Components Of Information Processing Research Articles

Rules for dissipationless topotronics.

Topological systems hosting gapless boundary states have attracted huge attention as promising components for next-generation information processing, attributed to their capacity for dissipationless electronics. Nevertheless, recent theoretical and experimental inquiries have revealed the emergence of energy dissipation in precisely quantized electrical transport. Here, we present a criterion for the realization of truly no-dissipation design, characterized as Nin = Ntunl + Nbs, where Nin, Ntunl, and Nbs represent the number of modes participating in injecting, tunneling, and backscattering processes, respectively. The key lies in matching the number of injecting, tunneling, and backscattering modes, ensuring the equilibrium among all engaged modes inside the device. Among all the topological materials, we advocate for the indispensability of Chern insulators exhibiting higher Chern numbers to achieve functional devices and uphold the no-dissipation rule simultaneously. Furthermore, we design the topological current divider and collector, evading dissipation upon fulfilling the established criterion. Our work paves the path for developing the prospective topotronics.

High-efficiency nonlinear frequency conversion enabled by optimizing the ferroelectric domain structure in x-cut LNOI ridge waveguide

Abstract Photonic devices based on ferroelectric domain engineering in thin film lithium niobate are key components for both classical and quantum information processing. Periodic poling of ridge waveguide can avoid the selective etching effect of lithium niobate, however, the fabrication of high-quality ferroelectric domain is still a challenge. In this work, we optimized the applied electric field distribution, and rectangular inverted domain structure was obtained in the ridge waveguide which is beneficial for efficient nonlinear frequency conversions. Second harmonic confocal microscope, piezoresponse force microscopy, and chemical selective etching were used to characterize the inverted domain in the ridge waveguide. In addition, the performance of nonlinear frequency conversion of the periodically poled nano-waveguide was investigated through second harmonic generation, and the normalized conversion efficiency was measured to be 1,720 % W−1 cm−2, which is close to 60 % that of the theoretical value. The fabrication technique described in this work will pave the way for the development of high-efficiency, low-loss lithium niobate nonlinear photonic devices.

Electrophysiological correlates of (mis)judging social information.

Social information can be used to optimize decision-making. However, the simultaneous presentation of multiple sources of advice can lead to a distinction bias in judging the validity of the information. While the involvement of event-related potential (ERP) components in social information processing has been studied, how they are modulated by (mis)judging an advisor's information validity remains unknown. In two experiments participants performed a decision-making task with highly accurate or inaccurate cues. Each experiment consisted of an initial, learning, and test phase. During the learning phase, three advice cues were simultaneously presented and the validity of them had to be assessed. The effect of different cue constellations on ERPs was investigated. In the subsequent test phase, the willingness to follow or oppose an advice cue was tested. Results demonstrated the distinction bias with participants over or underestimating the accuracy of the most uncertain cues. The P2 amplitude was significantly increased during cue presentation when advisors were in disagreement as compared to when all were in agreement, regardless of cue validity. Further, a larger P3 amplitude during outcome presentation was found when advisors were in disagreement and increased with more informative cues. As such, the most uncertain cues were related to the smallest P3 amplitude. The findings hint at the possible role of P3 in judging and learning the predictability of social cues. This study provides novel insights into the role of P2 and P3 components during the judgment of social information validity.

Metasurface for programmable quantum algorithms with classical and quantum light

Abstract Metasurfaces have recently opened up applications in the quantum regime, including quantum tomography and the generation of quantum entangled states. With their capability to store a vast amount of information by utilizing the various geometric degrees of freedom of nanostructures, metasurfaces are expected to be useful for processing quantum information. Here, we propose and experimentally demonstrate a programmable metasurface capable of performing quantum algorithms using both classical and quantum light with single photons. Our approach encodes multiple programmable quantum algorithms and operations, such as Grover’s search algorithm and the quantum Fourier transform, onto the same metalens array on a metasurface. A spatial light modulator selectively excites different sets of metalenses to carry out the quantum algorithms, while the interference patterns captured by a single-photon camera are used to extract information about the output state at the selected output directions. Our programmable quantum metasurface approach holds promising potential as a cost-effective means of miniaturizing components for quantum computing and information processing.

Towards the application of evidence accumulation models in the design of (semi-)autonomous driving systems – an attempt to overcome the sample size roadblock

For the foreseeable future, automated vehicles (AVs) will coexist on the roads with human drivers. To avoid accidents, AVs will require knowledge on how human drivers typically make high-stakes and time-sensitive decisions (e.g., whether or not to brake). Providing such insights could be statistical models designed to explain human information processing and decision making. This paper attempts to address a roadblock that prevents one class of such "cognitive models", evidence accumulation models (EAMs), from being widely applied in the design of AV systems: their high demands for data. Specifically, we investigate whether Bayesian hierarchical modeling can be used to determine a person's characteristics, if we only have limited data about their behavior but extensive data on other (comparable) people's behaviors. Leveraging a simulation study and a reanalysis of experimental data, we find that most parameters of Decision Diffusion Models (a class of EAMs) – representing information processing components – can be adequately estimated with as few as 20 observations, if prior information regarding the decision-making processes of the population is incorporated. Subsequently, we discuss the implications of our findings for the modeling of traffic situations.

Rational Design of Stimuli-Responsive Inorganic 2D Materials via Molecular Engineering: Toward Molecule-Programmable Nanoelectronics.

The ability of electronic devices to act as switches makes digital information processing possible. Succeeding graphene, emerging inorganic 2D materials (i2DMs) have been identified as alternative 2D materials to harbor a variety of active molecular components to move the current silicon-based semiconductor technology forward to a post-Moore era focused on molecule-based information processing components. In this regard, i2DMs benefits are not only for their prominent physiochemical properties (e.g., the existence of bandgap), but also for their high surface-to-volume ratio rich in reactive sites. Nonetheless, since this field is still in an early stage, having knowledge of both i) the different strategies for molecularly functionalizing the current library of i2DMs, and ii) the different types of active molecular components is a sine qua non condition for a rational design of stimuli-responsive i2DMs capable of performing logical operations at the molecular level. Consequently, this Review provides a comprehensive tutorial for covalently anchoring ad hoc molecular components-as active units triggered by different external inputs-onto pivotal i2DMs to assess their role in the expanding field of molecule-programmable nanoelectronics for electrically monitoring bistable molecular switches. Limitations, challenges, and future perspectives of this emerging field which crosses materials chemistry with computation are critically discussed.

Flexible, Transparent, and Wafer-Scale Artificial Synapse Array Based on TiOx /Ti3 C2 Tx Film for Neuromorphic Computing.

A high-density neuromorphic computing memristor array based on 2Dmaterials paves the way for next-generation information-processing components and in-memory computing systems. However, the traditional 2D-materials-based memristor devices suffer from poor flexibility and opacity, which hinders the application of memristors in flexible electronics. Here, a flexible artificial synapse array based on TiOx /Ti3 C2 Tx film is fabricated by a convenient and energy-efficient solution-processing technique, which realizes high transmittance (≈90%) and oxidation resistance (>30 days). The TiOx /Ti3 C2 Tx memristor shows low device-to-device variability, long memory retention and endurance, a high ON/OFF ratio, and fundamental synaptic behavior. Furthermore, satisfactory flexibility (R = 1.0mm) and mechanical endurance (104 bending cycles) of the TiOx /Ti3 C2 Tx memristor are achieved, which is superior to other film memristors prepared by chemical vapor deposition. In addition, high-precision (>96.44%) MNIST handwritten digits recognition classification simulation indicates that the TiOx /Ti3 C2 Tx artificial synapse array holds promise for future neuromorphic computing applications, and provides excellent high-density neuron circuits for new flexible intelligent electronic equipment.

Pandemic Dining Dilemmas: Exploring the Determinants of Korean Consumer Dining-Out Behavior during COVID-19

Amid the COVID-19 pandemic’s significant impact on the dining-out industry, this study examined factors influencing consumers’ dining-out behavior changes using a unified theoretical framework based on the theory of planned behavior (TPB) and select components of the risk information seeking and processing (RISP) model. A quantitative research method was employed, analyzing 536 valid survey responses collected in South Korea in early 2021 using partial least squares structural equation modeling (PLS-SEM). Findings showed that consumer attitude, perceived control, and subjective norm positively influenced dining-out intention, supporting the TPB. However, risk information-seeking behavior discourages dining-out behavior without significantly affecting intention. Fear emerged as a determinant of dining-out intention, risk information-seeking behavior, and dining-out behavior, highlighting the importance of emotions over rational thinking. This study contributes to existing literature by incorporating dining-out intention, COVID-19-related information-seeking behavior, and fear as key antecedents of dining-out behavior during the pandemic, while validating formative indicators that constitute risk information-seeking behavior and dining-out behavior in the research model.

A Digital-Analog Bimodal Memristor Based on CsPbBr3 for Tactile Sensory Neuromorphic Computing.

Memristor with digital and analog bipolar bimodal resistive switching offers a promising opportunity for the information-processing component. However, it still remains a huge challenge that the memristor enables bimodal digital and analog types and fabrication of artificial sensory neural network system. Here, a proposed CsPbBr3 -based memristor demonstrates a high ON/OFF ratio (>103 ), long retention (>104 s), stable endurance (100 cycles), and multilevel resistance memory, which acts as an artificial synapse to realize fundamental biological synaptic functions and neuromorphic computing based on controllable resistance modulation. Moreover, a 5× 5 spinosum-structured piezoresistive sensor array (sensitivity of 22.4 kPa-1 , durability of 1.5×104 cycles, and fast response time of 2.43ms) is constructed as a tactile sensory receptor to transform mechanical stimuli into electrical signals, which can be further processed by the CsPbBr3 -based memristor with synaptic plasticity. More importantly, this artificial sensory neural network system combined the artificial synapse with 5×5 tactile sensing array based on piezoresistive sensors can recognize the handwritten patterns of different letters with high accuracy of 94.44% under assistance of supervised learning. Consequently, the digital-analog bimodal memristor would demonstrate potential application in human-machine interaction, prosthetics, and artificial intelligence.

Quantum Emitters with Narrow Band and High Debye-Waller Factor in Aluminum Nitride Written by Femtosecond Laser.

Solid-state quantum emitters (QEs) are central components for photonic-based quantum information processing. Recently, bright QEs in III-nitride semiconductors, such as aluminum nitride (AlN), have attracted increasing interest because of the mature commercial application of the nitrides. However, the reported QEs in AlN suffer from broad phonon side bands (PSBs) and low Debye-Waller factors. Meanwhile, there is also a need for more reliable fabrication methods of AlN QEs for integrated quantum photonics. Here, we demonstrate that laser-induced QEs in AlN exhibit robust emission with a strong zero phonon line, narrow line width, and weak PSB. The creation yield of a single QE could be more than 50%. More importantly, they have a high Debye-Waller factor (>65%) at room temperature, which is the highest result among reported AlN QEs. Our results illustrate the potential of laser writing to create high-quality QEs for quantum technologies and provide further insight into laser writing defects in relevant materials.

Constrained Learning With Non-Convex Losses

Though learning has become a core component of modern information processing, there is now ample evidence that it can lead to biased, unsafe, and prejudiced systems. The need to impose requirements on learning is therefore paramount, especially as it reaches critical applications in social, industrial, and medical domains. However, the non-convexity of most modern statistical problems is only exacerbated by the introduction of constraints. Whereas good unconstrained solutions can often be learned using empirical risk minimization, even obtaining a model that satisfies statistical constraints can be challenging. All the more so, a good one. In this paper, we overcome this issue by learning in the empirical dual domain, where constrained statistical learning problems become unconstrained and deterministic. We analyze the generalization properties of this approach by bounding the empirical duality gap -- i.e., the difference between our approximate, tractable solution and the solution of the original (non-convex) statistical problem -- and provide a practical constrained learning algorithm. These results establish a constrained counterpart to classical learning theory, enabling the explicit use of constraints in learning. We illustrate this theory and algorithm in rate-constrained learning applications arising in fairness and adversarial robustness.

Fatigue and Secondary Media Impacts in the Automated Vehicle: A Multidimensional State Perspective

Safety researchers increasingly recognize the impacts of task-induced fatigue on vehicle driving behavior. The current study (N = 180) explored the use of a multidimensional fatigue measure, the Driver Fatigue Questionnaire (DFQ), to test the impacts of vehicle automation, secondary media use, and driver personality on fatigue states and performance in a driving simulator. Secondary media included a trivia game and a cellphone conversation. Simulated driving induced large-magnitude fatigue states in participants, including tiredness, confusion, coping through self-comforting, and muscular symptoms. Consistent with previous laboratory and field studies, dispositional fatigue proneness predicted increases in state fatigue during the drive, especially tiredness, irrespective of automation level and secondary media. Similar to previous studies, automation slowed braking response to the emergency event following takeover but did not affect fatigue. Secondary media use relieved subjective fatigue and improved lateral control but did not affect emergency braking. Confusion was, surprisingly, associated with faster braking, and tiredness was associated with impaired control of lateral position of the vehicle. These associations were not moderated by the experimental factors. Overall, data support the use of multidimensional assessments of both fatigue symptoms and information-processing components for evaluating safety impacts of interventions for fatigue.

Information theoretic evidence for layer- and frequency-specific changes in cortical information processing under anesthesia.

Nature relies on highly distributed computation for the processing of information in nervous systems across the entire animal kingdom. Such distributed computation can be more easily understood if decomposed into the three elementary components of information processing, i.e. storage, transfer and modification, and rigorous information theoretic measures for these components exist. However, the distributed computation is often also linked to neural dynamics exhibiting distinct rhythms. Thus, it would be beneficial to associate the above components of information processing with distinct rhythmic processes where possible. Here we focus on the storage of information in neural dynamics and introduce a novel spectrally-resolved measure of active information storage (AIS). Drawing on intracortical recordings of neural activity in ferrets under anesthesia before and after loss of consciousness (LOC) we show that anesthesia- related modulation of AIS is highly specific to different frequency bands and that these frequency-specific effects differ across cortical layers and brain regions. We found that in the high/low gamma band the effects of anesthesia result in AIS modulation only in the supergranular layers, while in the alpha/beta band the strongest decrease in AIS can be seen at infragranular layers. Finally, we show that the increase of spectral power at multiple frequencies, in particular at alpha and delta bands in frontal areas, that is often observed during LOC ('anteriorization') also impacts local information processing-but in a frequency specific way: Increases in isoflurane concentration induced a decrease in AIS in the alpha frequencies, while they increased AIS in the delta frequency range < 2Hz. Thus, the analysis of spectrally-resolved AIS provides valuable additional insights into changes in cortical information processing under anaesthesia.

Arrayed waveguide lens for beam steering

Abstract Integrated planar lenses are critical components for analog optical information processing that enable a wide range of applications including beam steering. Conventional planar lenses require gradient index control which makes their on-chip realization challenging. Here, we introduce a new approach for beam steering by designing an array of coupled waveguides with segmented tails that allow for simultaneously achieving planar lensing and off-chip radiation. The proposed arrayed waveguide lens is built on engineering the evanescent coupling between adjacent channels to realize a photonic lattice with an equi-distant ladder of propagation constants that emulates the continuous parabolic index profile. Through coupled-mode analysis and full-wave numerical simulations, we show that selective excitation of waveguide channels enables beam steering with large field-of-views of ∼60°. The proposed arrayed waveguide lens can serve as a compact component in integrated photonic circuits for applications in imaging, sensing, and metrology.

The Field of Psychology Never Maxed out on the Ideas of Max Wertheimer: A New Look at Productive Thinking

The Field of Psychology Never Maxed out on the Ideas of Max Wertheimer: A New Look at <i>Productive Thinking</i>

First-order Layer in Artificial Pain Pathway

The neural mechanisms involved in pain perception consist of a pathway which carry signals from the periphery to the cerebral cortex. First-order pain neurons transduce the potentially damaging stimuli detected by the sensorial extremes into long-ranging electrical signals that are transmitted to higher order neurons where the organisation is more heterarchical, especially in the cerebral cortex. However, the first order neurones, as their name states, have a degree of branching which clearly identifies them as hierarchical elements in the arrangement of pain pathway. This research aims to develop an artificial neural pain pathway that mimics this biological process, in particular the first order neurones. First, the research proposes the periodogram method on the condition monitoring data with a minor malfunction and operational damage. As the pain is associated with actual or potential tissue damage, using such data from a machinery system can provide insights which can be used to improve the computational effectiveness. Then, a one-dimensional convolutional neural network model is introduced to represent the second and third orders of the pain pathway. The research findings found clear support for studying the similarities between the major components of biological information processing of tissue damage and statistical signal processing for damage estimation.

Exceptional point protected robust on-chip optical logic gates.

Optical logic gates are crucial components for information processing and communication using photons. Current optical logic gates typically rely on the light interference principle which requires an accurate manipulation of the dynamical phase of light, making the device quite sensitive to system disturbances such as fabrication errors. Here we introduce non-Hermitian principles into the design of optical logic gates that work in the signal transmission process. We propose an exclusive-orgate for silicon-on-insulator platform by employing the physics in the exceptional point (EP) encirclement process. The EP induced mode switching behavior is applied to manipulate the phase of light which is topologically protected by the energy surface around the EP. As a result, the performance of the device is found to be extremely robust to structural parameter disturbances. The proposed non-Hermitian principle is expected to find applications for other on-chip photonic devices toward high robust performance.

Single‐Photon Nonreciprocity with an Integrated Magneto‐Optical Isolator

AbstractNonreciprocal photonic devices are essential components of classical optical information processing. It is interesting and important to investigate their feasibility in the quantum world. In this work, a single‐photon non‐reciprocal dynamical transmission experiment has been performed with an on‐chip silicon nitride (SiN)‐based magneto‐optical (MO) isolator. The measured isolation ratio for single photons achieved is 12.33 dB, consistent with the result of the classical test, which proves the functionality of our on‐chip isolator. The quantum coherence of the passing single photons is further verified using high‐visibility quantum interference. This work will promote on‐chip nonreciprocal photonic devices within the integrated quantum circuits and help introduce novel phenomena in quantum information processes.

Deterministic and Universal Quantum Squeezing Gate with a Teleportation‐Like Protocol

AbstractSqueezing transformation as an essential component of quantum information processing, gives rise to the possibility to perform various tasks such as distributed quantum computation and the quantum‐logic gate. However, the reported squeezing gate with best performance so far is realized with low success probability, while the performance of the deterministic ones is currently circumscribed by the limited squeezing degree of the nonclassical ancilla. To address this issue, a new scheme of deterministic and universal quantum squeezing gate with the property of nonlocal operation owing to the teleportation‐like protocol is developed and demonstrated. A high‐fidelity squeezing operation, even when the level of target squeezing being up to 10 dB, is demonstrated where a squeezed state with nonclassical noise reduction of 6.5 dB is directly observed. Moreover, a high‐fidelity complex operation including a Fourier transformation and a phase squeezing gate are performed, exploring the potential of implementing the complex task of quantum information processing with the presented functional unit. The method can be applied to distributed quantum processor, the creation of exotic nonclassical states, and quantum error correction.

Systematic analysis of additive manufacturing for printing on semi-finished parts

Additive manufacturing has gained significant interest in the industry, and fused filament fabrication in particular is a promising technology. In order to use the full potential of additive manufacturing, deeper understanding in this process is necessary. For this purpose, a profound analysis according to VDI 2206 guideline (“Design methodology for mechatronic systems”) is conducted for printing on semi-finished parts with fused filament fabrication. It presents the correlation of mechanical, electrotechnical and information processing components to detect the function chain. The relevant information, energy and material flow within the overall system are identified. New functional problems emerge from the analysis. For instance, the binding of the printed segment to the semi-finishes part needs to be considered. Moreover, the determination of the position and geometry of the semi-finished part poses a challenge. The analysis shows that the system needs to be expanded. To be able to print on semi-finished parts, a monitoring system, an advanced slicing software and a tempering ambience is necessary.