Dichotomy Between Orbital and Magnetic Nematic Instabilities in BaFe2S3

Nematic orders emerge nearly universally in iron-based superconductors, but elucidating their origins is challenging because of intimate couplings between orbital and magnetic fluctuations. The iron-based ladder material BaFe2S3, which superconducts under pressure, exhibits antiferromagnetic order below TN ~ 117K and a weak resistivity anomaly at T* ~ 180K, whose nature remains elusive. Here we report angle-resolved magnetoresistance (MR) and elastoresistance (ER) measurements in BaFe2S3, which reveal distinct changes at T*. We find that MR anisotropy and ER nematic response are both suppressed near T*, implying that an orbital order promoting isotropic electronic states is stabilized at T*. Such an isotropic state below T* competes with the antiferromagnetic order, which is evidenced by the nonmonotonic temperature dependence of nematic fluctuations. In contrast to the cooperative nematic orders in spin and orbital channels in iron pnictides, the present competing orders can provide a new platform to identify the separate roles of orbital and magnetic fluctuations.Comment: 7 pages 5 figures, to be published in Phys. Rev. Re

Real-Time Reduction of Task-Related Scalp-Hemodynamics Artifact in Functional Near-Infrared Spectroscopy with Sliding-Window Analysis

Functional near-infrared spectroscopy (fNIRS) is an effective non-invasive neuroimaging technique for measuring hemoglobin concentration in the cerebral cortex. Owing to the nature of fNIRS measurement principles, measured signals can be contaminated with task-related scalp blood flow (SBF), which is distributed over the whole head and masks true brain activity. Aiming for fNIRS-based real-time application, we proposed a real-time task-related SBF artifact reduction method. Using a principal component analysis, we estimated a global temporal pattern of SBF from few short-channels, then we applied a general linear model for removing it from long-channels that were possibly contaminated by SBF. Sliding-window analysis was applied for both signal steps for real-time processing. To assess the performance, a semi-real simulation was executed with measured short-channel signals in a motor-task experiment. Compared with conventional techniques with no elements of SBF, the proposed method showed significantly higher estimation performance for true brain activation under a task-related SBF artifact environment

A Cluster-Aware Transfer Learning for Bayesian Optimization of Personalized Preference Models

Obtaining personalized models of the crowd is an important issue in various applications, such as preference acquisition and user interaction customization. However, the crowd setting, in which we assume we have little knowledge about the person, brings the cold start problem, which may cause avoidable unpreferable interactions with the people. This paper proposes a cluster-aware transfer learning method for the Bayesian optimization of personalized models. The proposed method, called Cluster-aware Bayesian Optimization, is designed based on a known feature: user preferences are not completely independent but can be divided into clusters. It exploits the clustering information to efficiently find the preference of the crowds while avoiding unpreferable interactions. The results of our extensive experiments with different data sets show that the method is efficient for finding the most preferable items and effective in reducing the number of unpreferable interactions