Maximum margin classifier working in a set of strings

Numbers and numerical vectors account for a large portion of data. However, recently the amount of string data generated has increased dramatically. Consequently, classifying string data is a common problem in many fields. The most widely used approach to this problem is to convert strings into numerical vectors using string kernels and subsequently apply a support vector machine that works in a numerical vector space. However, this non-one-to-one conversion involves a loss of information and makes it impossible to evaluate, using probability theory, the generalization error of a learning machine, considering that the given data to train and test the machine are strings generated according to probability laws. In this study, we approach this classification problem by constructing a classifier that works in a set of strings. To evaluate the generalization error of such a classifier theoretically, probability theory for strings is required. Therefore, we first extend a limit theorem on the asymptotic behavior of a consensus sequence of strings, which is the counterpart of the mean of numerical vectors, as demonstrated in the probability theory on a metric space of strings developed by one of the authors and his colleague in a previous study [18]. Using the obtained result, we then demonstrate that our learning machine classifies strings in an asymptotically optimal manner. Furthermore, we demonstrate the usefulness of our machine in practical data analysis by applying it to predicting protein--protein interactions using amino acid sequences.Comment: This manuscript has been withdrawn because the experiments in Section 6 are insufficien

Hes6 acts in a positive feedback loop with the neurogenins to promote neuronal differentiation

During the development of the vertebrate nervous system, neurogenesis is promoted by proneural bHLH proteins such as the neurogenins, which act as potent transcriptional activators of neuronal differentiation genes. The pattern by which these proteins promote neuronal differentiation is thought to be governed by inhibitors, including a class of transcriptional repressors called the WRPW-bHLH proteins, which are similar to Drosophila proteins encoded by hairy and genes in the enhancer of split complex (E-(SPL)-C). Here, we describe the isolation and characterization of Hes6, which encodes a novel WRPW-bHLH protein expressed during neurogenesis in mouse and Xenopus embryos. We show that Hes6 expression follows that of neurogenins but precedes that of the neuronal differentiation genes. We provide several lines of evidence to show that Hes6 expression occurs in developing neurons and is induced by the proneural bHLH proteins but not by the Notch pathway. When ectopically expressed in Xenopus embryos, Hes6 promotes neurogenesis. The properties of Hes6 distinguish it from other members of the WRPW-bHLH family in vertebrates, and suggest that it acts in a positive-feedback loop with the proneural bHLH proteins to promote neuronal differentiation

Hydrodynamic collective effects of active proteins in biological membranes

Lipid bilayers forming biological membranes are known to behave as viscous 2D fluids on submicrometer scales; usually they contain a large number of active protein inclusions. Recently, it has been shown [Proc. Nat. Acad. Sci. USA 112, E3639 (2015)] that such active proteins should in- duce non-thermal fluctuating lipid flows leading to diffusion enhancement and chemotaxis-like drift for passive inclusions in biomembranes. Here, a detailed analytical and numerical investigation of such effects is performed. The attention is focused on the situations when proteins are concentrated within lipid rafts. We demonstrate that passive particles tend to become attracted by active rafts and are accumulated inside them.Comment: 12 pages, 7 figure

Non-equilibrium and non-linear stationary state in thermoelectric materials

Efficiency of thermoelectric materials is characterized by the figure of merit Z. Z has been believed to be a peculiar material constant. However, the accurate measurements in the present work reveal that Z has large size dependence and a non-linear temperature distribution appears as stationary state in the thermoelectric material. The observation of these phenomena is achieved by the Harman method. This method is the most appropriate way to investigate the thermoelectric properties because the dc and ac resistances are measured by the same electrode configuration. We describe the anomalous thermoelectric properties observed in mainly (Bi,Sb)2Te3 by the Harman method and then insist that Z is not the peculiar material constant but must be defined as the physical quantity dependent of the size and the position in the material.Comment: 9 pages, 4 figures. submitted to Applied Physics Lette

Bifurcation in the angular velocity of a circular disk propelled by symmetrically distributed camphor pills

We studied rotation of a disk propelled by a number of camphor pills symmetrically distributed at its edge. The disk was put on a water surface so that it could rotate around a vertical axis located at the disk center. In such a system, the driving torque originates from surface tension difference resulting from inhomogeneous surface concentration of camphor molecules released from the pills. Here we investigated the dependence of the stationary angular velocity on the disk radius and on the number of pills. The work extends our previous study on a linear rotor propelled by two camphor pills [Phys. Rev. E, 96, 012609 (2017)]. It was observed that the angular velocity dropped to zero after a critical number of pills was exceeded. Such behavior was confirmed by a numerical model of time evolution of the rotor. The model predicts that, for a fixed friction coefficient, the speed of pills can be accurately represented by a function of the linear number density of pills. We also present bifurcation analysis of the conditions at which the transition between a standing and a rotating disk appears.Comment: 14 pages, 8 figure

Spin-glass-like behavior of Ge:Mn

We present a detailed study of the magnetic properties of low-temperature-molecular-beam-epitaxy grown Ge:Mn dilute magnetic semiconductor films. We find strong indications for a frozen state of Ge_{1-x}Mn_{x}, with freezing temperatures of T_f=12K and T_f=15K for samples with x=0.04 and x=0.2, respectively, determined from the difference between field-cooled and zero-field-cooled magnetization. For Ge_{0.96}Mn_{0.04}, ac susceptibility measurements show a peak around T_f, with the peak position T'_f shifting as a function of the driving frequency f by Delta T_f' / [T_f' Delta log f] ~ 0.06, whereas for sample Ge_{0.8}Mn_{0.2} a more complicated behavior is observed. Furthermore, both samples exhibit relaxation effects of the magnetization after switching the magnitude of the external magnetic field below T_f which are in qualitative agreement with the field- and zero-field-cooled magnetization measurements. These findings consistently show that Ge:Mn exhibits a frozen magnetic state at low temperatures and that it is not a conventional ferromagnet.Comment: Revised version contains extended interpretation of experimental dat