Solving non-standard problems by a computer system

2021 ◽  
Author(s):  
A.N. Orekhov
Keyword(s):  
Computer System ◽  
Theoretical Description ◽  
Learning Networks ◽  
Standard Problem ◽  
Artificial Neurons ◽  
Wide Range ◽  
Basic Parameters ◽  
Human Thinking ◽  
Psychological Knowledge ◽  
The One

On the one hand, modern psychology presents a wide range of opinions from the complete denial of the possibility of an adequate theoretical description of the mental in mathematical terms to the recognition of the timeliness and even inevitability of such a description. On the other hand, many developers of traditional AI, i.e. systems based on rules, as well as systems based on deep learning networks of artificial neurons, and their various hybrids either use, most often subconsciously, the most primitive psychological concepts, or believe that they do not need psychological knowledge at all. Therefore, the problem consists of two interrelated parts. The first is whether it is possible to create algorithms of human thinking that are adequate to the facts known in psychology on the basis of the general theory of the psyche, which widely uses the mathematical apparatus. The second is whether it is possible to create a computer system based on these algorithms that can solve the most difficult (non-standard) problems in different fields of knowledge, using what most researchers refer to as "common sense". The goal of the article is to create a computer system capable of solving non-standard problems in natural Russian, using algorithms of human thinking and check its basic parameters. AlNikOr – computer system is created. AlNikOr can solve non-standard problems in natural Russian, using algorithms of human thinking. Its efficiency is shown by the example of solving a non-standard problem in physics. Computer systems based on AlNikOr can be used to solve real non-standard problems in various fields of science and technology.

General Trend of Negative Transference Number in Li Salt/Ionic Liquid Mixtures

2019 ◽  
Author(s):  
Nicola Molinari ◽  
Jonathan P. Mailoa ◽  
Boris Kozinsky
Keyword(s):  
Ionic Liquid ◽  
Transference Number ◽  
Liquid Mixtures ◽  
Single Linkage ◽  
Self Diffusion ◽  
Wide Range ◽  
Single Linkage Cluster ◽  
Concentrated Solution ◽  
The One ◽  
Dynamics Simulations

We show that strong cation-anion interactions in a wide range of lithium-salt/ionic liquid mixtures result in a negative lithium transference number, using molecular dynamics simulations and rigorous concentrated solution theory. This behavior fundamentally deviates from the one obtained using self-diffusion coefficient analysis and agrees well with experimental electrophoretic NMR measurements, which accounts for ion correlations. We extend these findings to several ionic liquid compositions. We investigate the degree of spatial ionic coordination employing single-linkage cluster analysis, unveiling asymmetrical anion-cation clusters. Additionally, we formulate a way to compute the effective lithium charge that corresponds to and agrees well with electrophoretic measurements and show that lithium effectively carries a negative charge in a remarkably wide range of chemistries and concentrations. The generality of our observation has significant implications for the energy storage community, emphasizing the need to reconsider the potential of these systems as next generation battery electrolytes.<br>

QCD at Fixed Order: Processes

2018 ◽  
Author(s):  
John Campbell ◽  
Joey Huston ◽  
Frank Krauss
Keyword(s):  
Hadron Collider ◽  
Detailed Comparison ◽  
Theoretical Description ◽  
Higgs Bosons ◽  
Final States ◽  
Collider Physics ◽  
Wide Range ◽  
Theoretical Predictions ◽  
Fixed Order ◽  
The Subject

At the core of any theoretical description of hadron collider physics is a fixed-order perturbative treatment of a hard scattering process. This chapter is devoted to a survey of fixed-order predictions for a wide range of Standard Model processes. These range from high cross-section processes such as jet production to much more elusive reactions, such as the production of Higgs bosons. Process by process, these sections illustrate how the techniques developed in Chapter 3 are applied to more complex final states and provide a summary of the fixed-order state-of-the-art. In each case, key theoretical predictions and ideas are identified that will be the subject of a detailed comparison with data in Chapters 8 and 9.

scholarly journals A Generalization Performance Study Using Deep Learning Networks in Embedded Systems

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1031
Author(s):  
Joseba Gorospe ◽  
Rubén Mulero ◽  
Olatz Arbelaitz ◽  
Javier Muguerza ◽  
Miguel Ángel Antón
Keyword(s):  
Deep Learning ◽  
Embedded Systems ◽  
Embedded System ◽  
General Purpose ◽  
Learning Networks ◽  
Performance Study ◽  
Learning Techniques ◽  
Wide Range ◽  
Learning Architectures

Deep learning techniques are being increasingly used in the scientific community as a consequence of the high computational capacity of current systems and the increase in the amount of data available as a result of the digitalisation of society in general and the industrial world in particular. In addition, the immersion of the field of edge computing, which focuses on integrating artificial intelligence as close as possible to the client, makes it possible to implement systems that act in real time without the need to transfer all of the data to centralised servers. The combination of these two concepts can lead to systems with the capacity to make correct decisions and act based on them immediately and in situ. Despite this, the low capacity of embedded systems greatly hinders this integration, so the possibility of being able to integrate them into a wide range of micro-controllers can be a great advantage. This paper contributes with the generation of an environment based on Mbed OS and TensorFlow Lite to be embedded in any general purpose embedded system, allowing the introduction of deep learning architectures. The experiments herein prove that the proposed system is competitive if compared to other commercial systems.

scholarly journals Modes of Interaction in Naturally Occurring Medical Encounters With General Practitioners: The “One in a Million” Study

2021 ◽  
pp. 104973232199379
Author(s):  
Olaug S. Lian ◽  
Sarah Nettleton ◽  
Åge Wifstad ◽  
Christopher Dowrick
Keyword(s):  
General Practitioners ◽  
Narrative Analysis ◽  
Mode Competition ◽  
General Applicability ◽  
Naturally Occurring ◽  
Wide Range ◽  
The One ◽  
Narrative Mode ◽  
Modes Of Interaction

In this article, we qualitatively explore the manner and style in which medical encounters between patients and general practitioners (GPs) are mutually conducted, as exhibited in situ in 10 consultations sourced from the One in a Million: Primary Care Consultations Archive in England. Our main objectives are to identify interactional modes, to develop a classification of these modes, and to uncover how modes emerge and shift both within and between consultations. Deploying an interactional perspective and a thematic and narrative analysis of consultation transcripts, we identified five distinctive interactional modes: question and answer (Q&A) mode, lecture mode, probabilistic mode, competition mode, and narrative mode. Most modes are GP-led. Mode shifts within consultations generally map on to the chronology of the medical encounter. Patient-led narrative modes are initiated by patients themselves, which demonstrates agency. Our classification of modes derives from complete naturally occurring consultations, covering a wide range of symptoms, and may have general applicability.

scholarly journals Entangling Lattice-Trapped Bosons with a Free Impurity: Impact on Stationary and Dynamical Properties

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 290
Author(s):  
Maxim Pyzh ◽  
Kevin Keiler ◽  
Simeon I. Mistakidis ◽  
Peter Schmelcher
Keyword(s):  
Structural Changes ◽  
Many Body ◽  
Wide Range ◽  
Entropy Measures ◽  
Particle Level ◽  
The Many ◽  
The One ◽  
Tunneling Dynamics ◽  
The Individual ◽  
Trapped Bosons

We address the interplay of few lattice trapped bosons interacting with an impurity atom in a box potential. For the ground state, a classification is performed based on the fidelity allowing to quantify the susceptibility of the composite system to structural changes due to the intercomponent coupling. We analyze the overall response at the many-body level and contrast it to the single-particle level. By inspecting different entropy measures we capture the degree of entanglement and intraspecies correlations for a wide range of intra- and intercomponent interactions and lattice depths. We also spatially resolve the imprint of the entanglement on the one- and two-body density distributions showcasing that it accelerates the phase separation process or acts against spatial localization for repulsive and attractive intercomponent interactions, respectively. The many-body effects on the tunneling dynamics of the individual components, resulting from their counterflow, are also discussed. The tunneling period of the impurity is very sensitive to the value of the impurity-medium coupling due to its effective dressing by the few-body medium. Our work provides implications for engineering localized structures in correlated impurity settings using species selective optical potentials.

scholarly journals Bio-Inspired Modality Fusion for Active Speaker Detection

2021 ◽  
Vol 11 (8) ◽  
pp. 3397
Author(s):  
Gustavo Assunção ◽  
Nuno Gonçalves ◽  
Paulo Menezes
Keyword(s):  
Superior Colliculus ◽  
Visual Information ◽  
Human Beings ◽  
Validation Process ◽  
Detection Approach ◽  
Wide Range ◽  
Speaker Detection ◽  
The One ◽  
The Brain ◽  
Fusion Ability

Human beings have developed fantastic abilities to integrate information from various sensory sources exploring their inherent complementarity. Perceptual capabilities are therefore heightened, enabling, for instance, the well-known "cocktail party" and McGurk effects, i.e., speech disambiguation from a panoply of sound signals. This fusion ability is also key in refining the perception of sound source location, as in distinguishing whose voice is being heard in a group conversation. Furthermore, neuroscience has successfully identified the superior colliculus region in the brain as the one responsible for this modality fusion, with a handful of biological models having been proposed to approach its underlying neurophysiological process. Deriving inspiration from one of these models, this paper presents a methodology for effectively fusing correlated auditory and visual information for active speaker detection. Such an ability can have a wide range of applications, from teleconferencing systems to social robotics. The detection approach initially routes auditory and visual information through two specialized neural network structures. The resulting embeddings are fused via a novel layer based on the superior colliculus, whose topological structure emulates spatial neuron cross-mapping of unimodal perceptual fields. The validation process employed two publicly available datasets, with achieved results confirming and greatly surpassing initial expectations.

scholarly journals β-Lactoglobulin Adsorption Layers at the Water/Air Surface: 5. Adsorption Isotherm and Equation of State Revisited, Impact of pH

2021 ◽  
Vol 5 (1) ◽  
pp. 14
Author(s):  
Georgi G. Gochev ◽  
Volodymyr I. Kovalchuk ◽  
Eugene V. Aksenenko ◽  
Valentin B. Fainerman ◽  
Reinhard Miller
Keyword(s):  
Theoretical Description ◽  
Fluid Interfaces ◽  
Interfacial Behavior ◽  
Current State ◽  
Air Interface ◽  
Direct Measurements ◽  
Wide Range ◽  
Dilational Viscoelasticity ◽  
Parameter Values ◽  
Β Lactoglobulin

The theoretical description of the adsorption of proteins at liquid/fluid interfaces suffers from the inapplicability of classical formalisms, which soundly calls for the development of more complicated adsorption models. A Frumkin-type thermodynamic 2-d solution model that accounts for nonidealities of interface enthalpy and entropy was proposed about two decades ago and has been continuously developed in the course of comparisons with experimental data. In a previous paper we investigated the adsorption of the globular protein β-lactoglobulin at the water/air interface and used such a model to analyze the experimental isotherms of the surface pressure, Π(c), and the frequency-, f-, dependent surface dilational viscoelasticity modulus, E(c)f, in a wide range of protein concentrations, c, and at pH 7. However, the best fit between theory and experiment proposed in that paper appeared incompatible with new data on the surface excess, Γ, obtained from direct measurements with neutron reflectometry. Therefore, in this work, the same model is simultaneously applied to a larger set of experimental dependences, e.g., Π(c), Γ(c), E(Π)f, etc., with E-values measured strictly in the linear viscoelasticity regime. Despite this ambitious complication, a best global fit was elaborated using a single set of parameter values, which well describes all experimental dependencies, thus corroborating the validity of the chosen thermodynamic model. Furthermore, we applied the model in the same manner to experimental results obtained at pH 3 and pH 5 in order to explain the well-pronounced effect of pH on the interfacial behavior of β-lactoglobulin. The results revealed that the propensity of β-lactoglobulin globules to unfold upon adsorption and stretch at the interface decreases in the order pH 3 > pH 7 > pH 5, i.e., with decreasing protein net charge. Finally, we discuss advantages and limitations in the current state of the model.

scholarly journals ASCU-Net: Attention Gate, Spatial and Channel Attention U-Net for Skin Lesion Segmentation

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 501
Author(s):  
Xiaozhong Tong ◽  
Junyu Wei ◽  
Bei Sun ◽  
Shaojing Su ◽  
Zhen Zuo ◽  
...  
Keyword(s):  
Skin Lesion ◽  
Contextual Information ◽  
Skin Lesions ◽  
Medical Image Segmentation ◽  
Learning Networks ◽  
Wide Range ◽  
Accurate Performance ◽  
Relevant Field ◽  
Skin Smooth ◽  
Smooth Transitions

Segmentation of skin lesions is a challenging task because of the wide range of skin lesion shapes, sizes, colors, and texture types. In the past few years, deep learning networks such as U-Net have been successfully applied to medical image segmentation and exhibited faster and more accurate performance. In this paper, we propose an extended version of U-Net for the segmentation of skin lesions using the concept of the triple attention mechanism. We first selected regions using attention coefficients computed by the attention gate and contextual information. Second, a dual attention decoding module consisting of spatial attention and channel attention was used to capture the spatial correlation between features and improve segmentation performance. The combination of the three attentional mechanisms helped the network to focus on a more relevant field of view of the target. The proposed model was evaluated using three datasets, ISIC-2016, ISIC-2017, and PH2. The experimental results demonstrated the effectiveness of our method with strong robustness to the presence of irregular borders, lesion and skin smooth transitions, noise, and artifacts.

Numerical investigation of the propagation of shock waves in rigid porous materials: development of the computer code and comparison with experimental results

1996 ◽  
Vol 324 ◽  
pp. 163-179 ◽  
Author(s):  
A. Levy ◽  
G. Ben-Dor ◽  
S. Sorek
Keyword(s):  
Porous Materials ◽  
Initial Conditions ◽  
Computer Code ◽  
Experimental Results ◽  
Numerical Code ◽  
Materials Development ◽  
Numerical Predictions ◽  
Wide Range ◽  
Dimensional Version ◽  
The One

The governing equations of the flow field which is obtained when a thermoelastic rigid porous medium is struck head-one by a shock wave are developed using the multiphase approach. The one-dimensional version of these equations is solved numerically using a TVD-based numerical code. The numerical predictions are compared to experimental results and good to excellent agreements are obtained for different porous materials and a wide range of initial conditions.

scholarly journals Two-Segment Foot Model for the Biomechanical Analysis of Squat

2017 ◽  
Vol 2017 ◽  
pp. 1-11
Author(s):  
E. Panero ◽  
L. Gastaldi ◽  
W. Rapp
Keyword(s):  
Body Weight ◽  
Inverse Dynamics ◽  
Statistical Significance ◽  
Lower Limbs ◽  
Biomechanical Analysis ◽  
Wide Range ◽  
Foot Model ◽  
Partial Body ◽  
The One ◽  
Midtarsal Joint

Squat exercise is acquiring interest in many fields, due to its benefits in improving health and its biomechanical similarities to a wide range of sport motions and the recruitment of many body segments in a single maneuver. Several researches had examined considerable biomechanical aspects of lower limbs during squat, but not without limitations. The main goal of this study focuses on the analysis of the foot contribution during a partial body weight squat, using a two-segment foot model that considers separately the forefoot and the hindfoot. The forefoot and hindfoot are articulated by the midtarsal joint. Five subjects performed a series of three trials, and results were averaged. Joint kinematics and dynamics were obtained using motion capture system, two force plates closed together, and inverse dynamics techniques. The midtarsal joint reached a dorsiflexion peak of 4°. Different strategies between subjects revealed 4° supination and 2.5° pronation of the forefoot. Vertical GRF showed 20% of body weight concentrated on the forefoot and 30% on the hindfoot. The percentages varied during motion, with a peak of 40% on the hindfoot and correspondently 10% on the forefoot, while the traditional model depicted the unique constant 50% value. Ankle peak of plantarflexion moment, power absorption, and power generation was consistent with values estimated by the one-segment model, without statistical significance.

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