Compact semiconductor device models are essential for efficiently designing and analyzing large circuits. However, traditional compact model development requires a large amount of manual effort and can span many years. Moreover, inclusion of new physics (eg, radiation effects) into an existing compact model is not triv…
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A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,657 papers · 148 categories
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48 results for “model compactness”
Low-Dimensional High-Fidelity Kinetic Models for NOX Formation by a Compute Intensification Methodphysics.chem-ph
Compact models for NOX formation during methane combustion are created using a new algorithm.
problem Creating accurate models for NOX formation during complex combustion processes.
method Adapted Machine Learning Optimization of Chemical Kinetics (MLOCK) algorithm with Latin Square method for virtual reaction network generation.
result Compact models with high fidelity (>75%) in reproducing industry-defined performance targets are generated.
New method for pricing options in stochastic volatility models.
problem Pricing options in models with stochastic volatility.
method Time-adaptive, high-order compact finite difference scheme.
result Extends fourth-order multistep methods to stochastic volatility models.
Compact learning results across various loss functions.
problem Understanding sample complexity in transductive learning.
method Analyzing finite projections and sample complexities for different loss functions.
result Exact compactness of sample complexity holds broadly across realizable and agnostic learning.
The Bäcklund problem is solved for both the compact and noncompact versions of the Ishimori (2+1)-dimensional nonlinear spin model. In particular, a realization of the arising Bäcklund algebra in the form of an infinite-dimensional loop Lie algebra of the Kač--Moody type is provided.
We give necessary conditions for the existence of a compact manifold locally modelled on a given homogeneous space, which generalize some earlier results, in terms of relative Lie algebra cohomology. Applications include both reductive and nonreductive cases. For example, we prove that there does not exist a compact ma…
Paper proves structure of compact Kähler 3-folds with specific bundles.
problem Characterizing compact Kähler 3-folds with nef anti-canonical bundles.
method Minimal Model Program, positivity of direct image sheaves, Q-conic bundles, orbifold vector bundles.
result Compact Kähler 3-folds with nef anti-canonical bundles are essentially one of three types.
Geometrically revisits and models homogeneous spaces of compact Lie group .
problem Classifying homogeneous reductive spaces of compact Lie group .
method Geometrical approach to revisit and model the spaces.
result Explicit relations among geometric models of the spaces.
We prove a smooth compactness theorem for the space of embedded self-shrinkers in $\RR^3$. Since self-shrinkers model singularities in mean curvature flow, this theorem can be thought of as a compactness result for the space of all singularities and it plays an important role in studying generic mean curvature flow.
We construct a family of compact almost Calabi--Yau manifolds of complex dimension 3 and therein a corresponding family of compact special Lagrangians with one-point singularities modelled upon that T^2-cone constructed by Harvey--Lawson and characterized by Haskins as a stable T^2-cone in the terminology by Joyce.
In this article, a three-time levels compact scheme is proposed to solve the partial integro-differential equation governing the option prices under jump-diffusion models. In the proposed compact scheme, the second derivative approximation of unknowns is approximated by the value of unknowns and their first derivative …
This paper reviews methods to create compact neural networks for IoT applications.
problem Complex deep neural networks are costly and slow, hindering real-world deployment.
method Automatic synthesis of compact, accurate DNN/LSTM models.
result Compact neural networks reduce energy consumption, memory, and inference time.
GOTabPFN improves tabular model performance with compact tokenization for HDLSS data.
problem Making tabular models effective for high-dimensional, low-sample size data without retraining.
method Introducing Graph-guided Ordering with Local Refinement (GO-LR) and Neuro-Inspired Subunit Compression (NSC) to create compact meta-features.
result GOTabPFN improves stability and accuracy in tabular benchmarks with compact tokenization.
New compact Weyl-parallel manifolds discovered in all dimensions n≥5.
problem Finding compact Weyl-parallel manifolds in all metric signatures and dimensions.
method Diffeomorphic to torus bundles over the circle, constructed from quotient-manifolds of model manifolds with discrete isometry groups.
result Existence of compact Weyl-parallel manifolds in all indefinite metric signatures in dimensions n≥5.
Compact DNNs increase memory footprint and reduce energy efficiency.
problem Designing compact deep neural networks (DNNs) for improved energy efficiency.
method Evaluation of recently proposed compact DNNs on a Tesla P100 GPU.
result Higher number of activations and memory footprint lead to reduced energy efficiency.
In this note we establish several versions of a compactness theorem for submanifolds. In particular we require only bounds on the second fundamental form and do not assume volume or diameter bounds. As an application we prove a compactness theorem for mean curvature flows and use it to construct smooth blow-up limits a…
We will construct surfaces of revolution with finite total curvature whose Gauss curvatures are not bounded. Such a surface of revolution is employed as a reference surface of comparison theorems in radial curvature geometry. Moreover, we will prove that a complete non-compact Riemannian manifold M is homeomorphic to t…
New -instantons constructed on Joyce's manifold.
problem Constructing -instantons on Joyce's compact manifold.
method Gluing non-flat connections on local model spaces to a flat connection on the -orbifold.
result More than 20,000 new four-parameter families of -instantons.
In a previous paper, we obtained a cohomological obstruction to the existence of compact manifolds locally modelled on a homogeneous space. In this paper, we give a classification of the semisimple symmetric spaces to which this obstruction is applicable.
Given a compact, connected Lie group , we use principal -bundles to construct manifolds with prescribed finite-dimensional algebraic models. Conversely, let be a compact, connected, smooth manifold which supports an almost free -action. Under a partial formality assumption on the orbit space and a regulari…
We investigate the finiteness structure of a complete non-compact -dimensional Riemannian manifold whose radial curvature at a base point of is bounded from below by that of a non-compact von Mangoldt surface of revolution with its total curvature greater than . We show, as our main theorem, that all Buse…
We prove the existence and uniqueness of geometric models of local isometry classes of locally homogeneous spaces with sectional curvature . Moreover, we show that the set of geometric models is compact in the pointed -topology.
Compactness theorem for 3-manifold Floer theory defined by Fueter sections.
problem Defining a new 3-manifold Floer theory with compact counts.
method Counting Fueter sections of hyperkähler bundles over 3-manifolds.
result Proved a compactness theorem for k=2.
We extend the scheme developed in B. Düring, A. Pitkin, "High-order compact finite difference scheme for option pricing in stochastic volatility jump models", 2019, to the so-called stochastic volatility with contemporaneous jumps (SVCJ) model, derived by Duffie, Pan and Singleton. The performance of the scheme is asse…
Toward Development of Machine Learned Techniques for Production of Compact Kinetic Modelsphysics.chem-ph
Automated method creates compact chemical models from detailed ones, reducing complexity and improving accuracy.
problem Creating accurate low-dimensional chemical kinetic models from detailed ones is time-consuming and requires expert knowledge.
method Machine Learned Optimisation of Chemical Kinetics (MLOCK) algorithm systematically perturbs sub-models to find optimal compact models.
result Compact models (15 species) retain ~87% fidelity to detailed models, outperforming previous methods.
New neural networks for non-commutative data.
problem No existing neural networks suitable for non-commutative data.
method Developed compact matrix quantum group equivariant neural networks.
result Characterized weight matrices for easy compact matrix quantum groups.
We introduce dropout compaction, a novel method for training feed-forward neural networks which realizes the performance gains of training a large model with dropout regularization, yet extracts a compact neural network for run-time efficiency. In the proposed method, we introduce a sparsity-inducing prior on the per u…
We address the problem of finding conditions under which a compact Lorentzian manifold is geodesically complete, a property, which always holds for compact Riemannian manifolds. It is known that a compact Lorentzian manifold is geodesically complete if it is homogeneous, or has constant curvature, or admits a time-like…
Continual lifelong learning is essential to many applications. In this paper, we propose a simple but effective approach to continual deep learning. Our approach leverages the principles of deep model compression, critical weights selection, and progressive networks expansion. By enforcing their integration in an itera…
This work extends PAC-Bayesian learning guarantees to non-compact symmetries and non-invariant data.
problem Lack of theoretical guarantees explaining the benefits of symmetries in machine learning models.
method Adapting and tightening PAC-Bayes bounds for non-compact symmetries and non-invariant data distributions.
result Theoretical evidence that symmetric models are preferable for symmetric data, beyond compact groups and invariant distributions.
In this paper, we study the evolution of one forms under Ricci flow with bounded curvature on a non-compact Rimennian manifold. We show on such a manifold that the norm of a smooth one form with compact support is non-increasing along the Ricci flow with bounded curvature. The norm is showed to…
Non-collapsed finite time singularities of the Ricci flow on compact Kähler surfaces are of Type Imath.DG
Ricci flow singularities on compact Kähler surfaces are of Type I.
problem Understanding finite time singularities of Ricci flow on compact Kähler surfaces.
method Analyzing the Type I property of singularities.
result Non-collapsed finite time singularities are of Type I.
The paper constructs monopole Floer homology for specific 3-manifolds and surfaces.
problem Constructing monopole Floer homology for compact 3-manifolds with toroidal boundaries.
method Using gauged Landau-Ginzburg models to study Seiberg-Witten moduli spaces.
result Finite energy solutions on are trivial, and small energy solutions on have exponentially decaying energy.
New indices for determining cluster compactness and separability.
problem Challenges in identifying true clusters in data sets.
method Developed absolute cluster indices to measure compactness and separability.
result Demonstrated improved performance compared to existing indices.
Discriminative Multi-level Reconstruction under Compact Latent Space for One-Class Novelty Detectioncs.LG
In one-class novelty detection, a model learns solely on the in-class data to single out out-class instances. Autoencoder (AE) variants aim to compactly model the in-class data to reconstruct it exclusively, thus differentiating the in-class from out-class by the reconstruction error. However, compact modeling in an im…
Bayesian neural network predicts planetary instability.
problem Predicting planetary instability in compact systems.
method Novel Bayesian neural network trained on raw orbital elements.
result Model predicts planetary instability times with high accuracy and robust generalization.
We evaluate the hedging performance of a high-order compact finite difference scheme from [4] for option pricing in Bates model. We compare the scheme's hedging performance to standard finite difference methods in different examples. We observe that the new scheme outperforms a standard, second-order central finite dif…
Neural SDEs model suicide risk with compact state space constraints.
problem Modeling suicide risk with irregular, noisy, and partially observed data.
method Developed neural SDEs confined to compact state spaces, addressing domain constraints and numerical stability.
result Improved forecasts and optimization dynamics over standard models on EMA datasets.
Measurements of cosmic microwave background (CMB) anisotropy are ideal experiments for discovering the non-trivial global topology of the universe. To evaluate the CMB anisotropy in multiply-connected compact cosmological models, one needs to compute the eigenmodes of the Laplace-Beltrami operator. Using the direct bou…
Detects outliers in VAE latent space by identifying vacant holes.
problem Outliers detection in VAE latent space.
method Compactness enforced via Alexandroff extension and fixed Lipschitz continuity.
result Anomalous inputs land on latent holes, enabling successful identification.
Improves deep learning robustness by enforcing local and global compactness.
problem Deep neural networks' vulnerability to adversarial attacks.
method Proposes Adversary Divergence Reduction Network (ADRN) that enforces local/global compactness and clustering assumption.
result Augmenting adversarial training with ADRN components improves robustness.
New local method solves Yamabe problems on compact and non-compact manifolds.
problem Yamabe problems on compact and non-compact manifolds.
method Local method for compact and non-compact manifolds.
result Generalizes Brezis and Nirenberg's nonlinear eigenvalue problem to subsets of manifolds.
LoMC: Localized Multidirectional Correction for Refusal Suppression in Routed Foundation Modelsstat.ML
Localized Multidirectional Correction improves non-refusal target-response behavior in foundation models.
problem Controlled post-training refusal suppression in routed MoE and hybrid-MoE foundation models.
method Introduce Localized Multidirectional Correction (LoMC), a support-gated intervention framework.
result Substantially improves non-refusal target-response behavior while maintaining general capability under a compact intervention footprint.
Compact curve solution emerges from non-compact curve.
problem Constructing solutions from non-compact curves.
method Slingshot solution to curve shortening flow.
result Compact embedded solution exists for a finite time.
Learning Compact Physics-Aware Delayed Photocurrent Models Using Dynamic Mode Decompositionphysics.comp-ph
Compact models learn photocurrent dynamics from radiation-induced excess carrier density.
problem Accurate but computationally expensive physics-based photocurrent models for semiconductor devices.
method Dynamic Mode Decomposition (DMD) for learning reduced order models from internal state data.
result Physics-aware, compact delayed photocurrent models accurately approximate internal excess carrier dynamics.
New infinite families of flat spaces found from symmetric spaces.
problem Finding new flat homogeneous spaces.
method Starting from compact symmetric spaces, constructing infinite families of compact homogeneous spaces with invariant Bismut connections.
result Infinite families of compact homogeneous spaces with vanishing Ricci tensor.
Compact metrics on Heisenberg manifolds have a specific condition for being relatively compact.
problem Conditions for relatively compact sets of left invariant metrics on Heisenberg manifolds.
method Necessary and sufficient condition for relatively compact sets of left invariant metrics.
result A condition for a set of left invariant metrics to be relatively compact in the moduli space.
Compact models for methane/air combustion reduce complexity without sacrificing accuracy.
problem Creating accurate, computationally efficient models for methane combustion.
method Data-oriented three-step methodology: 1) Remove non-essential species, 2) Numerically optimize to key species profiles, 3) Machine learning to refine parameters.
result Produced 19 and 15 species compact models that outperform current state-of-the-art models in accuracy and range of conditions.