Miracle-dz/UniFAR-FTUs huggingface dataset:
Inferring MicroRNA Targets Based on Restricted Boltzmann Machines

Predicting the miRNA-target interactions (MTIs) is a critical task for elucidating mechanistic roles of miRNAs in pathophysiology. However, most existing techniques have a higher false positive because the precise miRNA target mechanisms are poorly known. Considering that ensemble methods can take advantage of the complementary knowledge in different methods, we propose an alternative optimization framework, Inferring MiRNA Targets based on Restricted Boltzmann Machines (IMTRBM), to enhance the accuracy of previous prediction results. First, the proposed method directly constructs a weighted MTI network though the results predicted by individual methods and each miRNA target pair is weighted based on the frequency appearing in these results. Second, we transform the miRNA-target prediction problem into a complete bipartite graph model, named restricted Boltzmann machine, and utilize a practical learning procedure to train our model and make predictions. Our results show that the algorithm outperforms individual miRNA-target prediction approach in the number of validated miRNA targets at cutoffs of top list. Moreover, our framework can tolerate the decrease and increase of predicted MTIs and even discover new miRNA targets, which have been a challenge to predict for any individual methods. Finally, for the miRNAs that are not appearing in IMTRBM, we design a new method to supplement IMTRBM based on the intuition that similar miRNAs have similar functions, which also achieves a comparable result. The source code of IMTRBM is available at https://github.com/liuying201705/IMTRBM.
Reliable Control Policy of Cyber-Physical Systems Against a Class of Frequency-Constrained Sensor and Actuator Attacks

This paper is concerned with reliable control problems of cyber-physical systems against a class of frequency-constrained sensor and actuator attacks. We consider a continuous-time linear physical system equipped with an observer-based abnormal detector, and it is assumed that control signals and partial sensor outputs transmitted via network layers are vulnerable to cyber attacks. With the use of detection mechanisms of the abnormal monitor, an upper bound of the worst stealthy attacks is obtained, which is composed of the information of the detector’s threshold, the attack’s structure, and frequency characteristic. By exploiting the bound information, a novel attack compensator, which can stabilize the system with a nearly desired system performance, is proposed for a situation where an attack may occur without triggering an alarm. Finally, the effectiveness of the proposed control policy is verified by a numerical example.
Optimal Robust Output Containment of Unknown Heterogeneous Multiagent System Using Off-Policy Reinforcement Learning

This paper investigates optimal robust output containment problem of general linear heterogeneous multiagent systems (MAS) with completely unknown dynamics. A model-based algorithm using offline policy iteration (PI) is first developed, where the <inline-formula> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula>-copy internal model principle is utilized to address the system parameter variations. This offline PI algorithm requires the nominal model of each agent, which may not be available in most real-world applications. To address this issue, a discounted performance function is introduced to express the optimal robust output containment problem as an optimal output-feedback design problem with bounded <inline-formula> <tex-math notation="LaTeX">${\mathcal {L}_{2}}$ </tex-math></inline-formula>-gain. To solve this problem online in real time, a Bellman equation is first developed to evaluate a certain control policy and find the updated control policies, simultaneously, using only the state/output information measured online. Then, using this Bellman equation, a model-free off-policy integral reinforcement learning algorithm is proposed to solve the optimal robust output containment problem of heterogeneous MAS, in real time, without requiring any knowledge of the system dynamics. Simulation results are provided to verify the effectiveness of the proposed method.
Transfer Independently Together: A Generalized Framework for Domain Adaptation

Currently, unsupervised heterogeneous domain adaptation in a generalized setting, which is the most common scenario in real-world applications, is under insufficient exploration. Existing approaches either are limited to special cases or require labeled target samples for training. This paper aims to overcome these limitations by proposing a generalized framework, named as transfer independently together (TIT). Specifically, we learn multiple transformations, one for each domain (independently), to map data onto a shared latent space, where the domains are well aligned. The multiple transformations are jointly optimized in a unified framework (together) by an effective formulation. In addition, to learn robust transformations, we further propose a novel landmark selection algorithm to reweight samples, i.e., increase the weight of pivot samples and decrease the weight of outliers. Our landmark selection is based on graph optimization. It focuses on sample geometric relationship rather than sample features. As a result, by abstracting feature vectors to graph vertices, only a simple and fast integer arithmetic is involved in our algorithm instead of matrix operations with float point arithmetic in existing approaches. At last, we effectively optimize our objective via a dimensionality reduction procedure. TIT is applicable to arbitrary sample dimensionality and does not need labeled target samples for training. Extensive evaluations on several standard benchmarks and large-scale datasets of image classification, text categorization and text-to-image recognition verify the superiority of our approach.
The Research Pyramid: A Framework for Accounting Information Systems Research

This paper extends Sowa's (1997) Meaning Triangle to develop a framework for accounting information systems (AIS) research—the Research Pyramid. This framework identifies relationships between objects in economic reality, people's concepts of economic reality, symbols used to record and represent economic reality, and the resultant accounting information systems that capture and present data about economic reality. The Research Pyramid has two major uses. First, the paper illustrates how the Research Pyramid can be used to identify new research questions to extend existing research streams. To be used in this manner, existing AIS research is classified along each of the edges of the Research Pyramid. Once an area of the literature has been analyzed, the edges that have not been studied extensively reveal potential primitive mappings for future exploration. Second, each primitive mapping is evaluated to identify which of four research methodologies (design science, field studies, survey research, and labor.
Adaptive Neural Dynamic Surface Control for Nonstrict-Feedback Systems With Output Dead Zone

This paper focuses on the problem of adaptive output-constrained neural tracking control for uncertain nonstrict-feedback systems in the presence of unknown symmetric output dead zone and input saturation. A Nussbaum-type function-based dead-zone model is introduced such that the dynamic surface control approach can be used for controller design. The variable separation technique is employed to decompose the unknown function of entire states in each subsystem into a series of smooth functions. Radial basis function neural networks are utilized to approximate the unknown black-box functions derived from Young’s inequality. With the help of auxiliary first-order filters, the dimensions of neural network input are reduced in each recursive design. A main advantage of the proposed method is that for an $n$ -order nonlinear system, only one adaptation parameter needs to be tuned online. It is rigorously shown that the proposed output-constrained controller guarantees that all the closed-loop signals are semiglobal uniformly ultimately bounded and the tracking error never violates the output constraint.
A Novel Deep Learning Approach for Recognizing Stereotypical Motor Movements within and across Subjects on the Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by persistent difficulties including repetitive patterns of behavior known as stereotypical motor movements (SMM). So far, several techniques have been implemented to track and identify SMMs. In this context, we propose a deep learning approach for SMM recognition, namely, convolutional neural networks (CNN) in time and frequency-domains. To solve the intrasubject SMM variability, we propose a robust CNN model for SMM detection within subjects, whose parameters are set according to a proper analysis of SMM signals, thereby outperforming state-of-the-art SMM classification works. And, to solve the intersubject variability, we propose a global, fast, and light-weight framework for SMM detection across subjects which combines a knowledge transfer technique with an SVM classifier, therefore resolving the “real-life” medical issue associated with the lack of supervised SMMs per testing subject in particular. We further show that applying transfer learning across domains instead of transfer learning within the same domain also generalizes to the SMM target domain, thus alleviating the problem of the lack of supervised SMMs in general.
MITT: Medical Image Tracking Toolbox

Over the years, medical image tracking has gained considerable attention from both medical and research communities due to its widespread utility in a multitude of clinical applications, from functional assessment during diagnosis and therapy planning to structure tracking or image fusion during image-guided interventions. Despite the ever-increasing number of image tracking methods available, most still consist of independent implementations with specific target applications, lacking the versatility to deal with distinct end-goals without the need for methodological tailoring and/or exhaustive tuning of numerous parameters. With this in mind, we have developed the medical image tracking toolbox (MITT)—a software package designed to ease customization of image tracking solutions in the medical field. While its workflow principles make it suitable to work with 2-D or 3-D image sequences, its modules offer versatility to set up computationally efficient tracking solutions, even for users with limited programming skills. MITT is implemented in both C/C++ and MATLAB, including several variants of an object-based image tracking algorithm and allowing to track multiple types of objects (i.e., contours, multi-contours, surfaces, and multi-surfaces) with several customization features. In this paper, the toolbox is presented, its features discussed, and illustrative examples of its usage in the cardiology field provided, demonstrating its versatility, simplicity, and time efficiency.
Ultrasonic Microelastography to Assess Biomechanical Properties of the Cornea

Objective: To both qualitatively and quantitatively investigate corneal biomechanical properties through an ultrasonic microelastography imaging system, which is potentially useful in the diagnosis of diseases, such as keratoconus, postrefractive keratectasia, and tracking treatment such as cross-linking surgery. Methods: Our imaging system has a dual-frequency configuration, including a 4.5 MHz ring transducer to push the tissue and a confocally aligned 40 MHz needle transducer to track micron-level displacement. Two-dimensional/three-dimensional acoustic radiation force impulse (ARFI) imaging and Young's modulus in the region of interest were performed on ex vivo porcine corneas that were either cross-linked using formalin solution or preloaded with intraocular pressure (IOPs) from 5 to 30 mmHg. Results: The increase of corneal stiffness and the change in cross-linked volume following formalin crosslinking could be precisely observed in the ARFI images and reflected by the reconstructed Young's modulus while the B-mode structural images remained almost unchanged. In addition, the relationship between the stiffness of the cornea and IOPs was investigated among 12 porcine corneas. The corneal stiffness is significantly different at various IOPs and has a tendency to become stiffer with increasing IOP. Conclusion: Our results demonstrate the principle of using ultrasonic microelastography techniques to image the biomechanical properties of the cornea. Integrating high-resolution ARFI imaging labeled with reconstructed Young's modulus and structural imaging of the cornea can potentially lead to a routinely performed imaging modality in the field of ophthalmology.
Recursive Neural Networks for Density Estimation Over Generalized Random Graphs

Structured data in the form of labeled graphs (with variable order and topology) may be thought of as the outcomes of a random graph (RG) generating process characterized by an underlying probabilistic law. This paper formalizes the notions of generalized RG (GRG) and probability density function (pdf) for GRGs. Thence, a “universal” learning machine (combining the encoding module of a recursive neural network and a radial basis functions’ network) is introduced for estimating the unknown pdf from an unsupervised sample of GRGs. A maximum likelihood training algorithm is presented and constrained so as to ensure that the resulting model satisfies the axioms of probability. Techniques for preventing the model from degenerate solutions are proposed, as well as variants of the algorithm suitable to the tasks of graphs classification and graphs clustering. The major properties of the machine are discussed. The approach is validated empirically through experimental investigations in the estimation of pdfs for synthetic and real-life GRGs, in the classification of images from the Caltech Benchmark data set and molecules from the Mutagenesis data set, and in clustering of images from the LabelMe data set.
Detecting and Locating Gastrointestinal Anomalies Using Deep Learning and Iterative Cluster Unification

This paper proposes a novel methodology for automatic detection and localization of gastrointestinal (GI) anomalies in endoscopic video frame sequences. Training is performed with weakly annotated images, using only image-level, semantic labels instead of detailed, and pixel-level annotations. This makes it a cost-effective approach for the analysis of large videoendoscopy repositories. Other advantages of the proposed methodology include its capability to suggest possible locations of GI anomalies within the video frames, and its generality, in the sense that abnormal frame detection is based on automatically derived image features. It is implemented in three phases: 1) it classifies the video frames into abnormal or normal using a weakly supervised convolutional neural network (WCNN) architecture; 2) detects salient points from deeper WCNN layers, using a deep saliency detection algorithm; and 3) localizes GI anomalies using an iterative cluster unification (ICU) algorithm. ICU is based on a pointwise cross-feature-map (PCFM) descriptor extracted locally from the detected salient points using information derived from the WCNN. Results, from extensive experimentation using publicly available collections of gastrointestinal endoscopy video frames, are presented. The data sets used include a variety of GI anomalies. Both anomaly detection and localization performance achieved, in terms of the area under receiver operating characteristic (AUC), were >80%. The highest AUC for anomaly detection was obtained on conventional gastroscopy images, reaching 96%, and the highest AUC for anomaly localization was obtained on wireless capsule endoscopy images, reaching 88%.
Hierarchical Bayesian Inference and Learning in Spiking Neural Networks

Numerous experimental data from neuroscience and psychological science suggest that human brain utilizes Bayesian principles to deal the complex environment. Furthermore, hierarchical Bayesian inference has been proposed as an appropriate theoretical framework for modeling cortical processing. However, it remains unknown how such a computation is organized in the network of biologically plausible spiking neurons. In this paper, we propose a hierarchical network of winner-take-all circuits which can carry out hierarchical Bayesian inference and learning through a spike-based variational expectation maximization (EM) algorithm. Particularly, we show how the firing activities of spiking neurons in response to the input stimuli and the spike-timing-dependent plasticity rule can be understood, respectively, as variational E-step and M-step of variational EM. Finally, we demonstrate the utility of this spiking neural network on the MNIST benchmark for unsupervised classification of handwritten digits.
Competition:

MOTIVATION • Quality of Service (QoS) provision in Wireless Sensor Networks (WSNs) is extremely challenging because of: – resource constraints of sensor nodes – influential changes in the environment – dynamic network topology – redundant data • Stringent QoS metrics are required for mission-critical applications, e.g., in wireless industry, smart grids, cooperative driving, etc. – high reliability – low latency – high energy efficiency – high robustness against interference
Kernel Clustering: Density Biases and Solutions

Kernel methods are popular in clustering due to their generality and discriminating power. However, we show that many kernel clustering criteria have density biases theoretically explaining some practically significant artifacts empirically observed in the past. For example, we provide conditions and formally prove the density mode isolation bias in kernel K-means for a common class of kernels. We call it Breiman’s bias due to its similarity to the histogram mode isolation previously discovered by Breiman in decision tree learning with Gini impurity. We also extend our analysis to other popular kernel clustering methods, e.g., average/normalized cut or dominant sets, where density biases can take different forms. For example, splitting isolated points by cut-based criteria is essentially the sparsest subset bias, which is the opposite of the density mode bias. Our findings suggest that a principled solution for density biases in kernel clustering should directly address data inhomogeneity. We show that density equalization can be implicitly achieved using either locally adaptive weights or locally adaptive kernels. Moreover, density equalization makes many popular kernel clustering objectives equivalent. Our synthetic and real data experiments illustrate density biases and proposed solutions. We anticipate that theoretical understanding of kernel clustering limitations and their principled solutions will be important for a broad spectrum of data analysis applications across the disciplines.
Fast Forward-Dynamics Tracking Simulation: Application to Upper Limb and Shoulder Modeling

Objective: Musculoskeletal simulation can be used to estimate muscle forces in clinical movement studies. However, such simulations typically only target movement measurements and are not applicable to force exertion tasks which are commonly used in rehabilitation therapy. Simulations can also produce nonphysiological joint forces or be too slow for real-time clinical applications, such as rehabilitation with real-time feedback. The objective of this study is to propose and evaluate a new formulation of forward-dynamics assisted tracking simulation that incorporates measured reaction forces as targets or constraints without any additional computational cost. Methods: We illustrate our method with idealized proof-of-concept models and evaluate it with two upper limb cases: Tracking of hand reaction forces during an isometric force-generation task and constraining glenohumeral joint reaction forces for stability during arm elevation. Results: We show that the addition of reaction force optimization terms within our simulations generates plausible muscle force predictions for these tasks, which are strongly related to reaction forces in addition to movement. Execution times for all models tested were not different when run with or without the reaction force optimization term, ensuring that the simulations are fast enough for real-time clinical applications. Conclusion: Our novel reaction force optimization term leads to more realistic shoulder reaction forces, without any additional computational costs. Significance: Our formulation is not only valuable for shoulder simulations, but could be used in various clinical situations (e.g., for different joints and rehabilitation therapy tasks) where the direction and/or magnitude of reaction forces are of interest.
Automatic Croup Diagnosis Using Cough Sound Recognition

Objective: Croup, a respiratory tract infection common in children, causes an inflammation of the upper airway restricting normal breathing and producing cough sounds typically described as seallike “barking cough. ” Physicians use the existence of barking cough as the defining characteristic of croup. This paper aims to develop automated cough sound analysis methods to objectively diagnose croup. Methods: In automating croup diagnosis, we propose the use of mathematical features inspired by the human auditory system. In particular, we utilize the cochleagram for feature extraction, a time-frequency representation where the frequency components are based on the frequency selectivity property of the human cochlea. Speech and cough share some similarities in the generation process and physiological wetware used. As such, we also propose the use of mel-frequency cepstral coefficients which has been shown to capture the relevant aspects of the short-term power spectrum of speech signals. Feature combination and backward sequential feature selection are also experimented with. Experimentation is performed on cough sound recordings from patients presenting various clinically diagnosed respiratory tract infections divided into croup and non-croup. The dataset is divided into training and test sets of 364 and 115 patients, respectively, with automatically segmented cough sound segments. Results: Croup and non-croup patient classification on the test dataset with the proposed methods achieve a sensitivity and specificity of 92.31% and 85.29%, respectively. Conclusion: Experimental results show the significant improvement in automatic croup diagnosis against earlier methods. Significance: This paper has the potential to automate croup diagnosis based solely on cough sound analysis.