A Linear Programming Approach for Molecular QSAR analysis

Small molecules in chemistry can be represented as graphs. In a quantitative structure-activity relationship (QSAR) analysis, the central task is to find a regression function that predicts the activity of the molecule in high accuracy. Setting a QSAR as a primal target, we propose a new linear programming approach to the graph-based regression problem. Our method extends the graph classification algorithm by Kudo et al. (NIPS 2004), which is a combination of boosting and graph mining. Instead of sequential multiplicative updates, we employ the linear programming boosting (LP) for regression. The LP approach allows to include inequality constraints for the parameter vector, which turns out to be particularly useful in QSAR tasks where activity values are sometimes unavailable. Furthermore, the efficiency is improved significantly by employing multiple pricing

Rotationally-Driven Fragmentation for the Formation of the Binary Protostellar System L1551 IRS 5

Either bulk rotation or local turbulence is widely invoked to drive fragmentation in collapsing cores so as to produce multiple star systems. Even when the two mechanisms predict different manners in which the stellar spins and orbits are aligned, subsequent internal or external interactions can drive multiple systems towards or away from alignment thus masking their formation process. Here, we demonstrate that the geometrical and dynamical relationship between the binary system and its surrounding bulk envelope provide the crucial distinction between fragmentation models. We find that the circumstellar disks of the binary protostellar system L1551 IRS 5 are closely parallel not just with each other but also with their surrounding flattened envelope. Measurements of the relative proper motion of the binary components spanning nearly 30 yr indicate an orbital motion in the same sense as the envelope rotation. Eliminating orbital solutions whereby the circumstellar disks would be tidally truncated to sizes smaller than are observed, the remaining solutions favor a circular or low-eccentricity orbit tilted by up to $\sim$25$^\circ$ from the circumstellar disks. Turbulence-driven fragmentation can generate local angular momentum to produce a coplanar binary system, but which bears no particular relationship with its surrounding envelope. Instead, the observed properties conform with predictions for rotationally-driven fragmentation. If the fragments were produced at different heights or on opposite sides of the midplane in the flattened central region of a rotating core, the resulting protostars would then exhibit circumstellar disks parallel with the surrounding envelope but tilted from the orbital plane as is observed.Comment: Accepted for publication in Ap

Space-efficient Feature Maps for String Alignment Kernels

String kernels are attractive data analysis tools for analyzing string data. Among them, alignment kernels are known for their high prediction accuracies in string classifications when tested in combination with SVM in various applications. However, alignment kernels have a crucial drawback in that they scale poorly due to their quadratic computation complexity in the number of input strings, which limits large-scale applications in practice. We address this need by presenting the first approximation for string alignment kernels, which we call space-efficient feature maps for edit distance with moves (SFMEDM), by leveraging a metric embedding named edit sensitive parsing (ESP) and feature maps (FMs) of random Fourier features (RFFs) for large-scale string analyses. The original FMs for RFFs consume a huge amount of memory proportional to the dimension d of input vectors and the dimension D of output vectors, which prohibits its large-scale applications. We present novel space-efficient feature maps (SFMs) of RFFs for a space reduction from O(dD) of the original FMs to O(d) of SFMs with a theoretical guarantee with respect to concentration bounds. We experimentally test SFMEDM on its ability to learn SVM for large-scale string classifications with various massive string data, and we demonstrate the superior performance of SFMEDM with respect to prediction accuracy, scalability and computation efficiency.Comment: Full version for ICDM'19 pape

More supplements to a class of logarithmically completely monotonic functions associated with the gamma function

In this article, a necessary and sufficient condition and a necessary condition are established for a function involving the gamma function to be logarithmically completely monotonic on $(0,\infty)$. As applications of the necessary and sufficient condition, some inequalities for bounding the psi and polygamma functions and the ratio of two gamma functions are derived.Comment: 8 page

Protostellar collapse: rotation and disk formation

We present some important conclusions from recent calculations pertaining to the collapse of rotating molecular cloud cores with axial symmetry, corresponding to evolution of young stellar objects through classes 0 and begin of class I. Three main issues have been addressed: (1) The typical timescale for building up a preplanetary disk - once more it turned out that it is of the order of one free-fall time which is decisively shorter than the widely assumed timescale related to the so-called 'inside-out collapse'; (2) Redistribution of angular momentum and the accompanying dissipation of kinetic (rotational) energy - together these processes govern the mechanical and thermal evolution of the protostellar core to a large extent; (3) The origin of calcium-aluminium-rich inclusions (CAIs) - due to the specific pattern of the accretion flow, material that has undergone substantial chemical and mineralogical modifications in the hot (exceeding 900 K) interior of the protostellar core may have a good chance to be advectively transported outward into the cooler remote parts (beyond 4 AU, say) of the growing disk and to survive there until it is incorporated into a meteoritic body.Comment: 4 pages, 4 figure

Kinematic Structure of Molecular Gas around High-mass Star YSO, Papillon Nebula, in N159 East in the Large Magellanic Cloud

We present the ALMA Band 3 and Band 6 results of 12CO(2-1), 13$CO(2-1), H30alpha recombination line, free-free emission around 98 GHz, and the dust thermal emission around 230 GHz toward the N159 East Giant Molecular Cloud (N159E) in the Large Magellanic Cloud (LMC). LMC is the nearest active high-mass star forming face-on galaxy at a distance of 50 kpc and is the best target for studing high-mass star formation. ALMA observations show that N159E is the complex of filamentary clouds with the width and length of ~1 pc and 5 pc - 10 pc, respectively. The total molecular mass is 0.92 x 10^5 Msun from the 13CO(2-1) intensity. N159E harbors the well-known Papillon Nebula, a compact high-excitation HII region. We found that a YSO associated with the Papillon Nebula has the mass of 35 Msun and is located at the intersection of three filamentary clouds. It indicates that the formation of the high-mass YSO was induced by the collision of filamentary clouds. Fukui et al. 2015 reported a similar kinematic structure toward a YSO in the N159 West region which is another YSO that has the mass larger than 35 Msun in these two regions. This suggests that the collision of filamentary clouds is a primary mechanism of high-mass star formation. We found a small molecular hole around the YSO in Papillon Nebula with sub-pc scale. It is filled by free-free and H30alpha emission. Temperature of the molecular gas around the hole reaches ~ 80 K. It indicates that this YSO has just started the distruction of parental molecular cloud.Comment: 28 pages, 7 figures. Submitted to Ap

Probabilistic Clustering of Time-Evolving Distance Data

We present a novel probabilistic clustering model for objects that are represented via pairwise distances and observed at different time points. The proposed method utilizes the information given by adjacent time points to find the underlying cluster structure and obtain a smooth cluster evolution. This approach allows the number of objects and clusters to differ at every time point, and no identification on the identities of the objects is needed. Further, the model does not require the number of clusters being specified in advance -- they are instead determined automatically using a Dirichlet process prior. We validate our model on synthetic data showing that the proposed method is more accurate than state-of-the-art clustering methods. Finally, we use our dynamic clustering model to analyze and illustrate the evolution of brain cancer patients over time