scholarly journals Symmetry, Combinatorics, Artificial Intelligence, Music and Spectroscopy

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1850
Author(s):  
Krishnan Balasubramanian
Keyword(s):  
Artificial Intelligence ◽  
Music Theory ◽  
Esr Spectra ◽  
Machine Construction ◽  
Multiple Quantum ◽  
Magic Squares ◽  
Machine Perception ◽  
Musical Patterns ◽  
Spectroscopic Signatures ◽  
Musical Chords

Symmetry forms the foundation of combinatorial theories and algorithms of enumeration such as Möbius inversion, Euler totient functions, and the celebrated Pólya’s theory of enumeration under the symmetric group action. As machine learning and artificial intelligence techniques play increasingly important roles in the machine perception of music to image processing that are central to many disciplines, combinatorics, graph theory, and symmetry act as powerful bridges to the developments of algorithms for such varied applications. In this review, we bring together the confluence of music theory and spectroscopy as two primary disciplines to outline several interconnections of combinatorial and symmetry techniques in the development of algorithms for machine generation of musical patterns of the east and west and a variety of spectroscopic signatures of molecules. Combinatorial techniques in conjunction with group theory can be harnessed to generate the musical scales, intensity patterns in ESR spectra, multiple quantum NMR spectra, nuclear spin statistics of both fermions and bosons, colorings of hyperplanes of hypercubes, enumeration of chiral isomers, and vibrational modes of complex systems including supergiant fullerenes, as exemplified by our work on the golden fullerene C150,000. Combinatorial techniques are shown to yield algorithms for the enumeration and construction of musical chords and scales called ragas in music theory, as we exemplify by the machine construction of ragas and machine perception of musical patterns. We also outline the applications of Hadamard matrices and magic squares in the development of algorithms for the generation of balanced-pitch chords. Machine perception of musical, spectroscopic, and symmetry patterns are considered.

Music Theory, Phenomenology, and Modes of Perception

1986 ◽  
Vol 3 (4) ◽  
pp. 327-392 ◽  
Author(s):  
David Lewin
Keyword(s):  
Artificial Intelligence ◽  
Music Theory ◽  
Formal Model ◽  
Music Analysis ◽  
The Other ◽  
Musical Activity ◽  
Literary Theories

Recent years have seen an increasing influence on music theory of perceptual investigations that can be called phenomenological in the sense of Husserl, either explicitly or implicitly. The trend is problematic, particularly in what one might call its sociology, but it is also very promising. Potential or at least metaphorical links with Artificial Intelligence are especially suggestive. A formal model for "musical perceptions," incorporating some of the promising features, reveals interesting things in connection with Schubert's song Morgengruβ. The model helps to circumvent some traditional difficulties in the methodology of music analysis. But the model must be used with caution since, like other perceptual theories, it appears to make " listening" a paradigmatic musical activity. Composer/ performer/playwright/actor/director/poet can be contrasted here to listener/reader. The two genera can be compared in the usual ways, but also in some not-so-usual ways. The former genus may be held to be perceiving in the creative act, and some influential contemporary literary theories actually prefer members of this genus to those of the other as perceivers. The theories can be modified, I believe, to allow a more universal stance that also regards acts of analytic reading/listening as poetry.

scholarly journals ELEMENTS OF MUSIC BASED ON ARTIFICIAL INTELLIGENCE

2020 ◽  
Vol 4 (2) ◽  
pp. 30-32
Author(s):  
Alberto Moreno
Keyword(s):  
Artificial Intelligence ◽  
Music Theory ◽  
Research Field ◽  
Current Status ◽  
Element Analysis ◽  
Music Signal ◽  
Processing Theory ◽  
Calculation System ◽  
Music Information ◽  
Music Audio

Thus, for the current status of research and practical music audio processing needs, this paper argues, the music element analysis technology is the key to this research field, and on this basis, proposes a new framework music processing – Music calculation system, the core objective is to study intelligently and automatically identifies various elements of music information and analyze the information used in constructing the music content, and intelligent retrieval method translated. To achieve the above core research objectives, the paper advocates will be closely integrated music theory and calculation methods, the promotion of integrated use of music theory, cognitive psychology, music, cognitive science, neuroscience, artificial intelligence, signal processing theory to solve the music signal analysis identify the problem.

scholarly journals Movement, activity and action: the role of knowledge in the perception of motion

1997 ◽  
Vol 352 (1358) ◽  
pp. 1257-1265 ◽  
Author(s):  
Aaron F. Bobick
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Recent Work ◽  
Motion Perception ◽  
Action Understanding ◽  
Movement Activity ◽  
Machine Perception ◽  
Geometric Techniques ◽  
Gesture Understanding

This paper presents several approaches to the machine perception of motion and discusses the role and levels of knowledge in each. In particular, different techniques of motion understanding as focusing on one of movement, activity or action are described. Movements are the most atomic primitives, requiring no contextual or sequence knowledge to be recognized; movement is often addressed using either view–invariant or view–specific geometric techniques. Activity refers to sequences of movements or states, where the only real knowledge required is the statistics of the sequence; much of the recent work in gesture understanding falls within this category of motion perception. Finally, actions are larger–scale events, which typically include interaction with the environment and causal relationships; action understanding straddles the grey division between perception and cognition, computer vision and artificial intelligence. These levels are illustrated with examples drawn mostly from the group's work in understanding motion in video imagery. It is argued that the utility of such a division is that it makes explicit the representational competencies and manipulations necessary for perception.

scholarly journals The potential of artificial intelligence in enhancing adult weight loss: A scoping review

2021 ◽  
pp. 1-59
Author(s):  
Han Shi Jocelyn CHEW ◽  
Wei How Darryl ANG ◽  
Ying LAU
Keyword(s):  
Artificial Intelligence ◽  
Weight Loss ◽  
Scoping Review ◽  
Prediction Models ◽  
Predictive Analytics ◽  
Self Regulation ◽  
Self Control ◽  
Average Weight ◽  
Machine Perception ◽  
Management Interventions

Abstract Objective: To present an overview of how artificial intelligence (AI) could be used to regulate eating and dietary behaviours, exercise behaviours and weight loss. Design: A scoping review of global literature published from inception to 15 December 2020 was conducted according to Arksey and O’Malley’s five-step framework. Eight databases (CINAHL, Cochrane–Central, Embase, IEEE Xplore, PsycINFO, PubMed, Scopus and Web of Science) were searched. Included studies were independently screened for eligibility by two reviewers with good interrater reliability (k= 0.96). Results: 66 out of 5573 potential studies were included, representing more than 2,031 participants. Three tenets of self-regulation were identified - self-monitoring (n=66, 100%), optimization of goal-setting (n=10, 15.2%) and self-control (n= 10, 15.2%). Articles were also categorised into three AI applications namely machine perception (n=50), predictive analytics only (n=6), and real-time analytics with personalised micro-interventions (n=10). Machine perception focused on recognizing food items, eating behaviours, physical activities and estimating energy balance. Predictive analytics focused on predicting weight loss, intervention adherence, dietary lapses and emotional eating. Studies on the last theme focused on evaluating AI-assisted weight management interventions that instantaneously collected behavioural data, optimised prediction models for behavioural lapse events and enhance behavioural self-control through adaptive and personalized nudges/prompts. Only six studies reported average weight losses (2.4% to 4.7%) of which two were statistically significant. Conclusion: The use of AI for weight loss is still undeveloped. Based on this study findings, we proposed a framework on the applicability of AI for weight loss but cautioned its contingency upon engagement and contextualisation.

scholarly journals Symmetry and Combinatorial Concepts for Cyclopolyarenes, Nanotubes and 2D-Sheets: Enumerations, Isomers, Structures Spectra & Properties

Symmetry ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 34
Author(s):  
Krishnan Balasubramanian
Keyword(s):  
Generating Functions ◽  
Machine Learning Techniques ◽  
Machine Construction ◽  
Multiple Quantum ◽  
Spectral Techniques ◽  
Learning Techniques ◽  
Recent Developments ◽  
Topological Measures ◽  
Metal Organic ◽  
Vertex Partitions

This review article highlights recent developments in symmetry, combinatorics, topology, entropy, chirality, spectroscopy and thermochemistry pertinent to 2D and 1D nanomaterials such as circumscribed-cyclopolyarenes and their heterocyclic analogs, carbon and heteronanotubes and heteronano wires, as well as tessellations of cyclopolyarenes, for example, kekulenes, septulenes and octulenes. We establish that the generalization of Sheehan’s modification of Pólya’s theorem to all irreducible representations of point groups yields robust generating functions for the enumeration of chiral, achiral, position isomers, NMR, multiple quantum NMR and ESR hyperfine patterns. We also show distance, degree and graph entropy based topological measures combined with techniques for distance degree vector sequences, edge and vertex partitions of nanomaterials yield robust and powerful techniques for thermochemistry, bond energies and spectroscopic computations of these species. We have demonstrated the existence of isentropic tessellations of kekulenes which were further studied using combinatorial, topological and spectral techniques. The combinatorial generating functions obtained not only enumerate the chiral and achiral isomers but also aid in the machine construction of various spectroscopic and ESR hyperfine patterns of the nanomaterials that were considered in this review. Combinatorial and topological tools can become an integral part of robust machine learning techniques for rapid computation of the combinatorial library of isomers and their properties of nanomaterials. Future applications to metal organic frameworks and fullerene polymers are pointed out.

scholarly journals Detecting Spurious Correlations With Sanity Tests for Artificial Intelligence Guided Radiology Systems

2021 ◽  
Vol 3 ◽  
Author(s):  
Usman Mahmood ◽  
Robik Shrestha ◽  
David D. B. Bates ◽  
Lorenzo Mannelli ◽  
Giuseppe Corrias ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Best Practices ◽  
Learning System ◽  
Clinical Validation ◽  
Efficacy And Safety ◽  
Analytical Validation ◽  
Validation Data ◽  
Machine Perception ◽  
Computed Tomography Scans ◽  
Development Data

Artificial intelligence (AI) has been successful at solving numerous problems in machine perception. In radiology, AI systems are rapidly evolving and show progress in guiding treatment decisions, diagnosing, localizing disease on medical images, and improving radiologists' efficiency. A critical component to deploying AI in radiology is to gain confidence in a developed system's efficacy and safety. The current gold standard approach is to conduct an analytical validation of performance on a generalization dataset from one or more institutions, followed by a clinical validation study of the system's efficacy during deployment. Clinical validation studies are time-consuming, and best practices dictate limited re-use of analytical validation data, so it is ideal to know ahead of time if a system is likely to fail analytical or clinical validation. In this paper, we describe a series of sanity tests to identify when a system performs well on development data for the wrong reasons. We illustrate the sanity tests' value by designing a deep learning system to classify pancreatic cancer seen in computed tomography scans.

Developing and Delivering a High School Artificial Intelligence Course in Blended and Online Learning Environments

2020 ◽  
pp. 255-261
Author(s):  
Antonios Karampelas
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
High School ◽  
High School Students ◽  
Learning Environments ◽  
Community Schools ◽  
School Students ◽  
Online Learning Environments ◽  
Online Delivery ◽  
Machine Perception

The paper outlines the development and delivery of Artificial Intelligence to high school students of the American Community Schools of Athens, either as an independent course, or as part of a S.T.E.A.M. course, and the respective instructional design. The topics developed – Impact of Artificial Intelligence, Machine Perception, and Machine Learning – are discussed, including relevant assessments. Additionally, the transition to the online delivery of Artificial Intelligence is presented, followed by reflective views on student learning and suggested future steps.

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