Early detection of high fall risk is an essential component of fall prevention in older adults.
Wearable sensors can provide valuable insight into daily-life activities; biomechanical …

The rapid adoption and the diversification of cloud computing technology exacerbate the
importance of a sound experimental methodology for this domain. This work investigates …

Abstract We introduce Clifford Group Equivariant Neural Networks: a novel approach for
constructing $\mathrm {O}(n) $-and $\mathrm {E}(n) $-equivariant models. We identify and …

We present the Siamese Continuous Bag of Words (Siamese CBOW) model, a neural
network for efficient estimation of high-quality sentence embeddings. Averaging the …

The AI community is increasingly putting its attention towards combining symbolic and
neural approaches, as it is often argued that the strengths and weaknesses of these …

We study the problem of combining neural networks with symbolic reasoning. Recently
introduced frameworks for Probabilistic Neurosymbolic Learning (PNL), such as …

Sampling-based inference techniques are central to modern cosmological data analysis;
these methods, however, scale poorly with dimensionality and typically require approximate …

Increased peripheral performance is causing strain on the memory subsystem of modern
processors. For example, available DRAM throughput can no longer sustain the traffic of a …

Fast-advancing mobile communication technologies have increased the scale of the Internet
of Things (IoT) dramatically. However, this poses a tough challenge to the robustness of IoT …

Auto-scalers for clouds promise stable service quality at low costs when facing changing
workload intensity. The major public cloud providers provide trigger-based auto-scalers …