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Yuta Kawamoto

Publications and source records attributed to Yuta Kawamoto.

3 recordsLinked to original sources

Rules Create Unequal Rewards: Elite Tennis Players Allocate Resources Efficiently

In many competitive settings, from education to politics, rules do not reward effort evenly, and thresholds (e.g., grade cutoffs or electoral majorities) make some moments disproportionately important. Success thus depends on efficiently allocating limited resources. However, empirical demonstration has been difficult because effort allocation is rarely observable and feedback is often delayed, limiting our understanding of expertise. Professional tennis provides an ideal natural experiment. Because each game resets after a player wins four points and points in a lost game are wasted, the value of a point varies sharply across scores. Efficient allocation should therefore win games without wasting points, conserving resources for future games. Such allocation manifests in score-dependent point-winning probabilities, from which we derive each player's Pareto frontier-the theoretical limit of the trade-off between game-winning probability and the expected points per game. Here, we show that top players operate closer to this frontier, converting points to game wins more efficiently. Optimal strategies reduce the probability of winning points when the player is far behind (e.g.,0-2, 0-3). This behavior is psychologically difficult-letting go of the current game-but represents a rational energy conservation strategy. Top players exhibit this pattern especially in return games, where winning points is harder than in service games, requiring them to drastically vary their efforts, consistent with game-theoretic predictions. These findings suggest that elite performance reflects efficient adaptation to rule-created value structures; knowing when to give up may be as fundamental to expertise as knowing when to compete.

cs.GT

Convergence Analysis of SGD under Expected Smoothness

Stochastic gradient descent (SGD) is the workhorse of large-scale learning, yet classical analyses rely on assumptions that can be either too strong (bounded variance) or too coarse (uniform noise). The expected smoothness (ES) condition has emerged as a flexible alternative that ties the second moment of stochastic gradients to the objective value and the full gradient. This paper presents a self-contained convergence analysis of SGD under ES. We (i) refine ES with interpretations and sampling-dependent constants; (ii) derive bounds of the expectation of squared full gradient norm; and (iii) prove $O(1/K)$ rates with explicit residual errors for various step-size schedules. All proofs are given in full detail in the appendix. Our treatment unifies and extends recent threads (Khaled and Richtárik, 2020; Umeda and Iiduka, 2025).

cs.LG

Model-Checking in the Loop Model-Based Testing for Automotive Operating Systems

While vehicles have primarily been controlled through mechanical means in years past, an increasing number of embedded control systems are being installed and used, keeping pace with advances in electronic control technology and performance. Automotive systems consist of multiple components developed by a range of vendors. To accelerate developments in embedded control systems, industrial standards such as AUTOSAR are being defined for automotive systems, including the design of operating system and middleware technologies. Crucial to ensuring the safety of automotive systems, the operating system is foundational software on which many automotive applications are executed. In this paper, we propose an integrated model-based method for verifying automotive operating systems; our method is called Model-Checking in the Loop Model-Based Testing (MCIL-MBT). In MCIL-MBT, we create a model that formalizes specifications of automotive operating systems and verifies the specifications via model-checking. Next, we conduct model-based testing with the verified model to ensure that a specific operating system implementation conforms to the model. These verification and testing stages are iterated over until no flaws are detected. Our method has already been introduced to an automotive system supplier and an operating system vendor. Through our approach, we successfully identified flaws that were not detected by conventional review and testing methods.

cs.SE