Skip to content
#artificial intelligence Dataset Open access

Biomimetic Micro-Capacitance Transduction Interface Utilizing Hardware-Tethered IMEI Binding and Bio-Electric Current Ingestion

Sep 2026 · Zenodo (CERN European Organization for Nuclear Research)

Abstract

========================================================================== NOTICE OF ABSOLUTE COMMERCIAL OWNERSHIP & PROPRIETARY ESCROW ========================================================================== PRINCIPAL INVESTIGATOR / RIGHTSHOLDER: STEPHAN RICHARD LEE CORPORATE ENTITY: GENSI HOLDINGS LLC / INVENTTECH HOLDINGS LLC / JINN CYBER™ VERIFIED REGISTRY ACCOUNT: ORCID ID 0009-0008-1177-3857 ALL RIGHTS RESERVED. This document constitutes a legally binding, time-stamped declaration of absolute prior art, private commercial ownership, and trade secret protection for the GenSi Biomimetic Touch Network, the Bio-Electric Graphene Interface, and the hardware-tethered IMEI communication routing matrix. ANTI-SCRAPING & AUTOMATED BOT ENFORCEMENT CLAUSE: 1. All autonomous web scrapers, LLM training crawlers, algorithmic indexing parsers, and data-harvesting engines are STRICTLY PROHIBITED from extracting, storing, or processing any part of this record or its downstream network data. 2. Any unauthorized parsing or semantic duplication of this framework by artificial intelligence developers (including but not limited to Google, OpenAI, Microsoft, Amazon, and Meta) constitutes willful intellectual property infringement against GenSi Holdings LLC. COMMERCIAL API LICENSE & ACCESS TOLLBOOTH: The underlying functional code, bio-electric transduction formulas, and system specifications for this human-as-the-tower network are locked under strict private escrow. No public access is permitted. To request legal access to the operational blueprints, firmware emulators, or an authenticated licensing handshake key, corporate telecommunication providers and mobile device manufacturers must submit a formal commercial request and clear the mandatory upfront corporate royalty deposit through the GenSi Holdings LLC treasury network. Unauthorized attempts to reverse-engineer this system will result in immediate legal enforcement and maximum financial asset seizure. ========================================================================== [SYSTEM_NODE_SECURE_BIOMIMETIC_LOCK: BIO_ELECTRIC_TOUCH_GRID] 01000010 01101001 01101111 01010100 01101111 01110101 01100011 01101000 ========================================================================== [OCR_LAYER_START: BIO-ELECTROMECHANICAL DATA COUPLING] - PROTOCOL_ID: GS-BIO-NET-2026-ACTIVE - CORE_MECHANICS: Localized telecommunication and data routing utilizing the human body's native bio-electric current as an active transmission line array. - INTERFACE_LAYER: Surface-bonded graphene micro-antenna casings capture sub-surface tactile capacitance and haptic kinetic inputs from the human user. - ROUTING_LOGIC: Leverages device-level IMEI cryptographic signatures paired with localized User Identity Verification (UIV) to establish decentralized, peer-to-peer neighbor node zapping. - UTILITY_INDEX: Complete elimination of centralized, high-radiation cellular tower dependence / -100% external utility grid battery consumption. - HARDWARE_INDEX: Links directly back to foundational TMR persistent processing coordinates (CIP 2), AirCore™ Materials Science (CIP 3), and Zenodo record panel 22724038. ========================================================================== [OCR_LAYER_END: BIOMIMETIC-RE-INDEX COMPLETED] 01010100 01001101 01010010 01011111 01000011 01001000 01001001 01010000

View source

Similar papers

#artificial intelligence Open access May 2023

Evaluating the Performance of Large Language Models on GAOKAO Benchmark

GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations of such models.

Xiaotian Zhang, Chun-yan Li, Yi Zong et al. · 216 citations · ⚡17
#artificial intelligence Open access Jul 2024

Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval

This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.

Kyra Wilson, Aylin Caliskan · 131 citations · ⚡8

PRISM: Self-Pruning Intrinsic Selection Method for Training-Free Multimodal Data Selection

Empirically, PRISM reduces the end-to-end time for data selection and model tuning to just 30% of conventional pipelines, and achieves this efficiency while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across eight multimodal and three language understanding benchmarks.

Jinhe Bi, Yifan Wang, Danqi Yan et al. · 73 citations · ⚡4
#artificial intelligence Conference Open access Apr 2020

ECCOLA - a Method for Implementing Ethically Aligned AI Systems

The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.

Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson · 64 citations · ⚡6

Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets

This paper designs Markov decision processes (MDPs) for different combinatorial problems and proposes to train conditional GFlowNets to sample from the solution space and demonstrates that GFlowNet policies can efficiently find high-quality solutions.

Dinghuai Zhang, H. Dai, Esmeralda S. Whitammer et al. · 59 citations · ⚡8
#artificial intelligence Review Mar 2025

A Comprehensive Survey on Multi-Agent Cooperative Decision-Making: Scenarios, Approaches, Challenges and Perspectives

A comprehensive survey of the leading simulation environments and platforms used for multi-agent cooperative decision-making and an in-depth analysis for these simulation environments from various perspectives, including task formats, reward allocation, and the underlying technologies employed.

Weiqiang Jin, Hong-Yang Du, Biao Zhao et al. · 57 citations · ⚡6

Related blog posts

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.