Skip to content

Category

data science

3,683 papers

#data science Open access Oct 2026

TMC-LewisGen: Automated Generation of Lewis-like Structures for Transition-Metal Complexes.

Lewis structures constitute a foundational language in chemical education, mechanistic reasoning, synthesis, and modern cheminformatics. With the accelerating shift toward data-driven and AI-assisted molecular science, the ability to rapidly generate Lewis-like representations, including RDKit-parsable SMILES, has beco...

Farshad Shiri, Jing-Yuan Zhu, Sin-Hang Tong et al. · 0 citations
#data science Open access Oct 2026

CROSS-FOUNDRY QUANTUM NANO PROCESS PORTABILITY AND STANDARDS AT THE LIMIT Research positioning This monograph proposes a falsifiable research architecture for transferring quantum-nanofabrication capability across different foundries without confusing the numerical replication of a proprietary recipe with the reconstruction of its underlying physical intent. The central question is whether a quantum device can be rebuilt using different equipment, materials supply chains, calibration services, and engineering teams while meeting an independently preregistered quantum-functional acceptance threshold. The core coupling is Physical Intent × Reference Transfer × Quantum Reconstruction. The research treats process intent as a typed, auditable relationship between target material states, geometric and interface constraints, measurement references, failure modes, and terminal quantum function. It treats reference transfer as an uncertainty-bearing chain that may fail under correlated errors or the withdrawal of the original provider. It treats reconstruction as a device-level empirical claim rather than a similarity score between recipes. Research structure and methods The work contains 82 research chapters with five dedicated sections per chapter, a 30-part mathematical prelude, 200 Research Gates, and 33 Phoenix Answer Embryos. Each Research Gate specifies a challengeable question and a proposed measurement, rival explanation, baseline, stopping condition, and evidence artifact. Each Phoenix Answer Embryo is provisional: it may be falsified, divided, reconfigured, archived, or replaced. The 233 associated equation signposts are mathematical research prompts rather than claims of 233 new physical laws. In addition to chapter-level equations, the argument draws on statistical metrology, physical invariants, uncertainty propagation, causal identification, optimization, materials physics, and structured knowledge transfer. Equation validity remains conditional on its stated assumptions and measurement domain. The proposed three-foundry protocol is intentionally severe. Foundry A defines a quantum device, its physical-intent package, reference samples, tolerances, and frozen end-point test. Foundry B receives the package and attempts reconstruction without tacit tuning or unpublished support from the originator. Foundry C receives a minimal transferable specification and attempts independent reproduction, falsification, or a documented branch. Tests distinguish transfer of physical function from favorable selection, equipment sameness, common calibration ancestry, unreported supplier dependencies, and changes in acceptance thresholds. Geometry alone is insufficient: the terminal quantum-function criterion must remain meaningful for the declared device platform, including its measurement uncertainty, readout conditions, and lifetime or aging limitations. The eight thematic parts examine process-intent ontology; materials and interface invariants; reference traceability and uncertainty; recipe translation and interoperable data contracts; quantum qualification; blind cross-foundry experiments; governance and negative-result preservation; and successor science. The proposed Foundry Transfer Capsule combines an intent-to-observable ledger, reference structures, versioned metrology contracts, failure exemplars, qualification tests, safety and rights constraints, and successor reimplementation instructions. Vendor exit, supplier substitution, unavailable calibration services, unrecorded operator assistance, and third-party reinterpretation are treated as stress tests, not footnotes.

CROSS-FOUNDRY QUANTUM NANO PROCESS PORTABILITY AND STANDARDS AT THE LIMIT Research positioning This monograph proposes a falsifiable research architecture for transferring quantum-nanofabrication capability across different foundries without confusing the numerical replication of a proprietary recipe with the reconstru...

33 · 0 citations
#data science Open access Oct 2026

COUSINE v1.0.0-site: model code for "Optimizing carbon dioxide removal in agricultural soils: A new mechanistic model of enhanced rock weathering"

Enhanced rock weathering (ERW) has the potential for gigatonne-scale CO2 removal (CDR) annually by introducing finely crushed alkaline minerals into global agricultural soils. However, questions remain about how soil inorganic carbon forms across complex conditions in cropland soils. Here we present COUSINE (COUpled So...

Feng Tao, Benjamin Z. Houlton · 0 citations
#data science Review Open access Oct 2026

A Single Exercise Session Elicits a Transient Kynurenine Metabolic Signature in Health and Disease: A Systematic Review and Meta‐Analysis

Exercise induces heterogeneous changes in KYN metabolism, characterized by reduced TRP and increased KYN/TRP ratio and downstream metabolites, which are transient and context-dependent, reflecting acute changes in metabolic flux rather than sustained pathway remodeling.

M. V. dos Santos Rangel, G. Paz, Maria das Graças C Souza et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Symmetry-Compatible Principle for Optimizer Design: Embeddings, LM Heads, SwiGLU MLPs, and MoE Routers

A striking geometric disparity has long persisted in the practice of deep learning. While modern neural network architectures naturally exhibit rich symmetry and equivariance properties, popular optimizers such as Adam and its variants operate inherently coordinate-wise, rendering them unable to respect the equivarianc...

Tim Tsz-Kit Lau, Weijie Su · 0 citations
#machine learning Preprint Open access Oct 2026

Sharp Statistical Thresholds for Attributed Gaussian Graph Matching

We investigate the problem of matching networks where both edge weights and node features are correlated following a specific node correspondence. While prior work has established sharp thresholds for matching either graphs or features individually, the joint setting has been significantly less explored. In this study,...

Mohammad Hassan Ahmad Yarandi, Luca Ganassali · 0 citations

Is Flow Matching Just Trajectory Replay for Sequential Data?

This work derives the optimal velocity field targeted by the empirical FM objective on sequential data and shows that the resulting sampler is an ODE whose dynamics describe a nonparametric, memory-augmented continuous-time dynamical system.

Soon Hoe Lim, Shi-Zhen Lin, Michael W. Mahoney et al. · 4 citations
#machine learning Preprint Open access Oct 2026

Beyond Sustainable Development Goal (SDG) Accelerators: Directed Evidence for Adaptive Sustainability Portfolios

Governments and development partners often seek Sustainable Development Goal (SDG) "accelerators" whose progress can propagate gains across human well-being, institutions, resources, and environmental priorities. Such claims require evidence on direction, timing, robustness, and trade-offs, not only network connectivit...

Md Muhtasim Munif Fahim, Md Jahid Hasan Imran, Md. Naim Molla et al. · 0 citations
#machine learning Preprint Open access Oct 2026

Noise-Aware Differentially Private Variational Inference

Differential privacy (DP) provides robust privacy guarantees for statistical inference, but this can lead to unreliable results and biases in downstream applications. While several noise-aware approaches have been proposed which integrate DP perturbation into the inference, they are limited to specific types of simple...

Talal Alrawajfeh, Joonas J\"alk\"o, Antti Honkela · 0 citations
#machine learning Preprint Open access Oct 2026

Comparing SGLD and a fixed-noise predictor-corrector adaptation in canonical joint energy-based models on CIFAR-10

Joint Energy-Based Models (JEM) unify classification and generation within a single network and support out-of-distribution (OOD) detection. Canonical JEM training relies on stochastic gradient Langevin dynamics (SGLD); a theoretically motivated alternative, the Predictor-Corrector (PC) sampler, has not previously unde...

Dmytro Knopov · 0 citations
#machine learning Preprint Open access Oct 2026

Gauge-Invariant, Parameter-Insensitive Regularization for Potential Recovery from Flow on Directed Graphs

Recovering a latent potential from observed flow on a directed graph (a discrete Poisson problem with Dirichlet boundaries) is ill-posed, and the standard fix backfires: ridge regularization shrinks toward a gauge-meaningless origin, collapsing and reversing the recovered ordering ($+0.81\to-0.42$ rank correlation agai...

Mohammad Forouhesh · 0 citations

From tech blogs

See all →
GPT-Lab Nov 3, 2026

Sovereignty Is the Option to Leave

Is your company’s AI sovereign? A practical guide for CIOs on AI vendor lock-in, EU Data Act switching, cloud sovereignty, and testing your exit plan before you need it. The post Sovereignty Is the Option to Leave appeared first on GPT-Lab.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.