Cross-View Action Recognition from Temporal Self-Similarities

This paper concerns recognition of human actions under view changes. We explore self-similarities of action sequences over time and observe the striking stability of such measures across views. Building upon this key observation we develop an action descriptor that captures the structure of temporal similarities and dissimilarities within an action sequence. Despite this descriptor not being strictly view-invariant, we provide intuition and experimental validation demonstrating the high stability of self-similarities under view changes. Self-similarity descriptors are also shown stable under action variations within a class as well as discriminative for action recognition. Interestingly, self-similarities computed from different image features possess similar properties and can be used in a complementary fashion. Our method is simple and requires neither structure recovery nor multi-view correspondence estimation. Instead, it relies on weak geometric cues captured by self-similarities and combines them with machine learning for efficient cross-view action recognition. The method is validated on three public datasets, it has similar or superior performance compared to related methods and it performs well even in extreme conditions such as when recognizing actions from top views while using side views for training only

Composition and antimicrobial activity of the essential oil of Thymus fontanesii

The yield of oil isolated by hydrodistillation from aerial parts of Thymus fontanesii Boiss. et Reut. (Lamiaceae) growing wild in Djelfa (Algeria) was 0.9%. Analysis of the oil by gas chromatography (GC) and gas chromatography/mass spectrometry (GC/MS) revealed the identification of 47 components accounting for 98.5% of the total oil. The oil was found to be rich in monoterpenes (91.5%) with major constituents being thymol (29.3%), γ-terpinene (21.7%), p-cymene (15.9%), and thymol methyl ether (11.4%), while a smaller amount of linalool (4.8%) and β-caryophyllene (2.9%) were detected. According to the antimicrobial study using the disk diffusion method and the agar dilution assay-minimal inhibitory concentration (MIC), the oil showed strong in vitro growth inhibition activity against Gram-negative bacteria and antifungal activity. The oil exhibited the maximum antifungal activity against Mucor ramaniamus (MIC = 0.2 μL/mL). © 2006 Informa Healthcar

Studies on the essential oil composition and antimicrobial activity of Thymus algeriensis Boiss

Many species of Thymus have been widely used in Algerian folk medicine as astringent, expectorant and cicatrising agents. This study was designed to investigate for the first time the essential oil of the aerial parts of wild growing Thymus algeriensis endemic in North Africa. The volatile oil obtained by hydrodistillation was characterized by the physico-chemical properties, GC and GC/MS techniques. The yield of the oil was 1.13% (w/w), based on dry weight. Fifty-five components, representing 94.3% of the total oil, were identified. The oil was distinguished by its high content of oxygenated monoterpenes (79.5%) and was found to possess the following major components: linalool (47.3%), thymol (29.2%) and p-cymene (6.8%). Furthermore, the oil was tested for antimicrobial activity against four bacteria, two fungi and two yeasts. This oil exhibited a significant in vitro antimicrobial activity against Bacillus subtilis (MIC = 0.5 μL/mL), as well as against all yeast and all filamentous fungi tested (MIC = 0.5 and 1.0 μL/mL). © 2006 Elsevier Ltd. All rights reserve

Chemical composition of the essential oil of Salvia officinalis from Algeria

Sage (Salvia spp.), belonging to the Lamiaceae family, is an aromatic plant and includes about 900 species that grow in several regions all over the world [1]. This genus showed a very high variability of both morphological and cariological characters [2]. Since ancient times, this genus has been used in folk medicine and as a flavoring agen

Provenance opportunities for WS-VLAM:An exploration of an e-Science and an e-Business approach

Scientific applications are frequently modeled as a workflow that is executed under the control of a workflow management system. One crucial requirement during the execution is the validation of the generated results and the traceability of the experiment execution path. The automated tracking and storage of provenance information during workflow execution could satisfy this requirement. To collect provenance data using the Grid-enabled scientific workflow management system WS-VLAM, experimentation was made with two different implementations of the provenance concepts. The first one adopts the Open Provenance Model (OPM) as basis to represent, store, and share scientific experiments metadata using the Provenance Layer Infrastructure for e-Science Resources (PLIER). The second one is the history-tracing XML (HisT) which was developed for e-Business provenance. HisT provides a specific model to store provenance data within layered XML documents, whereby each layer is related to one individual workflow task. This paper explores these two provenance models by using an example workflow application and describes how they are integrated into WS-VLAM including implementation details of the provenance architecture. It finally gives a comparison of the two different approaches with a special regard to provenance for human actors

Essential oils from wild populations of Algerian Lavandula sotechas L. : composition, chemical variability and in vitro biological properties

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