Related papers: VieM v1.00 -- Vienna Mapping and Sparse Quadratic …

Sparse support vector machine (SVM) is a popular classification technique that can simultaneously learn a small set of the most interpretable features and identify the support vectors. It has achieved great successes in many real-world…

In this paper we introduce a software package, the Very Long Baseline Interferometry Network SIMulator (VNSIM), which provides an integrated platform assisting radio astronomers to design the Very Long Baseline Interferometry (VLBI)…

This is a user guide for the first version of our developed Maple library, named Singularity. The first version here is designed for the qualitative study of local real zeros of scalar smooth maps. This library will be extended for symbolic…

Embeddings are powerful tools for transforming complex and unstructured data into numeric formats suitable for computational analysis tasks. In this work, we use multiple embeddings for similarity calculations to be applied in bibliometrics…

The past decades have witnessed the rapid development of image and video coding techniques in the era of big data. However, the signal fidelity-driven coding pipeline design limits the capability of the existing image/video coding…

In recent years, State Space Models (SSMs) with efficient hardware-aware designs, known as the Mamba deep learning models, have made significant progress in modeling long sequences such as language understanding. Therefore, building…

The visual appearance of a product significantly influences purchase decisions on e-commerce websites. We propose a novel framework VASG (Visually Aware Skip-Gram) for learning user and product representations in a common latent space using…

The effectiveness of Voting Advice Applications (VAA) is often compromised by the length of their questionnaires. To address user fatigue and incomplete responses, some applications (such as the Swiss Smartvote) offer a condensed version of…

Designing unimodular waveforms with a desired beampattern, spectral occupancy and orthogonality level is of vital importance in the next generation Multiple-Input Multiple-Output (MIMO) radar systems. Motivated by this fact, in this paper,…

Street view imagery (SVI), largely captured via outfitted fleets or mounted dashcams in consumer vehicles is a rapidly growing source of geospatial data used in urban sensing and development. These datasets are often collected…

This specification defines a protocol for retrieving image data from a variety of astronomical image repositories through a uniform interface. The interface is meant to be reasonably simple to implement by service providers. A query…

Robust tooling and publicly available pre-trained models have helped drive recent advances in mechanistic interpretability for language models. However, similar progress in vision mechanistic interpretability has been hindered by the lack…

Test beam data reconstruction is a task that requires a large amount of flexibility due to the heterogeneous data acquisition environments found in these experiments. Often, detectors with different readout schemes such as triggered,…

Learning interpretable and disentangled representations of data is a key topic in machine learning research. Variational Autoencoder (VAE) is a scalable method for learning directed latent variable models of complex data. It employs a clear…

Bird's-eye-view (BEV) map layout estimation requires an accurate and full understanding of the semantics for the environmental elements around the ego car to make the results coherent and realistic. Due to the challenges posed by occlusion,…

Variable selection techniques have become increasingly popular amongst statisticians due to an increased number of regression and classification applications involving high-dimensional data where we expect some predictors to be unimportant.…

In order to make the task, description of planning domains and problems, more comprehensive for non-experts in planning, the visual representation has been used in planning domain modeling in recent years. However, current knowledge…

We show how a neural network can be trained to Wiener filter masked CMB maps to high accuracy. We propose an innovative neural network architecture, the WienerNet, which guarantees linearity in the data map. Our method does not require…

In the context of autonomous navigation, effectively conveying abstract navigational cues to agents in dynamic environments presents significant challenges, particularly when navigation information is derived from diverse modalities such as…

Support Vector Machines (SVMs) are a relatively new supervised classification technique to the land cover mapping community. They have their roots in Statistical Learning Theory and have gained prominence because they are robust, accurate…