Let be a compact complex Calabi-Yau 4-fold. Under certain assumptions, we define Donaldson-Thomas type deformation invariants ( invariants) by studying moduli spaces of solutions to the Donaldson-Thomas equations on . We also study sheaves counting problems on local Calabi-Yau 4-folds. We relate …
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The article finds non-Abelian DT-instantons on non-Kähler manifolds.
problem Finding solutions to DT-instanton equations on non-Kähler manifolds.
method Constructing examples of DT-instantons for homogeneous almost Hermitian structures on the manifold of full flags in C^3.
result Explicit classification and phenomena of reducibility and disappearance of DT-instantons.
New method builds hyperbolic spheres with controlled holonomy.
problem Creating hyperbolic spheres with specific holonomy properties.
method Gluing simple building blocks to form hyperbolic cone spheres.
result Any Deroin-Tholozan representation can be realized as cone sphere holonomy.
DeepDTA predicts drug-target binding affinities using deep learning.
problem Predicting the continuum of binding strength values between drugs and targets.
method Uses deep learning, specifically CNNs, to model 1D representations of drug and target sequences.
result Deep learning model outperforms state-of-the-art methods in predicting DT binding affinities.
Enhanced VAE with DT improves flexibility in latent variable modeling.
problem Limitations of VAE's diagonal covariance matrix in matching true posterior distribution.
method Proposes dyadic transformation (DT) to model multivariate normal distributions.
result DT enhances posterior flexibility and achieves competitive results.
Study evaluates digital transformation impact on financial performance using LLMs.
problem Measuring and understanding the impact of digital transformation on financial performance.
method Constructed DT indicators from company reports; analyzed effects of different digital technologies.
result Digital transformation improves financial performance, but varies by technology.
This research assesses uncertainty quantification and sensitivity analysis for DTs in nuclear fuel performance.
problem Understanding the reliability and performance of advanced nuclear fuels using DTs.
method Introduces ML-based uncertainty quantification and sensitivity analysis methods applied to BISON fuel performance code.
result Demonstrates the effectiveness of DTs in multi-criteria decision-making for nuclear fuel performance.
Mathematical advances needed for Digital Twins, differing from traditional models.
problem Foundational mathematical advances required for Digital Twins.
method Multi-scale, multi-physics modeling and coupling, different reliability criteria and uncertainty assessments.
result AI/ML methods can perform well in biomedical problems but fail in simple engineering systems.
Boosted DT classifiers become DP with new calibrated loss.
problem Making boosted DT classifiers differentially private.
method Crafted M-loss and objective calibration.
result Significantly outperforms random forests in DP settings.
Deep Neural Operator Driven Real Time Inference for Nuclear Systems to Enable Digital Twin Solutionsstat.ML
DeepONet accelerates nuclear DT inference with high accuracy and efficiency.
problem Real-time prediction and model evaluation in nuclear systems.
method Deep Neural Operator (DeepONet) for surrogate modeling.
result DeepONet outperforms traditional ML methods in accuracy and speed.
New operators defined on supermanifolds, linked to Darboux transformations.
problem Defining and analyzing differential operators on supermanifolds.
method Defining non-degenerate operators, using super Wronskians and Berezinians, applying Darboux transformations.
result Every Darboux transformation corresponds to an invariant subspace and can be expressed by a super-Wronskian formula.
The paper constructs Darboux transforms for a specific hierarchy and its related flows.
problem Constructing and analyzing Darboux transforms for the -hierarchy.
method Using loop group factorization, the authors construct Darboux transforms and provide Permutability and scaling formulas.
result Explicit soliton solutions are constructed and provided for specific flows.
Study DT invariants on Calabi-Yau 4-folds, linking to 3-folds.
problem Counting torsion sheaves on Calabi-Yau 4-folds.
method Use Atiyah class reduction to relate 4-fold DT to 3-fold DT.
result DT invariants on Calabi-Yau 4-folds can be related to 3-folds.
GDT improves reinforcement learning by matching future state information efficiently.
problem Efficient learning of multi-task policies from trajectory data.
method Generalized Decision Transformer (GDT) for offline hindsight information matching.
result GDT enables effective offline multi-task state-marginal matching and imitation learning.
DTS improves inference-time alignment of diffusion models with less compute.
problem Inference-time alignment of diffusion models suffers from inaccurate value estimation and inefficient reuse of past computations.
method Diffusion Tree Sampling (DTS) uses a tree-based approach to propagate terminal rewards and iteratively refine value estimates.
result DTS produces asymptotically exact samples and matches the FID of best-performing baselines with up to 10x less compute.
A new Bayesian model improves dynamic texture segmentation.
problem Automatic selection of DTs in video sequences.
method Joint Dirichlet process mixture and GDTM approach with VBEM and RTSS.
result The proposed algorithm outperforms previous methods in efficiency and accuracy.
The monetary growth orderq-fin.GN
Growth of monetary assets and debts is commonly described by the formula of compound interest which for the case of continuous compounding is the exponential growth law. Its differential form is dc/dt = i c where dc/dt describes the rate of monetary growth, i the compounded interest rate and c the actual principal. Exp…
New rule reduces exploration regret to logarithmic, improving bad episode handling.
problem Improving exploration regret in average reward MDPs.
method Replacing Doubling Trick with Vanishing Multiplicative rule in EVI-based algorithms.
result Regret is logarithmic under the new rule, significantly better than linear.
Paper proposes DG-ETC for online submodular maximization with stochastic bandit feedback.
problem Online unconstrained submodular maximization with stochastic bandit feedback.
method Double-Greedy - Explore-then-Commit (DG-ETC) approach.
result DG-ETC achieves logarithmic regret for -approximate pseudo-regret.
DTS improves robustness of bandit algorithms in nonstationary environments.
problem Brittle behavior of multi-armed bandit algorithms in nonstationary exogenous factors.
method Deconfounded Thompson Sampling (DTS) that projects population-level performance while controlling for context.
result DTS provides resilience to exogenous variation and balances exploration and exploitation.
For a given time horizon DT, this article explores the relationship between the realized volatility (the volatility that will occur between t and t+DT), the implied volatility (corresponding to at-the-money option with expiry at t+DT), and several forecasts for the volatility build from multi-scales linear ARCH process…
Researchers use DT to transfer policies from one environment to another using causal reasoning.
problem Adapting to changes in environmental dynamics in reinforcement learning.
method Applying causal counterfactual reasoning to Decision Transformer (DT) architecture for policy transfer.
result DT successfully transfers a learned policy to new environments while retaining most of the reward.
Enhances RL in target domains with limited data using augmented return.
problem Utilize data from an accessible source domain to improve policy learning in a target domain with scarce data.
method Return Augmented Decision Transformer (REAG) method, which augments the return in the source domain to align with the target domain's optimal trajectory distribution.
result The proposed REAG method achieves the same level of suboptimality as without a dynamics shift, enhancing DT type frameworks' performance in off-dynamics RL.
Optimal model improves AUC, recall, and F1 score for class-imbalanced business risk.
problem Improving prediction of class-imbalanced business risk.
method Resampling, regularization, and model ensembling techniques.
result Boosting on DT with SMOTE oversampling achieves AUC, recall, and F1 score of 0.8633, 0.9260, and 0.8907, respectively.
Transform ANNs into interpretable decision trees.
problem Lack of interpretability in ANNs.
method Developed two MDT algorithms: EC-DT and Extended C-Net.
result Extended C-Net generates the most compact and effective trees.
Paper defines new risk measures for elliptical distributions.
problem Risk measurement for elliptical distributions.
method DTM, DTS, DTK definitions and formula derivation for specific distributions.
result Explicit formulas for DTE, DTV, DTS, and DTK for various distributions.
Study computes isoperimetric profiles in low-dimensional Riemannian products.
problem Estimating isoperimetric profiles in Riemannian products.
method Symmetrization techniques for product manifolds.
result Explicit lower bounds for isoperimetric profiles in and .
Mathematical structures of non-perturbative topological string theory: from GW to DT invariantshep-th
Mathematical structures link Gromov-Witten to Donaldson-Thomas invariants.
problem Understanding non-perturbative topological string theory.
method Borel summation of Gromov-Witten potential and analysis of Stokes phenomena.
result Stokes phenomena encode Donaldson-Thomas invariants of the resolved conifold.
Proposes DTS framework to predict CTR by tracking user interest evolution over time.
problem Predicting CTR by ignoring dynamic user interest changes over time.
method Integrates time information using ODEs in a neural network to model interest evolution.
result Achieves superior CTR prediction performance compared to existing methods.
This study examines hyperparameter tuning for CART and C4.5 DT algorithms.
problem Finding optimal hyperparameters for DT algorithms to improve predictive performance.
method Comprehensive empirical study with 94 datasets, using IRACE for tuning.
result Different HP profiles provide significant improvements for CART, but less for C4.5.
Scaling theory of temporal correlations and size dependent fluctuations in the traded value of stocksphysics.soc-ph
Records of the traded value f_i(t) of stocks display fluctuation scaling, a proportionality between the standard deviation sigma(i) and the average <f(i)>: sigma(i) ~ f(i)^alpha, with a strong time scale dependence alpha(dt). The non-trivial (i.e., neither 0.5 nor 1) value of alpha may have different origins and provid…
Proposes DT-LET for better transfer learning between domains with different resolutions.
problem Transfer learning between domains with different resolutions often fails due to mismatched hidden layers.
method Develops DT-LET model to find the best matching layers for transfer based on high-level feature correspondence.
result DT-LET achieves superior results in cross-domain recognition/classification tasks, validating the necessity of layer correspondence searching.
Study biharmonic submanifolds in warped product structures.
problem Characterize biharmonic submanifolds in warped product spaces.
method Analyze tension and bitension fields, relate to warping function and geometry of submanifolds.
result Characterize tangentially and normally biharmonic cases via differential conditions on the warping function.
Proof of wall-crossing formula using spectral networks.
problem Proving the Kontsevich-Soibelman wall-crossing formula.
method Path-lifting rules for spectral networks, convergence justification.
result Definition and justification of path lifting rules for spectral networks.
Decision tree predicts DV recidivism with interpretable models.
problem Predicting DV re-offending to aid risk assessment and victim protection.
method Employed decision tree induction to balance accuracy and interpretability, addressing class imbalance and feature selection.
result Achieved comparable accuracy with 3 features and understandable 4-node trees.
Dehn filling Dehn twistsmath.GR
The study examines the properties of mapping class groups under specific Dehn twist subgroups.
problem Characterizing the structure of mapping class groups under Dehn twist subgroups.
method Analyzes the mapping class group for various and , using properties of hyperbolic graphs and actions.
result The mapping class group is hyperbolic in low complexity cases.
Stochastic volatility of financial markets as the fluctuating rate of trading: an empirical studyphysics.soc-ph
We present an empirical study of the subordination hypothesis for a stochastic time series of a stock price. The fluctuating rate of trading is identified with the stochastic variance of the stock price, as in the continuous-time random walk (CTRW) framework. The probability distribution of the stock price changes (log…
Random Forests are reinterpreted as generative models to handle missing data and detect outliers.
problem Handling missing features and detecting outliers in Random Forests.
method Interpreting Random Forests as Probabilistic Circuits and applying marginalisation for missing data.
result GeDTs and GeFs can handle missing data and detect outliers under certain assumptions.
We develop DTs for PDE models using KL-NN and TL, analyzing TL's moment equations and one-shot learning for exactness.
problem Creating accurate digital twins for systems governed by PDEs under changing conditions.
method We use KL-NN surrogate models and transfer learning to construct DTs, analyzing the moment equations and proposing one-shot and few-shot learning methods.
result For linear PDEs, one-shot TL is exact; for nonlinear PDEs, some parameters can be transferred with minimal error.
GANs generate training data for machine learning tasks.
problem Imbalanced data sets and sensitive information.
method Generative Adversarial Networks (GANs) to create artificial training data.
result A Decision Tree classifier trained on GAN-generated data achieved similar or better accuracy and recall than on original data.
Neural networks estimate SDEs with jump noise using a Tamed-Milstein scheme.
problem Estimating drift and diffusion functions in SDEs with jump noise.
method Tamed-Milstein scheme with neural networks as non-parametric approximators.
result Flexible estimation of complex nonlinear dynamics in systems with state-dependent noise.
Neural Decision Treesstat.ML
Proposes neural decision trees combining neural networks and decision trees.
problem Combining neural networks and decision trees for improved modeling power.
method Integrates multilayer perceptrons into decision tree structure with weight sharing.
result Demonstrates improved modeling power and flexibility over standard decision trees and MLPs.
We review a construction of hyperkahler metrics proposed in joint work of Davide Gaiotto, Greg Moore and the author. A key ingredient in this construction is a collection of integer "DT invariants" obeying the wall-crossing formula of Kontsevich-Soibelman.
Study online pricing with contextual elasticity and heteroscedastic valuation.
problem Online contextual dynamic pricing with customer decision based on features and price.
method Introduced a novel approach to modeling customer demand with feature-based price elasticity and heteroscedastic noise. Proposed an efficient algorithm called Pricing with Perturbation (PwP).
result Proved an regret bound for the algorithm, matching a lower bound of .
Proportional transaction costs present difficult theoretical problems in trading algorithm design, on account of their lack of analytical tractability. The author derives a solution of DT-NT-DT form for an arbitrary model in which the the traded asset has diffusive dynamics described by one or more stochastic risk fact…
This paper considers the problem of learning the parameters in Bayesian networks of discrete variables with known structure and hidden variables. Previous approaches in these settings typically use expectation maximization; when the network has high treewidth, the required expectations might be approximated using Monte…
Study shows market volatility arises from riskless opportunities not being available.
problem Origins of market volatility in finance and stochastic differential equations.
method Game-theoretic framework, focusing on fractal dimensions and riskless opportunities.
result Riskless opportunities for making money lead to market volatility, which is incompatible with high volatility.
LDTA expands LDA's topic modeling capacity with tree-structured priors.
problem Limited expressiveness of Dirichlet priors in LDA for complex topic relationships.
method Introduces Latent Dirichlet-Tree Allocation (LDTA) with Dirichlet-Tree (DT) priors, and develops universal mean-field variational inference and Expectation Propagation.
result LDTA enables expressive, tree-structured priors over topic proportions, expanding modeling capacity of LDA.