Co-registration of Laser Altimeter Tracks with Digital Terrain Models and Applications in Planetary Science

We have derived algorithms and techniques to precisely co-register laser altimeter profiles with gridded Digital Terrain Models (DTMs), typically derived from stereo images. The algorithm consists of an initial grid search followed by a least-squares matching and yields the translation parameters at sub-pixel level needed to align the DTM and the laser profiles in 3D space. This software tool was primarily developed and tested for co-registration of laser profiles from the Lunar Orbiter Laser Altimeter (LOLA) with DTMs derived from the Lunar Reconnaissance Orbiter (LRO) Narrow Angle Camera (NAC) stereo images. Data sets can be co-registered with positional accuracy between 0.13 m and several meters depending on the pixel resolution and amount of laser shots, where rough surfaces typically result in more accurate co-registrations. Residual heights of the data sets are as small as 0.18 m. The software can be used to identify instrument misalignment, orbit errors, pointing jitter, or problems associated with reference frames being used. Also, assessments of DTM effective resolutions can be obtained. From the correct position between the two data sets, comparisons of surface morphology and roughness can be made at laser footprint- or DTM pixel-level. The precise co-registration allows us to carry out joint analysis of the data sets and ultimately to derive merged high-quality data products. Examples of matching other planetary data sets, like LOLA with LRO Wide Angle Camera (WAC) DTMs or Mars Orbiter Laser Altimeter (MOLA) with stereo models from the High Resolution Stereo Camera (HRSC) as well as Mercury Laser Altimeter (MLA) with Mercury Dual Imaging System (MDIS) are shown to demonstrate the broad science applications of the software tool

Explanatory debugging: Supporting end-user debugging of machine-learned programs

Many machine-learning algorithms learn rules of behavior from individual end users, such as task-oriented desktop organizers and handwriting recognizers. These rules form a “program” that tells the computer what to do when future inputs arrive. Little research has explored how an end user can debug these programs when they make mistakes. We present our progress toward enabling end users to debug these learned programs via a Natural Programming methodology. We began with a formative study exploring how users reason about and correct a text-classification program. From the results, we derived and prototyped a concept based on “explanatory debugging”, then empirically evaluated it. Our results contribute methods for exposing a learned program's logic to end users and for eliciting user corrections to improve the program's predictions

Effect of the frustration of psychological needs on addictive behaviors in mobile videogamers. The mediating role of use expectancies and time spent gaming

Casual videogames (CVGs), played on smartphones, are becoming increasingly popular, especially among females and adults. Whereas the addictive potential of online (computer) videogames is well-established, there is yet insufficient evidence for Internet gaming disorder (IGD) in mobile gamers and for the mediating role of some mechanisms involved. The aim of this study is to investigate the impact of the frustration of psychological needs on mobile videogamers' addictive behavior as well as the role of cognitions (game-use expectancies) and behaviors (time spent playing) through a hypothesized serial mediation model, while controlling for important correlates, such as game genre, age, gender and payment during play. A total of 471 mobile game users (211 males) with an average age of 21.73 replied to an online survey containing sociodemographic and game variables, the Need Satisfaction and Frustration Scale (NSFS), the Internet Gaming Disorder Scale-Short Form (IGDS9-SF) and a slightly modified version of the Internet Use Expectancies Scale (IUES). The results corroborate the negative effects of need frustration on IGD among mobile gamers and clarify the role of time spent playing and game-use expectancies in the development of IGD, highlighting the important role of cognitions in this relationship. We conclude that both the time spent playing and game-use expectancies should be important targets for clinical interventions, even though they are not included in the diagnostic criteri

The need for international planetary cartography planning and cooperation

Cartography is fundamental to planetary science and as such, a lack of appropriate consideration of this foundation can have and has had serious and expensive consequences to both the scientific return from planetary missions and the safety of future lander missions. In this abstract we highlight the need for, and recommend cooperative planning of, such cartographic work at the national and international level. In an effort to support the planetary exploration initiatives of the various spacefaring nations, we detail specific negative consequences of not properly accounting for cartographic constraints during mission planning and execution. We will also pose several unanswered questions that must be addressed before new exploration efforts should commence. To assure the best possible return on space exploration investments, we recommend that the following planetary cartographic issues be considered: 1. Adequate resources for mapping at all stages from mission design through calibration, operations, development of processing algorithms and software, and processing to archiving; 2. Easy access to data sets and metadata from all nations; consistent (or at least well-documented) data formats; consistent cartographic standards; 3. Cooperation and support leading to the joint analysis of data sets from many nations, in turn leading to integration in a single cartographic coordinate framework at known accuracy levels, and the ability to leverage the powerful synergistic value of multiple data sets. Possible actions that could be taken to achieve these goals will also be presented

Integrating rich user feedback into intelligent user interfaces

The potential for machine learning systems to improve via a mutually beneficial exchange of information with users has yet to be explored in much detail. Previously, we found that users were willing to provide a generous amount of rich feedback to machine learning systems, and that the types of some of this rich feedback seem promising for assimilation by machine learning algorithms. Following up on those findings, we ran an experiment to assess the viability of incorporating real-time keyword-based feedback in initial training phases when data is limited. We found that rich feedback improved accuracy but an initial unstable period often caused large fluctuations in classifier behavior. Participants were able to give feedback by relying heavily on system communication in order to respond to changes. The results show that in order to benefit from the user’s knowledge, machine learning systems must be able to absorb keyword-based rich feedback in a graceful manner and provide clear explanations of their predictions

Optimizing the distribution of tie points for the bundle adjustment of hrsc image mosaics

For a systematic mapping of the Martian surface, the Mars Express orbiter is equipped with a multi-line scanner: Since the beginning of 2004 the High Resolution Stereo Camera (HRSC) regularly acquires long image strips. By now more than 4, 000 strips covering nearly the whole planet are available. Due to the nine channels, each with different viewing direction, and partly with different optical filters, each strip provides 3D and color information and allows the generation of digital terrain models (DTMs) and orthophotos. To map larger regions, neighboring HRSC strips can be combined to build DTM and orthophoto mosaics. The global mapping scheme Mars Chart 30 is used to define the extent of these mosaics. In order to avoid unreasonably large data volumes, each MC-30 tile is divided into two parts, combining about 90 strips each. To ensure a seamless fit of these strips, several radiometric and geometric corrections are applied in the photogrammetric process. A simultaneous bundle adjustment of all strips as a block is carried out to estimate their precise exterior orientation. Because size, position, resolution and image quality of the strips in these blocks are heterogeneous, also the quality and distribution of the tie points vary. In absence of ground control points, heights of a global terrain model are used as reference information, and for this task a regular distribution of these tie points is preferable. Besides, their total number should be limited because of computational reasons. In this paper, we present an algorithm, which optimizes the distribution of tie points under these constraints. A large number of tie points used as input is reduced without affecting the geometric stability of the block by preserving connections between strips. This stability is achieved by using a regular grid in object space and discarding, for each grid cell, points which are redundant for the block adjustment. The set of tie points, filtered by the algorithm, shows a more homogenous distribution and is considerably smaller. Used for the block adjustment, it yields results of equal quality, with significantly shorter computation time. In this work, we present experiments with MC-30 half-tile blocks, which confirm our idea for reaching a stable and faster bundle adjustment. The described method is used for the systematic processing of HRSC data.DLR/50 QM 1601BMWi/50 QM 160

End-user feature labeling: a locally-weighted regression approach

When intelligent interfaces, such as intelligent desktop assistants, email classifiers, and recommender systems, customize themselves to a particular end user, such customizations can decrease productivity and increase frustration due to inaccurate predictions - especially in early stages, when training data is limited. The end user can improve the learning algorithm by tediously labeling a substantial amount of additional training data, but this takes time and is too ad hoc to target a particular area of inaccuracy. To solve this problem, we propose a new learning algorithm based on locally weighted regression for feature labeling by end users, enabling them to point out which features are important for a class, rather than provide new training instances. In our user study, the first allowing ordinary end users to freely choose features to label directly from text documents, our algorithm was both more effective than others at leveraging end users' feature labels to improve the learning algorithm, and more robust to real users' noisy feature labels. These results strongly suggest that allowing users to freely choose features to label is a promising method for allowing end users to improve learning algorithms effectively

End-User Feature Labeling via Locally Weighted Logistic Regression

Applications that adapt to a particular end user often make inaccurate predictions during the early stages when training data is limited. Although an end user can improve the learning algorithm by labeling more training data, this process is time consuming and too ad hoc to target a particular area of inaccuracy. To solve this problem, we propose a new learning algorithm based on Locally Weighted Logistic Regression for feature labeling by end users, enabling them to point out which features are important for a class, rather than provide new training instances. In our user study, the first allowing ordinary end users to freely choose features to label directly from text documents, our algorithm was more effective than others at leveraging end users’ feature labels to improve the learning algorithm. Our results strongly suggest that allowing users to freely choose features to label is a promising method for allowing end users to improve learning algorithms effectively

Marco Polo: near Earth object sample return mission

Marco Polo is a joint European-Japanese mission of sample return from a Near Earth Object. The Marco Polo proposal was submitted to ESA on July 2007 in the framework of the Cosmic Vision 2015-2025 context, and on October 2007 passed the first evaluation process. The primary objectives of this mission is to visit a primitive NEO, belonging to a class that cannot be related to known meteorite types, to characterize it at multiple scales, and to bring samples back to Earth. Marco Polo will give us the first opportunity for detailed laboratory study of the most primitive materials that formed the planets. This will allow us to improve our knowledge on the processes which governed the origin and early evolution of the Solar System, and possibly of the life on Earth