Researchers create exact minimal surfaces with helical motifs in biological structures.
problem Analyzing helical motifs in minimal surfaces of biological structures.
method Developed a method to construct exact minimal surfaces with arbitrary helical motifs.
result Exact minimal surfaces with helical motifs can be created and analyzed.
Study harmonic surfaces in 3D space, proving superposition principle.
problem Understanding harmonic surfaces in R3. method Using harmonic Enneper immersions and superposition principle.
result Minimal and maximal surfaces can be decomposed into harmonic components.
Solvents can induce helical knots in simulated biopolymer tubes.
problem Understanding how solvents influence the folding of biopolymers.
method Computer simulations using morphometric solvation techniques.
result Solvents can induce complex helical structures, including knots.
Method finds motifs in knowledge graphs, revealing their structure.
problem Identifying meaningful subunits in knowledge graphs.
method Inspired by simple graphs, the approach uses compression techniques to find motifs.
result Motifs found reflect the basic structure of the graph.
Improved scaffold generation for protein motifs using SE(3) flow matching.
problem Lack of structural diversity in generated scaffolds for protein motifs.
method Extended FrameFlow for motif-scaffolding with motif amortization and motif guidance.
result 2.5 times more designable and unique motif-scaffolds compared to state-of-the-art.
MotiFiesta learns network motifs efficiently.
problem Efficiently identifying network motifs in real-world datasets.
method Formulated as a node labelling task, using machine learning.
result Demonstrated efficient motif discovery on challenging datasets.
Networks are a fundamental tool for modeling complex systems in a variety of domains including social and communication networks as well as biology and neuroscience. Small subgraph patterns in networks, called network motifs, are crucial to understanding the structure and function of these systems. However, the role of…
Paper proposes DTW-SOM for visual exploration of time-series motifs.
problem Exploring motifs extracted by time-series motif discovery algorithms.
method Adapted Self-Organizing Map (DTW-SOM) using Dynamic Time Warping distance.
result DTW-SOM effectively visualizes motifs from time-series data.
In this paper, in Euclidean n -space, we investigate the relation between slant helices and spherical helices. Moreover, in E n, we show that a slant helix and the tangent indicatrix of the slant helix have the same axis (or direction). Also, we give the important relations between slant helices, spherical helices in E…
The study characterizes helices in Euclidean and hyperbolic spaces.
problem Characterizing helices in Euclidean and hyperbolic spaces.
method Analyzing Killing vector fields associated with rotations in both spaces.
result Helices in hyperbolic space are geodesics on suitable surfaces.
New clustering methods use motifs to organize networks.
problem Organizing directed graphs efficiently.
method Construct clustering methods parametrized by motifs.
result New clustering methods can organize networks.
In this study, we give definitions and characterizations of eikonal slant helices, eikonal Darboux helices and non-normed eikonal Darboux helices in 3-dimensional pseudo- Riemannian manifold M . We show that every eikonal slant helix is also an eikonal Darboux helix for timelike and spacelike curves. Furthermore, we ob…
MMGAN creates graphs with higher-order motifs for better network simulation.
problem Generative models fail to capture higher-order connectivity patterns in real-world networks.
method Combines multiple biased random walks to capture different motif structures.
result Outperforms NetGAN at creating graphs with accurate network motif statistics.
The paper defines Vn-slant helices in a lightlike cone and their curvature functions.
problem Understanding Vn-slant helices in a lightlike cone Qn+1.
method Defined Vn-slant helices and their harmonic curvature functions in Qn+1, expressed differential equations, and provided conditions for being Vn-slant helices.
result Differential equations of harmonic curvature functions and necessary conditions for Vn-slant helices in Qn+1.
Study classifies polyharmonic helices in various space forms.
problem Classifying polyharmonic helices in different space forms.
method Derived classification results for polyharmonic helices in space forms.
result Polyharmonic helices of arbitrary order in space forms of negative curvature are geodesics.
Behaviors of several laboratory animals can be modeled as sequences of stereotyped behaviors, or behavioral motifs. However, identifying such motifs is a challenging problem. Behaviors have a multi-scale structure: the animal can be simultaneously performing a small-scale motif and a large-scale one (e.g. \textit{chewi…
MDF represents time series motifs as images for improved classification.
problem Classifying time series data with high-order patterns.
method Motif Difference Field (MDF) using Fully Convolutional Networks (FCN).
result MDF outperforms other methods on UCR time series datasets.
In this paper, we define slant helices in three dimensional Lie Groups with a bi-invariant metric and obtain a characterization of slant helices. Moreover, we give some relations between slant helices and their involutes, spherical images.
odeN efficiently approximates multiple temporal motifs in large networks.
problem Efficiently counting multiple temporal motifs in large temporal networks.
method odeN is a sampling-based algorithm that provides accurate probabilistic approximations of motif counts.
result odeN provides accurate approximations of motif counts in a fraction of the time needed by state-of-the-art methods.
Building on previous results on the quadratic helicity in magnetohydrodynamics (MHD) we investigate particular minimum helicity states. Those are eigenfunctions of the curl operator and are shown to constitute solutions of the quasi-stationary incompressible ideal MHD equations. We then show that these states have inde…
Study on null helices in semi-Riemannian manifolds with special submanifolds.
problem Investigating geometric properties of null helices on totally umbilical submanifolds in 3D semi-Riemannian manifolds.
method Using the null Frenet frame and degenerate metric condition, equations and invariants characterizing null helices are derived.
result Equations and invariants characterizing null helices on totally umbilical submanifolds in 3D semi-Riemannian manifolds are obtained.
The paper investigates polyharmonic helices in 3D solvable Lie group Sol_3 and Euclidean spheres.
problem Existence and classification of polyharmonic helices of order r.
method Analytical and geometric approaches, including Lie group theory and Euclidean sphere analysis.
result Complete classification of proper r-harmonic helices in Sol_3 and new examples in Bianchi-Cartan-Vranceanu spaces.
Complex systems, such as airplanes, cars, or financial markets, produce multivariate time series data consisting of a large number of system measurements over a period of time. Such data can be interpreted as a sequence of states, where each state represents a prototype of system behavior. An important problem in this …
Characterizes concircular helices in space forms and ruled hypersurfaces.
problem Understanding concircular hypersurfaces and helices in space forms.
method Characterization through differential equations and ruled hypersurfaces.
result Concircular helices are geodesics of concircular surfaces.
Synthetic Petri Dish predicts neural architecture performance faster.
problem Expensive NAS evaluation process with ground-truth data.
method Instantiates motifs in small networks, evaluates with few synthetic samples.
result Significantly higher accuracy in predicting motif performance.
Optimized concentric helices minimize the ropelength of non-alternating torus knots.
problem Optimizing the ropelength of non-alternating torus knots.
method Optimized geometry and combinatorics of concentric helices.
result Optimized ropelength of concentric helices is approximately 7.83Q^(3/2).
Paper discovers manoeuvres from vehicle telematics data.
problem Analyzing driving behaviour from vehicle data.
method Used motif detection in time-series with a modified EMD algorithm.
result Validated motif discovery for complex manoeuvres.
The helicity of a vector field is a measure of the average linking of pairs of integral curves of the field. Computed by a six-dimensional integral, it is widely useful in the physics of fluids. For a divergence-free field tangent to the boundary of a domain in 3-space, helicity is known to be invariant under volume-pr…
In this work, we studied the properties of the spherical indicatrices of involute curve of a space curve and presented some characteristic properties in the cases that involute curve and evolute curve are slant helices and helices, spherical indicatrices are slant helices and helices and we introduced new representatio…
Weaved helices form mechanically stable 3D structures.
problem Creating stable 3D structures from helical elements.
method Exploiting screw symmetry and invariant cylindrical rod packing to form triply periodic arrangements.
result Demonstrated nineteen triply periodic arrangements of interwoven helices.
A motif-based framework identifies local spillover structures in financial markets.
problem Aggregate risk spillovers obscure local interaction patterns in systemic risk.
method Develops a motif-based framework using multiscale backbones and colored motifs.
result Motif-based portfolios outperform traditional benchmarks on risk-adjusted returns.
The present paper attempts to show an alternative approach with regards to rational Pythagorean-hodograph (PH) curves and especially more natural approach for rational PH helices (i.e. rational helices). It exploits geometric features of rational helices to obtain a simpler construction of these curves and apply this t…
New method learns diverse protein scaffolds for motif design.
problem Designing long, diverse protein scaffolds for specific motifs.
method E(3)-equivariant graph neural network for diffusion modeling.
result First to guarantee conditional sampling from diffusion models.
PGEL learns embeddings to diversify protein motifs while maintaining biological function.
problem Generating diverse protein structures while preserving biological function.
method Embedding learning framework that enhances motif diversity in a diffusion model's frozen denoiser.
result PGEL achieves greater structural diversity, better designability, and improved self-consistency compared to partial diffusion.
Model improves robustness of neural network sequences without transition failures.
problem Learning and generating complex sequences of motor primitives without interference.
method Inspired by thalamocortical circuit, uses specific module for motif transitions.
result Improved robustness of sequence generation with no transition failures.
In this paper, we give some characterizations for spacelike helices in Minkowski space-time. We find the differential equations characterizing the spacelike helices and also give the integral characterizations for these curves in Minkowski space-time.
New method clusters weighted directed networks using motifs.
problem Clustering directed networks fails to consider higher-order structure and edge weights.
method Motif-based weighted spectral clustering with new matrix formulae.
result Scalable and effective clustering on large graphs and real-world data.
In this work, we give some new characterizations for inclined curves and slant helices in n-dimensional Euclidean space E^{n}. Morever, we consider the pre-characterizations about inclined curves and slant helices and reconfigure them.
New method uses diffusion models to generate proteins with specific motifs.
problem Generating proteins with specific functional substructures (motifs) using diffusion models.
method Adapting SMC-aided diffusion posterior samplers to zero-shot scaffolding tasks.
result Proposed potentials and samplers improve performance in generating proteins with desired motifs.
New method discovers time series motifs under DTW, significantly reducing computations.
problem Discovering time series motifs under DTW is computationally challenging.
method Exact scalable method using novel lower bounds hierarchy.
result Prunes up to 99.99% of DTW computations under realistic settings.
Proposes a motif-preserving Graph Neural Network for financial default prediction.
problem Weak connectivity and imbalance in motif patterns in graph-based models.
method MotifGNN with curriculum learning to capture higher-order topology structures.
result Significantly improved financial default prediction accuracy on public and industrial datasets.
Characterizes concircular helices and surfaces in 3D space.
problem Understanding concircular helices and surfaces in Euclidean 3-space.
method Characterization through differential equations and ruled surfaces.
result Characterizes concircular helices and surfaces in 3D space.
In this paper, we introduce the notion of motif closure and describe higher-order ranking and link prediction methods based on the notion of closing higher-order network motifs. The methods are fast and efficient for real-time ranking and link prediction-based applications such as web search, online advertising, and re…
Generalized belief propagation converges to optimal solutions on graphs with motifs.
problem Understanding belief propagation on loopy graphs.
method Study of generalized belief propagation on graphs with motifs.
result Generalized belief propagation converges to the global optimum of the Bethe free energy.
In this paper, we define some new associated curves as integral curves of a vector field generated by Frenet vectors of tangent indicatrix of a curve in Euclidean 3-space. We give some relationships between curvatures of these curves. By using these associated curves, we give some methods to construct helices and slant…
Time Series Motif Discovery (TSMD) is defined as searching for patterns that are previously unknown and appear with a given frequency in time series. Another problem strongly related with TSMD is Word Segmentation. This problem has received much attention from the community that studies early language acquisition in ba…
A new algebraic method for computing helicity is developed, by discovering a relationship between helicity of fluid mechanics and algebraic polynomial invariants of knot theory. We have constructed a topological invariant tH(L) for a link L of knots, where H is the helicity of a …
Paper constructs motifs from planar tilings for DP weaves and polycatenanes.
problem Creating complex entangled structures from periodic tilings.
method Combinatorial methodology using polygonal link transformations.
result Predicting the type of motif from a given tiling and polygonal link method.