Miracle-dz/UniFAR-FTUs huggingface dataset:
Cloud Modeling Languages by Example

Recently, several proposals towards a cloud modeling language have emerged. As they address the diversity of cloud environments, it is not surprising that these modeling languages support different scenarios. Using a by-example approach based on the scenario of software migration, we demonstrate their representational capabilities and review them according to characteristics common to all modeling languages as well as specific to the cloud computing domain. We report on our findings and present research guidelines for future efforts towards a better alignment of the different cloud modeling languages.
The parameters of ballpen ink examinations.

Prior to about 1950, the inks encountered on questioned documents were usually fluid inks of the washable dyestuff or iron gallotannate varieties. Analysis of these inks included observation under various wavelengths of light ranging from ultraviolet (UV) to infrared (IR), photography with selected filters, and chemical spot tests to detect metals such as iron, vanadium, and chromium. Since about 1953, the ballpen has taken over the position formerly occupied by the fluid ink pen.
Eurographics/ Ieee-vgtc Symposium on Visualization 2010 Non-iterative Second-order Approximation of Signed Distance Functions for Any Isosurface Representation

Signed distance functions (SDF) to explicit or implicit surface representations are intensively used in various computer graphics and visualization algorithms. Among others, they are applied to optimize collision detection, are used to reconstruct data fields or surfaces, and, in particular, are an obligatory ingredient for most level set methods. Level set methods are common in scientific visualization to extract surfaces from scalar or vector fields. Usual approaches for the construction of an SDF to a surface are either based on iterative solutions of a special partial differential equation or on marching algorithms involving a polygonization of the surface. We propose a novel method for a non-iterative approximation of an SDF and its derivatives in a vicinity of a manifold. We use a second-order algebraic fitting scheme to ensure high accuracy of the approximation. The manifold is defined (explicitly or implicitly) as an isosurface of a given volumetric scalar field. The field may be given at a set of irregular and unstructured samples. Stability and reliability of the SDF generation is achieved by a proper scaling of weights for the Moving Least Squares approximation, accurate choice of neighbors, and appropriate handling of degenerate cases. We obtain the solution in an explicit form, such that no iterative solving is necessary, which makes our approach fast.
Cleaning Training-Datasets with Noise-Aware Algorithms

We introduce a novel learning algorithm for noise elimination. Our algorithm is based on the re-measurement idea for the correction of erroneous observations and is able to discriminate between noisy and noiseless observations by using kernel methods. We apply our noise-aware algorithms to several domains including: astronomy, face recognition and ten machine learning benchmark datasets. Experimental results adding noise and useful anomalies to the data show that our algorithm improves data quality, without having to eliminate any observation from the original dataset
A Survey on Research on the Application-Layer Traffic Optimization (ALTO) Problem

A significant part of the Internet traffic today is generated by peer- to-peer (P2P) applications used traditionally for file-sharing, and more recently for real-time communications and live media streaming. Such applications discover a route to each other through an overlay network with little knowledge of the underlying network topology. As a result, they may choose peers based on information deduced from empirical measurements, which can lead to suboptimal choices. We refer to this as the Application Layer Traffic Optimization (ALTO) problem. In this draft we present a survey of existing literature on discovering topology characteristics.
Botulinum Neurotoxins and Botulism: A Novel Therapeutic Approach

Specific treatment is not available for human botulism. Current remedial mainstay is the passive administration of polyclonal antibody to botulinum neurotoxin (BoNT) derived from heterologous species (immunized animal or mouse hybridoma) together with supportive and symptomatic management. The antibody works extracellularly, probably by blocking the binding of receptor binding (R) domain to the neuronal receptors; thus inhibiting cellular entry of the holo-BoNT. The antibody cannot neutralize the intracellular toxin. Moreover, a conventional antibody with relatively large molecular size (150 kDa) is not accessible to the enzymatic groove and, thus, cannot directly inhibit the BoNT zinc metalloprotease activity. Recently, a 15–20 kDa single domain antibody (VHH) that binds specifically to light chain of BoNT serotype A was produced from a humanized-camel VH/VHH phage display library. The VHH has high sequence homology (>80%) to the human VH and could block the enzymatic activity of the BoNT. Molecular docking revealed not only the interface binding between the VHH and the toxin but also an insertion of the VHH CDR3 into the toxin enzymatic pocket. It is envisaged that, by molecular linking the VHH to a cell penetrating peptide (CPP), the CPP-VHH fusion protein would be able to traverse the hydrophobic cell membrane into the cytoplasm and inhibit the intracellular BoNT. This presents a novel and safe immunotherapeutic strategy for botulism by using a cell penetrating, humanized-single domain antibody that inhibits the BoNT by means of a direct blockade of the groove of the menace enzyme.
A Behavioural Bayesian Method for Sample Size Determination in Clinical Trials

1 SUMMARY When a new clinical treatment is being considered trials are carried out to estimate the increase in performance which i s l i k ely to result if the new treatment were to replace the treatment in current use. An important feature of the analysis in this paper is that the nal decision whether to use the new treatment is taken by potential users and their advisers on the basis of the strength of the evidence provided by the trial. The data are assumed to come from a normal distribution for which the mean and variance are both unknown. The optimal sample size is obtained by minimizing the expected total cost, which is the cost of the trial minus the resulting beneet from subsequent use of the new treatment. There is no widely used method for determining sample size which balances costs and beneets in this way. Typically the sample size is calculated on the basis of the required power at some value of the diierence in eeect. This is a fairly arbitrary criterion in comparison to the one proposed here.
Automated seizure detection in scalp EEG using multiple wavelet scales

The proposed research work designs a detector algorithm for automatic detection of epileptic seizures. In this work a wavelet based feature extraction technique has been adopted. Epochs of EEG are decomposed using discrete wavelet transform (DWT) up to 5 level of wavelet decomposition. Relative values of energy and a normalized coefficient of variation (NCOV) based measure, (σ<sup>2</sup>/μ<sub>a</sub>) are computed on the wavelet coefficients acquired in the frequency range of 0-32 Hz from both seizure and non-seizure segments. The performance of NCOV over the traditionally used coefficient of variation, COV (σ<sup>2</sup>/μ<sup>2</sup>) was studied. The feature NCOV yielded better performance than the commonly used COV, σ<sup>2</sup>/μ<sup>2</sup>. The algorithm was evaluated on 5 subjects from CHB-MIT scalp EEG database.
Outdoor‐based Experiential Training: Improving Transfer of Training Using Virtual Reality

Outdoor‐based experiential training has become a popular technique for training managers by directly involving them in the training process. Outdoor‐based programmes offer training in a fresh and motivating setting, tend to eliminate many of the role conflicts from the day‐to‐day work setting, and emphasize many of the interpersonal skills so important in today′s work setting. Unfortunately, the value of these programmes is often questioned by many trainers and managers. Do the skills learned in the outdoors actually transfer to the work setting, or do they simply work only in the outdoors? Presents a new addition to the outdoor training programme called virtual reality training. While not for all organizations, virtual reality training recreates the participant′s own work environment in a developmental setting, and helps to increase the transfer of training to the work setting. Presents actual examples of virtual reality programmes so the readers can evaluate the effectiveness of this type of training fo.
Revision of VLT/UVES constraints on a varying fine-structure constant

We critically review the current null results on a varying fine-structure constant, a, derived from Very Large Telescope (VLT)/Ultraviolet and Visual Echelle Spectrograph (UVES) quasar absorption spectra, focusing primarily on the many-multiplet analysis of 23 absorbers from which Chand et al. reported a weighted mean relative variation of Aa/a = (-0.06 ± 0.06) x 10 -5 . Our analysis of the same reduced data, using the same fits to the absorption profiles, yields very different individual Δα/α values with uncertainties typically larger by a factor of ∼3. We attribute the discrepancies to flawed parameter estimation techniques in the original analysis and demonstrate that the original Aa/a values were strongly biased towards zero. Were those flaws not present, the input data and spectra should have given a weighted mean of Δα/α = (-0.44 ± 0.16) x 10 -5 . Although this new value does reflect the input spectra and fits (unchanged from the original work - only our analysis is different), we do not claim that it supports previous Keck/High Resolution Echelle Spectrograph (HIRES) evidence for a varying a: there remains significant scatter in the individual Aa/a values which may stem from the overly simplistic profile fits in the original work. Allowing for such additional, unknown random errors by increasing the uncertainties on Aa/a to match the scatter provides a more conservative weighted mean, Aa/a = (-0.64 ± 0.36) x 10 -5 . We highlight similar problems in other current UVES constraints on varying a and argue that comparison with previous Keck/HIRES results is premature.
CONSTRUCTION AND USE OF IMITATING MODEL IN THE PROCESS OF MULTI-CRITERIA OPTIMIZATION OF THE SEPARATE-FLOW NOZZLE

There is considered a procedure of an optimum aerodynamic design of exhaust nozzles for highbypass-ratio turbofans (BPR8) using numerical simulation of viscous flows on the base of RANS equations with q- turbulence model. Problem of multi-criterion optimization of nozzle configuration is considered in severe statement with taking into account real restrictions on the acceptable range of control parameters variation. In order to simplify solution of optimization problems and decrease volume of parametric calculations, functional correlation between nozzle characteristics and the control parameters is presented as a multifactor simulation model.
Hierarchical Optimization-based Planning for High-DOF Robots

We present an optimization-based motion planning algorithm for high degree-of-freedom (DOF) robots in dynamic environments. Our formulation decomposes the highdimensional motion planning problem into a sequence of low-dimensional sub-problems. We compute collision-free and smooth paths using optimization-based planning and trajectory perturbation for each sub-problem. The high-DOF robot is treated as a tightly coupled system, and we use constrained coordination in an incremental manner to plan its motion. We highlight the performance on robots with tens of DOFs and demonstrate performance benefits over prior methods.
Using Diffusion Geometric Coordinates for Hyperspectral Imagery Representation

Modeling hyperspectral imagery via nonlinear manifold learning approaches can successfully capture the intrinsic geometries of the underlying complex high-dimensional data, which gives the state-of-the-art hyperspectral imagery representation. In this letter, we demonstrate that diffusion geometric coordinates can also represent hyperspectral imagery in a concise way and reveal much more significant structures than traditional linear methods. This diffusion framework tries to form a diffusion operator on the investigated hyperspectral imagery which simulates Markov random walk on the constructed affinity graph. The diffusion geometric coordinates derived from diffusion maps of the hyperspectral data incorporate the intrinsic geometries well where much more details about species-level spatial distributions are revealed in our experiments which show better classification results than principle component analysis (PCA). For 105- 106 or even larger imagery, by exploiting the backbone approach, the computation complexity and memory requirement of the full-scene computation and representation are tractable, which shows the potential significant usefulness in the hyperspectral remote sensing field.
A Bayesian Approach toward Active Learning for Collaborative Filtering

Collaborative filtering is a useful technique for exploiting the preference patterns of a group of users to predict the utility of items for the active user. In general, the performance of collaborative filtering depends on the number of rated examples given by the active user. The more the number of rated examples given by the active user, the more accurate the predicted ratings will be. Active learning provides an effective way to acquire the most informative rated examples from active users. Previous work on active learning for collaborative filtering only considers the expected loss function based on the estimated model, which can be misleading when the estimated model is inaccurate. This paper takes one step further by taking into account of the posterior distribution of the estimated model, which results in more robust active learning algorithm. Empirical studies with datasets of movie ratings show that when the number of ratings from the active user is restricted to be small, active learning methods only based on the estimated model don't perform well while the active learning method using the model distribution achieves substantially better performance.
De novo DNA methylation through the 5′-segment of the H19 ICR maintains its imprint during early embryogenesis

Genomic imprinting is a major monoallelic gene expression regulatory mechanism in mammals, and depends on gamete-specific DNA methylation of specialized cis-regulatory elements called imprinting control regions (ICRs). Allele-specific DNA methylation of the ICRs is faithfully maintained at the imprinted loci throughout development, even in early embryos where genomes undergo extensive epigenetic reprogramming, including DNA demethylation, to acquire totipotency. We previously found that an ectopically introduced H19 ICR fragment in transgenic mice acquired paternal allele-specific methylation in the somatic cells of offspring, whereas it was not methylated in sperm, suggesting that its gametic and postfertilization modifications were separable events. We hypothesized that this latter activity might contribute to maintenance of the methylation imprint in early embryos. Here, we demonstrate that methylation of the paternally inherited transgenic H19 ICR commences soon after fertilization in a maternal DNMT3A- and DNMT3L-dependent manner. When its germline methylation was partially obstructed by insertion of insulator sequences, the endogenous paternal H19 ICR also exhibited postfertilization methylation. Finally, we refined the responsible sequences for this activity in transgenic mice and found that deletion of the 5′ segment of the endogenous paternal H19 ICR decreased its methylation after fertilization and attenuated Igf2 gene expression. These results demonstrate that this segment of the H19 ICR is essential for its de novo postfertilization DNA methylation, and that this activity contributes to the maintenance of imprinted methylation at the endogenous H19 ICR during early embryogenesis. Highlighted article: In the mouse early embryo, H19 ICR imprinting is achieved through maternally inherited DNMT3A- and DNMT3L-mediated de novo methylation and requires a specific 5′ region of the locus.
The E3 ligase RNF43 inhibits Wnt signaling downstream of mutated β-catenin by sequestering TCF4 to the nuclear membrane

Cancer-associated mutations in RNF43 prevent its inhibitory actions on the Wnt–β-catenin pathway in the nucleus. RNF43 halts Wnt at the nucleus Wnt signaling is critical to development and is often reactivated in cancer. The E3 ubiquitin ligase RNF43 inhibits Wnt–β-catenin signaling. Rather than promoting the degradation of cell surface Wnt receptors, Loregger et al. found that RNF43 sequestered TCF4, a β-catenin partner and transcription factor, at the nuclear membrane through a mechanism independent of its E3 ligase function. When expressed in frog embryos, RNF43 bearing mutations like those found in human gastrointestinal tumors increased Wnt signaling. Coexpression of wild-type RNF43 suppressed Wnt signaling even in cells with a constitutively active mutant of β-catenin, indicating tumor-suppressive activity of RNF43 functioning downstream of β-catenin. Given its fundamental role in development and cancer, the Wnt–β-catenin signaling pathway is tightly controlled at multiple levels. RING finger protein 43 (RNF43) is an E3 ubiquitin ligase originally found in stem cells and proposed to inhibit Wnt signaling by interacting with the Wnt receptors of the Frizzled family. We detected endogenous RNF43 in the nucleus of human intestinal crypt and colon cancer cells. We found that RNF43 physically interacted with T cell factor 4 (TCF4) in cells and tethered TCF4 to the nuclear membrane, thus silencing TCF4 transcriptional activity even in the presence of constitutively active mutants of β-catenin. This inhibitory mechanism was disrupted by the expression of RNF43 bearing mutations found in human gastrointestinal tumors, and transactivation of the Wnt pathway was observed in various cells and in Xenopus embryos when the RING domain of RNF43 was mutated. Our findings indicate that RNF43 inhibits the Wnt pathway downstream of oncogenic mutations that activate the pathway. Mimicking or enhancing this inhibitory activity of RNF43 may be useful to treat cancers arising from aberrant activation of the Wnt pathway.
Relationship Between Perfectionism and Agoraphobia in Patients with Panic Disorder

We investigated the relationship between perfectionism and agoraphobia in 59 panic disorder patients with agoraphobia, 44 panic disorder patients without agoraphobia and 35 healthy controls using Frost's Multidimensional Perfectionism Scale. The panic disorder with agoraphobia group scored significantly higher than the panic disorder group or the healthy controls on the Frost's Multidimensional Perfectionism Scale subscales regarding concern over mistakes, personal standards, parental criticism and on the total Frost's Multidimensional Perfectionism Scale score. In logistic regression analyses, the Frost's Multidimensional Perfectionism Scale total score was the only predictor variable that made a significant unique contribution to the prediction of agoraphobia. These results suggest that perfectionistic beliefs and tendencies may be involved in the development and maintenance of agoraphobia in patients with panic disorder and thus should be considered as a target for cognitive behavioural therapy.
EnViBo: Embedded network for vital sign and Biomedical Signal Monitoring

Ambulatory monitoring of vital sign and biomedical signals covers a wide spectrum of applications in different contexts nowadays and has been a research topic during the last decade. The plethora of IEEE 802.X communication standards, favors greatly the development of wireless sensor networks ranging from body-area to wide-area coverage. The use of gateways to connect wireless networks with infrastructure networks increase the range of coverage of monitoring applications, and allows getting data remotely through interfaces running on devices like desktop computers, laptops, tablets and smartphones. We present a monitoring platform called EnViBo which is based on a three level architecture; this platform integrates sensor nodes that use moderate-cost elements and Microchip's IEEE 802.15. 4-based royaltie-free protocol named MiWi P2P for communications. The progresses in fields like embedded systems, energy harvesting solutions, microelectromechanical systems and wearable sensors, allows creating applications supported by EnViBo platform which can be both cost and operationally effective.
A hybrid power-saving protocol by dual-channel and dual-transmission-range clustering for IEEE 802.11-based MANETs

Purpose – There are two types of medium access control (MAC) layer power‐saving (PS) protocols for IEEE 802.11‐based mobile ad hoc networks: synchronous and asynchronous ones. This paper seeks to propose a hybrid PS protocol to take advantages of both types of protocols.Design/methodology/approach – The protocol utilizes the concept of dual‐channel and dual‐transmission‐range clustering. It divides all the hosts into clusters. Each cluster has a head and all the heads are organized as a virtual backbone to help route data. The protocol also utilizes the cluster head dismissal mechanism to avoid the ever‐increasing of cluster heads and to adapt to topology changing.Findings – Simulation results demonstrate that the proposed protocol is more power‐efficient and more scalable than related protocols.Originality/value – The proposed protocol is applicable to MANETs composed of hosts with single IEEE 802‐11 network interface card.
Voltage-controlled inversion of tunnel magnetoresistance in epitaxial nickel/graphene/MgO/cobalt junctions

We report on the fabrication and characterization of vertical spin-valve structures using a thick epitaxial MgO barrier as spacer layer and a graphene-passivated Ni film as bottom ferromagnetic electrode. The devices show robust and scalable tunnel magnetoresistance, with several changes of sign upon varying the applied bias voltage. These findings are explained by a model of phonon-assisted transport mechanisms that relies on the peculiarity of the band structure and spin density of states at the hybrid graphene|Ni interface.
Access and Resistance to Dominant Forms of Discourse: Critical Literacy and “At Risk” High School Students

In an effort to both challenge and engage adolescent students deemed to be academically “at risk,” the purpose of this study was to examine the possibility of creating a pedagogical bridge between dominant (school sanctioned) and non-dominant (non-school) forms of literacy. The study was conducted within a setting where high school students were invited to co-construct a literacy group with two primary adult mentors: an education professor from a local state university and the school's Communities in Schools Youth Coordinator. Through an examination of student response to texts containing themes of social justice, findings for this study reveal that creating contexts where “at risk” adolescents can explore marginalized positionalities presented in a variety of genres of text creates a bridge between dominant and non-dominant forms of discourse. Implications for the role of critical literacy—taking action to foster a more just society—are presented as a key component for creating this bridge.