Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning from history: the need for a synthetic approach to human cognition

History does repeat itself if it comes to how cognitive science develops new research lines. Consider attention, one of the first experimentally researched psychological phenomenon. While William James still thought that “everyone knows what attention is,” it took us more than 100 years to learn that attention is actually not one coherent thing but falls into very different subfunctions, which again are subserved by various processes. It seems only natural that a young, developing scientific discipline needs some time to learn how to deal with phenomena, translate them into useful scientific concepts, and investigate the underlying mechanisms. And yet, we apparently did not learn much from this quest, as witnessed by the very similar developments in research on phenomena with a younger history. Developments showing the same problematic tendencies that have troubled attention research and slowed down its progress for/by many years. In the following, we shall try to capture the typical way new research lines develop—the analytical approach, as we call it, by using attention as an example. Then we focus on three pitfalls in this process that seem particularly difficult to circumvent and present an alternative research strategy—the synthetic approach.
An Efficient Burst Transmission Scheme for Wireless Sensor Networks

This paper addresses link quality fluctuation and its impact on the packet delivery capacity of wireless sensor networks. Independent studies have previously confirmed that link quality fluctuates even in a static deployment and understanding stable durations, good and bad alike, can contribute to the efficient transmission of packets. We propose a two stage Markov model to characterise link quality fluctuation and to determine when and for how long nodes should transmit packets in burst. Both to develop and test our model, we deployed a wireless sensor network consisting of 14 nodes in a garden and transmitted more than 120,000 packets with different links. The experiment results confirm that our approach improved the packet delivery capacity of the links by up to 40% when compared with a baseline and by up to 25% when compared with a scheme that employs conditional distribution functions.
Capturing and automating performance diagnosis: the Poirot approach

Performance diagnosis, the process of finding and explaining performance problems, is an important part of parallel programming. Effective performance diagnosis requires that the programmer plan an appropriate method, and manage the experiments required by that method. This paper presents Poirot, an architecture to support performance diagnosis. It explains how the architecture helps automatically, adaptably plan and manage the diagnosis process. The paper evaluates the generality and practicality of Poirot, by reconstructing diagnosis methods found in several published performance tools. <<ETX>>
A robust data-driven version of the Berlin Method

In this paper a robust data-driven procedure for decomposing seasonal time series based on a generalized Berlin Method (BV, Berliner Verfahren) as proposed by Heiler and Michels (1994) is discussed. The basic robust algorithm used here is an adaptation of the LOWESS (LOcally Weighted Scatterplot Smoothing) procedure (Cleveland, 1979). For selecting the optimal bandwidth the simple double smoothing rule (Heiler and Feng, 1999) is used. The optimal order of the local polynomial is selected with a BIC criterion. The proposed procedure is applied to the macroeconomic time series used in the recent empirical studies carried out by the German Federal Statistical Office (Speth, 1994 and Hopfner, 1998).
Adaptive Scheduling for On-Demand Time-Critical Information Dissemination over Data Broadcast Channel

Modern information dissemination services can dynamically produce real-time data that is valid and useful depending on users’ on-demand data requests. Information servers must adopt a scheduling approach to maintain timely data access and system performance. Most previous studies on scheduling such data discuss on-demand data broadcasting and real-time task processing with various performance metrics. This paper exploits the attributes of request urgency, service productivity, and access fairness, and proposes an adaptive scheduling scheme for delivering on-demand, time-critical data objects over a data broadcast channel. In terms of request satisfaction and mean access time, performance results show that the proposed scheduling approach is amenable under dynamic on-demand time-critical requests, simultaneously achieving a low mean access time and request deadline miss rate.
Mentoring and Beginning Teachers' Workplace Learning

Mentoring has been the focus of much attention in the recent literature on initial teacher education and induction and as such has become a 'foundation stone' of collaborative endeavours between universities and schools in the facilitation of teacher development. In 1998 some 220 beginning teachers and 245 supervisors and mentors in New South Wales government schools were surveyed and beginning teachers' professional learning observed closely in six case study schools in different settings across the state. One-way analysis of variance (ANOVA) and multivariate analysis of variance (MANOVA) indicated the relevance of internship to initial teacher education programs and established the importance of mentoring support in beginning teachers' professional learning in the induction year. The case studies also identified key practices, conditions and professional interactions that sustained transmission, transactional and transformational approaches to teacher learning. The complementary qualitative and quantitative methodology provided strong evidence of the importance of the mentoring strategy.
Yeast Colonies: A Model for Studies of Aging, Environmental Adaptation, and Longevity

When growing on solid surfaces, yeast, like other microorganisms, develops organized multicellular populations (colonies and biofilms) that are composed of differentiated cells with specialized functions. Life within these populations is a prevalent form of microbial existence in natural settings that provides the cells with capabilities to effectively defend against environmental attacks as well as efficiently adapt and survive long periods of starvation and other stresses. Under such circumstances, the fate of an individual yeast cell is subordinated to the profit of the whole population. In the past decade, yeast colonies, with their complicated structure and high complexity that are also developed under laboratory conditions, have become an excellent model for studies of various basic cellular processes such as cell interaction, signaling, and differentiation. In this paper, we summarize current knowledge on the processes related to chronological aging, adaptation, and longevity of a colony cell population and of its differentiated cell constituents. These processes contribute to the colony ability to survive long periods of starvation and mostly differ from the survival strategies of individual yeast cells.
Subarcsecond radio observations of the dwarf starburst galaxy NGC 3077

We present the first subarcsecond radio observations of the nearby dwarf starburst galaxy NGC 3077 obtained with the MERLIN interferometer. We have detected two resolved sources which are coincident with the positions of two discrete X-ray sources detected by Chandra. One of the radio sources is associated with a supernova remnant (SNR) and the observed radio flux is consistent with having a non-thermal origin. The age of the SNRs of about 760 yr is between the average age of the SNRs detected in M82 and those detected in the Milky Way and the Large Magellanic Cloud. We use this detection to calculate a star formation rate ‐ ‐
Activation and reconfiguration of fictive feeding by the octopamine-containing modulatory OC interneurons in the snail Lymnaea.

We describe the role of the octopamine-containing OC interneurons in the buccal feeding system of Lymnaea stagnalis. OC neurons are swallowing phase interneurons receiving inhibitory inputs in the N1 and N2 phases, and excitatory inputs in the N3 phase of fictive feeding. Although the OC neurons do not always fire during feeding, the feeding rate is significantly (P < 0.001) higher when both SO and OC fire in each cycle than when only the SO fires. In 28% of silent preparations, a single stimulation of an OC interneuron evokes the feeding pattern. Repetitive stimulation of the OC interneuron increases the proportion of responsive preparations to 41%. The OC interneuron not only changes both the feeding rate and reconfigures the pattern. Depolarization of the OC interneurons increases the feeding rate and removes the B3 motor neuron from the firing sequence. Hyperpolarization slows it down (increasing the duration of N1 and N3 phases) and recruits the B3 motor neuron. OC interneurons form synaptic connections onto buccal motor neurons and interneurons but not onto the cerebral (cerebral giant cell) modulatory neurons. OC interneurons are electrically coupled to all N3 phase (B4, B4Cl, B8) feeding motor neurons. They form symmetrical connections with the N3p interneurons having dual electrical (excitatory) and chemical (inhibitory) components. OC interneurons evoke biphasic synaptic inputs on the protraction phase interneurons (SO, N1L, N1M), with a short inhibition followed by a longer lasting depolarization. N2d interneurons are hyperpolarized, while N2v interneurons are slowly depolarized and often fire a burst after OC stimulation. Most motor neurons also receive synaptic responses from the OC interneurons. Although OC and N3p interneurons are both swallowing phase interneurons, their synaptic contacts onto follower neurons are usually different (e.g., the B3 motor neurons are inhibited by OC, but excited by N3p interneurons). Repetitive stimulation of OC interneuron facilitates the excitatory component of the biphasic responses evoked on the SO, N1L, and N1M interneurons, but neither the N2 nor the N3 phase interneurons display a similar longer-lasting excitatory effect. OC interneurons are inhibited by all the buccal feeding interneurons, but excited by the serotonergic modulatory CGC neurons. We conclude that OC interneurons are a new kind of swallowing phase interneurons. Their connections with the buccal feeding interneurons can account for their modulatory effects on the feeding rhythm. As they contain octopamine, this is the first example in Lymnaea that monoaminergic modulation and reconfiguration are provided by an intrinsic member of the buccal feeding network.
High Density Shapes Using Photometric Stereo and Laser Range Sensor under Unknown Light-Source Direction

Much research is in progress on the acquisition of high-density three-dimensional shapes by acquiring and combining shape data and normal data. The method proposed in this paper estimates normals to object surfaces by employing the photometric stereo method and combines the estimation with the three-dimensional shape acquired by a laser range sensor. Although the photometric stereo method presumes that light-source directions for each image are known, the proposed method uses its light-source direction estimates. By linearizing the images as preprocessing, specular reflection and shadow effects within the image are removed and the precision for the light-source direction estimations is increased. Since the proposed method does not require the light-source directions to be known, it offers the advantage of broad applicability for measurement work.
Angiogenesis and Heparanase Activity Assays for in Vitro Tumor Growth and Metastasis Using Novel Identification of Sulfated Oligosaccharide-based Inhibitors of Updated Version

Inhibitors of tumor angiogenesis and metastasis are rapidly emerging as important new drug candidates for cancer therapy. To facilitate the identification of such drugs, we recently developed novel and rapidin vitro assays for human angiogenesis and for the extracellular matrix-degrading enzyme heparanase, which has been implicated in tumor metastasis. In this study, sulfated oligosaccharides, which are structural mimics of heparan sulfate, were investigated as drug candidates because these compounds may interfere with heparan sulfate recognition by many angiogenic growth factors and may inhibit cleavage of heparan sulfate by heparanase. In the preliminary screening studies, it was found that inhibitory activity in both assay systems was critically dependent on chain length and degree of sulfation, highly sulfated linear oligosaccharides of five or more monosaccharides in length being the most active. However, two sulfated oligosaccharides stood out as potential antitumor drugs, phosphomannopentaose sulfate (PI-88) and maltohexaose sulfate, both of these compounds having the important property of simultaneously being potent inhibitors of in vitro angiogenesis and heparanase activity. Due to the ease of manufacture of the starting material, phosphomannopentaose, PI-88 was studied in more detail. PI-88 was shown to inhibit the primary tumor growth of the highly invasive rat mammary adenocarcinoma 13762 MAT by ;50%, inhibit metastasis to the draining popliteal lymph node by ;40%, and reduce the vascularity of tumors by;30%, all of these effects being highly significant. Acute hematogenous metastasis assays also demonstrated that PI-88 was a potent ( >90%) inhibitor of blood-borne metastasis. Thus, by the use of novel in vitro screening procedures, we have identified a promising antitumor agent.
Neuro-Fuzzy Network Based On Extended Kalman Filtering for Financial Time Series

— The neural network's performance can be measured by efficiency and accuracy. The major disadvantages of neural network approach are that the generalization capability of neural networks is often significantly low, and it may take a very long time to tune the weights in the net to generate an accurate model for a highly complex and nonlinear systems. This paper presents a novel Neuro-fuzzy architecture based on Extended Kalman filter. To test the performance and applicability of the proposed neuro-fuzzy model, simulation study of nonlinear complex dynamic system is carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction of financial time series. A benchmark case studie is used to demonstrate that the proposed model is a superior neuro-fuzzy modeling technique.
Ciliobrevins as tools for studying dynein motor function

Dyneins are a small class of molecular motors that bind to microtubules and walk toward their minus ends. They are essential for the transport and distribution of organelles, signaling complexes and cytoskeletal elements. In addition dyneins generate forces on microtubule arrays that power the beating of cilia and flagella, cell division, migration and growth cone motility. Classical approaches to the study of dynein function in axons involve the depletion of dynein, expression of mutant/truncated forms of the motor, or interference with accessory subunits. By necessity, these approaches require prolonged time periods for the expression or manipulation of cellular dynein levels. With the discovery of the ciliobrevins, a class of cell permeable small molecule inhibitors of dynein, it is now possible to acutely disrupt dynein both globally and locally. In this review, we briefly summarize recent work using ciliobrevins to inhibit dynein and discuss the insights ciliobrevins have provided about dynein function in various cell types with a focus on neurons. We temper this with a discussion of the need for studies that will elucidate the mechanism of action of ciliobrevin and as well as the need for experiments to further analyze the specificity of ciliobreviens for dynein. Although much remains to be learned about ciliobrevins, these small molecules are proving themselves to be valuable novel tools to assess the cellular functions of dynein.
Parental involvement buffers associations between pump duration and metabolic control among adolescents with type 1 diabetes.

OBJECTIVES To examine pump duration associations with adolescents' metabolic control and whether parental involvement moderated this association. METHODS This study used a cross-sectional sample of 10- to 14-year-olds with diabetes (N = 252, 53.6% female) and parents' reported parental involvement; HbA1c was obtained from medical records. Half (50.8%) were on an insulin pump (continuous subcutaneous insulin infusion, CSII), with the remainder prescribed multiple daily injections (MDI). RESULTS Adolescents on CSII displayed better HbA1c than those on MDI. A curvilinear association revealed that participants on CSII for <2 years showed a positive pump duration-HbA1c association, while those on CSII longer showed no association. Parental involvement interacted with pump duration to predict HbA1c. Pump duration was associated with poorer HbA1c only when parents were relatively uninvolved. CONCLUSIONS Within the limitations of a cross-sectional design, data suggest that adolescents on CSII have better HbA1c than those on MDI, but may experience a period of deterioration that can be offset by parental involvement.
EM-based stepwise regression imputation using standard and robust methods

Imputation of missing values is one of the major tasks for data pre-processing in many areas. Whenever imputation of data from official statistics comes into mind, several (additional) challenges almost always arise, like large data sets, data sets consisting of a mixture of different variable types, or data outliers. The aim of this contribution is to propose an automatic algorithm called IRMI for iterative model-based imputation using robust methods, encountering for the mentioned challenges, and to provide a software tool in R. This algorithm is compared to the algorithm IVEWARE, which is the " recommended software " for imputations in international and national statistical institutions. Using artificial data and real data sets from official statistics and other fields, the advantages of IRMI over IVEWARE – especially with respect to robustness – are demonstrated.
Random subspaces of the instance and principal component spaces for ensembles

In machine learning accurate predictors may be obtained by combining predictions of an ensemble of accurate and diverse predictors. Ensembles are efficiently constructed with the random subspace method (RSM) performed in the instance or in the principal components (PCs) spaces. In this paper, we extend RSM to explore the synergy in the characteristics of these two spaces, with a method referred to as RSM-IPCS. Using 24 datasets from the UCI machine learning repository, we show an enhanced performance of RSM-IPCS in comparison to the original RSM and RSM in PCs space, in terms of higher accuracy and smaller variances. Since RSM-IPCS exhibited at least a similar performance to the best method in a separate space, it opens the way for optimization of ensembles based on the combination of multiple spaces.
Parametric Fixed-Point Iteration Implemented in C-XSC Evgenija Popova

Consider linear systems whose matrix and right-hand side vector depend affinelinearly on parameters varying within prescribed intervals. We present a C-XSC [9] implementation of a parametric fixed-point iteration method for the verified enclosure of the parametric solution set. Input data for the system have an entirely numerical representation by dense twodimensional matrices. Some specific features of the corresponding algorithm concerning sharp enclosure of the contracting matrix, epsilon inflation, and inner approximation of the solution enclosure are discussed. Numerical examples illustrate the application of the presented software and the discussed specific features of the algorithm. Our software seems to be the first open source software delivering verified results in the field of parametric linear systems (inner and outer estimations for the hull of the solution set). The source code is available at http://www.math.uni-wuppertal.de/wrswt/xsc/cxsc − oftware.html . The software will be developed further.
On the Dynamic Optimization of Biped Robot

 Abstract—This paper concentrates on three important points: the selection of the suitable direct method used for suboptimal control of the biped robot, the selection of the appropriate nonlinear programming (NLP) algorithm that searches for the global minimum rather than the local minimum, and the effect of different constraints on the energy of the biped robot. To perform the mentioned points, the advantages and disadvantages of the optimal control methods were illustrated. The inverse-dynamics based optimization is preferred because of the ability to convert the original optimal control into algebraic equations which are easy to deal with. The inverse-dynamics-based optimization was classified as spline and the finite difference based optimization. Due to the easy use of the latter, it was used for investigating seven cases with different constraints for 6-DOF biped robot during the single support phase (SSP). Hybrid genetic-sequential quadratic programming (GA-SQP) was used for simulation of the target robot with MATLAB. It can be concluded that more imposed constraints on the biped robot, more energy is needed. In general, more energy can be required in the case of (1) restriction of the swing foot to be level to the ground and (2) reducing the hip height or constraining the hip to move in constant height.
Efficient hardware-based nonintrusive dynamic application profiling

Application profiling—the process of monitoring an application to determine the frequency of execution within specific regions—is an essential step within the design process for many software and hardware systems. Profiling is often a critical step within hardware/software partitioning utilized to determine the critical kernels of an application. In this article, we present an innovative, nonintrusive dynamic application profiler (DAProf) capable of profiling an executing application by monitoring the application's short backward branches, function calls, and function returns. The resulting profile information provides an accurate characterization of the frequently executed loops within the application providing a breakdown of loop executions versus loop iterations per execution. DAProf achieves excellent profiling accuracy with an average accuracy of 98% for loop executions, 97% for average iterations per execution, and 95% for percentage of execution time. In addition, the presented dynamic application profiler incurs as little as 11% area overhead compared to an ARM9 microprocessor. DAProf is ideally suited for rapidly profiling software applications and dynamic optimization approaches such as dynamic hardware/software partitioning in which detailed loop execution information is needed to provide accurate performance estimates.