Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Class Probabilities to Map Gradual Transitions in Shrub Vegetation from Simulated EnMAP Data

Monitoring natural ecosystems and ecosystem transitions is crucial for a better understanding of land change processes. By providing synoptic views in space and time, remote sensing data have proven to be valuable sources for such purposes. With the forthcoming Environmental Mapping and Analysis Program (EnMAP), frequent and area-wide mapping of natural environments by means of high quality hyperspectral data becomes possible. However, the amplified spectral mixing due to the sensor’s ground sampling distance of 30 m on the one hand and the patterns of natural landscapes in the form of gradual transitions between different land cover types on the other require special attention. Based on simulated EnMAP data, this study focuses on mapping shrub vegetation along a landscape gradient of shrub encroachment in a semi-arid, natural environment in Portugal. We demonstrate how probability outputs from a support vector classification (SVC) model can be used to extend a hard classification by information on shrub cover fractions. This results in a more realistic representation of gradual transitions in shrub vegetation maps. We suggest a new, adapted approach for SVC parameter selection: During the grid search, parameter pairs are evaluated with regard to the prediction of synthetically mixed test data, representing shrub to non-shrub transitions, instead of the hard classification of original, discrete test data. Validation with an unbiased, equalized random sampling shows that the resulting shrub-class probabilities from adapted SVC more accurately represent shrub cover fractions (mean absolute error/root mean squared error of 16.3%/23.2%) compared to standard SVC (17.1%/29.5%). Simultaneously, the discrete classification output was considerably improved by incorporating synthetic mixtures into parameter selection (averaged F1 accuracies increased from 72.4% to 81.3%). Based on our findings, the integration of synthetic mixtures into SVC parameterization allows the use of SVC for sub-pixel cover fraction estimation and, this way, can be recommended for deriving improved qualitative and quantitative descriptions of gradual transitions in shrub vegetation. The approach is therefore of high relevance for mapping natural ecosystems from future EnMAP data.
Applying transformational leadership theory to coaching research in youth sport: A systematic literature review

There is growing recognition that Transformational Leadership theory (TFL) holds significant potential for coaching research in youth sport. While the existing literature offers insight into the types of athlete outcomes that are associated with TFL, studies evaluating how these outcomes can be acquired are limited. The purpose of the present study was to synthesise and integrate research across a variety of disciplines (e.g. organisational psychology, health care and promotion, education, and sport and exercise psychology) examining the processes by which TFL influences followers’ (i.e. employees, students, patients, athletes, etc.) psychosocial development. A systematic search was conducted of six electronic databases covering a wide range of disciplines. Peer-reviewed, original studies published in English were included in this review. The initial search yielded 2077 papers, of which 151 met the selection criteria and were retained for analysis. A descriptive, content analysis-based approach was used to assess emerging patterns in research design and study findings. Results revealed numerous processes at the intrapersonal, interpersonal, and environmental levels that contributed to the relationships between TFL and follower development. A conceptual model of these processes is proposed, along with recommendations for future coaching research in youth sport.
Effect of Syzygium cumini and Bauhinia forficata aqueous-leaf extracts on oxidative and mitochondrial parameters in vitro

Aqueous-leaf extract of Syzygium cumini and Bauhinia forficata are traditionally used in the treatment of diabetes and cancer, especially in South America, Africa, and Asia. In this study, we analyzed the effects of these extracts on oxidative and mitochondrial parameters in vitro, as well as their protective activities against toxic agents. Phytochemical screenings of the extracts were carried out by HPLC analysis. The in vitro antioxidant capacities were compared by DPPH radical scavenging and Fe2+ chelating activities. Mitochondrial parameters observed were swelling, lipid peroxidation and dehydrogenase activity. The major chemical constituent of S. cumini was rutin. In B. forficata were predominant quercetin and gallic acid. S. cumini reduced DPPH radical more than B. forficata, and showed iron chelating activity at all tested concentrations, while B. forficata had not similar property. In mitochondria, high concentrations of B. forficata alone induced a decrease in mitochondrial dehydrogenase activity, but low concentrations of this extract prevented the effect induced by Fe2++H2O2. This was also observed with high concentrations of S. cumini. Both extracts partially prevented the lipid peroxidation induced by Fe2+/citrate. S. cumini was effective against mitochondrial swelling induced by Ca2+, while B. forficata alone induced swelling more than Ca2+. This study suggests that leaf extract of S. cumini might represent a useful therapeutic for the treatment of diseases related with mitochondrial dysfunctions. On the other hand, the consumption of B. forficata should be avoided because mitochondrial damages were observed, and this possibly may pose risk to human health.
Gender recognition on real world faces based on shape representation and neural network

Gender as a soft biometric attribute has been extensively investigated in the domain of computer vision because of its numerous potential application areas. However, studies have shown that gender recognition performance can be hindered by improper alignment of facial images. As a result, previous experiments have adopted face alignment as an important stage in the recognition process, before performing feature extraction. In this paper, the problem of recognizing human gender from unaligned real world faces using single image per individual is investigated. The use of feature descriptor to form shape representation of face images with any arbitrary orientation from the cropped version of Labeled Faces in the Wild (LFW) dataset is proposed. By combining the feature extraction technique with artificial neural network for classification, a recognition rate of 89.3% is attained.
Mechanical Properties of Organelles Driven by Microtubule-Dependent Molecular Motors in Living Cells

The organization of the cytoplasm is regulated by molecular motors which transport organelles and other cargoes along cytoskeleton tracks. Melanophores have pigment organelles or melanosomes that move along microtubules toward their minus and plus end by the action of cytoplasmic dynein and kinesin-2, respectively. In this work, we used single particle tracking to characterize the mechanical properties of motor-driven organelles during transport along microtubules. We tracked organelles with high temporal and spatial resolutions and characterized their dynamics perpendicular to the cytoskeleton track. The quantitative analysis of these data showed that the dynamics is due to a spring-like interaction between melanosomes and microtubules in a viscoelastic microenvironment. A model based on a generalized Langevin equation explained these observations and predicted that the stiffness measured for the motor complex acting as a linker between organelles and microtubules is ∼ one order smaller than that determined for motor proteins in vitro. This result suggests that other biomolecules involved in the interaction between motors and organelles contribute to the mechanical properties of the motor complex. We hypothesise that the high flexibility observed for the motor linker may be required to improve the efficiency of the transport driven by multiple copies of motor molecules.
Splashing on elastic membranes: The importance of early-time dynamics

We study systematically the effect of substrate compliance on the threshold for splashing of a liquid drop using an elastic membrane under variable tension. We find that the splashing behavior is strongly affected by the tension in the membrane and splashing can be suppressed by reducing this tension. The deflection of the membrane upon droplet impact is measured using a laser sheet, and the results allow us to estimate the energy absorbed by the film upon drop impact. Measurements of the velocity and acceleration of the spreading drop after impact indicate that the splashing behavior is set at very early times after, or possibly just before, impact, far before the actual splash occurs. We also provide a model for the tension dependence of the splashing threshold based on the pressure in the drop upon impact that takes into account the interplay between membrane tension and drop parameters.
Differential Expression of Enzymes Associated with Serine/Glycine Metabolism in Different Breast Cancer Subtypes

Purpose Glycine and serine are well-known, classic metabolites of glycolysis. Here, we profiled the expression of enzymes associated with serine/glycine metabolism in different molecular subtypes of breast cancer and discuss their potential clinical implications. Methods We used western blotting and immunohistochemistry to examine five serine-/glycine-metabolism–associated proteins (PHGDH, PSAT, PSPH, SHMT, and GLDC) in six breast cancer cell lines and 709 breast cancer cases using tissue microarray (TMA). Results PHGDH and PSPH, associated with serine metabolism, were highly expressed in the TNBC cells. GLDC, associated with glycine metabolism, was highly expressed in HER-2-positive MDA-MB-453 and TNBC-related MDA-MB-435S. TMA showed that the TNBC-type breast cancer tissues highly expressed PHGDH, PSPH, and SHMT1, but not the luminal-A-type tissues (p<0.001). PSPH and SHMT1 expression in the tumor stroma of HER-2-type cancers was the highest, but the luminal-A tissues showed the lowest expression (p<0.001). GLDC was most frequently expressed in cancer cells and stroma of the HER-2-positive cancers and least frequently in TNBC (p<0.001). By Cox multivariate analysis, tumor PSPH positivity (hazard ratio [HR]: 2.068, 95% confidence interval [CI]: 1.049–4.079, p = 0.036), stromal PSPH positivity (HR: 2.152, 95% CI: 1.107–4.184, p = 0.024), and stromal SHMT1 negativity (HR: 2.142, 95% CI: 1.219–3.764, p = 0.008) were associated with short overall survival. Conclusions Expression of serine-metabolism–associated proteins was increased in TNBC and decreased in the luminal-A cancers. Expression of glycine-metabolism–associated proteins was high in the tumor and stroma of HER-2-positive cancers.
Leakage grading of inputs to CMOS logic

Supply voltages and threshold voltages continue t o be aggressively scaled down in order t o obtain power reduction, performance improvement, and increasing integration density. This leads to leakage current becoming a much more significant component of power than it has been in the past. We have previously shown that substantial leakage reduction can be achieved in single Vt circuits by turning off stitcks of transistors. A theoretical model was also derived which predicts the quiescent leakage current and the idle time required to reach quiescent levels. In this report, we will review the leakage estimation model, outline a method for evaluating the leakage assocated with an input vector, and use the model to identify inputs which minimize leakage in a variety of test cases.
Introduction to Factor Analysis

The factor loadings k are parameters to be estimated that tap how the unobserved factors account for the observed variables: the larger the values of k, the more a particular variable is said to ‘‘load’’ on the corresponding factor. Note that the factor loadings k vary across survey items, but not across individuals. Put differently, items vary in the way they are explained by the underlying factors, but the relationships between underlying factors and observed responses is constant across individuals (hence the absence of an i subscript indexing k). Note also that there are fewer underlying factors than there are variables (p < k), consistent with the notion that like any statistical procedure, factor analysis is a device for ‘‘data reduction’’, taking a possibly rich though unwieldy set of survey responses and summarizing them with a simpler underlying structure.
On Probability Estimation via Relative Frequencies and Discount

Probability estimation is an elementary building block of every statistical data compression algorithm. In practice probability estimation is often based on relative letter frequencies which get scaled down, when their sum is too large. Such algorithms are attractive in terms of memory requirements, running time and practical performance. However, there still is a lack of theoretical understanding. In this work we formulate a typical probability estimation algorithm based on relative frequencies and frequency discount, Algorithm RFD. Our main contribution is its theoretical analysis. We show that Algorithm RFD performs almost as good as any piecewise stationary model with either bounded or unbounded letter probabilities. This theoretically confirms the recency effect of periodic frequency discount, which has often been observed empirically.
A Theory of Load Adjustments and its Implications for Congestion Control

Multiplicative Increase (MI), Additive Increase (AI), and Multiplicative Decrease (MD) are linear adjustments used extensively in networking. However, their properties are not fully understood. We analyze responsiveness (time for the total load to reach the target load), smoothness (maximal size of the total load oscillations aft er reaching the target load), fairing speed (speed of convergence to equal individual loads) and scalabilities of MAIMD algorithms, which generalize AIMD algorithms via optional inclusion of MI. We prove that an MAIMD can provide faster asymptotic fairing than a less smooth AIMD. Furthermore, we discover that loads under a specific M AIMD converge from any initial state to the same periodic patt ern, called a canonical cycle. While imperfectly correlated with smoothness, the canonical cycle reliably predicts the asymptotic fairing speed. We also show that AIMD algorithms offe r the best trade-off be tween smoothness and responsiveness. The n, we introduce smoothness-responsiveness diagrams to investigate MAIMD scalabilities. Finally, we discuss implications of the theory for the practice of congestion control. Read complete abstract on page 2.
Centrality for graphs with numerical attributes

Identification of important actors in social networks is a hard task but with various interesting applications such as in information recommendation or for viral marketing. Existing centrality measures evaluate the importance of an actor in considering only the structural positions regardless of prior information on these actors such as their popularity, accessibility or behavior. A few measures have been proposed for weighted networks, notably the three common measures of centrality: degree, closeness, and betweenness. However, these extended versions have solely focused on the weights of ties and not on the attributes of nodes. This article proposes generalizations that combine these both aspects. We present a set of measures, based on conventional centrality indicators, suited to weighted attributed graphs where the nodes are characterized by attributes. We illustrate the benefits of this approach on real attributed graphs. Experiments have validated the contribution of the links weights and attributes, especially for the detection of information broadcasters in social networks.
Context selection for linguistic data fusion

Reports generated by soldiers are common in time-critical military environments. Data fusion systems that attempt to process those reports must maintain the context for each set of observations to avoid inaccurate state estimates. This paper analyzes the selection and assignment of topical context under a Bayesian methodology. We present several techniques to decrease the hypothesis space and heuristics that apply specifically to military reporting environments. Using a data set consisting of semi-structured reports, we show that this approach allows accurate assignment of topical context even when the context is only implied rather than given explicitly.
The z = 0 Galaxy Luminosity Function. I. Techniques for Identification of Dwarf Galaxies at ∼10 Mpc

We present a program to study the galaxy luminosity function of the Leo I and Coma I Groups at ∼10 Mpc. We have surveyed over 7 deg2 in Leo I and ∼11 deg2 in Coma I. In this paper, we detail the method we have developed and implemented for identifying on morphological grounds low surface brightness, MR < -10, dwarf galaxies at a distance of 10 Mpc. We also describe extensive Monte Carlo simulations of artificial galaxies, which we use to tune our detection algorithms and evaluate our detection efficiency and parameter recovery as a function of R-band central surface brightness μR(0) and total magnitude RT. We find for a subset of our Leo I data that at the 90% completeness level, we can detect dwarfs comparable to Antlia and Sculptor. Finally, we describe preliminary follow-up observations that confirm we are detecting dwarf spheroidal galaxies in Leo I at 10 Mpc.
Genetic Deletion of the Desmosomal Component Desmoplakin Promotes Tumor Microinvasion in a Mouse Model of Pancreatic Neuroendocrine Carcinogenesis

We used the RIP1-Tag2 (RT2) mouse model of islet cell carcinogenesis to profile the transcriptome of pancreatic neuroendocrine tumors (PNET) that were either non-invasive or highly invasive, seeking to identify pro- and anti-invasive molecules. Expression of multiple components of desmosomes, structures that help maintain cellular adhesion, was significantly reduced in invasive carcinomas. Genetic deletion of one of these desmosomal components, desmoplakin, resulted in increased local tumor invasion without affecting tumor growth parameters in RT2 PNETs. Expression of cadherin 1, a component of the adherens junction adhesion complex, was maintained in these tumors despite the genetic deletion of desmoplakin. Our results demonstrate that loss of desmoplakin expression and resultant disruption of desmosomal adhesion can promote increased local tumor invasion independent of adherens junction status.
A Wireless Secure Remote Access Architecture Implementing Role Based Access Control: WiSeR

— In this study, we propose a network architecture for providing secure access to information resources of enterprise network from remote locations in a wireless fashion. Our proposed architecture offers a very promising solution for organizations which are in need of a secure, flexible and cost-effective remote access methodology. Security of the proposed architecture is based on Virtual Private Network technology and a special role based access control mechanism with location and time constraints. The flexibility mainly comes from the use of Internet as the communication medium and cost-effectiveness is due to the possibility of in-house implementation of the proposed architecture.
On combining i-vectors and discriminative adaptation methods for unsupervised speaker normalization in DNN acoustic models

In automatic speech recognition (ASR), adaptation and adaptive training techniques are used to perform speaker normalization. Previous methods mainly focus on using these techniques in isolation. In contrast, this paper investigates two approaches to improve the ASR performance by combining i-vector based speaker adaptive training in deep neural network (DNN) acoustic models with discriminative adaptation techniques. First, we combine these techniques by interpolating the decoding lattices of i-vector based systems with the decoding lattices of a discriminatively adapted model. Then, we combine these methods by discriminatively adapting the i-vector based system in unsupervised fashion. Our experiments on TED-LIUM dataset show that compared with a strong speaker independent baseline, lattice interpolation and adaptation of the i-vector systems achieve 12.0% and 15.6% relative improvements, respectively. Moreover, in comparison to the i-vector based systems, lattice interpolation reported a 4.5% relative improvement while discriminatively adapting the i-vector system reported a 8.3% relative improvement.
Comparing design of experiments and evolutionary approaches to multi-objective optimisation of sensornet protocols

The lifespan, and hence utility, of sensornets is limited by the energy resources of individual motes. Network designers seek to maximise energy efficiency while maintaining an acceptable network Quality of Service. However, the interactions between multiple tunable protocol parameters and multiple sensornet performance metrics are generally complex and unknown. In this paper we address this multi-dimensional optimisation problem by two distinct approaches. Firstly, we apply a Design Of Experiments approach to obtain a generalised linear interaction model, and from this derive an estimated near-optimal solution. Secondly, we apply the Two-Archive evolutionary algorithm to improve solution quality for a specific problem instance. We demonstrate that, whereas the first approach yields a more generally applicable solution, the second approach yields a broader range of viable solutions at potentially lower experimental cost.
User Interface for Volume Rendering in Virtual Reality Environments

Volume Rendering applications require sophisticated user interaction for the definition and refinement of transfer functions. Traditional 2D desktop user interface elements have been developed to solve this task, but such concepts do not map well to the interaction devices available in Virtual Reality environments. In this paper, we propose an intuitive user interface for Volume Rendering specifically designed for Virtual Reality environments. The proposed interface allows transfer function design and refinement based on intuitive two-handed operation of Wand-like controllers. Additional interaction modes such as navigation and clip plane manipulation are supported as well. The system is implemented using the Sony PlayStation Move controller system. This choice is based on controller device capabilities as well as application and environment constraints. Initial results document the potential of our approach.
Interdisziplinäres Problembasiertes Lernen - Kompetenzen fördern, Zukunft gestalten

In einer Kombination aus PBL und Interdisziplinarem Lernen arbeiten im Lehr-Lern-Format Interdisziplinares Problembasiertes Lernen ( iPBL ) Studierende in interdisziplinar zusammengesetzten Gruppen an gesellschaftlichen Schlusselproblemen mit Hilfe von acht Analyse-Schritten. Evaluationsergebnisse weisen darauf hin, dass Studierende im Vergleich zu klassischen Lehr-Lern-Formaten hohere Fach-, Methoden-, Kommunikations- und Personalkompetenzen entwickeln. Geeignete Tutor-Formen werden vorgestellt und evaluiert. 13.05. 2016 | Mirjam Brasler & Jan Dettmers (Hamburg)
Supervised scaled regression clustering: an alternative to neural networks

Describes a method for the supervised training of regression systems that can be an alternative to feedforward artificial neural networks (ANNs) trained with the backpropagation algorithm. The proposed methodology is a hybrid structure based on supervised clustering with genetic algorithms and local learning. Supervised scaled regression clustering with genetic algorithms (SSRCGA) offers certain advantages related to robustness, generalization performance, feature selection, explanative behavior, and the additional flexibility of defining the fitness function and the regularization constraints. Computational results of SSRCGA are compared with backpropagation trained ANNs on a real-life environmental multivariate regression task.