Reviewerly/exHarmony huggingface dataset:
Breaking the limits of purification: postselection enhances heat-bath algorithmic cooling

Abstract Quantum technologies require pure states, which are often generated by extreme refrigeration. Heat-bath algorithmic cooling is the theoretically optimal refrigeration technique: it shuttles entropy from a multiparticle system to a thermal bath, thereby generating a quantum state with a high degree of purity. Here, we show how to surpass this hitherto-optimal technique by taking advantage of a single binary-outcome measurement. Our protocols can create arbitrary numbers of pure quantum states without any residual mixedness by using a recently discovered device known as a quantum switch to put two operations in superposition, with postselection certifying the complete purification.
Are Concept Drift Detectors Reliable Alarming Systems? - A Comparative Study

As machine learning models increasingly replace traditional business logic in the production system, their lifecycle management is becoming a significant concern. Once deployed into production, the machine learning models are constantly evaluated on new streaming data. Given the continuous data flow, shifting data, also known as concept drift, is ubiquitous in such settings. Concept drift usually impacts the performance of machine learning models, thus, identifying the moment when concept drift occurs is required. Concept drift is identified through concept drift detectors. In this work, we assess the reliability of concept drift detectors to identify drift in time by exploring how late are they reporting drifts and how many false alarms are they signaling. We compare the performance of the most popular drift detectors belonging to two different concept drift detector groups, error rate-based detectors and data distribution-based detectors. We assess their performance on both synthetic and real-world data. In the case of synthetic data, we investigate the performance of detectors to identify two types of concept drift, abrupt and gradual. Our findings aim to help practitioners understand which drift detector should be employed in different situations and, to achieve this, we share a list of the most important observations made throughout this study, which can serve as guidelines for practical usage. Furthermore, based on our empirical results, we analyze the suitability of each concept drift detection group to be used as an alarming system.
Improving the Segmentation Accuracy of Ovarian-Tumor Ultrasound Images Using Image Inpainting

Diagnostic results can be radically influenced by the quality of 2D ovarian-tumor ultrasound images. However, clinically processed 2D ovarian-tumor ultrasound images contain many artificially recognized symbols, such as fingers, crosses, dashed lines, and letters which assist artificial intelligence (AI) in image recognition. These symbols are widely distributed within the lesion’s boundary, which can also affect the useful feature-extraction-utilizing networks and thus decrease the accuracy of lesion classification and segmentation. Image inpainting techniques are used for noise and object elimination from images. To solve this problem, we observed the MMOTU dataset and built a 2D ovarian-tumor ultrasound image inpainting dataset by finely annotating the various symbols in the images. A novel framework called mask-guided generative adversarial network (MGGAN) is presented in this paper for 2D ovarian-tumor ultrasound images to remove various symbols from the images. The MGGAN performs to a high standard in corrupted regions by using an attention mechanism in the generator to pay more attention to valid information and ignore symbol information, making lesion boundaries more realistic. Moreover, fast Fourier convolutions (FFCs) and residual networks are used to increase the global field of perception; thus, our model can be applied to high-resolution ultrasound images. The greatest benefit of this algorithm is that it achieves pixel-level inpainting of distorted regions without clean images. Compared with other models, our model achieveed better results with only one stage in terms of objective and subjective evaluations. Our model obtained the best results for 256 × 256 and 512 × 512 resolutions. At a resolution of 256 × 256, our model achieved 0.9246 for SSIM, 22.66 for FID, and 0.07806 for LPIPS. At a resolution of 512 × 512, our model achieved 0.9208 for SSIM, 25.52 for FID, and 0.08300 for LPIPS. Our method can considerably improve the accuracy of computerized ovarian tumor diagnosis. The segmentation accuracy was improved from 71.51% to 76.06% for the Unet model and from 61.13% to 66.65% for the PSPnet model in clean images.
EgoFish3D: Egocentric 3D Pose Estimation From a Fisheye Camera via Self-Supervised Learning

Egocentric vision has gained increasing popularity recently, opening new avenues for human-centric applications. However, the use of the egocentric fisheye cameras allows wide angle coverage but image distortion is introduced along with strong human body self-occlusion imposing significant challenges in data processing and model reconstruction. Unlike previous work only leveraging synthetic data for model training, this paper presents a new real-world EgoCentric Human Pose (ECHP) dataset. To tackle the difficulty of collecting 3D ground truth using motion capture systems, we simultaneously collect images from a head-mounted egocentric fisheye camera as well as from two third-person-view cameras, circumventing the environmental restrictions. By using self-supervised learning under multi-view constraints, we propose a simple yet effective framework, namely EgoFish3D, for egocentric 3D pose estimation from a single image in different real-world scenarios. The proposed EgoFish3D incorporates three main modules. 1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">The third-person-view module</i> takes two exocentric images as input and estimates the 3D pose represented in the third-person camera frame; 2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the egocentric module</i> predicts the 3D pose in the egocentric camera frame; and 3) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">the interactive module</i> estimates the rotation matrix between the third-person and the egocentric views. Experimental results on our ECHP dataset and existing benchmark datasets demonstrate the effectiveness of the proposed EgoFish3D, which can achieve superior performance to existing methods.
Augmented Reality in Breast Surgery Education

Augmented reality (AR) has demonstrated a potentially wide range of benefits and educational applications in the virtual health ecosystem. The concept of real-time data acquisition, machine learning-aided processing, and visualization is a foreseen ambition to leverage AR applications in the healthcare sector. This breakthrough with immersive technologies like AR, mixed reality, virtual reality, or extended reality will hopefully initiate a new surgical era: that of the use of the so-called surgical metaverse.This paper focuses on the future use of AR in breast surgery education describing two potential applications (surgical remote telementoring and impalpable breast cancer localization using AR), along with the technical needs to make it possible.Surgical telementoring and impalpable tumors noninvasive localization are two examples that can have success in the future provided the improvements in both data transformation and infrastructures are capable to overcome the current challenges and limitations.
Automatic Modulation Classification for Underwater Acoustic Communication Signals Based on Deep Complex Networks

Automatic modulation classification (AMC) is an important method for monitoring and identifying any underwater communication interference. Since the underwater acoustic communication scenario is full of multi-path fading and ocean ambient noise (OAN), coupled with the application of modern communication technology, which is usually susceptible to environmental influences, automatic modulation classification (AMC) becomes particularly difficult when it comes to an underwater scenario. Motivated by the deep complex networks (DCN), which have an innate ability to process complex data, we explore DCN for AMC of underwater acoustic communication signals. To integrate the signal processing method with deep learning and overcome the influences of underwater acoustic channels, we propose two complex physical signal processing layers based on DCN. The proposed layers include a deep complex matched filter (DCMF) and deep complex channel equalizer (DCCE), which are designed to remove noise and reduce the influence of multi-path fading for the received signals, respectively. Hierarchical DCN is constructed using the proposed method to achieve better performance of AMC. The influence of the real-world underwater acoustic communication scenario is taken into account; two underwater acoustic multi-path fading channels are conducted using the real-world ocean observation dataset, white Gaussian noise, and real-world OAN are used as the additive noise, respectively. Contrastive experiments show that the AMC based on DCN can achieve better performance than the traditional deep neural network based on real value (the average accuracy of the DCN is 5.3% higher than real-valued DNN). The proposed method based on DCN can effectively reduce the influence of underwater acoustic channels and improve the AMC performance in different underwater acoustic channels. The performance of the proposed method was verified on the real-world dataset. In the underwater acoustic channels, the proposed method outperforms a series of advanced AMC method.
PoeticAR: Reviving Traditional Poetry of the Heritage Site of Jichang Garden via Augmented Reality

As a famed Chinese classical garden, the Jichang Garden was a constant inspiration to many poets in its hundreds of years' history, who composed a rich body of poems—a valuable intangible cultural heritage. While tourists tend to pay attention to tangible natural scenery and historical architectures, they often neglect intangible cultural heritage—poems. We interviewed 23 tourists and found that augmented reality (AR) was viable for tourists to enjoy the physical scenery and the poetry simultaneously. We developed an initial prototype of PoeticAR, which presents poems based on physical scenery to enhance tourists' cultural and aesthetic experience. We further revised the prototype based on the ideas generated from a workshop with 18 tourists. We conducted a between-subject user study with 30 tourists to compare PoeticAR with Video. Results showed that PoeticAR significantly motivated tourists' interest in poems, enhanced the cultural and aesthetic tour experience in Jichang Garden, and increased awareness of Intangible Cultural Heritage of Cultural Heritage sites.
Theoretical Grounds of Shared Intentionality for Neuroscience in Devel-oping Bioengineering Systems

The article discusses a definition of shared intentionality that reflects recent discoveries for inspiring further translational research in developing bioengineering systems based on human-computer interaction. The child's cognition begins through shared intentionality that occurs in child-caregiver interaction when communication via sensory cues is impossible. There needs to be more knowledge on how it appears. This article argues that shared intentionality is collaborative interactions in which participants share the essential sensory stimulus of the actual cognitive problem. This social bond enables ecological training of the immature organism, starting at the reflexes stage of development, for processing the organization, identification, and interpretation of sensory information in developing perception. In nature, shared intentionality appears in mother-child dyads in increasing interpersonal dynamics due to mechanisms of cell coupling that provide an ecological developmental template. Knowledge about neurophysiological processes occurring during pre-perceptual communication can contribute to advances in bioengineering systems.
Uncertainty Exploration: Toward Explainable SAR Target Detection

Deep learning-based synthetic aperture radar (SAR) target detection has been developed for years, with many advanced methods proposed to achieve higher indicators of accuracy and speed. In spite of this, the current deep detectors cannot express the reliability and interpretation in trusting the predictions, which are crucial especially for those ordinary users without much expertise in understanding SAR images. To achieve explainable SAR target detection, it is necessary to answer the following questions: how much should we trust and why cannot we trust the results. With this purpose, we explore the uncertainty for SAR target detection in this article by quantifying the model uncertainty and explaining the ignorance of the detector. First, the Bayesian deep detectors (BDDs) are constructed for uncertainty quantification, answering how much to trust the classification and localization result. Second, an occlusion-based explanation method (U-RISE) for BDD is proposed to account for the SAR scattering features that cause uncertainty or promote trustworthiness. We introduce the probability-based detection quality (PDQ) and multielement decision space for evaluation besides the traditional metrics. The experimental results show that the proposed BDD outperforms the counterpart frequentist object detector, and the output probabilistic results successfully convey the model uncertainty and contribute to more comprehensive decision-making. Furthermore, the proposed U-RISE generates an attribution map with intuitive explanations to reveal the complex scattering phenomena about which BDD is uncertain. We deem our work will facilitate explainable and trustworthy modeling in the field of SAR image understanding and increase user comprehension of model decisions.
A user-knowledge vector space reconstruction model for the expert knowledge recommendation system

Expert Knowledge Recommendation System (EKRS) is an intelligent research assistance system. The system is formed by mapping two sets of conceptual spaces through Institutional Repository (IR) and Core Resource Dataset (CRD) in 2018. The user knowledge pattern matching (UKPM) of EKRS has problems such as uncertain user knowledge text matching, slow update of expert knowledge, and inability to accurately track user knowledge. This paper establishes a user knowledge vector space reconstruction model (UKVSM) through the following steps to solve the above problems. Firstly, the text feature items of IR and CRD are reconstructed and the depth and density correction coefficient matrix of the original node of the text semantic meaning is calculated based on the similarity of feature items of the semantic layer. Secondly, in order to improve the efficiency of UKPM exact matching, the Lagrangian relaxation algorithm (LRA) is used to optimize the two sets of knowledge matching strategies. Finally, the real data set is extracted from the EKRS platform, and the model and algorithm proposed in this paper are tested and verified respectively, and compared with other methods. Experiments show that reconstruction model can improve the accuracy of user knowledge task assignment in EKRS, while LRA can improve the efficiency of model solving.
A robust estimator of mutual information for deep learning interpretability

We develop the use of mutual information (MI), a well-established metric in information theory, to interpret the inner workings of deep learning models. To accurately estimate MI from a finite number of samples, we present GMM-MI (pronounced $``$Jimmie$"$), an algorithm based on Gaussian mixture models that can be applied to both discrete and continuous settings. GMM-MI is computationally efficient, robust to the choice of hyperparameters and provides the uncertainty on the MI estimate due to the finite sample size. We extensively validate GMM-MI on toy data for which the ground truth MI is known, comparing its performance against established mutual information estimators. We then demonstrate the use of our MI estimator in the context of representation learning, working with synthetic data and physical datasets describing highly non-linear processes. We train deep learning models to encode high-dimensional data within a meaningful compressed (latent) representation, and use GMM-MI to quantify both the level of disentanglement between the latent variables, and their association with relevant physical quantities, thus unlocking the interpretability of the latent representation. We make GMM-MI publicly available.
Exploring Mission Design for Imaging Spectroscopy Retrievals for Land and Aquatic Ecosystems

Abstract The retrieval algorithms used for optical remote sensing satellite data to estimate Earth's geophysical properties have specific requirements for spatial resolution, temporal revisit, spectral range and resolution, and instrument signal‐to‐noise ratio (SNR) performance to meet biogeoscience objectives. Studies to estimate surface properties from hyperspectral data use a range of algorithms sensitive to various sources of spectroscopic uncertainty, which are in turn influenced by mission architecture choices. Retrieval algorithms vary across scientific fields and may be more or less sensitive to mission architecture choices that affect spectral, spatial, or temporal resolutions and spectrometer SNR. We used representative remote sensing algorithms across terrestrial and aquatic study domains to inform aspects of mission design that are most important for impacting accuracy in each scientific area. We simulated the propagation of uncertainties in the retrieval process including the effects of different instrument configuration choices. We found that retrieval accuracy and information content degrade consistently at &gt;10 nm spectral resolution, &gt;30 m spatial resolution, and &gt;8‐day revisit. In these studies, the noise reduction associated with lower spatial resolution improved accuracy vis à vis high spatial resolution measurements. The interplay between spatial resolution, temporal revisit, and SNR can be quantitatively assessed for imaging spectroscopy missions and used to identify key components of algorithm performance and mission observing criteria.
Time-optimal control of two-level quantum systems by piecewise constant pulses

We apply an extension of the Pontryagin maximum principle to derive time-optimal controls of two-level quantum systems by means of piecewise constant pulses. Global optimal solutions are obtained for state-to-state transfer in the cases with one and two controls. Exact quantum speed limits are established as a function of the sampling period. We observe numerically an exponential convergence towards the minimum time in the continuous limit when this period goes to zero. We show that this convergence is only polynomial for a linearized quantum system. We discuss the experimental impact of this result.
Machine learning for optical quantum metrology

<i>Advanced Photonics</i>, co-published by SPIE and Chinese Laser Press, is a highly selective, Gold Open Access, international journal publishing innovative research in all areas of optics and photonics, including fundamental and applied research.
A framework for the efficient enhancement of non-uniform illumination underwater image using convolution neural network

In this paper, the non-uniform illumination enhancement problem of underwater images under the artificial light sources conditions is investigated based on Convolution Neural Network (CNN). First, we propose a trainable end-to-end enhancer called NUIENet, for enhancing the non-uniform illumination of underwater images. The proposed model consists of correction network and fusion layers. The correction network adopts the encoder–decoder structure with skip connections to enhance the features of different channels in the HSV domain, and then these enhanced features are fused by the fusion layers to obtain the desired high-quality images. Second, we built an underwater images dataset using Generative Adversarial Network (GAN) and Gaussian Function. Finally, both qualitative and quantitative experimental results show that the proposed method can produce better performance compared to other state-of-the-art enhancement methods on both real-word and synthetic underwater dataset.
Formación docente, trabajo, profesión en el contexto de la pandemia y post-pandemia

Learning and the challenges of the teaching job are not only framed within the teaching profession, but also respond to complex needs that arise from students in their development contexts. As new educational, political and economic scenarios emerge, the challenges for teachers are diverse because they are responsible for responding to the community and society as a whole. The current scenarios are very different from the initial ones of the school, which to this day pursue the purpose of seeking answers. The aim of the work was to identify what students learn and at the same time what are the challenges of the work, profession and teacher training after the pandemic. The teacher is a subject that society demands a particular profile and a degree of responsibility that does not happen with other professions, since he/she has a very complex task that requires many elements. It is necessary in this context to train teachers in a curriculum that develops both deep knowledge of content areas and effective pedagogical understanding, including digital technologies in a cross-cutting manner, with clear learning objectives that include knowledge, skills and attitudes. The study also focuses on the context of COVID-19, being necessary to introduce technologies in a transversal way in initial and continuous teacher training throughout the educational system, but also computational thinking.
Exploring the role of virtual reality in military decision training

Introduction: Simulation methods, including physical synthetic environments, already play a substantial role in human skills training in many industries. One example is their application to developing situational awareness and judgemental skills in defence and security personnel. The rapid development of virtual reality technologies has provided a new opportunity for performing this type of training, but before VR can be adopted as part of mandatory training it should be subjected to rigorous tests of its suitability and effectiveness. Methods: In this work, we adopted established methods for testing the fidelity and validity of simulated environments to compare three different methods of training use-of-force decision making. Thirty-nine dismounted close combat troops from the UK’s Royal Air Force completed shoot/don’t-shoot judgemental tasks in: i) live fire; ii) virtual reality; and iii) 2D video simulation conditions. A range of shooting accuracy and decision-making metrics were recorded from all three environments. Results: The results showed that 2D video simulation posed little decision-making challenge during training. Decision-making performance across live fire and virtual reality simulations was comparable but the two may offer slightly different, and perhaps complementary, methods of training judgemental skills. Discussion: Different types of simulation should, therefore, be selected carefully to address the exact training need.