Effects of Na2SiO3 on anodization of Mg-Al-Zn alloy in 3 M KOH solution

The anodic behavior of Mg-Al-Zn alloy (AZ91D) under low potential electrolysis in 3 M KOH solutions was studied with and without addition of 0.5-5 M Na₂SiO₃. Anodic films incorporating silicon were formed during electrolysis, and the films formed under constant potential electrolysis at 4 V in 3 M KOH solution with Na₂SiO₃ were uniform and thicker than the films formed without Na₂SiO₃. A few at% of silicon was present as Mg₂SiO₄ in the films, although the main compound was Mg(OH)₂. The corrosion resistance of the films formed in solutions with Na₂SiO₃ increased in an anodic polarization test in 0.1 M KCl solution.     

Predicting viewer-perceived activity/dominance in soccer games with stick-breaking HMM using data from a fixed set of cameras

We attempted to predict activity/dominance for soccer games, where activity is defined as the degree of activity of the game as perceived by the viewer, whereas dominance is the degree at which the viewer perceives a particular team to dominate over the other team. Such activity/dominance information would help a layman viewer understand the game. It would also enable construction of an automatic digest creation system that extracts scenes having high activity/dominance. There are two facets of this study: 1. The main part of the underlying prediction model consists of a Stick-Breaking Hidden Markov Model, where the data automatically estimates the number of states of the Markov process behind the data. 2. The data used in this paper is vector time-series data consisting of player, referee, and ball positions, together with team information, acquired by a set of fixed cameras. The problem was approached with a Bayesian framework where learning and prediction were implemented by three different methods: Markov Chain Monte Carlo, Expectation Maximization, and Variational Bayes. The proposed method was tested using a dataset consisting of 10 professional soccer games and was compared against standard regression methods.     

Studying re-opened bugs in open source software

Bug fixing accounts for a large amount of the software maintenance resources. Generally, bugs are reported, fixed, verified and closed. However, in some cases bugs have to be re-opened. Re-opened bugs increase maintenance costs, degrade the overall user-perceived quality of the software and lead to unnecessary rework by busy practitioners. In this paper, we study and predict re-opened bugs through a case study on three large open source projects—namely Eclipse, Apache and OpenOffice. We structure our study along four dimensions: (1) the work habits dimension (e.g., the weekday on which the bug was initially closed), (2) the bug report dimension (e.g., the component in which the bug was found) (3) the bug fix dimension (e.g., the amount of time it took to perform the initial fix) and (4) the team dimension (e.g., the experience of the bug fixer). We build decision trees using the aforementioned factors that aim to predict re-opened bugs. We perform top node analysis to determine which factors are the most important indicators of whether or not a bug will be re-opened. Our study shows that the comment text and last status of the bug when it is initially closed are the most important factors related to whether or not a bug will be re-opened. Using a combination of these dimensions, we can build explainable prediction models that can achieve a precision between 52.1–78.6 % and a recall in the range of 70.5–94.1 % when predicting whether a bug will be re-opened. We find that the factors that best indicate which bugs might be re-opened vary based on the project. The comment text is the most important factor for the Eclipse and OpenOffice projects, while the last status is the most important one for Apache. These factors should be closely examined in order to reduce maintenance cost due to re-opened bugs.     

Expectation-maximization algorithms for inference in Dirichlet processes mixture

Mixture models are ubiquitous in applied science. In many real-world applications, the number of mixture components needs to be estimated from the data. A popular approach consists of using information criteria to perform model selection. Another approach which has become very popular over the past few years consists of using Dirichlet processes mixture (DPM) models. Both approaches are computationally intensive. The use of information criteria requires computing the maximum likelihood parameter estimates for each candidate model whereas DPM are usually trained using Markov chain Monte Carlo (MCMC) or variational Bayes (VB) methods. We propose here original batch and recursive expectation-maximization algorithms to estimate the parameters of DPM. The performance of our algorithms is demonstrated on several applications including image segmentation and image classification tasks. Our algorithms are computationally much more efficient than MCMC and VB and outperform VB on an example.     

Lithography with UV-LED array for curved surface structure

The technology to fabricate high-aspect ratio and micro curved surface structures is needed for optical device fabrication such as micro lens, light guiding device, and so on. In this study, micro curved surface structures were fabricated by using novel lithography technique with UV-LED array and rotary stage (Hanai et al. in Proceedings of the 22nd sensor symposium, pp 516–519, 2005). Smooth surface structures can be fabricated in this technique, because UV-LED which has wide directivity characteristics makes the difference of the UV dose. In addition, the structures can be formed with high uniformity in large area by only one exposure process, because rotation reduces the unevenness of exposure. This technique can control the shapes of structures by changing exposure time and applying gray scale mask method (Waits et al. in Sensors Actuat A 119:245–253, 2005). We fabricated structures of hemisphere, semi-cylinder, and semi-cone by using this technique. High-aspect ratio structures were obtained and they had smooth curved surface. Then, they were used for micro lens fabrication. By using molding technique, micro lenses of the UV curable resin were fabricated on the silicon wafer.     

Cylindrical coils created with 3D X-ray lithography and metallization

The demand for microactuators is increasing recently. The key technology to realizing practical microactuators is microfabrication process. In the production of microminiature components, the technologies for processing high-aspect-ratio structures are essential. As one of these technologies, the LIGA process is widely known. Our laboratory researches the LIGA process to three-dimensional microfabrication and established the cylindrical-microcoil production process. In this paper, we have fabricated the cylindrical-microcoil for the solenoidal electromagnetic type microactuator. We designed and analyzed microactuators, and fabricated and evaluated microactuator coils produced by the combination of three-dimensional X-ray lithography and level copper plating. We succeeded in creating threaded groove-shaped structures with 10 μm line width, 20 μm pitch, and aspect ratio of 5 on the surface of an acrylic pipe by means of three-dimensional X-ray lithography. As a measure to suppress void generation, which is one of the shortcomings of electrolytic plating processes, the sputtering apparatus and plating equipment were improved, a pretreatment process was additionally provided, and the actual electrolytic plating method was improved. As a result, a void-free metallic deposit could be formed on a thin coil line. The processing technology enables the formation of thin-wire coil lines whose current paths feature a large allowable current-carrying capacity, enabling the production of miniature, high-output microactuators.     

Reflection-grating self-pumped phase conjugation with optical feedback free of input conditions

A reflection-grating self-pumped phase-conjugate mirror is demonstrated with a photorefractive KNbO(3) crystal in a novel ring configuration. The feedback beam in the ring configuration is made to track the input probe beam automatically so that the phase conjugation is sustained without readjustment of the optical feedback loop when the angle or the position of the input probe beam is changed. The tracking feedback is realized by means of a simple three-lens erect imaging system. The angular and the positional acceptances for a tightly focused input beam of 50-μm diameter are Δθ(x). = 55 mrad, Δθ(y), = 67 mrad, and Δ(x) = 2.1 mm, Δ(y) = 4.2 mm, respectively.     

Three-dimensional board-to-board free-space optical interconnects and their application to the prototype multiprocessor system: COSINE-III

A prototype multiprocessor system using three-dimensional board-to-board free-space optical interconnects is constructed for the first time to our knowledge. In the system, 64 processing units form a three-dimensional mesh processor network with the help of bidirectional board-to-board free-space optical interconnects. A theoretical analysis shows that the three-dimensional board-to-board freespace optical interconnects effectively solve common interconnection problems such as wiring congestion, signal delay, and clock skew. The prototype system, COSINE-III, is confirmed to work well as a multiprocessor system. The system is also shown to be easy to extend to a larger and more flexible system.     

Development of an epileptic seizure prediction algorithm using R–R intervals with self-attentive autoencoder

Epilepsy is a neurological disorder that may affect the autonomic nervous system (ANS) from 15 to 20 min before seizure onset, and disturbances of ANS affect R–R intervals (RRI) on an electrocardiogram (ECG). This study aims to develop a machine learning algorithm for predicting focal epileptic seizures by monitoring R–R interval (RRI) data in real time. The developed algorithm adopts a self-attentive autoencoder (SA-AE), which is a neural network for time-series data. The results of applying the developed seizure prediction algorithm to clinical data demonstrated that it functioned well in most patients; however, false positives (FPs) occurred in specific participants. In a future work, we will investigate the causes of FPs and optimize the developing seizure prediction algorithm to further improve performance using newly added clinical data.

     

3He in 99.5% Porous Aerogel at the Normal-Superfluid Transition

We have used a torsional oscillator to measure the superfluid density and dissipation near the superfluid transition of ³ He in aerogel of 99.5% porosity. We used a new cell (constructed at Penn State) for which the aerogel was grown in the pores of a 100 m silver sinter. The cell was tested with ⁴ He and showed no signs of the second-sound resonances that have interfered with previous torsional oscillator measurements. The measurements with ³ He, presented here, were taken at pressures of 1.34 and 4.13 bars. We observed values of _s / in the T 0 limit of 0.05 and 0.14 respectively. Our measurements show an increase in the dissipation on warming through T _c . This series of measurements is ongoing and temperature sweeps at various pressures are planned.