Medusa detects significant modules in diverse biological data, improving gene-disease association predictions.
problem Ignoring semantic meanings in data modeling limits the value of diverse biological data.
method Medusa combines collective matrix factorization with submodular optimization to detect significant modules.
result Medusa outperforms methods ignoring semantic meanings in predicting gene-disease associations.
VGAE learns gene-disease associations from networks, predicting disease-genes.
problem Predicting gene-disease associations from disease-gene networks.
method Introducing VGAE, a variational graph auto-encoder for disease-gene prediction.
result VGAE and C-VGAE outperform baseline methods in disease-gene prediction.
Framework predicts logical queries on incomplete knowledge graphs.
problem Complex logical queries involving multiple unobserved edges and entities.
method Low-dimensional embeddings and learned geometric operations.
result Efficient predictions with linear time complexity in query variables.
dCMF learns shared latent representations from multiple matrices, improving predictive modeling.
problem Learning from multiple heterogeneous data sources, especially non-linear interactions.
method Develops a deep-learning based method (dCMF) for unsupervised learning of multiple shared representations.
result dCMF significantly outperforms previous CMF algorithms in integrating heterogeneous data.
Generative model designs drug combinations for improved efficacy and reduced side effects.
problem Designing effective drug combinations to overcome resistance and reduce side effects.
method Developed a deep generative model using HVGAE and a novel reward system.
result Network-principled drug combinations show reduced toxicity and potential for new strategies.
New method discovers nonlinear associations using copula entropy.
problem Linear association measures have theoretical limitations.
method Proposes a new method using copula entropy.
result Demonstrates more meaningful nonlinear associations.
A novel cross-modal auto-encoder associates different data types efficiently.
problem Cross-modal data association in heterogeneous datasets.
method Bayesian inference framework with variational auto-encoders and associators.
result Successfully associates visual and auditory data with minimal paired data.
Study explores associated groups of symmetric quandles and their properties.
problem Understanding the structure and relationships of symmetric quandles and their associated groups.
method Group-theoretic analysis and characterization of associated groups.
result Characterization and properties of associated groups of symmetric quandles.
When response variables are nominal and populations are cross-classified with respect to multiple polytomies, questions often arise about the degree of association of the responses with explanatory variables. When populations are known, we introduce a nominal association vector and matrix to evaluate the dependence of …
We study deformations of associative submanifolds Y3⊂M7 of a G2 manifold M7. We show that the deformation space can be perturbed to be smooth, and it can be made compact and zero dimensional by constraining it with an additional equation. This allows us to associate local invariants to associative subm…
This study addresses transitions in conically singular associative submanifolds and their desingularizations.
problem Counting closed associative submanifolds of G2-manifolds and understanding transitions arising from degenerations. method Analysis of moduli spaces, transversality results, and desingularization techniques for conically singular associative submanifolds.
result For generic co-closed G2-structures, there are no CS associative submanifolds with stability-index greater than 0 or 1. Study of associative submanifolds in Berger space SO(5)/SO(3).
problem Characterizing and classifying associative submanifolds in Berger space.
method Geometric correspondence with pseudo-holomorphic curves, analysis of special Gauss maps.
result Existence of infinitely many topological types of compact associative 3-folds.
Model for associative submanifolds in K3 fibrations.
problem Understanding singularity formation in associative submanifolds.
method Graphs in a 3-manifold with locally gradient flow lines.
result Produces analogues of known singularity formation phenomena.
This paper studies the associativity of gluing of trajectories in Morse theory. We show that the associativity of gluing follows from of the existence of compatible manifold with face structures on the compactified moduli spaces. Using our previous work, we obtain the associativity of gluing in certain cases. In partic…
It is well-known that in any codimension a simply connected Euclidean minimal surface has an associated one-parameter family of minimal isometric deformations. In this paper, we show that this is just a special case of the associated family to any simply connected elliptic surface for which all curvature ellipses of a …
A new method counts associative submanifolds and Seiberg-Witten monopoles.
problem Counting associative submanifolds and Seiberg-Witten monopoles in G2-manifolds.
method Floer homology groups generated by associative submanifolds and solutions of Seiberg-Witten equations.
result Construction of Floer homology groups associated with G2-manifolds.
Study constructs associative submanifolds in G2-manifolds from orbifolds.
problem Constructing associative submanifolds in G2-manifolds from orbifolds. method Using Joyce's generalised Kummer construction.
result The volume of associative submanifolds tends to zero as they approach orbifolds.
DEDACT breaks down feature importance into direct and associative components.
problem Lack of clear distinction between direct and associative feature importance.
method DEDACT framework to decompose direct and associative importance measures.
result Provides insight into sources of prediction-relevant information and feature pathways.
The paper studies prolongations of Lie algebras associated with pseudo H-type Lie algebras.
problem Investigating prolongations of Lie algebras associated with pseudo H-type Lie algebras. method Analyzing prolongations of associated fundamental graded Lie algebra and associated conformal pseudo-subriemannian fundamental graded Lie algebra.
result The prolongation of the associated conformal pseudo-subriemannian fundamental graded Lie algebra coincides with that of the associated fundamental graded Lie algebra under certain conditions.
Extracts biological context from biomedical texts to associate with events.
problem Identifying biological context and associating it with biochemical events in texts.
method Analyzed an annotated corpus and developed classifiers using syntactic, distance, and frequency features.
result Developed and evaluated classifiers for context-event association.
Method constructs rigid associative submanifolds in twisted G2-manifolds.
problem Constructing rigid associative submanifolds in twisted G2-manifolds.
method Introducing a gluing theorem for asymptotically cylindrical associative submanifolds in ACyl G2-manifolds.
result Yields many new topological types of rigid associative submanifolds.
Proofs and descriptions of totally geodesic submanifolds in symmetric spaces.
problem Classifying totally geodesic submanifolds in symmetric spaces.
method Independent proof and descriptions using algebraic and geometric properties.
result Natural descriptions and classifications of totally geodesic submanifolds.
DeepDA uses LSTM to track multiple targets in clutter.
problem NP-hard combinatorial optimization in multi-target tracking with clutter.
method LSTM-based deep learning for data association.
result Significant performance on association ratio, target ID switching, and time-consuming tracking.
The associator of a non-associative algebra is the curvature of the Hochschild quasi-complex. The relationship ``curvature-associator'' is investigated. Based on this generic example, we extend the geometric language of vector fields to a purely algebraic setting, similar to the context of Gerstenhaber algebras. We int…
Bayesian approach to data association using Gaussian processes.
problem Separating data from different generating processes.
method Fully Bayesian approach with Gaussian process priors for structure encoding and doubly stochastic variational inference.
result Efficient learning scheme for deep Gaussian process priors.
A local classification of the Hermitian manifolds with flat associated connection is given. Hermitian manifolds admitting locally a conformal metric with flat associated connection are characterized by a curvature identity. Locally conformal Kaehler manifolds as well as Hermitian surfaces with vanishing associated conf…
Framework uncovers symmetric and asymmetric species associations from data.
problem Retrieving bidirectional species associations from co-occurrence data.
method Machine learning framework modeling latent embeddings and joint generative model.
result Framework successfully recovers known symmetric and asymmetric associations.
Deep learning optimizes user association in Massive MIMO networks.
problem Optimizing user cell association for maximum sum-rate in Massive MIMO networks.
method Training a deep neural network to learn optimal association rules based on user positions.
result The neural network achieves the same performance as traditional optimization methods with reduced computational complexity.
OMBA learns product and user representations for better online market basket analysis.
problem Limited ability to uncover rarely occurring and temporal associations in MBA.
method Jointly learns product and user representations, captures temporal dynamics, scalable online method.
result OMBA outperforms state-of-the-art methods by 21% on real-world datasets.
Computes quandle associated groups using group homology.
problem Computing associated groups of quandles.
method Using group homology theory to describe and compute associated groups.
result Computed second quandle homology groups of specific quandle families.
A new framework learns shared features from multi-view data with many-to-many associations.
problem Learning shared features from multi-view data with many-to-many associations.
method Probabilistic Multi-view Graph Embedding (PMvGE) using neural networks.
result PMvGE outperforms existing multi-view methods in large-scale datasets.
Develops G-MLKM for better data-target association in constrained spaces.
problem Data-target association problem in constrained spaces with limited sensor information.
method Graph-based multi-layer k-means++ (G-MLKM) method, including MLKM for local space and G-MLKM for general constrained space.
result Improves data-target association accuracy through error correction mechanisms.
New algebraic structure for vector bundles with special properties.
problem Developing new algebraic structures for vector bundles.
method Introducing para-associative algebroids and showing local triviality conditions.
result Existence of a differential connection is necessary and sufficient for local triviality.
New mechanics on non-associative octonions discovered.
problem Discrete mechanics on non-associative groups.
method Generalized Lagrangian and Hamiltonian mechanics to non-associative objects.
result Discrete mechanics on unitary octonions achieved.
Associative submanifolds of the 7-sphere S^7 are 3-dimensional minimal submanifolds which are the links of calibrated 4-dimensional cones in R^8 called Cayley cones. Examples of associative 3-folds are thus given by the links of complex and special Lagrangian cones in C^4, as well as Lagrangian submanifolds of the near…
Drinfeld associator is a key tool in computing the Kontsevich integral of knots. A Drinfeld associator is a series in two non-commuting variables, satisfying highly complicated algebraic equations - hexagon and pentagon. The logarithm of a Drinfeld associator lives in the Lie algbera L generated by the symbols a,b,c mo…
Graph network predicts circRNA-disease associations using multi-source similarity features.
problem Identifying circRNA-disease associations is challenging and time-consuming.
method Proposes a graph convolution network framework using multi-source similarity information.
result Framework predicts circRNA-disease associations with promising results and outperforms existing methods.
The paper explores new rules for analyzing label rankings and pairwise preferences.
problem Mining patterns in multi-target relations for label ranking.
method Developed two types of association rules: Label Ranking Association Rules (LRAR) and Pairwise Association Rules (PAR). Conducted sensitivity analysis on similarity measures.
result Both LRAR and PAR show potential in analyzing multi-target relations.
Poisson algebra is usually defined to be a commutative algebra together with a Lie bracket, and these operations are required to satisfy the Leibniz rule. We describe Poisson structures in terms of a single bilinear operation. This enables us to explore Poisson algebras in the realm of non-associative algebras. We stud…
We consider minimal immersions in MxR. We study existence and uniqueness of associate and conjugate isometric immersions to a given minimal surface. We use the theory of univalent harmonic map between surfaces. Then we study the geometry of associate minimal vertical graphs. We prove that an associate surface of a vert…
The aim of this paper is to present the stochastic Poisson equations associated to Lie algebroids. The stochastic Poisson equations associated to a refinement of a concrete principal bundle are determined.
This paper gives two methods for constructing associative 3-folds in R^7, based around the fundamental idea of evolution equations, and uses these methods to construct examples of these geometric objects. The paper is a generalisation of the work by Joyce in math.DG/0008021, math.DG/0008155, math.DG/0010036 and math.DG…
Survey of recent measures of association, including a new coefficient.
problem Exploring new measures of association in statistics.
method Survey and introduction of a new correlation coefficient.
result Proposed a new extension of the correlation coefficient to standard Borel spaces.
We solve the regularized Knizhnik-Zamolodchikov equation and find an explicit expression for the Drinfeld associator. We restrict to the case of the fundamental representation of gl(N). Several tests of the results are presented. It can be explicitly seen that components of this solution for the associator coincide w…
The paper studies scaling laws for associative memory mechanisms.
problem Understanding and optimizing learning and memorization processes.
method High-dimensional matrices of outer products of embeddings, relating to transformer models. Derived scaling laws with sample and parameter sizes. Extensive numerical experiments.
result Precise scaling laws and statistical efficiency of estimators.
The article develops deformation theory for ACyl associative submanifolds in ACyl G2-manifolds.
problem Deformation theory of ACyl associative submanifolds in ACyl G2-manifolds.
method Study of moduli spaces with fixed and varying asymptotic data, computing virtual dimensions.
result The moduli space of ACyl associative submanifolds embeds as a Lagrangian submanifold in the moduli space of holomorphic curves.
Paper studies non-associativity in quantum systems with magnetic fields.
problem Non-associativity of magnetic translations in quantum systems.
method Quantum field theory approach with n-component fermions. result Non-associativity described by a 3-cocycle of Rn with values in S1. A neural network learns word-referent associations across various contexts.
problem Learning word-referent associations in different contexts.
method A biologically inspired multi-layered architecture that takes images and phonemes as input, builds representations, and adjusts prototypes based on current context.
result The model achieves up to 78% accuracy in ambiguous situations and mimics human learning patterns.