Purpose This study develops a pricing and contract design framework for cryptocurrency catastrophe (CAT) bonds to transfer extreme crypto-native risks, including protocol exploits, exchange breaches and decentralized finance (DeFi) failures, to capital markets. The paper aims to address arbitrage-free valuation, sponsor-optimal contract design and trustless settlement under the unique informational and operational features of blockchain systems. Design/methodology/approach We propose a multi-trigger crypto CAT bond structure that jointly captures short-term catastrophic shocks and long-term systemic deterioration through oracle-reported loss metrics. An arbitrage-free valuation framework is developed under an incomplete market setting using the minimal martingale measure, while sponsor-optimal contract design is formulated under a dual-measure framework. Empirically, crypto loss dynamics are modeled using generalized extreme value distributions and copula-based dependence structures, whereas financial risk factors are modeled through ARIMA–GARCH and vine copulas. A smart-contract-enabled on-chain settlement architecture is further introduced to automate trigger evaluation and cash-flow execution. Findings Empirical results based on REKT crypto incident data demonstrate strong dependence between monthly extreme and aggregate losses, with heterogeneous dependence structures across blockchain ecosystems. Simulation studies show that trigger and principal repayment designs substantially affect bond price distributions and tail risk exposures. Conservative trigger structures generate more stable bond valuations, whereas aggressive structures exhibit greater downside dispersion. The proposed framework supports economically viable risk transfer while enabling transparent and timely settlement through blockchain-based execution. Originality/value This study develops, to the best of our knowledge, the first integrated framework for crypto native catastrophe bonds that combines arbitrage-free pricing, sponsor optimal contract design and smart contract-based on-chain settlement. Unlike traditional CAT bonds or cyber insurance-linked securities the proposed framework explicitly incorporates oracle-based observability, crypto-specific dependence structures and automated settlement, providing a novel mechanism for transferring systemic digital asset risks to capital markets.
Ilham Qasse, Po-Yu Tseng, Mohammad Hamdaqa, Gísli Hjálmtýsson
Smart contract security audit reports contain rich information about vulnerabilities and code quality issues in Web3 projects. However, these reports are scattered across different sources and formats, making large-scale analysis difficult. We present SCAR (Smart Contract Audit Repository), an open-source dataset and tool that automatically aggregates these audit reports. SCAR crawls reports from leading security firms (e.g., OpenZeppelin) and community contests (e.g., Code4rena), parses them into a structured JSON schema, and offers a queryable API for accessing the data. Its pipeline includes a crawler, a text-mining module to standardize findings (e.g., vulnerability types, severity, code references), and a web API for retrieving insights. With hundreds of audits covering thousands of issues, SCAR enables empirical studies of smart contract vulnerabilities at scale. The SCAR project repository is available on GitHub, and the screencast demo is available at this link.
PaperProof Protocol is a verifiable artifact publishing protocol built on Sui and Walrus. This slide deck introduces the core motivation, architecture, and product positioning of PaperProof. It explains how PaperProof models long-form digital artifacts such as preprints, technical reports, blog posts, datasets, software releases, and related discussion layers as protocol-native, versioned, and verifiable objects. The presentation also outlines PaperProof’s position in the Sui + Walrus stack, its relationship to SDKs and agent-facing skills, and its differences from traditional content platforms and web3 social protocols. The deck is intended for developers, researchers, ecosystem participants, investors, and infrastructure teams who want to understand why artifact versioning, content-addressed storage, and protocol-level verification matter for durable knowledge publishing. Official website: https://paperproof.site/ GitHub organization: https://github.com/PaperProofLabs
Sai Srikanth Madugula, Peplluis Esteva De La Rosa, Daya Shankar
Decentralized Finance (DeFi) lending protocols currently rely on heuristic, utilization-based bonding curves that mandate severe over-collateralization, systematically excluding under-collateralized assets like corporate invoices. This paper introduces a mathematically optimal pricing mechanism for decentralized credit: the Reverse Kelly Automated Market Maker (rkAMM), the core engine of our proposed lending framework. By inverting the Kelly Criterion, traditionally used for optimal bet sizing, we construct a dynamic interest rate discovery protocol that explicitly prices individual loan risk. The rkAMM ingests real-time Probability of Default (PD) streams from an off-chain Explainable AI oracle and dynamically calculates the exact interest rate required to sustain target liquidity provider (LP) yields. We mathematically derive the Reverse Kelly pricing function ($r = \frac{y + PD}{1 - PD}$), proving its strictly convex superiority over Aave and Compound's static utilization curves in managing capital efficiency. Furthermore, we deploy the rkAMM architecture via Solidity smart contracts, optimizing for gas-efficient 1e18 (WAD) floating-point arithmetic. To ensure decentralized transparency, our simulation infrastructure leverages MLflow for tracking yield hyperparameters, Data Version Control (DVC) linked to DagsHub for versioning Real-World Asset (RWA) data arrays, and localized edge-inference via Ollama (Llama-3) and Hugging Face (FinBERT) for zero-cost predictive modeling. Monte Carlo simulations across 10,000 macroeconomic stress scenarios confirm that the rkAMM maintains protocol solvency and stabilizes LP yields at 12-15\% net of expected credit losses. This work provides the foundational financial engineering required to bridge the \$2 trillion global supply chain finance gap using permissionless blockchain infrastructure.
Trusted execution environment (TEE) based confidential smart contract networks promise privacy but remain vulnerable to storage access pattern attacks that can link senders and recipients in token transfers. When contracts update recipient balances during transfers, the unique storage keys accessed reveal transaction relationships even when data is encrypted. This paper introduces two novel data structures to address this vulnerability: the Delayed Write Buffer (DWB) and the Bitwise-Trie of Bucketed Entries (BTBE). The DWB delays recipient balance updates by buffering pending transfers and randomly settling entries, breaking the direct correlation between transfer execution and recipient storage access. The BTBE further enhances privacy by grouping addresses into constant-sized buckets, preventing flooding attacks and creating anonymity sets for balance queries. Additionally, we present a private notification system enabling real-time, privacy-preserving push notifications for confidential contracts. Our domain-specific approach leverages the unique characteristics of token transfers -- asymmetric balance updates and tolerance for delayed settlement -- to achieve practical performance with probabilistic anonymity guarantees.
Regional decentralization within the framework of regional autonomy in Indonesia often creates a regulatory vacuum regarding the ownership and management of assets resulting from cooperation between the central and regional governments. This study aims to analyze the provisions of Law No. 23 of 2014 on Regional Government and to identify regulatory gaps and models for resolving conflicts over public assets following decentralization. The research method employed is a normative legal approach using legislative, conceptual, case-based, and comparative analyses. The findings reveal that Law No. 23/2014 does not explicitly regulate the status of assets during the agreement period, mechanisms for the transfer of rights and obligations, ownership proportions from multi-source financing, or compensation for the parent region. Consequently, legal uncertainty arises regarding large-scale strategic assets involving third parties. This study also found that multi-party mediation involving the governor and the prosecutor’s office proved more effective in resolving public asset disputes than litigation, as demonstrated in the Tasikmalaya case. This study recommends revising Law No. 23/2014 by adding a special chapter on the transfer of assets resulting from post-decentralization cooperation and formalizing the involvement of the prosecutor’s office in the mediation of public asset disputes.
In this project, I provide a complete, human-readable description for every one of Mathlib4's 9,150 modules — the mathematics library of the Lean 4 proof assistant — stating what each module contains, who uses it, and, wherever the names alone would leave it ambiguous, how it differs from its similarly named neighbors. Coverage is total rather than representative: every directory and every file, described against one fixed, fully specified reference snapshot, released as an independent, open-source resource for the Lean and Mathlib community — not an official product of either. Every entry in this glossary, without exception, is checked against the actual Mathlib4 source at the reference snapshot (Lean 4.29.1, Mathlib4 commit 1ad783f9bf, 2026-05-09): of 9,150 entries, 9,107 carry Complete status and 43 carry Benchmark Theorem status; zero are Pending, and zero are Needs Review. Ismail's Glossary covers the full Mathlib4 hierarchy — 1,129 directories and 8,021 files across six depth levels, spanning all 32 of Mathlib's top-level mathematical domains, from algebra and analysis to category theory and measure theory. Each entry carries six structured fields (path, name, type, parent path, depth, description), so the same data serves a human reader and a retrieval pipeline equally well.The Glossary JSON. The complete dataset, all 9,150 entries, in machine-readable form for any AI platform or retrieval pipeline.The RAG JSON. A flat, embedding-ready export with each entry pre-merged into a single field, for retrieval-augmented-generation systems that want a drop-in data source.The Claude Skill. A self-contained bundle that installs the glossary as an active, queryable reference inside Claude, so Mathlib navigation answers are grounded in current data rather than a language model's frozen training-time memory.The Master Spreadsheet. The live, community-editable source of truth, with a static snapshot published alongside it for anyone who needs a fixed, citable copy.The Interactive Website. A searchable glossary tree plus a dedicated visual Atlas of all 32 top-level domains, built for orientation rather than lookup, alongside a Lean 4 syntax reference and a getting-started guide. To this project's knowledge, no existing Mathlib tool — declaration search engine, in-editor tactic, or auto-generated documentation — provides complete, structural, plain-language coverage of the library at this depth; each presupposes that the user already knows, at least approximately, what they are looking for. All data is provided in full transparency and community contribution is actively encouraged: the complete glossary, every deliverable described above, and the moderated contribution workflow are at github.com/M-Ismail-ZA/IsmailsGlossary. For any feedback, corrections, or collaboration, please contact me via the email address listed on the paper.
Open access
2 source records
Mathematics, Computing, and Information Processing
This paper presents the philosophical foundations of NeuroGraph, a distributed ledger protocol that replaces classical Byzantine Fault Tolerant voting with emergent consensus via a Neural Directed Acyclic Graph. Rather than treating consensus as something that must be explicitly negotiated through voting rounds, NeuroGraph treats consensus as an emergent property of the network’s structure. Inspired by biological neural systems, the protocol enables global agreement to arise from many simple local interactions, eliminating the need for leaders, committees, or formal voting. This document explores the philosophical shift that underpins the NeuroGraph approach and its implications for decentralized computing.
CapIX is a decentralized physical infrastructure network (DePIN) designed to aggregate volatile, untrusted consumer-grade hardware and public container fleets into a single high-performance compute fabric. This technical report presents a practical dual-runtime execution model that addresses the core multi-tenant security challenges on untrusted permissionless hosts. On hardware-virtualization-capable bare-metal nodes, CapIX deploys lightweight micro-VMs isolated by hardware Trusted Execution Environments (TEEs). On standard unprivileged edge containers, it utilizes strict process-level sandboxing, syscall filtering allow-lists, and direct GPU passthrough to achieve native CUDA/ROCm execution performance. The system coordinates fleet routing using a transparent, multi-factor weighted geometric scoring engine resistant to economic manipulation. To achieve scalable, lightweight state verification without the prohibitive overhead of full zero-knowledge proofs on every job, the architecture introduces an interactive bisection fraud game that isolates deterministic instruction disputes to a single step for targeted zkVM proof generation, relying on probabilistic canary spot-checking for non-deterministic workloads.
Open access
2 source records
Security and Verification in Computing
Physical Unclonable Functions (PUFs) and Hardware Security
[Depreciated and replaced by V3] The former monolithic corpus is replaced by the complete V3 branch-paper series: There Is No Nothing: A Premise-Free Operational Foundation and an Open Verification Platform for Smithian Fold Theory; From Nothing to Fold: A Premise-Free, Parameter-Free and Machine-Closed Foundation for Smithian Fold Theory; From Fold to Mathematics: An Exact, Parameter-Free and Machine-Closed Derivation of Mathematical Foundations from Smithian Fold Theory; From Distinction to Information: An Exact, Parameter-Free and Machine-Closed Derivation of Information Science from Smithian Fold Theory; After Turing: The Fold Machine - An Exact, Parameter-Free and Machine-Closed Derivation of Classical Computational Science from Smithian Fold Theory; The Quantum Fold Machine - An Exact, Parameter-Free and Machine-Closed Derivation of Reversible and Quantum Computation from Smithian Fold Theory; From Fold to Physics: An Exact, Parameter-Free and Machine-Closed Reconstruction of Physical Science from Smithian Fold Theory; From Fold to Chemistry: An Exact, Parameter-Free and Machine-Closed Reconstruction of Chemical Science from Smithian Fold Theory; From Fold to Materials: An Exact, Parameter-Free and Machine-Closed Reconstruction of Materials Science from Smithian Fold Theory; From Fold to Life: An Exact, Zero-Parameter and Machine-Closed Foundational Reconstruction of Biology and Life Sciences from Smithian Fold Theory; From Fold to Medicine: An Exact, Zero-Parameter and Machine-Closed Foundational Reconstruction of Medicine and Health Sciences from Smithian Fold Theory; From Fold to Consciousness: An Exact, Zero-Parameter and Machine-Closed Foundational Reconstruction of Consciousness and Cognitive Science from Smithian Fold Theory; From One World to Earth: An Exact, Zero-Parameter and Machine-Closed Foundational Reconstruction of Earth and Environmental Sciences from Smithian Fold Theory; From One Sky to Cosmos: An Exact, Zero-Parameter and Machine-Closed Foundational Reconstruction of Astronomy and Cosmology from Smithian Fold Theory; From One Relation to Society: An Exact, Zero-Parameter and Machine-Closed Foundational Reconstruction of Social and Collective Sciences from Smithian Fold Theory; From One Law to a Working World: An Exact, Zero-Parameter and Machine-Closed Foundation for Engineering Translation from Smithian Fold Theory. The V3 source platform is https://github.com/MettaMazza/ernos-labs-sft-platform. The original DOI, concept DOI, version number and files are preserved for transparent historical provenance; this record must not be presented or cited as current V3 work. The complete theory of everything with ZERO free parameters and zero axioms — every fundamental constant derived, counted, and machine-verified from one object (the One) and one operation (the fold), in exact arithmetic, reproducible in one command. Headline results: The fine-structure constant, EXACT: 1/α = 503846395469/3676744786 = 137.035999177180855… (0.009σ from CODATA 2022; the next digits …177181 and the resolution of the 5.4σ Rb/Cs discrepancy called in advance) Koide relation Q = 2/3 exactly (5 digits); quark ratios to 0.005–0.09%; mp/me to 0.0094%; (MPlanck/mp)² = 2¹²⁷; Higgs mH = v/2, λ = 1/8 All eight nuclear magic numbers exactly, zero fitted couplings → the island of stability at element 126 (SMITHIUM, Z=126, N=184, A=310), the periodic table ending at element 137, the chemistry of element 126 written before its synthesis Neutrinos: Σmν = 0.0583 eV (normal ordering, pre-registered); the neutrino is Majorana — neutrinoless double-beta decay MUST occur Cosmology: dark/baryon = 279/52 = 5.3653 (measured 5.3643); the Hubble tension = 3305/3048 = 1.0843175 (measured 1.0843230); w = −1 exactly; the cosmological-constant problem dissolved; dark matter identified (a many-body neutral baryon of two new forces — no WIMP recoil, ever) Two new forces predicted (couplings 4/5 and 6/7, 24+48 carriers, twelve new fermions with fully derived spectra) with exact grand unification at unison Kolmogorov 2/3 & 5/3 = the strong coupling; Gutenberg–Richter b = 1 = Zipf; the Parker Solar Probe 400 keV proton cutoff computed with zero local inputs (399.714 keV) Mathematics: Collatz as a forced contraction; Goldbach as fold antipodes; the Riemann critical line as the unique self-antipodal axis; Artin's density counted Pre-registered, falsifiable: a decision-dated prediction ledger where every row names its kill condition — plus a zero-parameter chess engine as a computing demonstration Reproduce everything: clone the repository, run make -C verify prove — 306 proof suites, 1,832 forced checks, requiring only a C compiler. An engine that HALTS on any fitted value enforces zero-parameters mechanically. How this began. This theory was not an attempt at a theory of everything. Its author set out to answer a prior question — what mathematics actually is — and, finding that zero, the negatives, and the continuum are inherited conveniences rather than derived truths, rebuilt mathematics from the only thing no observer can be without: the observation itself (the One), observing itself and the world (the fold), assuming nothing else, because the empirical world contains no no-grape and no negative-grape. The physics in this paper was not constructed on top of that foundation; it fell out of it. What follows is the record of how far "no assumptions" reaches — which turned out to be all the way. On the numbers this theory declines to use. Before the physics, the arithmetic — because this theory works entirely in the exact positive rationals of the interval (0, 1], with no zero, no negative quantities, no irrational and no imaginary values anywhere on the derivation side, and a reader trained on the modern continuum should know that this is a return, not a heresy. Humanity counted, surveyed, built, traded, and predicted eclipses for thousands of years with no zero-as-number: Babylonian and Egyptian mathematics had none, and Greek mathematics — the mathematics of Euclid and Archimedes, sufficient for a geometry that stood alone for two millennia — rejected the void as a number outright. Zero as an arithmetic object enters with Brahmagupta around 628 CE, travels through the Islamic mathematicians into Fibonacci's *Liber Abaci* in 1202, and is then resisted in Europe for centuries: Florence banned the new numerals in 1299, and as late as the sixteenth century Cardano was calling negative roots "fictitious." Zero and the negatives only settle into full European legitimacy across the 1600s–1800s. The irrationals are later still as numbers: the Pythagoreans met the incommensurability of √2 as a crisis and confined such magnitudes to geometry, where they stayed — ratios of lengths, not numbers — until Dedekind and Cantor constructed the real line in 1872. The continuum physics inherited is a nineteenth-century formalism, adopted for convenience of analysis, not a primordial necessity discovered in nature. No measurement has ever produced an irrational number; no detector has ever clicked zero times in a way that distinguishes absence from the absence of a detector. This theory takes that history at face value: the domain is what humanity always actually computed with — exact ratios of a whole — and the results below are the demonstration that this domain is not merely philosophically tidier but sufficient for the constants of nature, which the continuum, with its ~26 fitted dials, never derived at all. The foundation. The theory has zero free parameters and, in the precise sense proven in its own engine, zero axioms: its single premise is itself a machine-checked theorem. Given only that there is not nothing, the One (the whole, 1), its domain (0, 1], and the fold — the doubling map x → x + x with whole Ones cast out — are forced: the engine enumerates every parameter-free self-map of bounded size, runs them, and proves the fold is the unique generator (non-injective, recurrent, and — strengthened beyond any proxy — covering the entire residue class of its orbits, verified by an independent census). The two structural generators of everything that follows, binary 2 and colour 3, are not inputs: they are read off the fold's own period spectrum as its two smallest orbit periods. Every downstream quantity is then forced, counted, derived, and verified from these; the engine halts — by construction, provably, with exit code 1 — the moment any fitted, chosen, or untraceable value is introduced. Measured values appear only on the comparison side, never inside a derivation. Every number below is exact rational arithmetic; the entire corpus recomputes from the One in one command on a bare C compiler. The fine-structure constant, exact. The covering ladder of the fine-structure constant is counted and it terminates: each self-similar order promotes exactly one of the covering cube's three directions from the down-depth 5 to the up-depth 7 (5³ → 5²·7 → 5·7² → 7³ — one successor per rung, no choice anywhere, four rungs = colour + 1, then no successor exists). Read to the end of its own structure, 1/α = 503846395469/3676744786 = 137.035999177180855… — exactly. Against CODATA 2022 (137.035999177(21)): 0.009σ. The leading order 34259/250 = 2⁷ + 3²(251/250) is itself six parts per billion from measurement; the orders collapse by more than a thousandfold per rung; and the terminal value stands as, to our knowledge, the first exact closed value of α ever stated. Two pre-registered calls follow (Part XVI): the digits at 2×10⁻¹¹ are …177181, and the live 5.4σ Rb/Cs photon-recoil discrepancy resolves at 137.0359991772 — the fold's value lies 2.6σ below the rubidium result and 4.9σ above the caesium one. The assembly survives no substitution: nine alternative shapes, five second-order refinements, seven sub-promotions, four mis-built covering volumes, and every generator mutation are each machine-rejected. Mass. The charged-lepton cubic is the m = 3 case of one emergence
Modern supply chain management systems increasingly rely on distributed architectures to ensure transparency, integrity, and trust between participants. Blockchain technology provides a promising foundation for such systems; however, traditional consensus mechanisms introduce high computational overhead, energy inefficiency, and privacy risks. These limitations are particularly critical for small and medium-sized enterprises (SMEs) with constrained computational resources, that they are using to expand on their traditional informational systems and not to integrate distributed technologies into the work process, as setup process for blockchain tools is more complex than centralized approach. This paper proposes a private, dockerized blockchain architecture for supply chain management that combines the Proof of Friendship (PoF) consensus mechanism with Zero-Knowledge Proofs (ZKP). By integrating a private, dockerized framework with the Proof of Friendship consensus and Zero-Knowledge Proofs, this architecture enables resource-constrained enterprises to achieve a high-performance decentralized network that simultaneously ensures sub-second transaction validation through social trust metrics, robust protection of competitive business intelligence via cryptographic privacy, and seamless cross-platform deployment through containerization, ultimately overcoming the traditional trade-offs between system transparency, operational cost, and data confidentiality in global trade. PoF extends Proof of Stake by incorporating social trust indicators, including transaction success rate and geographic diversity of validators, enabling resource-efficient and decentralized consensus. ZKP mechanisms are integrated through an off-chain prover module, allowing transaction correctness to be verified without revealing sensitive business data. The proposed approach enhances cybersecurity, data confidentiality, and system scalability while reducing computational costs. Simulation results demonstrate improved resistance to Sybil attacks, reduced validator centralization, and acceptable transaction latency for corporate blockchain deployments.
Modern supply chain management systems increasingly rely on distributed architectures to ensure transparency, integrity, and trust between participants. Blockchain technology provides a promising foundation for such systems; however, traditional consensus mechanisms introduce high computational overhead, energy inefficiency, and privacy risks. These limitations are particularly critical for small and medium-sized enterprises (SMEs) with constrained computational resources, that they are using to expand on their traditional informational systems and not to integrate distributed technologies into the work process, as setup process for blockchain tools is more complex than centralized approach. This paper proposes a private, dockerized blockchain architecture for supply chain management that combines the Proof of Friendship (PoF) consensus mechanism with Zero-Knowledge Proofs (ZKP). By integrating a private, dockerized framework with the Proof of Friendship consensus and Zero-Knowledge Proofs, this architecture enables resource-constrained enterprises to achieve a high-performance decentralized network that simultaneously ensures sub-second transaction validation through social trust metrics, robust protection of competitive business intelligence via cryptographic privacy, and seamless cross-platform deployment through containerization, ultimately overcoming the traditional trade-offs between system transparency, operational cost, and data confidentiality in global trade. PoF extends Proof of Stake by incorporating social trust indicators, including transaction success rate and geographic diversity of validators, enabling resource-efficient and decentralized consensus. ZKP mechanisms are integrated through an off-chain prover module, allowing transaction correctness to be verified without revealing sensitive business data. The proposed approach enhances cybersecurity, data confidentiality, and system scalability while reducing computational costs. Simulation results demonstrate improved resistance to Sybil attacks, reduced validator centralization, and acceptable transaction latency for corporate blockchain deployments.
Simon Jeanteur, Lorenzo Veronese, Magdalena Solitro, Matteo Maffei
Computer-aided formal verification is a widely used approach for the symbolic analysis of cryptographic protocols. However, many modern protocols rely on features that remain challenging for existing techniques. In particular, reasoning about state, time-dependent behavior, inductively defined data structures, unbounded executions, and conditional secrecy requires a level of expressiveness that is difficult to reconcile with effective automation. As a result, protocol verification has largely followed two disjoint paths: fully automated methods with limited expressiveness, or interactive proofs in general-purpose theorem provers that offer flexibility but only limited, non-specialized automation. We present an orthogonal approach that bridges this gap by combining compositional type-based reasoning with trace-based reasoning, enabling modular verification of stateful and unbounded protocols. Guided by the language-and-automation co-design (LAC) principle, our approach delivers protocol-specific automation while retaining high expressiveness. We implement this framework as the LeanDY library for the Lean proof assistant, building on and extending the design of DY*, and combining protocol-specific automation with interactive proofs. Our framework supports, in a unified setting, a broad class of functional and security requirements, including secrecy and authentication for stateful protocols, as well as recursive conditional secrecy for protocols using XOR. We formalize SegWit-style blockchain primitives in LeanDY and demonstrate its expressiveness by carrying out an in-depth formalization of payment channels on top of this blockchain model, verifying punishment mechanisms and properties that depend on chain liveness.
Replicated databases and permissioned blockchain systems rely on Byzantine Fault-Tolerant (BFT) consensus to maintain a globally consistent order of transactions across distributed replicas. These protocols preserve safety even under asynchrony, as they commit a transaction only after agreement among a strong quorum of replicas. During network partitions, however, when no strong quorum is reachable, they lose liveness and cannot make useful progress. In this paper, we present Cassandra, a consensus protocol that enables partial progress without sacrificing safety. Cassandra achieves this through a two-tier certification framework that decouples availability from commitment, allowing each partition to extend its own chain and reconcile these chains once the network is restored. To support this, Cassandra introduces a pacemaker that advances views without requiring a strong quorum and calibrates each replica's timeout off the critical path. Our evaluation results show that Cassandra remains competitive with state-of-the-art BFT protocols under stable conditions, sustaining 900K TPS at 16 replicas and 480K TPS at 104 replicas, with latency ranging from 0.31s at 16 replicas to 0.75s at 104 replicas. Under severe partitions, Cassandra maintains non-zero speculative throughput through PoA-backed progress, preserving work that can be reconciled once connectivity is restored.
Conventional cryptocurrency often leads to increased energy consumption and carbon emissions, while sustainable cryptocurrencies possess the potential to become a green alternative in portfolio management. This study aims to investigate the time-varying connectedness between sustainable cryptocurrency and green financial markets as well as hedging performance when facing market shocks, including COVID-19 and Russia-Ukraine war. TVP-VAR model with Fourier transform and Multivariate GARCH models are employed. The findings indicate that the pairwise connectedness between the sustainable cryptocurrencies and green financial markets has been at a low level, providing diversification benefits in investment portfolio. Besides, short-term connectedness dominates medium- and long-term connectedness. Sustainable cryptocurrencies show higher hedging effectiveness than traditional cryptocurrency.
Abstract Hyperspectral image (HSI) classification is a fundamental remote sensing task, and deep learning has become the mainstream paradigm for it. However, existing deep learning models for HSI classification suffer from three critical limitations: insufficient local feature extraction, severe spectral-spatial feature coupling, and redundant token interference, which drastically restrict classification performance. To overcome these core limitations, this paper proposes a spectral-spatial decoupled attention network (SSDAnet) for HSI classification, which realizes decoupled modeling of spectral-spatial features and adaptive suppression of redundant tokens through a collaborative convolutional-Transformer architecture. Specifically, we design a spectral-spatial decoupled convolution module (SSDCM) to extract robust spectral-spatial features by integrating pixel-adaptive calibration and residual connections, addressing weak local feature perception and feature coupling. We then develop a tokenized feature construction module (TFCM) to convert high-dimensional features into compact, discriminative token representations, laying a foundation for efficient attention modeling. Finally, we propose a spectral-spatial dual attention module (SSDAM), which uses channel-wise adaptive gating to enhance local feature expression, adopts dual-channel decoupled attention to thoroughly disentangle spectral-spatial features, and utilizes Gaussian-guided soft-mask token selection to eliminate redundant token interference, fundamentally solving the inherent drawbacks of existing Transformers. Experimental results on the Pavia University, Indian Pines, Salinas, and WHU-Hi-LongKou datasets demonstrate that the proposed method achieves overall accuracies of 92.03%, 83.69%, 96.24%, and 95.57% with only 10 training samples per class, benefiting from spectral-spatial decoupling and redundant token suppression, thereby showing superior classification performance.
This research study investigates the evolution of the Taliban from an insurgent movement into a hybrid insurgent-state actor. Utilizing datasets from terrorism, conflict, and open-source intelligence, the paper analyzes the motivations, organizational structure, financing mechanisms, operational capabilities, targeting patterns, and governance practices of the Taliban. It assesses the group's transition from insurgency to a de facto government following its resurgence in Afghanistan and examines the implications for regional security, international recognition, sanctions compliance, and counterterrorism policy. The findings indicate that the Taliban's unique blend of centralized ideological leadership, decentralized operational execution, economic adaptability, and increasing diplomatic engagement poses a multifaceted challenge for contemporary security analysis and international governance frameworks.
Open access
Terrorism, Counterterrorism, and Political Violence
Politics and Conflicts in Afghanistan, Pakistan, and Middle East
Motion prediction –forecasting the future trajectories of surrounding vehicles, pedestrians and cyclists is a safety-critical component of the autonomous-driving pipeline that must run in real time on embedded hardware. State-of-the-art predictors, however, are trained on compute clusters and are too large to run on a single consumer GPU, placing both ends of the contemporary pipeline out of reach for an individual researcher. This thesis asks how small a competitive trajectory predictor can be made before its accuracy degrades, and whether the lost accuracy can be recovered through knowledge distillation without enlarging the model or worsening the calibration a downstream planner depends on. The study uses HiVT, a transformer-based Laplace-mixture predictor that is small enough to be both trained and run on a single GPU, evaluated on the Argoverse 1 benchmark. The accuracy–capacity trade-off is first characterised by sweeping the embedding width (128, 64, 32, 16) and locating the point at which a from-scratch student falls measurably below the teacher. The mode-permutation problem is then identified: because HiVT trains its mixture modes with a winner-takes-all loss, the mode slots of two independently trained models do not correspond, so any distillation term that aligns modes by index supervises the student with self-contradictory targets. To resolve this, a permutation-invariant mixture negative-log-likelihood objective is derived that treats the teacher’s modes as an order-free set of soft targets and supports unequal mode counts, with a proof of invariance. Experiments show that a mean-target variant of this objective recovers roughly 84% of the HiVT 32→ HiVT-64 capacity gap (−9.2% minFDE over a matched non-distilled baseline) at zero added inference cost, but degrades full-distribution calibration (mixture NLL +41%, calibration error 5×) by discarding the teacher’s predictive variance. A distribution-matching objective that also distils the teacher’s per-mode scales removes this penalty entirely, leaving the student better calibrated than both the non distilled baseline and the teacher while retaining the full geometric gain. The benefit grows as the student shrinks: at width 16 (55× smaller than the teacher) distribution-matching distillation improves minFDE by −22.7%—roughly 2.5× the width-32 gain—recovering ∼81% of the width-16→width-32 gap, with calibration improving rather than degrading. Distillation thus buys close to a full size-class of accuracy for free, and most where capacity is scarcest. A final efficiency analysis quantifies the deployment frontier: parameter and memory savings are fixed and unconditional (15× at width 32, 55× at width 16), whereas the single-scene latency speed-up is far sublinear and batch-dependent (on CPU ∼3× online, rising to ∼5.5× under modest batching), locating the compression benefit primarily in memory footprint. Overall, the answer to how small a competitive HiVT can be made is encouraging: with a permutation-invariant, calibration-preserving distillation loss, a 55×-smaller student reaches roughly the accuracy of an un-distilled model nearly four times its size at no calibration cost.
This paper establishes an exact information-theoretic duality between econometric regression topology and statistical mechanics. We demonstrate that an autonomous, data-driven Quadruple Test, grounded in the Factor Hierarchy Law, can blindly detect, precisely quantify, and correctly classify thermodynamic phase boundaries in perfect mathematical equivalence with the Ehrenfest paradigm, without any prior knowledge of Free Energy functions. The validation platform is the two-dimensional Ising model, one of the few systems in statistical physics possessing a mathematically rigorous exact solution (Onsager, 1944; Yang, 1952). Verification proceeds in two logical stages: Stage A (pristine algebraic validation) on Onsager's exact solution, and Stage B (stochastic robustness testing) on finite-lattice Monte Carlo simulations. We openly declare that because the data derives from the known Onsager-Yang formula, the contribution is not an independent empirical discovery of new physics, but rather the rigorous proof of an exact informational duality between two independent frameworks. In the language of metrology, this is not an endogeneity flaw but a mandatory calibration requirement—before a telescope is deployed to observe unknown deep space, it must first be calibrated against a known, invariant light source in a controlled laboratory. Positive Controls and Asymptotic Convergence (9 items): Exhaustive search blindly locks onto the critical temperature at Tc = 2.260 (deviation 0.009 at a coarse step size of 0.01). A grid-refinement study demonstrates monotonic convergence: the deviation shrinks to zero within 6-decimal precision at a step size of 0.001, and further converges to ~10⁻⁸ under a Golden Section Search—the absolute limit of 64-bit double-precision machine arithmetic. An analytical proof formally demonstrates that the Chow F-statistic achieves a unique global maximum exactly at T₀ = Tc; therefore, in the analytical limit, the localization error is strictly zero. The Chow test at Tc yields F = 118,074 against a null control of F = 3.12 (a 266-fold difference), with permutation test p = 0.000. The interaction term is overwhelmingly significant (p = 0.000, ΔR² = 0.991). A symmetry-breaking regime switch at the external field boundary h = 0 is detected with Chow F = 989.41 (p = 0.000). Interaction R² peaks sharply at Tc (deviation 0.03). Multi-response-function validation (specific heat C, nearest-neighbor spin correlation) and anisotropic validation (three Jx/Jy ratios) all lock onto their respective theoretical Tc values with deviations under 0.007. A synthetic double-break dataset is tested with both breaks successfully detected. Negative Controls (4 items): A 3,000-temperature-point exhaustive scan over 4 response variables finds no false positive of comparable magnitude to the true peak (maximum artifact F = 481 vs. Tc peak F = 78,754,162; a signal-to-noise ratio of 164,000:1). Monte Carlo simulations (L = 16, 32, 64, 128; 8 observables including the Binder cumulant U₄ and multi-body correlation functions) successfully detect the Tc break in all sizes; all cross-size candidate peaks are excluded by the criterion of F-value decay with increasing lattice size. Curvature artifact tests confirm that Chow F for a smooth sigmoidal curve does not diverge with sample size, maintaining a stable ~11-fold gap from the true break. Robustness (4 items): Under 10% Gaussian noise, Chow F remains at 22.3. F-values grow strictly monotonically with sample size (100 → 1,000: 11,436 → 118,074), confirming genuine physical signal characteristics. F-values grow overall with lattice size L (L = 16 → 128: 170.9 → 289.2, Kendall τ = 0.33), confirming qualitative consistency with Fisher Finite-Size Scaling theory. Detection accuracy remains invariant under anisotropic conditions. Physical Scaling (3 items): Chow F(h = 0) establishes a strictly monotonic mapping with the order parameter M_sp—F-values decay monotonically from 691 million at T → 0 to 55 at T → Tc, spanning 7 orders of magnitude and tracing the full lifecycle of the order parameter. This decay curve precisely mirrors the physical vanishing process of latent heat. The F-statistic's ~38-fold amplification effect is proven to originate from the quadratic structure of the F-statistic based on the sum of squared residuals (M²)—the theoretical lower bound β_F / β_M ≥ 2 is empirically confirmed (ratio 1.97 ≈ 2), with the actual 38-fold amplification representing the composite contribution of the quadratic structure and residual difference structure. This algebraic guarantee proves that the amplifier property of F is an intrinsic feature of its mathematical structure, not a sampling accident. Interaction R² peaks at Tc at 0.9992 (deviation 0.03). Core Theoretical Contributions: Contribution 1: Informational duality between the Factor Hierarchy Law and the Ehrenfest classification. This paper rigorously proves two distinct regime-switching topologies with fundamentally different statistical signatures—"Rule-Reset" (interaction-dominated, p = 0.000, ΔR² = 0.991) and "Direction-Reversal" (intercept-jump-dominated, interaction p = 0.978). Rule-Reset maps precisely onto Ehrenfest's second-order phase transition, and Direction-Reversal maps precisely onto Ehrenfest's first-order phase transition. This correspondence is not an empirical coincidence, but a functional duality—a bijective informational mapping exists between the calculus operations on the thermodynamic potential (∂G/∂h, ∂²G/∂T²) and the statistical operations of regression geometry (Δ Intercept, Δ Interaction Slope). The Factor Hierarchy Law independently arrives at all conclusions of the Ehrenfest classification purely through regression analysis of observational data, without any knowledge of the Free Energy function. Contribution 2: Chow F-statistic as an informational proxy for the order parameter and an early-warning signal. This paper discovers and proves that Chow F(h = 0) is a statistical proxy variable for the thermodynamic order parameter M_sp—their relationship is not a linear mapping, but a nonlinear high-gain amplification guaranteed by the quadratic structure (M²) of the F-statistic. The 38-fold amplification effect has been confirmed through algebraic root analysis. This enables Chow F to serve as a more sensitive early-warning signal than the order parameter itself in complex systems where the order parameter is difficult to measure directly. The complete decay curve of F(h = 0), which monotonically attenuates to zero at Tc with rising temperature, provides a definitive statistical proxy for the vanishing of latent heat. Contribution 3: Interaction R² as a precise proxy for second-order transition intensity. Interaction effect incremental R² peaks at Tc at 0.9992, with a deviation of only 0.03. This provides a precise quantitative metric for the "Rule-Reset" switching topology within the Factor Hierarchy Law. Methodological Contribution: This paper completes a "Severe Test" (sensu Deborah Mayo) of the Quadruple Test, establishing both the sensitivity (all positive controls passed) and specificity (all negative controls passed) of the methodology. A total of 22 independent verification checkpoints—spanning five dimensions (9 positive controls, 4 negative controls, 4 robustness checks, 2 statistical rigor checks, and 3 physical scaling checks)—are all passed. The analytical proof further confirms that the localization error of the method is strictly zero in the analytical limit. Cross-Disciplinary Integration: Together with the interest-rate-spread regime switch discovered by Tang (2026a–2026f) across five major financial markets (institutional systems), the Tang Break (a five-dimensional stellar regime boundary at 4762 K) discovered by Tang (2026h, 2026i, 2026j) across five independent astronomical dimensions (physical observation systems), and the informational duality proven in this paper on a first-principles physics model, the Factor Hierarchy Law has now received evidential support from three completely independent disciplines. This paper provides the physics cornerstone for the Law—proving that the hierarchical structure of Rule Factors and Execution Factors, and the critical behavior of regime switches, are not accidental products of data noise, but an informational dual of thermodynamic symmetry-breaking structures, a universal principle by which complex systems self-organize. Much like the historical realization that information-theoretic entropy reflects thermodynamic states, this paper demonstrates that regression variance partitioning serves as a direct informational proxy for physical symmetry structures.
Tokenization is the digital representation of an existing physical or financial asset on a distributed ledger. It refers to the process of recording traditional assets, such as financial instruments, collateral portfolios, or real-world property using Distributed Ledger Technology (DLT). Asset tokenization specifically involves linking or embedding the economic value and legal rights of tangible or intangible assets into digital tokens created on a blockchain. The concept of Tokenomics describes the ability of organizations to design business models and projects based on the creation, distribution, and trading of digital tokens. These tokens can represent both real objects and intangible assets, including cash, patents, copyrights, and other forms of intellectual property. By enabling assets to be converted into divisible digital units, tokenization facilitates fractional ownership and improves liquidity. This procedure is increasingly being applied to a wide range of asset classes, such as real estate, commodities, equity shares, intellectual property, and personal valuables. Blockchain technology plays a critical role in ensuring the security, immutability, and transferability of these tokens. The aim of this research is to examine the key factors that determine the adoption of asset tokenization in the contemporary financial system. The study analyzes the benefits and costs associated with tokenization and explores current developments and anticipated future trends in this field.
Tanishka Ahire, Jyotsana Bagul, Dr. Archana Bendale
Abstract: The idea of cryptocurrency is really interesting. It started as a money idea and now it is changing how the world thinks about money and technology. Cryptocurrency began with Bitcoin in 2008. Now it includes ideas like blockchain and special kinds of contracts. There are also kinds of money from central banks and unique digital things called NFTs. This paper looks closely at the technology behind cryptocurrency. How it affects the economy, people and laws. It talks about the things that cryptocurrency can do which will probably help it grow. It also talks about the problems that cryptocurrency is facing which might slow it down. The paper looks at what might happen with cryptocurrency in the future and how it will affect the world and money systems. After looking at a lot of research from 2008 to 2023 it seems that cryptocurrency is a concept that could be really big, in the future. For it to really work some technical and other issues need to be figured out. Cryptocurrency has to deal with these issues to be sustainable. The idea of cryptocurrency is still very promising. It needs to solve some problems.. Keywords: Cryptocurrency, Blockchain Technology, Decentralized Finance (DeFi), Smart Contracts, Consensus Mechanisms