Model Checking with Program Slicing Based on Variable Dependence Graphs

In embedded control systems, the potential risks of software defects have been increasing because of software complexity which leads to, for example, timing related problems. These defects are rarely found by tests or simulations. To detect such defects, we propose a modeling method which can generate software models for model checking with a program slicing technique based on a variable dependence graph. We have applied the proposed method to one case in automotive control software and demonstrated the effectiveness of the method. Furthermore, we developed a software tool to automate model generation and achieved a 35% decrease in total verification time on model checking.Comment: In Proceedings FTSCS 2012, arXiv:1212.657

A Coupled Spintronics Neuromorphic Approach for High-Performance Reservoir Computing

The rapid development in the field of artificial intelligence has increased the demand for neuromorphic computing hardware and its information-processing capability. A spintronics device is a promising candidate for neuromorphic computing hardware and can be used in extreme environments due to its high resistance to radiation. Improving the information-processing capability of neuromorphic computing is an important challenge for implementation. Herein, a novel neuromorphic computing framework using spintronics devices is proposed. This framework is called coupled spintronics reservoir (CSR) computing and exploits the high-dimensional dynamics of coupled spin-torque oscillators as a computational resource. The relationships among various bifurcations of the CSR and its information-processing capabilities through numerical experiments are analyzed and it is found that certain configurations of the CSR boost the information-processing capability of the spintronics reservoir toward or even beyond the standard level of machine learning networks. The effectiveness of our approach is demonstrated through conventional machine learning benchmarks and edge computing in real physical experiments using pneumatic artificial muscle-based wearables, which assist human operations in various environments. This study significantly advances the availability of neuromorphic computing for practical uses

Repression of factor VIII inhibitor development with apoptotic factor VIII-expressing embryonic stem cells

Development of factor VIII (fVIII)-neutralizing antibodies, called inhibitors, is a challenging problem in the management of hemophilia A patients. We explored the possibility of pretreatment with apoptotic fVIII-expressing embryonic stem (ES) cells to prevent the development of fVIII inhibitors. Murine ES cells integrated with the human F8 gene were differentiated into embryoid bodies, dissociated to a single cell suspension, subjected to hypo-osmotic shock to induce apoptosis, and intraperitoneally injected into hemophilia A mice. Inhibitors were induced by periodic intraperitoneal injections of recombinant human fVIII (rhfVIII). In the groups in which intraperitoneal injections of rhfVIII began at 1-3 weeks after pretreatment, the titers of inhibitors were significantly lower after the third administration of rhfVIII compared with that in the control group in which apoptotic Ainv18 ES cells (without the human F8 gene) were used for pretreatment, and continued to show lower levels until the sixth administration of rhfVIII. These results suggest that pretreatment with apoptotic hfVIII-expressing ES cells might be promising for the prevention of fVIII inhibitor development in hemophilia A patients

A rare case of concomitant huge exophytic gastrointestinal stromal tumor of the stomach and Kasabach-Merritt phenomenon

<p>Abstract</p> <p>Background</p> <p>We report an extremely rare case of concomitant huge exophytic GIST of the stomach and Kasabach-Merritt phenomenon (KMP).</p> <p>Case presentation</p> <p>The patient was a 67-year-old man experiencing abdominal distension since September 2006. A physical examination revealed a 25 × 30 cm hard mass that was palpable in the middle and lower left abdomen minimal intrinsic mobility and massive ascites. Since the admitted patient was diagnosed with DIC, surgery could not be performed. The patient received a platelet transfusion and the DIC was treated. Due to this treatment, the platelet count recovered to 7.0 × 10⁴; tumor resection was performed at 16 days after admission. Laparotomy revealed a huge extraluminal tumor arising from the greater curvature of the stomach that measured 25 × 30 cm and had not ruptured into the peritoneal cavity or infiltrated other organs. Partial gastric resection was performed. The resected mass measured 25 × 25 × 20 cm. In cross section, the tumor appeared hard and homogenous with a small polycystic area. Histopathology of the resected specimen showed large spindle cell GIST with >5/50 HPF (high-power field) mitotic activity. The postoperative course was uneventful, and the coagulopathy improved rapidly.</p> <p>Conclusion</p> <p>Since the characteristic of tumor in this case was hypervascularity with bleeding and necrotic lesions, coagulopathy was thought to be caused by the trapping of platelets within a large vasculized tumor mass.</p

Automatic Network Structure-based Clustering of Multivariate Time Series

本論文では，ネットワーク構造を持つ多次元時系列データのためのパターン検出手法であるNGLについて述べる．NGLは，時間変化するネットワーク構造を持つ多次元時系列データが与えられたときに，その時系列データの中から重要なネットワーク構造を発見し，それらの情報を要約，表現する．具体的に，提案手法は，(a)多次元時系列データからネットワーク構造に基づいた解釈性の高いクラスタを発見する．(b)その際に最適な分割点とクラスタ数を自動的に決定する．すなわち，事前情報の付与が必要ない．そして，(c)自動決定アルゴリズムにより高精度なクラスタリングを実現する．人工データを用いた精度評価実験では最新の既存手法と比較して提案手法が大幅な精度向上を達成していることを明らかにした．また，実データを用いた実験ではNGLが解釈性の高いクラスタを発見していることを確認した．In this paper we present NGL, pattern mining algorithm for multiple time series data with underlying network structures. Our method has the following properties: (a) Interpretable: it provides interpretable network structures for the data; (b) Automatic: it determines the optimal cut points and the number of clusters automatically; (c) Accurate: it provides reliable clustering performance thanks to the automated algorithm. We evaluate our NGL algorithm on synthetic datasets, outperforming state-of-the-art baselines in terms of accuracy. And extensive experiments on real datasets demonstrate that NGL does indeed obtain interpretable network structure clusters