Miracle-dz/UniFAR-FTUs huggingface dataset:
Sparse Aperture Inverse Synthetic Aperture Radar Imaging Based on Gridless Compressive Sensing

Under the condition of sparse aperture, the traditional inverse synthetic aperture radar (ISAR) imaging method suffers from the sidelobe. Compressive sensing (CS) is a method to solve this problem. The traditional CS algorithm assumes that the strong scattering point of the target is located on the grid. Yet in practice, the distribution of the scattering point is continuous. If it deviates from the grid, the performance of CS will deteriorate. Therefore, a sparse aperture ISAR imaging method based on gridless CS is proposed in this paper. The method does not need to discretize the scene and use atomic norm minimization as sparse constraint. By solving semidefinite program, the position of scattering centers can be estimated directly in continuous space, which can solve the problem of basis mismatch effectively. The simulation results validate the feasibility of this algorithm.
Cross-layer QoS enabled SDN-like publish/subscribe communication infrastructure for IoT

Publish/subscribe paradigm is often adopted to create the communication infrastructure of the Internet of Things (IoT) for many clients to access enormous real-time sensor data. However, most current publish/subscribe middlewares are based on traditional ossified IP networks, which are difficult to enable Quality of Service (QoS). How to design the next generation publish/subscribe middleware has become an urgent problem. The emerging Software Defined Networking (SDN) provides new opportunities to improve the QoS of publish/subscribe facilities for delivering events in IoT owing to its customized programmability and centralized control. We can encode event topics, priorities and security policies into flow entries of SDN-enabled switches to satisfy personalized QoS needs. In this paper, we propose a cross-layer QoS enabled SDN-like publish/subscribe communication infrastructure, aiming at building an IoT platform to seamlessly connect IoT services with SDN networks and improving the QoS of delivering events. We first present an SDN-like topic-oriented publish/subscribe middleware architecture with a cross-layer QoS control framework. Then we discuss prototype implementation, including topic management, topology maintenance, event routing and policy management. In the end, we use differentiated services and cross-layer access control as cross-layer QoS scenarios to verify the prototype. Experimental results show that our middleware is effective.
On Delay Elements in Boundary Scan Cells for Delay Testing of 3D IC Interconnection

For testing delay faults in 3D IC interconnection, we have proposed a DFT (Design-for- Testability) method for TSVs using a modified boundary scan circuit with embedded Time-to-Digital Converter (TDCBS). A TDCBS cell has a delay element to form a delay line. In this paper, for improving delay resolution, delay gates that have small propagation delay time are investigated and implemented as a delay line. In order to prevent pulse shrinking of transition signal through a delay line, the proposed cell is designed to reduce the difference in transition delay between the delay for rising transition and for falling transition. The measurement results for an experimental chip show the effectiveness of our new design.
Preliminary Study on Production of Coarse and Fine Crackles in Respiration Using a Model of Communication Theory

This study described an original one-cycle sine wave passing the chest wall to be a coarse crackle or a fine crackle for the examination of the reason of the crackle formation. The results explain the differences between the definitions of crackle which were proposed by Sovijarvi et al. and Reichert et al. Lung sounds with crackles were synthesized by computer simulation. The results show that higher frequency (more than 100 Hz) carriers of bursts penetrate the equipollent bandpass filter of the chest wall directly while the coarse crackles occurred. Consequently, this communication system model supports the formation of normal lung sounds, wheezes, and crackles by the modulation theory and filter from the macro perspective.
Learning Believable Player Movement Patterns from Human Data in a Soccer Game

Player movement patterns are one of the behavioral traits immediately visible to an observer. Thus, a soccer AI system striving for believable (human-like) behavior must ensure the believability of player movements. We show how tracking data of real human players in soccer can be used to create a case-based reasoning AI system, able to simulate realistic player movements in a computer soccer game. Our results are confirmed with a direct comparison of actions made by AI-controlled players and professional athletes.
Electric Load Forecasting Based on CEEMDAN and LSSVM Optimized by Cuckoo Search Algorithm

In the paper, short-term electric load forecasting (STLF) by optimization method is studied. First, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to decompose the short-term electric load data into some intrinsic mode functions (IMF): high frequency sequence (HF), the low frequency sequence (LF) and the trend term (T). Then, the least squares support vector machine model (LSSVM) optimized by the cuckoo search algorithm (CS) is utilized to predict electric load. Moreover, the final prediction value is obtained by the add integration method. Last but not least, the five benchmark models are added as the comparison model. The results show that the hybrid integrated model has higher horizontal precision and direction accuracy than other benchmark models. The results demonstrate that the accuracy of the model with decomposition method is higher than that without decomposition method.
Graph Wavelet-Based Multilevel Graph Coarsening and Its Application in Graph-CNN for Alzheimer’s Disease Detection

Along with the classical applications like graph partitioning, graph visualization, etc., graph coarsening has been recently applied in graph convolutional neural network (GCNN) architectures to perform the pooling operation in the graph domain. In this paper, we propose a novel two-stage graph coarsening method rooted on the graph signal processing with its application in the GCNN architecture. In the first stage of coarsening, the graph wavelet transform (GWT) based features are used to obtain a coarsened graph which preserves the topological characteristics of the original graph. In the second stage, the coarsening problem is formulated as an optimization problem where the reduced Laplacian operator at each level is obtained as a restriction of the original Laplacian operator to a specified subspace that also maximizes the topological similarity. The performance of the proposed coarsening algorithm is quantified in the general coarsening context using different graph coarsening quality measures. Its effectiveness as a pooling operator in GCNN is validated by applying it for the graph coarsening operation in the GCNN architecture. This modified GCNN architecture is then used as a graph signal classifier for the early detection of Alzheimer’s disease. The results show that the proposed coarsening method outperforms state-of-the-art methods, both in the general coarsening context and as a pooling operator in the GCNN architecture.
Run-Time Reconfigurable MPSoC-Based On-Board Processor for Vision-Based Space Navigation

This paper describes a reconfigurable architecture for an on-board processor to be used in space exploration critical systems. It relies on, a dynamically reconfigurable multi-accelerator hardware architecture that provides transparent reconfiguration and scalable performance, dependability, and power consumption, at run-time. The architecture is integrated under an RTEMS operating system, which manages reconfiguration and fault mitigation in a fully-compatible way with space requirements. In this work, the proposed processor is used to implement a vision-based navigation system, providing fully autonomous adaptation to different phases of a space mission timeline or events that the spacecraft may encounter during its lifespan. Results show that reconfigurability enables the practical usage of Commercial Off-The-Shelf (COTS) Multiprocessor Systems-on-Chips (MPSoCs) in real scenarios.
Supervised Nonnegative Tucker Decomposition for Computational Phenotyping

With the availability of Electronic Health Records (EHR) data, lots of predictive tasks in medical practice seem solvable by building predictive models. However, EHR data always contains various medical concepts (e.g., diagnosis, medicines, lab tests) with high dimensions and mass correlations among them. To avoid the curse of dimensionality, the representation learning or dimensional reduction process is needed before prediction. Traditional methods either could not mine the correlations of different dimensions in datasets or lack of interpretability. Therefore we proposed a supervised tucker tensor decomposition (SNTD) which is guided by the prediction tasks for representation learning. Detailly speaking, SNTD constrains factor matrices by adding the label information of task and generating regularization term towards the non-negative tucker decomposition (NTD). Compared to the CP decomposition based methods, SNTD has better performance for that more flexible core tensor can contribute to the better learning of correlations among dimensions largely. We also demonstrate the accuracy and interpretability of our approach on an real-world EHR data for hospitalization task. Our result shows SNTD can not only obtain better performance of prediction than baseline methods, but also achieve the interpretability of representations.
Using Floating-Point Intervals for Non-Modular Computations in Residue Number System

The residue number system (RNS) provides parallel, carry-free, and high-speed arithmetic and is therefore a good tool for high-performance computing. However, operations such as magnitude comparison, sign computation, overflow detection, scaling, and division are difficult to perform in RNS, since it is problematic to determine the magnitude of an RNS number. In order to resolve this problem, we propose to compute the interval evaluation of the fractional representation of an RNS number in floating-point arithmetic of limited precision. No matter what the size $n$ of the moduli set and dynamic range, only small arithmetic operations are required, and most of the computations are performed in parallel with $n$ threads, which allows for efficient implementation of our method on many general-purpose computing platforms. Using this method, we propose new algorithms for magnitude comparison and general division in RNS and implement them for GPUs using the CUDA platform. We evaluate the performance of our algorithms on an NVIDIA GTX 1080 GPU using sets of 4 to 256 RNS moduli that provide dynamic ranges from 64 to 4096 bits. Experimental results show that the proposed new algorithms are efficient for large moduli sets and clearly outperform the existing RNS magnitude comparison and division algorithms in terms of execution time.
Comprehensive Review of Pulse Repetitions Interval (PRI) Classification Schemes

This research is focused on comprehensive analysis of the Pulse Repetition Interval (PRI) Classification schemes; applied to the intercepted pulses by an Electronic Intelligence (ELINT) receiver / Radar Warning Receiver. The intercepted pulse train is interleaved in time; these pulses are first pre-sorted on the basis of some fundamental parameters of the captured pulses termed as Pulse Descriptor Words (PDWs); then de-interleaved on the basis of Statistical Time of Arrival (TOA) analysis. The first step after de-interleaving and association of PDWs to candidate emitters is the PRI classification, which then serves as one of the most important parameters for emitter identification. There are a total of six different PRI types (e.g. Constant, Staggered, Jittered, Sliding, Dwell & Switch and Sinusoidal). The PRI classification schemes found in literature are neither comprehensive (i.e. able to differentiate between all six types) nor computationally efficient as most of them employ machine learning techniques. A PRI classification technique was proposed by the authors, which employed feature extraction out of these pulse trains followed by simple threshold based hierarchical classification on top of the feature set to classify all six types of PRIs. A comparison of this scheme with all other schemes not only proves this scheme superior to other schemes in most of the test cases but also proves it to be computationally efficient due to its simplistic approach. A key parameter used for comparison is PRI Classification rate; determined on the basis of synthetic data generated in MATLAB. Robustness of these algorithms were also tested against impurities like Missing pulses, Spurious pulses and Incidental jitter which are inherent part of the ELINT receiver / RWR.
HeteroOS: OS Design for Heterogeneous Memory Management in Datacenters

Heterogeneous memory management combined with server virtualization in datacenters is expected to increase the software and OS management complexity. State-of-the-art solutions rely exclusively on the hypervisor (VMM) for expensive page hotness tracking and migrations, limiting the benefits from heterogeneity. To address this, we design HeteroOS, a novel application-transparent OS-level solution for managing memory heterogeneity in virtualized systems. The HeteroOS design first makes the guest-OSes heterogeneityaware, and then extracts rich OS-level information about applications' memory usage to place data in the 'right' memory, avoiding page migrations. When such proactive placements are not possible, HeteroOS combines the power of the guest-OSes' information about applications with the VMM's hardware control to track for hotness and migrate only performance-critical pages. Finally, HeteroOS also designs an efficient heterogeneous memory sharing across multiple guest-VMs. Evaluation of HeteroOS with memory, storage, and network-intensive datacenter applications show up to 2x performance improvement compared to the state-of-the-art VMMexclusive approach.
Therapeutic Benefit of Intravenous Administration of Bone Marrow Stromal Cells After Cerebral Ischemia in Rats

Background and Purpose—We tested the hypothesis that intravenous infusion of bone marrow derived–marrow stromal cells (MSCs) enter the brain and reduce neurological functional deficits after stroke in rats. Methods—Rats (n=32) were subjected to 2 hours of middle cerebral artery occlusion (MCAO). Test groups consisted of MCAO alone (group 1, n=6); intravenous infusion of 1×106 MSCs at 24 hours after MCAO (group 2, n=6); or infusion of 3×106 MSCs (group 3, n=7). Rats in groups 1 to 3 were euthanized at 14 days after MCAO. Group 4 consisted of MCAO alone (n=6) and group 5, intravenous infusion of 3×106 MSCs at 7 days after MCAO (n=7). Rats in groups 4 and 5 were euthanized at 35 days after MCAO. For cellular identification, MSCs were prelabeled with bromodeoxyuridine. Behavioral tests (rotarod, adhesive-removal, and modified Neurological Severity Score [NSS]) were performed before and at 1, 7, 14, 21, 28, and 35 days after MCAO. Immunohistochemistry was used to identify MSCs or cells derived from MSCs in bra.
Plasma Adenosine Levels Increase in Patients With Chronic Heart Failure

Background Adenosine is believed to be cardioprotective; however, it has not been elucidated whether the plasma adenosine level is increased in chronic heart failure. Methods and Results Seventy-one patients attending a specialized heart failure clinic during a 6-month period were grouped according to the cause of chronic heart failure and the New York Heart Association function class. There were 40 patients with chronic heart failure due to ischemic heart diseases and 31 patients with valvular heart diseases and dilated cardiomyopathy. Control subjects consisted of 64 healthy laboratory staff members and patients without chronic heart failure. We found that the plasma adenosine levels were increased in patients with ischemic and nonischemic heart failure (218±23 and 211±21 nmol/L, respectively, versus 62±3 nmol/L for healthy subjects) and that the extent of increases in adenosine levels correlated well with the severity of chronic heart failure. Conclusions We conclude that adenosine levels in the system.
Towards intervention-centric causal reasoning in learning agents

Interventions are central to causal learning and reasoning. Yet ultimately an intervention is an abstraction: an agent embedded in a physical environment (perhaps modeled as a Markov decision process) does not typically come equipped with the notion of an intervention -- its action space is typically ego-centric, without actions of the form `intervene on X'. Such a correspondence between ego-centric actions and interventions would be challenging to hard-code. It would instead be better if an agent learnt which sequence of actions allow it to make targeted manipulations of the environment, and learnt corresponding representations that permitted learning from observation. Here we show how a meta-learning approach can be used to perform causal learning in this challenging setting, where the action-space is not a set of interventions and the observation space is a high-dimensional space with a latent causal structure. A meta-reinforcement learning algorithm is used to learn relationships that transfer on observational causal learning tasks. This work shows how advances in deep reinforcement learning and meta-learning can provide intervention-centric causal learning in high-dimensional environments with a latent causal structure.
Short-Circuit Calculations for a Transmission Line in the Algerian Power Network Compensated by Thyristor Controlled Voltage Regulator

In this paper, the impact of using the Thyristor Controlled Voltage Regulator (TCVR) on short-circuit calculations is studied for a high voltage transmission line in case of a phase to earth fault in the presence of a fault resistance (RF). A 400 kV single transmission line, in the Algerian power network, is used for this study where the line is compensated by TCVR FACTS device to control the voltage and active power of the line. The impact of the fault resistance is studied to reflect its effect on short-circuit calculations including symmetrical current components, transmission line currents, voltage symmetrical components, and transmission line voltages, without and with TCVR. The obtained simulation results match the presented theoretical analysis.
Metadata of the chapter that will be visualized in SpringerLink

This paper puts forward a vision of a universal ontology (UO) aiming at solving, or at least greatly alleviating, the semantic integration problem in the field of conceptual modeling and the understandability problem in the field of the semantic web. So far it has been assumed that the UO is not feasible in practice, but we think that it is time to revisit that assumption in the light of the current state-of-the-art. This paper aims to be a step in this direction. We try to make an initial proposal of a feasible UO. We present the scope of the UO, the kinds of its concepts, and the elements that could comprise the specification of each concept. We propose a modular structure for the UO consisting of four levels. We argue that the UO needs a complete set of concept composition operators, and we sketch three of them. We also tackle a few issues related to the feasibility of the UO, which we think that they could be surmountable. Finally, we discuss the desirability of the UO, and we explain why we conjecture that there are already organizations that have the knowledge and resources needed to develop it, and that might have an interest in its development in the near future.
Effects of buddhist-based, nurse-delivered program on psychological symptom cluster and spiritual well-being of Thai women with breast cancer undergoing chemotheraphy : A randomized controlled trial

This single-blinded randomized controlled trial with a repeated measures design was aimed at determining the effects of a Buddhist-based, nurse-delivered program on the psychological symptom cluster and spiritual well-being of Thai women with breast cancer undergoing chemotherapy. The sample consisted of 93 women with stage I-III breast cancer who were undergoing chemotherapy at the Chemotherapy Infusion Center of a university hospital in southern Thailand. The subjects were randomly assigned to either the experimental or control group. The experimental group (n = 45) received usual care plus the Buddhist-based, nurse-delivered program involving the following three main activities: raising self-awareness, integrated Buddhist principle of Four Noble Truths, and self-reflection regarding psychological symptom cluster and the progress of Buddhist practices. The control group (n = 48) received usual care only. The instrument consisted of 1) the Psychological Symptom Subscale (MSAS-PSYCH) and 2) Thai Spiritual Well-being Assessment Tool Patients
Iranian brown propolis possesses neuroprotective effect against ischemic neuronal damage in mice

Introduction: Stroke is one of the leading causes of death and disability worldwide. Propolis, a polyphenol-rich resinous product processed by honeybees from a variety of plant sources, has a set of biological activities. We investigated the neuroprotective effect of Iranian brown propolis (IBP) in a mouse model of permanent middle cerebral artery occlusion (MCAO). Methods: Experimentally, water extracts of propolis (WEPs) were obtained from Kerman (KeWEP) and Khorasan Razavi (KhWEP) provinces, Iran. The chemical characterization and total polyphenol content of WEPs were determined using the Folin–Ciocalteu assay and gas chromatography-mass spectrometry (GC-MS). Animals were divided into eight experimental groups including: sham, control, and three groups each of which KeWEP- and KhWEP-treated mice. The drugs were administered at doses of 30, 100 and 200 mg/kg, intraperitoneally (IP), during four different time points. Infarct volume and brain edema were measured at 48 h. Behavioral tests were evaluated at 4, 24 and 48-hour post stroke. Results: The total polyphenol content was 1100 and 1400 mg/L in KhWEP and KeWEP respectively. Compared to the control group, the doses of 100 and 200 mg/kg in both samples decreased infarct volume. Brain edema was also reduced in all treatment groups. The dose of 200 mg/kg in both samples and 100 mg/kg in the KeWEP-treated group significantly increased grasping ability. Sensory-motor function was improved in all groups, too. Conclusion: These results suggest that IBP may reduce ischemic brain injury by its neuroprotective effect on focal cerebral ischemia.