arXiv research

LASER compresses recursive model activations by exploiting their low-dimensional structure.

This paper improves Gaussian process predictions by integrating prior knowledge.

Many mobile robots rely on 2D laser scanners for localization, mapping, and navigation. However, those sensors are unable to correctly provide distance to obstacles such as glass panels and tables whose actual occupancy is invisible at the height the sensor is measuring. In this work, instead of estimating the distance…

Study pentagon growth with laser-cut models.

This work uses neural density estimation to analyze laser-induced breakdown spectroscopy data, enabling accurate predictions and uncertainty quantification.

In microsurgery, lasers have emerged as precise tools for bone ablation. A challenge is automatic control of laser bone ablation with 4D optical coherence tomography (OCT). OCT as high resolution imaging modality provides volumetric images of tissue and foresees information of bone position and orientation (pose) as we…

Ultra-short laser pulses with femtosecond to attosecond pulse duration are the shortest systematic events humans can create. Characterization (amplitude and phase) of these pulses is a key ingredient in ultrafast science, e.g., exploring chemical reactions and electronic phase transitions. Here, we propose and demonstr…

A RL approach optimizes metal AM process parameters for consistent melt pool depth.

INNs improve acceptance rates in electron spectra analysis.

The paper analyzes the performance of delay-based reservoir computing using eigenvalue analysis.

Convolutional neural network localizes OD and fovea in UWFoV-SLO images.

Paper uses CNN to predict process parameters from molten pool data in WLAM.

Manifold methods improve amino acid classification in LIBS spectra.

In this contribution, we present a novel approach for segmenting laser radar (lidar) imagery into geometric time-height cloud locations with a fully convolutional network (FCN). We describe a semi-supervised learning method to train the FCN by: pre-training the classification layers of the FCN with image-level annotati…

The paper extends IPC framework to stationary physical systems and validates it with a photonic system.

Unsupervised learning classifies transient noise in gravitational wave detectors.

Measures DNA quality degradation effects.

Modeling coating degradation with fewer data points.

Advanced 3D metrology technologies such as Coordinate Measuring Machine (CMM) and laser 3D scanners have facilitated the collection of massive point cloud data, beneficial for process monitoring, control and optimization. However, due to their high dimensionality and structure complexity, modeling and analysis of point…

Additive Manufacturing (AM) is a manufacturing paradigm that builds three-dimensional objects from a computer-aided design model by successively adding material layer by layer. AM has become very popular in the past decade due to its utility for fast prototyping such as 3D printing as well as manufacturing functional p…

Paper uses deep learning to model systems with degrading behavior.

Sparse matrix decomposition identifies key design variables for ICF experiments.

Statistical test detects model degradations in optimized language models.

BNCR-GAN improves GANs to generate clean images from degraded inputs.

Framework optimizes battery storage for markets by separating long-term degradation from short-term market dynamics.

Clever sampling methods can be used to improve the handling of big data and increase its usefulness. The subject of this study is remote sensing, specifically airborne laser scanning point clouds representing different classes of ground cover. The aim is to derive a supervised learning model for the classification usin…

Paper develops a hybrid DNN approach for RUL prediction with adaptive drift.

A method for inferring ground-truth signals from degraded sensor data.

Study investigates how errors in reinsurance parameters degrade optimal solutions.

Paper uses neural networks to predict NOx emissions from gas turbines.

The study focuses on the experiment of using three different smartphones to collect acceleration data from vibration for the road roughness detection. The Android operating system is used in the application. The study takes place on asphaltic pavement of the expressway system of Thailand, with 9 km distance. The run ve…

The paper starts with a brief review of present understanding of income distributions; especially with regard to recent work in the field of econophysics that draws parallels between income, wealth and energy distributions. Examples of alternative energy distributions found in physical systems are discussed, and how th…

This research detects and identifies human-made objects in 3D point clouds using novel methods.

Paper predicts bearing degradation stages for pharmaceutical industry maintenance.

Tying knots and linking microscopic loops of polymers, macromolecules, or defect lines in complex materials is a challenging task for material scientists. We demonstrate the knotting of microscopic topological defect lines in chiral nematic liquid crystal colloids into knots and links of arbitrary complexity by using l…

Paper proposes a new regularization method to prevent model degradation under distribution shifts.

In this paper, benefiting from the strong ability of deep neural network in estimating non-linear functions, we propose a discriminative embedding function to be used as a feature extractor for clustering tasks. The trained embedding function transfers knowledge from the domain of a labeled set of morphologically-disti…

Analyzes layer-wise quantization effects in neural networks.

The paper proposes a method to detect relevant model degradations without over-alerting.

This paper introduces a new Urban Point Cloud Dataset for Automatic Segmentation and Classification acquired by Mobile Laser Scanning (MLS). We describe how the dataset is obtained from acquisition to post-processing and labeling. This dataset can be used to learn classification algorithm, however, given that a great a…

Deep learning predicts RNA degradation from crowdsourced data.

Proposes a federated learning approach for RUL prediction from nonparametric degradation and failure signals.

Paper presents a probabilistic diagnostic model for identifying and treating supervised learning degradation issues.

Study optimizes HTL-free PSCs with MWCNTs, improving efficiency and stability.

CRC improves multivariate forecasting accuracy without risking performance degradation.

This research improves asset life prediction by integrating deep learning with mixture distributions.

Current Flash X-ray single-particle diffraction Imaging (FXI) experiments, which operate on modern X-ray Free Electron Lasers (XFELs), can record millions of interpretable diffraction patterns from individual biomolecules per day. Due to the stochastic nature of the XFELs, those patterns will to a varying degree includ…

Refines deep generative models to improve data density precision.