Low-level Components Research Articles

Trajectories and revolutions in popular melody based on U.S. charts from 1950 to 2023

In the past century, the history of popular music has been analyzed from many different perspectives, with sociologists, musicologists and philosophers all offering distinct narratives characterizing the evolution of popular music. However, quantitative studies on this subject began only in the last decade and focused on features extracted from raw audio, which limits the scope to low-level components of music. The present study investigates the evolution of a more abstract dimension of popular music, specifically melody, using a new dataset of popular melodies spanning from 1950 to 2023. To identify "melodic revolutions", changepoint detection was applied to a multivariate time series comprising features related to the pitch and rhythmic structure of the melodies. Two major revolutions in 1975 and 2000 and one smaller revolution in 1996, characterized by significant decreases in complexity, were located. The revolutions divided the time series into three eras, which were modeled separately with autoregression, linear regression and vector autoregression. Linear regression of autoregression residuals underscored inter-feature relationships, which become stronger in post-2000 melodies. The overriding pattern emerging from these analyses shows decreasing complexity and increasing note density in popular melodies over time, especially since 2000.

Formally verifying decompositions of stochastic specifications

According to the principles of compositional verification, verifying that lower-level components satisfy their specification ensures that the whole system satisfies its top-level specification. The key step is to ensure that the lower-level specifications constitute a correct decomposition of the top-level specification. In a non-stochastic context, such decomposition can be analyzed using techniques of theorem proving. In industrial applications, especially in safety-critical systems, specifications are often of stochastic nature, for example, giving a bound on the probability that a system failure will occur before a given time. A decomposition of such a specification requires techniques beyond traditional theorem proving. The first contribution of the paper is a theoretical framework that allows the representation of, and reasoning about, stochastic and timed behavior of systems as well as specifications for such behavior. The framework is based on traces that describe the continuous-time evolution of a system, and specifications are formulated using timed automata combined with probabilistic acceptance conditions. The second contribution is a novel approach to verifying decompositions of such specifications by reducing the problem to checking emptiness of the solution space for a system of linear inequalities.

Towards ultra-flexibility: a framework for evaluating the cyber-physical continuum in flexible production systems

Flexibility is often cited as a desirable key characteristic of modern production systems. In ultra-flexible production, machinery and layouts are in a constant state of adaptation to accommodate changing orders, varying products, or evolving conditions. Cyber-physical integration has been proposed as a potential approach to increasing system flexibility with Cyber-Physical Production Systems (CPPS) and Digital Twins (DT) as central concepts. While numerous architectures, frameworks and approaches have been proposed for CPPS and DT development, further research is motivated regarding the development of a requirement-based framework that links together the high-level system property of flexibility and lower-level system components, enabling the analysis, prescription and comparison of systems. Such a framework could enable manufacturers to continuously evaluate and improve manufacturing systems’ flexibility as well as make informed design decisions. Ultimately enhancing system flexibility and responsiveness to changing production conditions. This study aims to initiate the development and formulation of such a requirements-based framework linking flexibility and lower-level system components. Additionally, it seeks to introduce the concept of a ”cyber-physical continuum,” which the study aims to define as a potential quantifiable indicator reflecting flexibility within production systems. This is achieved by leveraging prior CPPS research based on high-level system requirements. These requirements were expanded by branching each requirement into lower-level components creating a more granular scope and providing a finer lens for analysis and assessment. The framework was then applied to assess a high-mix, low-volume manufacturing scenario. Application of the preliminary framework in the case study indicates its potential utility in providing a useful view of the cyber-physical content of a system. Moreover, it serves as a valuable guide for pinpointing areas for improvement and development. By developing a framework that seamlessly links high-level flexibility requirements with detailed implementation requirements, systems can be comprehensively evaluated, methodically prescribed, and effectively compared. As future work, further refinement and validation of this framework will be crucial to ensuring its validity and applicability across diverse manufacturing contexts.

Reductionism and proxy failure: From neuroscience to target-based drug discovery.

Reductionist methodologies reduce phenomena to some of their lower-level components. Researchers gradually shift their focus away from observing the actual object of study toward investigating and optimizing such lower-level proxies. Following reductionism, these proxies progressively diverge further from the original object of study. We vividly illustrate this in the evolution of target-based drug discovery from rational and phenotypic drug discovery.

Editor's Introduction: Best Papers from the 20th International Conference on Cognitive Modeling.

The International Conference on Cognitive Modelling is dedicated to understanding how the complex processes of the mind can be explained in terms of detailed inner processing. In this issue, we present four representative papers of this field of research from our 20th meeting, ICCM 2022. This meeting was our first hybrid meeting, with a virtual version happening July 11-15, 2022, and an in-person event from July 23-27, 2022, held in Toronto, Canada. The four papers presented here were the top-ranked papers across both the virtual and in-person events. Three of the papers develop novel computational theories about low-level components within the mind and how those components result in high-level phenomena such as motivation, anhedonia, and attention. The final paper demonstrates the use of cognitive modeling to develop novel explanations of a paired associate learning task, and uses those insights to develop and explain human performance in a more complex version of that task.

Formula omitted]: A template to build verified thread-local interfaces with software scheduler abstractions

This paper presents ThreadAbs, an extension of the layer-based software formal verification toolkit CCAL (Gu et al., 2018). ThreadAbs is specifically designed to provide better expressiveness and proof management for thread abstraction in multithreaded libraries. Thread abstraction isolates the behavior of each thread from others when providing a top-level formal specification for software. Compared to the original CCAL, ThreadAbs offers significant improvements in this regard.CCAL is a verification framework that enables a layered approach to building certified software, as demonstrated by multiple examples (Gu et al. 2016; Li et al. 2021; Shin et al. 2019). Obviously, its main targets usually include multithread libraries, which support significant improvement in the utilization and isolation of system resources. However, it poses new challenges for formal verification. Firstly, it requires a sudden change in the granularity of concurrency during the implementation and verification of the target software. Typically, systems are associated with software schedulers that are built on top of several underlying components in the system (e.g., thread spawn, yield, sleep, and wake-up). Due to the software scheduler, these systems can be divided into low-level components consisting of modules that the software scheduler depends on (e.g., allocators for shared resources and scheduling queues) and high-level components that use software schedulers (e.g., condition variables, semaphores, and IPCs). Therefore, software formal verification on those systems has to provide proper method to deal with those distinct features, which is usually abstracting other threads’ behavior as much as possible to provide an independent thread model and its formal specification. Secondly, it requires handling side effects from other threads, such as dynamic resource allocation from parents with proper isolation of all threads from each other.CCAL has limited support for two crucial aspects of formal verification in multithreaded systems. Firstly, its previous thread abstraction method does not handle the side effects caused by a parent thread during dynamic initial state allocation properly. Secondly, the proofs produced by CCAL are tied to CertiKOS, which makes it challenging to use them for similar proofs that use CCAL as their verification toolkit. To address these issues, we introduce ThreadAbs, a new mechanized methodology that provides proper thread abstraction to reason about multithreaded programs in conjunction with CCAL. We also extend the previous CertiKOS proof with ThreadAbs to demonstrate its usability and expressiveness.

Surrogate Ensemble-Assisted Hyper-Heuristic Algorithm for Expensive Optimization Problems

This paper proposes a novel surrogate ensemble-assisted hyper-heuristic algorithm (SEA-HHA) to solve expensive optimization problems (EOPs). A representative HHA consists of two parts: the low-level and the high-level components. In the low-level component, we regard the surrogate-assisted technique as a type of search strategy and design the four search strategy archives: exploration strategy archive, exploitation strategy archive, surrogate-assisted estimation archive, and mutation strategy archive as low-level heuristics (LLHs), each archive contains one or more search strategies. Once the surrogate-assisted estimation archive is activated to generate the offspring individual, SEA-HHA first selects the dataset for model construction from three principles: All Data, Recent Data, and Neighbor, which correspond to the global and the local surrogate model, respectively. Then, the dataset is randomly divided into training and validation data, and the most accurate model built by polynomial regression (PR), support vector regression (SVR), and Gaussian process regression (GPR) cooperates with the infill sampling criterion is employed for solution estimation. In the high-level component, we design a random selection function based on the pre-defined probabilities to manipulate a set of LLHs. In numerical experiments, we compare SEA-HHA with six optimization techniques on 5-D, 10-D, and 30-D CEC2013 benchmark functions and three engineering optimization problems with only 1000 fitness evaluation times (FEs). The experimental and statistical results show that our proposed SEA-HHA has broad prospects for dealing with EOPs.

Program generation meets program verification: A case study on number-theoretic transform

Program generation allows us to produce high-performance code specialized to each application domain. Although it has had great success in various domains, it remains to be seen whether it is effective for cryptography, where the correctness of programs is indispensable.This work presents a unified approach to program generation, analysis, and verification. Our target is Number-Theoretic Transform (NTT), a key building block of several candidates in Post Quantum Cryptography. We developed a program-generation framework based on the typed tagless-final style, and obtained highly efficient implementations with vector instructions such as AVX2 and AVX-512. The framework allows us to implement a program analyzer for integer-overflow analysis. By combining a brute-force analysis with the standard static interval analysis, we have obtained more precise results than the state-of-the-art static analyzer, which let us find a new optimization.We have verified the generated program using the framework. While verifying the generated program all at once is intractable, we can separately verify the low-level components and the high-level algorithm, thanks to the compositional nature of our program generator. We have used an SMT solver for the former and a custom symbolic interpreter for the latter to prove the functional correctness of the generated program successfully.

The NEF-SPA Approach as a Framework for Developing a Neurobiologically Inspired Spiking Neural Network Model for Speech Production.

The computer-based simulation of the whole processing route for speech production and speech perception in a neurobiologically inspired way remains a challenge. Only a few neural based models of speech production exist, and these models either concentrate on the cognitive-linguistic component or the lower-level sensorimotor component of speech production and speech perception. Moreover, these existing models are second-generation neural network models using rate-based neuron approaches. The aim of this paper is to describe recent work developing a third-generation spiking-neuron neural network capable of modeling the whole process of speech production, including cognitive and sensorimotor components. Our neural model of speech production was developed within the Neural Engineering Framework (NEF), incorporating the concept of Semantic Pointer Architecture (SPA), which allows the construction of large-scale neural models of the functioning brain based on only a few essential and neurobiologically well-grounded modeling or construction elements (i.e., single spiking neuron elements, neural connections, neuron ensembles, state buffers, associative memories, modules for binding and unbinding of states, modules for time scale generation (oscillators) and ramp signal generation (integrators), modules for input signal processing, modules for action selection, etc.). We demonstrated that this modeling approach is capable of constructing a fully functional model of speech production based on these modeling elements (i.e., biologically motivated spiking neuron micro-circuits or micro-networks). The model is capable of (i) modeling the whole processing chain of speech production and, in part, for speech perception based on leaky-integrate-and-fire spiking neurons and (ii) simulating (macroscopic) speaking behavior in a realistic way, by using neurobiologically plausible (microscopic) neural construction elements. The model presented here is a promising approach for describing speech processing in a bottom-up manner based on a set of micro-circuit neural network elements for generating a large-scale neural network. In addition, the model conforms to a top-down design, as it is available in a condensed form in box-and-arrow models based on functional imaging and electrophysiological data recruited from speech processing tasks.

A multilevel account of hippocampal function in spatial and concept learning: Bridging models of behavior and neural assemblies.

A complete neuroscience requires multilevel theories that address phenomena ranging from higher-level cognitive behaviors to activities within a cell. We propose an extension to the level of mechanism approach where a computational model of cognition sits in between behavior and brain: It explains the higher-level behavior and can be decomposed into lower-level component mechanisms to provide a richer understanding of the system than any level alone. Toward this end, we decomposed a cognitive model into neuron-like units using a neural flocking approach that parallels recurrent hippocampal activity. Neural flocking coordinates units that collectively form higher-level mental constructs. The decomposed model suggested how brain-scale neural populations coordinate to form assemblies encoding concept and spatial representations and why so many neurons are needed for robust performance at the cognitive level. This multilevel explanation provides a way to understand how cognition and symbol-like representations are supported by coordinated neural populations (assemblies) formed through learning.

Developing behavioural activation for people with acquired brain injury: a qualitative interpretive description study of barriers and facilitators to activity engagement

BackgroundAcquired brain injuries (ABI) from stroke, head injury, or resected brain tumours are associated with poor emotional wellbeing and heightened risk of mood disorder. Common sequalae of ABI, such as poor attention and memory, can create barriers to the efficacy of cognitively demanding mood interventions, such as Cognitive Behavioural Therapy (CBT). Behavioural Activation (BA), where individuals plan and engage in reinforcing activities, is a promising alternative due to lower cognitive demands. However, BA was initially developed in clinical populations without ABI where the primary barriers to activity engagement were low mood and anxious avoidance. Additionally, BA can incorporate a range of techniques (e.g., mood monitoring, activity scheduling, targeting avoidance, contingency management) and psychoeducational topics (e.g., mindfulness, managing uncertainty; social/communication skills). Exploring barriers and facilitators to adopting specific BA components in ABI is an important aim.MethodsSemi-structured interviews were conducted with purposively selected ABI survivors (N = 16) with both low and high depressive symptoms, and family members (N = 7). Questions focused on routine and enjoyable activities, and feedback on 10 different BA techniques and associated psychoeducational topics. Transcripts were analysed using an interpretive description framework. Analysis was informed by field notes, reflexivity diaries, and peer debriefing.ResultsThe final constructed framework, Creating Sustainable Engagement, comprises a two-tier hierarchy. Higher-level themes concerned core perspectives of BA, regardless of BA component discussed. This included identifying optimal time windows for different BA components (Right Tool at the Right Time), that BA components should, at least initially, not be burdensome or fatiguing (Perceived Effort), that emotional readiness to confront activity-mood relationships should be addressed (Emotional Impact), and that planned BA activities be consistent with individual values (Relation to Values). Lower-level themes concerned specific BA components: Of these, activity scheduling, procedures targeting avoidance, managing uncertainty and social/communication skills were generally well-received, while mood monitoring, contingency management, and mindfulness had mixed feedback.ConclusionsBA is a widely scalable intervention that can be adapted for ABI. This study provides a novel framework on implementing a range of BA components in ABI and adds to the limited evidence on which components may be particularly suitable.

Edutainment in social studies learning: Can it develop critical thinking skills and creativity?

This study is a response to the current conditions of social studies teaching and learning in Indonesia, which are considered tedious, emphasizing low-level components of intellectual growth. Such conditions necessitate the development of the Social Sciences teachers’ creativity to create enjoyable learning that accommodates students' competency attainment. This study aims to investigate, analyze, and characterise edutainment-based social science learning innovations adopted in junior high school. Research and development were employed in the study. It began with a preliminary investigation, followed by constructing an educational entertainment model. According to the study's findings, there are three forms of edutainment development in social science education. These include instructional games, edutainment through the "If History" technique, and edutainment through creative writing techniques. These three methods were found to be promoting students’ participation in the learning process through engaging activities and exploring content familiar to the student's context. This type of research has the potential to develop a creative and innovative Social Science learning model in junior secondary education. Keywords: education in the social sciences, edutainment, learning model

Integrated Evaluation of the Aeroacoustics and Psychoacoustics of a Single Propeller.

Aeroacoustic noise in multiple rotor drones has been increasingly recognized as a crucial issue, while noise reduction is normally associated with a trade-off between aerodynamic performance and sound suppression as well as sound quality improvement. Here, we propose an integrated methodology to evaluate both aeroacoustics and psychoacoustics of a single propeller. For a loop-type propeller, an experimental investigation was conducted in association with its aerodynamic and acoustic characteristics via a hover stand test in an anechoic chamber; the psychoacoustic performance was then examined with psychoacoustic annoyance models to evaluate five psychoacoustic metrics comprising loudness, fluctuation strength, roughness, sharpness, and tonality. A comparison of the figure of merit (FM), the overall sound pressure level (OASPL) and psychoacoustic metrics was undertaken among a two-blade propeller, a four-blade propeller, the loop-type propeller, a wide chord loop-type propeller, and a DJI Phantom III propeller, indicating that the loop-type propeller enables a remarkable reduction in OASPL and a noticeable improvement in sound quality while achieving comparable aerodynamic performance. Furthermore, the psychoacoustic analysis demonstrates that the loop-type propeller can improve the psychological response to various noises in terms of the higher-level broadband and lower-level tonal noise components. It is thus verified that the integrated evaluation methodology of aeroacoustics and psychoacoustics can be a useful tool in the design of low-noise propellers in association with multirotor drones.

PHM Digital Hierarchy of Needs

In today’s modern world, with the abundance of digital data, data science is identified as a rigorous discipline and machine learning (ML) techniques are commonly used. The Prognostics & Health Management (PHM) field can successfully be executed utilizing a foundational approach where a digital hierarchy of needs is established for successful implementation of PHM on a large-scale system. This paper rationalizes the digital hierarchy of needs as it applies to PHM, explains how each foundational concept is essential, and builds upon the base-level concepts through analysis and implementation.
 First, this paper expounds how the integration of digital data from the lower-level components to the system level is critical for the success of PHM-enabled Systems. Once established, arguments will be presented for mapping the appropriate fault data to the corresponding components for the purpose of correlating failures to fault data. Subsequently, a case is presented for using real data to conduct Fault Detection, Fault Isolation, and simple prognostics analyses. Advanced PHM analyses can then be conducted utilizing data science and machine learning techniques with the intent of predictive maintenance analysis. Lastly, an argument is presented answering the need for an approach to implement real-time predictive activities once the complex analysis is validated and verified.
 This concept can be seen graphically in the figure below. Data integration is the foundation of the pyramid, supporting the identification of fault and parametric data, and followed by Fault Detection, Fault Isolation, and Simple Prognostics. The top tiers of the pyramid then can integrate complex analysis such as ML, and real-time implementation.

Quantifying Micromolar Crystallinity in Pharmaceutical Materials Utilizing 19F Solid-State NMR.

Quantifying low-level components in solid-state analysis presents a significant challenge for most thermal, diffractometric, vibrational, and spectroscopic techniques. In pharmaceutical analysis, identifying and quantifying the physical form of the drug substance in solid dosages is a critical task to ensure the quality of drug products. For example, recrystallization of active pharmaceutical ingredients in amorphous solid dispersions can compromise the stability and bioavailability of drug products. Herein, we have developed and demonstrated fluorine-19 solid-state nuclear magnetic resonance (19F ssNMR) methods and pushed the boundary to quantify minor crystalline contents in amorphous pharmaceuticals. Calibration curves suggest that 19F direct polarization and 1H-19F cross-polarization ssNMR can readily quantify 0.1% w/w crystalline compound I, a commercial fluorinated drug molecule developed by Merck & Co., Inc., Rahway, NJ, U.S.A., in its amorphous formulation. 1H-19F multiple cross-polarization (MultiCP) has been implemented, for the first time, and compared with conventional cross-polarization methods. Most importantly, a relaxation-filtered 19F ssNMR method was utilized to unambiguously identify and quantify as low as 0.04% w/w crystalline components, that is, 6 μmol in a 100 mg tablet at 25% drug loading, by suppressing the signal from the amorphous counterpart. Such a low level of detection offers high confidence and sensitivity to quantify trace amounts of phase change in pharmaceutical amorphous materials in the solid state, which can facilitate formulation development as well as quality control.

A Real-Time Digital Twin and Neural Net Cluster-Based Framework for Faults Identification in Power Converters of Microgrids, Self Organized Map Neural Network

In developing distribution networks, the deployment of alternative generation sources is heavily motivated by the growing energy demand, as by environmental and political motives. Consequently, microgrids are implemented to coordinate the operation of these energy generation assets. Microgrids are systems that rely on power conversion technologies based on high-frequency switching devices to generate a stable distribution network. However, disrupting scenarios can occur in deployed systems, causing faults at the sub-component and the system level of microgrids where its identification is an economical and technological challenge. This paradigm can be addressed by having a digital twin of the low-level components to monitor and analyze their response and identify faults to take preventive or corrective actions. Nonetheless, accurate execution of digital twins of low-level components in traditional simulation systems is a difficult task to achieve due to the fast dynamics of the power converter devices, leading to inaccurate results and false identification of system faults. Therefore, this work proposes a fault identification framework for low-level components that includes the combination of Real-Time systems with the Digital Twin concept to guarantee the dynamic consistency of the low-level components. The proposed framework includes an offline trained Self Organized Map Neural Network in a hexagonal topology to identify such faults within a Real-Time system. As a case study, the proposed framework is applied to a three-phase two-level inverter connected to its digital model in a Real-Time simulator for open circuit faults identification.

Integration and expression: The complementary functions of self-reflection.

No construct is more central to personality than the person's self-concept. Higher-order domains of self-assessment, including self-perceived skills, traits, and values, are expressed in action and provide frames of reference for deciding whether to accept or reject personally relevant social feedback. To perform these functions in a consistent manner, the domains of self-concept need to achieve coherence, with the components of each domain sufficiently integrated to provide an unequivocal platform for decision making and behavior. This depiction implies two functions of self-reflection-one focused on forging coherence in self-concept and the other focusing on the expression of domains that have achieved coherence. We refer to these two modes of self-reflection, respectively, as integration and expression. Both modes can be understood in terms of a spotlight of attention that focuses on different regions of the self-structure. In the integration mode, the spotlight converges on incoherent regions of self-concept to eliminate inconsistencies among the lower-level components. In the expression mode, the spotlight converges on coherent regions of self-concept that can provide an unequivocal platform for decision making and effective action. Using agent-based modeling, we illustrate the operation of both modes, discuss the conditions that differentially activate them, and develop their implications for personality dynamics.

The Contribution of Radical Knowledge and Character Recognition to L2 Chinese Reading Comprehension.

Although there have been extensive theoretical discussions on the various component skills needed in comprehending texts in L2 English and L1 Chinese, empirical investigations on the component skills of L2 Chinese are scarce. This study attempts to fill in this gap by investigating the direct and indirect contributions of two lower-level latent component skills, radical knowledge and character recognition, to L2 Chinese passage-level reading comprehension. Radical knowledge was measured by a Receptive Semantic Radical Knowledge Test and a Semantic Radical Meaning Matching Test. Character recognition was assessed by a Lexical Decision Test and a Character Knowledge Test. Two tests, a Multiple-choice Test and a Cloze Test, were adopted to measure textual reading comprehension. The data were collected from 209 learners of Chinese as a second language (CSL). Radical knowledge was found to have a significant direct effect on character recognition and significant indirect effect on L2 Chinese reading through the mediation of character recognition. Character recognition was found to have a significant direct effect on reading comprehension. Taken together, this study suggests the importance of lower-level character and sub-character component skills to L2 Chinese reading.

Numerical investigation and experimental verification of the proposed robot leg virtual model

Modeling and simulation studies are significant in the early stages of the design process, since there is no prototype. For instance, dynamic systems such as control systems can be developed rapidly and effectively with the simulation studies. However, the validity of the results obtained from the simulation studies performed with the dynamic model should be verified before deepening the design studies. After all, the accuracy of the results of the performed simulations depends on the accuracy of the obtained model. In this paper, studies on the numerical analysis of the proposed leg model to be used in a quadruped robot, and experimental verification of the dynamic model obtained by this means are presented. In this context, a simulation model has been constructed by using the physical and mathematical model that includes all the major components of the proposed robot leg in order to evaluate the performances of different algorithms in the design studies. In order to evaluate the validity of the simulation model, a series of tests were carried out on the experimental setup and the obtained results were demonstrated both numerically and graphically. According to the obtained results, it was seen that the built simulation model was valid, and matched with the real system and its physical trends. Finally, after discussing the reliability and validity of the proposed robot leg experimental and simulation setup, low-level components affecting controller performance in hydraulically actuated robots are discussed and recommendations made.

Review of Traction System, Energy Efficiency and Electrical Design of Automated Guided Vehicles in Automotive Industry

Automated guided vehicles (AGVs) have been part of every production plant because of the increasing demand for production speed, efficiency and production throughput. Therefore, an AGV is part of the automation system in production plants. In that sense, AGVs can be considered as programmable and configurable robots because every production has specific AGV performance requirements such as AGV speed, positioning accuracy, maximum load, maximum speed, and safety standards. In this study, comprehensive system parameters are studied to provide a guideline to researchers and application engineers working on AGVs. Regarding the traction system, energy efficiency solutions, failure risks of AGV motors, and electrical design issues are the dedicated sections in this paper. On a global scale, cyber security and climate change are also two critical parameters that should be considered for component specifications of every AGV. As a case study in automotive production plants, an AGV developed in Ford Otosan R&D Center is presented. To this end, this paper is intended to be a design guideline considering the relationship between the upper-level software interacting with the environment and lower-level electrical components exposed to mechanical load.