Learning over Molecules: Representations and Kernels

In this paper, we tackle machine learning over molecular space by considering three representations for molecules: (1) a vector of molecular properties that we treat as predictor variables, (2) a graph that captures the relationship between individual atoms in a molecule, and (3) a cheminformatic fingerprint that “identifies” a molecule. We assess the viability of each representation by training a model to predict energy values. In particular, we look a class of models that use kernel methods, whereby the prediction algorithm relies on a similarity measure between training data. On a subset of the Harvard Clean Energy Project (CEP) database, we find a simple fingerprint similarity kernel to be the fastest and most accurate for predicting HOMO-LUMO energy gap values

Itinerant quantum critical point with frustration and non-Fermi-liquid

Employing the self-learning quantum Monte Carlo algorithm, we investigate the frustrated transverse-field triangle-lattice Ising model coupled to a Fermi surface. Without fermions, the spin degrees of freedom undergoes a second-order quantum phase transition between paramagnetic and clock-ordered phases. This quantum critical point (QCP) has an emergent U(1) symmetry and thus belongs to the (2+1)D XY universality class. In the presence of fermions, spin fluctuations introduce effective interactions among fermions and distort the bare Fermi surface towards an interacting one with hot spots and Fermi pockets. Near the QCP, non-Fermi-liquid behavior are observed at the hot spots, and the QCP is rendered into a different universality with Hertz-Millis type exponents. The detailed properties of this QCP and possibly related experimental systems are also discussed.Comment: 9 pages, 8 figure

Id1 induces apoptosis through inhibition of RORgammat expression

<p>Abstract</p> <p>Background</p> <p>Basic helix-loop-helix E proteins are transcription factors that play crucial roles in T cell development by controlling thymocyte proliferation, differentiation and survival. E protein functions can be repressed by their naturally occurring inhibitors, Id proteins (Id1-4). Transgenic expression of Id1 blocks T cell development and causes massive apoptosis of developing thymocytes. However, the underlying mechanisms are not entirely understood due to relatively little knowledge of the target genes regulated by E proteins.</p> <p>Results</p> <p>We designed a unique strategy to search for genes directly controlled by E proteins and found RORγt to be a top candidate. Using microarray analyses and reverse-transcriptase PCR assays, we showed that Id1 expression diminished RORγt mRNA levels in T cell lines and primary thymocytes while induction of E protein activity restored RORγt expression. E proteins were found to specifically bind to the promoter region of RORγt, suggesting their role in activating transcription of the gene. Functional significance of E protein-controlled RORγt expression was established based on the finding that RORγt rescued apoptosis caused by Id1 overexpression. Furthermore, expression of RORγt prevented Id1-induced p38 MAP kinase hyper-activation.</p> <p>Conclusion</p> <p>These results suggest that E protein-dependent RORγt gene expression aids the survival of developing thymocytes, which provides a possible explanation for the massive apoptosis found in Id1 transgenic mice.</p

Prediction for Irregular Ocean Wave and Floating Body Motion by Regularization: Part 1. Irregular Wave Prediction

Ocean waves can be explained in terms of many factors, including wave spectrum, which has the characteristics of wave height and periodicity, directional spreading function, which has a directional property, and random phase, which randomly represents a certain property. Under the assumption of a linear system, ocean waves show irregular behaviours, which can be observed in the forms of wave spectrum, directional spreading function, and complex phase calculations using the method of linear superposition. Ocean waves, which include a variety of periodic elements, exhibit direct proportionality between their period and propagation velocity. The purpose of this study was to understand the phase components of the period and to make exact calculations on the deterministic phase in order to make predictions on ocean waves. However, measurements of actual ocean waves exist only in the form of information on wave elevation, so we faced an inverse problem of having to analyse this information and calculate the deterministic phase. Regularization was used as part of the solution, and various methods were used to obtain stable values

Prediction for Irregular Ocean Wave and Floating Body Motion by Regularization: Part 2. Motion Prediction

In the analysis of the motion of a floating body, the domains can broadly be divided into the frequency domain and the time domain. The essence of the frequency domain analysis lies in calculating the hydrodynamic coefficient from the equation of motion, which has six degrees of freedom, by applying several methods. In this research, Bureau Veritas’s “HydroStar” software was used, and the comparison and the verification were carried out by experiments. For the time domain analysis, we used an existing method proposed by Cummins and made motion predictions by using deterministic random phases calculated in the time domain calculations of the excitation force. Lastly, the potential of wave and motion predictions was verified through the data obtained from a motion analysis experiment using a tension leg platform in the context of irregular waves