Unusual weak magnetic exchange in two different structure types: YbPt2_2Sn and YbPt2_2In

We present the structural, magnetic, thermodynamic, and transport properties of the two new compounds YbPt$_2$Sn and YbPt$_2$In. X-ray powder diffraction shows that they crystallize in different structure types, the hexagonal ZrPt$_2$Al and the cubic Heusler type, respectively. Despite quite different lattice types, both compounds present very similar magnetic properties: a stable trivalent Yb$^{3+}$, no evidence for a sizeable Kondo interaction, and very weak exchange interactions with a strength below 1K as deduced from specific heat $C(T)$. Broad anomalies in $C(T)$ suggest short range magnetic ordering at about 250mK and 180mK for YbPt$_2$Sn and YbPt$_2$In, respectively. The weak exchange and the low ordering temperature result in a large magnetocaloric effect as deduced from the magnetic field dependence of $C(T)$, making these compounds interesting candidates for magnetic cooling. In addition we found in YbPt$_2$In evidences for a charge density wave transition at about 290K. The occurrence of such transitions within several RET$_2$X compound series (RE = rare earth, T = noble metal, X = In, Sn) is analyzed.Comment: 16 pages, 7 figure

Zoom-SVD: Fast and Memory Efficient Method for Extracting Key Patterns in an Arbitrary Time Range

Given multiple time series data, how can we efficiently find latent patterns in an arbitrary time range? Singular value decomposition (SVD) is a crucial tool to discover hidden factors in multiple time series data, and has been used in many data mining applications including dimensionality reduction, principal component analysis, recommender systems, etc. Along with its static version, incremental SVD has been used to deal with multiple semi infinite time series data and to identify patterns of the data. However, existing SVD methods for the multiple time series data analysis do not provide functionality for detecting patterns of data in an arbitrary time range: standard SVD requires data for all intervals corresponding to a time range query, and incremental SVD does not consider an arbitrary time range. In this paper, we propose Zoom-SVD, a fast and memory efficient method for finding latent factors of time series data in an arbitrary time range. Zoom-SVD incrementally compresses multiple time series data block by block to reduce the space cost in storage phase, and efficiently computes singular value decomposition (SVD) for a given time range query in query phase by carefully stitching stored SVD results. Through extensive experiments, we demonstrate that Zoom-SVD is up to 15x faster, and requires 15x less space than existing methods. Our case study shows that Zoom-SVD is useful for capturing past time ranges whose patterns are similar to a query time range.Comment: 10 pages, 2018 ACM Conference on Information and Knowledge Management (CIKM 2018

Reversible and irreversible magnetocaloric effect: The cases of rare-earth intermetallics YbPt2Sn and Ce0.5La0.5B6

Magnetocaloric effect (MCE) has drawn much attention because its magnetic cooling property enables refrigeration without producing noxious gas or using rapidly depleting resources. However, applications for everyday life are yet distant. In addition, we need to understand more about the practical aspect of the MCE. Here, we introduce a phenomenological model to explain the quasi-adiabatic MCE. Correction factors to the equilibrium thermodynamic feature implied by the entropy landscape are devised in analytic forms. To demonstrate the validity of the model, the MCE from two different materials is investigated. The recently discovered metallic paramagnet, YbPt2Sn, shows a linear and reversible MCE which is typical of a paramagnetic system and suitable for cryogenics without ³He. On the other hand, a complex-phase material, Ce0.5La0.5B6, exhibits a pronounced irreversible MCE especially across a magnetic phase boundary. A term that describes the field induced heating near a phase transition turns out to be essential in resolving the irreversible, non-equilibrium MCE

Disease-specific induced pluripotent stem cells: a platform for human disease modeling and drug discovery

The generation of disease-specific induced pluripotent stem cell (iPSC) lines from patients with incurable diseases is a promising approach for studying disease mechanisms and drug screening. Such innovation enables to obtain autologous cell sources in regenerative medicine. Herein, we report the generation and characterization of iPSCs from fibroblasts of patients with sporadic or familial diseases, including Parkinson's disease (PD), Alzheimer's disease (AD), juvenile-onset, type I diabetes mellitus (JDM), and Duchenne type muscular dystrophy (DMD), as well as from normal human fibroblasts (WT). As an example to modeling disease using disease-specific iPSCs, we also discuss the previously established childhood cerebral adrenoleukodystrophy (CCALD)- and adrenomyeloneuropathy (AMN)-iPSCs by our group. Through DNA fingerprinting analysis, the origins of generated disease-specific iPSC lines were identified. Each iPSC line exhibited an intense alkaline phosphatase activity, expression of pluripotent markers, and the potential to differentiate into all three embryonic germ layers: the ectoderm, endoderm, and mesoderm. Expression of endogenous pluripotent markers and downregulation of retrovirus-delivered transgenes [OCT4 (POU5F1), SOX2, KLF4, and c-MYC] were observed in the generated iPSCs. Collectively, our results demonstrated that disease-specific iPSC lines characteristically resembled hESC lines. Furthermore, we were able to differentiate PD-iPSCs, one of the disease-specific-iPSC lines we generated, into dopaminergic (DA) neurons, the cell type mostly affected by PD. These PD-specific DA neurons along with other examples of cell models derived from disease-specific iPSCs would provide a powerful platform for examining the pathophysiology of relevant diseases at the cellular and molecular levels and for developing new drugs and therapeutic regimens

S3CMTF: Fast, accurate, and scalable method for incomplete coupled matrix-tensor factorization.

How can we extract hidden relations from a tensor and a matrix data simultaneously in a fast, accurate, and scalable way? Coupled matrix-tensor factorization (CMTF) is an important tool for this purpose. Designing an accurate and efficient CMTF method has become more crucial as the size and dimension of real-world data are growing explosively. However, existing methods for CMTF suffer from lack of accuracy, slow running time, and limited scalability. In this paper, we propose S3CMTF, a fast, accurate, and scalable CMTF method. In contrast to previous methods which do not handle large sparse tensors and are not parallelizable, S3CMTF provides parallel sparse CMTF by carefully deriving gradient update rules. S3CMTF asynchronously updates partial gradients without expensive locking. We show that our method is guaranteed to converge to a quality solution theoretically and empirically. S3CMTF further boosts the performance by carefully storing intermediate computation and reusing them. We theoretically and empirically show that S3CMTF is the fastest, outperforming existing methods. Experimental results show that S3CMTF is up to 930× faster than existing methods while providing the best accuracy. S3CMTF shows linear scalability on the number of data entries and the number of cores. In addition, we apply S3CMTF to Yelp rating tensor data coupled with 3 additional matrices to discover interesting patterns

Synthesis of PTFE based Air Cathode for Metal Air Battery

A large number of researchers devotes deep study to reducing the contact resistance and improving the durability of air cathode. Air cathode consists of gas diffusion layer, current collector and catalytic layers. The network structure (gas diffusion layer, GDL) of Air cathode plays an important role in metal-air battery. This GDL makes the air-cathode semi-permiable. It means that H2O does not pass through GDL layer but O2 moleecules can pass the layer. For that reason, the optimization of sintering condition is very important process in manufacturing Air cathode. This article is about the dependence of discharge property of magnesium air-battery to its sinter-ability. Thus in order to observe any changes in the discharge property, sinter-ability, a cost-effective method was designed in the air cathode production

Prediction of electrolyte refresh time for Mg-Air battery

In order to understand when to replace the electrolyte of the Mg-Air battery, the effect of the surface area of Metal anode according to the consumption. For each capacity of the Mg-Air battery pack on the electrolyte concentration was calculated, the timing of electrolyte replacement was predicted

A study on the electrolyte circulation effect of Mg-air battery

The theoretical discharge energy density of the Mg-Air battery, which is attracting attention as a next-generation battery, was modelled and the efficiency of electrolyte circulation was compared. After that, 180kWh class power facility was built using Mg-Air batteries, and the effect of electrolyte removal according to the circulation method was compared and investigated

Additional file 1: Table S1. of Annotating activation/inhibition relationships to protein-protein interactions using gene ontology relations

Lists all possible combinations of GO relations where relation reasoning can be applied. Figure S1-S2. shows ROC and PRC along with their area under the curves obtained from cross-validation, and independent test results. (PDF 799 kb