Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive clock synchronization in sensor networks

Recent advances in technology have made low cost, low power wireless sensors a reality. Clock synchronization is an important service in any distributed system, including sensor network systems. Applications of clock synchronization in sensor networks include data integration in sensors, sensor reading fusion, TDMA medium access scheduling, and power mode energy saving. However, for a number of reasons, standard clock synchronization protocols are unsuitable for direct application in sensor networks. We introduce the concept of adaptive clock synchronization based on the need of the application and the resource constraint in the sensor networks. We describe a probabilistic method for clock synchronization that uses the higher precision of receiver-to-receiver synchronization, as described in reference broadcast synchronization (RBS) protocol. This deterministic protocol is extended to provide a probabilistic bound on the accuracy of the clock synchronization, allowing for a tradeoff between accuracy and resource requirement. Expressions to convert service specifications (maximum clock synchronization error and confidence probability) to actual protocol parameters (minimum number of messages and synchronization overhead) are derived. Further, we extend this protocol for maintaining clock synchronization in a multihop network.
A broadband high linearity inductorless CMOS variable gain amplifier for square kilometer array

This paper presents a high-bandwidth 2 GHz CMOS variable gain amplifier (VGA) for using in the band of square kilometer array (SKA) range that achieves an 18 dB dynamic range (DR) while powered from a single 1.8 V supply. This VGA capability is obtained by employing a passive digital T-bridge attenuator followed by a fixed amplifier, the use of attenuator in the front end of the VGA results in a high linearity of 28 dBm to 34 dBm over the full DR. In contrast to traditional VGAs [5], [6], the VGA exhibits 30-50 dB more in DR while its bandwidth and IP3 extends below the presented.
Numerical Simulation in Combustion Space of an Oxy-fuel Glass Furnace with Different Jet Angles of Auxiliary Oxygen

Numerical simulation in the combustion space of an oxy-fuel glass furnace was carried out. In order to obtain better combustion efficiency, the cases by adding auxiliary oxygen inlet with different jet angles of auxiliary oxygen from 0 degrees to 90 degrees were compared. The gas phase is expressed with two-equation model, while the combustion with non-premixed model and the radiation with DO radiation model. According to the simulation results, the way adding auxiliary oxygen inlet contributes to improve combustion efficiency and achieve appropriate temperature distribution. The flame shape keeps relative rational in small angles (from 0 degrees to 50 degrees) but becomes irregular after 50 degrees. Especially in 30 degrees, not only the heat transfer rate to the bottom surface reaches a maximum value, but also the average temperature of the cross middle slice of burners height is high.
Data-Driven speech parameter generation for russian text-to-speech system

We propose a speech parameter generation approach for Russian based on hidden Markov models. The speech parameter sequence is generated from HMMs whose observation vectors contain speech characteristics. As a baseline we use the spectrum represented by mel-frequency cepstral coefficients (MFCC), pitch and duration parameters. All of them can be easily complemented by any other parameters, improving the quality. For the creation of the voice model we use linguistic and prosodic features which are the observations of every allophone in the utterance. This paper also presents the results of research into selecting the most effective features to characterize an allophone. Experimental results show that Russian speech can be successfully parameterized and an arbitrary utterance can be synthesized from the generated parameters.
Nonnegative Matrix Factorization with Earth Mover's Distance metric

Nonnegative Matrix Factorization (NMF) approximates a given data matrix as a product of two low rank nonnegative matrices, usually by minimizing the L2 or the KL distance between the data matrix and the matrix product. This factorization was shown to be useful for several important computer vision applications. We propose here a new NMF algorithm that minimizes the Earth Mover's Distance (EMD) error between the data and the matrix product. We propose an iterative NMF algorithm (EMD NMF) and prove its convergence. The algorithm is based on linear programming. We discuss the numerical difficulties of the EMD NMF and propose an efficient approximation. Naturally, the matrices obtained with EMD NMF are different from those obtained with L2 NMF. We discuss these differences in the context of two challenging computer vision tasks - texture classification and face recognition - and demonstrate the advantages of the proposed method.
Cancer log-kill revisited.

At the root of science lie basic rules, if we can discover or deduce them. This is not an abstract project but practical; if we can understand the why then perhaps we can rationally intervene. One of the unifying unsolved problems in physics is the hypothetical "Theory of Everything. " In a similar vein, we can ask whether our own field contains such hidden fundamental truths and, if so, how we can use them to develop better therapies and outcomes for our patients. Modern oncology has developed as drugs and translational science have matured over the 50 years since ASCO's founding, but almost from that beginning tumor modeling has been a key tool. Through this general approach Norton and Simon changed our understanding of cancer biology and response to therapy when they described the fit of Gompertzian curves to both clinical and animal observations of tumor growth. The practical relevance of these insights has only grown with the development of DNA sequencing promising a raft of new targets (and drugs). In that regard, Larry Norton's contribution to this year's Educational Book reminds us to always think creatively about the fundamental problems of tumor growth and metastases as well as therapeutic response. Demonstrating the creativity and thoughtfulness that have marked his remarkable career, he now incorporates a newer concept of self-seeding to further explain why Gompertzian growth occurs and, in the process, provides a novel potential therapeutic target. As you read his elegantly presented discussion, consider how this understanding, wisely applied to the modern era of targeted therapies, might speed the availability of better treatments. But even more instructive is his personal model-not only the Norton-Simon Hypothesis-of how to live and approach science, biology, patients and their families, as well as the broader community. He shows that with energy, enthusiasm, optimism, intellect, and hard work we can make the world better. Clifford A. Hudis, MD, FACP, 2013-2014 ASCO President.
Near duplicate image matching techniques

Near duplicate image detection needs the matching of a bit altered images to the original image. This will help in the detection of forged images. A great deal of effort has been dedicated to visual applications that need efficient image similarity metrics and signature. Digital images can be easily edited and manipulated owing to the great functionality of image processing software. This leads to the challenge of matching somewhat altered images to their originals, which is termed as near duplicate image detection. This paper discusses the literature reviewed on the development of several image matching algorithms. This paper encompasses 2 sections. Section 1 is the introduction. Section 2 discusses the literature reviewed on the development of image matching algorithms.
A New Nearby Candidate Star Cluster in Ophiuchus at d ≃ 170 pc

The recent discoveries of nearby star clusters and associations within a few hundred pc of the Sun, as well as the order of magnitude difference in the formation rates of the embedded and open cluster populations, suggests that additional poor stellar groups are likely to be found at surprisingly close distances to the Sun. Here I describe a new nearby stellar aggregate found by virtue of the parallel proper motions, similar trigonometric parallaxes, and consistent color-magnitude distribution of its early-type members. The 120 Myr old group lies in Ophiuchus at d ≃ 170 pc, with its most massive member being the 4th magnitude post-main-sequence B8 II-III star μ Oph. The group may have escaped previous notice due to its nonnegligible extinction (AV ≃ 0.9 mag). If the group was born with a normal initial mass function, and the nine B- and A-type systems represent a complete system of intermediate-mass stars, then the original population was probably of order ∼200 systems. The age and space motion of the new cluster are very similar to those of the Pleiades, the α Per cluster, and the AB Dor moving group, suggesting that these aggregates may have formed in the same star-forming complex some ∼108 yr ago.
Fault Localization for Dynamic Web Application

Fault localization is the problem of formative where in the source code modifies has to be completed in order to fix the sensed failures. The cause of the failure is called as fault that also called as bug. Based on the dynamic execution of the web application segregate the source cause of fault by fault localization techniques. To localize faults efficiently in web applications, some fault-localization algorithms can be improved by using a comprehensive domain for conditional and function-call statements and using a source mapping but did not focus on cross based language in web application. This study use traverse analysis based technique for fault localization. A traverse analysis is constructed for each executable statement and to determine the suspiciousness of the corresponding statement. In addition, to use dynamic test generation based on heuristic search strategies. This approach directed by the control flow graph of the program beneath test.
Striatal dopamine ramping may indicate flexible reinforcement learning with forgetting in the cortico-basal ganglia circuits

It has been suggested that the midbrain dopamine (DA) neurons, receiving inputs from the cortico-basal ganglia (CBG) circuits and the brainstem, compute reward prediction error (RPE), the difference between reward obtained or expected to be obtained and reward that had been expected to be obtained. These reward expectations are suggested to be stored in the CBG synapses and updated according to RPE through synaptic plasticity, which is induced by released DA. These together constitute the 'DA=RPE' hypothesis, which describes the mutual interaction between DA and the CBG circuits and serves as the primary working hypothesis in studying reward learning and value-based decision making. However, recent work has revealed a new type of DA signal that appears not to represent RPE. Specifically, it has been found in a reward-associated maze task that striatal DA concentration primarily shows a gradual increase towards the goal. We explored whether such ramping DA could be explained by extending the 'DA=RPE' hypothesis by taking into account biological properties of the CBG circuits. In particular, we examined effects of possible time-dependent decay of DA-dependent plastic changes of synaptic strengths by incorporating decay of learned values into the RPE-based reinforcement learning model and simulating reward learning tasks. We then found that incorporation of such a decay dramatically changes the model's behavior, causing gradual ramping of RPE. Moreover, we further incorporated magnitude-dependence of the rate of decay, which could potentially be in accord with some past observations, and found that near-sigmoidal ramping of RPE, resembling the observed DA ramping, could then occur. Given that synaptic decay can be useful for flexibly reversing and updating the learned reward associations, especially in case the baseline DA is low and encoding of negative RPE by DA is limited, the observed DA ramping would be indicative of the operation of such flexible reward learning.
Performance-driven architectural refactoring through bidirectional model transformations

The generation of performance models from architectural models has been tackled with well-founded approaches in the last decade, whereas there is a clear lack of automation in the backward path that brings the analysis results back to the software architecture. It is common to iteratively modify a (generated) performance model until performance indices meet the requirements. However, propagating the performance model modifications back to the original architectural model is a complex problem. In this paper we make a first step in this direction, in that we use the JTL language for specifying a bidirectional model transformation between UML models and Queueing Networks, so working towards an automated round-trip process between software architectural models and performance models.
On the effectiveness of sybil defenses based on online social networks

A Sybil attack can inject many forged identities (called Sybils) to subvert a target system. Among various defense approaches, of particular attention are those that explore the online social networks (OSNs) of users in a target system to detect or tolerate Sybil nodes. Albeit different in their working principle, all these approaches assume it is difficult for an attacker to create attack edges to connect Sybils with honest users. However, researchers have found that an attacker can employ simple strategies to obtain many attack edges. In this work we revisit the state-of-the-art, OSN-based Sybil defenses, and point out their strengths and weaknesses due to the impact of the new properties. We find these defense approaches are vulnerable to attackers under the new scenario, and in many cases a Sybil node only needs to obtain a handful of attack edges to disguise itself as a benign node.
Financial Development and Innovation: Cross-Country Evidence

We examine how financial market development affects technological innovation. Using a large data set that includes 32 developed and emerging countries and a fixed effects identification strategy, we identify economic mechanisms through which the development of equity markets and credit markets affects technological innovation. We show that industries that are more dependent on external finance and that are more high-tech intensive exhibit a disproportionally higher innovation level in countries with better developed equity markets. However, the development of credit markets appears to discourage innovation in industries with these characteristics. Our paper provides new insights into the real effects of financial market development on the economy.
Cognition of Korean-English secondary school teachers about intercultural dimension in EFL teaching

Choi, Yuri. 2016. Cognition of Korean-English Secondary School Teachers about Intercultural Dimension in EFL teaching. Master’s Thesis in Education. University of Jyväskylä. Department of Education. The importance of intercultural competence in education has been recognized in order to promote students’ tolerance and empathy towards others in the globalized world. Being the first and foremost foreign language subject in Korea, English classroom is considered to be the right place to address different cultures and the intercultural competence. Then, how English teachers understand intercultural competence would be important since it would greatly affect their teaching. The aim of this study is to investigate how Korean-English secondary school teachers understand intercultural dimension in EFL teaching in relation to their teaching context; and how their reported practices look like. 37 teachers participated in online survey (Google Forms) with closed and open questions. The quantitative data were used to describe teachers’ general understanding, and analyzed together with the qualitative data by means of thematic analysis. The findings suggest that secondary Korean-English teachers have high appreciation and desire of integrating intercultural dimension in English teaching, but the gap exists between the cognition and the reported practice due to some constraining factors. This study tries to connect teacher cognition of culture teaching, national curriculum, intercultural dimension, their reported practice, and the contextual factors that affect teaching practices, in order to provide the overall and comprehensive picture of English education in Korea. Some suggestions were also made for better future of our English education; regarding culture teaching in English classroom, teacher education and training, the need for teachers’ active attitude in teaching, and the assessment system.
Fast edge-preserving envelope for dynamic range compression

High dynamic range scenes usually associate with abrupt illumination changes. In order to characterize such discontinuities in illumination, an algorithm for edge preserving image envelope estimation is proposed in this paper. The computational procedure follows the fast iterative approximate Gaussian envelope for Retinex theory, which is proposed by McCann and Sobel. Inspired by bilateral filtering, a contrast weight function is then introduced to preserve edges when computing the image envelope. The proposed envelope estimator is then adopted in dynamic range compression. Fine local contrast and low total dynamic range can be simultaneously obtained using the proposed algorithm. Meanwhile, the undesirable artifacts are avoided. Moreover, the algorithm inherits the high efficiency of McCann-Sobel Retinex algorithm.
Rough set‐based proteochemometrics modeling of G‐protein‐coupled receptor‐ligand interactions

G‐Protein‐coupled receptors (GPCRs) are among the most important drug targets. Because of a shortage of 3D crystal structures, most of the drug design for GPCRs has been ligand‐based. We propose a novel, rough set‐based proteochemometric approach to the study of receptor and ligand recognition. The approach is validated on three datasets containing GPCRs. In proteochemometrics, properties of receptors and ligands are used in conjunction and modeled to predict binding affinity. The rough set (RS) rule‐based models presented herein consist of minimal decision rules that associate properties of receptors and ligands with high or low binding affinity. The information provided by the rules is then used to develop a mechanistic interpretation of interactions between the ligands and receptors included in the datasets. The first two datasets contained descriptors of melanocortin receptors and peptide ligands. The third set contained descriptors of adrenergic receptors and ligands. All the rule models induced from these datasets have a high predictive quality. An example of a decision rule is “If R1_ligand(Ethyl) and TM helix 2 position 27(Methionine) then Binding(High). ” The easily interpretable rule sets are able to identify determinative receptor and ligand parts. For instance, all three models suggest that transmembrane helix 2 is determinative for high and low binding affinity. RS models show that it is possible to use rule‐based models to predict ligand‐binding affinities. The models may be used to gain a deeper biological understanding of the combinatorial nature of receptor‐ligand interactions. Proteins 2006. © 2006 Wiley‐Liss, Inc.
Gray Level Based Face Detection Using Template Face Mask and L1 norm

In today's world, importance of biometric studies is increasing day by day. Biometric face recognition study is the most widely used method in the legal environment. Commonly, face image uses in all identification (IDs, driver's license, passport, etc.). In particularly, to improve the social security of city life, automatic face detection and face recognition systems are needed and there is a lot of study for this purpose. So we developed a template matching method and performed to obtain face image from entrance picture. Average face mask was obtained using hundred people normalized face image. Face mask has been applied to entrance picture and the shortest distance part of the entrance picture matched the face mask. When applying of the face mask to entrance picture, corner detection and difference matrix methods performed together improving the achievement. In additionally, entrance picture background, luminance conditions have the negative effect that has been tried to choose simple background entrance picture. Realized in our study could be based for further study on face detection-face identification. In addition, the system could be developing further using the artificial intelligence (AI) methods.
Complexity effects on end user understanding of data models : An experimental comparison of large data model representation methods

This paper describes a laboratory experiment which evaluates the effectiveness of different representation methods for end user understanding of large data models. Data model understanding is evaluated in terms of: xComprehension performance: the ability to answer questions about the data model xVerification performance: the ability to identify discrepancies between the data model and a set of user requirements in textual form. This is the first empirical comparison of large data model representation techniques that has been conducted in over two decades of research in this area. The results suggest that there are significant complexity effects on end user understanding of data models. By reducing a data model to "chunks" of manageable size, both comprehension and verification performance can be significantly improved. This finding has implications for other graphical notations used in IS development.
Discrete calculus of variation for homographic configurations in celestial mechanics

We provide in this paper the discrete equations of motion for the newtonian $n$-body problem deduced from the quantum calculus of variations (Q.C.V.) developed in \cite{Cre,CFT,RS1,RS2}. These equations are brought into the usual lagrangian and hamiltonian formulations of the dynamics and yield sampled functional equations involving generalized scale derivatives. We investigate especially homographic solutions to these equations that we obtain by solving algebraic systems of equations similar to the classical ones. When the potential forces are homogeneous, homographic solutions to the discrete and classical equations may be related through an explicit expansion factor that we provide. Consequently, perturbative equations both in lagrangian and hamiltonian formalisms are deduced.