Miracle-dz/UniFAR-FTUs huggingface dataset:
A Causal Analysis of Observed Declines in Managed Honey Bees (Apis mellifera)

ABSTRACT The European honey bee (Apis mellifera) is a highly valuable, semi-free-ranging managed agricultural species. While the number of managed hives has been increasing, declines in overwinter survival, and the onset of colony collapse disorder in 2006, precipitated a large amount of research on bees’ health in an effort to isolate the causative factors. A workshop was convened during which bee experts were introduced to a formal causal analysis approach to compare 39 candidate causes against specified criteria to evaluate their relationship to the reduced overwinter survivability observed since 2006 of commercial bees used in the California almond industry. Candidate causes were categorized as probable, possible, or unlikely; several candidate causes were categorized as indeterminate due to lack of information. Due to time limitations, a full causal analysis was not completed at the workshop. In this article, examples are provided to illustrate the process and provide preliminary findings, using three candidate causes. Varroa mites plus viruses were judged to be a “probable cause” of the reduced survival, while nutrient deficiency was judged to be a “possible cause. ” Neonicotinoid pesticides were judged to be “unlikely” as the sole cause of this reduced survival, although they could possibly be a contributing factor.
A Shortest Tree Algorithm for Optimal Assignments Across Space and Time in a Distributed Processor System

The problem of optimally assigning the modules of a program over the processors of an inhomogeneous distributed processor system is analyzed. The objective is to assign modules, wherever possible, to the processors on which they execute most rapidly while taking into account the overhead of interprocessor communication. Factors contributing to the cost of an assignment are 1) the amount of computation required by each module, 2) the amount of data transmitted between each pair of modules, 3) the speed of each processor, and 4) the speed of the communication link between each pair of processors.
UHYG at the NTCIR-12 MobileClick Task: Link-based Ranking on iUnit-Page Bipartite Graph

We participated in the iUnit ranking subtask and the iUnit summarization subtask of the NTCIR-12 MobileClick for the Japanese and English languages. Our strategy is based on link analysis on an iUnit-page bipartite graph. First, we constructed an iUnit-page bipartite graph considering the entailment relationship between the iUnits and the pages. Then, we ranked the iUnits by their scores based on link analysis. For the iUnit ranking subtask, we examined three types of entailment relationships and three types of link analysis, the degree of nodes, PageRank, and HITS. For the iUnit summarization subtask, we propose an intent-sensitive PageRank that is an extended version of the topic-sensitive PageRank based on the probability that users visit pages in a search result page.
Behavioral Effects of Post-employment Restrictions † A. a Broader Approach of Dynamic Investment

Traditional economic models view post-employment restrictions as necessary constraints stemming from the assumption that absent such contractual protections, employers would under-invest in R&D and employee training. This article enriches the analysis of human capital investment by exploring effects on employee motivation while keeping in mind the aggregate dimension of mobility over time. The article reports original experimental research demonstrating that contractual background affects motivation and performance: in a pure effort task, individuals abandoned the task at higher rates, spent less time on it, and failed more frequently to find the correct solution when bound by post-employment restrictions. At the same time, some of these effects, including time on task and quality of performance, largely disappeared in a task that invoked internal talent and creativity. The article links recent empirical evidence about positive spillovers with a behavioral analysis that suggests further positive effects, together offering a richer perspective of the costs and benefits of post-employment restrictions. 3 It is an immutable law in business that words are words, explanations are explanations, promises are promises but only performance is reality. 1. Introduction How much of human productive action is a function of intrinsic interest in the task at hand and how much is affected by external rewards, promises, and constraints? This Article provides a nuanced view of the costs and benefits of post-employment restrictions. Traditional economic models view post-employment restrictions, primarily covenants not-to-compete, as necessary limitations stemming from the assumption that absent such contractual protections, employers would under-invest in research, development, and employee training because of the increased risk that these employees will leave and use that training as competitors (Rubin and Shed 1981; Glick et al. 2002). This Article seeks to enrich the analysis of human capital development by studying the motivational effects of post-employment restrictions on performance during employment while attending to the dynamic nature of the employment market. The attempt is to test the following assumption: while post-employment restrictions may encourage firms to invest in employee skill and research and development (R&D), such restrictions may discourage employees from investing in their own human capital and work performance. The Article reports the findings of an experimental study designed to observe the effects of post-employment restrictions on task performance. The reported lab experiment was incentive 4 compatible, providing pay for performance to the 1028 study participants. The results demonstrate that under certain conditions of contractual restrictions, when tasks involve pure effort …
Confirming the influence of educational background in pair-design knowledge through experiments

The sharing of tacit knowledge is a strategic factor for the success of software process, from a number of perspectives: training, project assimilation, and reducing noise in knowledge transfer. Pair programming is supposed to be a practice suitable for this purpose. Unfortunately, the building of tacit knowledge is determined by factors that are difficult to isolate and capture because they concern personal attitude and capability. Thus, we have focused on the possible causes forming the individual ability, that can be isolated and studied, such as the individual education background. We have applied the practice of working in pairs to the design phase. We have made an experiment and a replica in academic environment, in order to understand the relationship between the building of knowledge through the practice and the individual background. In this paper we discuss the replica and compare the results with the first experiment's ones.
Intelligent GPS-less Speed Detection and clustering in VANET

Vehicular Ad hoc NETwork (VANET) has emerged as a new wireless network for vehicular communications. To provide a flexible and high reliable communication service in VANET, vehicles are clustered to construct many small networks (clusters) so that channel interferences and flooding messages can be limited. This research presents a novel Multi-resolution Relative Speed Detection (MRSD) model to improve the clustering algorithm in VANET without using GPS. MRSD uses the Moving Average Convergence Divergence (MACD), the Momentum of Received Signal Strength (MRSS), and Artificial Neural Networks (ANNs) to estimate the motion state and the relative speed of a vehicle based purely on Received Signal Strength. The proposed MRSD model is accurate with the assistance of the intelligent classification, and incurs less overhead in the cluster head election than that of other algorithms.
Advanced DSA in Web Services on Mining – Spatial LBS-Case Study

The Web Services paradigm assures to enable rich flexible and dynamic interoperation of highly distributed, heterogeneous network enabled services. The idea of Web Services Mining that it makes use of the findings in the field of data mining and applies them to the world of Web Services. The emerging concept of Semantic Web Services aims at more sophisticated Web Services technologies: on basis of Semantic Description Frameworks, Intelligent mechanisms are envisioned for Discovery, Composition, and contracting of Web Services. The aim of semantic web is not only to support to access information on the web but also to support its usage. Geospatial Semantic Web is an augmentation to the Semantic Web that adds geospatial abstractions, as well as related reasoning, representation and query mechanisms. Web Service Security represents a key requirement for today’s distributed interconnected digital world and for the new generations, Web 2.0 and Semantic Web. To date, the problem of security has been investigated very much in the context of standardization efforts; Personal judgments are made usually based on the sensitivity of the information and the reputation of the party to which the information is to be disclosed. On the privacy front, this means that privacy invasion would net more quality and sensitive personal information. In this paper, we had implemented a case study on integrated privacy issues of Spatial Semantic Web Services Mining. Initially we improved privacy of Geospatial Semantic Layer. Finally, we implemented a Location Based System and improved its digital signature capability, using advanced Digital Signature standards.
The Effects of Self-Esteem, Variety of Sexual Experience, and Erotophilia on Sexual Satisfaction in Sexually Active Heterosexuals

Little research has investigated components of sexual satisfaction in nonclinical populations. We studied 99 male and 99 female undergraduates on several factors thought to contribute to healthy sexuality and sexual satisfaction, specifically, self-esteem, variety of sexual experience, and sexual attitudes—erotophilia-erotophobia. Results showed that individuals with high self-esteem and those with a wide variety of sexual experience also havehigh sexual satisfaction, but erotophilic versus erotophobic attitudes did not affect the degree of sexual satisfaction.
Knowledge Management and the Development of Dynamic Capabilities : An Empirical Analysis in SMEs

Knowledge Management projects and practices can serve as a means to develop dynamic capabilities in order to stimulate exploration and exploitation. The aim of this paper is to analyse how KM projects can contribute to the development of dynamic capabilities and how they can stimulate the development of new routines for exploration and exploitation. The paper is based on four case studies of SMEs that have implemented KM projects. Results show that under some conditions KM projects can give an important contribution to the development of dynamic capabilities for both exploration and exploitation.
Using uncorrelated discriminant analysis for tissue classification with gene expression data

The classification of tissue samples based on gene expression data is an important problem in medical diagnosis of diseases such as cancer. In gene expression data, the number of genes is usually very high (in the thousands) compared to the number of data samples (in the tens or low hundreds); that is, the data dimension is large compared to the number of data points (such data is said to be undersampled). To cope with performance and accuracy problems associated with high dimensionality, it is commonplace to apply a preprocessing step that transforms the data to a space of significantly lower dimension with limited loss of the information present in the original data. Linear discriminant analysis (LDA) is a well-known technique for dimension reduction and feature extraction, but it is not applicable for undersampled data due to singularity problems associated with the matrices in the underlying representation. This paper presents a dimension reduction and feature extraction scheme, called uncorrelated linear discriminant analysis (ULDA), for undersampled problems and illustrates its utility on gene expression data. ULDA employs the generalized singular value decomposition method to handle undersampled data and the features that it produces in the transformed space are uncorrelated, which makes it attractive for gene expression data. The properties of ULDA are established rigorously and extensive experimental results on gene expression data are presented to illustrate its effectiveness in classifying tissue samples. These results provide a comparative study of various state-of-the-art classification methods on well-known gene expression data sets
Buffering study over intermediate hops including packet retransmission

Sequential decoding is one of the most important convolutional code's algorithms that can work in intermediate hops efficiently. It is of interest since its error detection and correction techniques are considered to be variable and dependent to channel state. In this paper, we conduct a new buffering study over intermediate hops' systems that employ sequential decoding algorithms. As a result of that, we find a closed form expression for the expected buffer occupancy under different assumptions. Our study can be applicable to any network environments that are affected by noise which consequently leads to high packet error rate. The results are presented and explained when considering different system's parameters.
Reliability Analysis: System Models

Reliability analysis is a design-time activity. The goal is to anticipate problems and suggest product improvements before manufacturing and delivery. Enhanced quality and customer satisfaction along with reduced life-cycle costs are the motivation. Analysts employ models of physical systems to determine points of vulnerability and failure probabilities. Unfortunately, the models of complex systems--those with dependency cycles among components--necessarily lead to ambiguous analyses. Below, the source of these ambiguities is identified and some AI and expert systems techniques are suggested to help deal with them.
Efficient blind dereverberation framework for automatic speech recognition

A speech signal captured by a distant microphone is generally smeared by reverberation, which severely degrades Automatic Speech Recognition (ASR) performance. In this paper, we propose a novel and practical single channel dereverberation scheme, which utilizes the relationship between speech and re-verberation. A dereverberation ﬁlter derived by the proposed method is capable of efﬁciently suppressing late reﬂections, which constitute a major cause of ASR performance degradation. The proposed algorithm can achieve effective derever-beration with a more reasonable computational complexity than conventional methods. Experimental results reveal a substantial improvement in ASR performance even in severely reverberant environments
Genomic and Molecular Characterization of Miltefosine Resistance in Leishmania infantum Strains with Either Natural or Acquired Resistance through Experimental Selection of Intracellular Amastigotes

During the last decade miltefosine (MIL) has been used as first-line treatment for visceral leishmaniasis in endemic areas with antimonial resistance, but a decline in clinical effectiveness is now being reported. While only two MIL-resistant Leishmania infantum strains from HIV co-infected patients have been documented, phenotypic MIL-resistance for L. donovani has not yet been identified in the laboratory. Hence, a better understanding of the factors contributing to increased MIL-treatment failure is necessary. Given the paucity of defined MIL-resistant L. donovani clinical isolates, this study used an experimental amastigote-selected MIL-resistant L. infantum isolate (LEM3323). In-depth exploration of the MIL-resistant phenotype was performed by coupling genomic with phenotypic data to gain insight into gene function and the mutant phenotype. A naturally MIL-resistant L. infantum clinical isolate (LEM5159) was included to compare both datasets. Phenotypically, resistance was evaluated by determining intracellular amastigote susceptibility in vitro and actual MIL-uptake. Genomic analysis provided supportive evidence that the resistance selection model on intracellular amastigotes can be a good proxy for the in vivo field situation since both resistant strains showed mutations in the same inward transporter system responsible for the acquired MIL-resistant phenotype. In line with previous literature findings in promastigotes, our data confirm a defective import machinery through inactivation of the LiMT/LiRos3 protein complex as the main mechanism for MIL-resistance also in intracellular amastigotes. Whole genome sequencing analysis of LEM3323 revealed a 2 base pair deletion in the LiMT gene that led to the formation an early stop codon and a truncation of the LiMT protein. Interestingly, LEM5159 revealed mutations in both the LiMT and LiRos3 genes, resulting in an aberrant expression of the LiMT protein. To verify that these mutations were indeed accountable for the acquired resistance, transfection experiments were performed to re-establish MIL-susceptibility. In LEM3323, susceptibility was restored upon expression of a LiMT wild-type gene, whereas the MIL-susceptibility of LEM5159 could be reversed after expression of the LiRos3 wild-type gene. The aberrant expression profile of the LiMT protein could be restored upon rescue of the LiRos3 gene both in the LEM5159 clinical isolate and a ΔLiRos3 strain, showing that expression of LdMT is dependent on LdRos3 expression. The present findings clearly corroborate the pivotal role of the LiMT/LiRos3 complex in resistance towards MIL.
The Relational Vector-Space Model

This paper addresses the classification of linked entities. Weintroduce a relational vector (VS) model (in analogy to theVS model used in information retrieval) that abstracts the linkedstructure, representing entities by vectors of weights. Givenlabeled data as background knowledge training data, classificationprocedures can be defined for this model, including astraightforward, "direct" model using weighted adjacency vectors.Using a large set of tasks from the domain of company affiliationidentification, we demonstrate that such classification procedurescan be effective. We then examine the method in more detail,showing that as expected the classification performance correlateswith the- relational auto correlation of the data set. We then turnthe tables and use the relational VS scores as a way toanalyze/visualize the relational autocorrelation present in acomplex linked structure. The main contribution of the paper 1s tointroduce the relational VS model as a potentially useful additionto the toolkit for relational data mining. It could provide usefulconstructed features for domains with low to moderate relationalautocorrelation; it may be effective by itself for domains with high levels of relational autocorrelation, and it provides a usefulabstraction for analyzing the properties of linked data.
Dynamic Staffing and Rescheduling in Software Project Management: A Hybrid Approach

Resource allocation could be influenced by various dynamic elements, such as the skills of engineers and the growth of skills, which requires managers to find an effective and efficient tool to support their staffing decision-making processes. Rescheduling happens commonly and frequently during the project execution. Control options have to be made when new resources are added or tasks are changed. In this paper we propose a software project staffing model considering dynamic elements of staff productivity with a Genetic Algorithm (GA) and Hill Climbing (HC) based optimizer. Since a newly generated reschedule dramatically different from the initial schedule could cause an obvious shifting cost increase, our rescheduling strategies consider both efficiency and stability. The results of real world case studies and extensive simulation experiments show that our proposed method is effective and could achieve comparable performance to other heuristic algorithms in most cases.
Automatically Extending the Lexicon for Parsing

This paper describes a method for automatically extending the lexicon of wide-coverage parsers. The method is an extension to the automatic detection of coverage problems of natural language parsers, based on large amounts of raw text (van Noord 2004). The goal is to extend grammar coverage, focusing in particular on the acquisition of lexical information for missing and incomplete lexicon entries (including subcategorization frames). In order to assign lexical entries for unknown words, or for words for which the lexicon only contains a subset of its possible lexical categories, we propose to apply a parser to a set of unannotated sentences containing the unknown word, or to a set of unannotated sentences (found by error mining) in which the word apparently was used with a missing lexical category. The parser will assign all universal lexical categories to the problematic word. Once the parser has found a result for the sentence, it can output the lexical category that was eventually used in its best parse. If this process is repeated for a large enough sample of sentences, it is expected that either a single or a small number of lexical categories can then be identified which are to be taken as the correct lexical categories of this word. A maximum entropy classifier is trained to select the correct lexical categories.
Aire controls the differentiation program of thymic epithelial cells in the medulla for the establishment of self-tolerance

The roles of autoimmune regulator (Aire) in the expression of the diverse arrays of tissue-restricted antigen (TRA) genes from thymic epithelial cells in the medulla (medullary thymic epithelial cells [mTECs]) and in organization of the thymic microenvironment are enigmatic. We approached this issue by creating a mouse strain in which the coding sequence of green fluorescent protein (GFP) was inserted into the Aire locus in a manner allowing concomitant disruption of functional Aire protein expression. We found that Aire+ (i.e., GFP+) mTECs were the major cell types responsible for the expression of Aire-dependent TRA genes such as insulin 2 and salivary protein 1, whereas Aire-independent TRA genes such as C-reactive protein and glutamate decarboxylase 67 were expressed from both Aire+ and Aire− mTECs. Remarkably, absence of Aire from mTECs caused morphological changes together with altered distribution of mTECs committed to Aire expression. Furthermore, we found that the numbers of mTECs that express involucrin, a marker for terminal epidermal differentiation, were reduced in Aire-deficient mouse thymus, which was associated with nearly an absence of Hassall's corpuscle-like structures in the medulla. Our results suggest that Aire controls the differentiation program of mTECs, thereby organizing the global mTEC integrity that enables TRA expression from terminally differentiated mTECs in the thymic microenvironment.