Information Processing Paradigm Research Articles

Electric Love Therapy

AbstractIn the early 1970s, as the industrial economy crumbled around Detroit and its suburbs, Dr. H. C. Tien, an independent electroconvulsive therapist who ran the Michigan Institute of Psychosynthesis in Lansing, MI, advocated a “cybernetic” approach to family psychiatry. In Tien’s practice, one can learn how the liberatory kernel of religion and the truth of sexual difference—key components of moral treatment in nineteenth-century asylum reform—became amplified by emerging paradigms of neural nets and information processing. In Tien’s practice—what he called Electric Love Therapy—electric shock treatment became a technology of conversion and, more precisely, of sexual differentiation and spiritual cultivation. Tien’s is a disturbing example of how the regulation of sexuality and gender as private matters serves as resource and spur to secular demands to proprietize religion as an interior matter.

Design and Automation for Quantum Computation and Quantum Technologies

Quantum computation is a promising field of research that will shift the computation paradigm for data and information processing with quantum parallelism. This Special Issue of the IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS (JETCAS) is dedicated to showcasing the latest research progress on the design and automation for quantum computation and quantum technologies.

Taxonomy of Load Balancing Practices in the Cloud Computing Paradigm

Rapid growth in communication technology allows users location-independent access to IT infrastructure at pay-per-use via cloud computing. This has paved a new paradigm in information processing for the consumers. Due to Cloud's inherent characteristics, most service providers shift to the Cloud and its data centers. To retain Cloud's services' reliability, it's essential to carry out the minimum latency tasks and cost-effectively. Various techniques to improve performance and use of assets are focused on load control, task management, resource management, service quality, and workload management. Data load balancing helps data centers to avoid overload/underload of virtual machines, a difficulty in the world of cloud computing. This study reports a state-of-the-art analysis of current load balancing approaches', problems, and complexities to design more successful algorithms.

Artificial Intelligence (AI) and Machine Learning for Multimedia and Edge Information Processing

The advancements and progress in artificial intelligence (AI) and machine learning, and the numerous availabilities of mobile devices and Internet technologies together with the growing focus on multimedia data sources and information processing have led to the emergence of new paradigms for multimedia and edge AI information processing, particularly for urban and smart city environments. Compared to cloud information processing approaches where the data are collected and sent to a centralized server for information processing, the edge information processing paradigm distributes the tasks to multiple devices which are close to the data source. Edge information processing techniques and approaches are well suited to match current technologies for Internet of Things (IoT) and autonomous systems, although there are many challenges which remain to be addressed. The motivation of this paper was to survey these new paradigms for multimedia and edge information processing from several technological perspectives including: (1) multimedia analytics on the edge empowered by AI; (2) multimedia streaming on the intelligent edge; (3) multimedia edge caching and AI; (4) multimedia services for edge AI; and (5) hardware and devices for multimedia on edge intelligence. The review covers a wide spectrum of enabling technologies for AI and machine learning for multimedia and edge information processing.

Robust Preparation of Wigner-Negative States with Optimized SNAP-Displacement Sequences

Hosting nonclassical states of light in three-dimensional microwave cavities has emerged as a promising paradigm for continuous-variable quantum information processing. Here we experimentally demonstrate high-fidelity generation of a range of Wigner-negative states useful for quantum computation, such as Schrödinger-cat states, binomial states, Gottesman-Kitaev-Preskill states, as well as cubic phase states. The latter states have been long sought after in quantum optics and have never been achieved experimentally before. We use a sequence of interleaved selective number-dependent arbitrary phase (SNAP) gates and displacements. We optimize the state preparation in two steps. First we use a gradient-descent algorithm to optimize the parameters of the SNAP and displacement gates. Then we optimize the envelope of the pulses implementing the SNAP gates. Our results show that this way of creating highly nonclassical states in a harmonic oscillator is robust to fluctuations of the system parameters such as the qubit frequency and the dispersive shift.1 MoreReceived 10 December 2021Revised 19 April 2022Accepted 8 June 2022DOI:https://doi.org/10.1103/PRXQuantum.3.030301Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum information processing with continuous variablesQuantum Information

Towards Secure and Intelligent Internet of Health Things: A Survey of Enabling Technologies and Applications

With the growth of computing and communication technologies, the information processing paradigm of the healthcare environment is evolving. The patient information is stored electronically, making it convenient to store and retrieve patient information remotely when needed. However, evolving the healthcare systems into smart healthcare environments comes with challenges and additional pressures. Internet of Things (IoT) connects things, such as computing devices, through wired or wireless mediums to form a network. There are numerous security vulnerabilities and risks in the existing IoT-based systems due to the lack of intrinsic security technologies. For example, patient medical data, data privacy, data sharing, and convenience are considered imperative for collecting and storing electronic health records (EHR). However, the traditional IoT-based EHR systems cannot deal with these paradigms because of inconsistent security policies and data access structures. Blockchain (BC) technology is a decentralized and distributed ledger that comes in handy in storing patient data and encountering data integrity and confidentiality challenges. Therefore, it is a viable solution for addressing existing IoT data security and privacy challenges. BC paves a tremendous path to revolutionize traditional IoT systems by enhancing data security, privacy, and transparency. The scientific community has shown a variety of healthcare applications based on artificial intelligence (AI) that improve health diagnosis and monitoring practices. Moreover, technology companies and startups are revolutionizing healthcare with AI and related technologies. This study illustrates the implication of integrated technologies based on BC, IoT, and AI to meet growing healthcare challenges. This research study examines the integration of BC technology with IoT and analyzes the advancements of these innovative paradigms in the healthcare sector. In addition, our research study presents a detailed survey on enabling technologies for the futuristic, intelligent, and secure internet of health things (IoHT). Furthermore, this study comprehensively studies the peculiarities of the IoHT environment and the security, performance, and progression of the enabling technologies. First, the research gaps are identified by mapping security and performance benefits inferred by the BC technologies. Secondly, practical issues related to the integration process of BC and IoT devices are discussed. Third, the healthcare applications integrating IoT, BC, and ML in healthcare environments are discussed. Finally, the research gaps, future directions, and limitations of the enabling technologies are discussed.

Frequency-bin entanglement from domain-engineered down-conversion

Frequency encoding is quickly becoming an attractive prospect for quantum information protocols owing to larger Hilbert spaces and increased resilience to noise compared to other photonic degrees of freedom. To fully make use of frequency encoding as a practical paradigm for quantum information processing, an efficient and simple source of frequency entanglement is required. Here, we present a single-pass source of discrete frequency-bin entanglement that does not use filtering or a resonant cavity. We use a domain-engineered nonlinear crystal to generate an eight-mode frequency-bin entangled source at telecommunication wavelengths. Our approach leverages the high heralding efficiency and simplicity associated with bulk crystal sources.

High-Performance Reservoir Computing With Fluctuations in Linear Networks.

Reservoir computing has emerged as a powerful machine learning paradigm for harvesting nontrivial information processing out of disordered physical systems driven by sequential inputs. To this end, the system observables must become nonlinear functions of the input history. We show that encoding the input to quantum or classical fluctuations of a network of interacting harmonic oscillators can lead to a high performance comparable to that of a standard echo state network in several nonlinear benchmark tasks. This equivalence in performance holds even with a linear Hamiltonian and a readout linear in the system observables. Furthermore, we find that the performance of the network of harmonic oscillators in nonlinear tasks is robust to errors both in input and reservoir observables caused by external noise. For any reservoir computing system with a linear readout, the magnitude of trained weights can either amplify or suppress noise added to reservoir observables. We use this general result to explain why the oscillators are robust to noise and why having precise control over reservoir memory is important for noise robustness in general. Our results pave the way toward reservoir computing harnessing fluctuations in disordered linear systems.

Investigating information processing paradigm to predict performance in emerging firms: the mediating role of technological innovation

PurposeThis paper aims to investigate the influence of the information processing paradigm (information sharing and quality) on firm performance using organizational information processing theory (OIPT) in emerging marketing. This research also pursues to explore the underpinning mediation mechanism of technological innovation in the association between the information processing paradigm and firm performance.Design/methodology/approachUsing the survey method, this study recruited 331 textile SME entrepreneurs and validated the proposed model and hypotheses in AMOS.FindingsThe outcomes reveal that information sharing, information quality and technological innovation are positively related to firm performance, while technological innovation mediates the association between the information processing paradigm (i.e. information sharing and information quality) and firm performance.Practical implicationsThe findings enable the firms to bring technological innovation and realize the best performance by seeking and sharing valuable information across the firm. The managers should ensure a culture of quality information sharing, improve coordination among departments and discourage irrelevant/fake information. Such practices are beneficial for firm decision-makers when formulating business plans and focusing on communication mechanisms that will assist them in attaining higher innovation levels and firm performance.Originality/valueThis paper theoretically and empirically contributes to examining the influence of the information processing paradigm on firm performance, which was ignored in information and performance management literature. Moreover, to the best of the author’s knowledge, this is the first study that investigates the mediation mechanism between the information processing paradigm and firm performance.

On Information Granulation via Data Clustering for Granular Computing-Based Pattern Recognition: A Graph Embedding Case Study

Granular Computing is a powerful information processing paradigm, particularly useful for the synthesis of pattern recognition systems in structured domains (e.g., graphs or sequences). According to this paradigm, granules of information play the pivotal role of describing the underlying (possibly complex) process, starting from the available data. Under a pattern recognition viewpoint, granules of information can be exploited for the synthesis of semantically sound embedding spaces, where common supervised or unsupervised problems can be solved via standard machine learning algorithms. In this work, we show a comparison between different strategies for the automatic synthesis of information granules in the context of graph classification. These strategies mainly differ on the specific topology adopted for subgraphs considered as candidate information granules and the possibility of using or neglecting the ground-truth class labels in the granulation process. Computational results on 10 different open-access datasets show that by using a class-aware granulation, performances tend to improve (regardless of the information granules topology), counterbalanced by a possibly higher number of information granules.

Prospect and challenges of analog switching for neuromorphic hardware

To inaugurate energy-efficient hardware as a solution to complex tasks, information processing paradigms shift from von Neumann to non-von Neumann computing architectures. Emerging electronic devices compete with speed, energy, and performance to revolutionize the neural hardware system where training and inference must achieve milestones. In this Perspective, we discuss the essential criteria for training and inference in various nonvolatile neuromorphic systems such as filamentary resistive switching, interfacial resistive switching, electrochemical random-access memory, and ferroelectric memory. We present a holistic analysis of technical requirements to design ideal neuromorphic hardware in which linearity is the critical aspect during training, whereas retention is the essential criterion of inference. Finally, we evaluate the prospect of a futuristic neuromorphic hardware system by optimizing the training and inference dilemma.

100 Hertz frame-rate switching three-dimensional orbital angular momentum multiplexing holography via cross convolution

The orbital angular momentum (OAM) of light has been implemented as an information carrier in OAM holography. Holographic information can be multiplexed in theoretical unbounded OAM channels, promoting the applications of optically addressable dynamic display and high-security optical encryption. However, the frame-rate of the dynamic extraction of the information reconstruction process in OAM holography is physically determined by the switching speed of the incident OAM states, which is currently below 30 Hz limited by refreshing rate of the phase-modulation spatial light modulator (SLM). Here, based on a cross convolution with the spatial frequency of the OAM-multiplexing hologram, the spatial frequencies of an elaborately-designed amplitude distribution, namely amplitude decoding key, has been adopted for the extraction of three-dimensional holographic information encoded in a specific OAM information channel. We experimentally demonstrated a dynamic extraction frame rate of 100 Hz from an OAM multiplexing hologram with 10 information channels indicated by individual OAM values from –50 to 50. The new concept of cross convolution theorem can even provide the potential of parallel reproduction and distribution of information encoded in many OAM channels at various positions which boosts the capacity of information processing far beyond the traditional decoding methods. Thus, our results provide a holographic paradigm for high-speed 3D information processing, paving an unprecedented way to achieve the high-capacity short-range optical communication system.

Practical Guide for Building Superconducting Quantum Devices

Quantum computing offers a powerful new paradigm of information processing that has the potential to transform a wide range of industries. In the pursuit of the tantalizing promises of a universal quantum computer, a multitude of new knowledge and expertise has been developed, enabling the construction of novel quantum algorithms as well as increasingly robust quantum hardware. In particular, we have witnessed rapid progress in the circuit quantum electrodynamics (cQED) technology, which has emerged as one of the most promising physical systems that is capable of addressing the key challenges in realizing full-stack quantum computing on a large scale. In this Tutorial, we present some of the most crucial building blocks developed by the cQED community in recent years and a précis of the latest achievements towards robust universal quantum computation. More importantly, we aim to provide a synoptic outline of the core techniques that underlie most cQED experiments and offer a practical guide for a novice experimentalist to design, construct, and characterize their first quantum device.11 MoreReceived 9 October 2020DOI:https://doi.org/10.1103/PRXQuantum.2.040202Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum computationQuantum information with solid state qubitsQuantum Information

From Engel to Enactivism

In this article we offer a two-part commentary on Bolton and Gillett’s reconceptualization of Engel’s biopsychosocial model. In the first section we present a conceptual and historical assessment of the biopsychosocial model that differs from the analysis by Bolton and Gillett. Specifically, we point out that Engel in his vision of the biopsychosocial model was less concerned with the ontological possibility and nature of psychosocial causes, and more concerned with psychosocial influences in the form of illness interpretation and presentation, sick role, seeking or rejection of care, the doctor-patient therapeutic relationship, and role of personality factors and family relationships in recovery from illness, etc. On the basis of this assessment, we then question Bolton and Gillett’s restricted focus on accounting for biopsychosocial causal interactions. The second section compares Bolton and Gillett’s account with a recent enactivist account of mental disorder that tackles similar conceptual problems of causal interactions. Bolton and Gillett’s utilize elements of the 4E cognition, but they combine these proto-ideas with an information-processing paradigm. Given their explicit endorsement of 4E approaches to mind and cognition, we illustrate some key ways in which a more fleshed out enactive account, particularly one that doesn’t rely on notions of information-processing, differs from the account proposed by Bolton and Gillett.

Stochastic facilitation in heteroclinic communication channels.

Biological neural systems encode and transmit information as patterns of activity tracing complex trajectories in high-dimensional state spaces, inspiring alternative paradigms of information processing. Heteroclinic networks, naturally emerging in artificial neural systems, are networks of saddles in state space that provide a transparent approach to generate complex trajectories via controlled switches among interconnected saddles. External signals induce specific switching sequences, thus dynamically encoding inputs as trajectories. Recent works have focused either on computational aspects of heteroclinic networks, i.e., Heteroclinic Computing, or their stochastic properties under noise. Yet, how well such systems may transmit information remains an open question. Here, we investigate the information transmission properties of heteroclinic networks, studying them as communication channels. Choosing a tractable but representative system exhibiting a heteroclinic network, we investigate the mutual information rate (MIR) between input signals and the resulting sequences of states as the level of noise varies. Intriguingly, MIR does not decrease monotonically with increasing noise. Intermediate noise levels indeed maximize the information transmission capacity by promoting an increased yet controlled exploration of the underlying network of states. Complementing standard stochastic resonance, these results highlight the constructive effect of stochastic facilitation (i.e., noise-enhanced information transfer) on heteroclinic communication channels and possibly on more general dynamical systems exhibiting complex trajectories in state space.

Non-Markovian momentum computing: Thermodynamically efficient and computation universal

All computation is physically embedded. Reflecting this, a growing body of results embraces rate equations as the underlying mechanics of thermodynamic computation and biological information processing. Strictly applying the implied continuous-time Markov chains, however, excludes a universe of natural computing. We show that expanding the toolset to continuous-time hidden Markov chains substantially removes the constraints. The general point is made concrete by our analyzing two eminently-useful computations that are impossible to describe with a set of rate equations over the memory states. We design and analyze a thermodynamically-costless bit flip, providing a first counterexample to rate-equation modeling. We generalize this to a costless Fredkin gate---a key operation in reversible computing that is computation universal. Going beyond rate-equation dynamics is not only possible, but necessary if stochastic thermodynamics is to become part of the paradigm for physical information processing.

Printable, ultralow-power ternary synaptic transistors for multifunctional information processing system

With the advent of artificial intelligence (AI) and the Internet of Things (IoT), the development of advanced printable synaptic devices has exhibited significant improvements, especially in facile-processed artificial neural networks with versatile information storage and neuromorphic computational capabilities. This enables the further development of the skin-like sensor and information processing systems. Here, we propose a multifunctional synaptic transistor based on a ternary organic semiconductor (OSC)-polymer-inorganic perovskite quantum dot (IPQD) photosensitive layer. Due to the dual optics-absorption functions of the OSCs and IPQDs, the synaptic device demonstrates the lowest power consumption (~0.11 fJ) and significant optically synaptic-like behavior with tunable short-term and long-term neuroplasticity. By adding polymer, the device stability and printability of the ternary solution for printable devices are improved. We further characterize the properties of the synaptic device, the learning-forgetting behavior, Morse code information processing and the pattern-recognition capability tested on the MNIST dataset are demonstrated. Our results open the new pathway for skin-like bioinspired multifunctional information processing paradigms. • OSC-polymer-IPQDs ternary layer is designed. • Ultralow operation voltage of −0.01 V and the lowest power consumption of ~0.11 fJ per spike. • Learning-forgetting behavior, Morse code processing, and pattern-recognition capability. • Printability of OSC-polymer-IPQDs solution for printable devices.

Toward a richer understanding of human cognition: Unleashing the full potential of the concurrent information-processing paradigm

One of the most influential working hypotheses in psychology, to this day, is that human information processing in higher-order cognition (e.g., judgment and decision-making) is constrained by having to process objects serially, one at a time. However, a rather large body of research, accumulated over the past 50 years, has demonstrated that serial-processing models provide a poor descriptive account of human information processing. An alternate to the serial-processing view is that people can process information concurrently; many cognitive processes can advance independently of each other even if the system involves only a single central information-processing unit (i.e., central executive). Perhaps this general idea can be advanced beyond its present standing and is to provide new powerful tools in the pursuit of understanding human behavior. To this end, the article provides a review of (i) the conceptual differences between different types of processing (serial, concurrent, parallel), (ii) recent advancements in the field of computer science, and (iii) existing research on human information-processing, which is in line with the advancements in computer science. Finally, the article provides a discussion of outstanding research questions gleaned from these reviews—questions that could stimulate entirely new research programs.

One Transistor One Electrolyte‐Gated Transistor Based Spiking Neural Network for Power‐Efficient Neuromorphic Computing System

AbstractNeuromorphic computing powered by spiking neural networks (SNN) provides a powerful and efficient information processing paradigm. To harvest the advantage of SNNs, compact and low‐power synapses that can reliably practice local learning rules are required, posing significant challenges to the conventional silicon‐based platform in terms of area‐ and energy‐efficiency, as well as computing throughput. Here, electrolyte‐gated transistors (EGTs) paired with transistors are employed to implement power‐efficient neuromorphic computing systems. The one‐transistor‐one‐EGT (1T1E) synapse not only alleviates the self‐discharging of EGT but also provides a flexible and efficient way to practice the important spike‐timing‐dependent plasticity learning rule. Based on that, an SNN with a temporal coding scheme is implemented for associative memory that can learn and recover images of handwritten digits with high robustness. Thanks to the temporal coding scheme and low operation current of EGTs, the energy‐efficiency of 1T1E‐based SNN is ≈30× lower than that of the prevalent rate coding scheme, and the peak performance is estimated to be 2 pJ/SOP (picojoule per synaptic operation) at the training phase and 80 TOPs−1 W−1 (tera operations per second per watt) at inference phase, respectively. These results pave the way for power‐efficient neuromorphic computing systems with wide applications for edge computing.

Quantum computing models for artificial neural networks

Neural networks are computing models that have been leading progress in Machine Learning (ML) and Artificial Intelligence (AI) applications. In parallel, the first small-scale quantum computing devices have become available in recent years, paving the way for the development of a new paradigm in information processing. Here we give an overview of the most recent proposals aimed at bringing together these ongoing revolutions, and particularly at implementing the key functionalities of artificial neural networks on quantum architectures. We highlight the exciting perspectives in this context, and discuss the potential role of near-term quantum hardware in the quest for quantum machine learning advantage.