Frequency domain theory of nonlinear Volterra systems based on parametric characteristic analysis.

The frequency domain methods tor linear systems are well accepted by engineers and have been widely applied in engineering practice because the transfer function of linear systems can always provide a coordinate-free and equivalent description for system characteristics and are convenient to be used for the system analysis and design. Although the analysis and design of linear systems in the frequency domain have been well established and the frequency domain methods for nonlinear systems have aheady been investigated for many years, the frequency domain analysis for nonlinear systems is far from being fully developed

MMNet: Multi-Mask Network for Referring Image Segmentation

Referring image segmentation aims to segment an object referred to by natural language expression from an image. However, this task is challenging due to the distinct data properties between text and image, and the randomness introduced by diverse objects and unrestricted language expression. Most of previous work focus on improving cross-modal feature fusion while not fully addressing the inherent uncertainty caused by diverse objects and unrestricted language. To tackle these problems, we propose an end-to-end Multi-Mask Network for referring image segmentation(MMNet). we first combine picture and language and then employ an attention mechanism to generate multiple queries that represent different aspects of the language expression. We then utilize these queries to produce a series of corresponding segmentation masks, assigning a score to each mask that reflects its importance. The final result is obtained through the weighted sum of all masks, which greatly reduces the randomness of the language expression. Our proposed framework demonstrates superior performance compared to state-of-the-art approaches on the two most commonly used datasets, RefCOCO, RefCOCO+ and G-Ref, without the need for any post-processing. This further validates the efficacy of our proposed framework.Comment: 10 pages, 5 figure

EAVL: Explicitly Align Vision and Language for Referring Image Segmentation

Referring image segmentation aims to segment an object mentioned in natural language from an image. A main challenge is language-related localization, which means locating the object with the relevant language. Previous approaches mainly focus on the fusion of vision and language features without fully addressing language-related localization. In previous approaches, fused vision-language features are directly fed into a decoder and pass through a convolution with a fixed kernel to obtain the result, which follows a similar pattern as traditional image segmentation. This approach does not explicitly align language and vision features in the segmentation stage, resulting in a suboptimal language-related localization. Different from previous methods, we propose Explicitly Align the Vision and Language for Referring Image Segmentation (EAVL). Instead of using a fixed convolution kernel, we propose an Aligner which explicitly aligns the vision and language features in the segmentation stage. Specifically, a series of unfixed convolution kernels are generated based on the input l, and then are use to explicitly align the vision and language features. To achieve this, We generate multiple queries that represent different emphases of the language expression. These queries are transformed into a series of query-based convolution kernels. Then, we utilize these kernels to do convolutions in the segmentation stage and obtain a series of segmentation masks. The final result is obtained through the aggregation of all masks. Our method can not only fuse vision and language features effectively but also exploit their potential in the segmentation stage. And most importantly, we explicitly align language features of different emphases with the image features to achieve language-related localization. Our method surpasses previous state-of-the-art methods on RefCOCO, RefCOCO+, and G-Ref by large margins.Comment: 10 pages, 4 figures. arXiv admin note: text overlap with arXiv:2305.1496

MAMO: Masked Multimodal Modeling for Fine-Grained Vision-Language Representation Learning

Multimodal representation learning has shown promising improvements on various vision-language tasks. Most existing methods excel at building global-level alignment between vision and language while lacking effective fine-grained image-text interaction. In this paper, we propose a jointly masked multimodal modeling method to learn fine-grained multimodal representations. Our method performs joint masking on image-text input and integrates both implicit and explicit targets for the masked signals to recover. The implicit target provides a unified and debiased objective for vision and language, where the model predicts latent multimodal representations of the unmasked input. The explicit target further enriches the multimodal representations by recovering high-level and semantically meaningful information: momentum visual features of image patches and concepts of word tokens. Through such a masked modeling process, our model not only learns fine-grained multimodal interaction, but also avoids the semantic gap between high-level representations and low- or mid-level prediction targets (e.g. image pixels), thus producing semantically rich multimodal representations that perform well on both zero-shot and fine-tuned settings. Our pre-trained model (named MAMO) achieves state-of-the-art performance on various downstream vision-language tasks, including image-text retrieval, visual question answering, visual reasoning, and weakly-supervised visual grounding

Formulation of a new gradient descent MARG orientation algorithm: case study on robot teleoperation

We introduce a novel magnetic angular rate gravity (MARG) sensor fusion algorithm for inertial measurement. The new algorithm improves the popular gradient descent (ʻMadgwick’) algorithm increasing accuracy and robustness while preserving computa- tional efficiency. Analytic and experimental results demonstrate faster convergence for multiple variations of the algorithm through changing magnetic inclination. Furthermore, decoupling of magnetic field variance from roll and pitch estimation is pro- ven for enhanced robustness. The algorithm is validated in a human-machine interface (HMI) case study. The case study involves hardware implementation for wearable robot teleoperation in both Virtual Reality (VR) and in real-time on a 14 degree-of-freedom (DoF) humanoid robot. The experiment fuses inertial (movement) and mechanomyography (MMG) muscle sensing to control robot arm movement and grasp simultaneously, demon- strating algorithm efficacy and capacity to interface with other physiological sensors. To our knowledge, this is the first such formulation and the first fusion of inertial measure- ment and MMG in HMI. We believe the new algorithm holds the potential to impact a very wide range of inertial measurement applications where full orientation necessary. Physiological sensor synthesis and hardware interface further provides a foundation for robotic teleoperation systems with necessary robustness for use in the field

Preparation and Tests of MR Fluids With CI Particles Coated With MWNTs

The magnetorheological (MR) fluid is a typical smart material, whose shear yield stress can be adjusted through changing the strength of external magnetic field, and the changing process only takes a few milliseconds. The MR fluid is composed of micro/nanometer ferromagnetic particles, carrier fluids, and some additives. Among them, the performance of ferromagnetic particles will mainly affect the sedimentation stability and the magnetic saturation of the MR fluid. Therefore, the ferromagnetic particles are expected to have characteristics of both low density and high magnetism. In this paper, the multi-walled carbon nanotubes (MWNTs) were adopted to coat on the carbonyl iron (CI) particles with grafting technology using ultrasonication and mechanical stirring. The coated CI particles with perfect core-shell structure were developed and the influence of the dosages of grafting agent and MWNTs were tested. And then, MR fluids with CI particles coated with MWNTs were established and the coating effect was studied through surface topography particle density, and magnetic properties of composite magnetic particles and stability tests of the prepared MR fluids. The results showed that although the magnetic saturation of the prepared MR fluids with CI particles coated with MWNTs would reduce slightly, the particles density and the adsorption force between the particles were decreased effectively, which are both advantageous to the improvement of the sedimentation stability of MR fluids

CatNorth: An Improved Gaia DR3 Quasar Candidate Catalog with Pan-STARRS1 and CatWISE

A complete and pure sample of quasars with accurate redshifts is crucial for quasar studies and cosmology. In this paper, we present CatNorth, an improved Gaia DR3 quasar candidate catalog with more than 1.5 million sources in the 3$\pi$ sky built with data from Gaia, Pan-STARRS1, and CatWISE2020. The XGBoost algorithm is used to reclassify the original Gaia DR3 quasar candidates as stars, galaxies, and quasars. To construct training/validation datasets for the classification, we carefully built two different master stellar samples in addition to the spectroscopic galaxy and quasar samples. An ensemble classification model is obtained by averaging two XGBoost classifiers trained with different master stellar samples. Using a probability threshold of $p_{\mathrm{QSO\_mean}}>0.95$ in our ensemble classification model and an additional cut on the logarithmic probability density of zero proper motion, we retrieved 1,545,514 reliable quasar candidates from the parent Gaia DR3 quasar candidate catalog. We provide photometric redshifts for all candidates with an ensemble regression model. For a subset of 89,100 candidates, accurate spectroscopic redshifts are estimated with the Convolutional Neural Network from the Gaia BP/RP spectra. The CatNorth catalog has a high purity of > 90% while maintaining high completeness, which is an ideal sample to understand the quasar population and its statistical properties. The CatNorth catalog is used as the main source of input catalog for the LAMOST phase III quasar survey, which is expected to build a highly complete sample of bright quasars with $i < 19.5$.Comment: 24 pages, 13 figures, submitted to AAS journals. Table 4 (The CatNorth quasar candidate catalog) is available at https://nadc.china-vo.org/res/r101313

A multimechanistic antibody targeting receptor-binding sites potently cross-protects against influenza B viruses

流感病毒HA是研制流感药物和流感疫苗的重要靶标，但HA具有高度变异性，如何在高变异HA中找到不变之处，即高度保守表位，是研制流感特效药物和广谱疫苗的关键。近年来国外报道的流感HA广谱中和单抗的识别位点均在较为保守的HA茎部区，而针对流感病毒与细胞受体结合部位的HA头部区尤其是RBS区，一直未能发现广谱中和抗体。夏宁邵教授团队通过探索多种免疫策略和筛选策略，成功筛选出一株广谱中和单抗12G6，识别一个位于HA头部RBS上的全新保守性表位。体外实验显示12G6人源化改造的C12G6抗体能高效中和1940-2016年间世界各地历年流行的代表三个遗传变异亚系的18个乙型流感病毒代表株对细胞的感染，并能保护小鼠致死性感染，治疗效果显著优于已报道的代表性抗体以及抗流感药物；C12G6与“达菲”联合用药具有明显的协同效果。此外，雪貂感染模型的预防和治疗效果进一步证实了C12G6作为抗体药物的治疗潜能。研究还显示该表位是病毒感染复制的关键表位，该位点的突变会造成病毒毒力显著下降。最后，研究揭示了C12G6通过五种不同的抗病毒作用机制发挥作用，提示其高效的抗病毒活性得益于多机制协同效应，这也是目前国内外第一次发现一个流感抗体能通过如此全面的抗病毒机制发挥作用。 该发现为研制能抵抗各种变异株的乙型流感特效治疗药物和通用疫苗带来新希望。 该研究工作依托分子疫苗学和分子诊断学国家重点实验室（厦门大学）、国家传染病诊断试剂与疫苗工程技术研究中心、厦门大学养生堂生物药物联合实验室完成。陈毅歆副教授、夏宁邵教授为该研究论文的共同通讯作者。在读博士研究生沈晨光、陈俊煜、李睿、王国松和硕士研究生张梦娅等为共同第一作者。【Abstract】Influenza B virus causes considerable disease burden worldwide annually, highlighting the limitations of current influenza vaccines and antiviral drugs. In recent years, broadly neutralizing antibodies (bnAbs) against hemagglutinin (HA) have emerged as a new approach for combating influenza. We describe the generation and characterization of a chimeric monoclonal antibody, C12G6, that cross-neutralizes representative viruses spanning the 76 years of influenza B antigenic evolution since 1940, including viruses belonging to the Yamagata, Victoria, and earlier lineages. Notably, C12G6 exhibits broad cross-lineage hemagglutination inhibition activity against influenza B viruses and has higher potency and breadth of neutralization when compared to four previously reported influenza B bnAbs. In vivo, C12G6 confers stronger cross-protection against Yamagata and Victoria lineages of influenza B viruses in mice and ferrets than other bnAbs or the anti-influenza drug oseltamivir and has an additive antiviral effect when administered in combination with oseltamivir. Epitope mapping indicated that C12G6 targets a conserved epitope that overlaps with the receptor binding site in the HA region of influenza B virus, indicating why it neutralizes virus so potently. Mechanistic analyses revealed that C12G6 inhibits influenza B viruses via multiple mechanisms, including preventing viral entry, egress, and HA-mediated membrane fusion and triggering antibody-dependent cell-mediated cytotoxicity and complement-dependent cytotoxicity responses. C12G6 is therefore a promising candidate for the development of prophylactics or therapeutics against influenza B infection and may inform the design of a truly universal influenza vaccine.This research was supported by grants from the National Natural Science Foundation of China (31670934 and 81371817), the Ministry of Science and Technology of the People’s Republic of China (2011ZX09102-009-12 and 2012DFH30020), the Research Grants Council of the Hong Kong Special Administrative Region (7629/13M, 17103214, and 17154516), and a sponsored research agreement from Sanofi Pasteur. 研究工作得到了香港大学新发传染病国家重点实验室和赛诺菲巴斯德公司的技术支持和帮助，获得国家自然科学基金、新药创制国家科技重大专项、科技部对港科技合作项目等课题资助