Demo Data from SIGIR 2026 / reviewers in the wild / expert
Masaki Tsukamoto
dblp:97/8300
· DBLP profile ↗
2ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0002-8359-3141ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 since 2021Theory of computation · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Theoretical computer science
1 paper |
Coding theory · 54% Mathematical optimization · 23% Information theory · 23% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Mathematical optimization
dynamical systems |
0.3 | 1 | 2018 | From Rate Distortion Theory to Metric Mean Dimension: Variational Principle · IEEE Trans. Inf. Theory 2018 |
Information theory › ergodic theory
metric mean dimension |
0.3 | 1 | 2018 | From Rate Distortion Theory to Metric Mean Dimension: Variational Principle · IEEE Trans. Inf. Theory 2018 |
Coding theory › source coding › rate-distortion theory
rate-distortion function |
0.3 | 1 | 2018 | From Rate Distortion Theory to Metric Mean Dimension: Variational Principle · IEEE Trans. Inf. Theory 2018 |
Coding theory › source coding
rate-distortion theory |
0.3 | 1 | 2018 | From Rate Distortion Theory to Metric Mean Dimension: Variational Principle · IEEE Trans. Inf. Theory 2018 |
Coding theory › source coding
lossy source coding |
0.1 | 1 | 2018 | From Rate Distortion Theory to Metric Mean Dimension: Variational Principle · IEEE Trans. Inf. Theory 2018 |
Methods — techniques the papers use, named apart from their topics
variational principle · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Graph optimization algorithm using symmetry and host bias for low-latency indirect networkabstractIt is known that an indirect network with a small host-to-host Average Shortest Path Length (h-ASPL) improves overall system performance in a parallel computer system . As a means to discuss such indirect networks in graph theory, the Order/Radix Problem (ORP) has been proposed. ORP involves finding a graph with a minimum h-ASPL that satisfies a given number of hosts and radix. A graph in ORP represents an indirect network and has two types of vertices: host and switch. We propose an optimization algorithm to generate graphs with a sufficiently small h-ASPL. The primary features of the proposed algorithm are the symmetry of the graph and the bias of the hosts adjacent to each switch. These features reduce the computational time to calculate the h-ASPL and improve the search performance of the algorithm. The performance of the proposed algorithm is evaluated using problems presented by Graph Golf, an international ORP competition. Our results show that the proposed algorithm can generate graphs with a smaller h-ASPL than the existing algorithm. To evaluate the performance of the graphs generated by the proposed algorithm, we use the parallel simulation framework SimGrid and the parallel benchmark collection NAS Parallel Benchmarks. Our results also show that the graphs generated by the proposed algorithm have higher performance than those generated by the existing algorithm. Masahiro Nakao, Masaki Tsukamoto, Yoshiko Hanada, Keiji Yamamoto |
Parallel Comput. | 2 |
| 2018 | From Rate Distortion Theory to Metric Mean Dimension: Variational PrincipleabstractThe purpose of this paper is to point out a new connection between information theory and dynamical systems. In the information theory side, we consider rate distortion theory, which studies lossy data compression of stochastic processes under distortion constraints. In the dynamical systems side, we consider mean dimension theory, which studies how many parameters per iterate we need to describe a dynamical system. The main results are new variational principles connecting rate distortion function to metric mean dimension. Elon Lindenstrauss, Masaki Tsukamoto |
IEEE Trans. Inf. Theory | 2 |