Computational Theory Research Articles

Feature-Specific Salience Maps in Human Cortex.

Priority map theory is a leading framework for understanding how various aspects of stimulus displays and task demands guide visual attention. Per this theory, the visual system computes a priority map, which is a representation of visual space indexing the relative importance, or priority, of locations in the environment. Priority is computed based on both salience, defined based on image-computable properties; and relevance, defined by an individual's current goals, and is used to direct attention to the highest-priority locations for further processing. Computational theories suggest that priority maps identify salient locations based on individual feature dimensions (e.g., color, motion), which are integrated into an aggregate priority map. While widely accepted, a core assumption of this framework, the existence of independent feature dimension maps in visual cortex, remains untested. Here, we tested the hypothesis that retinotopic regions selective for specific feature dimensions (color or motion) in human cortex act as neural feature dimension maps, indexing salient locations based on their preferred feature. We used fMRI activation patterns to reconstruct spatial maps while male and female human participants viewed stimuli with salient regions defined by relative color or motion direction. Activation in reconstructed spatial maps was localized to the salient stimulus position in the display. Moreover, the strength of the stimulus representation was strongest in the ROI selective for the salience-defining feature. Together, these results suggest that feature-selective extrastriate visual regions highlight salient locations based on local feature contrast within their preferred feature dimensions, supporting their role as neural feature dimension maps.SIGNIFICANCE STATEMENT Identifying salient information is important for navigating the world. For example, it is critical to detect a quickly approaching car when crossing the street. Leading models of computer vision and visual search rely on compartmentalized salience computations based on individual features; however, there has been no direct empirical demonstration identifying neural regions as responsible for performing these dissociable operations. Here, we provide evidence of a critical double dissociation that neural activation patterns from color-selective regions prioritize the location of color-defined salience while minimally representing motion-defined salience, whereas motion-selective regions show the complementary result. These findings reveal that specialized cortical regions act as neural "feature dimension maps" that are used to index salient locations based on specific features to guide attention.

PENDAMPINGAN KEGIATAN OLIMPIADE SISWA NASIONAL BIDANG KOMPUTER PADA SISWA SMA NEGERI 1 PRAMBANAN SLEMAN

The National Student Olympiad (OSN) is an academic competition held annually by the Indonesian Ministry of Education and Culture for high school students. As one of the public schools in Sleman, SMA Negeri 1 Prambanan always sends students to the OSN. Preparatory efforts are always made so that students can be maximised in participating in the OSN. The OSN mentoring activity for the SMA Negeri 1 Prambanan team in 2023, especially in the computer field, was held at the AKPRIND Institute of Science & Technology computer laboratory. This activity aims to make students have an overview of OSN questions and the process of solving answers, especially programming questions. By discussing OSN material, the expected benefits include, among others, increasing student learning motivation. By understanding the types of questions and the process of solving them, students can feel more prepared and motivated to take part in the OSN. The mentoring was carried out with a training method by explaining the material and discussing OSN questions in 2022. The training material provided starts from basic computer theory, programming logic to programming material using the C++ language. The results of the activity, students have an overview of OSN questions and the process of solving answers, especially programming questions. Students' understanding, especially programming questions, increased and students' understanding of OSN questions and the process of solving answers, especially programming questions, can provide positive benefits for learning abilities and student achievement.

The fundamental theory of quantum computing, applications in practical fields, and future challenges

Building on the fundamental principles of quantum computing, this paper sequentially presents the development of quantum computing and its associated application domains. We also analyze and compare the differences between quantum and classical computing, highlighting the benefits of quantum computing for specific problems. To comprehend the advantages of quantum computing in particular fields, extensive research has been conducted in three areas: cryptography, chemistry, and artificial intelligence. We also discuss the wider applications of this emerging technology for future investigation, considering the current state of quantum computing development and anticipated trends.

Multi-scale study of turbulent mass transfer process in different shaped-adsorbent packed bed

Insufficient consideration of the coupled effect of multi-scale and turbulent mass transfer impedes thorough understanding of the dynamic adsorption process in the fixed bed. To solve this problem, a novel PR-CMT model, integrated particle resolution method and computational mass transfer theory, is proposed and verified to simulate the gas adsorption process on particles by combining the computational mass transfer method and the particle dissolution method. The proposed model not only rigorously accounts for the turbulent diffusion by incorporating the equation of concentration variance c2‾ and concentration variance dissipation εc, but also establishes connections between different scales of fixed-bed particle-fluid systems. Through this work, it is found that there is a preferred fluid path in the particle filling structure, which can lead to significant changes in local mass transfer performance. Additionally, the turbulent diffusion enhances the mass and heat transfer between the gas and particles, resulting in a more uniform distribution in the radial direction. Using the proposed model, the effects of the adsorbent characteristics on the multi-scale adsorption process, such as the adsorbent porosity and shape, are further examined. The results indicate that increasing the adsorbent porosity leads to enhanced overall adsorption rate in the fixed bed, and the turbulent diffusion is strongest in the fixed bed with cylinder adsorbents.

Phase behaviour of semiflexible lattice polymers in poor-solvent solution: Mean-field theory and Monte Carlo simulations.

We study a solution of interacting semiflexible polymers with curvature energy in poor-solvent conditions on the d-dimensional cubic lattice using mean-field theory and Monte Carlo computer simulations. Building upon past studies on a single chain, we construct a field-theory representation of the system and solve it within a mean-field approximation supported by Monte Carlo simulations in d = 3. A gas-liquid transition is found in the temperature-density plane that is then interpreted in terms of real systems. Interestingly, we find this transition to be independent of the bending rigidity. Past classical Flory-Huggins and Flory mean-field results are shown to be particular cases of this more general framework. Perspectives in terms of guiding experimental results towards optimal conditions are also proposed.

The impact of intelligent customer service agents’ initial response on consumers’ continuous interaction intention

Intelligent customer service agents (ICSAs) have been widely used as automated service assistants in e-commerce. Although ICSAs are highly valued by enterprises, consumers have lower continuous interaction intention for them. The initial response content will largely affect consumers' continuous interaction intention, which directly determines the ICSAs' efficiency. Therefore, making an effective strategy for ICSAs' initial response has become a focus for enterprises. Previous studies mainly investigated ICSAs from the perspective of the complete interaction, and ignored the initial interaction. Based on the theory of computers as social actors and the stereotype content model, this study investigates the impact of ICSAs' initial response content (functional content vs. social content) on consumers' continuous interaction intention. Through two online scenario-based experiments, the results show that initial response content has a significant influence on consumers' continuous intention. The initial response content impacts consumers' trust and affect through perceived warmth and perceived competence, which further influence consumers’ continuous interaction intention. Furthermore, the functional response content has a greater impact on perceived competence, while the social response content has a stronger influence on perceived warmth. In addition, the effect of initial response content on perceived warmth and on perceived competence are moderated by interaction initiation modes. These findings enrich the literature on ICSAs and offer practical implications for e-retailers seeking to make effective initial response strategies.

Computational Group Theory and Quantum-Era Cryptography

This paper provides an overview of the significant role of computational group theory in cryptography. Group theory plays a crucial role in various cryptographic applications, such as key exchange, encryption, and digital signatures. This paper examines the fundamental concepts, algorithms, and applications of computational group theory in cryptography, using polycyclic groups with a focus on key findings and recent developments in quantum-era cryptography.

Investigating the mental health of university students during the COVID-19 pandemic in a UK university: a machine learning approach using feature permutation importance

Mental wellbeing of university students is a growing concern that has been worsening during the COVID-19 pandemic. Numerous studies have gathered empirical data to explore the mental health impact of the pandemic on university students and investigate factors associated with higher levels of distress. While the online questionnaire survey has been a prevalent means to collect data, regression analysis has been observed a dominating approach to interpret and understand the impact of independent factors on a mental wellbeing state of interest. Drawbacks such as sensitivity to outliers, ineffectiveness in case of multiple predictors highly correlated may limit the use of regression in complex scenarios. These observations motivate the underlying research to propose alternative computational methods to investigate the questionnaire data. Inspired by recent machine learning advances, this research aims to construct a framework through feature permutation importance to empower the application of a variety of machine learning algorithms that originate from different computational frameworks and learning theories, including algorithms that cannot directly provide exact numerical contributions of individual factors. This would enable to explore quantitative impact of predictors in influencing student mental wellbeing from multiple perspectives as a result of using different algorithms, thus complementing the single view due to the dominant use of regression. Applying the proposed approach over an online survey in a UK university, the analysis suggests the past medical record and wellbeing history and the experience of adversity contribute significantly to mental wellbeing states; and the frequent communication with families and friends to keep good relationship as well as regular exercise are generally contributing to improved mental wellbeing.

Research on innovative model of piano informatization teaching under the background of big data and soft computing

The continuous emergence of new social media has promoted the interaction of data in life. In this context, big data technology has gradually become a type of technology with special value in the field of education. It realizes business decision-making through the collection and analysis of specific types of data. The application of technology to the field of education can realize the analysis of students’ learning needs and classroom teaching efficiency, while the application of big data technology to piano teaching can strengthen the combination of piano teaching and technology, and at the same time provide more data analysis and decision support for the whole process of piano teaching. Soft computing simplifies the calculation process by combining a variety of sophisticated algorithms. Soft computing is similar to human brain processing and perception. It can complete multi-variable complex calculations through low-cost and uncertain processes. With the continuous integration, the variables generated in the teaching process gradually increase, and the application of soft computing technology can effectively solve the complex calculation problems caused by many variables in the analysis of piano teaching. This paper first analyzes the basic theory of big data, soft computing and piano informatization teaching, and then obtains the problems existing in the current piano informatization teaching through questionnaire survey and analysis, and designs piano informatization, teaching innovation model based on big data and soft computing technology respectively. Through the post-investigation of the piano informatization teaching institutions combined with technical strategies, it is found that the degree of match between the piano informatization teaching content of the institution and the students’ learning needs, the evaluation accuracy of piano teachers’ teaching effects, and the students’ piano learning performance have improved.

Microfluidic Printing of Vertically-Oriented Nanosheets/MOFs Hetero-Interface for Intensive Pseudocapacitive Storage.

Efficient manufacture of electroactive vertically-oriented nanosheets with enhanced electrolyte mass diffusion and strong interfacial redox dynamics is critical for realizing high energy density of miniature supercapacitor (SC), but still challenging. Herein, microfluidic droplet printing is developed to controllably construct vertically-oriented graphene/ZIF-67 hetero-microsphere (VAGS/ZIF-67), where the ZIF-67 is coordinately grown on vertically-oriented graphene framework via Co─O─C bonds. The VAGS/ZIF-67 shows ordered porous channel, high electroactivity and strong interfacial interaction, providing rapid electrolyte diffusion dynamics and high faradaic capacitance in KOH solution (1674 F g-1 , 1004 C g-1 ), which are verified by computational fluid dynamics (CFD) and density functional theory (DFT). Moreover, the VAGS/ZIF-67 based SC exhibits large energy density (100Wh kg-1 ), excellent durability (10000 cycles and high/low temperature), and robust power-supply applications in portable electronics.

Outlier detection for incomplete real-valued data via rough set theory and granular computing

Outlier detection is an important topic in data mining. An information system (IS) is a database that shows relationships between objects and attributes. A real-valued information system (RVIS) is an IS whose information values are real numbers. People often encounter missing values during data processing. A RVIS with the miss values is an incomplete real-valued information system (IRVIS). Due to the presence of the missing values, the distance between two information values is difficult to determine, so the existing outlier detection rarely considered an IS with the miss values. This paper investigates outlier detection for an IRVIS via rough set theory and granular computing. Firstly, the distance between two information values on each attribute of an IRVIS is introduced, and the parameter λ to control the distance is given. Then, the tolerance relation on the object set is defined according to the distance, and the tolerance class is obtained, which is regarded as an information granule. After then, λ-lower and λ-upper approximations in an IRVIS are put forward. Next, the outlier factor of every object in an IRVIS is presented. Finally, outlier detection method for IRVIS via rough set theory and granular computing is proposed, and the corresponding algorithms is designed. Through the experiments, the proposed method is compared with other methods. The experimental results show that the designed algorithm is more effective than some existing algorithms in an IRVIS. It is worth mentioning that for comprehensive comparison, ROC curve and AUC value are used to illustrate the advantages of the proposed method.

Preface

Welcome to 2023 International Conference on Computational and Mathematical Methods in Engineering, which was held in Qingdao, China on 29-30 July 2023! As the editor of this proceeding, I’m honored to present this special issue to you.The CMME2023 aims to provide a platform for scholars and researchers in related fields to exchange ideas and present their research results. It covers engineering issues and is designed to provide readers with an opportunity to gain a comprehensive understanding of cutting-edge research.With the rapid development of technology, engineering computing plays a crucial role in the field of natural science and practical applications. Through the cross-integration of computer science, mathematics and engineering, improve the theory and application of engineering computing. The study of engineering computing and mathematical methods, not only helps us better understand and explain phenomena, but also provides us with opportunities to optimize design and improve efficiency.We are committed to publishing high-quality, original, and practical articles, covering a wide range of research fields, such as structural mechanics, fluid mechanics, electronics and communication engineering, etc. Meanwhile, we also welcome submissions from different backgrounds and professional fields, and promote interdisciplinary collaboration and knowledge sharing.To ensure the scientific and reliable content, journal quality and academic standards, all papers undergone peer review by using a rigorous peer review system. Each submission is reviewed by anonymous experts and will only be accepted for publication after passing rigorous review. This guarantees the quality and accuracy of the published articles and allows readers to rely on these research results with confidence. We would like to express our deep gratitude to the authors who participated in this CMME, as well as all the anonymous reviewers, who filled in the content of this proceeding and provided valuable comments and suggestions.Here, you will find a series of inspiring and forward-looking articles covering the latest research in various fields. We hope that these articles will stimulate your thinking and provide you with valuable reference and guidance as you practice engineering computing and mathematical methods. We also hope that through the promotion and dissemination of this proceeding, we can further promote the development of the field of engineering computing and make positive contributions to solving major problems in engineering practice.Finally, I would like to thank all the contributing authors, reviewers and editorial team for their hard work and support. Sincerely hope that CMME2023 can provide valuable information for your research and practice in related fields and make positive contribution. We can’t wait to see you at next CMME 2024!CMME2023 Organizing Committee2 August 2023

A role for cortical interneurons as adversarial discriminators.

The brain learns representations of sensory information from experience, but the algorithms by which it does so remain unknown. One popular theory formalizes representations as inferred factors in a generative model of sensory stimuli, meaning that learning must improve this generative model and inference procedure. This framework underlies many classic computational theories of sensory learning, such as Boltzmann machines, the Wake/Sleep algorithm, and a more recent proposal that the brain learns with an adversarial algorithm that compares waking and dreaming activity. However, in order for such theories to provide insights into the cellular mechanisms of sensory learning, they must be first linked to the cell types in the brain that mediate them. In this study, we examine whether a subtype of cortical interneurons might mediate sensory learning by serving as discriminators, a crucial component in an adversarial algorithm for representation learning. We describe how such interneurons would be characterized by a plasticity rule that switches from Hebbian plasticity during waking states to anti-Hebbian plasticity in dreaming states. Evaluating the computational advantages and disadvantages of this algorithm, we find that it excels at learning representations in networks with recurrent connections but scales poorly with network size. This limitation can be partially addressed if the network also oscillates between evoked activity and generative samples on faster timescales. Consequently, we propose that an adversarial algorithm with interneurons as discriminators is a plausible and testable strategy for sensory learning in biological systems.

Theory of additive complementary dual codes, constructions and computations

We study complementary dual Fq-linear codes defined over an extension field Fqm, which we refer to as additive complementary dual (ACD) codes. We first consider duality of this class of codes with respect to a general inner product on Fqmn, which covers the commonly studied cases such as the trace-Euclidean and Hermitian inner products. In general this inner product is not necessarily symmetric, hence we define the notions of left/right hulls and left/right duals of an additive code. This leads to one-sided ACD codes, for which we prove a characterization result in terms of the generator matrix, extending the analogous characterization of Massey for linear complementary dual (LCD) codes. Then we focus on constructions and parameters of ACD codes with respect to trace-Euclidean, Hermitian and Galois inner products. We prove how LCD codes yield ACD codes relative to inner products considered in this work, make some observations on MDS subclass of ACD codes and present a construction of an ACD code by expanding another ACD code. We provide extensive computational results and tables on ACD codes over F4,F8 and F9. Our computations yield many MDS ACD codes and also examples of ACD codes, which have more codewords than the comparable LCD codes with the same length and minimum distance.

The ZX-calculus as a language for topological quantum computation

Unitary fusion categories formalise the algebraic theory of topological quantum computation. These categories come naturally enriched in a subcategory of the category of Hilbert spaces, and by looking at this subcategory, one can identify a collection of generators for implementing quantum computation. We represent such generators for the Fibonacci and Ising models, namely the encoding of qubits and the associated braid group representations, with the ZX-calculus and show that in both cases, the Yang–Baxter equation is directly connected to an important rule in the complete ZX-calculus known as the P-rule, which enables one to interchange the phase gates defined with respect to complementary bases. In the Ising case, this reduces to a familiar rule relating two distinct Euler decompositions of the Hadamard gate as Z- and X-phase gates, whereas in the Fibonacci case, we give a previously unconsidered exact solution of the P-rule involving the Golden ratio. We demonstrate the utility of these representations by giving graphical derivations of the single-qubit braid equations for Fibonacci anyons and the single- and two-qubit braid equations for Ising anyons. We furthermore present a fully graphical procedure for simulating and simplifying braids with the ZX-representation of Fibonacci anyons.

Understanding Polaritonic Chemistry from Ab Initio Quantum Electrodynamics.

In this review, we present the theoretical foundations and first-principles frameworks to describe quantum matter within quantum electrodynamics (QED) in the low-energy regime, with a focus on polaritonic chemistry. By starting from fundamental physical and mathematical principles, we first review in great detail ab initio nonrelativistic QED. The resulting Pauli-Fierz quantum field theory serves as a cornerstone for the development of (in principle exact but in practice) approximate computational methods such as quantum-electrodynamical density functional theory, QED coupled cluster, or cavity Born-Oppenheimer molecular dynamics. These methods treat light and matter on equal footing and, at the same time, have the same level of accuracy and reliability as established methods of computational chemistry and electronic structure theory. After an overview of the key ideas behind those ab initio QED methods, we highlight their benefits for understanding photon-induced changes of chemical properties and reactions. Based on results obtained by ab initio QED methods, we identify open theoretical questions and how a so far missing detailed understanding of polaritonic chemistry can be established. We finally give an outlook on future directions within polaritonic chemistry and first-principles QED.

Exploring new depths: Applying machine learning for the analysis of student argumentation in chemistry

AbstractConstructing arguments is essential in science subjects like chemistry. For example, students in organic chemistry should learn to argue about the plausibility of competing chemical reactions by including various sources of evidence and justifying the derived information with reasoning. While doing so, students face significant challenges in coherently structuring their arguments and integrating chemical concepts. For this reason, a reliable assessment of students' argumentation is critical. However, as arguments are usually presented in open‐ended tasks, scoring assessments manually is resource‐consuming and conceptually difficult. To augment human diagnostic capabilities, artificial intelligence techniques such as machine learning or natural language processing offer novel possibilities for an in‐depth analysis of students' argumentation. In this study, we extensively evaluated students' written arguments about the plausibility of competing chemical reactions based on a methodological approach called computational grounded theory. By using an unsupervised clustering technique, we sought to evaluate students' argumentation patterns in detail, providing new insights into the modes of reasoning and levels of granularity applied in students' written accounts. Based on this analysis, we developed a holistic 20‐category rubric by combining the data‐driven clusters with a theory‐driven framework to automate the analysis of the identified argumentation patterns. Pre‐trained large language models in conjunction with deep neural networks provided almost perfect machine‐human score agreement and well‐interpretable results, which underpins the potential of the applied state‐of‐the‐art deep learning techniques in analyzing students' argument complexity. The findings demonstrate an approach to combining human and computer‐based analysis in uncovering written argumentation.

Towards a model of human-cyber–physical automata and a synthesis framework for control policies

Advances in research and increasing applications of Cyber–Physical Systems (CPSs) show the need to consider factors of humans in the loop. This has led to the growing research focus on Human-Cyber–Physical Systems (HCPSs). In general, humans in an HCPS interact with both the cyber and physical systems, as well as among the humans themselves. For a better understanding, correct design, development, operation, and maintenance of HCPSs, a computational theory based on a computational model is required. This paper presents our initial work towards a model of human-cyber–physical automata (HCPA). We consider an HCPS as a combination of a human-physical system (HPS) and a CPS in which the control switches between the humans and the machines. We define an HCPA by connecting the automaton of the HPS and the automaton of the CPS through a switch control automaton. The switch control automaton makes switching decision in some critical states shared by the HPS and the CPS. Our theorem shows that the control switching between the HPS and the CPS increases the probability of satisfying a given property. We model the behaviour of a human in specified applications or even in carry out specific tasks, instead of general human intelligence. Therefore, a human can make mistakes to decision making and thus it is a probabilistic automaton with learning ability. The switching between the human and the machine is modelled by an oracle. The oracle learns about the human behaviour, the machine behaviour, as well as the environment to make the control decisions. To generate the control policies of the human and the oracle, we propose a synthesis framework to maximize the probability of the satisfaction of a property specified in Linear Temporal Logic (LTL) by the HCPA. We present a prototype implementation of the framework by extending the model-free reinforcement learning (RL) algorithm and model-free deep-RL algorithm, and our experiment shows that our synthesis framework is effective in obtaining switch policies.

Research on Influencing Factors of Electricity Consumption in China Based on Grey Theory and Big Data Technology

With the development and application of computer theory, electronic information technology, economics and other disciplines, more and more models are being used to discuss electricity consumption factors. Thesis based on actual data of China, this paper discusses the influence of economic growth, electricity price, urbanization level, industrial structure, science and technology investment level and opening degree on electric power consumption level of our country, and analyses the influence of grey correlation analysis method. To drive electricity consumption, the first is to optimize and upgrade the industrial structure, and the second is to further improve the level of urbanization. The research results of this paper have certain reference value for relevant managers and government departments to formulate policies.

Analog Programmable‐Photonic Computation

AbstractDigital electronics is a technological cornerstone in this modern society that has covered the increasing demand for computing power during the last decades thanks to a periodic doubling of transistor density in integrated circuits. Currently, such scaling law is reaching its fundamental limit, leading to the emergence of a large gamut of applications that cannot be supported by digital electronics, specifically, those that involve real‐time multi‐data processing, e.g., medical diagnostic imaging, robotic control, and autonomous driving, among others. In this scenario, an analog computing approach implemented in a real‐time reconfigurable nonelectronic hardware such as programmable integrated photonics (PIP) can be more efficient than digital electronics to perform these emerging applications. However, actual analog computing models such as quantum and neuromorphic computation were not conceived to extract the unique benefits of PIP (and integrated photonics in general). Here, the foundations of a new computation theory are presented, termed Analog Programmable‐Photonic Computation (APC), explicitly designed to unleash the full potential of PIP technology. Interestingly, APC enables overcoming basic theoretical and technological limitations of existing computational models and can be implemented in other technologies (e.g., in electronics, acoustics or using metamaterials), consequently exhibiting the potential to spark a ground‐breaking impact on the information society.