Algorithmic trading systems on DEXs reject most candidate tokens, but the counterfactual outcome of rejected candidates is rarely measured.
problem Measuring the counterfactual outcome of rejected tokens in algorithmic trading systems on decentralized exchanges.
method Post-Rejection Follow-up Sampling (PRFS) introduces a methodology for collecting and analyzing the data needed to evaluate filter precision against actual market outcomes of rejected candidates.
result PRFS produces the data needed to evaluate filter precision against actual market outcomes of rejected candidates, not against synthetic backtest reconstructions.
The paper audits trading filters, finding a high save-to-miss ratio.
problem Improving the efficiency and accuracy of trading filters in decentralized exchanges.
method A precision audit of filter rules against real trading data, classifying rejection events.
result Conservative save-to-miss ratio of 3.7 : 1, with wider interpretation of 14.8 : 1.
Deep Bayesian models estimate causal effects for dynamic treatment regimes over long follow-up times.
problem Challenges in causal effect estimation for dynamic treatment regimes with long follow-up times.
method Combining outcome regression models with deep Bayesian models for high-dimensional features.
result Stable and accurate dynamic causal effect estimation from observational data, especially with long-term follow-up.
Study compares resampling methods for rare event prediction in longitudinal studies.
problem Predicting rare events in longitudinal follow-up studies.
method Comparison of resampling methods to improve standard regression models.
result Effect of sampling rate on model predictive performance.
Ensemble model predicts AD progression from CN status with high accuracy.
problem Early prediction of clinical progression from cognitively normal to mild cognitive impairment or Alzheimer's disease.
method Ensemble survival analysis combining penalized Cox regression, advanced survival models, and aggregation techniques.
result Ensemble model achieved peak C-index of 0.907 and integrated time-dependent AUC of 0.904, outperforming baseline models.
Analyzing electronic health records (EHR) poses significant challenges because often few samples are available describing a patient's health and, when available, their information content is highly diverse. The problem we consider is how to integrate sparsely sampled longitudinal data, missing measurements informative …
Develops new methods for risk-aware decision-making in medical bandits.
problem Risk-averse decision-making in medical contexts with limited data.
method Safe, anytime-valid concentration bounds, risk-aware contextual bandits, nonparametric algorithms.
result Improved decision-making algorithms for postoperative patient follow-up.
Noninvasive MR-guided focused ultrasound (MRgFUS) treatments are promising alternatives to the surgical removal of malignant tumors. A significant challenge is assessing the viability of treated tissue during and immediately after MRgFUS procedures. Current clinical assessment uses the nonperfused volume (NPV) biomarke…
We show that there are no tight nonholomorphic maps from irreducible domains into exceptional codomains, the only exception being the already known tight nonholomorphic maps from the Poincare disc. This follows up on previous work by the first author where this was shown for classical codomains.
Corrects errors in previous studies on metric measure spaces.
problem Identifies and corrects errors in previous research on metric measure spaces.
method Corrigendum to previous publications in Acta Math, JFA.
result Corrects errors in previous studies on metric measure spaces.
The present paper is a follow up of our paper \cite{nS}. We investigate here the maximization of higher order eigenvalues in a conformal class on a smooth compact boundaryless Riemannian surface. Contrary to the case of the first nontrivial eigenvalue as shown in \cite{nS}, bubbling phenomena appear.
This paper is a follow-up to our joint paper with I. Agol, P. Storm and K. Whyte "Finiteness of arithmetic hyperbolic reflection groups". The main purpose is to investigate the effective side of the method developed there and its possible application to the problem of classification of arithmetic hyperbolic reflection …
New method allows generating independent data matrices from summary statistics.
problem Generating independent data matrices from summary statistics like mean and covariance.
method Thinning a Wishart random matrix based on sample mean and covariance.
result It is possible to generate two independent data matrices from summary statistics.
Chronic Pulmonary Aspergillosis (CPA) is a complex lung disease caused by infection with Aspergillus. Computed tomography (CT) images are frequently requested in patients with suspected and established disease, but the radiological signs on CT are difficult to quantify making accurate follow-up challenging. We propose …
New FX option interpolations impact implied volatilities.
problem Different interpolations of FX option quotes lead to varying implied volatilities.
method Analysis of various exact interpolations of broker quotes.
result Different interpolations result in different implied volatilities.
Optimizes data labeling for causal effect estimation with missing outcomes.
problem Estimating causal effects with missing outcome data and budget constraints.
method Optimizes batch sampling probability to minimize variance of causal inference estimator.
result Achieves lower mean-squared error with fewer labeled data points.
The application of existing methods for constructing optimal dynamic treatment regimes is limited to cases where investigators are interested in optimizing a utility function over a fixed period of time (finite horizon). In this manuscript, we develop an inferential procedure based on temporal difference residuals for …
Study uses ML to predict non-participation in ELSA COVID-19 follow-up studies.
problem Predicting non-participation in follow-up surveys due to various factors.
method Used machine learning algorithms including KNN, RF, AdaBoost, logistic regression, NN, and SVC.
result Random Forest (RF) outperforms other models in balanced accuracy.
There is currently great interest in applying neural networks to prediction tasks in medicine. It is important for predictive models to be able to use survival data, where each patient has a known follow-up time and event/censoring indicator. This avoids information loss when training the model and enables generation o…
RED-2400 is a public benchmark of trading events from a Solana exchange, labeled by algorithmic rejection.
problem Analyzing algorithmically-rejected trading events for insights into market dynamics.
method Public dataset of 6,660 algorithmically-rejected trading events, linked to post-rejection price and liquidity trajectories.
result First window of a planned series of datasets extending the time horizon and enabling regime-stratified analysis.
Survey on machine learning from very few samples.
problem Learning and generalizing from very few samples.
method Comprehensive review of 300+ FSL papers.
result Meta learning based FSL approaches are emphasized.
In this paper we present an algorithm for rapid Bayesian analysis that combines the benefits of nested sampling and artificial neural networks. The blind accelerated multimodal Bayesian inference (BAMBI) algorithm implements the MultiNest package for nested sampling as well as the training of an artificial neural netwo…
The BBF, SABR, and rough SABR formulas provide nearly arbitrage-free implied vol approximations.
problem Arbitrage in implied volatility calculations.
method Analytical proofs for BBF, SABR, and rough SABR formulas under specific models.
result These formulas offer asymptotically arbitrage-free approximations of implied volatility.
The paper explores how deep generative models can improve performance on imbalanced datasets.
problem Imbalanced datasets lead to poor classifier performance and misleading evaluation metrics.
method The paper studies deep generative models for oversampling minority classes in imbalanced tabular data.
result Deep generative models can improve performance on imbalanced classification tasks, but require different under- and oversampling ratios.
New algorithm poLinUCB improves online learning in content recommendation platforms.
problem Improving efficiency in content recommendation platforms with post-serving context.
method Novel contextual bandit problem with post-serving contexts and a new algorithm, poLinUCB.
result Achieves tight regret under standard assumptions and significant benefit of utilizing post-serving contexts.
The aim of this text is to provide an elementary and self-contained exposition of Gromov's argument on topological overlap (the presentation is based on Gromov's work, as well as two follow-up papers of Matousek and Wagner, and of Dotterrer, Kaufman and Wagner). We also discuss a simple generalization in which the vert…
New method for causal inference in survival outcomes using RDD.
problem Censoring in time-to-event analyses.
method Nonparametric approach with doubly robust censoring corrections.
result Higher efficiency and robustness to misspecification.
Predicting an individual's risk of experiencing a future clinical outcome is a statistical task with important consequences for both practicing clinicians and public health experts. Modern observational databases such as electronic health records (EHRs) provide an alternative to the longitudinal cohort studies traditio…
New estimator for survival function with missing not at random censoring indicators.
problem Estimating survival function with missing not at random censoring indicators.
method Proposes a new estimator based on a conditional copula model for the missingness mechanism.
result Provides a new method for estimating conditional survival function with MNAR censoring indicators.
This is a follow up of our previous paper - Trybuła and Zawisza \cite{TryZaw}, where we considered a modification of a monotone mean-variance functional in continuous time in stochastic factor model. In this article we address the problem of optimizing the mentioned functional in a market with a stochastic interest rat…
Alternative proof for links not smoothly slice in S2imesS2.
problem Existence of links not smoothly slice in S2imesS2. method Alternative proof using Miyazaki and Yasuhara's result.
result Existence of links not smoothly slice in S2imesS2. A new method joins two arcs with a degree of freedom.
problem Joining two arcs with a precise point.
method Geometric approach using tangent vectors and points.
result A novel method to determine the join point.
Competition aims to develop sample-efficient reinforcement learning methods.
problem Limited access to samples in reinforcement learning.
method Develops algorithms leveraging human demonstrations in Minecraft.
result Fosters innovation in sample-efficient reinforcement learning.
A recent paper (arxiv.org:1810.00025) studied properties of a compactification of the moduli space of irreducible Hermitian-Yang-Mills connections on a hermitian bundle over a projective algebraic manifold. In this follow-up note, we show that the Yang-Mills flow at infinity on the space of semistable integrable connec…
We give a partial characterization of bordered Floer homology in terms of sutured Floer homology. The bordered algebra and modules are direct sums of certain sutured Floer complexes. The algebra multiplication and algebra action correspond to a new gluing map on SFH. It is defined algebraically, and is a special case o…
Explores tensor products in hyperdimensional computing.
problem Understanding tensor products in hyperdimensional computing.
method Generalized results from graph embeddings to vector symbolic architectures and hyperdimensional computing.
result Tensor product is the most general and expressive representation with errorless unbinding and detection.
Predicts the age of astronomical transients from real-time data.
problem Improving understanding of transients and their progenitor systems.
method Bayesian probabilistic recurrent neural network.
result Accurately predicts the age of transients with robust uncertainties.
Proposes a deep learning method for modeling dynamic individual-level latent trajectories with changing parameters.
problem Modeling longitudinal data with changing individual-level dynamics parameters.
method Combines deep learning for dimensionality reduction and differential equations for dynamic modeling, allowing different parameters for sub-periods.
result Successfully identifies dynamic parameters and predictors of resilience.
This is an expository paper which gives a proof of the Atiyah-Singer index theorem for Dirac operators, presenting the theorem as a computation of the K-homology of a point. This paper and its follow up ("K-homology and index theory II: Elliptic Operators") was written to clear up basic points about index theory that a…
In recent years, besides the medical treatment methods in medical field, Computer Aided Diagnosis (CAD) systems which can facilitate the decision making phase of the physician and can detect the disease at an early stage have started to be used frequently. The diagnosis of Idiopathic Pulmonary Fibrosis (IPF) disease by…
In this follow up work to [45, 33, 32, 46] we introduce and study a notion of geodesic stability restricted to rays with prescribed singularity types. A number of notions of interest fit into this framework, in particular algebraic- and transcendental K-polystability, equivariant K-polystability, and the geodesic K-pol…
Study on scalar curvature in wedge spaces with existence and obstruction results.
problem Existence and obstructions of scalar curvature in wedge spaces.
method Utilized established tools for wedge spaces including Yamabe, elliptic, and index theories.
result Provided existence and obstruction results for scalar curvature under suitable positivity assumptions.
Patients suffering from obstructive sleep apnea are mainly treated with continuous positive airway pressure (CPAP). Good compliance with this therapy is broadly accepted as more than 4h of CPAP average use nightly. Although it is a highly effective treatment, compliance with this therapy is problematic to achieve with …
CUBE explains models by balanced experiments and contrasts.
problem Post-hoc explanation of trained predictive models.
method Design-based framework using balanced low-high probes.
result Reveals dominant learned effect structure and clarifies query efficiency.
This paper aims to analyse adolescents' road accidents in Tuscany. The analysis is based on the Database Edit of Osservatorio di Epidemiologia della Toscana. Complexity and heterogeneity of Edit's data represet an interesting scope to apply Machine Learning methods. In particular, in this paper is proposed an analysis …
Imputation with missing-indicators improves machine learning performance, especially for categorical attributes.
problem Missing values in datasets can be lost through imputation, potentially reducing model performance.
method Combines missing-indicator approach with imputation strategies on real datasets.
result Missing-indicators generally increase classification performance, especially for categorical attributes.
Mammography is the most effective and available tool for breast cancer screening. However, the low positive predictive value of breast biopsy resulting from mammogram interpretation leads to approximately 70% unnecessary biopsies with benign outcomes. Data mining algorithms could be used to help physicians in their dec…
Unified framework for understanding GRPO as U-statistic.
problem Theoretical properties of GRPO remain less studied.
method Unified framework through classical U-statistics.
result GRPO is asymptotically equivalent to an oracle policy gradient algorithm.