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.
The study detects and classifies touch gestures with high accuracy.
Designs a Cellular Automata rule for forming touching loop patterns.
A successful grasp requires careful balancing of the contact forces. Deducing whether a particular grasp will be successful from indirect measurements, such as vision, is therefore quite challenging, and direct sensing of contacts through touch sensing provides an appealing avenue toward more successful and consistent …
Paper teaches robots to play piano with touch and learning.
We propose a fully automatic method for learning gestures on big touch devices in a potentially multi-user context. The goal is to learn general models capable of adapting to different gestures, user styles and hardware variations (e.g. device sizes, sampling frequencies and regularities). Based on deep neural networks…
User interfaces provide an interactive window between physical and virtual environments. A new concept in the field of human-computer interaction is a soft user interface; a compliant surface that facilitates touch interaction through deformation. Despite the potential of these interfaces, they currently lack a signal …
TACTO simulates high-resolution touch sensing for robotics.
Proposes a calibration method for various volatility models.
Lipschitz equivalence of self-similar sets is an important area in the study of fractal geometry. It is known that two dust-like self-similar sets with the same contraction ratios are always Lipschitz equivalent. However, when self-similar sets have touching structures the problem of Lipschitz equivalence becomes much …
RaSE screens variables via random subspaces, identifying joint effects.
The problem of learning a sparse model is conceptually interpreted as the process of identifying active features/samples and then optimizing the model over them. Recently introduced safe screening allows us to identify a part of non-active features/samples. So far, safe screening has been individually studied either fo…
New Bayesian optimization models for efficient material screening.
This paper treats the problem of screening for variables with high correlations in high dimensional data in which there can be many fewer samples than variables. We focus on threshold-based correlation screening methods for three related applications: screening for variables with large correlations within a single trea…
Statistical inference can be computationally prohibitive in ultrahigh-dimensional linear models. Correlation-based variable screening, in which one leverages marginal correlations for removal of irrelevant variables from the model prior to statistical inference, can be used to overcome this challenge. Prior works on co…
New screening rules improve lasso model fitting efficiency.
A new screening rule 'dynamic Sasvi' improves sparse optimization speed.
In the present paper, we introduce screen transversal lightlike submanifolds of metallic semi-Riemannian manifolds with its subclasses, namely screen transversal anti-invariant, radical screen transversal and isotropic screen transversal lightlike submanifolds, and give an example. We show that there do not exist co-is…
New AI platform screens portfolios for desirable firms and news.
A variable screening procedure via correlation learning was proposed Fan and Lv (2008) to reduce dimensionality in sparse ultra-high dimensional models. Even when the true model is linear, the marginal regression can be highly nonlinear. To address this issue, we further extend the correlation learning to marginal nonp…
Recent computational strategies based on screening tests have been proposed to accelerate algorithms addressing penalized sparse regression problems such as the Lasso. Such approaches build upon the idea that it is worth dedicating some small computational effort to locate inactive atoms and remove them from the dictio…
A new screening method for high-dimensional data reduces computational cost.
We study safe screening for metric learning. Distance metric learning can optimize a metric over a set of triplets, each one of which is defined by a pair of same class instances and an instance in a different class. However, the number of possible triplets is quite huge even for a small dataset. Our safe triplet scree…
In this paper we develop the notion of screen isoparametric hypersurface for null hypersurfaces of Robertson-Walker spacetimes. Using this formalism we derive Cartan identities for the screen principal curvatures of null screen hypersurfaces in Lorentzian space forms and provide a local characterization of such hypersu…
A new method for virtual drug screening detects top treatments.
A new screening rule improves lasso solving speed.
Recently, to solve large-scale lasso and group lasso problems, screening rules have been developed, the goal of which is to reduce the problem size by efficiently discarding zero coefficients using simple rules independently of the others. However, screening for overlapping group lasso remains an open challenge because…
Safe sample screening improves RSVM performance without sacrificing accuracy.
The main purpose of the present paper is to study the geometry of screen transversal lightlike submanifolds and radical screen transversal lightlike submanifolds and screen transversal anti-invariant lightlike submanifolds of Golden Semi-Riemannian manifolds. We investigate the geometry of distributions and obtain nece…
In data sets with many more features than observations, independent screening based on all univariate regression models leads to a computationally convenient variable selection method. Recent efforts have shown that in the case of generalized linear models, independent screening may suffice to capture all relevant feat…
Introduces screening rules for non-convex Lasso problems.
Efficiently allocate budgets for LLM-assisted virtual screening to reduce costs.
Deep learning predicts breast cancer with high accuracy from patient data.
New rules reduce SLOPE model fitting time by screening out irrelevant variables.
A new method reduces feature screening cost from to .
The paper studies half-lightlike submanifolds in Lorentzian manifolds with specific distributions.
To find efficient screening methods for high dimensional linear regression models, this paper studies the relationship between model fitting and screening performance. Under a sparsity assumption, we show that a subset that includes the true submodel always yields smaller residual sum of squares (i.e., has better model…
Deep CNN model improves breast cancer screening exam classification.
New types of null hypersurfaces found in Sasakian space-forms.
Variable screening is a fast dimension reduction technique for assisting high dimensional feature selection. As a preselection method, it selects a moderate size subset of candidate variables for further refining via feature selection to produce the final model. The performance of variable screening depends on both com…
This paper introduces LR-FFS for robust feature screening in federated learning under label shift.
The paper introduces a new screening method for faster L1 regularization.
Model shows screening for infectious disease is hard but Thompson sampling works well.
A new method reduces feature size in CRFs for faster training.
After the surface theory of Möbius geometry, this study concerns a pair of conformally immersed surfaces in -sphere. Two new invariants and associated with them are introduced as well as the notion of touch and co-touch. This approach is helpful in research about transforms of certain surface classes. As an …
The paper explores null hypersurfaces in Lorentzian manifolds using geometric immersions.
Study Einstein warped-product manifolds with specific curvature conditions.
New models compare mammograms to improve cancer diagnosis.
Ghost points affect stability in finite difference schemes for diffusion equations.