the challenge of limited sample size in detection of AD, which is a common issue but lack of adequate attention. Furthermore, this paper sheds new light on effective use of multi-source data for neurological disease diagnosis.
Noninvasive Treatment-Efficacy Evaluation for HIFU Therapy Based on Magneto-Acousto-Electrical Tomography

Objective: As a novel noninvasive modality of oncotherapy or stroke treatment, high-intensity focused ultrasound (HIFU) has drawn more and more attention in the past decades. Whereas, real-time temperature monitoring and treatment-efficacy evaluation are still the key issues for HIFU therapy. Methods: Based on the temperature–conductivity relation of tissues with a sharp conductivity variation of irreversible thermocoagulation at 69 °C, a noninvasive method of treatment-efficacy evaluation for HIFU ablation using the magneto-acousto-electrical tomography (MAET) technology is theoretically studied. By applying the nonlinear Khokhlov–Zabolotskaya–Kuznetsov equation and Pennes equation, a cylindrical model is established to simulate the distributions of pressure, temperature, and conductivity with the consideration of harmonic components. Results: The MAET signals are simulated to analyze the characteristics of the peak amplitude and the axial interval of the two clusters generated by the conductivity boundary of HIFU ablation. Conclusion: The axial interval can be used as the indictor to evaluate the size of HIFU ablation with the minimum axial width of one wavelength. Significance: The favorable results demonstrate the feasibility of real-time treatment-efficacy evaluation for HIFU therapy using the MAET technology and suggest potential applications in clinical practice.
Chronic Kidney Disease and Cardiovascular Disease: Is there Any Relationship?

Introduction: Chronic Kidney Disease is a growing health burden world wide. Traditional and mutual risk factors between CVD and CKD are age, hypertension, diabetes mellitus, dyslipidem-ia, tobacco use, family history and male gender. In this review, we will focus on whether or not early CKD is an important risk factor for the presence, severity and progression of CVD. Specifically, we will examine both traditional and novel risk factors of both CKD and CVD and how they relate to each other. Conclusion: We will also assess if early treatment of CKD, intensive compared to standard, has an important effect on the halt of the development of CKD as well as CVD. Insights into the pathogene-sis and early recognition of CKD as well as the importance of novel kidney biomarkers will be point-ed out. Also, common pathogenetic mechanisms between CKD and CVD will be discussed
Tensor Sparse Representation for 3-D Medical Image Fusion Using Weighted Average Rule

Objective: The technique of fusing multimodal medical images into single image has a great impact on the clinical diagnosis. The previous works mostly concern the two-dimensional (2-D) image fusion performed on each slice individually, that may destroy the 3-D correlation across adjacent slices. To address this issue, this paper proposes a novel 3-D image fusion scheme based on Tensor Sparse Representation (TSR). Methods: First, each medical volume is arranged as a three-order tensor, and represented by TSR with learned dictionaries. Second, a novel “weighted average” rule, calculated from the tensor sparse coefficients using 3-D local-to-global strategy. The weights are then employed to combine the multimodal medical volumes through weighted average. Results: The visual and objective comparisons show that the proposed method is competitive to the existing methods on various medical volumes in different imaging modalities. Conclusion: The TSR-based 3-D fusion approach with weighted average rule can preserve the 3-D structure of medical volume, and reduce the low contrast and artifacts in fused product. Significance: The designed weights offer the effective assigned weights and accurate salience levels measure, which can improve the performance of fusion approach.
Generalized Hidden-Mapping Transductive Transfer Learning for Recognition of Epileptic Electroencephalogram Signals

Electroencephalogram (EEG) signal identification based on intelligent models is an important means in epilepsy detection. In the recognition of epileptic EEG signals, traditional intelligent methods usually assume that the training dataset and testing dataset have the same distribution, and the data available for training are adequate. However, these two conditions cannot always be met in practice, which reduces the ability of the intelligent recognition model obtained in detecting epileptic EEG signals. To overcome this issue, an effective strategy is to introduce transfer learning in the construction of the intelligent models, where knowledge is learned from the related scenes (source domains) to enhance the performance of model trained in the current scene (target domain). Although transfer learning has been used in EEG signal identification, many existing transfer learning techniques are designed only for a specific intelligent model, which limit their applicability to other classical intelligent models. To extend the scope of application, the generalized hidden-mapping transductive learning method is proposed to realize transfer learning for several classical intelligent models, including feedforward neural networks, fuzzy systems, and kernelized linear models. These intelligent models can be trained effectively by the proposed method even though the data available are insufficient for model training, and the generalization abilities of the trained model is also enhanced by transductive learning. A number of experiments are carried out to demonstrate the effectiveness of the proposed method in epileptic EEG recognition. The results show that the method is highly competitive or superior to some existing state-of-the-art methods.
Spike Timing or Rate? Neurons Learn to Make Decisions for Both Through Threshold-Driven Plasticity

Spikes play an essential role in information transmission in central nervous system, but how neurons learn from them remains a challenging question. Most algorithms studied how to train spiking neurons to process patterns encoded with a sole assumption of either a rate or a temporal code. Is there a general learning algorithm capable of processing both codes regardless of the intense debate on them within neuroscience community? In this paper, we propose several threshold-driven plasticity algorithms to address the above question. In addition to formulating the algorithms, we also provide proofs with respect to several properties, such as robustness and convergence. The experimental results illustrate that our algorithms are simple, effective and yet efficient for training neurons to learn spike patterns. Due to their simplicity and high efficiency, our algorithms would be potentially beneficial for both software and hardware implementations. Neurons with our algorithms can also detect and recognize embedded features from a background sensory activity. With the as-proposed algorithms, a single neuron can successfully perform multicategory classifications by making decisions based on its output spike number in response to each category. Spike patterns being processed can be encoded with both spike rates and precise timings. When afferent spike timings matter, neurons will automatically extract temporal features without being explicitly instructed as to which point to fire.