Reviewerly/exHarmony huggingface dataset:
Over-the-Air Federated Learning with Compressed Sensing: Is Sparsification Necessary?

Over-the-Air (OtA) Federated Learning (FL) refers to an FL system where multiple agents apply OtA computation for transmitting model updates to a common edge server. Two important features of OtA computation, namely linear processing and signal-level superposition, motivate the use of linear compression with compressed sensing (CS) methods to reduce the number of data samples transmitted over the channel. The previous works on applying CS methods in OtA FL have primarily assumed that the original model update vectors are sparse, or they have been sparsified before compression. However, it is unclear whether linear compression with CS-based reconstruction is more effective than directly sending the non-zero elements in the sparsified update vectors, under the same total power constraint. In this study, we examine and compare several communication designs with or without sparsification. Our findings demonstrate that sparsification before compression is not necessary. Alternatively, sparsification without linear compression can also achieve better performance than the commonly considered setup that combines both.
Cyber Physical System Information Collection: Robot Location and Navigation Method Based on QR Code

In this paper, we propose a method to estimate the exact location of a camera in a cyber-physical system using the exact geographic coordinates of four feature points stored in QR codes(Quick response codes) and the pixel coordinates of four feature points analyzed from the QR code images taken by the camera. Firstly, the P4P(Perspective 4 Points) algorithm is designed to uniquely determine the initial pose estimation value of the QR coordinate system relative to the camera coordinate system by using the four feature points of the selected QR code. In the second step, the manifold gradient optimization algorithm is designed. The rotation matrix and displacement vector are taken as the initial values of iteration, and the iterative optimization is carried out to improve the positioning accuracy and obtain the rotation matrix and displacement vector with higher accuracy. The third step is to convert the pose of the QR coordinate system with respect to the camera coordinate system to the pose of the AGV(Automated Guided Vehicle) with respect to the world coordinate system. Finally, the performance of manifold gradient optimization algorithm and P4P analytical algorithm are simulated and compared under the same conditions.One can see that the performance of the manifold gradient optimization algorithm proposed in this paper is much better than that of the P4P analytic algorithm when the signal-to-noise ratio is small.With the increase of the signal-to-noise ratio,the performance of the P4P analytic algorithm approaches that of the manifold gradient optimization algorithm.when the noise is same,the performance of manifold gradient optimization algorithm is better when there are more feature points.
MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Images

This paper introduces an innovative methodology for producing high-quality 3D lung CT images guided by textual information. While diffusion-based generative models are increasingly used in medical imaging, current state-of-the-art approaches are limited to low-resolution outputs and underutilize radiology reports' abundant information. The radiology reports can enhance the generation process by providing additional guidance and offering fine-grained control over the synthesis of images. Nevertheless, expanding text-guided generation to high-resolution 3D images poses significant memory and anatomical detail-preserving challenges. Addressing the memory issue, we introduce a hierarchical scheme that uses a modified UNet architecture. We start by synthesizing low-resolution images conditioned on the text, serving as a foundation for subsequent generators for complete volumetric data. To ensure the anatomical plausibility of the generated samples, we provide further guidance by generating vascular, airway, and lobular segmentation masks in conjunction with the CT images. The model demonstrates the capability to use textual input and segmentation tasks to generate synthesized images. The results of comparative assessments indicate that our approach exhibits superior performance compared to the most advanced models based on GAN and diffusion techniques, especially in accurately retaining crucial anatomical features such as fissure lines, airways, and vascular structures. This innovation introduces novel possibilities. This study focuses on two main objectives: (1) the development of a method for creating images based on textual prompts and anatomical components, and (2) the capability to generate new images conditioning on anatomical elements. The advancements in image generation can be applied to enhance numerous downstream tasks.
Adaptive variational ground state preparation for spin-1 models on qubit-based architectures

We apply the adaptive variational quantum imaginary time evolution (AVQITE) method to prepare ground states of one-dimensional spin $S=1$ models. We compare different spin-to-qubit encodings (standard binary, Gray, unary, and multiplet) with regard to the performance and quantum resource cost of the algorithm. Using statevector simulations we study two well-known spin-1 models: the Blume-Capel model of transverse-field Ising spins with single-ion anisotropy, and the XXZ model with single-ion anisotropy. We consider system sizes of up to $20$ qubits, which corresponds to spin-$1$ chains up to length $10$. We determine the dependence of the number of CNOT gates in the AVQITE state preparation circuit on the encoding, the initial state, and the choice of operator pool in the adaptive method. Independent on the choice of encoding, we find that the CNOT gate count scales cubically with the number of spins for the Blume-Capel model and quartically for the anistropic XXZ model. However, the multiplet and Gray encodings present smaller prefactors in the scaling relations. These results provide useful insights for the implementation of AVQITE on quantum hardware.
Varying Entropy Degrees of Freedom Effects in Low-Scale Leptogenesis

We analyse in detail the effect of varying entropy degrees of freedom on low-scale leptogenesis models. As an archetypal model, we consider the Tri-Resonant Leptogensis${}$ (TRL) scenario introduced recently by the authors, where the neutrino-Yukawa coupling matrix is dictated by an approximate $\mathbb{Z}_n$ discrete symmetry (with $n=3,6$). TRL models exhibit no preferred direction in the leptonic flavour space and have the remarkable feature that leptogenesis can successfully take place even if all light neutrinos are strictly massless up to one-loop order. Most interestingly, for TRL scenarios with heavy Majorana neutrinos lighter than 100 GeV, temperature varying degrees of freedom associated with the entropy of the plasma have a dramatic impact on the predictions of the Baryon Asymmetry in the Universe (BAU), and may sensitively depend on the freeze-out sphaleron temperature $T_{\rm sph}$. We find that this is a generic feature of most freeze-out low-scale leptogenesis models discussed in the literature. In the same context, we consider heavy-neutrino scenarios realising dynamics related to critical unstable qudits in the thermal plasma and assess their significance in generating the BAU. The phenomenological implications of TRL scenarios at the intensity and high-energy frontiers are analysed.
Stochastic interpolants with data-dependent couplings

Generative models inspired by dynamical transport of measure -- such as flows and diffusions -- construct a continuous-time map between two probability densities. Conventionally, one of these is the target density, only accessible through samples, while the other is taken as a simple base density that is data-agnostic. In this work, using the framework of stochastic interpolants, we formalize how to \textit{couple} the base and the target densities, whereby samples from the base are computed conditionally given samples from the target in a way that is different from (but does preclude) incorporating information about class labels or continuous embeddings. This enables us to construct dynamical transport maps that serve as conditional generative models. We show that these transport maps can be learned by solving a simple square loss regression problem analogous to the standard independent setting. We demonstrate the usefulness of constructing dependent couplings in practice through experiments in super-resolution and in-painting.
Modular Speech-to-Text Translation for Zero-Shot Cross-Modal Transfer

Recent research has shown that independently trained encoders and decoders, combined through a shared fixed-size representation, can achieve competitive performance in speech-to-text translation. In this work, we show that this type of approach can be further improved with multilingual training. We observe significant improvements in zero-shot cross-modal speech translation, even outperforming a supervised approach based on XLSR for several languages.
Pre-skills of reading and writing in children with developmental language disorder and children with typical language development

&amp;lt;p&amp;gt;Introduction. In addition to the development of spoken language, typical intellectual abilities, preservation of the senses of hearing and sight, phonological awareness and the ability of graphomotor expression are necessary for mastering the skills of reading and writing. With this in mind, the aim of this research is to determine the level of phonological awareness and the ability of graphomotor expression in children with developmental language disorder (DLD) and children with typical language development (TLD). Methods. The sample consisted of 46 children aged five to seven years, who were divided into two groups based on the status of speech and language abilities. The first group consisted of 23 children with DLD, and the second of 23 children with TLD. The FONT test was used to assess phonological awareness, and the Prediction Test for Dysgraphia was used to assess graphomotor abilities. Results. The results of the research show that children with DLD performed poorly on phonological awareness tasks compared to TLD children. Age proved to be a significant factor in differences in the development of phonological awareness. Achievements on the Prediction Test for Dysgraphia show that children with DLD have almost twice the weaker ability of graphomotor expression compared to TLD children. Furthermore, the research results show that there are significant differences in the level of development of graphomotor skills between children of different ages. Also, no significant difference in achievements on the Prediction Test for Dysgraphia or on the FONT test has been found in relation to gender. Conclusion. Children with developmental language disorder had significantly lower achievements on the phonological awareness test and the Prediction Test for Dysgraphia compared to children with typical language development.&amp;lt;/p&amp;gt;
Half-Hop: A graph upsampling approach for slowing down message passing.

Message passing neural networks have shown a lot of success on graph-structured data. However, there are many instances where message passing can lead to over-smoothing or fail when neighboring nodes belong to different classes. In this work, we introduce a simple yet general framework for improving learning in message passing neural networks. Our approach essentially upsamples edges in the original graph by adding "slow nodes" at each edge that can mediate communication between a source and a target node. Our method only modifies the input graph, making it plug-and-play and easy to use with existing models. To understand the benefits of slowing down message passing, we provide theoretical and empirical analyses. We report results on several supervised and self-supervised benchmarks, and show improvements across the board, notably in heterophilic conditions where adjacent nodes are more likely to have different labels. Finally, we show how our approach can be used to generate augmentations for self-supervised learning, where slow nodes are randomly introduced into different edges in the graph to generate multi-scale views with variable path lengths.
Pose-Free Generalizable Rendering Transformer

In the field of novel-view synthesis, the necessity of knowing camera poses (e.g., via Structure from Motion) before rendering has been a common practice. However, the consistent acquisition of accurate camera poses remains elusive, and errors in pose extraction can adversely impact the view synthesis process. To address this challenge, we introduce PF-GRT, a new Pose-Free framework for Generalizable Rendering Transformer, eliminating the need for pre-computed camera poses and instead leveraging feature-matching learned directly from data. PF-GRT is parameterized using a local relative coordinate system, where one of the source images is set as the origin. An OmniView Transformer is designed for fusing multi-view cues under the pose-free setting, where unposed-view fusion and origin-centric aggregation are performed. The 3D point feature along target ray is sampled by projecting onto the selected origin plane. The final pixel intensities are modulated and decoded using another Transformer. PF-GRT demonstrates an impressive ability to generalize to new scenes that were not encountered during the training phase, without the need of pre-computing camera poses. Our experiments with zero-shot rendering on the LLFF, RealEstate-10k, Shiny, and Blender datasets reveal that it produces superior quality in generating photo-realistic images. Moreover, it demonstrates robustness against noise in test camera poses. Code is available at https://zhiwenfan.github.io/PF-GRT/.
An Analysis of Ordinances on Lifelong Education in China: Focusing on Shanghai

Legislation plays an indispensable role in the development of lifelong education. This study aims to provide implications for the construction of the legal system of lifelong education in China by analyzing the Shanghai Lifelong Education Ordinance. Therefore, this study explored the Shanghai Ordinance on Lifelong Education content from promoting organizations and networks, financial support, strategies and projects of lifelong education. The results of the analysis were as follows: First, the ordinance has formulated a lifelong education management mechanism centered on local government and clarified the basic direction of resource integration. Second, a mechanism for ensuring lifelong education funding has been established, in which financial contributions from the government are the main agency, complemented by numerous contributions from society and individuals. Third, diversify lifelong education programs, emphasize the protection of the learning rights of disadvantaged groups, and initially form a system of academic achievement certification and a mechanism for monitoring and evaluation. Based on the results, discussions and implications were suggested.
An Improved Optimization Dung Beetle Algorithm

The dung beetle optimization technique has the issue of arbitrarily initializing the population while solving complex problems, leading to unstable search results and limited global search capability. This article suggests three-improvement version of the dung beetle optimization method in light of this. First, this study employs the TENT chaotic map with strong ergodicity to generate the initial population and uses the Reverse learning technique in order to increase the randomness of the population and obtain a suitable initial solution position. Second, this paper uses the Levy flight strategy to optimize the algorithm weight and the Levy flight improvement weight strategy to maintain a high global development ability in the algorithm optimization due to the original algorithm’s weak ability to jump out of local optimization and the lack of randomness. Finally, in order to increase the optimization speed of the algorithm, a new address update method is proposed to address the drawbacks of the original algorithm’s address update approach. The improved optimization algorithm incorporates both local exploration and global optimization characteristics after 10 benchmark function tests and comparison with other algorithms. This enhances the algorithm’s capacity to jump out of local optima and gives it good robustness and optimization accuracy.
Vehicle Target Detection in Complex Scene SAR Images Based on Co-saliency

Complex features and topographies such as urban areas, forests, and mountains in complex scenes cause many types of SAR image clutter and interference, which makes it more difficult to detect vehicle targets. Aiming at the problem of vehicle target detection in complex scenarios, this paper proposes a SAR image vehicle target detection algorithm based on co-saliency, which uses auxiliary data and data to be measured to calculate co-saliency, obtains the salient features of the target to be measured to complete the detection task, and can effectively detect vehicle targets with the assistance of a small number of vehicle target slices. The co-saliency is applied to the SAR image target detection task, and the preliminary co-saliency map is obtained by calculating the contrast features and corresponding features of the image. Then the weights generated by the co-saliency map of the auxiliary data are used to further suppress the background and enhance the target, so as to improve the detection performance. Experimental verification is performed on the SAR images of the measured complex scenes, and the performance of the proposed method is verified. The experimental results confirm that compared with the traditional CFAR algorithm and significance algorithm, the detection accuracy of the co-saliency algorithm is improved by at least 0.469, and the F1-score is improved by at least 0.561.
Image Segmentation Using Attribute Network

Image segmentation is one of the key research topics in image analysis and pattern recognition. In recent years, with the rapid development of complex network theory, many researchers have applied complex network theory to the fields of image analysis and computer vision. Existing methods for image segmentation using complex networks only consider the connections between nodes, ignoring the characteristics of the nodes, which means that some important information of the image is lost during the modeling process, and affects the image segmentation performance. In this paper, both the connections between nodes and the characteristics of the nodes themselves are considered, an image topology network is constructed for the connections between nodes, and an attribute network is constructed for the features of nodes. Thus, an image segmentation algorithm using attribute networks (SAINS) is proposed in this paper. The SAINS method can effectively segment image without specifying the number of segmented regions. Experimental results show that the proposed algorithm SAINS has better image segmentation accuracy than GFCNIS and LDCS algorithm.
Does One Keyboard Fit All? Comparison and Evaluation of Device-Free Augmented Reality Keyboard Designs

Virtual keyboard designs are widely discussed with the increasing prevalence of head-mounted and lightweight Mixed Reality devices. However, isolated design suggestions with distinct implementations may lack comparability in terms of performance, learnability, and user preference. We compare three promising device-free text-entry solutions for Augmented Reality (AR) on the Microsoft HoloLens 2. The virtual keyboards comprise dwell-based eye-gaze input, eye-gaze with pinch-gesture-commit input, and mid-air tap typing on virtual QWERTY-keyboards. We conducted a controlled within-subjects lab experiment with 27 subjects measuring typing performance, task load, usability, and preference across the three keyboards. Users state distinct preferences for the respective keyboards and weight the advantages and disadvantages differently. Considering diverse usage scenarios, subjects would even prefer these input modes over speech or physical keyboard input. The results indicate that virtual keyboard design shall be tailored to individual user preferences. Therefore, this study provides essential insights into designing AR keyboards for heterogeneous user groups.
Graph Convolutional Network with Learnable Message Propagation Mechanism

In recent years, graph neural network has become the main paradigm for solving graph analysis tasks, which can easily process high-dimensional data and has a powerful fitting capability. Recent works on graph neural networks have successfully transferred the convolution network in computer vision to graph. Graph convolution network (GCN) has become the classical network framework in graph neural networks due to its simple aggregation approach and favorable theoretical support. When the original graph data are constructed, an adjacency matrix is used to represent the topology, where 0 or 1 indicates whether there is a connection between nodes. Moreover, GCN aggregates node attributes only depends on adjacency matrix. Although it can learn a mapping function, its message propagation mechanism is fixed for a given adjacency matrix. However, for specific downstream tasks, we expect to propagate messages relevant to the downstream task, while a fixed aggregation mode cannot handle this. To this end, we propose a graph convolution neural network with a learnable message propagation mechanism. The original adjacency matrix is adjusted through a learnable weight, so that the message propagation mechanism better adapts to downstream tasks. Experimental results show that the proposed model achieves significant performance in node classification.
Efficient Storage and Analysis of Videos through Motion-Based Frame Removal

Abstract The storage and analysis of CCTV footage pose a significant challenge in modern surveillance systems. This paper presents an innovative and modern solution for efficient storage optimization and analysis through motion-based frame removal techniques. By leveraging the OpenCV library redundant frames are removed, resulting in substantial storage reduction without losing important motion events. The methodology uses Python’s OpenCV library with optional technologies such as Django, SQLite, and Bootstrap, ensuring reliability, scalability, usability, and effective data visualisation. Experimental results have demonstrated the efficacy across different camera setups in different environments and their ability to preserve critical information. The approach offers a practical solution for decreasing cost, improving analysis efficiency, and enhancing usability. This research contributes to the field of CCTV surveillance by providing a practical and industry-oriented solution. The findings inspire further improvements in storage optimization and pave the way for more intelligent surveillance systems.