Online Learning with Ensembles

Supervised online learning with an ensemble of students randomized by the choice of initial conditions is analyzed. For the case of the perceptron learning rule, asymptotically the same improvement in the generalization error of the ensemble compared to the performance of a single student is found as in Gibbs learning. For more optimized learning rules, however, using an ensemble yields no improvement. This is explained by showing that for any learning rule $f$ a transform $\tilde{f}$ exists, such that a single student using $\tilde{f}$ has the same generalization behaviour as an ensemble of $f$-students.Comment: 8 pages, 1 figure. Submitted to J.Phys.

On the random neighbor Olami-Feder-Christensen slip-stick model

We reconsider the treatment of Lise and Jensen (Phys. Rev. Lett. 76, 2326 (1996)) on the random neighbor Olami-Feder-Christensen stik-slip model, and examine the strong dependence of the results on the approximations used for the distribution of states p(E).Comment: 6pages, 3 figures. To be published in PRE as a brief repor

Optimisation of on-line principal component analysis

Different techniques, used to optimise on-line principal component analysis, are investigated by methods of statistical mechanics. These include local and global optimisation of node-dependent learning-rates which are shown to be very efficient in speeding up the learning process. They are investigated further for gaining insight into the learning rates' time-dependence, which is then employed for devising simple practical methods to improve training performance. Simulations demonstrate the benefit gained from using the new methods.Comment: 10 pages, 5 figure

Phase transitions in optimal unsupervised learning

We determine the optimal performance of learning the orientation of the symmetry axis of a set of P = alpha N points that are uniformly distributed in all the directions but one on the N-dimensional sphere. The components along the symmetry breaking direction, of unitary vector B, are sampled from a mixture of two gaussians of variable separation and width. The typical optimal performance is measured through the overlap Ropt=B.J* where J* is the optimal guess of the symmetry breaking direction. Within this general scenario, the learning curves Ropt(alpha) may present first order transitions if the clusters are narrow enough. Close to these transitions, high performance states can be obtained through the minimization of the corresponding optimal potential, although these solutions are metastable, and therefore not learnable, within the usual bayesian scenario.Comment: 9 pages, 8 figures, submitted to PRE, This new version of the paper contains one new section, Bayesian versus optimal solutions, where we explain in detail the results supporting our claim that bayesian learning may not be optimal. Figures 4 of the first submission was difficult to understand. We replaced it by two new figures (Figs. 4 and 5 in this new version) containing more detail

On-Line AdaTron Learning of Unlearnable Rules

We study the on-line AdaTron learning of linearly non-separable rules by a simple perceptron. Training examples are provided by a perceptron with a non-monotonic transfer function which reduces to the usual monotonic relation in a certain limit. We find that, although the on-line AdaTron learning is a powerful algorithm for the learnable rule, it does not give the best possible generalization error for unlearnable problems. Optimization of the learning rate is shown to greatly improve the performance of the AdaTron algorithm, leading to the best possible generalization error for a wide range of the parameter which controls the shape of the transfer function.)Comment: RevTeX 17 pages, 8 figures, to appear in Phys.Rev.

Unconventional multiband superconductivity with nodes in single-crystalline SrFe2(As_0.65P_0.35)2 as seen via 31P-NMR and specific heat

We report 31P-NMR and specific heat measurements on an iron (Fe)-based superconductor SrFe2(As0.65P0.35)2 with Tc=26 K, which have revealed the development of antiferromagnetic correlations in the normal state and the unconventional superconductivity(SC) with nodal gap dominated by the gapless low-lying quasiparticle excitations. The results are consistently argued with an unconventional multiband SC state with the gap-size ratio of different bands being significantly large; the large full gaps in s\pm-wave state keep Tc high, whereas a small gap with a nodal-structure causes gapless feature under magnetic field. The present results will develop an insight into the strong material dependence of SC-gap structure in Fe-based superconductors.Comment: 6 pages, 5 figures, 1 tabl

脳梗塞モデルラットにおける虚血後の時期依存的な抗炎症性M2マクロファージ活性化変調の役割

Cerebral ischemia triggers inflammatory changes, and early complications and unfavorable outcomes of endovascular thrombectomy for brain occlusion promote the recruitment of various cell types to the ischemic area. Although anti-inflammatory M2-type macrophages are thought to exert protective effects against cerebral ischemia, little has been clarified regarding the significance of post-ischemic phase-dependent modulation of M2-type macrophages. To test our hypothesis that post-ischemic phase-dependent modulation of macrophages represents a potential therapy against ischemic brain damage, the effects on rats of an M2-type macrophage-specific activator, Gc-protein macrophage-activating factor (GcMAF), were compared with vehicle-treated control rats in the acute (day 0–6) or subacute (day 7–13) phase after ischemia induction. Acute-phase GcMAF treatment augmented both anti-inflammatory CD163+M2-type- and pro-inflammatory CD16+ M1-type macrophages, resulting in no beneficial effects. Conversely, subacute-phase GcMAF injection increased only CD163+ M2-type macrophages accompanied by elevated mRNA levels of arginase-1 and interleukin-4. M2-type macrophages co-localized with CD36+ phagocytic cells led to clearance of the infarct area, which were abrogated by clodronate-liposomes. Expression of survival-related molecules on day 28 at the infarct border was augmented by GcMAF. These data provide new and important insights into the significance of M2-type macrophage-specific activation as post-ischemic phase-dependent therapy

Treatment with the PPARγ Agonist Pioglitazone in the Early Post-ischemia Phase Inhibits Pro-inflammatory Responses and Promotes Neurogenesis Via the Activation of Innate- and Bone Marrow-Derived Stem Cells in Rats

Neurogenesis is essential for a good post-stroke outcome. Exogenous stem cells are currently being tested to promote neurogenesis after stroke. Elsewhere, we demonstrated that treatment with the PPARγ agonist pioglitazone (PGZ) before cerebral ischemia induction reduced brain damage and activated survival-related genes in ovariectomized (OVX) rats. Here, we tested our hypothesis that post-ischemia treatment with PGZ inhibits brain damage and contributes to neurogenesis via activated stem cells. Bone marrow (BM) cells of 7-week-old Wistar female rats were replaced with BM cells from green fluorescent protein-transgenic (GFP+BM) rats. Three weeks later, they were ovariectomized (OVX/GFP+BM rats). We subjected 7-week-old Wistar male and 13-week-old OVX/GFP+BM rats to 90-min cerebral ischemia. Male and OVX/GFP+BM rats were divided into two groups, one was treated with PGZ (2.5 mg/kg/day) and the other served as the vehicle control (VC). In both male and OVX/GFP+BM rats, post-ischemia treatment with PGZ reduced neurological deficits and the infarct volume. In male rats, PGZ decreased the mRNA level of IL-6 and M1-like macrophages after 24 h. In OVX/GFP+BM rats, PGZ augmented the proliferation of resident stem cells in the subventricular zone (SVZ) and the recruitment of GFP+BM stem cells on days 7–14. Both types of proliferated stem cells migrated from the SVZ into the peri-infarct area. There, they differentiated into mature neurons, glia, and blood vessels in association with activated Akt, MAP2, and VEGF. Post-ischemia treatment with PGZ may offer a new avenue for stroke treatment through contribution to neuroprotection and neurogenesis