Miracle-dz/UniFAR-FTUs huggingface dataset:
One Gene is Not Enough : Transgene Detection , Expression and Control

It is not surprising that recently reported successes with sheep and cattle nuclear transfer have rekindled interest in examining new methodologies for producing transgenic animals. The proponents of nuclear transfer and other new approaches in part justify their efforts by pointing to the inefficiency of producing transgenic livestock. The techniques for producing transgenic livestock have remained basically unchanged since the first transgenic farm animals were reported in 1985 (Hammer et al., 1985). A review of the transgenic livestock literature suggests that the inefficiency of producing transgenic animals, by pronuclear microinjection, can be attributed to poor embryo survival, poor transgene integration rates and unpredictable transgene behaviour (Wall, 1996). At a minimum, performing nuclear transfer with genetically engineered nuclei will eliminate the inefficiency related to poor transgene integration rates. The impact of nuclear transfer on embryo/fetal survival and on the ability to select integrations with predictable transgene expression is still unknown. Therefore, until the new approaches are validated and are shown to increase the overall efficiency, we believe it is prudent to review some of the proposed strategies for improving the efficiency of current technology. Furthermore, no matter which way genes are introduced, their behaviour will be dictated by still ill-defined transgene design criteria. Though we are unable to offer a resolution to the vagaries of transgene design, it is our hope that some of the molecular approaches discussed will stimulate readers to re-evaluate their transgene design guidelines. The title of this chapter was chosen in recognition of that fact that from the very beginning of transgenic animal technology, transgenes (fusion
Assessing Dimensionality of a Set of Items - Comparison of Different Approaches

Abstract : This study examines the performance of the following four methodologies for assessing unidimensionality: DIMTEST, Holland and Rosenbaum's approach, linear factor analysis, and nonlinear factor analysis. Each method is examined and compared with other methods on simulated data sets and on real data sets. Seven data sets, all with 2000 examinees, were generated: three unidimensional, and four two-dimensional data sets. Two levels of correlation between abilities were considered: p=. 3 and p=. 7. Eight different real data sets were used: four of them were expected to be unidimensional, and the other four were expected to be two-dimensional. Findings suggest that, while the linear factor analysis often overestimated the number of underlying dimensions, the other three methods correctly confirmed unidimensionality but differed in their ability to detect lack of unidimensionality. DIMTEST showed excellent power in detecting lack of unidimensionality; Holland and Rosenbaum's and nonlinear factor analysis approaches showed good power, provided the correlation between abilities was low.
Consumer Responses to Voluntary Nutrition Labeling in the Presence of Information Spillovers

Consumers often rely on product quality disclosures to make their purchase decisions. Voluntary nutrition labeling (VNL), a means of nutrition quality disclosure, was introduced in the last decade by leading food manufacturers and selected food retailers. To the extent that consumers make inferences based on VNL disclosures as they consider buying non-VNL food products, information spillovers occur. However, previous studies on the effectiveness of VNL programs have not considered information spillover effects. In this study, we examine consumer responses to VNL by incorporating both product participation and information spillover effects. Empirical results from the U.S. ready-to-eat cereal market confirm a positive participation effect of VNL and a strong negative spillover effect on non-VNL products (non-participating food products), especially those that are relatively unhealthy (high in sugar, saturated fat and/or sodium). These findings indicate that ignoring the effects of VNL information spillovers in models of consumer choices may lead to underestimation of the impact of VNL on consumer valuation of participating products and overestimation of consumer valuation of non-participating products, suggesting that an incentive in a firmâs voluntary participation in self-regulated labeling programs may be to avoid negative treatment externalities. Overall, this article shows the importance of including spillover effects in the evaluation of VNL programs and that VNL could play a positive role in improving the healthiness of consumer choices.
Advanced Monte Carlo Methods

The goal of Monte Carlo simulations is usually to represent the equilibrium ensemble, without requiring that the succession of simulation microstates bears any resemblance to the actual dynamics of the system. The resulting freedom to invent Monte Carlo moves (consistent with detailed balance, of course) has resulted in many ingenious techniques for more rapid sampling of equilibrium ensembles. Lattice models in particular are simpler than more microscopically accurate representations of molecules. As a consequence, more sophisticated Monte Carlo techniques can be devised for lattice models to cope with various circumstances in which straightforward Monte Carlo simulation is frustratingly slow to equilibrate, or to explore configuration space. One such circumstance is simulation near the critical point. Fluctuations in a system in the onephase region near the critical point become large in spatial extent and relax very slowly, because the driving force for fluctuations to relax depends on their free energy cost, which becomes small near the critical point. This tendency for long-wavelength fluctuations to relax slowly as we approach the critical point is called “critical slowing down”; we shall discuss it more systematically in the next chapter. For now, it su ces to observe that critical slowing down is problematic for conventional Monte Carlo simulations, in which we flip spins one at a time. For such problems, methods have been devised in which entire clusters of spins can be flipped in a single move (Section 8.1). Phase-separated systems, in which the configuration is essentially described by the shapes of interfaces between the coexisting phases, is another circumstance in which straightforward lattice Monte Carlo is very slow to explore equilibrium. Often, for such systems we would prefer to conserve the number of A and B species, for example in simulating the properties of a single interface between phases. Simple Monte Carlo moves attempting to exchange two randomly chosen sites will typically fail to be accepted, unless they have the e↵ect of moving the interface a bit. Again for lattice models, special techniques have been developed to keep track of which exchange moves are most likely to succeed, and attempt these more often (Section 8.2.1). For any equilibrium simulation technique, whether molecular dynamics or Monte Carlo, we often want to explore how the properties of the system vary with temperature, field, or some other parameter in the Hamiltonian. For example, in searching for the spinodal or phase boundary in a simulation, we evidently must vary the temperature in some systematic way. At first sight, it would seem that each time we want to change the temperature even a little bit, we must perform a new simulation, since the microstates in the partition function are weighted di↵erently at each temperature. Fortunately, there is a technique for “reweighting” histograms of values for observables (energy, pressure, composition, . . . ) from simulations performed at one temperature, to obtain a histogram as if we had simulated at a nearby temperature (Section 8.3).
Automated Diatom Classification (Part A): Handcrafted Feature Approaches

This paper deals with automatic taxa identification based on machine learning methods. The aim is therefore to automatically classify diatoms, in terms of pattern recognition terminology. Diatoms are a kind of algae microorganism with high biodiversity at the species level, which are useful for water quality assessment. The most relevant features for diatom description and classification have been selected using an extensive dataset of 80 taxa with a minimum of 100 samples/taxon augmented to 300 samples/taxon. In addition to published morphological, statistical and textural descriptors, a new textural descriptor, Local Binary Patterns (LBP), to characterize the diatom’s valves, and a log Gabor implementation not tested before for this purpose are introduced in this paper. Results show an overall accuracy of 98.11% using bagging decision trees and combinations of descriptors. Finally, some phycological features of diatoms that are still difficult to integrate in computer systems are discussed for future work.
Application of Hybrid Meta-Heuristic Techniques for Optimal Load Shedding Planning and Operation in an Islanded Distribution Network Integrated with Distributed Generation

In a radial distribution network integrated with distributed generation (DG), frequency and voltage instability could occur due to grid disconnection, which would result in an islanded network. This paper proposes an optimal load shedding scheme to balance the electricity demand and the generated power of DGs. The integration of the Firefly Algorithm and Particle Swarm Optimization (FAPSO) is proposed for the application of the planned load shedding and under frequency load shedding (UFLS) scheme. In planning mode, the hybrid optimization maximizes the amount of load remaining and improves the voltage profile of load buses within allowable limits. Moreover, the hybrid optimization can be used in UFLS scheme to identify the optimal combination of loads that need to be shed from a network in operation mode. In order to assess the capabilities of the hybrid optimization, the IEEE 33-bus radial distribution system and part of the Malaysian distribution network with different types of DGs were used. The response of the proposed optimization method in planning and operation were compared with other optimization techniques. The simulation results confirmed the effectiveness of the proposed hybrid optimization in planning mode and demonstrated that the proposed UFLS scheme is quick enough to restore the system frequency without overshooting in less execution time.
An Analysis of the Relationship between Environmental Motivations and Iso14001 Certification

The objectives of this paper are both to identify the environmental motivations associated with the decision to initiate the process for ISO14001 certification and to check whether such motivations undergo any changes once the company obtains its certification and experiences the actual effects of this process. Based on the work of Bansal and Roth, we distinguish between ethical, competitive and relational motivations, and compare the importance of these motivations in four company stages with respect to the ISO14001 certification: not considered, considered, in process and awarded. This comparison was performed through a logit analysis applied over the data supplied by 184 Spanish manufacturers from three different industrial sectors. It was concluded that the decision to pursue the ISO14001 certification responds to ethical and competitive motivations, and that once the company gets its certification, the company portfolio of environmental motivations does not change significantly.
KAIST Graduate School of Management WORKING PAPER SERIES Bounded Momentum in the Formation Strategies: The Case of Alliances

Building on the literatures on strategic momentum and interfirm cooperation, we develop and test a model of alliance formation that incorporates the distinction between alliances encompassing different corporate functions. We theorize that differences in functional purposes shape the learning and coordination effects that generate type-specific bounded momentum, where in the case of alliances bounded momentum refers to the pattern whereby past alliances encourage new alliance formation up to some point and discourage it beyond that point. We predict that the bounded momentum effects will arise at the firm and industry levels, and that their strength will depend on whether a new alliance involves the same function as previous alliances. Empirically, we examine the successive alliances formed by 32 international airlines through 1994. We find that a firm's past production (marketing) alliances create bounded momentum for its subsequent production (marketing) alliances but not for its subsequent marketing (production) alliances. We also present results about the bounded momentum generated by other firms’ alliances. The study sheds light on the dynamics of alliances at multiple levels of analysis, and on firm and industry effects in corporate strategy. We discuss implications for research on other forms of strategic actions too.
[CL-AFF Shared Task] Multi-label Text Classification Using an Emotion Embedding Model

In this paper, we propose a deep learning model-based approach that combines a language embedding model and an emotion embedding model in the classiﬁcation of text for the CL-AFF Shared Task 2020. The task aims to predict the disclosure and supportiveness labels of the comments (to the posts) in the OﬀMyChest dataset which consists of a small labeled dataset and a large unlabeled dataset. We investigate the eﬀectiveness of the BERT, Glove, and Emotional Glove embedding models, to represent the text for label prediction. We also propose to use the original posts in the dataset as contextual information. We evaluated our approach and report the results.
Automatic Aspect-Based Sentiment Analysis (AABSA) from Customer Reviews

Online review platforms provide enormous information for users to evaluate products and services. However, the sheer volume of reviews can create information overload that could increase user search costs and cognitive burden. To reduce information overload, in this paper, we propose an Automatic Aspect-Based Sentiment Analysis (AABSA) model to automatically identify key aspects from Chinese online reviews and conduct aspect-based sentiment analysis. We create a hierarchical structure of hypernyms and hyponyms, apply deep-learning-based representation learning and clustering to identify aspects that are the core content in the reviews, and then calculate the sentiment score of each aspect. To evaluate the performance of the identiﬁed aspects, we use an econometric model to estimate the impact of each aspect on product sales. We collaborate with one of Asia’s largest online shopping platforms and employ the model in its product review tagging system to help consumers search for product aspects. Compared with benchmark models, our model is both more eﬀective, because it creates a more comprehensive list of aspects that are indicative of customer needs, and more eﬃcient because it is fully automated without any human labor cost.
A report of the CL-Aff OffMyChest Shared Task: Modeling Supportiveness and Disclosure

This overview describes the oﬃcial results of the CL-Aﬀ Shared Task 2020 – #OﬀMyChest. The dataset comprised a semi-supervised classiﬁcation task, and an open-ended knowledge modeling task on a dataset of Reddit comments with annotations crowdsourced from Amazon Mechanical Turk. The Shared Task was organized as a part of the 3 rd Workshop on Aﬀective Content Analysis @ AAAAI-20, held in New York, USA, on February 7, 2020. This paper compares the participating systems in terms of their accuracy and F-1 scores at predicting diﬀer-ent facets of self-disclosure. Feedback from the system runs was used to weed out labeling errors in the test set. The annotated test and training datasets, instructions, and the scripts used for evaluation are available at the GitHub repository.
A Deep Learning Approach Towards Multimodal Stress Detection

Several studies that emerged from the ﬁelds of psychology and medical sciences during recent years have highlighted the impact that stress can have on human health and behavior. Wearable technologies and sensor-based monitoring have shown promising results towards assessing, monitoring and potentially preventing high-risk situations that may occur as a result of fatigue, poor health, or other similar conditions caused by excessive amounts of stress. In this paper, we present our initial steps in developing a deep-learning based approach that can assist with the task of multimodal stress detection. Our results indicate the promise of this direction, and point to the need for further investigations to better understand the role that deep-learning approaches can play in developing generalizable architectures for multimodal aﬀective computing. For our experiments we use the MuSE dataset – a rich resource designed to understand the correlations between stress and emotion – and evaluate our methods on eight diﬀerent information signals captured from 28 individuals.
Context-aware Multimodal Learning Analytics Taxonomy

: Analysis of learning interactions can happen for different purposes. As educational practices increasingly take place in hybrid settings, data from both spaces are needed. At the same time, to analyse and make sense of machine aggregated data afforded by Technology-Enhanced Learning (TEL) environments, contextual information is needed. We posit that human labelled (classroom observations) and automated observations (multimodal learning data) can enrich each other. Researchers have suggested learning design (LD) for contextualisation, the availability of which is often limited in authentic settings. This paper proposes a Context-aware MMLA Taxonomy, where we categorize systematic documentation and data collection within different research designs and scenarios, paying special attention to authentic classroom contexts. Finally, we discuss further research directions and challenges.
Measuring the eGovernment Readiness: Proposed Framework

Information and communication technology can make speedy changes in government processes, particularly in developing countries.in addition, there are many factors that effect on the implementation of eGovernment, because of these, this study carried out a framework to check eGovernment readiness by using the most important factors to measure eReadiness namely Technical, Organizational and Cultural. The result shows that the trust of the Internet and technology is very important to the wide-spread acceptance of eGovernment. because of that, Users need the Data and information protection must be safe from the unauthorized. Also, increasing the training and education factor will positively increase the decentralization factor. ICT infrastructure alone cannot achieve the implementation of eGovernment without a conscious policy designed to transform both the role of ICT in administration and the training and orientation of staff to imbibe innovations. it will be easier for people to be involved with new technology when all the essential things for life are covered. Computer and Internet experience impact eReadiness. public sector organizations should pay more attention to the recruitment and training of so-called big data talent or data scientists. In addition, there is a need to feel that
Quasiparticles and phonon satellites in spectral functions of semiconductors and insulators: Cumulants applied to full first principles theory and Fr\"ohlich polaron

The electron-phonon interaction causes thermal and zero-point motion shifts of electron quasiparticle (QP) energies $\epsilon_k(T)$. Other consequences of interactions, visible in angle-resolved photoemission spectroscopy (ARPES) experiments, are broadening of QP peaks and appearance of sidebands, contained in the electron spectral function $A(k,\omega)=-{\Im m}G_R(k,\omega) /\pi$, where $G_R$ is the retarded Green's function. Electronic structure codes (e.g. using density-functional theory) are now available that compute the shifts and start to address broadening and sidebands. Here we consider MgO and LiF, and determine their nonadiabatic Migdal self energy. The spectral function obtained from the Dyson equation makes errors in the weight and energy of the QP peak and the position and weight of the phonon-induced sidebands. Only one phonon satellite appears, with an unphysically large energy difference (larger than the highest phonon energy) with respect to the QP peak. By contrast, the spectral function from a cumulant treatment of the same self energy is physically better, giving a quite accurate QP energy and several satellites approximately spaced by the LO phonon energy. In particular, the positions of the QP peak and first satellite agree closely with those found for the Fr\"ohlich Hamiltonian by Mishchenko $\textit{et al.}$ (2000) using diagrammatic Monte Carlo. We provide a detailed comparison between the first-principles MgO and LiF results and those of the Fr\"ohlich Hamiltonian. Such an analysis applies widely to materials with infra-red active phonons. We also compare the retarded and time-ordered cumulant treatments: they are equivalent for the Fr\"ohlich Hamiltonian, and only slightly differ in first-principles electron-phonon results for wide-band gap materials.
NUMERICAL PREDICTION OF FLUID FLOW AND HEAT TRANSFER IN WELDING WITH A MOVING HEAT SOURCE

An improved computational analysis is presented for the detailed prediction of heat transfer, phase change, and fluid flow during welding with a moving heat source. The governing equations are formulated in a reference frame attached to the heat source. A special feature in the formulation is that the primary unknown velocity is the convective velocity of the fluid with the motion of the heat source, resulting in additional source terms in the equations. The equations are solved using a control-volume-based computational method. The temperature and velocity fields and the time-temperature data are presented for two welding velocities to demonstrate the results using the new technique.