Reviewerly/exHarmony huggingface dataset:
Heisenberg scaling precision in multi-mode distributed quantum metrology

We propose an $N$-photon Gaussian measurement scheme which allows the estimation of a parameter $\varphi$ encoded into a multi-port interferometer with a Heisenberg scaling precision (i.e. of order $1/N$). In this protocol, no restrictions on the structure of the interferometer are imposed other than linearity and passivity, allowing the parameter $\varphi$ to be distributed over several components. In all previous proposals Heisenberg scaling has been obtained provided that both the input state and the measurement at the output are suitably adapted to the unknown parameter $\varphi$. This is a serious drawback which would require in practice the use of iterative procedures with a sequence of trial input states and measurements, which involve an unquantified use of additional resources. Remarkably, we find that only one stage has to be adapted, which leaves the choice of the other stage completely arbitrary. We also show that our scheme is robust against imperfections in the optimized stage. Moreover, we show that the adaptive procedure only requires a preliminary classical knowledge (i.e to a precision $1/\sqrt{N}$) on the parameter, and no further additional resources. As a consequence, the same adapted stage can be employed to monitor with Heisenberg-limited precision any variation of the parameter of the order of $1/\sqrt{N}$ without any further adaptation.
Phases of learning dynamics in artificial neural networks in the absence or presence of mislabeled data

Abstract Despite the tremendous success of deep neural networks in machine learning, the underlying reason for their superior learning capability remains unclear. Here, we present a framework based on statistical physics to study the dynamics of stochastic gradient descent (SGD), which drives learning in neural networks. Using the minibatch gradient ensemble, we construct order parameters to characterize the dynamics of weight updates in SGD. In the case without mislabeled data, we find that the SGD learning dynamics transitions from a fast learning phase to a slow exploration phase, which is associated with large changes in the order parameters that characterize the alignment of SGD gradients and their mean amplitude. In a more complex case, with randomly mislabeled samples, the SGD learning dynamics falls into four distinct phases. First, the system finds solutions for the correctly labeled samples in phase I; it then wanders around these solutions in phase II until it finds a direction that enables it to learn the mislabeled samples during phase III, after which, it finds solutions that satisfy all training samples during phase IV. Correspondingly, the test error decreases during phase I and remains low during phase II; however, it increases during phase III and reaches a high plateau during phase IV. The transitions between different phases can be understood by examining changes in the order parameters that characterize the alignment of the mean gradients for the two datasets (correctly and incorrectly labeled samples) and their (relative) strengths during learning. We find that individual sample losses for the two datasets are separated the most during phase II, leading to a data cleansing process that eliminates mislabeled samples and improves generalization. Overall, we believe that an approach based on statistical physics and stochastic dynamic systems theory provides a promising framework for describing and understanding learning dynamics in neural networks, which may also lead to more efficient learning algorithms.
Possibilities of Application of Augmented Reality in Different Branches of Education

Augmented reality has a great impact on the student in the presentation of educational material: objects of augmented reality affect the development of facial expressions, attention, stimulate thinking, and increase the level of understanding of information. Its implementation in various spheres has indisputable advantages: realism, clarity, application in many industries, information completeness and interactivity. That is why the study presents the possibilities of using augmented reality in the study of mathematics, anatomy, physics, chemistry, architecture, as well as in other fields. The comparison of domestic and foreign proposals for augmented reality is presented. The use of augmented reality in various fields (technology, entertainment, science and medicine, education, games, etc.) should be well thought out and pedagogically appropriate. That is why in the future it is planned to conduct research on the feasibility of using augmented reality and to develop elements of augmented reality accordingly.
Global-to-local region-based indicator embedded in edge-based level set model for segmentation

Image segmentation is an essential analysis tool in the field of computer vision, and the level set method has been widely used in image segmentation. Specifically, the edge-based level set models can reduce many undesired regions because they mainly rely on the edge information. However, the edge-based level set models are usually sensitive to the initial condition, which limits their application. To overcome this shortcoming, a global-to-local region-based indicator is designed in this paper, which is utilized to embed the region information into the edge-based models. Unlike the edge-based indicator frequently used in the edge-based models, the proposed region-based indicator can allow bidirectional motion of the active contour curve according to the region information. In general, the proposed region-based indicator can intrinsically incorporate the edge information and region information into one single energy function. Experimental results on synthetic images, natural images and medical images validate the effectiveness of the proposed method. Compared with some other level set models, the proposed method generally achieves better performance.
Critiquing for Music Exploration in Conversational Recommender Systems

Dialogue-based conversational recommender systems allow users to give language-based feedback on the recommended item, which has great potential for supporting users to explore the space of recommendations through conversation. In this work, we consider incorporating critiquing techniques into conversational systems to facilitate users' exploration of music recommendations. Thus, we have developed a music chatbot with three system variants, which are respectively featured with three different critiquing techniques, i.e., user-initiated critiquing (UC), progressive system-suggested critiquing (Progressive SC), and cascading system-suggested critiquing (Cascading SC). We conducted a between-subject study (N=107) to compare these three types of systems with regards to music exploration in terms of user perception and user interaction. Results show that both UC and SC are useful for music exploration, while users perceive higher diversity of recommendations with the system that offers Cascading SC and perceive more serendipitous with the system that offers Progressive SC. In addition, we find that the critiquing techniques significantly moderate the relationships between some interaction metrics (e.g., number of listened songs, number of dialogue turns) and users' perceived helpfulness and serendipity during music exploration.
Quantum nondemolition measurement based generation of entangled states in two Bose–Einstein condensates

Abstract We theoretically study a scheme for generating entanglement between two Bose–Einstein condensates (BECs). The scheme involves placing two BECs in the path of a Mach–Zehnder interferometer, where the coherent light interacts with the atoms due to a quantum nondemolition Hamiltonian. In contrast to standard approaches where a Holstein–Primakoff approximation is used, we use an exact wavefunction approach where the atoms can be initialized in an arbitrary state and the light–atom interaction times can be arbitrary. In the short time regime, it is possible to construct a very simple approximate theory for the overall effect of the scheme: amplitudes in the superposition between the two BECs with unequal spin eigenvalues are damped. We analyze the types of correlations, entanglement, Einstein–Podolsky–Rosen (EPR) steering, and Bell correlations that are produced and show that the state is similar to a spin-EPR state. Using a two-pulse sequence the correlations can be dramatically improved, where the state further approaches a spin-EPR state.
Interpreting the Latent Space of GANs via Measuring Decoupling

With the success of generative adversarial networks (GANs) on various real-world applications, the controllability and security of GANs have raised more and more concerns from the community. Specifically, understanding the latent space of GANs, i.e., obtaining the completely decoupled latent space, is essential for applications in some secure scenarios. At present, there is no quantitative method to measure the decoupling of latent space, which is not conducive to the development of the community. In this article, we propose two methods to measure the sensitivity of latent dimensions: one is a sequential intervention method, and the other is an optimization-based method that measures the sensitivity in both the value and the direction. With these two methods, the decoupling of latent space can be measured by the sparsity of the sensitivity vector obtained. The effectiveness of the proposed methods has been verified by experiments on the representative GANs. Code will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/iceli1007/latent-analysis-of</uri> .
Contact Tracing Made Un-relay-able

Automated contact tracing is a key solution to control the spread of airborne transmittable diseases: it traces contacts among individuals in order to alert people about their potential risk of being infected. The current SARS-CoV-2 pandemic put a heavy strain on the healthcare system of many countries. Governments chose different approaches to face the spread of the virus and the contact tracing apps were considered the most effective ones. In particular, by leveraging on the Bluetooth Low-Energy technology, mobile apps allow to achieve a privacy-preserving contact tracing of citizens. While researchers proposed several contact tracing approaches, each government developed its own national contact tracing app.
Securing multiple-single-channel color image using unequal spectrum decomposition and 2D-SLIM biometric keys

A novel multiple-single-channel color image cryptosystem based on unequal spectrum decomposition (USD) and 2D sine improved 1ogistic iterative chaotic map with infinite collapse modulation map (2D-SLIM) is proposed. In this method, R, G, and B channels of each color image of an authorized user is fused with corresponding LL sub-band of a gray-scale carrier image by inverse discrete wavelet transform (DWT) to obtain single-channel and watermarked image. The individual biometric key of an authorized user is produced by his/her phase-encoded irisprint and then modulated by chaotic random phase mask (CRPM). So the parameters of CRPM are used as decryption keys with the uniqueness of the irisprint. The watermarked image as input image is normalized, multiplied with biometric key, and then Fresnel transformed. The Fresnel spectrum is divided into two complex-value masks by using unequal spectrum decomposition in which one phase mask is exploited as decryption key and other as ciphertext. The final ciphertext is obtained by adding individual ciphertexts. The encryption and decryption process can be realized with a hybrid optoelectronic system. Numerical simulations have been performed to verify the validity and feasibility of the proposed system.
Autoencoder framework based on orthogonal projection constraints improves anomalies detection

In this study, we propose a novel autoencoder framework based on orthogonal projection constraint (OPC) for anomaly detection (AD) on both complex image and vector datasets. Orthogonal projection is useful to capture the null subspace that consists of noisy information for AD, which is explicitly ignored in the existing approaches. The exploration of double subspaces, called normal space (NS) and abnormal space (AS) can improve the discriminative manifold information. Therefore, in this study, autoencoder framework based on the OPC learning method is proposed that combines the orthogonal subspace score and the reconstruction error score in the target tasks for AD. To the best of our knowledge, this is the first study that introduces an autoencoder-based model with two orthogonal subspaces for AD. Through the orthogonality, the anomaly-free data and abnormal⧹nosiy information are projected into the NS and the AS, respectively. Thus, it potentially addresses the problem of the distribution of generative model by combining the abilities of two subspaces that can appropriately learn the features and establish a strict boundaries around the normal data. For image datasets, we propose a convolutional autoencoder based on OPC. Additionally, the generalization and adaptability of the proposed method in AD was investigated using vector datasets by implementing a fully-connected layer-based OPC in the encoder-decoder structure. The effectiveness of the proposed framework for AD was evaluated through the comparison with state-of-the-art approaches.
Resolution of fuzzy relation equations with increasing operations over complete lattices

In this paper, we discuss the resolution of max - M compositional and min - M compositional fuzzy relation equations over complete lattices , where M is a left-increasing or right-increasing binary operation . The solvability, the existence of the least and the greatest solutions, and the unique solvability of these fuzzy relation equations are characterized.
Photon-Number-Dependent Hamiltonian Engineering for Cavities

Cavity resonators are promising resources for quantum technology, while native nonlinear interactions for cavities are typically too weak to provide the level of quantum control required to deliver complex targeted operations. Here we investigate a scheme to engineer a target Hamiltonian for photonic cavities using ancilla qubits. By off-resonantly driving dispersively coupled ancilla qubits, we develop an optimized approach to engineering an arbitrary photon-number-dependent (PND) Hamiltonian for the cavities while minimizing the operation errors. The engineered Hamiltonian admits various applications including canceling unwanted cavity self-Kerr interactions, creating higher-order nonlinearities for quantum simulations, and designing quantum gates resilient to noise. Our scheme can be implemented with coupled microwave cavities and transmon qubits in superconducting circuit systems.
Real-Time Data Scheduling of Flexible Job in Papermaking Workshop Based on Deep Learning and Improved Fuzzy Algorithm

The traditional real-time data scheduling method ignores the optimization process of job data that leads to delayed delivery, high inventory cost, and low utilization rate of equipment. This paper proposes a novel real-time data scheduling method based on deep learning and an improved fuzzy algorithm for flexible operations in the papermaking workshop. The algorithm is divided into three parts: the first part describes the flexible job shop scheduling problem; the second part constructs the fuzzy scheduling model of flexible job data in papermaking workshop; and finally the third part uses a genetic algorithm to obtain the optimal solution of fuzzy scheduling of flexible job data in papermaking workshop. The results show that the optimal solution is obtained in 48 seconds at the 23rd attempt (iteration) under the application of the proposed method. This result is much better than the three traditional scheduling methods with which we compared our results. Hence, this paper improves the work efficiency and quality of papermaking workshop and reduces the operating cost of the papermaking enterprise.
Discriminative Multi-View Dynamic Image Fusion for Cross-View 3-D Action Recognition

Dramatic imaging viewpoint variation is the critical challenge toward action recognition for depth video. To address this, one feasible way is to enhance view-tolerance of visual feature, while still maintaining strong discriminative capacity. Multi-view dynamic image (MVDI) is the most recently proposed 3-D action representation manner that is able to compactly encode human motion information and 3-D visual clue well. However, it is still view-sensitive. To leverage its performance, a discriminative MVDI fusion method is proposed by us via multi-instance learning (MIL). Specifically, the dynamic images (DIs) from different observation viewpoints are regarded as the instances for 3-D action characterization. After being encoded using Fisher vector (FV), they are then aggregated by sum-pooling to yield the representative 3-D action signature. Our insight is that viewpoint aggregation helps to enhance view-tolerance. And, FV can map the raw DI feature to the higher dimensional feature space to promote the discriminative power. Meanwhile, a discriminative viewpoint instance discovery method is also proposed to discard the viewpoint instances unfavorable for action characterization. The wide-range experiments on five data sets demonstrate that our proposition can significantly enhance the performance of cross-view 3-D action recognition. And, it is also applicable to cross-view 3-D object recognition. The source code is available at https://github.com/3huo/ActionView.
Identification and Classification of Textual Aggression in Social Media: Resource Creation and Evaluation

Recently, social media has gained substantial attention as people can share opinions, expressions, emotions and carry out meaningful interactions through it spontaneously. Unfortunately, with this rapid advancement, social media misuse has also been proliferated, which leads to an increase in aggressive, offensive and abusive activities. Most of these unlawful activities performed through textual communication. Therefore, it is monumental to create intelligent systems that can identify and classify these texts. This paper presents an aggressive text classification system in Bengali. To serve our purpose a corpus (hereafter we called, ‘ATxtC’) is developed using hierarchical annotation schema that contains 7591 annotated texts (3888 for aggressive and 3703 for non-aggressive). Furthermore, the proposed system can classify aggressive Bengali text into religious, gendered, verbal and political aggression classes. Data annotation obtained a 0.74 kappa score in coarse-grained and 0.61 kappa score in fine-grained categories, which ensures the data’s acceptable quality. Several classification algorithms such as LR, RF, SVM, CNN and BiLSTM are implemented on AtxtC. The experimental result shows that the combined CNN and BiLSTM model achieved the highest weighted \(f_1\) score of 0.87 (identification task) and 0.80 (classification task).
Smartphone Apps for Learning Progress and Course Revision

Smartphones as our permanent companion seem to be an expedient choice for the implementation of e-learning tools in the form of smartphone apps due to their convenience and accessibility. We hence present two recently published smartphone apps that were developed for an introductory control course. The apps serve the purposes of (i) improving learning progress and (ii) revision of course content between lectures. In this paper, we explain how the apps align with our lecture Introduction to Automatic Control, put them into context of our e-learning strategy and describe our pursued goals. Moreover, a survey among third year control students is discussed, which supports the presented approach. Finally, we discuss and compare the mean (smartphone app) to other measures, considering both content and goals.
FORM: Follow the Online Regularized Meta-Leader for Cold-Start Recommendation

Meta-learning based recommendation systems alleviate the cold-start problem through a bi-level meta-optimization process. Recommendation borrows prior experience from pre-trained static system-level parameters and fine-tunes the model in user-level for new users. However, it is more natural for the system to sample users in a dynamic online sequence in most real-world recommendation systems, which brings further challenges for existing meta-learning based recommendation: system-level updates begins before user-level recommendation models have converged on the whole time series; stable and randomness-resistant bi-level gradient descent approaches are missing in the current meta-learning framework; evaluation on learning abilities across different users are lacked for exploring the diversities of different users.
GAU-Net: U-Net Based on Global Attention Mechanism for brain tumor segmentation

Abstract Deep learning has shown great advantages in biomedical image segmentation. The classic model U-Net uses a stacked encoding-decoding structure of convolution operations for feature extraction and pixel-level classification. The stacking of convolutional layers can expand the receptive field, but it is still a local operation and cannot capture long-distance dependence. Therefore, in this work, we propose a Global Attention Mechanism that combines channel attention module and spatial attention module and integrates different convolutions in it. Besides, we design a residual module for the traditional up and down sampling blocks. And finally, we combine them with U-Net to propose a new global attention network GAU-Net. We perform experiments on the dataset BraTS2018. Our model has increased the mIoU from 0.65 to 0.75 with only 5.4% of U-Net parameters. At the same time, the inference time is also significantly shortened with relatively good performance.
CLEAR: Contrastive-Prototype Learning with Drift Estimation for Resource Constrained Stream Mining

Non-stationary data stream mining aims to classify large scale online instances that emerge continuously. The most apparent challenge compared with the offline learning manner is the issue of consecutive emergence of new categories, when tackling non-static categorical distribution. Non-stationary stream settings often appear in real-world applications, e.g., online classification in E-commerce systems that involves the incoming productions, or the summary of news topics on social networks (Twitter). Ideally, a learning model should be able to learn novel concepts from labeled data (in new tasks) and reduce the abrupt degradation of model performance on the old concept (also named catastrophic forgetting problem). In this work, we focus on improving the performance of the stream mining approach under the constrained resources, where both the memory resource of old data and labeled new instances are limited/scarce. We propose a simple yet efficient resource-constrained framework CLEAR to facilitate previous challenges during the one-pass stream mining. Specifically, CLEAR focuses on creating and calibrating the class representation (the prototype) in the embedding space. We first apply the contrastive-prototype learning on large amount of unlabeled data, and generate the discriminative prototype for each class in the embedding space. Next, for updating on new tasks/categories, we propose a drift estimation strategy to calibrate/compensate for the drift of each class representation, which could reduce the knowledge forgetting without storing any previous data. We perform experiments on public datasets (e.g., CUB200, CIFAR100) under stream setting, our approach is consistently and clearly better than many state-of-the-art methods, along with both the memory and annotation restriction.