Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning With Kernels: A Local Rademacher Complexity-Based Analysis With Application to Graph Kernels

When dealing with kernel methods, one has to decide which kernel and which values for the hyperparameters to use. Resampling techniques can address this issue but these procedures are time-consuming. This problem is particularly challenging when dealing with structured data, in particular with graphs, since several kernels for graph data have been proposed in literature, but no clear relationship among them in terms of learning properties is defined. In these cases, exhaustive search seems to be the only reasonable approach. Recently, the global Rademacher complexity (RC) and local Rademacher complexity (LRC), two powerful measures of the complexity of a hypothesis space, have shown to be suited for studying kernels properties. In particular, the LRC is able to bound the generalization error of an hypothesis chosen in a space by disregarding those ones which will not be taken into account by any learning procedure because of their high error. In this paper, we show a new approach to efficiently bound the RC of the space induced by a kernel, since its exact computation is an NP-Hard problem. Then we show for the first time that RC can be used to estimate the accuracy and expressivity of different graph kernels under different parameter configurations. The authors’ claims are supported by experimental results on several real-world graph data sets.
Adaptive Asymptotic Neural Network Control of Nonlinear Systems With Unknown Actuator Quantization

In this paper, we propose an adaptive neural-network-based asymptotic control algorithm for a class of nonlinear systems subject to unknown actuator quantization. To this end, we exploit the sector property of the quantization nonlinearity and transform actuator quantization control problem into analyzing its upper bounds, which are then handled by a dynamic loop gain function-based approach. In our adaptive control scheme, there is only one parameter required to be estimated online for updating weights of neural networks. Within the framework of Lyapunov theory, it is shown that the proposed algorithm ensures that all the signals in the closed-loop system are ultimately bounded. Moreover, an asymptotic tracking error is obtained by means of introducing Barbalat’s lemma to the proposed adaptive law.
Multilabel Prediction via Cross-View Search

Embedding methods have shown promising performance in multilabel prediction, as they are able to discover the label dependence. However, most methods ignore the correlations between the input and output, such that their learned embeddings are not well aligned, which leads to degradation in prediction performance. This paper presents a formulation for multilabel learning, from the perspective of cross-view learning, that explores the correlations between the input and the output. The proposed method, called Co-Embedding (CoE), jointly learns a semantic common subspace and view-specific mappings within one framework. The semantic similarity structure among the embeddings is further preserved, ensuring that close embeddings share similar labels. Additionally, CoE conducts multilabel prediction through the cross-view <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> nearest neighborhood (<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>NN) search among the learned embeddings, which significantly reduces computational costs compared with conventional decoding schemes. A hashing-based model, i.e., Co-Hashing (CoH), is further proposed. CoH is based on CoE, and imposes the binary constraint on continuous latent embeddings. CoH aims to generate compact binary representations to improve the prediction efficiency by benefiting from the efficient <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>NN search of multiple labels in the Hamming space. Extensive experiments on various real-world data sets demonstrate the superiority of the proposed methods over the state of the arts in terms of both prediction accuracy and efficiency.
Type 2 Diabetes and Asthma: Systematic Review of the Bidirectional Relationship.

BACKGROUND AND OBJECTIVE Obesity is an important contributor to the risk of both asthma and Type 2 Diabetes (T2DM). However, it has been suggested that T2DM and asthma are also independently associated. The aim of this systematic review was to synthesize the evidence for an independent relationship between T2DM and asthma. METHODS MEDLINE and EMBASE were searched for studies reporting the relationship between asthma and T2DM in adults. Given a potential bidirectional relationship, articles relating to T2DM as a risk factor for asthma, and asthma as a risk factor for T2DM were examined separately. RESULTS Eight studies were identified for inclusion in the review (n=2,934,399 participants). Four studies examined incident diabetes in those with asthma. The pooled (random effects model) adjusted hazard ratio for incident T2DM in asthma was 1.37 (95%CI 1.12-1.69; p <0.001) after controlling for BMI. Four studies reported prevalence or incidence rates of asthma in people with T2DM; higher rates of asthma in those with T2DM were reported in all four studies. Meta-analysis of results was not possible due to methodological heterogeneity. The quality of included studies was good, but due to small numbers, publication bias cannot be excluded. CONCLUSION The published literature suggests a bidirectional independent relationship between T2DM and asthma, although we cannot exclude publication bias.
Ballistocardiogram-Based Approach to Cuffless Blood Pressure Monitoring: Proof of Concept and Potential Challenges

<italic>Objective:</italic> The goal was to propose and establish the proof of concept of an ultraconvenient cuffless blood pressure monitoring approach based on the ballistocardiogram. <italic>Methods:</italic> The proposed approach monitors blood pressure by exploiting two features in the whole-body head-to-foot ballistocardiogram measured using a force plate: the time interval between the first (“I”) and second (“J”) major waves (“I–J interval”) for diastolic pressure and the amplitude between the J and third major (“K”) waves (“J–K amplitude”) for pulse pressure. The efficacy of the approach was examined in 22 young healthy volunteers by investigating the diastolic pressure monitoring performance of pulse transit time, pulse arrival time, and ballistocardiogram's I–J interval, and the systolic pressure monitoring performance of pulse transit time and I–J interval in conjunction with ballistocardiogram's J–K amplitude. <italic>Results:</italic> The I–J interval was comparable to pulse transit time and pulse arrival time in monitoring diastolic pressure, and the J–K amplitude could provide meaningful improvement to pulse transit time and I–J interval in monitoring systolic pressure. <italic>Conclusion:</italic> The ballistocardiogram may contribute toward ultraconvenient and more accurate cuffless blood pressure monitoring. <italic> Significance:</italic> The proposed approach has potential to complement the pulse transit time technique for cuffless blood pressure monitoring in two ways. First, it may be integrated with pulse transit time to enable independent monitoring of diastolic and systolic pressures via the J–K amplitude. Second, it may even enable diastolic and systolic pressure monitoring from the ballistocardiogram alone.
On-chip communication for neuro-glia networks

Hardware has become more prone to faults as a result of geometric scaling, wear-out and faults caused during the manufacturing process, therefore, the reliability of hardware is reliant on the need to continually adapt to faults. A computational model of biological self-repair in the brain, derived from observing the role of astrocytes (a glial cell found in the mammalian brain), has captured self-repair within models of neural networks known as neuro-glia networks. This astrocyte-driven repair process can address the issues of faulty synapse connections between neurons. These astrocyte cells are distributed throughout a neuro-glia network and regulate synaptic activity, and it has been observed in computational models that this can result in a fine-grained self-repair process. Therefore, mapping neuro-glia networks to hardware provides a strategy for achieving self-repair in hardware. The internal interconnecting of these networks in hardware is a challenge. Previous work has focused on addressing neuron to astrocyte communication (local), however, the global self-repair process is dependent on the communication infrastructure between astrocyte-to-astrocyte; e.g. astrocyte network. This study addresses the key challenge of providing a scalable communication interconnect for global astrocyte network requirements and how it integrates with existing local communication mechanism. Area/power results demonstrate scalable implementations with the ring topology while meeting timing requirements.
Spatial and Temporal Downsampling in Event-Based Visual Classification

As the interest in event-based vision sensors for mobile and aerial applications grows, there is an increasing need for high-speed and highly robust algorithms for performing visual tasks using event-based data. As event rate and network structure have a direct impact on the power consumed by such systems, it is important to explore the efficiency of the event-based encoding used by these sensors. The work presented in this paper represents the first study solely focused on the effects of both spatial and temporal downsampling on event-based vision data and makes use of a variety of data sets chosen to fully explore and characterize the nature of downsampling operations. The results show that both spatial downsampling and temporal downsampling produce improved classification accuracy and, additionally, a lower overall data rate. A finding is particularly relevant for bandwidth and power constrained systems. For a given network containing 1000 hidden layer neurons, the spatially downsampled systems achieved a best case accuracy of 89.38% on N-MNIST as opposed to 81.03% with no downsampling at the same hidden layer size. On the N-Caltech101 data set, the downsampled system achieved a best case accuracy of 18.25%, compared with 7.43% achieved with no downsampling. The results show that downsampling is an important preprocessing technique in event-based visual processing, especially for applications sensitive to power consumption and transmission bandwidth.
Lateral root growth in Arabidopsis is controlled by short and long distance signaling through the LRR RLKs XIP1/CEPR1 and CEPR2

ABSTRACT Plants rely on lateral roots to explore their soil environment and to maximize their uptake of essential minerals and water. Here we present evidence that the receptor kinases XIP1/CEPR1 and CEPR2 regulate both the initiation of lateral root primordia and emergence of lateral roots locally in the root, while also controlling lateral root extension in response to shoot-derived sucrose in Arabidopsis plants. In addition, mutation of both of these receptors prevents seedlings from responding to sucrose in the media, resulting in longer lateral roots. These results, combined with previous data, establish XIP1/CEPR1 and CEPR2-dependent roles in short- and long-distance pathways regulating different stages of lateral root growth.
Adaptive Structure Discovery for Multimedia Analysis Using Multiple Features

Multifeature learning has been a fundamental research problem in multimedia analysis. Most existing multifeature learning methods exploit graph, which must be computed beforehand, as input to uncover data distribution. These methods have two major problems confronted. First, graph construction requires calculating similarity based on nearby data pairs by a fixed function, e.g., the RBF kernel, but the intrinsic correlation among different data pairs varies constantly. Therefore, feature learning based on such predefined graphs may degrade, especially when there is dramatic correlation variation between nearby data pairs. Second, in most existing algorithms, each single-feature graph is computed independently and then combine them for learning, which ignores the correlation between multiple features. In this paper, a new unsupervised multifeature learning method is proposed to make the best utilization of the correlation among different features by jointly optimizing data correlation from multiple features in an adaptive way. As opposed to computing the affinity weight of data pairs by a fixed function, the weight of affinity graph is learned by a well-designed optimization problem. Additionally, the affinity graph of data pairs from different features is optimized in a global level to better leverage the correlation among different channels. In this way, the adaptive approach correlates the features of all features for a better learning process. Experimental results on real-world datasets demonstrate that our approach outperforms the state-of-the-art algorithms on leveraging multiple features for multimedia analysis.
Barrier Lyapunov Function Based Learning Control of Hypersonic Flight Vehicle With AOA Constraint and Actuator Faults

This paper investigates a fault-tolerant control of the hypersonic flight vehicle using back-stepping and composite learning. With consideration of angle of attack (AOA) constraint caused by scramjet, the control laws are designed based on barrier Lyapunov function. To deal with the unknown actuator faults, a robust adaptive allocation law is proposed to provide the compensation. Meanwhile, to obtain good system uncertainty approximation, the composite learning is proposed for the update of neural weights by constructing the serial–parallel estimation model to obtain the prediction error which can dynamically indicate how the intelligent approximation is working. Simulation results show that the controller obtains good system tracking performance in the presence of AOA constraint and actuator faults.
Do post-traumatic pain and post-traumatic stress symptomatology mutually maintain each other? A systematic review of cross-lagged studies

Abstract After traumatic exposure, individuals are at risk of developing symptoms of both pain and post-traumatic stress disorder (PTSD). Theory and research suggest a complex and potentially mutually maintaining relationship between these symptomatologies. However, findings are inconsistent and the applied methods are not always well suited for testing mutual maintenance. Cross-lagged designs can provide valuable insights into such temporal associations, but there is a need for a systematic review to assist clinicians and researchers in understanding the nature of the relationship. Thus, the aim of this systematic review was to identify, critically appraise, and synthesize results from cross-lagged studies on pain and PTSD symptomatology to assess the evidence for longitudinal reciprocity and potential mediators. Systematic searches resulted in 7 eligible studies that were deemed of acceptable quality with moderate risk of bias using the cohort study checklist from Scottish Intercollegiate Guidelines Network. Furthermore, synthesis of significant pathways in the cross-lagged models showed inconsistent evidence of both bidirectional and unidirectional interaction patterns between pain and PTSD symptomatology across time, hence not uniformly supporting the theoretical framework of mutual maintenance. In addition, the synthesis suggested that hyperarousal and intrusion symptoms may be of particular importance in these cross-lagged relationships, while there was inconclusive evidence of catastrophizing as a mediator. In conclusion, the findings suggest an entangled, but not necessarily mutually maintaining relationship between pain and PTSD symptomatology. However, major variations in findings and methodologies complicated synthesis, prompting careful interpretation and heightening the likelihood that future high-quality studies will change these conclusions.
Regularized Gaussian Mixture Model for High-Dimensional Clustering

Finding low-dimensional representation of high-dimensional data sets is an important task in various applications. The fact that data sets often contain clusters embedded in different subspaces poses barrier to this task. Driven by the need in methods that enable clustering and finding each cluster’s intrinsic subspace simultaneously, in this paper, we propose a regularized Gaussian mixture model (GMM) for clustering. Despite the advantages of GMM, such as its probabilistic interpretation and robustness against observation noise, traditional maximum-likelihood estimation for GMMs shows disappointing performance in high-dimensional setting. The proposed regularization method finds low-dimensional representations of the component covariance matrices, resulting in better estimation of local feature correlations. The regularization problem can be incorporated in the expectation maximization algorithm for maximizing the likelihood function of a GMM, with the ${M}$ -step modified to incorporate the regularization. The ${M}$ -step involves a determinant maximization problem, which can be solved efficiently. The performance of the proposed method is demonstrated using several simulated data sets. We also illustrate the potential value of the proposed method in applications using four real data sets.
Phenotype Extraction Based on Word Embedding to Sentence Embedding Cascaded Approach

As a significant determinant in the development of named entity recognition, phenotypic descriptions are normally presented differently in biomedical literature with the use of complicated semantics. In this paper, a novel approach has been proposed to identify plant phenotypes by adopting word embedding to sentence embedding cascaded approach. We make use of a word embedding method to find high-frequency phenotypes with original sentences used as input in a sentence embedding method. In doing so, a variety of complicated phenotypic expressions can be recognized accurately. Besides, the state-of-the-art word representation models have been compared and among them, skip-gram with negative sampling was selected with the best performance. To evaluate the performance of our approach, we applied it to the dataset composed of 56 748 PubMed abstracts of model organism Arabidopsis thaliana. The experiment results showed that our approach yielded the best performance, as it achieved a 2.588-fold increase in terms of the number of new phenotypic descriptions when compared to the original phenotype ontology.
Intensity-curvature highlight of human brain magnetic resonance imaging vasculature

This paper uses the concept of intensity-curvature to highlight human brain vasculature imaged through magnetic resonance imaging (MRI). Two model functions are fitted to the MRI data. The model functions are: 1) the bivariate cubic polynomial (B32D), 2) the bivariate cubic Lagrange polynomial (G42D). The concept of intensity-curvature entails the calculation of the classic-curvature and the two intensity-curvature terms (ICTs): before and after interpolation. When the two intensity-curvature terms are calculated on a pixel-by-pixel basis across the image, they become two additional images. Through the use of the aforementioned ICT images it is possible to highlight and filter the human brain vasculature imaged with MRI. Moreover, the inverse Fourier transformation of the difference between the k-space of the MRI and the k-space of the ICT provides vessels identification. In essence, this research presents evidence that MRI images of the human brain can be studied through two additional domains: the intensity-curvature terms.
Event-Based Synchronization Control for Memristive Neural Networks With Time-Varying Delay

In this paper, we investigate the global synchronization control problem for memristive neural networks (MNNs) with time-varying delay. A novel event-triggered controller is introduced with the linear diffusive term and discontinuous sign term. In order to greatly reduce the computation cost of the controller under certain event-triggering condition, two event-based control schemes are proposed with static event-triggering condition and dynamic event-triggering condition. Some sufficient conditions are derived by these control schemes to ensure the response MNN to be synchronized with the driving one. Furthermore, under certain event-triggering conditions, a positive lower bound is achieved for the interexecution time to guarantee that Zeno behavior cannot be executed. Finally, numerical simulations are provided to substantiate the effectiveness of the proposed theoretical results.
Synchronization Conditions for a Multirate Kuramoto Network With an Arbitrary Topology and Nonidentical Oscillators

This paper presents methods to find a positively invariant set (PIS) and derive conditions on the edge weights for a multirate Kuramoto oscillator network to achieve synchronization. These methods can be applied to a network with an arbitrary topology and nonidentical oscillators. The proposed methods are based on the construction of energy functions for this type of network. Two different conditions on the edge weights are provided by using graph spectral properties, or alternatively by analyzing a path set of a graph. These methods provide flexibility in checking the edge weight conditions. We further improve the estimate of the PIS for the multirate Kuramoto network when its damping coefficients are greater than a certain value. The effectiveness and conservativeness of the proposed methods are demonstrated by simulation studies. A comparison with another method from the existing literature shows that our method gives less conservative estimate of the PIS.
Empirical Bayesian Light-Field Stereo Matching by Robust Pseudo Random Field Modeling

Light-field stereo matching problems are commonly modeled by Markov Random Fields (MRFs) for statistical inference of depth maps. Nevertheless, most previous approaches did not adapt to image statistics but instead adopted fixed model parameters. They explored explicit vision cues, such as depth consistency and occlusion, to provide local adaptability and enhance depth quality. However, such additional assumptions could end up confining their applicability, e.g. algorithms designed for dense view sampling are not suitable for sparse one. In this paper, we get back to MRF fundamentals and develop an empirical Bayesian framework—Robust Pseudo Random Field—to explore intrinsic statistical cues for broad applicability. Based on pseudo-likelihoods with hidden soft-decision priors, we apply soft expectation-maximization (EM) for good model fitting and perform hard EM for robust depth estimation. We introduce novel pixel difference models to enable such adaptability and robustness simultaneously. Accordingly, we devise a stereo matching algorithm to employ this framework on dense, sparse, and even denoised light fields. It can be applied to both true-color and grey-scale pixels. Experimental results show that it estimates scene-dependent parameters robustly and converges quickly. In terms of depth accuracy and computation speed, it also outperforms state-of-the-art algorithms constantly.
Quantized Control of Markov Jump Nonlinear Systems Based on Fuzzy Hidden Markov Model

This paper considers the problem of asynchronous guaranteed cost control (GCC) for nonlinear Markov jump systems with stochastic quantization. Hidden Markov model is used to describe the nonsynchronous controller and the random quantization phenomenon. Based on Takagi–Sugeno fuzzy technique and Lyapunov function approach, a sufficient condition is obtained, which can not only ensure the asymptotic stability of the closed-loop system and existence of the desired controller, but also can yield the minimal upper bound of GCC performance. Finally, two examples are provided to demonstrate the correctness and reliability of our developed approaches.