Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Single-Fault Detection Technique for Micro-fluidic Based Biochips

This paper presents a new fault detection technique for digital micro-fluidic based biochips. Due to recent advances in microfluidics, digital micro-fluidic biochips are revolutionizing laboratory procedures, e.g., biosensing, clinical diagnostics etc. Because of the underlying mixed-technology and mixed energy domains, biochips exhibit unique failure mechanisms and defects. Off-line and on-line test techniques are required to ensure system dependability. In this paper, a new technique is presented to traverse all the cells and cell boundary, for the purpose of detecting a single fault within the bio-chip. Experimental results show that the proposed technique improves the fault detection time to a great extent.
Unifying user-based and item-based collaborative filtering approaches by similarity fusion

Memory-based methods for collaborative filtering predict new ratings by averaging (weighted) ratings between, respectively, pairs of similar users or items. In practice, a large number of ratings from similar users or similar items are not available, due to the sparsity inherent to rating data. Consequently, prediction quality can be poor. This paper re-formulates the memory-based collaborative filtering problem in a generative probabilistic framework, treating individual user-item ratings as predictors of missing ratings. The final rating is estimated by fusing predictions from three sources: predictions based on ratings of the same item by other users, predictions based on different item ratings made by the same user, and, third, ratings predicted based on data from other but similar users rating other but similar items. Existing user-based and item-based approaches correspond to the two simple cases of our framework. The complete model is however more robust to data sparsity, because the different types of ratings are used in concert, while additional ratings from similar users towards similar items are employed as a background model to smooth the predictions. Experiments demonstrate that the proposed methods are indeed more robust against data sparsity and give better recommendations.
The causal manipulation and Bayesian estimation of chain event graphs

Discrete Bayesian Networks (BNs) have been very successful as a framework both for inference and for expressing certain causal hypotheses. In this paper we present a class of graphical models called the chain event graph (CEG) models, that generalises the class of discrete BN models. This class is suited for representing conditional independence and sample space structures of asymmetric models. It retains many useful properties of discrete BNs, in particular admitting conjugate estimation. It provides a flexible and expressive framework for representing and analysing the implications of causal hypotheses, expressed in terms of the effects of a manipulation of the generating underlying system.We prove that, as for a BN, identifiability analyses of causal effects can be performed through examining the topology of the CEG graph, leading to theorems analogous to the Backdoor theorem for the BN.
A discriminative approach for speaker selection in speaker de-identification systems

Speaker de-identification is an interesting and newly investigated task in speech processing. In the current implementations, this task is based on transforming one speaker speech to another speaker in order to hide the speaker identity. In this paper we present a discriminative approach for human speaker selection for speaker de-identification. We used two modules, a speaker identification system and a speaker transformation one, to select the most appropriate speaker to transform the source speaker speech from a set of speakers. In order to select the target speaker, we minimize the identification confidence of the transformed speech as the source speaker and maximize the confusion about the transformed speech membership to the rest of the speaker models and the identiication conidence of the re-transformed speech using the source speaker model. These three factors are combined to achieve overall optimization performance in order to select the best target speaker to transform the source.
Multi-dimensional exploration of API usage

This paper is concerned with understanding API usage in a systematic, explorative manner for the benefit of both API developers and API users. There exist complementary, less explorative methods, e.g., based on code search, code completion, or API documentation. In contrast, our approach is highly interactive and can be seen as an extension of what IDEs readily provide today. Exploration is based on multiple dimensions: i) the hierarchically organized scopes of projects and APIs; ii) metrics of API usage (e.g., number of project classes extending API classes); iii) metadata for APIs; iv) project- versus API-centric views. We also provide the QUAATLAS corpus of Java projects which enhances the existing QUALITAS corpus to enable API-usage analysis. We implemented the exploration approach in an open-source, IDE-like, Web-enabled tool EXAPUS.
Conceptual density functional theory.

I. Introduction: Conceptual vs Fundamental andComputational Aspects of DFT1793II. Fundamental and Computational Aspects of DFT 1795A. The Basics of DFT: The Hohenberg−KohnTheorems1795B. DFT as a Tool for Calculating Atomic andMolecular Properties: The Kohn−ShamEquations1796C. Electronic Chemical Potential andElectronegativity: Bridging Computational andConceptual DFT1797III. DFT-Based Concepts and Principles 1798A. General Scheme: Nalewajski’s ChargeSensitivity Analysis1798B. Concepts and Their Calculation 18001. Electronegativity and the ElectronicChemical Potential18002. Global Hardness and Softness 18023. The Electronic Fukui Function, LocalSoftness, and Softness Kernel18074. Local Hardness and Hardness Kernel 18135. The Molecular Shape FunctionsSimilarity 18146. The Nuclear Fukui Function and ItsDerivatives18167. Spin-Polarized Generalizations 18198. Solvent Effects 18209. Time Evolution of Reactivity Indices 1821C. Principles 18221. Sanderson’s Electronegativity EqualizationPrinciple18222. Pearson’s Hard and Soft Acids andBases Principle18253. The Maximum Hardness Principle 1829IV. Applications 1833A. Atoms and Functional Groups 1833B. Molecular Properties 18381. Dipole Moment, Hardness, Softness, andRelated Properties18382. Conformation 18403. Aromaticity 1840C. Reactivity 18421. Introduction 18422. Comparison of Intramolecular ReactivitySequences18443. Comparison of Intermolecular ReactivitySequences18494. Excited States 1857D. Clusters and Catalysis 1858V. Conclusions 1860VI. Glossary of Most Important Symbols andAcronyms1860VII. Acknowledgments 1861VIII. Note Added in Proof 1862IX. References 1865
Connection between Alcohol Consumption and Aggression in a Population of Romanian Students

Nowadays there is global concern regarding the connection between alcohol consumption and aggressive behavior among young individuals. The aim of the present study is to examine if there is a direct link between increased alcohol intake and increased aggression among Romanian students. On a sample comprised of 772 Romanian students, were measured socio-demographic and economic data, information about family environment, assessment of health risk behavior and the level of aggression using Buss-Perry Aggression Questionnaire (BPAQ). The Chi square test (χ2), Goodman and Kruskal's gamma test, and multinomial logistic regression were used for statistical analysis, which led to proving the existence of a direct association between aggression and patterns and frequency of drinking. Patterns of drinking are significantly influenced by gender, smoking, time spent in clubs and restaurants, and physical abuse by parents during childhood. Alcohol abuse represents a factor more specific to men, influenced by low parental control and domestic violence. Social-cultural life led to benefits over level of aggressive behavior. For population of the Romanian students analyzed, the alcohol intake and level of aggression are directly connected.
Nonlinear Fading Channel Equalization of BPSK Signals Using Multiplicative Neuron Model

A high order feed forward neural network architecture with optimum number of nodes is used for adaptive channel equalization in this paper.The replacement of summation at each node by multiplicatio n results in more powerful mapping because of its capability of processing higher-order information from training data. The equalizer is tested on Rayleigh fading channel with BPSK signals. Performance comparison with recurrent radial basis function (RRBF) neural network show that the proposed equalizer provides compact architecture and satisfactory results in terms of bit error rate performance at various levels of signal to noise ratios for a Rayleigh fading channel.
The Initial Mass Functions of Four Embedded Stellar Clusters

We present near-infrared J, H, and K images of four embedded stellar clusters in the Galaxy. We find a significant fraction of pre-main-sequence stars present in at least one of the clusters. For the clusters dominated by main-sequence stars, we determine the initial mass function (IMF) both by using the K luminosity function and a global extinction correction and by deriving individual extinction corrections for each star based on their placement in the K versus H - K color-magnitude diagram. Based on our IMFs we find a significant discrepancy between the mean IMF derived via the different methods, suggesting that taking individual extinctions into account is necessary to correctly derive the IMF for an embedded cluster.
Cooperative Multiagent Search for Portfolio Selection

We present a new multiagent modelfor themulti-period portfolio selection problem. Individual agents receive a share of initial wealth, and follow an investment strategy that adjusts their portfolio as they observe movements of the market over time. The agents share their wealth at the end of the (cid:12)nal investment period. We show that a multiagent system can outperform a single agent that invests all the wealth in a simple stochastic market environment. Furthermore, a cooperative multiagent system, with a simple communication mechanism of explicit hint exchange, achieves a further increase in performance. Finally we show that communication is redundant in a more realistic market that satis(cid:12)es the constraints between volatility and return implied by the Capital Asset Pricing Model.
Classification of varying length time series using example-specific adapted Gaussian mixture models and support vector machines

In this paper, we propose a hybrid framework that first uses an adapted Gaussian mixture model based method to represent a varying length sequence of feature vectors as a fixed length pattern and then uses a discriminative model for classification of varying length patterns of long duration. In the conventional GMM-UBM (Gaussian mixture model-Universal background model) based classifier, a UBM is built using feature vectors of all classes. In the proposed approach, a GMM is built for each class using the feature vectors of all the patterns of that class. Then an adapted GMM is built for each example in the training data set using the GMM built for the class to which the example belongs to. The log-likelihood of a pattern for a given example-specific adapted GMM model is used as a score. A similarity based score vector is obtained by applying a pattern to the adapted GMMs of the patterns in the training set. A test pattern is also represented using a score vector. Support vector machine is then used for classification of score vector representation of varying length patterns. Our studies on speech emotion recognition and audio clip classification tasks show that the proposed method gives a significantly improved classification performance compared to the conventional GMM based classifiers.
On the Momentum Diffusion of Radiating Ultrarelativistic Electrons in a Turbulent Magnetic Field

Here we investigate some aspects of stochastic acceleration of ultrarelativistic electrons by magnetic turbulence. In particular, we discuss the steady state energy spectra of particles undergoing momentum diffusion due to resonant interactions with turbulent MHD modes, taking rigorously into account direct energy losses connected with different radiative cooling processes. For the magnetic turbulence we assume a given power spectrum of the type W(k) ∝ k−q. In contrast to the previous approaches, however, we assume a finite range of turbulent wavevectors k, consider a variety of turbulence spectral indices 1 ⩽ q⩽ 2, and concentrate on the case of a very inefficient particle escape from the acceleration site. We find that for different cooling and injection conditions, stochastic acceleration processes tend to establish a modified ultrarelativistic Maxwellian distribution of radiating particles, with the high-energy exponential cutoff shaped by the interplay between cooling and acceleration rates. For example, if the timescale for the dominant radiative process scales with the electron momentum as ∝pr, the resulting electron energy distribution is of the form ne(p) ∝ p2exp [ − (1/a)(p/peq)a] , where a = 2 − q − r and peq is the equilibrium momentum defined by the balance between the stochastic acceleration and energy loss timescales. We also discuss in more detail the synchrotron and inverse-Compton emission spectra produced by such an electron energy distribution, taking into account Klein-Nishina effects. We point out that the curvature of the high-frequency segments of these spectra, even though they are produced by the same population of electrons, may be substantially different between the synchrotron and inverse-Compton components.
Hyaluronan fragments generated by sperm-secreted hyaluronidase stimulate cytokine/chemokine production via the TLR2 and TLR4 pathway in cumulus cells of ovulated COCs, which may enhance fertilization

The toll-like receptor (TLR) system is expressed in cumulus cells of ovulated cumulus-oocyte complexes (COCs) and is activated by bacterial lipopolysaccharides (LPS). However, the endogenous ligand(s) for the TLRs and the physiological role(s) in ovulated COCs remain to be defined. Based on reports that hyaluronan fragments can activate TLR2 and TLR4 in macrophages, and that ovulated COCs are characterized by a hyaluronan-rich matrix, we cultured ovulated mouse COCs with purified hyaluronan fragments, treated them with purified hyaluronidase or exposed them to sperm as a physiologically relevant source of hyaluronidase. Hyaluronan fragments or hyaluronidase activated the NFκB pathway and induced Il6, Ccl4 and Ccl5 mRNA expression within 2 hours. Anti-TLR2 and anti-TLR4 neutralizing antibodies significantly suppressed hyaluronan fragment- and hyaluronidase-induced activation of the NFκB pathway and the expression of these genes. When ovulated COCs were cultured with sperm, the expression and secretion of cytokine/chemokine family members were induced in a time-dependent manner that could be blocked by TLR2/TLR4 antibodies or by a hyaluronan-blocking peptide (Pep-1). The chemokines secreted from TLR2/TLR4-stimulated COCs activated cognate chemokine receptors (CCRs) localized on sperm and induced sperm protein tyrosine phosphorylation, which was used as an index of capacitation. Significantly, in vitro fertilization of COC-enclosed oocytes was reduced by the TLR2/TLR4 neutralizing antibodies or by Pep-1. From these results, we propose that TLR2 and TLR4 present on cumulus cells were activated by the co-culture with sperm in a hyaluronan fragment-dependent manner, and that chemokines secreted from COCs induced sperm capacitation and enhanced fertilization, providing evidence for a regulatory loop between sperm and COCs during fertilization.
On Time-Varying Bit-Allocation Maintaining Stability and Performance: A Convex Parameterization

In this paper, we analyze and derive conditions for stability of a feedback system in which the plant and feedback controller are separated by a noiseless finite-rate communication channel. We allow for two deterministic classes of reference inputs to excite the system, and derive sufficient conditions for input-output (IO) stability as a function of the encoding strategy and controller. We first construct an encoder as a quantizer that can have infinite memory and can be time-varying, in that the strategy it follows to allocate a total of R bits to its inputs, is a function of time. This construction of the quantizer leads to the result that the set of allocation strategies that maintains stability for each class of reference signals is convex, allowing the search for the most efficient strategy to ensure stability to be formulated as a convex optimization problem. We then synthesize quantizers and time-varying controllers to minimize the rate required for stability and to track commands. Examples presented in this paper demonstrate how this framework enables computationally efficient methods for simultaneously designing quantizers and controllers for given plants. Furthermore, we observe that our finite memory quantizers that minimize the rate required for stability do not reduce to trivial memoryless bit-allocation strategies.
The Pragmatics of yani as a Parenthetical Marker in Turkish : Evidence from the METU

Resumé This paper examines the use of parenthetical yani (‘that is’ or ‘in other words’) in written Turkish discourse with a view of investigating its contribution to the ‘what is said’ and the ‘what is inferrable’ in discourse. The paper maintains that parenthetical yani serves to constrain the metarepresentation of referents and contributes to the derivation of implied meaning at the intentional level of the discourse, thereby often producing rhetorical, emotive effects. The study concludes that yani enters discourse processing at both the conceptual and the procedural level and that a strict categorization of discourse markers as belonging to the conceptual or the procedural group is not possible.
Conceptual database security access permissions

High-level conceptual database design is a widespread method in database built with conceptual models we will illustrate the "mini world" of the database via Database Management System (DBMS) in an independent form. The form will be mapped by the use of a mapping method to reach a DBMS specific model. The database designer should keep in mind both data and functional requirements throughout the whole process. We will also indicate some database security aspects in our model. Database applications usually have to meet high security level, therefore we must protect the database and the data stored in the database against those who do not have the appropriate access permissions. The design could be established of the access permission system with the specification of the project which may reach the implementation through building the conceptual model. In our approach we illustrate this access design method from high-level to the implementation. First we have to define the user groups and their roles. In connection with the conceptual model (Enhanced Entity Relationship Model) we recommend the use of an improved model, which already includes the privileges of the users, too. These permissions can be represented in a conceptual access matrix model. At this level the privileges of the users are still DBMS-independent, then by mapping, it reaches the low-level database model (relational model), which can be accompanied with the access matrix model. This is the point where access permissions become connected to DBMS-specific elements. Finally, we point out a certain realizations of the access control permissions.
Are state mindfulness and state work engagement related during the workday?

ABSTRACT Given the growing interest in mindfulness in the workplace and the established importance of work engagement for work-related health and well-being, we explore the relationships between these 2 positive psychological states as they vary naturalistically over the workday. Utilising data from 3 measurement occasions per day (before work, mid-workday, and end-workday), we track natural variations in state mindfulness and state work engagement to create a fine-grained picture about their reciprocal relationships within the workday. A sample of 94 university employees completed multiple diary entries per day, for 10 working days. Data were analysed using multilevel structural equation modelling in MPlus, testing within-person pathways between mindfulness and work engagement across the day. Although state work engagement was a predictor of subsequent state mindfulness, state mindfulness did not predict subsequent state work engagement when controlling for earlier within-day work engagement. Overall, the within-day associations between mindfulness and work engagement were weak. We note the high within-day stabilities of state mindfulness and state work engagement, and discuss whether job and personal resources as well as task features might be moderators in the relationships between these constructs.
Multi-Resolution Image Morphing

The proposed technique extends conventional image morphing algorithms by offering additional control over the processing of the individual multi-resolutions that are used in the wavelet representation when moving from the source to the destination image. This gives the animator the possibility to introduce a disparity between the morphing of global and detail features that can lead to interesting new effects that are not achievable with traditional methods. The calculation of an intermediate image in conventional image morphing algorithms is done in three steps: firstly the determination of feature correspondence and of an intermediate feature geometry, secondly the warping of the source and the destination images to intermediate images that match the feature geometry, and finally the blending of the two warped images. The proposed algorithm alters the third step by making the blending dependent on the multi-resolution levels using a new specialized 2D wavelet transformation.