String (computer science)

About: String (computer science) is a research topic. Over the lifetime, 19430 publications have been published within this topic receiving 333247 citations. The topic is also known as: str & s.

Modular decorative light system

Abstract: A sculptable, decorative light system includes a number of modules of differing lengths which can respond to commands either in parallel or time delay to create pre-selected light colors and sequences. Three miniature connectors are used to create a direct current fed system which has the potential to even out a current demand. Interconnectable lamp modules are provided in sets of 1, 2, 3, 4, and more lengths to create decorative light displays. Each lamp module or string of modules may include a male/plug end and a female/receptacle end.

Method and device for translating a source text into a target using modeling and dynamic programming

Abstract: For translating a word-organized source text into a word-organized target text through mapping of source words on target words, both a translation model and a language model are used. In particular, alignment probabilities are ascertained between various source word & target word pairs, whilst preemptively assuming that alignment between such word pairs is monotonous through at least substantial substrings of a particular sentence. This is done by evaluating incrementally statistical translation performance of various target word strings, deciding on an optimum target word string, and outputting the latter.

String Stable Model Predictive Cooperative Adaptive Cruise Control for Heterogeneous Platoons

Abstract: Cooperative adaptive cruise control (CACC) is a potential solution to decrease traffic jams caused by shock waves, increase the road capacity, decrease fuel consumption and improve safety. This paper proposes an integrated solution to a combination of four challenges in these CACC systems. One of the technological challenges is how to guarantee string stability (the ability to avoid amplification of dynamic vehicle responses along the string of vehicles) under nominal operational conditions. The second challenge is how to apply this solution to heterogeneous vehicles. The third challenge is how to maintain confidentiality of the vehicle parameters. Finally, the fourth challenge is to find a method which improves robustness against wireless packet loss. This paper proposes a model predictive control approach in combination with a feed-forward control design, which is based on a shared vector of predicted accelerations over a finite time horizon. This approach is shown to be applicable to a heterogeneous sequence of vehicles, while the vehicle parameters remain confidential. In previous works such an approach has shown to increase robustness against packet losses. Conditions for string stability are presented for the nominal operational conditions. Experimental results are presented and indeed demonstrate string stable behavior.

Ensemble clustering using semidefinite programming with applications

Abstract: In this paper, we study the ensemble clustering problem, where the input is in the form of multiple clustering solutions. The goal of ensemble clustering algorithms is to aggregate the solutions into one solution that maximizes the agreement in the input ensemble. We obtain several new results for this problem. Specifically, we show that the notion of agreement under such circumstances can be better captured using a 2D string encoding rather than a voting strategy, which is common among existing approaches. Our optimization proceeds by first constructing a non-linear objective function which is then transformed into a 0-1 Semidefinite program (SDP) using novel convexification techniques. This model can be subsequently relaxed to a polynomial time solvable SDP. In addition to the theoretical contributions, our experimental results on standard machine learning and synthetic datasets show that this approach leads to improvements not only in terms of the proposed agreement measure but also the existing agreement measures based on voting strategies. In addition, we identify several new application scenarios for this problem. These include combining multiple image segmentations and generating tissue maps from multiple-channel Diffusion Tensor brain images to identify the underlying structure of the brain.

On variance-based subtyping for parametric types

Abstract: We develop the mechanism of variant parametric types, inspired by structural virtual types by Thorup and Torgersen, as a means to enhance synergy between parametric and inclusive polymorphism in object-oriented languages. Variant parametric types are used to control both subtyping between different instantiations of one generic class and the visibility of their fields and methods. On one hand, one parametric class can be used as either covariant, contravariant, or bivariant by attaching a variance annotation-which can be either +, -, or *, respectively-to a type argument. On the other hand, the type system prohibits certain method/field accesses through variant parametric types, when those accesses can otherwise make the program unsafe. By exploiting variant parametric types, a programmer can write generic code ions working on a wide range of parametric types in a safe way. For instance, a method that only reads the elements of a container of strings can be easily modified so that it can accept containers of any subtype of string. The theoretical issues are studied by extending Featherweight GJ-an existing core calculus for Java with generics-with variant parametric types. By exploiting the intuitive connection to bounded existential types, we develop a sound type system for the extended calculus.