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Byzantine Consensus in the Partially Authenticated Setting
(Association for Computing Machinery, 2026-07-01) Lenzen, Christoph; Loss, Julian; Shi, Kecheng; Wagner, Benedikt; Department of Computer Science
Byzantine Agreement and Broadcast are traditionally studied in one of two extremes: the authenticated setting, where a public key infrastructure (PKI) enables universally verifiable signatures and yields higher fault tolerance, and the unauthenticated setting, where no PKI is available and resilience necessarily drops. Motivated by Proof-of-Stake blockchains, where only a stable subset of participants (e.g., validators) have registered long-term keys while others do not, we initiate a systematic study of consensus in the partially authenticated setting, where a subset of parties are registered in a PKI and the remaining parties are unregistered.We provide a nearly complete feasibility characterization of the resilience as a function of the number s of registered parties among n total parties. First, we show that Byzantine Agreement or Byzantine Broadcast with an unregistered sender is possible if and only if t ≤ max{⌈s/2⌉, ⌈n/3⌉} - 1, matching a simple protocol and an impossibility bound. Second, for Byzantine Broadcast with a registered sender, we give a deterministic synchronous broadcast protocol tolerating up to t ≤ s + ⌈(n - s)/3⌉ - 1 Byzantine faults (equivalently, 3t < n + 2s); while we present the binary case in the main body for clarity, our techniques extend to an efficient multivalued protocol. We complement this with a matching lower bound in a strengthened leakage model in which the adversary learns each party's private state at the end of every round, ruling out both deterministic protocols and randomized protocols that rely only on short-lived secrets and the basic signing/verification interface.
Verk
Buffering strategies in the VUCA and BANI world
(2026-12) Tync, Łukasz; Kuchta, Dorota; Fridgeirsson, Thordur Vikingur; Department of Engineering
In an era characterised by volatility, uncertainty, complexity, and ambiguity (VUCA), further compounded by brittleness, anxiety, non-linearity, and incomprehensibility (BANI), it has become increasingly important to investigate how buffering strategies enable organisations to maintain resilience and sustainability. This study explores both traditional and emerging concepts of organisational buffers, with the aim of identifying their forms and assessing their role in contemporary management practice. Adopting a dual-method approach, the research combines conventional and AI-assisted literature analysis with qualitative interviews conducted with organisational leaders. AI tools were employed to detect both explicit and implicit buffering strategies in the academic literature, revealing a wide spectrum of structural, human resource, psychological, and strategic buffers. Complementing this, a pilot study based on semi-structured interviews offered practical insights into real-world buffering practices. The findings demonstrate that organisations actively deploy a variety of buffering mechanisms, including scenario planning, vertical integration, skill diversity, psychological safety, and experience-based leadership. The study advances resilience theory by laying the groundwork for a comprehensive and typologically expanded framework of buffering strategies applicable across diverse organisational contexts. It further underscores the crucial interplay between technological tools and human judgement in identifying adaptive strategies that foster long-term sustainability in turbulent environments.
Verk
Breast Ultrasound AI Under Dataset Shift : A Patient-Leakage-Aware Benchmark
(2026-05) Wang, Lulu; Department of Engineering
Background: Artificial intelligence (AI) has shown promise in breast ultrasound image analysis, but most evidence still comes from single-dataset studies. Clinical translation requires evaluation under heterogeneous acquisition and curation conditions. This study presents a patient-leakage-aware, reproducible benchmark for breast ultrasound AI under dataset shift, with emphasis on external generalization, calibration, and confidence-related behavior. Methods: A reproducible benchmark framework was developed using patient-level splitting, internal testing, pairwise cross-dataset evaluation, whole-image and region-of-interest (ROI) input strategies, calibration analysis, targeted ROI-margin sensitivity analysis, representative explainable AI visualization, and an auxiliary lesion-versus-normal confidence-based analysis. Four public breast ultrasound datasets (BUSI, BUS-UCLM, BUS-BRA, and BrEaST) were harmonized for a primary benign-versus-malignant lesion classification task. Normal images were excluded from the primary endpoint and used only in auxiliary analyses when sufficient numbers were available. Results: Cross-dataset testing was weaker on average than internal testing, with mean raw AUROC decreasing from 0.801 to 0.719 and mean balanced accuracy from 0.723 to 0.635. ROI input improved external performance, especially for the vision transformer, increasing mean external AUROC from 0.666 to 0.805 and mean external balanced accuracy from 0.594 to 0.713 relative to whole-image input. Temperature scaling improved calibration-related metrics, reducing mean external expected calibration error from 0.180 to 0.150 and mean external negative log-likelihood from 0.848 to 0.682. Conclusions: This study establishes a reproducible benchmark for evaluating breast ultrasound AI under dataset shift, with explicit attention to patient-level leakage control, external validity, and reliability of predicted probabilities.
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Author Correction : Global burden of amphetamine, cannabis, cocaine and opioid use in 204 countries, 1990–2023: a Global Burden of Disease Study (Nature Medicine, (2026), 32, 2, (527-544), 10.1038/s41591-025-04137-0)
(2026-07) GBD 2023 Substance Use Collaborators; Department of Psychology
Correction to: Nature Medicinehttps://doi.org/10.1038/s41591-025-04137-0, published online 16 January 2026. In the original version of this article, errors and omissions were identified in the Competing Interests statements of several authors among the GBD 2023 Substance Use Collaborators. Following a comprehensive review of all competing interest declarations, corrections were required for nine authors. The competing interests statement for S. Afzal was incomplete. The statement has been amended to include grants or contracts from the Dean Office, Institute of Public Health Lahore. The competing interests statement for L. Monasta was incomplete. The statement has been amended to include support for the present manuscript from the Italian Ministry of Health to the Institute for Maternal and Child Health – IRCCS Burlo Garofolo, with payments made to the institution under project RC 34/2027; all outside the submitted work. The competing interests statement for J.P.S. was incomplete. The statement has been amended to include support for the present manuscript from the Portuguese Foundation for Science and Technology, including payment of salary under contract reference 2021.01789.CEECIND/CP1662/CT0014; all outside the submitted work. The competing interests statement for L.M.L.R.S. contained inaccuracies regarding the sources of grants or contracts. The statement has been corrected to accurately reflect support received from SPRINT – Sport Physical Activity and Health Research & Innovation Center, Polytechnic of Guarda, 6300-559 Guarda, Portugal and RISE–Health, Faculty of Health Sciences, University of Beira Interior, 6201-506 Covilhã, Portugal. The competing interests statement for J.A.S. was incomplete. The statement has been amended to include stock or stock options in Atyr Pharmaceuticals. The competing interests statement for A.C.T. contained an incorrect NIH grant number. The grant number has been corrected from K34DA061696 to K24DA061696. The competing interests statements for B. Oancea, K. Krishan, and I. N. Soyiri were omitted from the version of this article initially published. The statements have now been added and read as follows: “B.O. reports grants from the Core Program within the Romanian National Research, Development, and Innovation Plan 2022–2027, carried out with the support of MRID, project no. 23020101 (SIA-PRO), contract no. 7N/2022, and project PNRR-I8 no. 842027778, contract no. 760096. K. Krishan reports non-financial support from the UGC Centre of Advanced Study (CAS II), awarded to the Department of Anthropology, and support from the RUSA 2.0 grant awarded by the Ministry of Education to Panjab University, Chandigarh, India; all outside the submitted work. I.N.S. reports a leadership or fiduciary role in another board, society, committee, or advocacy group, paid or unpaid, as a trustee of the Citizens Advice Bureau for Hull & East Riding, United Kingdom.” These errors have now been corrected in the HTML and PDF versions of the article. No other parts of the article have been changed.
Verk
Attribute Conditioning is insensitive to cue competition and is not predicted by the Big Five Personality Traits
(2026-05) Quigley, Martyn; Dymond, Simon; Kiely, Katie; Bradley, Alex; Haselgrove, Mark; Department of Psychology
When a neutral stimulus is paired with a stimulus denoting an attribute, the neutral stimulus inherits that attribute (i.e., Attribute Conditioning; AC). The current experiments examined whether this effect is sensitive to cue competition, specifically blocking (Experiment 1, n = 245) and overshadowing (Experiment 2, n = 213), and whether personality traits can predict this effect (n = 458). Participants were shown cartoon images of people (CSs) paired with healthy or unhealthy foods (USs) and completed the Big Five Inventory. An AC effect was evident—people paired with healthy foods were rated healthier than people paired with unhealthy foods. However, there was no evidence of cue competition or personality traits impacting the AC effect, although females displayed a stronger AC effect than males. These findings indicate that AC is a robust phenomenon of relevance to social learning processes but is insensitive to factors that influence other forms of conditioning.

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