Neural networks predict ODT formulations, reducing development time.
problem Efficiently predicting ODT formulations for quality control.
method Artificial Neural Network (ANN) and Deep Neural Network (DNN) techniques.
result DNN model outperformed ANN in predicting ODT disintegrating time.
AI helps complete ancient tablets with missing parts.
problem Fragmented ancient tablets lack information.
method Used recurrent neural networks to model ancient language.
result Automatic completion of ancient texts possible.
New tools for constructing disintegrations and studying their modes.
problem Difficulty in constructing disintegrations and understanding their modes.
method Developed comprehensive mathematical tools for constructing disintegrations and analyzing their modes.
result Disagreement between restricted density and disintegration density in certain cases.
AI detects oral pre-cancerous lesions with high accuracy.
problem Manual screening of oral cavity cancer is expensive and lacks specialists.
method Deep convolutional neural networks (DCNNs) using transfer learning.
result DCNN models achieve high accuracy in distinguishing between benign and pre-cancerous tongue lesions.
Digital RNN improves dysgraphia detection in handwriting tests.
problem Early detection and remediation of handwriting difficulties.
method Recurrent Neural Network (RNN) model using a graphics tablet.
result RNN diagnoses dysgraphia with over 90% accuracy.
Proves uniqueness of barycenters on manifolds without restrictions.
problem Finding unique barycenters on complex geometric spaces.
method Introduces new disintegrated Monge-Kantorovich metrics for barycenter problems.
result Uniqueness of barycenters on connected, complete Riemannian manifolds.
A new method splits diffusion operators on principal bundles, leading to disintegration theorems.
problem Diffusion operators on principal bundles with constant rank.
method Defining semi-connections and splitting diffusion operators into horizontal and vertical components.
result A disintegration theorem for the law of diffusion operators on principal bundles.
New PAC-Bayesian bounds provide practical guarantees for neural networks.
problem Loose derandomization step in PAC-Bayesian bounds for deterministic models.
method Introduce disintegrated PAC-Bayesian bounds for deterministic models.
result Significant practical improvement over state-of-the-art bounds.
Automated process links oral health to systemic conditions using machine learning.
problem Correlating oral health with systemic health conditions.
method Intraoral fluorescent biomarker imaging, machine learning segmentation, and clinical examination.
result Machine learning classifier achieved AUC of 0.677, indicating a learned association between disease signatures in images and periodontal disease.
Classifies invariant measures on specific character varieties.
problem Classifying invariant probability measures on character varieties.
method Measure disintegration along transverse Lagrangian tori fibrations.
result Ergodic measures are either counting measures on finite orbits or Liouville measures.
Study arbitrage theory without numéraire, generalizing NUPBR.
problem Arbitrage theory in markets without numéraire.
method Disintegration of probability space into crash times.
result Generalization of NUPBR to no unbounded profits with bounded risk.
ABC improves uncertainty quantification in LLMs for clinical diagnostics.
problem Overconfident and poorly calibrated estimates of LLMs in clinical domains.
method Approximate Bayesian Computation (ABC) for likelihood-free inference.
result Improves accuracy by up to 46.9%, reduces Brier scores by 74.4%, and enhances calibration.
Estimates network structure and interaction rules from multiple agent trajectories.
problem Modeling multi-agent systems on networks from data.
method Jointly infers network topology and interaction kernels using non-convex optimization.
result ORALS estimator is consistent and asymptotically normal under coercivity conditions.
The paper extends localisation technique to multiple constraints in Euclidean spaces.
problem Proving log-concavity of conditional measures in decomposed convex sets.
method Defining partitions of maximal closed convex sets and proving log-concavity of conditional measures.
result Existence of a partition and log-concavity of conditional measures for almost every set of the partition.
What happens when the Supreme Court of the United States decides a case impacting one or more publicly-traded firms? While many have observed anecdotal evidence linking decisions or oral arguments to abnormal stock returns, few have rigorously or systematically investigated the behavior of equities around Supreme Court…
We consider the question of learning in general topological vector spaces. By exploiting known (or parametrized) covariance structures, our Main Theorem demonstrates that any continuous linear map corresponds to a certain isomorphism of embedded Hilbert spaces. By inverting this isomorphism and extending continuously, …
For quantitative structure-property relationship (QSPR) studies in chemoinformatics, it is important to get interpretable relationship between chemical properties and chemical features. However, the predictive power and interpretability of QSPR models are usually two different objectives that are difficult to achieve s…
Improved bounds on learning algorithms' performance using conditional mutual information.
problem Bounding the generalization error of learning algorithms.
method Introducing conditional mutual information and disintegrated mutual information to tighten bounds.
result New bounds are tighter than previous ones, especially for noisy, iterative algorithms.
Study estimates treatment effect on survival outcomes using targeted maximum likelihood estimation.
problem Estimating treatment effect on time-to-event outcomes in clinical settings.
method Divided into three phases: estimation, feature selection, and targeted maximum likelihood estimation.
result Method performs well in high sample size or event rate conditions.
Study heat content on RCD(K,N) spaces with specific boundary conditions.
problem Analyzing heat content in RCD(K,N) spaces with irregular boundaries.
method Proved first-order asymptotics using measured interior geodesic condition.
result Established first-order heat content asymptotics on RCD(K,N) spaces.
New bounds improve neural network generalization through slicing.
problem Difficulty in evaluating mutual information in high dimensions for neural networks.
method Slicing the parameter space and using disintegrated mutual information and k-sliced mutual information.
result Slicing improves generalization and offers significant computational and statistical advantages.
New bounds for model generalization under deterministic gradient descent.
problem Establishing generalization bounds for models trained with gradient descent methods.
method PAC-Bayesian bounds for deterministic optimisation algorithms.
result Fully computable bounds that depend on initial distribution and Hessian.
The study proves curvature bounds for quotient spaces of isometric actions.
problem Proving curvature bounds for quotient spaces of isometric actions.
method Disintegrate absolutely continuous measures and define a functional to prove curvature bounds.
result Necessary and sufficient conditions for Ricci curvature to be bounded below.
Study SRB measures for Anosov actions on manifolds.
problem Characterize SRB measures for Anosov actions.
method Use Ruelle-Taylor resonances and properties of Sinai-Ruelle-Bowen measures.
result SRB measures have properties like smooth disintegrations, positive basins, and are unique under certain conditions.
Deep learning improves Android malware detection.
problem Detecting and preventing Android malware.
method Review of static, dynamic, and hybrid deep learning approaches.
result Identifies strengths and weaknesses of deep learning methods.
Paper develops a new generalization bound using PAC-Bayes theory and Gibbs distributions.
problem Limits of traditional generalization bounds due to complexity measures.
method Leverages PAC-Bayes bounds with Gibbs distributions to derive a flexible generalization bound.
result Derives a generalization bound that can adapt to both hypothesis class and task complexity.
New risk measures control subgroup imbalances, improving PAC-Bayesian bounds.
problem Insufficient risk bounds for subgroup imbalances in data.
method Introduce constrained f-entropic risk measures and derive PAC-Bayesian bounds.
result First disintegrated PAC-Bayesian guarantees beyond standard risks.
Automated suggestions help train technicians diagnose incidents faster.
problem Manual and time-consuming incident diagnosis by train maintenance technicians.
method Developed and deployed a learning machine to suggest diagnostics to technicians.
result The model refines its accuracy through feedback from experts and uses feature engineering.
Sparse algorithms reduce model size for faster keyword spotting.
problem Large models are hard to deploy on resource-limited devices.
method Apply sparse algorithms to reduce parameter count in DNN KWS models.
result Sparse models perform better with minimal parameter loss.
DataLearner simplifies data mining on Android devices.
problem Lack of general-purpose data-mining tools for mobile devices.
method Augments Weka engine with Charles Sturt University algorithms, providing 40 mining algorithms.
result Delivers classification accuracy similar to PCs/laptops with acceptable speed and battery life.
The curve graph and related graphs are hyperbolic and have quasi-tree fibers.
problem Understanding the structure of the curve graph and related graphs.
method Analyzing a sequence of graphs with Lipschitz maps and proving hyperbolicity and quasi-tree properties.
result The graphs in the sequence are hyperbolic and have quasi-tree fibers, leading to bounds on asymptotic dimension and acylindrical actions.
DL-Droid detects Android malware using deep learning and real devices.
problem Sophisticated Android malware detection challenges traditional methods.
method Deep learning system with stateful input generation on real devices.
result DL-Droid achieves up to 99.6% detection rate with dynamic + static features.
Paper addresses the disparity between sampled and mean representations in disentangled learning.
problem Disparity between sampled and mean representations in disentangled learning.
method Proposes a method to eliminate the disparity by proving and utilizing the relationship between total correlation of sampled and mean representations for multivariate normal distributions.
result Demonstrates that a factorized mean representation can have lower total correlation than the sampled representation.
This study uses bandit algorithms to predict Warfarin dosages more accurately.
problem Determining the correct initial Warfarin dosage is challenging due to patient variability and adverse effects.
method Developed and evaluated linear bandit algorithms on real data from PharmGKB.
result Proposed algorithms outperformed fixed-dose and clinical algorithms.
Survey of big data in cyber-physical systems, including data security and green challenges.
problem Managing vast amounts of data in cyber-physical systems.
method Taxonomy and overview of data collection, storage, access, processing, and analysis.
result First panoramic survey on big data for CPS, addressing cybersecurity and green challenges.
Trade finance history traced from medieval origins to modern markets.
problem Evolution and standardization of trade finance products.
method Historical analysis of market structures and regulatory changes.
result Global trade finance market evolved from local to centralized, then decentralized.
Study finds non-adherence to schizophrenia meds leads to earlier adverse events.
problem Impact of medication non-adherence on adverse outcomes in schizophrenia patients.
method Survival analysis, causal inference methods (T-learner, S-learner, nearest neighbor matching), different amounts of longitudinal information.
result Non-adherence to schizophrenia meds advances adverse events by 1 to 4 months.
New bounds using samplewise evaluated CMI for deep neural networks.
problem Improving generalization bounds for deep neural networks.
method Introduced a new family of information-theoretic generalization bounds using samplewise evaluated conditional mutual information (CMI).
result The new bounds can be tighter than previous ones for deep neural networks.
Develops a new framework for conditional independence.
problem Generalizing previous notions of conditional independence.
method Introduces transition probability spaces and transitional random variables.
result Satisfies all desired relevance relations except symmetry.
Machine learning and topological data analysis identify unique geometric and topological features of human papillae.
problem Identifying unique features of human papillae across individuals.
method 3D microscopic scans, machine learning, discrete differential geometry, computational topology, persistent homology.
result Persistent homology features of papillae shape predict papillae type with high accuracy and can identify individuals with high accuracy.
Convolutional neural networks improve biopsy image classification for diagnosing Celiac Disease and Environmental Enteropathy.
problem Diagnosing Celiac Disease and Environmental Enteropathy from biopsy images due to histopathologic overlap.
method Proposed a convolutional neural network (CNN) to classify duodenal biopsy images.
result The proposed model achieves high accuracy in classifying biopsy images for CD, EE, and healthy controls.
The paper studies optimal transport for vector measures and confirms a conjecture about their conditional measures.
problem Optimal transport of vector measures and conditional measures.
method Developed a theory of optimal transport for vector measures and used it to answer a conjecture.
result The conditional measures of vector measures have total mass zero under certain conditions.
Study approximates operators on labelled conditional distributions for non-exchangeable systems.
problem Approximating operators on constrained probability measures for non-exchangeable systems.
method Combines cylindrical approximations and DeepONet-type neural architecture for finite-dimensional representations.
result Establishes a universal approximation theorem for continuous operators on Mλ. Study improves drug prediction accuracy for pharmacokinetic parameters.
problem Limited accuracy in predicting pharmacokinetic parameters.
method Integrated transfer learning and multitask learning approach.
result Improved model generalization and predictive ability.
DKF uses nonlinear, Gaussian approximations for better neural decoding.
problem Improving neural decoding for brain-computer interfaces.
method Developed a Discriminative Kalman Filter (DKF) for nonlinear, non-Gaussian state estimation.
result DKF successfully enabled quadriplegic users to control devices using mental imagery.
Software and hardware co-design and optimization of HPC systems has become intolerably complex, ad-hoc, time consuming and error prone due to enormous number of available design and optimization choices, complex interactions between all software and hardware components, and multiple strict requirements placed on perfor…
New method reduces memory usage for Bayesian inverse problems on large grids.
problem Solving large-scale linear inverse problems with Gaussian process priors.
method Implicit representation of posterior covariance matrices, sequential disintegrations of Gaussian measures.
result Significant reduction in uncertainty for high-density regions estimation.
The paper analyzes financial market turbulence using mathematical physics.
problem Understanding price fluctuations caused by information asymmetry.
method Spectrum analysis to decompose pricing patterns.
result Identifies phase correlations in financial stock market turbulence.