Abstract: A semiconductor structure is formed with first and second semiconductor wafers and a redistribution layer. The first semiconductor wafer is formed with a first active layer and a first interconnect layer. The second semiconductor wafer is formed with a second active layer and a second interconnect layer. The second semiconductor wafer is inverted and bonded to the first semiconductor wafer, and a substrate is removed from the second semiconductor wafer. The redistribution layer redistributes electrical connective pad locations on a side of the second semiconductor wafer. The redistribution layer also electrically contacts the first interconnect layer through a hole in the second active layer and the second interconnect layer.

Abstract: An integrated circuit assembly includes an insulating layer having a having a first surface and a second surface, where the first surface of the insulating layer is less than 10 microns below an upper plane of the integrated circuit assembly. An active layer contacts the first surface of the insulating layer. A metal bond pad is electrically connected to the active layer and formed on the second surface of the insulating layer, and is also electrically connected to a printed circuit board. A method of fabricating an integrated circuit assembly includes coupling a handle wafer to the active layer of a semiconductor-on-insulator wafer, removing the substrate of the semiconductor-on-insulator, forming a bond pad connecting to the active layer on the exposed insulator surface, bonding the bond pad to a printed circuit board using a solder bump, and removing the handle wafer.

Abstract: A multiple model data exploration system and method for running multiple machine-learning models simultaneously to understand and explore data. Embodiments of the system and method allow a user to gain a greater understanding of the data and to gain new insights into their data. Embodiments of the system and method also allow a user to interactively explore the problem and to navigate different views of data. Many different classifier training and evaluation experiments are run simultaneously and results are obtained. The results are aggregated and visualized across each of the experiments to determine and understand how each example is classified for each different classifier. These results then are summarized in a variety of ways to allow users to obtain a greater understanding of the data both in terms of the individual examples themselves and features associated with the data.

Abstract: Descried herein are various technologies to visualizing data in a spreadsheet application. Customized visualizations are generated by binding a user-defined equation with a parameter of a primitive visual shape that has been selected by the user to visualized a dataset. The user-defined equation takes as input a value of data in a cell of the spreadsheet application, and the primitive visual shape is displayed to the user as a function of the output of the user-defined equation.

Abstract: A latent topic labels text mining system and method to mine and analyze the content of textual data. Embodiments of the system and method are particularly well suited for use on microblog data to help people identify posts they want to read and to find people that they want to follow. Embodiments of the system and method use a modified Labeled LDA technique (called an L+LDA technique) that analyzes content using a combination of labeled and latent topics. The resultant data is assigned labels one of four labels to generate a lower-dimensional representation of the data that the individual words in a microblog post. This learned topic representation is used to characterize, summarize, filter, find, suggest, and compare the content of microblog posts. Embodiments of the system and method also include visualization techniques such as a tag cloud visualization that is used to visualize microblogging data.

Abstract: A multiple model data exploration system and method for running multiple machine-learning models simultaneously to understand and explore data. Embodiments of the system and method allow a user to gain a greater understanding of the data and to gain new insights into their data. Embodiments of the system and method also allow a user to interactively explore the problem and to navigate different views of data. Many different classifier training and evaluation experiments are run simultaneously and results are obtained. The results are aggregated and visualized across each of the experiments to determine and understand how each example is classified for each different classifier. These results then are summarized in a variety of ways to allow users to obtain a greater understanding of the data both in terms of the individual examples themselves and features associated with the data.

Abstract: The pluggable web-based visualization technique described herein pertains to a method for providing pluggable web-based visualizations for applications. The method selects visualizations from the web to be embedded into a host application on a desktop computing device. The visualizations can be plugged in to a variety of host applications. This empowers end-users, application designers, and visualization designers by allowing greater reuse of existing code. Additionally, end-users do not have to wait for new revisions of existing applications to use the latest techniques. Designers of domain specific visualizations can work on just the visualization and have them incorporated into a variety of different host applications. Users can perform local processing and visualizations on their own machine, yet obtain new visualizations from the web where they can be updated more frequently and where special purpose visualizations are available.