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Search Results: 1 - 10 of 30 matches for " Hachiya "
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Field-induced reentrant superconductivity in thin films of nodal superconductor
M. Hachiya,K. Aoyama,R. Ikeda
Physics , 2013, DOI: 10.1103/PhysRevB.88.064519
Abstract: Previous works on nodal d-wave superconductors have shown that a Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) like modulated superconducting (SC) state can be realized with no magnetic field when quasiparticles acquire an additional linear term in the wavevector in their dispersion. In the present work, stability of such a novel modulated SC state in an artificial film against an applied magnetic field is studied. As a reflection of the presence of the two different FFLO states, one close to zero field and the other at the high field end, in a single field v.s. temperature phase diagram of thin films, the conventional uniform SC state generally tends to appear as a reentrant ordered phase bounded by the normal phase in {\it lower} fields.
Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting
Ning Xie,Hirotaka Hachiya,Masashi Sugiyama
Computer Science , 2012, DOI: 10.1587/transinf.E96.D.1134
Abstract: Oriental ink painting, called Sumi-e, is one of the most appealing painting styles that has attracted artists around the world. Major challenges in computer-based Sumi-e simulation are to abstract complex scene information and draw smooth and natural brush strokes. To automatically find such strokes, we propose to model the brush as a reinforcement learning agent, and learn desired brush-trajectories by maximizing the sum of rewards in the policy search framework. We also provide elaborate design of actions, states, and rewards tailored for a Sumi-e agent. The effectiveness of our proposed approach is demonstrated through simulated Sumi-e experiments.
Feature Selection via L1-Penalized Squared-Loss Mutual Information
Wittawat Jitkrittum,Hirotaka Hachiya,Masashi Sugiyama
Computer Science , 2012, DOI: 10.1587/transinf.E96.D.1513
Abstract: Feature selection is a technique to screen out less important features. Many existing supervised feature selection algorithms use redundancy and relevancy as the main criteria to select features. However, feature interaction, potentially a key characteristic in real-world problems, has not received much attention. As an attempt to take feature interaction into account, we propose L1-LSMI, an L1-regularization based algorithm that maximizes a squared-loss variant of mutual information between selected features and outputs. Numerical results show that L1-LSMI performs well in handling redundancy, detecting non-linear dependency, and considering feature interaction.
Adipose Tissue Remodeling as Homeostatic Inflammation
Michiko Itoh,Takayoshi Suganami,Rumi Hachiya,Yoshihiro Ogawa
International Journal of Inflammation , 2011, DOI: 10.4061/2011/720926
Abstract: Evidence has accumulated indicating that obesity is associated with a state of chronic, low-grade inflammation. Obese adipose tissue is characterized by dynamic changes in cellular composition and function, which may be referred to as “adipose tissue remodeling”. Among stromal cells in the adipose tissue, infiltrated macrophages play an important role in adipose tissue inflammation and systemic insulin resistance. We have demonstrated that a paracrine loop involving saturated fatty acids and tumor necrosis factor-α derived from adipocytes and macrophages, respectively, aggravates obesity-induced adipose tissue inflammation. Notably, saturated fatty acids, which are released from hypertrophied adipocytes via the macrophage-induced lipolysis, serve as a naturally occurring ligand for Toll-like receptor 4 complex, thereby activating macrophages. Such a sustained interaction between endogenous ligands derived from parenchymal cells and pathogen sensors expressed in stromal immune cells should lead to chronic inflammatory responses ranging from the basal homeostatic state to diseased tissue remodeling, which may be referred to as “homeostatic inflammation”. We, therefore, postulate that adipose tissue remodeling may represent a prototypic example of homeostatic inflammation. Understanding the molecular mechanism underlying homeostatic inflammation may lead to the identification of novel therapeutic strategies to prevent or treat obesity-related complications. 1. Introduction The metabolic syndrome is a constellation of visceral fat obesity, insulin resistance, atherogenic dyslipidemia, and hypertension, which all independently increase the risk of atherosclerotic diseases [1–5]. The adipose tissue secretes a number of bioactive substances or adipocytokines, and unbalanced production of pro- and anti-inflammatory adipocytokines in obese adipose tissue may critically contribute to many aspects of the metabolic syndrome [1–5]. Obesity is now viewed as a state of systemic, chronic low-grade inflammation [1–4]. In contrast to acute inflammation which resolves by an active termination program, chronic inflammation may involve persistent stress and/or impaired resolution process, thereby resulting in functional maladaptation and tissue remodeling [6]. On the other hand, during the course of obesity, adipose tissue is characterized by adipocyte hypertrophy, followed by increased angiogenesis, immune cell infiltration, and extracellular matrix overproduction [1, 2, 7, 8], which may be referred to as adipose tissue remodeling. Pathogen sensors or pattern-recognition
Murasaki: A Fast, Parallelizable Algorithm to Find Anchors from Multiple Genomes
Kris Popendorf,Hachiya Tsuyoshi,Yasunori Osana,Yasubumi Sakakibara
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0012651
Abstract: With the number of available genome sequences increasing rapidly, the magnitude of sequence data required for multiple-genome analyses is a challenging problem. When large-scale rearrangements break the collinearity of gene orders among genomes, genome comparison algorithms must first identify sets of short well-conserved sequences present in each genome, termed anchors. Previously, anchor identification among multiple genomes has been achieved using pairwise alignment tools like BLASTZ through progressive alignment tools like TBA, but the computational requirements for sequence comparisons of multiple genomes quickly becomes a limiting factor as the number and scale of genomes grows.
Primary hepatic leiomyosarcoma: Case report and literature review
Nairuthya Shivathirthan,Junji Kita,Yukihiro Iso,Hiroyuki Hachiya
World Journal of Gastrointestinal Oncology , 2011, DOI: 10.4251/wjgo.v3.i10.148
Abstract: Primary hepatic leiomyosarcoma are rare tumors with less than 30 cases reported in the English literature. Non specific presentations and often diagnosis delayed until they reach a large size, is the norm with therapy leading to an often dismal prognosis. A 67-year-old man presented complaining of abdominal pain and a palpable abdominal mass since Jan 2010. Abdominal ultrasonography and abdominal computed tomography revealed a large tumor in the left lobe of the liver. Surgical exploration was undertaken and an extended left hepatectomy with extension onto the dorsal part of segment 8 preserving the MHV with partial resection of segment 6 was undertaken. The weight of the resected specimen was 1300 g of the left lobectomy specimen and 8 g of the segment 6 partial resection specimen. The pathology report confirmed the diagnosis of leiomyosarcoma. On immunohistochemistry, the tumor cells were positive for smooth muscle actin stain. The patient is on regular follow up and is currently 9 mo post resection with no evidence of recurrence. We report the case of a resected primary hepatic leiomyosarcoma and emphasize the need for a global database for these rare tumors to promote a better and broader understanding of this less understood subject.
Cisto sacular congênito da laringe Congenital laryngeal saccular cyst
Luiz Ubirajara Sennes,Rui Imamura,Ronaldo Frizzarini,Adriana Hachiya
Brazilian Journal of Otorhinolaryngology , 2012, DOI: 10.1590/s1808-86942012000300025
Abstract:
Ballistic spin resonance in multisubband quantum wires
Marco O. Hachiya,Gonzalo Usaj,J. Carlos Egues
Physics , 2013, DOI: 10.1103/PhysRevB.89.125310
Abstract: Ballistic spin resonance was experimentally observed in a quasi-one-dimensional wire by Frolov et al. [Nature (London) 458, 868 (2009)]. The spin resonance was generated by a combination of an external static magnetic field and the oscillating effective spin-orbit magnetic field due to periodic bouncings of the electrons off the boundaries of a narrow channel. An increase of the D'yakonov-Perel spin relaxation rate was observed when the frequency of the spin-orbit field matched that of the Larmor precession frequency around the external magnetic field. Here we develop a model to account for the D'yakonov-Perel mechanism in multisubband quantum wires with both the Rashba and Dresselhaus spin-orbit interactions. Considering elastic spin-conserving impurity scatterings in the time-evolution operator (Heisenberg representation), we extract the spin relaxation time by evaluating the time-dependent average of the spin operators. The magnetic field dependence of the nonlocal voltage, which is related to the spin relaxation time behavior, shows a wide plateau, in agreement with the experimental observation. This plateau arises due to injection in higher subbands and small-angle scattering. In this quantum mechanical approach, the spin resonance occurs near the spin-orbit induced energy anticrossings of the quantum wire subbands with opposite spins. We also predict anomalous dips in the spin relaxation time as a function of the magnetic field in systems with strong spin-orbit couplings.
Non-monotonic spin relaxation and decoherence in graphene quantum dots with spin-orbit interactions
Marco O. Hachiya,Guido Burkard,J. Carlos Egues
Physics , 2013, DOI: 10.1103/PhysRevB.89.115427
Abstract: We investigate the spin relaxation and decoherence in a single-electron graphene quantum dot with Rashba and intrinsic spin-orbit interactions. We derive an effective spin-phonon Hamiltonian via the Schrieffer-Wolff transformation in order to calculate the spin relaxation time T_1 and decoherence time T_2 within the framework of the Bloch-Redfield theory. In this model, the emergence of a non-monotonic dependence of T_1 on the external magnetic field is attributed to the Rashba spin-orbit coupling-induced anticrossing of opposite spin states. A rapid decrease of T_1 occurs when the spin and orbital relaxation rates become comparable in the vicinity of the spin-mixing energy-level anticrossing. By contrast, the intrinsic spin-orbit interaction leads to a monotonic magnetic field dependence of the spin relaxation rate which is caused solely by the direct spin-phonon coupling mechanism. Within our model, we demonstrate that the decoherence time T_2 ~ 2 T_1 is dominated by relaxation processes for the electron-phonon coupling mechanisms in graphene up to leading order in the spin-orbit interaction. Moreover, we show that the energy anticrossing also leads to a vanishing pure spin dephasing rate for these states for a super-Ohmic bath.
Parametric Return Density Estimation for Reinforcement Learning
Tetsuro Morimura,Masashi Sugiyama,Hisashi Kashima,Hirotaka Hachiya,Toshiyuki Tanaka
Computer Science , 2012,
Abstract: Most conventional Reinforcement Learning (RL) algorithms aim to optimize decision-making rules in terms of the expected returns. However, especially for risk management purposes, other risk-sensitive criteria such as the value-at-risk or the expected shortfall are sometimes preferred in real applications. Here, we describe a parametric method for estimating density of the returns, which allows us to handle various criteria in a unified manner. We first extend the Bellman equation for the conditional expected return to cover a conditional probability density of the returns. Then we derive an extension of the TD-learning algorithm for estimating the return densities in an unknown environment. As test instances, several parametric density estimation algorithms are presented for the Gaussian, Laplace, and skewed Laplace distributions. We show that these algorithms lead to risk-sensitive as well as robust RL paradigms through numerical experiments.
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