Uniform Micro-Patterning of an Arbitrary Surface

According to the literature, creating specific micro-level patterns on some surfaces can significantly reduce friction. To this effect, a method is presented to create a regular pattern of micro-level indentations on any irregular surface. Creating a uniform pattern on a regular surface is possible using commercial CAD software, where regular surface is the surface obtained by extrusion or revolution of a 2D sketch along any curve. But, it is complicated and often incorrect for irregular surfaces. The thesis presents the approach followed to create parameterized regular patterns on arbitrary surfaces. Three different algorithms are presented, each achieving a progressively increased quality solution. The last and best method provides a set of points with their corresponding normals to the surface to enable the creation of the patterning feature. The algorithm reads an STL file, a format neutral output of any CAD software and implements the method on the approximated surface. Each facet surface upon which the pattern has to be created is sliced by planes at specific distances from each other. The intersections of the facets and the planes are calculated and chains are formed from the intersections in each plane. Points are interpolated at the required pitch in different chains formed at the intersection of a single plane and the facets. This procedure is repeated for each plane. Thus, a pattern of points of specified pitch distance that can be as low as microns can be generated. Given specifications of a machine, this method generates the X, Y, and Z translations and the axis rotation angles needed to generate a g-code specific to a micro-milling machine. This code can be used directly for any metal removing process that has to create micro-level indentations on an arbitrary surface. If instead, the features are protrusions on some irregular surface, then the resultant points obtained with the developed approach can be used to apply the pattern at each of the identified locations

Uniform Micro-Patterning of an Arbitrary Surface

According to the literature, creating specific micro-level patterns on some surfaces can significantly reduce friction. To this effect, a method is presented to create a regular pattern of micro-level indentations on any irregular surface. Creating a uniform pattern on a regular surface is possible using commercial CAD software, where regular surface is the surface obtained by extrusion or revolution of a 2D sketch along any curve. But, it is complicated and often incorrect for irregular surfaces. The thesis presents the approach followed to create parameterized regular patterns on arbitrary surfaces. Three different algorithms are presented, each achieving a progressively increased quality solution. The last and best method provides a set of points with their corresponding normals to the surface to enable the creation of the patterning feature. The algorithm reads an STL file, a format neutral output of any CAD software and implements the method on the approximated surface. Each facet surface upon which the pattern has to be created is sliced by planes at specific distances from each other. The intersections of the facets and the planes are calculated and chains are formed from the intersections in each plane. Points are interpolated at the required pitch in different chains formed at the intersection of a single plane and the facets. This procedure is repeated for each plane. Thus, a pattern of points of specified pitch distance that can be as low as microns can be generated. Given specifications of a machine, this method generates the X, Y, and Z translations and the axis rotation angles needed to generate a g-code specific to a micro-milling machine. This code can be used directly for any metal removing process that has to create micro-level indentations on an arbitrary surface. If instead, the features are protrusions on some irregular surface, then the resultant points obtained with the developed approach can be used to apply the pattern at each of the identified locations

Adaptive Curves for Optimally Efficient Market Making

Automated Market Makers (AMMs) are essential in Decentralized Finance (DeFi) as they match liquidity supply with demand. They function through liquidity providers (LPs) who deposit assets into liquidity pools. However, the asset trading prices in these pools often trail behind those in more dynamic, centralized exchanges, leading to potential arbitrage losses for LPs. This issue is tackled by adapting market maker bonding curves to trader behavior, based on the classical market microstructure model of Glosten and Milgrom. Our approach ensures a zero-profit condition for the market maker’s prices. We derive the differential equation that an optimal adaptive curve should follow to minimize arbitrage losses while remaining competitive. Solutions to this optimality equation are obtained for standard Gaussian and Lognormal price models using Kalman filtering. A key feature of our method is its ability to estimate the external market price without relying on price or loss oracles. We also provide an equivalent differential equation for the implied dynamics of canonical static bonding curves and establish conditions for their optimality. Our algorithms demonstrate robustness to changing market conditions and adversarial perturbations, and we offer an on-chain implementation using Uniswap v4 alongside off-chain AI co-processors

Adaptive Curves for Optimally Efficient Market Making

Automated Market Makers (AMMs) are essential in Decentralized Finance (DeFi) as they match liquidity supply with demand. They function through liquidity providers (LPs) who deposit assets into liquidity pools. However, the asset trading prices in these pools often trail behind those in more dynamic, centralized exchanges, leading to potential arbitrage losses for LPs. This issue is tackled by adapting market maker bonding curves to trader behavior, based on the classical market microstructure model of Glosten and Milgrom. Our approach ensures a zero-profit condition for the market maker's prices. We derive the differential equation that an optimal adaptive curve should follow to minimize arbitrage losses while remaining competitive. Solutions to this optimality equation are obtained for standard Gaussian and Lognormal price models using Kalman filtering. A key feature of our method is its ability to estimate the external market price without relying on price or loss oracles. We also provide an equivalent differential equation for the implied dynamics of canonical static bonding curves and establish conditions for their optimality. Our algorithms demonstrate robustness to changing market conditions and adversarial perturbations, and we offer an on-chain implementation using Uniswap v4 alongside off-chain AI co-processors

CCR5AS lncRNA variation differentially regulates CCR5, influencing HIV disease outcome.

Multiple genome-wide studies have identified associations between outcome of human immunodeficiency virus (HIV) infection and polymorphisms in and around the gene encoding the HIV co-receptor CCR5, but the functional basis for the strongest of these associations, rs1015164A/G, is unknown. We found that rs1015164 marks variation in an activating transcription factor 1 binding site that controls expression of the antisense long noncoding RNA (lncRNA) CCR5AS. Knockdown or enhancement of CCR5AS expression resulted in a corresponding change in CCR5 expression on CD4+ T cells. CCR5AS interfered with interactions between the RNA-binding protein Raly and the CCR5 3' untranslated region, protecting CCR5 messenger RNA from Raly-mediated degradation. Reduction in CCR5 expression through inhibition of CCR5AS diminished infection of CD4+ T cells with CCR5-tropic HIV in vitro. These data represent a rare determination of the functional importance of a genome-wide disease association where expression of a lncRNA affects HIV infection and disease progression

Thinking Fast and Slow: Data-Driven Adaptive DeFi Borrow-Lending Protocol

Decentralized finance (DeFi) borrowing and lending platforms are crucial to the decentralized economy, involving two main participants: lenders who provide assets for interest and borrowers who offer collateral exceeding their debt and pay interest. Collateral volatility necessitates over-collateralization to protect lenders and ensure competitive returns. Traditional DeFi platforms use a fixed interest rate curve based on the utilization rate (the fraction of available assets borrowed) and determine over-collateralization offline through simulations to manage risk. This method doesn't adapt well to dynamic market changes, such as price fluctuations and evolving user needs, often resulting in losses for lenders or borrowers. In this paper, we introduce an adaptive, data-driven protocol for DeFi borrowing and lending. Our approach includes a high-frequency controller that dynamically adjusts interest rates to maintain market stability and competitiveness with external markets. Unlike traditional protocols, which rely on user reactions and often adjust slowly, our controller uses a learning-based algorithm to quickly find optimal interest rates, reducing the opportunity cost for users during periods of misalignment with external rates. Additionally, we use a low-frequency planner that analyzes user behavior to set an optimal over-collateralization ratio, balancing risk reduction with profit maximization over the long term. This dual approach is essential for adaptive markets: the short-term component maintains market stability, preventing exploitation, while the long-term planner optimizes market parameters to enhance profitability and reduce risks. We provide theoretical guarantees on the convergence rates and adversarial robustness of the short-term component and the long-term effectiveness of our protocol. Empirical validation confirms our protocol’s theoretical benefits