129 research outputs found
Approximately Stable Matchings with Budget Constraints
This paper considers two-sided matching with budget constraints where one
side (firm or hospital) can make monetary transfers (offer wages) to the other
(worker or doctor). In a standard model, while multiple doctors can be matched
to a single hospital, a hospital has a maximum quota: the number of doctors
assigned to a hospital cannot exceed a certain limit. In our model, a hospital
instead has a fixed budget: the total amount of wages allocated by each
hospital to doctors is constrained. With budget constraints, stable matchings
may fail to exist and checking for the existence is hard. To deal with the
nonexistence of stable matchings, we extend the "matching with contracts" model
of Hatfield and Milgrom, so that it handles approximately stable matchings
where each of the hospitals' utilities after deviation can increase by factor
up to a certain amount. We then propose two novel mechanisms that efficiently
return such a stable matching that exactly satisfies the budget constraints. In
particular, by sacrificing strategy-proofness, our first mechanism achieves the
best possible bound. Furthermore, we find a special case such that a simple
mechanism is strategy-proof for doctors, keeping the best possible bound of the
general case.Comment: Accepted for the 32nd AAAI Conference on Artificial Intelligence
(AAAI2018). arXiv admin note: text overlap with arXiv:1705.0764
Z-score-based modularity for community detection in networks
Identifying community structure in networks is an issue of particular
interest in network science. The modularity introduced by Newman and Girvan
[Phys. Rev. E 69, 026113 (2004)] is the most popular quality function for
community detection in networks. In this study, we identify a problem in the
concept of modularity and suggest a solution to overcome this problem.
Specifically, we obtain a new quality function for community detection. We
refer to the function as Z-modularity because it measures the Z-score of a
given division with respect to the fraction of the number of edges within
communities. Our theoretical analysis shows that Z-modularity mitigates the
resolution limit of the original modularity in certain cases. Computational
experiments using both artificial networks and well-known real-world networks
demonstrate the validity and reliability of the proposed quality function.Comment: 8 pages, 10 figure
Additive Approximation Algorithms for Modularity Maximization
The modularity is a quality function in community detection, which was
introduced by Newman and Girvan (2004). Community detection in graphs is now
often conducted through modularity maximization: given an undirected graph
, we are asked to find a partition of that maximizes
the modularity. Although numerous algorithms have been developed to date, most
of them have no theoretical approximation guarantee. Recently, to overcome this
issue, the design of modularity maximization algorithms with provable
approximation guarantees has attracted significant attention in the computer
science community.
In this study, we further investigate the approximability of modularity
maximization. More specifically, we propose a polynomial-time
-additive approximation algorithm for the
modularity maximization problem. Note here that
holds. This improves the current best additive approximation error of ,
which was recently provided by Dinh, Li, and Thai (2015). Interestingly, our
analysis also demonstrates that the proposed algorithm obtains a nearly-optimal
solution for any instance with a very high modularity value. Moreover, we
propose a polynomial-time -additive approximation algorithm for the
maximum modularity cut problem. It should be noted that this is the first
non-trivial approximability result for the problem. Finally, we demonstrate
that our approximation algorithm can be extended to some related problems.Comment: 23 pages, 4 figure
The Densest Subgraph Problem with a Convex/Concave Size Function
we propose a linear-programming-based polynomial-time exact algorithm. It should be emphasized that this algorithm obtains not only an optimal solution to the problem but also subsets of vertices corresponding to the extreme points of the upper convex hull of {(|S|, w(S)) | S subseteq V }, which we refer to as the dense frontier points. We also propose a flow-based combinatorial exact algorithm for unweighted graphs that runs in O(n^3) time. Finally, we propose a nearly-linear-time 3-approximation algorithm
Stochastic Solutions for Dense Subgraph Discovery in Multilayer Networks
Network analysis has played a key role in knowledge discovery and data
mining. In many real-world applications in recent years, we are interested in
mining multilayer networks, where we have a number of edge sets called layers,
which encode different types of connections and/or time-dependent connections
over the same set of vertices. Among many network analysis techniques, dense
subgraph discovery, aiming to find a dense component in a network, is an
essential primitive with a variety of applications in diverse domains. In this
paper, we introduce a novel optimization model for dense subgraph discovery in
multilayer networks. Our model aims to find a stochastic solution, i.e., a
probability distribution over the family of vertex subsets, rather than a
single vertex subset, whereas it can also be used for obtaining a single vertex
subset. For our model, we design an LP-based polynomial-time exact algorithm.
Moreover, to handle large-scale networks, we also devise a simple, scalable
preprocessing algorithm, which often reduces the size of the input networks
significantly and results in a substantial speed-up. Computational experiments
demonstrate the validity of our model and the effectiveness of our algorithms.Comment: Accepted to WSDM 202
EFFECTS OF FASTING ON PRAVASTATIN DISPOSITION IN PERFUSED RAT LIVER
Objective: Various nutrients such as glucose and cholesterol affect the expression of hepatic transporters. Although the pharmacokinetics of some drugs is affected by fasting, the fasting effects on drug hepatic disposition via alterations in transporters are unclear. Organic anion-transporting polypeptides and multidrug resistance-associated protein 2 (Mrp2/Abcc2) expressed in the liver are involved in hepatic disposition of pravastatin.Methods: An in situ perfused rat liver system was established. The mRNA and protein levels of transporters in the liver were examined by real-time reverse transcription PCR and western blotting. The localization of Mrp2 in hepatocytes was determined by immunostaining.Results: Pravastatin was rapidly eliminated from the perfusate. The cumulative biliary excretion amounts of pravastatin in fasting rats were significantly lower from 10 min compared with control. In fasting rats, the area under the plasma concentration-time curve (AUC)0‒∞ of pravastatin in the perfusate was significantly decreased, and hepatic clearance (CLh) and hepatic corrected clearance (CLcor) were significantly increased. The biliary clearance (CLbile) in fasting rats tended to decrease compared with that in control rats. Protein expression levels of transporters were unchanged after fasting. Confocal microscopy revealed a disruption of Mrp2 and ZO-1 colocalization in the liver of fasting rats.Conclusion: The biliary excretion of pravastatin was inhibited by fasting via decreased Mrp2 localization on the canalicular membrane
Spatially Resolved Spectral Imaging by A THz-FEL
Using the unique characteristics of the free-electron-laser (FEL), we successfully
performed high-sensitivity spectral imaging of different materials in the terahertz (THz) and
far-infrared (FIR) domain. THz imaging at various wavelengths was achieved using in situ
spectroscopy by means of this wavelength tunable and monochromatic source. In particular, owing
to its large intensity and directionality, we could collect high-sensitivity transmission imaging of
extremely low-transparency materials and three-dimensional objects in the 3–6 THz range. By
accurately identifying the intrinsic absorption wavelength of organic and inorganic materials, we
succeeded in the mapping of spatial distribution of individual components. This simple imaging
technique using a focusing optics and a raster scan modality has made it possible to set up and
carry out fast spectral imaging experiments on different materials in this radiation facility
Identification of a novel intronic enhancer responsible for the transcriptional regulation of musashi1 in neural stem/progenitor cells
<p>Abstract</p> <p>Background</p> <p>The specific genetic regulation of neural primordial cell determination is of great interest in stem cell biology. The Musashi1 (Msi1) protein, which belongs to an evolutionarily conserved family of RNA-binding proteins, is a marker for neural stem/progenitor cells (NS/PCs) in the embryonic and post-natal central nervous system (CNS). Msi1 regulates the translation of its downstream targets, including <it>m-Numb </it>and <it>p21 </it>mRNAs. <it>In vitro </it>experiments using knockout mice have shown that Msi1 and its isoform Musashi2 (Msi2) keep NS/PCs in an undifferentiated and proliferative state. Msi1 is expressed not only in NS/PCs, but also in other somatic stem cells and in tumours. Based on previous findings, Msi1 is likely to be a key regulator for maintaining the characteristics of self-renewing stem cells. However, the mechanisms regulating <it>Msi1 </it>expression are not yet clear.</p> <p>Results</p> <p>To identify the DNA region affecting <it>Msi1 </it>transcription, we inserted the fusion gene <it>ffLuc</it>, comprised of the fluorescent <it>Venus </it>protein and firefly <it>Luciferase</it>, at the translation initiation site of the mouse <it>Msi1 </it>gene locus contained in a 184-kb bacterial artificial chromosome (BAC). Fluorescence and Luciferase activity, reflecting the <it>Msi1 </it>transcriptional activity, were observed in a stable BAC-carrying embryonic stem cell line when it was induced toward neural lineage differentiation by retinoic acid treatment. When neuronal differentiation was induced in embryoid body (EB)-derived neurosphere cells, reporter signals were detected in Msi1-positive NSCs and GFAP-positive astrocytes, but not in MAP2-positive neurons. By introducing deletions into the BAC reporter gene and conducting further reporter experiments using a minimized enhancer region, we identified a region, "D5E2," that is responsible for <it>Msi1 </it>transcription in NS/PCs.</p> <p>Conclusions</p> <p>A regulatory element for <it>Msi1 </it>transcription in NS/PCs is located in the sixth intron of the <it>Msi1 </it>gene. The 595-bp D5E2 intronic enhancer can transactivate <it>Msi1 </it>gene expression with cell-type specificity markedly similar to the endogenous Msi1 expression patterns.</p
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