arXiv subjects

Takeshi Miki

Publications and source records attributed to Takeshi Miki.

6 recordsLinked to original sources

arXiv2026

Hybrid eTFCE-GRF: Exact Cluster-Size Retrieval with Analytical p-Values for Voxel-Based Morphometry

Threshold-free cluster enhancement (TFCE) integrates cluster extent across thresholds to improve voxel-wise neuroimaging inference, but permutation testing makes it prohibitively slow for large datasets. Probabilistic TFCE (pTFCE) uses analytical Gaussian random field (GRF) p-values but discretises the threshold grid. Exact TFCE (eTFCE) eliminates discretisation via a union-find data structure but still requires permutations. We combine eTFCE's union-find for exact cluster-size retrieval with pTFCE's analytical GRF inference. The union-find builds the cluster hierarchy in one pass over sorted voxels and enables exact size queries at any threshold; GRF theory then converts these sizes to analytical p-values without permutations. Validation on synthetic phantoms (64^3, 80 subjects): FWER controlled at nominal level (0/200 null rejections, 95% CI [0.0%, 1.9%]); power matches baseline pTFCE (Dice >= 0.999); smoothness error below 1%; concordance r > 0.99. On UK Biobank (N=500) and IXI (N=563), significance maps form strict subsets of reference R pTFCE, which supports conservative error control. Implemented in pytfce (pip install pytfce): baseline completes whole-brain VBM in ~5s (75x faster than R pTFCE), hybrid in ~85s (4.6x faster) with exact cluster sizes; both >1000x faster than permutation TFCE.

eess.IV↗

arXiv2026

Implementation Risk in Portfolio Backtesting: A Previously Unquantified Source of Error

Portfolio backtesting is the primary tool for evaluating investment strategies before deployment, yet practitioners implicitly assume that different engines produce identical results for the same strategy. we formalise implementation risk, the systematic divergence in backtested portfolio metrics arising solely from differences in how engines implement the same logical strategy, and propose four metrics grounded in metrology to quantify it: engine sensitivity, implementation uncertainty interval, divergence amplification factor, and conclusion stability index. we execute 15 benchmark strategies through five independent open-source engines on 30 non-overlapping stratified asset buckets comprising 180 s&p 500 stocks under four transaction-cost regimes. at zero cost, all five engines agree exactly (maximum divergence 0.000%), isolating transaction-cost implementation as the sole source of disagreement. under nonzero costs, divergence is structured and predictable (spearman rho = 0.93 with cost intensity), remaining below 0.75 percentage points for most strategies but reaching 3.71% for high-turnover rotation strategies. source-code forensics uncovered seven previously undocumented defects across three engines, abstracted into a five-category failure-mode taxonomy. all engines agree on the sign of every performance metric (conclusion stability index = 1), so implementation risk does not alter investment decisions for the strategies studied but introduces measurable ambiguity in performance attribution. code and benchmark data are publicly available.

q-fin.PM↗

arXiv2025

Cyberoception: Finding a Painlessly-Measurable New Sense in the Cyberworld Towards Emotion-Awareness in Computing

In Affective computing, recognizing users' emotions accurately is the basis of affective human-computer interaction. Understanding users' interoception contributes to a better understanding of individually different emotional abilities, which is essential for achieving inter-individually accurate emotion estimation. However, existing interoception measurement methods, such as the heart rate discrimination task, have several limitations, including their dependence on a well-controlled laboratory environment and precision apparatus, making monitoring users' interoception challenging. This study aims to determine other forms of data that can explain users' interoceptive or similar states in their real-world lives and propose a novel hypothetical concept "cyberoception," a new sense (1) which has properties similar to interoception in terms of the correlation with other emotion-related abilities, and (2) which can be measured only by the sensors embedded inside commodity smartphone devices in users' daily lives. Results from a 10-day-long in-lab/in-the-wild hybrid experiment reveal a specific cyberoception type "Turn On" (users' subjective sensory perception about the frequency of turning-on behavior on their smartphones), significantly related to participants' emotional valence. We anticipate that cyberoception to serve as a fundamental building block for developing more "emotion-aware", user-friendly applications and services.

cs.HC↗

arXiv2024

How to quantify interaction strengths? A critical rethinking of the interaction Jacobian and evaluation methods for non-parametric inference in time series analysis

Quantifying interaction strengths between state variables in dynamical systems is essential for understanding ecological networks. Within the empirical dynamic modeling approach, multivariate S-map infers the interaction Jacobian from time series data without assuming specific dynamical models. This approach enables the non-parametric statistical inference of interspecific interactions through state space reconstruction. However, deviations in the biological interpretation and numerical implementation of the interaction Jacobian from its mathematical definition pose challenges. We mathematically reintroduce the interaction Jacobian using differential quotients, uncovering two problems: (1) the mismatch between the interaction Jacobian and its biological meaning complicates comparisons between interspecific and intraspecific interactions; (2) the interaction Jacobian is not fully implemented in the parametric Jacobian numerically derived from given parametric models, especially using ordinary differential equations. As a result, model-based evaluations of S-map methods become inappropriate. To address these problems, (1) we propose adjusting the diagonal elements of the interaction Jacobian by subtracting 1 to resolve the comparability problem between inter- and intraspecific interaction strengths. Simulations of population dynamics showed that this adjustment prevents overestimation of intraspecific interaction strengths. (2) We introduce an alternative parametric Jacobian and then cumulative interaction strength (CIS), providing a more rigorous benchmark for evaluating S-map methods. Furthermore, we demonstrated that the numerical gap between CIS and the existing parametric Jacobian is substantial in realistic scenarios, suggesting CIS as preferred benchmark. These solutions offer a clearer framework for developing non-parametric approaches in ecological time series analysis.

q-bio.PE↗

arXiv2021

Reconstructing large networks with time-varying interactions

Reconstructing interactions from observational data is a critical need for investigating natural biological networks, wherein network dimensionality (i.e. number of interacting components) is usually high and interactions are time-varying. These pose a challenge to existing methods that can quantify only small interaction networks or assume static interactions under steady state. Here, we proposed a novel approach to reconstruct high-dimensional, time-varying interaction networks using empirical time series. This method, named "multiview distance regularized S-map", generalized the state space reconstruction to accommodate high dimensionality and overcome difficulties in quantifying massive interactions with limited data. When we evaluated this method using the time series generated from a large theoretical model involving hundreds of interacting species, estimated interaction strengths were in good agreement with theoretical expectations. As a result, reconstructed networks preserved important topological properties, such as centrality, strength distribution and derived stability measures. Moreover, our method effectively forecasted the dynamic behavior of network nodes. Applying this method to a natural bacterial community helped identify keystone species from the interaction network and revealed the mechanisms governing the dynamical stability of bacterial community. Our method overcame the challenge of high dimensionality and disentangled complex time-varying interactions in large natural dynamical systems.

q-bio.PE↗

arXiv2019

Ecosystem-level stabilizing effects of biodiversity via nutrient-diversity feedbacks in multitrophic systems

Statistical averaging and asynchronous population dynamics as portfolio mechanisms are considered as the most important processes with which biodiversity contributes to ecosystem stability. However, portfolio theories usually regard biodiversity as a fixed property, but overlook the dynamics of biodiversity altered by other ecosystem components. Here, we proposed a new mechanistic food chain model with nutrient-diversity feedback to investigate how dynamics of phytoplankton species diversity determines ecosystem stability. Our model focuses on nutrient, community biomass of phytoplankton and zooplankton, and phytoplankton species richness. The model assumes diversity effects of phytoplankton on trophic interaction strength along plankton food chain: phytoplankton diversity influences nutrient uptake by phytoplankton and zooplankton grazing on phytoplankton, which subsequently affects nutrient level and community biomass of phytoplankton and zooplankton. The nutrient level in turn affects phytoplankton diversity. These processes collectively form feedbacks between phytoplankton diversity and dynamics of plankton and nutrient. More importantly, nutrient-diversity feedback introduced additional temporal variabilities in community biomass, which apparently implies a destabilizing effect of phytoplankton diversity on ecosystem. However, the variabilities made ecosystems more robust against extinction of plankton because increasing phytoplankton diversity facilitates resource consumptions when consumers prone to extinct; while, reducing diversity weakens destabilizing dynamics caused by over-growth. Our results suggest the presence of a novel stabilizing effect of biodiversity acting through nutrient-diversity feedback, being independent of portfolio mechanisms.

q-bio.PE↗