In this paper, we consider non developable ruled surface with spacelike ruling, timelike ruling, respectively. We give the relations between the structure functions with the curvature and torsion of the striction line of the timelike and spacelike non developable ruled surfaces. Also, we have calculated the gaussian an…
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48 results for “rule structures”
Valuation Reveals Uncertaintyq-fin.MF
Paper recovers uncertainty from dynamic valuation rules.
problem Recovering latent uncertainty from observable valuation rules.
method Developed procedures to identify and characterize uncertainty structures from valuation rules.
result Valuation rules contain sufficient information to identify and recover uncertainty structures.
We show that derivations of the differential structure of a subcartesian space satisfy the chain rule and have maximal integral curves.
Discrete structure rules for validating molecular structures are usually limited to fulfillment of the octet rule or similar simple deterministic heuristics. We propose a model, inspired by language modeling from natural language processing, with the ability to learn from a collection of undirected molecular graphs, en…
Combining deep neural networks with structured logic rules is desirable to harness flexibility and reduce uninterpretability of the neural models. We propose a general framework capable of enhancing various types of neural networks (e.g., CNNs and RNNs) with declarative first-order logic rules. Specifically, we develop…
Stable generalized complex structures on certain surfaces are constant.
problem Existence of stable generalized complex structures on ruled surfaces.
method Analysis of sphere bundles over surfaces of genus ≥2.
result Stable generalized complex structures on these surfaces are of constant type.
Study infinite symplectic forms on ruled surfaces.
problem Uniqueness of symplectic structures on four-manifolds.
method Provided an infinite family of non-isotopic symplectic forms.
result Symplectic forms on ruled surfaces are pairwise non-isotopic.
Subdivision rules create sequences of nested cell structures on CW-complexes, and they frequently arise from groups. In this paper, we develop several tools for classifying subdivision rules. We give a criterion for a subdivision rule to represent a Gromov hyperbolic space, and show that a subdivision rule for a hyperb…
In this paper we study the topology of the space $\I_ω$ of complex structures compatible with a fixed symplectic form , using the framework of Donaldson. By comparing our analysis of the space $\I_ω$ with results of McDuff on the space $\cat J_ω$ of compatible almost complex structures on rational ruled surfaces, we…
All finite subdivision rules are combinatorially equivalent to three-dimensional subdivision rulesmath.GT
Finite subdivision rules in high dimensions can be difficult to visualize and require complex topological structures to be constructed explicitly. In many applications, only the history graph is needed. We characterize the history graph of a subdivision rule, and define a combinatorial subdivision rule based on such gr…
In recent years, probabilistic forecasting is an emerging topic, which is why there is a growing need of suitable methods for the evaluation of multivariate predictions. We analyze the sensitivity of the most common scoring rules, especially regarding quality of the forecasted dependency structures. Additionally, we pr…
Axiomatic Market Makingq-fin.MF
Axiomatizes the bid-ask market maker's quoting rule
problem Axiomatizing the quoting rule of a market maker
method Eight natural axioms and six environmental assumptions
result A unique three-parameter family emerges
The paper develops methods to derive mixed superposition rules for Lie systems and applies them to various physical systems.
problem Finding general solutions for Lie systems.
method Develops mixed superposition rules for Lie systems with imprimitive Lie algebras and semidirect sums.
result Extends coalgebra method to Lie systems of partial differential equations.
We propose a new framework for deriving screening rules for convex optimization problems. Our approach covers a large class of constrained and penalized optimization formulations, and works in two steps. First, given any approximate point, the structure of the objective function and the duality gap is used to gather in…
Study of ants' movement rules on a 6D space, revealing distribution structures and singular trajectories.
problem Understanding the movement patterns of ants in a 6D space.
method Analyzing mechanical system rules to derive distribution structures and singular trajectories.
result Distributions and singular trajectories of ants' movement rules in a 6D space.
Wittgenstein's Rule Following evolves datasets by extrapolating structural descriptors.
problem Generating meaningful continuations of evolving datasets.
method Wittgenstein's Rule Following (WRF) uses structural descriptors to extrapolate trajectories and average historical descriptors.
result WRF can generate meaningful continuations of evolving datasets.
DCR improves interpretability of concept-based models by using neural networks to build rule structures.
problem Inability of concept-based models to provide transparent decision processes.
method DCR uses neural networks to build syntactic rule structures using concept embeddings and executes these rules on concept truth degrees.
result DCR improves interpretability by up to 25% on challenging benchmarks and discovers meaningful logic rules.
SIRUS creates interpretable rules from random forests for regression.
problem Lack of interpretability in complex machine learning models.
method Random forest with rule extraction for stability and simplicity.
result SIRUS produces stable and interpretable rule sets.
Study path geometries with constant torsion and cone structures.
problem Characterizing path geometries with nontrivial torsion.
method Introducing constant torsion, establishing correspondence with cone structures, describing in terms of integrable systems.
result Path geometries with constant torsion correspond to cone structures on homogeneous ruled surfaces.
Equiaffine geometry of level sets and ruled hypersurfaces with equiaffine mean curvature zeromath.DG
Basic aspects of the equiaffine geometry of level sets are developed systematically. As an application there are constructed families of -dimensional nondegenerate hypersurfaces ruled by -planes, having equiaffine mean curvature zero, and solving the affine normal flow. Each carries a symplectic structure with r…
pRSL combines probabilistic rules to improve multi-label classification.
problem Modeling the structure between multi-label classes for better performance.
method Uses probabilistic propositional logic rules and belief propagation to combine predictions from multiple classifiers.
result pRSL achieves state-of-the-art performance on various benchmark datasets.
Rule-State Inference (RSI): A Bayesian Framework for Compliance Monitoring in Rule-Governed Domainscs.LG
RSI uses Bayesian inference to monitor compliance in rule-governed domains.
problem Structural obstacles in compliance monitoring, including unlabeled outcomes and selective withholding of evidence.
method Rule-State Inference (RSI) treats formalized rules as Bayesian priors and infers compliance states through mean-field variational inference.
result RSI delivers formal guarantees of adaptability, consistency, and convergence, validated on a synthetic enterprise benchmark.
FIRE extracts interpretable rules from tree ensembles.
problem Building sparse and interpretable rule sets from tree ensembles.
method Optimization-based framework with fusion regularization and sparsity-inducing penalties.
result FIRE outperforms state-of-the-art rule ensemble algorithms.
New characterizations of ruled real hypersurfaces in complex projective space found.
problem Characterizing ruled real hypersurfaces in complex projective space.
method Defined tensor fields related to Levi-Civita and generalized Tanaka-Webster connections and studied the structure operator.
result Obtained new characterizations of ruled real hypersurfaces in complex projective space.
In high dimensional settings, sparse structures are crucial for efficiency, either in term of memory, computation or performance. In some contexts, it is natural to handle more refined structures than pure sparsity, such as for instance group sparsity. Sparse-Group Lasso has recently been introduced in the context of l…
The Rules-and-Facts Model for Simultaneous Generalization and Memorization in Neural Networksstat.ML
RAF model explains neural networks' dual rule learning and fact memorization.
problem Understanding how neural networks learn rules and memorize facts simultaneously.
method Introduces the Rules-and-Facts (RAF) model to bridge generalization and memorization.
result Characterizes conditions for simultaneous rule learning and fact memorization in neural networks.
Novel approach for creating interpretable classifiers using bilevel optimization of split-rules in NLDTs.
problem Creating highly accurate and easily interpretable classifiers for practical applications.
method Representing classifiers as assemblies of simple mathematical rules using NLDTs with evolutionary bilevel optimization.
result The approach ensures interpretability while achieving high accuracy on various classification problems.
New examples of extremal Kähler metrics on blow-ups of parabolic ruled surfaces are constructed. The method is based on the gluing construction of Arezzo, Pacard and Singer. This enables to endow ruled surfaces of the form with special parabolic structures such that the associated iter…
Learning curves theory for hierarchically compositional data with power-law distributed featuresstat.ML
Unified theory for neural scaling laws in hierarchically compositional data.
problem Understanding neural scaling laws in hierarchically compositional data.
method Probabilistic context-free grammars and power-law distributed production rules.
result Unified learning curve behavior for classification and next-token prediction tasks.
FedRule uses graph neural networks to recommend rules for smart homes without centralizing data.
problem Manual rule setup for smart devices is inefficient and privacy-compromising.
method FedRule constructs user-specific graphs for rule recommendation, using federated learning to protect privacy.
result FedRule achieves comparable performance to centralized methods and outperforms others.
Screening rules help identify active sets in optimization problems.
problem Identifying active sets in optimization problems.
method Screening rules based on subdifferential sets and optimality conditions.
result The number of iterations needed depends only on the convergence rate.
Neural production systems learn visual dynamics by applying rule templates to entities.
problem Modeling interactions among entities in structured visual environments.
method Inspired by production systems, the paper uses rule templates to bind placeholder variables to specific entities, scoring and applying the best fitting rules to update entity properties.
result The architecture achieves robust future-state prediction and extrapolation from simple to complex environments, outperforming GNNs.
Generative models learn rules at different timescales, revealing a 'innovation window'.
problem Generative models' convergence to empirical training distribution rather than population distribution.
method Rule-valid synthetic tasks, analyzing and across training timescales.
result The 'innovation window' widens with increasing dataset size and narrows with rule complexity.
Proposes PRMs for interpreting financial risk concept drift.
problem Concept drift in high-stakes predictions like credit risk.
method Probabilistic Rule Models (PRMs) using Markov Logic Networks.
result Interpretable rules explain borrower risk changes.
We study some properties of decomposable exact Lagrangian cobordisms between Legendrian links in with the standard contact structure. In particular, for any decomposable exact Lagrangian filling of a Legendrian link , we may obtain a normal ruling of associated with . We prove that the asso…
A new screening rule improves SLOPE efficiency for high-dimensional data.
problem Efficiently selecting relevant predictors in high-dimensional data.
method Developed a screening rule for SLOPE based on its subdifferential.
result The screening rule improves SLOPE's performance significantly in high-dimensional settings.
Study of timelike surfaces with time-minimizing rulings in Newtonian and relativistic spacetimes.
problem Understanding time-minimizing paths in spacetime geometries.
method Constructing timelike surfaces ruled by geodesics of Finsler or Jacobi metrics.
result Explicit examples of brachistochrone-ruled timelike surfaces in Minkowski and Schwarzschild spacetimes.
Method integrates logical rules into neural multi-hop reasoning for drug repurposing.
problem Capturing long-range dependencies in biomedical data.
method Combines logical rules with neural multi-hop reasoning using reinforcement learning.
result Our method outperforms baseline methods in drug repurposing tasks.
Nonrigidity of flat ribbonsmath.DG
Study of flat ribbons constructed along curves in 3D space.
problem Determine the conditions for a ruled structure to form a flat ribbon.
method Investigate the ruled structure of flat ribbons and calculate energy bounds.
result There exists a well-defined flat ribbon only up to an initial condition.
Log-symplectic structures are Poisson structures that are determined by a symplectic form with logarithmic singularities. We construct moduli spaces of curves with values in a log-symplectic manifold. Among the applications, we classify symplectically ruled log-symplectic manifolds (both orientable and non-orientab…
In this paper we apply Donaldson's general moment map framework for the action of a symplectomorphism group on the corresponding space of compatible (almost) complex structures to the case of rational ruled surfaces. This gives a new approach to understanding the topology of their symplectomorphism groups, based on a r…
Lifted Relational Neural Networks (LRNNs) describe relational domains using weighted first-order rules which act as templates for constructing feed-forward neural networks. While previous work has shown that using LRNNs can lead to state-of-the-art results in various ILP tasks, these results depended on hand-crafted ru…
We present the design and implementation of a custom discrete optimization technique for building rule lists over a categorical feature space. Our algorithm produces rule lists with optimal training performance, according to the regularized empirical risk, with a certificate of optimality. By leveraging algorithmic bou…
TransINT embeds KGs by preserving implication rules, outperforming existing methods.
problem Embedding KGs while preserving relation implications for better access and analysis.
method Isomorphic intersections of linear subspaces with shared parameters for missing facts.
result Significant performance improvement in link prediction and triple classification.
Let be a ruled surface over a curve of genus . We prove that has a scalar-flat Hermitian metric if and only if and where is an intrinsic number depends on the complex structure of .
In this paper, we propose an efficient algorithm for mining novel `Set of Contrasting Rules'-pattern (SCR-pattern), which consists of several association rules. This pattern is of high interest due to the guaranteed quality of the rules forming it and its ability to discover useful knowledge. However, SCR-pattern has n…
For one-dimensional systems of conservation laws admitting two additional conservation laws we assign a ruled surface of codimension two in projective space. We call two such systems dual if the corresponding ruled surfaces are dual. We show that a Hamiltonian system is autodual, its ruled surface sits in some quadric,…
New scoring rule predicts causal relations from data with selection bias.
problem Discovering causal relations from independence constraints under selection bias and confounding.
method Local Y-Structure patterns and a scoring rule for Y-Structures.
result Y-Structure scoring rule successfully predicts causal relations in real-world data.