Miracle-dz/UniFAR-FTUs huggingface dataset:
Individual experience and evolutionary history of predation affect expression of heritable variation in fish personality and morphology

Predation plays a central role in evolutionary processes, but little is known about how predators affect the expression of heritable variation, restricting our ability to predict evolutionary effects of predation. We reared families of three-spined stickleback Gasterosteus aculeatus from two populations—one with a history of fish predation (predator sympatric) and one without (predator naive)—and experimentally manipulated experience of predators during ontogeny. For a suite of ecologically relevant behavioural (‘personality’) and morphological traits, we then estimated two key variance components, additive genetic variance (VA) and residual variance (VR), that jointly shape narrow-sense heritability (h2= VA/(VA + VR)). Both population and treatment differentially affected VA versus VR, hence h2, but only for certain traits. The predator-naive population generally had lower VA and h2 values than the predator-sympatric population for personality behaviours, but not morphological traits. Values of VR and h2 were increased for some, but decreased for other personality traits in the predator-exposed treatment. For some personality traits, VA and h2 values were affected by treatment in the predator-naive population, but not in the predator-sympatric population, implying that the latter harboured less genetic variation for behavioural plasticity. Replication and experimental manipulation of predation regime are now needed to confirm that these population differences were related to variation in predator-induced selection. Cross-environment genetic correlations (rA) were tight for most traits, suggesting that predator-induced selection can affect the evolution of the same trait expressed in the absence of predators. The treatment effects on variance components imply that predators can affect evolution, not only by acting directly as selective agents, but also by influencing the expression of heritable variation.
Eurographics Symposium on Point-based Graphics (2005) Computing Variation Modes for Point Set Surfaces

Point sets have become a popular shape representation. In this paper, we present a novel approach to computing variation modes for point set surfaces, and represent the point set surface as a linear combination of the variation modes, called a generative representation for the point set surface. Given a point set, our approach consists of two steps: The first is to produce a set of new samples with increasing smoothness and less detailed features. We use a modified smoothing method based on moving least squares (MLS) surface to produce the samples. The second is to arrange the shape vectors of the new samples together with the original point set into a matrix, and then compute the singular value decomposition of the matrix, producing a set of variation modes (the eigen vectors). Using the variation modes and the generative representation, we can easily synthesize new shapes. Typical applications are low/high/band pass filtering as well as denoising and detail enhancement in multiple scales.
LAND: Locality Aware Networks for Distributed Hash Tables

This paper proposes the first peer-to-peer network and lookup algorithm that has worst case constant distortion. The construction uses a constant expected number of links. The design lends itself to dynamic deployment, and has a simple and easy to verify proof. The construction embraces the two-tier architecture of current peer-to-peer networks, where stronger and stable nodes serve as ultra-peers, and other (e.g., transient home users) are regular peers.
Content Classification of Multimedia Documents using Partitions of Low-Level Features

JVRB, 3(2006), no. 6. - Audio-visual documents obtained from German TV news are classified according to the IPTC topic categorization scheme. To this end usual text classification techniques are adapted to speech, video, and non-speech audio. For each of the three modalities word analogues are generated: sequences of syllables for speech, “video words” based on low level color features (color moments, color correlogram and color wavelet), and “audio words” based on low-level spectral features (spectral envelope and spectral flatness) for non-speech audio. Such audio and video words provide a means to represent the different modalities in a uniform way. The frequencies of the word analogues represent audio-visual documents: the standard bag-of-words approach. Support vector machines are used for supervised classification in a 1 vs. n setting. Classification based on speech outperforms all other single modalities. Combining speech with non-speech audio improves classification. Classification is further improved by supplementing speech and non-speech audio with video words. Optimal F-scores range between 62% and 94% corresponding to 50% - 84% above chance. The optimal combination of modalities depends on the category to be recognized. The construction of audio and video words from low-level features provide a good basis for the integration of speech, non-speech audio and video.
A Comparative Study of Transmembrane Diffusion and Permeation of Ibuprofen across Synthetic Membranes Using Franz Diffusion Cells

Synthetic membranes used in Franz diffusion cells for topical formulation quality assessment should provide least resistance to drug diffusion. In this study, the diffusion rates of ibuprofen across thirteen membranes were determined using Franz diffusion cells. Correlation of the membrane thickness, pore size and MWCO with drug fluxes was also made. The drug diffusion results showed that the porous membranes were categorized into high-flux (8–18 mg/cm2/h) and low-flux (0.1–3 mg/cm2/h) membranes. The drug fluxes did not show strong correlations (r2 < 0.99) with membrane parameters. Synthetic membranes can give variable drug fluxes, thus investigators should be careful in choosing membrane for formulation quality assessment.
Divergent sexual selection via male competition: ecology is key

Sexual selection and ecological differences are important drivers of speciation. Much research has focused on female choice, yet the role of male competition in ecological speciation has been understudied. Here, we test how mating habitats impact sexual selection and speciation through male competition. Using limnetic and benthic species of threespine stickleback fish, we find that different mating habitats select differently on male traits through male competition. In mixed habitat with both vegetated and open areas, selection favours two trait combinations of male body size and nuptial colour: large with little colour and small with lots of colour. This matches what we see in reproductively isolated stickleback species, suggesting male competition could promote trait divergence and reproductive isolation. In contrast, when only open habitat exists, selection favours one trait combination, large with lots of colour, which would hinder trait divergence and reproductive isolation. Other behavioural mechanisms in male competition that might promote divergence, such as avoiding aggression with heterospecifics, are insufficient to maintain separate species. This work highlights the importance of mating habitats in male competition for both sexual selection and speciation.
Translational Control via the Mammalian Target of Rapamycin Pathway Is Critical for the Formation and Stability of Long-Term Fear Memory in Amygdala Neurons

The mammalian target of rapamycin kinase (mTOR) regulates protein synthesis in neurons at the translational level through phosphorylation of several intracellular targets. Recent work in invertebrates indicates that mTOR-dependent translational control may be critical for the induction and maintenance of activity-dependent synaptic plasticity underlying memory formation. Here, we report that training rats in a simple fear conditioning procedure evokes a time-dependent increase in the phosphorylation of p70s6 kinase, a major direct downstream target of mTOR. When the activation of mTOR was prevented by posttraining injection of rapamycin into the amygdala, formation of the memory and the increase in p70s6 kinase phosphorylation was attenuated. Furthermore, when rapamycin was applied to the amygdala after the recall of a previously stored fear memory, subsequent retention was disrupted, indicating that local translational control at active synapses is required for the stability as well as the formation of long-term memory in this system.
Design of proportional-integral observer for unknown input descriptor systems

This note presents simple methods to design full- and reduced- order proportional integral observer for unknown inputs (UI) descriptor systems. Sufficient conditions for the existence of the observer are given and proven. The observer is solvable by any pole placement algorithm, it achieves a posteriori robustness state and UI estimation versus to time varying parameters and bounded nonlinear UI. An illustrative example is included.
Semantic Orthogonality of Type Disciplines

We consider a version of PCF, and prove, using both syntactic and semantic means, that the operational equivalences of the base language are preserved when the language is extended with sum and product types, with polymorphic types, and with recursive types. These theorems show that the additions to the type systems are orthogonal to the original language.
Detecting Eye Fixations by Projection Clustering

The identification of the components of eye movements (fixations and saccades) is an essential part in the analysis of visual behavior because these types of movements provide the basic elements used by further investigations of human vision. However, many of the algorithms that detect fixations present some problems (consistency, robustness, many input parameters). In this article we present a new eye fixation identification technique that is based on clustering of eye positions using projections and projection aggregation.
A G ] 2 9 Ju n 20 05 FOURIER MUKAI TRANSFORMS FOR GORENSTEIN SCHEMES

We extend to singular schemes with Gorenstein singularities or fibered in schemes of that kind Bondal and Orlov’s criterion for an integral functor to be fully faithful. We also contemplate a criterion for equivalence. We offer a proof that is new even if we restrict to the smooth case. In addition, we prove that for proper Gorenstein morphisms, a relative integral functor is fully faithful if and only if its restriction to each fibre also is it. These results imply the invertibility of the usual relative Fourier-Mukai transform for an elliptic fibration as a direct corollary.
Complex Dynamics of Pell Sequence

Binet formula for Pell sequence is viewed as a function of complex variable. In this paper the study of attracting and repelling fixed points of Pell sequence is presented with the complex dynamics resulting in the escape time images. A study of orbits of the Binet type formula is presented in the paper. Besides this, a new class of Mandelbrot sets is also studied for the Mann-iterates.
An Effective Algorithm for Ink-Bleed through Removal in Document Images

When documents are written on both sides, quite often ink bleeds through the paper. This is a common phenomenon with old documents and low quality paper. With the presence of increased bleed-through, reading and deciphering the text becomes tedious. In this paper two methods are proposed for ink–bleed through removal. The first method is founded on Blind Segmentation Approach which exploits information from one side of the document and second method is based on the exploitation of information from both sides of the scanned document and founded on a registration process which aims at working on both the recto and verso sides simultaneously. These two methods are further compared with Otsu’s method which performs enhancement through thresholding.
An XML based framework for self-describing parallel I/O data

File I/O data is interpreted by high performance parallel/distributed applications mostly as a sequence of arbitrary bits. This leads to the situation where data is 'volatile' information that has meaning only with the respective application responsible for it; a fact that contradicts the physical storage paradigm. We claim that this situation must change and see the necessity to follow an approach - known from the database world - to interpret data as 'a model of reality'. To achieve this we add semantic knowledge to the datafiles. Thus we propose an XML based language, which provides homogeneous framework for describing data on all interpretative levels from physical representation to logical information. In our framework physical files are consequently augmented by interpretative information specified in the proposed language. This approach delivers, beside semantic knowledge on the data, the advantages of persistence and portability.
Mitigation Strategies for Pandemic Influenza A: Balancing Conflicting Policy Objectives

Mitigation of a severe influenza pandemic can be achieved using a range of interventions to reduce transmission. Interventions can reduce the impact of an outbreak and buy time until vaccines are developed, but they may have high social and economic costs. The non-linear effect on the epidemic dynamics means that suitable strategies crucially depend on the precise aim of the intervention. National pandemic influenza plans rarely contain clear statements of policy objectives or prioritization of potentially conflicting aims, such as minimizing mortality (depending on the severity of a pandemic) or peak prevalence or limiting the socio-economic burden of contact-reducing interventions. We use epidemiological models of influenza A to investigate how contact-reducing interventions and availability of antiviral drugs or pre-pandemic vaccines contribute to achieving particular policy objectives. Our analyses show that the ideal strategy depends on the aim of an intervention and that the achievement of one policy objective may preclude success with others, e.g., constraining peak demand for public health resources may lengthen the duration of the epidemic and hence its economic and social impact. Constraining total case numbers can be achieved by a range of strategies, whereas strategies which additionally constrain peak demand for services require a more sophisticated intervention. If, for example, there are multiple objectives which must be achieved prior to the availability of a pandemic vaccine (i.e., a time-limited intervention), our analysis shows that interventions should be implemented several weeks into the epidemic, not at the very start. This observation is shown to be robust across a range of constraints and for uncertainty in estimates of both R0 and the timing of vaccine availability. These analyses highlight the need for more precise statements of policy objectives and their assumed consequences when planning and implementing strategies to mitigate the impact of an influenza pandemic.
SVMs Modeling for Highly Imbalanced Classification

Traditional classification algorithms can be limited in their performance on highly unbalanced data sets. A popular stream of work for countering the problem of class imbalance has been the application of a sundry of sampling strategies. In this paper, we focus on designing modifications to support vector machines (SVMs) to appropriately tackle the problem of class imbalance. We incorporate different ldquorebalancerdquo heuristics in SVM modeling, including cost-sensitive learning, and over- and undersampling. These SVM-based strategies are compared with various state-of-the-art approaches on a variety of data sets by using various metrics, including G-mean, area under the receiver operating characteristic curve, F-measure, and area under the precision/recall curve. We show that we are able to surpass or match the previously known best algorithms on each data set. In particular, of the four SVM variations considered in this paper, the novel granular SVMs-repetitive undersampling algorithm (GSVM-RU) is the best in terms of both effectiveness and efficiency. GSVM-RU is effective, as it can minimize the negative effect of information loss while maximizing the positive effect of data cleaning in the undersampling process. GSVM-RU is efficient by extracting much less support vectors and, hence, greatly speeding up SVM prediction.
The impact of externally supplied protein on root and phytohormone in endangered species cercidiphyllum japonicum cutting seedling

Whether soaking or injected by BSA, low to intermediate (≦ 7.58 μ M) concentrations of BSA improved rooting rate of cuttings and higher (≧ 15.15 μ M) concentrations of BSA significantly decreased the rooting rate of cuttings. The effect on the rooting rate by the method of BSA injected are significant than that of BSA soaking cuttings. Both of soaked and injected cuttings with low to intermediate (≦ 7.58 μ M) concentrations of BSA markedly increased the average number of lateral roots and the average root length. The treatment of the injection had a remarkable effect on the average number of lateral roots and average root length than that of soaking whether the low or high concentration of BSA. The magnitude of changes of the endogenous GA, ABA, ZR and IAA of the cuttings were bigger in the cuttings treated by injecting than that of soaking in each stage after cutting. This study adds the understanding that organic nitrogen effects on the cuttings root formation, growth and endogenous hormone changes.
The evolving threat of influenza viruses of animal origin and the challenges in developing appropriate diagnostics.

BACKGROUND An H1N1 subtype of swine origin caused the first influenza pandemic in this century. This pandemic strain was a reassortant of avian, swine, and human influenza viruses. Many diagnostic laboratories were overwhelmed by the testing demands related to this pandemic. Nevertheless, there remains the threat of other animal influenza viruses, such as highly pathogenic H5N1. As a part of pandemic preparedness, it is essential to identify the diagnostic challenges that will accompany the next pandemic. CONTENT We discuss the natural reservoir of influenza viruses and the possible role of livestock in the emergence of pandemic strains. The current commonly used molecular tests for influenza diagnosis or surveillance are also briefly reviewed. Some of these approaches are also used to detect animal viruses. Unfortunately, owing to a lack of systematic surveillance of animal influenza viruses, established tests may not be able to detect pandemic strains that have yet to emerge from the animal reservoir. Thus, multiple strategies need to be developed for better identification of influenza viruses. In addition, molecular assays for detection of mutations associated with antiviral resistance and for viral segment reassortments should also be encouraged. SUMMARY Influenza viruses are highly dynamic viruses. Regular and systematic influenza surveillance in both humans and animals is essential to provide a more comprehensive picture of the prevalent influenza viruses. To better prepare for the next pandemic, we should develop some simple and easy-to-use tests for characterizing newly emerging influenza viruses.
Path-Based Program Repair

We propose a path-based approach to program repair for imperative programs. Our repair framework takes as input a faulty program, a logic specification that is refuted, and a hint where the fault may be located. An iterative abstraction refinement loop is then used to repair the program: in each iteration, the faulty program part is re-synthesized considering a symbolic counterexample, where the control-flow is kept concrete but the data-flow is symbolic. The appeal of the idea is two-fold: 1) the approach lazily considers candidate repairs and 2) the repairs are directly derived from the logic specification. In contrast to prior work, our approach is complete for programs with finitely many control-flow paths, i.e., the program is repaired if and only if it can be repaired at the specified fault location. Initial results for small programs indicate that the approach is useful for debugging programs in practice.
Development of travel speed detection method in welding simulator using augmented reality

This paper explains about travel speed detection method that can be applied as the welding simulator using augmented reality. In welding process, the travel speed is an important parameter that influences the welding quality. In the future, this simulator can be used in welder training with a relatively low cost. This method uses ARToolkit, OpenGL library, and Autodesk 3Ds Max software for building the simulator. This method is the development of welding simulator that will show the deviation and accuracy of moving marker detection. This method uses differences in distance of the coordinate per unit time algorithm, taken from the amount of frames per second (FPS) of a camera. After this method was successfully built, the measurement data is taken to analyze the accuracy and the number of error in speed detection by the simulator from the actual speed with different light intensity parameter. The result is that the error in speed detection is not too large, so this simulator was successfully built and it can be developed further to get more sophisticated features on welding process in the future.
On Peer-to-Peer relays for VoIP applications

As VoIP applications become increasingly popular, voice quality remains the user's biggest concern. Peer-to-Peer network can potentially obtain qualified relay nodes (QRNs) to make path switching when the QoS of a default path degrades. Locating quality relay nodes rapidly and inexpensively however is a very challenging task. In this paper, we design and develop a sophisticated system to take snapshots of network status among PlanetLab nodes. We collect the 1-hop QRNs of all low quality sessions (LQSs) in a network snapshot. Based on collected data, we evaluate the state-of-the-art relay node locating approaches (e.g. EDR, EBP and ASAP)
Constrained optimization based on a multiobjective evolutionary algorithm

A criticism of evolutionary algorithms (EAs) might be the lack of efficient and robust generic methods to handle constraints. The most widespread approach for constrained search problems is to use penalty methods. EAs have received increased interest during the last decade due to the ease of handling multiple objectives. A constrained optimization problem or an unconstrained multiobjective problem may in principle be two different ways to pose the same underlying problem. In this paper, an alternative approach for the constrained optimization problem is presented. The method is a variant of a multiobjective real coded genetic algorithm (GA) inspired by the penalty approach. It is evaluated on six different constrained single objective problems found in the literature. The results show that the proposed method performs well in terms of efficiency, and that it is robust for a majority of the test problems.
Teilhabe nach Schlaganfall – Validierung der deutschen Übersetzung des IMPACT-S

Zusammenfassung Ziel: Ziel dieser Studie war es, ein zuverlässiges ICF-basiertes Instrument zur Erfassung aller 9 Lebensdomänen der ICF zur Verfügung zu stellen. Aufgrund konzeptueller wie auch psychometrischer Stärken wurde der „ICF Measure of Participation and ACTivities questionnaire – Screener“ (IMPACT-S) ins Deutsche übersetzt und validiert. Methodik: In dieser multizentrischen Studie wurden 93 Betroffene während ihres stationären Reha-Aufenthaltes nach erstem Schlaganfall (Phase C und D) kontaktiert und zu 2 Messzeitpunkten 4–6 Monate postalisch nach dem Akutereignis befragt. Ergebnisse: Die interne Konsistenz der einzelnen IMPACT-S-Domänen und der beiden Subskalen war als befriedigend bis sehr gut einzuschätzen (0,72–0,94; Globalskala: 0,97). Ebenso zeigte sich eine gute bis sehr gute Test-Retestreliabilität. Schlussfolgerung: Die vorliegende deutsche Übersetzung und Validierung des IMPACT-S bildet die 9 Teilhabe-Domänen der ICF umfassend ab und erfüllt die statistischen Gütekriterien.