Miracle-dz/UniFAR-FTUs huggingface dataset:
Modulatory effects of FMRF-NH2 on outward currents and oscillatory activity in heart interneurons of the medicinal leech

Using single-electrode voltage clamp, heart interneurons of the medicinal leech were shown to possess both a rapidly inactivating outward current, IA, and a more slowly inactivating outward current, IK. IA and IK could be separated by their voltage sensitivity and kinetic properties. FMRF-NH2 (Phe-Met-Arg-Phe-NH2) modulates IK by shifting both steady state activation and inactivation to more hyperpolarized potentials, but it does not affect the time constants. IA and IK appear to use K+ as a charge carrier; a change in the external [K+] produced a shift in the apparent reversal potential in the direction predicted with potassium as the charge carrier. Both IA and IK are sensitive to tetraethylammonium (TEA) and 4-aminopyridine (4- AP), and TEA and 4-AP both interfere with the effects of FMRF-NH2 on IK. The biophysical properties of IA and of IK in the presence and absence of FMRF-NH2 were incorporated into a Hodgkin-Huxley model of these currents that could reproduce voltage-clamp data. FMRF-NH2 produces two apparently dissimilar effects on the heartbeat rhythm-- acceleration and disruption. We suggest that both effects could result from the hyperpolarizing shifts in steady state activation and inactivation of IK.
SATP-a transport protocol for ATM-intranets

This paper proposes a new transport protocol, namely SATP (simple ATM transport protocol), designed for ATM based intranets. It is adapted to the specific characteristics of ATM and can be used transparently from any application as an alternative to TCP which is known to be not very suitable for ATM. Transparent means that the use of SATP does not require any modification and recompilation of applications, which can benefit from the better performance of SATP in the case of ATM-based intranets. Beside the implementation and integration of the protocol, some performance results are presented.
DOLLY: An experimental evaluation of distributed node positioning framework in wireless sensor networks

The capability of nodes in self-configuration becomes one of the challenges in wireless sensor networks localization scheme. We propose DOLLY (Distributed Node Localization System), a new platform of distributed localization scheme which is implemented in each node to estimate its position with the assistance of reference nodes. We evaluate the comprehensive performance of the proposed scheme in various observation areas, including distance estimation, position calculation and packet delivery. The evaluation result of the proposed scheme is compared with the measurement result obtained by Imote2. The result shows that in the three environmental conditions, i.e. outdoor LOS, indoor LOS and NLOS, the platform provides a good performance for distance estimation processing. However, the execution time of distributed localization algorithm in our proposed platform is rather high. Overall the proposed scheme has sufficient reliability in distributed processing of node positioning in wireless sensor networks.
Adaptive Refinement of Search Patterns for Distributed Information Gathering

Information brokerage systems support distributed information gathering for data collections stored within the World Wide Web or for other on-line data repositories. These systems present a unified face of the underlying heterogeneity, both in terms of the access/search protocols and in terms of the data schemata. Typically, the user may input some values (e.g., interpreted as strings within a fulltext search), and may refine/relax them once she gets too many/little results for her query. Adaptiveness with respect to a refinement of attributes can improve the precision of the searches dramatically. This paper discusses adaptive refinement of attributepatterns together with its main implications in the context of the Constraint-Based Knowledge Broker system. It presents an implementation for distributed information gathering, and motivates design choices through concrete examples.
Apperceiving visual elements in human-technology interaction design

Silvennoinen, Johanna Maria Apperceiving Visual Elements in Human-technology Interaction Design Jyväskylä: University of Jyväskylä, 2017, 83 p. (+ included articles) (Jyväskylä Studies in Computing ISSN 1456-5390; 261) ISBN 978-951-39-7001-7 (nid.) ISBN 978-951-39-7002-4 (PDF) Visual design of technological artefacts is an integral part of peoples’ experiences in technology-interaction. Visual product properties are capable of eliciting affective responses and multisensorial experiences in humantechnology interaction. Current research in the field of human-technology interaction focuses on visual, emotional and multisensory aspects of interaction in addition to functionality and usability. However, the focus has not been on how performative aspects of visual elements affect technology-interaction as a cognitive sense making process shaping human experiences. To design technological contact points to be made sense of, the substance of visual representations requires clarification to conduct argument-based technologydesign, and to base design solutions on scientific results. Therefore, research is required to explicate what visual experience is in human-technology interaction, how its constituent factors and underlying dynamics can be studied, and how to design with this research-based knowledge. In this thesis, visual elements contributing to cognitive and affective processes of visual experience in human-technology interaction are examined from an interactionist perspective. The focus is on the role of visual elements in visual usability, aesthetic appeal and emotional aspects in experiencing technological artefacts and to explicate visual experience in appraising visual stimuli via apperception. The explication of visual experience as a cognitiveaffective process contributes fundamentally to how visual representations of technological artefacts are made sense of and experienced, and thus, provides a basis for argument-based visual technology-design. As a result of an interactionist approach in examining visual element appraisals in humantechnology interaction, a theoretical framework is presented. The framework integrates different dimensions of visual experience with interactionist methodological position and functions as a basis for argument-based visual design. The constituent dimensions of visual experience are visual usability and aesthetic appeal, which are the concepts with which visual experiences are studied and explicated in detail as operationalised in the experiments presented in the seven attached articles. The framework of visual experience can be utilised as a discourse tool in research and design of visual technological designs, and as an explanatory framework for argument-based visual design.
Functional Information, Biomolecular Messages and Complexity of BioSequences and Structures

In the quest for a mathematical measure able to capture and shed light on the dual notions of information and complexity in biosequences, Hazen et al. have introduced the notion of Functional In- formation (FI for short). It is also the result of earlier considerations and findings by Szostak and Carothers et al. Based on the experiments by Charoters et al., regarding FI in RNA binding activities, we decided to study the relation existing between FI and classic measures of com- plexity applied on protein-DNA interactions on a genome-wide scale. Us- ing classic complexity measures, i.e, Shannon entropy and Kolmogorov Complexity as both estimated by data compression, we found that FI applied to protein-DNA interactions is genuinely different from them. Such a fact, together with the non-triviality of the biological function considered, contributes to the establishment of FI as a novel and useful measure of biocomplexity. Remarkably, we also found a relationship, on a genome-wide scale, between the redundancy of a genomic region and its ability to interact with a protein. This latter finding justifies even more some principles for the design of motif discovery algorithms. Finally, our experiments bring to light methodological limitations of Linguistic Complexity measures, i.e., a class of measures that is a function of the vocabulary richness of a sequence. Indeed, due to the technology and as- sociated statistical preprocessing procedures used to conduct our studies, i.e., genome-wide ChIP-chip experiments, that class of measures cannot give any statistically significant indication about the relation between complexity and function. A serious limitation due to the widespread use of the technology.
Generalized fuzzy rough sets based on fuzzy coverings

This paper further studies the fuzzy rough sets based on fuzzy coverings. We first present the notions of the lower and upper approximation operators based on fuzzy coverings and derive their basic properties. To facilitate the computation of fuzzy coverings for fuzzy covering rough sets, the concepts of fuzzy subcoverings, the reducible and intersectional elements, the union and intersection operations are provided and their properties are discussed in detail. Afterwards, we introduce the concepts of consistent functions and fuzzy covering mappings and provide a basic theoretical foundation for the communication between fuzzy covering information systems. In addition, the notion of homomorphisms is proposed to reveal the relationship between fuzzy covering information systems. We show how large-scale fuzzy covering information systems and dynamic fuzzy covering information systems can be converted into small-scale ones by means of homomorphisms. Finally, an illustrative example is employed to show that the attribute reduction can be simplified significantly by our proposed approach.
Transcriptional and Translational Regulation of α-Acetolactate Decarboxylase of Lactococcus lactissubsp. lactis

ABSTRACT The α-acetolactate decarboxylase (ALDC) gene, aldB, is the penultimate gene of the leu-ilv-ald operon, which encodes the three branched-chain amino acid (BCAA) biosynthesis genes in Lactococcus lactis. Its product plays a dual role in the cell: (i) it catalyzes the second step of the acetoin pathway, and (ii) it controls the pool of α-acetolactate during leucine and valine synthesis. It can be transcribed from the two promoters present upstream of the leu and ilv genes (P1 and P2) or independently under the control of its own promoter (P3). In this paper we show that the production of ALDC is limited by two mechanisms. First, the strength of P3 decreases greatly during starvation for BCAAs and under other conditions that generally provoke the stringent response. Second, although aldB is actively transcribed from P1 and P2 during BCAA starvation, ALDC is not significantly produced from these transcripts. The aldB ribosome binding site (RBS) appears to be entrapped in a stem-loop, which is itself part of a more complex RNA folding structure. The function of the structure was studied by mutagenesis, using translational fusions with luciferase genes to assess its activity. The presence of the single stem-loop entrapping the aldB RBS was responsible for a 100-fold decrease in the level of aldB translation. The presence of a supplementary secondary structure upstream of the stem-loop led to an additional fivefold decrease of aldB translation. Finally, the translation of the ilvA gene terminating in the latter structure decreased the level of translation of aldBfivefold more, leading to the complete extinction of the reporter gene activity. Since three leucines and one valine are present among the last six amino acids of the ilvA product, we propose that pausing of the ribosomes during translation could modulate the folding of the messenger, as a function of BCAA availability. The purpose of the structure-dependent regulation could be to ensure the minimal production of ALDC required for the control of the acetolactate pool during BCAA synthesis but to avoid its overproduction, which would dissipate acetolactate. Large amounts of ALDC, necessary for operation of the acetoin pathway, could be produced under favorable conditions from the P3 transcripts, which do not contain the secondary structures.
Dynamic programming algorithm optimization for spoken word recognition

This paper reports on an optimum dynamic progxamming (DP) based time-normalization algorithm for spoken word recognition. First, a general principle of time-normalization is given using time-warping function. Then, two time-normalized distance definitions, called symmetric and asymmetric forms, are derived from the principle. These two forms are compared with each other through theoretical discussions and experimental studies. The symmetric form algorithm superiority is established. A new technique, called slope constraint, is successfully introduced, in which the warping function slope is restricted so as to improve discrimination between words in different categories. The effective slope constraint characteristic is qualitatively analyzed, and the optimum slope constraint condition is determined through experiments. The optimized algorithm is then extensively subjected to experimental comparison with various DP-algorithms, previously applied to spoken word recognition by different research groups. The experiment shows that the present algorithm gives no more than about two-thirds errors, even compared to the best conventional algorithm.
Partnering with Indigenous student co-researchers: improving research processes and outcomes

Objective To examine the contribution of student co-researchers to a community-based participatory Photovoice investigation of Indigenous children's food-related lived experience. We examine co-researchers’ contributions to the research process, their role in knowledge co-generation and dissemination, and factors that fostered research partnership with the teenage co-researchers. Methods High school students attending a First Nation community school in Canada were trained as research partners. They contributed to aspects of research design, conducted interviews with grades 3 and 4 Photovoice participants, and participated in data analysis and the development of a culturally relevant photobook. The study was initiated by the community's research committee. It is informed by critical consciousness theory and the positive youth development framework. Results Student co-researchers incorporated culturally appropriate strategies as they interviewed participants. Co-researchers adopted conversational approaches, built rapport by articulating personal and cultural connections, and engaged in mentoring and health promotion as they interviewed participants. They made critical contributions to dissemination by developing photobook content that promoted the importance of traditional foods and the vital role of family and community in healthy eating practices. Relationships and “dialogic” space were important to building partnership with and promoting capacity development among youth co-researchers. Conclusions Partnership between university researchers and Indigenous student co-researchers holds great promise for health promotion in communities. Co-researchers developed research and leadership skills, gained understanding of health challenges facing their community, and initiated health and cultural promotion through the project's Photobook. This investigation supports the powerful potential of student co-researchers to meaningfully contribute to research processes and to build knowledge that is relevant and credible both within and outside of their communities. Findings have implications for youth, communities and researchers.
Using the Windowed means paradigm for single trial P300 detection

The Windowed means paradigm is a method for slow-changing cortical potentials feature extraction, most importantly in reaction to events. It has been successfully used for various brain-computer interfaces. The objective of this paper was to evaluate if this paradigm is also appropriate for P300 brain-computer interfaces. The modified method was tested on five healthy subjects. The optimal selection of parameters was discussed. The Windowed means paradigm was successful for the P300 detection on the testing data-set.
The statistical dependence of auroral absorption on geomagnetic and solar wind parameters

Data from the Imaging Riometer for Ionospheric Studies (IRIS) at KilpisjFinland, have been compiled to form statistics of auroral absorption based on seven years of observations. In a previous study a linear relationship be- tween the logarithm of the absorption and the Kp index pro- vided a link between the observations of precipitation with the level of geomagnetic activity. A better fit to the absorp- tion data is found in the form of a quadratic in Kp for eight magnetic local time sectors. Past statistical investigations of absorption have hinted at the possibility of using the solar wind velocity as a proxy for the auroral absorption, although the lack of available satellite data made such an investigation difficult. Here we employ data from the solar wind monitors, WIND and ACE, and derive a linear relationship between the solar wind velocity and the cosmic noise absorption at IRIS for the same eight magnetic local time sectors. As far as the authors are aware this is the first time that in situ measure- ments of the solar wind velocity have been used to create a di- rect link with absorption on a statistical basis. The results are promising although, it is clear that some other factor is neces- sary in providing reliable absorption predictions. Due to the substorm related nature of auroral absorption, this is likely formed by the recent time history of the geomagnetic activ- ity, or by some other indicator of the energy stored within the magnetotail. For example, a dependence on the southward IMF (interplanetary magnetic field) is demonstrated with ab- sorption increasing with successive decreases in Bz; a north- ward IMF appears to have little effect and neither does the eastward component, By .
4-sensor camera calibration for image representation invariant to shading, shadows, lighting, and specularities

Most lighting can be accurately modeled using a simplified Planckian function. If we form logarithms of color ratios of camera sensor values, then in a Lambertian plus specular two-lobe model of reflection the temperature-dependent term is separate and is seen as a straight line: i.e., changing lighting amounts to changing each pixel value in a straight line, for a given camera. Here we use a 4-sensor camera. In this case, forming color ratios reduces the dimensionality to 3. Applying logarithms and projecting onto the plane in the 3D color space orthogonal to the light-change direction results in an image representation that is invariant to illumination change. For a given camera, the position of the specular point in the 2D plane is always the same, independent of the lighting. Thus a camera calibration produces illumination invariance at a single pixel. In the plane, matte surfaces reduce to points and specularities are almost straight lines. Extending each pixel value back to the matte position, postulated to be the maximum radius from the fixed specular point, at any angle in the 2D plane, removes specularity. Thus images are independent of shading (by forming ratios), independent of shadows (by making them independent of illumination temperature) and independent of specularities. The method is examined by forming 4D images from hyperspectral images, using real camera sensors, with encouraging results.
Topological inference for EEG and MEG

Neuroimaging produces data that are continuous in one or more dimensions. This calls for an inference framework that can handle data that approximate functions of space, for example, anatomical images, time-frequency maps and distributed source reconstructions of electromagnetic recordings over time. Statistical parametric mapping (SPM) is the standard framework for whole-brain inference in neuroimaging: SPM uses random field theory to furnish p-values that are adjusted to control family-wise error or false discovery rates, when making topological inferences over large volumes of space. Random field theory regards data as realizations of a continuous process in one or more dimensions. This contrasts with classical approaches like the Bonferroni correction, which consider images as collections of discrete samples with no continuity properties (i.e., the probabilistic behavior at one point in the image does not depend on other points). Here, we illustrate how random field theory can be applied to data that vary as a function of time, space or frequency. We emphasize how topological inference of this sort is invariant to the geometry of the manifolds on which data are sampled. This is particularly useful in electromagnetic studies that often deal with very smooth data on scalp or cortical meshes. This application illustrates the versatility and simplicity of random field theory and the seminal contributions of Keith Worsley (1951-2009), a key architect of topological inference.
On the Consistency of Certain Identification Methods for Linear Parameter Varying Systems

The consistency of certain identification methods for Linear Parameter Varying systems is considered. More precisely, methods for the identification of SISO input-output models are analysed. In order to perform a consistency analysis the application of ergodicity is required, which is not obviously applicable with these types of time-varying systems. It is therefore shown that, when the scheduling parameter satisfies certain conditions, ergodicity type results can be applied to the methods considered. An analysis is then carried out for two cases: that of noise-free measurements of the scheduling parameter, and then the more realistic case of noisy scheduling parameter measurements. The latter is shown to be an errors-in-variables type problem. In both cases the least squares technique is shown to typically give biased estimates and the instrumental variables method is proposed as a way of resolving this. The analysis carried out is reinforced by results in simulation.
Deep learning for human activity recognition: A resource efficient implementation on low-power devices

Human Activity Recognition provides valuable contextual information for wellbeing, healthcare, and sport applications. Over the past decades, many machine learning approaches have been proposed to identify activities from inertial sensor data for specific applications. Most methods, however, are designed for offline processing rather than processing on the sensor node. In this paper, a human activity recognition technique based on a deep learning methodology is designed to enable accurate and real-time classification for low-power wearable devices. To obtain invariance against changes in sensor orientation, sensor placement, and in sensor acquisition rates, we design a feature generation process that is applied to the spectral domain of the inertial data. Specifically, the proposed method uses sums of temporal convolutions of the transformed input. Accuracy of the proposed approach is evaluated against the current state-of-the-art methods using both laboratory and real world activity datasets. A systematic analysis of the feature generation parameters and a comparison of activity recognition computation times on mobile devices and sensor nodes are also presented.