This research establishes a formal topological framework for managing non- stationary market assets in portfolios by synthesizing high-dimensional chaotic dy- namics with industrial quality control and cryptographic verification. We introduce the Hala Operator as a state-dependent regulator capable of inducing Successive Controlled Collapse (SCC)—a process that maps continuous chaotic flows onto discrete, stable fixed-point constellations. By utilizing Taguchi Design of Experiments (DoE) for off-market robustness and Zero-Knowledge SNARKs for execution privacy, we provide a mathematically rigorous solution to the "Newtonian Trap" of market unpredictability. Formal proofs of global stability, dimension collapse via divergence analysis, and the uniqueness of the discrete constellation are presented.
Nicholas Brandt, Miguel Cueto Noval, Christoph U. Günther, Akın Ünal · 5 authors
CVRFs are PRFs that unify the properties of verifiable and constrained PRFs. Since they were introduced concurrently by Fuchsbauer and Chandran-Raghuraman-Vinayagamurthy in 2014, it has been an open problem to construct CVRFs without using heavy machinery such as multilinear maps, obfuscation or functional encryption. We solve this problem by constructing a prefix-constrained verifiable PRF that does not rely on the aforementioned assumptions. Essentially, our construction is a verifiable version of the Goldreich-Goldwasser-Micali PRF. To achieve verifiability we leverage degree-2 algebraic PRGs and bilinear groups. In short, proofs consist of intermediate values of the Goldreich-Goldwasser-Micali PRF raised to the exponents of group elements. These outputs can be verified using pairings since the underlying PRG is of degree 2. We prove the selective security of our construction under the Decisional Square Diffie-Hellman (DSDH) assumption and a new assumption, which we dub recursive Decisional Diffie-Hellman (recursive DDH). We prove the soundness of recursive DDH in the generic group model assuming the hardness of the Multivariate Quadratic (MQ) problem and a new variant thereof, which we call MQ+. Last, in terms of applications, we observe that our CVRF is also an exponent (C)VRF in the plain model. Exponent VRFs were recently introduced by Boneh et al. (Eurocrypt’25) with various applications to threshold cryptography in mind. In addition to that, we give further applications for prefix-CVRFs in the blockchain setting, namely, stake-pooling and compressible randomness beacons.
This paper mainly studies the retailer's reorder quantity and the profits of retailer, supplier and entire supply chain with inventory financing under incomplete information based on entropy. First we establish a inventory financing model to derive the reorder quantity of retailer under centralized supply chain and decentralized supply chain respectively. Then introduce the maximum entropy method to predict the market demand distribution. Fianlly, the results of our experiment indicate that information value can be higher when the market demand fluctuates severely and the best reorder quantity decision for retailer should be considered from entire supply chain except the fluncation of demand is big under incomplete information, while the retailer's decentralized decision under incomplete infromation will lead the entire supply chian to the worst profit.
Overconfidence is a universal psychological behavior. Overconfidence on demand awareness will have a significant impact on operation decisions. The supplier estimated the demand with excessive precision which influences the inventory financing decision-making deeply. We built the demand function based on the supplier’s overconfidence. Then we established the retailer, supplier, and the Bank’s profit function, respectively. Through the analysis of the bilevel Stackelberg game, we obtained the order quantity of the retailer with the capital constraint, the wholesale price of overconfident supplier, and the loan-to-value ratio of Bank, and we analyzed the influence of overconfidence on the decision variables. We have several findings as follows. First, the overconfidence makes the decisions of the retailer, supplier, and Bank deviate from the rational decisions. Second, the space of the market profit will affect the decision variables in the joint decision-making. Third, the financing supply chain (including the Bank and supply chain) should have a positive attitude towards the overconfidence of the supplier. Forth, in the joint decision-making, the supplier need determines the buyback price according to the capital demand; and in the decentralized decision-making, the supplier should try to use high buyback price strategy.