Metric-based Techniques Research Articles

Friedmann-like universes with torsion

We consider spatially homogeneous and isotropic cosmologies with non-vanishing torsion, which assumes a specific form due to the high symmetry of these universes. Using covariant and metric-based techniques, we derive the torsional versions of the continuity, the Friedmann and the Raychaudhuri equations. These show how torsion can drastically change the standard evolution of the Friedmann models, by playing the role of the spatial curvature or that of the cosmological constant. We find, for example, that torsion alone can lead to exponential expansion and thus make the Einstein–de Sitter universe look like the de Sitter cosmos. Also, by modifying the expansion rate of the early universe, torsion could have affected the primordial abundance of helium-4. We show, in particular, that torsion can reduce the production of primordial helium-4, unlike other changes to the standard thermal history of the universe. These theoretical results allow us to impose strong observational bounds on the relative strength of the associated torsion field, confining its ratio to the Hubble rate within the narrow interval (-,0.005813,,+,0.019370) around zero. Finally, turning to static spacetimes, we demonstrate that there exist torsional analogues of the Einstein static universe with all three types of spatial geometry. These models can be stable when the torsion field and the universe’s spatial curvature have the appropriate profiles.

Подход к обнаружению анти-паттернов в сервис-ориентированных системах

A service-based approach is a method to develop and integrate program products in a modular manner where each component is available through any net and has the possibility of being dynamically collaborated with other services of the system at run time. That approach is becoming widely adopted in industry of software engineering because it allows the implementation of distributed systems characterized by high quality. Quality attributes can be about the system (e.g., availability, modifiability), business-related aspects (e.g., time to market) or about the architecture (e.g., correctness, consistency). Maintaining quality-attributes on a high level is critical issue because service-based systems lack central control and authority, have limited end-to-end visibility of services, are subject to unpredictable usage scenarios and support dynamic system composition. The constant evolution in systems can easily deteriorate the overall architecture of the system and thus bad design choices, known as anti-patterns, may appear. These are the patterns to be avoided. If we study them and are able to recognize them, then we should be able to avoid them. Knowing bad practices is perhaps as valuable as knowing good practices. With this knowledge, we can re-factor the solution in case we are heading towards an anti-pattern. As with patterns, anti-pattern catalogues are also available. In case of continues evolution of systems, it is metric-based techniques that can be applied to obtain valuable, factual insights into how services work. Given the clear negative impact of anti-patterns, there is a clear and urgent need for techniques and tools to detect them. The article will focus on rules to recognize symptoms of anti-patterns in service-based environment, automated approaches to detection and applying metric-based techniques to this analysis.

Change Point Determination in Audio Data Using Auditory Features

Abstract The study is aimed to investigate the properties of auditory-based features for audio change point detection process. In the performed analysis, two popular techniques have been used: a metric-based approach and the ΔBIC scheme. The efficiency of the change point detection process depends on the type and size of the feature space. Therefore, we have compared two auditory-based feature sets (MFCC and GTEAD) in both change point detection schemes. We have proposed a new technique based on multiscale analysis to determine the content change in the audio data. The comparison of the two typical change point detection techniques with two different feature spaces has been performed on the set of acoustical scenes with single change point. As the results show, the accuracy of the detected positions depends on the feature type, feature space dimensionality, detection technique and the type of audio data. In case of the ΔBIC approach, the better accuracy has been obtained for MFCC feature space in the most cases. However, the change point detection with this feature results in a lower detection ratio in comparison to the GTEAD feature. Using the same criteria as for ΔBIC, the proposed multiscale metric-based technique has been executed. In such case, the use of the GTEAD feature space has led to better accuracy. We have shown that the proposed multiscale change point detection scheme is competitive to the ΔBIC scheme with the MFCC feature space.

Metric-based detection of robot kidnapping with an SVM classifier

Kidnapping occurs when a robot is unaware that it has not correctly ascertained its position, potentially causing severe map deformation and reducing the robot’s functionality. This paper presents metric-based techniques for real-time kidnap detection, utilising either linear or SVM classifiers to identify all kidnapping events during the autonomous operation of a mobile robot. In contrast, existing techniques either solve specific cases of kidnapping, such as elevator motion, without addressing the general case or remove dependence on local pose estimation entirely, an inefficient and computationally expensive approach. Three metrics that measured the quality of a pose estimate were evaluated and a joint classifier was constructed by combining the most discriminative quality metric with a fourth metric that measured the discrepancy between two independent pose estimates. A multi-class Support Vector Machine classifier was also trained using all four metrics and produced better classification results than the simpler joint classifier, at the cost of requiring a larger training dataset. While metrics specific to 3D point clouds were used, the approach can be generalised to other forms of data, including visual, provided that two independent ways of estimating pose are available.

A Hybrid Approach for Fast and Accurate Trace Signal Selection for Post-Silicon Debug

A major challenge in post-silicon debug is the lack of observability to the internal signals of a chip. Trace buffer technology provides one venue to address this challenge by online tracing of a few selected state elements. Due to the limited bandwidth of the trace buffer, only a few state elements can be selected for tracing. Recent research has focused on automated trace signal selection problem to maximize restoration of the untraced state elements using the few traced signals. Existing techniques can be categorized into high quality but slow simulation-based techniques and lower quality but much faster metric-based techniques. This paper presents a new trace signal selection technique which has comparable or better quality than simulation-based while it has a fast runtime, comparable to the metric-based techniques.