Miracle-dz/UniFAR-FTUs huggingface dataset:
Varactor-Based Dynamic Load Modulation of High Power Amplifiers

In this work, dynamic load modulation of high power amplifiers using a varactor-based tunable matching network is presented. The feasibility of dynamic tuning and efficiency enhancement of this technique is demonstrated using a modular design approach for two existing high efficiency power amplifiers (PA), a 7-W class-E, and a 10-W class-J power amplifier PA at 1 GHz. For this purpose and for each of the PAs, a simple quasi-static inverse model is developed allowing an efficiency-optimized control of the PA and the varactor-based tunable matching network. Modulated measurements using a single carrier WCDMA signal with 11.3 dB peak-to-average ratio (PAR) indicate about 10 to 14 percentage units improvements in the average power-added efficiency (PAE) for the complete architecture.
Political Brand Communities as Social Network Clusters: Winning and Trailing Candidates in the GOP 2016 Primary Elections

This study integrates a brand community theoretical framework and a social networks approach to the study of political marketing on social media. It analyzes Twitter activity by and about all Republican candidates during the month of January 2016 prior to the Iowa primary caucus. Specifically, it identifies key patterns of information flow and political brand community emergence surrounding each candidate as well as brand social mediators who play influential roles in content flow. Findings detect network clusters - subgroups of siloed users - who converse to exchange information with one another rather than with disconnected others in any given political brand community. We classify these clusters in two groups: direct communities that surround a brand – here, a candidate – and indirect communities that consist of users who talk about the political brands but are not directly connected to the brand. Within this fragmented community structure, brand social mediators have the power to bridge direct and indirect brand communities across the network. Among the winning and trailing brand communities, different sets of brand social mediators, patterns of information flow, and network structures emerge. The findings suggest that the more successful a political brand is in voter polls, the weaker the social ties and relationships are within the brand communities. The interactions between political brands and their direct vs. indirect communities also exhibit different patterns of information flow. While direct brand communities demonstrate a higher level of reciprocity, indirect brand communities show a higher level of density. Implications and directions for future research are discussed.
Learning the Latent Topics for Question Retrieval in Community QA

Community-based Question Answering (cQA) is a popular online service where users can ask and answer questions on any topics. This paper is concerned with the problem of question retrieval. Question retrieval in cQA aims to find historical questions that are semantically equivalent or relevant to the queried questions. Although the translation-based language model (Xue et al., 2008) has gained the state-of-the-art performance for question retrieval, they ignore the latent topic information in calculating the semantic similarity between questions. In this paper, we propose a topic model incorporated with the category information into the process of discovering the latent topics in the content of questions. Then we combine the semantic similarity based latent topics with the translation-based language model into a unified framework for question retrieval. Experiments are carried out on a real world cQA data set from Yahoo! Answers. The results show that our proposed method can significantly improve the question retrieval performance of translation-based language model.
Infrared and optical spectroscopy of type Ia supernovae in the nebular phase

We present near-infrared (NIR) spectra for Type Ia supernovae al epochs of 13 to 338 d after maximum blue light. Some contemporary optical spectra are also shown. All the NIR spectra exhibit considerable structure throughout the J, H and K bands. In particular, they exhibit a flux 'deficit' in the J band which persists as late as 175 d. This is responsible for the well-known red J-H colour, To identify the emission features and test the Ni-56 hypothesis for the explosion and subsequent light curve, we compare the NIR and optical nebular-phase data with a simple non-LTE nebular spectral model. We find that many of the spectral features are due to iron-group elements, and that the J-band deficit is due to a lack of emission lines from species that dominate the rest of the IR/optical spectrum. Nevertheless, some emission is unaccounted for, possibly due to inaccuracies in the cobalt atomic data. For some supernovae, blueshifts of 1000-3000 km s(-1) are seen in infrared and optical features at 3 months. We suggest that this is due to clumping in the ejecta. The evolution of the cobalt/iron mass ratio indicates that Co-56-decay dominates the abundances of these elements, The absolute masses of iron-group elements which we derive support the basic thermonuclear explosion scenario for Type Ia supernovae. A core-collapse origin is less consistent with our data.
Characterisation and modeling of mismatch in MOS transistors for precision analog design

A characterization methodology is presented that accurately predicts the mismatch in drain current over a wide operating range using a minimum set of measured data. The physical causes of mismatch are discussed in detail for both p- and n-channel devices. Statistical methods are used to develop analytical models that relate the mismatch to the device dimensions. It is shown that these models are valid for small-geometry devices only. Extensive experimental data from a 3-/spl mu/m CMOS process are used to verify the models. The application of the transistor matching studies to the design of a high-performance digital-to-analog converter (DAC) is discussed. A circuit design methodology is presented that highlights the close interaction between the circuit yield and the matching accuracy of devices. It has been possible to achieve a circuit yield of greater than 97% as a result of the knowledge generated regarding the matching behavior of transistors and due to the systematic design approach.
Toll-Like Receptor Ligands Induce Expression of the Costimulatory Molecule CD155 on Antigen-Presenting Cells

Genotoxic stress and RAS induce the expression of CD155, a ligand for the immune receptors DNAM-1, CD96 and TIGIT. Here we show that antigen-presenting cells upregulate CD155 expression in response to Toll-like receptor activation. Induction of CD155 by Toll-like receptors depended on MYD88, TRIF and NF-κB. In addition, IRF3, but not IRF7, modulated CD155 upregulation in response to TLR3 signals. Immunization of CD155-deficient mice with OVA and the TLR9 agonist CpG resulted in increased OVA-specific IgG2a/c titers when compared to wild type mice. Splenocytes of immunized CD155-deficient mice secreted lower levels of IL-4 and fewer IL-4 and GATA-3 expressing CD4+ T cells were present in the spleen of Cd155−/− mice. Our data suggest that CD155 regulates Th2 differentiation. Targeting of CD155 in immunization protocols using peptides may represent a promising new approach to boost protective humoral immunity in viral vaccines.
Improving of handwritten Tunisian City names recognition based on Factorial Hidden Markov Model

Hidden Markov Models (HMMs) are now widely used for off-line Arabic handwriting recognition. Actually, classical HMMs are one-dimensional models, that is why to process an Arabic word image we have developed a discrete Dynamic Bayesian Network (DBN). The DBNs are an extension and a generalization of the classical HMMs, which can model the interaction between several observations and state sequences. In our study, we have represented words by factorizing two streams in different manners, where the interaction is achieved through the causal influence between observable variables in the first model and state variables in the second one. The aim of this is to consider the two flows of information together: The observations on the columns (as well as lines) are obtained by scanning the image horizontally (and also vertically) by a uniform sliding window. We have compared the two models on the recognition of off-line Arabic handwritten words. The experiments show that the first model is better and more adapted to our task than the second one.
Mutability , Conceptual Transformation , and Context

Features differ in their mutability. For example, a robin could still be a robin even if it lacked a red breast; but it would probably not count as one if it lacked bones. I have hypothesized (Love & Sloman, 1995) that features are immutable to the extent other features depend on them. We can view a feature's mutability as a measure of transformational difficulty. In deriving new concepts, we often transform existing concepts (e.g. we can go from thinking about a robin to thinking about a robin without a red breast). The difficulty of this transformation, as measured by reaction time, increases with the immutability of the feature transformed. Conceptual transformations are strongly affected by context, but in a principled manner, also explained by feature dependency structure. A detailed account of context's effect on mutability is given, as well as corroborating data. I conclude by addressing how mutability-dependency theory can be applied to the study of similarity, categorization, conceptual combination, and
Evaluating online learning: a review of studies on e-testing in the university context

Nowadays, the class size and the growing number of undergraduate students generate unprecedented problems of management, monitoring and evaluation of learning. From this point of view technologies may provide opportunities which deserve to be investigated for their potential to contribute to solve problems related to the management of large size classes. Since many teachers show resistance towards the replacement of traditional exams with e-assessment, little effort has been made for the innovation of the evaluation procedures at the university level. However, several studies have been carried out in this field. This paper provides an overview of the current electronic evaluation activity in the university context. Data shows benefits related to students’ monitoring for the simplification of procedures and the reduction of teachers’ cognitive load. Moreover students appreciate the electronic mode for the possibility to obtain immediate feedback. The challenges concern the technological infrastructure, the scalability and safe use. Valutare l’apprendimento online: una rassegna degli studi sull’e-testing nel contesto universitario Oggigiorno le dimensioni delle classi e il numero crescente di studenti in ambito universitario pongono inediti problemi di gestione, monitoraggio e valutazione degli apprendimenti. La tecnologia da questo punto di vista rappresenta un’opportunita che necessita di essere indagata perche potenzialmente in grado di contribuire alla risoluzione delle problematiche connesse alla gestione di una grande mole di iscritti. Poiche molti docenti manifestano diffidenza verso la sostituzione degli esami tradizionali con modalita elettroniche di valutazione, pochi sforzi sono stati compiuti in questi anni per l’innovazione delle procedure valutative. Tuttavia, molti sono gli studi realizzati in questo campo. Questo lavoro fornisce una panoramica dell’attuale attivita di valutazione elettronica nel contesto universitario. Dai dati raccolti emergono benefici in relazione al monitoraggio degli studenti per la semplificazione delle procedure e la riduzione del carico di lavoro dei docenti. Inoltre gli studenti apprezzano la modalita elettronica per la possibilita di ottenere feedback immediati. Le sfide riguardano l’infrastruttura tecnologica, la scalabilita e la sicurezza.
Online generation of reachable grasps for dexterous manipulation using a representation of the reachable workspace

In service robotic tasks, the ability to grasp and handle objects is mandatory. Short response times with respect to execution of commanded tasks are necessary. Planning in general and grasp planning in particular should happen online. We extend the online grasp planner by Borst et al. [1] to generate reachable grasps while preserving the integrity and modularity of the grasp planner. To achieve this a representation of the reachable space of a robot arm is used to determine a grasp's reachability. Furthermore we show the influence of obstacles on the reachability throughout the workspace. A method to include obstacles into the representation of reachability is sketched. The resulting representation is used by the grasp planner. The performance of the algorithms is evaluated by measuring their computation times. Even in the worst case our grasp planner outperforms comparable state of the art approaches.
On Estimating L 22 Divergence

We give a comprehensive theoretical characterization of a nonparametric estimator for the L 22 divergence between two continuous distributions. We ﬁrst bound the rate of convergence of our estimator, showing that it is √ n -consistent provided the densities are sufﬁciently smooth. In this smooth regime, we then show that our estimator is asymptotically normal, construct asymptotic conﬁdence intervals, and establish a Berry-Ess´een style inequality characterizing the rate of convergence to normality. We also show that this estimator is minimax optimal.
miR-9 inhibits Schwann cell migration by targeting Cthrc1 following sciatic nerve injury

ABSTRACT The regulative effects of microRNAs (miRNAs) on responses of Schwann cells to a nerve injury stimulus are not yet clear. In this study, we noted that the expression of eight miRNAs was downregulated at different time points following rat sciatic nerve transection, and found that 368 potential targets of these eight miRNAs were mainly involved in phenotypic modulation of Schwann cells. Of these miRNAs, miR-9 was identified as an important functional regulator of Schwann cell migration that was a crucial regenerative response of Schwann cells to nerve injury. In vitro, upregulated expression of miR-9 inhibited Schwann cell migration, whereas silencing of miR-9 promoted Schwann cell migration. Intriguingly, miR-9 exerted this regulative function by directly targeting collagen triple helix repeat containing protein 1 (CTHRC1), which in turn inactivated downstream Rac1 GTPase. Rac1 inhibitor reduced the promotive effects of anti-miR-9 on Schwann cell migration. In vivo, high expression of miR-9 reduced Schwann cell migration within a regenerative nerve microenvironment. Collectively, our results confirmed the role of miR-9 in regulating Schwann cell migration after nerve injury, thus offering a new approach to peripheral nerve repair.
Diabetic Gastrointestinal Neuropathy: Elusive Diagnosis and Difficult Treatment

Diabetic Gastrointestinal Neuropathy is an elusive diagnosis and often overlooked. The affected patients do not connect the symptoms with diabetes and undergo a painstaking and expensive search for other disorders of the digestive tract. Diabetic Gastrointestinal Neuropathy additionally causes a baffling glucose instability that further aggravates the condition. The treatment is difficult. The less severe cases respond to prokinetic treatment, but when the disease is advanced the only available therapy is the insertion of a pacemaker.
Memex: A Browsing Assistant for Collaborative Archiving and Mining of Surf Trails

Keyword indices, topic directories, and link-based rankings are used to search and structure the rapidly growing Web today. Surprisingly little use is made of years of browsing experience of millions of people. Indeed, this information is routinely discarded by browsers. Even deliberate bookmarks are stored in a passive and isolated manner. All this goes against Vannevar Bush’s dream of the Memex : an enhanced supplement to personal and community memory. We propose to demonstrate the beginnings of a ‘Memex’ for the Web: a browsing assistant for individuals and groups with focused interests. Memex blurs the articial distinction between browsing history and deliberate bookmarks. The resulting glut of data is analyzed in a number of ways at the individual and community levels. Memex constructs a topic directory customized to the community, mapping their interests naturally to nodes in this directory. This lets the user recall topic-based browsing contexts by asking questions like \What trails was I following when I was last surng about classical music? " and \What are some popular pages in or near my community’s recent trail graph related to music? " 1 Motivation Three paradigms have emerged for exploring the Web: keyword search, directory browsing, and following links. Popular search engine and directory sites are visited tens of millions of times per day. We speculate that the total number of clicks per day is orders of magnitude larger. This third source of information, the browsing history of millions of Web users over several years, an information source that dwarfs the scale of the Web itself, is almost entirely discarded by browsers as ‘history’. Deliberate ‘bookmarks’ are preserved, but passively, in browser-dependent formats; this separates them from the dominant world of HTML hypermedia, even if their owners were willing to share them (as they are, in our experience, with all but a small section of their browsing activity).
Data-Driven Monitoring for Cloud Compute Systems

The end-to-end monitoring of inter-dependent applications in the cloud is challenging. Difficulties arise from the complexity of computations and the highly distributed nature of the deployment. Due to the lack of a comprehensive observability solution, it is very difficult to apply autonomous mechanisms to ensure service guarantees in the cloud. To tackle the problem, we propose the method of data-driven monitoring, that provides a detailed, live view on how data is flowing through a possibly complex compute system. The method is based on the tracing of individual input events and the collection of resource usage metrics along the paths. By reconstructing causal and temporal relationships, we can detect degradations in performance, pinpoint root causes and apply corrective actions before end-to-end requirements are endangered. To demonstrate the potential of the concept, we created a prototype implementation in a big data compute platform, and also developed two automated optimization algorithms.
Developing Cognitive and Motor Creativity in Children Through an Exercise Program Using Nonlinear Pedagogy Principles

Movement creativity is a key factor in motor development in children, yet very few exercise programs promote the development of motor creativity in children. To address this gap in the literature, this study tested the effectiveness of a creative exercise program on children’s motor and cognitive creativity, and their probability of adaptation to exercise challenges. A total of 140 fourth-grade students from 8 elementary schools (Mage = 9.56 ± 0.61 years) were randomly allocated to a conventional or creative program for 10 sessions over a period of 3 months. A nonlinear pedagogical framework was used to educate coaches and to transform the conventional program into a creative one. Following the 3-months intervention, children in the creative program displayed higher originality in thinking and improved fluency and flexibility in movement than children in the conventional program. No differences were observed on the probability of adaptation to exercise challenges between both programs. The findings revealed that introducing elements of the nonlinear pedagogy into a conventional exercise program can increase children’s cognitive and motor creativity. This study provides educators with evidenced-based guidelines for maintaining, or even fostering, both cognitive and motor creativity in children. Manipulation of constrains, variability, improvisation, and problem solving appear to be key principles for developing creativity among children.
Development of Rational Rail Network Topology for High-Speed and Conventional Trains Based on Bacterial Foraging Optimization

Development of projects on higher speeds of passenger trains on the world railways requires investigation into the balanced combination of routes for high-speed and conventional trains considering railway network topology. The objective of the study is consideration of railway network peculiarities regarding transportation demand by designing a mathematical model to find an optimal passenger train flow distribution. In order to formalize the process of a simultaneous search for the rail passenger network topology and determination of the most probable distribution within the formed train flow network for high-speed and conventional trains it has been proposed to use a criterion as a system entropy adapted to the rail passenger-oriented transportation. The concept of entropy is based on isomorphism in systems, which allows monitoring and implementing the link between a micro and macro level in the passenger transportation system. To solve the mathematical model the authors have used an optimization method based on Bacterial Foraging Optimization (BFO). The implementation of the mathematical model based on BFO will make it possible to theoretically substantiate the efficiency of existing and promising projects on higher speeds of passenger trains on the rail transport regarding adaptation of the network topology to transportation demand.
Finite-temperature depolarization in half metals

The temperature dependence of the spin polarization of type-I half-metallic ferromagnets is investigated and compared with that of other magnetic materials, such as semimetals, strong and weak ferromagnets, and exchange-enhanced Pauli paramagnets. Stable atomic moments, as realized by strong intra-atomic exchange, exhibit a nonzero spin-down density of states (DOS) at finite temperatures. This thermal spin mixing means that the conductivity of the ‘insulating’ spin channel is always nonzero and that half-metallic ferromagnetism is an idealized limit. At zero temperature, similar effects are caused by intersublattice interactions, spin–orbit coupling and crystal imperfections. With increasing interatomic hopping, the moment becomes unstable, and Stoner-type thermal excitations yield an additional reduction of the spin polarization. In the Stoner limit, the hybridization gap closes far below the Curie temperature, and the corresponding transition temperature T* increases with increasing hybridization gap and decreasing band width. Correlations are analysed by a version of the Kondo model and by an unrestricted Hartree–Fock approximation, and it is argued that correlations are less important than the leading one-electron contributions.
Model Reference Adaptive Control for Actuators of a Biped Robot Locomotion

Many robotics problems do not take dynamics of actuators into account in the formulation of the co ntrol solution. The fallacy is in assuming that desired f orces/torques can be instantaneously and accurately generated. In practice actuator dynamics may be unknown and can have significant transient effect on the overall results. This paper presents a Model Reference Adaptive Controller for the actuators of a robotic biped walker. The actuators learn and produce the torque required for a walking cycle presented in the form of a torque reference model. The actuators of the biped adapt themselves to match with the desired torque reference model with the aid of Lyapunov stability criterion. This control scheme for the biped robot is simulated on a sagitt al plane. The results verify the MRAC scheme for the actuators.