A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper investigates a new type of elastica formed during beam rupture.
Simulating dynamic rupture propagation is challenging due to the uncertainties involved in the underlying physics of fault slip, stress conditions, and frictional properties of the fault. A trial and error approach is often used to determine the unknown parameters describing rupture, but running many simulations usuall…
Bayesian neural networks improve earthquake rupture prediction and uncertainty estimation.
Beam search is widely used for approximate decoding in structured prediction problems. Models often use a beam at test time but ignore its existence at train time, and therefore do not explicitly learn how to use the beam. We develop an unifying meta-algorithm for learning beam search policies using imitation learning.…
Adversarial attacks reduce deep learning beam selection performance in mmWave 5G.
Federated LIDAR aided beam selection reduces mmWave beam search overhead.
Improved neural keyphrase generation by beam search with reward functions.
Beam search improves feature selection for better model performance.
Paper tackles robust prediction of nuclear reactor materials under scarce data.
Beam search improves UQ in LLMs by reducing duplicates and variance.
Optimizes tree models for better beam search performance.
Physics-informed model predicts beam stiffness and monitors structural health.
Achilles Tendon Rupture (ATR) is one of the typical soft tissue injuries. Rehabilitation after such a musculoskeletal injury remains a prolonged process with a very variable outcome. Accurately predicting rehabilitation outcome is crucial for treatment decision support. However, it is challenging to train an automatic …
Generalizes beam models to include curvature and torsion.
Attention-based encoder decoder network uses a left-to-right beam search algorithm in the inference step. The current beam search expands hypotheses and traverses the expanded hypotheses at the next time step. This traversal is implemented using a for-loop program in general, and it leads to speed down of the recogniti…
Gaussian beams describe the amplitude and phase of rays and are widely used to model acoustic propagation. This paper describes four new results in the theory of Gaussian beams. (1) A new version of the Červený equations for the amplitude and phase of Gaussian beams is developed by applying the equivalence of Hamilton-…
We propose a method to build quantum memristors in quantum photonic platforms. We firstly design an effective beam splitter, which is tunable in real-time, by means of a Mach-Zehnder-type array with two equal 50:50 beam splitters and a tunable retarder, which allows us to control its reflectivity. Then, we show that th…
A novel beam training scheme optimizes multi-hop THz communications with up to 75% performance gain.
The article recovers tensor fields from partial data using weighted divergent ray transforms.
We introduce a new sampling method for large language models that balances diversity and parallelism.
New algorithm optimizes beam and rate allocation in mmWave systems for multiple users.
Restricted Boltzmann Machines (RBMs) are a class of generative neural network that are typically trained to maximize a log-likelihood objective function. We argue that likelihood-based training strategies may fail because the objective does not sufficiently penalize models that place a high probability in regions where…
Method reconstructs aneurysm growth history from patient parameters using physics-informed autoencoder.
This paper generalizes Michell Truss to higher dimensions using geometric measure theory.
Paper improves EEG-based speech recognition using CTC and beam search.
Physics-constrained neural nets solve EM fields of charged particle beams.
In this thesis, we consider the suitability of using the charged cold fluid model in the description of ultra-relativistic beams. The method that we have used is the following. Firstly, the necessary notions of kinetic theory and differential geometry of second order differential equations are explained. Then an averag…
The well-known Gumbel-Max trick for sampling from a categorical distribution can be extended to sample elements without replacement. We show how to implicitly apply this 'Gumbel-Top-' trick on a factorized distribution over sequences, allowing to draw exact samples without replacement using a Stochastic Beam Sea…
Transmission imaging, as an important imaging technique widely used in astronomy, medical diagnosis, and biology science, has been shown in [49] quite different from reflection imaging used in our everyday life. Understanding the structures of images (the prior information) is important for designing, testing, and choo…
New method improves autofocus in CBCT scans by 93%.
If a variational problem comes with no boundary conditions prescribed beforehand, and yet these arise as a consequence of the variation process itself, we speak of a free boundary values variational problem. Such is, for instance, the problem of finding the shortest curve whose endpoints can slide along two prescribed …
In this work we simulate null geodesics for the Bonnor massive dipole metric by implementing a symbolic-numerical algorithm in Sage and Python. This program is also capable of visualizing in 3D, in principle, the geodesics for any given metric. Geodesics are launched from a common point, collectively forming a cone of …
Bardo Composer generates tabletop RPG music based on player speech.
Scanning Transmission Electron Microscopy (STEM) has become the main stay for materials characterization on atomic level, with applications ranging from visualization of localized and extended defects to mapping order parameter fields. In the last several years, attention was attracted by potential of STEM to explore b…
Neural text generation is a key tool in natural language applications, but it is well known there are major problems at its core. In particular, standard likelihood training and decoding leads to dull and repetitive outputs. While some post-hoc fixes have been proposed, in particular top- and nucleus sampling, they …
A new framework predicts hidden Markov model regimes online.
We study the calibration of several state of the art neural machine translation(NMT) systems built on attention-based encoder-decoder models. For structured outputs like in NMT, calibration is important not just for reliable confidence with predictions, but also for proper functioning of beam-search inference. We show …
Sharp stability estimate for tensor tomography in non-positive curvature.
Study timelike bounce in charged null dust collapse, identifying key surfaces.
Sequence-to-Sequence (seq2seq) modeling has rapidly become an important general-purpose NLP tool that has proven effective for many text-generation and sequence-labeling tasks. Seq2seq builds on deep neural language modeling and inherits its remarkable accuracy in estimating local, next-word distributions. In this work…
Modeling human language learning with multi-checkpoint machine translation.
OKRidge solves sparse ridge regression problems for nonlinear systems.
ASKAP observes a region of the Galactic plane, identifying 3963 radio sources.
Active learning improves neutron spectroscopy experiments by automating measurement selection.
This paper considers the problem of low-dimensional visualisation of very high dimensional information sources for the purpose of situation awareness in the maritime environment. In response to the requirement for human decision support aids to reduce information overload (and specifically, data amenable to inter-point…
A major challenge in X-ray computed tomography (CT) is reducing radiation dose while maintaining high quality of reconstructed images. To reduce the radiation dose, one can reduce the number of projection views (sparse-view CT); however, it becomes difficult to achieve high-quality image reconstruction as the number of…
New seq2seq model can copy entire spans, outperforming simpler models in editing tasks.
A neural program synthesis method with iterative fix operations.