Content-adaptive lenticular prints

Lenticular prints are a popular medium for producing automultiscopic glasses-free 3D images. The light field emitted by such prints has a fixed spatial and angular resolution. We increase both perceived angular and spatial resolution by modifying the lenslet array to better match the content of a given light field. Our optimization algorithm analyzes the input light field and computes an optimal lenslet size, shape, and arrangement that best matches the input light field given a set of output parameters. The resulting emitted light field shows higher detail and smoother motion parallax compared to fixed-size lens arrays. We demonstrate our technique using rendered simulations and by 3D printing lens arrays, and we validate our approach in simulation with a user study

Wearable Bluetooth Sensors for Capturing Relational Variables and Temporal Variability in Relationships: A Construct Validation Study

The advent of wearable sensor technologies has the potential to transform organizational research by offering the unprecedented opportunity to collect continuous, objective, highly granular data over extended time periods. Recent evidence has demonstrated the potential utility of Bluetooth-enabled sensors, specifically, in identifying emergent networks via colocation signals in highly controlled contexts with known distances and groups. Although there is proof of concept that wearable Bluetooth sensors may be able to contribute to organizational research in highly controlled contexts, to date there has been no explicit psychometric construct validation effort dedicated to these sensors in field settings. Thus, the two studies described here represent the first attempt to formally evaluate longitudinalBluetooth data streams generated in field settings, testing their ability to (a) show convergent validity with respect to traditional self-reports of relational data; (b) display discriminant validitywith respect to qualitative differences in the nature of alternative relationships (i.e., advice vs. friendship); (c) document predictive validity with respect to performance; (d) decompose variance in network-related measures into meaningful within- and between-unit variability over time; and (e) complement retrospective self-reports of time spent with different groups where there is a “ground truth” criterion. Our results provide insights into the validity of Bluetooth signals with respect to capturing variables traditionally studied in organizational science and highlight how the continuous data collection capabilities made possible by wearable sensors can advance research far beyond that of the static perspectives imposed by traditional data collection strategies

Interactive effects of multiple organizational climates on employee innovative behavior in entrepreneurial firms: A cross-level investigation

Given that organizations need to manage complex situations, multiple organizational climates can coexist and these climates can jointly influence employee behaviors. However, the mechanisms through which the latter relationships operate are poorly understood. We take a multilevel approach to examine the mechanisms that link organizational innovative climate and employee innovative behavior, and the moderating effects of organizational proactive and risk-taking climates on these relationships. Using multisource data from 105 managers and 39 CEOs, we found that innovative climate was positively related to employee innovative behavior indirectly through employee passion for inventing. In addition, the relationship between innovative climate and passion for inventing became stronger as proactive climate increased, and the relationship between passion for inventing and employee innovative behavior became stronger as risk-taking climate increased. Our study contributes to entrepreneurial research by highlighting the interactive effects of multiple organizational climates on employee innovative behavior

Wearable Bluetooth Sensors for Capturing Relational Variables and Temporal Variability in Relationships: A Construct Validation Study

The advent of wearable sensor technologies has the potential to transform organizational research by offering the unprecedented opportunity to collect continuous, objective, highly granular data over extended time periods. Recent evidence has demonstrated the potential utility of Bluetooth-enabled sensors, specifically, in identifying emergent networks via colocation signals in highly controlled contexts with known distances and groups. Although there is proof of concept that wearable Bluetooth sensors may be able to contribute to organizational research in highly controlled contexts, to date there has been no explicit psychometric construct validation effort dedicated to these sensors in field settings. Thus, the two studies described here represent the first attempt to formally evaluate longitudinalBluetooth data streams generated in field settings, testing their ability to (a) show convergent validity with respect to traditional self-reports of relational data; (b) display discriminant validitywith respect to qualitative differences in the nature of alternative relationships (i.e., advice vs. friendship); (c) document predictive validity with respect to performance; (d) decompose variance in network-related measures into meaningful within- and between-unit variability over time; and (e) complement retrospective self-reports of time spent with different groups where there is a “ground truth” criterion. Our results provide insights into the validity of Bluetooth signals with respect to capturing variables traditionally studied in organizational science and highlight how the continuous data collection capabilities made possible by wearable sensors can advance research far beyond that of the static perspectives imposed by traditional data collection strategies.This article is published as Matusik, J. G., Heidl, R., Hollenbeck, J. R., Yu, A., Lee, H. W., & Howe, M. (2019). Wearable bluetooth sensors for capturing relational variables and temporal variability in relationships: A construct validation study. Journal of Applied Psychology, 104(3), 357-387. Doi: 10.1037/apl0000334. Posted with permission. </p

A compiler for 3D machine knitting

© 2016 Copyright held by the owner/author(s). Publication rights licensed to ACM. Industrial knitting machines can produce finely detailed, seamless, 3D surfaces quickly and without human intervention. However, the tools used to program them require detailed manipulation and understanding of low-level knitting operations. We present a compiler that can automatically turn assemblies of high-level shape primitives (tubes, sheets) into low-level machine instructions. These high-level shape primitives allow knit objects to be scheduled, scaled, and otherwise shaped in ways that require thousands of edits to low-level instructions. At the core of our compiler is a heuristic transfer planning algorithm for knit cycles, which we prove is both sound and complete. This algorithm enables the translation of high-level shaping and scheduling operations into needle-level operations. We show a wide range of examples produced with our compiler and demonstrate a basic visual design interface that uses our compiler as a backend

Computational design of metallophone contact sounds

Metallophones such as glockenspiels produce sounds in response to contact. Building these instruments is a complicated process, limiting their shapes to well-understood designs such as bars. We automatically optimize the shape of arbitrary 2D and 3D objects through deformation and perforation to produce sounds when struck which match user-supplied frequency and amplitude spectra. This optimization requires navigating a complex energy landscape, for which we develop Latin Complement Sampling to both speed up finding minima and provide probabilistic bounds on landscape exploration. Our method produces instruments which perform similarly to those that have been professionally-manufactured, while also expanding the scope of shape and sound that can be realized, e.g., single object chords. Furthermore, we can optimize sound spectra to create overtones and to dampen specific frequencies. Thus our technique allows even novices to design metallophones with unique sound and appearance.National Science Foundation (U.S.) (CAREER-1453101)National Science Foundation (U.S.) (IIS-1116619)National Science Foundation (U.S.) (IIS 1447344)United States. Air Force Research LaboratoryUnited States. Defense Advanced Research Projects Agency. MEMEX ProgramIntel Corporatio

Secretions from NF-κB activated PCa cells induce osteoclast-like cell formation.

<p>(A) Conditioned medium from NF-κB activated PCa cells induces osteoclast-like cell formation <i>in vitro</i>. Bone marrow-derived macrophages were treated with conditioned media from LNCaP, NF-κB activated LNCaP-EE, C4-2B and PC3 cells. TRAP staining was performed after 10 days of additional culture with conditioned media. Arrows indicate the osteoclast-like cells. (B) Activation of NF-κB signaling in PCa cells has no significant effect on the osteoblasts proliferation. Primary cultured osteoblasts were treated with conditioned media from NF-κB activated (LNCaP-EE, C4-2B-EV and PC3-EV) or inactivated (LNCaP-EV, C4-2B-KD and PC3-KD) PCa cells. Proliferation assay (MTT assay) was performed at 48 hours after treatment.</p