Reviewerly/exHarmony huggingface dataset:
Improved I-nice clustering algorithm based on density peaks mechanism

Recently, Masud et al. [M. A. Masud, J. Z. Huang, C. H. Wei, et al. I-nice: A new approach for identifying the number of clusters and initial cluster centres. Information Sciences 466 (2018) 129–151] proposed a parameter-free clustering algorithm, named I-nice, which can identify the number of clusters and initial cluster centres using observation points. Although the experiment presented good clustering performance of I-nice, there are two inherent limitations that can be further improved. One is that I-niceSO is sensitive to the position of the observation point, and the other is that the number of nearest neighbours affects the determination of high-density areas in I-niceMO. Inspired by density peaks clustering, we propose a density-peaks-based I-nice (I-niceDP) clustering algorithm to improve the existing I-nice clustering algorithm. In I-niceDP, we use density peaks to determine the number of clusters and cluster centres in the components of the gamma mixture model rather than the k-nearest neighbours method. The comparative results using I-niceSO and I-niceMO indicate that I-niceDP can more accurately identify the number of clusters and initial cluster centres for datasets with large cluster numbers. Furthermore, I-niceDP obtains higher normalised mutual information values in comparison with seven other clustering algorithms. The experimental results demonstrate the feasibility and effectiveness of the I-niceDP clustering algorithm.
Imaging through scattering media based on semi-supervised learning

We present a method for less-invasive imaging through scattering media. We use an image-to-image translation, which is called a cycle generative adversarial network (CycleGAN), based on semi-supervised learning with an unlabeled dataset. Our method was experimentally demonstrated by reconstructing object images displayed on a spatial light modulator between diffusers. In the demonstration, CycleGAN was trained with captured images and object candidate images that were not used for image capturing through the diffusers and were not paired with the captured images.
Category-specific Semantic Coherency Learning for Fine-grained Image Recognition

Existing deep learning based weakly supervised fine-grained image recognition (WFGIR) methods usually pick out the discriminative regions from the high-level feature (HLF) maps directly. However, as HLF maps are derived based on spatial aggregation of convolution which is basically a pattern matching process that applies fixed filters, it is ineffective to model visual contents of same semantic but varying posture or perspective. We argue that this will cause the selected discriminative regions of same sub-category are not semantically corresponding and thus degrade the WFGIR performance. To address this issue, we propose an end-to-end Category-specific Semantic Coherency Network (CSC-Net) to semantically align the discriminative regions of the same subcategory. Specifically, CSC-Net consists of: 1) Local-to-Attribute Projecting Module (LPM), which automatically learns a set of latent attributes via collecting the category-specific semantic details while eliminating the varying spatial distributions from the local regions. 2) Latent Attribute Aligning (LAA), which aligns the latent attributes to specific semantic via graph convolution based on their discriminability, to achieve category-specific semantic coherency; 3) Attribute-to-Local Resuming Module (ARM), which resumes the original Euclidean space of latent attributes and construct latent attribute aligned feature maps by a location-embedding graph unpooling operation. Finally, the new feature maps are used which applies the category-specific semantic coherency implicitly for more accurate discriminative regions localization. Extensive experiments verify that CSC-Net yields the best performance under the same settings with most competitive approaches, on CUB Bird, Stanford-Cars, and FGVC Aircraft datasets.
Improving the Performance of VGG Through Different Granularity Feature Combinations

Convolutional neural networks have achieved amazing success in many areas in recent years, and VGG is a widely used convolutional neural network model.However, it has some limitations, such as a large number of parameters, which take up significant memory and thus restrict its application in resource-constrained scenarios such as mobile devices and embedded systems.In convolutional neural network models, the different number of convolutional layers can extract different granularity features, which represent the different levels of importance in the image recognition process.Here, we propose a new VGG architecture with different granularity feature combinations that combine different granularity features from block1, block2, block3, block4, and block5 in VGG.Each block is followed by a local fully connected layer to reduce the dimensionality of the coarse and fine features, and five different granularity features are combined as the input of the first global fully connected layer.By combining the features of different blocks, it can increase information flow from a lower layer directly to a fully connected layer and increase feature reuse without adding too many connections.The addition of five local fully connected layers means an increase in parameters, so we reduce the neuron number in two global fully connected layers to reduce the number of parameters.The well-known datasets CIFAR-10 and MNIST were used to evaluate the network's classification performance.The experimental results show that the proposed model achieves better training and testing performance than traditional VGGs and reduces the number of parameters.
Person-Independent Facial Expression Recognition Based on Improved Local Binary Pattern and Higher-Order Singular Value Decomposition

The recognition rate of person-independent facial expression is generally not high, which limits the practical application of facial expression recognition.Aiming at this problem, this paper analyzes the reasons for the low recognition rate of person-independent facial expression, and proposes a recognition algorithm of person-independent facial expression based on improved LBP (Local Binary Pattern) and HOSVD (Higher-Order Singular Value Decomposition).The algorithm has the following contributions of facial expression recognition framework.In the stage of facial expression feature extraction, the transient features extracted by LDP(Local Directional Pattern) and the persistent features extracted by CBP(Centralized Binary Pattern) are integrated to improve the discrimination of facial expression features.Moreover, in the stage of facial expression classification and recognition, the traditional nearest neighbor classification is changed into k-nearest neighbor pre-classification, and the regional energy calculated by HOSVD is used to determine the similarity of two images for secondary classification.Finally, in the extended Cohn-Kanade dataset and Oulu-CASIA NIR&VIS facial expression database, the theoretical analysis and experimental results show that the method has better recognition effect for solving the problem of person-independent facial expression recognition.
Planning for the Known Unknown: Machine Learning for Human Healthcare Systems

Clinical medicine is an inexact science. In situations of uncertainty, we often ask an experienced colleague for a second opinion. But what if one could effectively call upon the experience of thou...
Classical simulation of Gaussian quantum circuits with non-Gaussian input states

We consider Gaussian quantum circuits supplemented with non-Gaussian input states and derive sufficient conditions for efficient classical strong simulation of these circuits. In particular, we generalise the stellar representation of continuous-variable quantum states to the multimode setting and relate the stellar rank of the input non-Gaussian states, a recently introduced measure of non-Gaussianity, to the cost of evaluating classically the output probability densities of these circuits. Our results have consequences for the strong simulability of a large class of near-term continuous-variable quantum circuits.
The Applications and Challenges of Quantum Teleportation

Abstract Quantum teleportation plays a crucial role in information science for its property of completely secure transmission. It was first demonstrated as a means of transferring the quantum state. Later, it has been extended greatly in the field of quantum computing, quantum network, and quantum communication and other fields. The first part of this paper will talk briefly about the basic theory of quantum teleportation and the applications that have been achieved in recent years in both commercial and scientific purposes. Next, the results of current experiments and the challenges that should be overcome in the future will be presented. The final section will be a discussion about the development of quantum teleportation and its future implementations.
Hallucinating Visual Instances in Total Absentia

In this paper, we investigate a new visual restoration task, termed as hallucinating visual instances in total absentia (HVITA). Unlike conventional image inpainting task that works on images with only part of a visual instance missing, HVITA concerns scenarios where an object is completely absent from the scene. This seemingly minor difference in fact makes the HVITA a much challenging task, as the restoration algorithm would have to not only infer the category of the object in total absentia, but also hallucinate an object of which the appearance is consistent with the background. Towards solving HVITA, we propose an end-to-end deep approach that explicitly looks into the global semantics within the image. Specifically, we transform the input image to a semantic graph, wherein each node corresponds to a detected object in the scene. We then adopt a Graph Convolutional Network on top of the scene graph to estimate the category of the missing object in the masked region, and finally introduce a Generative Adversarial Module to carry out the hallucination. Experiments on COCO, Visual Genome and NYU Depth v2 datasets demonstrate that the proposed approach yields truly encouraging and visually plausible results.
Desiging Social Play Things

Social play is a key factor in children's positive development. Children with different interactional styles, such as neurodivergent children, face challenges to create opportunities for playing with peers to practice and acquire complex social skills, putting their social and emotional wellbeing at risk. We report on the design of social play technologies that aim to scaffold and support, in non-normative ways, co-located play of heterogeneous, neurodiverse groups of children aged 6 to 8. In three case studies we describe the participatory design processes that led to the development of three prototypical social play things. We report on the methodological challenges to design with these highly diverse groups and critically reflect on negotiating design decisions across abilities, agendas and conceptions of play within the process. Drawing on our successes and failures in these three cases, we synthesise design guidance for creating social play technologies for diverse groups of children.
Universal Domain Adaptation through Self Supervision

Unsupervised domain adaptation methods traditionally assume that all source categories are present in the target domain. In practice, little may be known about the category overlap between the two domains. While some methods address target settings with either partial or open-set categories, they assume that the particular setting is known a priori. We propose a more universally applicable domain adaptation framework that can handle arbitrary category shift, called Domain Adaptative Neighborhood Clustering via Entropy optimization (DANCE). DANCE combines two novel ideas: First, as we cannot fully rely on source categories to learn features discriminative for the target, we propose a novel neighborhood clustering technique to learn the structure of the target domain in a self-supervised way. Second, we use entropy-based feature alignment and rejection to align target features with the source, or reject them as unknown categories based on their entropy. We show through extensive experiments that DANCE outperforms baselines across open-set, open-partial and partial domain adaptation settings. Implementation is available at this https URL.
Reliable Graph Neural Networks via Robust Aggregation

Perturbations targeting the graph structure have proven to be extremely effective in reducing the performance of Graph Neural Networks (GNNs), and traditional defenses such as adversarial training do not seem to be able to improve robustness. This work is motivated by the observation that adversarially injected edges effectively can be viewed as additional samples to a node's neighborhood aggregation function, which results in distorted aggregations accumulating over the layers. Conventional GNN aggregation functions, such as a sum or mean, can be distorted arbitrarily by a single outlier. We propose a robust aggregation function motivated by the field of robust statistics. Our approach exhibits the largest possible breakdown point of 0.5, which means that the bias of the aggregation is bounded as long as the fraction of adversarial edges of a node is less than 50\%. Our novel aggregation function, Soft Medoid, is a fully differentiable generalization of the Medoid and therefore lends itself well for end-to-end deep learning. Equipping a GNN with our aggregation improves the robustness with respect to structure perturbations on Cora ML by a factor of 3 (and 5.5 on Citeseer) and by a factor of 8 for low-degree nodes.
Development of a display system for visualization in medical Applications

Abstract Imaging capabilities have been enhanced by scan modality. A review and comparison of some of the most effective open source libraries and medical application development has been implemented. A system composed of Ultrasound-Diagnostic-Machine-CMS600B2, a core i7-9750h Intel laptop processor, an easy-to-use capture tool for converting ultrasound analog images to digital images stored in a laptop and VR receiver to imagine a full organ. The understanding of 3D medical information earned very little consideration when it was intended to look at the ability of investigators to solve problems in 3D space. The aim of this research is to develop powerful computational techniques for integrating, interpreting and analyzing images for creative use in clinical practice. It’s turning into extra common to speak regarding 3D, 4D images formed by modern imaging techniques. The potential will be demonstrated by the evolution of medical imaging and VR systems that led to this system.
DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles

Recent research finds CNN models for image classification demonstrate overlapped adversarial vulnerabilities: adversarial attacks can mislead CNN models with small perturbations, which can effectively transfer between different models trained on the same dataset. Adversarial training, as a general robustness improvement technique, eliminates the vulnerability in a single model by forcing it to learn robust features. The process is hard, often requires models with large capacity, and suffers from significant loss on clean data accuracy. Alternatively, ensemble methods are proposed to induce sub-models with diverse outputs against a transfer adversarial example, making the ensemble robust against transfer attacks even if each sub-model is individually non-robust. Only small clean accuracy drop is observed in the process. However, previous ensemble training methods are not efficacious in inducing such diversity and thus ineffective on reaching robust ensemble. We propose DVERGE, which isolates the adversarial vulnerability in each sub-model by distilling non-robust features, and diversifies the adversarial vulnerability to induce diverse outputs against a transfer attack. The novel diversity metric and training procedure enables DVERGE to achieve higher robustness against transfer attacks comparing to previous ensemble methods, and enables the improved robustness when more sub-models are added to the ensemble. The code of this work is available at this https URL
Improving Inference for Neural Image Compression

We consider the problem of lossy image compression with deep latent variable models. State-of-the-art methods build on hierarchical variational autoencoders (VAEs) and learn inference networks to predict a compressible latent representation of each data point. Drawing on the variational inference perspective on compression, we identify three approximation gaps which limit performance in the conventional approach: an amortization gap, a discretization gap, and a marginalization gap. We propose remedies for each of these three limitations based on ideas related to iterative inference, stochastic annealing for discrete optimization, and bits-back coding, resulting in the first application of bits-back coding to lossy compression. In our experiments, which include extensive baseline comparisons and ablation studies, we achieve new state-of-the-art performance on lossy image compression using an established VAE architecture, by changing only the inference method.
Almost optimal model-free reinforcement learning via reference-advantage decomposition

We study the reinforcement learning problem in the setting of finite-horizon episodic Markov Decision Processes (MDPs) with $S$ states, $A$ actions, and episode length $H$. We propose a model-free algorithm UCB-Advantage and prove that it achieves $\tilde{O}(\sqrt{H^2SAT})$ regret where $T = KH$ and $K$ is the number of episodes to play. Our regret bound improves upon the results of [Jin et al., 2018] and matches the best known model-based algorithms as well as the information theoretic lower bound up to logarithmic factors. We also show that UCB-Advantage achieves low local switching cost and applies to concurrent reinforcement learning, improving upon the recent results of [Bai et al., 2019].
Monotone operator equilibrium networks.

Implicit-depth models such as Deep Equilibrium Networks have recently been shown to match or exceed the performance of traditional deep networks while being much more memory efficient. However, these models suffer from unstable convergence to a solution and lack guarantees that a solution exists. On the other hand, Neural ODEs, another class of implicit-depth models, do guarantee existence of a unique solution but perform poorly compared with traditional networks. In this paper, we develop a new class of implicit-depth model based on the theory of monotone operators, the Monotone Operator Equilibrium Network (MON). We show the close connection between finding the equilibrium point of an implicit network and solving a form of monotone operator splitting problem, which admits efficient solvers with guaranteed, stable convergence. We then develop a parameterization of the network which ensures that all operators remain monotone, which guarantees the existence of a unique equilibrium point. Finally, we show how to instantiate several versions of these models, and implement the resulting iterative solvers, for structured linear operators such as multi-scale convolutions. The resulting models vastly outperform the Neural ODE-based models while also being more computationally efficient. Code is available at this http URL.
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts

Accurate estimation of aleatoric and epistemic uncertainty is crucial to build safe and reliable systems. Traditional approaches, such as dropout and ensemble methods, estimate uncertainty by sampling probability predictions from different submodels, which leads to slow uncertainty estimation at inference time. Recent works address this drawback by directly predicting parameters of prior distributions over the probability predictions with a neural network. While this approach has demonstrated accurate uncertainty estimation, it requires defining arbitrary target parameters for in-distribution data and makes the unrealistic assumption that out-of-distribution (OOD) data is known at training time. In this work we propose the Posterior Network (PostNet), which uses Normalizing Flows to predict an individual closed-form posterior distribution over predicted probabilites for any input sample. The posterior distributions learned by PostNet accurately reflect uncertainty for in- and out-of-distribution data -- without requiring access to OOD data at training time. PostNet achieves state-of-the art results in OOD detection and in uncertainty calibration under dataset shifts.
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels

Recently, different machine learning methods have been introduced to tackle the challenging few-shot learning scenario that is, learning from a small labeled dataset related to a specific task. Common approaches have taken the form of meta-learning: learning to learn on the new problem given the old. Following the recognition that meta-learning is implementing learning in a multi-level model, we present a Bayesian treatment for the meta-learning inner loop through the use of deep kernels. As a result we can learn a kernel that transfers to new tasks; we call this Deep Kernel Transfer (DKT). This approach has many advantages: is straightforward to implement as a single optimizer, provides uncertainty quantification, and does not require estimation of task-specific parameters. We empirically demonstrate that DKT outperforms several state-of-the-art algorithms in few-shot classification, and is the state of the art for cross-domain adaptation and regression. We conclude that complex meta-learning routines can be replaced by a simpler Bayesian model without loss of accuracy.
Objective Mismatch in Model-based Reinforcement Learning

Model-based reinforcement learning (MBRL) has been shown to be a powerful framework for data-efficiently learning control of continuous tasks. Recent work in MBRL has mostly focused on using more advanced function approximators and planning schemes, with little development of the general framework. In this paper, we identify a fundamental issue of the standard MBRL framework -- what we call the objective mismatch issue. Objective mismatch arises when one objective is optimized in the hope that a second, often uncorrelated, metric will also be optimized. In the context of MBRL, we characterize the objective mismatch between training the forward dynamics model w.r.t.~the likelihood of the one-step ahead prediction, and the overall goal of improving performance on a downstream control task. For example, this issue can emerge with the realization that dynamics models effective for a specific task do not necessarily need to be globally accurate, and vice versa globally accurate models might not be sufficiently accurate locally to obtain good control performance on a specific task. In our experiments, we study this objective mismatch issue and demonstrate that the likelihood of one-step ahead predictions is not always correlated with control performance. This observation highlights a critical limitation in the MBRL framework which will require further research to be fully understood and addressed. We propose an initial method to mitigate the mismatch issue by re-weighting dynamics model training. Building on it, we conclude with a discussion about other potential directions of research for addressing this issue.