Miracle-dz/UniFAR-FTUs huggingface dataset:
The Random-Cluster Model

The class of random-cluster models is a unification of a variety of stochastic processes of significance for probability and statistical physics, including percolation, Ising, and Potts models; in addition, their study has impact on the theory of certain random combinatorial structures, and of electrical networks. Much (but not all) of the physical theory of Ising/Potts models is best implemented in the context of the random-cluster representation. This systematic summary of random-cluster models includes accounts of the fundamental methods and inequalities, the uniqueness and specification of infinite-volume measures, the existence and nature of the phase transition, and the structure of the subcritical and supercritical phases. The theory for two-dimensional lattices is better developed than for three and more dimensions. There is a rich collection of open problems, including some of substantial significance for the general area of disordered systems, and these are highlighted when encountered. Amongst the major open questions, there is the problem of ascertaining the exact nature of the phase transition for general values of the cluster-weighting factor q, and the problem of proving that the critical random-cluster model in two dimensions, with 1 ≤ q ≤ 4, converges when re-scaled to a stochastic Lowner evolution (SLE). Overall the emphasis is upon the random-cluster model for its own sake, rather than upon its applications to Ising and Potts systems.
Universalizing BulTreeBank: a Linguistic Tale about Glocalization

The paper presents the strategies and conversion principles of BulTreeBank into Universal Dependencies annotation scheme. The mappings are discussed from linguistic and technical point of view. The mapping from the original resource to the new one has been done on morphological and syntactic level. The first release of the treebank was issued in May 2015. It contains 125 000 tokens, which cover roughly half of the corpus data.
Bioactive Peptides

The increased consumer awareness of the health promoting effects of functional foods and nutraceuticals is the driving force of the functional food and nutraceutical market. Bioactive peptides are known for their high tissue affinity, specificity and efficiency in promoting health. For this reason, the search for food-derived bioactive peptides has increased exponentially. Over the years, many potential bioactive peptides from food have been documented; yet, obstacles such as the need to establish optimal conditions for industrial scale production and the absence of well-designed clinical trials to provide robust evidence for proving health claims continue to exist. Other important factors such as the possibility of allergenicity, cytotoxicity and the stability of the peptides during gastrointestinal digestion would need to be addressed. This review discusses our current knowledge on the health effects of food-derived bioactive peptides, their processing methods and challenges in their development.
Anatomic Invasive Depth Predicts Delayed Cervical Lymph Node Metastasis of Tongue Squamous Cell Carcinoma

Delayed cervical lymph node metastasis (CLNM) is the most negative prognostic factor of tongue squamous cell carcinoma (SCC). This study analyzed the relationship between clinicopathologic factors, including anatomic invasive depth (AID), and CLNM. A total of 212 patients with clinically node-negative (cN0) tongue SCC who had undergone partial glossectomy through the mouth were eligible for this retrospective study. The deepest portions where tongue SCC cells invaded as determined by microscopic analyses were classified into 5 categories, including epithelial and submucosal tissue, lateral extrinsic tongue muscle (ETM), intrinsic tongue muscles (ITM), paralingual and sublingual spaces, and medial ETM according to AID. We examined the relationship between clinicopathologic factors including AID and delayed CLNM. Multivariate analysis demonstrated that AID was an independent predictive factor for delayed CLNM (P=0.0022; odds ratio=7.1). Deeper invasion than ITM, including ITM, paralingual and sublingual spaces, and medial ETM, had high sensitivity and negative predictive value for delayed CLNM (94.4% and 95.7%, respectively). Precise elucidation of AID may be useful for the preoperative decision for performing elective neck dissection. None of 11 patients in whom the deepest portion where tumor invaded to lateral ETM (according to AID) showed delayed CLNM, although tongue SCC T4a tumor is defined by the presence of invasion of cancer cells to ETM. Tumors with invasion to lateral ETM might have to be excluded from the pathologic T4a category.
Existence and regularity of higher critical points for elliptic free boundary problems

Existence and regularity of minimizers in elliptic free boundary problems have been extensively studied in the literature. We initiate the corresponding study of higher critical points by considering a superlinear free boundary problem related to plasma confinement. The associated energy functional is nondifferentiable, and therefore standard variational methods cannot be used directly to prove the existence of critical points. Here we obtain a nontrivial generalized solution $u$ of mountain pass type as the limit of mountain pass points of a suitable sequence of $C^1$-functionals approximating the energy. We show that $u$ minimizes the energy on the associated Nehari manifold and use this fact to prove that it is nondegenerate. We use the nondegeneracy of $u$ to show that it satisfies the free boundary condition in the viscosity sense. Moreover, near any free boundary point that has a measure-theoretic normal, the free boundary is a smooth surface, and hence the free boundary condition holds in the classical sense.
Evaluation of In Vitro Cytotoxicity of Zinc Oxide (Zno) Nanoparticles Using Human Cell Lines

The aim of the present study was to evaluate the in vitro toxicity of Zinc Oxide (ZnO) nanoparticles against human alveolar epithelial (A549) cells and human embryonic kidney cells (HEK cells). The toxic effects of nanoparticles were analyzed after 24 hours of incubation with different cell lines using trypan blue dye exclusion method. Incubation of Zinc Oxide (ZnO) nanoparticles with different cells produced a dose dependent inhibition of growth of the cells. The TC50 values (toxic concentration 50 i.e. concentration of particles inducing 50% cell mortality) of Zinc Oxide (ZnO) nanoparticles were found in the range of 33 37 μg/ml.
Can You Hear Me Now

Boone County Amateur Radio Emergency Services (BCARES) conducted their 2016 Simulated Emergency Test (SET) as the “Can You Hear Me Now? -SET, 5 November 2016. In an unprecedented collaboration with the Boone County Office of Emergency Management the SET exercise was designed to access BCARES radio coverage across the 650 square mile county. The OEM, in cooperation with the County Public Works, identified 28 locations where radio coverage could be problematic, or significant in support of emergency operations of these agencies. The challenge would be, how well and by what means could BCARES members with their normal response VHF/UHF radios communicate. Some challenges were obvious with several of these locations in the Missouri River bottoms, and along the Katy Trail.
Modeling of Optimal Targeted Therapies Using Drug-Loaded Magnetic Nanoparticles for Liver Cancer

To enhance locoregional therapies for liver cancer treatment, we propose in this study a mathematical model to optimize the transcatheter arterial delivery of therapeutical agents. To maximize the effect of the treatment and minimize adverse effects on the patient, different mathematical models of the tumor growth are considered in this study to find the optimal number of the therapeutic drug-loaded magnetic nanoparticles to be administered. Three types of therapy models are considered, e.g., angiogenesis inhibition therapy, chemotherapy and radiotherapy. We use state-dependent Riccati equations (SDRE) as an optimal control methodology framework to the Hahnfeldt's tumor growth formulation. Based on this, design optimal rules are derived for each therapy to reduce the growth of a tumor through the administration of appropriate dose of antiangiogenic, radio- and chemo-therapeutic agents. Simulation results demonstrate the validity of the proposed optimal delivery approach, leading to reduced intervention time, low drug administration rates and optimal targeted delivery.
Smooth Imitation Learning for Online Sequence Prediction

We study the problem of smooth imitation learning for online sequence prediction, where the goal is to train a policy that can smoothly imitate demonstrated behavior in a dynamic and continuous environment in response to online, sequential context input. Since the mapping from context to behavior is often complex, we take a learning reduction approach to reduce smooth imitation learning to a regression problem using complex function classes that are regularized to ensure smoothness. We present a learning meta-algorithm that achieves fast and stable convergence to a good policy. Our approach enjoys several attractive properties, including being fully deterministic, employing an adaptive learning rate that can provably yield larger policy improvements compared to previous approaches, and the ability to ensure stable convergence. Our empirical results demonstrate significant performance gains over previous approaches.
Molecular Interactions between NAFLD and Xenobiotic Metabolism

Non-alcoholic fatty liver disease (NAFLD), the hepatic manifestation of the metabolic syndrome, is a complex multifactorial disease characterized by metabolic deregulations that include accumulation of lipids in the liver, lipotoxicity, and insulin resistance. The progression of NAFLD to non-alcoholic steatohepatitis and cirrhosis, and ultimately to carcinomas, is governed by interplay of pro-inflammatory pathways, oxidative stress, as well as fibrogenic and apoptotic cues. As the liver is the major organ of biotransformation, deregulations in hepatic signaling pathways have effects on both, xenobiotic and endobiotic metabolism. Several major nuclear receptors involved in the transcription and regulation of phase I and II drug metabolizing enzymes and transporters also have endobiotic ligands including several lipids. Hence, hepatic lipid accumulation in steatosis and NAFLD, which leads to deregulated activation patterns of nuclear receptors, may result in altered drug metabolism capacity in NAFLD patients. On the other hand, genetic and association studies have indicated that a malfunction in drug metabolism can affect the prevalence and severity of NAFLD. This review focuses on the complex interplay between NAFLD pathogenesis and drug metabolism. A better understanding of these relationships is a prerequisite for developing improved drug dosing algorithms for the pharmacotherapy of patients with different stages of NAFLD.
MicroRNAs and Their Regulatory Role in Sugarcane

Sugarcane, one of the most photosynthetically efficient crops, is an important source of sugar and feedstock for green energy and co-generation. The high level of polyploidy and genomic peculiarities in this crop point towards a complex mechanism of regulation for the economically important traits like sugar content, cane yield related traits, resistance to biotic and abiotic stresses etc. The regulatory pathways for these traits comprise of a number of genes, transcription factors and different categories of RNAs like small interference RNAs (siRNAs), and Micro RNAs (miRNAs). MicroRNAs (miRNAs) are found to play an important regulatory role in many crops. As in other crops, several miRNAs have been identified in sugarcane too and these are speculated to have a role in regulating the various metabolic processes. Role of miRNAs in relation to drought tolerance has been studied to a great extent in this crop. miRNAs have been predicted to be linked to expression of other traits like disease resistance, salinity tolerance, waterlogging and axillary bud growth in sugarcane. miRNAs can have a significant role in biomass production in sugarcane, as reported in several biofuel crops. Till now, miRNAs linked to sugar accumulation have not been identified in sugarcane, but studies suggest an important role for miRNAs in sugar metabolic pathway in crops like Sorghum and switch grass. It is presumed that in sugarcane too, sugar accumulation as well as the other important metabolic pathways might be regulated to some extent by the miRNAs. The review examines the progress made in understanding the miRNA regulation in sugarcane and the extent to which miRNA mediated regulation can be utilized in sugarcane improvement.
Diffusion and its Application in NiMnGa Alloys

Heusler NiMnGa alloys are often categorized as ferromagnetic shape memory alloys or magnetocaloric materials, which are important for both practical applications and fundamental research. The NiMnGa alloys undergo a series of diffusion and diffusionless transformation from high temperature to low temperature. Among these transformation, martensitic transformation from austenitic phase to martensitic phase is critical in determining the properties of the alloys. Although martensitic transformation is considered diffusionless, diffusion also has important applications in the research of NiMnGa alloysDiffusion couples along with equilibrium alloys have been used to determine the ternary phase diagrams in NiMnGa alloys. Phase diagrams are important in selecting NiMnGa alloys, in particular two-phase NiMnGa alloys for practical applications. Furthermore, the diffusion couples effectively assist in the determination of compositions that exhibit martensitic transformation temperature near room temperature. Diffusion coefficients have been assessed for NiMnGa alloys. Tracer diffusivity of Ni, Mn and Ga was reported in a wide temperature range and followed Arrhenius behavior. Two different activation energies were obtained, corresponding to B2 and L21 crystal structure, respectively. Interdiffusion coefficients for NiMnGa alloys with B2 crystal structure are measured, which showed that Ni diffuses the fastest, followed by Mn then Ga. The diffusion coefficients provide useful information for fabricating NiMnGa alloys through diffusional process.A combinatorial approach involving diffusion couples and advance characterization has been developed to investigate the mechanical properties, microstructure and crystallography of NiMnGa alloys rapidly and systematically over a large compositional range. The composition-dependent modulus and hardness for NiMnGa alloys was extracted from the diffusion couples with the help of nanoindentation. Martensitic phases with non-modulated and various modulated crystal structures, and austenitic phase were identified in the interdiffusion zones by transmission electron microscopy. The results demonstrate the capability of using diffusion couples to speed up the discovery of new NiMnGa alloys or other similar alloys showing martensitic transformation.
Graft vasculopathy in the skin of a human hand allograft: implications for diagnosis of rejection of vascularized composite allografts

Whereas vascularized composite allografts often undergo acute rejections early in the postgraft period, rejection manifesting with severe vascular changes (graft vasculopathy) has only been observed on three occasions in humans. We report a hand‐allografted patient who developed severe rejection following discontinuation of the immunosuppressive treatment. It manifested clinically with erythematous maculopapules on the skin and pathologically with graft vasculopathy that affected both large vessels and smaller cutaneous ones. The observation that graft vasculopathy can affect skin vessels shows that it is amenable to diagnosis with usual skin biopsy as recommended for the follow‐up of these allografts. Graft vasculopathy developing in the setting of vascularized composite allografts likely represents chronic rejection due to under‐immunosuppression and, if confirmed, should be included in a future update of the Banff classification of vascularized composite allograft rejection.
Global nursing shortages

In October 2001 government chief nurses and other delegates from 66 countries met to discuss how best to deal with a common challenge—the global growth of nursing shortages.1 Nursing shortages in the United Kingdom and elsewhere have been a repetitive phenomenon, usually due to an increasing demand for nurses outstripping static or a more slowly growing supply.2 This time the situation is more serious. Demand continues to grow, while projections for supply point to actual reductions in the availability of nurses in some developed and developing countries. Some health systems are also coping with the legacy of ill conceived reform projects of the 1990s, which demotivated and disenfranchised nurses and other staff. Developed countries are facing a demographic double whammy. The United States, United Kingdom, Australia, Canada, and other countries have an ageing nursing workforce, caring for increasing numbers of elderly people.3–5 The challenge is how to replace the many nurses who will retire over the decade. Some countries also have to cope …
Vibration Analysis Based Interturn Fault Diagnosis in Induction Machines

A vibration analysis based interturn fault diagnosis of induction machines is proposed in this paper, using a neural-network-based scheme, constituting of two parts. The first part finds out the optimum network size of the probabilistic neural network (PNN) using the Orthogonal Least Squares Regression algorithm. This judges the size of the PNN, with an effort to reduce the computation. The feature extraction to model the PNN is made meaningful using dual tree complex wavelet transform (DTCWT), which is nearly shift invariant analytical wavelet transform, giving a true representation of the input space. In the second part, preprocessing using principal component analysis is suggested as an effective way to further reduce the dimension of the feature set and size of the PNN without compromising the performance. The sensitivity, specificity, and accuracy show that the vibration signatures capture the fault more effectively (especially by the axial and radial ones), under varying supply-frequency and load conditions. A comparison with traditional discrete wavelet transform proves the applicability of the proposed scheme. A comparative evaluation with feedforward neural network and naïve Bayes scheme brings out the advantage of the proposed optimized DTCWT-PNN based technique over other machine learning approaches.
Peak power constrained closed-form transceiver designs for MIMO AF relaying systems with direct link

In this paper, we propose a new relay transceiver design based on the minimum mean-squared error criterion for non-regenerative multiple-input multiple-output relaying systems with direct link. Since norm power constraint at the relay node does not restrict the output power at each antenna element in designing the relay transceiver, we consider maximum eigenvalue constraint (MVC) which imposes a limit on the peak power of the output. To this end, we first derive the optimal structure of the relay transceiver under the MVC. Then, by introducing an upper bound of the mean-squared error which makes the problem tractable, we provide a closed-form relay transceiver design with the MVC. From simulation results, it is confirmed that the proposed relay transceiver technique demonstrates a significant performance improvement compared to conventional schemes.
Diagnosis and treatment of mitochondrial myopathies

Abstract Mitochondrial disorders are a heterogeneous group of disorders resulting from primary dysfunction of the respiratory chain. Muscle tissue is highly metabolically active, and therefore myopathy is a common element of the clinical presentation of these disorders, although this may be overshadowed by central neurological features. This review is aimed at a general medical and neurologist readership and provides a clinical approach to the recognition, investigation, and treatment of mitochondrial myopathies. Emphasis is placed on practical management considerations while including some recent updates in the field.
Neuroblast pattern and identity in the Drosophila tail region and role of doublesex in the survival of sex-specific precursors

The central nervous system is composed of segmental units (neuromeres), the size and complexity of which evolved in correspondence to their functional requirements. In Drosophila, neuromeres develop from populations of neural stem cells (neuroblasts) that delaminate from the early embryonic neuroectoderm in a stereotyped spatial and temporal pattern. Pattern units closely resemble the ground state and are rather invariant in thoracic (T1-T3) and anterior abdominal (A1-A7) segments of the embryonic ventral nerve cord. Here, we provide a comprehensive neuroblast map of the terminal abdominal neuromeres A8-A10, which exhibit a progressively derived character. Compared with thoracic and anterior abdominal segments, neuroblast numbers are reduced by 28% in A9 and 66% in A10 and are almost entirely absent in the posterior compartments of these segments. However, all neuroblasts formed exhibit serial homology to their counterparts in more anterior segments and are individually identifiable based on their combinatorial code of marker gene expression, position, delamination time point and the presence of characteristic progeny cells. Furthermore, we traced the embryonic origin and characterised the postembryonic lineages of a set of terminal neuroblasts, which have been previously reported to exhibit sex-specific proliferation behaviour during postembryonic development. We show that the respective sex-specific product of the gene doublesex promotes programmed cell death of these neuroblasts in females, and is needed for their survival, but not proliferation, in males. These data establish the terminal neuromeres as a model for further investigations into the mechanisms controlling segment- and sex-specific patterning in the central nervous system.
Deep Learning vs. traditional Machine Learning algorithms used in Credit Card Fraud Detection

With the continuing growth of E-commerce, credit card fraud has evolved exponentially, where people are using more on-line services to conduct their daily transactions. Fraudsters masquerade normal behaviour of customers to achieve unlawful gains. Fraud patterns are changing rapidly where fraud detection needs to be re-evaluated from a reactive to a proactive approach. In recent years Deep Learning has gained lot of popularity in image recognition, speech recognition and natural language processing. This paper seeks to understand how Deep Learning can be helpful in finding fraud in credit card transactions and compare Deep Learning against several state of the art algorithms (RF, GBM, GLM) and sampling methods (Over, Under, Hybrid, SMOTE and ROSE) used in fraud detection. The results show that Deep Learning performed best with the highest Recall (accuracy of identifying fraudulent transactions), which means lowest financial losses to the company. However, Deep Learning achieved the lowest Precision rate (classified more legitimate transactions as fraudulent), which can cause customer dissatisfaction. Among other chosen classifiers, oversampling method performed best in terms of AUC, precision was highest for GLM and F-Score was highest for model trained using ROSE sampling method. Recall and Precision both have high cost, so there cannot be any trade of one against the other. Selecting the best classifier to identify fraud is based on the business goal.