Miracle-dz/UniFAR-FTUs huggingface dataset:
The spatial metaphor for user interfaces: experimental tests of reference by location versus name

The enduring dichotomy between spatial and symbolic modes of representation and retrieval acquires an added pragmatic dimension through recent developments in computer-based information retrieval. The standard name-based approach to object reference is now supplemented on some systems by a spatial alternative-often driven by an office or desktop metaphor. Little rigorous evidence is available, however, to support the supposition that spatial memory in itself is more effective than symbolic memory. The accuracy of spatial versus symbolic reference was assessed in three experiments. In Experiment 1 accuracy of location reference in a location-only filing condition was initially comparable to that in a name-only condition, but deteriorated much more rapidly with increases in the number of objects filed. In Experiment 2 subjects placed objects in a two-dimensional space containing landmarks (drawings of a desk, table, filing cabinets, etc.) designed to evoke an office metaphor, and in Experiment 3 subjects placed objects in an actual, three-dimensional mock office. Neither of these enhancements served to improve significantly the accuracy of location reference, and performance remained below that of a name-only condition in Experiment 1. The results raise questions about the utility of spatial metaphor over symbolic filing and highlight the need for continuing research in which considerations of technological and economic feasibility are balanced by considerations of psychological utility.
Identification of a positive regulator of the cell cycle ubiquitin-conjugating enzyme Cdc34 (Ubc3)

The Cdc34 (Ubc3) ubiquitin-conjugating enzyme from Saccharomyces cerevisiae plays an essential role in the progression of cells from the G1 to S phase of the cell division cycle. Using a high-copy suppression strategy, we have identified a yeast gene (UBS1) whose elevated expression suppresses the conditional cell cycle defects associated with cdc34 mutations. The UBS1 gene encodes a 32.2-kDa protein of previously unknown function and is identical in sequence to a genomic open reading frame on chromosome II (GenBank accession number Z36034). Several lines of evidence described here indicate that Ubs1 functions as a general positive regulator of Cdc34 activity. First, overexpression of UBS1 suppresses not only the cell proliferation and morphological defects associated with cdc34 mutants but also the inability of cdc34 mutant cells to degrade the general amino acid biosynthesis transcriptional regulator, Gcn4. Second, deletion of the UBS1 gene profoundly accentuates the cell cycle defect when placed in combination with a cdc34 temperature-sensitive allele. Finally, a comparison of the Ubs1 and Cdc34 polypeptide sequences reveals two noncontiguous regions of similarity, which, when projected onto the three-dimensional structure of a ubiquitin-conjugating enzyme, define a single region situated on its surface. While cdc34 mutations corresponding to substitutions outside this region are suppressed by UBS1 overexpression, Ubs1 fails to suppress amino acid substitutions made within this region. Taken together with other findings, the allele specificity exhibited by UBS1 expression suggests that Ubs1 regulates Cdc34 by interaction or modification.
Analysis for Optimal Degree of Replication

Replication technologies are widely used in distributed systems to improve performance in terms of availability and scalability. We analyze cost of replication in various aspects, which help us to choose proper degree of replication in error-prone systems. Our proposed cost model and algorithm to choose proper degree of replication is simple. However, it would help us to decide whether or not to replicate and, if replicate, degree of replication in our system design.
An Eecient Korean Morphological Analysis Using Exclusive Information

The conventional Korean morphological analysis generates all possible candidates only to accept the correct ones, creating a signiicant amount of implausible candidates. Hence, the string processing time and the dictionary access frequency in analyzing a word are aggravated. To lower the above ineeciency, this paper proposes an eecient Korean morphological analysis method using exclusive information, which represents the exclusive relation between speciic phoneme, speciic syllable, speciic string and each part of speech. We discuss how it can reduce the implausible candidates in analyzing a word. The result of the experiment showed that Korean morphological analysis using exclusive information decreased 40.16% of the dictionary access frequency by reducing implausible candidates.
Exploring the performance benefits of heterogeneity and reconfigurable architectures in a commodity cloud

In this paper we evaluate the potential of running a compute-intensive simulation on a heterogeneous cluster built from CPU, GPU and FPGA devices. We do so by augmenting a commercially available cluster of CPUs and GPUs with an FPGA device and running a distributed n-body simulation on top of Spark for unconventional cores (SparkCL) on the three different types of computing architectures. We show that given an algorithm with a sufficiently high compute intensity, such as pairwise additive n-body, we can significantly increase performance and performance per watt in comparison to running the same algorithm on a homogeneous CPU based cluster. In addition, we show the potential of using FPGAs in future commodity heterogeneous clusters alongside CPUs and GPUs.
Two-staged nonlinear acoustic echo cancellation and its convergence analysis

Hand-free telephone system commonly drives the power-amplification and loudspeaker into saturated nonlinear region such that performance of conventional acoustic echo cancellation (AEC) is degraded. A computationally efficient cascade AEC model is used to include a memoryless piecewise linear (PWL) processor and a linear filter. In order to overcome the start-up divergence problem encountered in nonlinear AEC adaptation, a two-stage update scheme is adopted that starts with the linear filter update, and then joint adaptation of PWL and linear coefficients follows. Theoretical convergence analysis and stability criterion will be derived and validated by the computer simulation.
SAR image change detection by likelihood ratio test in multi-temporal time series

This paper presents a change detection method between two Synthetic Aperture Radar (SAR) images with similar incidence angles and using a likelihood ratio test (LRT). To address the composite hypothesis problem of the LRT, we propose to replace the noise-free values by their estimated results. Thus, a multi-temporal non local means denoising method proposed in [1] is used in this paper to estimate the noise-free values using both spatial and temporal information. The change detection results show the effective performance of the proposed method compared with the state of the art ones, such as log-ratio operator and generalized likelihood ratio test.
IDEAL framework for surgical innovation 3: randomised controlled trials in the assessment stage and evaluations in the long term study stage

The complexity of surgical procedures often poses challenges for conducting a rigorous and comprehensive evaluation. This paper considers the final two IDEAL stages of surgical innovation. Surgical randomised controlled trials are often challenging to undertake and require careful consideration of the intervention definition, who should deliver it, and the impact of surgeon and patient preferences. In the long term study stage, better monitoring of surgical procedures is needed, along with improved surveillance of devices.
Neural dimensionality reduction for document processing

Document processing usually gives rise to high-dimension representation vectors which are redundant and costly to process. Reducing dimensionality would be appropriate, but standard factor analysis methods such as PCA cannot deal with vectors of very high dimension. We have used instead an adaptive neural network technique (the Generalized Hebbian Algorithm) to extract the first principal components of a text corpus in order to represent documents economically. The approach is efficient and gives good results in a real Web page clustering application.
Mining influence in evolving entities: A study on stock market

Mining influence in evolving entities is an important but challenging task, partly due to complex nature of it. In this paper, we mainly focus on the following problems on it with respect to stock market: (1) How to identify pairs of stocks that influence one another; (2) How to quantify the influence and capture group effects and dynamic nature of influence of each stock; (3) How to adopt approximate approaches so that we can improve the efficiency of the proposed model. To tackle these problems, a novel graph-based mining method, which utilizes time series and volume information collaboratively is proposed, and several optimized algorithms are presented. Besides, two extended metrics to capture the dynamic and group nature of influence based on the model are derived. Furthermore, we also suggest a potential application of the model to stock price prediction. The experimental results on both synthetic and real data sets verify the effectiveness and efficiency of our approach. Some insights on this paper can be the ideas of analyzing the influence of evolving entities using the social network analysis methods.
Function and control of human invasive trophoblast subtypes: Intrinsic vs. maternal control

ABSTRACT The establishment of a functional placenta is pivotal for normal fetal development and the maintenance of pregnancy. In the course of early placentation, trophoblast precursors differentiate into highly invasive trophoblast subtypes. These cells, referred to as extravillous trophoblasts (EVTs), penetrate the maternal uterus reaching as far as the inner third of the myometrium. One of the most fundamental functions of EVTs is the transformation of spiral arteries to establish the uteroplacental blood circulation assuring an adequate nutrient and gas supply to the developing fetus. To achieve this, specialized EVT subpopulations interact with maternal immune cells, provoke elastolysis in the arterial wall and replace the endothelial cells lining the spiral arteries to induce intraluminal vascular remodeling. These and other trophoblast-mediated processes are tightly controlled by paracrine signals from the maternal decidua and furthermore underlie an intrinsic cell-type specific program. Various severe pregnancy complications such as preeclampsia or intrauterine growth retardation are associated with abnormal EVT function, shallow invasion, and decreased blood flow to the placenta. Hence a better understanding of human trophoblast invasion seems mandatory to improve therapeutic intervention. This approach, however, requires a profound knowledge of the human placenta, its various trophoblast subtypes and in particular a better understanding of the regulatory network that controls the invasive phenotype of EVTs.
DBit : A Methodology for Comparing Content Distribution Networks

Content distribution networks (CDNs) are increasingly used to deliver high-performance web services. As the volume of content grows, these networks constantly evolve to improve performance. Their performance is difficult to characterize because it depends on factors including the distribution of clients, the geographical scope of the CDN, the specific way in which the content delivery is engineered, and so on. In this paper, we ask the following question: given two CDNs, how do we determine whether one CDN’s user-perceived performance is significantly better than another? In a departure from prior work, we propose a methodology called DBit that identifies distributional differences in user-perceived performance, and we apply it to several CDNs that serve photos. Using 14.5 million photo fetches on PlanetLab from three popular CDNs over a period of 8 months, we find that DBit can identify significant performance differences not just between CDNs, but also across time and location. To assess the robustness of our findings, we complement the PlanetLab dataset with data from home networks in 1470 ASes worldwide
Lake metabolism: Relationships with dissolved organic carbon and phosphorus

Recent literature has suggested that for many lakes and rivers, the respiratory breakdown of organic matter (R) exceeds production of organic matter by photosynthesis (gross primary production [GPP]) within the water body. This metabolic balance (GPP > R; “heterotrophy”) implies that allochthonous organic matter supports a portion of the aquatic ecosystem's respiration. Evidence that many lakes are heterotrophic comes from diverse approaches, and debate remains over the circumstances in which heterotrophy exists. The methods used to estimate GPP and R and the limited extent of lake types studied, especially with respect to dissolved organic carbon (DOC) and total phosphorus (TP) concentrations, are two reasons for differing conclusions. We deployed O2 and CO2 sondes to measure diel gas dynamics in the surface waters of 25 lakes. From these data, we calculated GPP, R, and net ecosystem production (NEP = GPP − R). Over the broad range in TP and DOC among the lakes, diel CO2 and O2 changed on a near 1 : 1 molar ratio. Metabolism estimates from the two gases were comparable, except at high pH. Most lakes in our data set had negative NEP, but GPP and R appeared to be controlled by different factors. TP correlated strongly with GPP, whereas DOC correlated with R. At low DOC concentrations, GPP and R were nearly equal, but, at higher DOC, GPP and R uncoupled and lakes had negative NEP. Strong correlations between lake metabolism and landscape related variables suggest that allochthonous carbon influences lake metabolism.
Coroners and Justice Act 2009 – Special Measures Directions Take Two: Entrenching Unequal Access to Justice?

This article maps and analyses the changes made by the Coroners and Justice Act 2009 to existing Special Measures Directions for child witnesses, child defendants and complainants of sexual assault under the Youth Justice and Criminal Evidence Act 1999. Adult defendants suffering from some form of significant mental impairment are for the first time made eligible to apply for leave to testify using the live link and with the assistance of an intermediary. In addition, the 2009 Act deems witnesses to violent offences against the person involving the use of firearms or knives to be intimidated and hence automatically eligible for Special Measures. The article concludes that the measures for defendants do not go far enough and are susceptible to challenge under ECHR Article 6, and perhaps go too far in introducing anomalies in the treatment of different categories of intimidated witnesses. Four flow diagrams map the changes ro the Special Measures Regime.
Duopolistic Investment Opportunities under Agency Risk: Designing Contracts for Optimal Investment Decisions

Real options theory frequently assumes that investment decisions are directly taken by the owners or that, if there is a manager, he is fully aligned with them. However, empirical literature demonstrates that managers may reveal misaligned interests, which may deteriorate the value of the firm, consequently having a major impact on value maximizing decisions, namely, on the optimal timing to invest. This paper provides a contribute to the existing real options by dropping this assumption in the non-exclusive option case, specifically providing a duopolistic leader-follower framework where an agency problem between the owners of the option and their respective managers, is embodied and solved by an optimal labor contract scheme that aims to eliminate inadequate actions from the managers. In this model, both firms shareholders need not to follow the future evolution of project value drivers in order to guarantee optimal behavior. An analytical application is provided, being shown that even small deviations from the optimal compensation scheme may lead to highly sub-optimal decisions. JEL classification: G31; D82.
Presentation-Based Learning and Peer Evaluation to Enhance Active Learning and Self-Confidence in the Financial Management Classroom

Purpose – The purpose of this study was to identify an effective learning strategy for students to engage authentically in their learning process.This study also aimed to introduce an innovative active learning approach to enhance students’ involvement and to strengthen self-confidence throughout their learning in the Financial Management classroom.The approach involved the adoption of structured and accountable presentation-based learning activities.Methodology – This study used action research design involving an iterative process of self-reflective cycles: planning, acting, observing and reflecting. The research was accomplished in two cycles. Three Financial Management classes of an undergraduate program consisting of 120 students in the first cycle and 110 students in the second cycle participated in this research as part of regular classroom activities. Semi-structured questionnaire, lecturer observation, and interviews were used as the method for data collection.The quantitative and qualitative data were analysed accordingly. Findings – The results indicate that the approach adopted in this research reasonably enabled students to be actively engaged in their learning process and nullified free-riding learning behaviour among them. It also provided a chance for students to exercise their self-regulated learning towards becoming more independent learners and increased their confidence to speak and participate in class.Significance – This research contributes to the improvement of teaching practices in higher education, particularly in Financial Management instruction. The results imply that providing some freedom for students to creatively design and be accountable for their own learning has great potential to enhance their authentic active learning and self-confidence.
The GID1-Mediated Gibberellin Perception Mechanism Is Conserved in the Lycophyte Selaginella moellendorffii but Not in the Bryophyte Physcomitrella patens[W]

In rice (Oryza sativa) and Arabidopsis thaliana, gibberellin (GA) signaling is mediated by GIBBERELLIN-INSENSITIVE DWARF1 (GID1) and DELLA proteins in collaboration with a GA-specific F-box protein. To explore when plants evolved the ability to perceive GA by the GID1/DELLA pathway, we examined these GA signaling components in the lycophyte Selaginella moellendorffii and the bryophyte Physcomitrella patens. An in silico search identified several homologs of GID1, DELLA, and GID2, a GA-specific F-box protein in rice, in both species. Sm GID1a and Sm GID1b, GID1 proteins from S. moellendorffii, showed GA binding activity in vitro and interacted with DELLA proteins from S. moellendorffii in a GA-dependent manner in yeast. Introduction of constitutively expressed Sm GID1a, Sm G1D1b, and Sm GID2a transgenes rescued the dwarf phenotype of rice gid1 and gid2 mutants. Furthermore, treatment with GA4, a major GA in S. moellendorffii, caused downregulation of Sm GID1b, Sm GA20 oxidase, and Sm GA3 oxidase and degradation of the Sm DELLA1 protein. These results demonstrate that the homologs of GID1, DELLA, and GID2 work in a similar manner in S. moellendorffii and in flowering plants. Biochemical studies revealed that Sm GID1s have different GA binding properties from GID1s in flowering plants. No evidence was found for the functional conservation of these genes in P. patens, indicating that GID1/DELLA-mediated GA signaling, if present, differs from that in vascular plants. Our results suggest that GID1/DELLA-mediated GA signaling appeared after the divergence of vascular plants from the moss lineage.
Access and Funding in Public Higher Education— the 2011 National Survey

With Pell Grants cut, tuition rising at more the double the rate of inflation, and with state funding for both college budgets and student aid cut or stagnating, students and their families are being squeezed. Also squeezed in the protracted recession are funds for workforce development and lifelong learning that build economic competitiveness. These trends likely mean first, that college access is shrinking overall, and second,
Current knowledge on pathogenesis and immunodiagnosis of paracoccidioidomycosis.

: Host-parasite relationship and immunodiagnostic testing in paracoccidioidomycosis have been extensively investigated in recent years. We review the major advances in the understanding of pathogenesis of the disease with emphasis on the sequential steps in granuloma formation and the envolvement of immunological mechanisms in host defenses against the parasite. In addition, the several immunodiagnostic tests used for diagnosis and in the follow-up of patients are commented upon and critically analysed.
Identifying writing profiles in game-based language learning using data mining

Video games are effective tools to engage students in foreign language learning. Research has shown that not all students benefit from video games in the same way nor at the same pace. We have created a 3D video game, based on a virtual world platform, that aims at fostering students' writing competence. It requires learners to play collaboratively a game, interacting and communicating through text chats. To analyse the game's impact on students' language learning and especially on their writing skills, we use data mining clustering techniques. The aim is firstly, to classify students according to their learning outcomes and secondly, to compare grammar, vocabulary and writing skills before and after the experience. The target group consisted of more than 100 students of an A1 level (CEFR) German language course. Results show that not all students improve to a similar degree, but instead there are students who might better develop their writing competences by using other learning approaches.
The hidden experts in software-engineering communication: NIER track

Sharing knowledge in a timely fashion is important in distributed software development. However, because experts are difficult to locate, developers tend to broadcast information to find the right people, which leads to overload and to communication breakdowns. We study the context in which experts are included in an email discussion so that team members can identify experts sooner. In this paper, we conduct a case study examining why people emerge in discussions by examining email within a distributed team. We find that people emerge in the following four situations: when a crisis occurs, when they respond to explicit requests, when they are forwarded in announcements, and when discussants follow up on a previous event such as a meeting. We observe that emergent people respond not only to situations where developers are seeking expertise, but also to execute routine tasks. Our findings have implications for expertise seeking and knowledge management processes.
A Model-Based System Supporting Automatic Self-Regeneration of Critical Software

We describe a system under development that provides the ability to incrementally and pervasively increase the robustness of complex systems. The system is based upon model-based programming tools that enable the highlevel specification of self-deprecating and self-reconfiguring methods along with model-based executives that provide safe, optimal dispatching of functionally redundant methods, and that reason from component service models to continuously monitor, diagnose, regenerate and optimize function.