Computational Paradigms Research Articles

Electronic structure simulations in the cloud computing environment.

The transformative impact of modern computational paradigms and technologies, such as high-performance computing (HPC), quantum computing, and cloud computing, has opened up profound new opportunities for scientific simulations. Scalable computational chemistry is one beneficiary of this technological progress. The main focus of this paper is on the performance of various quantum chemical formulations, ranging from low-order methods to high-accuracy approaches, implemented in different computational chemistry packages and libraries, such as NWChem, NWChemEx, Scalable Predictive Methods for Excitations and Correlated Phenomena, ExaChem, and Fermi-Löwdin orbital self-interaction correction on Azure Quantum Elements, Microsoft's cloud services platform for scientific discovery. We pay particular attention to the intricate workflows for performing complex chemistry simulations, associated data curation, and mechanisms for accuracy assessment, which is demonstrated with the Arrows automated workflow for high throughput simulations. Finally, we provide a perspective on the role of cloud computing in supporting the mission of leadership computational facilities.

High‐Resolution Optical Convolutional Neural Networks Using Phase‐Change Material‐Based Microring Hybrid Waveguides

In the More‐than‐Moore era, the explosive growth of data and information has driven the exploration of alternative non‐von Neumann computational paradigms. Photonic neuromorphic computing has emerged as a promising approach, offering high speed, wide bandwidth, and massive parallelism. Herein, a high‐resolution optical convolutional neural network (OCNN) is introduced using phase‐change material Ge2Sb2Te5 (GST)‐based microring hybrid waveguides. This on‐chip optical computing platform integrates GST into photonic devices, enabling versatile programming and in‐memory computing capabilities. Central to this platform is a photonic convolutional computational kernel, constructed from photonic switching cells embedded with GST on a microring resonator. This programmable photonic switch leverages the refractive index modulation during the GST phase transition to achieve up to 64 discrete levels of transmission contrast, suitable for representing matrix elements in neural network algorithms with 6‐bit resolution. Using these matrix elements, an OCNN capable of performing parallelized image edge detection and digital recognition tasks with high accuracy is demonstrated. The architecture is scalable for large‐scale photonic neural networks, offering ultrahigh computational throughput, a compact design, complementary metal‐oxide‐semiconductor‐compatible fabrication, and broad bandwidth.

Investigation of temperature assisted corrosion failure of an aircraft turbine blade

In this study, the root cause of a recurring corrosion problem at the tip of an internally cooled high-pressure turbine blade (HPT) of a turbofan engine was investigated. A two-pronged strategy was utilized involving (i) experimental techniques to identify the blade material and corrosion products, and (ii) computational methods to analyze the fluid flow over the blade. In the experimental thrust, an array of techniques was employed to determine the chemical composition, microstructure, corrosion products and microhardness values while the airflow, pressure distribution and thermal profile over the blade were found through computational paradigms. The blade was a Ni-based superalloy containing typical gamma (γ) and gamma prime (γ′) phases, with an average microhardness value of 389 HV at room temperature. SEM analysis of the corroded area revealed oxides of iron, calcium, magnesium, aluminium, and silicon (FCMAS) most of which were not part of the base alloy. To determine the temperature profile and pressure distribution on the blade, a conjugate heat transfer analysis using Ansys CFX was conducted. A combination of experimental data from the engine ground running test station and analytical equations was used to find the required boundary conditions for the computational analysis. Pressure distribution over the entire blade was found which showed higher flow velocities and cross flows at the blade tip which potentially cause erosion at that region in the presence of impurities in the incoming air. Temperature distribution over the entire blade was also obtained, and it was found that the highest temperatures for the internally cooled blade occurred at the tip region which were in the range of ∼1060 °C–1250 °C (average ∼1114 °C). The temperature on the tip was high enough to cause the melting of the impurities that ingress the coating and cause its degradation during thermal cycling. This exposes the base alloy to FCMAS and causes corrosion. These high temperatures (1060–1250 °C) also induced phase transformation and increased the brittleness (∼22 % increase in hardness) which aggravated the presence of corrosion and resulted in crack formation and blade rejection.

Computational psychiatry and the evolving concept of a mental disorder

As a discipline, psychiatry is in the process of finding the right set of concepts to organize research and guide treatment. Dissatisfaction with the status quo as expressed in standard manuals has animated a number of computational paradigms, each proposing to rectify the received concept of mental disorder. We explore how different computational paradigms: normative modeling, network theory and learning-theoretic approaches like reinforcement learning and active inference, reconceptualize mental disorders. Although each paradigm borrows heavily from machine learning, they differ significantly in their methodology, their preferred level of description, the role they assign to the environment and, especially, the degree to which they aim to assimilate psychiatric disorders to a standard medical disease model. By imagining how these paradigms might evolve, we bring into focus three rather different visions for the future of psychiatric research. Although machine learning plays a crucial role in the articulation of these paradigms, it is clear that we are far from automating the process of conceptual revision. The leading role continues to be played by the theoretical, metaphysical and methodological commitments of the competing paradigms.

Reinforced steering Evolutionary Markov Chain for high-dimensional feature selection

The increasing accessibility of extensive datasets has amplified the importance of extracting insights from high-dimensional data. However, the task of selecting relevant features in these high-dimensional spaces is made more difficult due to the curse of dimensionality. Although Evolutionary Algorithms (EAs) have shown promise in the literature for feature selection, creating EAs for high dimensions is still challenging. To address the problem of feature selection in high dimensions, a novel concept of Evolutionary Reinforced Markov Chain is proposed in this paper. The proposed work has the following contributions and merits: (i) The paradigms of evolutionary computation, reinforcement learning, and Markov chain are incorporated into an integrational framework for feature selection in high dimensional spaces in a recursive manner. (ii) To support the global convergence of the algorithm and manage its computational complexity, a restricted group of the most effective agents is maintained within the evolutionary population. (iii) The dynamic Markov chain process efficiently manages agent evolution and communication, ensuring effective navigation through the search space. (iv) Agents moving in the right way are rewarded with an increase in their associated transition probability, while the agents going in the wrong direction are discouraged with a decrease in their associated transition probabilities; this promotes the establishment of an equilibrium state and leads to convergence. (v) The effective size of successful agents is reduced recursively while progressing through different states to further facilitate the speed of convergence and decrease the number of features. (vi) The performance comparison with state-of-the-art feature selection methods shows a significant improvement and promise of the proposed method over the existing methods.

Trapped Ion Quantum Computing: A Framework for Addressing Security Vulnerabilities

Trapped ion quantum computing has the potential to revolutionize computational paradigms. As the adoption of this technology grows, so does the need for stringent scrutiny of its involvement in cybersecurity, especially when it has implications in national defense or critical infrastructure. While trapped ion quantum computing offers transformative capabilities, it is vital to carefully examine the potential vulnerabilities associated with its use and patch them before implementing this powerful technology. In this paper, we examine the potential vulnerabilities in trapped ion quantum computing systems and propose a framework for addressing them. This framework includes risk assessment for evaluating vulnerabilities, threat modeling for identifying exploits, and prevention and mitigation for reducing their impact.

Bridging the Clinical-Computational Transparency Gap in Digital Pathology.

Computational pathology combines clinical pathology with computational analysis, aiming to enhance diagnostic capabilities and improve clinical productivity. However, communication barriers between pathologists and developers often hinder the full realization of this potential. To propose a standardized framework that improves mutual understanding of clinical objectives and computational methodologies. The goal is to enhance the development and application of computer-aided diagnostic (CAD) tools. The article suggests pivotal roles for pathologists and computer scientists in the CAD development process. It calls for increased understanding of computational terminologies, processes, and limitations among pathologists. Similarly, it argues that computer scientists should better comprehend the true use cases of the developed algorithms to avoid clinically meaningless metrics. CAD tools improve pathology practice significantly. Some tools have even received US Food and Drug Administration approval. However, improved understanding of machine learning models among pathologists is essential to prevent misuse and misinterpretation. There is also a need for a more accurate representation of the algorithms' performance compared to that of pathologists. A comprehensive understanding of computational and clinical paradigms is crucial for overcoming the translational gap in computational pathology. This mutual comprehension will improve patient care through more accurate and efficient disease diagnosis.

Assessing and advancing the potential of quantum computing: A NASA case study

Quantum computing is one of the most enticing computational paradigms with the potential to revolutionize diverse areas of future-generation computational systems. While quantum computing hardware has advanced rapidly, from tiny laboratory experiments to quantum chips that can outperform even the largest supercomputers on specialized computational tasks, these noisy-intermediate scale quantum (NISQ) processors are still too small and non-robust to be directly useful for any real-world applications. In this paper, we describe NASA’s work in assessing and advancing the potential of quantum computing. We discuss advances in algorithms, both near- and longer-term, and the results of our explorations on current hardware as well as with simulations, including illustrating the benefits of algorithm-hardware co-design in the NISQ era. This work also includes physics-inspired classical algorithms that can be used at application scale today. We discuss innovative tools supporting the assessment and advancement of quantum computing and describe improved methods for simulating quantum systems of various types on high-performance computing systems that incorporate realistic error models. We provide an overview of recent methods for benchmarking, evaluating, and characterizing quantum hardware for error mitigation, as well as insights into fundamental quantum physics that can be harnessed for computational purposes.

Improved synergistic swarm optimization algorithm to optimize task scheduling problems in cloud computing

Cloud computing has emerged as a cornerstone technology for modern computational paradigms due to its scalability and flexibility. One critical aspect of cloud computing is efficient task scheduling, which directly impacts system performance and resource utilization. In this paper, we propose an enhanced optimization algorithm tailored for task scheduling in cloud environments. Building upon the foundation of the Jaya algorithm and Synergistic Swarm Optimization (SSO), our approach integrates a Levy flight mechanism to enhance exploration-exploitation trade-offs and improve convergence speed. The Jaya algorithm's ability to exploit the current best solutions is complemented by the SSO's collaborative search strategy, resulting in a synergistic optimization framework. Moreover, the incorporation of Levy flights injects stochasticity into the search process, enabling the algorithm to escape local optima and navigate complex solution spaces more effectively. We evaluate the proposed algorithm against state-of-the-art approaches using benchmark task scheduling problems in cloud environments. Experimental results demonstrate the superiority of our method in terms of solution quality, convergence speed, and scalability. Overall, our proposed Improved Jaya Synergistic Swarm Optimization Algorithm offers a promising solution for optimizing TSCC (TSCC), contributing to enhanced resource utilization and system performance in cloud-based applications. The proposed method got 88 % accuracy overall and 10 % enhancement compared to the original method.

Holonomic swap and controlled-swap gates of neutral atoms via selective Rydberg pumping

Holonomic quantum computing offers a promising paradigm for quantum computation due to its error resistance and the ability to perform universal quantum computations. Here, we propose a scheme for the rapid implementation of a holonomic swap gate in neutral atomic systems, based on the selective Rydberg pumping mechanism. By employing time-dependent soft control, we effectively mitigate the impact of off-resonant terms even at higher driving intensities compared to time-independent driving. This approach accelerates the synthesis of logic gates and passively reduces the decoherence effects. Furthermore, by introducing an additional atom and applying the appropriate driving field, our scheme can be directly extended to implement a three-qubit controlled-swap gate. This advancement makes it a valuable tool for quantum state preparation, quantum switches, and a variational quantum algorithm in neutral atom systems.

Video anomaly detection guided by clustering learning

With the fuzzy boundary between normal and abnormal video data, which cannot be well distinguished by most methods, anomaly detection in video requires better characterization of the data. First we give a convolution-enhanced self-attentive video auto-encoder based on the U-Net architecture, which can extract richer image features. Secondly we design a dual-scale feature clustering structure for this encoder, which simultaneously compresses the channel and spatial structure features of the image to represent the features to obtain good coding characteristics and expand the boundary between normal and abnormal data. We also verify that our approach is equivalent to a class of auto-encoders for memory-guided learning. Finally, in the reconstruction task, since video auto-encoders are capable of triggering temporal time leakage phenomena that can lead to network performance degradation, we propose an anomaly score computation paradigm for video auto-encoders that utilizes the average frame anomaly score of a video clip to compute the first frame anomaly score in that video clip.Extensive experiments on three benchmark datasets show that our method outperforms most existing methods on large datasets with complex patterns. The code will be published at the following link: Anomaly-detection-guided-by-clustering-learning

Comparing Adiabatic Quantum Computers for satellite images feature extraction

Adiabatic Quantum Computers (AQCs) are special-purpose devices that promise to speed up the resolution of hard combinatorial optimization problems by exploiting quantum mechanical phenomena. Despite representing one of the most mature quantum computational paradigms, AQCs are often benchmarked on spin-glass problems that conveniently mimic their internal structure, and their performances are usually compared to those of suboptimal Simulated Annealing solvers. In this work, we evaluate the capabilities of AQCs to extract features from a dataset of low-resolution satellite images of airplanes, exploiting an approach based on matrix factorization. We assess the performance of three generations of quantum devices provided by the D-Wave company by analyzing their behavior via selected evaluation metrics as a function of the problem size. We also compare against classical results obtained with a commercial solver. Additionally, we outline a parameter-tuning procedure that allows for harnessing the full potential of AQCs. Although the quantum devices still tend to underperform their classical counterparts, we observe the two most recent AQCs exhibiting superior performance for the tested instances with the smallest size. Additionally, our results indicate incremental performance improvements across new AQCs generations. These observations suggest cautious optimism about the potential future applicability of such computational tools.

The Neural Orchestra: Cognitive Instrumentalities

Abstract Over the past several decades, the field of cognitive neuroscience has drawn with increasing frequency on the figure of the “neural orchestra,” a metaphor mapping the activities of localized cortical areas onto sections of a musical ensemble. Popularized by (among others) the neuroscientist Elkhonon Goldberg, the trope is often hailed by contemporary scientists as a new invention—a modern alternative to computational paradigms of cognition. And yet, as I argue here, the concept of the brain orchestra is far from new. Neural-orchestral cartographies originated in the phrenological theory of Franz Joseph Gall in the 1810s, then threaded through medical, ethnographic, anthropological, and pedagogical writing of the later nineteenth century, from the phreno-magnetic tracts of Mariano Cubí y Soler to the psychiatric diagnoses of Théodule-Armand Ribot. Fusing metaphysical concepts of harmony with theories of hierarchical cerebral organology, symphonic minds were products of an epistemological overlap between neural and musical discourses. Crucial to both domains was the newly powerful figure of the podium conductor, conceived as an overarching form of attentional control yoking players into powerful cognitive, musical, and political wholes. The result was a musical neurophysiology refracted through the lenses of imperial technocracy and authoritarian forms of centralization. Today, the historical and neuropolitical forces that generated the Romantic mind orchestra have been largely forgotten, but they continue to exert a spectral influence, hovering behind our conception of the “well-conducted mind,” our descriptions of “orchestrated” attention, and our psychopolitical fear of distraction.

Deep Structural Knowledge Exploitation and Synergy for Estimating Node Importance Value on Heterogeneous Information Networks

The classic problem of node importance estimation has been conventionally studied with homogeneous network topology analysis. To deal with practical network heterogeneity, a few recent methods employ graph neural models to automatically learn diverse sources of information. However, the major concern revolves around that their fully adaptive learning process may lead to insufficient information exploration, thereby formulating the problem as the isolated node value prediction with underperformance and less interpretability. In this work, we propose a novel learning framework namely SKES. Different from previous automatic learning designs, SKES exploits heterogeneous structural knowledge to enrich the informativeness of node representations. Then based on a sufficiently uninformative reference, SKES estimates the importance value for any input node, by quantifying its informativeness disparity against the reference. This establishes an interpretable node importance computation paradigm. Furthermore, SKES dives deep into the understanding that "nodes with similar characteristics are prone to have similar importance values" whilst guaranteeing that such informativeness disparity between any different nodes is orderly reflected by the embedding distance of their associated latent features. Extensive experiments on three widely-evaluated benchmarks demonstrate the performance superiority of SKES over several recent competing methods.

Quantum circuit mapping for universal and scalable computing in MZI-based integrated photonics.

Linear optical quantum computing (LOQC) offers a quantum computation paradigm based on well-established and robust technology and flexible environmental conditions following DiVincenzo's criteria. Within this framework, integrated photonics can be utilized to achieve gate-based quantum computing, defining qubits by path-encoding, quantum gates through the use of Mach-Zehnder interferometers (MZIs), and measurements through single-photon detectors. In particular, universal two-qubit gates can be achieved by suitable structures of MZIs together with post-selection or heralding. The most resource-efficient choice is given by the post-selected Controlled-Z (CZ) gate. However, this implementation is characterized by a design which has a non-regular structure and cannot be cascaded. This limits the implementation of large-scale LOQC. Starting from these issues, we suggest an approach to move toward a universal and scalable LOQC on the integrated photonic platform. First of all, choosing the post-selected CZ as a universal two-qubit gate, we extend the path-encoded dual-rail qubit to a triplet of waveguides, composed of an auxiliary waveguide and the pair of waveguides corresponding to the qubit basis states. Additionally, we introduce a swap photonic network that maps the regularly-labeled structure of the new path-encoded qubits to the structure needed for the post-selected CZ. We also discuss the optical swap gate that allows the connection of non-nearest neighbor path-encoded qubits. In this way, we can deterministically exchange the locations of the qubits and execute controlled quantum gates between any path-encoded qubits. Next, by truncating the auxiliary waveguides after any post-selected CZ, we find that it is possible to cascade this optical gate when it acts on different pairs that share only one qubit. Finally, we show the Bell state and the Greenberger-Horne-Zeilinger (GHZ) state generation circuits implementing the regular structure, the cascading procedure of post-selected CZ and the optical swap.

Nearly computable real numbers

We call a sequence ( a n ) n of elements of a metric space nearly computably Cauchy if for every increasing computable function r : N → N the sequence ( d ( a r ( n + 1 ) , a r ( n ) ) ) n converges computably to 0. We show that there exists an increasing sequence of rational numbers that is nearly computably Cauchy and unbounded. Then we call a real number α nearly computable if there exists a computable sequence ( a n ) n of rational numbers that converges to α and is nearly computably Cauchy. It is clear that every computable real number is nearly computable, and it follows from a result by Downey and LaForte (Theoretical Computer Science 284 (2002) 539–555) that there exists a nearly computable and left-computable number that is not computable. We observe that the set of nearly computable real numbers is a real closed field and closed under computable real functions with open domain, but not closed under arbitrary computable real functions. Among other things we strengthen results by Hoyrup (Theory of Computing Systems 60 (2017) 396–420) and by Stephan and Wu (In New computational paradigms. First conference on computability in Europe, CiE 2005, Proceedings (2005) 461–469 Springer) by showing that any nearly computable real number that is not computable is weakly 1-generic (and, therefore, hyperimmune and not Martin-Löf random) and strongly Kurtz random (and, therefore, not K-trivial), and we strengthen a result by Downey and LaForte (Theoretical Computer Science 284 (2002) 539–555) by showing that no promptly simple set can be Turing reducible to a nearly computable real number.

A Comparison between Invariant and Equivariant Classical and Quantum Graph Neural Networks

Machine learning algorithms are heavily relied on to understand the vast amounts of data from high-energy particle collisions at the CERN Large Hadron Collider (LHC). The data from such collision events can naturally be represented with graph structures. Therefore, deep geometric methods, such as graph neural networks (GNNs), have been leveraged for various data analysis tasks in high-energy physics. One typical task is jet tagging, where jets are viewed as point clouds with distinct features and edge connections between their constituent particles. The increasing size and complexity of the LHC particle datasets, as well as the computational models used for their analysis, have greatly motivated the development of alternative fast and efficient computational paradigms such as quantum computation. In addition, to enhance the validity and robustness of deep networks, we can leverage the fundamental symmetries present in the data through the use of invariant inputs and equivariant layers. In this paper, we provide a fair and comprehensive comparison of classical graph neural networks (GNNs) and equivariant graph neural networks (EGNNs) and their quantum counterparts: quantum graph neural networks (QGNNs) and equivariant quantum graph neural networks (EQGNN). The four architectures were benchmarked on a binary classification task to classify the parton-level particle initiating the jet. Based on their area under the curve (AUC) scores, the quantum networks were found to outperform the classical networks. However, seeing the computational advantage of quantum networks in practice may have to wait for the further development of quantum technology and its associated application programming interfaces (APIs).

Awareness about Artificial Neural Network (ANN) use in health system among physicians in Benghazi

Artificial neural networks are computational paradigms based on mathematical models that unlike traditional computing have a structure and operation that resembles that of the mammal brain. One of the original aims of artificial neural networks (ANN) was to understand and shape the functional characteristics and computational properties of the brain when it performs cognitive processes such as sensorial perception, concept categorization, concept association and learning. However, today one of the most uses of ANN in the health system are Diagnosis, predication and classification by using different and recent techniques.This is a paper pilot orientation use of ANN in the health system among physicians in Benghazi as a tool in their diagnostic field. Our target population was physicians in the Benghazi medical center and polyclinics with different professions, where the participates 200 with different age groups by filling one of two types of survey (online- in person). Based on the results of surveyBased on the results of survey we found 89% of physicians didn’t have any information about ANN, 95% didn’t read any paper about ANN technology, 98% didn’t use ANN as tool in diagnostic field, in addition, 60% they think it has value in using as tool in diagnostic field.

TLPGNN : A Lightweight Two-Level Parallelism Paradigm for Graph Neural Network Computation on Single and Multiple GPUs

Graph Neural Networks (GNNs) are an emerging class of deep learning models specifically designed for graph-structured data. They have been effectively employed in a variety of real-world applications, including recommendation systems, drug development, and analysis of social networks. The GNN computation includes regular neural network operations and general graph convolution operations, which take most of the total computation time. Though several recent works have been proposed to accelerate the computation for GNNs, they face the limitations of heavy pre-processing, low efficiency atomic operations, and unnecessary kernel launches. In this paper, we design TLPGNN , a lightweight two-level parallelism paradigm for GNN computation. First, we conduct a systematic analysis of the hardware resource usage of GNN workloads to understand the characteristics of GNN workloads deeply. With the insightful observations, we then divide the GNN computation into two levels, i.e., vertex parallelism for the first level and feature parallelism for the second. Next, we employ a novel hybrid dynamic workload assignment to address the imbalanced workload distribution. Furthermore, we fuse the kernels to reduce the number of kernel launches and cache the frequently accessed data into registers to avoid unnecessary memory traffic. To scale TLPGNN to multi-GPU environments, we propose an edge-aware row-wise 1-D partition method to ensure a balanced workload distribution across different GPU devices. Experimental results on various benchmark datasets demonstrate the superiority of our approach, achieving substantial performance improvement over state-of-the-art GNN computation systems, including Deep Graph Library (DGL), GNNAdvisor, and FeatGraph, with speedups of 6.1 ×, 7.7 ×, and 3.0 ×, respectively, on average. Evaluations of multiple-GPU TLPGNN also demonstrate that our solution achieves both linear scalability and a well-balanced workload distribution.

Criticality in FitzHugh-Nagumo oscillator ensembles: Design, robustness, and spatial invariance

Reservoir computing is an efficient and flexible framework for decision-making, control, and signal processing. It uses a network of interacting components varying from abstract nonlinear dynamical systems to physical substrates. Despite recent progress, the hardware implementation with inherent parameter variability and uncertainties, such as those mimicking the properties of living organisms’ nervous systems, remains an active research area. To address these challenges, we propose a constructive approach using a network of FitzHugh-Nagumo oscillators, exhibiting criticality across a broad range of resistive coupling strengths and robustness without specific parameter tuning. Additionally, the network’s activity demonstrates spatial invariance, offering freedom in choosing readout nodes. We introduce an alternative characterization of criticality by analyzing power dissipation, and demonstrate that criticality supports the robustness of the classification accuracy with respect to the readout shrinkage. Our results indicate criticality as a valuable property for classification problems, and provides design concepts for bio-inspired computational paradigms.