Reviewerly/exHarmony huggingface dataset:
On the Working Principle of the Hopfield Neural Networks and its Equivalence to the GADIA in Optimization

Hopfield neural networks (HNNs) are one of the most well-known and widely used kinds of neural networks in optimization. In this article, the author focuses on building a deeper understanding of the working principle of the HNN during an optimization process. Our investigations yield several novel results giving some important insights into the working principle of both continuous and discrete HNNs. This article shows that what the traditional HNN actually does as energy function decreases is to divide the neurons into two classes in such a way that the sum of biased class volumes is minimized (or maximized) regardless of the types of the optimization problems. Introducing neuron-specific class labels, the author concludes that the traditional discrete HNN is actually a special case of the greedy asynchronous distributed interference avoidance algorithm (GADIA) [17] of Babadi and Tarokh for the 2-class optimization problems. The computer results confirm the findings.
Self-Motion-Assisted Tensor Completion Method for Background Initialization in Complex Video Sequences

The background Initialization (BI) problem has attracted the attention of researchers in different image/video processing fields. Recently, a tensor-based technique called spatiotemporal slice-based singular value decomposition (SS-SVD) has been proposed for background initialization. SS-SVD applies the SVD on the tensor slices and estimates the background from low-rank information. Despite its efficiency in background initialization, the performance of SS-SVD requires further improvement in the case of complex sequences with challenges such as stationary foreground objects (SFOs), illumination changes, low frame-rate, and clutter. In this paper, a self-motion-assisted tensor completion method is proposed to overcome the limitations of SS-SVD in complex video sequences and enhance the visual appearance of the initialized background. With the proposed method, the motion information, extracted from the sparse portion of the tensor slices, is incorporated with the low-rank information of SS-SVD to eliminate existing artifacts in the initiated background. Efficient blending schemes between the low-rank (background) and sparse (foreground) information of the tensor slices is developed for scenarios such as SFO removal, lighting variation processing, low frame-rate processing, crowdedness estimation, and best frame selection. The performance of the proposed method on video sequences with complex scenarios is compared with the top-ranked state-of-the-art techniques in the field of background initialization. The results not only validate the improved performance over the majority of the tested challenges but also demonstrate the capability of the proposed method to initialize the background in less computational time.
Cross-Modality Knowledge Transfer for Prostate Segmentation from CT Scans

Creating large scale high-quality annotations is a known challenge in medical imaging. In this work, based on the CycleGAN algorithm, we propose leveraging annotations from one modality to be useful in other modalities. More specifically, the proposed algorithm creates highly realistic synthetic CT images (SynCT) from prostate MR images using unpaired data sets. By using SynCT images (without segmentation labels) and MR images (with segmentation labels available), we have trained a deep segmentation network for precise delineation of prostate from real CT scans. For the generator in our CycleGAN, the cycle consistency term is used to guarantee that SynCT shares the identical manually-drawn, high-quality masks originally delineated on MR images. Further, we introduce a cost function based on structural similarity index (SSIM) to improve the anatomical similarity between real and synthetic images. For segmentation followed by the SynCT generation from CycleGAN, automatic delineation is achieved through a 2.5D Residual U-Net. Quantitative evaluation demonstrates comparable segmentation results between our SynCT and radiologist drawn masks for real CT images, solving an important problem in medical image segmentation field when ground truth annotations are not available for the modality of interest.
Generating NOON states in circuit QED using a multiphoton resonance in the presence of counter-rotating interactions

The NOON states are valuable quantum resources, which have a wide range of applications in quantum communication, quantum metrology, and quantum information processing. Here we propose a fast, concise, and reliable protocol for deterministically generating the NOON states of two resonators coupled to a single $▵$-type superconducting qutrit. In particular, we derive the effective Hamiltonians at the points of the multiphoton resonances by virtue of the strong counter-rotating interaction between the resonator modes and the qutrit. Based on these crucial effective Hamiltonians, our protocol simplifies the previous ones using the single-photon resonance and consequently reduces the number of operations for state preparation. To test the robustness of this protocol, we analyze the effects from both the decoherence including dissipation and dephasing and the crosstalk of resonator modes on the state fidelity through a Lindblad master equation in the eigenbases of the full Hamiltonian.
Melakukan Penelitian Tindakan Kelas

The paper explores the classroom action research as one of research methodologies. This kind of research methodology aims to solve learning and teaching problems and improve the quality of education. There are four models of classroom action research, namely Kurt Lewin model, Stephen Kemmis and Robyn McTaggart model, Margaret Riel model, and Robert P. Pelton model. To make the models easy to apply, the authors show an example of classroom action research framework.
A Survey on Face Recognition Using Convolutional Neural Network

Face recognition is an important concept, which has generally considered in the course of recent decades. Generally image location can be considered as an extraordinary sort of item recognition in PC vision. In this paper, we explore one of the vital and very effective systems for conventional item discovery using convolutional neural network (CNN) method, that is, a gentle classifier development for resolving the substance identification problem. That in recognizing the face of images as the problem is very difficult one, and so far no quality results are been obtained. Usually, this problem splits into distinctive sub-issues, to make it simpler to work predominantly identification of face of a picture pursued by the face acknowledgment itself. There are several tasks to perform in between such as partial image face detection or extracting more features from them. Many years there are numerous calculations and systems have been utilized such as eigenfaces or active shape model, principal component analysis (PCA), K-nearest neighbour (KNN), and local binary pattern histograms (LBPH), but accurate results have not been identified. However because of the drawbacks of previously mentioned techniques in my study, I want to use CNN in deep learning to obtain best results.
Examining teachers’ technological pedagogical and content knowledge in the era of cloud pedagogy

With the ongoing innovation of instructional technologies there has been an emerging call to examine what types of knowledge teachers require to survive in the era of cloud pedagogy. In response to this call we proposed a research model – TLPACK – which is based on technological pedagogical content knowledge (TPACK), information communication technologies - technological pedagogical content knowledge (ICT-TPCK), and education technology, pedagogy and didactics, academic subject-matter discipline, educational psychology and educational sociology knowledge (TPACK-XL), to explore the types of knowledge that teachers at various levels – from kindergarten to post-secondary level – should equip themselves with in detail. TLPACK consists of five constructs (technology knowledge, learner knowledge, pedagogy knowledge, academic discipline, content knowledge, and context knowledge) but in total the TLPACK scale comprises 39 items. All items were converged based on the viewpoints of five experts from academia and practice following six rounds of the Delphi method, and the finalised version was prepared for reliability and validity examination. Proportional stratified sampling was adopted to conduct a questionnaire survey among teachers from kindergarten to post-secondary levels in Taiwan (n = 301). Rigorous statistical analyses were undertaken to examine the reliability and validity of this new model. Based on the results of statistical analyses, including item analysis, exploratory factor analysis, and confirmatory factor analysis, it is reasonable to state that the proposed TLPACK scale has good reliability and validity for practical use. The conclusion and limitations of this study were drawn based on the extracted results, and suggestions for future study are reported at the end of this report.Keywords: Delphi method; hospitality education; ICT-TPCK; TLPACK; TPACK; TPACK-XL
Application of Improved Astar Algorithm in Global Path Planning of Unmanned Vehicles

Path planning is a key part of ensuring that smart vehicles are safe and fast to reach their destination. Currently, global path planning mainly uses Astar algorithm for search planning. Astar algorithm is a heuristic search algorithm, which has been widely used in path planning, but it has many disadvantages, such as: low search efficiency, poor real-time performance, and there are no constraints in search. In view of the shortcomings of Astar algorithm, this paper presents Bidirectional search-Binary tree Astar algorithm(BB-Astar), BB-Astar algorithm has following improvements: First, binary tree data structure is added to the open table of Astar algorithm to optimize the efficiency of the algorithm; Second, use bidirectional search strategy to further improve the efficiency of the algorithm; Thirdly, two kinds of constraint conditions, which must pass through a certain node and a certain road section, are set in the algorithm to make the algorithm more practical. Simulation results show that the search efficiency of the BB-Astar algorithm is better than classic Astar algorithm, and the search with constraints can be completed.
Quantum bandits

We consider the quantum version of the bandit problem known as {\em best arm identification} (BAI). We first propose a quantum modeling of the BAI problem, which assumes that both the learning agent and the environment are quantum; we then propose an algorithm based on quantum amplitude amplification to solve BAI. We formally analyze the behavior of the algorithm on all instances of the problem and we show, in particular, that it is able to get the optimal solution quadratically faster than what is known to hold in the classical case.
Multi-path x-D recurrent neural networks for collaborative image classification

With the rapid development of image acquisition and storage, multiple images per class are commonly available for computer vision tasks (e.g., face recognition, object detection, medical imaging, etc.). Recently, the recurrent neural network (RNN) has been widely integrated with convolutional neural networks (CNN) to perform image classification on ordered (sequential) data. In this paper, by permutating multiple images as multiple dummy orders, we generalize the ordered “RNN+CNN” design (longitudinal) to a novel unordered fashion, called Multi-path x-D Recurrent Neural Network (MxDRNN) for image classification. To the best of our knowledge, few (if any) existing studies have deployed the RNN framework to unordered intra-class images to leverage classification performance. Specifically, multiple learning paths are introduced in the MxDRNN to extract discriminative features by permutating input dummy orders. Eight datasets from five different fields (MNIST, 3D-MNIST, CIFAR, VGGFace2, and lung screening computed tomography) are included to evaluate the performance of our method. The proposed MxDRNN improves the baseline performance by a large margin across the different application fields (e.g., accuracy from 46.40% to 76.54% in VGGFace2 test pose set, AUC from 0.7418 to 0.8162 in NLST lung dataset). Additionally, empirical experiments show the MxDRNN is more robust to category-irrelevant attributes (e.g., expression, pose in face images), which may introduce difficulties for image classification and algorithm generalizability. The code is publicly available.
A greedy stacking algorithm for model ensembling and domain weighting

Abstract Objective Because it is impossible to know which statistical learning algorithm performs best on a prediction task, it is common to use stacking methods to ensemble individual learners into a more powerful single learner. Stacking algorithms are usually based on linear models, which may run into problems, especially when predictions are highly correlated. In this study, we develop a greedy algorithm for model stacking that overcomes this issue while still being very fast and easy to interpret. We evaluate our greedy algorithm on 7 different data sets from various biomedical disciplines and compare it to linear stacking, genetic algorithm stacking and a brute force approach in different prediction settings. We further apply this algorithm on a task to optimize the weighting of the single domains (e.g., income, education) that build the German Index of Multiple Deprivation (GIMD) to be highly correlated with mortality. Results The greedy stacking algorithm provides good ensemble weights and outperforms the linear stacker in many tasks. Still, the brute force approach is slightly superior, but is computationally expensive. The greedy weighting algorithm has a variety of possible applications and is fast and efficient. A python implementation is provided.
An Extended Exposure Fusion and its Application to Single Image Contrast Enhancement

Exposure Fusion is a high dynamic range imaging technique fusing a bracketed exposure sequence into a high quality image. In this paper, we provide a refined version resolving its out-of-range artifact and its low-frequency halo. It improves on the original Exposure Fusion by augmenting contrast in all image parts. Furthermore, we extend this algorithm to single exposure images, thereby turning it into a competitive contrast enhancement operator. To do so, bracketed images are first simulated from a single input image and then fused by the new version of Exposure Fusion. The resulting algorithm competes with state of the art image enhancement methods.
Synthetic gauge field and chiral physics on two-leg superconducting circuits

The gauge field is essential for exploring novel phenomena in modern physics. However, it has not been realized in the recent breakthrough experiment on two-leg superconducting circuits with transmon qubits [Y. Ye et al., Phys. Rev. Lett. 123, 050502 (2019)]. Here we present an experimentally feasible method to achieve the synthetic gauge field by introducing ac microwave driving in each qubit. In particular, the effective magnetic flux per plaquette achieved can be tuned independently by properly choosing the driving phases. Moreover, the ground-state chiral currents for single- and two-qubit excitations are obtained and the Meissner-vortex phase transition is found. In the Meissner phase, the ground-state chiral current increases as the magnetic flux increases, while it decreases in the vortex phase. In addition, chiral dynamics, which depends crucially on the initial state of the system, is also revealed. Finally, the possible experimental observations of the chiral current and dynamics are addressed. Our results provide a new route to explore novel many-body properties induced by the interplay of the gauge field, two-leg hoppings, and interaction of photons on superconducting circuits.
Assessing Disease Exposure Risk With Location Histories And Protecting Privacy: A Cryptographic Approach In Response To A Global Pandemic

Governments and researchers around the world are implementing digital contact tracing solutions to stem the spread of infectious disease, namely COVID-19. Many of these solutions threaten individual rights and privacy. Our goal is to break past the false dichotomy of effective versus privacy-preserving contact tracing. We offer an alternative approach to assess and communicate users' risk of exposure to an infectious disease while preserving individual privacy. Our proposal uses recent GPS location histories, which are transformed and encrypted, and a private set intersection protocol to interface with a semi-trusted authority. There have been other recent proposals for privacy-preserving contact tracing, based on Bluetooth and decentralization, that could further eliminate the need for trust in authority. However, solutions with Bluetooth are currently limited to certain devices and contexts while decentralization adds complexity. The goal of this work is two-fold: we aim to propose a location-based system that is more privacy-preserving than what is currently being adopted by governments around the world, and that is also practical to implement with the immediacy needed to stem a viral outbreak.
Simultaneous transformation and rounding (STAR) models for integer-valued data

We propose a simple yet powerful framework for modeling integer-valued data, such as counts, scores, and rounded data.The datagenerating process is defined by Simultaneously Transforming and Rounding (star) a continuous-valued process, which produces a flexible family of integer-valued distributions capable of modeling zero-inflation, bounded or censored data, and over-or underdispersion.The transformation is modeled as unknown for greater distributional flexibility, while the rounding operation ensures a coherent integer-valued data-generating process.An efficient MCMC algorithm is developed for posterior inference and provides a mechanism for adaptation of successful Bayesian models and algorithms for continuous data to the integer-valued data setting.Using the star framework, we design a new Bayesian Additive Regression Tree model for integer-valued data, which demonstrates impressive predictive distribution accuracy for both synthetic data and a large healthcare utilization dataset.For interpretable regression-based inference, we develop a star additive model, which offers greater flexibility and scalability than existing integer-valued models.The star additive model is applied to study the recent decline in Amazon river dolphins.
Switchable Next-Nearest-Neighbor Coupling for Controlled Two-Qubit Operations

In a superconducting quantum processor with nearest neighbor coupling, the dispersive interaction between adjacent qubits can result in an effective next-nearest-neighbor coupling whose strength depends on the state of the intermediary qubit. Here, we theoretically explore the possibility of engineering this next-nearest-neighbor coupling to implement controlled two-qubit operations where the intermediary qubit controls the operation on the next-nearest neighbor pair of qubits. In particular, in a system comprising two types of superconducting qubits with anharmonicities of opposite-sign arranged in an -A-B-A- pattern, where the unwanted static ZZ coupling between adjacent qubits could be heavily suppressed, a switchable coupling between the next-nearest-neighbor qubits can be achieved via the intermediary qubit, the qubit state of which functions as an on/off switch for this coupling. Therefore, depending on the adopted activating scheme, various controlled two-qubit operations such as controlled-iSWAP gate can be realized, potentially enabling circuit depth reductions as to a standard decomposition approach for implementing generic quantum algorithms.
FPGA Implementation of CNN Algorithm for Detecting Malaria Diseased Blood Cells

In this study, Field Programmable Gate Array (FPGA) implementation of Convolutional Neural Network (CNN) for classification of malaria diseased cell is done. The hardware is designed and implemented on Xilinx Zynq-7000 FPGA using Very High-Speed Integrated Circuit Hardware Description Language (VHDL). In accordance with this purpose, Convolutional Neural Network (CNN) classification method used by image processing to make it easier for experts to comment on diseased cells. The classification method allows us to make a simpler interpretation by classifying complex images. Thanks to this research, it facilitates early diagnosis using image processing in the medical field as soon as reduces death and treatment costs. According to the experimental results, the accuracy rate for finding malaria diseased cell using CNN method is 94.76% for 200 8x8 binary images. The average execution time of CNN algorithm using Matlab on desktop PC is 174 microseconds. On the other hand, the maximum achievable frequency on Zynq FPGA is 168MHz (i.e. the longest critical path is 5.93 nanoseconds). The occupied area of CNN on Xilinx Zynq FPGA is only 783 six-input Look Up Tables (LUTs) of 17600, which is about 4.34% of Xilinx Zynq-7000 (XC7Z010-1CLG400C) FPGA.
IQA: Interactive query construction in semantic question answering systems

Semantic Question Answering (SQA) systems automatically interpret user questions expressed in a natural language in terms of semantic queries. This process involves uncertainty, such that the resulting queries do not always accurately match the user intent, especially for more complex and less common questions. In this article, we aim to empower users in guiding SQA systems towards the intended semantic queries through interaction. We introduce IQA - an interaction scheme for SQA pipelines. This scheme facilitates seamless integration of user feedback in the question answering process and relies on Option Gain - a novel metric that enables efficient and intuitive user interaction. Our evaluation shows that using the proposed scheme, even a small number of user interactions can lead to significant improvements in the performance of SQA systems.
Convergence Behavior of DNNs with Mutual-Information-Based Regularization

Information theory concepts are leveraged with the goal of better understanding and improving Deep Neural Networks (DNNs). The information plane of neural networks describes the behavior during training of the mutual information at various depths between input/output and hidden-layer variables. Previous analysis revealed that most of the training epochs are spent on compressing the input, in some networks where finiteness of the mutual information can be established. However, the estimation of mutual information is nontrivial for high-dimensional continuous random variables. Therefore, the computation of the mutual information for DNNs and its visualization on the information plane mostly focused on low-complexity fully connected networks. In fact, even the existence of the compression phase in complex DNNs has been questioned and viewed as an open problem. In this paper, we present the convergence of mutual information on the information plane for a high-dimensional VGG-16 Convolutional Neural Network (CNN) by resorting to Mutual Information Neural Estimation (MINE), thus confirming and extending the results obtained with low-dimensional fully connected networks. Furthermore, we demonstrate the benefits of regularizing a network, especially for a large number of training epochs, by adopting mutual information estimates as additional terms in the loss function characteristic of the network. Experimental results show that the regularization stabilizes the test accuracy and significantly reduces its variance.