The paper introduces a new language for semi-supervised learning tasks.
problem Handling semi-supervised learning with declarative constraints.
method Developed a declarative language for modeling both supervised and semi-supervised learning tasks, including heuristics and combining multiple heuristics.
result Improved performance on relation-extraction tasks for real-world domains.
The paper classifies links based on signature and crossing number properties.
problem Understanding the relationship between signature and crossing number of knots and links.
method Refined theorems and comprehensive classification of links with specific properties.
result Identification of all links where signature + crossing number = 2, closures of positive 3-braids.
We investigate the geometry of hyperbolic knots and links whose diagrams have a high amount of twisting of multiple strands. We find information on volume and certain isotopy classes of geodesics for the complements of these links, based only on a diagram. The results are obtained by finding geometric information on ge…
Using computer calculations and working with representatives of pretzel tangles we established general adequacy criteria for different classes of knots and links. Based on adequate graphs obtained from all Kauffman states of an alternating link we defined a new numerical invariant: adequacy number, and computed adequac…
New method computes knot invariants using free group automorphisms.
problem Computing knot invariants efficiently and accurately.
method Using representations of braid groups by automorphisms of a free group.
result Compared isotopic invariants to Alexander polynomials.
New methods show hyperbolicity of Brunnian links.
problem Determining hyperbolicity of Brunnian links.
method Geometric/combinatorial topology techniques applied to links with at least one unknotted component.
result Determine hyperbolicity for all Brunnian links.
Paper generalizes pretzel links using spatial graphs.
problem Classical pretzel links need a generalization.
method Introduces graph-pretzel links based on spatial graph projections.
result Constructs an infinite family of distinct ribbon knots.
Study investigates deep learning for scalable long-term traffic predictions in large transport networks.
problem Scalable long-term traffic predictions in large transport networks.
method Investigated deep learning models for link-based predictions, including clustering and graph convolutional approaches.
result Deep learning models can be useful for long-term large-scale traffic prediction, but simpler predictors are satisfactory for short-term forecasting.
Following the recent work by T.-H. Chan in [HOMFLY polynomial of some generalized Hopf links, J. Knot Theory Ramif. 9 (2000) 865--883] on reverse string parallels of the Hopf link we give an alternative approach to finding the Homfly polynomials of these links, based on the Homfly skein of the annulus. We establish tha…
We propose a non-parametric link prediction algorithm for a sequence of graph snapshots over time. The model predicts links based on the features of its endpoints, as well as those of the local neighborhood around the endpoints. This allows for different types of neighborhoods in a graph, each with its own dynamics (e.…
In these notes, I will sketch a new approach to Khovanov homology of knots and links based on counting the solutions of certain elliptic partial differential equations in four and five dimensions. The equations are formulated on four and five-dimensional manifolds with boundary, with a rather subtle boundary condition …
We introduce an alternative approach to the third order helicity of a volume preserving vector field B, which leads us to a lower bound for the L2-energy of B. The proposed approach exploits correspondence between the Milnor μˉ123-invariant for 3-component links and the homotopy invariants of maps to con…
A symmetric matrix invariant is defined for oriented link diagrams.
problem Defining an invariant for oriented link diagrams.
method Defining a symmetric map τD from regions of an oriented link diagram to Z[x], corrected by the writhe. result The negative signature of τD, corrected by the writhe, conjecturally equals twice the Tristram-Levine signature function. Abstract summarizes level-rank duality in knot and link invariants.
problem Distinguishing torus knots and links from hyperbolic ones.
method Chern-Simons theory and tables of knot invariants.
result Criterion to distinguish torus knots and links from hyperbolic ones.
Study on random alternating link diagrams and their hyperbolic volumes.
problem Understanding the relationship between the combinatorial structure and hyperbolic volume of random links.
method Model based on random 4-valent maps, analyzing alternating and nonalternating diagrams.
result Expected hyperbolic volume is asymptotically linear in the number of crossings for random alternating diagrams.
Spring-electrical models predict network links based on node proximity.
problem Predicting links in networks.
method Spring-electrical models applied to network layouts.
result The Euclidean distance in network layouts correlates with link probabilities.
Investor clusters analyzed in Helsinki Stock Exchange IPOs.
problem Lack of research on investor behavior in IPOs.
method Statistically validated network method to infer investor links based on trade timing.
result Large network structures form in IPO and mature companies, with evidence of institutional herding.
New invariant for special alternating links based on graph Laplacian.
problem Developing an invariant for special alternating links.
method Using the Laplacian matrix of the Tait graph, invariant is defined.
result A specific quadratic trace expression is invariant under flype moves.
New measures assess differences in causal graphs' separations.
problem Evaluating causal discovery algorithms' output.
method Proposes new distance measures capturing causal graphs' separations.
result Proposed distances assess differences in causal graphs' separations.
New model for links uses meander diagrams and combinatorics.
problem Modeling and analyzing random links.
method Random meander model based on meander diagrams and graphs, proving properties using combinatorics.
result Trivial links are unlikely, and there's a lower bound on non-isotopic knots.
New skein invariants defined for links using a novel procedure.
problem Defining new skein invariants for links.
method Applying skein relation to distinct component crossings, then evaluating resulting knots with given invariants.
result Generalizations of known skein invariants for classical links.
New theory for unoriented virtual links, extending classical invariants.
problem Invariants for unoriented virtual links not previously defined.
method Developed a new Khovanov homology theory for unoriented virtual links.
result Unoriented Jones polynomial for virtual links is a new invariant.
The paper derives formulas for braid index of alternating links.
problem Determining the braid index of alternating links.
method Diagrammatic approach based on minimum projections.
result Explicit formulas for braid index of many alternating links.
New method calculates number of components in twisted torus links.
problem Determining the number of components of twisted torus links.
method Euclidean algorithm-like procedure based on parameters.
result Number of components is a multiple of gcd(p, q, r, s).
A CNN model learns complex relationships in knowledge graphs.
problem Exploring complex relationships between entities and relationships in knowledge graphs.
method A Convolutional Neural Network (CNN) is used to learn entity and relationship representations in knowledge graphs.
result The proposed model outperforms state-of-the-art models on exploring unseen relationships.
New models predict interactions in multilayer networks more accurately.
problem Predicting interactions in multilayer networks.
method Introduce two stochastic block models for multilayer and temporal networks; develop scalable algorithms for inferring parameters.
result Modeling all layers simultaneously results in more accurate link prediction.
SELO model predicts link signs better than SDGNN using subgraph encoding and linear optimization.
problem Inferring the sign of links in signed networks with limited sign data.
method Subgraph Encoding via Linear Optimization (SELO) approach to learn edge embeddings.
result SELO model outperforms state-of-the-art methods on multiple real-world signed networks.
InteractE improves link prediction in KGs by increasing feature interactions.
problem Improving link prediction in knowledge graphs by inferring missing links.
method Feature permutation, novel feature reshaping, circular convolution.
result InteractE outperforms ConvE on multiple KG datasets.
The data in many disciplines such as social networks, web analysis, etc. is link-based, and the link structure can be exploited for many different data mining tasks. In this paper, we consider the problem of temporal link prediction: Given link data for times 1 through T, can we predict the links at time T+1? If our da…
Paper ranks influential Tor Darknet onion domains using content features.
problem Measuring influence of criminal onion domains in Tor Darknet.
method Content-based features from multiple sources, Learning-to-Rank approach.
result Listwise approach outperforms other methods with NDCG of 0.95 for top-10.
Construct algorithms for Frobenius manifolds and residue pairings on Calabi-Yau varieties.
problem Construct algorithms for Frobenius manifolds and residue pairings on Calabi-Yau varieties.
method Analyze a dGBV algebra and introduce weak primitive forms.
result Explicit algorithms for Frobenius manifolds and residue pairings.
Paper presents a new framework for sequence classification.
problem Sequence classification in real-world applications.
method Reference-based sequence classification framework.
result New sequence classification algorithms achieve comparable accuracy.
Dual-stage sEMG classification improves gesture recognition accuracy.
problem Improving accuracy in hand gesture recognition from sEMG signals.
method Dual-stage classification approach: first stage groups similar activities, second stage classifies within groups.
result Dual-stage classification yields significantly higher accuracy than single-stage approach.
A novel method for classification with rejection using ensemble of cost-sensitive classifiers.
problem Avoid risky misclassification in error-critical applications.
method Learning an ensemble of cost-sensitive classifiers.
result Improved classification accuracy and flexibility in loss selection.
The number of possible methods of generalizing binary classification to multi-class classification increases exponentially with the number of class labels. Often, the best method of doing so will be highly problem dependent. Here we present classification software in which the partitioning of multi-class classification…
Few-shot image classification is improved by correcting CNNs' texture bias.
problem Few-shot image classification performance is hindered by CNNs' texture bias.
method Corrected CNNs' texture bias using a simpler method than state-of-the-art approaches.
result State-of-the-art performance on miniImageNet task achieved.
Paper introduces LPCs for robust classification with performance bounds.
problem Conventional classification techniques constrain rules and use surrogate losses.
method Robust risk minimization (RRM) for unconstrained classification rules, optimizing 0-1 loss.
result LPCs provide performance bounds and competitive performance with state-of-the-art techniques.
New NHCAs improve multi-category classification efficiency.
problem Efficient multi-category classification for real-world problems.
method Twin SVM (TWSVM), Generalized eigenvalue proximal SVM (GEPSVM), Regularized GEPSVM (RegGEPSVM), and Improved GEPSVM (IGEPSVM) with OAA, BT, and TDS approaches.
result TDS-TWSVM outperforms other methods in classification accuracy.
Paper compares XGB and BPNN for music style classification.
problem Efficient music style classification using different methods.
method Feature extraction for timbral texture, rhythmic content, and pitch content; comparative evaluation of XGB and BPNN.
result XGB outperforms BPNN for small datasets in music classification.
Deep reinforcement learning improves classification accuracy for imbalanced datasets.
problem Imbalanced datasets challenge conventional classification algorithms.
method Formulated as a sequential decision-making process, solved using deep Q-learning network.
result Proposed model outperforms other imbalanced classification algorithms.
Classifies Lie algebra realizations by vector fields.
problem Classifying Lie algebra realizations by vector fields.
method Generalized correspondence between classification of transitive local realizations and subalgebras, formulated a reasonable classification problem, presented an algorithm for construction.
result Algorithm for constructing classification of general realizations.
Classification outperforms regression in portfolio construction, yielding higher Sharpe ratios.
problem Determining which machine learning approach (classification vs. regression) is more effective for portfolio construction.
method Used stacking ensemble of gradient boosted tree, random forest, and neural network models.
result Classification yields higher Sharpe ratios and economically significant alphas compared to regression.
C-HMCNN(h) improves HMC classification by leveraging class hierarchy.
problem Hierarchical multi-label classification with class hierarchy constraints.
method Exploits class hierarchy to produce coherent predictions for multi-label classification.
result C-HMCNN(h) outperforms state-of-the-art models in HMC classification.
Advances few-shot classification by treating it as supervised learning and proposing new training techniques.
problem Formulating the ability of humans to learn from limited data in machine learning.
method Formulated few-shot classification as a supervised learning problem and introduced multi-episode and cross-way training techniques.
result Proposed training strategies accelerate the training process without accuracy loss.
Improves NILM with multi-label SRC, outperforming state-of-the-art.
problem Non-intrusive load monitoring (NILM) for energy disaggregation.
method Modified multi-label sparse representation based classification (SRC).
result Significant improvement over state-of-the-art techniques with minimal training data.
New approach improves classification guarantees by focusing on direction rather than regression risk.
problem Improving classification guarantees in binary classification problems.
method Establishing a geometric distinction between classification and regression, leveraging scale invariance.
result Improved guarantees for classification risk compared to regression risk.
Study selective classification with halfspaces, achieving error bounds under Gaussian distributions.
problem Modeling relationships in subsets of data defined by selection rules.
method Sparse linear classifiers for subsets defined by halfspaces, focusing on Gaussian feature distributions.
result First PAC-learning algorithm for homogeneous halfspace selectors with error guarantee $\bigO*{\sqrt{\mathrm{opt}}}$.
New method for robust trajectory classification without parameters.
problem Trajectory classification accuracy and robustness.
method Parameter-free approach to find best trajectory partition and dimension combination.
result Promising preliminary results show improved robustness.