Study predicts success of crypto-tokens on Pump.fun platform.
problem Identify factors affecting the success of new crypto-tokens.
method Build predictive models using bonding curve mechanism and structural/behavioral variables.
result Conditional variables significantly improve the predictive power of token success.
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.
Survival analysis of 832,941 Solana token launches shows a significant decline in graduation rate.
problem Analyzing the survival rate of Solana token launches and identifying factors affecting graduation.
method Survival analysis using Kaplan-Meier and Cox proportional-hazards models.
result The survival rate of Solana token launches has declined significantly, with a 3.18x decrease from previous rates.
We discuss fun problems, vaguely related to notions and theorems of a course in differential geometry. This paper can be regarded as a weekend "treasure chest" supplementing the course weekday lecture notes. The problems and solutions are not original, while their relation to the course might be so.
While pump-and-dump schemes have attracted the attention of cryptocurrency observers and regulators alike, this paper represents the first detailed empirical query of pump-and-dump activities in cryptocurrency markets. We present a case study of a recent pump-and-dump event, investigate 412 pump-and-dump activities org…
A new knot invariant is fast, strong, topologically meaningful, and fun.
problem Computing and understanding knot invariants efficiently and comprehensively.
method Developed a pair of polynomial knot invariants Θ=(Δ,θ) that are fast, strong, and topologically meaningful.
result Θ is a powerful knot invariant with separation power greater than other known invariants.
Twitter promotes cryptocurrency pump-and-dumps, affecting trading behavior and returns.
problem The influence of Twitter on cryptocurrency pump-and-dump events.
method Analysis of abnormal returns, trading volume, and tweet activity.
result Investors relying on Twitter information sell later, leading to significant losses.
Detects and traces masterminds behind cryptocurrency pump-and-dump schemes.
problem Identifying and tracing the entities organizing cryptocurrency manipulation.
method Collects real-time data from social networks and cryptocurrency markets, constructs temporal attributed graphs, and uses GNN to identify masterminds.
result Achieves higher F1 scores and precision than state-of-the-art fraud detection methods, detects 438 masterminds.
Paper detects pump and dump schemes in cryptocurrencies.
problem Cryptocurrency market manipulation by communities.
method Analyzed communities, reported case studies, introduced real-time detection approach.
result Real-time detection approach outperforms current methods.
Predicts cryptocurrency pump probability using sequence-based neural networks.
problem Detecting pump-and-dump schemes in cryptocurrency markets.
method Developed a sequence-based neural network (SNN) that encodes historical P&D events into sequences for prediction.
result SNN improves prediction accuracy by leveraging positional attention to extract useful information.
Study analyzes cryptocurrency pump-and-dump dynamics using minute-level data.
problem Identifying and quantifying insider trading in cryptocurrency markets.
method Algorithmic identification of insider volume spikes, conservative profit bounds calculation, social-media verification.
result Median returns above 100%, upper-quartile returns exceeding 2000% for insider profits.
Interest surrounding cryptocurrencies, digital or virtual currencies that are used as a medium for financial transactions, has grown tremendously in recent years. The anonymity surrounding these currencies makes investors particularly susceptible to fraud---such as "pump and dump" scams---where the goal is to artificia…
Detects crypto pump-and-dump schemes with a thresholding-based model.
problem Detecting genuine anomalies from minor trading fluctuations.
method Combining threshold-based criteria with EWMA and volatility measures.
result Balances high true-positive detection with minimal noise.
Neural network with data augmentation improves multi-stage pump prediction accuracy.
problem Predicting multi-stage pump external characteristics with high accuracy.
method Neural network model with data augmentation for multi-objective prediction.
result Neural network model with data augmentation outperforms other models in accuracy.
Novel framework identifies pump-specific deterioration rates using Bayesian hierarchical hazard modeling and causal discovery.
problem Challenges in asset management due to heterogeneous deterioration rates in pump equipment.
method Bayesian hierarchical hazard modeling with causal discovery, GPU-accelerated No-U-Turn Sampling (NUTS), and DirectLiNGAM.
result Identified striking heterogeneity in deterioration rates, with negative effects 400 times larger than positive effects.
Study improves detection of cryptocurrency pump-and-dump schemes.
problem Class imbalance in P&D detection due to rare events.
method Synthetic Minority Oversampling Technique (SMOTE) and ensemble learning models.
result XGBoost and LightGBM achieved high recall rates (94.87% and 93.59%) with strong F1-scores.
This paper presents a novel Inter Catchment Wastewater Transfer (ICWT) method for mitigating sewer overflow. The ICWT aims at balancing the spatial mismatch of sewer flow and treatment capacity of Wastewater Treatment Plant (WWTP), through collaborative operation of sewer system facilities. Using a hydraulic model, the…
New AI method improves anomaly detection across different IIoT sensors.
problem Poor performance of anomaly detection models when applied to different machines.
method Robust AI method using pre-processing and multiple models on different pumps.
result Models perform well across different environments and types of pumps.
We present a novel technique to solve the problem of managing optimally a pumped hydroelectric storage system. This technique relies on representing the system as a stochastic optimal control problem with state constraints, these latter corresponding to the finite volume of the reservoirs. Following the recent level-se…
We point out a simple equities trading strategy that allows a sufficiently large, market-neutral, quantitative hedge fund to achieve outsized returns while simultaneously contributing significantly to increasing global wealth inequality. Overnight and intraday return distributions in major equity indices in the United …
The paper models cryptocurrency market bubbles using agent-based models.
problem Understanding speculative bubbles in cryptocurrency markets.
method Agent-based models and ODE models to estimate return rates and market values.
result Formulated formula for total system risk.
Model financial markets with social media influences using hierarchical networks.
problem Understanding social media's impact on financial markets.
method Agent-based model with hierarchical influence network.
result Model accurately simulates real-world financial market behaviors.
Abstract perspective on quadratic programming for optimal portfolio allocation.
problem Optimal allocation problems in long portfolio theory.
method Using maximum principles and distinguished boundaries in reproducing kernel Hilbert spaces.
result Support of an optimal distribution lies in a variety intersecting a distinguished boundary.
One of the best ways for developers to test and improve their skills in a fun and challenging way are programming challenges, offered by a plethora of websites. For the inexperienced ones, some of the problems might appear too challenging, requiring some suggestions to implement a solution. On the other hand, tagging p…
In this note, we concentrate on the sub-Laplace on the nilpotent Lie group of rank two, which is the infinitesimal generator of the diffusion generated by n Brownian motions and their 2n(n−1) Lévy area processes, which is the simple extension of the sub-Laplace on the Heisenberg group H. In order …
New model explains price dynamics of Bitcoin with psychological factors.
problem Understanding price variations in cryptocurrency markets with psychological factors.
method Extended agent-based model with heterogeneous psychological parameters.
result Model shows diverse dynamics based on psychological correlation.
Deep RL improves blood glucose control for T1D patients.
problem Managing blood glucose levels for people with type 1 diabetes.
method Developed deep reinforcement learning techniques for automated blood glucose control.
result Deep RL approach outperforms baseline control algorithms, reducing glycemic risk and hypoglycemia.
AR app enhances young children's understanding of Wudhu.
problem Teaching Wudhu to young children is challenging.
method Needs analysis, system design, implementation, testing (Black Box Testing).
result AR technology improves children's learning interest and understanding of Wudhu.
Paper verifies RNNs using automata learning and model checking.
problem Verifying the correctness of RNNs is challenging.
method Learn a deterministic finite automaton from RNN, use model checking for verification.
result Can discover and generalize counterexamples to faulty flows.
Factory machinery is prone to failure or breakdown, resulting in significant expenses for companies. Hence, there is a rising interest in machine monitoring using different sensors including microphones. In the scientific community, the emergence of public datasets has led to advancements in acoustic detection and clas…
Unified PCA framework on flag manifolds for robust data analysis.
problem Outliers and manifold data in PCA.
method Generalization of PCA to flag manifolds, optimization problems, and tangent-PCA integration.
result Novel robust and dual geodesic PCA variations.
Modeling dynamic groundwater markets with price formation and trading strategies.
problem Understanding competitive effects in environmental markets with groundwater banking.
method Stochastic models and game theory with machine learning algorithms.
result Sub-game perfect Nash equilibria characterized by groundwater price processes.
Detects anomalies in stock and crypto data with high accuracy.
problem Identifying rare or unexpected events in time series data.
method Uses signature or randomized signature methods for anomaly detection.
result Achieves F1 scores up to 88% in identifying pump and dump attempts.
The study analyzes how deep neural networks treat instances with regular and irregular patterns.
problem Understanding how deep neural networks handle both common and rare patterns in data.
method Characterizes instances using a consistency score based on training data sets of varying sizes.
result The consistency score identifies out-of-distribution and mislabeled examples, distinguishing them from strongly regular examples.
We propose a model for equity trading in a population of agents where each agent acts to achieve his or her target stock-to-bond ratio, and, as a feedback mechanism, follows a market adaptive strategy. In this model only a fraction of agents participates in buying and selling stock during a trading period, while the re…
Study chaotic dynamics in social stratification models leading to thermalization and turbulence.
problem Understanding social stratification dynamics through chaotic nonlinear systems.
method Modeling social network links with oscillators and energies, studying Hamiltonian evolution and nonlinear interactions.
result Chaotic dynamics leads to dynamical thermalization and Kolmogorov-Zakharov turbulence, with implications for wealth inequality.
Paper presents first model extraction attack against DRL models.
problem Stealing DRL models from interaction with environment.
method Imitation learning approach to recover DRL models from predicted actions.
result Successfully recovers DRL models with high fidelity and accuracy.
The paper introduces Robust Correlated Equilibrium for games with time-varying costs and proposes an algorithm to achieve it.
problem Games with time-varying costs and disturbances.
method Proposes Robust Correlated Equilibrium and a decentralized algorithm to learn optimal strategies.
result The algorithm converges to the Robust Correlated Equilibrium, showing no regret for each controller.
We model human decision-making behaviors in a risk-taking task using inverse reinforcement learning (IRL) for the purposes of understanding real human decision making under risk. To the best of our knowledge, this is the first work applying IRL to reveal the implicit reward function in human risk-taking decision making…
Study shows how to motivate AI agents like humans through learning from demonstrations.
problem Transfer human-like intrinsic motivation to AI agents in RL.
method Inverse RL approach to learn an exploration bonus from demonstrations.
result AI agents can learn and use complex exploration behaviors from demonstrations.
Theoretical understanding of deep learning is one of the most important tasks facing the statistics and machine learning communities. While deep neural networks (DNNs) originated as engineering methods and models of biological networks in neuroscience and psychology, they have quickly become a centerpiece of the machin…
This paper optimizes kernel and acquisition functions for high-dimensional Bayesian Optimization.
problem Bayesian Optimization struggles with high-dimensional problems due to computational inefficiency.
method The paper leverages the additionality of the objective function to map kernel and acquisition functions in lower-dimensional subspaces, improving efficiency.
result Efficient optimization of acquisition function in high-dimensional problems.
Transformers can be hard to interpret due to complex optima.
problem Interpretability methods fail to capture the full complexity of Transformer models.
method Theoretical analysis and synthetic experiments on Dyck language learning.
result Attention patterns can be nearly randomized while preserving network functionality.
Modern treatments for Type 1 diabetes (T1D) use devices known as artificial pancreata (APs), which combine an insulin pump with a continuous glucose monitor (CGM) operating in a closed-loop manner to control blood glucose levels. In practice, poor performance of APs (frequent hyper- or hypoglycemic events) is common en…
Flashot visualizes Flash Loan attacks in DeFi systems.
problem Lack of standard tools to study Flash Loan attacks in DeFi.
method Proposes Flashot, a prototype to transparently illustrate asset flows.
result Demonstrates the effectiveness of Flashot in studying Flash Loan attacks.
Hydropower reduces system electricity price and volatility, especially at extreme levels.
problem Impact of hydropower on system electricity price and volatility.
method Robust statistical analysis using multiple linear regression and quantile regression.
result Hydropower reduces system electricity price and volatility, especially at extreme levels.
Study reveals widespread manipulation of meme coins, leading to significant economic losses.
problem Widespread manipulation of meme coins leading to economic losses.
method Cross-chain analysis of 34,988 tokens across Ethereum, BNB Smart Chain, Solana, and Base.
result 82.8% of high-return tokens show evidence of artificial growth strategies.
HyPV-LEAD detects cryptocurrency anomalies proactively, improving financial security.
problem Cryptocurrency anomalies like mixing, fraud, and pump-and-dump operations are hard to detect due to class imbalance and temporal volatility.
method HyPV-LEAD integrates lead time into anomaly detection through window-horizon modeling, Peak-Valley sampling, and hyperbolic embedding.
result HyPV-LEAD achieves a PR-AUC of 0.9624 on Bitcoin transaction data, significantly outperforming state-of-the-art methods.