To scale quantum computers to useful levels, we must build networks of quantum computational nodes that can share entanglement for use in distributed forms of quantum algorithms. In one proposed architecture, node-to-node entanglement is created when...
3D point cloud-based place recognition is highly demanded by autonomous driving in GPS-challenged environments and serves as an essential component (i.e. loop-closure detection) in lidar-based SLAM systems. This paper proposes a novel approach, named...
Between 1995 and 2009, electron temperature (Te) measurements of more than 15000 plasmas produced in the Joint European Torus (JET) have been carefully reviewed using the two main diagnostics available over this time period: Michelson interferometer...
Many large scale problems in computational fluid dynamics such as uncertainty quantification, Bayesian inversion, data assimilation and PDE constrained optimization are considered very challenging computationally as they require a large number of exp...
It is one of the world's most ethnically diverse and multicultural nations, the product of large-scale immigration. Canada's long and complex relationship with the United States has had a significant …
Regularization of quantum field theories introduces a mass scale which breaks axial rotational and scaling invariances. We demonstrate from first principles that axial torsion and torsion trace modes have non-transverse vacuum polarization tensors,...
Social media and other platforms rely on automated detection of abusive content to help combat disinformation, harassment, and abuse. One common approach is to check user content for similarity against a server-side database of problematic items. How...
Primordial nucleosynthesis is a success story of the standard big bang (SBB) cosmology. We explore nucleosynthesis in possible models very different from SBB in which the cosmological scale factor increases linearly with time right through the peri...
1. SBB Ae 6/6 no. 11429, named after the city of Altdorf, in a vibrant red color scheme. 2. Prussian P8 with a small goods train. Characteristic for this class are the unequeal distances between the ...
Accelerating research on Large Multimodal Models (LMMs) in non-English languages is crucial for enhancing user experiences across broader populations. In this paper, we introduce JMMMU (Japanese MMMU), the first large-scale Japanese benchmark designe...
We present STORM, a spatio-temporal reconstruction model designed for reconstructing dynamic outdoor scenes from sparse observations. Existing dynamic reconstruction methods often rely on per-scene optimization, dense observations across space and ti...
Understanding historical forest dynamics, specifically changes in forest biomass and carbon stocks, has become critical for assessing current forest climate benefits and projecting future benefits under various policy, regulatory, and stewardship sce...
1. consisting of or forming a large mass; bulky and heavy: massive columns. 2. imposingly large or prominent: a massive forehead. 3. large in scale, amount, or degree: a massive dose. 4. great in …
In this paper, we use our recently developed theory for the backward-in-time (BIT) relative dispersion of inertial particles in turbulence (Bragg \emph{et al.}, Phys. Fluids 28, 013305, 2016) to develop the theoretical model by Pan \& Padoan (J. Flui...
Previous studies have shown that at the small-scales of stably stratified turbulence, the scale-dependent buoyancy flux reverses sign, such that there is a conversion of turbulent potential energy (TPE) back into turbulent kinetic energy (TKE) at the...
An increasing number of data science and machine learning problems rely on computation with tensors, which better capture the multi-way relationships and interactions of data than matrices. When tapping into this critical advantage, a key challenge i...
MoE facilitates the development of large models by making the computational complexity of the model no longer scale linearly with increasing parameters. The learning sparse gating network selects a set of experts for each token to be processed; howev...
Low-rank matrix estimation is a canonical problem that finds numerous applications in signal processing, machine learning and imaging science. A popular approach in practice is to factorize the matrix into two compact low-rank factors, and then optim...
Many problems in data science can be treated as estimating a low-rank matrix from highly incomplete, sometimes even corrupted, observations. One popular approach is to resort to matrix factorization, where the low-rank matrix factors are optimized vi...
in miniature, of a reduced size; on a small scale: a terrarium resembling a jungle in miniature.