Machine learning techniques are being increasingly used as flexible non-linear fitting and prediction tools in the physical sciences. Fitting functions that exhibit multiple solutions as local minima can be analysed in terms of the corresponding machine learning landscape. Methods to explore and visualise molecular pot…
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48 results for “Interdisciplinary Perspectives”
PyKale bridges interdisciplinary ML with Python, enabling accurate predictions.
problem Cross-disciplinary barriers in machine learning.
method Knowledge-aware machine learning on graphs, images, texts, and videos.
result Enables multimodal learning and transfer learning with latest deep learning models.
Can an interdisciplinary field contribute to one of the parent disciplines from which it emerged?physics.soc-ph
In the light of contemporary discussions of inter and transdisciplinarity, this paper approaches econophysics and sociophysics to seek a response to the question -- whether these interdisciplinary fields could contribute to physics and economics. Drawing upon the literature on history and philosophy of science, the pap…
YouTube presents an unprecedented opportunity to explore how machine learning methods can improve healthcare information dissemination. We propose an interdisciplinary lens that synthesizes machine learning methods with healthcare informatics themes to address the critical issue of developing a scalable algorithmic sol…
NESYM combines AI and Earth models for new climate insights.
problem Replacing traditional Earth models with AI.
method Neural Earth System Modelling (NESYM) integrating AI and climate models.
result Artificial intelligence may render traditional models obsolete.
The paper presents instructive interdisciplinary applications of constrained mechanics calculus in economics on a level appropriate for the undergraduate physics education. The aim of the paper is: 1. to meet the demand for illustrative examples suitable for presenting the background of the highly expanding research fi…
High-dimensional data models, often with low sample size, abound in many interdisciplinary studies, genomics and large biological systems being most noteworthy. The conventional assumption of multinormality or linearity of regression may not be plausible for such models which are likely to be statistically complex due …
In these notes we give an interdisciplinary result which links the geometric concept of minimal surfaces with generalized harmonic functions.
Information geometry offers new tools for statistical analysis.
problem Statistical analysis of probability distributions.
method Geometric perspective on statistical manifolds.
result New applications in radar sensing, signal processing, etc.
This paper clarifies deep learning for IS scholars.
problem Limited IS contributions in deep learning.
method Systematic review and framework development.
result Clear guidelines for IS scholars to make DL contributions.
Coherent Patterns in Nuclei and in Financial Marketsphysics.data-an
In the area of traditional physics the atomic nucleus belongs to the most complex systems. It involves essentially all elements that characterize complexity including the most distinctive one whose essence is a permanent coexistence of coherent patterns and of randomness. From a more interdisciplinary perspective, thes…
Examines performance metrics for ML models in engineering.
problem Identifying proper assessment metrics for ML models.
method Analyzes commonly-used metrics for regression and classification.
result Identifies key performance metrics for ML models.
Lecture notes introduce Abelian differentials and their flat surfaces, focusing on families and Teichmüller dynamics.
problem Study of Abelian differentials and their geometric properties.
method Associate flat surfaces to Abelian differentials and analyze their families under action.
result Properties of orbit of Abelian differentials under Teichmüller dynamics.
Concepts of polymer statistical topologycond-mat.stat-mech
I review few conceptual steps in analytic description of topological interactions, which constitute the basis of a new interdisciplinary branch in mathematical physics, "Statistical Topology", emerged at the edge of topology and statistical physics of fluctuating non-phantom rope-like objects. This new branch is called…
Econophysics is a new research field, which makes an attempt to bring economics in the fold of natural sciences or specifically attempts for a "physics of economics". The term Econophysics was formally born in Kolkata in 1995. The entry on Econophysics in The New Palgrave Dictionary of Economics, 2nd Ed., Vol 2, Macmil…
Survey of theoretical foundations for policy optimization in control.
problem Understanding the theoretical properties of gradient-based methods in control and reinforcement learning.
method Interdisciplinary review of optimization landscape, convergence, and sample complexity for various control problems.
result Recent theoretical results on stability and robustness in learning-based control.
STS clarifies chaos and stochastic dynamics, linking algebraic topology and physics.
problem Chaos and stochastic dynamics in arbitrary form SDEs.
method Supersymmetric theory of stochastic dynamics (STS) using generalized transfer operator (GTO) and topological field theories (TFT).
result Positive 'pressure' in GTOs corresponds to spontaneous breakdown of topological supersymmetry, explaining 1/f noise.
This paper analyzes machine learning workflows in climate modeling.
problem Challenges in integrating machine learning with climate modeling.
method Analysis of case studies focusing on design patterns and workflow structure.
result Synthesis of workflow design patterns across diverse projects in ML-enabled climate modeling.
Data of sequential nature arise in many application domains in forms of, e.g. textual data, DNA sequences, and software execution traces. Different research disciplines have developed methods to learn sequence models from such datasets: (i) in the machine learning field methods such as (hidden) Markov models and recurr…
OceanForecastBench offers a comprehensive benchmark for data-driven ocean forecasting models.
problem Lack of open-source, standardized benchmarks for data-driven ocean forecasting models.
method Proposes OceanForecastBench, a benchmark with high-quality data and evaluation pipeline.
result Offers the most comprehensive benchmarking framework for data-driven ocean forecasting.
AI+MPS workshop aims to strengthen AI's role in science.
problem AI's potential to enhance scientific discovery and education.
method Proposes activities and strategic priorities to strengthen AI-MPS link.
result AI and science are becoming increasingly intertwined.
Most of the econometric and econophysics models have been borrowed from the statistical physics, and as a cosequence, a new interdisciplinary science called econophysics has emerged. In this paper we planned to extend the analogy between different economic processes or phenomena and processes and phenomena from differe…
Highly comparative time-series analysis: The empirical structure of time series and their methodsphysics.data-an
The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording, and analyzing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods ha…
Classic economic science is reaching the limits of its explanatory powers. Complexity science uses an increasingly larger set of different methods to analyze physical, biological, cultural, social, and economic factors, providing a broader understanding of the socio-economic dynamics involved in the development of nati…
From Ecology to Finance (and Back?): Recent Advancements in the Analysis of Bipartite Networksphysics.soc-ph
Bipartite networks provide an insightful representation of many systems, ranging from mutualistic networks of species interactions to investment networks in finance. The analysis of their topological structures has revealed the ubiquitous presence of properties which seem to characterize many - apparently different - s…
Identifying Complexity by Means of Matricescond-mat.soft
Complexity is an interdisciplinary concept which, first of all, addresses the question of how order emerges out of randomness. For many reasons matrices provide a very practical and powerful tool in approaching and quantifying the related characteristics. Based on several natural complex dynamical systems, like the str…
Traffic signal control is an important and challenging real-world problem, which aims to minimize the travel time of vehicles by coordinating their movements at the road intersections. Current traffic signal control systems in use still rely heavily on oversimplified information and rule-based methods, although we now …
Quant 4.0 uses AI to automate, explain, and incorporate knowledge in investment.
problem Limitations of deep learning in quant investment.
method Automated AI, Explainable AI, Knowledge-driven AI.
result Improves investment decision-making through automation, interpretability, and prior knowledge integration.
While artificial intelligence (AI) and other automation technologies might lead to enormous progress in healthcare, they may also have undesired consequences for people working in the field. In this interdisciplinary study, we capture empirical evidence of not only what healthcare work could be automated, but also what…
Detecting depression early from social media texts.
problem Early diagnosis and prevention of depression.
method Topic analysis and learned confidence scores.
result Achieved good results compared to state of the art.
Machine learning techniques are being applied to scientific fields, showing promise and challenges.
problem Applying machine learning to scientific data poses challenges in universality and robustness.
method Critical analysis of anomaly detection techniques, focusing on data universality, robustness, and transferability.
result Machine learning techniques show potential but also present domain-specific challenges.
In the face of growing needs for water and energy, a fundamental understanding of the environmental impacts of human activities becomes critical for managing water and energy resources, remedying water pollution, and making regulatory policy wisely. Among activities that impact the environment, oil and gas production, …
Tensor analysis tackles complex multidimensional data across fields.
problem Efficiently extracting information from high-dimensional data.
method Interdisciplinary approach combining statistics, optimization, and numerical linear algebra.
result Significant progress in tensor analysis over the last decade.
Commentary on Cheng's fairness comparison between tests and AI.
problem Distinction between equality and equity in fairness.
method Systematic comparison of test fairness and algorithmic fairness.
result Importance of causality in fairness research.
Designing a new drug is a lengthy and expensive process. As the space of potential molecules is very large (10^23-10^60), a common technique during drug discovery is to start from a molecule which already has some of the desired properties. An interdisciplinary team of scientists generates hypothesis about the required…
Marketing analytics is a diverse field, with both academic researchers and practitioners coming from a range of backgrounds including marketing, expert systems, statistics, and operations research. This paper provides an integrative review at the boundary of these areas. The aim is to give researchers in the intelligen…
This paper argues for more realistic human models in RL.
problem Current RL models oversimplify human feedback, ignoring personal, contextual, and dynamic aspects.
method Calls for interdisciplinary research on human feedback in RL.
result Realistic human models are needed for robust human-in-the-loop RL systems.
Beyond Sentiment Classification: A Generative Framework for Emotion Intensity Evaluation in Textcs.CL
Generative model evaluates text emotion intensity, outperforming classification.
problem Limitations of discrete emotion classification in applied domains.
method Fine-tuning generative language models to output continuous emotion intensity scores.
result Generative model outperforms classification baselines and reveals generalization capabilities.
This paper analyzes crypto white papers under MiCAR, highlighting NLP's role.
problem Regulatory changes in crypto white papers under MiCAR.
method Survey of existing NLP applications, analysis of MiCAR changes.
result NLP can assist in regulatory compliance and white paper analysis.
The purpose of the New York Workshop on Computer, Earth and Space Sciences is to bring together the New York area's finest Astronomers, Statisticians, Computer Scientists, Space and Earth Scientists to explore potential synergies between their respective fields. The 2011 edition (CESS2011) was a great success, and we w…
Over the last decades, the distribution of income and wealth has been deteriorating in many countries, leading to increased inequalities within and between societies. This tendency has revived the interest in the subject greatly, yet it still receives very little attention within the realm of mainstream economic thinki…
The global financial system can be represented as a large complex network in which banks, hedge funds and other financial institutions are interconnected to each other through visible and invisible financial linkages. Recently, a lot of attention has been paid to the understanding of the mechanisms that can lead to a b…
Study finds significant price declines and capital reallocation from centralized to decentralized exchanges after FTX collapse.
problem Quantifying trust dynamics and redistribution between centralized and decentralized exchanges.
method Interdisciplinary approach combining causal inference and computational text analysis.
result Significant price declines and capital reallocation from centralized to decentralized exchanges following the FTX collapse.
The implementation of artificial neural networks in hardware substrates is a major interdisciplinary enterprise. Well suited candidates for physical implementations must combine nonlinear neurons with dedicated and efficient hardware solutions for both connectivity and training. Reservoir computing addresses the proble…
The current economic crisis has provoked an active response from the interdisciplinary scientific community. As a result many papers suggesting what can be improved in understanding of the complex socio-economics systems were published. Some of the most prominent papers on the topic include (Bouchaud, 2009; Farmer and …
Development of efficient business process models and determination of their characteristic properties are subject of intense interdisciplinary research. Here, we consider a business process model as a directed graph. Its nodes correspond to the units identified by the modeler and the link direction indicates the causal…
Fatal accidents are a major issue hindering the wide acceptance of safety-critical systems using machine-learning and deep-learning models, such as automated-driving vehicles. Quality assurance frameworks are required for such machine learning systems, but there are no widely accepted and established quality-assurance …
NodeSig efficiently computes binary node embeddings for scalable graph analysis.
problem Scalability issues in graph representation learning models.
method NodeSig uses random walk diffusion probabilities and stable random projections to compute binary node embeddings efficiently.
result NodeSig achieves a good balance between accuracy and efficiency on node classification and link prediction tasks.