Chinese spelling correction is a foundational task in natural language processing that aims to detect and correct spelling errors in text. Most spelling corrections in Chinese used multimodal information to model the relationship between incorrect and correct characters. However, feature information mismatch occured during fusion result from the different sources of features, causing the importance relationships between different modalities to be ignored, which in turn restricted the model from learning in an efficient manner. To this end, this paper proposes a multimodal language model-based Chinese spelling corrector, named as MISpeller. The method, based on ChineseBERT as the basic model, allows the comprehensive capture and fusion of character semantic information, phonetic information and graphic information in a single model without the need to construct additional neural networks, and realises the phenomenon of unequal fusion of multi-feature information. In addition, in order to solve the overcorrection issues, the replication mechanism is further introduced, and the replication factor is used as the dynamic weight to efficiently fuse the multimodal information. The model is able to control the proportion of original characters and predicted characters according to different input texts, and it can learn more specifically where errors occur. Experiments conducted on the SIGHAN benchmark show that the proposed model achieves the state-of-the-art performance of the F1 score at the correction level by an average of 4.36%, which validates the effectiveness of the model.

In this work, we propose a 1T1R ReRAM CiM architecture for Hyperdimensional Computing (HDC). The number of Source Lines and Bit Lines is reduced by introducing memory cells that are connected in series, which is especially advantageous when using a 3D implementation. The results of CiM operations contain errors, but HDC is robust against them, so that even if the XNOR operation has an error of 25%, the inference accuracy remains above 90%.

Integrating RGB and event sensors improves object detection accuracy, especially during the night, due to the high-dynamic range of event camera. However, introducing an event sensor leads to an increase in computational resources, which makes the implementation of RGB-event fusion multi-modal AI to CiM difficult. To tackle this issue, this paper proposes RGB-Event fusion Multi-modal analog Computation-in-Memory (CiM), called REM-CiM, for multi-modal edge object detection AI. In REM-CiM, two proposals about multi-modal AI algorithms and circuit implementation are co-designed. First, Memory capacity-Efficient Attentional Feature Pyramid Network (MEA-FPN), the model architecture for RGB-event fusion analog CiM, is proposed for parameter-efficient RGB-event fusion. Convolution-less bi-directional calibration (C-BDC) in MEA-FPN extracts important features of each modality with attention modules, while reducing the number of weight parameters by removing large convolutional operations from conventional BDC. Proposed MEA-FPN w/ C-BDC achieves a 76% reduction of parameters while maintaining mean Average Precision (mAP) degradation to < 2.3% during both day and night, compared with Attentional FPN fusion (A-FPN), a conventional BDC-adopted FPN fusion. Second, the low-bit quantization with clipping (LQC) is proposed to reduce area/energy. Proposed REM-CiM with MEA-FPN and LQC achieves almost the same memory cells, 21% less ADC area, 24% less ADC energy and 0.17% higher mAP than conventional FPN fusion CiM without LQC.

This paper introduces a wireless-powered relay transceiver designed to extend 5G millimeter-wave coverage. It employs an on-chip butler matrix, enabling beam control-free operation. The prototype includes PCB array antennas and on-chip butler matrix and rectifiers manufactured using a Si CMOS 65 nm process. The relay transceiver performs effectively in beam angles from -45° to 45°. In the 24 GHz wireless power transmission (WPT) mode, it generates 0.12 mW with 0 dBm total input power, boasting an RF-DC conversion efficiency of 12.2%. It also demonstrates communication performance at 28 GHz in both RX and TX modes with a 100 MHz bandwidth and 64QAM modulation.

This paper proposes a novel configuration for a wideband single-carrier transmitter using a sub-terahertz frequency. For wideband single-carrier transmission over a bandwidth of several gigahertz, the frequency response non-flatness derived from transmitter components in an operating band seriously deteriorates the transmission quality due to inter-symbol interference. A promising approach to address this problem is equalizing the frequency response non-flatness at the transmitter. The proposed novel configuration has a feedback route for calculating the inverse frequency response and multiplying it with a transmission signal spectrum in the frequency domain. Moreover, we verify that employing the proposed transmitter configuration simplifies the receiver configuration by lowering the calculation complexity to minimize the inter-symbol interference to meet the signal-to-interference-and-noise ratio requirements. To confirm the feasibility of the proposed configuration, the transmission quality obtained using the proposed configuration is measured and evaluated. Experimental results confirm that the proposed configuration improves the error vector magnitude value to over 5 dB for a 10 Gbaud transmission and the transmission data rate of 25 Gbps.

The load-independent zero-voltage switching class-E inverter has garnered considerable interest as an essential component in wireless power transfer systems. This inverter achieves high efficiency across a broad spectrum of load conditions by incorporating a load adjustment circuit (LAC) subsequent to the resonant filter. Nevertheless, the presence of the LAC influences the output impedance of the inverter, thereby inducing a divergence between the targeted and observed output power, even in ideal lossless simulations. Consequently, iterative adjustments to component values are required via an LC element implementation. We introduce a novel design methodology that incorporates an external quality factor on the side of the resonant filter, inclusive of the LAC. Thus, the optimized circuit achieves the intended output power without necessitating alterations in component values.

In Japan, research on spatial transmission Wireless Power Transfer/Transmission (WPT) for long-distance power transmission has been conducted ahead of the rest of the world; however, until 2022, there has been no category under the Radio Law, and it has been treated as an experimental station. The authors are working on Japanese institutionalization (revision of ministerial ordinances) and global standardization of this spatial transmission WPT for social implementation. This paper describes the Japanese and international institutionalization and standardization trends. In addition, as the latest trend in R&D trends, as the next step of institutionalization, the author introduces two national projects that are being worked on by industry, academia, and government for Step 2, which can be used for a wider range of applications by relaxing the scope of use and restrictions from Step 1, which has various restrictions. The first is about the Cross-ministerial Strategic Innovation Promotion Program (SIP) Phase 2. In SIP Phase 2, we conducted R&D on “WPT system for sensor networks and mobile devices”. This R&D is research on detecting and avoiding people so that radio exposure does not exceed protection guidelines and detecting incumbent radios and avoiding harmful interference so that more power can be transmitted under coexistence conditions. The other is “Research and Development for Expansion of Radio Resources” to be conducted by the Ministry of Internal Affairs and Communications (MIC), which is scheduled for four years from FY2022. This is also a more concrete research and development project for Step 2 institutionalization, along with the results of the SIP mentioned above.

This paper delves into the utilisation of the subgradient method with geometrically decaying stepsize for Bilinear Form Identification. We introduce the iterative Wiener Filter, an l2 regression method, and highlight its limitations when confronted with noise, particularly heavy-tailed noise. To address these challenges, the paper suggests employing the l1 regression method with a subgradient method utilizing a geometrically decaying step size. The effectiveness of this approach is compared to existing methods, including the ALS algorithem. The study demonstrates that the l1 algorithm, especially when paired with the proposed subgradient method, excels in stability and accuracy under conditions of heavy-tailed noise. Additionally, the paper introduces the standard rounding procedure and the S-outlier bound as relaxations of traditional assumptions. Numerical experiments provide support and validation for the presented results.

In this paper, the Towards High Performance Voxel-based 3D Object Detection (Voxel-RCNN) three-dimensional (3D) point cloud object detection model is used as the benchmark network. Aiming at the problems existing in the current mainstream 3D point cloud voxelization methods, such as the backbone and the lack of feature expression ability under the bird’s-eye view (BEV), a high-performance voxel-based 3D object detection network (Reinforced Voxel-RCNN) is proposed. Firstly, a 3D feature extraction module based on the integration of inverted residual convolutional network and weight normalization is designed on the 3D backbone. This module can not only well retain more point cloud feature information, enhance the information interaction between convolutional layers, but also improve the feature extraction ability of the backbone network. Secondly, a spatial feature-semantic fusion module based on spatial and channel attention is proposed from a BEV perspective. The mixed use of channel features and semantic features further improves the network’s ability to express point cloud features. In the comparison of experimental results on the public dataset KITTI, the experimental results of this paper are better than many voxel-based methods. Compared with the baseline network, the 3D average accuracy and BEV average accuracy on the three categories of Car, Cyclist, and Pedestrians are improved. Among them, in the 3D average accuracy, the improvement rate of Car category is 0.23%, Cyclist is 0.78%, and Pedestrians is 2.08%. In the context of BEV average accuracy, enhancements are observed: 0.32% for the Car category, 0.99% for Cyclist, and 2.38% for Pedestrians. The findings demonstrate that the algorithm enhancement introduced in this study effectively enhances the accuracy of target category detection.

For flight simulators, it is crucial to create three-dimensional terrain using clear remote sensing images. However, due to haze and other contributing variables, the obtained remote sensing images typically have low contrast and blurry features. In order to build a flight simulator visual system, we propose a deep learning-based dehaze model for remote sensing images dehazing. An encoder-decoder architecture is proposed that consists of a multiscale fusion module and a gated large kernel convolutional attention module. This architecture can fuse multi-resolution global and local semantic features and can adaptively extract image features under complex terrain. The experimental results demonstrate that, with good generality and application, the model outperforms existing comparison techniques and achieves high-confidence dehazing in remote sensing images with a variety of haze concentrations, multi-complex terrains, and multi-spatial resolutions.

Aiming at the problem that large-scale traffic data lack labels and take too long for feature extraction in network intrusion detection, an unsupervised intrusion detection method ACOPOD based on Adam asymmetric autoencoder and COPOD (Copula-Based Outlier Detection) algorithm is proposed. This method uses the Adam asymmetric autoencoder with a reduced structure to extract features from the network data and reduce the data dimension. Then, based on the Copula function, the joint probability distribution of all features is represented by the edge probability of each feature, and then the outliers are detected. Experiments on the published NSL-KDD dataset with six other traditional unsupervised anomaly detection methods show that ACOPOD achieves higher precision and has obvious advantages in running speed. Experiments on the real civil aviation air traffic management network dataset further prove that the method can effectively detect intrusion behavior in the real network environment, and the results are interpretable and helpful for attack source tracing.

This article describes the idea of utilizing Attested Execution Secure Processors (AESPs) that fit into building a secure Self-Sovereign Identity (SSI) system satisfying Sybil-resistance under permissionless blockchains. Today’s circumstances requiring people to be more online have encouraged us to address digital identity preserving privacy. There is a momentum of research addressing SSI, and many researchers approach blockchain technology as a foundation. SSI brings natural persons various benefits such as owning controls; on the other side, digital identity systems in the real world require Sybil-resistance to comply with Anti-Money-Laundering (AML) and other needs. The main idea in our proposal is to utilize AESPs for three reasons: first is the use of attested execution capability along with tamper-resistance, which is a strong assumption; second is powerfulness and flexibility, allowing various open-source programs to be executed within a secure enclave, and the third is that equipping hardware-assisted security in mobile devices has become a norm. Rafael Pass et al.’s formal abstraction of AESPs and the ideal functionality $\color{brown}{\mathcal{G}_\mathtt{att}}$ enable us to formulate how hardware-assisted security works for secure digital identity systems preserving privacy under permissionless blockchains mathematically. Our proposal of the AESP-based SSI architecture and system protocols, $\color{blue}{\Pi^{\mathcal{G}_\mathtt{att}}}$, demonstrates the advantages of building a proper SSI system that satisfies the Sybil-resistant requirement. The protocols may eliminate the online distributed committee assumed in other research, such as CanDID, because of assuming AESPs; thus, $\color{blue}{\Pi^{\mathcal{G}_\mathtt{att}}}$ allows not to rely on multi-party computation (MPC), bringing drastic flexibility and efficiency compared with the existing SSI systems.

This letter proposes a scheme for the backward transmission of the propagation-delay based three-user X channel, which is reciprocal to the forward transmission. The given scheme successfully delivers 10 expected messages in 6 time-slots by cyclic interference alignment without loss of degrees of freedom, which supports efficient bidirectional transmission between the two ends of the three-user X channel.

In 2005, Nandi introduced a class of double-block-length compression functions hπ(x) := (h(x), h(π(x))), where h is a random oracle with an n-bit output and π is a non-cryptographic public permutation. Nandi demonstrated that the collision resistance of hπ is optimal if π has no fixed point in the classical setting. Our study explores the collision resistance of hπ and the Merkle-Damgård hash function using hπ in the quantum random oracle model. Firstly, we reveal that the quantum collision resistance of hπ may not be optimal even if π has no fixed point. If π is an involution, then a colliding pair of inputs can be found for hπ with only O(2n/2) queries by the Grover search. Secondly, we present a sufficient condition on π for the optimal quantum collision resistance of hπ. This condition states that any collision attack needs Ω(22n/3) queries to find a colliding pair of inputs. The proof uses the recent technique of Zhandry’s compressed oracle. Thirdly, we show that the quantum collision resistance of the Merkle-Damgård hash function using hπ can be optimal even if π is an involution. Finally, we discuss the quantum collision resistance of double-block-length compression functions using a block cipher.

The dispersion problem is a variant of facility location problems, that has been extensively studied. Given a polygon with n edges on a plane we want to find k points in the polygon so that the minimum pairwise Euclidean distance of the k points is maximized. We call the problem the k-dispersion problem in a polygon. Intuitively, for an island, we want to locate k drone bases far away from each other in flying distance to avoid congestion in the sky. In this paper, we give a polynomial-time approximation scheme (PTAS) for this problem when k is a constant and ε < 1 (where ε is a positive real number). Our proposed algorithm runs in O(((1/ε)2 + n/ε)k) time with 1/(1 + ε) approximation, the first PTAS developed for this problem. Additionally, we consider three variations of the dispersion problem and design a PTAS for each of them.

Melanin, which is responsible for the appearance of spots and freckles, is an important indicator in evaluating skin condition. To assess the efficacy of cosmetics, skin condition scoring is performed by analyzing the distribution and amount of melanin from microscopic images of the stratum corneum cells. However, the current practice of diagnosing skin condition using stratum corneum cells images relies heavily on visual evaluation by experts. The goal of this study is to develop a quantitative evaluation system for skin condition based on melanin within unstained stratum corneum cells images. The proposed system utilizes principal component regression to perform five-level scoring, which is then compared with visual evaluation scores to assess the system’s usefulness. Additionally, we evaluated the impact of indicators related to melanin obtained from images on the scores, and verified which indicators are effective for evaluation. In conclusion, we confirmed that scoring is possible with an accuracy of more than 60% on a combination of several indicators, which is comparable to the accuracy of visual assessment.

Stack Overflow, a leading Q&A platform for developers, is a substantial reservoir of Python code snippets. Nevertheless, the incompatibility issues between Python versions, particularly Python 2 and Python 3, introduce substantial challenges that can potentially jeopardize the utility of these code snippets. This empirical study dives deep into the challenges of Python version inconsistencies on the interpretation and application of Python code snippets on Stack Overflow. Our empirical study exposes the prevalence of Python version compatibility issues on Stack Overflow. It further emphasizes an apparent deficiency in version-specific identification, a critical element that facilitates the identification and utilization of Python code snippets. These challenges, primarily arising from the lack of backward compatibility between Python’s major versions, pose significant hurdles for developers relying on Stack Overflow for code references and learning. This study, therefore, signifies the importance of proactively addressing these compatibility issues in Python code snippets. It advocates for enhanced tools and strategies to assist developers in efficiently navigating through the Python version complexities on platforms like Stack Overflow. By highlighting these concerns and providing a potential remedy, we aim to contribute to a more efficient and effective programming experience on Stack Overflow and similar platforms.

This study investigates a two/three-dimensional (2D/3D) symbol-mapping technique that evaluates data transmission quality based on a four-level pulse-amplitude modulation (PAM-4) symbol transition. Multi-dimensional symbol transition mapping facilitates the visualization of the degree of interference (ISI). The simulation and experimental results demonstrated that the 2D symbol mapping can evaluate the PAM-4 data transmission quality degraded by ISI and visualize the equalization effect. Furthermore, potential applications of 2D mapping and its extension to 3D mapping were explored.

In high-speed data communication systems, it is important to evaluate the quality of the transmitted signal at the receiver. At a high-speed data rate, the transmission line characteristics act as a high-frequency attenuator and contribute to the intersymbol interference (ISI) at the receiver. To evaluate ISI conditions, eye diagrams are widely used to analyze signal quality and visualize the ISI effect as an eye-opening rate. Various types of on-chip eye-opening monitors (EOM) have been proposed to adjust waveform-shaping circuits. However, the eye diagram evaluation of multi-valued signaling becomes more difficult than that of binary transmission because of the complicated signal transition patterns. Moreover, in severe ISI situations where the eye is completely closed, eye diagram evaluation does not work well. This paper presents a novel evaluation method using Two-dimensional(2D) symbol mapping and a linear mixture model (LMM) for multi-valued data transmission. In our proposed method, ISI evaluation can be realized by 2D symbol mapping, and an efficient quantitative analysis can be realized using the LMM. An experimental demonstration of four leveled pulse amplitude modulation(PAM-4) data transmission using a Cat5e cable 100 m is presented. The experimental results show that the proposed method can extract features of the ISI effect even though the eye is completely closed in the server condition.

This paper proposes a decomposition method for symmetric multiple-valued functions. It decomposes a given symmetric multiple-valued function into three parts. By using suitable decision diagrams for the three parts, we can represent symmetric multiple-valued functions compactly. By deriving theorems on sizes of the decision diagrams, this paper shows that space complexity of the proposed representation is low. This paper also presents algorithms to construct the decision diagrams for symmetric multiple-valued functions with low time complexity. Experimental results show that the proposed method represents randomly generated symmetric multiple-valued functions more compactly than the conventional representation method using standard multiple-valued decision diagrams. Symmetric multiple-valued functions are a basic class of functions, and thus, their compact representation benefits many applications where they appear.