Miracle-dz/UniFAR-FTUs huggingface dataset:
Targeted Test Generation for Actor Systems

This paper addresses the problem of targeted test generation for actor systems. Speciﬁcally, we propose a method to support generation of system-level tests to cover a given code location in an actor system. The test generation method consists of two phases. First, static analysis is used to construct an abstraction of an entire actor system in terms of a message ﬂow graph (MFG). An MFG captures potential actor interactions that are deﬁned in a program. Second, a backwards symbolic execution (BSE) from a target location to an “entry point” of the actor system is performed. BSE uses the MFG constructed in the ﬁrst phase of our targeted test generation method to guide execution across actors. Because concurrency leads to a huge search space which can potentially be explored through BSE, we prune the search space by using two heuristics combined with a feedback-directed technique. We implement our method in Tap , a tool for Java Akka programs, and evaluate Tap on the Savina benchmarks as well as four open source projects. Our evaluation shows that the Tap achieves a relatively high target coverage (78% on 1,000 targets) and detects six previously unreported bugs in the subjects.
fpgaConvNet: Mapping Regular and Irregular Convolutional Neural Networks on FPGAs

Since neural networks renaissance, convolutional neural networks (ConvNets) have demonstrated a state-of-the-art performance in several emerging artificial intelligence tasks. The deployment of ConvNets in real-life applications requires power-efficient designs that meet the application-level performance needs. In this context, field-programmable gate arrays (FPGAs) can provide a potential platform that can be tailored to application-specific requirements. However, with the complexity of ConvNet models increasing rapidly, the ConvNet-to-FPGA design space becomes prohibitively large. This paper presents fpgaConvNet, an end-to-end framework for the optimized mapping of ConvNets on FPGAs. The proposed framework comprises an automated design methodology based on the synchronous dataflow (SDF) paradigm and defines a set of SDF transformations in order to efficiently navigate the architectural design space. By proposing a systematic multiobjective optimization formulation, the presented framework is able to generate hardware designs that are cooptimized for the ConvNet workload, the target device, and the application’s performance metric of interest. Quantitative evaluation shows that the proposed methodology yields hardware designs that improve the performance by up to $6.65\times $ over highly optimized graphics processing unit designs for the same power constraints and achieve up to $2.94\times $ higher performance density compared with the state-of-the-art FPGA-based ConvNet architectures.
MfeCNN: Mixture Feature Embedding Convolutional Neural Network for Data Mapping

Data mapping plays an important role in data integration and exchanges among institutions and organizations with different data standards. However, traditional rule-based approaches and machine learning methods fail to achieve satisfactory results for the data mapping problem. In this paper, we propose a novel and sophisticated deep learning framework for data mapping called mixture feature embedding convolutional neural network (MfeCNN). The MfeCNN model converts the data mapping task to a multiple classification problem. In the model, we incorporated multimodal learning and multiview embedding into a CNN for mixture feature tensor generation and classification prediction. Multimodal features were extracted from various linguistic spaces with a medical natural language processing package. Then, powerful feature embeddings were learned by using the CNN. As many as 10 classes could be simultaneously classified by a softmax prediction layer based on multiview embedding. MfeCNN achieved the best results on unbalanced data (average F1 score, 82.4%) among the traditional state-of-the-art machine learning models and CNN without mixture feature embedding. Our model also outperformed a very deep CNN with 29 layers, which took free texts as inputs. The combination of mixture feature embedding and a deep neural network can achieve high accuracy for data mapping and multiple classification.
Adaptive Neural Control of Pure-Feedback Nonlinear Systems With Event-Triggered Communications

This paper is concerned with the adaptive event-triggered control problem for a class of pure-feedback nonlinear systems. Unlike the existing results where the control execution is periodic, the new proposed scheme updates the controller and the neural network weights only when desired control specifications cannot be guaranteed. Clearly, this can largely reduce the amount of transmission data. Besides, since the event-trigger error is discontinuous because of the event-triggering mechanism, the stability analysis in the classical sense may not be guaranteed. To solve this problem, we formulate the event-triggered network control systems as a nonlinear impulsive dynamical system, and a novel Lyapunov theorem is used to show the stability properties of the closed-loop systems. Finally, two simulation examples are given to illustrate the effectiveness of the theoretical results.
Automatic Screening of Sleep Apnea Patients Based on the SpO2 Signal

Objective: This paper presents a methodology to automatically screen for sleep apnea based on the detection of apnea and hypopnea events in the blood oxygen saturation (SpO2) signal. Methods: It starts by detecting all desaturations in the SpO2 signal. From these desaturations, a total of 143 time-domain features are extracted. After feature selection, the six most discriminative features are used to construct classifiers to predict if desaturations are caused by respiratory events. From these, a random forest classifier yielded the best classification performance. The number of desaturations, classified as caused by respiratory events per hour of recording, can then be used as an estimate of the apnea-hypopnea index (AHI), and to predict whether or not a patient suffers from sleep apnea-hypopnea syndrome (SAHS). All classifiers were developed based on a subset of 500 subjects of the Sleep Heart Health Study (SHHS) and tested on three different datasets, containing 8052 subjects in total. Results: An averaged desaturation classification accuracy of 82.8% was achieved over the different test sets. Subjects having SAHS with an AHI greater than 15 can be detected with an average accuracy of 87.6%. Conclusion: The achieved SAHS screening outperforms SpO2 methods from the literature on the SHHS test dataset. Moreover, the robustness of the method was shown when tested on different independent test sets. Significance: These results show that an algorithm based on simple features of SpO2 desaturations can outperform more elaborate methods in the detection of apneic events and the screening of SAHS patients.
PhotoRecomposer: Interactive Photo Recomposition by Cropping

We present a visual analysis method for interactively recomposing a large number of photos based on example photos with high-quality composition. The recomposition method is formulated as a matching problem between photos. The key to this formulation is a new metric for accurately measuring the composition distance between photos. We have also developed an earth-mover-distance-based online metric learning algorithm to support the interactive adjustment of the composition distance based on user preferences. To better convey the compositions of a large number of example photos, we have developed a multi-level, example photo layout method to balance multiple factors such as compactness, aspect ratio, composition distance, stability, and overlaps. By introducing an EulerSmooth-based straightening method, the composition of each photos is clearly displayed. The effectiveness and usefulness of the method has been demonstrated by the experimental results, user study, and case studies.
ELF: Extract Landmark Features By Optimizing Topology Maintenance, Redundancy, and Specificity

Feature selection is the process of selecting a subset of landmark features for model construction when there are many features and a comparatively few samples. The far-reaching development technologies such as biological sequencing at single cell level make feature selection a more challenging work. The difficulty lies in four facts: those features measured are in high dimension and with noise; dropouts make the data much sparse; many features are either redundant or irrelevant; and samples are not well-labeled in the experiments. Here, we propose a new model called ELF (Extract Landmark Features) to address the above challenges. ELF aims to simultaneously maximize topology maintenance to keep the pairwise relationships among samples, minimize feature redundancy to diversify the features, and maximize feature specificity to make every selected feature more representative. This makes ELF a nonlinear combinatorial optimization. To solve this difficult problem, we propose a heuristic algorithm based on greedy strategy. We show ELF's outstanding performance on two single cell RNA-seq datasets. One is the direct reprogramming from mouse embryonic fibroblasts to induced neuron and the other is hepatoblast differentiation. ELF is able to choose only hundreds of landmark genes to maintain the cells’ correlativity. Topology maintenance, redundancy removal, and specificity each plays its important role in selecting landmark features and revealing cells’ biological functions. In addition, ELF can be generally applied in other scenarios. We demonstrate that ELF can reveal pivotal pixel in writing region and human face in two public image datasets. We believe that ELF is a useful tool to obtain more interpretable results by revealing key features while clustering the samples well.
Large-Scale Robust Semisupervised Classification

Semisupervised learning aims to leverage both labeled and unlabeled data to improve performance, where most of them are graph-based methods. However, the graph-based semisupervised methods are not capable for large-scale data since the computational consumption on the construction of graph Laplacian matrix is huge. On the other hand, the substantial unlabeled data in training stage of semisupervised learning could cause large uncertainties and potential threats. Therefore, it is crucial to enhance the robustness of semisupervised classification. In this paper, a novel large-scale robust semisupervised learning method is proposed in the framework of capped <inline-formula> <tex-math notation="LaTeX">$\ell _{\boldsymbol {2,p}}$ </tex-math></inline-formula>-norm. This strategy is superior not only in computational cost because it makes the graph Laplacian matrix unnecessary, but also in robustness to outliers since the capped <inline-formula> <tex-math notation="LaTeX">$\ell _{\boldsymbol {2,p}}$ </tex-math></inline-formula>-norm used for loss measurement. An efficient optimization algorithm is exploited to solve the nonconvex and nonsmooth challenging problem. The complexity of the proposed algorithm is analyzed and discussed in theory detailedly. Finally, extensive experiments are conducted over six benchmark data sets to demonstrate the effectiveness and superiority of the proposed method.
Consensus of Leader-Following Multiagent Systems: A Distributed Event-Triggered Impulsive Control Strategy

This paper investigates the leader-following consensus problem of multiagent systems using a distributed event-triggered impulsive control method. For each agent, the controller is updated only when some state-dependent errors exceed a tolerable bound. The control inputs will be carried out by actor only at event triggering impulsive instants. According to the Lyapunov stability theory and impulsive method, several sufficient criteria for leader-following consensus are derived. Also, it is shown that continuous communication of neighboring agents can be avoided, and Zeno-behavior can be excluded in our schema. The results are illustrated through several numerical simulation examples.
Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation

Objective: Deep learning based methods for retinal vessel segmentation are usually trained based on pixel-wise losses, which treat all vessel pixels with equal importance in pixel-to-pixel matching between a predicted probability map and the corresponding manually annotated segmentation. However, due to the highly imbalanced pixel ratio between thick and thin vessels in fundus images, a pixel-wise loss would limit deep learning models to learn features for accurate segmentation of thin vessels, which is an important task for clinical diagnosis of eye-related diseases. Methods: In this paper, we propose a new segment-level loss which emphasizes more on the thickness consistency of thin vessels in the training process. By jointly adopting both the segment-level and the pixel-wise losses, the importance between thick and thin vessels in the loss calculation would be more balanced. As a result, more effective features can be learned for vessel segmentation without increasing the overall model complexity. Results: Experimental results on public data sets demonstrate that the model trained by the joint losses outperforms the current state-of-the-art methods in both separate-training and cross-training evaluations. Conclusion: Compared to the pixel-wise loss, utilizing the proposed joint-loss framework is able to learn more distinguishable features for vessel segmentation. In addition, the segment-level loss can bring consistent performance improvement for both deep and shallow network architectures. Significance: The findings from this study of using joint losses can be applied to other deep learning models for performance improvement without significantly changing the network architectures.
HeadFusion: 360<inline-formula><tex-math notation="LaTeX">${^\circ }$</tex-math> <alternatives><inline-graphic xlink:href="yu-ieq1-2841403.gif"/></alternatives></inline-formula> Head Pose Tracking Combining 3D Morphable Model and 3D Reconstruction

Head pose estimation is a fundamental task for face and social related research. Although 3D morphable model (3DMM) based methods relying on depth information usually achieve accurate results, they usually require frontal or mid-profile poses which preclude a large set of applications where such conditions can not be garanteed, like monitoring natural interactions from fixed sensors placed in the environment. A major reason is that 3DMM models usually only cover the face region. In this paper, we present a framework which combines the strengths of a 3DMM model fitted online with a prior-free reconstruction of a 3D full head model providing support for pose estimation from any viewpoint. In addition, we also proposes a symmetry regularizer for accurate 3DMM fitting under partial observations, and exploit visual tracking to address natural head dynamics with fast accelerations. Extensive experiments show that our method achieves state-of-the-art performance on the public BIWI dataset, as well as accurate and robust results on UbiPose, an annotated dataset of natural interactions that we make public and where adverse poses, occlusions or fast motions regularly occur.
Amyotrophic Lateral Sclerosis Mortality in the United States, 2011–2014

Background: The International Classification of Disease, 10th Revision (ICD-10) did not include a code specific for Amyotrophic lateral sclerosis (ALS) until 2017. Instead, code G12.2 included both ALS and other motor neuron diseases (MND). Our objective was to determine US mortality rates for ALS exclusively by excluding other MND and progressive supranuclear palsy. Methods: All mortality data coded as G12.2 under the pre-2017 rubric were obtained for 2011–2014. Deaths without ALS listed in one of the un-coded cause-of-death fields were excluded. ALS death rates per 100,000 persons were age-adjusted to the 2000 US standard population using the direct method. Results: The proportion of excluded records coded G12.2 but not ALS was 0.21, resulting in 24,328 ALS deaths. The overall age-adjusted mortality rate was 1.70 (95% CI 1.68–1.72). The rate among males was 2.09 (95% CI 2.05–2.12) and females was 1.37 (95% CI 1.35–1.40). The overall rate among whites was 1.84, blacks 1.03, and other races 0.70. For both sexes and all races, the rate increased with age and peaked among 75–79 year-olds. Rates tended to be greater in states at higher latitudes. Conclusions: Previous reports of ALS mortality in the United States showed similar age, sex, and race distributions but with greater age-adjusted mortality rates due to the inclusion of other diseases in the case definition. When using ICD-10 data collected prior to 2017, additional review of multiple-cause of death data is required for the accurate estimation of ALS deaths.
Learning Kinematic Structure Correspondences Using Multi-Order Similarities

In this paper, we present a novel framework for finding the kinematic structure correspondences between two articulated objects in videos via hypergraph matching. In contrast to appearance and graph alignment based matching methods, which have been applied among two similar static images, the proposed method finds correspondences between two dynamic kinematic structures of heterogeneous objects in videos. Thus our method allows matching the structure of objects which have similar topologies or motions, or a combination of the two. Our main contributions can be summarised as follows: (i) casting the kinematic structure correspondence problem into a hypergraph matching problem by incorporating multi-order similarities with normalising weights, (ii) introducing a structural topology similarity measure by aggregating topology constrained subgraph isomorphisms, (iii) measuring kinematic correlations between pairwise nodes, and (iv) proposing a combinatorial local motion similarity measure using geodesic distance on the Riemannian manifold. We demonstrate the robustness and accuracy of our method through a number of experiments on synthetic and real data, outperforming various other state of the art methods. Our method is not limited to a specific application nor sensor, and can be used as building block in applications such as action recognition, human motion retargeting to robots, and articulated object manipulation amongst others.
Runtime Programmable and Memory Bandwidth Optimized FPGA-Based Coprocessor for Deep Convolutional Neural Network

The deep convolutional neural network (DCNN) is a class of machine learning algorithms based on feed-forward artificial neural network and is widely used for image processing applications. Implementation of DCNN in real-world problems needs high computational power and high memory bandwidth, in a power-constrained environment. A general purpose CPU cannot exploit different parallelisms offered by these algorithms and hence is slow and energy inefficient for practical use. We propose a field-programmable gate array (FPGA)-based runtime programmable coprocessor to accelerate feed-forward computation of DCNNs. The coprocessor can be programmed for a new network architecture at runtime without resynthesizing the FPGA hardware. Hence, it acts as a plug-and-use peripheral for the host computer. Caching is implemented for input features and filter weights using on-chip memory to reduce the external memory bandwidth requirement. Data are prefetched at several stages to avoid stalling of computational units and different optimization techniques are used to efficiently reuse the fetched data. Dataflow is dynamically adjusted in runtime for each DCNN layer to achieve consistent computational throughput across a wide range of input feature sizes and filter sizes. The coprocessor is prototyped using Xilinx Virtex-7 XC7VX485T FPGA-based VC707 board and operates at 150 MHz. Experimental results show that our implementation is $15{\times}$ energy efficient than highly optimized CPU implementation and achieves consistent computational throughput of more than 140 G operations/s for a wide range of input feature sizes and filter sizes. Off-chip memory transactions decrease by $111{\times}$ due to the use of the on-chip cache.
Representing Medical Images With Encoded Local Projections

This paper introduces the “encoded local projections” (ELP) as a new dense-sampling image descriptor for search and classification problems. The gradient changes of multiple projections in local windows of gray-level images are encoded to build a histogram that captures spatial projection patterns. Using projections is a conventional technique in both medical imaging and computer vision. Furthermore, powerful dense-sampling methods, such as local binary patterns and the histogram of oriented gradients, are widely used for image classification and recognition. Inspired by many achievements of such existing descriptors, we explore the design of a new class of histogram-based descriptors with particular applications in medical imaging. We experiment with three public datasets (IRMA, Kimia Path24, and CT Emphysema) to comparatively evaluate the performance of ELP histograms. In light of the tremendous success of deep architectures, we also compare the results with deep features generated by pretrained networks. The results are quite encouraging as the ELP descriptor can surpass both conventional and deep descriptors in performance in several experimental settings.
Distinct roles for IκB kinases alpha and beta in regulating pulmonary endothelial angiogenic function during late lung development

Pulmonary angiogenesis is essential for alveolarization, the final stage of lung development that markedly increases gas exchange surface area. We recently demonstrated that activation of the nuclear factor kappa‐B (NFκB) pathway promotes pulmonary angiogenesis during alveolarization. However, the mechanisms activating NFκB in the pulmonary endothelium, and its downstream targets are not known. In this study, we sought to delineate the specific roles for the NFκB activating kinases, IKKα and IKKβ, in promoting developmental pulmonary angiogenesis. Microarray analysis of primary pulmonary endothelial cells (PECs) after silencing IKKα or IKKβ demonstrated that the 2 kinases regulate unique panels of genes, with few shared targets. Although silencing IKKα induced mild impairments in angiogenic function, silencing IKKβ induced more severe angiogenic defects and decreased vascular cell adhesion molecule expression, an IKKβ regulated target essential for both PEC adhesion and migration. Taken together, these data show that IKKα and IKKβ regulate unique genes in PEC, resulting in differential effects on angiogenesis upon inhibition, and identify IKKβ as the predominant regulator of pulmonary angiogenesis during alveolarization. These data suggest that therapeutic strategies to specifically enhance IKKβ activity in the pulmonary endothelium may hold promise to enhance lung growth in diseases marked by altered alveolarization.
Tri-net for Semi-Supervised Deep Learning

Deep neural networks have witnessed great successes in various real applications, but it requires a large number of labeled data for training. In this paper, we propose tri-net, a deep neural network which is able to use massive unlabeled data to help learning with limited labeled data. We consider model initialization, diversity augmentation and pseudo-label editing simultaneously. In our work, we utilize output smearing to initialize modules, use fine-tuning on labeled data to augment diversity and eliminate unstable pseudo-labels to alleviate the influence of suspicious pseudo-labeled data. Experiments show that our method achieves the best performance in comparison with state-of-the-art semi-supervised deep learning methods. In particular, it achieves 8.30% error rate on CIFAR-10 by using only 4000 labeled examples.