Anonymized traders' wallet addresses reveal predictive trading information.
problem Adverse selection and return predictability in public trading environments.
method Reconstructed full-depth limit order book from 17.1 billion messages and 14.3 million orders.
result Wallets' activity correlates with price movements and improves return prediction.
Public trading wallets reveal price information not captured by anonymous data.
problem Informed traders' anonymity in public exchanges.
method Reconstructed full-depth limit order book from 17.1 billion messages.
result Wallets' aggressive orders predict returns, with a 13.2% gain over anonymous benchmarks.
Study identifies 1,012 persistent wallet cohorts on Solana pump.fun, showing coordinated buying behavior.
problem Understanding coordinated buying behavior on Solana pump.fun.
method Two-stage detection pipeline: first-buyer-window extraction followed by persistent-cohort surfacing via graph co-occurrence.
result 1,012 persistent wallet cohorts identified, showing systematic co-buying across multiple launches.
Paper introduces OCRR Score for quantifying DeFi wallet credit risk.
problem Inability to assess credit risk in decentralized finance.
method Probabilistic measure based on historical and predictive on-chain activity.
result Dynamic adjustment of LTV and LT based on wallet risk profile.
The paper assesses VASPs' solvency using multiple data sources.
problem Insolvency risk in VASPs without systematic auditing.
method Cross-referencing cryptoasset wallets, balance sheets, and supervisory data.
result Inconsistent data between DLT transactions and balance sheets for some VASPs.
Digital money could reduce germ spread during coronavirus.
problem Spreading of germs via paper money during coronavirus.
method Policy recommendations for mobile wallets, digital currencies, and data protection.
result Adopting digital money can help reduce germ spread.
ZAPs rewards users based on activity, making them less susceptible to bots and sybil attacks.
problem Incentive programs in DeFi are vulnerable to bots and sybil operations.
method ZAPs combines percentile normalization, two-layer weighting, and anomaly detection.
result ZAPs reduces adversarial reward capture by 30-90 percent.
Agent-to-agent finance aims to manage payments and trust for AI agents.
problem Managing financial interactions between autonomous AI agents.
method Develops agent-to-agent finance concept and explores blockchain solutions.
result Agent-to-agent finance can address coordination frictions in financial markets.
Paper evaluates deadline-ILS on insider trading contracts, finding it distinguishes signals from noise.
problem Deadlines in insider trading contracts and information leakage detection.
method Empirical evaluation using FFIC dataset, hazard-rate estimation, cross-market wallet analysis.
result Deadline-ILS distinguishes signal from proxy artefact, with a significant shift in magnitude.
Paper introduces SCI to distinguish market signals from coordination.
problem Unclear signals in prediction markets.
method Formalizes SCI, introduces weighted and time-varying extensions.
result Discriminates between market signals and coordination.
AnChain.AI detects NFT wash trading with 0.14% of transactions flagged.
problem NFT market manipulation through wash trading.
method Algorithm flags transactions within 30 days of repurchase.
result 0.14% of NFT transactions are involved in wash trading.
Study reveals structure of Bitcoin's crypto flow network.
problem Understanding crypto flows among Bitcoin users.
method Blockchain data, user identification, network construction, bow-tie structure, Hodge decomposition, non-negative matrix factorization.
result Users are located in upstream, downstream, and core of the crypto flow network.
We explore inverse and quanto inverse crypto options, their pricing, and applications.
problem Market incompleteness in crypto options trading.
method Comparison of direct and inverse options, and introduction of currency-protected 'quanto' options.
result Pricing and hedging characteristics of inverse and quanto inverse options in a Black-Scholes framework.
Three methods detect informed trading on prediction markets, each focusing on different aspects.
problem Detecting informed trading in decentralized prediction markets.
method Composite screen, event-level sign-randomization test, and Information Leakage Score (ILS) framework.
result Different methods detect informed trading on prediction markets, each focusing on different aspects.
Inspection-L detects illicit cryptocurrency transactions using GNNs and self-supervised learning.
problem Detect illicit cryptocurrency transactions for anti-money laundering.
method Graph Neural Network (GNN) framework based on self-supervised Deep Graph Infomax (DGI) and Graph Isomorphism Network (GIN) with supervised learning algorithms.
result Inspection-L outperforms state-of-the-art methods in key classification metrics.
Framework scores DeFi users based on liquidity and trading behavior.
problem Distinguishing between liquidity provision and active trading in DeFi.
method Rule-based decomposition, deep residual neural network, pool-level context.
result Deep residual neural network improves user scoring and risk assessment.
Study reveals dynamic causal relationships between Ethereum transaction fees and economic subsystems.
problem Historical gas fee volatility caused economic disequilibria and stakeholder challenges.
method Time-varying Granger causality analysis using data on active wallets and transaction volume.
result Dynamic bidirectional causal relationships between transaction fees and economic subsystems across Ethereum.
Study of Polymarket's prediction market microstructure using tick-level order book data.
problem Understanding the microstructure of decentralized prediction markets.
method Analysis of a continuous tick-level order book feed and on-chain trade records.
result Trade direction inferred from Polymarket's public order-book feed disagrees with on-chain data in ~59% of cases.
Hop Sampling improves GNNs in non-stationary environments by preventing overfitting.
problem Non-stationary environments cause concept drift, making GNNs overfit to training graphs.
method Randomly selects the number of propagation steps in GNNs to prevent overfitting.
result Improves GNNs' prediction accuracy by 7.97% and 16.93% in LINE Coupon recommender systems.
Deep learning generates addresses for under-addressed cities.
problem Lack of street addresses in developing countries.
method Deep learning applied to satellite images to generate addresses.
result Algorithm generates addresses for neighborhoods and regions.
Paper proposes a deep learning model for understanding e-commerce addresses.
problem Challenges in parsing shipping addresses with no fixed format.
method Combines NLP techniques with pre-processing steps for addresses, uses RoBERTa for vector representations.
result RoBERTa model achieves 90% accuracy in sub-region classification for North and South Indian cities.
Paper proposes new features to improve Bitcoin address classification.
problem Identifying criminal activities in Bitcoin transactions.
method New features based on transaction history summarized by block height.
result Improved performance of Bitcoin address classification significantly.
Survey of deep learning methods for recommender systems challenges.
problem Cold start and candidate generation challenges in recommender systems.
method Taxonomy of deep learning techniques addressing cold start and candidate generation problems.
result Taxonomy of deep learning techniques to address recommender systems challenges.
Kernel Mean Matching enhances GANs for content-addressable generation.
problem Creating models that can generate images consistent with specified examples.
method Kernel Mean Matching applied to GANs.
result The method generates images consistent with specified input sets while maintaining original model quality.
ETHGamDet detects crypto gambling contracts and addresses.
problem Identifying crypto gambling contracts and addresses.
method Multi-modal retrieval methods, LightGBM model with memory components.
result ETHGamDet achieves high F1-scores in address and contract classification.
Chaos and nonlinear economic dynamics are addressed for a quantum coupled map lattice model of an artificial economy, with quantized supply and demand equilibrium conditions. The measure theoretic properties and the patterns that emerge in both the economic business volume dynamics' diagrams as well as in the quantum m…
Classifies Bitcoin addresses based on their balance functions.
problem Understanding and identifying Bitcoin addresses used for illicit activities.
method Functional data analysis to extract features from balance functions.
result Functional principal components improve prediction accuracy.
Paper introduces a method to assess liquidity risk in meme tokens using entity-linked address analysis.
problem High market volatility and vulnerability to manipulation in meme tokens.
method Multi-dimensional approach integrating fund flow analysis, behavioral similarity, and anomalous transaction detection.
result Significant disparities between apparent and actual liquidity in meme token markets.
ARMIN improves memory efficiency and lightness in neural networks.
problem Complex memory addressing and inefficient memory information exploitation in previous MANNs.
method ARMIN uses hidden state for automatic memory addressing and a novel RNN cell for memory integration.
result ARMIN achieves lower computational overhead and similar performances compared to vanilla LSTM.
This paper tackles quantizing memory-augmented neural networks, improving their performance and energy efficiency.
problem Quantizing memory-augmented neural networks (MANNs) poses unique challenges not seen in other network types.
method Identified memory addressing as the main issue, proposed a robust quantization method for MANNs.
result Achieved a 22x computation-energy gain with 8-bit fixed-point and binary quantization.
1. Translated by Thomas E. Cecil, Department of Mathematics and Computer Science, College of the Holy Cross, Worcester, MA 01610, USA; E-mail address: cecil@mathcs.holycross.edu 2. Typed by Wenjiao Yan, School of Mathematical Sciences, Laboratory of Mathematics and Complex Systems, Beijing Normal University, Beijing 10…
Corrects errors in Hans' pseudocovering spaces paper.
problem Mathematical errors and citation issues in Hans' pseudocovering spaces paper.
method Identifies and corrects errors in Hans' previous work.
result Addresses mathematical and citation errors in Hans' pseudocovering spaces paper.
This work addresses fairness constraints for multiple subpopulations in machine learning models.
problem Fairness constraints for multiple subpopulations in machine learning models.
method Constraining the expected outcome of subpopulations in kernel regression and decision tree regression, specifically random forests and boosted trees.
result The proposed solution does not affect the computational or memory complexity of decision trees and can be easily integrated post training.
In this paper, we address the issue of quaternionic Toledo invariant to study the character variety of two dimensional complex hyperbolic uniform lattices into SU(n,2). We construct four distinct representations to prove that the character variety contains at least four distinct components. We also address the existe…
New federated conformal prediction method addresses label shift for uncertainty quantification.
problem Label shift in federated learning and its impact on uncertainty quantification.
method Quantile regression-based federated conformal prediction method with privacy constraints.
result Method provides valid coverage of prediction sets and differential privacy guarantees.
This study examines non-retail trading on Polymarket, revealing unique behavior patterns and structural limitations.
problem Lack of address-level quote-lifecycle data in Polymarket prediction markets.
method Empirical analysis of 13 million order-filled events using DBSCAN clustering on a six-feature fill-side vector.
result Non-retail behavior is uni-modal, contradicting previous archetypal hypotheses.
New method improves blockchain analysis by handling temporal changes and scalability.
problem Limited focus on evolving nature and scalability of blockchain transaction networks.
method Incremental approach with Metropolis-Hastings random walks.
result Comparable performance in node classification tasks with reduced computational overhead.
New method improves causal effect estimation by addressing imbalance in training data.
problem Imbalance between treatment and control groups in training data.
method Combines distributionally robust optimization and weight regularization.
result Consistent improvements over existing methods in experiments.
GRAND treats GNNs as PDE discretizations, addressing graph learning issues.
problem Graph learning issues like depth, oversmoothing, and bottlenecks.
method Models GNNs as a continuous diffusion process, treating them as PDE discretizations.
result Linear and nonlinear versions of GRAND achieve competitive results on graph benchmarks.
Corrects group discrimination in scores with minimal individual error.
problem Addressing group discrimination within a score function.
method Analytical solution for two populations, linear programming for n populations.
result An approximate solution with high precision can be computed.
Study addresses RTB model performance drops due to distribution shifts.
problem Distribution shifts between training and target environments in RTB markets.
method Applies Exponential Tilt Reweighting Alignment (ExTRA) algorithm to estimate and correct model weights.
result Demonstrates improved RTB model performance using ExTRA algorithm.
The study explores maximal symmetry in Ricci solitons on Lie groups.
problem Maximal symmetry in left-invariant Riemannian metrics and Ricci solitons.
method Analysis of left-invariant metrics and Ricci solitons on Lie groups, using tools from previous work on Einstein metrics.
result Expanding homogeneous Ricci solitons have maximal isometry algebras but not always maximal isometry groups.
Paper optimizes hyperspherical prototypes for better class separation.
problem Previous HPL approaches either lack principled optimisation or are limited to one latent dimension.
method Develops a principled optimisation procedure and uses linear block codes to create well-separated prototypes in various dimensions.
result Optimal prototype placement is characterized with achievable and converse bounds, showing near-optimality.
FedFaiREE addresses fairness in decentralized learning with small samples.
problem Ensuring fairness in decentralized federated learning with limited data.
method FedFaiREE is a post-processing algorithm for distribution-free fair learning in decentralized settings with small samples.
result FedFaiREE provides theoretical guarantees for both fairness and accuracy in decentralized environments.
IntelligentPooling improves treatment decisions in mHealth.
problem Optimizing treatment decisions in mobile health with limited data and non-stationary responses.
method Generalized Thompson-Sampling bandit algorithms to IntelligentPooling, addressing differential response, limited data, and non-stationary responses.
result IntelligentPooling achieves 26% lower regret compared to state-of-the-art methods.
A new approach improves numerical tabular data imputation by addressing diffusion models' limitations.
problem Inaccurate and difficult training in numerical tabular data imputation.
method Kernelized Negative Entropy-regularized Wasserstein gradient flow Imputation (KnewImp) based on Wasserstein gradient flow (WGF) framework.
result KnewImp significantly outperforms existing methods in numerical tabular data imputation.
DoubleGen addresses bias in generative modeling of counterfactuals.
problem Bias in generative models for counterfactual outcomes.
method Doubly robust framework that modifies generative modeling training objectives to mitigate confounding and misspecification biases.
result Successfully addresses confounding bias even if only one auxiliary model is correct.
Researchers improve NCE by addressing its flat loss landscape issues.
problem NCE's poor performance due to an ill-behaved loss landscape.
method Introduced eNCE with an exponential loss and normalized gradient descent.
result Proven that landscape issues arise from inappropriate noise distribution.