Tunneling Conductance Dependence of the Tunneling Spectra in Bi2Sr2CaCu2O8

We have measured the tunneling spectra in Bi₂Sr₂CaCu₂O₈ with a scanning tunneling microscope(STM) at various tip-sample distances by changing the tunneling conductance in a controlled manner. When the tunneling conductance is increased from 1×10^–9 to 1×10^–5 S, the spectra do not show changes in particular. However, the gap value decreases steeply and the asymmetric back ground density of states turns inverted V-shaped one above 6×10^–4 S. The changes in the tunneling spectra at the high tunneling conductances are explained by the enhancement of the local carrier density induced by the pressure that the STM tip applied to the sample.     

Effective lightning protection schemes for distribution line

The causes of lightning outage are subdivided into direct lightning strokes and induced lightning strokes, which are identified by the characteristics of the lightning overvoltage. In the past, lightning protection devices were directed mainly toward the latter, and attention has been focused on the installation of lightning protection devices, ground wires, and reinforcement of insulators. However, lightning outages continue to occur. Thus it is extremely important to clarify the fault characteristics of lightning surges and to study the effectiveness of various lightning protection devices by considering both direct lightning stroke and induced lightning stroke in order to prevent lightning outage in the future. In this research, the electromagnetic transients program (EMTP) has been applied to the direct lightning stroke, and the induced lightning outage analysis program for multiple conductor systems has been applied to the induced lightning stroke to study the effectiveness of lightning protection devices provided by combination of various lightning protection devices. The most effective lightning protection schemes are analyzed and evaluated based on verification tests on the full scale models as well as economic considerations.     

Correlations Among Maximum Likelihood and Weighted/Unweighted Least Squares Estimators in Factor Analysis

The asymptotic correlations among maximum likelihood (ML) and various least squares (LS) estimators in factor analysis are derived. The LS estimators include the unweighted (ULS) and weighted estimators for unstandardized variables and the ULS estimators for standardized variables. The derived formulas cover the cases with restrictions on parameters. Numerical examples with simulations are provided to confirm the accuracy of the formulas and the influence of scales on the asymptotic correlations.

     

Software‐processed edge‐ and level‐adaptive overdrive (SELAO) method for high‐quality motion pictures

Abstract— In this paper, a software‐processed edge‐ and level‐adaptive overdrive (SELAO) method, which is a novel overdrive technique that utilizes not only a temporal change of gray levels but also a spatial edge intensity of motion pictures, is proposed. The SELAO method is a software video‐processing technology to improve motion‐picture quality rendered on LCDs more than is possible with a conventional SLAO method without edge‐adaptive overdrive, and it works in real time on commonly used personal computers (PCs).     

A case of inflammatory pseudotumor of the liver causing elevated serum CA19-9 levels

Inflammatory pseudotumor of the liver is a rare lesion characterized by proliferating fibrovascular tissue admixed with inflammatory cells. A 50-yr-old Japanese man was hospitalized because of upper abdominal pain and high fever. Computed tomography revealed a poorly demarcated, low density mass in the left lobe of the liver, and abnormal laboratory findings included WBC 9340/mm3, CRP 10.5 mg/dl, and marked elevation of CA19-9 to 1167.9 U/ml. Endoscopic retrograde cholangiography showed irregularity of the intrahepatic bile duct of the left lateral segment, and the lateral segmental branches of the portal vein were not visualized on the venous phase of abdominal angiography. Ultrasound-guided liver biopsy was performed, but malignant disease, including intrahepatic cholangiocarcinoma, could not be completely ruled out. The patient underwent left hepatic lobectomy with lymph node dissection. Histopathological examination yielded a definitive diagnosis of inflammatory pseudotumor. The lesion was immunohistochemically stained for CA19-9 by the ABC method, and the biliary epithelium in severely inflamed portal canals was found to be positive. The markedly elevated preoperative level of CA 19-9 decreased to almost within the normal range and the patient remains well 2 yr 9 months after surgery, without any complications.     

Stochastic Resonance in Recurrent Neural Network with Hopfield-Type Memory

Stochastic resonance (SR) is known as a phenomenon in which the presence of noise helps a nonlinear system in amplifying a weak (under barrier) signal. In this paper, we investigate how SR behavior can be observed in practical autoassociative neural networks with the Hopfield-type memory under the stochastic dynamics. We focus on SR responses in two systems which consist of three and 156 neurons. These cases are considered as effective double-well and multi-well models. It is demonstrated that the neural network can enhance weak subthreshold signals composed of the stored pattern trains and have higher coherence abilities between stimulus and response.     

Evidence for a Jahn-Teller distortion of Cs2NaHoCl6 at 150 mK observed by X-ray diffractometry

By means of SR X-ray diffractometry, cooperative Jahn-Teller distortion as a structural phase transformation of the first kind is directly confirmed by a splitting of Laue spots. It is understood that the degenerate ground state of a nonmagnetic doublet (₃) of Ho³⁺ in a cubic Ho-elpasolite, Fm3m(O _h ⁵ ), at least above 600 mK, is lifted up by a distortion below 150 mK, which was predicted by a splitting of the NQR spectrum and anomalies in the specific heat and magnetic susceptibility. This structural transformation shows not only the lowest transition temperature up to now, but also the greatest hysteresis specified by a Schottky-type excitation. The magnitude of the relative distortion (1/1) has been roughly estimated to be on the 2.0 · 10^–3 order of magnitude, which is much larger than the predicted value obtained from a point charge approximation. The pecularities of this transition are discussed from the viewpoint that the general theory of a structural transformation, established convincingly at high temperatures could not be easily applied.     

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.

     

Improvement of a neural network-based motion generator with bimanual coordination for upper limb prosthesis

We propose to redesign a neural network used as a motion generator with bimanual coordination for upper limb prosthesis in order to improve its learning capability. We assumed that the wearer of the prosthesis was a unilateral amputee. In our previous work, we proposed a prosthesis control system using a neural network that learned bimanual coordination in order to implement smooth motion with both hands. However, the previously proposed system has the problem that a neural network cannot generate the desired motion of the prosthesis in special cases. The reason is that the motion generator calculates the desired posture of the prosthesis from the current posture of the healthy arm only, regardless of the current posture of the prosthesis. We propose to use the current posture of both the healthy arm and the prosthesis as neural network inputs in order to solve this problem. In this article, we show that a single neural network whose input was the current posture of both arms could learn the relationships of the coordinated motions of holding boxes of different sizes, and the newly proposed system can calculate the desired motion of the prosthesis in special cases through computer simulations.     

Basic constructs of the SIM operating system

The basic constructs of SIMPOS (Sequential Inference Machine Programming and Operating System) are explained. SIMPOS is an operating system for a super-personal computer (SIM), based on logic programming language (a modified Prolog, called KL0). Our design principle is simplicity both in concept and structure. The entire system will be constructed using these basic constructs.