arXiv research

Novel framework identifies pump-specific deterioration rates using Bayesian hierarchical hazard modeling and causal discovery.

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…

Twitter promotes cryptocurrency pump-and-dumps, affecting trading behavior and returns.

Detects and traces masterminds behind cryptocurrency pump-and-dump schemes.

Paper detects pump and dump schemes in cryptocurrencies.

Predicts cryptocurrency pump probability using sequence-based neural networks.

Study predicts success of crypto-tokens on Pump.fun platform.

Study analyzes cryptocurrency pump-and-dump dynamics using minute-level data.

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.

Study improves detection of cryptocurrency pump-and-dump schemes.

A multi-objective prediction method of multi-stage pump method based on neural network with data augmentation is proposed. In order to study the highly nonlinear relationship between key design variables and centrifugal pump external characteristic values (head and power), the neural network model (NN) is built in comp…

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.

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…

Study identifies 1,012 persistent wallet cohorts on Solana pump.fun, showing coordinated buying behavior.

The paper models cryptocurrency market bubbles using agent-based models.

Model financial markets with social media influences using hierarchical networks.

Survival analysis of 832,941 Solana token launches shows a significant decline in graduation rate.

New model explains price dynamics of Bitcoin with psychological factors.

Deep RL improves blood glucose control for T1D patients.

Paper verifies RNNs using automata learning and model checking.

Deep RL predicts equipment maintenance from sensor data.

Humans are accustomed to environments that contain both regularities and exceptions. For example, at most gas stations, one pays prior to pumping, but the occasional rural station does not accept payment in advance. Likewise, deep neural networks can generalize across instances that share common patterns or structures,…

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…

Predicting unscheduled breakdowns of plasma etching equipment can reduce maintenance costs and production losses in the semiconductor industry. However, plasma etching is a complex procedure and it is hard to capture all relevant equipment properties and behaviors in a single physical model. Machine learning offers an …

Modeling dynamic groundwater markets with price formation and trading strategies.

Detects anomalies in stock and crypto data with high accuracy.

We classify real hypersurfaces in CP^2and CH^2 equipped with pseudo-parallel structure Jacobi operator.

Given a complex manifold $M$ equipped with a holomorphic action of a connected complex Lie group $G$, and a holomorphic principal $H$--bundle $E_H$ over $X$ equipped with a $G$--connection $h$, we investigate the connections on the principal $H$--bundle $E_H$ that are (strongly) adapted to $h$. Examples are provided by…

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.

We show that the Teichmüller space of a surface without boundary and with punctures, equipped with Thurston's metric is the limit (in an appropriate sense) of Teichmüller spaces of surfaces with boundary, equipped with their arc metrics, when the boundary lengths tend to zero. We use this to obtain a result on the tran…

The present paper deals with the Killing correspondence between some Finsler spaces. We consider a Finsler space equipped with a $β$-change of metric and study the Killing correspondence between the original Finsler space and the Finsler space equipped with $β$-change of metric. We obtain necessary and sufficient condi…

Deep learning improves oilfield equipment maintenance and reduces downtime.

The paper introduces Robust Correlated Equilibrium for games with time-varying costs and proposes an algorithm to achieve it.

A hypercomplex manifold is a manifold equipped with three complex structures I, J, K satisfying the quaternionic relations. Let M be a 4-dimensional compact smooth manifold equipped with a hypercomplex structure, and E be a vector bundle on M. We show that the moduli space of anti-self-dual connections on E is also hyp…

We prove that the Teichmüller space of surfaces with given boundary lengths equipped with the arc metric (resp. the Teichmüller metric) is almost isometric to the Teichmüller space of punctured surfaces equipped with the Thurston metric (resp. the Teichmüller metric).

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…

New methods improve tool-to-tool matching in semiconductor manufacturing.

We study three dimensional real hypersurfaces in CP^2 and CH^2 equipped with $xi$-parallel structure Jacobi operator. We prove that they are Hopf hypersurfaces and if additional $α\neq0$, we classify them.

In this paper, we generalize the geometry of the product pseudo-Riemannian manifold equipped with the product Poisson structure (\cite{Nas2}) to the geometry of a warped product of pseudo-Riemannian manifolds equipped with a warped Poisson structure. We construct three bivector fields on a product manifold and show tha…

Remaining Useful Life (RUL) of an equipment or one of its components is defined as the time left until the equipment or component reaches its end of useful life. Accurate RUL estimation is exceptionally beneficial to Predictive Maintenance, and Prognostics and Health Management (PHM). Data driven approaches which lever…

Study examines Lie algebroids with homological sections, generalizing Q-manifolds and Lie superalgebras.

This paper optimizes kernel and acquisition functions for high-dimensional Bayesian Optimization.

Transformers can be hard to interpret due to complex optima.

We define and make an initial study of (even) Riemannian supermanifolds equipped with a homological vector field that is also a Killing vector field. We refer to such supermanifolds as Riemannian Q-manifolds. We show that such Q-manifolds are unimodular, i.e., come equipped with a Q-invariant Berezin volume.

Graph neural networks improve equipment health monitoring from multisensor data.