Framework assesses treatment effects by risk groups in observational studies.
problem Evaluating treatment effects in observational studies with risk stratification.
method Five-step framework for risk-based assessment of treatment effect heterogeneity.
result Low-risk patients received negligible absolute benefits, while high-risk patients had pronounced effects.
Deep neural network identifies potential SARS-CoV-2 inhibitors.
problem Finding novel therapies for SARS-CoV-2.
method Used ChemAI, a deep neural network trained on 220M data points, to screen and rank one billion molecules from the ZINC database.
result Identified 30,000 top-ranked compounds for further bioassays.
Deep generative model discovers inhibitors for unknown targets.
problem Discovering novel inhibitor molecules for unknown drug targets.
method Deep generative framework trained on protein sequences, small molecules, and interactions.
result Micromolar-level inhibition observed for two out of four synthesized candidates, including activity against SARS-CoV-2 variants.
QSAR models struggle to predict activity cliffs, but graph isomorphism features improve AC-sensitivity.
problem QSAR models struggle to predict activity cliffs (ACs).
method Nine distinct QSAR models combining molecular representation methods and regression techniques.
result Graph isomorphism features improve AC-sensitivity.
We used machine learning methods to predict NaV1.7 inhibitors and found the model RF-CDK that performed best on the imbalanced dataset. Using the RF-CDK model for screening drugs, we got effective compounds K1. We use the cell patch clamp method to verify K1. However, because the model evaluation method in this article…
The optimization of algorithm (hyper-)parameters is crucial for achieving peak performance across a wide range of domains, ranging from deep neural networks to solvers for hard combinatorial problems. The resulting algorithm configuration (AC) problem has attracted much attention from the machine learning community. Ho…
ACS is an interactive framework for model-free selection with guaranteed error control.
problem Model-free selection with rigorous error control.
method Adaptive conformal selection with human-in-the-loop data exploration and new information incorporation.
result ACS provides concrete selection algorithms for various goals, including model update/selection, diversified selection, and incorporating new data.
We construct a functor AC(−,−) from the category of path connected spaces X with a base point x to the category of simply connected spaces. The following are the main results of the paper: (i) If X is a Peano continuum then AC(X,x) is a cell-like Peano continuum; (ii) If X is n−dimensional then AC(X,x)…
Paper proposes a fast data-driven AC-OPF method using sparse hybrid Gaussian processes.
problem Optimizing electricity generation and delivery under generation uncertainty in modern power grids.
method Data-driven approach using sparse hybrid Gaussian processes to model power flow equations.
result Shows up to two times faster and more accurate solutions compared to state-of-the-art methods.
A virtual knot that has a homologically trivial representative K in a thickened surface Σ×[0,1] is said to be an almost classical (AC) knot. K then bounds a Seifert surface F⊂Σ×[0,1]. Seifert surfaces of AC knots are useful for computing concordance invariants and slice ob…
Paper accelerates nonlinear mapping in online systems with lower time complexity.
problem Speeding up nonlinear mapping in online systems.
method Integrates an acceleration module into Dendrite Net (DD) to reduce time complexity.
result DD with AC has lower time complexity while maintaining nonlinear mapping and system identification properties.
This paper improves sample complexity for AC and NAC algorithms under Markovian sampling.
problem Improving sample complexity for actor-critic and natural actor-critic algorithms.
method Characterizes convergence rate and sample complexity under Markovian sampling and mini-batch data.
result Improves sample complexity for AC and NAC algorithms by orders of magnitude.
We construct monopoles in any asymptotically conical (AC) 3-manifold X with b2(X)=0. For sufficiently large mass, our construction covers an open set in the moduli space of monopoles. We also give a more general construction of Dirac monopoles in any AC manifold, which may be useful for generalizing our result t…
Develops a machine learning approach for solving AC-OPF problems.
problem Nonlinear and computationally demanding AC chance-constrained OPF problem.
method Uses Gaussian process regression to approximate AC power flow equations.
result Demonstrates competitive and promising results compared to state-of-the-art approaches.
We give a description of Gray AC^{\perp} manifolds (M,g) whose Ricci tensor has two eigenvalues of multiplicity 1 and dim M-1.
New quantum models unify Alexander and generalized Alexander polynomials for AC links.
problem Defining and distinguishing AC links and virtual knots.
method Generalizing AC links to virtual tangles and using quantum supergroups.
result Generalized Alexander polynomials are distinct from Alexander polynomials for AC links.
Mathematical Reinforcement Learning faces a 'Two-Hump' problem due to sparse rewards and a scarcity of intermediate 'hard-but-solvable' instances.
problem Mathematical search problems in Reinforcement Learning
method Novel data generation techniques and algorithmic enhancements
result Substantial performance improvements over previous baselines
New methods solve saddle point problems without line search.
problem Solving saddle point problems efficiently and adaptively.
method Auto-conditioned primal-dual hybrid gradient (AC-PDHG) and auto-conditioned ADMM (AC-ADMM) methods.
result Methods achieve optimal complexity and convergence guarantees.
ACE improves counterfactual explanations with fewer model queries.
problem Inefficient sampling for counterfactual explanations in machine learning models.
method Adaptive sampling combining Bayesian estimation and stochastic optimization.
result ACE achieves superior evaluation efficiency compared to state-of-the-art methods.
In this paper, we develop an online method that leverages machine learning to obtain feasible solutions to the AC optimal power flow (OPF) problem with negligible optimality gaps on extremely fast timescales (e.g., milliseconds), bypassing solving an AC OPF altogether. This is motivated by the fact that as the power gr…
We study the {\it arc and curve} complex AC(S) of an oriented connected surface S of finite type with punctures. We show that if the surface is not a sphere with one, two or three punctures nor a torus with one puncture, then the simplicial automorphism group of AC(S) coincides with the natural image of the exten…
Machine learning and deep learning have gained popularity and achieved immense success in Drug discovery in recent decades. Historically, machine learning and deep learning models were trained on either structural data or chemical properties by separated model. In this study, we proposed an architecture training simult…
In this paper, we introduce Anomaly Contribution Explainer or ACE, a tool to explain security anomaly detection models in terms of the model features through a regression framework, and its variant, ACE-KL, which highlights the important anomaly contributors. ACE and ACE-KL provide insights in diagnosing which attribut…
New example of non-Kähler soliton with Kähler-like behavior at infinity.
problem Constructing non-Kähler expanding gradient Ricci solitons.
method Asymptotically conical (AC) construction with Kähler tangent cone at infinity.
result Example of a non-Kähler soliton with a Kähler-like behavior at infinity.
The ACS criterion is verified for specific hypersurfaces in unit spheres.
problem Verifying the ACS criterion for minimal isoparametric hypersurfaces in unit spheres.
method Moment-relaxation technique and explicit extremal configurations.
result The ACS condition holds under specific conditions on principal curvatures.
This work analyzes how neural networks learn representations in actor-critic algorithms.
problem Theoretical support for neural AC algorithms is limited to linear function approximations.
method Mean-field analysis of a two-timescale learning AC algorithm with overparameterized networks.
result Neural AC finds the globally optimal policy at a sublinear rate in the continuous-time and infinite-width limiting regime.
New method uses Riemannian geometry to quantify molecular shapes.
problem Quantifying molecular similarity for drug discovery.
method Riemannian geometry and Kähler quantization (KQMolSA).
result KQMolSA method compares well to existing shape similarity methods.
Connectedness proved for Zd actions on 1D manifolds by C2 diffeomorphisms.
problem Connectedness of Zd actions by C2 diffeomorphisms on 1D manifolds. method Proved connectedness through continuous paths of C1+ac diffeomorphisms. result Connectedness of Zd actions by C2 diffeomorphisms on 1D manifolds. GE2E-AC improves accent classification by focusing on accent embeddings.
problem Training models to predict accent type can lead to learning irrelevant features.
method GE2E-AC trains models to extract accent embeddings, making them closer for the same accent class.
result GE2E-AC outperforms baseline models trained with conventional loss.
We consider the deformation theory of asymptotically conical (AC) and of conically singular (CS) G2-manifolds. In the AC case, we show that if the rate of convergence ν to the cone at infinity is generic in a precise sense and lies in the interval (−4,0), then the moduli space is smooth and we compute its dimen…
Paper uses Gaussian processes to solve AC-OPF with renewable uncertainty.
problem Optimizing power grids with fluctuating renewable sources.
method Data-driven approach using Gaussian processes.
result Efficiently solves chance-constrained AC-OPF with uncertainty.
Ultrasound diagnosis is routinely used in obstetrics and gynecology for fetal biometry, and owing to its time-consuming process, there has been a great demand for automatic estimation. However, the automated analysis of ultrasound images is complicated because they are patient-specific, operator-dependent, and machine-…
Anomaly detection is one of the frequent and important subroutines deployed in large-scale data processing systems. Even being a well-studied topic, existing techniques for unsupervised anomaly detection require storing significant amounts of data, which is prohibitive from memory and latency perspective. In the big-da…
ACE improves GBI for simulators by approximating cost functions, making inference more efficient.
problem Inference for misspecified simulators is overly restrictive.
method Amortized cost estimation (ACE) for Generalized Bayesian Inference (GBI).
result ACE provides accurate cost predictions and more efficient inference.
Paper analyzes convergence rates of two time-scale AC and NAC algorithms.
problem Finite-sample convergence rate analysis of two time-scale AC and NAC algorithms.
method Developed novel techniques for bias error and convergence rate analysis.
result Established non-asymptotic convergence rates for two time-scale AC and NAC.
ACE models allow flexible conditioning and prediction of latent variables.
problem Lack of flexibility in conditioning and prediction of latent variables in probabilistic models.
method Introduces Amortized Conditioning Engine (ACE) that explicitly represents latent variables and allows runtime conditioning and prediction.
result ACE models outperform existing methods in diverse tasks like image completion, classification, Bayesian optimization, and simulation-based inference.
This paper improves convergence bounds for AC and NAC algorithms with function approximation.
problem Improving convergence bounds for actor-critic algorithms with function approximation.
method Non-asymptotic analysis of AC and NAC algorithms with compatible function approximation.
result Eliminates the term ε_critic from the error bounds while maintaining best known sample complexities.
New method uses Riemannian geometry to describe molecular shapes.
problem Predicting drug-like molecules using shape similarity.
method Riemannian geometry applied to molecular surfaces.
result RGMolSA method captures molecular shape effectively.
Study on Hermitian Calabi functional in complexified orbits of symplectic manifolds.
problem Analyzing the Hermitian Calabi functional on complexified orbits of symplectic manifolds.
method Explicit formula for Hessian of Hermitian Calabi functional, semi-positive definiteness proof, and weak parabolicity of Hermitian Calabi flow.
result Hessian of Hermitian Calabi functional is semi-positive definite on complexified orbits.
This work uses a SI-DNN to predict AC-OPF solutions efficiently.
problem Efficiently predicting AC-OPF solutions in real-time power systems.
method Sensitivity-Informed Deep Neural Network (SI-DNN) for AC-OPF.
result SI-DNN can predict AC-OPF solutions with better generalization and constraint satisfaction.
Sensors which use electromagnetic induction (EMI) to excite a response in conducting bodies have long been investigated for subsurface explosive hazard detection. In particular, EMI sensors have been used to discriminate between different types of objects, and to detect objects with low metal content. One successful, p…
ACE improves GFlowNet exploration efficiency by balancing complementary search strategies.
problem Efficient exploration of diverse high-probability regions in GFlowNets.
method Adaptive Complementary Exploration (ACE) trains a separate GFlowNet to search underexplored regions.
result Significantly improves approximation accuracy and diverse state discovery.
Researchers found a family of Sp(2)-invariant solitons for Laplacian flow.
problem Analyzing Sp(2)-invariant solitons for Laplacian flow on the 4-sphere.
method Used mathematical analysis and asymptotic cone determination.
result Identified a 1-parameter family of Sp(2)-invariant expanding solitons with specific asymptotic behavior.
We construct infinitely many new 1-parameter families of simply connected complete noncompact G_2-manifolds with controlled geometry at infinity. The generic member of each family has so-called asymptotically locally conical (ALC) geometry. However, the nature of the asymptotic geometry changes at two special parameter…
Obstructs 2-torsion in rational knot concordance group.
problem Identifying 2-torsion elements in rational knot concordance group.
method Localized von Neumann ρ-invariant.
result Provides an obstruction for knots of order 2 in algebraic rational concordance group from being of finite order in rational knot concordance group.
Uniqueness proven for specific types of geometric structures.
problem Proving uniqueness of asymptotically conical gradient shrinking solitons.
method Extends Kotschwar and Wang's argument for uniqueness of AC gradient shrinking Ricci solitons.
result G_2-structures are equivalent if asymptotically conical and asymptotic to the same closed G_2-cone.
This work uses SVM to identify track component failures in AC Track Circuits.
problem Detecting and identifying specific track component failures in AC Track Circuits.
method Applied SVM classifier to STDS track circuit data.
result Successfully classified 15 different track component failures.
Proposes a method to assess unobserved confounding effects in causal inference.
problem Assessing unobserved confounding in causal inference studies.
method Copula-based normalizing flows with sensitivity parameter ρ. result Estimates average causal effect (ACE) as a function of unobserved confounding strength.