Miracle-dz/UniFAR-FTUs huggingface dataset:
Inflation in no-scale String Models

This thesis studies the question of how de Sitter vacua and slow-roll inflation may be realized in string-motivated models. More specifically, we consider 4d N = 1 supergravity theories (without vector multiplets) with Kähler potentials which are ‘no-scale’ at leading order. Such theories frequently arise in the moduli sector of string compactifications. We discuss a condition on the scalar geometry (defined by the Kähler potential) and on the direction of supersymmetry breaking in the scalar manifold, which has to be met in order for the average of the masses of the sGoldstinos to be positive, and hence for metastable vacua to be possible. This condition also turns out to be necessary for the existence of trajectories admitting slow-roll inflation. Its implications for certain scalar manifolds which arise from Calabi-Yau string compactifications are discussed. In particular, for two-moduli models arising from compactifications of heteroticand type IIB string theory, a simple criterion on the intersection numbers needs to be satisfied for possible de Sitter phases to exist. In addition, we show that subleading corrections breaking the no-scale property may allow the condition on the scalar geometry to be fulfilled, even when it is violated at leading order. Finally, we develop a procedure to construct superpotentials for a given viable Kähler potential, such that the scalar potential has a realistic local minimum. We propose two-moduli models, with superpotentials which could arise from flux backgrounds and non-perturbative effects, which have a viable vacuum without employing subleading corrections or an uplifting sector.
1 Designing Privacy-aware Internet of Things Applications

Internet of Things (IoT) applications typically collect and analyse personal data that can be used to derive sensitive information about individuals. However, thus far, privacy concerns have not been explicitly considered in software engineering processes when designing IoT applications. In this paper, we explore how a Privacy-by-Design (PbD) framework, formulated as a set of guidelines, can help software engineers to design privacy-aware IoT applications. We studied the utility of our proposed PbD framework by studying how software engineers use it to design IoT applications. We also explore the challenges in using set of guidelines to influence the IoT applications design process. This paper also highlights the benefits of providing a framework that helps software engineers explicitly consider privacy for IoT applications and also surfaced a number of challenges associated with our approach. Our studies show that PbD framework significantly increase both novice and expert software engineers’ ability to design privacy aware IoT applications.
Ja n 20 08 Three regularization models of the Navier-Stokes equations

Jonathan Pietarila Graham, Darryl D. Holm, Pablo D. Mininni, and Annick Pouquet National Center for Atmospheric Research, P.O. Box 3000, Boulder, Colorado 80307, USA∗ currently at Max-Planck-Institut für Sonnensystemforschung, 37191 Katlenburg-Lindau, Germany 3 Department of Mathematics, Imperial College London, London SW7 2AZ, UK 4 Computer and Computational Science Division, Los Alamos National Laboratory , Los Alamos, NM 87545, USA 5 Departamento de F́ısica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, 1428 Buenos Aires, Argentina. (Dated: November 8, 2018)
Isolation and characterization of acid soluble collagen (ASC) and pepsin soluble collagen (PSC) extracted from silver catfish (Pangasius sp.) skin

The aims of this study are to isolate and characterize acid soluble collagen (ASC) and pepsin soluble collagen (PSC) extracted from silver catfish (Pangasius sp.) skin. Isolated ASC and PSC collagen were characterized in terms of chemical composition (moisture, protein, fat and ash content), protein concentration, functional group, solubility, and morphological properties as compared to commercial collagen. Yields of ASC and PSC were 4.27% and 2.27%, respectively. The chemical compositions of raw skin were 34.64%, 2.81%, 3.68%, and 0.31%, while the chemical compositions of ASC and PSC were 94.21%, 3.48%, 0.81%, 59.15%, and 88.25%, 3.46%, 0.92%, and 29.24%, for moisture, protein, fat, and ash, respectively. ASC and PSC had protein concentrations of 2.27 mg/mL and 2.70 mg/mL, respectively. Functional group analysis revealed that both isolated collagens exhibited Amide A, II and III as a fingerprint for collagen structure. The highest solubility was found at pH 4 for ASC, pH 1 for PSC, and pH 5 for commercial collagen. The morphology of the isolated collagens was porous and they contained fibril. In conclusion, the characteristics of the isolated ASC and PSC from silver catfish (Pangasius sp.) skin indicate that value-added collagen can be produced from the alternative source of freshwater fish.
Role of [56_TD$DIFF]intrinsic factors in impulsive buying decision: An empirical study of young consumers

Theprimary aimof the current researchwas to study the effect of various intrinsic factors on consumer decisionmaking vis-à-vis [41_TD$DIFF]impulsive buying tendencies. After employing EFA and CFA on 630 consumers in the different parts of Jammu and Kashmir, results showed that intrinsic factors significantly influence the Impulsive Buying Decision. The application of Structural Equation Modeling disintegrated intrinsic factors into positive and negative influencers of [62_TD$DIFF]impulsive buying behaviour. The present study has significant bearing in consumer world as it has highlighted through a model for how intrinsic factors shape the buying tendencies of a young consumer. Through the application of Multi Group Analysis, a comparison has been drawn between impulsive buying behaviour and various intrinsic factors across males and females taken as two different consumer groups. Overall results have been found significant and could well be adopted for strategymaking by various stake holders in the field of consumerpsychology andconsumer behaviour to figure out the effects of intrinsic factors on buying behaviour. ©2017HolySpiritUniversityofKaslik. ProductionandhostingbyElsevierB.V. This is anopen access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-ncnd/4.0/). a r t i c l e i n f o
Fine-structure molecular epidemiological analysis of Staphylococcus aureus recovered from cows

Sixty-three Staphylococcus aureus isolates recovered from bovine sources in the USA and the Republic of Ireland were characterized by multilocus enzyme electrophoresis (MLEE), ribotyping, and random ampliﬁed polymorphic DNA polymerase chain reaction (RAPD–PCR) typing at two separate laboratories. The S . aureus isolates were assigned by MLEE to 10 electrophoretic types (ETs) (Index of Discrimination, D (cid:175) 0 (cid:177) 779). In contrast, the same isolates were assigned to 13 ribotypes ( D (cid:175) 0 (cid:177) 888), and to 12 RAPD types ( D (cid:175) 0 (cid:177) 898). A common clone, ET3, of worldwide distribution, was represented by six distinct combinations of ribotypes and RAPD types. S . aureus clones recovered from cows in Ireland were also associated with mastitis in dairy cows in the USA. These ﬁndings are consistent with the hypothesis that only a few specialized clones of S . aureus are responsible for the majority of cases of bovine mastitis, and that these clones have a broad geographic distribution.
Title Concurrent Single-Label Image Classification andAnnotation via Efficient Multi-Layer Group Sparse Coding

We present a multi-layer group sparse coding framework for concurrent single-label image classification and annotation. By leveraging the dependency between image class label and tags, we introduce a multi-layer group sparse structure of the reconstruction coefficients. Such structure fully encodes the mutual dependency between the class label, which describes image content as a whole, and tags, which describe the components of the image content. Therefore we propose a multi-layer group based tag propagation method, which combines the class label and subgroups of instances with similar tag distribution to annotate test images. To make our model more suitable for nonlinear separable features, we also extend our multilayer group sparse coding in the Reproducing Kernel Hilbert Space (RKHS), which further improves performances of image classification and annotation. Moreover, we also integrate our multi-layer group sparse coding with kNN strategy, which greatly improves the computational efficiency. Experimental results on the LabelMe, UIUC-Sports and NUS-WIDE-Object databases show that our method outperforms the baseline methods, and achieves excellent performances in both image classification and annotation tasks.
A Summary of the 4th International Workshop on Recovering 6D Object Pose

This document summarizes the 4th International Workshop on Recovering 6D Object Pose which was organized in conjunction with ECCV 2018 in Munich. The workshop featured four invited talks, oral and poster presentations of accepted workshop papers, and an introduction of the BOP benchmark for 6D object pose estimation. The workshop was attended by 100+ people working on relevant topics in both academia and industry who shared up-to-date advances and discussed open problems.
United States Patent ( 19 ) Lonberg et al . ( 54 ) TRANSGENIC NON-HUMAN ANIMALS CAPABLE OF PRODUCING

63 Continuation-in-part of Ser. No. 990,860, Dec. 16, 1992, Pat. No. 5,454,806, which is a continuation-in-part of Ser. No. 904,068, Jun. 23, 1992, which is a continuation-in-part of Ser. No. 853,408, Mar. 18, 1992, which is a continuation in-part of Ser. No. 834,539, Feb. 5, 1992, which is a continuation-in-part of Ser. No. 810,279, Dec. 17, 1991, Pat. No. 5,569,825, which is a continuation-in-part of Ser. No. 575,962, Aug. 31, 1990, abandoned, which is a continuation in-part of Ser. No. 574,748, Aug. 29, 1990, abandoned.
Robust Canonical Texture-based Face Alignment : A landmark-free pose estimation approach for 3 D model registration

Registering a 3D facial model onto a 2D image is important for constructing pixel-wise correspondences between different facial images. The registration is based on a 3× 4 dimensional projection matrix, which is obtained from pose estimation. Conventional pose estimation approa-ches employ facial landmarks to determine the coefficients inside the projection matrix and are sensitive to missing or incorrect landmarks. In this paper, we propose a landmark-free pose estimation method. The method can be used to estimate the matrix when facial landmarks are not available. Experimental results show that the proposed method outperforms several landmark-free pose estimation methods and obtains competitive accuracy in terms of estimating pose parameters. The method is also demonstrated to be effective inside a 3D-aided face recognition pipeline, whose rank-1 identification rate is close to the conventional pipeline that uses landmarks to estimate head poses. This material is based upon work supported by the U.S. Department of Homeland Security under Grant Award Number 2015-ST-061BSH001. This grant is awarded to the Borders, Trade, and Immigration (BTI) Institute: A DHS Center of Excellence led by the University of Houston, and includes support for the project “Image and Video Person Identification in an Operational Environment: Phase I” awarded to the University of Houston. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the U.S. Department of Homeland Security. Yuhang Wu · Shishir K. Shah · Ioannis A. Kakadiaris (*) Computational Biomedicine Lab Department of Computer Science, University of Houston 4849 Calhoun Road, Houston, TX, U.S.A, 77004 E-mail: {ywu35,sshah,ikakadia}@central.uh. edu Nomenclature PNS Pose-normalized space AFM Annotated Face Model T-AFM Texture of Annotated Face Model RDD Rotation determined decomposition TBB Target Bounding Box SDM Supervised Descent Method GSDM Global Supervised Descent Method RSSDM Random Subspace Supervised Descent Method 2dSC Two-dimensional Sparse Coding G3D Generic 3D model PS3D Personalized 3D model CTFA Canonical Texture-based Face Alignment RCTFA Robust Canonical Texture-based Face Alignment
MICROSATELLITE MARKER BASED GENETIC DIVERSITY ANALYSIS IN BROWN PLANT HOPPER RESISTANT RICE GENOTYPES

Genetic improvement mainly depends on extent of genetic variability present in the population. In the present study, a total of 38 SSR markers were used across 27 rice genotypes for their discrimination. Among these 21 markers showed polymorphism and number of alleles ranged from 2 to 5 with an average of 2.9 alleles per locus. The overall size of amplified fragments ranged from 95 to 270 bp. The polymorphic information content values varied from 0.14 to 0.72 with an average of 0.48 per locus. Cluster analysis was used to group cultivars by constructing dendrogram based on SSR markers analysis. All studied genotypes grouped into two main clusters at 23% similarity based on Jaccard’s similarity index with additional sub clusters within each group. Based on this study, the larger range of similarity values for related cultivars using microsatellites provides greater confidence for the assessment of genetic diversity and relationships, which can be used in future breeding programs. Introduction Rice (Oryza sativa L.) as a major cereal crop, is one of the most diversified crop species due to its adaptation to a wide range of geographical, ecological and climatic regions. Rice is the most economically important food crop in many developing countries and has also become major crop in many developed countries, where its consumption has increased considerably, particularly in North America and the European Union (EU) due to food diversification and immigration. Rice provides about two-third of the caloric intake in Asia and one-third of caloric intake in Africa and Latin America (Shastry et al. 2000). India is remarkably rich in rice diversity, including cultivars, land races, wild and weedy relatives. Brown plant hopper (BPH) is one of the most devastating pests of rice and can cause yield loss up to 60% (Panda and Khush 1995). The use of resistant varieties is an economical and effective way to control BPH. Consistent efforts have been made to identify genes for BPH resistance from various sources to develop resistant varieties. Genetic diversity is a pre-requisite for any crop improvement program as it helps in estimating and establishing of genetic relationship in germplasm collection, identifying diverse parental combinations to create segregating progenies with maximum genetic variability and superior recombinations for further selection and introgressing desirable genes from diverse germplasm (Islam et al. 2012). There are several ways for estimation of diversity in germplasm, such as evaluation of phenotypic variation, biochemical and DNA polymorphisms. However, both phenotypic and biochemical characterizations are unreliable because they are environmentally challenged, labour demanding, numerically and phenologically limited. In contrast, DNA-based molecular markers are ubiquitous, repeatable, stable and highly reliable. Molecular markers are powerful tools in the assessment of genetic variation and in the elucidation of genetic relationships within and among *Author for correspondence: < mailto:elizabeth_06r@yahoo.co.in>
Voluntary Disclosure by Activist Shareholders ∗

Corporate governance theory models activists as privately informed minority stakeholders who use persuasion over other investors to enact their agendas at a target firm. Consistent with this idea, we find that activists frequently release open letters aimed at other investors that are associated with significant stock price movements, decreased bid-ask spreads, and key activism outcomes such as directorship wins, strategic shifts, and proxy advisor recommendations. Managers commonly respond to activists’ letters, thus creating a public debate about the target’s future. These findings suggest that activists use open letters to inform other investors and facilitate change at their targets.
Dynamic Trust Management of Semi-Automated Complex Systems

Supervis ory Control and Data Acquisition (SCADA) systems are widely used in industry to manage systems ranging from electricity networks to chemical plants. However they are vulnerable to security breaches from insiders (the people operating the system). In addition, the increased complexity of the managed system may result in unreliable and false feedback to the operator. This paper describes a framework for automated trust management and privilege re-allocation within the monitoring system, based on the feedback about the performance of the monitored system. The framework uses an agent-based architecture for system monitoring, together with Bayesian networks and workflows for reasoning about the trust levels of different actors in the system.
Energy Consumption in Internet of Things (IOT)

An Internet of Things (IoT) architecture consists of a wide range of Internet-connected devices or things. The IoT is a broad term referring to applications as diverse as Internetconnected vehicles, consumer electronics and smart phones. IoT refers not only to personal computers and mobile phones connected through the Internet, but also to the wireless interconnection of all of the billions of ‘things’ and devices through the internet or local area networks that is done to increase efficient utilization [1, 2], as shown in (Figure 1). Internet of Things (IoT) is a network that enables new forms of communication between people and things and between things themselves. Each of the things or objects in IoT communicates with the others and plays a defined role [3]. Wireless nodes equipped with sensors are among the things and devices on the IoT. Wireless sensor nodes connected to a network collect information about the environment surrounding the sensor node. Many types of sensors are used by wireless sensor nodes, including those for measuring temperature, humidity, illumination, motion, pressure, stress, distortion, position, flow rate, and gas. The sensor is the most important component in the (IOT), So to increase the number, the accuracy of collected data we need to increase the number of sensor nodes [2].
Fe b 20 15 CSMA-SIC : Carrier Sensing with Successive Interference Cancellation

Successive interference cancellation (SIC) is a physical layer technique that enables the decoders to decode multiple simultaneously transmitted signals. The complicated model of SIC requires careful design of the MAC protocol and accurate adjustment of transmission parameters. We propose a new MAC protocol, known as CSMA-SIC, that employs the multi-packet reception capability of SIC. The proposed protocol adjusts the transmission probabilities to achieve throughput optimality.
parse dimensionality reduction of hyperspectral image based on emi-supervised local Fisher discriminant analysis

This paper presents a novel sparse dimensionality reduction method of hyperspectral image based on semi-supervised local Fisher discriminant analysis (SELF). The proposed method is designed to be especially effective for dealing with the out-of-sample extrapolation to realize advantageous complementarities between SELF and sparsity preserving projections (SPP). Compared to SELF and SPP, the method proposed herein offers highly discriminative ability and produces an explicit nonlinear feature parsity preserving projections (SPP) imensionality reduction emi-supervised local Fisher discriminant nalysis (SELF) mapping for the out-of-sample extrapolation. This is due to the fact that the proposed method can get an explicit feature mapping for dimensionality reduction and improve the classification performance of classifiers by performing dimensionality reduction. Experimental analysis on the sparsity and efficacy of low dimensional outputs shows that, sparse dimensionality reduction based on SELF can yield good classification results and interpretability in the field of hyperspectral remote sensing.
No . 06-014 The Importance of Equity Finance for R & D Activity – Are There Differences Between Young and OldCompanies ?

This paper analyzes the importance of equity finance for the R&D activity of small and medium-sized enterprises. We use information on almost 6000 German SMEs from a company survey. Using the intensity of banking competition at the district level as instrument to control for endogeneity, we find that a higher equity ratio is conducive to more R&D for young but not for old companies. Equity may be a constraining factor for young companies which have to rely on the original equity investment of their owners since they have not yet accumulated retained earnings and can relay less on outside financing. The positive influence is found for R&D intensity but not for the decision whether to perform R&D. Equity financing is therefore especially important for the most innovative, young companies. JEL classification: G 32, O 32
Using Prior Information on Parameters to Eliminate Dependence on Initial Values in Fitting Coxian Phase Type Distributions to Length of Stay Data in Healthcare Settings

Background: Modeling length of stay (LOS) data in healthcare settings using Coxian phase type (PH) distributions is becoming increasingly popular. However, dependence on initial values is a persistent difficulty in parameter estimations. This paper explores the utility of prior information on the parameters to address this difficulty. Methods: Maximum likelihood methods were used to estimate parameters of PH distributions that best fit simulated datasets with various sample sizes arising from PH distributions of various numbers of phases and parameters, using randomly generated initial values. Estimated values for the parameters resulting from different initial values were compared to the known values to assess the extent to which estimates depend on initial values; the impacts of sample sizes, existence of prior information, as well as the number of parameters with prior information were assessed. Results: Without prior information, parameter estimates depend on initial values for all PH distributions and all sample sizes. Prior information on one or more parameters led to more concentrated estimates, with higher number of parameters with prior information or larger sample sizes leading to more concentrated estimates. For example, with a sample size of 500, the estimates for a parameter with known value of 0.706 without prior information had a wide range of 1.523; using prior information for two parameters narrowed that range down to 0.156. For 3-phase PH distributions, prior information on 3 parameters appeared to be sufficient to eliminate dependence on initial values, even for small sample sizes. For 4-phase PH distributions, prior information on 5 parameters and a moderate sample size were needed to eliminate such dependence. Conclusions: Combination of prior information on parameters and sufficient sample sizes can eliminate dependence on initial values in fitting PH distributions to LOS data.