Improved Approximation Algorithms for k-Submodular Function Maximization

This paper presents a polynomial-time $1/2$-approximation algorithm for maximizing nonnegative $k$-submodular functions. This improves upon the previous $\max\{1/3, 1/(1+a)\}$-approximation by Ward and \v{Z}ivn\'y~(SODA'14), where $a=\max\{1, \sqrt{(k-1)/4}\}$. We also show that for monotone $k$-submodular functions there is a polynomial-time $k/(2k-1)$-approximation algorithm while for any $\varepsilon>0$ a $((k+1)/2k+\varepsilon)$-approximation algorithm for maximizing monotone $k$-submodular functions would require exponentially many queries. In particular, our hardness result implies that our algorithms are asymptotically tight. We also extend the approach to provide constant factor approximation algorithms for maximizing skew-bisubmodular functions, which were recently introduced as generalizations of bisubmodular functions

NASDA's earth observation satellite data archive policy for the earth observation data and information system (EOIS)

NASDA's new Advanced Earth Observing Satellite (ADEOS) is scheduled for launch in August, 1996. ADEOS carries 8 sensors to observe earth environmental phenomena and sends their data to NASDA, NASA, and other foreign ground stations around the world. The downlink data bit rate for ADEOS is 126 MB/s and the total volume of data is about 100 GB per day. To archive and manage such a large quantity of data with high reliability and easy accessibility it was necessary to develop a new mass storage system with a catalogue information database using advanced database management technology. The data will be archived and maintained in the Master Data Storage Subsystem (MDSS) which is one subsystem in NASDA's new Earth Observation data and Information System (EOIS). The MDSS is based on a SONY ID1 digital tape robotics system. This paper provides an overview of the EOIS system, with a focus on the Master Data Storage Subsystem and the NASDA Earth Observation Center (EOC) archive policy for earth observation satellite data

Oxidative steam reforming of methane over Ni/a-Al2O3 modified with trace Pd

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Development and single‐particle analysis of hybrid extracellular vesicles fused with liposomes using viral fusogenic proteins

Extracellular vesicles (EVs) have potential biomedical applications, particularly as a means of transport for therapeutic agents. There is a need for rapid and efficient EV-liposome membrane fusion that maintains the integrity of hybrid EVs. We recently described Sf9 insect cell-derived EVs on which functional membrane proteins were presented using a baculovirus-expression system. Here, we developed hybrid EVs by membrane fusion of small liposomes and EVs equipped with baculoviral fusogenic proteins. Single-particle analysis of EV-liposome complexes revealed controlled introduction of liposome components into EVs. Our findings and methodology will support further applications of EV engineering in biomedicine

A cell factory of Bacillus subtilis engineered for the simple bioconversion of myo-inositol to scyllo-inositol, a potential therapeutic agent for Alzheimer's disease

<p>Abstract</p> <p>Background</p> <p>A stereoisomer of inositol, <it>scyllo</it>-inositol, is known as a promising therapeutic agent for Alzheimer's disease, since it prevents the accumulation of beta-amyloid deposits, a hallmark of the disease. However, this compound is relatively rare in nature, whereas another stereoisomer of inositol, <it>myo</it>-inositol, is abundantly available.</p> <p>Results</p> <p><it>Bacillus subtilis </it>possesses a unique inositol metabolism involving both stereoisomers. We manipulated the inositol metabolism in <it>B. subtilis </it>to permit the possible bioconversion from <it>myo</it>-inositol to <it>scyllo</it>-inositol. Within 48 h of cultivation, the engineered strain was able to convert almost half of 10 g/L <it>myo</it>-inositol to <it>scyllo</it>-inositol that accumulated in the culture medium.</p> <p>Conclusions</p> <p>The engineered <it>B. subtilis </it>serves as a prototype of cell factory enabling a novel and inexpensive supply of <it>scyllo</it>-inositol.</p

Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network

Variational autoencoders (VAEs) are one of the deep generative models that have experienced enormous success over the past decades. However, in practice, they suffer from a problem called posterior collapse, which occurs when the encoder coincides, or collapses, with the prior taking no information from the latent structure of the input data into consideration. In this work, we introduce an inverse Lipschitz neural network into the decoder and, based on this architecture, provide a new method that can control in a simple and clear manner the degree of posterior collapse for a wide range of VAE models equipped with a concrete theoretical guarantee. We also illustrate the effectiveness of our method through several numerical experiments.Comment: accepted to ICML 2023, some notations adjusted from the submitted versio