In this paper, we analyze the nonlinear single pixel X-ray transform $K$ and study the reconstruction of $f$ from the measurement $Kf$. Different from the well-known X-ray transform, the transform $K$ is a nonlinear operator and uses a single detector that integrates all rays in the space. We derive stability estimates and an inversion of the linearization at zero. We also consider the case where we integrate along geodesics of a Riemannian metric. Moreover, we conduct several numerical experiments to corroborate the theoretical results.

This article proposes an identity-preserving conditional generative adversarial network (IPcGAN) for image-to-image translation. The proposed framework can learn the translation function without any corresponding image in two domains, and map a real image into a latent space and a conditional representation. Contrary to other works, we introduce a fine-tuning process and a novel joint-loss to preserve the persons original identity while maintaining realism in the synthetic faces. We evaluate the new model with feature-wise errors and demonstrate that it produces much better results than existing models.

The development of High-level programming languages(HLPL) is a long process of evolution, which has gone through procedure-oriented languages, object-oriented languages, script languages and visual & database languages. Throughout the process of evolution, coding in an efficient and convenient way is the primary impetus. Hence, the productivity should vary across different languages. When evaluating the productivity of developers coding in different programming languages, such variations are usually ignored, which may not reflect their actual coding efficiencies and make the developers feel unfair. Especially, ignoring the variations will lead to inappropriate baselines of process performance, thus may potentially reduce the effectiveness of quantitative management. In this paper, we conducted an empirical study to revisit the productivity variations across different programming languages based on the data of International Software Benchmarking Standards Group (ISBSG) and a software organization. We found that, in most language categories, the productivity is all significantly different in ISBSG and the organization respectively. In the software industry represented by ISBSG, the relative productivity levels are gradually increasing with the evolution of HLPL. However, for the organization, it does not always keep the same increasing trend, but the average productivity within the four categories is almost stable. The finding could guide the software organization to establish an appropriate productivity baseline.

Selection of the right user stories and planning their implementation for the next iteration is critical for success of extreme Programming (XP). Success here is measured by the total business value generated from all user stories implemented within time. The business value of an iteration is composed by the value of the individual user stories selected and additional value created from themes of user stories. In this paper, a method combining advanced search and risk analysis is proposed to support decision-making for the "best" set of user stories. The advanced search technique combines genetic search with subsequent application of the hill climbing technique. The top candidate solutions are further analyzed pro-actively in terms of their risk to be implementable in-time with the available effort. As a proof-of-concept, the applicability of the proposed method is applied for the planning of one iteration in a case study project with 40 user stories. As a result, a set of trade-off solutions is offered as decision support for XP teams.

Productivity is a crucial concern for most software organizations. It can help project managers to make project plan, supervise project progress, and measure the project members' performance. Thus it has been widely measured and analyzed by both industry and researchers. But in the actual software project management, the project data filled by the developers may be incomplete and imprecise. Especially it is very hard for the developers to give the precise work product scale of each task. Therefore, the productivity calculated basing on those data is also imprecise. To solve the problem, this paper presents a method for precise productivity estimation. The method calculates work product scale of each task using change set size information by rebuilding relationships between the tasks and the SVN commits, and then calculates the productivity. And an experimental study has been done basing on Qone. Qone is an integrated system for project management developed by Institute of Software Chinese Academy of Sciences (ISCAS). It has been used in more than 200 software companies in China.

Given a region q/sub R/ and a future timestamp q/sub T/, a "range aggregate" query estimates the number of objects expected to appear in q/sub R/ at time q/sub T/. Currently the only methods for processing such queries are based on spatio-temporal histograms, which have several serious problems. First, they consume considerable space in order to provide accurate estimation. Second, they incur high evaluation cost. Third, their efficiency continuously deteriorates with time. Fourth, their maintenance requires significant update overhead. Motivated by this, we develop Venn sampling (VS), a novel estimation method optimized for a set of "pivot queries" that reflect the distribution of actual ones. In particular, given m pivot queries, VS achieves perfect estimation with only O(m) samples, as opposed to O(2/sup m/) required by the current state of the art in workload-aware sampling. Compared with histograms, our technique is much more accurate (given the same space), produces estimates with negligible cost, and does not deteriorate with time. Furthermore, it permits the development of a novel "query-driven" update policy, which reduces the update cost of conventional policies significantly.

Nowadays, color screen, powerful computing processors, and high speed data communication protocols are supported by more and more mobile phones. Through these new technologies, it is possible for mobile phone users to download, view, and even edit images on their mobile phones. However, this poses a new challenge for mobile software developers if they want to allow several users to edit on the same image. Traditional methods (e.g., white board method) cannot be used under mobile environment due to its high communication cost. In this paper, we propose a cooperative editing tool based on an asynchronous method to greatly save the communication cost. The tool is successfully used as a plug-in in our Mobile Information and Resource Exchange System (MIRES).

Line detection algorithms constitute the basis fortechnical document analysis and recognition. Theperformance of these algorithms decreases as the qualityof the documents degrades. To test the robustness of linedetection algorithms under noisy circumstance, wepropose a document degradation mode, which simulatesnoise types that drawings may undergo during theirproduction, storage, photocopying, or scanning. Using ourmodel, a series of document images at various noise levelsand types can be generated for testing the performance ofline detection algorithms. To illustrate that our model isconsistent with real world noise types, we validated themethod by applying it to three line recognition algorithms.

Flash /spl trade/ is experiencing a breathtaking growth and has become one of the prevailing media formats on the Web. Unfortunately, little research effort has been dedicated to the retrieval of Flash movies by content, which is critical to the utilization of the enormous Flash resource. Unlike text or image, a Flash movie contains a number of components in different types such as objects, events and interactions, with the number ranging from tens to hundreds. How to index and store these components effectively and efficiently in a database is a big challenge. In this paper, we propose a novel star schema for database design and associated facilities, based on the movie's characteristics at object, event and interaction levels. Experiment results are given to show that our approach can retrieve more relevant movies, efficiently use the storage space and speed up the retrieval process.