Reviewerly/exHarmony huggingface dataset:
Hilbert’s and Takeda’s single-shot interferometry with a linear-carrier: a comparison

Abstract The purpose of the present work is to compare single-shot phase-retrieval methods based on Hilbert’s transform with Takeda’s spatial linear-carrier method. In addition, we briefly discuss the so-called slightly-off-axis method that involves two interferograms as an efficient way to remove background illumination bias. After carefully analyzing the assumptions implicit in both approaches, we demonstrate that the Hilbert transform based method is completely identical to the 30 years old Takeda’s interferometry with a spatial linear-carrier. Validation experiments are presented.
Correlation-aware Graph Data Augmentation with Implicit and Explicit Neighbors

In recent years, there has been a significant surge in commercial demand for citation graph-based tasks, such as patent analysis, social network analysis, and recommendation systems. Graph Neural Networks (GNNs) are widely used for these tasks due to their remarkable performance in capturing topological graph information. However, GNNs’ output results are highly dependent on the composition of local neighbors within the topological structure. To address this issue, we identify two types of neighbors in a citation graph: explicit neighbors based on the topological structure and implicit neighbors based on node features. Our primary motivation is to clearly define and visualize these neighbors, emphasizing their importance in enhancing graph neural network performance. We propose a Correlation-aware Network (CNet) to re-organize the citation graph and learn more valuable informative representations by leveraging these implicit and explicit neighbors. Our approach aims to improve graph data augmentation and classification performance, with the majority of our focus on stating the importance of using these neighbors, while also introducing a new graph data augmentation method. We compare CNet with state-of-the-art (SOTA) GNNs and other graph data augmentation approaches acting on GNNs. Extensive experiments demonstrate that CNet effectively extracts more valuable informative representations from the citation graph, significantly outperforming baselines. The code is available on public GitHub. 1
Attacking Click-through Rate Predictors via Generating Realistic Fake Samples

How to construct imperceptible (realistic) fake samples is critical in adversarial attacks. Due to the sample feature diversity of a recommender system (containing both discrete and continuous features), traditional gradient-based adversarial attack methods may fail to construct realistic fake samples. Meanwhile, most recommendation models adopt click-through rate (CTR) predictors, which usually utilize black-box deep models with discrete features as input. Thus, how to efficiently construct realistic fake samples for black-box recommender systems is still full of challenges. In this article, we propose a hierarchical adversarial attack method against black-box CTR models via generating realistic fake samples, named CTRAttack. To better train the generation network, the weights of its embedding layer are shared with those of the substitute model, with both the similarity loss and classification loss used to update the generation network. To ensure that the discrete features of the generated fake samples are all real, we first adopt the similarity loss to ensure that the distribution of the generated perturbed samples is sufficiently close to the distribution of the real features, and then the nearest neighbor algorithm is used to retrieve the most appropriate features for non-existent discrete features from the candidate instance set. Extensive experiments demonstrate that CTRAttack can not only effectively attack the black-box recommender systems but also improve the robustness of these models while maintaining prediction accuracy.
Dynamic Attentive Convolution for Facial Beauty Prediction

Facial Beauty Prediction (FBP) is a significant pattern recognition task that aims to achieve consistent facial attractiveness assessment with human perception. Currently, Convolutional Neural Networks (CNNs) have become the mainstream method for FBP. The training objective of most conventional CNNs is usually to learn static convolution kernels, which, however, makes the network quite difficult to capture global attentive information, and thus usually ignores the key facial regions, e.g., eyes, and nose. To tackle this problem, we devise a new convolution manner, Dynamic Attentive Convolution (DyAttenConv), which integrates the dynamic and attention mechanism into convolution in kernel-level, with the aim of enforcing the convolution kernels adapted to each face dynamically. DyAttenConv is a plug-and-play module that can be flexibly combined with existing CNN architectures, making the acquisition of the beauty-related features more globally and attentively. Extensive ablation studies show that our method is superior to other fusion and attention mechanisms, and the comparison with other state-of-the-arts also demonstrates the effectiveness of DyAttenConv on facial beauty prediction task.
Content-Adaptive Optimization Framework for Universal Deep Image Compression

While deep image compression performs better than traditional codecs like JPEG on natural images, it faces a challenge as a learning-based approach: compression performance drastically decreases for out-of-domain images. To investigate this problem, we introduce a novel task that we call universal deep image compression, which involves compressing images in arbitrary domains, such as natural images, line drawings, and comics. Furthermore, we propose a content-adaptive optimization framework to tackle this task. This framework adapts a pre-trained compression model to each target image during testing for addressing the domain gap between pre-training and testing. For each input image, we insert adapters into the decoder of the model and optimize the latent representation extracted by the encoder and the adapter parameters in terms of rate-distortion, with the adapter parameters transmitted per image. To achieve the evaluation of the proposed universal deep compression, we constructed a benchmark dataset containing uncompressed images of four domains: natural images, line drawings, comics, and vector arts. We compare our proposed method with non-adaptive and existing adaptive compression methods, and the results show that our method outperforms them. Our code and dataset are publicly available at https://github.com/kktsubota/universal-dic.
Rotation-Invariant Convolution Networks with Hexagon-Based Kernels

We introduce the Hexagonal Convolutional Neural Network (HCNN), a modified version of CNN that is robust against rotation. HCNN utilizes a hexagonal kernel and a multi-block structure that enjoys more degrees of rotation information sharing than standard convolution layers. Our structure is easy to use and does not affect the original tissue structure of the network. We achieve the complete rotational invariance on the recognition task of simple pattern images and demonstrate better performance on the recognition task of the rotated MNIST images, synthetic biomarker images and microscopic cell images than past methods, where the robustness to rotation matters.
High-Accuracy People Counting in Large Spaces Using Overhead Fisheye Cameras

Quantifying the number of people in various spaces of a commercial building is important for saving energy, optimizing space usage, and assisting with public safety. To accomplish these goals requires obtaining accurate, fine-grained counts in real time. However, existing methodologies are ineffective for covering large areas with high occupancy. We propose an occupancy-sensing system that uses multiple overhead fisheye cameras and state-of-the-art deep-learning algorithms to cover large spaces with high counting accuracy. Tested in a 2,000 ft2 space, our system shows 54% to 83% reduction in commonly-used error metrics compared to recent people-counting methods proposed for large-space scenarios. Our system is scalable to arbitrarily-large spaces; additional cameras can be integrated with minimal commissioning. We also introduce two new performance metrics for assessing counting accuracy that, unlike common metrics used to date, are independent of occupancy level and can be easily compared across different occupancy scenarios.
A novel approach for detecting deep fake videos using graph neural network

Abstract Deep fake technology has emerged as a double-edged sword in the digital world. While it holds potential for legitimate uses, it can also be exploited to manipulate video content, causing severe social and security concerns. The research gap lies in the fact that traditional deep fake detection methods, such as visual quality analysis or inconsistency detection, need help to keep up with the rapidly advancing technology used to create deep fakes. That means there's a need for more sophisticated detection techniques. This paper introduces an enhanced approach for detecting deep fake videos using graph neural network (GNN). The proposed method splits the detection process into two phases: a mini-batch graph convolution network stream four-block CNN stream comprising Convolution, Batch Normalization, and Activation function. The final step is a flattening operation, which is essential for connecting the convolutional layers to the dense layer. The fusion of these two phases is performed using three different fusion networks: FuNet-A (additive fusion), FuNet-M (element-wise multiplicative fusion), and FuNet-C (concatenation fusion). The paper further evaluates the proposed model on different datasets, where it achieved an impressive training and validation accuracy of 99.3% after 30 epochs.
What Happens Next? Resonance—Answer

Abstract I demonstrate in the answer some of the resonances that occur when the strings of the piano are free to vibrate.
Stabilizing Error Correction Mechanism in the Presence of Explosiveness

In the presence of explosiveness of the adjustment term in the error correction model, the adjustment of the dependent variable Y was too large and overshoots the equilibrium, creating a divergent pattern. The error correction model fails to capture the deviation from equilibrium appropriately, thereby resulting in overshooting of the model. In this paper, a new model to stabilize the explosiveness in an Error Correction model called the stabilizing Error Correction Mechanism was proposed. Mathematical methodology for obtaining the estimate of the model using the Ordinal Least Square method was derived. Error Correction model was used to model the relationship among the variables and the result was compared with the Stabilizing Error Correction Mechanism using root mean square error. A Monte-Carlo simulation was performed, and the stimulation results showed that the error correction model exhibited some explosiveness, and the damping coefficient of the stabilizing model exerted a stabilizing effect on the error correction mechanism, thereby reducing the overshooting in the error correction model. The proposed model contributed to a smoother and more stable response to deviations from the long-run equilibrium. The root mean square error of the stabilizing Error Correction model was observed to be 1.30663, 1.04533, 12.55786, 10.49876, 10.0034, and 19.41545 as compared to the adjustment model in the Error Correction model (60.6888, 35.5929, 315238, 24.31958, 10.1485 and 19.7687) when the persistence is high and . Therefore, the Stabilizing Error Correction model performs better than the Error Correction model.
Accurate Fourier-space statistics for line intensity mapping: Cartesian grid sampling without aliased power

Abstract Estimators for n-point clustering statistics in Fourier-space demand that modern surveys of large-scale structure be transformed to Cartesian coordinates to perform Fast Fourier Transforms (FFTs). In this work, we explore this transformation in the context of pixelised line intensity maps (LIM), highlighting potential biasing effects on power spectrum measurements. Current analyses often avoid a complete resampling of the data by approximating survey geometry as rectangular in Cartesian space, an increasingly inaccurate assumption for modern wide-sky surveys. Our simulations of a 20 × 20 deg2 21cm LIM survey at 0.34 &amp;lt; z &amp;lt; 0.54 show this assumption biases power spectrum measurements by ${&amp;gt;}\, 20~{{\%}}$ across all scales. We therefore present a more robust framework for regridding the voxel intensities onto a 3D FFT field by coordinate transforming large numbers of Monte-Carlo sampling particles. Whilst this unbiases power spectrum measurements on large scales, smaller-scale discrepancies remain, caused by structure smoothing and aliasing from separations unresolved by the grid. To correct these effects, we introduce modelling techniques, higher-order particle assignments, and interlaced FFT grids to suppress the aliased power. Using a Piecewise Cubic Spline (PCS) particle assignment and an interlaced FFT field, we achieve sub-percent accuracy up to 80% of the Nyquist frequency for our 21cm LIM simulations. We find a more subtle hierarchical improvement in results for higher-order assignment schemes, relative to the gains made for galaxy surveys, which we attribute to the extra complexity in LIM from additional discretising steps. Python code accompanying this paper is available at github.com/stevecunnington/gridimp.
Constructing new high-order polynomial chaotic maps and application in pseudorandom number generator

Abstract The chaotic map have been widely applied in fields such as pseudorandom number generation(PRNG) and image encryption due to its excellent chaotic performance. In this paper, two classes of high-order polynomial chaotic maps of special form are proposed based on the Li-Yorke theorem. The dynamic behavior of the proposed maps is numerically analyzed, including bifurcation and Lyapunov exponent, and the analysis results prove the validity of the proposed conclusions. The proposed polynomial chaotic maps have a larger parameters and chaotic range, as well as a more stable Lyapunov exponent. Furthermore, based on the coupling chaotic systems, we design a pseudorandom number generator(PRNG), and the number of chaos parameters are expanded through the coupling control parameters in a PRNG. Then the performance of the pseudorandom sequence generated by the PRNG is tested and analysed. The test and analysis results show that the pseudorandom sequence has favorabble security, structural complexity, and randomness. . Especially, the information entropy of 7.9998 and the key space size of 2^ 208 exceed the recently reported pseudorandom number generators(PRNGs). In comparison with other pseudorandom number generators(PRNGs) based on chaotic maps in the recent literature, this paper provides comprehensive performance test and analysis of the proposed PRNG and demonstrates its potential for cryptographic applications.
Hamiltonian Switching Control of Noisy Bipartite Qubit Systems

Abstract We develop a Hamiltonian switching ansatz for bipartite control that is inspired by the Quantum Approximate Optimization Algorithm (QAOA), to mitigate environmental noise on qubits. We demonstrate the control for a central spin coupled to bath spins via isotropic Heisenberg interactions, and then make physical applications to the protection of quantum gates performed on superconducting transmon qubits coupling to environmental two-level-systems (TLSs) through dipole-dipole interactions, as well as on such qubits coupled to both TLSs and a Lindblad bath. The control field is classical and acts only on the system qubits. We use reinforcement learning with policy gradient (PG) to optimize the Hamiltonian switching control protocols, using a fidelity objective for specific target quantum gates. &amp;#xD; We use this approach to demonstrate effective suppression of both coherent and dissipative noise, with numerical studies achieving target gate implementations with fidelities over 0.9999 (four nines) in the majority of our test cases and showing improvement beyond this to values of 0.999999999 (nine nines) upon a subsequent optimization by Gradient Ascent Pulse Engineering (GRAPE). We analyze how the control depth, total evolution time, number of environmental TLS, and choice of optimization method affect the fidelity achieved by the optimal protocols and reveal some critical behaviors of bipartite control of quantum gates.
The Applications based on Video Motion Magnification Techniques

This research study's major goal is to present an important overview of recent work on applications based on Video Motion magnification (VMM) approaches during the course of the last 10 years. Over the past few years, video motion magnification (VMM) technologies have attracted a lot of attention and research, particularly as applications based on video motion have become more and more necessary. With an increase in the number of recommended procedures, surveying and evaluation become necessary. In this study, we will highlight how the survey was focused on several articles that used motion video augmentation techniques in their applications. We contrast these applications as well.
A Method of Applying Virtual Reality Converged Remote Platform Based on Crawfish Optimization Algorithm to Improve ESN Network

INTRODCTION: Immersive teaching and learning methods based on virtual reality-integrated remote platforms not only allow foreign language learners to learn in a vivid and intuitive learning environment, but also provide good conditions for multi-channel perceptual experiences of foreign language learners in terms of sight, sound and touch.OBJECTIVES: To address the problems of insufficiently systematic analysis and quantification, poor robustness and low accuracy of analysis methods in current effect analysis methods.METHODS: This paper proposes an effect analysis method of virtual reality fusion remote platform based on crawfish optimization algorithm to improve echo state network. First, the effect analysis system is constructed by analyzing the process of virtual reality fusion remote platform and extracting the effect analysis influencing elements; then, the echo state network is improved by the crayfish optimization algorithm and the effect analysis model is constructed; finally, the high accuracy of the proposed method is verified by the analysis of simulation experiments.RESLUTS: The proposed method improves the accuracy of the virtual reality fusion remote platform effect analysis model, the analysis time is 0.002s, which meets the real-time requirements, and the number of optimization convergence iterations is 16, which is better than other algorithms.CONCLUSION: The problems of insufficiently systematic analytical quantification of effect analysis methods, poor robustness of analytical methods, and low accuracy have been solved.
The Impact of Rotation Model on Minimizing Inflectional Morphemes Errors in English Writing: A Comparative Study of Error Analysis

The study aims to analyze 12th-grade students’ writing errors related to inflectional morphemes. In addition, the research measures efficacy of the Rotation Model (RM) and the Grammar Translation Method (GTM) in minimizing writing errors among learners of English. Comparative studies are rare to find the effectiveness of two teaching models in the context of error analysis. Eighty-two samples were taken into consideration for randomize sampling. The present research is divided into two groups; the experimental group, consisting of 41 participants, provided with instruction via RM, and the control group, also comprising 41 individuals, being taught through GTM. Pre and post-test treatment data were collected and analyzed with the help of SPSS 22. The results revealed common errors with inflectional morphemes among both groups. Additionally, the mean values for each type of error were significantly minimized due to the post-instruction of RM compared to the GTM. The study’s findings largely devoted to pedagogical improvement among ESL learners’ writing. The findings are reviewed concerning future research directions and instructional strategies.
FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources

Recommendation in settings such as e-recruitment and online dating involves distributing limited opportunities, which differs from recommending practically unlimited goods such as in e-commerce or music recommendation. This setting calls for novel approaches to quantify and enforce fairness. Indeed, typical recommender systems recommend each user their top relevant items, such that desirable items may be recommended simultaneously to more and to less qualified individuals. This is arguably unfair to the latter. Indeed, when they pursue such a desirable recommendation (e.g. by applying for a job), they are unlikely to be successful. To quantify fairness in such settings, we introduce inferiority : a novel (un)fairness measure that quantifies the competitive disadvantage of a user for their recommended items. Inferiority is complementary to envy : a previously-proposed fairness notion that quantifies the extent to which a user prefers other users’ recommendations over their own. We propose to use both inferiority and envy in combination with an accuracy-related measure called utility : the aggregated relevancy scores of the recommended items. Unfortunately, none of these three measures are differentiable, making it hard to optimize them, and restricting their immediate use to evaluation only. To remedy this, we reformulate them in the context of a probabilistic interpretation of recommender systems, resulting in differentiable versions. We show how these loss functions can be combined in a multi-objective optimization problem that we call FEIR (Fairness through Envy and Inferiority Reduction), used as a post-processing of the scores from any standard recommender system. Experiments on synthetic and real-world data show that the proposed approach effectively improves the trade-offs between inferiority, envy and utility, compared to the naive recommendation and the state of the art method for the related problem of congestion alleviation in job recommendation. We discuss and enhance the practical impact of our findings on a wide range of real-world recommendation scenarios, and we offer implementations of visualization tools to render the envy and inferiority metrics more accessible.
HA-DQS-Net: dynamic query design based on transformer with hollow attention

A common problem in the field of object detection is that the image features could not be fully expressed. And another issue is that the static query selection in the detection transformer (DETR)-like models cannot adapt well to different datasets due to the fixed number of selected object queries. To solve these problems, hollow attention (HA) and dynamic query selection (DQS) modules were proposed, and a network HA-DQS-Net was further formed. HA integrates specially designed masks into self-attention to better combine channel and spatial directional feature information, thereby learning more complex and comprehensive target features. DQS improves the idea of static query selection in the current DETR-like model by dynamically selecting the number of object queries based on the actual number of targets in the image, which enhances the accuracy of the model. HA-DQS-Net, which combines the advantages of HA and DQS, has a competitive performance in the field of object detection. The excellent detection effectiveness of our viewpoint is validated based on PASVAL VOC and a homemade smoking dataset. It is worth noting that all APs have been improved when HA is applied to different DETR-like models, which improves the universality of the HA module.
Work In Progress: Implementing Team Projects in Online Courses - Balancing Individual Responsibility and Team Collaboration

In this role, she focuses on
Augmented Reality for Sustainable Collaborative Design

Her scholarship is motivated by the fundamental question of how