On the impact of serializing contention management on STM performance

Transactional memory (TM) is an emerging concurrent programming abstraction. Numerous software-based transactional memory (STM) implementations have been developed in recent years. STM implementations must guarantee transaction atomicity and isolation. In order to ensure progress, an STM implementation must resolve transaction collisions by consulting a contention manager (CM).     

Reader-based exploration of lexical cohesion

Lexical cohesion refers to the reader-perceived unity of text achieved by the author’s usage of words with related meanings (Halliday and Hasan, 1976). This article reports on an experiment with 22 readers aimed at finding lexical cohesive patterns in 10 texts. Although there was much diversity in peoples’ answers, we identified a common core of the phenomenon, using statistical analysis of agreement patterns and a validation experiment. The core data may now be used as a minimal test set for models of lexical cohesion; we present an example suggesting that models based on mutually exclusive lexical chains will not suffice. In addition, we believe that procedures for revealing and analyzing sub-group patterns of agreement described here may be applied to data collected in other studies of comparable size.

Reader-based exploration of lexical cohesion

Lexical cohesion refers to the reader-perceived unity of text achieved by the author’s usage of words with related meanings (Halliday and Hasan, 1976). This article reports on an experiment with 22 readers aimed at finding lexical cohesive patterns in 10 texts. Although there was much diversity in peoples’ answers, we identified a common core of the phenomenon, using statistical analysis of agreement patterns and a validation experiment. The core data may now be used as a minimal test set for models of lexical cohesion; we present an example suggesting that models based on mutually exclusive lexical chains will not suffice. In addition, we believe that procedures for revealing and analyzing sub-group patterns of agreement described here may be applied to data collected in other studies of comparable size.

Effects of THC on driving performance, physiological state and subjective feelings relative to alcohol

BACKGROUND: The effects of marijuana or THC on driving has been tested in several studies, but usually not in conjunction with physiological and subjective responses and not in comparison to alcohol effects on all three types of measures. OBJECTIVE: To assess the effects of two dosages of THC relative to alcohol on driving performance, physiological strain, and subjective feelings. METHOD: We tested the subjective feelings and driving abilities after placebo, smoking two dosages of THC (13 mg and 17 mg), drinking (0.05% BAC) and 24 h after smoking the high dose THC cigarette, while monitoring physiological activity of the drugs by heart rate. Fourteen healthy students, all recreational marijuana users, participated in the study. RESULTS: Both levels of THC cigarettes significantly affected the subjects in a dose-dependent manner. The moderate dose of alcohol and the low THC dose were equally detrimental to some of the driving abilities, with some differences between the two drugs. THC primarily caused elevation in physical effort and physical discomfort during the drive while alcohol tended to affect sleepiness level. After THC administration, subjects drove significantly slower than in the control condition, while after alcohol ingestion, subjects drove significantly faster than in the control condition. No THC effects were observed after 24 h on any of the measures.     

A NEW VERSION OF STRUCTURAL PERSISTENCE IN PREDICTION

Abstract. A data analysis estimation method called structural persistence is presented in this paper. Prediction applications of the method to time series with trend and seasonal components are discussed. The basic underlying assumption is that the structure of a given series does not change in the forecasting range. Therefore, when forecasts are made, the values which measure the structure (e.g. the trend shape) of the original and of the forecast-extended time series should be the same. We propose relaxed 'model-free' use of the principle which nevertheless provides an explicit prediction formula for the one-stepahead prediction P _{n +1}. Missing data can also be estimated using this method. The procedure is applied to some previously published time series data and the prediction results are compared with those obtained using the Box-Jenkins approach.     

Carbon and Nitrogen Based Nanosheets as Fluorescent Probes with Tunable Emission

2D carbon and nitrogen based semiconductors (CN) have attracted widespread attention for their possible use as low‐cost and environmentally friendly materials for various applications. However, their limited solution‐dispersibility and the difficulty in preparing exfoliated sheets with tunable photophysical properties restrain their exploitation in imaging‐related applications. Here, the synthesis of carbon and nitrogen organic scaffolds with highly tunable optical properties, excellent dispersion in water and DMSO, and good bioimaging properties is reported. Tailored photophysical and chemical properties are acquired by the synthesis of new starting monomers containing different substituent chemical groups with varying electronic properties. Upon monomer condensation at moderate temperature, 350 °C, the starting chemical groups are fully preserved in the final CN. The low condensation temperature and the effective molecular‐level modification of the CN scaffold lead to well‐dispersed photoluminescent CN thin sheets with a wide range of emission wavelengths. The good bioimaging properties and the tunable fluorescence properties are exemplified by in situ visualization of giant unilamellar vesicles in a buffered aqueous solution as a model system. This approach opens the possibility for the design of tailor‐made CN materials with tunable photophysical and chemical properties toward their exploitation in various fields, such as photocatalysis, bioimaging, and sensing.     

A survey on Mesh Segmentation Techniques

We present a review of the state of the art of segmentation and partitioning techniques of boundary meshes. Recently, these have become a part of many mesh and object manipulation algorithms in computer graphics, geometric modelling and computer aided design. We formulate the segmentation problem as an optimization problem and identify two primarily distinct types of mesh segmentation, namely part segmentation and surface‐patch segmentation. We classify previous segmentation solutions according to the different segmentation goals, the optimization criteria and features used, and the various algorithmic techniques employed. We also present some generic algorithms for the major segmentation techniques.     

WND-CHARM: Multi-purpose image classification using compound image transforms

We describe a multi-purpose image classifier that can be applied to a wide variety of image classification tasks without modifications or fine-tuning, and yet provide classification accuracy comparable to state-of-the-art task-specific image classifiers. The proposed image classifier first extracts a large set of 1025 image features including polynomial decompositions, high contrast features, pixel statistics, and textures. These features are computed on the raw image, transforms of the image, and transforms of transforms of the image. The feature values are then used to classify test images into a set of pre-defined image classes. This classifier was tested on several different problems including biological image classification and face recognition. Although we cannot make a claim of universality, our experimental results show that this classifier performs as well or better than classifiers developed specifically for these image classification tasks. Our classifier's high performance on a variety of classification problems is attributed to (i) a large set of features extracted from images; and (ii) an effective feature selection and weighting algorithm sensitive to specific image classification problems. The algorithms are available for free download from openmicroscopy.org.