Miracle-dz/UniFAR-FTUs huggingface dataset:
DNRLMF-MDA:Predicting microRNA-Disease Associations Based on Similarities of microRNAs and Diseases

MicroRNAs (miRNAs) are a class of non-coding RNAs about $\sim$22nt nucleotides. Studies have proven that miRNAs play key roles in many human complex diseases. Therefore, discovering miRNA-disease associations is beneficial to understanding disease mechanisms, developing drugs, and treating complex diseases. It is well known that it is a time-consuming and expensive process to discover the miRNA-disease associations via biological experiments. Alternatively, computational models could provide a low-cost and high-efficiency way for predicting miRNA-disease associations. In this study, we propose a method (called DNRLMF-MDA) to predict miRNA-disease associations based on dynamic neighborhood regularized logistic matrix factorization. DNRLMF-MDA integrates known miRNA-disease associations, functional similarity and Gaussian Interaction Profile (GIP) kernel similarity of miRNAs, and functional similarity and GIP kernel similarity of diseases. Especially, positive observations (known miRNA-disease associations) are assigned higher importance levels than negative observations (unknown miRNA-disease associations). DNRLMF-MDA computes the probability that a miRNA would interact with a disease by a logistic matrix factorization method, where latent vectors of miRNAs and diseases represent the properties of miRNAs and diseases, respectively, and further improve prediction performance via dynamic neighborhood regularized. The 5-fold cross validation is adopted to assess the performance of our DNRLMF-MDA, as well as other competing methods for comparison. The computational experiments show that DNRLMF-MDA outperforms the state-of-art method PBMDA. The AUC values of DNRLMF-MDA on three datasets are 0.9357, 0.9411, and 0.9416, respectively, which are superior to the PBMDA's results of 0.9218, 0.9187, and 0.9262. The average computation times per 5-fold cross validation of DNRLMF-MDA on three datasets are 38, 46, and 50 seconds, which are shorter than the PBMDA's average computation times of 10869, 916, and 8448 seconds, respectively. DNRLMF-MDA also can predict potential diseases for new miRNAs. Furthermore, case studies illustrate that DNRLMF-MDA is an effective method to predict miRNA-disease associations.
Graph-Theory-Based Decentralized Adaptive Output-Feedback Control for a Class of Nonlinear Interconnected Systems

This paper investigates the problem of decentralized output feedback control for a class of interconnected systems with unknown state-dependent interconnections. First, we design a new form of ${K}$ -filters with extra design parameters to compensate the unmeasurable states. Then by introducing a smooth function, we can design a decentralized output feedback control law by integrating the well-known backstepping framework. Furthermore, from the graph theory and Lyapunov function method, we analyze the stability and tracking property of the closed-loop systems. As an illustrative example, the proposed control scheme is applied to the controller design of a mass-spring-damper system.
Model-Based ILC with a Modified Q-Filter for Complex Motion Systems: Practical Considerations and Experimental Verification on a Wafer Stage

Iterative learning control (ILC) is one of the most popular tracking control methods for systems that repeatedly execute the same task. A system model is usually used in the analysis and design of ILC. Model-based ILC results in general in fast convergence and good performance. However, the model uncertainties and nonrepetitive disturbances hamper its practical applications. One of the commonly used solutions is the introduction of a low-pass filter, namely, the Q-filter. However, it is indicated in this paper that the existing Q-filter configurations compromise the servo performance, although improving the robustness. Motivated by the combination of performance and robustness, a novel Q-filter configuration in ILC is presented in this paper. Some practical considerations, such as the configuration of ILC in a feedback control system, the time delay compensation, and the learning coefficient, are provided in the implementation of the proposed ILC algorithm. The effectiveness and superiority of the proposed ILC versus existing Q-filter ILC are demonstrated by both theoretical analysis and experimental verification on a wafer stage.
A Neural Controller for Image-Based Visual Servoing of Manipulators With Physical Constraints

Main issues in visual servoing of manipulators mainly include rapid convergence of feature errors to zero and the safety of joints regarding joint physical limits. To address the two issues, in this paper, an image-based visual servoing scheme is proposed for manipulators with an eye-in-hand configuration. Compared with existing schemes, the proposed one does not require performing pseudoinversion for the image Jacobian matrix or inversion for the Jacobian matrix associated with the forward kinematics of the manipulators. Theoretical analysis shows that the proposed scheme not only guarantees the asymptotic convergence of feature errors to zero but also the compliance with joint angle and velocity limits of the manipulators. Besides, simulation results based on a PUMA560 manipulator with a camera mounted on the end effector verify the theoretical conclusions and the efficacy of the proposed scheme.
Variational Random Function Model for Network Modeling

Link prediction is a fundamental problem in network modeling. A family of link prediction approaches is to treat network data as an exchangeable array whose entries can be explained by random functions (e.g., block models and Gaussian processes) over latent node factors. Despite their powerful ability in modeling missing links, these models tend to have a large computational complexity and thus are hard to deal with large networks. To address this problem, we develop a novel variational random function model by defining latent Gaussian processes on exchangeable arrays. This model not only inherits the ability of Gaussian process to describe the nonlinear interactions between nodes, but also enjoys significant reduction on computational complexity. To further make the model scalable to large network data, we develop an efficient key-value-free strategy under the map-reduce framework to tremendously reduce the inference time. Experimental results on large network data have demonstrated both the efficacy and efficiency of the proposed method over state-of-the-arts methods in network modeling.
Crowd evacuation simulation based on emotion contagion

Simulation study on evacuation scenarios has an important influence on public safety. Unfortunately, the traditional evacuation-drill approach for emergency situations focuses on the physiological interaction between individuals/crowds, whereas the approach ignores psychological interaction. In this paper, we present an emotion contagion model that associates components, such as physiological demand, security requirement and personality traits, with individual agents comprising a crowd, and produces emergent behaviours in the crowd as a whole. The emotion contagion model realises the interaction of physiological and psychological factors. Personality traits are associated with emotions by using the OCEAN, OCC and PAD models. To reflect the complexity of emotion contagion of crowd, we consider three processes: contagion, update, and decay. We also consider the effect of emotions on the movement. Testing results demonstrate that the proposed method is effective and realistic for simulating the movement of heterogeneous crowd in emergency situations.
A Dual Tissue-Doppler Optical-Flow Method for Speckle Tracking Echocardiography at High Frame Rate

A coupled computational method for recovering tissue velocity vector fields from high-frame-rate echocardiography is described. Conventional transthoracic echocardiography provides limited temporal resolution, which may prevent accurate estimation of the 2-D myocardial velocity field dynamics. High-frame-rate compound echocardiography using diverging waves with integrated motion compensation has been shown to provide concurrent high-resolution B-mode and tissue Doppler imaging (TDI). In this paper, we propose a regularized least-squares method to provide accurate myocardial velocities at high frame rates. The velocity vector field was formulated as the minimizer of a cost function that is a weighted sum of: 1) the <inline-formula> <tex-math notation="LaTeX">${\ell }^{{2}}$ </tex-math></inline-formula>-norm of the material derivative of the B-mode images (optical flow); 2) the <inline-formula> <tex-math notation="LaTeX">${\ell }^{{2}}$ </tex-math></inline-formula>-norm of the tissue-Doppler residuals; and 3) a quadratic regularizer that imposes spatial smoothness and well-posedness. A finite difference discretization of the continuous problem was adopted, leading to a sparse linear system. The proposed framework was validated <italic>in vitro</italic> on a rotating disk with speeds up to 20 cm/s, and compared with speckle tracking echocardiography (STE) by block matching. It was also validated <italic>in vivo</italic> against TDI and STE in a cross-validation strategy involving parasternal long axis and apical three-chamber views. The proposed method based on the combination of optical flow and tissue Doppler led to more accurate time-resolved velocity vector fields.
Evaluation of Machine-Learning Approaches to Estimate Sleep Apnea Severity From At-Home Oximetry Recordings

Complexity, costs, and waiting list issues demand a simplified alternative for sleep apnea–hypopnea syndrome (SAHS) diagnosis. The blood oxygen saturation signal (SpO2) carries useful information about SAHS and can be easily acquired from overnight oximetry. In this study, SpO2 single-channel recordings from 320 subjects were obtained at patients’ homes and were used to automatically obtain statistical, spectral, nonlinear, and clinical SAHS-related information. Relevant, nonredundant data from these analyses were subsequently used to train and validate four machine-learning methods with the ability to classify SpO2 signals into one of the four SAHS-severity degrees (no-SAHS, mild, moderate, and severe). All the models trained (linear discriminant analysis, 1-vs-all logistic regression, Bayesian multilayer perceptron, and AdaBoost) outperformed the diagnostic ability of the conventionally used 3% oxygen desaturation index. An AdaBoost model built with linear discriminants as base classifiers reached the highest figures. It achieved 0.479 Cohen's κ in the SAHS severity classification, as well as 92.9%, 87.4%, and 78.7% accuracies in binary classification tasks using increasing severity thresholds (apnea–hypopnea index: 5, 15, and 30 events/hour, respectively). These results suggest that machine-learning can be used along with SpO2 information acquired at a patients’ home to help in SAHS diagnosis simplification.
The expression of MMP‐1 and MMP‐9 is up‐regulated by smooth muscle cells after their cross‐talk with macrophages in high glucose conditions

Patients with diabetes mellitus have an increased risk of myocardial infarction and coronary artery disease‐related death, exhibiting highly vulnerable plaques. Many studies have highlighted the major role of macrophages (MAC) and smooth muscle cells (SMC) and the essential part of metalloproteases (MMPs) in atherosclerotic plaque vulnerability. We hypothesize that in diabetes, the interplay between MAC and SMC in high glucose conditions may modify the expression of MMPs involved in plaque vulnerability. The SMC‐MAC cross‐talk was achieved using trans‐well chambers, where human SMC were grown at the bottom and human MAC in the upper chamber in normal (NG) or high (HG) glucose concentration. After cross‐talk, the conditioned media and cells were isolated and investigated for the expression of MMPs, MCP‐1 and signalling molecules. We found that upon cross‐talk with MAC in HG, SMC exhibit: (i) augmented expression of MMP‐1 and MMP‐9; (ii) significant increase in the enzymatic activity of MMP‐9; (iii) higher levels of soluble MCP‐1 chemokine which is functionally active and involved in MMPs up‐regulation; (iv) activated PKCα signalling pathway which, together with NF‐kB are responsible for MMP‐1 and MMP‐9 up‐regulation, and (v) impaired function of collagen assembly. Taken together, our data indicate that MCP‐1 released by cell cross‐talk in diabetic conditions binds to CCR2 and triggers MMP‐1 and MMP‐9 over‐expression and activity, features that could explain the high vulnerability of atherosclerotic plaque found at diabetic patients.
Removal of Eye Blink Artifacts From EEG Signals Using Sparsity

Neural activities recorded using electroencephalography (EEG) are mostly contaminated with eye blink (EB) artifact. This results in undesired activation of brain–computer interface (BCI) systems. Hence, removal of EB artifact is an important issue in EEG signal analysis. Of late, several artifact removal methods have been reported in the literature and they are based on independent component analysis (ICA), thresholding, wavelet transformation, etc. These methods are computationally expensive and result in information loss which makes them unsuitable for online BCI system development. To address the above problems, we have investigated sparsity-based EB artifact removal methods. Two sparsity-based techniques namely morphological component analysis (MCA) and K-SVD-based artifact removal method have been evaluated in our work. MCA-based algorithm exploits the morphological characteristics of EEG and EB using predefined Dirac and discrete cosine transform (DCT) dictionaries. Next, in K-SVD-based algorithm an overcomplete dictionary is learned from the EEG data itself and is designed to model EB characteristics. To substantiate the efficacy of the two algorithms, we have carried out our experiments with both synthetic and real EEG data. We observe that the K-SVD algorithm, which uses a learned dictionary, delivers superior performance for suppressing EB artifacts when compared to MCA technique. Finally, the results of both the techniques are compared with the recent state-of-the-art FORCe method. We demonstrate that the proposed sparsity-based algorithms perform equal to the state-of-the-art technique. It is shown that without using any computationally expensive algorithms, only with the use of over-complete dictionaries the proposed sparsity-based algorithms eliminate EB artifacts accurately from the EEG signals.
A Pilot Study of Impulse Radio Ultra Wideband Radar Technology as a New Tool for Sleep Assessment.

STUDY OBJECTIVES To validate Impulse radio ultra wideband pulse-doppler radar technology against polysomnography (PSG) for sleep assessment. METHODS In all, 12 participants were recruited and their overnight sleep was assessed both by a Novelda XeThru radar and PSG. Two subjects had two nightly recordings, whereas 10 had one recording. Epoch by epoch (30 seconds) comparisons from bedtime to rise time were conducted. Concordance was estimated in terms of the mean difference between the radar and the PSG estimates regarding sleep onset latency, wake time after sleep onset and total sleep time. In addition, accuracy, sensitivity, specificity and Cohen kappa were calculated. RESULTS The mean difference (minutes) between the radar and the PSG registrations was -5.7 minutes (standard deviation [SD] = 22.1 minutes) for sleep onset latency, 6.4 minutes (SD = 32.5 minutes) for wake after sleep onset, and 1.5 minutes (SD = 24.6 minutes) for total sleep time. The mean values obtained for accuracy, sensitivity, specificity and Cohen kappa were 0.931, 0.961, 0.695 and 0.670, respectively. CONCLUSION Impulse radio ultra wideband radar technology is a promising tool in terms of affordable and practical objective sleep assessment. Further technical development and more validation studies are needed in order to conclude about the utility potential of this device.
Quantized Feedback Control of Fuzzy Markov Jump Systems

This paper addresses the problem of quantized feedback control of nonlinear Markov jump systems (MJSs). The nonlinear plant is represented by a class of fuzzy MJSs with time-varying delay based on a Takagi–Sugeno fuzzy model. The quantized signal is utilized for control purpose and the sector bound approach is exploited to deal with quantization errors. By constructing a Lyapunov function which depends both on mode information and fuzzy basis functions, the reciprocally convex approach is used to derive the criterion which is able to ensure the stochastic stability with a predefined ${l_{2}-l_\infty }$ performance of the resulting closed-loop system. The design of the quantized feedback controller is then converted to a convex optimization problem, which can be handled through the linear matrix inequality technique. Finally, a simulation example is presented to verify the effectiveness and practicability of the proposed new design techniques.
Pólya Urn Latent Dirichlet Allocation: A Doubly Sparse Massively Parallel Sampler

Latent Dirichlet Allocation (LDA) is a topic model widely used in natural language processing and machine learning. Most approaches to training the model rely on iterative algorithms, which makes it difficult to run LDA on big corpora that are best analyzed in parallel and distributed computational environments. Indeed, current approaches to parallel inference either don't converge to the correct posterior or require storage of large dense matrices in memory. We present a novel sampler that overcomes both problems, and we show that this sampler is faster, both empirically and theoretically, than previous Gibbs samplers for LDA. We do so by employing a novel Pólya-urn-based approximation in the sparse partially collapsed sampler for LDA. We prove that the approximation error vanishes with data size, making our algorithm asymptotically exact, a property of importance for large-scale topic models. In addition, we show, via an explicit example, that—contrary to popular belief in the topic modeling literature—partially collapsed samplers can be more efficient than fully collapsed samplers. We conclude by comparing the performance of our algorithm with that of other approaches on well-known corpora.
Reach-Avoid Games With Two Defenders and One Attacker: An Analytical Approach

This paper considers a reach-avoid game on a rectangular domain with two defenders and one attacker. The attacker aims to reach a specified edge of the game domain boundary, while the defenders strive to prevent that by capturing the attacker. First, we are concerned with the barrier, which is the boundary of the reach-avoid set, splitting the state space into two disjoint parts: 1) defender dominance region (DDR) and 2) attacker dominance region (ADR). For the initial states lying in the DDR, there exists a strategy for the defenders to intercept the attacker regardless of the attacker’s best effort, while for the initial states lying in the ADR, the attacker can always find a successful attack strategy. We propose an attack region method to construct the barrier analytically by employing Voronoi diagram and Apollonius circle for two kinds of speed ratios. Then, by taking practical payoff functions into considerations, we present optimal strategies for the players when their initial states lie in their winning regions, and show that the ADR is divided into several parts corresponding to different strategies for the players. Numerical approaches, which suffer from inherent inaccuracy, have already been utilized for multiplayer reach-avoid games, but computational complexity complicates solving such games and consequently hinders efficient on-line applications. However, this method can obtain the exact formulation of the barrier and is applicable for real-time updates.
Targeted PCR Array Analysis of Genes in Innate Immunity and Glucocorticoid Signaling Pathways in Mice Cochleae Following Acoustic Trauma.

AIM To comprehensively analyze cochlear gene expressions related to innate immunity and glucocorticoid signaling at onset of acute noise-induced hearing loss (NIHL). BACKGROUND Recent studies suggested innate immunity is involved in the cochlear pathology of NIHL. Glucocorticoids may modulate immune actions in cochleae. METHODS Mice were exposed to 120 dB-octave band noise for 2 hours. Twelve hours later, a targeted PCR array analyzed cochlear expressions of 84 key genes in inflammation and immune pathways and 84 genes in the glucocorticoid signaling pathway. Real-time RT-PCR was used to analyze expression of two immune-related genes, Ccl12 and Glycam1, in noise-exposed cochleae with or without dexamethasone. RESULT In inflammatory and immune gene pathways, 31.0% (26/84 genes) were significantly upregulated (>2-fold change) or downregulated (<0.5-fold change) (p < 0.05) in noise-exposed cochleae compared with controls. Sixteen of these differentially expressed genes (DEGs) encoded chemokines. DEGs included Ccl12, Ccl2, Ccl4, Ccl7, Cxcl1, Cxcl10, and Ptgs2 (upregulated genes), and Ccr7, Cxcr2, Kng1, Ltb, and Tnfsf14 (downregulated genes). In the glucocorticoid signaling pathway, 92.9% (78/84 genes) were unchanged in noise-exposed cochleae without dexamethasone administration. Cochlear expressions of Ccl12 and Glycam1 were significantly upregulated by noise and downregulated by dexamethasone. CONCLUSION The targeted PCR array demonstrated that several dozen genes involved in innate immunity are actively regulated in cochleae with NIHL. The glucocorticoid signaling pathway was not endogenously regulated at 12 hours post-noise trauma. Systemic dexamethasone downregulated Ccl12 and Glycam1, which are upregulated in noise-exposed cochleae. These data may provide a basis for genomic medicine treatment of acute sensorineural hearing loss.
Spoofing Detection in Automatic Speaker Verification Systems Using DNN Classifiers and Dynamic Acoustic Features

With the development of speech synthesis technology, automatic speaker verification (ASV) systems have encountered the serious challenge of spoofing attacks. In order to improve the security of ASV systems, many antispoofing countermeasures have been developed. In the front-end domain, much research has been conducted on finding effective features which can distinguish spoofed speech from genuine speech and the published results show that dynamic acoustic features work more effectively than static ones. In the back-end domain, Gaussian mixture model (GMM) and deep neural networks (DNNs) are the two most popular types of classifiers used for spoofing detection. The log-likelihood ratios (LLRs) generated by the difference of human and spoofing log-likelihoods are used as spoofing detection scores. In this paper, we train a five-layer DNN spoofing detection classifier using dynamic acoustic features and propose a novel, simple scoring method only using human log-likelihoods (HLLs) for spoofing detection. We mathematically prove that the new HLL scoring method is more suitable for the spoofing detection task than the classical LLR scoring method, especially when the spoofing speech is very similar to the human speech. We extensively investigate the performance of five different dynamic filter bank-based cepstral features and constant Q cepstral coefficients (CQCC) in conjunction with the DNN-HLL method. The experimental results show that, compared to the GMM-LLR method, the DNN-HLL method is able to significantly improve the spoofing detection accuracy. Compared with the CQCC-based GMM-LLR baseline, the proposed DNN-HLL model reduces the average equal error rate of all attack types to 0.045%, thus exceeding the performance of previously published approaches for the ASVspoof 2015 Challenge task. Fusing the CQCC-based DNN-HLL spoofing detection system with ASV systems, the false acceptance rate on spoofing attacks can be reduced significantly.