arXiv subjects

Akihito Sudo

Publications and source records attributed to Akihito Sudo.

4 recordsLinked to original sources

arXiv2026

Mechanism-resolved second law for multipartite systems: An entropy-production ledger for correlation loss

Internal correlation among the subsystems of a many-body system with additive bare energies stores free energy at $k_B T$ per nat. Three one-step processes on three bits can share the same initial--final joint distribution yet differ in their minimum total entropy production, zero or $\ln 2$, depending on whether the mechanism causing the correlation loss itself reads the variable that carries it. Two such processes can further share a valid declaration of their reading patterns and the heat released on every trajectory, so that every per-stage balance and every fixed-block modularity value computed under that declaration coincides; the minimum total entropy production over implementations of their kernels still differs by $\ln 2$, the kernels remaining distinguishable only at the level of their full-state-space structure. We consider one synchronized step of a classical multipartite system in contact with a single heat bath. Each subsystem is updated by its own local mechanism, which reads a beginning-of-step snapshot of a fixed subset of the others under local detailed balance. We derive an exact entropy-production ledger for correlation loss that is valid for arbitrary reading patterns, including reciprocal ones, and is saturated by explicit processes. Under acyclic reading and for protocols that build no correlation erased within the same step, the ledger collapses to a mechanism-resolved second law. The total entropy production is bounded below by the destroyed correlation hidden from the mechanisms that caused each loss. For a fixed conversion task, the informed--blind gap along conversion chains is capped at $k_B T$ times the initial total correlation, and the cap is exact. Under a per-run work budget, information decides access rather than price. This unmeasured contrast identifies a candidate single-electron test.

cond-mat.stat-mech↗

arXiv2026

Thermodynamics of Learning: A Typed Four-Component Accounting of Memory, Fit, and Value

What a finite learning device has recorded and what will hold value for it on future tasks are not the same quantity. We develop a typed accounting for finite-state learning devices that separates four components: a training-side fit functional $Φ_{\mathrm{fit}}$, the record-correlation stock $J_{D}=I(M;D)$, an update-side search ledger $σ_{M}$, and an operational capital value $V(M;T,b)$. This value is the work gap between an informed protocol class and a blind class obtained by deleting the memory-read port and re-optimizing from scratch. (I) Separation: for every $n$, there is a device family on which record correlation and world correlation grow by $n\ln 2$ while the capital gain is exactly zero. In the $\mathrm{flat}^{*}$ regime, data-free updates never increase $V$. (II) Capitalization ledger: an exact $\mathrm{flat}^{*}$ extraction identity and a universal ledger identity give, for (F5$'$)-stable $M$-local updates under a no-discarded-record-correlation condition (f), the bound $η_{\mathrm{cap}}\le 1$ for the capitalization efficiency $η_{\mathrm{cap}}=ΔV/(k T\,σ_{M})$, together with necessary and sufficient conditions for equality. (III) Value retention: for the retention gap $L_{\mathrm{gen}}$ and retention ratio $ρ_{\mathrm{gen}}$ (the former carries no sign constraint; the latter is defined for positive training-side value and is not confined to $[0,1]$) we give a two-layer alignment domain: an exact exchange rate between value and the side-information-adjusted record fit $I(M';D\mid Y)$ without any record-side-information independence assumption, and a raw record-stock exchange rate under a joint side-information neutrality condition $(M,D)\perp Y$, whose boundary is marked by an explicit one-time-pad witness. These are statements about finite-device value retention under task-distribution shift, not a theory of statistical generalization.

cond-mat.stat-mech↗

arXiv2021

LassoLayer: Nonlinear Feature Selection by Switching One-to-one Links

Along with the desire to address more complex problems, feature selection methods have gained in importance. Feature selection methods can be classified into wrapper method, filter method, and embedded method. Being a powerful embedded feature selection method, Lasso has attracted the attention of many researchers. However, as a linear approach, the applicability of Lasso has been limited. In this work, we propose LassoLayer that is one-to-one connected and trained by L1 optimization, which work to drop out unnecessary units for prediction. For nonlinear feature selections, we build LassoMLP: the network equipped with LassoLayer as its first layer. Because we can insert LassoLayer in any network structure, it can harness the strength of neural network suitable for tasks where feature selection is needed. We evaluate LassoMLP in feature selection with regression and classification tasks. LassoMLP receives features including considerable numbers of noisy factors that is harmful for overfitting. In the experiments using MNIST dataset, we confirm that LassoMLP outperforms the state-of-the-art method.

cs.LG↗

arXiv2020

Chaos may enhance expressivity in cerebellar granular layer

Recent evidence suggests that Golgi cells in the cerebellar granular layer are densely connected to each other with massive gap junctions. Here, we propose that the massive gap junctions between the Golgi cells contribute to the representational complexity of the granular layer of the cerebellum by inducing chaotic dynamics. We construct a model of cerebellar granular layer with diffusion coupling through gap junctions between the Golgi cells, and evaluate the representational capability of the network with the reservoir computing framework. First, we show that the chaotic dynamics induced by diffusion coupling results in complex output patterns containing a wide range of frequency components. Second, the long non-recursive time series of the reservoir represents the passage of time from an external input. These properties of the reservoir enable mapping different spatial inputs into different temporal patterns.

q-bio.NC↗