Reviewerly/exHarmony huggingface dataset:
GPU-accelerated simulations of quantum annealing and the quantum approximate optimization algorithm

We study large-scale applications using a GPU-accelerated version of the massively parallel J\"ulich universal quantum computer simulator (JUQCS--G). First, we benchmark JUWELS Booster, a GPU cluster with 3744 NVIDIA A100 Tensor Core GPUs. Then, we use JUQCS--G to study the relation between quantum annealing (QA) and the quantum approximate optimization algorithm (QAOA). We find that a very coarsely discretized version of QA, termed approximate quantum annealing (AQA), performs surprisingly well in comparison to the QAOA. It can either be used to initialize the QAOA, or to avoid the costly optimization procedure altogether. Furthermore, we study the scaling of the success probability when using AQA for problems with 30 to 40 qubits. We find that the case with the largest discretization error scales most favorably, surpassing the best result obtained from the QAOA.
Deep Ladder-Suppression Network for Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) aims at learning a classifier for an unlabeled target domain by transferring knowledge from a labeled source domain with a related but different distribution. Most existing approaches learn domain-invariant features by adapting the entire information of the images. However, forcing adaptation of domain-specific variations undermines the effectiveness of the learned features. To address this problem, we propose a novel, yet elegant module, called the deep ladder-suppression network (DLSN), which is designed to better learn the cross-domain shared content by suppressing domain-specific variations. Our proposed DLSN is an autoencoder with lateral connections from the encoder to the decoder. By this design, the domain-specific details, which are only necessary for reconstructing the unlabeled target data, are directly fed to the decoder to complete the reconstruction task, relieving the pressure of learning domain-specific variations at the later layers of the shared encoder. As a result, DLSN allows the shared encoder to focus on learning cross-domain shared content and ignores the domain-specific variations. Notably, the proposed DLSN can be used as a standard module to be integrated with various existing UDA frameworks to further boost performance. Without whistles and bells, extensive experimental results on four gold-standard domain adaptation datasets, for example: 1) Digits; 2) Office31; 3) Office-Home; and 4) VisDA-C, demonstrate that the proposed DLSN can consistently and significantly improve the performance of various popular UDA frameworks.
An Instance Space Analysis of Regression Problems

The quest for greater insights into algorithm strengths and weaknesses, as revealed when studying algorithm performance on large collections of test problems, is supported by interactive visual analytics tools. A recent advance is Instance Space Analysis, which presents a visualization of the space occupied by the test datasets, and the performance of algorithms across the instance space. The strengths and weaknesses of algorithms can be visually assessed, and the adequacy of the test datasets can be scrutinized through visual analytics. This article presents the first Instance Space Analysis of regression problems in Machine Learning, considering the performance of 14 popular algorithms on 4,855 test datasets from a variety of sources. The two-dimensional instance space is defined by measurable characteristics of regression problems, selected from over 26 candidate features. It enables the similarities and differences between test instances to be visualized, along with the predictive performance of regression algorithms across the entire instance space. The purpose of creating this framework for visual analysis of an instance space is twofold: one may assess the capability and suitability of various regression techniques; meanwhile the bias, diversity, and level of difficulty of the regression problems popularly used by the community can be visually revealed. This article shows the applicability of the created regression instance space to provide insights into the strengths and weaknesses of regression algorithms, and the opportunities to diversify the benchmark test instances to support greater insights.
Research on the process of small sample non-ferrous metal recognition and separation based on deep learning

Consumption of copper and aluminum has increased significantly in recent years; therefore, recycling these elements from the end-of-life vehicles (ELVs) will be of great economic value and social benefit. However, the separation of non-ferrous materials is difficult because of their different sources, various shapes and sizes, and complex surface conditions. In experimental study on the separation of these materials, few non-ferrous metal scraps can be used. To address these limitations, a traditional image recognition model and a small sample multi-target detection model (which can detect multiple targets simultaneously) based on deep learning and transfer learning were used to identify non-ferrous materials. The improved third version of You Only Look Once (YOLOv3) multi-target detection model using data augmentation, the loss function of focal loss, and a method of adjusting the threshold of Intersection over Union (IOU) between candidate bound and ground truth bound has superior target detection performance than methods. We obtained a 95.3% and 91.4% accuracy in identifying aluminum and copper scraps, respectively, and an operation speed of 18 FPS, meeting the real-time requirements of a sorting system. By using the improved YOLOv3 multi-target detection algorithm and equipment operation parameters selected, the accuracy and purity of the separation system exceeded 90%, meeting the needs of actual production.
On The Stability of Graph Convolutional Neural Networks Under Edge Rewiring

Graph neural networks are experiencing a surge of popularity within the machine learning community due to their ability to adapt to nonEuclidean domains and instil inductive biases. Despite this, their stability, i.e., their robustness to small perturbations in the input, is not yet well understood. Although there exists some results showing the stability of graph neural networks, most take the form of an upper bound on the magnitude of change due to a perturbation in the graph topology. However, the change in the graph topology captured in existing bounds tend not to be expressed in terms of structural properties, limiting our understanding of the model robustness properties. In this work, we develop an interpretable upper bound elucidating that graph neural networks are stable to rewiring between high degree nodes. This bound and further research in bounds of similar type provide further understanding of the stability properties of graph neural networks.
EDBGAN: Image Inpainting via an Edge-Aware Dual Branch Generative Adversarial Network

As deep learning technology develops rapidly, image inpainting methods have made significant progress in generating reasonable contents for images with large and irregular holes. Nevertheless, existing methods either use one encoder-decoder to generate results in one step without making good use of structure features which are helpful, or adopt two encoder-decoders to recover structures and textures subsequently where the second encoder-decoder used for recovering textures relies heavily on the first encoder-decoder. Thus, an one-stage method which simultaneously utilizes structures and textures is promising. In this letter, we propose a dual branch encoder-decoder, whose texture branch and edge branch can simultaneously extract features from the masked RGB image and its corresponding edge map. Moreover, we propose a lightweight mutually guided attention block (MGAB), which makes features of two branches guide each other from low level to high level. At the end of two branches, we apply dual attention block (DAB) to perform feature fusion, which uses self-attention mechanism and lightweight attention mechanism on features of texture branch and edge branch, respectively. Extensive experiments demonstrate that our proposed method is effective in recovering edges and textures and achieves the state-of-the-art performance.
Planning-Augmented Hierarchical Reinforcement Learning

Planning algorithms are powerful at solving long-horizon decision-making problems but require that environment dynamics are known. Model-free reinforcement learning has recently been merged with graph-based planning to increase the robustness of trained policies in state-space navigation problems. Recent ideas suggest to use planning in order to provide intermediate waypoints guiding the policy in long-horizon tasks. Yet, it is not always practical to describe a problem in the setting of state-to-state navigation. Often, the goal is defined by one or multiple disjoint sets of valid states or implicitly using an abstract task description. Building upon previous efforts, we introduce a novel algorithm called Planning-Augmented Hierarchical Reinforcement Learning (PAHRL) which translates the concept of hybrid planning/RL to such problems with implicitly defined goal. Using a hierarchical framework, we divide the original task, formulated as a Markov Decision Process (MDP), into a hierarchy of shorter horizon MDPs. Actor-critic agents are trained in parallel for each level of the hierarchy. During testing, a planner then determines useful subgoals on a state graph constructed at the bottom level of the hierarchy. The effectiveness of our approach is demonstrated for a set of continuous control problems in simulation including robot arm reaching tasks and the manipulation of a deformable object.
The influence of mobile instant messaging with scientific approach on students’ critical-thinking skills in physics learning during covid-19 pandemic

Abstract The purpose of this study was to determine the influence of Mobile Instant Messaging with the scientific approach on critical-thinking skills on physics learning during the Covid-19 pandemic. The research method used was a quasi-experimental research with a non-equivalent control group design. The population of this study was all eleventh-grade science students Senior High School Bandar Sribhawono. The sampling technique used the purposive sampling technique. The instrument used a test in the form of essay questions to measure students’ critical-thinking skills. Based on the critical-thinking skills, the experimental class and control class obtained 72.60% and 61.30% in the basic support indicator. In elementary clarification indicator, the experimental class and control class obtained 64.40% and 51.90%. In advanced clarification indicator, the experimental class and control class obtained 62.20% and 45.30%. Indicator of inference, the experimental class and control class obtained 67.90 and 46.40%. Indicator of strategy and tactics, the experimental class and control class obtained 66.40% and 46.09%. The score of students’ critical thinking skills in both classes was tested with the independent sample t-test with the obtained sig value of 0.001 &lt;0.05. Thus, Ha was accepted which indicated that the students were able to think critically. In this study, it can be stated that there was an influence of mobile instant messaging with a scientific approach on students’ critical-thinking skills.
A transformation‐free linear regression for compositional outcomes and predictors

Compositional data are common in many fields, both as outcomes and predictor variables. The inventory of models for the case when both the outcome and predictor variables are compositional is limited, and the existing models are often difficult to interpret in the compositional space, due to their use of complex log-ratio transformations. We develop a transformation-free linear regression model where the expected value of the compositional outcome is expressed as a single Markov transition from the compositional predictor. Our approach is based on estimating equations thereby not requiring complete specification of data likelihood and is robust to different data-generating mechanisms. Our model is simple to interpret, allows for 0s and 1s in both the compositional outcome and covariates, and subsumes several interesting subcases of interest. We also develop permutation tests for linear independence and equality of effect sizes of two components of the predictor. Finally, we show that despite its simplicity, our model accurately captures the relationship between compositional data using two datasets from education and medical research.
Continual Activity Recognition with Generative Adversarial Networks

Continual learning is an emerging research challenge in human activity recognition (HAR). As an increasing number of HAR applications are deployed in real-world environments, it is important and essential to extend the activity model to adapt to the change in people’s activity routine. Otherwise, HAR applications can become obsolete and fail to deliver activity-aware services. The existing research in HAR has focused on detecting abnormal sensor events or new activities, however, extending the activity model is currently under-explored. To directly tackle this challenge, we build on the recent advance in the area of lifelong machine learning and design a continual activity recognition system, called HAR-GAN , to grow the activity model over time. HAR-GAN does not require a prior knowledge on what new activity classes might be and it does not require to store historical data by leveraging the use of Generative Adversarial Networks (GAN) to generate sensor data on the previously learned activities. We have evaluated HAR-GAN on four third-party, public datasets collected on binary sensors and accelerometers. Our extensive empirical results demonstrate the effectiveness of HAR-GAN in continual activity recognition and shed insight on the future challenges.
Tree-tensor-network classifiers for machine learning: From quantum inspired to quantum assisted

We describe a quantum-assisted machine learning (QAML) method in which multivariate data is encoded into quantum states in a Hilbert space whose dimension is exponentially large in the length of the data vector. Learning in this space occurs through applying a low-depth quantum circuit with a tree tensor network (TTN) topology, which acts as an unsupervised feature extractor to identify the most relevant quantum states in a data-driven fashion. This unsupervised feature extractor then feeds a supervised linear classifier and encodes the output in a small-dimensional quantum register. In contrast to previous work on \emph{quantum-inspired} TTN classifiers, in which the embedding map and class decision weights did not map the data to well-defined quantum states, we present an approach that can be implemented on gate-based quantum computing devices. In particular, we identify an embedding map with accuracy similar to exponential machines (Novikov \emph{et al.}, arXiv:1605.03795), but which produces valid quantum states from classical data vectors, and utilize manifold-based gradient optimization schemes to produce isometric operations mapping quantum states to a register of qubits defining a class decision. We detail methods for efficiently obtaining one- and two-point correlation functions of the decision boundary vectors of the quantum model, which can be used for model interpretability, as well as methods for obtaining classifications from partial data vectors. Further, we show that the use of isometric tensors can significantly aid in the human interpretability of the correlation functions extracted from the decision weights, and may produce models that are less susceptible to adversarial perturbations. We demonstrate our methodologies in applications utilizing the MNIST handwritten digit dataset and a multivariate timeseries dataset of human activity recognition.
Virtual Try-on Network With Attribute Transformation and Local Rendering

A virtual try-on network has gradually become a popular topic in recent years. It aims to transfer images of in-shop clothes onto the image of a target person. Owing to the diversity of clothing attributes, developing an image-based virtual try-on network is a complicated task for computers to perform and requires significant effort. Existing methods are unsatisfactory as they cannot preserve the characteristics of the clothes or the target person's identity well, thereby affecting the perception of the generated images; therefore, further research is required. To address this problem, we propose a novel try-on method that combines attribute transformation and local rendering. First, we employ pixel-level semantic segmentation to identify the try-on area and provide implementation conditions for local rendering. Second, we construct a learnable attribute transformation module to complete the try-on task for different attributes. Third, we use a learnable clothing warping module to fit the pose and figure of the target person well and establish a novel loss function, called modified style loss (M-SL), to handle clothes with rich details. Finally, we adopt a local rendering strategy, using which only renders the clothing area to ensure that the details of the non-target area are not lost. Extensive experiments are performed to test our method. The results demonstrate that our method outperforms other state-of-the-art methods.
Optimal Smooth Paths Based on Clothoids for Car-like Vehicles in the Presence of Obstacles

Automated Guided Vehicles are increasingly used for material transfer in factory and warehouse environments amongst humans and human operated vehicles. Safe and efficient operation is challenging when there is a mix of human and automated traffic as fixed guide paths can become blocked more frequently. In this work we aim to show smooth and efficient paths based on clothoid curves can be used to automatically plan diversions which can be traversed at high speed by automated fork-lift vehicles, which are car-like in the sense they have a limited turning radius and angular acceleration. The approach, based on numerical optimisation within convex region constraints is described in detail, and numerical results for curvature and sharpness are compared to a cubic spline on a small number of simulated environments. The clothoid spline is less affected, in terms of its objective function, by a shift in the obstacle boundaries than a cubic spline, for obstacle shifts below 0.5m. The clothoid spline takes longer to converge for but the output path has attractive qualities like lower peak sharpness, enabling high speed operation. The method is therefore most useful for applications where path quality is important and updates are required less frequently. Changing the objective function by increasing weighting parameter b allowed the path shape to be tuned to reduce the peak sharpness, at the cost of increasing the total length. With b > 100, convergence was poor because parts of the spline were pushed outside the assigned region, an artefact arising from the constraints only being enforced at the start and end of each segment. The analytical Jacobian of the constraints was effective at reducing the number of function evaluations to reach convergence.
Ensemble Inference Methods for Models With Noisy and Expensive Likelihoods

The increasing availability of data presents an opportunity to calibrate unknown parameters which appear in complex models of phenomena in the biomedical, physical, and social sciences.However, model complexity often leads to parameter-to-data maps which are expensive to evaluate and are only available through noisy approximations.This paper is concerned with the use of interacting particle systems for the solution of the resulting inverse problems for parameters.Of particular interest is the case where the available forward model evaluations are subject to rapid fluctuations, in parameter space, superimposed on the smoothly varying large-scale parametric structure of interest.A motivating example from climate science is presented, and ensemble Kalman methods (which do not use the derivative of the parameter-to-data map) are shown, empirically, to perform well.Multiscale analysis is then used to analyze the behavior of interacting particle system algorithms when rapid fluctuations, which we refer to as noise, pollute the large-scale parametric dependence of the parameter-to-data map.Ensemble Kalman methods and Langevin-based methods (the latter use the derivative of the parameter-to-data map) are compared in this light.The ensemble Kalman methods are shown to behave favorably in the presence of noise in the parameter-to-data map, whereas Langevin methods are adversely affected.On the other hand, Langevin methods have the correct equilibrium distribution in the setting of noise-free forward models, while ensemble Kalman methods only provide an uncontrolled approximation, except in the linear case.Therefore a new class of algorithms, ensemble Gaussian process samplers, which combine the benefits of both ensemble Kalman and Langevin methods, are introduced and shown to perform favorably.
Deep image enhancement for ill light imaging

Imaging in the natural scene under ill lighting conditions (e.g., low light, back-lit, over-exposed front-lit, and any combinations of them) suffers from both over- and under-exposure at the same time, whereas processing of such images often results in over- and under-enhancement. A single small image sensor can hardly provide satisfactory quality for ill lighting conditions with ordinary optical lenses in capturing devices. Challenges arise in the maintenance of a visual smoothness between those regions, while color and contrast should be well preserved. The problem has been approached by various methods, including multiple sensors and handcrafted parameters, but extant model capacity is limited to only some specific scenes (i.e., lighting conditions). Motivated by these challenges, in this paper, we propose a deep image enhancement method for color images captured under ill lighting conditions. In this method, input images are first decomposed into reflection and illumination maps with the proposed layer distribution loss net , where the illumination blindness and structure degradation problem can be subsequently solved via these two components, respectively. The hidden degradation in reflection and illumination is tuned with a knowledge-based adaptive enhancement constraint designed for ill illuminated images. The model can maintain a balance of smoothness and contribute to solving the problem of noise besides over- and under-enhancement. The local consistency in illumination is achieved via a repairing operation performed in the proposed Repair-Net . The total variation operator is optimized to acquire local consistency, and the image gradient is guided with the proposed enhancement constraint. Finally, a product of updated reflection and illumination maps reconstructs an enhanced image. Experiments are organized under both very low exposure and ill illumination conditions, where a new dataset is also proposed. Results on both experiments show that our method has superior performance in preserving structural and textural details compared to other states of the art, which suggests that our method is more practical in future visual applications.