The Displacement Current Is Not a Current ── Not One Charge Crosses between the Plates, and Still a Magnetic Field Stands ── The Field Computed from Inside and from Outside Meets Exactly at the Rim ── [Paper 267]
Aug 2026· Zenodo (CERN European Organization for Nuclear Research)
Quantum and Classical Electrodynamics
Abstract
Between the plates of a charging capacitor, not one charge crosses. And still a magnetic field stands there. This paper asks what, then, a current is──the answer is not the motion of charge. What closes Ampere's law is what has been called a current. No new mathematical theorem and no new law is claimed. Scope of this paper (scope note): No new mathematical theorem and no new law is claimed──the Ampere-Maxwell law, the displacement current, and the field of a parallel-plate capacitor are all standard. No electromagnetism is built──what is used is one line integral and the comparison of two expressions. Maxwell's equations are not derived──Paper 93 treats the discovery of the field and Paper 116 the exponent of c. This paper receives the equations as a premise and asks only after the standing of one term. No physical medium is posited for the displacement current──neither an ether nor a polarisation of the vacuum is invoked. Only the behaviour of a term in an equation is treated. The relativistic treatment is not entered──that displacement and conduction currents exchange with the observer is mentioned but not formalised. Edge effects are not treated──the plates are taken as large with no fringing, and the numbers are values inside that idealisation. The standing of epsilon_0 is not discussed──since the SI revision of 2019, epsilon_0 is a measured quantity. This paper uses it as a conversion constant and does not make its standing a subject. The 4pi in mu_0 is not called a discovery──mu_0=4pix10^-7 comes from the choice of units. Paper 116 treated the non-uniqueness of 4pi, and this paper follows it in writing that this one is a choice of units. Relation to earlier papers: Paper 02 showed that a pure solid angle 4pi appears in an inverse-square field──Section 6 here looks at the 4pi in mu_0 and writes that its standing is not the same. Paper 116 treated the non-uniqueness of 4pi──this paper follows that and claims no credit for the 4pi here. Paper 258 wrote the condition for 4pi to appear as three items──this paper stands on the side where the condition is not met. Paper 240 counted “mass” as seven things──this paper writes that “current” does not have one meaning. Paper 201 counted “complete” as four different claims──the same shape of division. What is added is stating explicitly that the crossing charge is 0 A, computing the field between the plates at each distance, confirming that the inside and outside expressions agree at 4.0000 muT on the rim, and treating the 4pi of mu_0 as a choice of units and writing that its standing differs from the 4pi of Paper 02. First, count the charge that crosses. The conduction current in the wire is 1 A, and the charge crossing between the plates is 0 A (Section 2). Second, this is the core of the paper. The charge is zero and a field stands anyway. With plates of radius 5 cm and I=1 A, the field 1 cm from the axis is 0.8000 muT (Section 3). Third, the field grows in proportion to the distance from the axis. At 1,2,3,4,5 cm it is 0.8000,1.6000,2.4000,3.2000,4.0000 muT──the same form as inside a current-carrying wire (Section 3). Fourth, the two meet exactly at the rim. At r=5 cm, the inside expression and the outside expression both give 4.0000 muT──the side where no charge crosses and the side where it does return the same value (Section 4). Fifth, epsilon_0 dPhi_E/dt equals the conduction current exactly. What makes them agree is the conversion constant epsilon_0=8.8541878x10^-12 (Section 5). Sixth, a 4pi sits here too. mu_0=4pix10^-7=1.2566371x10^-6──a solid angle sits inside the constant that fixes the size of the field (Section 6). the displacement current was not a current. The charge crossing between the plates is 0 A, and there is nothing to carry it. Still the field stands, rising in proportion to r and reaching 4.0000 muT at the rim──and computing with the outside expression, where charge does cross, returns the same 4.0000 muT. The epsilon_0 cancels and dQ/dt remains, so the two agree exactly and not approximately. So the word “current” carries two definitions──the motion of charge and what closes Ampere's law. And the 4pi inside mu_0 likewise differs in standing from the 4pi that came out of geometry──this one is embedded in the definition of a unit. One thing separates them──writing down which of the two definitions the word is being used in. Write it down, and the occasions for looking for charge separate from those for counting terms in an equation. Do not write it down, and one goes on searching for something crossing a place where nothing does. On the making of this work: The ideas and content of this work stem from the author's own considerations. Assistance from an AI (a large language model) was used for structuring, English translation, and checking the algebra. Any remaining errors or misinterpretations are solely the author's. Feedback and corrections are sincerely appreciated. ----- 充電中のコンデンサの板のあいだには、電荷が一つも渡っていない。それでも、そこには磁場が立っている。本稿が問うのは、では「電流」とは何なのかである──答は、電荷の移動ではない。アンペール則を閉じるものが電流と呼ばれている。新しい数学定理も新しい法則も主張しない。 本稿の射程(射程注記):新しい数学定理も新しい法則も主張しない──アンペール=マクスウェル則、変位電流、平行平板コンデンサの磁場は、いずれも標準的である。電磁気学を作らない──使うのは一つの周回積分と、二つの式の突き合わせだけである。マクスウェル方程式を導かない──論文93 が場という発見を、論文116 が c の冪を扱う。本稿は方程式を前提として受け取り、その一項の身分だけを問う。変位電流に物理的な媒質を仮定しない──エーテルも、真空の分極も持ち出さない。扱うのは式の項としての振る舞いだけである。相対論的な扱いに立ち入らない──変位電流と伝導電流が観測者によって入れ替わることには触れるが、定式化しない。端の効果を扱わない──板は十分大きく、縁の漏れは無視している。数値は理想化の内側の値である。 epsilon_0 の身分を論じない──2019 年のSI改定以降 epsilon_0 は測定量である。本稿は換算係数として使うだけで、その身分を主題にしない。 mu_0 の 4pi を発見だと言わない──mu_0=4pix10^-7 は単位系の取り方から来る。論文116 が 4pi の非一意性を扱っており、本稿もそれを踏まえて「単位の選択である」と書く。既刊との関係:論文02 は逆二乗場に純粋立体角 4pi が現れることを示した──本稿の第6節は mu_0 の中の 4pi を見るが、そちらは単位の選択であって同じ身分ではないと書く。論文116 は 4pi の非一意性を扱った──本稿はその指摘に従い、mu_0 の 4pi を手柄にしない。論文258 は 4pi が出る条件を三つに書き出した──本稿は条件を満たさない例の側に立つ。論文240 は「質量」が七つあることを数えた──本稿は「電流」が一つの意味ではないことを書く。論文201 は「完備」が四つの別の主張であることを数えた──同じ形の分け方である。加えたのは渡る電荷が 0 A であることを明示したこと、板の中の磁場を距離ごとに計算したこと、縁で内外の式が 4.0000 muT で一致することを確かめたこと、mu_0 の 4pi を単位の選択として扱い、論文02 の 4pi と身分が違うと書いたことである。 第一に、渡っている電荷を数える。導線を流れる伝導電流は 1 A、板のあいだを渡る電荷は 0 A である(第2節)。 第二に、これが本稿の芯である。電荷が 0 なのに磁場が立つ。板半径 5 cm、I=1 A で、中心から 1 cm の点の磁場は 0.8000 muT である(第3節)。 第三に、磁場は中心からの距離に比例して増える。1,2,3,4,5 cm で 0.8000,1.6000,2.4000,3.2000,4.0000 muT──導線の中の磁場と同じ形である(第3節)。 第四に、縁でぴたりと繋がる。板の縁 r=5 cm を、中の式で計算しても外の式で計算しても 4.0000 muT になる──電荷が渡っていない側と、渡っている側が、同じ値を返す(第4節)。 第五に、epsilon_0 dPhi_E/dt が伝導電流と厳密に一致する。一致させているのは epsilon_0=8.8541878x10^-12 という換算係数である(第5節)。 第六に、ここにも 4pi が座っている。 mu_0=4pix10^-7=1.2566371x10^-6──磁場の大きさを決めている定数の中に、球の立体角が入っている(第6節)。 変位電流は、電流ではなかった。板のあいだを渡る電荷は 0 A であり、運ぶものが何も無い。それでも磁場は立ち、r に比例して増え、縁で 4.0000 muT になる──そして電荷が渡っている外側の式で計算しても、同じ 4.0000 muT が返る。 epsilon_0 が約分されて dQ/dt が残るので、二つは近似ではなく厳密に一致している。つまり「電流」という語には二つの定義がある──電荷の移動と、アンペール則を閉じるものである。そして mu_0 の中の 4pi もまた、幾何から出た 4pi とは身分が違う──こちらは単位の定義に埋め込まれている。分けるものは一つ──その語をどちらの定義で使っているのかを書き出すこと。書き出せば、電荷を探すべき場面と、式の項を数えるべき場面が分かれる。書き出さなければ、何も渡っていない場所に、渡っているものを探し続けることになる。 作成にあたって:本稿の着想と内容は、著者自身の考察に基づくものです。文章の構成整理や英訳、数式の確認には AI(大規模言語モデル)の助力を得ました。最終的な内容の解釈や誤りがあれば、それらはすべて著者の責に帰します。お気づきの点があれば、ご教示いただければ幸いです。
This paper investigates a dynamic heterogeneous mobile edge computing network (HMECN), where mobile devices (MDs) could offload their full tasks to a small base station (SBS) directly or the macro base station (MBS) in direct or relay mode. As age of information (AoI) is a comprehensive and accurate metric to capture the freshness of computation results, we formulate a long-term weighted sum AoI (LWSA) minimization problem in the HMECN by jointly optimizing the offloading decisions of MDs as well as the bandwidth and computation resource allocation of all base stations, subject to energy, delay and peak AoI constraints. To address the formulated non-convex mixed integer nonlinear programming problem, we decompose it into the offloading decision optimization (ODO) top-problem and the resource allocation optimization (RAO) sub-problem. Based on the decomposition, we propose a federated learning (FL)-assisted hybrid DRL and convex approach that is comprised of a safe multi-agent DRL algorithm, convex optimization and FL. The ODO top-problem is solved by the safe multi-agent DRL algorithm, which strictly ensures that the actions of each agent do not exceed its energy constraint and then paves the way for using convex optimization to solve the RAO sub-problem. FL is used to alleviate the training instability problem aggravated by multi-agent settings via breaking the limitation of partial knowledge for each individual agent. Simulation results demonstrate the superiority of the proposed approach in terms of the LWSA, convergence, scalability and robustness in dynamic environments.
Xiaoying Liu, Junhao Zheng, Kechen Zheng et al.· IEEE Transactions on Mobile...· 8 citations
High-altitude airships (HAS) and uncrewed aerial vehicles (UAVs) equipped with Multiaccess Edge Computing (MEC) servers have emerged as promising aerial MEC nodes for providing task offloading (TO) services to intelligent mobile devices (IMDs) in post-disaster scenarios. HAS offers robust computing and energy resources, while UAVs provide flexible, low-altitude coverage for rapid deployment. However, direct task offloading from IMDs to HAS often leads to task failures due to high transmission delays. UAVs with limited onboard resources require to minimize resource waste. Additionally, IMDs in sparse areas face insufficient TO services due to unfair UAV coverage. This paper defines these challenges as a joint optimization problem involving TO, RA, and UAV coverage fairness. It proposes a cooperative aerial Multiaccess Edge Computing (AMEC) framework integrating HAS and UAVs to address the issue. Within this framework, a hybrid TO scheme is first developed to mitigate the high transmission delay between IMDs and HAS. Second, a Distance, Resource, Urgency-based Decision Mechanism (DRUDM) is designed to enhance the accuracy of UAVs in selecting target IMDs for TO services. Third, a Coverage Fairness Guarantee (CFG) strategy is proposed to optimize UAV flight trajectories, ensuring IMDs in sparse areas receive fair TO services. Finally, the joint optimization problem is modeled as a Multi-Agent Partially Observable Markov Decision Process (MA-POMDP), and a DRUDM–CFG algorithm is presented to efficiently solve this complex non-convex optimization problem. Experimental results demonstrate that the proposed algorithm outperforms other compared algorithms in task completion rate and average delay, benefiting from the DRUDM mechanism. Meanwhile, the CFG strategy effectively improves TO service fairness for IMDs in sparse areas.
Xiting Peng, Chuanqi Qin, Xiaoyu Zhang et al.· IEEE Transactions on Mobile...· 4 citations
Hyperbolic surfaces are a fundamental object in mathematics and play an increasingly important role in computational geometry and topology. A key ingredient in the design of efficient algorithms on such surfaces is the availability of a geometric discretization of controlled complexity. In this paper, we present the first algorithm for constructing e-nets on hyperbolic surfaces starting from a fundamental polygon representation. Our approach is based on Delaunay refinement and relies on maintaining Delaunay triangulations through edge flips. The size of an e-net cannot be bounded solely as a function of the genus because of the presence of arbitrarily long collars around short geodesics. To overcome this difficulty, we introduce the notion of a pseudo e-net, which decomposes the surface into e-thin cylinders together with a Delaunay triangulation over an e-net of the remaining thick part. As applications, we obtain algorithms for computing the length spectrum of an e-thick hyperbolic surface and for computing the systole from a pseudo log(sqrt(2))-net. These results demonstrate that Delaunay-based discretizations provide a practical and versatile framework for algorithmic computations on hyperbolic surfaces.
V. Delecroix, Vincent Despré, Camille Lanuel et al.· 3 citations
Future 6G networks are envisaged to tightly integrate communication, sensing, and computing, demanding real-time, intent-driven intelligence at the edge. While large language models (LLMs) excel in intent recognition and semantic reasoning, their application to real-time network lifecycle management at the edge is limited by heterogeneous application intents (APPIs), dynamic network conditions, and severe resource constraints. This paper proposes a novel lightweight LLM architecture, KGLlama-KD, that synergizes knowledge graphs (KGs) with knowledge distillation (KD) to enable intent-driven networking and enhance 6G edge intelligence. Specifically, a KG is constructed to formally describe the relationships among application scenarios, functional primitives, performance requirements within APPIs, and the correspondences between APPIs and network service requests (NSRs), thereby producing a structured intent training dataset. Building upon the Llama 3 foundation model, a two-phase optimization framework is designed to support lightweight edge deployment while preserving translation fidelity. The LLM is first fine-tuned with KG guidance and compressed via KD in the cloud, and then deployed on resource-constrained edge nodes to perform real-time, accurate, and efficient APPIs interpretation. Experiments validate that KGLlama-KD achieves 95% accuracy for APPI understanding, surpassing DeepSeek and Qwen by an average of 8%. The distilled model reduces inference latency by 60% compared to full-scale LLMs, fulfilling the sub-100 ms requirement for 6G latency-sensitive services.
Bing Wu, Sai Zou, Minghui Liwang et al.· IEEE Transactions on Mobile...· 3 citations
Dispersed computing has emerged as a promising paradigm that leverages underutilized resources from massive Internet of Things devices (IoTDs) to enhance the computing capacity at the network edge. However, existing works about the dispersed computing overlook the heterogeneous computing environment with parallel and serial computations and task reliability requirements for the hardware-constrained IoTDs, and they lack multi-objective optimization approaches to optimize the task offloading. To address the challenges, we propose a comprehensive scheme to achieve a delay-aware and economic-aware dispersed computing paradigm by using a multi-objective optimization approach. Particularly, we consider parallel processing at an edge server and serial processing at the lightweight IoTDs, and leverage the task redundancy to satisfy the task reliability requirements on the IoTD side. We further formulate a constrained multi-objective optimization problem (CMOP) aiming at jointly optimizing the task assignment, bandwidth allocation, and CPU frequency allocation to simultaneously minimize the total delay cost and the total charge cost of the tasks. To address the CMOP, we propose an improved constrained multi-objective evolutionary algorithm that employs a dual-population cooperative mechanism between two populations and a repairing constraint-handling technique. The dual-population cooperative mechanism can balance convergence toward Pareto optimality and solution diversity maintenance. The repairing constraint-handling technique is designed to guide solutions toward feasible regions, achieving efficient exploration of complex constrained search spaces. Simulation results demonstrate the superiority of our algorithm in seeking the better-converged and better-distributed Pareto optimal solutions to well address the tradeoffs between the two objectives.
Xumin Huang, Zexiong Wu, Chaoda Peng et al.· IEEE Transactions on Mobile...· 2 citations
The deflated-Welch statistic: a closed-form, guaranteed-level test for heteroscedastic one-way ANOVA 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.21908169. What this is The reproducibility deposit for the deflated-Welch statistic T_BB, a closed-form, guaranteed-level test for heteroscedastic one-way ANOVA (the Behrens–Fisher problem for k ≥ 3 groups). Welch's test becomes liberal under skew and unstable variance weights at small samples; T_BB = Q(s²)·exp(−R) keeps the ordinary group means and buys a guaranteed level by deflating the Welch quadratic by a Berger–Boos scale-inflation radius R. Three operating points are provided: a fixedcalibrated radius (κ_s), a design-adaptive near-guarantee radius (closed-form polygamma Cornish–Fisher with a finite-nkurtosis guard), and a fully proved smallest-eigenvalue radius R_eig (Gaussian, extended under bounded kurtosis). What the deposit contains Manuscript (author + anonymized) and a derivations supplement (DA1–DA13) plus a long-form derivations companion, covering: why Welch fails under skew in closed form; the Berger–Boos deflation and its exact worst-case radius; the polygamma-cumulant Cornish–Fisher radius with saddlepoint-exact normal backbone; the excess-kurtosis tail term with its finite-n upper-confidence guard; the imbalance correction; the fully proved smallest-eigenvalue radius (with the k-group multiplicity fix, free-β optimization, and the proved-under-bounded-kurtosis widening); and the k-sample Behrens–Fisher null distribution. Interactive demonstrator rerun_cochran/honest_anova.html — computes raw-mean Welch, the fixed / adaptive / proved T_BB radii, the estimand-changing transform routes, and the full routing receipt in the browser, reproducing the deposited Python. Its engine is extracted as a standalone Node module (m01A_anova_engine.js) and checked cell-by-cell against Python across an 84-design taxonomy (verify_anova_engine_taxonomy.py/.js, max |Δp| = 0.00000). Reproducibility scripts (rerun_cochran/, rerun/) — every reported number traces to a named, deterministically-seeded script (size/power/surface, the calibration and information-limit decompositions, the proved-radius verification, the imbalance calibration, the skew-router branch, and the figures). Real-data evidence — anova_flip_scan.py scans 2,783 public one-way layouts (254 datasets): guaranteed T_BBwithholds ~41% of Welch-significant calls, concentrated where the weight-instability screen fires, and never manufactures significance (Table 7 / Figure 15). Figures and the deterministic deposit builder (fixed timestamps → stable md5). All evaluation is simulation-based; the one empirical component is the public-dataset scan, which uses only openly distributed data. Code is released under the MIT License; text and figures under CC BY 4.0. Version history (consolidated changelog) Published version DOIs are marked ✅; the concept DOI above always resolves to the latest. Staged versions were rolled into the next published one unless noted. v1.0.77 ✅ 10.5281/zenodo.22167690 (2026-08-30): CSDA guide-for-authors conformance — abstract trimmed to 247 words (from 284), keywords cut to 7 (from 11), the withholding highlight shortened to ≤85 characters, and the arXiv PDF/source regenerated. No change to methods, results, figures, or code. v1.0.76 ✅ 10.5281/zenodo.22167536 (2026-08-30) — AI-disclosure heading aligned to Elsevier. The manuscript's declaration heading is now "Declaration of generative AI and AI-assisted technologies in the manuscript preparation process" (was "Use of generative AI"); the disclosure body is unchanged. Prepared alongside an Elsevier-compliant cover-letter variant and an EM suggested-reviewer sheet (both kept outside the deposit). docx/pdf rebuilt; deterministic md5 refreshed. v1.0.75 ✅ 10.5281/zenodo.22167304 (2026-08-30) — Submission-sharpening pass. Graphical abstract + Elsevier Highlights; figures and tables renumbered into reading order with per-table Source clauses; the validity–power frontier (Figure 8) now carries the proved R_eig operating point (100% validity, size-adjusted power 0.613, merge_tbb_proved_frontier.py); new Section 7 "Recovering power by design" + Table 8 (rc_anova_power_by_design.py); and a live required-n calculator in honest_anova.html (per-group and total n for 80% power, "power now @ total n"), with a numeric-heading CSS fix and the engine re-verified against Python at 0.00000. v1.0.74 ✅ 10.5281/zenodo.22165892 (2026-08-29) — Proved-under-bounded-kurtosis radius (DA12.6). The proved non-normal widening now keys on excess kurtosis, √(1 + κ̂·(n−1)/(2n)), from the exact Var(s²/σ²) = 2/(n−1) + κ/n, so symmetric heavy tails (Student-t) are covered where the old skew form √(1 + 0.75·skew²) under-covered; tbbProvedswitched to the kurtosis form across the demonstrator, engine, and Python truth (re-verified JS-vs-Python at 0.00000); new rc_anova_kurtosis_proof.py + deep-dive. v1.0.73 ✅ 10.5281/zenodo.22165709 (2026-08-29) — Reconstructed & verified demonstrator engine (standalone Node module + taxonomy verifier, max |Δp| = 0.00000 across 84 designs; Yuen zero-variance fix; T_BB-routed presets both directions); series-impact deep-dive (the corrected R_eig k-group multiplicity gap also reaches m03 and m01t). v1.0.72 (2026-08-29) — Title set to "The deflated-Welch statistic…"; corrected + optimized proved radius R_eig (β/k multiplicity fix + β-optimization, DA12); real-data Welch-vs-T_BB flip scan (2,783 layouts; Table 7 / Figure 15) + demonstrator imbalance-factor fix; long-form derivations companion. v1.0.71 / v1.0.70 (2026-08-21) — Zhang normal-reference comparator benchmarked on the efficiency frontier (valid on only 24% of designs, in the calibrated-liberal cluster); k = 2 adaptive-radius case-study fold (design-scaling vs shape-keying distinction). v1.0.69 ✅ 10.5281/zenodo.22035826 (2026-08-20) — HTML R1/R2 presentation pass + Figure 9 adaptive per-cluster label merge. v1.0.68 ✅ 10.5281/zenodo.22033737 (2026-08-20) — Companion consolidation into a single six-column Table 6; Figures 11–14 harmonized into one story. v1.0.67 / v1.0.65 / v1.0.60 (2026-08-19/20) — Guarded-reference naming-collision fix; the 40,000-replication expanded-frontier pin (Table 3 + Figure 8) with the symmetric-heteroscedastic skew-router branch; the mean-preserving lightened-R_eig do-not-use fallback. v1.0.59 ✅ 10.5281/zenodo.21995320 (2026-08-18) — Reporting standard + honest_anova.html demonstrator re-aligned to the current T_BB methods paper. v1.0.57 ✅ 10.5281/zenodo.21986847 (2026-08-17) — Reviewer-comprehension pass (multi-paragraph abstract, contributions list, trimmed captions); proved radius R_eig added as a Table 3 scorecard row; corner tail-index correction (N−k)/2 (low-order moments exist in every deployed design). v1.0.56–v1.0.49 (2026-08-16) — The k-sample Behrens–Fisher corner-distribution program: two-moment scaled-χ² corner reference, derived corner cumulants, the secular-eigenvalue law + closed CGF + power-law tail, consolidated into derivations DA13 with a prior-art/novelty audit. v1.0.48 ✅ 10.5281/zenodo.21963458 (2026-08-16) — The unifying λ(z) correction (a smooth instability-keyed deflation strength). v1.0.45 ✅ 10.5281/zenodo.21962965 (2026-08-16) — Atomic sparsity index + bootstrap-t edge hardening + shape-aware pooled standardized-residual bootstrap (SA-PSRB); multivariate transfer to m03. v1.0.44–v1.0.41 (2026-08-16) — Shape-moment re-injection order (skew is the sweet spot), validated and hardened pooled standardized-residual bootstrap, atomic weight-noise probes. v1.0.40 ✅ 10.5281/zenodo.21961667 (2026-08-16) — Log-domain weight-stabilization probe (negative for stabilization; clarifies the size-adjusted oracle ceiling); includes the oracle-power gap decomposition (≈92% conservatism, ≈8% estimation). v1.0.37 ✅ 10.5281/zenodo.21961327 (2026-08-16) — Residual-bootstrap qualification of the shoot-out + the first proved Gaussian smallest-eigenvalue radius R_eig (DA12, the p = 1 specialization of the m03 theorem). v1.0.36 (2026-08-15) — Figure 11 T_BB-region colour fix (amber, matching the routing figures). v1.0.27 ✅ 10.5281/zenodo.21908170 — Earlier published baseline of the deposit. Provenance: every number traces to a named, deterministically-seeded script listed in the manuscript Declarations; the demonstrator engine reproduces the deposited Python to max |Δp| = 0.00000 across the taxonomy verification. License. Code and scripts in the deposit are released under the MIT License; text and figures under CC BY 4.0. Reuse is permitted with attribution to the author and citation of the concept DOI above. How to cite. Dwyer, W. J. The deflated-Welch statistic: a closed-form, guaranteed-level test for heteroscedastic one-way ANOVA. Reproducibility deposit, Zenodo. https://doi.org/10.5281/zenodo.21908169
William Dwyer· Zenodo (CERN European Organi...· 2 citations
Related blog posts
Microsoft Research Blog· microsoft.comAug 31, 2026
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.