arXiv research

Paper proposes a framework to predict therapeutic properties of compounds.

Characterizes measures preserving compound mixed renewal process properties.

Analyzes compound interest with constant payments and interest rate.

STMT predicts compounds in unknown areas with trend reflection.

The study improves compound selection in in silico screening by focusing on model's ability to predict desirable outcomes.

In this paper, we introduce a new model for the risk process based on general compound Hawkes process (GCHP) for the arrival of claims. We call it risk model based on general compound Hawkes process (RMGCHP). The Law of Large Numbers (LLN) and the Functional Central Limit Theorem (FCLT) are proved. We also study the ma…

Mol-CycleGAN generates optimized molecules with similar structure.

Develops an efficient method for compound option valuation.

ASLA learns atomic structures using neural networks and reinforcement learning.

BBRT improves molecular properties through iterative translation.

A new method solves complex financial problems using deep learning.

Generative model tailors anticancer drugs based on transcriptomic data.

We propose a novel computational strategy for de novo design of molecules with desired properties termed ReLeaSE (Reinforcement Learning for Structural Evolution). Based on deep and reinforcement learning approaches, ReLeaSE integrates two deep neural networks - generative and predictive - that are trained separately b…

Study of Hawkes processes in limit order books for price volatility analysis.

Clarifies model-based RL's theoretical issues and counterexamples for popular losses.

A major challenge in computational chemistry is the generation of novel molecular structures with desirable pharmacological and physiochemical properties. In this work, we investigate the potential use of autoencoder, a deep learning methodology, for de novo molecular design. Various generative autoencoders were used t…

Timely assessment of compound toxicity is one of the biggest challenges facing the pharmaceutical industry today. A significant proportion of compounds identified as potential leads are ultimately discarded due to the toxicity they induce. In this paper, we propose a novel machine learning approach for the prediction o…

Generative model learns to create molecules with multiple properties using interpretable substructures.

In this paper, we obtain a property of the expectation of the inverse of compound Wishart matrices which results from their orthogonal invariance. Using this property as well as results from random matrix theory (RMT), we derive the asymptotic effect of the noise induced by estimating the covariance matrix on computing…

Paper proposes a deep Gaussian process for predicting formation energy of inorganic compounds.

Paper extends stochastic dominance for compound binomial distributions.

Study estimates Medallion's compounded return before fees at 31.8%.

This chapter is an attempt to present a mathematical theory of compound fractional Poisson processes. The chapter begins with the characterization of a well-known Lévy process: The compound Poisson process. The semi-Markov extension of the compound Poisson process naturally leads to the compound fractional Poisson proc…

Researchers calculated EVaR for various distributions using Lambert function.

This review introduces graph kernels for chemoinformatics.

Paper introduces a new model for cyber insurance pricing.

Generates natural product-like compounds using GPT models.

High throughput screening of compounds (chemicals) is an essential part of drug discovery [7], involving thousands to millions of compounds, with the purpose of identifying candidate hits. Most statistical tools, including the industry standard B-score method, work on individual compound plates and do not exploit cross…

Semi-supervised learning improves QSAR model predictions for novel compounds.

Normalized compound random measures are flexible nonparametric priors for related distributions. We consider building general nonparametric regression models using normalized compound random measure mixture models. Posterior inference is made using a novel pseudo-marginal Metropolis-Hastings sampler for normalized comp…

Compound Finance optimizes risk metrics for V3 protocol using Chainrisk simulations.

New Riemannian geometry for Compound Gaussian distributions applied to efficient change detection.

In this paper we assume the insurance wealth process is driven by the compound Poisson process. The discounting factor is modelled as a geometric Brownian motion at first and then as an exponential function of an integrated Ornstein-Uhlenbeck process. The objective is to maximize the cumulated value of expected discoun…

ChemGrapher uses deep learning to automatically convert chemical compound images into accurate graphs.

Under the Basel II standards, the Operational Risk (OpRisk) advanced measurement approach is not prescriptive regarding the class of statistical model utilised to undertake capital estimation. It has however become well accepted to utlise a Loss Distributional Approach (LDA) paradigm to model the individual OpRisk loss…

This study deals with the problem of pricing compound options when the underlying asset follows a mixed fractional Brownian motion with jumps. An analytic formula for compound options is derived under the risk neutral measure. Then, these results are applied to value extendible options. Moreover, some special cases of …

Novel model predicts anticancer compound sensitivity with high accuracy and interpretability.

Ethereum's Pectra upgrade introduces 0x02 compounding validators, offering higher stake and potential APR uplifts.

The paper develops generalization bounds for deep compound Gaussian neural networks.

New MGCPP model for order flow in financial markets.

The understanding of the type of inhibitory interaction plays an important role in drug design. Therefore, researchers are interested to know whether a drug has competitive or non-competitive interaction to particular protein targets. Method: to analyze the interaction types we propose factorization method Macau which …

The paper introduces ESG valuation in option pricing using binomial trees.

Study predicts shear stress in compound channels using data mining and machine learning.

Graph convolutional network (GCN) is generalization of convolutional neural network (CNN) to work with arbitrarily structured graphs. A binary adjacency matrix is commonly used in training a GCN. Recently, the attention mechanism allows the network to learn a dynamic and adaptive aggregation of the neighborhood. We pro…

We propose an analytical approach to the computation of tail probabilities of compound distributions whose individual components have heavy tails. Our approach is based on the contour integration method, and gives rise to a representation of the tail probability of a compound distribution in the form of a rapidly conve…

Study shows convergence rates for BSDEs approximated by compound Poisson processes.

CRYSPNet predicts crystal structures from chemical compositions.

Bayesian learning improves reliability of molecular predictions for hit compound discovery.