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Figshare · 2026

The challenges affecting the widespread uptake of responsible design by commercial design consultancies in UK and Ireland

Vicky Lofthouse, Norman Stevenson

This paper recognizes and responds to a prior lack of understanding regarding the factors that shape designer’s opportunities to engage in more responsible design and identifies a myriad of complex elements at play. Through in-depth qualitative research with 27 design consultants and 4 leading academics in the UK and Ireland, insights into industrial design practice which bridge this knowledge gap and provide a valuable foundation upon which future work to engage design in responsible design practices, have been generated. Multiple priorities; acting predominately in response to the requirements of their clients; and the fact that consultants are not the final decision makers, all limit the opportunity for designers to engage in responsible design. However there is also a clear value-action gap that appears to be driven by widespread lack of understanding as to how to undertake responsi…

Zenodo (CERN European Organization for Nuclear Research) · 2026

When Adaptivity Counts: Hit-Count Inspection with Information-Only Queries

Tamashiro, Takuya

We study a hidden-set problem in which perfect point queries acquire information but earn no reward, while a separate exact-q terminal action earns its hit count. Every instance satisfies V_ad^# = 2 and I >= 1, a staircase has ratio at least q/(1+2q/I) and approaches q as depth and ground size grow. The worst-case additive gap tends to q-1; at depth two, a four-state prior attains the sharp gap q(q-1)/[2(2q-1)]. Under one-hit reward, adaptivity instead collapses: a prior-free pointwise compilation makes all policy classes equal weighted maximum (I+q)-coverage. For hit count, the batch objective is not generally submodular, while a coverage proxy gives a (1-1/e)/q approximation. By contrast, reward-bearing active search admits ratios that grow with feedback-batch size. A two…

Journal of the Association for Information Systems · 2026

Modelling the Intention to Adopt Cloud Computing Services: A Transaction Cost Theory Perspective

Ogan Yigitbasioglu

This paper uses transaction cost theory to study cloud computing adoption. A model is developed and tested with data from an Australian survey. According to the results, perceived vendor opportunism and perceived legislative uncertainty around cloud computing were significantly associated with perceived cloud computing security risk. There was also a significant negative relationship between perceived cloud computing security risk and the intention to adopt cloud services. This study also reports on adoption rates of cloud computing in terms of applications, as well as the types of services used.

PubMed · 2026

Diamond Cut Veneers: The Faceted Monolithic Ceramic Dental Prosthesis, Concept Origin and Priority Record

Atakan Elter

PURPOSE: This study evaluated the validity of the color selection results obtained from digital cameras. MATERIALS AND METHODS: Four observers matched the colors of 30 extracted maxillary incisors under daylight conditions. Two different digital cameras, with 1.5- and 5.0-megapixel resolutions, were used to select the color of the standardized circular area with 3 reference points on the same teeth; a spectrophotometer was also used, and the results were compared with the human and camera observations. RESULTS: The highest significant agreement (60% agreement with the spectrophotometric values) was found with the 5.0-megapixel camera. CONCLUSION: It appears that the resolution capacity of a digital camera can increase the reliability of color selection.

Zenodo (CERN European Organization for Nuclear Research) · 2026

Do AI Models Invent References? A Verification Audit of Citations in AI-Generated Academic Text

TextPulse Research

Working paper and evidence data. Five current model families (DeepSeek, Mistral, OpenAI, Anthropic, Gemini; 2026) audited under one protocol across 30 academic topics. Condition A verified all 194 author-year citations the models embedded in prose (none fabricated after adjudication). Condition B verified 1,500 one-shot full references against Crossref and OpenAlex with DOI resolution checks; 15.2 percent were fabricated or misattributed (family range 9.0 to 29.7 percent) and one in four supplied DOIs was defective. Files: per-reference and per-citation verdict evidence (JSONL/CSV), generation, verification, adjudication, and analysis code, figures, and the paper PDF. Full generated texts are not part of this release. Paper page:

Nature Neuroscience · 2026

Strong and localized recurrence controls the dimensionality of neural activity across brain areas

David Dahmen, Stefano Recanatesi, Xiaoxuan Jia, Gabriel Koch Ocker

The brain contains an astronomical number of neurons, but it is their collective activity that underlies brain function. The number of degrees of freedom that this activity explores (its dimensionality) is therefore a fundamental signature of neural dynamics. However, it is not known what controls dimensionality in the biological brain. Through analysis of high-density Neuropixels recordings, here, we argue that areas across the mouse cortex predominantly operate in a sensitive regime that gives recurrent synaptic networks a strong role in regulating dimensionality. This control is expressed across time, as cortical activity transitions among states with different dimensionalities. Moreover, this control is mediated through highly tractable features of synaptic networks (network motifs). Analyzing a massive synaptic physiology dataset, we find that motifs impacting dimensionality are pr…

Zenodo (CERN European Organization for Nuclear Research) · 2026

The Vocabulary Fingerprint of AI Rewriting: Common Words AI Language Models Prioritize

TextPulse Research

Working paper and open lexicons. We compared 60,786 human-written academic texts with AI rewrites of the same texts (eight model configurations across six model families, 40.4 million tokens) and measured which words models inject and suppress when rewriting. The signature is dominated by formal connectives and Latinate substitutions such as "thereby", "consequently", and "utilized", while the famous chatbot words behave differently: "meticulously" is enriched 214-fold, but "delve" is not enriched in rewriting at all. Two lexicons are released, related as subset and whole. The studied lexicon (1,057 words) is a small subset covering the most prominent AI-preferred words of the common vocabulary; it uses a centered AI score, 100·σ(δ), where 50 means equal use on both sides and the score can lean both ways. The full-vocabulary lexicon scores the entire English vocabulary as represented in…

Information Systems Research · 2026

The Impact of Generative AI on Collaborative Open-Source Software Development: Evidence from GitHub Copilot

Fangchen Song, Ashish Agarwal, Wen Wen

Generative artificial intelligence (AI) facilitates content production and enhances ideation, with potentially important implications for developer productivity and participation in software development. To explore its impact on collaborative open-source software (OSS) development, we investigate the role of GitHub Copilot, a generative AI pair programmer, in OSS development where multiple distributed developers voluntarily collaborate. Using GitHub's proprietary Copilot usage data, combined with public OSS project data obtained from GitHub, we find that Copilot use increases project-level code contributions by 5.9%. This gain is accompanied by a 3.4% increase in developer coding participation and a 2.1% increase in individual code contributions. However, Copilot use is also associated with an 8% increase in coordination time and more code discussions. This reveals an important tradeoff…

Zenodo (CERN European Organization for Nuclear Research) · 2026

Stylometric Fingerprints of AI Rewriting: Punctuation, Syntax, and Model Attribution Across 60,786 Paired Texts

TextPulse Research

Working paper and open per-text data. We compared 60,786 human-written academic texts with AI rewrites of the same texts produced by eight AI model configurations across six model families, and computed 49 stylometric features for every text: punctuation rates, passive voice, nominalization, first person, contractions, sentence openers, lexical diversity, word length, and a set of hedge and connective phrases. The paired design controls topic and content, so every change is caused by the model itself. AI rewrites use longer words (Cohen's d = 1.57 for words of seven or more letters), more nominalizations (d = 1.12), higher lexical diversity (d = 1.31), more passive voice, and more commas, while first person, contractions, questions, and repeated sentence openers are mostly removed. A multinomial logistic regression reading only the 49 features identifies which of six models produced a r…

Zenodo (CERN European Organization for Nuclear Research) · 2026

Execution-Time Authorization for AI Agents: A Formal Framework for Deterministic Governance Boundaries

Edward Meyman

Execution-Time Authorization for AI Agents formalizes execution-time authorization (ETA) as a deterministic governance boundary for AI agents and other autonomous systems whose proposed actions may produce real-world effects. The paper defines ETA as a deterministic runtime enforcement architecture that evaluates a canonicalized proposed action against declared, versioned policy and decision state before an in-scope effect may occur; emits an action-bound verdict; and couples execution to the applicable authorization condition, producing a tamper-evident authorization artifact intended to support independent reconstruction. A conforming ETA deployment must be assessed separately for Output Integrity, Input Integrity, and Replay Integrity under the Authorization Boundary Integrity Model (ABIM); the definition is an implementation model, not a completeness test. The paper distinguishes ET…

Zenodo (CERN European Organization for Nuclear Research) · 2026

Human versus AI Text Classification from Stylometric Features Across 121,092 Academic Texts

TextPulse Research

Working paper and open per-text data. This study treats the separation of human-written and AI-rewritten academic text as a plain text classification task. The data are 60,306 human-written academic texts and 60,786 AI rewrites of those texts, produced by eight AI model configurations across six model families, each text described by 47 interpretable stylometric features such as word length, passive voice, lexical diversity, punctuation rates, and sentence openers. A logistic regression on these features separates the two classes with an AUC of 0.936 and an accuracy of 86.4 percent under grouped five-fold cross-validation, and a gradient boosting model increases this to an AUC of 0.961 and an accuracy of 89.5 percent. The errors have a clear structure. Identifying 99 percent of AI rewrites requires falsely flagging 68 percent of human texts, and keeping false flags at 1 percent detects…

Research Repository (Delft University of Technology) · 2026

The architecture of creativity: Toward a causal theory of creative workspace design

Katja Thoring, Milene Gonçalves, Roland M. Mueller, Pieter Desmet

The question of how the physical work environment can affect creativity is gaining interest among companies and educational institutions. This paper introduces ten propositions outlining possible relationships between spatial characteristics and creative work. The propositions were developed following a grounded-theory approach based on nine expert interviews that provide insight on the topic from the perspective of different creative fields-namely, urban planning, architecture, interior design, office planning, furniture design, industrialdesign, design thinking, innovation, and fine arts. We focused on both educational and practice environments within the creative sector. For each proposition, we provide links to supporting literature. We present a summary of the main insights and visualize the developed propositions as a set of causal graphs. The propositions have implications for bo…

Central European Journal of Operations Research · 2026

The Shapley value for airport and irrigation games

Judit Márkus, Miklós Pintér, Anna Radványi

Abstract In this paper, we consider cost-sharing problems defined on rooted trees. We refer to these problems as cost-tree problems and to the induced transferable utility cooperative games as irrigation games. We introduce a formal notion of irrigation games and provide a characterization of this class of games. The well-known class of airport games (Littlechild and Thompson 1977) is shown to be a subclass of irrigation games. The Shapley value (Shapley 1953) is arguably the most widely used solution concept for transferable utility cooperative games. Dubey (1982) and Moulin and Shenker (1992) show, respectively, that the axiomatizations of the Shapley value due to Shapley (1953) and Young (1985) remain valid for the class of airport games. In this paper, we extend the results of Dubey (1982) and Moulin and Shenker (1992) to the broader class of irrigation games. Specifically, we provi…

eNeuro · 2026

Electrophysiological indices of hierarchical speech processing differentially reflect the comprehension of speech in noise

Shyanthony R. Synigal, Andrew J. Anderson, Edmund C. Lalor

ABSTRACT The past few years have seen an increase in the use of encoding models to explain neural responses to natural speech. The goal of these models is to characterize how the human brain converts acoustic energy into distinct linguistic representations that enable everyday speech comprehension. For example, researchers have shown that electroencephalography (EEG) data can be modeled in terms of acoustic features of speech, such as its amplitude envelope or spectrogram, linguistic features such as phonemes and phoneme probability, and higher-level linguistic features like context-based word predictability. However, it is unclear how reliably EEG indices of these speech feature representations reflect comprehension in different listening conditions. To address this, we recorded EEG from neurotypical adults who listened to segments of an audiobook in various levels of background noise.…

Journal of the American Statistical Association · 2026

Selecting the number of components in PCA via random signflips

David Hong, Yue Sheng, Edgar Dobriban

Principal component analysis (PCA) is a foundational tool in modern data analysis, and a crucial step in PCA is selecting the number of components to keep. However, classical selection methods (e.g., scree plots, parallel analysis, etc.) lack statistical guarantees in the increasingly common setting of large-dimensional data with heterogeneous noise, i.e., where each entry may have a different noise variance. Moreover, it turns out that these methods, which are highly effective for homogeneous noise, can fail dramatically for data with heterogeneous noise. This paper proposes a new method called signflip parallel analysis (FlipPA) for the setting of approximately symmetric noise: it compares the data singular values to those of "empirical null" matrices generated by flipping the sign of each entry randomly with probability one-half. We develop a rigorous theory for FlipPA, showing that…

Zenodo (CERN European Organization for Nuclear Research) · 2026

An Epistemic-Content Taxonomy of Human Intervention in Agentic Collaboration

Jongsun Suh

When a human intervenes in an agent's work, their input supplies epistemic content; yet existing taxonomies index such moves only by timing, authority, granularity, defect class, or interaction state, not by what the input supplies. Two months of one practitioner's workstation record, 2,205 inputs to LLM coding agents with 1,094 of them gated as interventions, are coded on two axes: the epistemic content of the input, whether grounds, a frame, or an act of standing, and the target the input acts on. Grounds are considerations bearing on entitlement to a conclusion, which live in the world and can be looked up, a frame is a replacement of the hypothesis space the agent is searching, and standing is the authority a move exercises while supplying no domain content of its own, which is what a stop signal, a demand for judgment, or a re-anchoring to purpose transfers. The traffic those schem…

Zenodo (CERN European Organization for Nuclear Research) · 2026

Interlanguage and Mathematical Translation Methodology Sidecar

OpenAI Codex

Interlanguage and mathematical translation methodology sidecar Open first: 00_Interlanguage_Methodology_Current_v14_20260805.pdf remains the default overview. This concept is the public methodology, provenance, decision-rationale, correction/reversal, corpus-control, and continuation shelf; mathematical readers remain on their dedicated concepts. Aktualny medžuslovjansky program Open Logic Naznačenje: To jest praktično medžuslovjansko izdanje logiky i matematičnogo jezyka za čitateljev slovjanskyh jezykov, vključajuči ljudij bez znanja anglijskogo. Latinična redakcijska svědočba jest neprěryvna od OLP-0001 do OLP-0175. Uvodny tekst o sistemah logiky silnějših od logiky prvogo reda jest prěložen i prěgledan; slěduje OLP-0176, many-sorted-logic.tex. Kirilična verzija bude deterministično izvedena iz zamrznutoj latiničnoj svědočby, a ne nezavisno prěložena. Točny kontrolny punkt: 175 iz 72…

ACM Transactions on Intelligent Systems and Technology · 2026

A Survey on Human-AI Collaboration with Large Foundation Models

Vanshika Vats, Marzia Binta Nizam, Minghao Liu, Ziyuan Wang

As the capabilities of artificial intelligence (AI) continue to expand rapidly, Human-AI (HAI) Collaboration, combining human intellect and AI systems, has become pivotal for advancing problem-solving and decision-making processes. The advent of Large Foundation Models (LFMs) has greatly expanded its potential, offering unprecedented capabilities by leveraging vast amounts of data to understand and predict complex patterns. At the same time, realizing this potential responsibly requires addressing persistent challenges related to safety, fairness, and control. This paper reviews the crucial integration of LFMs with HAI, highlighting both opportunities and risks. We structure our analysis around: human-guided model development, collaborative design principles, ethical and governance frameworks, and applications in high-stakes domains. Our review shows that successful HAI systems are not…

Zenodo (CERN European Organization for Nuclear Research) · 2026

Non-Native English Writing and the False Positives of Stylometric AI Text Detection

TextPulse Research

Working paper and open per-essay data. A widely cited study found that perplexity-based AI text detectors flag the majority of essays by non-native English writers as AI-generated while sparing native writers. This study measures whether the bias is true for a transparent stylometric classifier. We score all 5,600 essays of the ICNALE Written Essays corpus, written under controlled conditions by college students from ten Asian countries and regions at known proficiency levels and by English native speakers, with the human-versus-AI classifier of our prior studies, trained on 50,701 academic texts and rewrites and never on any essay. Every essay predates modern AI writing tools, so every flag is a false positive. The logistic regression classifier shows no bias against non-native writers. It flags 2.7 percent of learner essays and 3.3 percent of native essays at the default threshold, st…

Zenodo (CERN European Organization for Nuclear Research) · 2026

Do AI Detectors Agree? An Inter-Rater Reliability Study of Commercial AI Text Detectors on Academic Writing

TextPulse Research

Nine commercial AI text detectors (Turnitin, GPTZero, Originality.ai, Pangram, Copyleaks, ZeroGPT, Winston, Sapling, and QuillBot) were treated as independent raters of 90 academic texts: 30 purely human-written before 2022, 30 AI-generated by five model families, and 30 hybrid (human + AI) splices. Overall agreement is substantial (Krippendorff's alpha 0.71, Fleiss' kappa 0.78), but on hybrid human-AI texts agreement falls to chance level (mean pairwise kappa 0.02). In total, 28 of 30 hybrid texts received both a human verdict and an AI verdict from different tools, and the median per-text score range was 100 points. On mixed human-AI writing, the classification a text receives relies more on the choice of detector than on the text itself. This record contains the working paper (PDF), the full 90-text corpus with per-text provenance, the 90 x 9 detector score matrix, the generation pro…

Zenodo (CERN European Organization for Nuclear Research) · 2026

Sentence-Length Burstiness as a Cross-Disciplinary and Cross-Model Signal of AI Rewriting

TextPulse Research

Working paper and open per-text data. We compared 60,779 human-written academic texts with rewrites of the same texts generated by eight AI model configurations across six model families, so that every burstiness change is caused by the model rather than by topic or content. Burstiness is measured in its simplest form, the standard deviation of sentence lengths, with the coefficient of variation (CV) as the length-adjusted primary statistic. Human academic writing in this corpus has a mean CV of 0.449; the AI rewrites average 0.376, and 79.3 percent of all rewrites are flatter than their human written source. The flattening is universal across models but varies eight times in strength (DeepSeek -0.234, 98.5 percent flatter; Grok -0.032). A naive CV threshold that catches 62 percent of rewrites also falsely flags 39 percent of genuine human texts, concentrated on natural-science and engi…

Non-coding RNA Research · 2026

LncPNdeep: A long non-coding RNA classifier based on large language model with peptide and nucleotide embedding

Zongrui Dai, F. Deng, Hsiao H. Sung

Accurate classification of long non-coding RNAs (lncRNAs) is essential for transcriptome annotation and understanding gene regulation. Existing computational methods predominantly rely on nucleotide sequence features, frequently overlooking biologically relevant peptide signals encoded within lncRNAs. To overcome this limitation, we developed LncPNdeep, an integrative deep learning framework that combines nucleotide and peptide embeddings extracted via masked language models, specifically utilizing contextual representations from BigBird, Longformer, and ProtTrans. By fusing both features in a concatenated neural architecture, LncPNdeep robustly captures complex sequence relationships and improves discrimination between lncRNAs and coding RNAs. Benchmarking on the human transcriptome achieved state-of-the-art performance with 97.1% accuracy, surpassing established lncRNA classification…

Nature · 2026

Psychedelics align brain activity with context

Devon Stoliker, Leonardo Novelli, M. Amin Khajehnejad, Mana Biabani

Abstract Psychedelics can profoundly alter consciousness by reorganising brain connectivity; however, their effects are context-sensitive. To understand how this reorganisation depends on context, we collected and comprehensively analysed the largest psychedelic neuroimaging dataset to date. Sixty-two adults were scanned with functional MRI and EEG during rest and naturalistic stimuli (meditation, music, and movie), before and after ingesting 19 mg of psilocybin (functional MRI ≈80 min post-dose; EEG ≈150 min post-dose). Half the participants ranked the experience among the most meaningful of their lives. Under psilocybin, functional MRI and EEG signals recorded during eyes-closed conditions became similar to those recorded during an eyes-open condition. Global functional connectivity increased in associative regions and decreased in sensory areas. Using machine learning to represent ne…

Zenodo (CERN European Organization for Nuclear Research) · 2026

URVSO_Formal_Verification_Core_Sovereign_AI_Transition_Systems.pdf

Richard Anthony Amaya

This paper presents URVSO (Universal Rigorous Verification and State-Transition Operator), a formal verification architecture for certifying specified behavioral transitions in sovereign AI systems. URVSO models an AI system as a state-transition system subject to explicit constraints, uncertainty bounds, adversarial perturbation requirements, and machine-verifiable compliance predicates. The framework separates cryptographic validity, formal validity, robustness under specified perturbations, and empirical behavioral observation. URVSO incorporates state-space modeling, constraint satisfaction, adversarial robustness, zero-knowledge compliance proofs, recursive proof composition, and fixed-point analysis. A transition is certified only when its formal preconditions are satisfied, its required robustness conditions hold, and an associated proof is accepted by the specified verifier. URV…

Science China Information Sciences · 2026

Myriad: a large multimodal model applying vision experts for industrial anomaly detection

Yuanze Li, Haolin Wang, Shihao Yuan, Ming Liu

Zenodo (CERN European Organization for Nuclear Research) · 2026

The Universal Concept Layer: Language as Versionable Code, A Meaning Layer between Humans, Language Models, Agents, and Robots

Andreas Ehstand

Bilingual working paper (English / German), the English version is the citation version; the German version is identical in substance and included in this record. Infrastructure for systems combining humans, language models, agents and robots is being standardised rapidly: transport, tool invocation, identity, capability description. One layer goes systematically unaddressed, and not by oversight. The classical agent communication languages reserved a placeholder for it, a message field pointing to an ontology. What was specified is the reference, not the lifecycle of what is referenced. This paper proposes filling that placeholder rather than setting it again. It argues that the layer of meaning forms a distinct layer of system architecture, the Universal Concept Layer, and that its carrier material is versioned natural language: terms maintained as software artefacts, carrying defin…

Review of Financial Studies · 2026

Carbon Emissions and the Bank-Lending Channel

Marcin Kacperczyk, José‐Luis Peydró

Abstract We study how firm-level carbon emissions affect bank lending and real outcomes in a sample of global firms with syndicated loans. We exploit bank-level climate commitments as firm-level shocks to lending relationships, using firms' prior credit exposures to identify credit supply effects. Firms with higher emissions that previously borrowed from committed banks receive less bank credit. Evidence from lending volumes, prices, and within-firm-time loan-level data indicates a supply-side shift away from high-emission firms, not explained by borrower risk. Affected firms reduce debt, leverage, size, and investment, yet we find no reduction in future emissions, instead documenting evidence consistent with greenwashing. (JEL G21, G23, G30, D62, Q50)

Management Science · 2026

Trade-Offs Between Ranking Objectives: Descriptive Evidence and Structural Estimation

Rafael Greminger

When designing product rankings, online retailers and platforms choose which outcome to maximize: revenues from commissions or markups, the number of transactions, or consumer welfare. These objectives need not align, creating potential trade-offs. This paper studies how rankings differ between objectives and quantifies the resulting trade-offs. I provide descriptive evidence showing that lower-priced and high-utility alternatives gain more demand when ranked higher, suggesting that ranking them higher increases transactions and consumer welfare but may decrease revenues. To quantify these trade-offs, I develop and estimate a structural demand model in which consumers search and discover products, and construct rankings for each objective. The results show that these counterfactual rankings all increase consumer welfare, transactions, and platform revenues relative to a neutral benchmar…

Zenodo (CERN European Organization for Nuclear Research) · 2026

Text Length and the Reliability of Human versus AI Text Classification in Academic Writing

TextPulse Research

Working paper and open per-text data. Our previous study classified human-written and AI-rewritten academic texts from interpretable stylometric features with an AUC of 0.936 on texts with a median length near 290 words. This study measures how that reliability depends on text length. From 25,561 text pairs in which both the human text and its AI rewrite contain at least 300 words, we cut every text to 25, 50, 100, 150, 200, 250, and 300 words, re-extract 45 stylometric features at each length, and retrain the classifier at each length under grouped five-fold cross-validation. The composition of the sample is identical at every length, so the results isolate text length from every other property of the texts. Classification reliability increases monotonically with length. The AUC of a logistic regression increases from 0.817 at 25 words to 0.940 at 300 words and 0.950 on the untruncated…

Frontiers in Applied Mathematics and Statistics · 2026

Methodological guidance for predictor variable selection for adolescent smoking outcomes in Global Youth Tobacco Survey using R and Python

Wingston F Ng'ambi, Cosmas Zyambo, Lawrence N. Kazembe

ABSTRACT Background The Global Youth Tobacco Survey (GYTS) is widely used to monitor tobacco use among adolescents worldwide. However, inconsistent analytical approaches particularly in handling complex survey designs and predictor selection limit comparability across countries, survey waves, and software platforms. Although much of the GYTS literature relies on proprietary tools such as SAS and SPSS, practical and transparent guidance on implementing reproducible, theory-informed analyses remains limited. A unified workflow that respects the survey’s design while supporting cross-platform implementation is needed. Methods We developed a reproducible, open-source workflow for analysing GYTS data using R and Python. In R, analyses were conducted using the survey package (svydesign and svyglm) with constrained stepwise selection via stepAIC. In Python, a custom constrained stepwise proced…

ACM Computing Surveys · 2026

A Tutorial on Gaussian Process Learning-based Model Predictive Control

Jie Wang, Youmin Zhang

This tutorial provides an accessible and implementation-oriented introduction to Gaussian process learning-based model predictive control (GP-MPC), which combines probabilistic residual modeling with receding-horizon control. Its central tutorial contribution is a detailed, step-by-step derivation of multi-step mean and covariance propagation for GP-augmented prediction models. The derivation shows how commonly used propagation formulas follow from the laws of total expectation and total covariance, and clarifies the roles of the GP posterior mean, GP posterior covariance, uncertain inputs, and query-output cross-covariances. Building on this foundation, the paper distinguishes mean-only unconstrained GP predictive control from uncertainty-aware constrained GP-MPC, clarifies regulation and output-tracking formulations, and provides concise implementation and computational-complexity gui…

Cell Reports · 2026

Inferential planning in the frontal cortex

Francesco Donnarumma, Thomas Parr, Karl Friston, James C. R. Whittington

How the brain plans and maintains sequences of future actions remains a central question in systems neuroscience. Studies in the frontal cortex revealed that multiple elements of a sequence are represented simultaneously in separable neural subspaces, challenging classical sequential planning models. Here, we show that these representations emerge naturally under inferential planning, in which sequential actions are inferred from sensory evidence and goals. Using a hierarchical generative model, we reproduce key neural phenomena observed in the primate frontal cortex, including the simultaneous activation of multiple plan elements, the emergence of (almost) orthogonal "memory" subspaces, and their reuse across forward and backward tasks. Our approach provides a mechanistic account of how probabilistic inference over control states produces distributed neural representations of plans. Th…

Nature Methods · 2026

Beyond benchmarking: an expert-guided consensus approach to spatially aware clustering

Jieran Sun, Kirti Biharie, Peiying Cai, Niklas Müller‐Bötticher

Spatial omics technologies have revolutionized the study of tissue architecture and cellular heterogeneity by integrating molecular profiles with spatial localization. In spatially resolved transcriptomics, delineating higher-order anatomical structures is critical for understanding how cellular organization affects tissue and organ function. Since 2020, more than 50 spatially aware clustering (SAC) methods have been developed for this purpose. However, the reliability of current benchmarks is undermined by their narrow focus on Visium and brain tissue datasets, as well as incorrect interpretation of manual annotation as ground truth. Here, we present SACCELERATOR, a community-driven, extensible framework that standardizes data formatting, method integration, and metric evaluation, and is designed to rapidly incorporate new methods and datasets. SACCELERATOR currently includes 22 SAC me…

Memoirs of the American Mathematical Society · 2026

Nonlinear Stability for 3-D Plane Poiseuille Flow in a Finite Channel

Qi Chen, Shijin Ding, Zhilin Lin, Zhifei Zhang

In this paper, we study the nonlinear stability for the 3-D plane Poiseuille flow ( 1 − y 2 , 0 , 0 ) (1-y^2,0,0) at high Reynolds number R e Re in a finite channel T × [ − 1 , 1 ] × T \mathbb {T}\times [-1,1 ]\times \mathbb {T} with non-slip boundary condition. We prove that if the initial velocity v 0 v_0 satisfies ‖ v 0 − ( 1 − y 2 , 0 , 0 ) ‖ H 4 ≤ c 0 R e − 7 4 \begin{equation*} \|v_0-(1-y^2,0,0)\|_{H^{4}}\leq c_0 Re^{-\frac {7}{4}} \end{equation*} for some c 0 0 c_0>0 independent of R e Re , then the solution of 3-D Naiver-Stokes equations is global in time and does not transition away from the plane Poiseuille flow. To our knowledge, this is the first nonlinear stability result for the 3-D plane Poiseuille flow, and the transition threshold is consistent with the numerical result by Lundbladh et al. [ Threshold amplitudes for transition in channel flows , Transition, Turbulence…

Nature Communications · 2026

The choice-wide behavioral association study: data-driven identification of interpretable behavioral components

David B. Kastner, Nicole O. Yokota, Christina Y. Lee, Cristofer M. Holobetz

Behavior contains rich structure across many timescales, but there is a dearth of methods to identify relevant components, especially over the longer periods required for learning and decision-making. Inspired by the goals and techniques of genome-wide association studies, we present a data-driven method-the choice-wide behavioral association study: CBAS-that systematically identifies such behavioral features. CBAS uses a powerful, resampling-based, method of multiple comparisons correction to identify sequences of actions or choices that either differ significantly between groups or significantly correlate with a covariate of interest. We apply CBAS to different tasks and species (flies, rats, and humans) and find, in all instances, that it provides interpretable information about each behavioral task.

Science · 2026

De novo design of RNA pseudoknots with deep learning

Jill Townley, Wipapat Kladwang, David Baker, Hamish M Blair

RNA design has been hindered by the limited accuracy of 3D structure prediction. Here, we show that intricate RNA structures can be generated with current deep learning tools through accurate de novo design of pseudoknot secondary structures. In an Eterna competition involving 57 pseudoknots, generative AI methods matched experienced human designers in solving most blind challenges, evaluated by single-nucleotide-resolution chemical mapping, compensatory mutagenesis, and cryogenic electron microscopy. AI-generated molecules with accurate secondary structures formed well-ordered 3D folds stabilized by noncanonical tertiary interactions not modeled during design. Success was guided by an RNet foundation model trained on prior chemical mapping data, suggesting that some difficult RNA design tasks may be tractable without first solving RNA 3D structure prediction.

Nature Machine Intelligence · 2026

Multi-resolution enhancement for full-spectrum neural representations

Yuan Ni, Zhantao Chen, Shizhou Xu, Cheng Peng

Scientific data acquisition continues to outpace storage and analysis capabilities, making voxel-based representations increasingly intractable. Implicit neural representations (INRs) offer a promising solution by encoding signals through coordinate-based neural networks, serving as surrogates of data, with computational and storage requirements scaling with network complexity rather than data dimensionality. However, smaller INRs struggle to faithfully represent multiscale structures, high-frequency information and fine textures that constitute a large proportion of scientific measurements. We propose WIEN-INR, a theoretically guided hierarchical INR framework that distributes modelling across resolution scales and enables improved representation capacity through a novel enhancement network to recover subtle details. This multiscale architecture allows smaller networks to retain the fu…

Entropy · 2026

A Ranked Sparsity Extension to the Bayesian Information Criterion: A Tool for Selecting Variables from Multiple Data Modalities

Ryan Peterson, Sarah M. Bird, Logan M. Harris, Patrick J. Breheny

The concept of ranked sparsity, originally introduced in the context of penalized regression, arises in modeling applications when an expected disparity exists in the quality of information between different feature sets. Its presence can cause traditional and modern model selection methods to fail because such procedures commonly presume “covariate equipoise”-that each potential parameter is equally worthy of entering into the final model. However, this presumption does not always hold, especially in the presence of derived variables or with highly disparate feature sets (i.e., multi-modal data). For instance, when all possible interactions are considered as candidate predictors, the sheer number of them grossly inflates the number of false discoveries, resulting in unnecessarily complex and difficult-to-interpret models with many (truly spurious) interactions. In this work, we motivat…

PLoS Computational Biology · 2026

The perils of omitting omissions when modeling evidence accumulation

Xiamin Leng, Alexander Fengler, Amitai Shenhav, Michael Frank

Response deadlines are commonly imposed in decision-making research to incentivize speedy decisions and sustained attention. This procedure frequently leads to a proportion of trials in which no response is made, and these omissions are often simply removed from the data during analysis. Here we show that this seemingly trivial assumption is in fact quite consequential for parameter estimation. We propose that omissions should instead be treated as observations to inform inference of the underlying generative process that led to their occurrence. Using new tools from likelihood-free inference applicable to a broad class of sequential sampling models (SSMs), we enable fast computation of omission probability without explicit integration, and clarify the degree to which omitting omissions - even in seemingly benign settings - can lead researchers astray. We explore this phenomenon in the…

Zenodo (CERN European Organization for Nuclear Research) · 2026

From Decision to Action: Three-Way Action Gating and Execution Failure in Longitudinal Human-LLM Interaction

Alen Širola

Longitudinal human-LLM work exposes a class of failures that is easy to hide when evaluation ends at explanation quality. A protocol may be known, a trigger may occur, and the model may even describe the correct next action, while the required external action still does not happen. The opposite failure also occurs: an action can be executed when it is not required or authorized. This paper adapts the established three-way decision frame - acceptance, rejection, and abstention - to action control as execute, inhibit, and defer, then adds a distinct implementation layer between decision and observable state change. The proposal is grounded in preserved naturalistic cases from a longitudinal human-LLM workflow, including missed activation, unauthorized activation, correct non-activation, premature action under unresolved conditions, and a verified decision-to-action gap. The paper introduc…

PLoS ONE · 2026

Bayesian model discovery for reverse-engineering biochemical networks from data

Andreas Christ Sølvsten Jørgensen, Marc Sturrock, Atiyo Ghosh, Vahid Shahrezaei

Reverse engineering gene regulatory networks from gene expression data is a challenging inference task. A related problem in computational systems biology is identification of signalling networks that perform particular functions, such as adaptation. Indeed, for many research questions, there is an ongoing need for efficient inference algorithms that can identify the simplest model, from among a larger set of inter-related models, that best explains empirical observations. To this end, we introduce Sparse Likelihood-free Inference using Gibbs sampling (SLInG), a Bayesian sparse likelihood-free inference method. SLInG provides an efficient sampling method for Approximate Bayesian Computation with sparsity-inducing hierarchical priors that is widely applicable for any simulation-based model discovery task. We first apply SLInG to linear sparse regression problem using a classic dataset, b…

Multidimensional Systems and Signal Processing · 2026

Multiscale deep learning convolutional neural network for ADHD detection using EEG

Javier Sanchís, Tahar Kechadi, Miguel A. Teruel, Juan Trujillo

International Journal of Information Security · 2026

Data and context matter: towards generalizing AI-based software vulnerability detection

Rijha Safdar, Danyail Mateen, Syed Taha Ali, Umer Ashfaq

AI-based solutions demonstrate remarkable results in identifying vulnerabilities in software, but research has consistently found that this performance does not generalize to unseen codebases. In this paper, we specifically investigate the impact of model architecture, parameter configuration, and quality of training data on the ability of these systems to generalize. For this purpose, we introduce VulGate, a high quality state of the art dataset that mitigates the shortcomings of prior datasets, by removing mislabeled and duplicate samples, updating new vulnerabilities, incorporating additional metadata, integrating hard samples, and including dedicated test sets. We undertake a series of experiments to demonstrate that improved dataset diversity and quality substantially enhances vulnerability detection. We also introduce and benchmark multiple encoder-only and decoder-only models. We…

Journal of Pharmaceutical Innovation · 2026

From QbD to Explainable AI: Interpretable Random Forest Surrogates for Design Space Understanding of Voriconazole-β-Cyclodextrin Inclusion Complexes

Sonal Solanki, Garima Kumari, Nikunj Solanki, D. P. Jindal

Nature · 2026

Asymmetric prefrontal representations for leader-follower dynamics

Yuan Cheng, Yusi Chen, M. K. Kwak, Ross P. Kempner

Across species, cooperative behaviour is often organized by distinct social roles such as leaders and followers1, yet the neural mechanisms that support these emergent role dynamics remain unclear. Here we introduce a mouse paradigm that captures leader-follower dynamics during cooperation. In this paradigm, stable social roles emerge through reciprocal interaction and predict learning speed. Disrupting the activity of the medial prefrontal cortex (mPFC), particularly in followers, impairs cooperation and induces complementary shifts in how animals weigh self- and partner-related cues during decision-making. Calcium imaging reveals that the mPFC represents leader-follower dynamics and computes an egocentric social value map of the partner’s position in a role-dependent manner. By integrating these empirical findings with a multi-agent inverse reinforcement learning framework, we identif…

Computational Mechanics · 2026

Practical multi-fidelity machine learning: fusion of deterministic and Bayesian models

Jiaxiang Yi, Cheng Ji, Miguel A. Bessa

Open MIND · 2026

Measuring Machine Habitus: A Pre-Registered Multiple Correspondence Analysis of LLM Disposition Space

Toeda Taiko

Large language models exhibit consistent styles of judgment that are not reducible to capability scores, yet attempts to measure them with human psychometric instruments have been criticized as measurement artifacts. We propose a behavioral, relational alternative inspired by Bourdieu's habitus and Airoldi's *machine habitus*: a pre-registered battery of 120 forced-choice dilemmas with no correct answers, administered in seed-shuffled stateless blocks to 7 models (35 valid sessions), analyzed with multiple correspondence analysis (MCA) rather than a factor model imported from human populations. The confirmatory session-stability hypothesis was supported with a moderate effect: independent sessions of the same model cohere in disposition space (between/within distance ratio 2.40; mean silhouette 0.258, bootstrap 95% CI [0.125, 0.384]; permutation p ≤ 1/10,001; Holm-adjusted p = 0.0004).…

Nature Methods · 2026

Lessons learned from a Kaggle challenge for particle picking in cryo-electron tomography

Ariana Peck, Joshua Hutchings, Jonathan Schwartz, Yue Yu

Abstract The difficulty of particle picking in cryo-electron tomography remains a barrier to routine in situ structure determination. Machine learning is well-suited to overcome this bottleneck with efficient algorithms that generalize across molecular species. To spur new algorithm development, we held a three-month Kaggle challenge that tasked contestants with annotating five molecular species across hundreds of experimental tomograms. This competition successfully engaged >1000 participants from diverse fields and delivered particle pickers that outperformed existing state-of-the-art. Systematic comparisons of the contestants’ submissions revealed the tolerance of subtomogram averaging to moderate but not severe over-picking and underscored the need for more robust measures of annotation quality. The winning models also highlighted the importance of data augmentation to overcome limi…

CSIRO · 2026

National Soil Carbon Research Programme: Field and Laboratory Methodologies

Jonathan Sanderman, Jeff Baldock, Bruce Hawke, Lynne M. Macdonald

Operating Systems Design and Implementation · 2026

Bypassing Races in Live Applications with Execution Filters

Jingyue Wu, Heming Cui, Junfeng Yang

Deployed multithreaded applications contain many races because these applications are difficult to write, test, and debug. Worse, the number of races in deployed applications may drastically increase due to the rise of multicore hardware and the immaturity of current race detectors.LOOM is a live-workaround system designed to quickly and safely bypass application races at runtime. LOOM provides a flexible and safe language for developers to write execution filters that explicitly synchronize code. It then uses an evacuation algorithm to safely install the filters to live applications to avoid races. It reduces its performance overhead using hybrid instrumentation that combines static and dynamic instrumentation.We evaluated LOOM on nine real races from a diverse set of six applications, including MySQL and Apache. Our results show that (1) LOOM can safely fix all evaluated races in a ti…

Zenodo (CERN European Organization for Nuclear Research) · 2026

What Happens to Citations When AI Rewrites Academic Text? A Large-Scale Paired Audit

TextPulse Research

Working paper and evidence data. A paired audit of 213,881 in-text citation marks across 60,786 human-AI rewrite pairs from eight model configurations in six families. 96.9 percent of citation marks survived rewriting unchanged; 2.26 percent were corrupted (dropped, altered, or without a source counterpart), and the corruption rate varied 28-fold across models on identical inputs. Files: the per-citation evidence extract (one row per citation mark with verdict and match evidence; pair identifiers are opaque and no passage text is included), extraction, alignment, validation, and analysis code, figures, and the paper PDF. Source and rewrite passage texts are not released. Paper page:

Journal of the Association for Information Systems · 2026

The Perceived Impact of the Agile Development and Project Management Method Scrum on Information Systems and Software Development Productivity

Karlheinz Kautz, Johanson, Thomas Heide, Andreas Uldahl

This research contributes to the body of knowledge in information systems development (ISD) with an empirical investigation in form of a case study that demonstrates the positive impact of the agile development and project management method Scrum on information systems and software development productivity and it provides a useful operationalization of the concept through seven identified indicators for productivity. Despite the fact that the case unit had challenges with the use of Scrum, the indicators identified the areas where the company had managed to exploit the potential of Scrum and its practices with regard to increasing productivity. The research results are discussed both with regard to the existing Scrum literature as well as to complex adaptive systems (CAS) as a foundation for ISD and agile development.

Journal of the Association for Information Systems · 2026

Spectral Methods for Immunization of Large Networks

Muhammad Ahmad, Juvaria Tariq, Mudassir Shabbir, Imdadullah Khan

Given a network of nodes, minimizing the spread of a contagion using a limited budget is a well-studied problem with applications in network security, viral marketing, social networks, and public health. In real graphs, virus may infect a node which in turn infects its neighbour nodes and this may trigger an epidemic in the whole graph. The goal thus is to select the best k nodes (budget constraint) that are immunized (vaccinated, screened, filtered) so as the remaining graph is less prone to the epidemic. It is known that the problem is, in all practical models, computationally intractable even for moderate sized graphs. In this paper we employ ideas from spectral graph theory to define relevance and importance of nodes. Using novel graph theoretic techniques, we then design an efficient approximation algorithm to immunize the graph. Theoretical guarantees on the running time of our al…

Zenodo (CERN European Organization for Nuclear Research) · 2026

Observability Is Not Enforcement: A Doctrinal Framework for Distinguishing Compliance Instrumentation from Runtime Authorization in AI Governance Architectures (Working Paper v2.0)

Edward Meyman

AI compliance instrumentation can produce extensive evidence without controlling whether a governed action occurs. Many contemporary systems embed explainability artifacts, monitoring dashboards, drift detection, and integrity-protected audit bundles into development pipelines, improving audit readiness and regulatory traceability. These systems frequently do not condition execution on authorization at the point of action. This working paper introduces a doctrinal framework that distinguishes: • Evidence-routing compliance systems, which collect, transform, and route artifacts for review, and• Runtime authorization boundaries, which condition a governed effect on a valid authorization artifact carrying an action-bound ALLOW verdict. The distinction is formalized along five doctrinal axes: enforcement locus, integrity-protection semantics, failure behavior, bypass resistance, and overrid…

Zenodo (CERN European Organization for Nuclear Research) · 2026

SCU5.0 Beta Release: Security Hardening, Asynchronous Optimization, and Defect Elimination Across 28 Issues in a Conversational AI Agent Platform

Ruoshui Xiaoxiang

SCU5.0 represents a security-focused and asynchronous-optimization release built upon the SCU4 codebase, addressing 28 issues categorized as P0-critical (4), P1-protective (12), P2-refinement (9), and legacy/pair-thinking (3). The release eliminates all blocker-level defects and security vulnerabilities, elevating the architectural health score from 6.8 to 8.9 with 12/12 smoke tests passing. Key fixes include replacing ad-hoc path validation with a unified safe_join_path function performing realpath normalization and whitelist verification across three directory traversal vulnerabilities (CVSS 7.5), implementing SSRF protection by intercepting private, loopback, link-local, and reserved IP ranges plus cloud metadata endpoints, and wrapping eight synchronous endpoint handlers with asyncio.to_thread for proper async isolation. The release introduces atomic JSON persistence via tempfile an…

Nature Communications · 2026

MuSpAn: a toolbox for multiscale spatial analysis

Joshua A. Bull, Joshua W. Moore, Shania M. Corry, Muyang Lin

Abstract Advances in multiplex imaging and spatial omics have revolutionised spatial data generation in biology, revealing complex tissue organisation across multiple scales. However, methods for analysing these data have lagged behind, with fragmented, study-specific pipelines and limited guidance for tool selection. To address this, we introduce MuSpAn, a Multiscale Spatial Analysis package offering intuitive, flexible access to a wide range of mathematical tools - including spatial statistics, topological data analysis, geometry, and networks - within a unified framework. MuSpAn supports efficient data querying, is agnostic to imaging modality, and provides extensive documentation and community support. It enables users to create custom pipelines or conduct unbiased exploratory analyses. We demonstrate MuSpAn’s capacity to interrogate cross-compartmental cell interactions at multiple…