The ecology of management concepts

How does the popularity of a concept depend on how it contrasts with and complements existing concepts? We argue that being similar to existing concepts, being located in a popular domain, and being combined with similar existing concepts are important for gaining attention early on but less important and even negative for sustaining popularity. To examine this question, we focus on the rise and fall of management concepts. We analyze data on the rise and fall of keywords in the Harvard Business Review between 1922 and 2010. Multiple tests confirm our hypotheses. The implication is that lessons learned from studies of popular concepts can be misleading as guides for how to make novel concepts popular

Comparison of Pixel-based Position Input and Direct Acceleration Input for Virtual Stick Balancing Tests

A virtual stick balancing environment is developed using a computer mouse as input device. The development process is presented both on the hardware and software level. Two possible concepts are suggested to obtain the acceleration of the input device: discrete differentiation of the cursor position measured in pixels on the screen and by direct measurements via an Inertial Measurement Unit (IMU). The comparison of the inputs is carried out with test measurements using a crank mechanism. The measured signals are compared to the prescribed motion of the mechanism and it is shown that the IMU-based input signal fits better to the prescribed motion than the pixel-based input signal. The pixel-based input can also be applied after additional filtering, but this presents an extra computational delay in the feedback loop

The Role of Human Fallibility in Psychological Research:A Survey of Mistakes in Data Management

Errors are an inevitable consequence of human fallibility, and researchers are no exception. Most researchers can recall major frustrations or serious time delays due to human errors while collecting, analyzing, or reporting data. The present study is an exploration of mistakes made during the data-management process in psychological research. We surveyed 488 researchers regarding the type, frequency, seriousness, and outcome of mistakes that have occurred in their research team during the last 5 years. The majority of respondents suggested that mistakes occurred with very low or low frequency. Most respondents reported that the most frequent mistakes led to insignificant or minor consequences, such as time loss or frustration. The most serious mistakes caused insignificant or minor consequences for about a third of respondents, moderate consequences for almost half of respondents, and major or extreme consequences for about one fifth of respondents. The most frequently reported types of mistakes were ambiguous naming/defining of data, version control error, and wrong data processing/analysis. Most mistakes were reportedly due to poor project preparation or management and/or personal difficulties (physical or cognitive constraints). With these initial exploratory findings, we do not aim to provide a description representative for psychological scientists but, rather, to lay the groundwork for a systematic investigation of human fallibility in research data management and the development of solutions to reduce errors and mitigate their impact

Learning Visual Clothing Style with Heterogeneous Dyadic Co-occurrences

With the rapid proliferation of smart mobile devices, users now take millions of photos every day. These include large numbers of clothing and accessory images. We would like to answer questions like `What outfit goes well with this pair of shoes?' To answer these types of questions, one has to go beyond learning visual similarity and learn a visual notion of compatibility across categories. In this paper, we propose a novel learning framework to help answer these types of questions. The main idea of this framework is to learn a feature transformation from images of items into a latent space that expresses compatibility. For the feature transformation, we use a Siamese Convolutional Neural Network (CNN) architecture, where training examples are pairs of items that are either compatible or incompatible. We model compatibility based on co-occurrence in large-scale user behavior data; in particular co-purchase data from Amazon.com. To learn cross-category fit, we introduce a strategic method to sample training data, where pairs of items are heterogeneous dyads, i.e., the two elements of a pair belong to different high-level categories. While this approach is applicable to a wide variety of settings, we focus on the representative problem of learning compatible clothing style. Our results indicate that the proposed framework is capable of learning semantic information about visual style and is able to generate outfits of clothes, with items from different categories, that go well together.Comment: ICCV 201

Measures on the square as sparse graph limits

We study a metric on the set of finite graphs in which two graphs are considered to be similar if they have similar bounded dimensional "factors". We show that limits of convergent graph sequences in this metric can be represented by symmetric Borel measures on [0, 1](2). This leads to a generalization of dense graph limit theory to sparse graph sequences. (C) 2019 Elsevier Inc. All rights reserved

Random walk of second class particles in product shock measures

We consider shock measures in a class of conserving stochastic particle systems on Z. These shock measures have a product structure with a step-like density profile and include a second class particle at the shock position. We show for the asymmetric simple exclusion process, for the exponential bricklayers' process, and for a generalized zero range process, that under certain conditions these shocks, and therefore the second class particles, perform a simple random walk. Some previous results, including random walks of product shock measures and stationary shock measures seen from a second class particle, are direct consequences of our more general theorem. Multiple shocks can also be handled easily in this framework. Similar shock structure is also found in a nonconserving model, the branching coalescing random walk, where the role of the second class particle is played by the rightmost (or leftmost) particle.Comment: Minor changes after referees' comment

SampleSizePlanner:A Tool to Estimate and Justify Sample Size for Two-Group Studies

Planning sample size often requires researchers to identify a statistical technique and to make several choices during their calculations. Currently, there is a lack of clear guidelines for researchers to find and use the applicable procedure. In the present tutorial, we introduce a web app and R package that offer nine different procedures to determine and justify the sample size for independent two-group study designs. The application highlights the most important decision points for each procedure and suggests example justifications for them. The resulting sample-size report can serve as a template for preregistrations and manuscripts

Learning Material-Aware Local Descriptors for 3D Shapes

Material understanding is critical for design, geometric modeling, and analysis of functional objects. We enable material-aware 3D shape analysis by employing a projective convolutional neural network architecture to learn material- aware descriptors from view-based representations of 3D points for point-wise material classification or material- aware retrieval. Unfortunately, only a small fraction of shapes in 3D repositories are labeled with physical mate- rials, posing a challenge for learning methods. To address this challenge, we crowdsource a dataset of 3080 3D shapes with part-wise material labels. We focus on furniture models which exhibit interesting structure and material variabil- ity. In addition, we also contribute a high-quality expert- labeled benchmark of 115 shapes from Herman-Miller and IKEA for evaluation. We further apply a mesh-aware con- ditional random field, which incorporates rotational and reflective symmetries, to smooth our local material predic- tions across neighboring surface patches. We demonstrate the effectiveness of our learned descriptors for automatic texturing, material-aware retrieval, and physical simulation. The dataset and code will be publicly available.Comment: 3DV 201