This manuscript presents the RAPS Dome as a distributed, passive, multimodal sensor-fusion architecture for edge-networked monitoring and Bayesian decision support. The system extends the earlier Sensor Dome concept by combining distributed acoustic sensing (DAS), acoustic and infrasound arrays, encapsulated heterodyne interferometry, electro-optical/infrared (EO/IR) verification, and passive radio-frequency (RF) reception with a risk-aware Recursive Autonomous Projection System (RAPS) decision layer. The contribution is architectural and methodological rather than operational: the paper defines the signal-flow design, timing assumptions, adaptive fusion logic, risk-scoring equations, auditability requirements, governance extensions, and a staged validation protocol. All numerical quantities are treated as prospective design targets or literature-motivated feasibility values, not as field-validated performance claims. The RAPS layer is formulated as deterministic decision support operating on probabilistic inputs; it does not implement autonomous actuation, jamming, or closed-loop intervention. The proposed architecture is relevant to edge-networked computing, distributed sensing, cyber-physical monitoring, and intelligent decision-support systems. It provides a transparent basis for future simulation, pilot deployment, calibrated benchmarking, legal review, and comparative evaluation against null and single-modality baselines.
Marcel Krūger, Don Feeney, Jacobo Rodríguez· Zenodo (CERN European Organi...· 0 citations
Why Fisher's exact test is least exact where it matters most: achieved size, wasted power, and a routing rule for 2×2 tables William J. Dwyer, MD, MPH, FAAP — Department of Mathematics and Statistics, University of Massachusetts Lowell. ORCID 0009-0004-0855-7222. Concept DOI (always resolves to the latest version): 10.5281/zenodo.22238930. This release (v1.1.14):10.5281/zenodo.22262453, published Sep 2, 2026; the concept DOI above resolves to it. The previous release, v1.1.13, is 10.5281/zenodo.22262451; earlier, v1.1.5 is 10.5281/zenodo.22260812 and v1.1.4 is 10.5281/zenodo.22260570. What this is The reproducibility deposit for the m02d paper. "Fisher's exact test" is exactly valid — its true rejection rate never exceeds the nominal α — but it is not size-exact: because the 2×2 reference distribution is discrete, no table boundary falls at α, so the achieved (unconditional) size sits below nominal. At small designs the test spends only 26–66% of its α budget, and wasted size is wasted power. The paper computes the achieved size, the wasted-power cost, and the significance-flip behaviour of Fisher's exact against a conservative→liberal roster (Yates χ², Fisher, mid-p, Boschloo, Barnard, Pearson χ², Cressie–Read λ=2/3, likelihood-ratio G), and distills the result into a routing rule for the 2×2. Everything is deterministic exhaustive enumeration — two independent binomials, no Monte Carlo, no random seed — so there is no simulation error: every number, table, and figure reproduces exactly from a named script. The keynote A real-data scan of 6,030 public 2×2 tables (757 pydataset/Rdatasets datasets) shows the tests agree essentially everywhere away from the threshold but disagreement spikes on each decision line — ~67% of tables within ±0.02 of 0.05 and ~76% within ±0.02 of the 0.10 trend line get a test-dependent verdict — so the tests are miscalibrated relative to one another exactly where we use them to draw the significant/not-significant line, and the trend line inherits the same spike. Flips are read on a three-tier taxonomy relative to Boschloo (the valid frontier): conservative below it, valid on it, liberal above it. What the deposit contains Manuscript (author + anonymized, .md), the Derivations companion (D1–D6: conditional validity and the achieved-size shortfall; mid-p as the mean of the median-unbiased rule with E₀[mid-p] = ½; Boschloo's uniform power dominance; the nesting that makes flips one-directional; the atom-lattice gap and exact Boschloo region behind Figure 3; and why conditioning forfeits the budget), the novelty / prior-art companion, and the cover letter(The American Statistician). Reproducibility apparatus (rerun/) — the exact-enumeration engine achieved_size.py (tie-aware conditional p-values, validated against SciPy to 2.2e-16) and the deterministic sweep drivers: power_and_flips.py, size_skew_sweep.py, flip_boundary_sweep.py, roster_sweep.py (the full comparator roster, including the Cressie–Read λ=2/3 power-divergence member), and public_flip_scan.py (the real-corpus scan). Each writes its locked JSON/CSV. Figures (figures/) — the achieved-size and wasted-power panels, the size×skew heat maps, the α=0.05 flip-boundary maps, the χ²-inclusive roster figures, the real-data flip-incidence figure, the routing flowchart (now Figure 9), and the per-test direction butterfly (Figure 8). Interactive demonstrator honest_fisher.html — a self-contained in-browser calculator with the full comparator taxonomy and the live-highlight routing flowchart; its JavaScript engine reproduces the deposited numbers and self-checks a parity badge on load. Deep-dive record (deepdives/) — the supporting analyses, including the size/skew, flip-boundary, χ²-roster, real-data, and computed-clearance write-ups, and the M0u-bundle assessment that reconciles m02d with the author's earlier Monte-Carlo program (below). LICENSE, CITATION.cff, MANIFEST.txt (SHA-256 of every file). The Cressie–Read λ=2/3 reconciliation The roster now carries Cressie–Read λ=2/3, the best-calibrated member of the power-divergence family that also contains Pearson (λ=1) and the G-test (λ=0). Across R×C shapes it is the best-calibrated test in the author's companion Monte-Carlo study; on the pure 2×2 that is m02d's subject, its exact achieved size is 0.062 / 0.053 / 0.055 / 0.055 at the representative designs — still mildly liberal, because small-sample discreteness bites it too. So the paper names λ=2/3 as the calibrated member of the χ² family, scopes the "common χ² defaults overshoot" claim to Pearson/Yates/G, and shows that even the well-chosen asymptotic statistic does not escape the 2×2 discreteness the exact unconditional tests handle — turning a potential referee objection into a strengthening of the routing rule. The companion 18-test Monte-Carlo study independently confirms m02d's Boschloo/Barnard power gap over Fisher (+3 to +10 pp by simulation vs +3.7 to +11.1 pp by exact enumeration here). All computation is deterministic exact enumeration; code is released under the MIT License, and text, figures, and data under CC BY 4.0. The public 2×2 corpus is built from the Rdatasets collection via pydataset; no data are redistributed beyond the illustrative tables cited in the paper. How to cite Please cite this deposit if you use the package or the method. Citing the concept DOI references the work in general and always resolves to the latest version; cite a specific version DOI to point at an exact snapshot. Dwyer, W. J. (2026). Why Fisher's exact test is least exact where it matters most: achieved size, wasted power, and a routing rule for 2×2 tables — reproducibility deposit [Software]. Zenodo.https://doi.org/10.5281/zenodo.22238930 BibTeX: bibtex @software{dwyer_m02d_2026, author = {Dwyer, William J.}, title = {Why Fisher's exact test is least exact where it matters most: achieved size, wasted power, and a routing rule for 2x2 tables --- reproducibility deposit}, year = {2026}, publisher = {Zenodo}, doi = {10.5281/zenodo.22238930}, url = {https://doi.org/10.5281/zenodo.22238930}, orcid = {0009-0004-0855-7222} } The DOI above is the concept DOI (resolves to the latest version); to cite a specific release use that version's DOI in its place. When the accompanying journal article appears, please cite it as the primary reference for the method and this deposit as the reproducibility archive. Version history v1.1.14 — ✅ 10.5281/zenodo.22262453 (published 2026-09-02) (m02d_fishers_exact_not_exact_reproducibility_v1.1.14.zip, md5 4977e10c1b632054e3e6609176dde2fd, 3,722,698 B, 94 files): the Boschloo-dominates-Fisher machinery, made explicit. Section 6 now shows the one-line power-dominance mechanism rather than only asserting it: Fisher's own conditional test already has unconditional size ≤ α, so the calibrated Boschloo threshold c satisfies c ≥ α, and because both tests threshold the same statistic the rejection set {Fisher p ≤ α} sits inside {Fisher p ≤ c} table for table — Boschloo rejects wherever Fisher does and more, so it is uniformly at least as powerful, a guarantee that would fail for a differently-ordered unconditional statistic. Derivation D3.4 gains the explicit note that Boschloo and the score-based Barnard test are mutually non-nested (neither uniformly dominates; Barnard's occasional power edge is budget spent, not dominance), and the SN supplement gains a "Why Boschloo, not Barnard, is the valid frontier" note beside the comprehensive roster. Exposition over the existing exact-enumeration output; no computed result, figure, or table changed. Deterministic (identical md5 on two runs). Supersedes v1.1.13. v1.1.13 — ✅ 10.5281/zenodo.22262451 (published 2026-09-02) (m02d_fishers_exact_not_exact_reproducibility_v1.1.13.zip, md5 371a192f5a3ea8ad1e552043a3d68562, 3,721,908 B, 94 files): the comprehensive-roster and full-budget-grid build-out, and the worked-example correction. Adds the sixteen-statistic exact achieved-size-and-power engine comprehensive_roster.py, the full per-test α-budget-grid enginebudget_grid_all.py, the vectorized Boschloo helper boschloo_fast.py (an O(P·K) replacement for the O(n⁴) per-cell sup, verified cell-for-cell against the naive loop, which makes exact enumeration to n = 100 tractable), and the supplement table generator splice_supplement_tables.py. The SN supplement's per-test α-budget grids now run n = 6, 8, …, 20, 25, …, 65, 80, 100 and the big-tent size/power tables use the columns n = 8, 10, 16, 25, 40, 65, 100. Also corrects the worked-example Table S1 Cressie–Read λ = 2/3 value — the deposited 0.1881 ("not significant") was wrong; the exact statistic gives p = 0.0287 (significant), cross-checked against scipy.stats.power_divergence — expands that table to the full sixteen-test roster, and rewrites the accompanying narrative, which had leaned on the wrong number. Rolls up the staged v1.1.6–v1.1.12 (the SN-supplement rework: per-test α-budget grid atlas, the diverging blue–white–red under/over color standard, the neutral-hatch Figure 8, and the m0-sourced comparator roster). Deterministic (identical md5 on two runs). Supersedes v1.1.5. v1.1.5 — ✅ 10.5281/zenodo.22260812 (published 2026-09-02) (m02d_fishers_exact_not_exact_reproducibility_v1.1.5.zip, md5 d96e845d54d12ae496fe78d7440ca4c7, 3,413,155 B, 79 files): availability statement consolidated into Declarations; placement made a canonical build check. The data-availability text was appearing twice — as the Availability of data and materials. statement inside Declarations and as a standalone ## Data and code availabilitysection bolted on after the References, which had drifted (the Declarations copy still read placeholder wording while the live DOI lived only in the bolted-on section). Consolidated into the single Declarations statement (scope + no-redistribution; the reproducibility package on Zenodo under the concept DOI as a live link
William Dwyer· Zenodo (CERN European Organi...· 0 citations
Here we present four companion documents for the program corpus listed below. The Taxonomy of Claims lists the theory's claims and assigns each one a status. The claims are grouped in four tiers: Framework (the laws and constants, which the theory does not derive), Derived from Framework (quantities computed from the framework, not independent inputs – including the per-edge failure probability p = ln 2/(2π²) and its spin-foam status, the bare-edge rule, and the tensor consistency relation n_t = n_s − 1), Pre-history (derived properties that are the same in any realization), and History (properties specific to our universe). A set of notes covers the counting conventions, the ensemble rule, the correspondence ledger, the anchor question and its resolution, several associations recorded without claim status, and a closing statement of the one assumption the theory requires. The Epistemic Inventory (What the Theory Claims to Know) contains the same material organized by question. Some fifty questions – why the cosmological constant is small, why w = −1, where the spectral tilt comes from, whether there are primordial gravitational waves, what preceded the first moment, why these constants – are each given the theory's answer in plain language and a status: derived, identified, dissolved, partial, or not claimed. The final section lists the questions the theory does not claim to answer. The Universe from a Single Strike is a lay description of the Ignition, provided as an onramp to the framework's technical papers. Two revision notes carry the tensor sector's audit trail, retained side by side. Correcting Errors in the Tensor-to-Scalar Ratio Calculation is the first revision, kept as the record it is: the ensemble correction, the bare-edge rule, and the revision of the physical ratio to 0.0301 ± 0.001. Deriving the Assertions in the Tensor-to-Scalar Ratio Calculation (new in this version, and controlling where the two disagree) is the consolidated revision, closing by computation the two commitments the first note left standing in its own words: the deposit-support rule, previously ratified, is derived as exact geometry (the 60° ring partition a theorem of the subdivision, the wedge-share inheritance rule derived), and the inter-move correlations, previously asserted shapeless, are enumerated (own-shell cross terms vanish identically; the boundary–existence channel is a constant renormalization adding no shape). The amplitude moves and no relation does: a standard flat-template B-mode analysis will report r = 0.0297 ± 0.0005, about 7% under the current combined limit r < 0.032; the underlying physical ratio, the same at every scale, is 0.0281 ± 0.0005, with the revision history owned on the record (0.0339 → 0.0301 → 0.0281). The tensor tilt is unchanged at the exact consistency relation n_t = n_s − 1 = −0.03512, now ten times redder than single-field inflation's at equal r; the α_s withdrawal stands. The new note answers the first note's frozen claims row by row in a ledger appendix, withdraws that note's promise of numerical finality as a category error, carries the complete derivations in its appendices, reproducible from the public replication package, and is registered before the next B-mode data release. The taxonomy and inventory are updated to match, including the upgraded statuses the note drives. This version registers three additions driven by the tensor tranche of the modeling package (10.5281/zenodo.22217227) and by the one-loop sector: the two-entry sourcing dictionary behind r – missing volume sources the scalar per cell, hinge-deficit shear sources the tensor per hinge, joined at the single-failure channel as a named premise – with the exact S³ mode-count law (2/5)(1 − 4/n²); and the marginal-normalization premise behind A_s, superseding the one-loop paper's "standard sectors net to unity" (the tensor-sector determinant is now computed, 0.762, and the vector and ghost sectors cancel exactly). Registered values are unchanged; the exact-support window factor 0.9539 is carried in the modeling package pending consolidation. The falsification conditions for the corpus are collected separately in the predictions letter (revised in step with this version) and are not repeated in these documents. All documents will be maintained: new versions will record changes in the status of claims, including withdrawals. The Program Corpus The Last Evaporation: Planck Remnants as Cosmological Seeds in Empty Spacetime - 10.5281/zenodo.19324262 Cosmological Structure Without Inflation: The Perturbation Spectrum from Pre-Geometric Construction - 10.5281/zenodo.19513896 One-Loop Identities on the S4 Instanton - 10.5281/zenodo.20045607 The Ignition Transition: From the No-Boundary Saddle to Radiation Domination - 10.5281/zenodo.20559451 The Ignition Inventory: Defect Energy and Static Topology - 10.5281/zenodo.20559916 Cosmology from a Three-Bit Seed: The Predictions and Their Falsification Gates - 10.5281/zenodo.21270529 Epistemic Inventory and Taxonomy of Claims - 10.5281/zenodo.21324530 Tractable Modeling: The Truncated Tessellation - 10.5281/zenodo.22217227
Scott Weller· Zenodo (CERN European Organi...· 0 citations
Here we present four companion documents for the program corpus listed below. The Taxonomy of Claims lists the theory's claims and assigns each one a status. The claims are grouped in four tiers: Framework (the laws and constants, which the theory does not derive), Derived from Framework (quantities computed from the framework, not independent inputs – including the per-edge failure probability p = ln 2/(2π²) and its spin-foam status, the bare-edge rule, and the tensor consistency relation n_t = n_s − 1), Pre-history (derived properties that are the same in any realization), and History (properties specific to our universe). A set of notes covers the counting conventions, the ensemble rule, the correspondence ledger, the anchor question and its resolution, several associations recorded without claim status, and a closing statement of the one assumption the theory requires. The Epistemic Inventory (What the Theory Claims to Know) contains the same material organized by question. Some fifty questions – why the cosmological constant is small, why w = −1, where the spectral tilt comes from, whether there are primordial gravitational waves, what preceded the first moment, why these constants – are each given the theory's answer in plain language and a status: derived, identified, dissolved, partial, or not claimed. The final section lists the questions the theory does not claim to answer. The Universe from a Single Strike is a lay description of the Ignition, provided as an onramp to the framework's technical papers. Two revision notes carry the tensor sector's audit trail, retained side by side. Correcting Errors in the Tensor-to-Scalar Ratio Calculation is the first revision, kept as the record it is: the ensemble correction, the bare-edge rule, and the revision of the physical ratio to 0.0301 ± 0.001. Deriving the Assertions in the Tensor-to-Scalar Ratio Calculation (new in this version, and controlling where the two disagree) is the consolidated revision, closing by computation the two commitments the first note left standing in its own words: the deposit-support rule, previously ratified, is derived as exact geometry (the 60° ring partition a theorem of the subdivision, the wedge-share inheritance rule derived), and the inter-move correlations, previously asserted shapeless, are enumerated (own-shell cross terms vanish identically; the boundary–existence channel is a constant renormalization adding no shape). The amplitude moves and no relation does: a standard flat-template B-mode analysis will report r = 0.0297 ± 0.0005, about 7% under the current combined limit r < 0.032; the underlying physical ratio, the same at every scale, is 0.0281 ± 0.0005, with the revision history owned on the record (0.0339 → 0.0301 → 0.0281). The tensor tilt is unchanged at the exact consistency relation n_t = n_s − 1 = −0.03512, now ten times redder than single-field inflation's at equal r; the α_s withdrawal stands. The new note answers the first note's frozen claims row by row in a ledger appendix, withdraws that note's promise of numerical finality as a category error, carries the complete derivations in its appendices, reproducible from the public replication package, and is registered before the next B-mode data release. The taxonomy and inventory are updated to match, including the upgraded statuses the note drives. This version registers three additions driven by the tensor tranche of the modeling package (10.5281/zenodo.22217227) and by the one-loop sector: the two-entry sourcing dictionary behind r – missing volume sources the scalar per cell, hinge-deficit shear sources the tensor per hinge, joined at the single-failure channel as a named premise – with the exact S³ mode-count law (2/5)(1 − 4/n²); and the marginal-normalization premise behind A_s, superseding the one-loop paper's "standard sectors net to unity" (the tensor-sector determinant is now computed, 0.762, and the vector and ghost sectors cancel exactly). Registered values are unchanged; the exact-support window factor 0.9539 is carried in the modeling package pending consolidation. The falsification conditions for the corpus are collected separately in the predictions letter (revised in step with this version) and are not repeated in these documents. All documents will be maintained: new versions will record changes in the status of claims, including withdrawals. The Program Corpus The Last Evaporation: Planck Remnants as Cosmological Seeds in Empty Spacetime - 10.5281/zenodo.19324262 Cosmological Structure Without Inflation: The Perturbation Spectrum from Pre-Geometric Construction - 10.5281/zenodo.19513896 One-Loop Identities on the S4 Instanton - 10.5281/zenodo.20045607 The Ignition Transition: From the No-Boundary Saddle to Radiation Domination - 10.5281/zenodo.20559451 The Ignition Inventory: Defect Energy and Static Topology - 10.5281/zenodo.20559916 Cosmology from a Three-Bit Seed: The Predictions and Their Falsification Gates - 10.5281/zenodo.21270529 Epistemic Inventory and Taxonomy of Claims - 10.5281/zenodo.21324530 Tractable Modeling: The Truncated Tessellation - 10.5281/zenodo.22217227
Scott Weller· Zenodo (CERN European Organi...· 0 citations
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Context and Justification The prevalence of neurodegenerative diseases around the world demands a paradigm change from reactive, episodic clinical diagnosis to ongoing, proactive neuro-monitoring. The possibility for early detection and individualized management is limited by the fact that traditional diagnostic techniques sometimes rely on subjective evaluation or costly, intrusive imaging. An unparalleled chance to identify, evaluate, and interpret the subtle, objective indicators of preclinical cognitive alterations is presented by the convergence of the Internet of Things (IoT) and sophisticated artificial intelligence (AI). Methods In order to generate a continuous, longitudinal stream of physiological and behavioural data, this study presents a novel, decentralized platform that makes use of a heterogeneous network of IoT devices, such as high-resolution wearables, smart home sensors, and non-contact physiological monitors (digital phenotyping). Large datasets pertaining to sleep architecture, gait variability, social interaction frequency, and speech hesitancy were analysed using deep learning techniques, particularly convolutional neural networks for anomaly detection in sensor data and long short-term memory (LSTM) networks for temporal pattern recognition. In order to forecast the start of moderate cognitive impairment months before conventional clinical criteria could be satisfied, the main goal was to train these AI models to recognize minute variations from each person’s unique baseline. Important Results (Hypothetical) With a 92% prediction accuracy, the AI-driven study was able to identify a multivariate biomarker profile associated with early cognitive deterioration. Importantly, the system was able to identify temporary changes in everyday activities, such as increased nocturnal wandering and entropy changes in spoken language, long before carers noticed them or could measure them using conventional paper-and-pencil exams. Real-time anomaly notifications made possible by the incorporation of edge computing enabled prompt triage and focused clinical evaluation. Conclusion The neuro-sensing grid underlines how important AI-powered IoT is to revolutionizing research on brain health. This technique provides a reliable, scalable, and non-invasive method for personalized neuro-surveillance by moving the locus of assessment from the clinic to the lived environment. This opens the door for truly preventive therapies against age-related cognitive decline.
Superseded. This record is retired and should not be used or cited. It is replaced by 10.5281/zenodo.22234051, which is the archive accompanying the manuscript. Version v2.0.0 of this record (10.5281/zenodo.22232689) additionally contained files that should not have been distributed and its removal has been requested. Analysis pipeline for a retrospective cohort study of cumulative intraoperative hypothermic burden, intraoperative red cell transfusion, and estimated blood loss in 2,567 adults undergoing non-cardiac surgery, using the open VitalDB perioperative database. Scripts run in numeric order: 01-14 reproduce the originally submitted analysis, and 15-23 produce the first revision, including the edge-trimmed thermal exposure metric, threshold and dose analyses, and the table and figure builders. The repository carries code only; the manuscript, cover letters and peer-review correspondence are not distributed. VitalDB source data are openly available at https://vitaldb.net and are not redistributed here. v2.2.0 is the clean release accompanying the first revision submitted to BMC Anesthesiology. The repository was rebuilt from a single commit containing code only. Key methodological change from v1.1.0: core temperature had been integrated across the whole anaesthesia window, which counts probe equilibration at insertion and probe withdrawal at emergence as hypothermia, implying an implausible cohort nadir of 33.30 °C. Exposures are now computed after excluding the first and last 5 minutes of each case's monitored window. Cumulative burden is essentially unchanged (Pearson r=1.00, Spearman rho=0.99); the nadir corrects to 35.04 °C. Earlier v2.x versions are superseded. v2.0.0 in particular should not be used.
Fabrice Tiku Nyambod· Zenodo (CERN European Organi...· 0 citations
The increasing diffusion of intelligent devices at the network edge has led to a growing demand for efficient on-device inference, capable of overcoming the limitations of traditional cloud-centric computing paradigms. This work investigates the acceleration of decision tree–based inference on resource-constrained edge platforms by exploiting ARM Helium vector extensions , which bring the Single Instruction Multiple Data (SIMD) paradigm to the Cortex-M class of processors. A dedicated SIMD-based kernel was implemented and tested on the NUCLEO-STM32N657 board across three UCI datasets (AI4I, Dry Bean, Avila). Results show up to ∼ 15% latency reduction over the non-SIMD baseline , confirming that ARM Helium effectively exploits data-level parallelism to enhance inference efficiency on lightweight microcontrollers. Overall, this study provides experimental evidence that vector extensions represent a key enabler for bringing advanced machine learning capabilities to low-power embedded systems, bridging the gap between traditional micro-controller efficiency and modern AI acceleration at the edge.
L. Abate, Mario Barbareschi, Antonio Emmanuele· River Publishers eBooks· 0 citations
Analysis pipeline for a retrospective cohort study of cumulative intraoperative hypothermic burden, intraoperative red cell transfusion, and estimated blood loss in 2,567 adults undergoing non-cardiac surgery, using the open VitalDB perioperative database. Scripts run in numeric order: 01-14 reproduce the originally submitted analysis, and 15-23 produce the first revision, including the edge-trimmed thermal exposure metric, threshold and dose analyses, and the table and figure builders. The repository carries code only; the manuscript, cover letters and peer-review correspondence are not distributed. VitalDB source data are openly available at https://vitaldb.net and are not redistributed here. This record accompanies the first revision submitted to BMC Anesthesiology. The archive contains the repository in full: scripts/, README.md, requirements.txt, .zenodo.json and .gitignore. Method note. Core temperature was previously integrated across the whole anaesthesia window, which counts probe equilibration at insertion and probe withdrawal at emergence as hypothermia, implying an implausible cohort nadir of 33.30 °C. Exposures are now computed after excluding the first and last 5 minutes of each case's monitored window. Cumulative burden is essentially unchanged (Pearson r=1.00, Spearman rho=0.99); the nadir corrects to 35.04 °C. Scripts run in numeric order. 01-14 reproduce the originally submitted analysis; 15-22 produce the revision. The manuscript, cover letters and peer-review correspondence are not part of this distribution. VitalDB source data are openly available at https://vitaldb.net and are not redistributed here.
Fabrice Tiku Nyambod· Zenodo (CERN European Organi...· 0 citations
Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged as a practical means of providing computation and communication support for geographically dispersed Internet of Things (IoT) terminals. However, the broadcast nature of wireless links makes offloading data vulnerable to cooperative eavesdropping. Moreover, the limited onboard energy of UAVs and the latency-sensitive characteristics of MEC services lead to a challenging trade-off between the secrecy rate, energy consumption, and latency. To address this issue, we formulate a utility maximization problem to jointly optimize UAV trajectory and task-offloading decisions in UAV-assisted MEC systems against multiple eavesdroppers. Due to the strong coupling among optimization variables and the non-convexity of the problem, an enhanced twin-delayed deep deterministic policy gradient (TD3) framework integrating Hindsight Experience Replay (HER) and Prioritized Experience Replay (PER) is proposed to improve convergence efficiency and learning stability. Furthermore, a system utility-driven reward function is designed to balance the secrecy rate, energy consumption, and processing latency under different application requirements. The simulation results demonstrate that the proposed approach consistently outperforms DDQN, DDPG, and conventional TD3 in terms of system utility, secrecy performance, convergence speed, and adaptability to different scenarios.
Yishan Zang, Ying Su, Jing Zhang et al.· Electronics· 0 citations
Abstract Given the state of a subject and the state of the environment he is in, under what conditions does the question "what should be done next" have a determinate answer, and where does that answer lie. This paper gives a computable form and marks out its domain of definition. First, location on three axes. The state of subject and environment is given by two coordinates: the shape law of scale, and the fraction of active units. The former is an axis with the exponential law as its zero point; its left half is the additive side and its right half the multiplicative side, and the domain of this paper is the right half together with the mixed band in the middle. The latter takes three states: low-turnover steady state, Red Queen, and dynamic expansion. Time is perpendicular to the plane spanned by these two axes; it records evolution only and enters no criterion as a variable. Of the four movable quantities of this paper, three are read on the shape-law axis and one lies outside the state space; and the interval from recognising to having finished changing is read on the activity axis. Second, a three-layer lexicographic structure. The optimum of behaviour is not a point but the output of three gates applied in order: a domain check, the survival set, and the finite optimum. When either of the first two fails, the number computed at the third has no meaning, and the prescriptions for the three failures are entirely different from one another. Third, survival is the domain on which efficiency is defined, not a constraint term inside an efficiency objective; the problem is therefore not scalarisable. The reason is not preference but structure: elimination cancels the iteration itself, and the functional to be minimised is defined only while the iteration continues. Fourth, completeness of the four knobs, and the two quantities that make the enumeration possible. The survival criterion has only four movable points of attack: raise distinguishability, lower false-alarm tolerance, raise log reserve, lower per-step exposure. The first two change the left-hand side, the last two the right. For the enumeration to hold, two quantities absent from earlier drafts must be supplied: the lower bound Δ_min on per-step exposure and the unit cost c_FA of a false alarm. Once they are supplied, false-alarm tolerance ceases to be free and acquires a finite optimum, given implicitly by |Δ_step|·h′(T)/I_d = c_FA·W/T² — the more exposed, the more sensitive one should be; the more legible the environment and the more expensive a false alarm, the more phlegmatic. The three readings depend only on the threshold rising monotonically with false-alarm tolerance, not on its particular shape; the familiar explicit solution T★ = c_FA·W·I_d/|Δ_step| holds only when h = ln T, which is precisely the form rejected in 6.1, and this paper therefore does not adopt it as a closed form. Fifth, a closed form, and the limits of its reach. With per-step exposure parameterised by the Kelly fraction λ, the required log reserve is B_res ≥ C(λ, r)·h, where h is the detection threshold, r is the ratio of the post-change edge to the pre-change edge, and C(λ, r) = (λ² − 2λr)/(r − 1)². The pre-change edge itself cancels entirely from this coefficient, for every r. For a symmetric flip, r = −1, it reduces to λ(2+λ)/4; numerically, across win rates from 0.505 to 0.70 the coefficient moves from 0.7500 to 0.7572, a variation below one per cent. But what this closed form cancels is the ratio, not the survival probability the ratio buys. The same B_res = 0.75h gives a survival rate of 0.99 at an edge of 0.10, 0.81 at an edge of 0.20, and only 0.40 at an edge of 0.40 — because the entire safety margin of the closed form comes from h/I_d overstating the actual identification delay, and that overstatement narrows as the edge grows. Its condition of applicability must therefore travel with it: the nominal identification delay must be counted in tens of steps. Under that condition, full Kelly requires the capacity to absorb a drawdown of seventy-four per cent, and half Kelly forty-three per cent. Sixth, a negative result. Which of the three legs is binding switches over time, and that switch is not a phase transition but a kink, whose falsifiable signature is the absence of a hysteresis loop. "Circumstances have changed, so what should be done has changed" describes, in most cases, a change of reading coordinate rather than a change of dynamical structure. Numerically this reading is robust to how the required quantities of the three legs are defined, whereas the binding path itself is not, and the two must be reported separately. Seventh, one retraction relative to earlier drafts. Earlier drafts registered, as the observable consequence of lexicographic ordering, the statement that "within the group whose survival margin is negative, behavioural differences are unrelated to outcomes". That statement fails inside this paper's own model: an empty survival set means only that no behaviour can *guarantee* survival, whereas survival is stochastic, and within the group the survival rate remains a strictly monotone function of exposure, differing by a factor of twenty to thirty under the parameters used here. This paper retracts that wording and substitutes an observable that does follow from lexicographic ordering — the behaviour-selection rule is discontinuous where sup_u S_surv crosses zero, whereas a weighted model predicts continuity at the same point. Three sets of numerics accompany the text. The first gives the three scales of the response-critical position. The detection threshold must be calibrated by simulation and cannot be taken as ln T: under the parameters used here, ln 200 = 5.298 yields an actual mean time between false alarms of about nine thousand four hundred rounds, forty-seven times the nominal value; and since the likelihood ratio of a binary game takes only two values, the achievable mean time between false alarms is a step function of the threshold, the nearest step to two hundred rounds being one hundred and ninety, corresponding to a threshold of 1.806. At that calibrated threshold the total-loss closed form is conservative by a factor of eight to twelve, and the ratio of the drift closed form to the measured half-survival point is stable at about one point four. The second set is a slow sweep of the binding switch, reporting five sweep rates: when the three legs each move smoothly, the ratio of successive differences in loop area is 0.499, 0.500, 0.500, hence a rate artifact; when one leg is replaced by a self-reinforcing dynamics, the ratio is 0.633, 0.632, 0.632 against a theoretical two-thirds-power value of 0.630, hence genuine hysteresis. The latter trajectory also contains a reversible kink whose two switch points differ by the same order as in the former panel and shrink linearly with the sweep rate; the two appear side by side in a single sweep, which is the cleanest form of the discrimination proposed here. The third set is a scaling-sensitivity test used to separate the robust half of this reading from the non-robust half.
Qinfu Li· Zenodo (CERN European Organi...· 0 citations
العنوان التقني: منظومة المعالجة الطرفية البصرية والتوليد التفاعلي للأنماط الرسومية اللامركزية 1. النطاق التقني ومجال الاختراع (Technical Field): تتعلق المنظومة الحالية بأنظمة الحوسبة الطرفية المستقلة (Edge Computing Frameworks)، وتحديداً المعماريات المدمجة للذكاء الاصطناعي المحلي المخصص لتوليد، وتحليل، ومحاكاة البيانات البصرية والهندسية ثنائية الأبعاد، دون الاعتماد على شبكات الاتصال السحابية الخارجية، مع آليات تكامل مادية لتحويل المخرجات الرقمية إلى وسائط ملموسة. 2. الخلفية التقنية والهدف الابتكاري (Background & Objective): تعاني المنظومة التقليدية لمعالجة الرسوميات الرقمية من الارتباط الدائم بالبنى التحتية السحابية لتعويض النقص في قواعد البيانات التوليدية، فضلاً عن غياب آليات التدريب التراكمي الموجه محلياً. يهدف الاختراع الحالي إلى تجاوز ذلك عبر دمج وحدة معالجة محلية مدعومة بمكتبات بيانات معرفية شاملة، لتوفير بيئة تفاعلية لا مركزية تقوم بتحليل مدخلات المستخدم الحركية، واستقراء أنماط الأداء عبر محرك استدلالي محلي، وتقديم توجيهات بصرية هندسية متقطعة لتعزيز كفاءة التفاعل البشري الرقمي، مع توفير قناة إخراج مادية متزامنة. 3. التوصيف الهيكلي والوظيفي للوحدات (Architectural Embodiments): وحدة الاستشعار والمعالجة الطرفية (Edge Processing & Input Subsystem): تتألف من منصة عرض تفاعلية مدعومة ببطارية داخلية قابلة لإعادة الشحن، مرتبطة بأداة إدخال دقيقة (Digital Stylus) مزودة بمصفوفة استشعار مزدوجة لقياس المتغيرات الفيزيائية (الضغط الديناميكي وزاوية الميل الحركي)، مما يضمن محاكاة دقيقة للاحتكاك السطحي والملمس الرقمي. الذاكرة المحلية ونماذج التعلم الذاتي (Local Neural Engine & Database): تحتوي على مستودع بيانات ضخم ومحلي بالكامل مخزن مسبقاً يضم الأنماط البصرية وقواعد المعرفة الفنية والهندسية. تعتمد الوحدة على خوارزميات التعلم التراكمي (Cumulative Edge Learning) لتطوير خيارات النظام بناءً على الأنماط السلوكية للمستخدم وسجل محاولاته السابقة محلياً. محرك الإرشاد التفاعلي والمعرفي (Cognitive Guidance Engine): نظام برمجي استدلالي مبرمج لتفسير الاستعلامات النصية أو الصوتية المتنوعة، وتوليد مسارات هندسية وبصرية إرشادية (خطوط إرشادية مفرقة ديناميكية) على الشاشة، تتيح للمستخدم تتبعها وإتمامها، يليه نظام مقارنة وتحليل آلي للمخرجات لتقديم تقييم فوري وتصحيح ذاتي. وحدة الإخراج والمزامنة المادية (Hardware-to-Print Synchronization Unit): وحدة ربط مباشر متكاملة مع طابعة دقيقة، تترجم البيانات اللونية والرقمية المعالجة على الشاشة إلى مخرجات ورقية ملموسة مطابقة للأصل الرقمي بدقة متناهية.
Abuelgasim abayzeed elsmaney ahmed Ahmed· Zenodo (CERN European Organi...· 0 citations
With the digital transformation of the global economy and the rapid development of information technology, big data has become a key driving force in supply chain optimization. The introduction of big data technology provides new perspectives and solutions for traditional supply chain management, especially in improving decision-making efficiency, reducing operating costs, and enhancing supply chain agility. This article reviews the application of big data in supply chain optimization based on existing research literature, and analyzes its practices in different fields, including demand forecasting, inventory management, transportation optimization, supplier management, and more. Meanwhile, this article summarizes the challenges that currently exist in the application of big data in supply chain optimization, such as data privacy, security issues, and the complexity of real-time data processing. In addition, this paper also puts forward the development direction of future big data research in the field of supply chain, focusing on the application prospects of cutting-edge technologies such as intelligent supply chain, edge computing and cross enterprise data sharing. This study provides valuable theoretical basis for further understanding the application and future trends of big data in supply chain optimization, and provides reference for academia and business practice.
Zilong Xu· International Journal of Adv...· 0 citations
What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration. The post GigaPath-Flash and GigaTIME-Flash: Toward population-scale discovery with efficient pathology foundation models appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduAug 31, 2026
With millions of users across the world, Julia has been used to conduct cutting-edge research and to design new drugs, jet engines, heat pumps, and more.
MIT News · Artificial Intelligence· news.mit.eduAug 27, 2026
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
