This paper reports on the details of the International Competition on Graph Counting Algorithms (ICGCA) held in 2023. The graph counting problem is to count the subgraphs satisfying specified constraints on a given graph. The problem belongs to #P-complete, a computationally tough class. Since many essential systems in modern society, e.g., infrastructure networks, are often represented as graphs, graph counting algorithms are a key technology to efficiently scan all the subgraphs representing the feasible states of the system. In the ICGCA, contestants were asked to count the paths on a graph under a length constraint. The benchmark set included 150 challenging instances, emphasizing graphs resembling infrastructure networks. Eleven solvers were submitted and ranked by the number of benchmarks correctly solved within a time limit. The winning solver, TLDC, was designed based on three fundamental approaches: backtracking search, dynamic programming, and model counting or #SAT (a counting version of Boolean satisfiability). Detailed analyses show that each approach has its own strengths, and one approach is unlikely to dominate the others. The codes and papers of the participating solvers are available: https://afsa.jp/icgca/.

Recently, multi-issue closed negotiations have attracted attention in multi-agent systems. In particular, multi-time and multilateral negotiation strategies are important topics in multi-issue closed negotiations. In multi-issue closed negotiations, an automated negotiating agent needs to have strategies for estimating an opponent's utility function by learning the opponent's behaviors since the opponent's utility information is not open to others. However, it is difficult to estimate an opponent's utility function for the following reasons: (1) Training datasets for estimating opponents' utility functions cannot be obtained. (2) It is difficult to apply the learned model to different negotiation domains and opponents. In this paper, we propose a novel method of estimating the opponents' utility functions using boosting based on the least-squares method and nonlinear programming. Our proposed method weights each utility function estimated by several existing utility function estimation methods and outputs improved utility function by summing each weighted function. The existing methods using boosting are based on the frequency-based method, which counts the number of values offered, considering the time elapsed when they offered. Our experimental results demonstrate that the accuracy of estimating opponents' utility functions is significantly improved under various conditions compared with the existing utility function estimation methods without boosting.

An Authenticated Encryption scheme is used to guarantee both privacy and authenticity of digital data. At FSE 2014, an authenticated encryption scheme called CLOC was proposed. CLOC is designed to handle short input data efficiently without needing heavy precomputation nor large memory. This is achieved by making various cases of different treatments in the encryption process depending on the input data. Five tweak functions are used to handle the conditional branches, and they are designed to satisfy 55 differential probability constraints, which are used in the security proof of CLOC. In this paper, we show that all these 55 constraints are necessary. This shows the design optimality of the tweak functions in CLOC in that the constraints cannot be relaxed, and hence the specification of the tweak functions cannot be simplified.

Network Coding-based Epidemic Routing (NCER) facilitates the reduction of data delivery delay in Delay Tolerant Networks (DTNs). The intrinsic reason lies in that the network coding paradigm avoids competitions for transmission opportunities between segmented packets of a large data file. In this paper, we focus on the impact of transmission competitions on the delay performance of NCER when multiple data files exist. We prove analytically that when competition occurs, transmitting the least propagated data file is optimal in the sense of minimizing the average data delivery delay. Based on such understanding, we propose a family of competition avoidance policies, namely the Least Propagated First (LPF) policies, which includes a centralized, a distributed, and a modified variants. Numerical results show that LPF policies can achieve at least 20% delay performance gain at different data traffic rates, compared with the policy currently available.

Network traffic load usually differs significantly at different times of a day due to users' different time-preference. Network congestion may happen in traffic peak times. In order to prevent this from happening, network service providers (NSPs) can either over-provision capacity for demand at peak times of the day, or use dynamic time-dependent pricing (TDP) scheme to reduce the demand at traffic peak times. Since over-provisioning network capacity is costly, many researchers have proposed TDP schemes to control congestion as well as to improve the revenue of NSPs. To the best of our knowledge, all the studies on TDP schemes consider only the monopoly or duopoly NSP case. In our previous work, the duopoly NSP case has been studied with the assumption that each NSP has complete information of quality of service (QoS) of the other NSP. In this paper, an oligopoly NSP case is studied. NSPs try to maximize their overall revenue by setting time-dependent price, while users choose NSPs by considering their own time preference, congestion status in the networks and the price set by the NSPs. The interactions among NSPs are modeled as an oligopoly Bertrand game. Firstly, assuming that each NSP has complete information of QoS of all NSPs, a unique Nash equilibrium of the game is established under the assumption that users' valuation of QoS is uniformly distributed. Secondly, the assumption of complete information of QoS of all NSPs is relaxed, and a learning algorithm is proposed for NSPs to achieve the Nash equilibrium of the game. Analytical and experimental results show that NSPs can benefit from TDP scheme, however, not only the competition effect but also the incomplete information among NSPs causes revenue loss for NSPs under the TDP scheme.

The maturation of inhibitory transmission through γ-aminobutyric acid (GABA) is required to induce ocular dominance (OD) plasticity in the visual cortex. However, only circuits that are mediated by specific GABAA receptors can selectively elicit OD plasticity, implying a role of local circuits involved in GABA inhibition in this process. In this study, in order to theoretically examine the effects of such local pathways associated with cortical inhibition on the induction of OD plasticity, we compared synaptic modification dynamics regulated by feedforward inhibition and those regulated by feedback inhibition. Feedforward inhibition facilitated competitive interactions between different groups of inputs conveying correlated activities, which were required for the emergence of experience-dependent plasticity. Conversely, feedback inhibition suppressed competitive interactions and prevented synapses from reflecting past sensory experience. Our results suggest that the balance between feedforward and feedback inhibition regulates the timing and level of cortical plasticity by modulating competition among synapses. This result suggests an importance of activity-dependent competition in experience-dependent OD plasticity, which is in line with the results of previous experiments.

Due to network users' different time-preference, network traffic load usually significantly differs at different time. In traffic peak time, network congestion may happen, which make the quality of service for network users deteriorate. There are essentially two ways to improve the quality of services in this case: (1) Network service providers (NSPs) over-provision network capacity by investment; (2) NSPs use time-dependent pricing (TDP) to reduce the traffic at traffic peak time. However, over-provisioning network capacity can be costly. Therefore, some researchers have proposed TDP to control congestion as well as improve the revenue of NSP. But to the best of our knowledge, all of the literature related time-dependent pricing scheme only consider the monopoly NSP case. In this paper, a duopoly NSP case is studied. The NSPs try to maximize their overall revenue by setting time-dependent price, while users choose NSP by considering their own preference, congestion status in the networks and the price set by the NSPs. Analytical and experimental results show that the TDP benefits the NSPs, but the revenue improvement is limited due to the competition effect.

Multi-Car Elevator (MCE) systems, which consist of several independent cars built in the same shaft, are being considered as the elevators of the next generation. In this paper, we present MceSim, a simulator of MCE systems. MceSim is an open source software available to the public, and it can be used as a common testbed to evaluate different control methods related to MCE systems. MceSim was used in the group controller performance competition: CST Solution Competition 2007. This experience has proven MceSim to be a fully functional testbed for MCE systems.

This paper presents a dynamic node decaying method (DNDM) for layered artificial neural networks that is suitable for classification problems. Our purpose is not to minimize the total output error but to obtain high generalization ability with minimal structure. Users of the conventional back propagation (BP) learning algorithm can convert their program to the DNDM by simply inserting a few lines. This method is an extension of a previously proposed method to more general classification problems, and its validity is tested with recent standard benchmark problems. In addition, we analyzed the training process and the effects of various parameters. In the method, nodes in a layer compete for survival in an automatic process that uses a criterion. Relatively less important nodes are decayed gradually during BP learning while more important ones play larger roles until the best performance under given conditions is achieved. The criterion evaluates each node by its total influence on progress toward the upper layer, and it is used as the index for dynamic competitive decaying. Two additional criteria are used: Generalization Loss to measure over-fitting and Learning Progress to stop training. Determination of these criteria requires a few human interventions. We have applied this algorithm to several standard benchmark problems such as cancer, diabetes, heart disease, glass, and iris problems. The results show the effectiveness of the method. The classification error and size of the generated networks are comparable to those obtained by other methods that generally require larger modification, or complete rewriting, of the program from the conventional BP algorithm.

Characteristics of the optical feedback noise in semiconductor lasers under superposition of the HF (High Frequency) current were experimentally examined and theoretically analyzed. The feedback noise was mostly suppressed by superposition of HF current, but still remained when frequency of the HF current coincided with a rational number of the round trip time period for the optical feedback in experimental measurement. Theoretical analysis was also given to explain these characteristic based on the mode competition theory of the semiconductor laser.

In this paper agents' interactions are defined in terms of cooperation, coordination and competition. As for cooperation and coordination problems, we focus on knowledge sharing of agents, define agencies as organizations of agents, propose a method to extract organizational knowledge for interacting agents. In case of competition, knowledge sharing is impossible. Therefore, modeling and formalization of strategic decision making and uncertainty management is required. We present an incomplete game theoretical based decision making method for competitive agents.

The need for electronic sealed-bid auction services with quantitative competition is increasing. This paper proposes a new method that combines one-way functions and a bit commitment technique for quantitative competitive sealed-bid auctions. Since each modular exponentiation is replaced with a one-way function, the proposed method's computational time is one forty thousandth that of the former methods and the proposed method suits mass bidder systems.

Generating conditions of the optical feedback noise in self-pulsing lasers were experimentally examined. The noise charcteristics were determined by changing the operating power, the feedback distance and the feedback ratio for several types of self-pulsing lasers. The idea of the effective modulation index was introduced to evaluate the generating conditions in an uniform manner based on the mode competition theory. Validity of the idea was experimentally confirmed for generation of noise.

A Multi-Agent Learning Language (MALL) is defined as being necessary for agents in environments where they encounter crucial situations in which they have to learn about the environment, other parties moves and strategies, and then construct an optimal plan. The language is based on two major factors, the level of certainty in fully monitoring (surveying) the agents and the environment, and optimal plan construction, in an autonomous way. Most of the work related to software agents is based on the assumption that other agents are trustworthy. In the growing Internet environment this may not be true. The proposed new learning language allows agents to learn about the environment and the strategies of their opponents while devising their own plans. The language is being tested in our project of software agents for Electronic Commerce that operates in various security zones. The language is flexible and adaptable to a variety of agents applications.

Theoretical calculations of the pulsing operation and the intensity noise under the optical feedback are demonstrated for operation of the self-sustained pulsation lasers. Two alternative models for the optical feedback effect, namely the time delayed injection model and the external cavity model, are applied in a combined manner to analyze the phenomena. The calculation starts by supposing the geometrical structure of the laser and the material parameters, and are ended by evaluating the noise. Characteristics of the feedback induced noise for variations of the operating parameters, such as the injection current, the feedback distance and the feedback ratio, are examined. A comparison to experimental data is also given to ensure accuracy of the calculation.

Competition in some telecommunication services has emerged in Japan since deregulation of telecommunication markets in 1985. Demand forecasting methods which take into account competition and investment plan based on it should be studied. There are many forecasting and network planning methods, but most of them do not take into account competition. Thus, in this paper, the competitive Bass model, attraction model, regression model and entropy model are discussed as forecasting methods which can be used under competitive environment. Most of the existing planning methods have treated costs and interest rates as deterministic values, but in fact they are not deterministic. Thus, we show a method which represents undefined factors by fuzzy numbers with triangular membership functions.

Mechanism of the noise generation caused by the optical feedback in semiconductor laser was experimentally determined. Two types of the mode competition phenomena were confirmed to be the generating mechanisms. Applicability of the self-sustained pulsation to be a noise reduction method was also discussed.

The generation mechanism for excess intensity noise due to optical feedback is analyzed theoretically and experimentally. Modal rate equations under the weakly coupled condition with external feedback are derived to include the mode competition phenomena in DFB and Fabry-Perot lasers. We found that the sensitivity of the external feedback strongly depends on design parameters of structure, such as the coupling constant of the corrugation, the facet reflection and the phase relation between the corrugation and the facet. A DFB laser whose oscillating wavelength is well adjusted to Bragg wavelength through insertion of a phase adjustment region becomes less sensitive to external optical feedback than a Fabry-Perot laser, but other types of DFB lasers revealing a stop band are more sensitive than the Fabry-Perot laser.