This paper constructs keen weakly reducible Heegaard splittings of arbitrary genus.
problem Creating Heegaard splittings with specific properties.
method Construction of keen weakly reducible splittings.
result Arbitrary genus Heegaard splittings can be constructed.
The paper extends keenness concept to bridge splittings and finds conditions for existence.
problem Extending keenness concept to bridge splittings and finding conditions for existence.
method Extending the concept of keenness to bridge splittings and proving existence conditions.
result Existence of strongly keen (g,b)-splitting of a link with distance n for certain integers g, b, and n. In this paper, we introduce a new concept of {\it strongly keen} for Heegaard splittings, and show that, for any integers n≥2 and g≥3, there exists a strongly keen Heegaard splitting of genus g whose Hempel distance is n.
The paper provides examples of keen weakly reducible bridge spheres for links in b-bridge position.
problem Characterizing and finding examples of keen weakly reducible bridge spheres.
method Analyzing bridge spheres and their properties in terms of compressing disks and width complex.
result Infinitely many examples of keen weakly reducible bridge spheres for links in b-bridge position.
KEEN Universe provides reproducible and transferable knowledge graph embeddings.
problem Lack of reproducibility and transferability in KGE experiments.
method Developed an ecosystem with Python packages for reproducible and transferable KGEs.
result KEEN Universe facilitates sharing of trained KGE models across different fields.
This paper presents a general solution for a recent model by Keen for endogenous money creation. The solution provides an analytic framework that explains all significant dynamical features of Keen's model and their parametric dependence, including an exact result for both the period and subsidence rate of the Great Mo…
Model analyzes inventory growth cycles with debt-financed investment.
problem Understanding inventory growth cycles in economies with debt financing.
method Continuous-time stock-flow consistent model for inventory dynamics.
result Model reveals Kitchin cycles in short-run dynamics.
We consider complex Fenchel-Nielsen coordinates on the quasi-Fuchsian space of punctured tori. These coordinates arise from a generalisation of Kra's plumbing construction and are related to earthquakes on Teichmueller space. They also allow us to interpolate between two coordinate systems on Teichmueller space, namely…
Study of complex moduli spaces for Kleinian groups.
problem Understanding moduli spaces of Kleinian groups.
method Review and extension of existing work on the Riley slice.
result Extension of Riley slice results to new Kleinian groups.
We study a monetary version of the Keen model by merging two alternative extensions, namely the addition of a dynamic price level and the introduction of speculation. We recall and study old and new equilibria, together with their local stability analysis. This includes a state of recession associated with a deflationa…
There are certain families of words and word sequences (words in the generators of a two-generator group) that arise frequently in the Teichm{ü}ller theory of hyperbolic three-manifolds and Kleinian and Fuchsian groups and in the discreteness problem for two generator matrix groups. We survey some of the families of su…
Investigates the impact of narrow banking on macroeconomics.
problem The risks and benefits of a full reserve requirement on demand deposits.
method Extended Goodwin-Keen model with time deposits and central bank reserves; numerical examples.
result Narrow banking does not reduce economic growth but improves financial stability.
Exact learning of tree-structured models with side info and noise.
problem Learning tree-structured graphical models with side information and noise.
method Probabilistic tools from strong large deviations theory.
result Exact asymptotics of structure learning from samples.
Novel methods combining autoregressive models and variable transformations improve density estimation.
problem Improving density estimation methods for complex data.
method Combining autoregressive models and variable transformations.
result Jointly leveraging transformations and autoregressive models improves performance.
BERT improves Chinese word segmentation performance.
problem Chinese word segmentation task.
method Applying BERT to CWS task using benchmark datasets.
result BERT can improve performance even with inconsistent labels.
An irreducible representation of the free group on two generators X,Y into SL(2,C) is determined up to conjugation by the traces of X,Y and XY. We study the diagonal slice of representations for which X,Y and XY have equal trace. Using the three-fold symmetry and Keen-Series pleating rays we locate those groups which a…
Identifies half-space neighborhoods of pleating rays in the Riley slice of Schottky groups.
problem Determining points in the Riley slice of Schottky groups.
method Adapting ideas from L. Keen and C. Series, identifying half-space neighborhoods of pleating rays.
result Provides a provable method to determine if a point is in the Riley slice.
We consider the following question: Which parameters in the extension of a rational pleating ray across the boundary of $\Cal M$, the Maskit embedding of the Teichmüller space of once punctured tori correspond to a Kleinian group? Using methods of Keen and Series and Wright we prove a local result, stating that on each…
Model explains stock price bubbles through debt crises and financial crashes.
problem Analyzing financial fragility and stock price bubbles.
method Stock-flow consistent model integrating macroeconomic and financial market dynamics.
result Model demonstrates how credit expansion and crash risk lead to recurrent boom-bust cycles.
Paper introduces new method for statistical inference with stochastic gradients.
problem Uncertainty quantification for solutions from iterative optimization methods.
method Moment-adjusted stochastic gradient descent.
result Established non-asymptotic theory for statistical inference.
Baccalaureate institutions seek to integrate statistics into data science.
problem Graduate statisticians losing ground in data science.
method Reviewing historical contributions of statisticians and calling for integration.
result Baccalaureate institutions need to integrate statistics into data science education.
A new neural network improves the accuracy of predicting constants of motion.
problem Discovering constants of motion in dynamical systems.
method A novel neural network architecture using SVD and a two-phase training algorithm.
result The new approach retains advantages of COMET and improves performance.
In [4]: `The Riley slice of Schottky space', (Proc. London Math. Soc. 69 (1994), 72-90), Keen and Series analysed the theory of pleating coordinates in the context of the Riley slice of Schottky space R, the deformation space of a genus two handlebody generated by two parabolics. This theory aims to give a complete des…
Measuring systemic risk or fragility of financial systems is a ubiquitous task of fundamental importance in analyzing market efficiency, portfolio allocation, and containment of financial contagions. Recent attempts have shown that representing such systems as a weighted graph characterizing the complex web of interact…
Brief introduction to deep learning for math students.
problem Understanding deep neural networks and training methods.
method Explains deep neural networks, training methods, and uses MATLAB and software.
result Illustrates the application of deep learning in image classification.
MassMutual uses neural network embeddings from financial news to predict downgrade risk.
problem Predicting downgrade risk in financial institutions using alternative data sources.
method Proposes a predictive downgrade model using neural network embeddings of financial news.
result Improves performance of benchmark model by more than 5 percent in terms of AUC and recall rate.
Challenge evaluates semantic code search using annotated corpus.
problem Evaluating relevant code from natural language queries.
method Release of CodeSearchNet Corpus and expert annotations.
result 99 queries with 4k relevance annotations for evaluation.
E-LTH finds winning tickets scalable across different network architectures.
problem Finding winning tickets in different network architectures efficiently.
method Tweaking winning tickets from one network to another.
result Winning tickets from one network can be stretched or squeezed into another network's subnetwork.
We survey some major contributions to Riemann's moduli space and Teichm{ü}ller space. Our report has a historical character, but the stress is on the chain of mathematical ideas. We start with the introduction of Riemann surfaces, and we end with the discovery of some of the basic structures of Riemann's moduli space a…
This study investigates transfer learning for medical image classification.
problem Limited data for training deep neural networks in medical domains.
method Transfer learning using various DNNs for diabetic retinopathy and macular edema.
result Transfer learning is feasible and promising for medical image classification.
Study develops time-continuous models and probabilistic descriptions for agent-based economic market models.
problem Formulating and describing agent-based economic market models in a time-continuous and probabilistic manner.
method Derived time-continuous formulations, discussed impact of time-scaling, proved stability, presented probabilistic descriptions using kinetic theory.
result Time-continuous formulations and probabilistic descriptions for agent-based economic market models.
Hybrid model combines interpretable and black-box models for better transparency and performance.
problem Balancing interpretability and predictive performance in machine learning models.
method Proposes a Hybrid Predictive Model (HPM) integrating an interpretable model with a black-box model, using principled objective functions and customized training algorithms.
result Hybrid models achieve an efficient trade-off between transparency and predictive performance.
The paper introduces BCART models for aggregate claim amount, improving frequency-severity and joint modeling.
problem Modeling aggregate claim amount with frequency-severity and joint dependencies.
method Developed three types of BCART models: frequency-severity, sequential, and joint models. Used various distributions for claim severity data.
result Weibull distribution outperforms gamma and lognormal for right-skewed, heavy-tailed claim severity data.
Boosts generative models by combining multiple meta-models.
problem Challenges in creating a single generative model that accurately represents complex data.
method Cascades multiple meta-models (like RBM and VAE) to create a stronger generative model.
result Derives a decomposable variational lower bound for training and evaluating the boosted model.
The paper uses model-based trees to create interpretable surrogate models for complex machine learning models.
problem Interpreting complex machine learning models.
method Using model-based trees to partition feature space and create interpretable models.
result Model-based trees generate optimal surrogate models that balance interpretability and performance.
Study on limits of community detection in various network models.
problem Limits of community detection in network models.
method Analysis of several network models including Stochastic Block Model, Exponential Random Graph Model, Latent Space Model, Directed Preferential Attachment Model, and Directed Small-world Model.
result Information-theoretic limits for recovery of node labels in network models.
Gauge Flow Models use a learnable Gauge Field in Generative Flow Models.
problem Improving generative model performance.
method Integrates a learnable Gauge Field into Flow ODEs.
result Gauge Flow Models outperform traditional Flow Models in Flow Matching experiments.
The study examines how model predictions hold up under model extensions.
problem Model predictions may not be robust under model extensions, limiting their applicability.
method The study uses causal ordering to assess robustness of qualitative model predictions and characterizes model extensions that preserve predictions.
result Conditions and techniques are provided to assess robustness of model predictions under model extensions.
MALC combines interpretable linear models with black-box models for better predictions and transparency.
problem Combining interpretability with black-box models for better predictions.
method Formulates MALC as a convex optimization problem and uses accelerated proximal gradient method for training.
result MALC provides an efficient frontier balancing prediction accuracy and transparency.
Alternative approach to model selection using transformation analysis.
problem Over-simplistic models lead to erroneous interpretations.
method Step-wise complexity reduction to identify simpler, better-interpretable models.
result Transformation models improve model fit and interpretability.
Revises Bayesian model averaging for foundation models.
problem Ensemble pre-trained and lightly-finetuned foundation models for improved classification performance.
method Introduces trainable linear classifiers and computationally cheaper model averaging scheme (OMA).
result Ensembled models can better predict on various datasets.
Paper introduces symmetric divergence link models for probability distributions.
problem Symmetric divergence measures for probability distributions.
method Two general classes of link models: one for survival functions and another for cumulative probability distribution functions.
result Advantages of symmetric divergence measures over asymmetric measures for model averaging and feature assessment.
The paper tests stock return models and uses LSTM to predict stock returns.
problem Validating stock return models and predicting stock returns.
method Used Fama-French three-factor, four-factor, and five-factor models; also used LSTM model.
result Fama-French five-factor model shows better validity for stock returns.
New method to handle credit portfolio model uncertainties.
problem Model risk in credit portfolio models.
method Demonstrates comprehensive yet easy-to-implement approach to uncertainty in model parameters.
result Comprehensive method to deal with model uncertainties.
A new neural network model predicts multi-symbol tokens over multiple scales.
problem Language modeling with improved flexibility and performance.
method A learned dictionary of multi-symbol tokens using BPE compression.
result The model outperforms LSTM on language modeling tasks, especially for smaller models.
Researchers review challenges in interpreting additive models, especially neural additive models.
problem Challenges in interpreting additive models, particularly neural additive models.
method Review of generalized additive models and discussion of nonidentifiability.
result Challenges in claiming interpretability or suitability for safety-critical applications of additive models.
Distill-and-Compare audits black-box models by training transparent models to mimic them.
problem Auditing proprietary, opaque black-box risk scoring models.
method Model distillation and comparison of transparent student models to black-box models.
result Identifies missing features in black-box models, improving transparency.
Proposes a decision-theoretic approach for enhancing model interpretability in Bayesian frameworks.
problem Challenges the traditional approach of restricting model structure for interpretability in Bayesian frameworks.
method Introduces an interpretability utility function and a two-step method involving a reference model and a proxy model.
result Demonstrates that the proposed method generates more accurate models with the same level of interpretability.