An intelligent decision support system and pervasive edge-computing framework that leverages smart glasses and a companion smartphone to infer emotional states from microscopic visual fixation patterns, establishing a physiologically interpretable, unobtrusive, and deployable paradigm for continuous real-time emotion monitoring.
Xiangyu Shen, Fei-Yang Deng, Zixi Dai et al.· 0 citations
Digital Twin (DT) technology, with Federated Learning (FL), enables decentralised and privacyaware intelligence of large-scale infrastructures of smart cities. Quantum computing compromises the existing cryptography systems that underlie existing FL frameworks. The current paper presents a Quantum-Secure FL (QSec-FL) architecture that incorporates both Post-Quantum Cryptography (PQC) and Quantum Key Distribution (QKD) as methods to ensure quantum-resistant communication between the IoT, edge, and cloud layers of 6G networks. The framework facilitates real-time DT synchronisation and ultra-reliable low-latency communication (URLLC) through adaptive model aggregation and secure key management. With the help of a large dataset of SmartCity-6G-QSec, QSec-FL features better robustness, synchronisation integrity, and intrusion resilience than traditional secure FL systems. Additionally, the framework is compliant with the standards of NIST PQC, ETSI QKD, and 3GPP Release 19, ensuring compatibility and readiness for use in future 6G-based infrastructures. QSec-FL is a quantum-resilient security system incorporating federated intelligence to establish a unified, dependable, real-time DT operation and safe AI-based decision-making of the advanced smart cities.
Wearable edge computing devices require novel hardware for high-efficiency data processing. In this work, we proposed novel textile in-memory computing memristor, exhibiting great potential in image compression applications for the first time. The device shows low operation voltage and reliable switching characteristics, paving the way for continuous analog modulation of input signals. Based on the experimental I-V curve model, 16-cell memristor array was constructed to validate analog multiplication and integration functions of memristor circuits. The measured current and voltage responses were further obtained from the practical circuit and showed good agreement with the theoretical results. To further evaluate the feasibility of realizing compressed sensing tasks, block-wise compressed sensing reconstruction based on memristor arrays was carried out in practical circuits. These results demonstrate the great potential of textile memristors in constructing next-generation wearable in-memory compressed sensing system.
Xingliang Shan, Wen-Xuan Chong, Xu-Fu Wang et al.· IEEE Electron Device Letters· 0 citations
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 Krüger, Don Feeney, Jacobo Rodríguez· Zenodo (CERN European Organi...· 0 citations
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This paper presents a novel quantum computation algorithm based on graph theory, aiming to simplify quantum computations by leveraging graph structure characteristics. Traditional quantum computing often struggles with efficiently utilizing graph structures, leading to complex algorithms. Our approach transforms the core quantum computation process into a graph representation, employing graph nodes and edges to design an optimized quantum computation. The core mechanism focuses on reducing computational complexity through the intelligent design of the graph structure itself. This paper details the algorithm's architecture, the rationale behind the graph representation, and the resulting efficiency gains compared to traditional methods. We present preliminary results demonstrating the effectiveness of this algorithm in solving a specific benchmark problem, highlighting its potential for advancing quantum computing.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the application of topological information to quantum computation, focusing on the evolution of quantum states through topological structures. The core claim is to design an algorithm that simulates the topological evolution of quantum states, allowing for predictive analysis of future quantum computations. Traditional quantum computation relies on classical physics models, but this research leverages quantum computation's inherent properties to offer a novel approach to simulating complex quantum systems. The algorithm employs a system of nodes and edges representing topological configurations, allowing for the observation of evolving topological structures. The paper details the design of a simulation framework, explores the impact of various topological parameters, and presents preliminary results demonstrating the algorithm's ability to predict state evolution. This work contributes to the development of robust quantum computing algorithms by focusing on the dynamic interplay of topological structures.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This chapter examines three technology families that have drawn attention from supply chain researchers and practitioners: blockchain, augmented and virtual reality (AR/VR), and edge computing. The argument is not simply that each yields operational benefits, but that their significance lies in addressing complementary problems within a single domain. Blockchain addresses trust and information integrity in multi-party networks; AR/VR addresses human interface challenges in complex environments; edge computing provides distributed computation enabling real-time responsiveness at scale. Drawing on recent work, the chapter traces how these technologies support the industrial metaverse. Sustainability, resilience, human-centred design, and infrastructure scalability are key themes. It also examines challenges—interoperability, governance gaps, cost barriers, and security exposures—and offers a coherent view of their convergence and implications.
Ashish Gupta, Ergashev Nuriddin Gayratovich, Begimov Uktam et al.· Advances in computational in...· 0 citations
A self-contained browser-based laboratory activity built around a hydrostatic transmission. A prime mover drives a fixed-displacement pump, the pump drives a fixed-displacement hydraulic motor through a hose, and an eddy-current brake absorbs the motor's output. Both machines are carried on trunnions with a torque arm resting on a load cell at a measured radius, so the power crossing into the fluid and the power crossing back out of it are separately weighed and the efficiency of the hydraulic link is measured rather than inferred. Energy is followed through four conversions — electrical to mechanical in the prime mover, mechanical to hydraulic in the pump, hydraulic back to mechanical in the motor, and mechanical to heat in the brake — with the loss and its mechanism named at every stage. The activity is organised around the two statements the whole of fluid power rests on: that the load sets the pressure, because a motor can only make torque by developing pressure across itself, and that the valve sets the speed, because whatever flow reaches the motor must be swallowed one displacement at a time. Most students expect a valve to be the pressure control, and the brake sweep settles that in minutes. In the simulation nothing is assumed. Viscosity follows the Walther relation fitted to the stated ISO grade; leakage in both machines is laminar and therefore viscosity dependent; the bypass valve is a sharp-edged orifice that seals on its seat; the relief valve cracks at its setting and passes more the further it is pushed above it; motor speed is solved from the flow balance, distinguishing a motor starved of flow from one stalled against the relief; and the prime mover carries a torque curve and a droop governor. Because leakage rises and viscous drag falls as the oil warms, the volumetric and mechanical efficiencies move against each other and their product passes through a maximum, which is the argument for fixing oil temperature in a test standard. The motor is instrumented twice over. A torque arm on its casing gives the torque the shaft actually delivers; a pressure gauge on each side of it, with the nameplate displacement, gives the torque the pressure accounts for. The two disagree by the mechanical efficiency, and comparing them lets students quantify the error introduced by the cheap, portable, industry-standard method that has to take that efficiency from a catalogue. On the reference data supplied, the catalogue figure is accurate to a fraction of a per cent at peak power and wrong by twenty-two per cent at light load. The torque arms can be switched off in the simulation, at which point the reported torque becomes an assumption with nothing on the rig able to check it. A needle valve downstream of the motor demonstrates separately that a restriction anywhere in the line loads the pump and not the motor, raising every pressure in the circuit while reducing the difference the motor converts into torque. All three interactive tools switch between SI and US customary units. The conversion is treated as a teaching point rather than a convenience: the constant in the displacement-torque relation changes from 20 pi to 2 pi between the systems because psi times cubic inches is already pound inches, and the 231, 1714 and 63025 that appear throughout American fluid power practice are shown to be unit conversions in disguise. Everything is stored and computed in SI so that no rounding accumulates from switching, and the test suite verifies that all dimensionless results are identical in both systems. The package includes a printable student handout typeset as an article, an instructor answer sheet that recomputes every result, curve, energy chain and model answer from the bench measurements entered rather than storing them, and a grading tool that audits a group's reported values against per-feature tolerances and computes every answer twice, once from the bench master readings and once from what the group wrote down, so that measurement error and arithmetic error can be graded apart. Every file is self-contained: no CDN, no build step, no server, no network access. A headless suite of 253 checks verifies the physics identities, the viscosity model, the flow balance across every combination of the two controls, the valve models, the governor, and both instructor tools.
A. Bulent Koc· Zenodo (CERN European Organi...· 0 citations
This technical report introduces a cloud-native architecture designed to implement the Cognitive Edge Emergence Model (CEEM). Traditional language model generation relies heavily on generic associations and external valence inferences. In contrast, this architecture establishes an individual-specific, self-reinforcing cognitive framework by decoupling semantic embedding from dynamic edge weight updates. The system utilizes FastAPI on Cloud Run for asynchronous request handling, Vertex AI (Gemini API) strictly for text embeddings without valence inference, and Cloud SQL with pgvector for similarity matching and structural graph persistence. State management dynamically controls cognitive bandwidth B(t) and temperature tau(B), while a dedicated Cloud Run service computes the continuous edge weight evolution via the differential equation: dw_ij / dt = eta * a_i(t) * a_j(t) - lambda * w_ij(t) where edge weights (w_ij) are reinforced exclusively through co-activation (a_i * a_j). This architecture operationalizes a structural profile that mirrors individual cognitive trajectories rather than generic statistical associations. Keywords: Cognitive Edge Emergence Model (CEEM) Cloud Architecture pgvector Valence-free Inference Structural Cognition Lab Neural-Symbolic Integration
Takahiro Ikeda· 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
FINDING: Selmer group ranks in quadratic twist families of elliptic curves are governed by symplectic root system symmetries, with mod-2 torsion modules exhibiting congruence structures tied to Weyl group actions. | MATH: 2-Selmer rank parity in quadratic twists \(E^{(d)}: dy^2 = x^3 + ax + b\) — the parity conjecture links \(\mathrm{rank}_2 \mathrm{Sel}(E^{(d)}) \equiv \mathrm{ord}_{s=1} L(E^{(d)},s) \pmod{2}\). Symplectic type of mod-\(p\) torsion: \(E[p] \cong \mathbb{F}_p^2\) with Weil pairing \(\langle \cdot,\cdot \rangle: E[p] \times E[p] \to \mu_p\), giving a symplectic structure on the Selmer group. Graph-theoretic algorithm for \(E_b: y^2 = x^3 + bx\) over \(\mathbb{Q}(i)\): \(\varphi\)-Selmer group computed via weighted graph \(G_b\) with vertices = odd Gaussian primes \(p \equiv 1 \pmod{4}\), edge weights = Legendre symbols \(\left(\frac{b}{p}\right)\). | CONNECTION: The symplectic group \(\mathrm{Sp}_{2g}(\mathbb{F}_2)\) acting on 2-Selmer groups has root system \(C_g\) (sy Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com
Andrew Stewart Caldin· Zenodo (CERN European Organi...· 0 citations
Macromolecular structure prediction remains limited by the $O(N^2)$ scaling of non-bonded interaction evaluations and the absence of strict physical boundary conditions in purely statistical deep-learning models. In the published SARS-CoV-2 spike glycoprotein protomer model (AF-P0DTC2), sequential $C\alpha$ distances reach $4.143\text{ \AA}$, exceeding the physical peptide bond tether constraint of $\delta_{\mathrm{tether}} \le 4.10\text{ \AA}$ by $0.043\text{ \AA}$. To address these scaling and fidelity constraints, we report a distributed 5-dimensional biophysical tensor architecture that unifies Euclidean backbone coordinates $(\mathbb{D}_1\text{--}\mathbb{D}_3)$ with continuous volumetric fields for Debye-screened electrostatics $(\mathbb{D}_4)$ and Kyte–Doolittle hydration $(\mathbb{D}_5)$. Thermodynamic feasibility is evaluated locally on edge compute nodes prior to network transmission, rejecting 53.8% of unviable proposals for ubiquitin ($N=76$) and 55.0% for the full spike protomer ($N=1{,}273$). For large targets ($N > 2{,}000$), the pairwise invariant calculation is dynamically chunked ($B=1{,}000$) and paged across a 477.5 GiB Host-RAM pool, eliminating GPU VRAM exhaustion while maintaining a block compute velocity of $56.9\text{ ms}$ with zero relative error in float64 accumulation against fully materialized distance matrices. An adversarial multi-agent review (Agents Alpha, Beta, Gamma) details the distinction between structural preservation ($\text{TM-score } 0.9998$ on accepted $0.03\text{--}0.20\text{ \AA}$ jitter) and ab initio prediction, rationalizes the operational Debye screening parameter ($\lambda_D = 9.0\text{ \AA}$), and documents host/GPU memory collisions during concurrent runtime execution. Complete coordinate streams, benchmark telemetry, and distributed orchestration routines are provided for independent verification.Author’s Note: To the Wet Lab Scientists and Researchers I am Robert J. Weber, and I built the system that generated the results shown here. My goal is simple: I am trying to help. Not for my sake or yours, but to help those afflicted by this horrible disease. I offer my work without worrying about profit, which is why I have posted everything openly. Humanity, and the families watching their loved ones suffer, are utterly worthy of this help. If we can create a treatment—or even a preventative measure that slows the progression—it would mean everything. To allow a husband or a wife to smile, to embrace, and to retain the functional love and recognition of the person they built a life with... who could ask for a better payment? RJW
Robert Weber· Zenodo (CERN European Organi...· 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.
