Coarse Graining and Reduced Order Models for Plume Ejection Dynamics

Oct 13, 2025 by talynt.org 8 views

Latest 15 Papers - March 09, 2025

Time Series: Unlocking the Power of Predictive Modeling

Time series analysis has become a crucial aspect of modern data science, with applications in finance, healthcare, and climate modeling. In this section, we will explore the latest research in time series analysis, focusing on papers that have been recently published on arXiv.

Coarse Graining and Reduced Order Models for Plume Ejection Dynamics

In the paper "Coarse graining and reduced order models for plume ejection dynamics" [1], the authors propose a novel approach to modeling plume ejection dynamics using coarse graining and reduced order models. The authors demonstrate the effectiveness of their approach using numerical simulations and compare it to traditional methods.

RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration

The paper "RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration" [2] presents a novel approach to anomaly detection using large language models (LLMs) and relation-aware graph (RAG) integration. The authors demonstrate the effectiveness of their approach using real-world datasets and compare it to traditional methods.

Bayesian Estimation of a Multivariate TAR Model When the Noise Process Distribution Belongs to the Class of Gaussian Variance Mixtures

In the paper "Bayesian estimation of a multivariate TAR model when the noise process distribution belongs to the class of Gaussian variance mixtures" [3], the authors propose a novel approach to estimating the parameters of a multivariate threshold autoregressive (TAR) model when the noise process distribution belongs to the class of Gaussian variance mixtures. The authors demonstrate the effectiveness of their approach using numerical simulations.

Federated Dynamic Modeling and Learning for Spatiotemporal Data Forecasting

The paper "Federated dynamic modeling and learning for spatiotemporal data forecasting" [4] presents a novel approach to federated dynamic modeling and learning for spatiotemporal data forecasting. The authors demonstrate the effectiveness of their approach using real-world datasets and compare it to traditional methods.

Interpretable Transformation and Analysis of Timelines through Learning via Surprisability

In the paper "Interpretable transformation and analysis of timelines through learning via surprisability" [5], the authors propose a novel approach to transforming and analyzing timelines using learning via surprisability. The authors demonstrate the effectiveness of their approach using real-world datasets.

CATCH: Channel-Aware Multivariate Time Series Anomaly Detection via Frequency Patching

The paper "CATCH: Channel-aware multivariate time series anomaly detection via frequency patching" [6] presents a novel approach to channel-aware multivariate time series anomaly detection using frequency patching. The authors demonstrate the effectiveness of their approach using real-world datasets and compare it to traditional methods.

Temporal Analysis of NetFlow Datasets for Network Intrusion Detection Systems

In the paper "Temporal analysis of NetFlow datasets for network intrusion detection systems" [7], the authors propose a novel approach to temporal analysis of NetFlow datasets for network intrusion detection systems. The authors demonstrate the effectiveness of their approach using real-world datasets.

Time-Varying Factor Augmented Vector Autoregression with Grouped Sparse Autoencoder

The paper "Time-varying factor augmented vector autoregression with grouped sparse autoencoder" [8] presents a novel approach to time-varying factor augmented vector autoregression using grouped sparse autoencoder. The authors demonstrate the effectiveness of their approach using numerical simulations.

Nonparametric Data Segmentation in Multivariate Time Series via Joint Characteristic Functions

In the paper "Nonparametric data segmentation in multivariate time series via joint characteristic functions" [9], the authors propose a novel approach to nonparametric data segmentation in multivariate time series using joint characteristic functions. The authors demonstrate the effectiveness of their approach using numerical simulations.

A Comprehensive Survey of Deep Learning for Multivariate Time Series Forecasting: A Channel Strategy Perspective

The paper "A comprehensive survey of deep learning for multivariate time series forecasting: a channel strategy perspective" [10] presents a comprehensive survey of deep learning for multivariate time series forecasting from a channel strategy perspective. The authors provide an overview of the current state of the art and identify future research directions.

Spectral Extremal Connectivity of Two-State Seizure Brain Waves

In the paper "Spectral extremal connectivity of two-state seizure brain waves" [11], the authors propose a novel approach to analyzing the spectral extremal connectivity of two-state seizure brain waves. The authors demonstrate the effectiveness of their approach using real-world datasets.

TimeFound: A Foundation Model for Time Series Forecasting

The paper "TimeFound: A foundation model for time series forecasting" [12] presents a novel approach to time series forecasting using a foundation model. The authors demonstrate the effectiveness of their approach using real-world datasets and compare it to traditional methods.

Data-Driven Identification of Nonlinear Dynamical Systems with LSTM Autoencoders and Normalizing Flows

In the paper "Data-driven identification of nonlinear dynamical systems with LSTM autoencoders and normalizing flows" [13], the authors propose a novel approach to data-driven identification of nonlinear dynamical systems using LSTM autoencoders and normalizing flows. The authors demonstrate the effectiveness of their approach using numerical simulations.

Sparse Estimation of Parameter Support Sets for Generalized Vector Autoregressions by Resampling and Model Aggregation

The paper "Sparse estimation of parameter support sets for generalized vector autoregressions by resampling and model aggregation" [14] presents a novel approach to sparse estimation of parameter support sets for generalized vector autoregressions using resampling and model aggregation. The authors demonstrate the effectiveness of their approach using numerical simulations.

Graph-Augmented LSTM for Forecasting Sparse Anomalies in Graph-Structured Time Series

In the paper "Graph-Augmented LSTM for forecasting sparse anomalies in graph-structured time series" [15], the authors propose a novel approach to forecasting sparse anomalies in graph-structured time series using graph-augmented LSTM. The authors demonstrate the effectiveness of their approach using real-world datasets.

Symbolic: Unlocking the Power of Symbolic Reasoning

Symbolic reasoning has become a crucial aspect of modern artificial intelligence, with applications in natural language processing, computer vision, and robotics. In this section, we will explore the latest research in symbolic reasoning, focusing on papers that have been recently published on arXiv.

Neural DNF-MT: A Neuro-Symbolic Approach for Learning Interpretable and Editable Policies

The paper "Neural DNF-MT: A neuro-symbolic approach for learning interpretable and editable policies" [16] presents a novel approach to learning interpretable and editable policies using neural DNF-MT. The authors demonstrate the effectiveness of their approach using real-world datasets.

Physically-Feasible Reactive Synthesis for Terrain-Adaptive Locomotion via Trajectory Optimization and Symbolic Repair

In the paper "Physically-feasible reactive synthesis for terrain-adaptive locomotion via trajectory optimization and symbolic repair" [17], the authors propose a novel approach to physically-feasible reactive synthesis for terrain-adaptive locomotion using trajectory optimization and symbolic repair. The authors demonstrate the effectiveness of their approach using numerical simulations.

From Metaphor to Mechanism: How LLMs Decode Traditional Chinese Medicine Symbolic Language for Modern Clinical Relevance

The paper "From metaphor to mechanism: how LLMs decode traditional Chinese medicine symbolic language for modern clinical relevance" [18] presents a novel approach to decoding traditional Chinese medicine symbolic language for modern clinical relevance using large language models (LLMs). The authors demonstrate the effectiveness of their approach using real-world datasets.

Training-Free Anomaly Event Detection via LLM-Guided Symbolic Pattern Discovery

In the paper "Training-free anomaly event detection via LLM-guided symbolic pattern discovery" [19], the authors propose a novel approach to training-free anomaly event detection using LLM-guided symbolic pattern discovery. The authors demonstrate the effectiveness of their approach using real-world datasets.

Code-as-Symbolic-Planner: Foundation Model-Based Robot Planning via Symbolic Code Generation

The paper "Code-as-symbolic-planner: foundation model-based robot planning via symbolic code generation" [20] presents a novel approach to foundation model-based robot planning using symbolic code generation. The authors demonstrate the effectiveness of their approach using numerical simulations.

SymbolFit: Automatic Parametric Modeling with Symbolic Regression

In the paper "SymbolFit: Automatic parametric modeling with symbolic regression" [21], the authors propose a novel approach to automatic parametric modeling using symbolic regression. The authors demonstrate the effectiveness of their approach using real-world datasets.

Large Language Models Meet Symbolic Provers for Logical Reasoning Evaluation

The paper "Large language models meet symbolic provers for logical reasoning evaluation" [22] presents a novel approach to logical reasoning evaluation using large language models and symbolic provers. The authors demonstrate the effectiveness of their approach using real-world datasets.

NeSyC: A Neuro-Symbolic Continual Learner for Complex Embodied Tasks in Open Domains

In the paper "NeSyC: A neuro-symbolic continual learner for complex embodied tasks in open domains" [23], the authors propose a novel approach to neuro-symbolic continual learning for complex embodied tasks in open domains. The authors demonstrate the effectiveness of their approach using real-world datasets.

Symmetry-Based Abstraction Algorithm for Accelerating Symbolic Control Synthesis

The paper "Symmetry-based abstraction algorithm for accelerating symbolic control synthesis" [24] presents a novel approach to accelerating symbolic control synthesis using symmetry-based abstraction. The authors demonstrate the effectiveness of their approach using numerical simulations.

NeuroSymAD: A Neuro-Symbolic Framework for Interpretable Alzheimer's Disease Diagnosis

In the paper "NeuroSymAD: A neuro-symbolic framework for interpretable Alzheimer's disease diagnosis" [25], the authors propose a novel approach to interpretable Alzheimer's disease diagnosis
Q&A: Time Series, Symbolic, and Logical Reasoning

In our previous article, we explored the latest research in time series analysis, symbolic reasoning, and logical reasoning. In this article, we will answer some of the most frequently asked questions about these topics.

Q: What is time series analysis?

A: Time series analysis is a branch of statistics that deals with the analysis and modeling of data that varies over time. It is used to identify patterns, trends, and correlations in data, and to make predictions about future values.

Q: What are some common applications of time series analysis?

A: Time series analysis has a wide range of applications, including:

Forecasting stock prices and economic indicators
Analyzing weather patterns and climate change
Understanding population growth and demographics
Identifying trends in consumer behavior and market research
Predicting energy consumption and demand

Q: What is symbolic reasoning?

A: Symbolic reasoning is a type of artificial intelligence that deals with the manipulation of symbols and logical rules to reason about complex problems. It is used to represent and reason about abstract concepts, such as numbers, words, and ideas.

Q: What are some common applications of symbolic reasoning?

A: Symbolic reasoning has a wide range of applications, including:

Natural language processing and language understanding
Computer vision and image recognition
Robotics and autonomous systems
Expert systems and decision support systems
Knowledge representation and reasoning

Q: What is logical reasoning?

A: Logical reasoning is a type of reasoning that deals with the use of logical rules and inference to arrive at conclusions. It is used to evaluate arguments, identify patterns, and make decisions.

Q: What are some common applications of logical reasoning?

A: Logical reasoning has a wide range of applications, including:

Artificial intelligence and machine learning
Expert systems and decision support systems
Natural language processing and language understanding
Computer vision and image recognition
Robotics and autonomous systems

Q: How do time series analysis, symbolic reasoning, and logical reasoning relate to each other?

A: Time series analysis, symbolic reasoning, and logical reasoning are all related to the field of artificial intelligence and machine learning. They are used to analyze and reason about complex data, and to make predictions and decisions.

Q: What are some of the challenges of time series analysis, symbolic reasoning, and logical reasoning?

A: Some of the challenges of time series analysis, symbolic reasoning, and logical reasoning include:

Handling large and complex datasets
Identifying patterns and trends in data
Making accurate predictions and decisions
Dealing with uncertainty and ambiguity
Evaluating the effectiveness of models and algorithms

Q: What are some of the future directions of time series analysis, symbolic reasoning, and logical reasoning?

A: Some of the future directions of time series analysis, symbolic reasoning, and logical reasoning include:

Developing more accurate and efficient models and algorithms
Incorporating more data and knowledge into models and algorithms
Improving the interpretability and explainability of models and algorithms
Developing more robust and reliable models and algorithms
Applying time series analysis, symbolic reasoning, and logical reasoning to new and emerging fields.

Q: How can I get started with time series analysis, symbolic reasoning, and logical reasoning?

A: To get started with time series analysis, symbolic reasoning, and logical reasoning, you can:

Take online courses and tutorials
Read books and research papers
Join online communities and forums
Participate in hackathons and competitions
Work on personal projects and applications.

Q: What are some of the most popular tools and software for time series analysis, symbolic reasoning, and logical reasoning?

A: Some of the most popular tools and software for time series analysis, symbolic reasoning, and logical reasoning include:

Python and R programming languages
TensorFlow and PyTorch machine learning frameworks
NumPy and Pandas data analysis libraries
Scikit-learn and Keras machine learning libraries
TensorFlow and PyTorch symbolic reasoning and logical reasoning libraries.

Q: What are some of the most popular applications of time series analysis, symbolic reasoning, and logical reasoning?

A: Some of the most popular applications of time series analysis, symbolic reasoning, and logical reasoning include:

Finance and banking
Healthcare and medicine
Energy and utilities
Transportation and logistics
Government and public policy.

Q: What are some of the most popular industries that use time series analysis, symbolic reasoning, and logical reasoning?

A: Some of the most popular industries that use time series analysis, symbolic reasoning, and logical reasoning include:

Finance and banking
Healthcare and medicine
Energy and utilities
Transportation and logistics
Government and public policy.

Q: What are some of the most popular companies that use time series analysis, symbolic reasoning, and logical reasoning?

A: Some of the most popular companies that use time series analysis, symbolic reasoning, and logical reasoning include:

Google and Amazon
Facebook and Microsoft
Apple and IBM
Tesla and Uber
Netflix and Spotify.

Q: What are some of the most popular research institutions that use time series analysis, symbolic reasoning, and logical reasoning?

A: Some of the most popular research institutions that use time series analysis, symbolic reasoning, and logical reasoning include:

Stanford University and MIT
Harvard University and UC Berkeley
Carnegie Mellon University and University of Michigan
University of Toronto and University of British Columbia
University of Cambridge and University of Oxford.