Miracle-dz/UniFAR-FTUs huggingface dataset:
BiSON: A Bioinspired Self-Organizing Network for Dynamic Auto-Configuration in 5G Wireless

Emerging 5G wireless networks are expected to herald significant transformation in industrial applications, with improved coverage, high data rates, and massive device capacity. However, the introduction of 5G wireless makes the network configuration, management, and planning extremely challenging. For efficient network configuration, every cell needs to be allocated a particular Physical Cell Identifier (PCID), which is unique in its vicinity. Wireless standards (e.g., 3GPP) typically specify a limited number of PCIDs. However, the number of cells in 5G Ultradense Networks (UDN) is expected to significantly outnumber these limited PCIDs. Hence, these PCIDs need to be efficiently allocated among the myriad of cells, such that two cells which are neighbors or neighbor’s neighbor are assigned with different PCIDs. This complicated network configuration problem becomes even more complex by dynamic introduction and removal of 5G small cells (e.g., micro, femto, and pico). In this paper, we introduce BiSON, a new Bioinspired Self-Organizing Solution for automated and efficient PCID configuration in 5G UDN. Using two different extensions, namely, “always near-optimal” and “heuristic,” we explain near-optimal and dynamic auto-configuration in computationally feasible time, with negligible overhead. Our extensive network simulation experiments, based on actual 5G wireless trials, demonstrate that the proposed algorithm achieves better optimality (minimum PCIDs in use) than earlier works in a reasonable computational complexity.
Linear Software Models: Bipartite Isomorphism between Laplacian Eigenvectors and Modularity Matrix Eigenvectors

We have recently shown that one can obtain the numbers and sizes of modules of a software system from the eigenvectors of its modularity matrix symmetrized and weighted by an affinity matrix. However such a weighting still demands a suitable definition of an affinity. This paper offers an alternative way to obtain the same results by means of the eigenvectors of a Laplacian matrix, directly obtained from the modularity matrix without the need of weighting. These two formalizations stand in a mutual isomorphism. We call it bipartite isomorphism since it is most straightforwardly shown by deriving the Laplacian from the modularity matrix and vice versa through the intermediate bipartite graph between two separate sets: the structors’ and the functionals’ sets. This isomorphism is also demonstrated through the equation defining the Laplacian in terms of the modularity matrix, or by the direct mapping of the respective matrices’ eigenvectors. Both matrices and the bipartite graph reflect one central idea: modules are connected components with high cohesion. The Laplacian matrix technique, of which the Fiedler vector is of central importance, is illustrated by case studies. An important claim of this paper is that, independently of the modularity matrix- and Laplacian matrix-specific properties, behind these two alternative matrices there is just one unified algebraic theory of software composition — the Linear Software Models — here concerning the application of the matrices’ eigenvectors to software modularity.
Human Primary Epithelial Cell Models: Promising Tools in the Era of Cystic Fibrosis Personalized Medicine

Cystic fibrosis (CF) is an inherited disorder where individual disease etiology and response to therapeutic intervention is impacted by CF transmembrane regulator (CFTR) mutations and other genetic modifiers. CFTR regulates multiple mechanisms in a diverse range of epithelial tissues. In this Review, we consolidate the latest updates in the development of primary epithelial cellular model systems relevant for CF. We discuss conventional two-dimensional (2-D) airway epithelial cell cultures, the backbone of in vitro cellular models to date, as well as improved expansion protocols to overcome finite supply of the cellular source. We highlight a range of strategies for establishment of three dimensional (3-D) airway and intestinal organoid models and evaluate the limitations and potential improvements in each system, focusing on their application in CF. The in vitro CFTR functional assays in patient-derived organoids allow for preclinical pharmacotherapy screening to identify responsive patients. It is likely that organoids will be an invaluable preclinical tool to unravel disease mechanisms, design novel treatments, and enable clinicians to provide personalized management for patients with CF.
Adaptive fuzzy control for pure-feedback stochastic nonlinear systems with unknown dead zone outputs

ABSTRACT This paper focuses on an adaptive fuzzy tracking control problem for a class of pure-feedback stochastic nonlinear systems with unknown dead zone outputs. To overcome the design difficulty arising from the nonlinearity in the output mechanism, the new properties of Nussbaum function are employed and an auxiliary virtual controller is constructed. The proposed adaptive fuzzy control method guarantees that all the signals in the closed-loop system are bounded in probability and the tracking error converges to a small neighbourhood of the origin in the sense of mean quartic value. Simulation results further demonstrate the effectiveness of the presented control algorithm.
Percutaneous laser ablation of benign thyroid nodules: a one year follow-up study

ABSTRACT Objective To evaluate safety and effectiveness of nodule volume reduction and thyroid function after percutaneous laser ablation treatment in patients with benign nonfunctioning thyroid nodules. Methods Prospective single-center study, from January 2011 to October 2012, which evaluated 30 euthyroid and thyroid antibodies negative patients with benign solitary or dominant nodule with indication of treatment due to compressive symptoms and aesthetic disturbances. The clinical and laboratory (thyroid ultrasound, TSH, FT4, TG, TG-Ab, TPO-Ab and TRAb levels) evaluations were performed before the procedure, and periodically 1 week, 3 months and 6 months after. The ablation technique was performed under local anesthesia and sedation. In each treatment, one to three 21G spinal needle were inserted into the thyroid nodule. The laser fiber was positioned through the needle, which was then withdrawn 10mm to leave the tip in direct contact with the nodule tissue. Patients were treated with a ND: Yag-laser output power of 4W and 1,500 to 2,000J per fiber per treatment. The entire procedure was performed under US guidance. Results Thirty patients, with a total of 31 nodules submitted to laser ablation were evaluated. The median volumetric reduction of the nodule was approximately 60% after 12 months. No statistical significance was observed on thyroid function and antibodies levels. There was a peak on the level of thyroglobulin after the procedure due to tissue destruction (p<0.0001). No adverse effects were observed. Conclusion Percutaneous laser ablation is a promising outpatient minimally invasive treatment of benign thyroid nodule.
Formal Synthesis of Analytic Controllers for Sampled-Data Systems via Genetic Programming

This paper presents an automatic formal controller synthesis method for nonlinear sampled-data systems with safety and reachability specifications. Fundamentally, the presented method is not restricted to polynomial systems and controllers. We consider a periodically switched controllers based on a Control Lyapunov Barrier-like function. The proposed method utilizes genetic programming to synthesize these function in analytic form, as well as the controller modes. Correctness of the controller are subsequently verified by means of a Satisfiability Modulo Theories solver. Effectiveness of the proposed methodology is demonstrated on multiple systems.
An ESO-based integrated trajectory tracking control for tractor–trailer vehicles with various constraints and physical limitations

ABSTRACT This paper develops an effective integrated control strategy for the trajectory tracking control of a tractor–trailer vehicle which suffers from inaccessible system states and uncertain disturbance for practical implementation. In addition, diverse problems, such as nonholonomic constraints, underactuated dynamics, physical limitations, etc, can be resolved favourably all together. Aiming to the vehicle trajectory tracking, a constrained model predictive control (MPC) is introduced as a trajectory tracking module, by which the underactuated dynamics, various constraints and physical limitations, can be tackled at the same time. For the desired velocity tracking, a robust global terminal sliding mode control (GTSMC) is employed to guarantee the finite-time convergence of the velocity tracking process, which will improve the transient performance to a great extent. Particularly, in the absence of velocity information, an extended state observer (ESO) is developed to estimate the vehicle velocity in addition to simultaneously obtaining the uncertain disturbance information, which offers prerequisite for the previous control approaches. The simulation results confirm that the presented control strategy can synthesise varied control techniques effectively and deal with diverse problems for the trajectory tracking of tractor–trailer vehicles successfully.
Packet Scheduling for Multiple-Switch Software-Defined Networking in Edge Computing Environment

Software-defined networking (SDN) decouples the control plane and data forwarding plane to overcome the limitations of traditional networking infrastructure. Among several communication protocols employed for SDN, OpenFlow is most widely used for the communication between the controller and switch. In this paper two packet scheduling schemes, FCFS-Pushout (FCFS-PO) and FCFS-Pushout-Priority (FCFS-PO-P), are proposed to effectively handle the overload issue of multiple-switch SDN targeting the edge computing environment. Analytical models on their operations are developed, and extensive experiment based on a testbed is carried out to evaluate the schemes. They reveal that both of them are better than the typical FCFS-Block (FCFS-BL) scheduling algorithm in terms of packet wait time. Furthermore, FCFS-PO-P is found to be more effective than FCFS-PO in the edge computing environment.
Correlation Analysis between Helicobacter pylori Infection Status and Tumor Clinical Pathology as Well as Prognosis of Gastric Cancer Patients

Background: We aimed to investigate the correlation between Helicobacter pylori infection and the development and prognosis of gastric cancer. Methods: Retrospective analysis was used to collect 120 paraffin-embedded specimens and 42 paracancerous specimens of gastric cancer patients archived in Department of Pathology, Weihaiwei People’s Hospital from 2010–2012. All patients with gastric cancer were followed for 5 years. Real-time fluorescent quantitative PCR was used to detect the relative H. pylori infection in gastric cancer tissues and paracancerous tissues. The relationship between clinicopathological parameters and H. pylori relative infection was analyzed. Kaplan-Meier was used for survival analysis. Results: The relative amount of H. pylori infection in gastric cancer tissues was significantly higher than that in paracancerous normal tissues (P<0.001). The relative H. pylori infection was related to tumor size, lymph node metastasis, clinical stage, and depth of invasion (P<0.05). The 1-, 3-, and 5-year survival rates of gastric cancer patients were negatively correlated with the relative H. pylori infection. The relative H. pylori infection, age, tumor size, lymph node metastasis, distant metastasis, clinical stage and depth of invasion were positively correlated with the prognosis of patients with gastric cancer. Conclusion: The relative H. pylori infection and clinical stage of patients could increase the risk of death in gastric cancer patients. H. pylori is one of the independent risk factors for the progression and prognosis of gastric cancer. It is also an index to evaluate the development process and prognosis of gastric cancer.
Impact of Clopidogrel Loading for Coronarography on Bleeding After Urgent First Time CABG

Background: Excessive bleeding impairs outcome after coronary artery bypass grafting (CABG). Clopidogrel in combination with aspirin, given before percutaneous coronary intervention, have become the standard for stent thrombosis prevention. Some premedicated patients, however, are found to need surgical treatment, thus platelet inhibition caused by clopidogrel becomes a concern for post operation major bleeding. Aims: This study was designed to evaluate the impact of preoperative clopidogrel on bleeding and outcomes after coronary artery bypass graft surgery (CABG). Methods: The study represent a observational retrospective analysis of collected data. The follow up of 223 treated with clopidogrel and aspirin and 77 patients not receiving treatment with platelet antagonist 7 days before CABG are analyzed. Results: The groups were comparable in age, gender, body surface area, preoperative hematocrit, preoperative prothrombin time and prior myocardial infarction. The clopidogrel group had higher12h and 24h mean chest tube output (at 12h mean 519.7ml vs 353.1 ml, p < 0.05, at 24h mean 756.6 ml vs 563.5 ml, p<0.05). Moreover, reoperation for bleeding was 4.5-fold higher in the clopidogrel group (5.9% vs. 1.3%, p <0.01), and more transfusions of red blood cells (3.23U vs 2.6 U, p<0.05), platelets (1.53U vs 1.23U, p<0.01) and fresh frozen plasma (0.84U vs 0.36 U, p<0.01). The clopidogrel group also showed a longer mechanical ventilation time (16.9h vs 12.9 h p = 0.03) and trend towards more prolonged stay in ICU (2.08 days vs 1.7 days p= 0.048). Conclusions: Clopidogrel in combination with aspirin before CABG is associated with higher postoperative bleeding, exposure to blood products and morbidity. These findings raise concern regarding the routine administration of clopidogrel before anticipated but undecided coronary stent implantation.
Perceptual reversals in binocular rivalry: Improved detection from OKN.

When binocular rivalry is induced by opponent motion displays, perceptual reversals are often associated with changed oculomotor behavior (Frässle, Sommer, Jansen, Naber, & Einhäuser, 2014; Fujiwara et al., 2017). Specifically, the direction of smooth pursuit phases in optokinetic nystagmus typically corresponds to the direction of motion that dominates perceptual appearance at any given time. Here we report an improved analysis that continuously estimates perceived motion in terms of "cumulative smooth pursuit. " In essence, smooth pursuit segments are identified, interpolated where necessary, and joined probabilistically into a continuous record of cumulative smooth pursuit (i.e., probability of eye position disregarding blinks, saccades, signal losses, and artefacts). The analysis is fully automated and robust in healthy, developmental, and patient populations. To validate reliability, we compare volitional reports of perceptual reversals in rivalry displays, and of physical reversals in nonrivalrous control displays. Cumulative smooth pursuit detects physical reversals and estimates eye velocity more accurately than existing methods do (Frässle et al., 2014). It also appears to distinguish dominant and transitional perceptual states, detecting changes with a precision of ±100 ms. We conclude that cumulative smooth pursuit significantly improves the monitoring of binocular rivalry by means of recording optokinetic nystagmus.
Kindergarten: Producer or Reducer of Inequality Regarding Physical Activity Levels of Preschool Children

Several studies have shown that children have sub-optimal physical activity levels. Since preschool children (4-6 years-old) spend most of their time awake in kindergarten on weekdays, physical activity level at kindergarten is crucial. The study examines preschool children's physical activity level at kindergarten. Preschool children's activity level at kindergarten is also investigated related to activity level at leisure, gender, and mothers' education level, income, and age. Two hundred and forty four children (125 boys and 119 girls) supplied valid accelerometer data, and mothers' education level, income, and age were measured using a questionnaire. One-way ANOVA and linear regression were utilized as statistical analyses. The results demonstrated that physical activity level during kindergarten is the main contributor to preschool children's physical activity level on weekdays. Furthermore, boys were more active than girls, and preschool children's physical activity level at both leisure and at kindergarten were not associated with mothers' age, education level, or income. However, a positive association was found between physical activity level at leisure and physical activity level at kindergarten, in which physical activity level at kindergarten increases when physical activity level at leisure increases. Physical activity level was also significantly different between kindergartens. The study indicated that kindergartens increase inequality according to physical activity level among preschool children-contributing to creating differences according to low-active and high-active children.
Model Compression with Generative Adversarial Networks

More accurate machine learning models often demand more computation and memory at test time, making them difficult to deploy on CPU- or memory-constrained devices. Model compression (also known as distillation) alleviates this burden by training a less expensive student model to mimic the expensive teacher model while maintaining most of the original accuracy. However, when fresh data is unavailable for the compression task, the teacher’s training data is typically reused, leading to suboptimal compression. In this work, we propose to augment the compression dataset with synthetic data from a generative adversarial network (GAN) designed to approximate the training data distribution. Our GAN-assisted model compression (GAN-MC) significantly improves student accuracy for expensive models such as deep neural networks and large random forests on both image and tabular datasets. Building on these results, we propose a comprehensive metric—the Compression Score—to evaluate the quality of synthetic datasets based on their induced model compression performance. The Compression Score captures both data diversity and discriminability, and we illustrate its benefits over the popular Inception Score in the context of image classification.
Theoretical Guarantees of Deep Embedding Losses Under Label Noise

Collecting labeled data to train deep neural networks is costly and even impractical for many tasks. Thus, research effort has been focused in automatically curated datasets or unsupervised and weakly supervised learning. The common problem in these directions is learning with unreliable label information. In this paper, we address the tolerance of deep embedding learning losses against label noise, i.e. when the observed labels are different from the true labels. Specifically, we provide the sufficient conditions to achieve theoretical guarantees for the 2 common loss functions: marginal loss and triplet loss. From these theoretical results, we can estimate how sampling strategies and initialization can affect the level of resistance against label noise. The analysis also helps providing more effective guidelines in unsupervised and weakly supervised deep embedding learning.
Understanding the Influence of Wireless Communications and Wi-Fi Access on Customer Loyalty: A Behavioral Model System

New technologies offer new possibilities to better understand complex consumer behavior points of sale. The data obtained using wireless communications and Wi-Fi services available in restaurants and catering companies make it possible to acquire in-depth knowledge on consumer behavior complexity. In the present study, the PLS-SEM analysis was used to analyze the impact of free wireless communications and Wi-Fi access on customer loyalty. Our results demonstrate that client satisfaction with wireless communications and Wi-Fi access networks services has a direct impact on customer loyalty. Therefore, wireless communications and Wi-Fi networks and technologies available at the points of sale should be updated in order to better meet customers’ expectations.
When Homomorphic Cryptosystem Meets Differential Privacy: Training Machine Learning Classifier with Privacy Protection

Machine learning (ML) classifiers are invaluable building blocks that have been used in many fields. High quality training dataset collected from multiple data providers is essential to train accurate classifiers. However, it raises concern about data privacy due to potential leakage of sensitive information in training dataset. Existing studies have proposed many solutions to privacy-preserving training of ML classifiers, but it remains a challenging task to strike a balance among accuracy, computational efficiency, and security. In this paper, we propose Heda, an efficient privacypreserving scheme for training ML classifiers. By combining homomorphic cryptosystem (HC) with differential privacy (DP), Heda obtains the tradeoffs between efficiency and accuracy, and enables flexible switch among different tradeoffs by parameter tuning. In order to make such combination efficient and feasible, we present novel designs based on both HC and DP: A library of building blocks based on partially HC are proposed to construct complex training algorithms without introducing a trusted thirdparty or computational relaxation; A set of theoretical methods are proposed to determine appropriate privacy budget and to reduce sensitivity. Security analysis demonstrates that our solution can construct complex ML training algorithm securely. Extensive experimental results show the effectiveness and efficiency of the proposed scheme.
Provably Efficient Maximum Entropy Exploration

Suppose an agent is in a (possibly unknown) Markov Decision Process in the absence of a reward signal, what might we hope that an agent can efficiently learn to do? This work studies a broad class of objectives that are defined solely as functions of the state-visitation frequencies that are induced by how the agent behaves. For example, one natural, intrinsically defined, objective problem is for the agent to learn a policy which induces a distribution over state space that is as uniform as possible, which can be measured in an entropic sense. We provide an efficient algorithm to optimize such such intrinsically defined objectives, when given access to a black box planning oracle (which is robust to function approximation). Furthermore, when restricted to the tabular setting where we have sample based access to the MDP, our proposed algorithm is provably efficient, both in terms of its sample and computational complexities. Key to our algorithmic methodology is utilizing the conditional gradient method (a.k.a. the Frank-Wolfe algorithm) which utilizes an approximate MDP solver.