We show that a finite numerical boundary slope of an essential surface in the exterior of a Montesinos knot is bounded above and below in terms of the numbers of positive/negative crossings of a specific minimal diagram of the knot.
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48 results for “positive-negative learning”
This work tackles domain shift in speech emotion recognition by proposing class-wise adversarial domain adaptation.
problem Domain shift between corpora poses a challenge for speech emotion recognition, especially for positive/negative emotions.
method Class-wise adversarial domain adaptation to reduce shift between different corpora.
result Our method is effective even with limited target labeled examples, as demonstrated on EMODB and Aibo corpora.
Theoretical analysis shows PU and NU learning can outperform PN learning under certain conditions.
problem Comparing PU and PN learning without negative data.
method Theoretical analysis based on upper bounds on estimation errors.
result Conditions under which PU and NU learning outperform PN learning are identified and proven.
Conformal qc geometry of spherical qc manifolds are investigated. We construct the qc Yamabe operators on qc manifolds, which are covariant under the conformal qc transformations. A qc manifold is scalar positive, negative or vanishing if and only if its qc Yamabe invariant is positive, negative or zero, respectively. …
Solves a Cauchy problem for minimal spacelike surfaces in 4D spacetime.
problem Constructing minimal spacelike surfaces in 4D spacetime.
method Defining isoclinic parametric surfaces and proving their relation to holomorphic functions, solving the Cauchy problem.
result Solves the Cauchy problem for minimal spacelike surfaces in .
We show that the hyperbolic volume of a hyperbolic knot is a quandle cocycle invariant. Further we show that it completely determines invertibility and positive/negative amphicheirality of hyperbolic knots.
The Yamabe operator and invariants on octonionic contact manifolds and convex cocompact subgroups of math.DG
The study constructs Yamabe operators on OC manifolds and proves their properties.
problem Investigating Yamabe operators on OC manifolds and their invariants.
method Construction and analysis of OC Yamabe operators, transformation formula proof, Green function construction.
result Yamabe operators on OC manifolds have specific scalar positivity properties.
In this note we prove that for any finite quandle and any 2-cocycle $φ\in Z^2_{Q\pm}(X; \mathds{Z})$, the cocycle invariant is trivial for all knots .
This paper proposes a new method to improve meta-learning models.
problem Improving generalization in meta-learning models.
method Gradient agreement optimization to control task weights in loss function.
result Meta-learning models with better generalization achieved.
Research shows SBP's tone impacts stock market returns positively or negatively.
problem Impact of State Bank of Pakistan's monetary policy communications on stock market.
method Sentiment analysis and high frequency stock market returns analysis.
result Positive or negative tone in SBP communications affects stock returns positively or negatively.
ChatGPT can summarize corporate disclosures more concisely and effectively, improving stock market reactions.
problem Information asymmetry and inefficiency in stock markets due to bloated disclosures.
method Comparing ChatGPT-generated summaries to original disclosures, analyzing their impact on stock market reactions.
result ChatGPT-generated summaries are more effective at explaining stock market reactions to disclosed information.
Model predicts stock correlations based on investors' expected returns.
problem Lack of microscopic explanation for stock correlations.
method Agent-based model derived from minority game.
result Stock returns are positively/negatively correlated when agents' expected returns for one stock are positively/negatively correlated with the historical return of the other.
New method uses random convex polytopes to measure representation quality.
problem Measuring the quality of deep learning representations.
method Random Polytope Descriptor method based on random convex polytopes.
result Regularization in autoencoders can degrade out-of-distribution detection.
The estimation of asset return distributions is crucial for determining optimal trading strategies. In this paper we describe the constrained mixture model, based on a mixture of Gamma and Gaussian distributions, to provide an accurate description of price trends as being clearly positive, negative or ranging while acc…
The study proves curvature bounds for hyperkähler manifolds.
problem Proving curvature invariants of hyperkähler manifolds.
method Analytical proof in complex dimension four, experimental proof in higher dimensions, verification for known manifolds.
result The conjectured curvature invariants are proven to be positive/negative for all known hyperkähler manifolds up to dimension eight.
Study relates Gaussian curvature signs to cuspidal edge types and geometric invariants.
problem Understanding the relationship between Gaussian curvature and singularities of Gauss maps of cuspidal edges.
method Analyzes geometric invariants and types of singularities of Gauss maps to define and characterize positivity/negativity of cusps.
result Defines and characterizes positivity/negativity of cusps of Gauss maps by geometric invariants of cuspidal edges, and shows relation between sign of cusps and Gaussian curvature.
Neural networks model future values in finance.
problem Modeling future values of financial portfolios.
method Deep learning with neural networks to parameterize future values, optimizing parameters.
result Obtained expected positive/negative exposures for specific financial products.
Paper proposes a fast, learning-rate free method for optimizing AUC.
problem Scalable optimization of AUC for large datasets.
method Sampling mini-batches of positive/negative pairs and computing U-statistics.
result Number of samples required is independent of the number of pairs available.
Recognizing the -horospherical manifold of Picard number 1 by varieties of minimal rational tangentsmath.AG
A -horospherical manifold is identified by its VMRT.
problem Recognizing -horospherical manifolds of Picard number 1.
method Using the method developed for symplectic Grassmannians, which involves constructing a flat Cartan connection and studying the positivity/negativity of vector bundles.
result The -horospherical manifold ${f X}$ is the only smooth projective variety with the property of being recognized by its VMRT.
Study reveals striking uniformity in triply graded link homology for specific braids.
problem Investigating the structure of reduced triply graded link homology in specific degrees.
method Diagrammatic approach to Hochschild cohomology of Soergel bimodules.
result Homology often zero, especially in negative braid case, with striking uniformity.
Paper uses RL to fool neural networks with minimal pixel changes.
problem Vulnerability of neural networks to adversarial attacks.
method Deep Q Learning (DQN) to generate adversarial images.
result DQN can fool neural networks with minimal pixel modifications.
Paper uses ensemblers to predict sepsis early from patient records.
problem Early detection of sepsis in patients.
method Imputation and weak ensembler technique applied to 40k patient records.
result Model achieved 93.45% accuracy and 0.271 utility score.
Research uses Twitter data to analyze public perception of city logistics.
problem Understanding public views on city logistics from multiple stakeholders.
method Collecting Twitter content, applying unsupervised learning and NLP.
result Built an Interest Map and determined sentiment of city logistics entries.
Paper introduces a new skein relation for multivariable polynomials of virtual links.
problem Understanding properties of virtual links through polynomial invariants.
method Developed a virtual skein relation for multivariable polynomials of virtual links.
result New skein relation for multivariable polynomials of virtual links.
The study examines conjugation curvature in a specific group, finding elements with various curvatures.
problem Analyzing conjugation curvature in a particular group structure.
method Examined elements in , calculated word length, and used density results.
result Found elements with positive, negative, and zero conjugation curvature.
New method accelerates large margin metric learning for nearest neighbor classification.
problem Efficiently learning metrics for nearest neighbor classification.
method Triplet mining and stratified sampling for large margin metric learning.
result Improved efficiency and scalability of optimization.
Negative step sizes improve second-order methods for neural networks.
problem Second-order methods discard negative curvature, limiting their effectiveness.
method Introduce negative step sizes in second-order methods combined with Wolfe line search.
result Negative step sizes lead to global convergence and improved performance.
The group SU(2)*SU(2) acts naturally on SL(2,C) by simultaneous right and left multiplication. We study the Kahler metrics invariant under this action using global Kahler potentials. The volume growth and various curvature quantities are then explicitly computable. Examples include metrics of positive, negative and zer…
New method handles structural uncertainty in graphs better than existing models.
problem Handling heterophily and structural noise in semi-supervised learning on graphs.
method Sparse signed message passing network that models a posterior distribution over signed adjacency matrices.
result Our method outperforms strong baseline models on heterophilic benchmarks under both synthetic and real-world structural noise.
Model predicts future term connections in biomedical research.
problem Capturing temporal dynamics and unobserved connections in biomedical term relationships.
method Variational inference model for positive-unlabeled learning on dynamic graphs.
result Model effectively predicts term relationships in real-world datasets.
Meta-learning method improves PU classification performance.
problem Improving binary classifiers from PU data in unseen tasks.
method Adapts model to PU data using related tasks and neural networks.
result Proposed method outperforms existing methods on synthetic and real-world datasets.
New SCI index bounds unknotting framed unknots.
problem Bounding unknotting framed unknots.
method Introducing Self-Crossing Index (SCI) and using it to provide bounds.
result SCI provides bounds for unknotting framed unknots.
Paper proposes a new approach to stabilize GAN training by treating generated data as unlabeled.
problem Traditional GAN training treats generated data as negative, ignoring their potential quality.
method Defines positive and unlabeled classification for GANs, treating generated data as unlabeled.
result PUGAN achieves comparable or better performance than sophisticated discriminator stabilization methods.
Self-PU combines self-training with PU learning for improved binary classification.
problem Binary classification from unlabeled data and few labeled positives.
method Integrates self-paced training, self-calibrated loss, and self-distillation.
result Significantly improved performance on Alzheimer's Disease classification.
This study examines how imbalanced training data affects author name disambiguation.
problem The impact of imbalanced training data on machine learning for author name disambiguation.
method Training three classifiers (Logistic Regression, Naïve Bayes, Random Forest) on multiple labeled datasets with various positive-negative training data ratios.
result Increasing negative training data can improve disambiguation performance but with diminishing returns.
Study finds conditions for conformal deformations to constant scalar curvature in conic metrics.
problem Finding conditions for conformal deformations to constant scalar curvature in conic metrics.
method Analyzes conformal deformations within a class of incomplete Riemannian metrics that generalize conic orbifold singularities.
result Determines sufficient conditions for the existence of a conformal deformation to a conic metric with constant scalar curvature -1.
Proposes new fairness definitions for classification tasks combining statistical and individual fairness.
problem Combining statistical and individual fairness in classification tasks.
method Designs an oracle-efficient algorithm for fair empirical risk minimization.
result The ERM solution generalizes to new individuals and tasks.
Merlin improves robustness of MTSF models to missing data.
problem Suboptimal forecasting performance due to unfixed missing rates in MTSF models.
method Offline knowledge distillation and multi-view contrastive learning.
result Merlin enhances robustness of MTSF models while preserving accuracy.
Analog of Kauffman bracket for non-orientable knots in thickened surface.
problem Defining an invariant for non-orientable knots in a non-orientable surface.
method Proposes an analog of the Kauffman bracket polynomial with modified sign rules.
result Polynomial is an isotopy invariant and independent of classical Kauffman for orientable covers.
ChatGPT predicts stock trends from Twitter sentiment, showing positive effects.
problem Predicting stock market trends using social media sentiment.
method Used ChatGPT for sentiment analysis of Twitter posts about Microsoft and Google.
result ChatGPT's predictions correlated positively with stock performance.
Modern classification problems frequently present mild to severe label imbalance as well as specific requirements on classification characteristics, and require optimizing performance measures that are non-decomposable over the dataset, such as F-measure. Such measures have spurred much interest and pose specific chall…
Paper tackles non-monotonic resource utilization in sequential decision-making.
problem Sequential decision-making under uncertainty with resource constraints.
method Introduces a new MDP policy with constant regret against LP relaxation.
result Develops a learning algorithm with logarithmic regret for unknown outcome distributions.
New method improves unsupervised feature learning for natural data.
problem Natural data's correlated and long-tail distribution challenges instance-level contrastive learning.
method Cross-level instance-group discrimination (CLD) to integrate between-instance similarity.
result CLD achieves new state-of-the-art performance on various datasets.
This paper classifies symmetries of biharmonic heat equations on surfaces of revolution.
problem Investigating symmetries of biharmonic heat equations on surfaces of revolution.
method Lie symmetry analysis to classify symmetries and derive invariant solutions.
result The biharmonic heat equation on a surface of revolution has the same Lie symmetries as the harmonic heat equation.
This paper analyzes optimal stopping regions for American options with Poisson exercise opportunities.
problem Analyzing the optimal stopping regions for American options with Poisson exercise opportunities.
method Computing identities related to the first Poisson arrival time to an interval and applying them to the computation of the optimal strategies.
result Explicit expressions of the stopping and continuation regions and the value function are obtained.
This paper tackles tweet classification by identifying purpose and position.
problem Difficulties in determining user intention and attitude in short, informal tweets.
method Transformed tweet classification into a multi-label problem and applied a multi-label classification method with post-processing.
result The method effectively classifies tweet purpose and position, outperforming individual classification methods.
This paper introduces new risk measures for systemic risk analysis.
problem Analyzing systemic risk in financial systems.
method Introducing conditional distortion risk measures and their properties.
result Presented sufficient conditions for ordering risk measures.
Let (M,g) be a compact Riemannian manifold with dimension n > 2. The Yamabe problem is to find a metric with constant scalar curvature in the conformal class of g, by minimizing the total scalar curvature. The proof was completed in 1984. Suppose (M',g') and (M'',g'') are compact Riemannian n-manifolds with constant sc…