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Novel approach for computing gradients of physical observables

Physical Review D 2023年第9期108卷 L091508-L091508页

作者： Simone Bacchio Computation-based Science and Technology Research Center The Cyprus Institute Nicosia Cyprus

We show that an infinitesimal step of gradient flow can be used for defining a novel approach for computing gradients of physical observables with respect to action parameters. Compared to the commonly used perturbative expansion, this approach does not require calculating any disconnected contribution or vacuum expectation value and can provide results up to 3 orders of magnitudes more precise. On the other hand, it requires a nontrivial condition to be satisfied by the flow action, the calculation of its force and its Laplacian, and the force of the observable, whose gradient needs to be measured. As a proof of concept, we measure gradients in β of Wilson loops in a four-dimensional SU(3) Yang-Mills theory simulated on a 164 lattice using the Wilson action.

关键词： Lattice QCD Lattice field theory Perturbation theory Machine learning

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AI for Air Quality: Leveraging Data Fusion for Deep Downscaling of Atmospheric Pollutants

AI for Air Quality: Leveraging Data Fusion for Deep Downscal...

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2022 MACLEAN: MAChine Learning for EArth ObservatioN Workshop, MACLEAN 2022

作者： Ashiotis, Giannis Tsigkanos, Evgenios Christoudias, Theodoros Nicolaou, Mihalis A. Computation-Based Science and Technology Research Center The Cyprus Institute Nicosia Cyprus Climate and Atmosphere Research Center The Cyprus Institute Nicosia Cyprus

Air pollution is detrimental to the environment and contributes to the global burden of disease. Accurate prediction of surface concentrations of atmospheric pollutants, including fine particulate matter (PM2.5) and gaseous N O2, is key to mitigating these harmful effects. However, most forecasts produced by state-of-the-art models exhibit biases and limitations due to low-resolution (typically ∼ 40 km) and intense computational requirements. While deep learning-based approaches promise cheap inference, and can inherently scale and assimilate large amounts of data, ground truth measurements are obtained by station networks that are costly and sparsely distributed spatially - making the training of data-hungry deep learning methods tedious. In this context, we propose a novel deep learning framework that can accurately downscale the resolution of surface concentrations down to 1 × 1 km by leveraging additional data sources of static and time-varying nature (such as elevation and land use type). To do so, we carefully design a tailored data alignment and sampling process that balances the distribution across land use type, enhancing model generalization. To handle ground truth sparsity, we propose a tailored loss function that leads the network to model correlations across data sources close to ground station, while encouraging it to approximate model-based outputs elsewhere. With a set of qualitative and quantitative experiments, we demonstrate that the proposed method outperforms all compared modeling- and learning-based approaches, in both the aerosol and gaseous phase, with a significant increase in accuracy (up to 19% for N O2 and 13% for PM2.5) - while generalizing the improvement across the various land use types. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Data fusion

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Symmetry Breaking in Geometric Quantum Machine Learning in the Presence of Noise

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PRX Quantum 2024年第3期5卷 030314-030314页

作者： Cenk Tüysüz Su Yeon Chang Maria Demidik Karl Jansen Sofia Vallecorsa Michele Grossi Zeuthen 15738 Germany Institut für Physik Geneva 1211 Switzerland Institute of Physics Computation-Based Science and Technology Research Center

Geometric quantum machine learning based on equivariant quantum neural networks (EQNNs) recently appeared as a promising direction in quantum machine learning. Despite encouraging progress, studies are still limited to theory, and the role of hardware noise in EQNN training has never been explored. This work studies the behavior of EQNN models in the presence of noise. We show that certain EQNN models can preserve equivariance under Pauli channels, while this is not possible under the amplitude damping channel. We claim that the symmetry breaks linearly in the number of layers and noise strength. We support our claims with numerical data from simulations as well as hardware up to 64 qubits. Furthermore, we provide strategies to enhance the symmetry protection of EQNN models in the presence of noise.

关键词： Artificial neural networks Machine learning Quantum algorithms Quantum algorithms for chemical calculations Quantum circuits Quantum coherence & coherence measures Quantum information theory

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Chiral carbon dots from glucose by room temperature alkaliassisted synthesis for electrocatalytic oxidation of tryptophan enantiomers

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Nano research 2023年第7期16卷 8929-8936页

作者： Mengling Zhang Xing Fan Xin Du Yurong Ma Xiting Wang Hui Huang Yang Liu Youyong Li Zhenhui Kang Macao Institute of Materials Science and Engineering(MIMSE) MUST-SUDA Joint Research Center for Advanced Functional MaterialsMacao University of Science and TechnologyTaipaMacao 999078China Institute of Functional Nano and Soft Materials(FUNSOM) Jiangsu Key Laboratory for Carbon-based Functional Materials and DevicesJoint International Research Laboratory of Carbon-Based Functional Materials and DevicesSoochow UniversitySuzhou 215123China Research Center for Carbon-based Electronics and Key Laboratory for the Physics and Chemistry of Nanodevices School of ElectronicsPeking UniversityBeijing 100871China

Chiral catalysis is one of the most direct and effective approach to obtain pure optical *** carbon dots(CDs)as carbon-based chiral catalysts show great potential in chiral ***,we report a facile one step base-catalyzed aldol condensation to fabricate the chiral CDs from glucose at ambient temperature and *** formation of chiral CDs involves the processes of isomerization and aldol *** chiral CDs have been demonstrated that they have selective capacity for electrocatalytic oxidization of tryptophan enantiomers.L type of CDs(LCDs)is more likely to catalyze L-tryptophan(Trp)than D-Trp with the selective factor(I_(L)/I_(D))of 1.60,whereas the D type of CDs(DCDs)tends to catalyze D-Trp(I_(L)/I_(D):0.63).Theoretical calculations combined with various contrast experiments(temperature and pH)demonstrate that the selectively electrocatalytic capacity of chiral CDs toward Trp isomers is due to the different hydrogen-bond interactions between chiral CDs and Trp.

关键词： chiral carbon dots synthesis mechanism electrocatalysis chiral catalysis

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Non-perturbative renormalization of quark and gluon operators using a gauge-invariant scheme 39

Non-perturbative renormalization of quark and gluon operator...

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39th International Symposium on Lattice Field Theory, LATTICE 2022

作者： Spanoudes, G. Alexandrou, C. Finkenrath, J. Hadjiyiannakou, K. Panagopoulos, H. Yamamoto, S. Computation-based Science and Technology Research Center The Cyprus Institute 20 Kavafi Str. Nicosia2121 Cyprus Department of Physics University of Cyprus NicosiaCY-1678 Cyprus

We present preliminary results for the renormalization functions (RFs) of a number of quark and gluon operators studied in lattice QCD using a gauge-invariant renormalization scheme (GIRS). GIRS is a variant of the coordinate-space renormalization prescription, in which Green's functions of gauge-invariant operators are calculated in position space. A novel aspect is that summations over different time slices of the positions of the operators are employed in order to reduce the statistical noise in lattice simulations. We test the reliability of this scheme by calculating RFs for the vector one-derivative quark bilinear operator, which enters the average momentum fraction of the nucleon. We use N f = 4 degenerate twisted mass clover-improved fermion ensembles of different volumes and lattice spacings. We also present first results of applying GIRS when operator mixing occurs: the mixing coefficients of the gluon and quark singlet energy-momentum tensor operators are evaluated by imposing appropriate renormalization conditions on the lattice. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

关键词： Gages

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Sinogram Denoise based on Fourier Neural Operators

Sinogram Denoise Based on Fourier Neural Operators

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2022 IEEE Nuclear science Symposium, Medical Imaging Conference, and Room Temperature Semiconductor Detector Conference, IEEE NSS MIC RTSD 2022

作者： Chrysostomou, Charalambos The Cyprus Institute Computation-Based Science and Technology Research Center 20 Konstantinou Kavafi Street Nicosia Aglantzia2121 Cyprus

ISBN: (纸本)9781665488723

A novel method for sinogram denoise based on Fourier Neural Operator (FNO) in SPECT imaging is presented. Projection data from software phantoms were used to train the proposed model. Generated software phantoms with various noise levels added were used to evaluate the method's efficacy in denoising SPECT sinograms. The resulting denoised sinograms are reconstructed using Ordered Subset Expectation Maximization (OSEM) and compared to the reconstructions of the original noised sinograms. The results show that the proposed method can denoise the sinograms and significantly improve current state-of-the-art methodologies, with significantly less training data than other machine learning approaches. Finally, results from real-world DAT-SPECT sinograms demonstrate the proposed method's efficacy and capability. © 2022 IEEE.

关键词： Convolutional Neural Networks (CNN) Ordered Subset Expectation Maximization (OSEM) Single Photon Emission Computerized Tomography (SPECT) SPECT Sinogram Denoise

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Masked Generative Light Field Prompting for Pixel-Level Structure Segmentations

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research 2024年第4期2024卷 533-544页

作者： Mianzhao Wang Fan Shi Xu Cheng Shengyong Chen The Engineering Research Center of Learning-Based Intelligent System(Ministry of Education) Tianjin University of TechnologyTianjin 300384China Key Laboratory of Computer Vision and System(Ministry of Education) Tianjin University of TechnologyTianjin 300384China School of Computer Science and Engineering Tianjin University of TechnologyTianjin 300384China

Pixel-level structure segmentations have attracted considerable attention,playing a crucial role in autonomous driving within the metaverse and enhancing comprehension in light field-based machine ***,current light field modeling methods fail to integrate appearance and geometric structural information into a coherent semantic space,thereby limiting the capability of light field transmission for visual *** this paper,we propose a general light field modeling method for pixel-level structure segmentation,comprising a generative light field prompting encoder(LF-GPE)and a prompt-based masked light field pretraining(LF-PMP)*** LF-GPE,serving as a light field backbone,can extract both appearance and geometric structural cues *** aligns these features into a unified visual space,facilitating semantic ***,our LF-PMP,during the pretraining phase,integrates a mixed light field and a multi-view light field *** prioritizes considering the geometric structural properties of the light field,enabling the light field backbone to accumulate a wealth of prior *** evaluate our pretrained LF-GPE on two downstream tasks:light field salient object detection and semantic *** results demonstrate that LF-GPE can effectively learn high-quality light field features and achieve highly competitive performance in pixel-level segmentation tasks.

关键词： prompt backbone integrate

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PIC Design for the Two-Channel Dual-Band SS-OCT System

PIC Design for the Two-Channel Dual-Band SS-OCT System

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2024 International Conference on Electrical Engineering and Photonics, EExPolytech 2024

作者： Stepanov, Ivan V. Lyubopytov, Vladimir S. Ivanov, Anton A. Grakhova, Elizaveta P. Ufa University of Science and Technology Research Lab. Sensor Systems Based on Devices of Integrated Photonics Ufa Russia

ISBN: (纸本)9798350388879

The paper presents the design of the photonic integrated circuit (PIC) for the dual-band swept-source optical coherence tomography (SS-OCT) system. The designed PIC includes two interferometric schemes for the 820-880 nm and 1260-1360 nm wavelength ranges and optical triggers for both schemes. We developed PIC for the silicon nitride platform because it provides lower losses than other integrated photonics platforms. We made design verification with numerical simulation in the Ansys Lumerical software. © 2024 IEEE.

关键词： Silicon nitride

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Personality Enhanced Emotion Generation Modeling for Dialogue Systems

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COGNITIVE computation 2024年第1期16卷 293-304页

作者： Liu, Zhiqiang Jia, Wenchao Zhou, Yutong Xu, Biqi Liu, Zhiqiang Wu, Zhuoyi College of Data Science and Application Inner Mongolia University of Technology Hohhot 010080 Inner Mongolia China Inner Mongolia Autonomous Region Engineering & Technology Research Centre of Big Data Based Software Service Hohhot 010080 Inner Mongolia China

Emotion plays a crucial role in human communication, as it adds depth and richness to conversations. In recent years, there has been growing interest in developing conversation systems with the ability to generate emotions. However, to create more engaging and realistic interactions, it is essential to consider the influence of personality on emotion generation. This paper proposes a novel approach that combines personality modeling with emotion generation for conversation systems. By incorporating personality traits into the emotion generation process, we aim to create more personalized and contextually appropriate emotional responses. Drawing from BigFive model and emotion computation techniques, our model takes into account individual differences in personality to generate emotions that align with each user's unique characteristics. Experiments show that combining emotion modeling with personality in a dialogue system helps improve the performance of emotion generation models. Additionally, it is also verified that our approach outperforms other baselines on several metrics.

关键词： Dialogue system Emotion generation Personality Anthropomorphism

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PIC-based Continuous Frequency Tuning of the Optoelectronic Oscillator 3

PIC-Based Continuous Frequency Tuning of the Optoelectronic ...

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3rd IEEE International Conference on Problems of Informatics, Electronics and Radio Engineering, PIERE 2024

作者： Ivanov, Vladislav V. Stepanov, Ivan V. Aleksakina, Yana V. Shaygardanova, Arina R. Khairetdinova, Adelya D. Voronkov, Grigory S. Ufa University of Science and Technology Research Lab. 'Sensor Systems Based on Devices of Integrated Photonics' Ufa Russia

ISBN: (纸本)9798331516321

The paper explores optoelectronic oscillator (OEO) output frequency control by adjusting the integrated optical time delay line in the OEO feedback loop. The developed mathematical model demonstrates the continuous nature of output OEO frequency tuning for the continuous phase incursion change in the OEO feedback loop. We created a photonic integrated circuit (PIC) numerical model for verification in the Ansys Lumerical Software. The PIC is designed for the generic silicon-on-insulator platform. We applied an unbalanced Mach-Zehnder interferometer with tunable couplers as an integrated optical time delay line. Numerical simulations showed that the applied time delay line provides an output OEO frequency change range of 360 MHz within a voltage range from -3.2 to 0.5 V. In this case, the time delay tuning step is 6 ps/V, and the corresponding output frequency step is 30 MHz/ps. For the precision digital-to-analog converter (DAC), an output OEO frequency tuning step is 7.2 kHz for the minimum DAC voltage step 40 μV. © 2024 IEEE.

关键词： Mach-Zehnder interferometers

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