Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and make testable models explain phenomena. Discovering equations, laws, principles are invariant, robust, causal been fundamental in physical sciences throughout centuries. Discoveries emerge from observing world and, when possible, performing interventions on system under study. With advent big data data-driven methods, fields equation discovery have...

We propose a new method to search for hypothetical scalar particles that have feeble interactions with standard-model particles. In the presence of massive bodies, these produce nonzero Yukawa-type scalar-field magnitude. Using radio-frequency spectroscopy data atomic dysprosium, as well clock data, we constrain field photon, electron, and nucleons range scalar-particle masses corresponding length scales >10 cm. limit mass m_{ϕ}→0, our derived limits on interaction parameters are...

We present a new method for linear and nonlinear, lagged contemporaneous constraint-based causal discovery from observational time series in the presence of latent confounders. show that existing methods such as FCI variants suffer low recall autocorrelated case identify effect size conditional independence tests main reason. Information-theoretical arguments can often be increased if parents are included conditioning sets. To early on, we suggest an iterative procedure utilizes novel...

In this work we study the quantum periods together with their Picard-Fuchs differential equations of Calabi-Yau fourfolds. contrast to threefolds, argue that large volume points fourfolds generically are regular singular operators non-maximally unipotent monodromy. We demonstrate property in explicit examples a single Kahler modulus. For these construct integral and global properties moduli space help numerical analytic continuation techniques. Furthermore, determine genus zero Gromov-Witten...

<p>The quest to understand cause and effect relationships is at the basis of scientific enterprise. In cases where classical approach controlled experimentation not feasible, methods from modern framework causal discovery provide an alternative way learn about observational, i.e., non-experimental data. Recent years have seen increasing interest in these various fields, for example climate Earth system sciences (where large scale often infeasible) as well machine learning...

Abstract Robust feature selection is vital for creating reliable and interpretable machine-learning (ML) models. When designing statistical prediction models in cases where domain knowledge limited underlying interactions are unknown, choosing the optimal set of features often difficult. To mitigate this issue, we introduce a multidata (M) causal approach that simultaneously processes an ensemble time series datasets produces single drivers. This uses discovery algorithms PC $ {}_1 or PCMCI...

Abstract The complex nature of many health problems necessitates the use systems thinking tools like causal loop diagrams (CLDs) to visualize underlying network and facilitate computational simulations potential interventions. However, construction CLDs is limited by constraints biases specific sources evidence. To address this, we propose a triangulation approach that integrates expert theory-driven group model building, literature review, data-driven discovery. We demonstrate utility this...

Applying advances in exact computations of supersymmetric gauge theories, we study the structure correlation functions two-dimensional N=(2,2) Abelian and non-Abelian theories. We determine universal relations among functions, which yield differential equations governing dependence theory ground state on Fayet–Iliopoulos parameters theory. For theories with a non-trivial infrared superconformal fixed point, these become Picard–Fuchs operators moduli-dependent vacuum Hilbert space...

We consider 1/4 BPS black hole solutions of ${\cal N}=2$ gauged supergravity in $AdS_4$. The near horizon geometry is $AdS_2 \times S^2$ and supersymmetry enhanced. In the first part paper we choose a moment map, which allows embedding this solution into sugra theory with hypermultiplet. then perform s-wave reduction at determine dilaton multiplet, couples to both metric gravitino fluctuations. second work Euclidean axial $\mathcal{N}=(2,2)$ JT show how add matter form covariantly twisted...

Abstract Teleconnections that link climate processes at widely separated spatial locations form a key component of the system. Their analysis has traditionally been based on means, climatologies, correlations, or spectral properties, which cannot always reveal dynamical mechanisms between different climatological processes. More recently, causal discovery methods either time series grid modes variability, estimated through dimension-reduction methods, have introduced. A major challenge in...

In this paper, we introduce a novel class of graphical models for representing time-lag specific causal relationships and independencies multivariate time series with unobserved confounders. We completely characterize these graphs show that they constitute proper subsets the currently employed model classes. As show, from one can thus draw stronger inferences—without additional assumptions. further representation Markov equivalence classes graphs. This contains more knowledge than what...

A structural vector autoregressive (SVAR) process is a linear causal model for variables that evolve over discrete set of time points and between which there may be lagged instantaneous effects. The qualitative structure an SVAR can represented by its finite directed graph, in link connects two processes whenever or effect them. At the graph level, compactly parameterised frequency domain. In this paper, we consider problem discovery estimation from spectral density, domain analogue auto...

Causal structure learning with data from multiple contexts carries both opportunities and challenges. Opportunities arise considering shared context-specific causal graphs enabling to generalize transfer knowledge across contexts. However, a challenge that is currently understudied in the literature impact of differing observational support between on identifiability graphs. Here we study detail recently introduced [6] graph objects capture mechanisms support, allowing for analysis larger...

Causal systems often exhibit variations of the underlying causal mechanisms between variables system. Often, these changes are driven by different environments or internal states in which system operates, and we refer to context as those that indicate this change mechanisms. An example relations soil moisture-temperature interactions their dependence on moisture regimes: Dry triggers a latent heat, while with wet do not feature such feedback, making it context-specific property. Crucially,...

<p>Understanding the cause and effect relationships that govern natural phenomena is central to scientific inquiry. While being gold standard for inferring causal relationships, there are many scenarios in which controlled experiments not possible. This example case most aspects of Earth's complex climate system. Causal then have be learned from statistical dependencies observational data, a task commonly referred as (observational)...

Robust feature selection is vital for creating reliable and interpretable Machine Learning (ML) models. When designing statistical prediction models in cases where domain knowledge limited underlying interactions are unknown, choosing the optimal set of features often difficult. To mitigate this issue, we introduce a Multidata (M) causal approach that simultaneously processes an ensemble time series datasets produces single drivers. This uses discovery algorithms PC1 or PCMCI implemented...

When dealing with time series data, causal inference methods often employ structural vector autoregressive (SVAR) processes to model time-evolving random systems. In this work, we rephrase recursive SVAR possible latent component as a linear Structural Causal Model (SCM) of stochastic on simple graph, the \emph{process graph}, that models every process single node. Using reformulation, generalise Wright's well-known path-rule for Gaussian SCMs newly introduced and express auto-covariance...

Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and make testable models explain phenomena. Discovering equations, laws principles are invariant, robust causal explanations world been fundamental in physical sciences throughout centuries. Discoveries emerge from observing and, when possible, performing interventional studies system under study. With advent big data use data-driven methods, equation...

Learning causal graphs from multivariate time series is a ubiquitous challenge in all application domains dealing with time-dependent systems, such as Earth sciences, biology, or engineering, to name few. Recent developments for this discovery learning task have shown considerable skill, notably the specific time-series adaptations of popular conditional independence-based framework. However, uncertainty estimation challenging methods. Here, we introduce novel bootstrap approach designed...