nils-herrmann/clean_scirepeval huggingface dataset:
Relationship Marketing das Management von Kundenbeziehungen

Die Beziehungsfuhrerschaft zum Kunden ruckt neben den klassischen Wettbewerbsvorteilen, wie der Qualitats und Kostenfuhrerschaft, als strategischer Erfolgsfaktor zunehmend in den Mittelpunkt. Deshalb gewinnt das Relationship Marketing verstanden als Management von Kundenbeziehungen fur Unternehmen an Bedeutung. Das Buch gibt einen umfassenden Uberblick uber die Grundlagen eines effektiven und effizienten Einsatzes des Relationship Marketing fur Unternehmen. Es entwickelt einen systematischen Managementansatz und stellt darauf aufbauend alle notwendigen Phasen des Managements von Kundenbeziehungen ausfuhrlich dar. Schwerpunkte liegen auf den folgenden Aspekten: " Analyse des Relationship Marketing " Strategische Ausrichtung und operative Umsetzung des Relationship Marketing " Implementierung und Kontrolle des Relationship Marketing " Institutionelle Besonderheiten des Relationship Marketing Studierende finden in dem Buch den State of the Art des Relationship Marketing. Praktiker erhalten einen detaillierten Uberblick uber den Managementprozess, zahlreiche Erfolgsbeispiele sowie die wichtigsten Zukunftstendenzen und Herausforderungen des Relationship Marketing. Aktuelle Entwicklungen des Relationship Marketing sind integriert, wie zum Beispiel Social Media Kommunikation oder Customer Experience Management. Ausgezeichnet mit dem Georg Bergler Preis der Gesellschaft fur Konsumforschung (GfK) des Fachverlags Handelsblatt und des Deutschen Marketing Verbands (DMV) fur das beste Marketing Fachbuch durch die gelungene Verbindung von Wissenschaft und Praxis.
Seasonal variation of organic matter concentration and characteristics in the Maji ya Chai River (Tanzania) Impact on treatability by ultrafiltration.
Venue: Water research

Many waters in Tanzania exhibit high concentrations of organic matter and dissolved contaminants such as fluoride. Due to bacteria and virus removal, ultrafiltration (UF) is an attractive option for drinking water treatment, and when coupled with adsorbents, may compete with other established processes like nanofiltration (NF) for lower contaminant concentrations. The results presented here examine the characteristics and treatability of tropical natural organic matter (NOM) by UF as a function of seasonal variation. The Tanzanian river Maji ya Chai was sampled monthly during one year. The composition of NOM in Maji ya Chai River is influenced strongly by precipitation. Total organic carbon (TOC) specific ultraviolet absorbance (SUVA) and concentration of allochthonous organics substances (such as humic substances (HS) are elevated in periods following high precipitation, while TOC is lower and contains more biopolymers in the dry seasons. UF experiments with two regenerated cellulose membranes of different molecular weight cut off (MWCO, 5 and 10 kDa) were conducted. UF is able to remove 50 95% of TOC with a seasonal variability of 10 20% Due to the remaining NOM in the water that would contribute to disinfection by product formation and bacterial regrowth, the physically disinfected water is more applicable for point of use systems than distribution or storage.
Human Movement Detection and Gait Periodicity Analysis Using Channel State Information
Venue: 2016 12th International Conference on Mobile Ad Hoc and Sensor Networks (MSN)

Under the influence of multipath effects and small scale fading, the robustness and reliability of the existing human detection methods based on radio frequency signals are easy to be impaired. In this paper, we propose a novel design that using the multi layer filtering of channel state information (CSI) to identify moving targets in dynamic environments and analyze the gait periodicity of human. We employ an efficient CSI subcarrier feature difference to the multi layer filtering method leveraging principal component analysis (PCA) and discrete wavelet transform (DWT) to eliminate the noises. Furthermore, we propose a profile matching mechanism for human detection and a periodicity analysis mechanism for human gait taking advantage of the above design. We evaluated it with the commodity Wi Fi infrastructures in different environments. Experimental results indicate that our approach performs identification of human with an average accuracy of 94%
The infrared moving object detection and security detection related algorithms based on W4 and frame difference

This paper presents the infrared moving object detection and security detection related algorithms in video surveillance based on the classical W4 and frame difference algorithm. Classical W4 algorithm is one of the powerful background subtraction algorithms applying to infrared images which can accurately, integrally and quickly detect moving object. However, the classical W4 algorithm can only overcome the deficiency in the slight movement of background. The error will become bigger and bigger for long term surveillance system since the background model is unchanged once established. In this paper, we present the detection algorithm based on the classical W4 and frame difference. It cannot only overcome the shortcoming of falsely detecting because of state mutations from background, but also eliminate holes caused by frame difference. Based on these we further design various security detection related algorithms such as illegal intrusion alarm, illegal persistence alarm and illegal displacement alarm. We compare our method with the classical W4, frame difference, and other state of the art methods. Experiments detailed in this paper show the method proposed in this paper outperforms the classical W4 and frame difference and serves well for the security detection related algorithms.
AUTOMATIC BUILDING EXTRACTION AND ROOF RECONSTRUCTION IN 3K IMAGERY BASED ON LINE SEGMENTS

We propose an image processing workflow to extract rectangular building footprints using georeferenced stereo imagery and a derivative digital surface model (DSM) product. The approach applies a line segment detection procedure to the imagery and subsequently verifies identified line segments individually to create a footprint on the basis of the DSM. The footprint is further optimized by morphological filtering. Towards the realization of 3D models, we decompose the produced footprint and generate a 3D point cloud from DSM height information. By utilizing the robust RANSAC plane fitting algorithm, the roof structure can be correctly reconstructed. In an experimental part, the proposed approach has been performed on 3K aerial imagery.
Surface reconstruction from image space adjacency of lines using breadth first plane search
Venue: 2016 IEEE International Conference on Robotics and Automation (ICRA)

In this paper, we propose a novel multi view method for surface reconstruction from matched line segments with applications to robotic mapping and image based rendering. Starting from 3D line segments and poses obtained via Line SLAM, we project segments from multiple frames into keyframes for image space analysis. For each keyframe, a grid of image faces is created by optimized intersection of the segment projection lines. These faces define a segment adjacency graph, wherein we perform our Breadth First Plane Search (BFPS) The found plane hypotheses are merged maximally with respect to a structure preserving criterion by growing coplanar regions across the graph. Hypotheses violating the visibility constraint are discarded based on fast per face and mostly nongeometrical evaluation of the scene and image graph. Finally, each image face gets back projected onto an optimal plane to obtain a 3D surface model. The presented system is a complete and automatic solution suitable for mapping an environment in real time scenarios like robotic exploration. We demonstrate the performance of our algorithm on several indoor scenes of varying complexity. Compared to a pure 3D analysis of segments, we see a speed up by one to almost two orders of magnitude, while still improving on reconstruction accuracy.
Comparison Between Two Generic 3d Building Reconstruction Approaches Point Cloud Based VS. Image Processing Based

This paper compares two generic approaches for the reconstruction of buildings. Synthesized and real oblique and vertical aerial imagery is transformed on the one hand into a dense photogrammetric 3D point cloud and on the other hand into photogrammetric 2.5D surface models depicting a scene from different cardinal directions. One approach evaluates the 3D point cloud statistically in order to extract the hull of structures, while the other approach makes use of salient line segments in 2.5D surface models, so that the hull of 3D structures can be recovered. With orders of magnitudes more analyzed 3D points, the point cloud based approach is an order of magnitude more accurate for the synthetic dataset compared to the lower dimensioned, but therefor orders of magnitude faster, image processing based approach. For real world data the difference in accuracy between both approaches is not significant anymore. In both cases the reconstructed polyhedra supply information about their inherent semantic and can be used for subsequent and more differentiated semantic annotations through exploitation of texture information.
BLACK BOX VARIATIONAL INFERENCE FOR STATE SPACE MODELS

Latent variable time series models are among the most heavily used tools from machine learning and applied statistics. These models have the advantage of learning latent structure both from noisy observations and from the temporal ordering in the data, where it is assumed that meaningful correlation structure exists across time. A few highly structured models, such as the linear dynamical system with linear Gaussian observations, have closed form inference procedures (e.g. the Kalman Filter) but this case is an exception to the general rule that exact posterior inference in more complex generative models is intractable. Consequently, much work in time series modeling focuses on approximate inference procedures for one particular class of models. Here, we extend recent developments in stochastic variational inference to develop a `black box' approximate inference technique for latent variable models with latent dynamical structure. We propose a structured Gaussian variational approximate posterior that carries the same intuition as the standard Kalman filter smoother but, importantly, permits us to use the same inference approach to approximate the posterior of much more general, nonlinear latent variable generative models. We show that our approach recovers accurate estimates in the case of basic models with closed form posteriors, and more interestingly performs well in comparison to variational approaches that were designed in a bespoke fashion for specific non conjugate models.
Health insurance coverage early release of estimates from the National Health Interview Survey, 2015

This report from the National Center for Health Statistics (NCHS) presents selected estimates of health insurance coverage for the civilian noninstitutionalized U.S. population based on data from the 2015 National Health Interview Survey (NHIS) along with comparable estimates from the 2010 2014 NHIS. Estimates for 2015 are based on data for 103,798 persons. Three estimates of lack of health insurance coverage are provided: (a) uninsured at the time of interview, (b) uninsured at least part of the year prior to interview (which includes persons uninsured for more than a year) and (c) uninsured for more than a year at the time of interview. Estimates of public and private coverage, coverage through exchanges, and enrollment in highdeductible health plans (HDHPs) and consumer directed health plans (CDHPs) are also presented. Detailed tables show estimates by selected demographics. Definitions are provided in the Technical Notes at the end of this report. This report is updated quarterly and is part of the NHIS Early Release (ER) Program, which releases updated selected estimates that are available from the NHIS website at http:/www.cdc.gov/nchs/nhis.htm. Estimates for each calendar quarter, by selected demographics, are also available as a separate set of tables through the ER Program. For more information about NHIS and the ER Program, see the Technical Notes and the Additional Early Release Program Products sections at the end of this report.
Unclonable Plasmonic Security Labels Achieved by Shadow Mask Lithography Assisted Self Assembly.
Venue: Advanced materials

An unclonable plasmonic anti counterfeiting strategy is demonstrated, which involves the use of molecule embedded metal@silica core shell nanoparticles as information carriers. A shadow mask lithography assisted self assembly is developed for the fabrication of the plasmonic security labels. The produced security labels show multiple sets of coding information that are highly unique, technically unreplicable, and can be robustly decoded by portable microscopes within seconds.
Mitigation of mask three dimensional induced phase effects by absorber optimization in ArFi and extreme ultraviolet lithography

We will summarize our work on mask topography induced effects over the last 5 years. We will give a full physical explanation of the effects that can be observed from exposed wafers in state of the art immersion and extreme ultraviolet photolithography. The mask topography induced phase leads to vertical and lateral displacements of the aerial image, resulting in feature dependent best focus and position. The feature dependency has been studied for gratings through pitch and size and for two trench arrangements. The physical explanation involves the analysis and quantification of phase effects in a similar way as was done for projection lens aberrations one decade ago. Phase effects, derived both from rigorous simulations and an analytical model, will be compared with exposure figure or merits (e.g. best focus per feature) and correlate well. Therefore, the analysis of mask topography induced phase and the reduction thereof by absorber thickness optimization can be used to drive lithography improvements.
DNA Origami Mask for Sub Ten Nanometer Lithography.
Venue: ACS nano

DNA nanotechnology is currently widely explored and especially shows promises for advanced lithography due to its ability to define nanometer scale features. We demonstrate a 9 x 14 nm(2) hole pattern transfer from DNA origami into an SiO2 layer with a sub 10 nm resolution using anhydrous HF vapor in a semiconductor etching machine. We show that the resulting SiO2 pattern inherits its shape from the DNA structure within a process time ranging from 30 to 60 s at an etching rate of 0.2 nm/s. At 600 s of etching, the SiO2 pattern meets corrosion and the overall etching reaction is blocked. These results, in addition to the entire surface coverage by magnesium occurring on the substrate at a density of 1.1 x 10(15) atom/cm(2) define a process window, fabrication rules, and limits for DNA based lithography.
Disordered nanostructures by hole mask colloidal lithography for advanced light trapping in silicon solar cells.
Venue: Optics express

We report on the fabrication of disordered nanostructures by combining colloidal lithography and silicon etching. We show good control of the short range ordered colloidal pattern for a wide range of bead sizes from 170 to 850 nm. The inter particle spacing follows a Gaussian distribution with the average distance between two neighboring beads (center to center) being approximately twice their diameter, thus enabling the nanopatterning with dimensions relevant to the light wavelength scale. The disordered nanostructures result in a lower integrated reflectance (8.1% than state of the art random pyramid texturing (11.7% when fabricated on 700 um thick wafers. When integrated in a 1.1 um thin crystalline silicon slab, the absorption is enhanced from 24.0% up to 64.3% The broadening of resonant modes present for the disordered nanopattern offers a more broadband light confinement compared to a periodic nanopattern. Owing to its simplicity, versatility and the degrees of freedom it offers, this potentially low cost bottom up nanopatterning process opens perspectives towards the integration of advanced light trapping schemes in thin solar cells.
Is increased antidepressant exposure a contributory factor to the obesity pandemic?
Venue: Translational Psychiatry

Major depressive disorder (MDD) and obesity are both common heterogeneous disorders with complex aetiology, with a major impact on public health. Antidepressant prescribing has risen nearly 400% since 1988, according to data from the Centers for Disease Control and Prevention (CDC) In parallel, adult obesity rates have doubled since 1980, from 15 to 30 percent, while childhood obesity rates have more than tripled. Rising obesity rates have significant health consequences, contributing to increased rates of more than thirty serious diseases. Despite the concomitant rise of antidepressant use and of the obesity rates in Western societies, the association between the two, as well as the mechanisms underlying antidepressant induced weight gain, remain under explored. In this review, we highlight the complex relationship between antidepressant use, MDD and weight gain. Clinical findings have suggested that obesity may increase the risk of developing MDD, and vice versa. Hypothalamic pituitary adrenal (HPA) axis activation occurs in the state of stress; concurrently, the HPA axis is also dysregulated in obesity and metabolic syndrome, making it the most well understood shared common pathophysiological pathway with MDD. Numerous studies have investigated the effects of different classes of antidepressants on body weight. Previous clinical studies suggest that the tricyclics amitriptyline, nortriptyline and imipramine, and the serotonin norepinephrine reuptake inhibitor mirtazapine are associated with weight gain. Despite the fact that selective serotonin reuptake inhibitor (SSRI) use has been associated with weight loss during acute treatment, a number of studies have shown that SSRIs may be associated with long term risk of weight gain; however, because of high variability and multiple confounds in clinical studies, the long term effect of SSRI treatment and SSRI exposure on body weight remains unclear. A recently developed animal paradigm shows that the combination of stress and antidepressants followed by long term high fat diet results, long after discontinuation of antidepressant treatment, in markedly increased weight, in excess of what is caused by high fat diet alone. On the basis of existing epidemiological, clinical and preclinical data, we have generated the testable hypothesis that escalating use of antidepressants, resulting in high rates of antidepressant exposure, might be a contributory factor to the obesity epidemic.
Media coverage and hospital notifications: Correlation analysis and optimal media impact duration to manage a pandemic.
Venue: Journal of theoretical biology

News reporting has the potential to modify a community's knowledge of emerging infectious diseases and affect peoples' attitudes and behavior. Here we developed a quantitative approach to evaluate the effects of media on such behavior. Statistically significant correlations between the number of new hospital notifications, during the 2009 A/H1N1 influenza epidemic in the Shaanxi province of China, and the number of daily news items added to eight major websites were found from Pearson correlation and cross correlation analyses. We also proposed a novel model to examine the implication for transmission dynamics of these correlations. The model incorporated the media impact function into the intensity of infection, and enhanced the traditional epidemic SEIR model with the addition of media dynamics. We used a nonlinear least squares estimation to identify the best fit parameter values in the model from the observed data. We also carried out the uncertainty and sensitivity analyses to determine key parameters during early phase of the disease outbreak for the final outcome of the outbreak with media impact. The findings confirm the importance of responses by individuals to the media reports, with behavior changes having important consequence for the emerging infectious disease control. Therefore, for mitigating emerging infectious diseases, media reports should be focused on how to guide people's behavioral changes, which are critical for limiting the spread of disease.
resilience and melancholy: pop music, feminism, neoliberalism

When critical theorists like Theodor Adorno and Max Horkheimer diagnosed the relationship between popular music and the broader social world, they argued that the lack of dissonance in mainstream songs reflected larger social pressures to conform, adjust and submit to the status quo. In her fascinating book Resilience and Melancholy, philosopher Robin James remixes this connection between music, society and power. According to James, contemporary music actually relies on noise and dissonance as resources for productivity, as raw materials for the reproduction of social life. In other words, contemporary songs 'incite damage for the purpose of recycling it. Noisemaking is the means of musical, cultural and social production' (p. 6) This trend of making noise useful and productive is emblematic of neo liberalism's demand to be resilient in the face of trauma and loss. As James puts it, 'resilience discourse is what ties contemporary pop music aesthetics to neoliberal capitalism and racism/sexism' (p. 6) While previous systems of power encouraged selves to avoid damage, neo liberalism or what she calls 'multi racial white supremacist patriarchy' instructs us to name, demonstrate and (visibly) overcome various forms of damage. This ability to overcome loss, especially by marginalised communities and individuals, is 'rewarded with increased human capital, status, and other forms of recognition and recompense, because this individual's own resilience boosts society's resilience' (p. 7) Throughout this exhilarating text, James provides provocative readings of songs and videos that perform the resiliency imperative. At the same time, she cites and interprets songs and artists that refuse to turn noise, death and loss into something profitable.
Topology Underlying GIBSON'S Echological Theory of Perception

During the exploration of the surrounding environment, the brain links together external inputs in a complex of sensations, giving rise to the perception of a persisting object. During imaginative processes, the same object can be recalled in mind even if it is out of sight. Here, Borsuk's theory of shape and the Borsuk Ulam theorem provide a mathematical foundation for Gibson's notion of persistence perception. Real scene visual signals are collectively the umbra of physical shapes impinging on the optic nerves, which we map to similar shape representations. We show how Gibson's ecological theory of perception accounts for our knowledge of world objects by borrowing a concept of invariance in topology. A series of transformations can be endlessly and gradually applied to a pattern, in particular to the shape of an object, without affecting its invariant properties, such as connectedness and boundedness of parts of a visual scene. In sum, high level representations of objects in our environment are mapped to simplified views (our interpretations) of the objects, in order to construct a symbolic representation of the environment. The fact that our perception of an object continues even when it is out of sight, can be explained by viewing regions on a sphere surface as multiple representations of object shapes. The representations can be projected continuously to an ecological object that we have seen and continue to see, thanks to the mapping from shapes in our memory to shapes in Euclidean space.