Miracle-dz/UniFAR-FTUs huggingface dataset:
Avian top predator and the landscape of fear: responses of mammalian mesopredators to risk imposed by the golden eagle

Top predators may induce extensive cascading effects on lower trophic levels, for example, through intraguild predation (IGP). The impacts of both mammalian and avian top predators on species of the same class have been extensively studied, but the effects of the latter upon mammalian mesopredators are not yet as well known. We examined the impact of the predation risk imposed by a large avian predator, the golden eagle (Aquila chrysaetos, L.), on its potential mammalian mesopredator prey, the red fox (Vulpes vulpes, L.), and the pine marten (Martes martes, L.). The study combined 23 years of countrywide data from nesting records of eagles and wildlife track counts of mesopredators in Finland, northern Europe. The predation risk of the golden eagle was modeled as a function of territory density, density of fledglings produced, and distance to nearest active eagle territory, with the expectation that a high predation risk would reduce the abundances of smaller sized pine martens in particular. Red foxes appeared not to suffer from eagle predation, being in fact most numerous close to eagle nests and in areas with more eagle territories. This is likely due to similar prey preferences of the two predators and the larger size of foxes enabling them to escape eagle predation risk. Somewhat contrary to our prediction, the abundance of pine martens increased from low to intermediate territory density and at close proximity to eagle nests, possibly because of similar habitat preferences of martens and eagles. We found a slightly decreasing trend of marten abundance at high territory density, which could indicate that the response in marten populations is dependent on eagle density. However, more research is needed to better establish whether mesopredators are intimidated or predated by golden eagles, and whether such effects could in turn cascade to lower trophic levels, benefitting herbivorous species.
Gesture therapy: A vision-based system for upper extremity stroke rehabilitation

Stroke is the main cause of motor and cognitive disabilities requiring therapy in the world. Therefor it is important to develop rehabilitation technology that allows individuals who had suffered a stroke to practice intensive movement training without the expense of an always-present therapist. We have developed a low-cost vision-based system that allows stroke survivors to practice arm movement exercises at home or at the clinic, with periodic interactions with a therapist. The system integrates a virtual environment for facilitating repetitive movement training, with computer vision algorithms that track the hand of a patient, using an inexpensive camera and a personal computer. This system, called Gesture Therapy, includes a gripper with a pressure sensor to include hand and finger rehabilitation; and it tracks the head of the patient to detect and avoid trunk compensation. It has been evaluated in a controlled clinical trial at the National Institute for Neurology and Neurosurgery in Mexico City, comparing it with conventional occupational therapy. In this paper we describe the latest version of the Gesture Therapy System and summarize the results of the clinical trail.
Immersion in Virtual Worlds - But not Second Life!

Previous attempts to quantify immersion have been pursued within the context of game virtual worlds where there is a clear outline of a goal. This paper seeks to investigate the problem of immersion measurement in an online based virtual world (ReactionGrid) where there is no distinct in-world goal and environmental context is less immersive as in a game environment. The experiment investigates participants' feelings towards their immersion experience while being in a virtual world. Our findings suggest that immersion mostly depends on co-presence and communication of users.
Design of a preamble signal for synchronization in ultra‐wideband noncoherent energy detection receivers

We present an effective preamble design scheme that can improve synchronization performance in ultra‐wideband noncoherent energy detection receivers. Focusing on the effect of the nonuniform energy allocation of a preamble in energy detection‐based synchronization, we propose a preamble energy allocation scheme. This scheme determines the energy distribution of a preamble by using a constrained optimization method. Through the proposed scheme, we promise that we can provide a generalized solution to design preambles for energy detection‐based synchronization, in that it is possible to design the power distribution of preamble pattern in various shapes according to the system's specification. In addition, we also suggest two preamble design scheme that finally decides the practical shape of a preamble from the determined energy distribution of a preamble. By providing preamble design examples, we show that preambles based on nonuniform energy distribution provide improved synchronization performance without any additional increase in receiver complexity in ultra‐wideband channel environments. Copyright © 2011 John Wiley & Sons, Ltd.
Rumination and Interpersonal Functioning in Perinatal Depression

Identifying vulnerability factors for antenatal depression is an important step in reducing depression rates. To date, research has focused on demographic and interpersonal risk factors for antenatal depression rather than cognitive factors. This study investigated the moderating role of social functioning on rumination and silencing the self in predicting prospective increases in depressive mood in a sample of pregnant women. Pregnant women at high risk for depression (n = 110) were assessed at baseline for symptoms of depressive mood, rumination, functioning aspects of perceived social support, and silencing the self. Depressive mood was measured again three months later. Social functioning moderated the effects of rumination and silencing the self on depression. For women low in social functioning, rumination was related to increases in depression while silencing the self was unrelated to rumination and depressive mood. High social functioning buffered the impact of rumination on prospective depressive.
Diversity in software engineering research

One of the goals of software engineering research is to achieve generality: Are the phenomena found in a few projects reflective of others? Will a technique perform as well on projects other than the projects it is evaluated on? While it is common sense to select a sample that is representative of a population, the importance of diversity is often overlooked, yet as important. In this paper, we combine ideas from representativeness and diversity and introduce a measure called sample coverage, defined as the percentage of projects in a population that are similar to the given sample. We introduce algorithms to compute the sample coverage for a given set of projects and to select the projects that increase the coverage the most. We demonstrate our technique on research presented over the span of two years at ICSE and FSE with respect to a population of 20,000 active open source projects monitored by Ohloh.net. Knowing the coverage of a sample enhances our ability to reason about the findings of a study. Furthermore, we propose reporting guidelines for research: in addition to coverage scores, papers should discuss the target population of the research (universe) and dimensions that potentially can influence the outcomes of a research (space).
Speech Scenario Adaptation and Discourse Topic Recognition on Mobile Smart Terminal

Context identifying based on speech data is important to social services and city management. In a complex application environment, a speech recognition system needs to address two main problems: background noises and large vocabulary search latency. We use the adjustment acoustic model to deal with the scenario adaptation, and we use adjustment dictionary and language module to solve the discourse topic recognition. As a case study, we design and implement a continuous language speech recognition system on a mobile smart terminal. Experiments show that the scene adaptation effectively improves the accuracy rate of speech recognition, and the discourse topic recognition verifies the recognition effectiveness of our speech recognition system.
White Dwarf Heating and Subsequent Cooling in Dwarf Nova Outbursts

We follow the time-dependent thermal evolution of a white dwarf (WD) undergoing sudden accretion in a dwarf nova outburst, using both simulations and analytic estimates. The post-outburst light curve clearly separates into early times when the WD flux is high and late times when the flux is near the quiescent level. The break between these two regimes, occurring at a time on the order of the outburst duration, corresponds to a thermal diffusion wave reaching the base of the freshly accreted layer. Our principal result is that long after the outburst, the fractional flux perturbation about the quiescent flux decays as a power law with time (and not as an exponential). We use this result to construct a simple fitting formula that yields estimates for both the quiescent flux and the accreted column, i.e., the total accreted mass divided by WD surface area. The WD mass is not well constrained by the late-time light curve alone, but it can be inferred if the accreted mass is known from observations. We compare our work with the well-studied outburst of WZ Sge, finding that the cooling is well described by our model, giving an effective temperature Teff = 14,500 K and accreted column Δy ≈ 106 g cm-2, in agreement with the modeling of Godon et al. To reconcile this accreted column with the accreted mass inferred from the bolometric accretion luminosity, a large WD mass, ≳1.1 M☉, is needed. Our power-law result is a valuable tool for making quick estimates of the outburst properties. We show that fitting the late-time light curve with this formula yields a predicted column within 20% of that estimated from our full numerical calculations.
Video Surveillance System for Security Applications

Computer Vision (CV) deals with replacement of human interpretation with computer based interpretation. It automatically analyses, reconstruct, and recognise objects in a scene from one or more images. Video surveillance is a topic in CV dealing with the monitoring of humans and their behaviours to analyze the habitual and unauthorized activities. An efficient video surveillance system detects moving foreground objects with lowest False Alarm Rates (FAR). This paper makes two proposals: one to detect the foreground in the video and the other to detect humans for surveillance applications. The proposed approach of foreground detection employs computations in YCbCr colour space for detecting moving objects in CCTVs. This system can handle slight camera movement and illumination changes. After foreground detection, the silhouette obtained is analysed and classified to determine whether it is humans or non-humans. In computer vision, usually human detection is based on human face, the head, and the entire body including legs as well as the human skin. In this work, the detection of humans is done based on the ratio of upper body and total height of silhouette. The precision and recall performance measures of the approach are computed and found to be superior to the existing Mixture of Gaussian approach in the literature.
Effects of communication environment, immediacy, and communication apprehension on cognitive and affective learning

This study explores relationships among immediacy, communication apprehension, and learning outcomes between two class formats: mixed-size sections (i.e., large-lecture/break-out sections) versus self-contained sections. The results indicated that students' cognitive learning outcomes were slightly greater in the mixed-size sections versus self-contained sections. In addition, affective learning decreased for all students from the first day of class, though it decreased slightly more for students in the large-lecture/break-out sections. When the teacher was perceived as highly immediate, however, there was no difference in affective learning due to format.
Geographical access to treatment for sex offenders under community supervision in Kentucky

Abstract As sex offenders are probated or paroled into the community, sex offender treatment and monitoring is often a condition of their release. In Kentucky, sex offenders are required to participate in community-based treatment for two years or more. However, some sex offenders are disadvantaged in accessing mandated treatment. This is a result of decisions concerning the placement of treatment programmes, the sex offenders' preference to return to communities where they can rely on family and other indigenous support networks, and some statutes (e.g. sex offender registration and residency restriction laws). This study utilises spatial methodologies, including an origin–destination (OD) matrix, to determine the time, in minutes, that sex offenders travel to sex offender treatment providers and non-spatial ordinary least squares (OLS) regression techniques to determine the association between family, neighbourhood and community characteristics on sex offenders’ travel time to treatment. Findings suggest that there is substantial disadvantage in treatment access, measured by travel time, for sex offenders who live in impoverished rural communities.
Support Vector Regression Based on Goal Programming and Multi-objective Programming

Support vector machine (SVM) is gaining much popularity as a powerful machine learning technique. SVM was originally developed for pattern classification and later extended to regression. One of main features of SVM is that it generalizes the maximal margin linear classifiers into high dimensional feature spaces through nonlinear mappings defined implicitly by kernels in the Hilbert space so that it may produce nonlinear classifiers in the original data space. On the oilier hand, the authors developed a family of various SVMs using multi-objective programming and goal programming (MOP/GF) techniques. This paper extends the family of SVM for classification to regression, and discusses their performance through numerical experiments.
Proteomic Data Analysis Optimization Using a Parallel MPI C Approach

Proteins and the networks they determine, called interactome networks, have received attention at an important degree during the last years, because they have been discovered to have an influence on some complex biological phenomena, such as problematic disorders like cancer. This paper presents a new parallel computation technique that allows for an accurate and fast analysis of the human interactome network to be conducted. It constitutes, essentially, a proteomic data analysis process that takes into consideration the sparse nature of interactome networks. Thus, the first stage of the analysis involves the parallel computation of each proteins betweenness centrality measure through a parallel sparse networks-dedicated approach. Then, the second phase detects the functionally-related communities of proteins. In order to accomplish this purpose, we make use of a community detection algorithm that is based on the edge betweenness calculation and that has been already described in one of the authors previous papers. The new protein data analysis technique was carefully tested on real biological data and the results acknowledge the existence of some important properties of those proteins that participate in the carcinogenesis process. Apart from being particularly useful for research purposes, the novel technique also speeds up the proteomic databases analysis process, as compared to our own sequential approach. The results of the comprehensive battery of tests that were applied prove some unique topological features of cancer mutated proteins, and a possible optimization solution for cancer drugs design is suggested.
Incorporating text analysis into evolution of social groups in blogosphere

Data reflecting social and business relations has often form of network of connections between entities (called social network). In such network important and influential users can be identified as well as groups of strongly connected users. Finding such groups and observing their evolution becomes an increasingly important research problem. One of the significant problems is to develop method incorporating not only information about connections between entities but also information obtained from text written by the users. Method presented in this paper combine social network analysis and text mining in order to understand groups evolution.
The ERI-6/7 Helicase Acts at the First Stage of an siRNA Amplification Pathway That Targets Recent Gene Duplications

Endogenous small interfering RNAs (siRNAs) are a class of naturally occuring regulatory RNAs found in fungi, plants, and animals. Some endogenous siRNAs are required to silence transposons or function in chromosome segregation; however, the specific roles of most endogenous siRNAs are unclear. The helicase gene eri-6/7 was identified in the nematode Caenorhabditis elegans by the enhanced response to exogenous double-stranded RNAs (dsRNAs) of the null mutant. eri-6/7 encodes a helicase homologous to small RNA factors Armitage in Drosophila, SDE3 in Arabidopsis, and Mov10 in humans. Here we show that eri-6/7 mutations cause the loss of 26-nucleotide (nt) endogenous siRNAs derived from genes and pseudogenes in oocytes and embryos, as well as deficiencies in somatic 22-nucleotide secondary siRNAs corresponding to the same loci. About 80 genes are eri-6/7 targets that generate the embryonic endogenous siRNAs that silence the corresponding mRNAs. These 80 genes share extensive nucleotide sequence homology and are poorly conserved, suggesting a role for these endogenous siRNAs in silencing of and thereby directing the fate of recently acquired, duplicated genes. Unlike most endogenous siRNAs in C. elegans, eri-6/7–dependent siRNAs require Dicer. We identify that the eri-6/7–dependent siRNAs have a passenger strand that is ∼19 nt and is inset by ∼3–4 nts from both ends of the 26 nt guide siRNA, suggesting non-canonical Dicer processing. Mutations in the Argonaute ERGO-1, which associates with eri-6/7–dependent 26 nt siRNAs, cause passenger strand stabilization, indicating that ERGO-1 is required to separate the siRNA duplex, presumably through endonucleolytic cleavage of the passenger strand. Thus, like several other siRNA–associated Argonautes with a conserved RNaseH motif, ERGO-1 appears to be required for siRNA maturation.
Biases in Automated Music Playlist Generation: A Comparison of Next-Track Recommending Techniques

Playlist generation is a special form of music recommendation where the problem is to create a sequence of tracks to be played next, given a number of seed tracks. In academia, the evaluation of playlisting techniques is often done by assessing with the help of information retrieval measures if an algorithm is capable of selecting those tracks that also a human would pick next. Such approaches however cannot capture other factors, e.g., the homogeneity of the tracks that can determine the quality perception of playlists. In this work, we report the results of a multi-metric comparison of different academic approaches and a commercial playlisting service. Our results show that all tested techniques generate playlists with certain biases, e.g., towards very popular tracks, and often create playlists continuations that are quite different from those that are created by real users.
Cyclin A Is a Mediator of p120E4F-Dependent Cell Cycle Arrest in G1

ABSTRACT E4F is a ubiquitously expressed GLI-Krüppel-related transcription factor which has been identified for its capacity to regulate transcription of the adenovirus E4 gene in response to E1A. However, cellular genes regulated by E4F are still unknown. Some of these genes are likely to be involved in cell cycle progression since ectopic p120 E4F expression induces cell cycle arrest in G1. Although p21 WAF1 stabilization was proposed to mediate E4F-dependent cell cycle arrest, we found that p120 E4F can induce a G1 block in p21−/− cells, suggesting that other proteins are essential for the p120 E4F -dependent block in G1. We show here that cyclin A promoter activity can be repressed by p120 E4F and that this repression correlates with p120 E4F binding to the cyclic AMP-responsive element site of the cyclin A promoter. In addition, enforced expression of cyclin A releases p120 E4F -arrested cells from the G1block. These data identify the cyclin A gene as a cellular target for p120 E4F and suggest a mechanism for p120 E4F -dependent cell cycle regulation.
Metaphysical presuppositions and scientific practices: Reductionism and organicism in cancer research

Metaphysical presuppositions are important for guiding scientific practices and research. The success of twentieth‐century biology, for instance, is largely attributable to presupposing that complex biological processes are reducible to elementary components. However, some biologists have challenged the sufficiency of reductionism for investigating complex biological phenomena and have proposed alternative presuppositions like organicism. In this article, contemporary cancer research is used as a case study to explore the importance of metaphysical presuppositions for guiding research. The predominant paradigm directing cancer research is the somatic mutation theory, in which mutated genes are presumed to be ultimately responsible for explaining carcinogenesis. This reductionistic approach to cancer has been criticised recently, and an organistic approach has been proposed. The article concludes with a discussion of the reciprocal interaction of metaphysical presuppositions and scientific practices for investigating cancer's complex nature.
Two-Dimensional Singular Value Decomposition ( 2 DSVD ) for 2 D Maps and Images

For a set of 1D vectors, standard singular value decomposition (SVD) is frequently applied. For a set of 2D objects such as images or weather maps, we form 2DSVD, which computes principal eigenvectors of rowrow and column-column covariance matrices, exactly as in the standard SVD. We study optimality properties of 2DSVD as low-rank approximation and show that it provides a framework unifying two recent approaches. Experiments on images and weather maps illustrate the usefulness of 2DSVD.
On the Practical Significance of Hindsight Bias: The Case of the Expectancy-Disconfirmation Model of Consumer Satisfaction

Abstract The implications of the phenomenon of hindsight bias for the expectancy-disconfirmation model of consumer satisfaction are investigated, and the moderating effects of choice (i.e., whether or not a product is selected on the basis of one′s expectations regarding the product′s likely performance) on the incidence of hindsight bias and on the relationships between expectations, performance, disconfirmation, and satisfaction are considered. A study dealing with consumers′ satisfaction with personalized envelopes shows that perceived performance biases people′s recall of their foresight expectations in a systematic way (hindsight bias), that hindsight, rather than foresight, expectations are the more potent influence on disconfirmation and satisfaction, and that choice moderates the degree of hindsight bias and the relationships between satisfaction and its antecedents.
A Review of Methods in Depression Research in Japan from 1990 to 2006

わが国での抑うつ研究は，どのようにして研究されているのかを調べるために，1990年から2006年に発刊された18の学術誌から，974の抑うつ研究を展望した。 抑うつ研究の特徴を捉えるために9つの変数に関してコード化基準を設け，各々の研究をコード化した。 分析の結果，抑うつ研究が報告される学術誌は，3タイプに分類できることが示された。 多くの事例研究は，精神疾患の診断基準や抑うつ尺度を利用せずに診断名を付けていることが明らかになった。 測定をしている研究では，19%は10項目未満で抑うつを測定していることが示された。 また，26%は連続変数で測定した抑うつ尺度を離散化していた。 これらの結果から導かれる，事例研究と尺度利用法の改善策について考察する。