University of Wisconsin-Madison

About: University of Wisconsin-Madison is a education organization based out in Madison, Wisconsin, United States. It is known for research contribution in the topics: Population & Gene. The organization has 108707 authors who have published 237594 publications receiving 11883575 citations.

Semantic feature production norms for a large set of living and nonliving things

Abstract: Semantic features have provided insight into numerous behavioral phenomena concerning concepts, categorization, and semantic memory in adults, children, and neuropsychological populations. Numerous theories and models in these areas are based on representations and computations involving semantic features. Consequently, empirically derived semantic feature production norms have played, and continue to play, a highly useful role in these domains. This article describes a set of feature norms collected from approximately 725 participants for 541 living (dog) and nonliving (chair) basic-level concepts, the largest such set of norms developed to date. This article describes the norms and numerous statistics associated with them. Our aim is to make these norms available to facilitate other research, while obviating the need to repeat the labor-intensive methods involved in collecting and analyzing such norms. The full set of norms may be downloaded from www.psychonomic.org/archive.

3D-HST WFC3-selected Photometric Catalogs in the Five CANDELS/3D-HST Fields: Photometry, Photometric Redshifts, and Stellar Masses

Abstract: The 3D-HST and CANDELS programs have provided WFC3 and ACS spectroscopy and photometry over ≈900 arcmin2 in five fields: AEGIS, COSMOS, GOODS-North, GOODS-South, and the UKIDSS UDS field. All these fields have a wealth of publicly available imaging data sets in addition to the Hubble Space Telescope (HST) data, which makes it possible to construct the spectral energy distributions (SEDs) of objects over a wide wavelength range. In this paper we describe a photometric analysis of the CANDELS and 3D-HST HST imaging and the ancillary imaging data at wavelengths 0.3-8 μm. Objects were selected in the WFC3 near-IR bands, and their SEDs were determined by carefully taking the effects of the point-spread function in each observation into account. A total of 147 distinct imaging data sets were used in the analysis. The photometry is made available in the form of six catalogs: one for each field, as well as a master catalog containing all objects in the entire survey. We also provide derived data products: photometric redshifts, determined with the EAZY code, and stellar population parameters determined with the FAST code. We make all the imaging data that were used in the analysis available, including our reductions of the WFC3 imaging in all five fields. 3D-HST is a spectroscopic survey with the WFC3 and ACS grisms, and the photometric catalogs presented here constitute a necessary first step in the analysis of these grism data. All the data presented in this paper are available through the 3D-HST Web site (http://3dhst.research.yale.edu).

Learning from Examples: Instructional Principles from the Worked Examples Research:

Abstract: Worked examples are instructional devices that provide an expert's problem solution for a learner to study. Worked-examples research is a cognitive-experimental program that has relevance to classroom instruction and the broader educational research community. A frame- work for organizing the findings of this research is proposed, leading to instructional design principles. For instance, one instructional design principle suggests that effective examples have highly integrated components. They employ multiple modalities in presentation and emphasize conceptual structure by labeling or segmenting. At the lesson level, effective instruction employs multiple examples for each conceptual problem type, varies example formats within problem type, and employs surface features to signal deep structure. Also, examples should be presented in close proximity to matched practice problems. More- over, learners can be encouraged through direct training or by the structure of the worked example to actively self:explain ...

Internationalization and Firm Governance: The Roles of CEO Compensation, Top Team Composition, and Board Structure

Abstract: Using the complementary lenses of information-processing and agency theories, this study tests the proposition that the complexity resulting from a firm's degree of internationalization will be acc...

Distributed optimization in sensor networks

Abstract: Wireless sensor networks are capable of collecting an enormous amount of data over space and time. Often, the ultimate objective is to derive an estimate of a parameter or function from these data. This paper investigates a general class of distributed algorithms for "in-network" data processing, eliminating the need to transmit raw data to a central point. This can provide significant reductions in the amount of communication and energy required to obtain an accurate estimate. The estimation problems we consider are expressed as the optimization of a cost function involving data from all sensor nodes. The distributed algorithms are based on an incremental optimization process. A parameter estimate is circulated through the network, and along the way each node makes a small adjustment to the estimate based on its local data. Applying results from the theory of incremental subgradient optimization, we show that for a broad class of estimation problems the distributed algorithms converge to within an /spl epsi/-ball around the globally optimal value. Furthermore, bounds on the number incremental steps required for a particular level of accuracy provide insight into the trade-off between estimation performance and communication overhead. In many realistic scenarios, the distributed algorithms are much more efficient, in terms of energy and communications, than centralized estimation schemes. The theory is verified through simulated applications in robust estimation, source localization, cluster analysis and density estimation.