Information Processing Demands Research Articles

Heralded Generation of Correlated Photon Pairs from CdS/CdSe/CdS Quantum Shells.

Quantum information processing demands efficient quantum light sources (QLS) capable of producing high-fidelity single photons or entangled photon pairs. Single epitaxial quantum dots (QDs) have long been proven to be efficient sources of deterministic single photons; however, their production via molecular-beam epitaxy presents scalability challenges. Conversely, colloidal semiconductor QDs offer scalable solution processing and tunable photoluminescence, but suffer from broader linewidths and unstable emissions. This leads to spectrally inseparable emission from exciton (X) and biexciton (XX) states, complicating the production of single photons and triggered photon pairs. Here, we demonstrate that colloidal semiconductor quantum shells (QSs) achieve significant spectral separation (∼75-80 meV) and long temporal stability of X and XX emissive states, enabling the observation of exciton-biexciton bunching in colloidal QDs. Our low-temperature single-particle measurements show cascaded XX-X emission of single photon pairs for over 200 s, with minimal overlap between X and XX features. The X-XX distinguishability allows for an in-depth theoretical characterization of cross-correlation strength, placing it in perspective with photon pairs of epitaxial counterparts. These findings highlight a strong potential of semiconductor quantum shells for applications in quantum information processing.

Non-volatile Fermi level tuning for the control of spin-charge conversion at room temperature

Current silicon-based CMOS devices face physical limitations in downscaling size and power loss, restricting their capability to meet the demands for data storage and information processing of emerging technologies. One possible alternative is to encode the information in a non-volatile magnetic state and manipulate this spin state electronically, as in spintronics. However, current spintronic devices rely on the current-driven control of magnetization, which involves Joule heating and power dissipation. This limitation has motivated intense research into the voltage-driven manipulation of spin signals to achieve energy-efficient device operation. Here, we show non-volatile control of spin-charge conversion at room temperature in graphene-based heterostructures through Fermi level tuning. We use a polymeric ferroelectric film to induce non-volatile charging in graphene. To demonstrate the switching of spin-to-charge conversion we perform ferromagnetic resonance and inverse Edelstein effect experiments. The sign change of output voltage is derived by the change of carrier type, which can be achieved solely by a voltage pulse. Our results provide an alternative approach for the electric-field control of spin-charge conversion, which constitutes a building block for the next generation of spin-orbitronic memory and logic devices.

Generation of a tripartite photonic state via a double-Λ configuration in a four-level system

Triphotons have a more abundant energy structure compared to biphotons. Furthermore, as the number of photons increases, excellent properties such as entangled multi-qubit states, high security, flexibility, and information capacity are observed. This leads to a growing demand for multi-body quantum information processing. Here, a method is proposed to generate a three-photon entangled state using a single six-wave mixing process in an atomic ensemble. The research examines the temporal correlation characteristics of the triphoton produced in photon coincidence counting measurements, with a focus on the linear and nonlinear susceptibilities of the six-wave mixing process. These properties primarily depend on the fifth-order nonlinear coupling coefficients responsible for the damping Rabi oscillations and the group delay determined by the longitudinal detuning function. To enhance the nonlinear interaction between the optical field and the atomic ensemble, placing the atomic ensemble in a high-quality cavity and utilizing laser cooling techniques to eliminate the internal Doppler broadening effect in the atomic gas hold promise.

All-Optical Control of Ultrafast Switching between the Hybridized Plasmonic Fields of Au Nanorod Dimer in fs-nm Scale with Dispersed Femtosecond Laser.

With the increasing demand for ultrafast communication and information processing in future optical chips, arbitrary manipulation of electromagnetic fields in the femtosecond-nanometer spatiotemporal scale has attracted great attention in integrated optics. Manipulation of the nanoscale light field in the real femtosecond temporal domain is challenging work. Here, we have demonstrated all-optical control of ultrafast switching between the hybridized plasmonic fields of a Au nanorod dimer in the fs-nm scale using a dispersed femtosecond laser and revealed the transformation process with ultrahigh spatiotemporal resolved technology via the combination of a pump-probe technique and photoemission electron microscopy (PEEM). The results show that we can actively and coherently control the transformation sequence and time (with the shortest temporal interval of around 15 fs) of the two hybridized modes in the Au nanorod dimer by tuning the dispersion of the laser pulse. The nanoscale light manipulation achieved by all-optical control may contribute to the design of high-speed miniaturized signal-processing systems.

Mapping drug smuggling networks in Japan: a social network analysis of trial documents

ABSTRACT This paper addresses a significant gap in drug market literature by examining high-level drug trafficking networks in Japan. We focus on three aspects: the structure of drug importation networks, the impact of transport methods on these structures, and the role of Japan’s mafia, the yakuza. Using novel statistical techniques that extend exponential random graph models (ERGMs) to multi-network samples, we analyse 573 Japanese trial documents on wholesale drug importation. We test the theory that trafficking operations with higher information-processing demands exhibit more efficiency-oriented network structures, while governance-type groups avoid expanding into trade activities. Our findings support these theories, showing that networks with higher information-processing demands are more efficiency-oriented but maintain security. Conversely, smaller networks with simpler transport methods prioritise security and concealment. Additionally, the yakuza do not organisationally engage in drug trafficking; when involved, yakuza members act as independent entrepreneurs.

New-Style Logic Operation and Neuromorphic Computing Enabled by Optoelectronic Artificial Synapses in an MXene/Y:HfO2 Ferroelectric Memristor.

Today's computing systems, to meet the enormous demands of information processing, have driven the development of brain-inspired neuromorphic systems. However, there are relatively few optoelectronic devices in most brain-inspired neuromorphic systems that can simultaneously regulate the conductivity through both optical and electrical signals. In this work, the Au/MXene/Y:HfO2/FTO ferroelectric memristor as an optoelectronic artificial synaptic device exhibited both digital and analog resistance switching (RS) behaviors under different voltages with a good switching ratio (>103). Under optoelectronic conditions, optimal weight update parameters and an enhanced algorithm achieved 97.1% recognition accuracy in convolutional neural networks. A new logic gate circuit specifically designed for optoelectronic inputs was established. Furthermore, the device integrates the impact of relative humidity to develop an innovative three-person voting mechanism with a veto power. These results provide a feasible approach for integrating optoelectronic artificial synapses with logic-based computing devices.

WITHDRAWN: Sustainable practices in entertainment computing

The increasing progression of the entertainment computing industry, covering primarily gaming, streaming, and virtual reality, results in detrimental consequences to the environment in terms of excessive energy demands for information processing and an increased amount of e-waste created by users. Sustainable entertainment computing is thus calling for improved strategies in management of such concerns with green solutions. This research work is aimed at identifying the approaches and tools which can be used to support the idea of sustainable computing in entertainment technology. The critical objective is to propose the use of energy efficient hardware and software and utilization of power in data centres from renewable energy resources. Lifecycle analysis, from production to disposal, is employed to assess and mitigate the environmental impact of both hardware and software. Furthermore, there are green certification and eco-labels on products and services to guarantee that, they are environmentally amenable. The findings suggest that several sustainable initiatives, namely development of energy-efficient hardware, optimized software and energy from renewable sources for data centres can be useful to minimize the entertainment computing sector’s impact on the environment. This paper provides a framework for entertainment technology companies towards more sustainable in their computing practices and therefore, adding to the growing list of organizations that are trying to push the entertainment industry towards a more sustainable future.

A CsPbBr3/CdS-based hybrid bidirectional optoelectronic device with light-emitting, modulation, and detection functions

Optoelectronic integrated circuits, with a broad photonic transportation bandwidth, have emerged as a promising solution to fulfill the escalating demands for high-volume information transportation and processing. However, challenges persist in developing optoelectronic integrated circuits based on low-dimensional nanostructures, including limited integration density and high energy consumption. Here, we demonstrate a bidirectional optoelectronic device by integrating a light-emitting/harvesting CsPbBr3 nanoplate with a waveguiding/modulating/detecting CdS nanobelt. By configuring the CsPbBr3 nanoplate in a Schottky-type device structure with a metal electrode, bright electroluminescence was attained at a bias voltage of 18 V. Thanks to the electric field-tuned phonon-coupling effect, the waveguided light in the CdS nanobelt exhibited a high modulation depth of up to 94%, rendering it an excellent building block as optical modulators and optical switches. Moreover, the integrated nanostructure device showcased functionality in the photodetection mode. The proposed device architecture holds promise for broader applications, potentially extending to other perovskite-coupled II–VI semiconductor optoelectronic integrated circuits for expanding integration capacity and enhancing optoelectronic performance.

Visual stimulation by extensive visual media consumption can be beneficial for motor learning

In this randomized controlled intervention trial, we investigated whether intense visual stimulation through television watching can enhance visual information processing and motor learning performance. 74 healthy young adults were trained in a motor skill with visual information processing demands while being accommodated in a controlled environment for five days. The experimental manipulation (n = 37) consisted of prolonged television watching (i.e., 8 h/day, + 62.5% on average) to induce intense exposure to visual stimulation. The control group (n = 37) did not consume visual media. The groups were compared by motor learning performance throughout the study as well as pre/post visual attention parameters and resting-state network connectivity in functional MRI. We found that the intervention group performed significantly better in the motor learning task (+ 8.21% (95%-CI[12.04, 4.31], t(70) = 4.23, p < 0.001) while showing an increased capacity of visual short-term memory (+ 0.254, t(58) = − 3.19, p = 0.002) and increased connectivity between visual and motor-learning associated resting-state networks. Our findings suggest that the human brain might enter a state of accentuated visuomotor integration to support the implementation of motor learning with visual information processing demands if challenged by ample input of visual stimulation. Further investigation is needed to evaluate the persistence of this effect regarding participants exposed to accustomed amounts of visual media consumption.Clinical Trials Registration: This trial was registered in the German Clinical Trials Register/Deutsches Register klinischer Studien (DRKS): DRKS00019955.

Don't throw the associative baby out with the Bayesian bathwater: Children are more associative when reasoning retrospectively under information processing demands.

Causal reasoning is a fundamental cognitive ability that enables individualsto learn about the complex interactions in the world around them. However, the mechanisms that underpin causal reasoning are not well understood. For example, it remains unresolved whether children's causal inferences are best explained by Bayesian inference or associative learning. The two experiments and computational models reported here were designed to examine whether 5- and 6-year-olds will retrospectively reevaluate objects-that is, adjust their beliefs about the causal status of some objects presented at an earlier point in time based on the observed causal status of other objects presented at a later point in time-when asked to reason about 3 and 4 objects and under varying degrees of information processing demands. Additionally, the experiments and models were designed to determine whether children's retrospective reevaluations were best explained by associative learning, Bayesian inference, or some combination of both. The results indicated that participants retrospectively reevaluated causal inferences under minimal information-processing demands (Experiment 1) but failed to do so under greater information processing demands (Experiment 2) and that their performance was better captured by an associative learning mechanism, with less support for descriptions that rely on Bayesian inference. RESEARCH HIGHLIGHTS: Five- and 6-year-old children engage in retrospective reevaluation under minimal information-processing demands (Experiment 1). Five- and 6-year-old children do not engage in retrospective reevaluation under more extensive information-processing demands (Experiment 2). Across both experiments, children's retrospective reevaluations were better explained by a simple associative learning model, with only minimal support for a simple Bayesian model. These data contribute to our understanding of the cognitive mechanisms by which children make causal judgements.

Laminar compartmentalization of attention modulation in area V4 aligns with the demands of visual processing hierarchy in the cortex

Attention selectively enhances neural responses to low contrast stimuli in visual area V4, a critical hub that sends projections both up and down the visual hierarchy. Veridical encoding of contrast information is a key computation in early visual areas, while later stages encoding higher level features benefit from improved sensitivity to low contrast. How area V4 meets these distinct information processing demands in the attentive state is unknown. We found that attentional modulation in V4 is cortical layer and cell-class specific. Putative excitatory neurons in the superficial layers show enhanced boosting of low contrast information, while those of deep layers exhibit contrast-independent scaling. Computational modeling suggested the extent of spatial integration of inhibitory neurons as the mechanism behind such laminar differences. Considering that superficial neurons are known to project to higher areas and deep layers to early visual areas, our findings suggest that the interactions between attention and contrast in V4 are compartmentalized, in alignment with the demands of the visual processing hierarchy.

Transparent artificial synapses based on Ag/Al-doped ZnO/ITO memristors for bioinspired neuromorphic computing

Today's computing systems to meet the enormous demands of information processing have driven the development of brain-inspired neuromorphic systems that can perform fast and energy-efficient calculations. In this study, Al-doped ZnO (AZO: ZnO to Al2O3 = 98:2 wt%) films with over 95 % light transmission were prepared by RF magnetron sputtering, and a structurally simple Ag/AZO/ITO memristor was fabricated. Moreover, the sustained potentiation/depression behavior of the memristor allows it to mimic artificial synapses that exhibit biological synaptic and neuronal functions such as short-term/long-term plasticity and paired-pulse facilitation.These results will contribute to the research and development of high-performance artificial synapses.

Support Vector Machine based Automatic Classification Method for IoT big Data Features

&lt;p&gt;As China’s information technology development shifts from a single high-speed growth stage to a multidimensional high-quality development stage, the Internet of Things (IoT) enters all aspects of life and becomes more and more popular. The demand for IoT big data information analysis and processing is increasing, and the important role of feature automatic classification methods becomes increasingly prominent. This research proposes SPO-SVM and WSPO-SVM models based on support vector machine for smart home environment monitoring data under the big data of Internet of Things, and then optimizes them with particle swarm optimization algorithm and adaptive method. Finally, the data set is selected for comparative experimental analysis of each optimization algorithm model. The experimental results show that the optimized WSPO-SVM model has less total misclassification and single class misclassification compared with other algorithms under Wine dataset. In cross-validation, both its classification accuracy performance outperformed other algorithms. Under 10 sets of smart home environment monitoring data sets, the WSPO-SVM algorithm model achieves 100% accuracy in 6 out of 10 test data sets, with an average accuracy of 97.67%, which is about 9% higher than the ordinary SVM algorithm model and about 15% higher than other feature classification algorithms. The experimental results prove that the WSPO-SVM algorithm can complete the feature classification work in the IoT big data environment, which meets the expectation.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

The Mediating Effects of Entrepreneurial Orientation Between Procedural Rationality and Strategic Decision-making Effectiveness

Researchers have shown interest in theory of organizational information processing and the resource-based theory, as they can both serve to provide competitive advantages for companies. Earlier studies have indicated that procedural rationality is used by firms to increase the demand for information processing when making effective strategic decisions. However, there is limited understanding of the mediating effects of entrepreneurial orientation as an intangible resource for organizations between procedural rationality and strategic decision-making effectiveness in the available studies in the literature. Thus, this study sheds light on the relationship between procedural rationality in strategic decision-making from organizations and their strategic decision-making effectiveness, taking into consideration the mediating effects of entrepreneurial orientation as an intangible resource for organizations. The research model was tested using a structural equation modelling design based on survey data from 162 companies that work under the umbrella of the Saudi financial sector, collated and analysed by software (SPSS and SmartPLS), the research found that procedural rationality in strategic decision-making within organizations positively affected their strategic decision-making effectiveness. Likewise, the research revealed that procedural rationality in strategic decision-making increased the level of entrepreneurial orientation. Moreover, the research findings showed that the high level of entrepreneurial orientation within organizations positively impacted their strategic decision-making effectiveness. These findings enrich the fields of strategic decision-making and procedural rationality through entrepreneurial orientation within organizations, this relationship should synchronize in function focusing on relevant information, data analytics, and strategic approach process. In light of this, this study brings scientific evidence from the perspective of financial companies within Saudi Arabia.

When Do Boards of Directors Contribute to Shareholder Value in Firms Targeted for Acquisition? A Group Information-Processing Perspective

We draw on group information-processing theory to investigate how target boards of directors may contribute to target value capture during the private negotiations phase in acquisitions. We view target boards as information-processing groups and private negotiations as information-processing tasks. We argue that target board meeting frequency is associated with increased processing—gathering, sharing, and analyzing—of acquisition-related information, which improves target bargaining and, ultimately, target value capture. We further posit that this value-enhancing effect of target board meeting frequency is more pronounced when target board composition improves the ability of target boards to process acquisition-related information. Finally, we expect that meeting frequency is more consequential for target bargaining and value capture when acquisition complexity imposes high information-processing demands on the target boards during private negotiations. Empirical evidence from a sample of acquisitions of publicly listed firms in the United States offers support for our group information-processing perspective on board contribution to shareholder value in firms targeted for acquisition. Funding: This work was supported by the Strategy Research Foundation [Dissertation Grant SRF-2015DP-0016]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.1643 .

Layered Gallium Monosulfide as Phase‐Change Material for Reconfigurable Nanophotonic Components On‐Chip

AbstractThe demand for information processing at ultrahigh speed with large data transmission capacity is continuously rising. Necessary building blocks for on‐chip photonic integrated circuits (PICs) are reconfigurable integrated low‐loss high‐speed modulators and switches. Phase change materials (PCMs) provide unique opportunities for integration into PICs. Here, the investigation of layered gallium monosulfide (GaS) as a novel low‐loss PCM from infrared to optical frequencies is pioneered, with high index contrast (Δn ≈0.5) at the optical telecommunication band. The GaS bandgap switches from 1.5 ± 0.2 eV for the amorphous state to 2.1 ± 0.1 eV for the crystalline state. It is demonstrated that the reversible GaS amorphous‐to‐crystalline phase transition can be operated thermally and by picosecond green (532 nm) laser irradiation. The design of a reconfigurable integrated optical modulator on‐chip based on Mach‐Zehnder Interferometers (MZI) with the GaS PCM cell deposited on one of the arms for application is presented at the telecommunication wavelength of λ = 1310 nm, where the standard single mode optical fiber exhibits zero chromatic dispersion, and at λ = 1550 nm, where a minimum optical loss of 0.22 dB km−1 is obtained. This opens the route to applications such as reconfigurable modulators, beam steering using phase modulation, and photonic neural networks.

Blind Post-Decision State-Based Reinforcement Learning for Intelligent IoT

Recent years have witnessed a renewed interest in reinforcement learning (RL) due to the rapid growth of the Internet-of-Things (IoT) and their associated intelligent information processing and decision-making demands. As the slow learning speed is one of the major stumbling blocks of the classic RL algorithms, substantial efforts have been devoted to developing faster RL algorithms. Among them, post-decision state (PDS) learning is a prominent one, which can often improve the learning speed by orders of magnitude by exploiting the structural property of the underlying Markov decision processes (MDPs). However, conventional PDS learning requires prior information about the PDS transition probability, which may not be always available in practice. To lift this limitation, a novel blind PDS (b-PDS) learning algorithm is proposed in this work by leveraging the generic two-timescale stochastic approximation framework. By introducing an extra estimating procedure about the PDS transition probability, b-PDS learning can achieve a similar improvement of learning speed as conventional PDS learning while excluding the need for prior information. In addition, by analyzing the globally asymptotically stable equilibrium of the corresponding ordinary differential equation (o.d.e.), the convergence and optimality of b-PDS learning are established. Moreover, extensive simulation results are provided to validate the effectiveness of the proposed algorithm. Over the considered random MDPs, it has been observed that, to reach 90% of the best possible time average reward, the proposed b-PDS learning can reduce the learning time by 70% compared to Q-learning and 30% compared to Dyna.

Preliminary evidence for progressions in ownership reasoning over the preschool period.

Defining developmental progressions can be an important step in identifying developmental precursors and mechanisms of change, within and across areas of reasoning. In one exploratory study, we examine whether the development of children's thinking about ownership follows a systematic progression wherein some components emerge reliably before others. We examine this issue in a sample of 72 children: 40 older 2-year-olds, Mage = 2.78 (.14); R = 2.50-3.00, and 32 older 4-year-olds, Mage = 4.77 (.16); R = 4.50-5.00, living in Michigan in the United States. We use a battery of four established ownership tasks that tested different aspects of children's ownership thinking. A Guttman test revealed a reliable sequence that explained 81.9% of children's performance. Namely, we discovered that identifying familiar owned objects emerged first, control of permission as a cue to ownership second, understanding ownership transfers third, and the tracking of sets of identical objects last. This ordering suggests two foundational ownership abilities on which more complex reasoning may be built: the ability to include information about familiar owners in children's mental models of objects and recognizing that control is central to ownership. The observed progression is an important first step toward developing a formal ownership scale. This study paves the way for mapping the conceptual and information-processing demands (e.g., executive functioning, memory) that likely underlie change in ownership thinking across childhood. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Harnessing Computational Complexity Theory to Model Human Decision-making and Cognition.

A central aim of cognitive science is to understand the fundamental mechanisms that enable humans to navigate and make sense of complex environments. In this letter, we argue that computational complexity theory, a foundational framework for evaluating computational resource requirements, holds significant potential in addressing this challenge. As humans possess limited cognitive resources for processing vast amounts of information, understanding how humans perform complex cognitive tasks requires comprehending the underlying factors that drive information processing demands. Computational complexity theory provides a comprehensive theoretical framework to achieve this goal. By adopting this framework, we can gain new insights into how cognitive systems work and develop a more nuanced understanding of the relation between task complexity and human behavior. We provide empirical evidence supporting our argument and identify several open research questions and challenges in applying computational complexity theory to human decision-making and cognitive science atlarge.

Utilizing Augmented Reality to Quantify Information Processing and Motor Performance in Military Personnel to Detect Mild Traumatic Brain Injury (P8-1.002)

<h3>Objective:</h3> Utilize augmented reality (AR) to create military specific scenarios that can be objectively quantified and used to facilitate the detection of and return-to-duty (RTD) following mild traumatic brain injury (mTBI). <h3>Background:</h3> Methods of detecting military mTBI are rooted in neuropsychological tests that measure a construct of executive or motor function. This approach does not lend itself to accurate detection or appropriate RTD. We have demonstrated the HoloLens 2 AR device provides accurate biomechanical data. Placing digital assets in a user’s real-world via AR technology creates an ecological environment to evaluate function. An AR Rifle qualification test (RQT) to evaluate dynamic movement, information processing, shooting performance and decision-making was developed. To evaluate performance in the context of a group or military unit, a room clearing assessment was created. <h3>Design/Methods:</h3> Data were gathered from 157 active-duty service members. The RQT was performed under single- and dual-task conditions. Five room clearing scenarios in which information processing and motor demands were manipulated were completed. Kinematic data from the HoloLens 2 provided biomechanical outcomes characterizing cognitive and motor performance. <h3>Results:</h3> Reaction time (RT) to firing and accuracy of target acquisition in the RQT did not differ between single- and dual-task conditions (<i>p&lt;0.05)</i>. Transition time from kneel to stand did not differ between conditions; however, dual-task sway path (41.2±17.5 cm) post-transition was increased compared to single-task (35.1±14.2 cm). There was a significant increase in processing time as the complexity of the room clearing scenarios increased. <h3>Conclusions:</h3> The objective quantification of information processing (RT) and motor (postural sway and movement time) function while performing ecological AR military tasks can enhance the detection of mTBI using objective functional metrics. The use of AR to deliver realistic digital experiences will facilitate appropriate RTD to ensure the safety of the service member and their unit. <b>Disclosure:</b> Mr. Kaya has nothing to disclose. Anson Rosenfeldt has received personal compensation for serving as an employee of Cleveland Clinic. Anson Rosenfeldt has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Enspire DBS. The institution of Anson Rosenfeldt has received research support from Michael J. Fox Foundation. The institution of Anson Rosenfeldt has received research support from National Institute of Health. The institution of Anson Rosenfeldt has received research support from Department of Defense. Anson Rosenfeldt has received intellectual property interests from a discovery or technology relating to health care. Mr. Waltz has nothing to disclose. Mrs. Jansen has nothing to disclose. Ms. Owen has nothing to disclose. Mrs. Dunlap has nothing to disclose. Karissa Hastilow has nothing to disclose. Mrs. Scelina has nothing to disclose. Mr. Zimmerman has nothing to disclose. Morgan McGrath has nothing to disclose. Amanda Penko has nothing to disclose. The institution of Dr. Alberts has received research support from NIH. Dr. Alberts has received intellectual property interests from a discovery or technology relating to health care. Dr. Alberts has received personal compensation in the range of $500-$4,999 for serving as a Member, Health and Wellness Council with Peloton Interactive.