We study matching markets in which institutions may have minimum and maximum quotas. Minimum quotas are important in many settings, such as hospital residency matching, military cadet matching, and school choice, but current mechanisms are unable to accommodate them, leading to the use of ad hoc solutions. We introduce two new classes of strategyproof mechanisms that allow for minimum quotas as an explicit input and show that our mechanisms improve welfare relative to existing approaches. Because minimum quotas cause a theoretical incompatibility between standard fairness and nonwastefulness properties, we introduce new second-best axioms and show that they are satisfied by our mechanisms. Last, we use simulations to quantify (1) the magnitude of the potential efficiency gains from our mechanisms and (2) how far the resulting assignments are from the first-best definitions of fairness and nonwastefulness. Combining both the theoretical and simulation results, we argue that our mechanisms will improve the performance of matching markets with minimum quota constraints in practice.

Innervation of the clinically normal human corneal epithelium was investigated utilizing immunohistochemical and electron microscopic techniques. All corneal epithelial sheets examined demonstrated neuron specific enolase (NSE: a non-specific marker for neural elements), calcitonin gene-related peptide (CGRP: a putative marker for sensory fibers), and tyrosine hydroxylase (TH: a marker for catecholaminergic nerves) immunoreactive fibers. NSE, CGRP, and TH fibers formed a dense basal epithelial plexus. The CGrp fibers tended to have beaded profiles, while TH fibers were smooth. Numerous free nerve endings originating from the basal epithelial plexus og NSE and CGRP fibers terminated throughout the thickness of epithelium. The densities of fibers in the basal epithelial nerve plexus were: NSE > CGRP > TH. Transmission electron microscopy demonstrated two types of epithelial nerve fibers, one containing large dense-core vesicles and another small dense-core visicles. Both types contained clear vesicles. These large and small dense-core vesicle fibers appeared to correspond to the CGRP and TH immunoreactive fibers, respectively. These results provide morphological baseline data on the normal sensory and sympathetic corneal epithelial innervation.

No abstract

Forming effective coalitions is a major research challenge in AI and multi-agent systems. Coalition Structure Generation (CSG) involves partitioning a set of agents into coalitions so that social surplus (the sum of the rewards of all coalitions) is maximized. A partition is called a coalition structure (CS). In traditional works, the value of a coalition is given by a black box function called a characteristic function. In this paper, we propose a novel formalization of CSG, i.e., we assume that the value of a characteristic function is given by an optimal solution of a distributed constraint optimization problem (DCOP) among the agents of a coalition. A DCOP is a popular approach for modeling cooperative agents, since it is quite general and can formalize various application problems in MAS. At first glance, this approach sounds like a very bad idea considering the computational costs, since we need to solve an NP-hard problem just to obtain the value of a single coalition. To optimally solve a CSG, we might need to solve O(2 n ) DCOP problem instances, where n is the number of agents. However, quite surprisingly, we show that an approximation algorithm, whose computational cost is about the same as solving just one DCOP, can find a CS whose social surplus is at least max(1/ n/2 , 1/(w * + 1)) of the optimal CS, where w * is the tree width of a constraint graph. Furthermore, we can generalize this approximation algorithm with a parameter k, i.e., the generalized algorithm can find a CS whose social surplus is at least max(k/ n/2 , k/(w * + 1)) of the optimal CS by exploring more search space. These results illustrate that the locality of interactions among agents, which is explicitly modeled in the DCOP formalization, is quite useful in developing efficient CSG algorithms with quality guarantees.

A highly safe 100 Wh-class laminated lithium ion battery (LIB) was developed. For ensuring safety of the LIB, a liquid electrolyte was quasi-solidified at silica surfaces. For the liquid electrolyte, a solvate ionic liquid (SIL), which is an equimolar complex of lithium bis(trifluoromethanesulfonyl)amide (LiTFSA) and tetraethylene glycol dimethyl ether (G4), Li(G4)TFSA, was used. For enhancing discharge-rate capability, Li(G4)TFSA was diluted by propylene carbonate (PC). Then, for enhancing cycle life, vinylene carbonate (VC) and hexafluorophosphate anion (PF 6 −)based salt were added for forming an solid-electrolyte interphase (SEI) on the graphite negative electrode and an AlF 3 at the surface of the aluminum current collector of the positive electrode, respectively. The assembled LIB exhibited initial discharge capacity of 32 Ah and coulombic efficiency of 76%. Regardless of high energy-type, the developed battery exhibited high discharge capacity of 26.2 Ah at 2 C. Its retention ratio of discharge capacity at the 118th cycle is high, i.e., 96%. The developed LIB (with energy density of 363 Wh L −1) generated neither fire nor smoke in a nail-penetration test. These results suggest that the developed LIB has high safety compared to a LIB comprised of a conventional organic liquid electrolyte.