A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
OOM-RL uses financial market losses to align AI agents in autonomous systems.
Bayesian method selects important covariates in modal regression.
Develops methods to learn centre groupings from summary statistics in multi-centre studies.
CDFM aims to unify causal discovery across diverse datasets.
Despite the functional success of deep neural networks (DNNs), their trustworthiness remains a crucial open challenge. To address this challenge, both testing and verification techniques have been proposed. But these existing techniques provide either scalability to large networks or formal guarantees, not both. In thi…
SDYNA is a general framework designed to address large stochastic reinforcement learning problems. Unlike previous model based methods in FMDPs, it incrementally learns the structure and the parameters of a RL problem using supervised learning techniques. Then, it integrates decision-theoric planning algorithms based o…
Curves can bound only finitely many developable surfaces.
The paper extends Cartan development to infinite dimensional Lie groups.
The paper studies singularities on parallels of tangent developable surfaces of frontal curves.
Survey on new Ricci flow techniques.
Survey of software developers' experience with Github Copilot tool.
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.
PrototypeML simplifies neural network design and development.
Study on insurance risk management and sustainable development.
Survey on DDVV-type inequalities, 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.
This paper reviews the checkered history of predictive distributions in statistics and discusses two developments, one from recent literature and the other new. The first development is bringing predictive distributions into machine learning, whose early development was so deeply influenced by two remarkable groups at …
Study shows 'Belt and Road' node cities boost digital finance in China.
Establishes existence of maximal globally hyperbolic development for Einstein equations.
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.
Motivated by applications in architecture and design, we present a novel method for increasing the developability of a B-spline surface. We use the property that the Gauss image of a developable surface is 1-dimensional and can be locally well approximated by circles. This is cast into an algorithm for thinning the Gau…
This article presents a theoretical model for a dynamic system based on sustainable development. Due to the relatively absence of theoretical studies and practical issues in the area of sustainable development, Romania aspires to the principles of sustainable development. Based on the concept as a process in which econ…
Simplifying machine learning (ML) application development, including distributed computation, programming interface, resource management, model selection, etc, has attracted intensive interests recently. These research efforts have significantly improved the efficiency and the degree of automation of developing ML mode…
Recent developments in Seiberg-Witten theory and relations with Complex Geometry.
This paper presents a simple model to measure the relative economic growth of economic systems. The model considers S-Shaped patterns of economic growth that, represented with a linear model, measure how an economic system grows in comparison with another one. In particular, this model introduces an approach which indi…
Constructs stochastic processes on sub-Riemannian manifolds using Cartan connections.
This chapter focuses on developing datasets for machine learning, addressing data preparation challenges.
This article reports recent developments of the research on Hamilton's Ricci flow and its applications.
The paper develops a causal machine learning framework to optimize aid allocation.
Defines metrics for Lorentzian spaces and explores maximal developments.
In this paper, the Almeida-Molino obstruction to developability of transversely complete foliations is extended to Lie groupoids.
The paper analyzes the current state of the world economy and offers a short-term forecast of its development. Our analysis of log-periodic oscillations in the DJIA dynamics suggests that in the second half of 2017 the United States and other more developed countries could experience a new recession, due to the third p…
In this paper we do the first large scale analysis of writing style development among Danish high school students. More than 10K students with more than 100K essays are analyzed. Writing style itself is often studied in the natural language processing community, but usually with the goal of verifying authorship, assess…
Proposes guidelines for developing medical AI products.
Framework for AI healthcare products from concept to market.
Examines insurance market development and similarity post-2004 EU enlargement.
New complete panel dataset for LMICs helps analyze innovation and development.
Composite development indicators used in policy making often subjectively aggregate a restricted set of indicators. We show, using dimensionality reduction techniques, including Principal Component Analysis (PCA) and for the first time information filtering and hierarchical clustering, that these composite indicators m…
The book explores essential stats and psychology for quantitative trading.
To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop new methods to generate adversarial examples as well as to develop new ways to defend against them. In this chapter, we describe the struct…
Develops arithmetic PDE geometry concepts like curvature and cohomology.
In this paper, we consider non developable ruled surface with spacelike ruling, timelike ruling, respectively. We give the relations between the structure functions with the curvature and torsion of the striction line of the timelike and spacelike non developable ruled surfaces. Also, we have calculated the gaussian an…
Machine learning workflow development is anecdotally regarded to be an iterative process of trial-and-error with humans-in-the-loop. However, we are not aware of quantitative evidence corroborating this popular belief. A quantitative characterization of iteration can serve as a benchmark for machine learning workflow d…
Satellite imagery helps assess sustainable development with machine learning.
Study finds long memory in some emerging Asian stocks but not in developed markets.
The aim of this paper is to identify the determinants of international stock markets integration. Intuitively we selected a great number of factors linked to financial integration. Then, we developed an international asset-pricing model with time-varying degree of integration. This model is estimated for 30 countries (…
We consider the sectoral composition of a country's GDP, i.e. the partitioning into agrarian, industrial, and service sectors. Exploring a simple system of differential equations we characterize the transfer of GDP shares between the sectors in the course of economic development. The model fits for the majority of coun…