The study clusters neighborhoods based on childhood vulnerability data and program retention rates.
problem Improving childhood outcomes and decision-making in early childhood development.
method Data-driven approaches combining public and private sources to visualize children's experiences and analyze program retention rates.
result Neighborhoods can be clustered based on childhood vulnerability, and certain programs have higher retention rates.
Study aims to predict childhood mental health trajectories using novel models.
problem Challenges in identifying and predicting childhood mental health issues.
method Developed latent class mixture models and Gaussian process models.
result Latent class mixture models predict trajectories faster with similar accuracy.
Review of childhood data for predicting overweight and obesity in later life.
problem Predicting overweight and obesity in later life using childhood data.
method Bibliographic searches and iterative searching of references.
result High performance prediction models often use short time periods or late childhood data.
AR app enhances young children's understanding of Wudhu.
problem Teaching Wudhu to young children is challenging.
method Needs analysis, system design, implementation, testing (Black Box Testing).
result AR technology improves children's learning interest and understanding of Wudhu.
3D CNN accurately classifies infant neurodevelopmental age from MRI scans.
problem Estimating neurodevelopmental age in infants from MRI data.
method 3D Convolutional Neural Network (3D CNN) trained on MRI images of 112 infants.
result 3D CNN achieves 99% sensitivity and 98.3% specificity in age classification.
Transformer model outperforms classical methods in childhood anemia prediction across diverse countries.
problem Generalizing childhood anemia prediction models across different countries and data scarcity.
method Transformer-based tabular foundation model compared to classical supervised methods using DHS data.
result Transformer model achieves lower Brier score and ECE in low-data settings, outperforming classical models.
Paper addresses measurement error in observational data, estimating effects of maternal smoking and opioid use on childhood obesity.
problem Systematic bias in inferences from observational datasets due to measurement error.
method Missing data view of measurement error problem; marginalizes latent true exposure; identifies outcome distribution under specific conditions.
result Method estimates effects of maternal smoking and opioid use on childhood obesity using only subject-reported data, refining estimates and consistency with existing literature.
BiLiNGAM model reveals brain emotion circuit development in adolescents.
problem Understanding brain emotion circuit development during adolescence.
method Bayesian incorporated linear non-Gaussian acyclic model (BiLiNGAM) for multiple DAGs estimation.
result BiLiNGAM reveals unique developmental hub structures and group-specific patterns in emotion-related intra- and inter-modular connectivity.
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.
Novel approach uses quasi-conformal geometry for OSA classification from cephalometry.
problem Classifying obstructive sleep apnea (OSA) based on craniofacial profiles.
method Quasi-conformal geometry for local deformation analysis of 15 landmark points in lateral cephalograms.
result Proposed model achieves 92.5% testing accuracy.
New method robust to random distributional shifts in prediction.
problem Random distributional shifts in real-world settings.
method Hybrid approach combining long-term and proxy outcomes.
result Hybrid approach yields lower mean-squared error than current methods.
The theory of complex hyperbolic discrete groups is still in its childhood but promises to grow into a rich subfield of geometry. In this paper I will discuss some recent progress that has been made on complex hyperbolic deformations of the modular group and, more generally, triangle groups. These are some of the simpl…
A ML-based method reconstructs 3D organ doses from 2D radiographs for pediatric abdominal radiotherapy.
problem Reconstructing detailed 3D dose distributions for childhood cancer survivors using limited 2D radiographs.
method Surrogate-free ML approach using 142 abdominal planning CTs, 300 artificial plans, and evolutionary algorithm.
result Accurate 3D dose reconstructions with MAEs ≤ 1.7 Gy for edge organs, validated on independent dataset.
Researchers develop a neural network that learns like humans, overcoming forgetting and structure issues.
problem Catastrophic forgetting and structure limitations in neural networks.
method Memory playback strategy and dynamic structure extension using conditional variational autoencoder (CVAE).
result The method effectively prevents forgetting and allows for dynamic network growth.
Paper tackles tensor decomposition for unaligned observations using RKHS and novel loss functions.
problem Tackles tensor decomposition for unaligned observations.
method Uses functions in RKHS to represent mode with unaligned observations, introduces versatile loss function, proposes optimization algorithm and stochastic gradient method.
result Demonstrates improved tensor decomposition efficiency and effectiveness with synthetic and real data.
Deep learning predicts asthma ED visits better than traditional methods.
problem Predicting asthma-related ED visits to improve patient management.
method Deep learning (Artificial Neural Networks) compared to Lasso logistic regression.
result Deep learning model (ANN) outperforms traditional Lasso logistic regression (AUC = 0.845 vs. AUC = 0.842).
Beta and gamma rhythms mediate different maturation trajectories of cortical networks.
problem Understanding how distinct cortical rhythms influence network maturation.
method Magnetoencephalography (MEG) to map frequency band-specific maturation from age 7 to 29 in 162 participants.
result Beta band mediated networks follow a linear trajectory, while gamma band networks follow an asymptotic one.
Sparse ELM classifier predicts brain ages from adolescent multimodal brain data.
problem Predicting brain ages from adolescent multimodal brain data with high accuracy.
method Sparse ELM classifier using residual errors for feature pruning.
result RES-ELM classifier outperforms conventional and sparse Bayesian learning ELM.
Combining experimental and observational data for long-term causal effects.
problem Estimating causal effects of treatment on long-term outcomes using mixed data types.
method Three approaches for fusing experimental and observational data: equal confounding, shared confounder, and proxy variables.
result Developed estimators for each approach and analyzed their robustness.
BIG-bench benchmarks language models, revealing their strengths and weaknesses.
problem Understanding and quantifying the capabilities of large language models.
method Developed the BIG-bench benchmark with 204 tasks from various domains, evaluated across multiple model sizes and types.
result Model performance improves with scale but remains poor, with breakthrough behaviors often involving multiple steps.
Efficiently solves Elastic Net in high dimensions with Newton method.
problem Feature selection in high-dimensional data with non-negligible collinearity.
method Semi-smooth Newton Augmented Lagrangian Method.
result Significantly reduces computational cost compared to competitors.
This paper introduces a novel recalibration method for multivariate forecasts.
problem Multivariate calibration for potentially misspecified models.
method Local mappings between marginal probability integral transform values and observed space, using K-nearest neighbors or normalizing flows.
result Demonstrated effectiveness on currency exchange rate and childhood malnutrition data.
Robot learns to imitate human interactions through deep learning.
problem Teaching robots to coordinate actions with human partners.
method Deep learning framework for motion embedding, prediction, and trajectory generation.
result Importance of predictive and adaptive components for successful imitation.
Study proposes deep learning techniques to diagnose and differentiate Celiac Disease and Environmental Enteropathy from biopsy images.
problem Challenging histopathologic overlap between Celiac Disease and Environmental Enteropathy in biopsy images.
method Color balancing and Random Multimodel Deep Learning (RMDL) architecture to address staining variability.
result Proposed deep learning techniques improve diagnosis accuracy of Celiac Disease and Environmental Enteropathy.
AI predicts dyslexia and dysgraphia in children.
problem Early detection and assessment of dyslexia and dysgraphia.
method Machine learning on datasets of children's handwriting and audio recordings.
result Preliminary model shows high performance in classifying dyslexic and dysgraphic children.
Natural experiment dataset reveals inconsistent treatment effect estimators.
problem Inconsistent results from over 20 estimators on a new dataset.
method Created a benchmark to evaluate estimator accuracy, derived variance formula, introduced new estimator.
result Doubly robust estimators outperform others by orders of magnitude.
The paper extends Cartan development to infinite dimensional Lie groups.
problem Generalizing Cartan development to infinite dimensional Lie groups.
method Generalization of Cartan development to infinite dimensional manifolds and Lie groups.
result The tangent mapping of a Cartan development is another Cartan development.
Curves can bound only finitely many developable surfaces.
problem Bounding developable surfaces with nonvanishing mean curvature.
method Proof of finiteness for developable surfaces with prescribed boundary curves.
result Generic curves bound only finitely many developable surfaces.
The paper studies singularities on parallels of tangent developable surfaces of frontal curves.
problem Understanding singularities on parallels of tangent developable surfaces.
method Generalization of tangent developable surfaces and parallel deformations for frontal curves in arbitrary dimensions.
result Classification of generic singularities on parallels of tangent developable surfaces for frontal curves in 3 or 4 dimensional Euclidean spaces.
Paper presents a model to measure economic growth and development.
problem Measuring relative economic growth of different systems.
method S-Shaped model with linear representation to indicate growth, development, or underdevelopment.
result Model accurately measures economic growth and development of regions and macro regions.
NIPS workshop focuses on ML for developing countries.
problem Addressing machine learning challenges in developing nations.
method Not specified in the abstract.
result Not specified in the abstract.
Survey of software developers' experience with Github Copilot tool.
problem Investigate developers' acceptance of AI-generated code.
method Survey with 18 questions distributed to 42 programmers.
result Mixed developer opinions, mostly positive but reluctance to use.
Survey on new Ricci flow techniques.
problem None explicitly stated.
method None explicitly stated.
result None explicitly stated.
Workshop on ML for developing countries to enhance sustainability.
problem Addressing challenges in machine learning for developing nations.
method Discussion and collaboration among researchers.
result Promoting sustainable impact through machine learning.
We provide a condition for spatial curves which rules out the development of a type I singularity. The condition is that after the last time for which an inflection point develops, if the torsion is ever everywhere non-negative, the curve cannot develop a type I singularity.
Method improves developability of B-spline surfaces for architectural design.
problem Developability of B-spline surfaces for architectural freeform surfaces.
method Algorithm to thin the Gauss image by increasing planarity of neighborhoods, allowing for paneling with specific developable types.
result Enforced developable panels for architectural design, reducing manufacturing costs.
PrototypeML simplifies neural network design and development.
problem Error-prone code and time-consuming model design.
method Visual interface for neural networks, abstracting PyTorch.
result Reduces model design and development time, easier debugging.
Study on insurance risk management and sustainable development.
problem Lack of attention to non-climate change aspects of sustainable development in insurance.
method Analysis of recent developments and legislative initiatives in insurance risk management.
result Strategies for small- and medium-sized enterprises to manage sustainable development risks.
Survey on DDVV-type inequalities, their history, and recent developments.
problem None explicitly stated; focuses on surveying existing work.
method None explicitly stated; focuses on surveying existing work.
result Survey of DDVV-type inequalities and their history and recent developments.
This manuscript develops the theory of agglomerative clustering with Bregman divergences. Geometric smoothing techniques are developed to deal with degenerate clusters. To allow for cluster models based on exponential families with overcomplete representations, Bregman divergences are developed for nondifferentiable co…
We develop a method to describe laws of random surfaces using surface holonomy.
problem Describing laws of random surfaces with structure.
method Introduce surface holonomy and develop expected surface developments.
result Expected surface development provides a structured description of random surface laws.
Survey finds developers iterate in ML workflows, aiming for benchmark.
problem Quantify iteration in ML workflow development.
method Conducted a survey of applied ML literature from five domains.
result Developers iterate in ML workflows, but extent varies by domain.
Study shows 'Belt and Road' node cities boost digital finance in China.
problem Impact of 'Belt and Road' node cities on digital inclusive finance.
method Descriptive analysis, literature review, theoretical model, differential differential method.
result Establishment of 'Belt and Road' node cities promotes digital inclusive finance in China, with Internet development as a mediating variable.
Developable surfaces near swallowtail singularities studied for flatness.
problem Understanding developable surfaces near swallowtail singularities.
method Introducing Darboux frames and invariants to study developable surfaces.
result New example of a frontal locally homeomorphic to a swallowtail.
Establishes existence of maximal globally hyperbolic development for Einstein equations.
problem Initial value problem for generalised Einstein equations.
method Generalised Lorentz gauge, adapted from Ringström's approach.
result Existence and geometric uniqueness of maximal globally hyperbolic development.
System detects overfitting in ML apps, improving quality and efficiency.
problem Overfitting in ML applications during continuous development.
method ease. ml/meter system for automated overfitting detection and measurement.
result Probabilistic overfitting signals for developers to take actions.
Survey article on Kahler-Einstein metrics and algebraic geometry.
problem Understanding Kahler-Einstein metrics in algebraic geometry.
method Not specified in the abstract, likely involves mathematical analysis and algebraic geometry techniques.
result Discussion of recent developments and challenges in the field.
We investigate the relationship among characteristic curves on developable surfaces. In case parameter curves coincide with these curves, we show that the base curve of a developable surface could be either a plane curve, a circular helix, a general helix or a slant helix.