Linear Complexity Lossy Compressor for Binary Redundant Memoryless Sources

A lossy compression algorithm for binary redundant memoryless sources is presented. The proposed scheme is based on sparse graph codes. By introducing a nonlinear function, redundant memoryless sequences can be compressed. We propose a linear complexity compressor based on the extended belief propagation, into which an inertia term is heuristically introduced, and show that it has near-optimal performance for moderate block lengths.Comment: 4 pages, 1 figur

The path-integral analysis of an associative memory model storing an infinite number of finite limit cycles

It is shown that an exact solution of the transient dynamics of an associative memory model storing an infinite number of limit cycles with l finite steps by means of the path-integral analysis. Assuming the Maxwell construction ansatz, we have succeeded in deriving the stationary state equations of the order parameters from the macroscopic recursive equations with respect to the finite-step sequence processing model which has retarded self-interactions. We have also derived the stationary state equations by means of the signal-to-noise analysis (SCSNA). The signal-to-noise analysis must assume that crosstalk noise of an input to spins obeys a Gaussian distribution. On the other hand, the path-integral method does not require such a Gaussian approximation of crosstalk noise. We have found that both the signal-to-noise analysis and the path-integral analysis give the completely same result with respect to the stationary state in the case where the dynamics is deterministic, when we assume the Maxwell construction ansatz. We have shown the dependence of storage capacity (alpha_c) on the number of patterns per one limit cycle (l). Storage capacity monotonously increases with the number of steps, and converges to alpha_c=0.269 at l ~= 10. The original properties of the finite-step sequence processing model appear as long as the number of steps of the limit cycle has order l=O(1).Comment: 24 pages, 3 figure

Correlation between direct dark matter detection and Br(B_s -> mu mu) with a large phase of B_s - anti-B_s mixing

We combine the analyses for flavor changing neutral current processes and dark matter solutions in minimal-type supersymmetric grand unified theory (GUT) models, SO(10) and SU(5), with a large B_s - anti-B_s mixing phase and large tan beta. For large tan beta, the double penguin diagram dominates the SUSY contribution to the B_s - anti-B_s mixing amplitude. Also, the Br(B_s -> mu mu) constraint becomes important as it grows as tan^6 beta, although it can still be suppressed by large pseudoscalar Higgs mass m_A. We investigate the correlation between B_s -> mu mu and the dark matter direct detection cross-section through their dependence on m_A. In the minimal-type of SU(5) with type I seesaw, the large mixing in neutrino Dirac couplings results in large lepton flavor violating decay process tau to mu gamma, which in turn sets upper bound on m_A. In the SO(10) case, the large mixing can be chosen to be in the Majorana couplings instead, and the constraint from Br(tau -> mu gamma) can be avoided. The heavy Higgs funnel region turns out to be an interesting possibility in both cases and the direct dark matter detection should be possible in the near future in these scenarios.Comment: 19 pages, 8 figure

Modification of the Unitarity Relation for sin2beta-Vub in Supersymmetric Models

Recently, a more than 2sigma discrepancy has been observed between the well measured inclusive value of Vub and the predicted value of Vub from the unitarity triangle fit using the world average value of sin2beta. We attempt to resolve this tension in the context of grand unified SO(10) and SU(5) models where the neutrino mixing matrix is responsible for flavor changing neutral current at the weak scale and the models with non-proportional A-terms (can be realized simply in the context of intersecting D-brane models) and investigate the interplay between the constraints arising from B_{s,d}-\bar B_{s,d} mixings, epsilon_K, Br(tau -> mu gamma), Br(mu -> e gamma) and a fit of this new discrepancy. We also show that the ongoing measurement of the phase of Bs mixing will be able to identify the grand unified model. The measurement of Br(tau -> e gamma) will also be able to test these scenarios, especially the models with non-proportional A-terms.Comment: 20 pages, 4 figures. Minor corrections, references adde

Statistical mechanics of lossy compression using multilayer perceptrons

Statistical mechanics is applied to lossy compression using multilayer perceptrons for unbiased Boolean messages. We utilize a tree-like committee machine (committee tree) and tree-like parity machine (parity tree) whose transfer functions are monotonic. For compression using committee tree, a lower bound of achievable distortion becomes small as the number of hidden units K increases. However, it cannot reach the Shannon bound even where K -> infty. For a compression using a parity tree with K >= 2 hidden units, the rate distortion function, which is known as the theoretical limit for compression, is derived where the code length becomes infinity.Comment: 12 pages, 5 figure

Suppressing Proton Decay in the Minimal SO(10) Model

We show that in a class of minimal supersymmetric SO(10) models which have been found to be quite successful in predicting neutrino mixings, all proton decay modes can be suppressed by a particular choice of Yukawa textures. This suppression works for contributions from both left and right operators for nucleon decay and for arbitrary \tan\beta. The required texture not only fits all lepton and quark masses as well as CKM parameters but it also predicts neutrino mixing parameter U_e3 and Dirac CP phase \sin|\delta_MNS| to be 0.07-0.09 and 0.3-0.7 respectively. We also discuss the relation between the GUT symmetry breaking parameters for the origin of these textures.Comment: 7 pages, 2 figure

Synapse efficiency diverges due to synaptic pruning following over-growth

In the development of the brain, it is known that synapses are pruned following over-growth. This pruning following over-growth seems to be a universal phenomenon that occurs in almost all areas -- visual cortex, motor area, association area, and so on. It has been shown numerically that the synapse efficiency is increased by systematic deletion. We discuss the synapse efficiency to evaluate the effect of pruning following over-growth, and analytically show that the synapse efficiency diverges as O(log c) at the limit where connecting rate c is extremely small. Under a fixed synapse number criterion, the optimal connecting rate, which maximize memory performance, exists.Comment: 15 pages, 16 figure

Parallel dynamics of continuous Hopfield model revisited

We have applied the generating functional analysis (GFA) to the continuous Hopfield model. We have also confirmed that the GFA predictions in some typical cases exhibit good consistency with computer simulation results. When a retarded self-interaction term is omitted, the GFA result becomes identical to that obtained using the statistical neurodynamics as well as the case of the sequential binary Hopfield model.Comment: 4 pages, 2 figure

Semiconductor Thermal Neutron Detector

The  CdTe  and  GaN  detector  with  a  Gd  converter  have  been developed  and  investigated  as  a  neutron  detector  for neutron  imaging.  The  fabricated  Gd/CdTe  detector  with  the  25  mm  thick  Gd  was  designed  on  the  basis  of  simulation results  of  thermal  neutron  detection  efficiency  and  spatial  resolution.  The  Gd/CdTe  detector  shows  the  detection  of neutron  capture  gamma  ray  emission  in  the  155Gd(n,  g)156Gd,  157Gd(n,  g)158Gd  and  113Cd(n,  g)114Cd  reactions  and characteristic X-ray emissions due to conversion-electrons generated inside the Gd film. The observed efficient thermal neutron detection with the Gd/CdTe detector shows its promise in neutron radiography application. Moreover, a BGaN detector has also investigated to separate neutron signal from gamma-ray clearly.&nbsp

Symmetric sequence processing in a recurrent neural network model with a synchronous dynamics

The synchronous dynamics and the stationary states of a recurrent attractor neural network model with competing synapses between symmetric sequence processing and Hebbian pattern reconstruction is studied in this work allowing for the presence of a self-interaction for each unit. Phase diagrams of stationary states are obtained exhibiting phases of retrieval, symmetric and period-two cyclic states as well as correlated and frozen-in states, in the absence of noise. The frozen-in states are destabilised by synaptic noise and well separated regions of correlated and cyclic states are obtained. Excitatory or inhibitory self-interactions yield enlarged phases of fixed-point or cyclic behaviour.Comment: Accepted for publication in Journal of Physics A: Mathematical and Theoretica