Search

Spiking neural network (SNN) has emerged as a promising paradigm in computational neuroscience and artificial intelligence, offering advantages such as low energy consumption and small memory footprint. However, their practical adoption is constrained by several challenges, prominently among them being performance optimization. In this study, we present a novel approach to enhance the performance of SNN for images through a new coding method that exploits bit plane representation. Our proposed technique is designed to improve the accuracy of SNN without increasing model size. Also, we investigate the impacts of color models of the proposed coding process. Through extensive experimental validation, we demonstrate the effectiveness of our coding strategy in achieving performance gain across multiple datasets. To the best of our knowledge, this is the first research that considers bit planes and color models in the context of SNN. By leveraging the unique characteristics of bit planes, we hope to unlock new potentials in SNNs performance, potentially paving the way for more efficient and effective SNNs models in future researches and applications.

Quantum state tomography is a crucial technique for characterizing the state of a quantum system, which is essential for many applications in quantum technologies. In recent years, there has been growing interest in leveraging neural networks to enhance the efficiency and accuracy of quantum state tomography. Still, many of them did not include mixed quantum state, since pure states are arguably less common in practical situations. In this research paper, we present two neural networks based approach for both pure and mixed quantum state tomography: Restricted Feature Based Neural Network and Mixed States Conditional Generative Adversarial Network, evaluate its effectiveness in comparison to existing neural based methods. We demonstrate that our proposed methods can achieve state-of-the-art results in reconstructing mixed quantum states from experimental data. Our work highlights the potential of neural networks in revolutionizing quantum state tomography and facilitating the development of quantum technologies., Comment: 10 pages, 5 figures, 17 illustration, 1 table

A theoretical study on electrical current fluctuations in a double quantum dot connected to electronic reservoirs is presented, with the aim of deriving the finite-frequency noise, the Fano factor and the $\Delta T$-noise. We establish a general expression for the noise in terms of Green functions in the double quantum dot and self-energies in the reservoirs. This result is then applied to model double quantum dots in various situations. For a non-interacting double quantum dot, we have highlighted several interesting features in the physical properties of this system. In particular, we have demonstrated the possibility of obtaining a significant reduction in zero-frequency noise and Fano factor either when the system is placed in a given operating regime, or when a temperature gradient is applied between the two reservoirs, resulting in a negative $\Delta T$-noise being generated. In addition, in the vicinity of honeycomb vertices, a sign change is observed in the finite-frequency cross-correlator between the two reservoirs, in contrast to what is obtained for the zero-frequency cross-correlator, which remains negative throughout the $(\varepsilon_1,\varepsilon_2)$-plane, $\varepsilon_{1,2}$ being the level energies in each of the two dots. By using an approximate first-level numerical approach, we finally study how the finite-frequency noise in a double quantum dot evolves under the influence of Coulomb interactions., Comment: 17 pages, 12 figures

In recent years, visual question answering (VQA) has attracted attention from the research community because of its highly potential applications (such as virtual assistance on intelligent cars, assistant devices for blind people, or information retrieval from document images using natural language as queries) and challenge. The VQA task requires methods that have the ability to fuse the information from questions and images to produce appropriate answers. Neural visual question answering models have achieved tremendous growth on large-scale datasets which are mostly for resource-rich languages such as English. However, available datasets narrow the VQA task as the answers selection task or answer classification task. We argue that this form of VQA is far from human ability and eliminates the challenge of the answering aspect in the VQA task by just selecting answers rather than generating them. In this paper, we introduce the OpenViVQA (Open-domain Vietnamese Visual Question Answering) dataset, the first large-scale dataset for VQA with open-ended answers in Vietnamese, consists of 11,000+ images associated with 37,000+ question-answer pairs (QAs). Moreover, we proposed FST, QuMLAG, and MLPAG which fuse information from images and answers, then use these fused features to construct answers as humans iteratively. Our proposed methods achieve results that are competitive with SOTA models such as SAAA, MCAN, LORA, and M4C. The dataset is available to encourage the research community to develop more generalized algorithms including transformers for low-resource languages such as Vietnamese., Comment: submitted to Elsevier

Visual Question Answering (VQA) is a challenging task of natural language processing (NLP) and computer vision (CV), attracting significant attention from researchers. English is a resource-rich language that has witnessed various developments in datasets and models for visual question answering. Visual question answering in other languages also would be developed for resources and models. In addition, there is no multilingual dataset targeting the visual content of a particular country with its own objects and cultural characteristics. To address the weakness, we provide the research community with a benchmark dataset named EVJVQA, including 33,000+ pairs of question-answer over three languages: Vietnamese, English, and Japanese, on approximately 5,000 images taken from Vietnam for evaluating multilingual VQA systems or models. EVJVQA is used as a benchmark dataset for the challenge of multilingual visual question answering at the 9th Workshop on Vietnamese Language and Speech Processing (VLSP 2022). This task attracted 62 participant teams from various universities and organizations. In this article, we present details of the organization of the challenge, an overview of the methods employed by shared-task participants, and the results. The highest performances are 0.4392 in F1-score and 0.4009 in BLUE on the private test set. The multilingual QA systems proposed by the top 2 teams use ViT for the pre-trained vision model and mT5 for the pre-trained language model, a powerful pre-trained language model based on the transformer architecture. EVJVQA is a challenging dataset that motivates NLP and CV researchers to further explore the multilingual models or systems for visual question answering systems. We released the challenge on the Codalab evaluation system for further research., Comment: VLSP2022 EVJVQA challenge

Given the high power density low discharge rate and decreasing cost rechargeable lithium-ion batteries LiBs have found a wide range of applications such as power grid level storage systems electric vehicles and mobile devices. Developing a framework to accurately model the nonlinear degradation process of LiBs which is indeed a supervised learning problem becomes an important research topic. This paper presents a classical-quantum hybrid machine learning approach to capture the LiB degradation model that assesses battery cell life loss from operating profiles. Our work is motivated by recent advances in quantum computers as well as the similarity between neural networks and quantum circuits. Similar to adjusting weight parameters in conventional neural networks the parameters of the quantum circuit namely the qubits degree of freedom can be tuned to learn a nonlinear function in a supervised learning fashion. As a proof of concept paper our obtained numerical results with the battery dataset provided by NASA demonstrate the ability of the quantum neural networks in modeling the nonlinear relationship between the degraded capacity and the operating cycles. We also discuss the potential advantage of the quantum approach compared to conventional neural networks in classical computers in dealing with massive data especially in the context of future penetration of EVs and energy storage., Comment: Accepted for 2023 IEEE Green Technology Conference, Denver, Colorado, USA

Recognizing handwriting images is challenging due to the vast variation in writing style across many people and distinct linguistic aspects of writing languages. In Vietnamese, besides the modern Latin characters, there are accent and letter marks together with characters that draw confusion to state-of-the-art handwriting recognition methods. Moreover, as a low-resource language, there are not many datasets for researching handwriting recognition in Vietnamese, which makes handwriting recognition in this language have a barrier for researchers to approach. Recent works evaluated offline handwriting recognition methods in Vietnamese using images from an online handwriting dataset constructed by connecting pen stroke coordinates without further processing. This approach obviously can not measure the ability of recognition methods effectively, as it is trivial and may be lack of features that are essential in offline handwriting images. Therefore, in this paper, we propose the Transferring method to construct a handwriting image dataset that associates crucial natural attributes required for offline handwriting images. Using our method, we provide a first high-quality synthetic dataset which is complex and natural for efficiently evaluating handwriting recognition methods. In addition, we conduct experiments with various state-of-the-art methods to figure out the challenge to reach the solution for handwriting recognition in Vietnamese., Comment: Accepted for publishing at the 16th International Conference on Computing and Communication Technologies (RIVF)

Image captioning is currently a challenging task that requires the ability to both understand visual information and use human language to describe this visual information in the image. In this paper, we propose an efficient way to improve the image understanding ability of transformer-based method by extending Object Relation Transformer architecture with Attention on Attention mechanism. Experiments on the VieCap4H dataset show that our proposed method significantly outperforms its original structure on both the public test and private test of the Image Captioning shared task held by VLSP., Comment: Accepted for publishing at the VNU Journal of Science: Computer Science and Communication Engineering

Efficient and accurate numerical algorithms are developed to solve a generalized Kirchhoff-Love plate model subject to three common physical boundary conditions: (i) clamped; (ii) simply supported; and (iii) free. We solve the model equation by discretizing the spatial derivatives using second-order finite-difference schemes, and then advancing the semi-discrete problem in time with either an explicit predictor-corrector or an implicit Newmark-Beta time-stepping algorithm. Stability analysis is conducted for the schemes and the results are used to determine stable time steps in practice. A series of carefully chosen test problems are solved to demonstrate the properties and applications of our numerical approaches. The numerical results confirm the stability and 2nd-order accuracy of the algorithms, and are also comparable with experiments for similar thin plates. As an application, we illustrate a strategy to identify the natural frequencies of a plate using our numerical methods in conjunction with a fast Fourier transformation (FFT) power spectrum analysis of the computed data. Then we take advantage of one of the computed natural frequencies to simulate the interesting physical phenomena known as resonance and beat for a generalized Kirchhoff-Love plate.

We study electrical and thermoelectrical properties for a double quantum dot system. We consider the cases of both single-level and multilevel quantum dots whatever the way they are coupled, either in a series or in a parallel arrangement. The calculations are performed by using the nonequilibrium Green function theory. In the case of a single-level double quantum dot, the problem is exactly solvable whereas for a multilevel double quantum dot, an analytical solution is obtained in the limit of energy-independent hopping integrals. { We present a detailed discussion about} the dependences of electrical conductance, zero-frequency charge susceptibility and Seebeck coefficient on the gate voltages applied to the dots, allowing us to derive the charge stability diagram. The findings are in agreement with the experimental observations notably with the occurrence of successive sign changes of the Seebeck coefficient when varying the gate voltages. We interpret the results in terms of the bonding and antibonding states produced by the level anticrossing effect which occurs in the presence of a finite interdot coupling. We show that at equilibrium the boundary lines between the domains with different dot occupancies in the charge stability diagram, take place when the bonding and antibonding state levels are aligned with the chemical potentials in the leads. Finally the total dot occupancy is found to be considerably reduced in the case in parallel compared with the case in series, { whenever} the level energies in each dot are equal. We interpret this dip as a direct manifestation of the interference effects occurring in the presence of the two electronic transmission paths provided by each dot., Comment: 18 pages, 13 figures

Recently, Vietnamese Natural Language Processing has been researched by experts in academic and business. However, the existing papers have been focused only on information classification or extraction from documents. Nowadays, with quickly development of the e-commerce websites, forums and social networks, the products, people, organizations or wonders are targeted of comments or reviews of the network communities. Many people often use that reviews to make their decision on something. Whereas, there are many people or organizations use the reviews to mislead readers. Therefore, it is so necessary to detect those bad behaviors in reviews. In this paper, we research this problem and propose an appropriate method for detecting Vietnamese reviews being spam or non-spam. The accuracy of our method is up to 90%., Comment: 6 pages, in Vietnamese

This paper focuses on energy recycling in full-duplex (FD) relaying multiple-input-multiple-output orthogonal frequency division multiplexing (OFDM) communication. The loop self-interference (SI) due to full-duplexing is seen as an opportunity for the energy-constrained relay node to replenish its energy requirement through wireless power transfer. In forwarding the source information to the destination, the FD relay can simultaneously harvest energy from the source wireless transmission and also through energy recycling from its own transmission. The objective is to maximize the overall spectral efficiency by designing the optimal power allocation over OFDM sub-carriers and transmit antennas. Due to a large number of sub-carriers, this design problem poses a large-scale nonconvex optimization problem involving a few thousand variables of power allocation, which is very computationally challenging. A new path-following algorithm is proposed, which converges to an optimal solution. This algorithm is very efficient since it is based on \textit{closed-form} calculations. Numerical results for a practical simulation setting show promising results by achieving high spectral efficiency.

This article presents original work combining a NURBS-based inverse analysis with both kinematic and constitutive nonlinearities to recover the applied loads and deformations of thin shell structures. The inverse formulation is tackled by gradient-based optimization algorithms based on computed and measured displacements at a number of discrete locations. The proposed method allows accurately recovering the target shape of shell structures such that instabilities due to snapping and buckling are captured. The results obtained show good performance and applicability of the proposed algorithms to computer-aided manufacturing of shell structures.

We calculate the charge susceptibility and the linear and differential conductances of a double quantum dot coupled to two metallic reservoirs both at equilibrium and when the system is driven away from equilibrium. This work is motivated by recent progress in the realization of solid state spin qubits. The calculations are performed by using the Keldysh nonequilibrium Green function technique. In the noninteracting case, we give the analytical expression for the electrical current and deduce from there the linear conductance as a function of the gate voltages applied to the dots, leading to a characteristic charge stability diagram. We determine the charge susceptibility which also exhibits peaks as a function of gate voltages. We show how the study can be extended to the case of an interacting quantum dot., Comment: 4 pages, 3 figures

The fluctuations of electrical current provide information on the dynamics of electrons in quantum devices. Understanding the nature of these fluctuations in a quantum dot is thus a crucial step insofar as this system is the elementary brick of quantum circuits. In this context, we develop a theory for calculating the quantum noise at finite frequency in a quantum dot connected to two reservoirs in the presence of interactions and for any symmetry of the couplings to the reservoirs. This theory is developed in the framework of the Keldysh non-equilibrium Green function technique. We establish an analytical expression for the quantum noise in terms of the various transmission amplitudes between the reservoirs and of some effective transmission coefficient which we define. We then study the noise as a function of the dot energy level and the bias voltage. The effects of both Coulomb interactions in the dot and asymmetric couplings with the reservoirs are characterized., Comment: 4 pages, 2 figures, in press in AIP Advances (2018)