Miracle-dz/UniFAR-FTUs huggingface dataset:
Services Scientific Principles Implementation into Talent Development Models: An Example of Synvision Technology Company

This study discloses the scope of services innovation of the case company, to understand how to proceed with the service of science, technology and services to construct value chain of Synvision Technology. The results of the entire interview by the following four points: (1) The value chain of services science, including core management structure, E-service management, quality certification and authentication services. (2) 5S for the management of core organizational culture perspective. (3) Through the information technology achieves the goal of improved services and importing ISO 9001, ISO 14001 and GSP mode of operation.
Msn2p and Msn4p Control a Large Number of Genes Induced at the Diauxic Transition Which Are Repressed by Cyclic AMP inSaccharomyces cerevisiae

ABSTRACT The multicopy suppressors of the snf1 defect, Msn2p and Msn4p transcription factors (Msn2/4p), activate genes through the stress-responsive cis element (CCCCT) in response to various stresses. This cis element is also the target for repression by the cyclic AMP (cAMP)-signaling pathway. We analyzed the two-dimensional gel electrophoresis pattern of protein synthesis of themsn2 msn4 double mutant and compared it with that of the wild-type strain during exponential growth phase and at the diauxic transition. Thirty-nine gene products (including those ofALD3, GDH3, GLK1, GPP2,HSP104, HXK1, PGM2,SOD2, SSA3, SSA4, TKL2,TPS1, and YBR149W) are dependent upon Msn2/4p for their induction at the diauxic transition. The expression of all these genes is repressed by cAMP. Thirty other genes identified during this study are still inducible in the mutant. A subset of these genes were found to be superinduced at the diauxic transition, and others were subject to cAMP repression (including ACH1,ADH2, ALD6, ATP2, GPD1,ICL1, and KGD2). We conclude from this analysis that Msn2/4p control a large number of genes induced at the diauxic transition but that other, as-yet-uncharacterized regulators, also contribute to this response. In addition, we show here that cAMP repression applies to both Msn2/4p-dependent and -independent control of gene expression at the diauxic shift. Furthermore, the fact that all the Msn2/4p gene targets are subject to cAMP repression suggests that these regulators could be targets for the cAMP-signaling pathway.
Ontology based Semantic Similarity Measure using Concept Weighting

Semantic similarity between the documents is essential when it is extracted from free text document. Representing the presence and absence of concept in binary format may not provide perfect accuracy. Concept weighting through term frequency will increase accuracy of clustered document. Concept weight is determined using term frequency and semantic distance. Semantic similarity of a concept is derived using ontology extracted from swoogle. Vector space model with parent-child (is-a) relationship ontology are exploited using protege. Term frequencies for the extracted concepts are calculated using text processing. In this paper Cosine similarity using concept weight measure is applied to find similarity between different documents. According to the similarity score, documents are clustered. In this paper a sample walkthrough for the proposed system has been discussed by comparing two documents.
A Unified Matching Framework for Multi-Flow Decode-and-Forward Cooperative Networks

Recent works have shown that cooperative diversity can be achieved by using relay selection (RS), distributed space-time coding (DSTC), and distributed beam-forming (DBF) in narrow-band decode-and-forward (DF) cooperative networks with one source-and-destination (s-d) pair. However, the joint resource allocation for broadband DF cooperative networks with multiple s-d flows has not received much attention yet. In this paper, a random hypergraph based unified matching framework is proposed, under which five feasible types of multi-flow DF cooperative networks will be considered. In each type, the maximum matching method will be applied to RS, DSTC, and DBF schemes so as to achieve the optimal channel allocation and relay selection with fairness assurance. By analyzing the properties of maximum matching, the outage probability of each s-d pair after resource allocation will be obtained. The results of diversity-multiplexing tradeoff will show that the proposed framework is capable of achieving the full frequency diversity and cooperative diversity for each s-d pair simultaneously, while the frequency multiplexing is equally shared. Based on the unified framework, the random rotation based parallel Hopcroft-Karp (R2PHK) algorithm will then be designed, which can work in each destination node independently, and shall enjoy a poly-logarithmic complexity O(log2loN), where N is the number of channels and lo is a constant.
The impact of parenthood on consumption:the new car buying experience

In this article the authors discuss the new car buying experience for expectant and new parents to identify the impact of becoming a parent on the values and attitudes they exhibit during the purchase experience and in the final choice they make. In particular, the authors contend that the introduction of a first baby to a family unit will influence the buying process as parents will use consumption to express values and to determine product usage. Firstly, literature delineating the buying process is reviewed, along with literature relating to value adjustment concerning the addition of a newborn to the family unit and the influences of impending or recent parenthood upon the purchase process for a new car. Following this discussion, research suggestions and managerial implications are discussed.
Eyes: Variety, Development and Evolution

The selective advantages of using light as a source of information are reflected in the diverse types of extant eyes. The physical properties of light restrict how it can be collected and processed, resulting in only eight known optical systems found in animals. Eyes develop through tissue rearrangement and differentiation. Our understanding of the source of genetic information used in developmental programs is growing rapidly and reveals distributions of gene expression with substantial overlap in both time and space. Specific genes and their products are used repeatedly, making causal relationships more difficult to discern. The phenomenon of groups of genes acting together seems to be the rule. Throughout evolution, particular genes have become associated with distinct aspects of eye development, and these suites of genes have been recruited repeatedly as new eyes evolved.
PAC-Bayesian Policy Evaluation for Reinforcement Learning

Bayesian priors offer a compact yet general means of incorporating domain knowledge into many learning tasks. The correctness of the Bayesian analysis and inference, however, largely depends on accuracy and correctness of these priors. PAC-Bayesian methods overcome this problem by providing bounds that hold regardless of the correctness of the prior distribution. This paper introduces the first PAC-Bayesian bound for the batch reinforcement learning problem with function approximation. We show how this bound can be used to perform model-selection in a transfer learning scenario. Our empirical results confirm that PAC-Bayesian policy evaluation is able to leverage prior distributions when they are informative and, unlike standard Bayesian RL approaches, ignore them when they are misleading.
Rural Opioid and Other Drug Use Disorder Diagnosis: Assessing Measurement Invariance and Latent Classification of DSM-IV Abuse and Dependence Criteria

The rates of non-medical prescription drug use in the United States (U.S.) have increased dramatically in the last two decades, leading to a more than 300% increase in deaths from overdose, surpassing motor vehicle accidents as the leading cause of injury deaths. In rural areas, deaths from unintentional overdose have increased by more than 250% since 1999 while urban deaths have increased at a fraction of this rate. The objective of this research was to test the hypothesis that cultural, economic, and environmental factors prevalent in rural America affect the rate of substance use disorder (SUD) in that population, and that diagnosis of these disorders across rural and urban populations may not be generalizable due to these same effects. This study applies measurement invariance analysis and factor analysis techniques: item response theory (IRT), multiple indicators, multiple causes (MIMIC), and latent class analysis (LCA), to the DSM-IV abuse and dependency diagnosis instrument. The sample used for the study was a population of adult past-year illicit drug users living in a rural or urban area drawn from the 2011-2012 National Survey on Drug Use and Health data files (N = 3,369| analyses 1 and 2; N = 12,140| analysis 3). Results of the IRT and MIMIC analyses indicated no significant variance in DSM item function across rural and urban subgroups ; however, several socio-demographic variables including age, race, income, and gender were associated with bias in the instrument. Latent class structures differed across the sub-3 groups in quality and number, with the rural sample fitting a 3-class structure and the urban fitting 6-class model. Overall the rural class structure exhibited less diversity and lower prevalence of SUD in multiple drug categories (e.g. cocaine, hallucinogens, and stimulants). This result suggests underlying elements affecting SUD patterns in the two populations. These findings inform the development of surveillance instruments, clinical services, and public health programming tailored to specific communities. and Charlie, the six lights that guide my way. 6 ACKOWLEDGEMENTS I would like to thank my committee (Drs Alamian, Pack, McBee, and Zheng) for all your efforts on my behalf. Through the process of developing this research I have been overwhelmed by your generosity and wisdom. I will always be grateful for the time you've each spent with me over the last three years. In addition I would like to thank Dr. Megan Quinn for her input in the early stages of the proposal development, …
SOFIE: a self-organizing framework for information extraction

This paper presents SOFIE, a system for automated ontology extension. SOFIE can parse natural language documents, extract ontological facts from them and link the facts into an ontology. SOFIE uses logical reasoning on the existing knowledge and on the new knowledge in order to disambiguate words to their most probable meaning, to reason on the meaning of text patterns and to take into account world knowledge axioms. This allows SOFIE to check the plausibility of hypotheses and to avoid inconsistencies with the ontology. The framework of SOFIE unites the paradigms of pattern matching, word sense disambiguation and ontological reasoning in one unified model. Our experiments show that SOFIE delivers high-quality output, even from unstructured Internet documents.
Collective Instance-Level Gene Normalization on the IGN Corpus

A high proportion of life science researches are gene-oriented, in which scientists aim to investigate the roles that genes play in biological processes, and their involvement in biological mechanisms. As a result, gene names and their related information turn out to be one of the main objects of interest in biomedical literatures. While the capability of recognizing gene mentions has made significant progress, the results of recognition are still insufficient for direct use due to the ambiguity of gene names. Gene normalization (GN) goes beyond the recognition task by linking a gene mention to a database ID. Unlike most previous works, we approach GN on the instance-level and evaluate its overall performance on the recognition and normalization steps in abstracts and full texts. We release the first instance-level gene normalization (IGN) corpus in the BioC format, which includes annotations for the boundaries of all gene mentions and the corresponding IDs for human gene mentions. Species information, along with existing co-reference chains and full name/abbreviation pairs are also provided for each gene mention. Using the released corpus, we have designed a collective instance-level GN approach using not only the contextual information of each individual instance, but also the relations among instances and the inherent characteristics of full-text sections. Our experimental results show that our collective approach can achieve an F-score of 0.743. The proposed approach that exploits section characteristics in full-text articles can improve the F-scores of information lacking sections by up to 1.8%. In addition, using the proposed refinement process improved the F-score of gene mention recognition by 0.125 and that of GN by 0.03. Whereas current experimental results are limited to the human species, we seek to continue updating the annotations of the IGN corpus and observe how the proposed approach can be extended to other species.
Visually guided landing of an unmanned aerial vehicle

We present the design and implementation of a real-time, vision-based landing algorithm for an autonomous helicopter. The landing algorithm is integrated with algorithms for visual acquisition of the target (a helipad) and navigation to the target, from an arbitrary initial position and orientation. We use vision for precise target detection and recognition, and a combination of vision and Global Positioning System for navigation. The helicopter updates its landing target parameters based on vision and uses an onboard behavior-based controller to follow a path to the landing site. We present significant results from flight trials in the field which demonstrate that our detection, recognition, and control algorithms are accurate, robust, and repeatable.
The Hammer and the Nail : Interdisciplinarity and Problem Solving in Sustainability Science

This is a thesis about interdisciplinarity, scientific integration, and problem solving in sustainability science. Sustainability science is an emerging and highly interdisciplinary field that seeks to integrate vastly differentiated bodies of knowledge in addressing the challenge of transitioning contemporary societies towards sustainability. Interdisciplinarity is paramount. Interdisciplinarity in general, and in the context of sustainability science in particular, has often been associated with solving particular problems and problem solving is one important theme in this thesis. A central idea that is developed is that of problem-feeding. Sometimes problems arise within one discipline that can only be solved with the help of another. This concept, that has predecessors in e.g. the work of Lindley Darden and Nancy Maull, is explored considerably. It is argued that in interdisciplinary contexts—such as sustainability science—where collaboration is sought it is important to maintain cross-boundary problem stability. That is to say, as the problem is transferred from one discipline to another transformations will often be necessary. These transformations then, need to be acceptable to all involved parties to maintain an active interdisciplinary connection. Another topic that is discussed both in the introductory essay and some of the papers included is that of scientific imperialism. Scientific imperialism—the infringement of one discipline upon the domain of another—is here suggested to be primarily a threat to in-terdisciplinary collaborations. A distinction is introduced between imperialist failures of expansionism and failures of replacement. These are labelled type-I and type-II imperialism respectively. Particular attention is devoted to the latter form. Type-II imperialism concerns cases where imperialist infringements fail as the imperial- izing framework replaces viable, or compatible alternatives. Such an error of replacement does, importantly, not imply that the framework or theory should be disregarded completely. This type of imperialist error can both be quite subtle, and damaging. For one, if one directs the attention to specific contexts knowledge is actually lost in the process. This is particularly serious in fields such as sustainability science that are, to such an large extent, aimed at influencing concrete policy. Finally, interdisciplinarity is difficult to achieve and in many cases represents a grand challenge in itself. There are however many different ways in which interdisciplinarity may be accomplished and different forms are suitable in different contexts. In a field such as sustainability science where complexity is such a prevalent feature, an inclusive, pluralist, approach is likely to be appropriate. (Less)
Diabetic Retinopathy using morphological operations and machine learning

Diabetic Retinopathy that is DR which is a eye disease that affect retina and further later at severe stage it lead to vision loss. Early detection of DR is helpful to improve the screening of patient to prevent further damage. Retinal micro-aneurysms, haemorrhages, exudates and cotton wool spots are kind of major abnormality to find the Non- Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR). The main objective of our proposed work is to detect retinal micro-aneurysms and exudates for automatic screening of DR using Support Vector Machine (SVM) and KNN classifier. To develop this proposed system, a detection of red and bright lesions in digital fundus photographs is needed. Micro-aneurysms are the first clinical sign of DR and it appear small red dots on retinal fundus images. To detect retinal micro-aneurysms, retinal fundus images are taken from Messidor, DB-rect dataset. After pre-processing, morphological operations are performed to find micro-aneurysms and then features are get extracted such as GLCM and Structural features for classification. In order to classify the normal and DR images, different classes must be represented using relevant and significant features. SVM gives better performance over KNN classifier.
Whom to Blame? Automatic Diagnosis of Performance Bottlenecks on Smartphones

The past decade has witnessed a tremendous growth in the variety and complexity of mobile applications (apps). Although considerable amount of efforts have been spent to improve app performance, smartphones nowadays still face many performance challenges. We discover that the resource contention of multiple running apps, caused by resource bottleneck(s), is a key factor that affects the smartphone performance. In this paper, we present APB, an A utomatic tool that detects Performance issues caused by resource B ottleneck(s) on commodity Android smartphones. APB employs an innovative bottleneck-hypersurface model to quantify performance issues given a specific system state. Then, based on the model, APB identifies a list of apps that contribute most to the resource contention, which can well inform the end user to take action such as killing background apps to resolve the performance issue. We implement APB on commodity Android platforms and widely evaluate its effectiveness with real user studies. Results show that APB outperforms three baseline approaches and helps users to improve smartphone performance by 10 to 67 percent, with less than 1 percent runtime overhead.
Analysis of Special Education Policy in Turkey and United States: Improving Turkey's Policy for Students with Intellectual Disability

The authors compare the special education law of Turkey with comparable law of the United States. The basis of the comparison is the legal framework represented by the six principles of the special education law of the United States, the Individuals with Disabilities Education Act. Within each principle, the special implications for students with intellectual disability are noted. Suggestions for improving Turkey's special education law are made. These relate principally to the principles of zero reject and issues related to discipline; appropriate education, and issues concerning related services and supplementary aids and services; least restrictive environment as related specifically to students with intellectual disability; procedural due process and issues related to notice from schools to parents and dispute resolution; and parent participation. By contrast, there seems to be no reason for Turkey to modify its law with respect to the principles related to nondiscriminatory evaluation. The analysis suggests that Turkey's special education law, being largely comparable with the United States' but still appropriate for Turkey as a homogeneous, single-language, Muslim country, may be appealing to countries that are similar to it and that, like Turkey, aspire for modernism in education.
A Birthday Repetition Theorem and Complexity of Approximating Dense CSPs

A $(k \times l)$-birthday repetition $\mathcal{G}^{k \times l}$ of a two-prover game $\mathcal{G}$ is a game in which the two provers are sent random sets of questions from $\mathcal{G}$ of sizes $k$ and $l$ respectively. These two sets are sampled independently uniformly among all sets of questions of those particular sizes. We prove the following birthday repetition theorem: when $\mathcal{G}$ satisfies some mild conditions, $val(\mathcal{G}^{k \times l})$ decreases exponentially in $\Omega(kl/n)$ where $n$ is the total number of questions. Our result positively resolves an open question posted by Aaronson, Impagliazzo and Moshkovitz (CCC 2014). As an application of our birthday repetition theorem, we obtain new fine-grained hardness of approximation results for dense CSPs. Specifically, we establish a tight trade-off between running time and approximation ratio for dense CSPs by showing conditional lower bounds, integrality gaps and approximation algorithms. In particular, for any sufficiently large $i$ and for every $k \geq 2$, we show the following results: - We exhibit an $O(q^{1/i})$-approximation algorithm for dense Max $k$-CSPs with alphabet size $q$ via $O_k(i)$-level of Sherali-Adams relaxation. - Through our birthday repetition theorem, we obtain an integrality gap of $q^{1/i}$ for $\tilde\Omega_k(i)$-level Lasserre relaxation for fully-dense Max $k$-CSP. - Assuming that there is a constant $\epsilon > 0$ such that Max 3SAT cannot be approximated to within $(1-\epsilon)$ of the optimal in sub-exponential time, our birthday repetition theorem implies that any algorithm that approximates fully-dense Max $k$-CSP to within a $q^{1/i}$ factor takes $(nq)^{\tilde \Omega_k(i)}$ time, almost tightly matching the algorithmic result based on Sherali-Adams relaxation.
Leveraging shape and depth in user authentication from in-air hand gestures

Depth-sensors, such as the Kinect, have predominately been used as a gesture recognition device. Recent works, however, have proposed using these sensors for user authentication using biometric modalities such as: face, speech, gait and gesture. The last of these modalities - gestures, used in the context of full-body and hand-based gestures, is relatively new but has shown promising authentication performance. In this paper, we focus on hand-based gestures that are performed in-air. We present a novel approach to user authentication from such gestures by leveraging a temporal hierarchy of depth-aware silhouette covariances. Further, we investigate the usefulness of shape and depth information in this modality, as well as the importance of hand movement when performing a gesture. By exploiting both shape and depth information our method attains an average 1.92% Equal Error Rate (EER) on a dataset of 21 users across 4 predefined hand-gestures. Our method consistently outperforms related methods on this dataset.