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.
Analysis finds no evidence of banks managing deposit run risk prior to 2023 Regional Banking Crisis.
I studied the convergence of regional house prices to national prices in USA by analyzing time-series of house price indices of 9 Census Divisions. I found the evidence of the convergence in some parts of the country using asymmetric unit root tests. The fact that the evidence of the convergence is not present in large…
DNNs improve accuracy by using more evidence from images.
A new method using mean shift clustering speeds up Bayesian evidence calculation.
The paper divides minimal hypersurfaces in a ball into two parts.
The aim of this paper is to present a further contribution to the analysis of absolute convergence (and), associated with the neoclassical theory, and conditional, associated with endogenous growth theory, of the sectoral productivity at regional level. Presenting some empirical evidence of absolute convergence of prod…
This work aims to study the Portuguese regional agglomeration process, using the linear form the New Economic Geography models that emphasize the importance of spatial factors (distance, costs of transport and communication) in explaining of the concentration of economic activity in certain locations. In a theoretical …
SVM predicts regional rainfall with varying accuracy, best in central US.
We prove that every plane passing through the origin divides an embedded compact free boundary minimal surface of the euclidean -ball in exactly two connected surfaces. We also show that if a region in the ball has mean convex boundary and contains a nullhomologous diameter, then this region is a closed halfball. Mo…
Reweighting improves risk bounds in certain data regions.
New sampling strategy improves TR algorithms for stochastic optimization.
Investors target specific regions of payoff distributions for portfolio optimization.
Proposes NRS to find flat minima in deep neural networks.
Bayesian framework for identifying localized regions of interest in dynamical systems.
We study the ellipticity and the ``Nekhoroshev stability'' (stability properties for finite, but very long, time scales) of the Riemann ellipsoids. We provide numerical evidence that the regions of ellipticity of the ellipsoids of types II and III are larger than those found by Chandrasekhar in the 60's and that all Ri…
We use PDPs with confidence bands to explain HPO results.
Paper develops a novel approach to identify clusters of features in multivariate extremes.
Digital transformation boosts corporate financial asset allocation, especially short-term.
We show that discrete synaptic weights can be efficiently used for learning in large scale neural systems, and lead to unanticipated computational performance. We focus on the representative case of learning random patterns with binary synapses in single layer networks. The standard statistical analysis shows that this…
A new algorithm detects changepoints in labeled and unlabeled data.
Develops a PIDE framework for option pricing with stochastic volatility and jumps.
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-p…
DASH improves ensemble generalizability by encouraging diverse, flat loss landscapes.
We give an algorithmic computation for the height of Kauffman's clock lattice obtained from a knot diagram with two adjacent regions starred and without crossing information specified. We show that this lattice is more familiarly the graph of perfect matchings of a bipartite graph obtained from the knot diagram by over…
Adaptive optimization methods bias neural network trajectories towards regions of lower local geometry.
Training dynamics reveal neural network complexity.
Study proposes a statistical test for Vision Transformer's attention mechanisms.
Convolutional neural networks have been used to achieve a string of successes during recent years, but their lack of interpretability remains a serious issue. Adversarial examples are designed to deliberately fool neural networks into making any desired incorrect classification, potentially with very high certainty. Se…
Based on the assumption that economic complexity is characterised by the interactions of economic agents (who) constantly change their actions and strategies in response to the outcome they mutually create, this paper presents how network models can be used a proxies for the mapping, quantification and analysis of Roma…
We present evidence that the best model for empirical volume-price distributions is not always the same and it strongly depends in (i) the region of the volume-price spectrum that one wants to model and (ii) the period in time that is being modelled. To show these two features we analyze stocks of the New York stock ma…
The relationship between international trade and foreign direct investment (FDI) is one of the main features of globalization. In this paper we investigate the effects of FDI on trade from a network perspective, since FDI takes not only direct but also indirect channels from origin to destination countries because of f…
Modeling bank leverage dynamics using dynamical systems and neural networks.
The goal of feature selection is to identify important features that are relevant to explain an outcome variable. Most of the work in this domain has focused on identifying globally relevant features, which are features that are related to the outcome using evidence across the entire dataset. We study a more fine-grain…
We introduce a general class of stochastic processes driven by a multifractional Brownian motion (mBm) and study the estimation problems of their pointwise Hölder exponents (PHE) based on a new localized generalized quadratic variation approach (LGQV). By comparing our suggested approach with the other two existing ben…
The paper explores how to handle uncertain evidence in probabilistic models.
Stock prices are known to exhibit non-Gaussian dynamics, and there is much interest in understanding the origin of this behavior. Here, we present a model that explains the shape and scaling of the distribution of intraday stock price fluctuations (called intraday returns) and verify the model using a large database fo…
New method uses Rashomon sets to improve Bayesian inference in factorial designs.
MIRA scores assess conditional distribution accuracy using joint samples.
Fact verification (FV) is a challenging task which requires to retrieve relevant evidence from plain text and use the evidence to verify given claims. Many claims require to simultaneously integrate and reason over several pieces of evidence for verification. However, previous work employs simple models to extract info…
Extensions of Veech groups using hierarchical hyperbolic spaces.
Evidence acquisition costs influence disclosure behavior and preference.
The paper identifies all link projections with isolate-region number one.
We study an adaptive source seeking problem, in which a mobile robot must identify the strongest emitter(s) of a signal in an environment with background emissions. Background signals may be highly heterogeneous and can mislead algorithms that are based on receding horizon control. We propose AdaSearch, a general algor…
Belief Propagation (BP) is one of the most popular methods for inference in probabilistic graphical models. BP is guaranteed to return the correct answer for tree structures, but can be incorrect or non-convergent for loopy graphical models. Recently, several new approximate inference algorithms based on cavity distrib…
We introduce a local move on a link diagram named a region freeze crossing change which is close to a region crossing change, but not the same. We study similarity and difference between region crossing change and region freeze crossing change.
In this paper we introduce evidence transfer for clustering, a deep learning method that can incrementally manipulate the latent representations of an autoencoder, according to external categorical evidence, in order to improve a clustering outcome. By evidence transfer we define the process by which the categorical ou…
Regionalization is the task of dividing up a landscape into homogeneous patches with similar properties. Although this task has a wide range of applications, it has two notable challenges. First, it is assumed that the resulting regions are both homogeneous and spatially contiguous. Second, it is well-recognized that l…
This article introduces a framework to estimate the value of evidence-based decision making.