Reviewerly/exHarmony huggingface dataset:
Colors of entangled two-photon absorption

Multiphoton absorption of entangled photons offers ways for obtaining unique information about chemical and biological processes. Measurements with entangled photons may enable sensing biological signatures with high selectivity and at very low light levels to protect against photodamage. In this paper, we present a theoretical and experimental study of the excitation wavelength dependence of the entangled two-photon absorption (ETPA) process in a molecular system, which provides insights into how entanglement affects molecular spectra. We demonstrate that the ETPA excitation spectrum can be different from that of classical TPA as well as that for one-photon resonant absorption (OPA) with photons of doubled frequency. These results are modeled by assuming the ETPA cross-section is governed by a two-photon excited state radiative linewidth rather than by electron-phonon interactions, and this leads to excitation spectra that match the observed results. Further, we find that the two-photon-allowed states with highest TPA and ETPA intensities have high electronic entanglements, with ETPA especially favoring states with the longest radiative lifetimes. These results provide concepts for the development of quantum light-based spectroscopy and microscopy that will lead to much higher efficiency of ETPA sensors and low-intensity detection schemes.
The Evaluation of Research-based Learning on ‘Moodle’ Learning Management System Using CIPP Models

The purpose of this study was to evaluate the quality of research-based learning conducted remotely through the use of the Moodle learning management system using the Context, Input, Process, and Product (CIPP) evaluation model.Data sources include lecturers, students, faculty documents, study program materials, and Moodle learning management systems (LMS) used in lectures.Data were gathered using the CIPP evaluation checklist ( 2002), semi-structured interviews, document analysis, and observation.The data were analyzed using content analysis.The findings indicate that learning occurs entirely within the Moodle LMS, which includes lecture tools, is guided by lecturers with subject-specific expertise, is collaborative within the lecture system, and is conducted using appropriate and effective virtual synchronous and self-directed asynchronous learning approaches.Additionally, employing various approaches and learning strategies has a major influence on students' abilities, attitudes, behaviors, and values when it comes to developing educational research and writing a quantitative research proposal.However, because proposal assignments are often assigned towards the program's ending, the time necessary to complete them is typically quick.This looks to be an omission of certain students' abilities.Slow learners will fall behind on these assignments.
FIND: Privacy-Enhanced Federated Learning for Intelligent Fake News Detection

The development and popularity of social networks have made information dissemination unprecedentedly convenient and speedy. However, the spread of fake news can often cause serious harm to society and individuals. Therefore, machine learning-based fake news detection methods have become increasingly important. The existing work often needs to collect sufficient user-side data for training, which also boosts the privacy leakage risk to the users. Therefore, this article proposes an intelligent fake news detection system based on federated learning (FL) called FIND, which can train a global model while keeping user data locally. At the same time, we also designed a sparsified update perturbation method to enhance the system security further. Finally, we conduct simulation experiments to study and discuss multiple acoustic factors and prove the feasibility of our system in terms of accuracy, security, and efficiency.
Enhanced Semantic Similarity Learning Framework for Image-Text Matching

Image-text matching is a fundamental task to bridge vision and language. The critical challenge lies in accurately learning the semantic similarity between these two heterogeneous modalities. For visual and textual features, existing methods typically default to a static dimensional correspondence mechanism, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., using a single dimension as the measure-unit to perform one-to-one correspondence, to examine semantic similarity, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., the cosine/Euclidean distance or the weighted similarity. In this paper, different from the single-dimensional correspondence with limited semantic expressive capability, we propose a novel enhanced semantic similarity learning (ESL), which generalizes both measure-units and their correspondences into a dynamic learnable framework to examine the multi-dimensional enhanced correspondence between visual and textual features. Specifically, we first devise the intra-modal multi-dimensional aggregators with iterative enhancing mechanism, which dynamically captures new measure-units integrated by hierarchical multi-dimensions, producing diverse semantic combinatorial expressive capabilities to provide richer and discriminative information for similarity examination. Then, we devise the inter-modal enhanced correspondence learning with sparse contribution degrees, which comprehensively and efficiently determines the cross-modal semantic similarity. Extensive experiments verify its superiority in achieving state-of-the-art performance. Codes will be released.
High-Resolution 3D MRI With Deep Generative Networks via Novel Slice-Profile Transformation Super-Resolution

High-resolution magnetic resonance imaging (MRI) sequences, such as 3D turbo or fast spin-echo (TSE/FSE) imaging, are clinically desirable but suffer from long scanning time-related blurring when reformatted into preferred orientations. Instead, multi-slice two-dimensional (2D) TSE imaging is commonly used because of its high in-plane resolution but is limited clinically by poor through-plane resolution due to elongated voxels and the inability to generate multi-planar reformations due to staircase artifacts. Therefore, multiple 2D TSE scans are acquired in various orthogonal imaging planes, increasing the overall MRI scan time. In this study, we propose a novel slice-profile transformation super-resolution (SPTSR) framework with deep generative learning for through-plane super-resolution (SR) of multi-slice 2D TSE imaging. The deep generative networks were trained by synthesized low-resolution training input via slice-profile downsampling (SP-DS), and the trained networks inferred on the slice profile convolved (SP-conv) testing input for 5.5x through-plane SR. The network output was further slice-profile deconvolved (SP-deconv) to achieve an isotropic resolution of (0.625mm) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . Compared to a state-of-the-art SR method and the networks trained by conventional downsampling, our SPTSR framework demonstrated excellent overall image quality with excellent sharpness, minimal artifacts, and low noise level, evaluated by two abdominal radiologists. The quantitative analysis cross-validated the expert reader study results. 3D simulation experiments confirmed the quantitative improvement of the proposed SPTSR and the effectiveness of the SP-deconv step, compared to 3D ground-truths. Ablation studies were conducted on the individual contributions of SP-DS and SP-conv, networks structure, training dataset size, and different slice profiles.
Affordance Diffusion: Synthesizing Hand-Object Interactions

Recent successes in image synthesis are powered by large-scale diffusion models. However, most methods are currently limited to either text- or image-conditioned generation for synthesizing an entire image, texture transfer or inserting objects into a user-specified region. In contrast, in this work we focus on synthesizing complex interactions (i.e., an articulated hand) with a given object. Given an RGB image of an object, we aim to hallucinate plausible images of a human hand interacting with it. We propose a two-step generative approach: a LayoutNet that samples an articulation-agnostic hand-object-interaction layout, and a ContentNet that synthesizes images of a hand grasping the object given the predicted layout. Both are built on top of a large-scale pretrained diffusion model to make use of its latent representation. Compared to baselines, the proposed method is shown to generalize better to novel objects and perform surprisingly well on out-of-distribution in-the-wild scenes of portable-sized objects. The resulting system allows us to predict descriptive affordance information, such as hand articulation and approaching orientation.
Decompose More and Aggregate Better: Two Closer Looks at Frequency Representation Learning for Human Motion Prediction

Encouraged by the effectiveness of encoding temporal dynamics within the frequency domain, recent human motion prediction systems prefer to first convert the motion representation from the original pose space into the frequency space. In this paper, we introduce two closer looks at effective frequency representation learning for robust motion prediction and summarize them as: decompose more and aggregate better. Motivated by these two insights, we develop two powerful units that factorize the frequency representation learning task with a novel decomposition-aggregation two-stage strategy: (1) frequency decomposition unit unweaves multi-view frequency representations from an input body motion by embedding its frequency features into multiple spaces; (2) feature aggregation unit deploys a series of intra-space and inter-space feature aggregation layers to collect comprehensive frequency representations from these spaces for robust human motion prediction. As evaluated on large-scale datasets, we develop a strong baseline model for the human motion prediction task that outperforms state-of-the-art methods by large margins: 8%∼12% on Human3.6M, 3%∼7% on CMU MoCap, and 7%∼10% on 3DPW.
RIATIG: Reliable and Imperceptible Adversarial Text-to-Image Generation with Natural Prompts

The field of text-to-image generation has made remarkable strides in creating high-fidelity and photorealistic images. As this technology gains popularity, there is a growing concern about its potential security risks. However, there has been limited exploration into the robustness of these models from an adversarial perspective. Existing research has primarily focused on untargeted settings, and lacks holistic consideration for reliability (attack success rate) and stealthiness (imperceptibility). In this paper, we propose RIATIG, a reliable and imperceptible adversarial attack against text-to-image models via inconspicuous examples. By formulating the example crafting as an optimization process and solving it using a genetic-based method, our proposed attack can generate imperceptible prompts for text-to-image generation models in a reliable way. Evaluation of six popular text-to-image generation models demonstrates the efficiency and stealthiness of our attack in both white-box and black-box settings. To allow the community to build on top of our findings, we've made the artifacts available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code is available at: https://github.com/WUSTL-CSPL/RIATIG.
Masked and Adaptive Transformer for Exemplar Based Image Translation

We present a novel framework for exemplar based image translation. Recent advanced methods for this task mainly focus on establishing cross-domain semantic correspondence, which sequentially dominates image generation in the manner of local style control. Unfortunately, cross-domain semantic matching is challenging; and matching errors ultimately degrade the quality of generated images. To overcome this challenge, we improve the accuracy of matching on the one hand, and diminish the role of matching in image generation on the other hand. To achieve the former, we propose a masked and adaptive transformer (MAT) for learning accurate cross-domain correspondence, and executing context-aware feature augmentation. To achieve the latter, we use source features of the input and global style codes of the exemplar, as sup-plementary information, for decoding an image. Besides, we devise a novel contrastive style learning method, for acquire quality-discriminative style representations, which in turn benefit high-quality image generation. Experimen-tal results show that our method, dubbed MATEBIT, performs considerably better than state-of-the-art methods, in diverse image translation tasks. The codes are available at https://github.com/AiArt-HDU/MATEBIT.
MMExit: Enabling Fast and Efficient Multi-modal DNN Inference with Adaptive Network Exits

Multi-modal DNNs have been demonstrated to outperform the best uni-modal DNNs by fusing information from different modalities. However, the performance improvement of multi-modal DNNs is always associated with an incredible increase in computational cost (e.g., network parameters, MAC operations, etc.) to handle more modalities, which ultimately makes them impractical for many real-world applications where computing capability is limited. In this paper, we propose MMExit, a multi-modal exit architecture that allows for computing appropriate modalities and layers to predict results for different data samples. To this end, we define a novel metric called utility of exit (UoE) to measure the correlations of performance and computational cost for different exits. We then use an equivalent modality serialization method to map the two-dimensional exit space into an equivalent linear space and rank the exits according to their UoE to achieve fast and adaptive inference. To train the MMExit network, we devise a joint loss function which synthesizes the features of different modalities and layers. Our results show that MMExit can slash up to 48.72% of MAC operations with the best performance compared to SOTA multi-modal architectures.
MwdpNet: towards improving the recognition accuracy of tiny targets in high-resolution remote sensing image

Abstract This study aims to develop a deep learning model to improve the accuracy of identifying tiny targets on high resolution remote sensing (HRS) images. We propose a novel multi-level weighted depth perception network, which we refer to as MwdpNet, to better capture feature information of tiny targets in HRS images. In our method, we introduce a new group residual structure, S-Darknet53, as the backbone network of our proposed MwdpNet, and propose a multi-level feature weighted fusion strategy that fully utilizes shallow feature information to improve detection performance, particularly for tiny targets. To fully describe the high-level semantic information of the image, achieving better classification performance, we design a depth perception module (DPModule). Following this step, the channel attention guidance module (CAGM) is proposed to obtain attention feature maps for each scale, enhancing the recall rate of tiny targets and generating candidate regions more efficiently. Finally, we create four datasets of tiny targets and conduct comparative experiments on them. The results demonstrate that the mean Average Precision (mAP) of our proposed MwdpNet on the four datasets achieve 87.0%, 89.2%, 78.3%, and 76.0%, respectively, outperforming nine mainstream object detection algorithms. Our proposed approach provides an effective means and strategy for detecting tiny targets on HRS images.
Zero-shot visual reasoning through probabilistic analogical mapping

Abstract Human reasoning is grounded in an ability to identify highly abstract commonalities governing superficially dissimilar visual inputs. Recent efforts to develop algorithms with this capacity have largely focused on approaches that require extensive direct training on visual reasoning tasks, and yield limited generalization to problems with novel content. In contrast, a long tradition of research in cognitive science has focused on elucidating the computational principles underlying human analogical reasoning; however, this work has generally relied on manually constructed representations. Here we present visiPAM (visual Probabilistic Analogical Mapping), a model of visual reasoning that synthesizes these two approaches. VisiPAM employs learned representations derived directly from naturalistic visual inputs, coupled with a similarity-based mapping operation derived from cognitive theories of human reasoning. We show that without any direct training, visiPAM outperforms a state-of-the-art deep learning model on an analogical mapping task. In addition, visiPAM closely matches the pattern of human performance on a novel task involving mapping of 3D objects across disparate categories.
Radial Basis Function Neural Network with Wavelet Transform for Power System Fault Identification and Classification

The three-phase fault detection and classification can be done by using radial basis function neural network with wavelet transform can be proposed in this paper.The Discrete Wavelet Transform (DWT) is used in implementation of wavelet approach and the radial basis function neural network (RBFNN) will be utilized to detect and classify different types of faults.This paper mainly focuses on identifying the faults and classification by obtaining detailed co-officiants of different types of faults.The RBFNN is used to overcome the drawback of wavelet transform for detecting and classification of faults.In this paper the concept of wavelet transforms and RBFNN with power system network is verified with MATLAB Simulink.The simulation results are obtained from wavelet transform technique and radial basis function neural network (RBFNN) for three phase fault detection and classification.
Credit Card Fraud Detection Using Machine Learning

Due to the pandemic in this time economic scenario increased, credit cards or debit cards use has become extremely commonplace due to online payments.Credit cards enable individuals to make large payments without having to carry huge amounts of money.The number of users is increasing, so credit card fraud also increased as well.The credentials on a credit card can be obtained fraudulently and used to defraud.So we're going to use Machine Learning Algorithms to collect data and overcome this issue.This project compares supervised algorithms like Logistic regression, Support vector machine, KNN, Decision Tree, Xgboost, etc., and finds the best model through hyper parameter tuning, Grid search and applies resampling techniques.
Reinforcement Learning for Selecting Custom Instructions under Area Constraint

Extensible processors, which combine programma-bility and efficiency, are emerging as a promising approach in the field of embedded computing. Automated synthesis of custom instructions from high-level application descriptions is a vital step involved in the design of extensible processors. In automated custom instruction synthesis, selecting custom instructions from a large set of candidates under area constraint is essentially a difficult combinatorial optimization problem. In this paper, we show that the custom instruction selection problem can be formulated as a sequential decision-making problem. Based on this formulation, we present three reinforcement learning-based approaches, namely SARSA, Q-learning, and Double Q-Learning, for solving the custom instruction selection problem. Moreover, we also perform a comprehensive analysis and com-parison of various combinations of learning specifications: the algorithm type and the update strategy for ε-greedy policy. The experiments with 45 test instances reveal that the SARSA, Q-learning, and Double Q-Learning algorithms outperform the meta-heuristic algorithm in terms of the overall performance gains by 26.9%,26.1% and 26.4% respectively. Among the three reinforcement learning algorithms, the SARSA algorithm slightly overwhelms the other two reinforcement learning algorithms. Furthermore, the experimental results suggest that the strategy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$F_3 : ε = κ^i,0 &lt; κ &lt; 1$</tex-math></inline-formula> is generally the most effective one for controlling the exploration and the exploitation of the learning processes.
Enhanced Global Path Planning Efficiency by Bidirectional A*, Gradient Descent, and Orientation Interpolation Algorithms

This paper presents an innovative approach to global path planning (GPP) that uniquely integrates an enhanced Bidirectional A* search algorithm, gradient descent smoothing, and orientation filter interpolation.These methods are applied within a quadratic approximation-based potential field and global cost map.The key idea is successfully integrating these powerful techniques, ensuring minimal impact on computational time while enhancing path size and smoothness.Rigorous testing in environments like the Amazon web services (AWS) RoboMaker Hospital Gazebo environment and the sections at the control and systems engineering building at Iraq's University of Technology (CSE-UOT) in Iraq shows significant improvement over existing planners like wavefront GPP and robot operating system (ROS) default GPP.The proposed algorithm reduced pathfinding time by approximately 60.3% and path size by 9.0%.These findings highlight the efficiency of the proposed global planner, suggesting it as a robust solution for autonomous robotic navigation in complex architectural scenarios.
On the quantum simulation of complex networks

Quantum walks provide a natural framework to approach graph problems with quantum computers, exhibiting speedups over their classical counterparts for tasks such as the search for marked nodes or the prediction of missing links. Continuous-time quantum walk algorithms assume that we can simulate the dynamics of quantum systems where the Hamiltonian is given by the adjacency matrix of the graph. It is known that such can be simulated efficiently if the underlying graph is row-sparse and efficiently row-computable. While this is sufficient for many applications, it limits the applicability for this class of algorithms to study real world complex networks, which, among other properties, are characterized by the existence of a few densely connected nodes, called hubs. In other words, complex networks are typically not row-sparse, even though the average connectivity over all nodes can be very small. In this work, we extend the state-of-the-art results on quantum simulation to graphs that contain a small number of hubs, but that are otherwise sparse. Hopefully, our results may lead to new applications of quantum computing to network science.