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.
Model predicts COVID-19 growth in Senegal, highlighting health care capacity importance.
Growth rate of the world Growth Domestic Product (GDP) is analysed to determine possible pathways of the future economic growth. The analysis is based on using the latest data of the World Bank and it reveals that the growth rate between 1960 and 2014 was following a trajectory approaching asymptotically a constant val…
We study the steady state solutions of a generalized logistic type equation on a complete Riemannian manifold. We provide sufficient conditions for existence, respectively non-existence of positive solutions, which depend on the relative size of the coefficients and their mutual interaction with the geometry of the man…
We present a novel methodology to determine the fundamental value of firms in the social-networking sector based on two ingredients: (i) revenues and profits are inherently linked to its user basis through a direct channel that has no equivalent in other sectors; (ii) the growth of the number of users can be calibrated…
A general nonlinear logistic equation has been proposed to model long-time saturation in industrial growth. An integral solution of this equation has been derived for any arbitrary degree of nonlinearity. A time scale for the onset of nonlinear saturation in industrial growth can be estimated from an equipartition cond…
The addition of wealth-attained advantage (WAA) to the Yard-Sale Model (YSM) of asset exchange has been demonstrated to induce wealth condensation. In a model of WAA for which the bias is a continuous function of the wealth difference of the transacting agents, the condensation was shown to arise from a second-order ph…
In this paper, we show how simple logistic growth that was studied intensively during the last 200 years in many domains of science could be extended in a rather simple way and with these extensions is capable to produce a collection of behaviors widely observed in an enormous number of real-life systems in Economics, …
Rapid growth in delivery and freight transportation is increasing in urban areas; as a result the use of delivery trucks and light commercial vehicles is evolving. Major cities can use traffic counting as a tool to monitor the presence of delivery vehicles in order to implement intelligent city planning measures. Class…
Enhances neural networks in hyperbolic space for better data structure capture.
In the framework of applying econophysics ideas in religious topics, the finances of the Antoinist religious movement organized in Belgium between 1920 and 2000 are studied. The interest of investigating financial aspects of such a, sometimes called, sect stems in finding characteristics of conditions and mechanisms un…
We introduce the logistic model of consumption growth, which captures a negative feedback loop preventing an unlimited growth of consumption due to finite biophysical resources of our planet. This simple dynamic model allows for derivation of the expression describing the declining long-term tail of a social discount c…
P.W. Anderson proposed the concept of complexity in order to describe the emergence and growth of macroscopic collective patterns out of the simple interactions of many microscopic agents. In the physical sciences this paradigm was implemented systematically and confirmed repeatedly by successful confrontation with rea…
This article derives prognostic expressions for the evolution of globally aggregated economic wealth, productivity, inflation, technological change, innovation and growth. The approach is to treat civilization as an open, non-equilibrium thermodynamic system that dissipates energy and diffuses matter in order to sustai…
On December 16, Zynga, the well-known social game developing company went public. This event is following other recent IPOs in the world of social networking companies, such as Groupon, Linkedin or Pandora to cite a few. With a valuation close to 7 billion USD at the time when it went public, Zynga has become the bigge…
New scaling laws explain deep learning performance growth.
We analyze an exhaustive data-set of new-cars monthly sales. The set refers to 10 years of Spanish sales of more than 6500 different car model configurations and a total of 10M sold cars, from January 2007 to January 2017. We find that for those model configurations with a monthly market-share higher than 0.1% the sale…
Maximum likelihood estimator performance in logistic regression analyzed.
Paper finds a lower bound for estimating low-rank matrices in logistic regression.
A new microeconomic model is presented that aims at a description of the long-term unit sales and price evolution of homogeneous non-durable goods in polypoly markets. It merges the product lifecycle approach with the price dispersion dynamics of homogeneous goods. The model predicts a minimum critical lifetime of non-…
Revises logistic-softmax likelihood for Bayesian meta-learning in few-shot classification.
Novel bounds for logistic regression coreset construction and feature selection.
Unified framework for sparse logistic regression with nonconvex regularization.
Study explores geometric structure and prior for beta-logistic distribution.
We comment on the fact that gradient ascent for logistic regression has a connection with the perceptron learning algorithm. Logistic learning is the "soft" variant of perceptron learning.
Paper explains learning property of logistic and softmax losses for balanced and imbalanced class data.
Improved sketching for logistic and regression with near-linear dimensions.
Safe screening rules reduce computation time in logistic regression with regularization.
Proposes logistic-beta process for modeling dependent probabilities with beta marginals.
Classification is the most important process in data analysis. However, due to the inherent non-convex and non-smooth structure of the zero-one loss function of the classification model, various convex surrogate loss functions such as hinge loss, squared hinge loss, logistic loss, and exponential loss are introduced. T…
Paper introduces imprecise logistic regression for handling uncertain data.
The study uses stock market indicators to forecast COVID-19 cases.
Improved regret bounds for logistic bandits via novel confidence set construction.
Improved confidence bounds for linear logistic model with applications to bandits.
A new data-oblivious sketch for logistic regression reduces data size while maintaining approximation accuracy.
The l1-regularized logistic regression (or sparse logistic regression) is a widely used method for simultaneous classification and feature selection. Although many recent efforts have been devoted to its efficient implementation, its application to high dimensional data still poses significant challenges. In this paper…
City Logistics is characterized by multiple stakeholders that often have different views of such a complex system. From a public policy perspective, identifying stakeholders, issues and trends is a daunting challenge, only partially addressed by traditional observation systems. Nowadays, social media is one of the bigg…
Logistic regression is a widely used method in several fields. When applying logistic regression to imbalanced data, for which majority classes dominate over minority classes, all class labels are estimated as `majority class.' In this article, we use an F-measure optimization method to improve the performance of logis…
Develops first optimal algorithm for logistic bandits.
A new algorithm approximates logistic regression probabilities efficiently.
New method improves transductive learning predictions with multiplicative oracle inequalities.
This paper presents a general theory that aims at explaining timescales observed empirically in technology transitions and predicting those of future transitions. This framework is used further to derive a theory for exploring the dynamics that underlie the complex phenomenon of irreversible and path dependent price or…
Paper explores connections between loss functions and consistency in binary classification and regression.
Prevalidated ridge regression simplifies logistic regression for high-dimensional data.
Study shows sample complexity for logistic regression with normal covariates.
Logistic regression gets a new, simpler uniform bound.
The paper reveals the hidden costs of digitizing commodity money and proposes a new stable-coin system.
Estimates future ICU demand using age-structured data and logistic modeling.
In this work, we present a globalized stochastic semismooth Newton method for solving stochastic optimization problems involving smooth nonconvex and nonsmooth convex terms in the objective function. We assume that only noisy gradient and Hessian information of the smooth part of the objective function is available via…