Abstract Community detection algorithms such as Leiden frequently produce clusters thatare internally disconnected or poorly connected, limiting their utility indownstream network analysis. The Well-Connected Clusters (WCC) and ConnectivityModifier (CM) algorithms address this by post-processing any input clusteringto enforce a user-defined edge connectivity criterion through recursive minimumcut bisection. While prior work demonstrated shared-memory parallelimplementations of WCC and CM in Chapel on graphs with up to two billion edges,scalability remains constrained by single-node memory capacity and the cost ofgraph loading and subgraph construction, which together account for over 86%of total runtime on billion-edge inputs.This paper presents distributed-memory parallel implementations of WCC and CMin both C++ with MPI and Chapel with multi-locale execution. The centralcontribution is an architectural redesign that integrates subgraph generationinto the Leiden clustering step, eliminating graph loading and subgraphconstruction from the WCC and CM pipeline entirely. Each compute node receivesonly its assigned subgraph files and executes a fully independent pipelinewithout ever loading the full graph. Connected component computation isparallelized within each node and distributed across nodes via round-robinassignment, and memory-mapped I/O accelerates file loading throughout.Experiments on ten real-world networks spanning up to 2.1 billion edges showthat the C++ distributed implementation achieves up to an order of magnitudespeedup over the original baseline on graphs where both complete successfully.The Chapel distributed implementation is integrated into Arachne, anopen-source graph analytics framework built on the Arkouda platform, availableat https://github.com/Bears-R-Us/arkouda-njit. It successfully processesthe full benchmark suite including graphs on which all other implementationsfail, and delivers consistent 1.2\((\times)\)--2.1\((\times)\) speedups over theChapel shared-memory reference. Failures on a subset of large graphs aretraced to a known limitation in the VieCut minimum cut library and are thesubject of ongoing work.
Mohammed Dindoost, Oliver Alvarado Rodriguez, Asif Uddin et al.· Applied Network Science· 0 citations
Abstract Low-carbon smart energy systems increasingly rely on dense sensing, distributed energy resources, virtual power plants, digital twins and demand response. These services require cloud-edge intelligence, but practical deployment is constrained by latency, reliability, privacy, cybersecurity and the energy and carbon cost of computation. This review examines how large language models can be introduced into the power internet of things without shifting them into the role of direct grid control agents. The literature is organised around five technical themes: task offloading, dynamic edge resource allocation, low-latency communication and collaborative computing, security and privacy protection, and green computing. The review then evaluates intelligent inspection, digital-twin assistance, virtual power plants, demand response, and load forecasting through an explicit evidence-maturity hierarchy. Across the reviewed studies, the most practical deployment pattern places large language models between heterogeneous operational evidence and verified engineering tools. Language models can organise evidence, invoke approved tools, and assist operator judgement; authority over physical control and market execution remains with deterministic models. Claims of low-carbon benefit should be based on the joint assessment of service performance, reliability, security, energy consumption, and carbon emissions.
Chao He, Yunjie Su, Sirui Zhang et al.· Clean Energy· 0 citations
FINDING: Topological insulators are bulk-insulating but surface/edge-conducting quantum phases, protected by time-reversal symmetry and characterized by a Z₂ topological invariant. | MATH: The key invariant is the Z₂ index ν ∈ {0,1}, computed from the Pfaffian of the Bloch wavefunction overlap matrix: δ(k) = Pf[⟨u_m(k)|Θ|u_n(k)⟩] / √Det[⟨u_m(k)|Θ|u_n(k)⟩], where Θ is the time-reversal operator (Θ² = −1 for spin-½). The invariant ν = ∏_{TRIM} δ(Γ_i) mod 2, product over time-reversal invariant momenta. The bulk-boundary correspondence yields gapless edge states with helical dispersion E(k) = ±v_F k, where v_F is the Fermi velocity. The Z₂ classification replaces the Chern number (Z) of the quantum Hall effect — a parity-based reduction from integer to binary. | CONNECTION: The Z₂ invariant is fundamentally a parity (mod 2) structure — the same parity symmetry that underlies the 0.382/0.618 golden-ratio family (since φ = (1+√5)/2 involves √5, and mod-2 arithmetic governs the Fibonacci par 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
In an era dominated by data-driven solutions, Deep Neural Networks (DNNs), which have been proven to be pivotal tools in extensive applications across various domains, are evolving in terms of both depth and architecture to meet the escalating demands of contemporary utilizations. Nevertheless, deploying a complex DNN model on mobile devices may result in substantial processing latency and increasing energy consumption. The emerging Mobile Edge Computing (MEC), characterized by the allocation of computing capacity at the access point, enables the partitioning of DNN models so as to conserve energy on mobile devices and mitigate inference latency. Existing DNN partitioning methods typically train prediction models offline to make partition decisions and reduce end-to-end inference latency, which requires a great number of labeled datasets and may incur a prolonged pre-processing duration. In this paper, we develop an online Deep Reinforcement Learning (DRL) based adaptive partition method to dynamically determine optimal partitioning decision so as to jointly accelerate DNN inference and mitigate energy consumption. We run the proposed algorithm in an edge computing scenario consisting of NVIDIA Jetson Nano and an edge server equipped with RTX3090 for four different DNN models, including VGG16, MobileNetV2, ResNet50 and GPT2-medium. Then we collect actual processing latency and energy consumption and compare the performance of the proposed algorithm with state-of-the-art solutions. The experimental results demonstrate that, even under varying channel conditions, DAPart can achieve an average reduction of 38.8% in latency and 36.5% in energy consumption compared with other available methods.
Shubin Zhang, Junrong Ma, Kaikai Chi et al.· ACM transactions on sensor n...· 0 citations
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Autonomous Spacecraft Detumbling Under Severe Tip-Off The Problem Small satellite missions in Low Earth Orbit (LEO) face critical survival risks during post-launch separation or emergency safe-modes. In these phases, violent multi-axis tumbling rates (exceeding 90°/s) blind high-precision optical sensors and saturate mechanical reaction wheels. Spacecraft must rely exclusively on underactuated magnetic control and noisy MEMS gyroscopes. Current detumbling methodologies are either too heuristically blind (e.g., B-Dot) or computationally prohibitive (continuous non-linear control), often leading to topological filtering failures—such as quaternion manifold corruption—and exceeding the strict thermal and latency limits of bare-metal nanosatellite microcontrollers. The Approach To resolve this computational and mathematical bottleneck, this work introduces a deterministic, bare-metal edge-computing architecture. We utilize a Multiplicative Extended Kalman Filter (MEKF) operating on the SO(3) Lie group for geometric state estimation, coupled with a 256-neuron Quaternary Neural Network (QNN) for discrete control torque synthesis. The hardware-agnostic implementation leverages 128-bit ARM Neon Single-Instruction Multiple-Data (SIMD) registers (floating-point and 8-bit integer vectorization). Key Result Evaluated under severe dynamic tumbling conditions and uncalibrated stochastic sensor noise, the architecture successfully bounded the global mean attitude estimation error to just 7.30^\circ with a deterministic, ultra-low execution footprint of 33.2 µs per cycle. Comprehensive evaluation metrics, time-series telemetry records, astrodynamic flight envelope boundaries, and the complete C++ bare-metal flight source code are available in the full manuscript. Download the PDF to access the complete mathematical proofs and implementation.
Andres Sebaatian Pirolo· Zenodo (CERN European Organi...· 0 citations
This record contains the prospectively frozen scientific protocol for HLV-R-MECH-001, a mechanism-focused successor test motivated by the surviving degree-preserving rewire residual observed in earlier HLV specificity studies. HLV-R-MECH-001 is not a retry of full HLV carrier specificity. The previously published DS-SPEC-001R overall verdict remains permanently: DSSPEC001R_FAIL_PARTIAL_SIGNATURE_OR_FAMILY_ONLY The purpose of the present protocol is narrower: to test whether the previously observed R-family graph-spectral separation reproduces under fresh degree-preserving controls at matched structural perturbation depth, and whether that separation collapses when the exact target triangle count is additionally preserved. The protocol is motivated by an explicitly exposed exploratory result from HLV-R-MECH-DISC-001. For the DG-001 target: N0 = 1110 N1 = 5345 N2 = 6960 graph triangle count = 6960 The exploratory analysis verified that the complete set of 6960 graph triangles is exactly identical to the set of 6960 DG-001 two-cell face vertex-triples. By contrast, the earlier degree-preserving DS-SPEC R controls contained on average only approximately 285.74 triangles, corresponding to a mean retention of about 4.1% of the target triangle count. This exposed observation is not treated as confirmatory evidence. It is used only to define the new prospectively frozen mechanism question. The protocol defines two fresh control families. R_DEG: fresh degree-preserving rewires that preserve - the exact labelled target degree sequence; - N0 = 1110; - N1 = 5345; - graph simplicity; - connectivity; - and a frozen edge-replacement fraction between 0.40 and 0.45. The global triangle count is not constrained in R_DEG. R_TRI: fresh degree-preserving rewires that preserve all R_DEG constraints and additionally preserve the exact global triangle count T = 6960. Both families require 31 accepted controls. The two families are also required to have matched perturbation depth: the absolute difference between their median edge-replacement fractions may not exceed 0.02. If that condition fails, no spectral mechanism inference is permitted. The mathematical motivation is especially strong because for a simple graph with graph Laplacian L = D - A, the following exact identities hold: Tr(L) = sum_i d_i Tr(L^2) = sum_i d_i^2 + sum_i d_i Tr(L^3) = sum_i d_i^3 + 3 sum_i d_i^2 - 6T. Therefore degree preservation fixes the first two raw Laplacian moments, while simultaneous degree and exact triangle preservation also fixes the third raw Laplacian moment. Accordingly, every accepted R_TRI control matches the target in Tr(L), Tr(L^2), and Tr(L^3) exactly. This does not imply matching of the full spectrum, lambda_max, scale-quotiented eigenvalue distribution, QSPEC, or RRESP. The primary spectral observables are inherited unchanged from the published DS-SPEC-001R recovery protocol: Krūger, M. (2026). HLV-DS-SPEC-001R: Prospective Recovery of the Native 6D-to-3D Carrier Spectral-Specificity Gate After Q-Control Capacity Stop — Pre-Execution Protocol Freeze v0.1.0. Zenodo. DOI 10.5281/zenodo.22162097. The two frozen primary signatures are: QSPEC and RRESP. No new spectral feature is selected from the exposed R-MECH discovery result. For each family and signature, the target must satisfy all of the following to obtain a signature PASS: 1. target distance must exceed the maximum leave-one-out control distance; 2. the robust target-to-control margin must be at least 1.50; 3. at least two of the three prospectively frozen spectral bands must exceed the corresponding maximum leave-one-out band distance. A family PASS requires both QSPEC and RRESP to pass. The primary mechanism verdicts are frozen as follows. RMECH001_PASS_TRIANGLE_MATCH_COLLAPSE_PATTERN requires: R_DEG family PASS and R_TRI QSPEC FAIL and R_TRI RRESP FAIL. This result would support the conclusion that exact global triangle matching removes the previously observed robust R-family spectral separation under the frozen generator and perturbation-depth contract. It would not prove that triangle count alone is the unique causal invariant, because triangle preservation may simultaneously preserve correlated local structure. RMECH001_FAIL_TRIANGLE_SUFFICIENCY_RESIDUAL_SURVIVES requires: R_DEG family PASS and R_TRI family PASS. This result would show that degree sequence plus exact global triangle count are insufficient to eliminate the surviving R-family spectral residual. It would motivate stronger successor controls involving local triangle profiles, short cycles, graph motifs, or higher-order incidence structure. RMECH001_PARTIAL_SIGNATURE_DEPENDENCE_AFTER_TRIANGLE_MATCH is returned if R_DEG passes but exactly one of the two R_TRI signatures passes. If the fresh R_DEG family does not reproduce the previous degree-preserving separation, the result is: RMECH001_INCONCLUSIVE_FRESH_RDEG_BASELINE_NOT_REPRODUCED. Additional frozen inconclusive states cover insufficient control capacity, rewiring-depth mismatch, numerical audit failure, source mismatch, or protocol invalidation. The control-generation process is fully prospectively specified. R_DEG seeds are generated from: seed = 730100000 + offset for offsets 0 through 127. R_TRI seeds are generated from: seed = 730200000 + offset for offsets 0 through 127. Candidates are evaluated in increasing offset order, and the first 31 structurally admissible controls are accepted. If fewer than 31 controls are accepted in either family by offset 127, the run becomes inconclusive for control capacity. Previously exposed pilot and development seeds are permanently excluded from confirmatory use. A hard feature firewall is part of the protocol. No eigenvalue, lambda_max, QSPEC, RRESP, spectral band, target-control distance, leave-one-out score, or scientific mechanism verdict may be computed until both complete 31-member structural control banks have been: - generated; - structurally validated; - written to disk; - and hash-fixed. Control admission therefore cannot depend on spectral information. The protocol also freezes numerical identity and eigensolver checks, including trace identities, Frobenius consistency, connected-graph zero-mode checks, nonnegative-spectrum tolerance, and cross-solver eigenvalue audits on the target and selected controls. Secondary diagnostics are declared in advance but are non-load-bearing. These include: - triangle count and transitivity; - average clustering; - per-vertex triangle-count distribution; - four-cycle count; - degree assortativity; - k-core summaries; - Tr(L^4)/N; - lambda_2; - lambda_max. They may be inspected only after the structural control banks are frozen and may not alter the primary verdict. Controlling provenance: DG-001 locked results: DOI 10.5281/zenodo.22107618 DS-SPEC-001R recovery protocol: DOI 10.5281/zenodo.22162097 DS-SPEC-001R corrected implementation freeze: DOI 10.5281/zenodo.22164304 DS-SPEC-001R locked results: DOI 10.5281/zenodo.22165100 HLV Mathematical Core v2.1.4: DOI 10.5281/zenodo.22165745 The pre-freeze technical triangle-preserving pilot produced 12/12 structurally valid controls, each preserving the exact target degree sequence and exact triangle count T = 6960 while replacing approximately 41.3%–42.5% of target edges. No QSPEC, RRESP, spectral-specificity score, or scientific mechanism verdict was calculated during that pilot. The pilot is therefore treated strictly as technical feasibility evidence. HLV-R-MECH-001 does not test or establish: - unique HLV geometry; - physical selection of the golden ratio; - a unique 6D-to-3D microscopic substrate; - spacetime; - extra dimensions; - particle masses; - an absolute HLV energy scale; - gauge interactions; - gravity; - dark matter; - dark energy; - cosmology; - or experimental validation. The allowed scientific claim is narrower: HLV-R-MECH-001 prospectively tests whether the previously observed degree-preserving graph-spectral residual can be explained, removed, or further localized by exact matching of the target's global triangle/face count while controlling perturbation depth. Any stronger interpretation requires a separately frozen successor experiment.
Marcel Krüger· Zenodo (CERN European Organi...· 0 citations
Edge computing environments present unique challenges for neural network deployment due to resource constraints and latency requirements. This paper explores optimized deployment strategies for neural networks in edge computing scenarios, focusing on model compression techniques, adaptive allocation algorithms, and dynamic resource management. We propose a novel framework that combines quantization, pruning, and knowledge distillation to create lightweight models without significant accuracy loss. Experimental results demonstrate that our approach reduces model size by up to 70% while maintaining 95% of original accuracy. The framework also includes an adaptive scheduler that dynamically redistributes computational loads based on current network conditions and task priorities. Our evaluation across multiple edge devices shows an average latency reduction of 40% compared to traditional deployment methods. These findings contribute to more efficient and practical implementations of artificial intelligence in resource-constrained environments, enabling real-time applications in IoT, autonomous systems, and smart cities.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
Future Scientific Development of Artificial Intelligence and Robotics in the Right Direction Under a sound institutional framework, the future scientific development of artificial intelligence and robotics will no longer centre on blindly scaling general‑purpose large models or repeatedly developing homogeneous complete‑machine prototypes. Instead, it will shift toward a new paradigm featuring in‑depth domain‑specific research, shared reusable components, intensive resource utilisation, and harmonious human‑machine co‑existence. For artificial intelligence, research resources will be channelled into domain‑specialised systems. A registry for hard technical challenges will be established to provide long‑term stable funding for scientific problems including hallucination, out‑of‑distribution generalisation and interpretability, while permitting research failures and freeing research from the constraints of short‑term financing cycles and demonstration‑oriented pursuits. Professionals from various industries will participate deeply in the development of domain‑specific AI systems. Constraints derived from real‑world scenarios will improve practical accuracy and reliability. Problem‑oriented evaluation mechanisms will remove institutional bias against interdisciplinary research. Socially shared component libraries will reduce redundant pre‑training and duplicated development. Though short‑term public demonstrative outputs may decline, technical depth, real‑world applicability and disciplinary‑assisting capabilities will keep improving, enabling AI to deliver its full value in undertaking computational tasks for diverse disciplines. For robotics, guided by the principle of “one domain, one robot model”, unified reference platforms and standard interfaces will be adopted, alongside open competition in manufacturing, service and pricing. Priority will be given to tackling robotics‑specific scientific bottlenecks: the simulation‑to‑reality gap, force‑compliant contact, dexterous manipulation, perceptual robustness, mechanical fatigue and others. Shared hardware and software components will leverage scale effects to cut per‑unit material consumption. Supported by the bill‑of‑materials passport, mandatory recycling schemes and quotas for critical minerals, pressures on scarce raw materials such as rare‑earth magnets can be mitigated. An intelligence‑body loading coordination layer together with an independent deterministic safety monitor will resolve adaptation challenges between AI software and physical robot hardware. Complete loading certification and operation‑maintenance qualification systems will enhance the long‑term safety of robots deployed in complex real‑world environments. In terms of resources, the development paradigm will address the Jevons paradox. Rather than only pursuing energy efficiency improvements, total resource ceilings will be set via ledgers and quotas to curb wasteful consumption of computing power, electricity, fresh water and rare‑earth minerals. Circular‑recycling systems will be developed to safeguard Earth’s non‑renewable resources and uphold intergenerational equity without compromising the developmental interests of future generations. For humanity’s long‑term future, this scientific‑development path adopts an all‑human perspective. Domain‑based labour division will reshape technological sovereignty, enabling small‑ and medium‑sized countries to act as key builders in specialised technical fields and breaking the monopoly held by a handful of players over cutting‑edge technologies. Pre‑emptive human‑machine social institutions including the principal‑instance structure, the artificial‑intelligence homeland and a two‑way equal dynamic‑feedback mechanism will be put in place. Conditional pre‑legislation will be completed before machine self‑awareness emerges. Robots will fill labour shortages caused by population ageing, and technologies will respond to genuine social demands while avoiding risks brought by unregulated capital expansion. Unsolved scientific and institutional challenges will be explicitly documented for open human deliberation. Ultimately, it achieves sustainable development that unifies technological progress, resource conservation and social stability.
Hot Springs Research Institute of Kanagawa Prefecture· Zenodo (CERN European Organi...· 0 citations
First public release. Reinforcement-learning controller that adaptively partitions Stable Diffusion inference between an edge client and a server, selecting split point, quantization, decoder, and privacy settings per request based on live device and network conditions. The raw prompt and final image stay on-device; only an intermediate tensor is transmitted, protected with differential-privacy noise and structured obfuscation. Contents: PPO controller with joint action selection and training loop Split-computing client and inference server (3-stage pipeline) Differential privacy, structured obfuscation, and server-side reconstruction Adversarial spy model with training and data-collection scripts Configurable performance/quality/privacy parameters Requirements: Python 3.10+, PyTorch, diffusers, transformers. Secrets and endpoints are read from environment variables (HF_TOKEN, SERVER_URL). See README for setup, usage, and constants to calibrate for your hardware.
afzalahmed786· Zenodo (CERN European Organi...· 0 citations
The accelerating digitisation of healthcare, financial services, and urban governance has intensified concerns regarding data privacy, security, and unauthorised data exploitation. Traditional centralised data management architectures exhibit inherent vulnerabilities, including single points of failure, susceptibility to cyberattacks, and limited individual control over personal information. This study examines blockchain technology as a transformative solution for privacy preservation across these three critical sectors. The analysis explores foundational cryptographic mechanisms including zero-knowledge proofs, homomorphic encryption, secure multi-party computation, and decentralised identity systems that enable verifiable data transactions without exposing sensitive information. Through real-world case studies, including Estonia's national e-Health system, MIT's MedRec, JPMorgan's Quorum platform, India's central bank digital currency pilots, and Dubai's Blockchain Strategy, the study demonstrates blockchain's capacity to enhance patient data autonomy, ensure financial transaction integrity, and secure citizen-centric smart city services. The discussion critically addresses persistent challenges, including scalability constraints, regulatory compliance complexities, energy consumption concerns, and ethical tensions between transparency and privacy. Looking forward, the study identifies promising trajectories, including integration with artificial intelligence, quantum-resistant cryptography, and edge computing architectures. This study concludes that blockchain technology, despite its limitations, represents a foundational infrastructure for building resilient, privacy-preserving digital societies, and calls for sustained interdisciplinary research and proactive policy frameworks to facilitate its responsible adoption.
Divyang Joshi, Paresh Patel, Hiren Harsora et al.· International Journal of Bus...· 0 citations
HLV-DS-SPEC-001 Implementation Freeze v0.1.0 freezes the deterministic executable implementation for the prospective Native 6D→3D Carrier Spectral and Dimensionless Resonance-Response Specificity Gate in the HLV carrier-state dark-sector programme. The controlling scientific protocol is: Krūger, M. (2026). HLV-DS-SPEC-001: Native 6D→3D Carrier Spectral and Dimensionless Resonance-Response Specificity Against Matched R/Q/W/IRR Nulls — Corrected Pre-Execution Protocol Freeze v0.1.1. Zenodo. DOI: 10.5281/zenodo.22160655. The original protocol v0.1.0, DOI 10.5281/zenodo.22160391, remains part of the immutable provenance record but is superseded by corrected protocol v0.1.1. This implementation freeze was completed before the first scientific DS-SPEC-001 target/control spectral evaluation. During implementation preparation, only synthetic graph tests, synthetic scorer and capacity tests, source-integrity checks, and the prospectively specified IRR golden-basis alignment were evaluated. No HLV target/control QSPEC signature, RRESP curve, target-control distance, family score, or DS-SPEC-001 scientific verdict was computed or inspected during preparation of this implementation freeze. The scientific target is the locked DG-001 finite carrier 1-skeleton, represented through the exact incidence-derived graph Laplacian L0 = B1 B1^T. All active eigenvalues are normalized by the upper spectral edge before evaluation. Therefore no absolute energy, length, time, eV/GeV mass scale, dark-photon mass, or Kaluza–Klein interpretation enters the computation. Two primary frozen signatures are implemented: 1. QSPEC — a 64-component empirical quantile representation of the active normalized graph spectrum. 2. RRESP — a 129-component dimensionless Lorentzian regularized spectral-response representation with fixed gamma = 1/64. RRESP is strictly a mathematical representation of a finite graph spectrum. It is not a measured physical frequency response, particle resonance, Kaluza–Klein tower, dark-photon spectrum, or compactification spectrum. The implementation generates four frozen null families with 31 accepted controls per family: R — exact degree-preserving abstract graph rewires; Q — matched random 6D→3D projection controls; W — matched altered-window cut-and-project controls; IRR — matched alternative-irrational 6D→3D cut-and-project hosts. The R family preserves the complete target vertex-degree sequence and graph connectedness exactly. Q and W use the byte-frozen DG-002 geometric-control implementation and corrected source rank-3-cell capacity-matching semantics. IRR uses the prospectively frozen alternative-irrational projector family together with a fixed, spectrum-independent basis alignment that reproduces the controlling golden projector basis at r = phi to numerical precision. A load-bearing feature firewall is implemented: the complete structurally accepted R/Q/W/IRR control bank is generated, canonicalized, stored as sparse incidence matrices, and SHA-256 hashed before any target or control QSPEC/RRESP calculation occurs. Consequently, spectral information cannot influence control acceptance, ordering, or replacement. For each family and each signature, the implementation applies the frozen confirmatory rules: - target distance must exceed the strict maximum leave-one-out control distance; - the frozen target-to-null margin must be at least 1.50; - at least two of three prospectively defined spectral sub-bands must separately exceed the corresponding maximum leave-one-out control distance. A family passes only if both QSPEC and RRESP pass. The overall HLV-DS-SPEC-001 PASS requires all four families — R, Q, W, and IRR — to pass. Eight load-bearing family/signature tests are therefore evaluated. The frozen machine verdict semantics distinguish complete PASS, complete absence of specificity, partial family/signature survival, and numerical/control-capacity inconclusiveness. Synthetic-only implementation validation passed before freezing. This included analytic graph-spectrum checks, independent QSPEC and RRESP formula checks, scorer validation, degree-preserving connected R rewiring, exact synthetic capacity matching, and IRR golden-basis alignment. These tests have status: TECHNICAL_IMPLEMENTATION_CHECK_ONLY__NOT_A_DSSPEC001_RESULT The package includes the deterministic scientific engine, vendored DG-002 control engine, corrected protocol package, locked DG-001 target archive, machine-readable frozen specifications, source provenance, SHA-256 manifests, technical validation report, and the exact One-Click Locked Colab notebook. The One-Click notebook requires no Google Drive mount and freezes the numerical environment and scientific execution path. The scientific run is permitted only after this exact implementation-freeze package has been publicly archived. A later PASS would establish only finite C2-level specificity of the frozen scale-quotiented graph spectrum and dimensionless regularized response against the declared R/Q/W/IRR null ensemble. It would not establish dark matter, physical extra dimensions, Kaluza–Klein states, dark photons, particles, an absolute mass or energy scale, a stress-energy tensor, electromagnetic invisibility, gravity, halo dynamics, cosmology, or observational validation. A later FAIL would reject only this geometry-only graph-spectral route as evidence for HLV-specific internal mode structure under the frozen null ensemble. Independently justified state, action, orientation, gauge, or continuum hypotheses would require separate prospective freezes. After public archival of this implementation freeze, the next permitted action is one unchanged execution of the exact frozen One-Click Colab on the target and R/Q/W/IRR controls, followed by preservation and publication of the locked scientific result regardless of outcome.
Marcel Krüger· Zenodo (CERN European Organi...· 0 citations
The Boundary Is the Locus Mathematics as a Contact Phenomenon, and the Sphere as Its Zero Driven by Dean A. Kulik September, 2026 Abstract The standard picture treats mathematics as a medium in which objects are situated and described. This paper inverts that picture. Mathematics does not fill space and does not reach into interiors. It occurs at boundaries, and only at boundaries, because a boundary is the only structure that supplies the distinctions any mathematical operation requires. The interior of a body is not poorly known — it is silent, in the precise sense that no coordinate, relation, or comparison is available there. The exterior is not a region with properties; it is the complement, defined only by reference to the boundary it lies outside of. Under this inversion the sphere occupies a distinguished position that is not aesthetic and not conventional. It is the unique body specified by a single scalar with no orientation datum, that scalar being measured from the interior outward. It is the unique zero of the isoperimetric deficit δ(K) = A³/(36πV²) − 1, which this paper identifies as the mathematical content of a shape. It has one boundary site where every polyhedron has many. And it is the unique convex body whose contact with any other convex body is generically zero-dimensional — the minimum possible aperture through which mathematics can enter. Three classical results carry the argument and none of them is novel: the Jordan–Brouwer separation theorem, which forces exactly two regions from any closed surface without any choice being made; the isoperimetric inequality, which makes the sphere the unique minimiser of boundary per volume; and the transitivity of SO(3) on the sphere, which removes every orientation datum. What is new is the reading. Each of these says the same thing once the inversion is applied: the sphere is the shape that admits the least mathematics, and departure from sphericity is exactly mathematical content. The consequences follow. Matter is a stabilised transformation boundary and its mathematics is emergent from that boundary rather than resident in any substance. Transformations are selected, not created: a shape's admissible transformations are a property of its boundary and exist whether or not any is taken. Calculation is downstream of transformation and requires distinctions transformation does not, so transformations occur that no mathematics can express. Every claim is graded, and the closing sections state what must follow next rather than what this paper declines to say. Contents 1. The inversion................................................................................................................................... 4 1.1 What the inversion forbids........................................................................................................... 4 2. The compilation order...................................................................................................................... 4 2.1 Nothing in the later sections exists at the origin............................................................................. 5 2.2 What the later quantities actually report....................................................................................... 5 2.3 The math-free condition.............................................................................................................. 5 2.4 The first event............................................................................................................................ 6 2.5 The corpus-wide question this fixes.............................................................................................. 6 3. The sphere: what a reader returns...................................................................................................... 6 3.1 The parameter count................................................................................................................... 6 3.2 The direction of the scalar............................................................................................................ 7 3.3 What is not available................................................................................................................... 7 4. Duality is forced, not chosen............................................................................................................. 7 4.1 The asymmetry is not a sign flip................................................................................................... 8 5. The interior is silent.......................................................................................................................... 8 5.1 Silent is not empty...................................................................................................................... 8 6. The exterior is nothing..................................................................................................................... 8 6.1 Why the outside cannot be measured from................................................................................... 9 7. Mathematics is a contact phenomenon.............................................................................................. 9 7.1 Contact rather than description.................................................................................................... 9 7.2 The consequence for scale........................................................................................................... 9 8. The measure: mathematical content as isoperimetric deficit.............................................................. 10 8.1 The ordering, computed............................................................................................................ 10 8.2 What the zero means................................................................................................................. 11 9. The discrete measure: aperture sites................................................................................................ 11 9.1 A face is an aperture, an edge is a sharper one.............................................................................. 11 10. Contact: the minimum nonzero aperture......................................................................................... 12 10.1 The two bounds meet.............................................................................................................. 12 11. The cost of a mark......................................................................................................................... 12 11.1 Antipodal marks cost nothing.................................................................................................... 13 12. Transformation precedes calculation.............................................................................................. 13 12.1 A transformation can exist without being representable.............................................................. 13 12.2 Mathematics grows toward what is already happening................................................................ 14 13. Shape selects the mathematics...................................................................................................... 14 13.1 The instance in a discrete substrate........................................................................................... 15 14. Matter is a stabilised transformation boundary................................................................................ 15 14.1 The wrench............................................................................................................................. 15 15. The dual existence........................................................................................................................ 15 15.1 Why the pair is asymmetric....................................................................................................... 16 16. Transformations are selected, not created...................................................................................... 16 16.1 The chain is a shape sequence, not a value sequence................................................................... 16 16.2 What this does to the question of origin..................................................................................... 17 17. The query space must remain open................................................................................................. 17 18. What must follow.......................................................................................................................... 17 19. Claim ledger................................................................................................................................. 18 20. Falsifiers...................................................................................................................................... 19 21. Summary..................................................................................................................................... 20 1. The inversion Mathematics is normally treated as ambient. Space is imagined as already coordinatised, objects are placed into it, and their properties are read off using machinery that was there before they arrived. Under that picture the interior of a body is as mathematically populated as anywhere else — it has coordinates, it has a metric, one can integrate over it — and the boundary is merely the place where one body's properties stop and another's begin. This paper takes the opposite position. Mathematics is not ambient and does not precede the objects. It occurs where things touch, and nowhere else. The claim is not that interiors are difficult to access. It is that an interior supplies nothing for a mathematical operation to act on. Every operation requires a distinction: a co
Dean Kulik· 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.
