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.
The excited states of polyatomic systems are rather complex, and often exhibit meta-stable dynamical behaviors. Static analysis of reaction pathway often fails to sufficiently characterize excited state motions due to their highly non-equilibrium nature. Here, we proposed a time series guided clustering algorithm to ge…
Study investigates XAI methods in clinical gait analysis.
Derives variance kernel for reaction boundary in financial models.
Derives operational-time variance kernel for reaction boundaries in financial markets.
BoostMD accelerates molecular dynamics simulations by 8x with ML force fields.
Global solutions and smoothing effects for reaction-diffusion equations on manifolds.
The constraint reaction force of ideal nonholonomic constraints in time-dependent mechanics on a configuration bundle is obtained. Using the vertical extension of Hamiltonian formalism to the vertical tangent bundle of , the Hamiltonian of a nonholonomic constrained system is constructed.
We apply the potential force estimation method to artificial time series of market price produced by a deterministic dealer model. We find that dealers' feedback of linear prediction of market price based on the latest mean price changes plays the central role in the market's potential force. When markets are dominated…
Bayesian optimization helps learn optimal controls for nuclear fusion reactions.
Machine learning (ML) techniques such as (deep) artificial neural networks (DNN) are solving very successfully a plethora of tasks and provide new predictive models for complex physical, chemical, biological and social systems. However, in most cases this comes with the disadvantage of acting as a black box, rarely pro…
This study compares different data preprocessing methods for gait analysis.
Neural models learn continuous-time Markov chain transition rates from data.
We use the formalism of Geometrothermodynamics to describe chemical reactions in the context of equilibrium thermodynamics. Any chemical reaction in a closed system is shown to be described by a geodesic in a dimensional manifold that can be interpreted as the equilibrium space of the reaction. We first show this i…
CRNN discovers chemical reaction pathways from data.
MEGAN models chemical reactions as graph edits, improving synthesis planning.
Upper bound on CRN reaction rates derived using information geometry.
Graphs predict reaction conditions for organic chemistry.
Weibull weight-scale parameter evolves during AdamW training, with alignment, injection, and decay forces driving its growth and relaxation.
Novel deep learning method predicts reaction coordinates and future MD trajectories.
Chemical reactions can be described as the stepwise redistribution of electrons in molecules. As such, reactions are often depicted using `arrow-pushing' diagrams which show this movement as a sequence of arrows. We propose an electron path prediction model (ELECTRO) to learn these sequences directly from raw reaction …
METRO predicts reactions using minimal templates, reducing computational overhead and achieving state-of-the-art results.
Chemical networks outperform spiking neural networks in classification tasks.
A molecule's geometry, also known as conformation, is one of a molecule's most important properties, determining the reactions it participates in, the bonds it forms, and the interactions it has with other molecules. Conventional conformation generation methods minimize hand-designed molecular force field energy functi…
We consider the problem of learning an interpretable potential energy function from a Hamiltonian system's trajectories. We address this problem for classical, separable Hamiltonian systems. Our approach first constructs a neural network model of the potential and then applies an equation discovery technique to extract…
Highly accurate potential energy surfaces are of key interest for the detailed understanding and predictive modeling of chemical systems. In recent years, several new types of force fields, which are based on machine learning algorithms and fitted to ab initio reference calculations, have been introduced to meet this r…
Bayesian inference for biochemical reaction networks using jump-diffusion approximations.
We generalize the reaction-diffusion model A + B -> 0 in order to study the impact of an excess of A (or B) at the reaction front. We provide an exact solution of the model, which shows that linear response breaks down: the average displacement of the reaction front grows as the square-root of the imbalance. We argue t…
The prediction of organic reaction outcomes is a fundamental problem in computational chemistry. Since a reaction may involve hundreds of atoms, fully exploring the space of possible transformations is intractable. The current solution utilizes reaction templates to limit the space, but it suffers from coverage and eff…
New method uses machine learning to analyze catalyst reactions.
Study traveling waves in hyperbolic space for Fisher-KPP equations.
The growing ubiquity of Social Media data offers an attractive perspective for improving the quality of machine learning-based models in several fields, ranging from Computer Vision to Natural Language Processing. In this paper we focus on Facebook posts paired with reactions of multiple users, and we investigate their…
Reaction prediction remains one of the major challenges for organic chemistry, and is a pre-requisite for efficient synthetic planning. It is desirable to develop algorithms that, like humans, "learn" from being exposed to examples of the application of the rules of organic chemistry. We explore the use of neural netwo…
Considered an important macroeconomic indicator, the Purchasing Managers' Index (PMI) on Manufacturing generally assumes that PMI announcements will produce an impact on stock markets. International experience suggests that stock markets react to negative PMI news. In this research, we empirically investigate the stock…
Study the singular limit of a boundary reaction equation, showing energy concentration and varifold support.
Hopfield networks improve reaction template prediction for few/zero-shot scenarios.
The study distills news sources to analyze stock reactions, finding sentiment has asymmetric and sector-specific effects.
Paper presents a multi-label topic model for financial texts with high performance and insights into market reactions.
Reactmine infers chemical reactions from time series data, overcoming sparse model limitations.
New model predicts chemical reactions with conditional graph logic networks.
Study shows neural operators can efficiently solve complex reaction-diffusion systems.
Study shows SEC crypto classification led to significant market reactions.
Deep RL finds efficient pathways for sugar to chemicals.
Bounds on chemical reaction network relaxation rates using convex analysis.
We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder-decoder architecture that consists of two recurrent neural networks, which has previously shown great success in…
We address the problem of optimal Central Bank intervention in the exchange rate market when interventions create feedback in the rate dynamics. In particular, we extend the work done on optimal impulse control by Cadenillas and Zapatero to incorporate temporary market reactions, of random duration and level, to Bank i…
There is an intuitive analogy of an organic chemist's understanding of a compound and a language speaker's understanding of a word. Consequently, it is possible to introduce the basic concepts and analyze potential impacts of linguistic analysis to the world of organic chemistry. In this work, we cast the reaction pred…
Researchers prove inequalities for reaction-diffusion systems using a new curvature-dimension condition.
Complex behaviour in many systems arises from the stochastic interactions of spatially distributed particles or agents. Stochastic reaction-diffusion processes are widely used to model such behaviour in disciplines ranging from biology to the social sciences, yet they are notoriously difficult to simulate and calibrate…