arXiv research

Paper uses VAEs to detect radar targets in complex noise.

SVDD and Deep SVDD improve radar target detection in clutter.

Video prediction models combined with planning algorithms have shown promise in enabling robots to learn to perform many vision-based tasks through only self-supervision, reaching novel goals in cluttered scenes with unseen objects. However, due to the compounding uncertainty in long horizon video prediction and poor s…

Bayesian nonparametric models improve tracking in cluttered environments.

We have developed an efficient algorithm for the maximum likelihood joint tracking and association problem in a strong clutter for GMTI data. By using an iterative procedure of the dynamic logic process "from vague-to-crisp," the new tracker overcomes combinatorial complexity of tracking in highly-cluttered scenarios a…

Robust STAP with coprime arrays reduces clutter using sparse modeling.

PiVoT improves real-time multi-object detection and tracking in clutter.

The Long Short-Term Memory (LSTM) neural network based data association algorithm named as DeepDA for multi-target tracking in clutters is proposed to deal with the NP-hard combinatorial optimization problem in this paper. Different from the classical data association methods involving complex models and accurate prior…

Analytical method approximates ELBO gradient in clutter problem.

Improved RL for grasping in cluttered scenes using state representation learning.

Contrast enhanced ultrasound is a radiation-free imaging modality which uses encapsulated gas microbubbles for improved visualization of the vascular bed deep within the tissue. It has recently been used to enable imaging with unprecedented subwavelength spatial resolution by relying on super-resolution techniques. A t…

Novel unsupervised MIG detectors improve signal detection in cluttered environments.

CVAE detects weak complex signals in maritime radar, improving detection over classical methods.

3D filament plots visualize curves in datasets, avoiding visual clutter.

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

Characterizes measures preserving compound mixed renewal process properties.

A network removes irrelevant structures from chest radiographs for better analysis.

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…

This work considers an estimation task in compressive sensing, where the goal is to estimate an unknown signal from compressive measurements that are corrupted by additive pre-measurement noise (interference, or clutter) as well as post-measurement noise, in the specific setting where some (perhaps limited) prior knowl…

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…

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…

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…

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

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

STMT predicts compounds in unknown areas with trend reflection.

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

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

In this paper, we implement multi-label neural networks with optimal thresholding to identify gas species among a multi gas mixture in a cluttered environment. Using infrared absorption spectroscopy and tested on synthesized spectral datasets, our approach outperforms conventional binary relevance - partial least squar…

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 …

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

Novel radar waveform design for autonomous vehicles.

A new method solves complex financial problems using deep learning.

With the rapid development of high-throughput technologies, parallel acquisition of large-scale drug-informatics data provides huge opportunities to improve pharmaceutical research and development. One significant application is the purpose prediction of small molecule compounds, aiming to specify therapeutic propertie…

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.

Object tracking is an ubiquitous problem that appears in many applications such as remote sensing, audio processing, computer vision, human-machine interfaces, human-robot interaction, etc. Although thoroughly investigated in computer vision, tracking a time-varying number of persons remains a challenging open problem.…

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.

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

The study addresses overlooked data-generating processes in time-series asset pricing.

Hybrid approach combines transformer and Bayesian filtering for robust multiple particle tracking.

With the advent of deep generative models in computational chemistry, in silico anticancer drug design has undergone an unprecedented transformation. While state-of-the-art deep learning approaches have shown potential in generating compounds with desired chemical properties, they disregard the genetic profile and prop…

Compound examines decentralized lending users and their short loan durations.