Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic lung segmentation in low-dose CT image with contrastive attention module

Automatic lung segmentation with severe pathology plays a significant role in the clinical application, which can save physicians’ efforts to annotate lung anatomy. Since the lung has fuzzy boundary in low-dose computed tomography (CT) images, and the tracheas and other tissues generally have the similar gray value as the lung, it is a challenging task to accurately segment lung. How to extract key features and remove background features is a core problem for lung segmentation. This paper introduces a novel approach for automatic segmentation of lungs in low-dose CT images. First, we propose a contrastive attention module, which generates a pair of foreground and background attention maps to guide feature learning of lung and background separately. Second, a triplet loss is used on three feature vectors from different regions to pull the features from the full image and the lung region close whereas pushing the features from background away. Our method was validated on a clinical data set of 78 CT scans using the four-fold cross validation strategy. Experimental results showed that our method achieved more accurate segmentation results than that of state-of-the-art approaches.
Extreme-Learning-Machine-Based Noniterative and Iterative Nonlinearity Mitigation for LED Communication Systems

This article concerns receiver design for light-emitting diode (LED) communications, where the LED nonlinearity can severely degrade the performance of the system. We propose extreme learning machine (ELM)-based noniterative and iterative receivers to effectively handle the LED nonlinearity and memory effects. For the iterative receiver design, we also develop a data-aided receiver, where data are used as virtual training sequence in ELM training. It is shown that the ELM-based receivers significantly outperform conventional polynomial-based receivers. Iterative receivers can achieve huge performance gain compared to noniterative receivers, and the data-aided receiver can reduce training overhead considerably. This article can also be extended to radio frequency communications, e.g., to deal with the nonlinearity of power amplifiers.
The Comparison and Analysis of Classic Convolutional Neural Network in the Field of Computer Vision

Convolutional neural network algorithm has been proposed as early as 20 years ago, but due to limited numerical computing ability and insufficient learning samples, it stays in the research stage, and is not popular in the application-end. In recent years, with the emergence of large-scale tagged data sets, the support of computer hardware and the improvement of related algorithms, convolutional neural networks have begun to be widely used in the field of pattern recognition and image processing. By analysing the development of CNN (Convolutional Neural Network) and two major models: VGGNet and ResNet, this paper demonstrates the importance of depth, width and residual learning in convolutional neural networks, and summarizes the trends of CNN structure and the basic paradigm of neural networks.
State-of-the-Art Robotic Devices for Wrist Rehabilitation: Design and Control Aspects

Robot-assisted physical therapy of the upper limb is becoming popular among the rehabilitation community. The wrist is the second most complicated joint in the upper limb after shoulder in terms of degrees of freedom. Several robotic devices have been developed during the past three decades for wrist joint rehabilitation. Intensive physical therapy and repetitive self-practice, with objective measurement of performance, could be provided by using these wrist rehabilitation robots at a low cost. There has been an increasing trend in the development of wrist rehabilitation robots to provide safe and customized therapy according to the disability level of patients. The mechanical design and control paradigms are two active fields of research undergoing rapid developments in the field of robot-assisted wrist rehabilitation. The mechanical design of these robots could be divided into the categories of end-effector based robots and wearable robotic orthoses. The control for these wrist rehabilitation robots could also be divided into the conventional trajectory tracking control mode and the assist-as-needed control mode for providing customized robotic assistance. This article presents a review of the mechanical design and control aspects of wrist rehabilitation robots. Experimental evaluations of these robots with healthy and neurologically impaired are also discussed along with the future directions of research in the design and control domains of wrist rehabilitation robots.
Automated segmentation of cardiac chambers from cine cardiac MRI using an adversarial network architecture

Cine cardiac magnetic resonance imaging (CMRI), the current gold standard for cardiac function analysis, provides images with high spatio-temporal resolution. Computing clinical cardiac parameters like ventricular blood-pool volumes, ejection fraction and myocardial mass from these high resolution images is an important step in cardiac disease diagnosis, therapy planning and monitoring cardiac health. An accurate segmentation of left ventricle blood-pool, myocardium and right ventricle blood-pool is crucial for computing these clinical cardiac parameters. U-Net inspired models are the current state-of-the-art for medical image segmentation. SegAN, a novel adversarial network architecture with multi-scale loss function, has shown superior segmentation performance over U-Net models with single-scale loss function. In this paper, we compare the performance of stand-alone U-Net models and U-Net models in SegAN framework for segmentation of left ventricle blood-pool, myocardium and right ventricle blood-pool from the 2017 ACDC segmentation challenge dataset. The mean Dice scores achieved by training U-Net models was on the order of 89.03%, 89.32% and 88.71% for left ventricle blood-pool, myocardium and right ventricle blood-pool, respectively. The mean Dice scores achieved by training the U-Net models in SegAN framework are 91.31%, 88.68% and 90.93% for left ventricle blood-pool, myocardium and right ventricle blood-pool, respectively.
Impulsive control and stability analysis of biped robot based on virtual constraint and adaptive optimization

This article is concerned with the problem of asymptotically stable periodic walking for a five‐link underactuated biped robot. Because of instantaneous change of two legs and complex dynamics during the walking process, the robot can be regarded as a nonlinear impulsive system. In order to make impulsive control of the robotic system, it is modeled as a rigid kinematic chain with Lagrange equations, which is strong coupling and hybrid. A novel state feedback controller is proposed to generate a closed‐loop periodic walking gait and track the desired trajectory of each joint. This controller is designed based on virtual constraint and hybrid zero dynamics. When the holonomic constraint of each actuator is zeroed by the controller, it can be regarded as an output. By using this method, the computation of the standard 5 degree of freedom (DOF) robot model can be reduced to 1 DOF. In addition, an adaptive optimization scheme is further investigated, which solves the problem of model parameter uncertainty. Finally, simulations are presented to illustrate the effectiveness of the proposed method.
On the Robustness of Cascade Diffusion under Node Attacks

How can we assess a network’s ability to maintain its functionality under attacks? Network robustness has been studied extensively in the case of deterministic networks. However, applications such as online information diffusion and the behavior of networked public raise a question of robustness in probabilistic networks. We propose three novel robustness measures for networks hosting a diffusion under the Independent Cascade (IC) model, susceptible to node attacks. The outcome of such a process depends on the selection of its initiators, or seeds, by the seeder, as well as on two factors outside the seeder’s discretion: the attack strategy and the probabilistic diffusion outcome. We consider three levels of seeder awareness regarding these two uncontrolled factors, and evaluate the network’s viability aggregated over all possible extents of node attacks. We introduce novel algorithms from building blocks found in previous works to evaluate the proposed measures. A thorough experimental study with synthetic and real, scale-free and homogeneous networks establishes that these algorithms are effective and efficient, while the proposed measures highlight differences among networks in terms of robustness and the surprise they furnish when attacked. Last, we devise a new measure of diffusion entropy that can inform the design of probabilistically robust networks.
The Last Mile: DSCSA Solution Through Blockchain Technology: Drug Tracking, Tracing, and Verification at the Last Mile of the Pharmaceutical Supply Chain with BRUINchain

Purpose As part of the FDA’s DSCSA Pilot Project Program, UCLA and its solution partner, LedgerDomain (collectively referred to as the team hereafter), focused on building a complete, working blockchain-based system, BRUINchain, which would meet all the key objectives of the Drug Supply Chain Security Act (DSCSA) for a dispenser operating solely on commercial off-the-shelf (COTS) technology. Methods The BRUINchain system requirements include scanning the drug package for a correctly formatted 2D barcode, flagging expired products, verifying the product with the manufacturer, and quarantining suspect and illegitimate products at the last mile: pharmacist to patient, the most complex area of the drug supply chain. The authors demonstrate a successful implementation where product-tracing notifications are sent automatically to key stakeholders, resulting in enhanced timeliness and reduction in paperwork burden. At the core of this effort was a blockchain-based solution to track and trace changes in custody of drug. As an immutable, time-stamped, near-real-time (50-millisecond latency), auditable record of transactions, BRUINchain makes it possible for supply chain communities to arrive at a single version of the truth. BRUINchain was tested using real data on real caregivers administering life-saving medications to real patients at one of the busiest pharmacies in the United States. Results In addition to communicating with the manufacturer directly for verification, BRUINchain also initiated suspect product notifications. During the study, a 100% success rate was observed for scanning, expiration detection, and counterfeit detection; and paperwork reduction from approximately 1 hour to less than a minute. Conclusions By automatically interrogating the manufacturer’s relational database with our blockchain-based system, our results indicate a projected DSCSA compliance cost of 17 cents per unit, and potentially much more depending on regulatory interpretation and speed of verification. We project that this cost could be reduced with manufacturers’ adoption of a highly performant, fully automated end-to-end system based on digital ledger technology (DLT). During an examination of the interoperability of such a system, we elaborate on its capacity to enable verification in real time without keeping humans in the loop, the key feature driving lower compliance cost. With 4.2 billion prescriptions being dispensed each year in the United States, DLT would not only reduce the projected per-unit cost to 13 cents per unit (saving $183 million in annual labor costs), but also serve as a major bulwark against bad or fraudulent transactions, reduce the need for safety stock, and enhance the detection and removal of potentially dangerous drugs from the drug supply chain to protect US consumers.
Cloud-based control of industrial cyber-physical systems

This paper presents an implementation of a control algorithm to a cloud system. The motivation is that cloud implementations of low-level systems in the production industry are gradually becoming more common. Microsoft Azure platform is utilized for the cloud-based control and the case is tested using a customized laboratory model, which can be presented as an agent in a typical production system. The model offers the regulation of a ball on an inclined surface and uses two asynchronous motors connected to frequency converters to control the position of the ball. These frequency converters are controlled by a Programmable Logic Controller (PLC). Windows Communication Foundation (WCF) services and Azure IoT Hub were selected to be used with the cloud-based control system. Experimental results have shown our solution can control the system with sampling period equal or higher than 100ms. The latency of WCF service is at around 100ms and latency of Azure IoT Hub is at around 1000ms, so the prediction algorithms could be implemented in the cloud for the latter. This research also shows the feasibility of migrating machine learning algorithms that demand high computing power to the cloud to reduce the computing burden on the local control units.
Design and development of pharmaceutical dosage forms for gene and siRNA delivery

These investigations are focused on the design and formulation of novel nonviral lipopolyamine vectors capable of efficiently and safely delivering DNA to the nucleus, and siRNA to the cytoplasm, in two tissue cultured (primary and cancer) cell lines. The thesis starts with a focussed literature review on the non-viral gene therapy (NVGT) vectors currently used in the formulation of DNA and siRNA. The first experimental part tests the ability of our novel lipospermines in NVGT, this includes structure-activity relationship (SAR) studies changing the: position, length, saturation or symmetry of the fatty chains of N4,N9-diacyl, N1,N12-diacyl and N4,N9-dialkyl spermines. The ability of these lipospermines in DNA condensation is investigated using ethidium bromide fluorescence-quenching, and gel electrophoresis (including gel shift and DNase protection) assays followed by nanoparticle characterization techniques (particle size and zeta potential). Transfection efficiency of pEGFP (using FACS) and cytotoxicity (using MTT) were studied in both cancer and primary cell lines and compared with Lipogen™ (N4,N9-dioleoyl spermine). Some of these novel lipospermines are shown to be as good as, but not better than N4,N9-dioleoyl spermine as efficient DNA transfecting agents. N4,N9-Dioleoyl spermine is the best transfecting agent from the all tested novel lipospermines displaying the lowest N/P ratio, highest transfection efficiency and the lowest cytotoxicity on both tested cell lines. We extended this SAR study to examine the same lipospermines in siRNA delivery. The ability of these compounds to bind siRNA was studied using the RiboGreen intercalation assay followed by similar nanoparticle characterization techniques. Transfection efficiency for delivery of Label IT® RNAi Delivery Control (using FACS) and cytotoxicity (MTT) were also studied in both cancer and primary cell lines, and compared with a market leader siRNA transfecting agent Trans-IT™. Twelve of these non-viral vectors, led by N4,N9-dieicosenoyl spermine and N4,N9-dierucoyl spermine, showed both transfection efficiency and cell viability over 75%.
Cost-Effective Cluster-Based Energy Efficient Routing for Green Wireless Sensor Network

The green Information and Communications Technologies (ICTs) have brought a revolution in uplifting the technology efficiently to facilitate human sector in the best possible way. Green Wireless Sensor Network (WSN) tactically focuses on improving the survival period of deployed nodes (as they have limited battery) in any target area. To address this concern, the main objective is to improve the routing in WSN. The cluster- based routing helps in acquiring the same with the appropriate Cluster Head (CH) selection. The use of energy heterogeneous nodes that normally comprise of high energy nodes, puts a lot of financial burden on the users as they incur a huge cost, thus becoming a bottleneck for the growth of green WSN. Therefore , another objective of the study is to reduce this cost involved in the network. A cost-effective routing protocol is proposed that introduces energy-efficient CH selection by incorporating parameters namely, node density, residual energy, the total energy of network and distance factor. Thus, the proposed protocol is termed as Cost-Effective Cluster-based Routing Protocol (CECRP) as it performs remarkably better with only two energy level nodes as compared to state-of-the-art protocols with three levels nodes. It can be encapsulated from the simulation results that CECRP outperforms state-of-theprotocols on different performance metrics. Furthermore, it is comprehended from the simulation results that CECRP proves to be 33.33% more cost-effective as compared to the competent protocols, hence CECRP favors the green WSN.
Estimating uncertainties in testing

The generally accepted approach to evaluating and expressing uncertainties in measurements undertaken by testing and calibration laboratories is given in The Guide to the expression of Uncertainty in Measurement, first published in 1993 by ISO, Geneva. That document, frequently referred to as The Guide or the GUM, presents a comprehensive study of these principles which, if followed, will ensure that measurement uncertainty is calculated and stated in a consistent manner in all situations. It is, however, a complex document and it has been found necessary to supplement it with simplified guidance for specific fields of measurement. This publication is one such guidance document that follows on from Stephanie Bell’s A Beginner’s Guide to Uncertainty of Measurement, developing treatment of the subject to a level intended for testing laboratories. It presents principles and guidance for the estimation of measurement uncertainty which are applicable to most fields of testing but is not intended for calibration laboratories, which normally need to have a greater depth of knowledge of these principles than is presented here. The principles and guidance given here are consistent with The Guide and with the requirements of ISO/IEC 17025, General requirements for the competence of testing and calibration laboratories where they relate to estimating and reporting uncertainty of measurements.
Working Conditions-Aware Fault Injection Technique

With new integration rates, the circuits sensitivity to environmental and working conditions has increased dramatically. Thus, presenting reliable, less consuming energy and error resilient architectures is being one of the major problems to deal with. Besides, evaluating robustness and effectiveness of the proposed architectures is also an urgent need. In this paper, we present an extension of SimpleScalar simulation tool having the ability to inject faults in a given cache architecture. We tried to focus on transient errors which are due to cosmic rays (soft errors) or to voltage scaling and high temperatures. During the fault injection process, there is no software traps and the target application isn't modified. Transient errors are modeled by a bit flip. The bit flip may occur in any part of the cache arrays and also in the cache blocks' address. In this paper we present an overview on the existing fault injection techniques. Then, we propose a fuzzy system which models the relationship between working conditions (the supply voltage and temperature) and the error probability. The impact of the injected faults on the cache performance as well as the experimental results are also shown. The results show that the impact of faults depends also on the application itself.
PerfJavaDoc: extending API documentation with performance information

: Javadoc is a documentation tool used for generating API documentation from Java source code. For some methods the generated documentation can contain description of the algorithm and its asymptotic complexity. However, such information is futile when the exact execution time of the method is needed with respect to certain critical characteristics. In this work we decided to enhance the Javadoc tool with a performance extension which permits to measure the performance of the method against the predeﬁned characteristics. These characteristics are speciﬁed in a workload generator, which is a method used to prepare the actual arguments and instance for the measured method. The separation of the measured code and the preparatory code allows developers to implement both parts clearly and easily, and also share the generator amongst multiple methods. The measuring itself is performed by a measuring server which may run on a dedicated reference machine, and was programmed to provide accurate results with respect to the Java platform.
Measuring information credibility in social media using combination of user profile and message content dimensions

Information credibility in social media is becoming the most important part of information sharing in the society. The literatures have shown that there is no labeling information credibility based on user competencies and their posted topics. This study increases the information credibility by adding new 17 features for Twitter and 49 features for Facebook. In the first step, we perform a labeling process based on user competencies and their posted topic to classify the users into two groups, credible and not credible users, regarding their posted topics. These approaches are evaluated over ten thousand samples of real-field data obtained from Twitter and Facebook networks using classification of Naive Bayes (NB), Support Vector Machine (SVM), Logistic Regression (Logit) and J48 algorithm (J48). With the proposed new features, the credibility of information provided in social media is increasing significantly indicated by better accuracy compared to the existing technique for all classifiers.
Determining prognosis of systemic candidiasis infection in rats with the polymerase chain reaction method

Candidiasis infections caused by the fungi Candida spp. are currently the most common systemic fungal infection in hospitalised patients. Diagnostic procedures involving culture or serological tests are either slow or lacking sensitivity and specificity. Thus polymerase chain reaction (PCR) assays which detect fungal DNA have been developed to provide a rapid, accurate diagnosis. In this study we evaluated a seminested PCR assay for the detection of systemic Candida albicans infection in laboratory animals. Adult Sprague-Dawley rats were infected with C. albicans by intravenous injection of yeast cells. Blood was collected from the animals every 3 to 4 days for extraction of DNA. Each week, 3 animals were sacrificed and their organs were cultured to confirm systemic C. albicans infection. The study period was 4 weeks (28 days). A seminested PCR was performed on DNA samples using universal fungal primers (ITS1 and ITS3) and a C. albicans specific primer (CALB1). The PCR product was detected by agarose gel electrophoresis. The PCR assay was able to detect C. albicans DNA in the blood samples of infected animals consistently from day 2 until day 25 post-infection. Organ culture of kidneys, liver and spleen revealed that the rats were systemically infected until day 21 post-infection and had recovered from infection (negative culture results) by day 28 post-infection. This study shows that the seminested PCR was an effective method to detect systemic C. albicans infection throughout the infection period. Therefore this assay is not only a diagnostic tool but has prognostic value as well for the monitoring of infection status.