An fpga implementation of decision tree classification

Data mining techniques are a rapidly emerging class of applications that have widespread use in several fields. One important problem in data mining is Classification, which is the task of assigning objects to one of several predefined categories. Among the several solutions developed, Decision Tree Classification (DTC) is a popular method that yields high accuracy while handling large datasets. However, DTC is a computationally intensive algorithm, and as data sizes increase, its running time can stretch to several hours. In this paper, we propose a hardware implementation of Decision Tree Classification. We identify the computeintensive kernel (Gini Score computation) in the algorithm, and develop a highly efficient architecture, which is further optimized by reordering the computations and by using a bitmapped data structure. Our implementation on a Xilinx Virtex-II Pro FPGA platform (with 16 Gini units) provides up to 5.58 × performance improvement over an equivalent software implementation.

液中オープンループ電位顕微鏡によるステンレス鋼のナノスケール腐食解析

13301甲第4408号博士（工学）金沢大学博士論文要旨Abstract 要約Outline 以下に掲載：ACS Nano 10(2) pp.2575-2583 2016. ACS Publications. 共著者：K. Honbo, S. Ogata, T. Kitagawa, N. Kobayashi, I. Sugimoto, S. Shima, A. Fukunaga, C. Takatoh, T. Fukum

A Sphingosine Kinase Form 2 Knockout Sensitizes Mouse Myocardium to Ischemia/Reoxygenation Injury and Diminishes Responsiveness to Ischemic Preconditioning

Sphingosine kinase (SphK) exhibits two isoforms, SphK1 and SphK2. Both forms catalyze the synthesis of sphingosine 1-phosphate (S1P), a sphingolipid involved in ischemic preconditioning (IPC). Since the ratio of SphK1 : SphK2 changes dramatically with aging, it is important to assess the role of SphK2 in IR injury and IPC. Langendorff mouse hearts were subjected to IR (30 min equilibration, 50 min global ischemia, and 40 min reperfusion). IPC consisted of 2 min of ischemia and 2 min of reperfusion for two cycles. At baseline, there were no differences in left ventricular developed pressure (LVDP), ± dP/dtmax, and heart rate between SphK2 null (KO) and wild-type (WT) hearts. In KO hearts, SphK2 activity was undetectable, and SphK1 activity was unchanged compared to WT. Total SphK activity was reduced by 53%. SphK2 KO hearts subjected to IR exhibited significantly more cardiac damage (37 ± 1% infarct size) compared with WT (28 ± 1% infarct size); postischemic recovery of LVDP was lower in KO hearts. IPC exerted cardioprotection in WT hearts. The protective effect of IPC against IR was diminished in KO hearts which had much higher infarction sizes (35 ± 2%) compared to the IPC/IR group in control hearts (12 ± 1%). Western analysis revealed that KO hearts had substantial levels of phosphorylated p38 which could predispose the heart to IR injury. Thus, deletion of the SphK2 gene sensitizes the myocardium to IR injury and diminishes the protective effect of IPC

A multi-sinusoidal compartment model as an alternative to the dispersion model for hepatic extraction kinetic analysis

The analysis of hepatic availability or hepatic extraction ratio as a function of hepatic blood flow and intrinsic clearance is important to estimate first-pass effects and oral bioavailability for pre-systemically-eliminating drugs. The dispersion model can afford more accurate analysis of hepatic availability than the other conventional models (well-stirred model, parallel-tube model). However, the model is rather complicate. In the present study, a simpler model was derived by assuming the sinusoidal space in the liver as a number of well-stirred compartments sequentially connected through the hepatic blood flow (multi-sinusoidal compartment model). In comparison with the other models, this model demonstrated hepatic outflow profiles very similar to those obtained by the dispersion model. It gave a simpler equation for hepatic availability, and improved the inaccuracy of the well-stirred model or the parallel tube model. The number of compartments, around 3, for this model corresponded to the dispersion number, 0.33, which has been reported to give the best fit to the rat hepatic outflow in the dispersion model. Similarly, the number of compartments, around 5 corresponded to the dispersion number, 0.17, which has been reported to give the best fit to the human hepatic elimination kinetics in the dispersion model.論文 (Article

SES: Sentiment Elicitation System for Social Media Data

Abstract—Social Media is becoming major and popu-lar technological platform that allows users discussing and sharing information. Information is generated and man-aged through either computer or mobile devices by one person and consumed by many other persons. Most of these user generated content are textual information, as So-cial Networks(Facebook, LinkedIn), Microblogging(Twitter), blogs(Blogspot, Wordpress). Looking for valuable nuggets of knowledge, such as capturing and summarizing sentiments from these huge amount of data could help users make informed decisions. In this paper, we develop a sentiment identification system called SES which implements three dif-ferent sentiment identification algorithms. We augment basic compositional semantic rules in the first algorithm. In the second algorithm, we think sentiment should not be simply classified as positive, negative, and objective but a continuous score to reflect sentiment degree. All word scores are calculated based on a large volume of customer reviews. Due to the special characteristics of social media texts, we propose a third algorithm which takes emoticons, negation word posi-tion, and domain-specific words into account. Furthermore, a machine learning model is employed on features derived from outputs of three algorithms. We conduct our experiments on user comments from Facebook and tweets from twitter. The results show that utilizing Random Forest will acquire a better accuracy than decision tree, neural network, and logistic regression. We also propose a flexible way to represent document sentiment based on sentiments of each sentence contained. SES is available online. Keywords-Social media, sentiment, rule, machine learning I

Visualizing Nanoscale Distribution of Corrosion Cells by Open-Loop Electric Potential Microscopy

Corrosion is a traditional problem but still one of the most serious problems in industry. To reduce the huge economic loss caused by corrosion, tremendous effort has been made to understand, predict and prevent it. Corrosion phenomena are generally explained by the formation of corrosion cells at a metal-electrolyte interface. However, experimental verification of their nanoscale distribution has been a major challenge owing to the lack of a method able to visualize the local potential distribution in an electrolytic solution. In this study, we have investigated the nanoscale corrosion behavior of Cu fine wires and a duplex stainless steel by in situ imaging of local corrosion cells by open-loop electric potential microscopy (OL-EPM). For both materials, potential images obtained by OL-EPM show nanoscale contrasts, where areas of higher and lower potential correspond to anodic areas (i.e., corrosion sites) and cathodic areas, respectively. This imaging capability allows us to investigate the real-time transition of local corrosion sites even when surface structures show little change. This is particularly useful for investigating reactions under surface oxide layers or highly corrosion-resistant materials as demonstrated here. The proposed technique should be applicable to the study of other redox reactions on a battery electrode or a catalytic material. The results presented here open up such future applications of OL-EPM in nanoscale electrochemistry. © 2016 American Chemical Society.Embargo Period 12 month