1 Introduction Artificial neural networks(ANNs,also NNs)have recently emerged as leading candidate models for deep learning,popularly used in various areas[1–3].Behind the enormous success,ANNs are generally with com...
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1 Introduction Artificial neural networks(ANNs,also NNs)have recently emerged as leading candidate models for deep learning,popularly used in various areas[1–3].Behind the enormous success,ANNs are generally with complicated structures,there being an intricate data flow through multiple linear or nonlinear components between the input layer and the output ***,it is pressing to evaluate how much a specific component contributes to the final output,termed the Credit Assignment Problem(CAP)[4]in this paper.
This paper proposes a novel hardware design to accelerate the Blake3 hash algorithm, a cryptographic hash function, on RISC-V architecture, using both vector extension (V1.0) and a specially designed Blake3 instructio...
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Maximal multi-photon entangled states,known as NOON states,play an essential role in quantum *** the number of photons growing,NOON states are becoming increasingly powerful and advantageous for obtaining supersensiti...
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Maximal multi-photon entangled states,known as NOON states,play an essential role in quantum *** the number of photons growing,NOON states are becoming increasingly powerful and advantageous for obtaining supersensitive and super-resolved *** this paper,we propose a universal scheme for generating three-and four-photon path-entangled NOON states on a reconfigurable photonic chip via photons subtracted from pairs and detected by heralding *** method is postselection free,enabling phase supersensitive measurements and sensing at the Heisenberg *** NOON-state generator allows for integration of quantum light sources as well as practical and portable precision phase-related measurements.
Emotion cause extraction(ECE)task that aims at extracting potential trigger events of certain emotions has attracted extensive attention ***,current work neglects the implicit emotion expressed without any explicit em...
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Emotion cause extraction(ECE)task that aims at extracting potential trigger events of certain emotions has attracted extensive attention ***,current work neglects the implicit emotion expressed without any explicit emotional keywords,which appears more frequently in application *** lack of explicit emotion information makes it extremely hard to extract emotion causes only with the local ***,an entire event is usually across multiple clauses,while existing work merely extracts cause events at clause level and cannot effectively capture complete cause event *** address these issues,the events are first redefined at the tuple level and a span-based tuple-level algorithm is proposed to extract events from different *** on it,a corpus for implicit emotion cause extraction that tries to extract causes of implicit emotions is *** authors propose a knowledge-enriched jointlearning model of implicit emotion recognition and implicit emotion cause extraction tasks(KJ-IECE),which leverages commonsense knowledge from ConceptNet and NRC_VAD to better capture connections between emotion and corresponding cause *** on both implicit and explicit emotion cause extraction datasets demonstrate the effectiveness of the proposed model.
Aviation accidents,referring to unexpected and undesirable events involving aircraft,often cause great damage to property and human *** from historical accidents is pivotal for improving safety in ***,aviation acciden...
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Aviation accidents,referring to unexpected and undesirable events involving aircraft,often cause great damage to property and human *** from historical accidents is pivotal for improving safety in ***,aviation accidents are typically documented and stored as unstructured or semi-structured free-text,rendering the ability to analyze such data a difficult *** study presents a novel framework that combines text mining and network analytics techniques to provide the ability to analyze aviation accident reports *** framework comprises a four-step modelling approach to:(1)the transformation of unstructured aviation safety report texts into structured numeric matrices using the TF-IDF matrix;(2)the identification of aviation accident topics using a structural topic model(STM);(3)the production of a word co-occurrence network(WCN)to determine the interrelations between aviation safety risk factors;and(4)quantitative analysis by technology of keywords to pinpoint key causal factors in aviation safety *** proposed framework is validated by analyzing aviation accident reports collected by the National Transportation Safety Board(NTSB).The results indicate that STM provides a more granular partitioning of topics and better distinguishes between similar events compared to traditional latent dirichlet allocation(LDA).Among the identified topics,“Fuel and Power”and“En-route Phase”have the highest occurrence rate according to ***,“Aircraft Crash”is the most prevalent topic in aviation accidents that resulted in fatal injuries,whereas the“Landing phase”is the most prevalent topic in nonfatal injuries on *** on the WCN,three centrality measures highlight“inspection of equipment”and“take off”as the most important risk factors in aviation *** proposed framework provides a comprehensive solution for in-depth analysis of aviation safety reports,offering decision support for aviation safety management and ac
We propose using wrist and ear-based sensing, via multiple novel and complementary modalities, to unobtrusively infer activity-aware, complex cognitive and affective states (such as confusion, boredom, and recall fail...
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Cyber-physical-human systems (CPHS) represent significant extensions of cyber-physical systems (CPS) to include aspects of human interactions and usage. A class of CPHS of interest here is smart products that offer se...
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The synthetic Floquet lattice,generated by multiple strong drives with mutually incommensurate frequencies,provides a powerful platform for quantum simulation of topological *** this study,we propose a 4-band tight-bi...
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The synthetic Floquet lattice,generated by multiple strong drives with mutually incommensurate frequencies,provides a powerful platform for quantum simulation of topological *** this study,we propose a 4-band tight-binding model of the Chern insulator with a Chern number C=±2 by coupling two layers of the half Bernevig–Hughes–Zhang lattice and subsequently mapping it onto the Floquet lattice to simulate its topological *** determine the Chern number of our Floquet-version model,we extend the energy pumping method proposed by Martin et al.[2017 ***.X 7041008]and the topological oscillation method introduced by Boyers et al.[2020 ***.125160505],followed by numerical simulations for both *** simulation results demonstrate the successful extraction of the Chern number using either of these methods,providing an excellent prediction of the phase diagram that closely aligns with the theoretical one derived from the original bilayer half Bernevig–Hughes–Zhang ***,we briefly discuss a potential experimental implementation for our *** work demonstrates significant potential for simulating complex topological matter using quantum computing platforms,thereby paving the way for constructing a more universal simulator for non-interacting topological quantum states and advancing our understanding of these intriguing phenomena.
Data-driven approaches have been shown to accelerate the process of high-fidelity design optimization, which has been successfully demonstrated in aerodynamic shape optimization (ASO) to support efficient and high per...
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Data-driven approaches have been shown to accelerate the process of high-fidelity design optimization, which has been successfully demonstrated in aerodynamic shape optimization (ASO) to support efficient and highperformance aircraft design. However, the high computational cost associated with generating aero dynamic data to build the required surrogate model contradicts our initial goal of achieving high-fidelity design with minimal computational expense. In this study, we exploit the capabilities of diffusion models in capturing complex high-dimensional data distributions to present an innovative conditional diffusion-based geometry sampler. Our research has led to the development of a comprehensive framework for high-fidelity data-driven ASO with reduced computational costs. This framework leverages multi-layer perception (MLP) as surrogate model and diffusion model as geometry sampler to achieve diverse and realistic geometric shapes while providing fine grained control over specific properties. Moreover, we employ a conditional diffusion-based geometry sampling method to significantly reduce the training set requirement for the surrogate model construction, achieving a50.5% decrease of the number of required data points. The effectiveness and capabilities of our proposed geometry sampling are validated using a high-dimensional dataset, demonstrating its potential to achieve high-fidelity ASO and multi-fidelity design exploration in a computationally efficient manner. In particular, the proposed geometry sampling method addresses several key aspects to support high-dimensional data-driven ASO and multi-fidelity design explorations, which include modeling the high-dimensional data distributions, sampling with time step and providing value function as the sampling guidance. This approach presents a comprehensive solution for effectively addressing the challenges associated with generating aerodynamic performance in high-dimensional and high-fidelity data. The ass
Integrated quantum frequency combs(QFCs)based on microring resonators supplies as an essential resource for expanding the Hilbert-space dimensionality for high-dimensional quantum computing and information *** this wo...
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Integrated quantum frequency combs(QFCs)based on microring resonators supplies as an essential resource for expanding the Hilbert-space dimensionality for high-dimensional quantum computing and information *** this work,we propose and demonstrate a reconfigurable ring resonator with tunable quality factors to efficiently increase the dimensionality of frequency entanglement,simultaneously,ensuring a high on-chip pair generation rate(PGR)and coincidence-to-accidental ratio(CAR).Our method exploits the asymmetric Mach-Zehnder interferometer instead of the traditional straight waveguide as the coupler of resonators which offer a tunable external coupling coefficient to modulate the quality factor to enlarge the QFCs’bandwidth and thus increase the dimensionality of frequency *** measured the QFCs’joint spectral intensity of 28 frequency pairs under various quality factors ranging from 16.6×10^(4) to 3.4×10^(4).Meanwhile,the measured Schmidt number increased from 11.01 to 24.77,denoting a huge expansion of the Hilbert-space dimensionality from 121 to a record number of 613 dimensions,which agrees well with our theoretical *** addition,the PGR and CAR-another two key parameters for high-quality QFCs-were all measured under different quality factors to verify that our method can significantly increase the Schmidt number and CAR while maintaining a high *** fact,bright QFCs with a total PGR of 4.3 MHz under a 0.48 mW pump power and a mean CAR of 1578 were simultaneously obtained at the highest Schmidt *** method is widely applicable to other material-based ring resonators and can act as a general solution for high-dimensional QFCs.
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