Defines cost of MEV and shows its relevance in various settings.
problem Excess value miners can realize by manipulating transaction order.
method Introduces a simple theoretical definition of cost of MEV, proves properties, and provides examples.
result Reveals the cost of MEV is related to the 'smoothness' of a function over the symmetric group.
Dark blockchain venues increase miners' profits but raise users' execution risk.
problem Exploitable information leakage in blockchain transactions.
method Economic incentive analysis and empirical study of dark venues.
result Dark venues increase miners' profits but raise users' execution risk.
This work introduces uncertainty principles to mitigate Maximal Extractable Value in blockchain systems.
problem Maximal Extractable Value (MEV) in decentralized systems due to transaction submission privacy and monopolist power.
method Unified approaches via uncertainty principles, akin to harmonic analysis and physics, to quantify trade-offs between transaction flexibility and user economic payoff.
result Demonstrates a quantitative trade-off between transaction flexibility and user economic payoff, analogous to the Nyquist-Shannon sampling theorem.
We investigate the structure of global inter-firm linkages using a dataset that contains information on business partners for about 400,000 firms worldwide, including all the firms listed on the major stock exchanges. Among the firms, we examine three networks, which are based on customer-supplier, licensee-licensor, a…
Fragmented exchanges arise due to speed advantages in high-activity regions.
problem Fragmentation of distributed securities exchanges due to speed advantages in high-activity regions.
method Economic model and Monte Carlo simulations of a decentralized exchange with two miner clusters.
result Speed advantage increases with infrastructure asymmetry between regions.
Style Miner generates stable and significant style factors for time series analysis.
problem Finding significant and stable explanatory factors in high-dimensional time series data.
method Proposes a reinforcement learning method to balance explanatory power and stability constraints.
result Outperforms existing methods by a large margin and achieves a 10% gain in R-squared explanatory power.
Maximal extractable value in CFMMs can degrade or improve routing quality, with reordering MEV showing logarithmic impact.
problem Maximal extractable value in constant function market makers (CFMMs) and its impact on routing quality.
method Game theoretic analysis of MEV in CFMMs, constructing price of anarchy and analyzing reordering MEV.
result Conditions under which reordering MEV shows logarithmic impact, and implications for MEV searchers and CFMM designers.
Research analyzes ethical concerns around MEV on blockchain and social media.
problem Fairness issues in transaction ordering on blockchain.
method Applied NLP methods to analyze topics in tweets on MEV.
result Tweets discussed ethical concerns like security, equity, and solutions to MEV.
Model infers mineral locations from geospatial data, improving predictions with auxiliary data.
problem Challenges in characterizing hidden mineral deposits underground.
method Generative modeling approach using masked and infilled geospatial maps.
result Models achieve Dice coefficients of 0.31 and recalls of 0.22 at 1×1 mi² resolution.
Dual random fields improve mineral potential predictions.
problem Limited understanding of multi-dimensional causalities and dependencies.
method Introduces dual random fields to pool response functions across the domain.
result Spatial inference and uncertainty assessment of response models and predictions.
Model shows how centralization occurs in cryptocurrency mining.
problem Centralization of reward and computational power in Bitcoin-like cryptocurrencies.
method Mean field game model to study miner competition and reward distribution.
result Heterogeneity of initial wealth leads to greater imbalance in reward distribution.
Game theory shows miners' hardware improvements don't centralize mining.
problem Decentralization of cryptocurrency mining.
method Game-theoretical model of mining efficiency and competition.
result Advancements in mining hardware efficiency do not lead to centralization.
A new sequencing rule prevents miners from front-running transactions in decentralized exchanges.
problem Miners exploit their privileged position to front-run transactions, leading to unfair profits.
method Introduce verifiable sequencing rules that constrain transaction execution order and are verifiable.
result A verifiable sequencing rule ensures users receive at least fair execution prices, preventing front-running.
Extends PoS proof-of-stake transaction fee mechanism with miner utility model.
problem Designing a transaction fee mechanism for PoS protocol that incorporates miner utility.
method Introduced a new mechanism (BSP(θ)) incorporating a parameter θ to ensure user and miner incentives.
result The new mechanism (BSP(θ)) satisfies user and miner incentives and contract proofness.
The paper analyzes how mutable blockchain protocols affect miner behavior and strategic stability.
problem The mutability of blockchain protocols undermines long-term planning and cooperative equilibria.
method Integrates Austrian capital theory with repeated game theory to examine miner behavior under different institutional conditions.
result Effective time preference increases when protocol rules are mutable, leading to political rent-seeking and undermining strategic coherence.
Study analyzes market co-movements in critical mineral investments using change point detection and cross-sectional analysis.
problem Market dynamics in critical mineral investments during significant global events.
method Combines change-point detection (PELT algorithm) with cross-sectional analysis on ESG-ranked ETFs.
result Investors herded during market downturns and shifted to anti-herding after positive news and geopolitical shocks.
Visual Human Activity Recognition (HAR) and data fusion with other sensors can help us at tracking the behavior and activity of underground miners with little obstruction. Existing models, such as Single Shot Detector (SSD), trained on the Common Objects in Context (COCO) dataset is used in this paper to detect the cur…
Bitcoin draws the highest degree of attention among cryptocurrencies, while coin mining is one of the most important fashion of profiting in the Bitcoin ecosystem. This paper constructs fresh coin circulation networks by tracking the fresh coin transfer routes with transaction referencing in Bitcoin blockchain. This pa…
CICLAD efficiently mines frequent closed itemsets from data streams with minimal memory usage.
problem Mining frequent closed itemsets from data streams is resource-intensive.
method CICLAD is an intersection-based sliding-window FCI miner that optimizes memory usage while maintaining performance.
result CICLAD achieves significantly lower memory footprint compared to existing methods.
Study on wealth and trading in PoS blockchain.
problem Decentralization and trading incentives in PoS.
method Analytic and stochastic tools, optimal control theory, mean field model.
result Miners balance PoS mining and trading for optimal strategy.
Ethereum tackles bribery in blockchain transactions with new fee mechanism.
problem Bribing miners in Ethereum blockchain to manipulate transaction fees.
method Filtered transactions, constructed proxies for bribery level, analyzed effects on blockchain and financial markets.
result Bribing affects Ethereum and other blockchains, influencing cryptocurrency, transaction stats, and network adoption.
A new algorithm discovers causal factors between T2DM and bone mineral density.
problem Discovering causal factors between T2DM and bone mineral density from clinical data.
method Prior-Knowledge-driven local Causal structure Learning (PKCL) algorithm.
result PKCL achieves more reliable results without long-standing medical experiments.
oPoW proposes a new PoW algorithm to reduce mining costs and environmental impact.
problem Scalability issues, environmental concerns, and systemic risks in Bitcoin PoW.
method oPoW is a novel PoW algorithm that shifts mining costs from electricity to hardware (CAPEX).
result oPoW reduces mining costs and improves network scalability, decentralization, and issuance.
DisCoveR efficiently discovers declarative process models from event logs.
problem Mining declarative process models from event logs efficiently and accurately.
method DisCoveR precisely formalizes an algorithm, uses a bit vector implementation, and rigorously evaluates performance.
result DisCoveR outperforms other declarative miners in accuracy and runtime.
Quantum Proof-of-Work uses boson sampling to secure blockchain consensus.
problem Ensuring secure and efficient blockchain consensus.
method Proposes using quantum boson sampling as a Proof-of-Work scheme for blockchain.
result Demonstrates a robust and energy-efficient PoW scheme.
This paper introduces a simple and efficient density estimator that enables fast systematic search. To show its advantage over commonly used kernel density estimator, we apply it to outlying aspects mining. Outlying aspects mining discovers feature subsets (or subspaces) that describe how a query stand out from a given…
Paper predicts stock market values using machine learning.
problem Predicting stock market values for Tehran stock exchange groups.
method Used machine learning algorithms including Decision Tree, Bagging, Random Forest, Adaptive Boosting, Gradient Boosting, XGBoost, Artificial neural network, Recurrent Neural Network, and Long short-term memory (LSTM).
result LSTM shows highest accuracy among all algorithms tested.
This paper analyzes the profitability of selfish mining on blockchain, considering the risk of ruin.
problem The profitability of selfish mining on blockchain, considering the risk of ruin.
method Formulated a stochastic model and used tools from applied probability and analysis to determine expected profit.
result Explicit expressions for expected profit under different scenarios were derived, identifying conditions for selfish mining as a strategic advantage.
This paper studies the optimal extraction and taxation of nonrenewable natural resources. It is well known that the market values of the main strategic resources such as oil, natural gas, uranium, copper,..., etc, fluctuate randomly following global and seasonal macroeconomic parameters, these values are modeled using …
Extraction of missing attribute values is to find values describing an attribute of interest from a free text input. Most past related work on extraction of missing attribute values work with a closed world assumption with the possible set of values known beforehand, or use dictionaries of values and hand-crafted featu…
Simplified NFT games discussed with methods for extracting value.
problem Issues influencing NFT games' structure and stability.
method Three methods for extracting value from NFT games.
result Various design constraints and mutual beneficial games.
Paper quantifies MEV on L2 networks, finding significant amounts on Polygon.
problem Lack of research on quantifying MEV on Ethereum Layer 2 networks.
method Analysis of Polygon's MEV, focusing on arbitrage opportunities and liquidations.
result Substantial MEV ($213 million) on L2s, mostly from arbitrage opportunities.
TabSODA improves imputation of surveys with skips and ordinal data.
problem Handling structural skips and ordinal responses in survey data.
method TabSODA uses an Elucidated Diffusion Model with skip pattern detection and ordinal awareness.
result TabSODA reduces ordinal missing-at-random (MACE) by up to 23.7% and improves categorical accuracy by up to 9%.
This paper studies the problem of optimally extracting nonrenewable natural resource in light of various financial and economic restrictions and constraints. Taking into account the fact that the market values of the main natural resources i.e. oil, natural gas, copper,...,etc, fluctuate randomly following global and s…
Private equity deals predict public market returns with up to 70% accuracy.
problem Predicting public market behavior from private equity transactions.
method Logit model using detailed analysis of private equity diligence process.
result Model predicts public market returns with up to 70% accuracy in specific sectors.
FedAMD framework improves federated learning with partial client participation.
problem Data heterogeneity and inactive client updates in partial client participation.
method Anchor sampling divides clients into anchor and miner groups, using large and small batches respectively.
result FedAMD achieves faster convergence and improved model performance compared to state-of-the-art methods.
Feature learning forms the cornerstone for tackling challenging learning problems in domains such as speech, computer vision and natural language processing. In this paper, we consider a novel class of matrix and tensor-valued features, which can be pre-trained using unlabeled samples. We present efficient algorithms f…
A problem of paramount importance in both pure (Restricted Invertibility problem) and applied mathematics (Feature extraction) is the one of selecting a submatrix of a given matrix, such that this submatrix has its smallest singular value above a specified level. Such problems can be addressed using perturbation analys…
Feature learning forms the cornerstone for tackling challenging learning problems in domains such as speech, computer vision and natural language processing. In this paper, we consider a novel class of matrix and tensor-valued features, which can be pre-trained using unlabeled samples. We present efficient algorithms f…
Machine learning methods have found many applications in Raman spectroscopy, especially for the identification of chemical species. However, almost all of these methods require non-trivial preprocessing such as baseline correction and/or PCA as an essential step. Here we describe our unified solution for the identifica…
Supervised linear feature extraction can be achieved by fitting a reduced rank multivariate model. This paper studies rank penalized and rank constrained vector generalized linear models. From the perspective of thresholding rules, we build a framework for fitting singular value penalized models and use it for feature …
Machine learning (ML) models may be deemed confidential due to their sensitive training data, commercial value, or use in security applications. Increasingly often, confidential ML models are being deployed with publicly accessible query interfaces. ML-as-a-service ("predictive analytics") systems are an example: Some …
TXtract extracts structured knowledge from thousands of product categories.
problem Extracting structured knowledge from diverse product categories in e-commerce.
method TXtract uses a taxonomy-aware model with category conditional self-attention and multi-task learning.
result TXtract outperforms state-of-the-art approaches by up to 10% in F1 and 15% in coverage across all categories.
Blockchain MEV is unaffected by ordering changes.
problem Maximizing arbitrage opportunities on blockchain exchanges.
method Formalized MEV, proved invariance under certain conditions.
result Maximal extractable value is invariant under changes in ordering mechanism.
Method generates natural ECGs with 25 interpretable features.
problem Lack of labeled ECGs for supervised learning and automatic diagnostics.
method Variational autoencoder for ECG generation and feature extraction.
result Low Maximum Mean Discrepancy (0.00383) indicates good ECG generation quality.
Multivariate time series classification is a high value and well-known problem in machine learning community. Feature extraction is a main step in classification tasks. Traditional approaches employ hand-crafted features for classification while convolutional neural networks (CNN) are able to extract features automatic…
Charts are an excellent way to convey patterns and trends in data, but they do not facilitate further modeling of the data or close inspection of individual data points. We present a fully automated system for extracting the numerical values of data points from images of scatter plots. We use deep learning techniques t…
This paper presents a novel method for information interpretability in an MC-SEFRON classifier. To develop a method to extract knowledge stored in a trained classifier, first, the binary-class SEFRON classifier developed earlier is extended to handle multi-class problems. MC-SEFRON uses the population encoding scheme t…