Miracle-dz/UniFAR-FTUs huggingface dataset:
Golden Indica and Japonica Rice Lines Amenable to Deregulation1

As an important step toward free access and, thus, impact of GoldenRice, a freedom-to-operate situation has been achieved for developing countries for the technology involved. Specifically, to carry the invention beyond its initial “proof-of-concept” status in a Japonica rice (Oryza sativa) cultivar, we report here on two transformed elite Indica varieties (IR64 and MTL250) plus one Japonica variety Taipei 309. Indica varieties are predominantly consumed in the areas with vitamin A deficiency. To conform with regulatory constraints, we changed the vector backbone, investigated the absence of beyond-border transfer, and relied on Agrobacterium tumefaciens-mediated transformation to obtain defined integration patterns. To avoid an antibiotic selection system, we now rely exclusively on phosphomannose isomerase as the selectable marker. Single integrations were given a preference to minimize potential epigenetic effects in subsequent generations. These novel lines, now in the T3 generation, are highly valuable because they are expected to more readily receive approval for follow-up studies such as nutritional and risk assessments and for breeding approaches leading to locally adapted variety development.
The Lead User Method: An Outline of Empirical Findings and Issues for Future Research

In order to reduce the risks of failure usually associated with NPD, leading companies such as 3M, HILTI, or Johnson&Johnson are increasingly working with so-called Lead Users. Their identification and involvement is supported by the Lead User method – a multi stage approach aiming to generate innovative new product concepts and to enhance the effectiveness of crossfunctional innovation teams. While the Lead User method is frequently cited in the literature, yet, there are only limited attempts to comprehensively discuss how this approach is embedded in theories and empirical findings of innovation and marketing research. Therefore the Lead User method is in the focus of the present paper, both with respect to its theoretical foundation and its implementation into the innovation management system. First, empirical research on user innovations is reviewed to clarify the theoretical foundation of the Lead User method. Second the attention is drawn to the Lead User practice by discussing the various process steps of this specific approach on the basis of two applications of the method. Based on this discussion, we outline open questions related with the practical implementation of the Lead User method in order to start an agenda for future research.
Optimal Joint Multiple Resource Allocation Method for Cloud Computing Environments

Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of config urable computing resources. To provide cloud computing ser vic s economically, it is important to optimize resource allocation under the assumption that the required resource can be ta ken from a shared resource pool. In addition, to be able to provide p rocessing ability and storage capacity, it is necessary to allocate b ndwidth to access them at the same time. This paper proposes an optimal resource allocation method for cloud computing environments. First, this paper dev elops a resource allocation model of cloud computing environments, a suming both processing ability and bandwidth are allocated simu ltaneously to each service request and rented out on an hourly ba sis. The allocated resources are dedicated to each service request. Ne x , this paper proposes an optimal joint multiple resource allocat ion method, based on the above resource allocation model. It is demo nstrated by simulation evaluation that the proposed method can reduce the request loss probability and as a result, reduce th e total resource required, compared with the conventional allocation method. Then, this paper defines basic principles and a measure f or achieving fair resource allocation among multiple users in a cloud computing environment, and proposes a fair joint multiple res ource allocation method. It is demonstrated by simulation evaluation s that the proposed method enables the fair resource allocatio n mong multiple users without a large decline in resource efficienc y.
Utilizing Extension Character 'Kashida' with Pointed Letters for Arabic Text Digital Watermarking

This paper exploits the existence of the redundant Arabic extension character, i.e. Kashida. We propose to use pointed letters in Arabic text with a Kashida to hold the secret bit ‘one’ and the un-pointed letters with a Kashida to hold ‘zero’. The method can be classified under secrecy feature coding methods where it hides secret information bits within the letters benefiting from their inherited points. This watermarking technique is found attractive too to other languages having similar texts to Arabic such as Persian and Urdu
Charles A. Dice Center for Research in Financial Economics Why do Hedge Funds' Worst Returns Cluster? Common Liquidity Shocks vs. Contagion

Using monthly hedge fund style indices representing eight different styles from January 1990 to August 2007, we find strong evidence of clustering of worst returns. Controlling for hedge fund risk factors identified in the literature, the average probability that a hedge fund style index has a worst return (lower 10% tail) increases from 2% to 19% as the number of other hedge fund style indices with worst returns increases from zero to seven. We investigate possible explanations for this clustering. Adverse shocks to asset and funding liquidity increase sharply the likelihood of simultaneous worst returns across hedge fund styles in the same month and in the next month. Specifically, large adverse shocks to credit spreads, prime broker stock prices, stock market liquidity, repo volume, and hedge fund flows are associated with an economically significant increase in the probability of clustering of worst returns. Though contagion is a possible explanation for the clustering we observe, we find at best mixed support for this explanation. ∗ Boyson is at Northeastern University, Stahel is at George Mason University, and Stulz is at The Ohio State University, NBER, and ECGI. Corresponding author is Boyson: n.boyson@neu.edu, (617) 373-4775. We wish to thank Stephen Brown, William Greene, David Hsieh, Andrew Karolyi, Marno Verbeek, and participants at seminars and conferences at the CREST-Banque de France conference on contagion, the FDIC, Maastricht University, the Ohio State University, RSM Erasmus University, and Wharton for useful discussions. We are also grateful to Rose Liao and Jerome Taillard for research assistance. We thank Lisa Martin at Hedge Fund Research for assistance regarding her firm’s products. All remaining errors are our own.
Recognizing Hand Gestures with Microsoft ’ s Kinect

In this paper, we propose and implement a novel method for recognizing hand gestures using rgb and depth data from Microsoft’s Kinect sensor. Our approach involves looking at specific hand motions, in addition to full body motions, to assist in the recognition of more refined gestures. With this approach, we are able to recognize ‘grasp’ and ‘drop’ gestures with over 90% accuracy.
Robust short-term load forecasting using projection statistics

It has been observed that the French electric load series possesses outliers and breaks. Outliers are deviant data points while breaks are lasting abrupt changes in the stochastic pattern of the series. It turns out that outliers and breaks significantly degrade the reliability and accuracy of conventional day-ahead estimation and forecasting methods. Robust methods are needed for this application. In this paper, we propose to use a robust diagnostic approach for which the identification of outliers and breaks is carried out via a robust multivariate estimation of location and covariance based on projection statistics (PS). The developed procedure consists of the following steps: (i) estimate the parameters and the order of a high order autoregressive AR(p*) by means of the PS, (ii) execute a robust filter cleaner to identify and reject the outliers, and (iii) apply a maximum-likelihood estimator defined at the Gaussian distribution that handles missing values. The performance of this method has been evaluated on the French electric demand in terms of execution time and forecasting accuracy. This approach improves the load forecasting quality for “normal days” and presents several interesting properties such as fast execution, good robustness, simplicity and easy on-line implementation. A novel multivariate approach is also proposed in order to deal with heteroscedasticity.
A Sensitive DNAzyme-Based Chiral Sensor for Lead Detection

A DNAzyme-based sensor for the determination and quantification of lead ions (Pb2+) has been established, which combines the recognition and catalysis of DNAzyme with the optical properties of nanomaterials. Circular dichroism (CD) signals were obtained by a DNAzyme-based assembly of asymmetric silver nanoparticle (AgNPs) dimers. A good linear relationship between CD signals and Pb2+ concentration was obtained ranging from 0.05 ng∙mL−1 to 10 ng∙mL−1 with a limit of detection (LOD) of 0.02 ng∙mL−1. The specificity of this sensor in lead ion detection was excellent, and a satisfactory recovery was obtained in the analysis of tap water samples. The proposed technique possesses both high sensitivity and good specificity, giving it great potential for the analysis of Pb2+ in water.
An Enhanced Load-Balancing Approach Using Detour in Ad-Hoc Networks

This paper proposes an enhanced load-balancing approach considering the on-going traffic sessions as well as new traffic sessions. In our scheme a congested node tries to escape from the congestion caused by the on-going traffic session by requesting phased detour to adjacent nodes. Like legacy load-balancing schemes, a potentially congested node avoids entering congestion state by dropping incoming RREQs for new traffic sessions. For efficient operation of the proposed load-balancing, each node monitors all the paths for ongoing traffic sessions continuously and uses congestion recognition mechanism. On this way, the proposed approach resolves congestion on the already configured path. We evaluate the performance in comparison with legacy schemes. According to simulation results, our scheme shows lowest packet latency and loss ratio with negligible increase of control overhead.
EXTRAGALACTIC CHEMICAL ABUNDANCES: DO H ii REGIONS AND YOUNG STARS TELL THE SAME STORY? THE CASE OF THE SPIRAL GALAXY NGC 300

We have obtained new spectrophotometric data for 28 H ii regions in the spiral galaxy NGC 300, a member of the nearby Sculptor Group. The detection of several auroral lines, including [O iii] λ4363, [S iii] λ6312, and [N ii] λ5755, has allowed us to measure electron temperatures and direct chemical abundances for the whole sample. We determine for the first time in this galaxy a radial gas-phase oxygen abundance gradient based solely on auroral lines, and obtain the following least-square solution: 12 + log(O/H) = 8.57(±0.02) − 0.41(±0.03)R/R25, where the galactocentric distance is expressed in terms of the isophotal radius R25. The characteristic oxygen abundance, measured at 0.4 × R25, is 12 + log(O/H) = 8.41. The gradient corresponds to −0.077 ± 0.006 dex kpc−1, and agrees very well with the galactocentric trend in metallicity obtained for 29 B and A supergiants in the same galaxy, −0.081 ± 0.011 dex kpc−1. The intercept of the regression for the nebular data virtually coincides with the intercept obtained from the stellar data, which is 8.59(±0.05). This allows little room for depletion of nebular oxygen onto dust grains, although in this kind of comparison we are somewhat limited by systematic uncertainties, such as those related to the atomic parameters used to derive the chemical compositions. We discuss the implications of our result with regard to strong-line abundance indicators commonly used to estimate the chemical compositions of star-forming galaxies, such as R23. By applying a few popular calibrations of these indices based on grids of photoionization models on the NGC 300 H ii region fluxes, we find metallicities that are higher by 0.3 dex (a factor of 2) or more relative to our nebular (Te based) and stellar ones. We detect Wolf–Rayet stellar emission features in ∼1/3 of our H ii region spectra, and find that in one of the nebulae hosting these hot stars the ionizing field has a particularly hard spectrum, as gauged by the “softness” parameter η = (O+/O++)/(S+/S++). We suggest that this is related to the presence of an early WN star. By considering a larger sample of extragalactic H ii regions we confirm, using direct abundance measurements, previous findings of a metallicity dependence of η, in the sense that softer stellar continua are found at high metallicity.
A Growing Cognitive Model of Motor Balance Skill: Architecture, Algorithm and Application1

The invention pertains to a method and system of inserting a timecode into a textual representation of a speech. The system includes a computer, a timecode generator, and videotape recorder. The computer is coupled with the timecode generator and the videotape recorder. The method includes the following steps. The timecode generator generates the timecode at a point in time. The point in time corresponds to a portion of the speech. The videotape recorder stores a video representation of the portion of the speech with the timecode. The computer receives a text data corresponding to the portion of the speech. The computer being responsive to receiving the text data, inserts a representation of the timecode into the text data.
Model Simulation for Test Execution Capacity Estimation

It is important for a company to know if it is able to attend the clients' requests on time and according to their expectations, mainly in competitive markets. A usual activity performed to ensure quality is the test activity. Software testing is been considered so important that organizations can allocate teams exclusively for test activities. In this context, it is important that test managers know the capacity of their test team. This paper defines a capacity model for test execution teams and presents the use of simulation technique for estimating teams' capacities for executing tests in mobile applications. We also report our experiences using this model in an organizational setting
Encoding Lineage in Scholarly Articles

The development of new scientiﬁc concepts today is an outcome of the accumulated knowledge built over time. Every scientiﬁc domain requires understanding of the trends of the dependencies between its subdomains. Analyses of trends to capture such dependencies using conventional document modeling techniques is a challenging task due to two reasons: (1) conventional vector-space modeling based representation of documents does not realize the history of the content, and (2) neither feature-level nor document-level causality is provided with any digital library metadata or citation network. In this paper, we propose an intuitive temporal representation of a scientiﬁc article that encodes inherent historic characteristics of the content. This intuitive representation of each document is then leveraged to discover causal relationships between scientiﬁc articles. In addition, we provide a mechanism to explore the lineage of each document in terms of other previously published documents, which illustrates how the theme of the document under analysis evolved over time. Empirical studies reported in the paper show that the proposed technique identiﬁes meaningful causal relationships and discovers meaningful lineage in the scientiﬁc literature that could not be discovered through the citation network of the articles.
Low-Molecular-Weight Polyethyleneimine Grafted Polythiophene for Efficient siRNA Delivery

Owing to its hydrophilicity, negative charge, small size, and labile degradation by endogenous nucleases, small interfering RNA (siRNA) delivery must be achieved by a carrier system. In this study, cationic copolymers composed of low-molecular-weight polyethylenimine and polythiophenes were synthesized and evaluated as novel self-tracking siRNA delivery vectors. The concept underlying the design of these copolymers is that hydrophobicity and rigidity of polythiophenes should enhance the transport of siRNA across the cell membrane and endosomal membrane. A gel retardation assay showed that the nanosized complexes formed between the copolymers and siRNA were stable even at a molar ratio of 1 : 2. The high cellular uptake (>80%) and localization of the copolymer vectors inside the cells were easily analyzed by tracking the fluorescence of polythiophene using fluorescent microscopy and cytometry. An in vitro luciferase knockdown (KD) assay in A549-luc cells demonstrated that the siRNA complexes with more hydrophobic copolymers achieved a higher KD efficiency of 52.8% without notable cytotoxicity, indicating protein-specific KD activity rather than solely the cytotoxicity of the materials. Our polythiophene copolymers should serve as novel, efficient, low cell toxicity, and label-free siRNA delivery systems.
Thin-skinned mass-wasting responsible for widespread deformation at Arenal volcano

The shape and stability of a volcano’s edifice depends on the relationship between eruption rate and the loss or redistribution of material due to erosion, mass-wasting or deformation. This work provides measurements of deformation and shallow mass-wasting at a stratovolcano immediately after an extended period of growth, and demonstrates that high rates of deformation can be associated with shallow edifice processes. We measure displacements and surface property changes on the upper flanks of Arenal, Costa Rica, after a ~40 year period of edifice growth. We present high-resolution satellite radar imagery of the 2011-2013 period that provides evidence of frequent rockfalls and of at least 16 slow-moving, shallow landslides (estimated to be 5-11 m thick, total volume = 2.4×107 m3 DRE). The 2012 Nicoya Earthquake (Mw 7.4) had no measurable impact on the velocities of sliding units at Arenal, but did result in an increase in the area affected by rockfall.
Adaptive model fusion for wearable sensors based human activity recognition

Wearable sensors based activity recognition is a research area where inertial measurement units based information is used to recognize human activities. While every human is different the usage of adaptive and personal models has become more attractive approach in the area. In this article, a novel solution is presented how to combine the human independent and personal models more effectively using self-organizing maps based distance as a selection criteria. Moreover, the selection can be done in real time and within wearable device itself. The results show that the approach clearly outperforms the posterior probability based approach in preserving the high recognition accuracy regardless of which model is used. In addition, it does not require so much memory capacity than Euclidean distance based selection.
Application of ensemble models in web ranking

One of the most important parts of search engines is the ranking unit. Many different classical ranking algorithms based on content (such as TF-IDF and BM25) and connectivity (such as HITS and PageRank) have been used in web search engines to find pages in response to a user query. Although these algorithms have been developed to improve retrieval results, none of them can take advantage of power of contents as well as useful link structures. Thus, it remains a challenging research question how to effectively combine these available information to maximize search accuracy. In this study, we investigate the application of different ensemble models in ranking algorithms. Some of them are simple such as Sum, Product and Borda rule, and the others are more complicated methods. We present three complex ensemble approaches. The first one is OWA operator to merge the results of various ranking algorithms. In the second approach, a state-of-the-art method, simulated click-through data, is used to learn how to combine many content and connectivity features of web pages. Moreover, we present a modified version of SVM classifier customized for ranking problems as the third complex fusion approach. The proposed methods are evaluated using the LETOR and dotIR benchmark data sets. The experimental results show that in most of the cases ensemble methods give better results and the improvements are very encouraging. These results also show that the OWA and SVM fusion methods are promising respect to other ensemble models.
An improved Fuzzy C-Means algorithm based on MapReduce

In order to solve the problem that the Fuzzy C-Means algorithm is sensitive to the initial clustering center, we use the Canopy algorithm to carry out the quick and rough clustering. At the same time, to avoid the blindness of the Canopy algorithm, we put forward an improved Canopy-FCM algorithm based on a max-min principle. In allusion to the problem that the FCM algorithm has high time complexity, this article use the parallel computing frame of MapReduce to design and realize the improved Canopy-FCM algorithm. Experimental results show: the improved Canopy-FCM algorithm based on MapReduce has better clustering quality and running speed than the Canopy-FCM and the FCM algorithm based on MapReduce, and the improved Canopy-FCM algorithm based on Hadoop has better speed-up ratio than the Canopy-FCM based on the Standalone mode.
Exploring how the individual consumer may resist toward foreign products: a qualitative research

The aim of this paper is to explore the ways by which Tunisian consumers may resist toward foreign products. The results obtained by inductive thematic analysis of 14 in-depth interviews present a new classification of consumer resistant behaviors within three main categories according to logical similarities between extracted themes and codes: basic, escalatory & compensatory groups. Two major types of relations were found between the three extracted themes in either progressive or synchronic associations. Results were discussed and future implications were assessed.
Physiological signals based human emotion Recognition: a review

Recent research in the field of Human Computer Interaction aims at recognizing the user's emotional state in order to provide a smooth interface between humans and computers. This would make life easier and can be used in vast applications involving areas such as education, medicine etc. Human emotions can be recognized by several approaches such as gesture, facial images, physiological signals and neuro imaging methods. Most of the researchers have developed user dependent emotion recognition system and achieved maximum classification rate. Very few researchers have tried to develop a user independent system and obtained lower classification rate. Efficient emotion stimulus method, larger data samples and intelligent signal processing techniques are essential for improving the classification rate of the user independent system. In this paper, we present a review on emotion recognition using physiological signals. The various theories on emotion, emotion recognition methodology and the current advancements in emotion research are discussed in subsequent topics. This would provide an insight on the current state of research and its challenges on emotion recognition using physiological signals, so that research can be advanced to obtain better recognition.