Towards deep-learning solutions for classification of automated snow height measurements (CleanSnow v1.0.2)

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Presents corrections to the paper, (Corrections to "Multi-Label Generalized Zero Shot Chest X-Ray Classification By Combining Image-Text Information With Feature Disentanglement"). ...

Accurate human trajectory prediction is one of the most crucial tasks for autonomous driving, ensuring its safety. Yet, existing models often fail to fully leverage the visual cues that humans subconsciously communicate when navigating the space. In this w ...

We prove local (in space and time) well-posedness for a mildly regularised version of the stochastic quantisation of the Yukawa(2) Euclidean field theory with a self-interacting boson. Our regularised dynamic is still singular but avoids non-local divergen ...

Training high-quality deep learning models is a challenging task due to computational and technical requirements. A growing number of individuals, institutions, and companies increasingly rely on pre-trained, third-party models made available in public rep ...

Many technologically important material properties are underpinned by disorder and short-range structural correlations; therefore, elucidating structure-property relationships in functional materials requires understanding both the average and the local st ...

We analyze and implement the kernel ridge regression (KR) method developed in Filipovic et al. (Stripping the discount curve-a robust machine learning approach. Swiss Finance Institute Research Paper No. 22-24. SSRN. https://ssrn.com/abstract=4058150, 2022 ...

Growing demand for multi-material Laser Powder Bed Fusion (LPBF) faces process control and quality monitoring challenges, particularly in ensuring precise material composition. This study explores optical and acoustic emission signals during LPBF processes ...

We present a study of the semileptonic decay D-0 -> pi(-)pi(0)e(+)nu(e) using an e(+)e(-) annihilation data sample of 7.93 fb(-1) collected at the center-of-mass energy of 3.773 GeV with the BESIII detector. The branching fraction of D-0 -> rho(770)(-) e(+ ...

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a communitywide challenge using the largest availab ...

High-frequency (HF) signals are ubiquitous in the industrial world and are of great use for monitoring of industrial assets. Most deep-learning tools are designed for inputs of fixed and/or very limited size and many successful applications of deep learnin ...

Manually segmenting multiple sclerosis (MS) cortical lesions (CLs) is extremely time consuming, and past studies have shown only moderate inter-rater reliability. To accelerate this task, we developed a deep-learning-based framework (CLAIMS: Cortical Lesio ...

Towards deep-learning solutions for classification of automated snow height measurements (CleanSnow v1.0.2)

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Remote control of chirality in hybrid perovskite materials

Multi-Label Generalized Zero Shot Chest X-Ray Classification By Combining Image-Text Information With Feature Disentanglement (vol 44, pg 31, 2025)

Social-Pose: Enhancing Trajectory Prediction with Human Body Pose

A Dynamical Yukawa2 Model

Do You Trust Your Model? Emerging Malware Threats in the Deep Learning Ecosystem

Unravelling the components of diffuse scattering using deep learning

Stripping the Swiss discount curve using kernel ridge regression

Qualify-as-you-go: sensor fusion of optical and acoustic signatures with contrastive deep learning for multi-material composition monitoring in laser powder bed fusion process

Study of the decay D0 → ρ(770)- e+νe

CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting

Fully learnable deep wavelet transform for unsupervised monitoring of high-frequency time series

Multiple sclerosis cortical lesion detection with deep learning at ultra-high-field MRI