Blog
On estimates for the discrete eigenvalues of two-dimensional quantum waveguides
In diesem Papier, we give upper estimates for the number and sum of eigenvalues below the bottom of the essential spectrum counting multiplicities of quantum waveguides in two dimensions. We consider both straight and curved waveguides of constant width, and the estimates...
Towards One-Stage End-to-End Table Structure Recognition with Parallel Regression for Diverse Scenarios
Table structure recognition aims to parse tables in unstructured data into machine-understandable formats. Recent methods address this problem through a two-stage process or optimized one-stage approaches. Jedoch, these methods either require multiple networks to be serially trained and perform more time-consuming sequential...
ESDiff: Encoding Strategy-inspired Diffusion Model with Few-shot Learning for Color Image Inpainting
Image inpainting is a technique used to restore missing or damaged regions of an image. Traditional methods primarily utilize information from adjacent pixels for reconstructing missing areas, while they struggle to preserve complex details and structures. Simultaneously, models based on deep learning...
Cooperative Task Offloading through Asynchronous Deep Reinforcement Learning in Mobile Edge Computing for Future Networks
Future networks (including 6G) are poised to accelerate the realisation of Internet of Everything. Jedoch, it will result in a high demand for computing resources to support new services. Mobile Edge Computing (MEC) is a promising solution, enabling to offload computation-intensive tasks...
TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction
Non-independent and identically distributed (Non-IID) data across edge clients have long posed significant challenges to federated learning (FL) training in edge computing environments. Prior works have proposed various methods to mitigate this statistical heterogeneity. While these works can achieve good theoretical performance,...
IRA: Adaptive Interest-aware Representation and Alignment for Personalized Multi-interest Retrieval
Online community platforms require dynamic personalized retrieval and recommendation that can continuously adapt to evolving user interests and new documents. Jedoch, optimizing models to handle such changes in real-time remains a major challenge in large-scale industrial settings. Um dies anzugehen, we propose...
Permeation and thermal desorption model of hydrogen in steel: a sensitivity analysis
This work presents a fully physical model of the hydrogen diffusion and trapping kinetics in metals, integrating permeation and thermal desorption within a unified framework. Based on the McNabb and Foster approach, it requires only binding energy and number density of trap...
Learning Isometric Embeddings of Road Networks using Multidimensional Scaling
The lack of generalization in learning-based autonomous driving applications is shown by the narrow range of road scenarios that vehicles can currently cover. A generalizable approach should capture many distinct road structures and topologies, as well as consider traffic participants, and dynamic...
Dynamic Membership for Regular Tree Languages
We study the dynamic membership problem for regular tree languages under relabeling updates: we fix an alphabet ${\Sigma}$ and a regular tree language $L$ over ${\Sigma}$ (expressed, z.B., as a tree automaton), we are given a tree $T$ with labels in ${\Sigma}$,...
Long-time asymptotics of the Sawada-Kotera equation on the line
The Sawada-Kotera (SK) equation is an integrable system characterized by a third-order Lax operator and is related to the modified Sawada-Kotera (mSK) equation through a Miura transformation. This work formulates the Riemann-Hilbert problem associated with the SK and mSK equations by using...