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.
We consider extensive data on Spanish international trades and population composition and, through statistical-mechanics and graph-theory driven analysis, we unveil that the social network made of native and foreign-born individuals plays a role in the evolution and in the diversification of trades. Indeed, migrants na…
New method detects long-term structures with internal dynamics in time series data.
This study explains RL training dynamics in LLMs, focusing on token-level optimization and reasoning pattern reshaping.
This paper is a contribution to interweaving two lines of research that have progressed in separate ways: network analyses of international trade and the literature on African trade and development. Gathering empirical data on African countries has important limitations and so does the space occupied by African countri…
ICE improves classification performance by leveraging internal patterns among instances.
This paper uses SampEn to measure and predict oil price volatility.
Study uses AI to predict changes in international public finances based on US markets.
We are interested in learning customers' video preferences from their historic viewing patterns and geographical location. We consider a Bayesian latent factor modeling approach for this task. In order to tune the complexity of the model to best represent the data, we make use of Bayesian nonparameteric techniques. We …
We present here a general framework and a specific algorithm for predicting the destination, route, or more generally a pattern, of an ongoing journey, building on the recent work of [Y. Lassoued, J. Monteil, Y. Gu, G. Russo, R. Shorten, and M. Mevissen, "Hidden Markov model for route and destination prediction," in IE…
We present a Bayesian tensor factorization model for inferring latent group structures from dynamic pairwise interaction patterns. For decades, political scientists have collected and analyzed records of the form "country took action toward country at time "---known as dyadic events---in order to form an…
Examines financial market patterns across 150 years and regions.
Deep learning models can overfit noisy data without losing generalization.
Proposes a framework to predict stock movements by integrating multi-order and internal dynamics.
This paper begins to explore the determinants of the topological properties of the international - trade network (ITN). We fit bilateral-trade flows using a standard gravity equation to build a "residual" ITN where trade-link weights are depurated from geographical distance, size, border effects, trade agreements, and …
IAs is well known, when D6 branes wrap a special lagrangian cycle on a non compact CY 3-fold in such a way that the internal string frame metric is Kahler there exists a dual description, which is given in terms of a purely geometrical eleven dimensional background with an internal metric of holonomy. It is also …
Despite the huge success of Long Short-Term Memory networks, their applications in environmental sciences are scarce. We argue that one reason is the difficulty to interpret the internals of trained networks. In this study, we look at the application of LSTMs for rainfall-runoff forecasting, one of the central tasks in…
DORA analyzes deep neural networks' internal representations to detect spurious correlations.
Armed conflict has led to an unprecedented number of internally displaced persons (IDPs) - individuals who are forced out of their homes but remain within their country. IDPs often urgently require shelter, food, and healthcare, yet prediction of when large fluxes of IDPs will cross into an area remains a major challen…
The International Trade Network (ITN) is the network formed by trade relationships between world countries. The complex structure of the ITN impacts important economic processes such as globalization, competitiveness, and the propagation of instabilities. Modeling the structure of the ITN in terms of simple macroeconom…
The international trade network (ITN) has received renewed multidisciplinary interest due to recent advances in network theory. However, it is still unclear whether a network approach conveys additional, nontrivial information with respect to traditional international-economics analyses that describe world trade only i…
Paper trains models to resist spurious patterns by having humans revise documents.
PG-IM uses neural-symbolic programs to manipulate images.
A data-driven approach predicts morphological development under structural instability.
Based on the approach of flow distances, the international trade flow system is studied from the perspective of multi-layer flow network. A model of multi-layer flow network is proposed for modelling and analyzing multiple types of flows in flow systems. Then, flow distances are introduced, and symmetric minimum flow d…
Proposes a new model for clustering multiplex networks with compositional data.
The paper proposes a new algorithm to select subsets of training data for better accuracy and explainability.
This review assesses deep-learning methods for complex sequential data.
New method for selecting clusters in residential electricity data.
QGMS framework detects market endpoints using geometric patterns.
Our work sheds new light on the role of oil prices in shaping the world economy by investigating flows of goods and services through global value chains between 1960 and 2011, by means of Markov Chain and network analysis. We show that over that time period the international division of labor and trade patterns are tig…
Internal Lagrangians derived from variational principles.
Model predicts internal fraud in retail banking is cyclical and influenced by corruption.
Assessing world-wide financial integration constitutes a recurrent challenge in macroeconometrics, often addressed by visual inspections searching for data patterns. Econophysics literature enables us to build complementary, data-driven measures of financial integration using graphs. The present contribution investigat…
New method maps global value chains at product level from trade data.
Nestedness has traditionally been used to detect assembly patterns in meta-communities and networks of interacting species. Attempts have also been made to uncover nested structures in international trade, typically represented as bipartite networks in which connections can be established between countries (exporters o…
Examines international taxation's impact on Georgian businesses.
Cryptocurrency market activity is decomposed into recurring and noise components, revealing patterns tied to macroeconomic reports.
The highly detailed international trade data among all countries in the world during 1971-2000 shows that the kinds of export goods and the logarithmic GDP (gross domestic production) of a country has an S-shaped relationship. This indicates all countries can be divided into three stages accordingly. First, the poor co…
Optimized execution model using interbank and internal liquidity.
This study assesses how economic shocks affect the efficiency and robustness of international pesticide trade networks.
Tests factor models by decomposing market into body and tail legs, revealing inconsistent results.
Decodes neural activity to assess latent states in real-world driving tasks.
There are few papers about the international trade of flowers, so it is believed that this paper, with this topic, could be an important contribution to the international scientific community. It is intended to analyze if the international trade flowers tendencies and policies are adapted to the actual world global con…
New spectral clustering for directed graphs reveals socio-economic patterns.
Deep generative models can emulate the perceptual properties of complex image datasets, providing a latent representation of the data. However, manipulating such representation to perform meaningful and controllable transformations in the data space remains challenging without some form of supervision. While previous w…
Deep learning is a form of machine learning for nonlinear high dimensional pattern matching and prediction. By taking a Bayesian probabilistic perspective, we provide a number of insights into more efficient algorithms for optimisation and hyper-parameter tuning. Traditional high-dimensional data reduction techniques, …
Spain uses DEA to select international markets for exports.
Improved speech recognition model with better performance.