Miracle-dz/UniFAR-FTUs huggingface dataset:
The hitchhiker's guide to Europe: the infection dynamics of an ongoing Wolbachia invasion and mitochondrial selective sweep in Rhagoletis cerasi

Wolbachia is a maternally inherited and ubiquitous endosymbiont of insects. It can hijack host reproduction by manipulations such as cytoplasmic incompatibility (CI) to enhance vertical transmission. Horizontal transmission of Wolbachia can also result in the colonization of new mitochondrial lineages. In this study, we present a 15‐year‐long survey of Wolbachia in the cherry fruit fly Rhagoletis cerasi across Europe and the spatiotemporal distribution of two prevalent strains, wCer1 and wCer2, and associated mitochondrial haplotypes in Germany. Across most of Europe, populations consisted of either 100% singly (wCer1) infected individuals with haplotype HT1, or 100% doubly (wCer1&2) infected individuals with haplotype HT2, differentiated only by a single nucleotide polymorphism. In central Germany, singly infected populations were surrounded by transitional populations, consisting of both singly and doubly infected individuals, sandwiched between populations fixed for wCer1&2. Populations with fixed infection status showed perfect association of infection and mitochondria, suggesting a recent CI‐driven selective sweep of wCer2 linked with HT2. Spatial analysis revealed a range expansion for wCer2 and a large transition zone in which wCer2 splashes appeared to coalesce into doubly infected populations. Unexpectedly, the transition zone contained a large proportion (22%) of wCer1&2 individuals with HT1, suggesting frequent intraspecific horizontal transmission. However, this horizontal transmission did not break the strict association between infection types and haplotypes in populations outside the transition zone, suggesting that this horizontally acquired Wolbachia infection may be transient. Our study provides new insights into the rarely studied Wolbachia invasion dynamics in field populations.
Multivariate Survival Analysis with Positive Stable

SUMMARY In this paper, we describe Bayesian modeling of dependent multivariate survival data using positive stable frailty distributions. A exible baseline hazard formulation using a piecewise exponential model with a correlated prior process is used. The estimation of the stable law parameter together with the parameters of the (conditional) proportional hazards model is facilitated by a modiied Gibbs sampling procedure. The methodology is illustrated on kidney infection data.
A STATISTICAL APPROACH FOR MULTILINGUAL DOCUMENT CLUSTERING AND TOPIC EXTRACTION FROM CLUSTERS

This paper describes a statistics-based methodology for document unsuper-vised clustering and cluster topics extraction. For this purpose, multiword lexical units (MWUs) of any length are automatically extracted from corpora using the LiPXtractor extractor — a language independent statistics-based tool. The MWUs are taken as base-features to describe documents. These features are transformed and a document similarity matrix is constructed. From this matrix, a reduced set of features is selected using an approach based on Principal Component Analysis. Then, using the Model Based Clustering Analysis software, it is possible to obtain the best number of clusters. Precision and Recall for document-cluster assignment range above 90%. Most important MWUs are extracted from each cluster and taken as document cluster topics. Results on new document classification will just be mentioned.
A Multi-agent System based on Implicit Culture

We present an implementation of a multi-agent system that aims at solving the problem of tacit knowledge transfer by means of experiences sharing. In particular, we consider experiences of use of pieces of information. Each agent incorporates a system for implicit culture support (SICS) whose goal is to realize the acceptance of the suggested information. The SICS permits a transparent (implicit) sharing of the information about the use, e.g., requesting and accepting pieces of information.
To beat or not to beat a tick: comparison of DNA extraction methods for ticks (Ixodes scapularis)

Background. Blacklegged ticks (Ixodes scapularis) are important disease vectors in the United States, known to transmit a variety of pathogens to humans, including bacteria, protozoa, and viruses. Their importance as a disease vector necessitates reliable and comparable methods for extracting microbial DNA from ticks. Furthermore, to explore the population genetics or genomics of this tick, appropriate DNA extraction techniques are needed for both the vector and its microbes. Although a few studies have investigated different methods of DNA isolation from ticks, they are limited in the number and types of DNA extraction and lack species-specific quantification of DNA yield. Methods. Here we determined the most efficient and consistent method of DNA extraction from two different developmental stages of I. scapularis—nymph and adult—that are the most important for disease transmission. We used various methods of physical disruption of the hard, chitinous exoskeleton, as well as commercial and non-commercial DNA isolation kits. To gauge the effectiveness of these methods, we quantified the DNA yield and confirmed the DNA quality via PCR of both tick and microbial genetic material. Results. DNA extraction using the Thermo GeneJET Genomic DNA Purification Kit resulted in the highest DNA yields and the most consistent PCR amplification when combined with either cutting or bead beating with select matrices across life stages. DNA isolation methods using ammonium hydroxide as well as the MoBio PowerSoil kit also produced strong and successful PCR amplification, but only for females. Discussion. We contrasted a variety of readily available methods of DNA extraction from single individual blacklegged ticks and presented the results through a quantitative and qualitative assessment.
Altered cerebral blood flow patterns associated with pathologic worry in the elderly

Background: Generalized anxiety disorder (GAD) is the most prevalent anxiety disorder among the elderly and has high functional and cognitive morbidity. However, late‐life GAD is relatively understudied and its functional neuroanatomy is uncharted. Several imaging studies have suggested abnormalities in the cognitive control systems of emotion regulation in anxiety disorders in young adults. The aim of this study was to examine the neural correlates of emotion regulation in late‐life GAD. Method: We compared 7 elderly GAD subjects and 10 elderly nonanxious comparison subjects using functional MRI. Regional cerebral blood flow (rCBF) was measured using pulsed arterial spin labeling perfusion MRI at rest and during an emotion regulation paradigm. Results: Relative to the rest condition, elderly nonanxious comparison subjects had increased rCBF during worry induction (WI) in the right insula, bilateral amygdala, and associative temporooccipital areas. Elderly GAD subjects had increased rCBF during WI in the associative temporooccipital areas, but not in the insula or the amygdala. During worry suppression (WS), elderly nonanxious comparison subjects had increased rCBF in the prefrontal cortex (PFC) and dorsal ACC. Elderly GAD subjects had no changes in rCBF during WS in the PFC. Conclusions: When attempting to regulate their emotional responses, elderly anxious subjects failed to activate prefrontal regions involved in the downregulation of negative emotions. These results, showing that elderly anxious subjects are not effectively engaging the PFC in suppressing worry, may be clinically relevant for developing personalized therapeutic strategies for the treatment of late‐life GAD. Depression and Anxiety 28:202–209, 2011. © 2011 Wiley‐Liss, Inc.
Enabling MAC Protocol Implementations on Software-Defined Radios

Over the past few years a range of new Media Access Control (MAC) protocols have been proposed for wireless networks. This research has been driven by the observation that a single one-size-fits-all MAC protocol cannot meet the needs of diverse wireless deployments and applications. Unfortunately, most MAC functionality has traditionally been implemented on the wireless card for performance reasons, thus, limiting the opportunities for MAC customization. Software-defined radios (SDRs) promise unprecedented flexibility, but their architecture has proven to be a challenge for MAC protocols. In this paper, we identify a minimum set of core MAC functions that must be implemented close to the radio in a high-latency SDR architecture to enable high performance and efficient MAC implementations. These functions include: precise scheduling in time, carrier sense, backoff, dependent packets, packet recognition, fine-grained radio control, and access to physical layer information. While we focus on an architecture where the bus latency exceeds common MAC interaction times (tens to hundreds of microseconds), other SDR architectures with lower latencies can also benefit from implementing a subset of these functions closer to the radio. We also define an API applicable to all SDR architectures that allows the host to control these functions, providing the necessary flexibility to implement a diverse range of MAC protocols. We show the effectiveness of our split-functionality approach through an implementation on the GNU Radio and USRP platforms. Our evaluation based on microbenchmarks and end-to-end network measurements, shows that our design can simultaneously achieve high flexibility and high performance.
CORFU: A Shared Log Design for Flash Clusters

CORFU organizes a cluster of flash devices as a single, shared log that can be accessed concurrently by multiple clients over the network. The CORFU shared log makes it easy to build distributed applications that require strong consistency at high speeds, such as databases, transactional key-value stores, replicated state machines, and metadata services. CORFU can be viewed as a distributed SSD, providing advantages over conventional SSDs such as distributed wear-leveling, network locality, fault tolerance, incremental scalability and geo-distribution. A single CORFU instance can support up to 200K appends/sec, while reads scale linearly with cluster size. Importantly, CORFU is designed to work directly over network-attached flash devices, slashing cost, power consumption and latency by eliminating storage servers.
Obligations in a Temporal Framework

In this paper we consider the notion of ought-to-do obligation in the context of several concepts of time. Starting from the relatively easy case of discrete time and a lockstep interpretation of actions, we experiment with several plausible deenitions and propose a generalised notion of obligation for dense time. A crucial ingredient of the deenition proposed is to consider a branching-time temporal operator in order to capture the obligation to a choice of actions adequately.
State-of-the-Art of Visualization for Eye Tracking Data

Eye tracking technology is becoming easier and cheaper to use, resulting in its increasing application to numerous ﬁelds of research. The data collected during an eye tracking experiment can be analyzed by statistical methods and/or with visualization techniques. Visualizations can reveal characteristics of ﬁxations, saccades, and scanpath structures. In this survey, we present an overview of visualization techniques for eye tracking data and describe their functionality. We classify the visualization techniques using nine categories. The categories are based on properties of eye tracking data, including aspects of the stimuli and the viewer, and on properties of the visualization techniques. The classiﬁcation of about 90 publications including technical as well as application papers with modiﬁcations of common visualization techniques are described in more detail. We ﬁnally present possible directions for further research in the ﬁeld of eye tracking data visualization. Categories and Subject Descriptors (according to ACM CCS) : [General and reference]: Document types—Surveys and overviews [Human-centered computing]: Visualization—Visualization techniques [Human-centered computing]: Visualization—Visualization design and evaluation methods
Exploring erp system outcomes in SMEs: a multiple case study

The purpose of this paper is to investigate Enterprise Resource Planning (ERP) system outcomes in the context of small and medium-sized enterprises (SMEs). Most of the former research on ERP outcomes is based on data from large enterprises, and this study examines how the SME context affects the ERP system outcomes and the related evaluation practices. The paper reports findings from a multiple case study of ERP implementation in four SMEs. The case companies put more emphasis on system and information quality improvements from the ERP systems, compared to individual and organizational outcomes. This can be related to the lack of a strategic perspective on ERP implementation in these companies, with replacement of the legacy systems serving as the main motivation for the implementation projects. Moreover, the findings indicate that the identified lack of ERP system evaluation practice in these SMEs can be explained by ownership type, resource constraints, limited IT competence, and status of the IT legacy systems in SMEs.
Syntactic open domain Arabic question/answering system for factoid questions

Information Retrieval systems have become a crucial part of any search engine, taking into consideration the increased number of pages and documents added to the World Wide Web every day. Question/Answering systems are specific to retrieve the most accurate answer among several documents; this paper will present an improved methodology and implementation of an open domain Arabic Question Answering System for factoid questions. The proposed system is based on Query Expansion ontology and an Arabic Stemmer. Basically the system mainly depends on AWN as a semantic Query Expansion and Khoja stemmer as a stemming system, to evaluate the system we used 56 translated CLEF and TREC questions, four types of test scenarios are performed. Four evaluation attributes were considered: Accuracy, Mean Reciprocal Rank, Answered Questions and recall; and encouraging results were reached with the proposed system.
FTCluster: Efficient Mining Fault-Tolerant Biclusters in Microarray Dataset

Biclustering is a popular method for micro array dataset analysis. It allows for condition set and gene set points clustering simultaneously. However, the noisy data in micro array may disturb the mining results. In order to reduce the influence of noise and find more biological biclusters, we propose an algorithm, FT Cluster, to mine fault-tolerant biclusters in micro array dataset. Unlike traditional fault-tolerant biclusters mining algorithms, FT Cluster uses several novel techniques to improve the efficiency. It also adopts several techniques to generate relaxed biclusters without candidate maintenance. The experimental results show FT Cluster is more effective than traditional algorithms. The biological significance of FT Cluster is evaluated by Gene Ontology and the results show FT Cluster can find larger biological relevant biclusters.
An Empirical Study on Consistency Management of Business and IT Process Models

Process models support the transition from business requirements to IT implementations. Organizations that adopt process modeling often maintain several co-existing models of the same business process. These models target different abstraction levels and stakeholder perspectives. Maintaining consistency among these models has become a major challenge for such organizations. Although several academic works have discussed this challenge, little empirical investigation exists on how people perform process model consistency management in practice. This paper aims to address this lack by presenting an in-depth empirical study of a business-driven engineering process deployed at a large company in the banking sector. We analyzed more than 70 business process models developed by the company, including their change history, with over 1000 change requests. We also interviewed 9 business and IT practitioners and surveyed 23 such practitioners to understand concrete difficulties in consistency management, the rationales for the specification-to-implementation refinements found in the models, strategies that the practitioners use to detect and fix inconsistencies, and how tools could help with these tasks. Our contributions are 1) an account of how business process models co-evolve and how their consistency is maintained in a concrete industrial setting; 2) a set of recurrent patterns used to refine business-level process specifications into IT-level models, and 3) a set of findings that confirm or contradict conventional wisdom on process model consistency management found in the literature.
Rumba: An online quality management system for approximate computing

Approximate computing can be employed for an emerging class of applications from various domains such as multimedia, machine learning and computer vision. The approximated output of such applications, even though not 100% numerically correct, is often either useful or the difference is unnoticeable to the end user. This opens up a new design dimension to trade off application performance and energy consumption with output correctness. However, a largely unaddressed challenge is quality control: how to ensure the user experience meets a prescribed level of quality. Current approaches either do not monitor output quality or use sampling approaches to check a small subset of the output assuming that it is representative. While these approaches have been shown to produce average errors that are acceptable, they often miss large errors without any means to take corrective actions. To overcome this challenge, we propose Rumba for online detection and correction of large approximation errors in an approximate accelerator-based computing environment. Rumba employs continuous lightweight checks in the accelerator to detect large approximation errors and then fixes these errors by exact re-computation on the host processor. Rumba employs computationally inexpensive output error prediction models for efficient detection. Computing patterns amenable for approximation (e.g., map and stencil) are usually data parallel in nature and Rumba exploits this property for selective correction. Overall, Rumba is able to achieve 2.1x reduction in output error for an unchecked approximation accelerator while maintaining the accelerator performance gains at the cost of reducing the energy savings from 3.2x to 2.2x for a set of applications from different approximate computing domains.
Semantic-based Audio Recognition and Retrieval

This study considers the problem of attaching meaning to non-speech sound. The purpose is to ably demonstrate automated annotation of a sound with a string of semantically appropriate words and also retrieval of sounds most relevant to a given textual query. This is achieved by constructing acoustic and semantic spaces from a database of sound and description pairs and using statistical models to learn similarity in each space. The spaces are then linked to allow retrieval in either direction. A key aspect is effective prediction of novel events through generalisation from known examples. The motivation and implementation of the system is described using such techniques and representations as Mel frequency cepstral coefficients, Gaussian mixture models, hierarchical clustering and latent semantic analysis. System results are evaluated with automatic classification measures and human judgements demonstrating that this is an effective method for annotation and retrieval of general sound.
Robustness of Productivity Estimates

Researchers interested in estimating productivity can choose from an array of methodologies, each with its strengths and weaknesses. Many methodologies are not very robust to measurement error in inputs. This is particularly troublesome, because fundamentally the objective of productivity measurement is to identify output differences that cannot be explained by input differences. Two other sources of error are misspecifications in the deterministic portion of the production technology and erroneous assumptions on the evolution of unobserved productivity. Techniques to control for the endogeneity of productivity in the firm's input choice decision risk exacerbating these problems. I compare the robustness of five widely used techniques: (a) index numbers, (b) data envelopment analysis, and three parametric methods: (c) instrumental variables estimation, (d) stochastic frontiers, and (e) semiparametric estimation. The sensitivity of each method to a variety of measurement and specification errors is evaluated using Monte Carlo simulations.
An arrival-based framework for human mobility modeling

Modeling human mobility is crucial in the performance analysis and simulation of mobile ad hoc networks, where contacts are exploited as opportunities for peer-to-peer message forwarding. The current approach to human mobility modeling has been based on continuously modifying models, trying to embed in them the newest features of mobility properties (e.g., visiting patterns to locations or inter-contact times) as they came up from trace analysis. As a consequence, typically these models are neither flexible (i.e., features of mobility cannot be changed without changing the model) nor controllable (i.e., the exact shape of mobility properties cannot be controlled directly). In order to take into account the above requirements, in this paper we propose a mobility framework whose goal is, starting from the stochastic process describing the arrival patterns of users to locations, to generate pairwise inter-contact times and aggregate inter-contact times featuring a predictable probability distribution. We validate the proposed framework by means of simulations. In addition, assuming that the arrival process of users to locations can be described by a Bernoulli process, we mathematically derive a closed form for the pairwise and aggregate inter-contact times, proving the controllability of the proposed approach in this case.
Scala Macros, a Technical Report

Metaprogramming is a powerful technique of software development, which allows to automate program generation. Applications of metaprogramming range from improving expressiveness of a programming language via deep embedding of domain-specific languages to boosting performance of produced code by providing programmer with finegrained control over compilation. In this report we introduce macros, facility that enables compile-time metaprogramming in the Scala programming language.
The semantic Web: what, why, how, and when

The semantic Web aims to enrich the existing Web with a layer of machine-interpretable metadata so that a computer program can draw conclusions (that is, derive new information) predictably (that is, all programs that receive this information draw the same conclusions from it). The semantic Web has two motivators. The first is data integration, which is a significant bottleneck in many IT applications. The second motivator is more intelligent support for end users. If computer programs can infer consequences of information on the Web, they can give better support in finding information, selecting information sources, personalizing information, combining information from different sources and so on. To achieve these goals, we must meet the challenges of providing i) a syntax for representing metadata ii) vocabularies for expressing the metadata iii) metadata for lots of Web pages
Power-efficient I/O design considerations for high-bandwidth applications

Power-efficiency results from several generations of I/O interfaces with specific goals are presented as well as the tradeoffs made within and across those designs. Foundational work in active-power reduction at a single rate for a symmetric system, the subsequent application of that work to a burst-mode asymmetric interface, and recent research on low-overhead bursting are discussed. Dynamic voltage frequency scaling and efficiency increases enabled by system level interconnect improvements are also considered as important techniques.