Pairs of iterators are ubiquitous throughout the C++ library. It is generally accepted that combining such a pair into a single entity usually termed Range delivers more concise and readable code. Defining the precise semantics of such Range concept proves surprisingly tricky, however. Theoretical considerations conflict with practical ones. Some design goals are mutually incompatible altogether.
Every program may encounter errors, some originating from internal bugs in the program, others coming from the environment the program is operating in. Ignoring all errors will make the program utterly unreliable, while treating every conceivable one introduces lots of extra complexity with little benefit. At think-cell, we have been using and refining our own principled approach to error handling, which we have not seen elsewhere. This lecture teaches our method, so that you in your next project, too, can write more reliable software with less effort.
Software patching is a powerful but potentially risky method for fixing bugs, adding functionality, and improving the usability or performance of software. This lecture looks at patching software where the source code is unavailable, an activity commonly referred to as hacking. We discuss why and when such activity may be necessary before looking in detail at the design and implementation of robust patches. We finish off by describing various tools and techniques that can be used to find suitable patching locations.
We have recently begun to port our software to other platforms. In this process, we discovered the world of pain that are iostreams and C-style I/O. In this talk we give a short overview over why we have banished iostreams from our code base and what we have replaced them with.
The C++ memory model defines how multiple threads interact with memory and shared data, enabling developers to reason about concurrent code in a platform independent way. The talk explains multi-threaded executions and data races in C++, how concurrent code is affected by compiler and hardware optimizations, and how to avoid undefined behavior by using locks and atomic operations. Then it focuses on different memory orders for atomic operations, their guarantees and performance implications.
We love C++ and use it daily. In this talk we try to explain why – despite its reputation for complexity – C++ is conceptually a much better language than Java. At least "better" in the sense that it allows the programmer to express her thoughts more clearly than would be possible in Java: C++ lets us programmers write abstractions without losing performance and C++ gives us a uniform resource handling mechanisms for all kinds of resources, not just memory.
This paper presents an efficient algorithm for a new variation of the point feature labeling problem. The goal is to position the largest number of point labels such that they do not intersect each other or their points. First we present an algorithm using a greedy algorithm with limited lookahead. We then present an algorithm that iteratively regroups labels, calling the first algorithm on each group, thereby identifying a close to optimal labeling order. The presented algorithm is being used in a commercial product to label charts, and our evaluation shows that it produces results far superior to those of other labeling algorithms.
This paper presents a smart algorithm for labeling column charts and their derivatives. To efficiently solve the problem, we separate it into two sub-problems. We first present a geometric algorithm to solve the problem of finding a good labeling for the labels of a single column, given that some other columns have already been labeled. We then present a strategy for finding a good order in which columns should be labeled, which repeatedly uses the first algorithm for some limited lookahead. The presented algorithm is being used in a commercial product to label charts, and has shown in practice to produce satisfactory results.
In this paper we introduce a new algorithm for image and video texture synthesis. In our approach, patch regions from a sample image or video are transformed and copied to the output and then stitched together along optimal seams to generate a new (and typically larger) output. In contrast to other techniques, the size of the patch is not chosen a-priori, but instead a graph cut technique is used to determine the optimal patch region for any given offset between the input and output texture. Unlike dynamic programming, our graph cut technique for seam optimization is applicable in any dimension. We specifically explore it in 2D and 3D to perform video texture synthesis in addition to regular image synthesis.
In this paper we present a new approach for generating controlled animations of video sprites. Video sprites are animations created by rearranging recorded video frames of a moving object. With our technique, the user can specify animations using a flexible cost function, which is automatically optimized by repeated replacement of video sprite subsequences.
This paper introduces a new type of medium, called a video texture, which has qualities somewhere between those of a photograph and a video. A video texture provides a continuous infinitely varying stream of images. While the individual frames of a video texture may be repeated from time to time, the video sequence as a whole is never repeated exactly. Video textures can be used in place of digital photos to infuse a static image with dynamic qualities and explicit action.