Supersymmetric Gauge Theories with Matters, Toric Geometries and Random Partitions

We derive the relation between the Hilbert space of certain geometries under the Bohr-Sommerfeld quantization and the perturbative prepotentials for the supersymmetric five-dimensional SU(N) gauge theories with massive fundamental matters and with one massive adjoint matter. The gauge theory with one adjoint matter shows interesting features. A five-dimensional generalization of Nekrasov's partition function can be written as a correlation function of two-dimensional chiral bosons and as a partition function of a statistical model of partitions. From a ground state of the statistical model we reproduce the polyhedron which characterizes the Hilbert space.Comment: 26 pages, 11 figures; v2 typos correcte

Rapid Integration of Multi-copy Transgenes Using Optogenetic Mutagenesis in Caenorhabditis elegans.

Stably transmitted transgenes are indispensable for labeling cellular components and manipulating cellular functions. In Caenorhabditis elegans, transgenes are generally generated as inheritable multi-copy extrachromosomal arrays, which can be stabilized in the genome through a mutagenesis-mediated integration process. Standard methods to integrate extrachromosomal arrays primarily use protocols involving ultraviolet light plus trimethylpsoralen or gamma- or X-ray irradiation, which are laborious and time-consuming. Here, we describe a one-step integration method, following germline-mutagenesis induced by mini Singlet Oxygen Generator (miniSOG). Upon blue light treatment, miniSOG tagged to histone (Histone-miniSOG) generates reactive oxygen species (ROS) and induces heritable mutations, including DNA double-stranded breaks. We demonstrate that we can bypass the need to first establish extrachromosomal transgenic lines by coupling microinjection of desired plasmids with blue light illumination on Histone-miniSOG worms to obtain integrants in the F3 progeny. We consistently obtained more than one integrant from 12 injected animals in two weeks. This optogenetic approach significantly reduces the amount of time and labor for transgene integration. Moreover, it enables to generate stably expressed transgenes that cause toxicity in animal growth

Hyperplane Arrangements and Locality-Sensitive Hashing with Lift

Locality-sensitive hashing converts high-dimensional feature vectors, such as image and speech, into bit arrays and allows high-speed similarity calculation with the Hamming distance. There is a hashing scheme that maps feature vectors to bit arrays depending on the signs of the inner products between feature vectors and the normal vectors of hyperplanes placed in the feature space. This hashing can be seen as a discretization of the feature space by hyperplanes. If labels for data are given, one can determine the hyperplanes by using learning algorithms. However, many proposed learning methods do not consider the hyperplanes' offsets. Not doing so decreases the number of partitioned regions, and the correlation between Hamming distances and Euclidean distances becomes small. In this paper, we propose a lift map that converts learning algorithms without the offsets to the ones that take into account the offsets. With this method, the learning methods without the offsets give the discretizations of spaces as if it takes into account the offsets. For the proposed method, we input several high-dimensional feature data sets and studied the relationship between the statistical characteristics of data, the number of hyperplanes, and the effect of the proposed method.Comment: 9 pages, 7 figure

Locality-Sensitive Hashing with Margin Based Feature Selection

We propose a learning method with feature selection for Locality-Sensitive Hashing. Locality-Sensitive Hashing converts feature vectors into bit arrays. These bit arrays can be used to perform similarity searches and personal authentication. The proposed method uses bit arrays longer than those used in the end for similarity and other searches and by learning selects the bits that will be used. We demonstrated this method can effectively perform optimization for cases such as fingerprint images with a large number of labels and extremely few data that share the same labels, as well as verifying that it is also effective for natural images, handwritten digits, and speech features.Comment: 9 pages, 6 figures, 3 table

Fungal Pathogens Infecting Soybean Aphid and Aphids on Other Crops Grown in Soybean Production Areas of Michigan

Seasonal prevalence of fungal pathogens infecting soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae), was assessed from 2004 to 2006 in two Michigan soybean production areas. In 2005 and 2006 field-collected soybean aphids were incubated, and fungal infection was detected at both sites early in August 2005 during soybean pod development and high soybean aphid densities. Significantly higher proportions of winged aphid morphs were infected (20 and 90% infection at the two sites) than wingless aphid morphs (1 and 3% infection). All cases of mycosis examined involved one pathogen species, Pandora neoaphidis (Remaudiére & Hennebert) Humber (Entomophthorales: Entomophthoraceae). In 2004 and 2005, we surveyed for pathogens of the soy- bean aphid in soybean as well as pathogens in other aphid species feeding on other crop plants (alfalfa, clover, corn, and wheat) by inspecting for sporulating aphid cadavers every 2 to 3 wk during the soybean growing season. Aphid ca- davers were most abundant in alfalfa, especially in August; were less common in clover, corn, and soybean; and were not found in wheat. Pandora neoaphidis was associated with cadavers of Acyrthosiphon pisum (Harris) (Hemiptera: Aphididae) in alfalfa and clover during the same period when soybean aphid infection was detected. Overall, mortality of soybean aphid and other aphid species due to fungal infection was confirmed in Michigan. The results also implicate infected winged soybean aphid morphs as potential agents for fungal dispersal, and A. pisum in alfalfa and clover as a source of fungal propagules for soybean aphid

RM-CVaR: Regularized Multiple β\beta-CVaR Portfolio

The problem of finding the optimal portfolio for investors is called the portfolio optimization problem. Such problem mainly concerns the expectation and variability of return (i.e., mean and variance). Although the variance would be the most fundamental risk measure to be minimized, it has several drawbacks. Conditional Value-at-Risk (CVaR) is a relatively new risk measure that addresses some of the shortcomings of well-known variance-related risk measures, and because of its computational efficiencies, it has gained popularity. CVaR is defined as the expected value of the loss that occurs beyond a certain probability level ($\beta$). However, portfolio optimization problems that use CVaR as a risk measure are formulated with a single $\beta$ and may output significantly different portfolios depending on how the $\beta$ is selected. We confirm even small changes in $\beta$ can result in huge changes in the whole portfolio structure. In order to improve this problem, we propose RM-CVaR: Regularized Multiple $\beta$-CVaR Portfolio. We perform experiments on well-known benchmarks to evaluate the proposed portfolio. Compared with various portfolios, RM-CVaR demonstrates a superior performance of having both higher risk-adjusted returns and lower maximum drawdown.Comment: accepted by the IJCAI-PRICAI 2020 Special Track AI in FinTec