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.
A new method for inferring latent states in Markov jump processes.
Derives operational-time variance kernel for reaction boundaries in financial markets.
Novel deep learning method predicts reaction coordinates and future MD trajectories.
Bayesian inference for biochemical reaction networks using jump-diffusion approximations.
We propose a new model for making generalizable and diverse retrosynthetic reaction predictions. Given a target compound, the task is to predict the likely chemical reactants to produce the target. This generative task can be framed as a sequence-to-sequence problem by using the SMILES representations of the molecules.…
Derives variance kernel for reaction boundary in financial models.
This paper provides a holistic study of how stock prices vary in their response to financial disclosures across different topics. Thereby, we specifically shed light into the extensive amount of filings for which no a priori categorization of their content exists. For this purpose, we utilize an approach from data mini…
Method learns latent dynamics of complex systems from noisy data.
Modeling high-frequency speculative markets as auction search processes.
A deep probabilistic model analyzes DNA-encoded library data for efficient screening.
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.
We suggest that the broad distribution of time scales in financial markets could be a crucial ingredient to reproduce realistic price dynamics in stylised Agent-Based Models. We propose a fractional reaction-diffusion model for the dynamics of latent liquidity in financial markets, where agents are very heterogeneous i…
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.
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.
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…
Learning from electronic medical records (EMR) is challenging due to their relational nature and the uncertain dependence between a patient's past and future health status. Statistical relational learning is a natural fit for analyzing EMRs but is less adept at handling their inherent latent structure, such as connecti…
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…
GD-VAEs learn dynamics from observations using geometric and topological information.
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.
Computer-assisted synthesis planning aims to help chemists find better reaction pathways faster. Finding viable and short pathways from sugar molecules to value-added chemicals can be modeled as a retrosynthesis planning problem with a catalyst allowed. This is a crucial step in efficient biomass conversion. The tradit…
Global solutions and smoothing effects for reaction-diffusion equations on manifolds.
We present an extended version of the recently proposed "LLOB" model for the dynamics of latent liquidity in financial markets. By allowing for finite cancellation and deposition rates within a continuous reaction-diffusion setup, we account for finite memory effects on the dynamics of the latent order book. We compute…
New model predicts chemical reactions with conditional graph logic networks.
Study shows neural operators can efficiently solve complex reaction-diffusion systems.
New method reduces PDE model parameters by 30% with sparsity.
Study shows SEC crypto classification led to significant market reactions.
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…
We propose a minimal theory of non-linear price impact based on a linear (latent) order book approximation, inspired by diffusion-reaction models and general arguments. Our framework allows one to compute the average price trajectory in the presence of a meta-order, that consistently generalizes previously proposed pro…
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…
DEMOTE uses neural diffusion-reaction processes to capture temporal dynamics in sparse tensor data.
INP accelerates stochastic simulations using deep Bayesian active learning.