Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum Field Theory of Open Spin Networks and New Spin Foam Models

We describe how a spin-foam state sum model can be reformulated as a quantum field theory of spin networks, such that the Feynman diagrams of that field theory are the spin-foam amplitudes. In the case of open spin networks, we obtain a new type of state-sum models, which we call the matter spin foam models. In this type of state-sum models, one labels both the faces and the edges of the dual two-complex for a manifold triangulation with the simple objects from a tensor category. In the case of Lie groups, such a model corresponds to a quantization of a theory whose fields are the principal bundle connection and the sections of the associated vector bundles. We briefly discuss the relevance of the matter spin foam models for quantum gravity and for topological quantum field theories.
ML estimation and LM tests for panel SUR with spatial lag and spatial errors: An application to hedonic housing prices in Paris

This paper proposes ML estimators for a panel SUR with both spatial lag and spatial error components. We study the general case where spatial effects are incorporated via spatial errors terms and via a spatial lag dependent variable and where the heterogeneity in the panel is incorporated via an error component specification. We generalize the approach of Wang and Kockelman (2007) and propose joint and conditional Lagrange Multiplier tests for spatial autocorrelation and random effects for this spatial SUR panel model. The small sample performance of the proposed estimators and tests are examined using Monte Carlo experiments. An empirical application to hedonic housing prices in Paris illustrate these methods.
Competitive priorities of enterprises in mainland China

Fierce competition in today's global markets has forced enterprises to improve their quality, cost, delivery, new product introduction speed, customer services, and the ability to be innovative. These six competitive factors are vital to the success of an enterprise. Enterprises in China are now facing a life and death transition in the dynamic market-driven economy. China's entry to the World Trade Organization (WTO) will create more fierce competition in China's market and will, inevitably, speed up the life and death process of Chinese enterprises. In this paper, we study the competitive priorities of 138 enterprises in mainland China by investigating the importance of these different priorities over the next 5 years, and their perceived strength relative to their primary competitors. We assess the reliability of the competitive priority instrument used in the Boston University Manufacturing Features Survey. The result shows that innovativeness, after-sale services, quality, and flexibility will become the most important competitive priorities among these Chinese enterprises in the next 5 years. Most of the companies also believe that they are stronger than their major competitors in the most important competitive priority areas. However, they believe that they have a lower level of relative strength in the area of innovativeness.
Semantic and Time-Dependent Expertise Profiling Models in Community-Driven Knowledge Curation Platforms

Online collaboration and web-based knowledge sharing have gained momentum as major components of the Web 2.0 movement. Consequently, knowledge embedded in such platforms is no longer static and continuously evolves through experts’ micro-contributions. Traditional Information Retrieval and Social Network Analysis techniques take a document-centric approach to expertise modeling by creating a macro-perspective of knowledge embedded in large corpus of static documents. However, as knowledge in collaboration platforms changes dynamically, the traditional macro-perspective is insufficient for tracking the evolution of knowledge and expertise. Hence, Expertise Profiling is presented with major challenges in the context of dynamic and evolving knowledge. In our previous study, we proposed a comprehensive, domain-independent model for expertise profiling in the context of evolving knowledge. In this paper, we incorporate Language Modeling into our methodology to enhance the accuracy of resulting profiles. Evaluation results indicate a significant improvement in the accuracy of profiles generated by this approach. In addition, we present our profile visualization tool, Profile Explorer, which serves as a paradigm for exploring and analyzing time-dependent expertise profiles in knowledge-bases where content evolves overtime. Profile Explorer facilitates comparative analysis of evolving expertise, independent of the domain and the methodology used in creating profiles.
A fast local gradient based super-resolution image reconstruction algorithm with fuzzy hyper-bias learning and sparse monitoring paradigm

Most of the image acquisition algorithms neglect the illumination problem like shadows and direction of illumination changes as well the image degradation caused by motion blur, noise introduction in several intermediate process. This paper presents a novel method of high resolution image generation by interpolating local gradient field and subsequent training of LR cluster patches by fuzzy learning. The query image, after several iterative resolution steps with help of fuzzy clustering, a penalty approach is associated as if a feedback path to eliminate the overestimation of HR pixels. A sparse monitoring for removing aliasing of samples is formulated to remove the gradual noising of pixels.
Dynamic deployment of wireless nodes for maximizing network lifetime in WSN

Wireless sensor networks (WSNs) often have many-to-one patterns and dynamic monitoring requirements, leading to energy imbalance. In this paper, we investigate the density in monitored area to determine the optimal deployment of nodes by taking into consideration of different requirements. By using our dynamic deployment scheme, energy balance can be achieved. Simulation results show that the dynamic placement performs better in prolonging the lifetime of WSNs.
Representation of the utility functional by two fuzzy integrals

In the cumulative prospect theory (CPT) the preference (utility functional) is represented by the difference of two Choquet integrals. We investigate representations of the corresponding functional by two fuzzy integrals, specially the difference representations of the asymmetric Choquet integral with respect to a signed fuzzy measure with bounded chain variation. We discuss non-monotone, real valued functionalL, which is revised monotone and its asymmetric Choquet integral-based representation.
Edinburgh Research Explorer Artificial Personality and Disfluency

The focus of this paper is artiﬁcial voices with different person-alities. Previous studies have shown links between an individual’s use of disﬂuencies in their speech and their perceived personality. Here, ﬁlled pauses ( uh and um ) and discourse markers ( like , you know , I mean ) have been included in synthetic speech as a way of creating an artiﬁcial voice with different personalities. We discuss the automatic insertion of ﬁlled pauses and discourse markers (i.e., ﬁllers) into otherwise ﬂuent texts. The automatic system is compared to a ground truth of human “acted” ﬁller insertion. Perceived personality (as deﬁned by the big ﬁve personality dimensions) of the synthetic speech is assessed by means of a standardised questionnaire. Synthesis without ﬁllers is compared to synthesis with either spontaneous or synthetic ﬁllers. Our ﬁndings explore how the inclusion of disﬂuencies inﬂuences the way in which subjects rate the perceived personality of an artiﬁcial voice.
The Truth is out: How Students REALLY Search

Part of the Library and Information Science Commons An indexed, print copy of the Proceedings is also available for purchase at: http://www.thepress. purdue.edu/series/charleston. You may also be interested in the new series, Charleston Insights in Library, Archival, and Information Sciences. Find out more at: http://www.thepress. purdue.edu/series/charleston-insights-library-archivaland-information-sciences.
Structural Mapping with Identical Elements Neural Network

In training two networks with shared weights on tasks that are analogous, the shared weights tend to encode the high level similarity between the two tasks. In this work, knowledge transfer was studies by investigating the structural mapping with the proposed identical elements neural network which features shared hidden layers. First, two networks were trained simultaneously on structurally analogous tasks. After it converged, cross computation was performed. The result shows that structural mapping between the two tasks can be observed from the activated outputs.
Instability from trade and democracy: the long-run effect of aid

Is there an effect of aid on democracy conditional on instability from trade? This paper reinvestigates the debated effect of aid on democracy with a new specification. We take advantage of previous empirical findings explaining the role of aid in mitigating the adverse effects of external shocks, and argue that in the long term, aggregate aid flows can potentially dampen the effects of terms-of-trade instability on the quality of democracy. An empirical investigation with data from 70 developing countries (28 of them African countries) over the period 1980-2003 (pooled in two twelve-years periods) provides supportive results. Moreover, the data suggest that terms-of- trade instability affects democracy through income instability. These results are robust to alternative specifications and to the use of different measures of aid intensity and democracy
Base Station ON-OFF Switching in 5G Wireless Systems: Approaches and Challenges

To achieve the expected 1000x data rates under the exponential growth of traffic demand, a large number of base stations (BS) or access points (AP) will be deployed in the fifth generation (5G) wireless systems, to support high data rate services and to provide seamless coverage. Although such BSs are expected to be small-scale with lower power, the aggregated energy consumption of all BSs would be remarkable, resulting in increased environmental and economic concerns. In existing cellular networks, turning off the under-utilized BSs is an efficient approach to conserve energy while preserving the quality of service (QoS) of mobile users. However, in 5G systems with new physical layer techniques and the highly heterogeneous network architecture, new challenges arise in the design of BS ON-OFF switching strategies. In this article, we begin with a discussion on the inherent technical challenges of BS ON-OFF switching. We then provide a comprehensive review of recent advances on switching mechanisms in different application scenarios. Finally, we present open research problems and conclude the paper.
Optimizing Area Under the ROC Curve using Ranking SVMs

Area Under the ROC Curve (AUC), often used for comparing classifiers, is a widely accepted performance measure for ranking instances. Many researches have studied optimization of AUC, usually via optimizing some approximation of a ranking function. Ranking SVMs are among the better performers but their usage in the literature is typically limited to learning a total ranking from partial rankings . In this paper, we observe that a ranking SVM is in fact a direct optimization of AUC via optimizing the Wilcoxon-MannWhitney statistic. We compare a linear ranking SVM with some well-known linear classifiers such as linear SVM, perceptron and logit in the context of binary classification. We show that an SVM optimized for ranking not only achieves better AUC than other linear classifiers on average, but also performs comparably in accuracy.
Robust precoded MIMO-OFDM for mobile frequency-selective wireless channels

We present a new multiple input multiple output (MIMO) technique, based on Walsh Hadamard Transform (WHT) precoding, to improve the robustness of space-frequency block coded orthogonal frequency division multiplexing (SFBC-OFDM) systems. The WHT is applied to the data symbols prior the Alamouti encoder at the transmitter and to the output of the Alamouti decoder at the receiver. The computational complexity of the proposed system is evaluated in terms of complex additions and multiplications where the numerical results show that the proposed system has a lower complexity as compared to other precoded OFDM systems. Moreover, the proposed system is highly robust to the channel time and frequency selectivity as compared to conventional SFBC, space time block coded (STBC) and other precoded OFDM systems.
Compiler-assisted dynamic power-aware scheduling for real-time applications

Managing power consumption while simultaneously delivering acceptable levels of performance is becoming a critical issue in several application domains such as wireless computing. We integrate compiler-assisted techniques with power-aware operating system services and present scheduling techniques to reduce energy consumption of applications that have deadlines. We show by simulation that our dynamic power management schemes dramatically decrease energy consumption .
The risk of clopidogrel resistance is associated with ABCB1 polymorphisms but not promoter methylation in a Chinese Han population

The goal of our study was to investigate the contribution of ABCB1 expression to the risk of clopidogrel resistance (CR). Platelets functions were measured using the Verify-Now P2Y12 assay. Applying Polymerase Chain Reaction–Restriction Fragment Length Polymorphism (PCR-RFLP), the single-nucleotide polymorphisms (SNPs) was tested. Using bisulphite pyrosequencing assay, we investigated the association of the ABCB1 DNA methylation levels and CR. It was shown that female, hypertension, and lower albumin levels increased the risk of CR (P<0.05). If patients did not have hypoproteinaemia or had hypertension, the SNP in rs1045642 was associated with CR (CC vs. TT: albumin ≥35, P = 0.042; hypertension, P = 0.045; C vs. T: albumin ≥35, P = 0.033; hypertension, P = 0.040). Additionally, the platelet inhibition of the CT+TT genotype in rs1128503 was larger than that of the CC genotype (P = 0.021). Multivariate logistic regression analysis showed that male, higher albumin and hsCRP decreased the risk of CR, and the stent size maybe positively correlated with CR. The SNP in rs1045642 was related to all-cause mortality (P = 0.024). We did not find any relationship between the methylation levels of the ABCB1 promoter and CR. In conclusions, our study indicated that ABCB1 polymorphisms might be useful in further evaluating the pathogenesis of CR.
Modeling thermal effects on nonlinear wave motion in biopolymers by a stochastic discrete nonlinear Schrödinger equation with phase damping

A mathematical model is introduced for weakly nonlinear wave phenomena in molecular systems like DNA and protein molecules that in- cludes thermal efiects: exchange of heat energy with the surrounding aqueous medium. The resulting equation is a stochastic discrete nonlinear Schrodinger equation with focusing cubic nonlinearity and \Thermal" terms modeling heat input and loss: PDSDNLS. New numerical methods are introduced to handle the unusual combination of a conservative equation, stochastic, and fully nonlinear terms. Some analysis is given of accuracy needs, and the special issues of time step adjustment in stochastic realizations. Numerical studies are presented of the efiects of thermalization on solitons, including damping induced self-trapping of wave energy, a discrete counterpart of single-point blowup. 1. Introduction. Pulse propagation in bio-polymers is of possibly great signifl- cance to energy transmission in protein and conformational change in DNA, and accurate mathematical modeling relies on nonlinear efiects and thermal efiects: en- ergy input to internal vibrational modes from heat in the aqueous medium of the molecule, and energy loss through radiation from such modes. The modeling and theory started with the work of Davydov (6, 7, 8) on protein molecules, giving continuum limit equations related to the (one-dimensional, focus- ing, cubic) nonlinear Schrodinger equation (NLS), with the sech soliton solutions of that equation playing an important role as pulses capable of carrying energy for signiflcant distances along molecules. Further developments by Scott, Eilbeck and Lomdahl (10, 15) emphasized spa- tially discrete models rather than continuum limits, and extended such models to other molecules. Indeed, such discrete NLS (DNLS) models seem to arise from a few generic features: dipole coupling along polymers interacting with localized vibration modes with faster time scales. An important feature revealed only by spatially discrete models is self-trapping driven by nonlinear self-focusing, in which energy from traveling pulses becomes localized in an oscillation at one or a few spatial nodes. More recent work by Bang, Christiansen, Giadidei, If, Johansson, Kivshar, Min- galeev, K. Rasmussen, Yakimenko (1, 5, 14) extends such modeling to DNA molecules, and adds thermal efiects, modeling thermal energy input and radiation as stochastic
Secure Ordered Bucketization

This study examines the ordered bucketization (OB) as a cryptographic object. In OB, plaintextspace is divided into p disjoint buckets, numbered from 1 to p, based on the order of the ranges that they cover. OB is quite useful in that a range query can be performed over encrypted data without the need to descrypt by attaching a bucket number to each ciphertext. Unfortunately, no research has been carried out on the security of OB in a cryptographic sense. This paper defines an encryption scheme with OB (EOB) and suggests a new security model for EOB, IND-OCPA-P, which assumes an adversary has reasonable power. Previous constructions proposed for efficient range queries were not secure in this model. Finally, an OB construction, in which the EOB implementation is secure on the IND-OCPA-P model, is proposed. In the proposed OB, p- 1 points are selected on the uniform distribution in the plaintext-space and the plaintext-space is divided based on the selected points. A bucket number is assigned to each divided range in ascending range order. With regard to the efficiency of a range query, the proposed OB guarantees reasonably good efficiency on range queries by showing that the distribution of a bucket size is not skewed.
A smoothed Minimum Mean-Square Error Log-Spectral Amplitude estimator for speech enhancement

Typical single-channel speech enhancement algorithms employ a multiplicative gain filter in the frequency-domain. This spectral gain is multiplied with the short-term spectrum of noisy speech in order to produce an estimate of the clean speech spectrum. More sophisticated techniques derived the calculation of this gain under various optimization criteria. This paper analyzes the use of smoothed gain filter for speech enhancement with the aim of gaining a better improvement in their performance. The smoothing technique is applied to the well-known Minimum Mean-Square Error short-time Log-Spectral Amplitude (MMSE-LSA) estimator. The performances of this technique are analyzed and compared with those of the classical MMSE-LSA estimator. Objective and subjective evaluation of the speech enhancement confirm superiority in noise reduction and quality of the enhanced speech.
IIP at SemEval-2016 Task 4: Prioritizing Classes in Ensemble Classification for Sentiment Analysis of Tweets

This paper describes the submission of team IIP in SemEval-2016 Task 4 Subtask A. The presented system is a novel weighted sum ensemble approach for sentiment analysis of short informal texts. The ensemble combines member classifiers that output classification confidence metrics. For the ensemble classification decision the members are combined by weights. In the presented approach the weights are derived to prioritize specific classes in multi-class classification. The presented results confirm that this improves results for the prioritized classes. The official task submission achieved a macro-averaged negative positive F1 of 57.4%. Post submission changes resulted in a F1 score of 60.2%. The evaluation also shows that the system outperforms other ensemble methods.
The Importance of Age and High Degree, in Protein-Protein Interaction Networks

Here we present an in-depth analysis of the protein age patterns found in the edge and triangle subgraphs of the yeast protein-protein interaction network (PIN). We assess their statistical significance both according to what would be expected by chance given the node frequencies found in the yeast PIN, and also, for the case of triangles, given the age frequencies observed in the currently available pairwise data. We find that pairwise interactions between Old proteins are over-represented even when controlling for high degree, and triangle interactions between Old proteins are over-represented even when controlling for pairwise interaction frequencies. There is evidence for negative selection of interactions between Middle-aged and Old proteins within triangles, despite pairwise Middle-Old interactions being common. Most triangles consist solely of vertices with high degree. Our findings point towards an architecture of the yeast PIN that is highly heterogeneous, having connected clumps which contain a large number of interacting Old proteins along with selective age-dependent interaction patterns. Supplementary Material is available online (www.liebertonline.com/cmb).
Run-time Modification of the Class Hierachy in a Live Java Run-time Modification of the Class Hierachy in a Live Java Development Environment Development Environment

Class hierarchy design is central to object-oriented software development. How-ever, it is sometimes di ﬃ cult for developers to anticipate all the implications of a design until implementation is underway. To support experimentation with di ﬀ erent designs, we extend prior work on live development environments to allow run-time modi ﬁ cation of the class hierarchy. The result is a more ﬂ uid object-oriented development process, in which immediate feedback from the executing program can be used to guide hierarchy design. This thesis presents a framework and developer support for run-time modi ﬁ cation of class inheritance relations in JPie, a live visual programming environment for Java. Most. Read complete abstract on page Read
A method based on the ideal and nadir solutions for stochastic MADM problems

Many real life decision making problems can be modeled as stochastic multi-attribute decision making (MADM) problems. A novel method for stochastic MADM problems is developed based on the ideal and nadir solutions as in the classical TOPSIS method. In a stochastic MADM problem, the evaluations of the alternatives with respect to the different attributes are represented by discrete stochastic variables. According to stochastic dominance rules, the probability distributions of the ideal and nadir variates, both are discrete stochastic variables, are defined and determined for a set of stochastic variables. A metric is proposed to measure the distance between two discrete stochastic variables. The ideal solution is a vector of ideal variates and the nadir solution is vector of nadir variates for the multiple attributes. As in the classical TOPSIS method, the relative closeness of an alternative is determined by its distances from the ideal and nadir solutions. The rankings of the alternatives are determined using the relative closeness. Examples are presented to illustrate the effectiveness of the proposed method. Through the examples, several significant advantages of the proposed method over some existing methods are discussed.