Miracle-dz/UniFAR-FTUs huggingface dataset:
Phenotypic Characterization of Circulating CD4+ T Cells in ANCA-Associated Vasculitis

T cell-mediated immune responses are thought to play an important role in the pathogenesis of anti-neutrophil cytoplasmic antibody- (ANCA-) associated vasculitides (AAV). CD4+ T cells can be divided into subsets depending on their expression of chemokine receptors. In this study, different CD4+ T cell populations in patients with AAV were analysed and compared to healthy blood donors as well as therapy controls. 18 patients with active AAV, 46 in remission, 21 healthy controls (HBD), and 15 therapy controls (TC) were enrolled. CD4+ T cells were divided into Th1, Th2, and Th17 cells and further subdivided into naïve, central memory, effector memory, and effector cells. Regulatory T cells were also analysed. Concentrations of cytokines and chemokines produced by the respective CD4+ T cell subset in plasma from 33 of the patients were measured by ELISA and compared to HBD. Clinical data were collected on all patients. CCL20 concentrations and percentages of Th17 cells (p = 0.019) were elevated in AAV patients compared to HBD. AAV patients had lower percentages of naïve CD4+ T cells (p = 0.0016) and a corresponding increase in proportion of effector memory CD4+ T cells when comparing to HBD (p = 0.027). Therapy controls showed similar results as AAV patients. In this study, we found that CD4+ T cell phenotype distribution is altered in AAV patients, in line with previously published work. However, no differences were found between AAV patients and TC, stressing the importance of treatment impact on this kind of studies.
Unsupervised Domain Adaptation for Neural Machine Translation

Impressive neural machine translation (NMT) results are achieved in domains with large-scale, high quality bilingual training corpora. However, transferring to a target domain with significant domain shifts but no bilingual training corpora remains largely unexplored. To address the aforementioned setting of unsupervised domain adaptation, we propose a novel adversarial training procedure for NMT to leverage the widespread monolingual data in target domain. Two discriminative networks, namely the domain discriminator and pair discriminator, are introduced to guide the translation model. The domain discriminator evaluates whether the sentences generated by the translation model are indistinguishable from the ones in target domain. The pair discriminator assesses whether the generated sentences are paired with the source-side sentences. The translation model acts as an adversary to the two discriminators, which aims to generate sentences uneasily discriminated by the discriminators. We tested our approach on Chinese-English and English-German translation tasks. Experimental results show that our approaches achieve great success in unsupervised domain adaptation for NMT.
Probabilistic Graph Embedding for Unsupervised Domain Adaptation

Unsupervised domain adaptation aims to predict unlabeled target domain data by taking advantaging of labeled source domain data. This problem is hard to solve mainly because of the limitation of target domain label and the difference between both domains. To overcome these two difficulties, many researchers have proposed to assign pseudo labels for the unlabeled target domain data and then project both domain data into a common subspace. The use of pseudo labels, however, lack of theoretical basis and thus may not gain ideal classification results eventually. Therefore, in this work we propose a novel method called Probabilistic Graph Embedding(PGE). PGE first derives probabilities that the target domain instances belong to each category, which is believed to be able to explore target domain better. We then obtain a projection matrix by constructing a within-class probabilistic graph. This projection matrix can embed both domains into a shared subspace where domain shift is largely diminished. Experiments on object recognition cross-domain datasets show that PGE is more effective and robust than the state-of-art unsupervised domain adaptation methods.
Optimization of Cognitive Radio by Parameter Adaptation

The rising cognitive radio has the capacity to regulate its radio parameters primarily based on pre-characterized targets by detecting the dynamic remote condition. There are a few parameters which assume a critical part in the productivity and execution of a framework such as Bit Error Rate (BER) and spectral efficiency. The point of this investigation is to minimize BER by utilizing Evolutionary Algorithms, for example, Firefly Algorithm (FA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) Algorithm and subsequently demonstrate the evaluation which is totally based on the convergence pace keeping in mind the end goal to locate the best reasonable calculation to limit the BER for the secondary users with decreased cost experienced by the primary users. The same procedure is repeated for achieving higher spectral efficiency by alternating required parameters of the Cognitive Radio.
Processing Retinal Images to Discover Diseases

The retina of a human eye consists of billion of photosensitive cells (rods and cones) and alternative nerve cells that acquire and arrange visual information. The retina of a human eye is a thin tissue layer on the inside back wall of your eye. Three of the most common retinal diseases are Diabetic Retinopathy, Glaucoma, and Cataract. The world is presently experiencing an epidemic of Diabetic Retinopathy (DR). Current predictions draw an estimation of doubling of the number affected from the current 170 million to an estimated 367 million by 2030. We propose a system wherein we extract blood vessels of the retina to detect eye diseases. Manually extracting the blood vessels of the human retina is a time-consuming task, and thus an automation of this process results in easy implementation of the work. This paper aims to design and consequently implement deep convolutional neural networks to identify the presence of an exudate, and thereby classify it into Diabetic Retinopathy, Glaucoma, and/or Cataract.
Buyang Huanwu Decoction Targets SIRT1/VEGF Pathway to Promote Angiogenesis After Cerebral Ischemia/Reperfusion Injury

Background: Ischemia stroke is known as the major cause of morbidity and mortality. Buyang Huanwu Decoction (BHD), a classical traditional Chinese medicine (TCM) formula, has been used to prevent and treat stoke for hundreds of years. The purpose of present study is to investigate the effects of BHD on angiogenesis in rats after cerebral ischemia/reperfusion (I/R) injury targeting Silent information regulator 1 (SIRT1) / Vascular endothelial growth factor (VEGF) pathway. Methods: The cerebral I/R injury model was induced by middle cerebral artery occlusion (MCAO). Adult Sprag-Dawley (SD) rats were randomly divided into five groups: sham group, normal saline (NS) group, BHD group, BHD+EX527 (SIRT1 specific inhibitor) group, and NS+EX527 group. Each group was divided into the subgroups according to 1, 3, 7, or 14 days time-point after cerebral ischemia/reperfusion, respectively. Neurological function score (NFS) was evaluated by the Rogers scale; microvascular density (MVD) in brain tissue around infarction area was observed by immunofluorescence; and the expression of SIRT1 and VEGF was assessed by Western Blot and Quantitative Real-time-PCR. Results: BHD can significantly improve NFS (P < 0.05), increase the MVD in the boundary ischemic area (P < 0.01) and elevate the expression of protein and mRNA of SIRT1 and VEGF following I/R injury (P < 0.01). In contrast, treatment with EX527 reversed the BHD-induced improvements in NFS (P < 0.01) and decreased the MVD (P < 0.01) and the expression of SIRT1 and VEGF (P < 0.05). Conclusion: BHD exerts neuroprotection targeting angiogenesis through the up-regulation of SIRT1/VEGF pathway against cerebral ischemic injury in rats.
Number of Connected Components in a Graph: Estimation via Counting Patterns

Due to the limited resources and the scale of the graphs in modern datasets, we often get to observe a sampled subgraph of a larger original graph of interest, whether it is the worldwide web that has been crawled or social connections that have been surveyed. Inferring a global property of the original graph from such a sampled subgraph is of a fundamental interest. In this work, we focus on estimating the number of connected components. It is a challenging problem and, for general graphs, little is known about the connection between the observed subgraph and the number of connected components of the original graph. In order to make this connection, we propose a highly redundant and large-dimensional representation of the subgraph, which at first glance seems counter-intuitive. A subgraph is represented by the counts of patterns, known as network motifs. This representation is crucial in introducing a novel estimator for the number of connected components for general graphs, under the knowledge of the spectral gap of the original graph. The connection is made precise via the Schatten $k$-norms of the graph Laplacian and the spectral representation of the number of connected components. We provide a guarantee on the resulting mean squared error that characterizes the bias variance tradeoff. Experiments on synthetic and real-world graphs suggest that we improve upon competing algorithms for graphs with spectral gaps bounded away from zero.
Roles of Myeloid-Derived Suppressor Cell Subpopulations in Autoimmune Arthritis

Emerging evidence suggests the promise of the use of myeloid-derived suppressor cells (MDSCs) in inflammatory disorders based on their unique immune-intervention properties. However, the roles of MDSCs in autoimmune arthritis are not completely understood. Indeed, their immunosuppressive functions in arthritic conditions remain controversial, with heterogeneity among MDSCs and differential effects among subpopulations receiving much attention. As a result, it is necessary to determine the roles of MDSC subpopulations in autoimmune arthritis to clarify their diagnostic and therapeutic potential. Interestingly, in the inflammation niche of autoimmune arthritis, each MDSC subpopulation can exhibit both alternatives of a given characteristic. Moreover, polymorphonuclear MDSCs (PMN-MDSCs) are likely to be more suppressive and stable compared with monocytic MDSCs (MO-MDSCs). Although various important cytokines associated with the differentiation of MDSCs or MDSC subpopulations from immature myeloid precursors, such as granulocyte colony-stimulating factor (G-CSF), have been largely applied in external inductive systems, their roles are not entirely clear. Moreover, MDSC-based clinical treatments in rheumatoid arthritis (RA) continue to represent a significant challenge, as also reported for other autoimmune diseases. In this review, we describe the effects and actions of MDSC subpopulations on the development of autoimmune arthritis and analyze several types of MDSC-based therapeutic strategies to provide comprehensive information regarding immune networks and a foundation for more effective protocols for autoimmune arthritis.
Evaluation of Virtual Customer Premises Equipment Prototype System with Open Source Software

Software defined networking and network functions virtualization enable network services to be provided more quickly and flexibly, and some network services are beginning to use these technologies. To create a new service with such technologies, this study treat virtual customer premises equipment (vCPE) that provides CPE functions as software on general-purpose servers. In this paper, a vCPE system is developed by using open source software (OSS) to reduce costs and provide flexibility for system deployment. Through this development, the issues with OSS-based systems are clarified and are considered how to cope with them. This paper also evaluated the practicality of OSS and performance of our OSS-based vCPE prototype system.
The Information Content of Multiword #Hashtags

In recent years data science had provided us many opportunities to uncover new social phenomena and behaviors in online social networks, and to utilize such information for business applications. One such interesting phenomenon is the use of hashtags to emphasize important content. In this paper, we evaluate the information content of hashtags for sentiment analysis applications. Specifically, we focus on multi-word hashtags, which challenge automated sentiment analysis methods. For this purpose, we develop a new algorithm to split multi-word hashtags into individual terms. We then compare the predictive accuracy of sentiment analysis with and without this finer-grained representation. We find that breaking down hashtags into multiple terms significantly improves the predictive accuracy of sentiment analysis procedures, and more generally, that hashtags are highly informative for sentiment analysis purposes.
Compression of Acoustic Model via Knowledge Distillation and Pruning

Recently, the performance of speech recognition system based on neural network has been greatly improved. Arguably, this huge improvement can be mainly attributed to deeper and wider layers. These systems are more difficult to be deployed on the embedded devices due to their large size and high computational complexity. To address these issues, we propose a method to compress deep feed-forward neural network (DNN) based acoustic model. In detail, a state-of-the-art acoustic model is trained as the baseline model. In this step, layer normalization is applied to accelerating the model convergence and improving the generalization performance. Knowledge distillation and pruning are then conducted to compress the model. Our final model can achieve 14.59× parameters reduction, 5× storage size reduction and comparable performance compared with the baseline model.
Design and Control Method of 25kW High Efficient EV Fast Charger

This paper discusses the design of a high efficient battery charger and control technique for EV fast charger. Existing EV fast charger systems used two-stage (AC/DC, DC/DC) as a single module to increase the output power. However, the existing method has the problem of increasing the unit price of EV fast charger as the number of AC/DC converters also increases. This study proposes overcoming this problem using a 25-kW high efficiency fast charger that uses a single AC/DC converter and four DC/DC converter modules. In addition, a converter with a module structure was applied for maximum efficiency tracking, and the control and design of the Vienna rectifier were conducted accordingly. Furthermore, the design of the three-level DC/DC converter was conducted to achieve high efficiency.
A Robust Non-Blind Image Watermarking Method Using 2-Level HWT-DCT

The current technology allows to distribute digital images quickly and easily over the internet. Some digital images have copyright that needs to be protected. Watermarking is a technique for protecting copyright. In order for copyright is not easily lost with image manipulation, then watermarking technique must be robust. This study proposes a combination of Discrete Cosine Transform (DCT) based on two levels of the Haar Wavelet Transform (HWT) on image watermarking method with the aim of improving aspects of robustness and imperceptibility. The first step, host image is transformed with DWT as much as two levels to get LL2 subband. LL2 subband is a subband containing the image core information of two levels of wavelet transformation. Furthermore, LL2 subband split into small sub-blocks with the size of 4 * 4. The sub-blocks are transformed again using DCT. Insertion of watermarks using alpha variables with a certain value on the DC coefficient matrix. This value is used for managing the quality and strength of the watermark. To test the imperceptibility and watermark resistance used measuring instruments MSE, PSNR, and correlation coefficient. Based on the simulation results obtained a watermarked image is quite satisfactory with a relatively good imperceptibility value and resistant to various attacks.
Development of Technology for the Identification of Model Parameters for Dendritic Structures Images

This work aims to increase the reliability of dendritic crystallogram’s images classification. Crystallographic methods are used for medical diagnosis and we propose here to improve the reliability of their classification through an improved description of de dendritic structures’ features. In this paper, we use the parameters of the mathematical model describing objects with dendritic structure. We developed a technology of parameters identification from a model image of dendritic structures, that was then implemented through the use of geometric and statistical features, together with a nearest neighbor classification algorithm.
Microbial Consortia: Promising Probiotics as Plant Biostimulants for Sustainable Agriculture

Department of Pharmacy, University of Naples Federico II, Naples, Italy, National Research Council, Institute for Sustainable Plant Protection, Portici, Italy, 3 Task Force on Microbiome Studies, University of Naples Federico II, Naples, Italy, Department of Agricultural Sciences, University of Naples Federico II, Portici, Italy, CIRAM-Interdepartmental Center for Environmental Research, University of Naples Federico II, Naples, Italy
Cost Minimization of GPS-GSM Based Vehicle Tracking System

Many vehicle tracking systems are based on Global Positioning System (GPS) and Global System for Mobile communications (GSM) due to the wide areas covered by these services. In these systems, the GPS coordinates of a moving vehicle is encapsulated in an SMS and transmitted to the monitoring server. In this paper, a GPS-GSM based system is designed and implemented to economically track vehicles moving over wide geographical areas. A scheme is proposed to reduce the number and cost of the used SMS messages. Performance evaluation under hypothetical and practical test cases showed the capability of the proposed system to achieve significant cost reduction. The proposed system also provides a trade-off between cost reduction and tracking accuracy. This enables the designer to select suitable system parameter values to minimize the cost for a given level of tracking accuracy.
JESS: Joint Entropy-Based DDoS Defense Scheme in SDN

Software-defined networking (SDN) is a communication paradigm that brings cost efficiency and flexibility through software-defined functions resident on centralized controllers. Although SDN applications are introduced in a limited scope with related technologies still under development, operational SDN networks already face major security threats. Therefore, comprehensive and efficient solutions are crucial. Especially, large-scale security threats such as distributed-denial-of-service (DDoS) attacks are jeopardizing safety and availability of data and services in these systems. A DDoS attack is aimed at making resources unavailable to legitimate users via overloading systems with excessive superfluous traffic from distributed sources. In this paper, we describe and evaluate a joint entropy-based security scheme (JESS) to enhance the SDN security with the aim of a reinforced SDN architecture against DDoS attacks. In particular, our proposed model devises a statistical solution to detect and mitigate these hazards. To the best of our knowledge, JESS is the first model that utilizes joint entropy for DDoS detection and mitigation in the SDN environment. Since it relies on a statistical model, it mitigates not only known attacks but also unfamiliar types in an efficient manner.
A New Distributed Model Predictive Control for Unconstrained Double-Integrator Multiagent Systems

In this paper, a distributed model predictive control (DMPC) is proposed for static formation of unconstrained double-integrator multiagent systems. The formation problem is formulated in the leader-follower framework, where the leaders have access to both their own and relative neighboring information, and the followers only have access to relative neighboring information. In the process of optimization, only current relative information is communicated between neighboring agents. For each agent, its predicted control vector and neighboring control vectors are regarded as decision variables for optimization, but only the own optimized current control of the agent is implemented. An analytical solution to the DMPC is obtained, and new sufficient conditions are given for achieving the static formation.
Research on Cogging Torque of PM Synchronous Motors Considering Load Condition

This paper investigates the change rules of cogging torque when the permanent magnet synchronous motor (PMSM) under load condition. The cogging torque is one of the most important components of the PMSM's torque ripple. The magnetic field maps show that the flux density on stator notch is influenced by the stator current, which causes the cogging torque increasement with stator current increases. The simulation of this paper shows that the amplitude of the cogging torque will increase with the increase of current.
An Ensemble Classifier Based on Feature Selection Using Ant Colony Optimization

An ensemble classifier based on feature selection is proposed to provide an effective methodology for classifying device states. We used Support Vector Machine (SVM) as base classifiers, and defined the classification performance and similarity measure to construct models using serial and parallel classifiers associated with the feature selection. We put forward Ant Colony Optimization (ACO) algorithms with three models, and verified the performance of the proposed ensemble classifier on vibration signals with five working states from a certain engine's cylinder head.
GAN-EM: GAN based EM learning framework

Expectation maximization (EM) algorithm is to find maximum likelihood solution for models having latent variables. A typical example is Gaussian Mixture Model (GMM) which requires Gaussian assumption, however, natural images are highly non-Gaussian so that GMM cannot be applied to perform image clustering task on pixel space. To overcome such limitation, we propose a GAN based EM learning framework that can maximize the likelihood of images and estimate the latent variables. We call this model GAN-EM, which is a framework for image clustering, semi-supervised classification and dimensionality reduction. In M-step, we design a novel loss function for discriminator of GAN to perform maximum likelihood estimation (MLE) on data with soft class label assignments. Specifically, a conditional generator captures data distribution for K classes, and a discriminator tells whether a sample is real or fake for each class. Since our model is unsupervised, the class label of real data is regarded as latent variable, which is estimated by an additional network (E-net) in E-step. The proposed GAN-EM achieves state-of-the-art clustering and semi-supervised classification results on MNIST, SVHN and CelebA, as well as comparable quality of generated images to other recently developed generative models.