arXiv research

This paper studies the interrelation between spot and futures prices in the two major rice markets in prewar Japan from the perspective of market efficiency. Applying a non-Bayesian time-varying model approach to the fundamental equation for spot returns and the futures premium, we detect when efficiency reductions in …

Research predicts rice prices in Banda Aceh post-COVID using ARIMA models.

Study compares machine learning and process-based models for predicting rice blast disease.

This paper examines the integration process of the Japanese major rice markets (Tokyo and Osaka) from 1881 to 1932. Using a non-Bayesian time-varying vector error correction model, we argue that the process strongly depended on the government's policy on the network system of the telegram and telephone; rice traders wi…

We investigate the relationship between market efficiency of rice futures transaction in Osaka and the Japanese government intervention in rice distributions by directly buying and selling rice during the interwar period, from the middle 1910s to 1939, considering the context of "discretion versus rules." We use a time…

This study analyzes how colonial rice trade in prewar Japan affected its rice market, considering several government interventions in the two rice futures exchanges in Tokyo and Osaka. We explore the interventions in the futures markets using two procedures. First, we measure the joint degree of efficiency in the marke…

Two impossibility theorems show formal alignment certification is impossible for AI systems.

The Lugannani-Rice formula is a saddlepoint approximation method for estimating the tail probability distribution function, which was originally studied for the sum of independent identically distributed random variables. Because of its tractability, the formula is now widely used in practical financial engineering as …

Uniform interpretation of group theory in manifold homeomorphisms.

Study improves paddy rice yield predictions in Peru using sparse regression and climatic variables.

The study of topological properties of random smooth maps, focusing on Kac-Rice formula and Betti numbers.

We extend Kac-Rice formula to compute expected intersections of random submanifolds.

Study finds intrinsic multifractality in maize and barley spot markets, but not in wheat and rice.

Method analyzes complexity of empirical risk landscapes for generalized linear models.

We study the Johansen-Ledoit-Sornette (JLS) model of financial market crashes (Johansen, Ledoit, and Sornette [2000] "Crashes as Critical Points." Int. J. Theor. Appl. Finan. 3(2) 219-255). On our view, the JLS model is a curious case from the perspective of the recent philosophy of science literature, as it is natural…

Study finds the number of modes in Gaussian kernel density estimators scales with sqrt(β log β).

MO-GP models fill gaps in biophysical data with across-domain info transfer.

The paper analyzes local minima in high-dimensional empirical risk minimization.

Formula for critical points of chi fields on manifolds.

We establish a few formulas that compute the volume of the zero-set (or nodal set) of a function on a compact Riemannian manifold as integrals of functionals of the function and its derivatives.

Develops calculus for random submanifolds using zonoids.

Study on variance of Laplace eigenfunctions on manifolds.

We analyze the landscape of empirical risk minimization for high-dimensional models, predicting phase transitions and critical point properties.

Recent work in distance metric learning has focused on learning transformations of data that best align with specified pairwise similarity and dissimilarity constraints, often supplied by a human observer. The learned transformations lead to improved retrieval, classification, and clustering algorithms due to the bette…

Non-convex optimization with local search heuristics has been widely used in machine learning, achieving many state-of-art results. It becomes increasingly important to understand why they can work for these NP-hard problems on typical data. The landscape of many objective functions in learning has been conjectured to …

Analyzing available FAO data from 176 countries over 21 years, we observe an increase of complexity in the international trade of maize, rice, soy, and wheat. A larger number of countries play a role as producers or intermediaries, either for trade or food processing. In consequence, we find that the trade networks bec…

Consider a $d\times d$ matrix $M$ whose rows are independent centered non-degenerate Gaussian vectors $ξ_1,...,ξ_d$ with covariance matrices $Σ_1,...,Σ_d$. Denote by $\mathcal{E}_i$ the location-dispersion ellipsoid of $ξ_i:\mathcal{E}_i={\mathbf{x}\in\mathbb{R}^d : \mathbf{x}^\topΣ_i^{-1} \mathbf{x}\leqslant1}$. We sh…

Study examines spillovers between BRICS and U.S. staple grain futures markets.

We study rough high-dimensional landscapes in which an increasingly stronger preference for a given configuration emerges. Such energy landscapes arise in glass physics and inference. In particular we focus on random Gaussian functions, and on the spiked-tensor model and generalizations. We thoroughly analyze the stati…

Gradient-based algorithms are effective for many machine learning tasks, but despite ample recent effort and some progress, it often remains unclear why they work in practice in optimising high-dimensional non-convex functions and why they find good minima instead of being trapped in spurious ones. Here we present a qu…

Study finds multifractal cross-correlations between agricultural markets and external uncertainties.

In this work we analyse quantitatively the interplay between the loss landscape and performance of descent algorithms in a prototypical inference problem, the spiked matrix-tensor model. We study a loss function that is the negative log-likelihood of the model. We analyse the number of local minima at a fixed distance …

The paper classifies U.S. crop types using hyperspectral satellite imagery.

Study on complexity of random polynomials with deterministic spikes, identifying phase transitions.

We establish formulas that give the intrinsic volumes, or curvature measures, of sublevel sets of functions defined on Riemannian manifolds as integrals of functionals of the function and its derivatives. For instance, in the Euclidean case, if $f \in \mathcal{C}^3(\mathbb{R}^n, \mathbb{R})$ and 0 is a regular value of…

Study analyzes climate impact on agricultural prices, offering insurance solutions.

Study analyzes landscape complexity of empirical loss functions with correlated data.

Russia-Ukraine conflict impacts global agricultural futures and spot markets' extreme risks.

Gradient boosting algorithm for spatial panel models improves estimation in high-dimensional settings.

New test detects sparse alternatives in Gaussian random fields.

Study measures risk spillovers between US and China's agricultural futures markets.

Proposes a new complex Gaussian distribution for better modeling of complex-valued signals.

(Frankle & Carbin, 2019) shows that there exist winning tickets (small but critical subnetworks) for dense, randomly initialized networks, that can be trained alone to achieve comparable accuracies to the latter in a similar number of iterations. However, the identification of these winning tickets still requires the c…

PolarBM models complex-valued audio signals in polar coordinates, improving over conventional methods.

Non-asymptotic tail bounds for Kostlan-Shub-Smale field on sphere

Study shows how price protection affects online learning algorithms for dynamic pricing.

AI generates theorems and proofs for training theorem provers.

Global inverse function theorem proved easily using Riemannian geometry.