A Frequency-Domain Path-Following Method for Discrete Data-Based Paths

This paper presents a novel frequency-domain approach for path following problem, specifically designed to handle paths described by discrete data. The proposed algorithm utilizes the fast Fourier Transform (FFT) to process the discrete path data, enabling the construction of a non-singular guiding vector field. This vector field serves as a reference direction for the controlled robot, offering the ability to adapt to different levels of precision. Additionally, the frequency-domain nature of the vector field allows for the reduction of computational complexity and effective noise suppression. The efficacy of the proposed approach is demonstrated through a numerical simulation, and theoretical analysis provides an upper bound for the ultimate mean-square path-following error

Non-singular Cooperative Guiding Vector Field Under a Homotopy Equivalence Transformation

The present article advances the concept of a non-singular cooperative guiding vector field under a homotopy equivalence transformation. Firstly, the derivation of a guiding vector field, based on a non-singular vector field, is elaborated to navigate a transformed path from another frame. The existence of such vector fields is also deliberated herein. Subsequently, a coordination vector field derived from the guiding vector field is presented, incorporating an in-depth analysis concerning the impact of the vector field parameters. Lastly, the practical implementation of this novel vector field is demonstrated by its applications to 2-D and 3-D cooperative moving path following issues, establishing its efficacy.Comment: 12 pages, 12 figures, submitting to TAC at presen

The Neutralization Epitope of Lactate Dehydrogenase-Elevating Virus Is Located on the Short Ectodomain of the Primary Envelope Glycoprotein

AbstractWe have measured by indirect ELISA the binding of neutralizing and non-neutralizing anti-lactate dehydrogenase-elevating virus (LDV) polyclonal and monoclonal antibodies to synthetic peptides representing unmodified hydrophilic segments of LDV proteins. Using this method a single neutralization epitope has been shown to be located in the very short (about 30 amino acid long) ectodomain of the primary envelope glycoprotein, VP-3P, encoded by ORF 5. Although the neutralization epitopes of neuropathogenic and non-neuropathogenic LDVs differ slightly in amino acid sequences, the neutralizing antibodies bind strongly to the epitopes of both groups of viruses. However, the neutralization epitopes of neuropathogenic and non-neuropathogenic LDVs are associated with different numbers of polylactosaminoglycan chains (1 and 3, respectively) which may affect the binding of neutralizing antibodies to the virions of these LDVs. The ELISA using synthetic peptides containing the neutralization epitope provides a novel, rapid, sensitive, and inexpensive method for quantitating LDV neutralizing antibodies in infected mice

Postpolypectomy Bleeding Prevention and More Complete Precancerous Colon Polyp Removal With Endoscopic Mucosal Stripping (EMS)

Background and Aims: Postpolypectomy bleeding and incomplete polyp removal are important complication and quality concerns of colonoscopy for colon cancer prevention. We investigated if endoscopic mucosal stripping (EMS) as a technical modification of traditional cold snare polypectomy to avoid submucosal injury during removal of non-pedunculated colon polyps could prevent postpolypectomy bleeding and facilitate complete polyp removal.Methods: This is an Internal Review Board exemption-granted retrospective analysis of 5,142 colonoscopies with snare polypectomy performed by one of the authors (ZJC) at Minnesota Gastroenterology ambulatory endoscopy centers during a 12-year period divided into pre-EMS era (2005–2012, n = 2,973) and EMS era (2013–2016, n = 2169) with systemic adoption of EMS starting 2013. Change in postpolypectomy bleeding rate before and after EMS adoption and EMS polypectomy completeness were evaluated.Results: Zero postpolypectomy bleeding case was found during EMS era (rate 0%) compared with 10 bleeding cases during pre-EMS era (rate 0.336%). This difference was statistically significant (P = 0.0055) and remained so after excluding 2 bleeding cases of pedunculated polyps (P = 0.012). All bleeding cases involved hot snare polypectomy. Histological examination of the involved polyps showed substantial submucosal vascular damage in contrast to a remarkable paucity of submucosa in comparable advanced polyps removed using EMS. Both biopsy and follow-up colonoscopy examination of the polypectomy sites confirmed that EMS more completely removed non-pedunculated advanced polyps.Conclusions: EMS polypectomy was effective in preventing postpolypectomy bleeding and facilitated complete polyp removal

Compression with Bayesian Implicit Neural Representations

Many common types of data can be represented as functions that map coordinates to signal values, such as pixel locations to RGB values in the case of an image. Based on this view, data can be compressed by overfitting a compact neural network to its functional representation and then encoding the network weights. However, most current solutions for this are inefficient, as quantization to low-bit precision substantially degrades the reconstruction quality. To address this issue, we propose overfitting variational Bayesian neural networks to the data and compressing an approximate posterior weight sample using relative entropy coding instead of quantizing and entropy coding it. This strategy enables direct optimization of the rate-distortion performance by minimizing the $\beta$-ELBO, and target different rate-distortion trade-offs for a given network architecture by adjusting $\beta$. Moreover, we introduce an iterative algorithm for learning prior weight distributions and employ a progressive refinement process for the variational posterior that significantly enhances performance. Experiments show that our method achieves strong performance on image and audio compression while retaining simplicity.Comment: Preprin

Learning to Detect Noisy Labels Using Model-Based Features

Label noise is ubiquitous in various machine learning scenarios such as self-labeling with model predictions and erroneous data annotation. Many existing approaches are based on heuristics such as sample losses, which might not be flexible enough to achieve optimal solutions. Meta learning based methods address this issue by learning a data selection function, but can be hard to optimize. In light of these pros and cons, we propose Selection-Enhanced Noisy label Training (SENT) that does not rely on meta learning while having the flexibility of being data-driven. SENT transfers the noise distribution to a clean set and trains a model to distinguish noisy labels from clean ones using model-based features. Empirically, on a wide range of tasks including text classification and speech recognition, SENT improves performance over strong baselines under the settings of self-training and label corruption