Adaptive locks: Combining transactions and locks for efficient concurrency

Transactional memory is being advanced as an alternative to traditional lock-based synchronization for concurrent programming. Transactional memory simplifies the programming model and maximizes concurrency. At the same time, transactions can suffer from interference that causes them to often abort, from heavy overheads for memory accesses, and from expressiveness limitations (e.g., for I/O operations). In this paper we propose an adaptive locking technique that dynamically observes whether a critical section would be best executed transactionally or while holding a mutex lock. The critical new elements of our approach include the adaptivity logic and cost–benefit analysis, a low-overhead implementation of statistics collection and adaptive locking in a full C compiler, and an exposition of the effects on the programming model. In experiments with both micro and macrobenchmarks we found adaptive locks to consistently match or outperform the better of the two component mechanisms (mutexes or transactions). Compared to either mechanism alone, adaptive locks often provide 3-to-10x speedups. Additionally, adaptive locks simplify the programming model by reducing the need for fine-grained locking: with adaptive locks, the programmer can specify coarse-grained locking annotations and often achieve fine-grained locking performance due to the transactional memory mechanisms.     

Identification of a golf swing robot using soft computing approach

Golf swing robots have been recently developed in an attempt to simulate the ultra high-speed swing motions of golfers. Accurate identification of a golf swing robot is an important and challenging research topic, which has been regarded as a fundamental basis in the motion analysis and control of the robots. But there have been few studies conducted on the golf swing robot identification, and comparative analyses using different kinds of soft computing methodologies have not been found in the literature. This paper investigates the identification of a golf swing robot based on four kinds of soft computing methods, including feedforward neural networks (FFNN), dynamic recurrent neural networks (DRNN), fuzzy neural networks (FNN) and dynamic recurrent fuzzy neural networks (DRFNN). The performance comparison is evaluated based on three sets of swing trajectory data with different boundary conditions. The sensitivity of the results to the changes in system structure and learning rate is also investigated. The results suggest that both FNN and DRFNN can be used as a soft computing method to identify a golf robot more accurately than FFNN and DRNN, which can be used in the motion control of the robot.     

Compressive multi-spectral imaging using self-correlations of images based on hierarchical joint sparsity models

We propose a novel multi-spectral imaging method based on compressive sensing (CS). In CS theory, the enhancement of signal sparsity is important for accurate signal reconstruction. The main novelty of the proposed method is the employment of a self-correlation of an image, that is a local intensity similarity and multi-spectral correlation, to enhance the sparsity of the multi-spectral image to be recovered. Local intensity similarity, which is based on the concept that spatial changes in intensity are likely to be similar within local regions, contributes to sparsity enhancement. Furthermore, we exploit multi-spectral correlation to improve the sparsity of the multi-spectral components to be recovered. In order to simultaneously exploit different types of characteristics (i.e., local intensity similarity and multi-spectral correlation) for representing a signal as sufficiently sparse, we introduce a hierarchical joint sparsity model in the CS image recovery process. Our experiments show that the use of a self-correlation significantly improves the performance of multi-spectral image reconstruction.     

Molecular dynamics simulations of diffusion of submonolayer polar liquid lubricant films on solid surfaces

A fundamental understanding of the diffusion phenomena of submonolayer polar liquid films is important for achieving reliable lubrication between moving mechanical parts separated by a nanometer-sized gap. To acquire this understanding, we conducted molecular dynamics (MD) simulations of diffusion phenomena of submonolayer polar perfluoropolyether (PFPE) Zdol films on solid surfaces. To improve the accuracy of these simulations, we developed an all-atom model that includes hydrogen-bond potential and refined atomic charges for Zdol molecules and tested it through MD simulations of spreading of step-shaped submonolayer PFPE films. Our MD simulations reproduced the experimentally observed effects of polar end groups on the diffusion speed and molecular conformation of Zdol. We then conducted MD simulations of self-diffusion of submonolayer Zdol films; these simulations demonstrated that as the thickness of the submonolayer Zdol films decreases, molecular conformation becomes flatter and the self-diffusion coefficient decreases. These changes in molecular conformation partially explain our experimental finding that the spreading of step-shaped submonolayer polar PFPE films slows down with decreasing initial thickness.     

Implementation of a decision support system using an interactive large-scale high-resolution display

In this research, we propose and evaluate a decision support system using an interactive large-scale high-resolution display. This decision support system supports the summarization and decision-making of a large amount of disaster information during the occurrence of a large-scale natural disaster. Municipal employees at the disaster control headquarters can display disaster information on the large-scale display with a touch or flick on a laptop or tablet. To evaluate the operability, readability, functionality, and necessity of the decision support system, we surveyed 23 municipal employees in the disaster prevention division using a questionnaire. The system received a great evaluation in all the evaluation items.     

Limitations on post-processing assisted quantum programming

A quantum multimeter is a programmable device that can implement measurements of different observables depending on the programming quantum state inserted into it. The advantage of this arrangement over a single-purpose device is in its versatility: one can realize various measurements simply by changing the programming state. The classical manipulation of measurement output data is known as post-processing. In this work we study the post-processing assisted quantum programming, which is a protocol where quantum programming and classical post-processing are combined. We provide examples showing that these two processes combined can be more efficient than either of them used separately. Furthermore, we derive an inequality relating the programming resources to their corresponding programmed observables, thereby enabling us to study the limitations on post-processing assisted quantum programming.     

Closed Form Solution of Local Shape from Shading at Critical Points

In the theory of shape from shading, behaviours of the local solution around a critical point of the image play an important role. This paper shows that the second derivatives of the object surface can be locally determined at these image critical points. Closed form expressions of the surface second derivatives in terms of the second derivatives of the image brightness and of the reflectance map are shown. They are derived as follows: By differentiating the image irradiance equation twice at an image critical point, a set of polynomial equations is obtained that contains the second derivatives of the surface, of the image brightness and of the reflectance map. Regarding these equations as simultaneous equations for unknown surface second derivatives, they are algebraically solved and their explicit expressions are derived. Such a derivation is possible only at image critical points and is impossible at any other image point. The applicability of the derived expressions to noisy images is tested using synthetic images.     

Straight-through microchannel devices for generating monodisperse emulsion droplets several microns in size

The authors recently proposed a promising technique for producing monodisperse emulsions using a straight-through microchannel (MC) device composed of an array of microfabricated oblong holes. This research developed new straight-through MC devices with tens of thousands of oblong channels of several microns in size on a silicon-on-insulator plate, and investigated the emulsification characteristics using the microfabricated straight-through MC devices. Monodisperse oil-in-water (O/W) and W/O emulsions with average droplet diameters of 4.4–9.8 μm and coefficients of variation of less than 6% were stably produced using surface-treated straight-through MC devices that included uniformly sized oblong channels with equivalent diameters of 1.7–5.4 μm. The droplet size of the resultant emulsions depended greatly on the size of the preceding oblong channels. The emulsification process using the straight-through MC devices developed in this research had very high apparent energy efficiencies of 47–60%, defined as (actual energy input applied to droplet generation/theoretical minimum energy input necessary for making droplets) × 100. Straight-through MC devices with numerous oblong microfluidic channels also have great potential for increasing the productivity of monodisperse fine emulsions.     

Case learning for CBR-based collision avoidance systems

With the rapid development of case-based reasoning (CBR) techniques, CBR has been widely applied to real-world applications such as collision avoidance systems. A successful CBR-based system relies on a high-quality case base, and a case creation technique for generating such a case base is highly required. In this paper, we propose an automated case learning method for CBR-based collision avoidance systems. Building on techniques from CBR and natural language processing, we developed a methodology for learning cases from maritime affair records. After giving an overview on the developed systems, we present the methodology and the experiments conducted in case creation and case evaluation. The experimental results demonstrated the usefulness and applicability of the case learning approach for generating cases from the historic maritime affair records.