Reviewerly/exHarmony huggingface dataset:
Quantum natural gradient generalized to noisy and nonunitary circuits

Variational quantum algorithms are promising tools whose efficacy depends on their optimization method. For noise-free unitary circuits, the quantum generalization of natural gradient descent has been introduced and shown to be equivalent to imaginary time evolution: the approach is effective due to a metric tensor reconciling the classical parameter space to the device's Hilbert space. Here we generalize quantum natural gradient to consider arbitrary quantum states (both mixed and pure) via completely positive maps; thus our circuits can incorporate both imperfect unitary gates and fundamentally nonunitary operations such as measurements. We employ the quantum Fisher information (QFI) as the core metric in the space of density operators. A modification of the error suppression by derangements (ESD) and virtual distillation (VD) techniques enables an accurate and experimentally efficient approximation of the QFI via the Hilbert-Schmidt metric tensor using prior results on the dominant eigenvector of noisy quantum states. Our rigorous proof also establishes the fundamental observation that the geometry of typical noisy quantum states is (approximately) identical in either the Hilbert-Schmidt metric or as characterized by the QFI. In numerical simulations of noisy quantum circuits we demonstrate the practicality of our approach and confirm it can significantly outperform other variational techniques.
Early stopping by correlating online indicators in neural networks

In order to minimize the generalization error in neural networks, a novel technique to identify overfitting phenomena when training the learner is formally introduced. This enables support of a reliable and trustworthy early stopping condition, thus improving the predictive power of that type of modeling. Our proposal exploits the correlation over time in a collection of online indicators, namely characteristic functions for indicating if a set of hypotheses are met, associated with a range of independent stopping conditions built from a canary judgment to evaluate the presence of overfitting. That way, we provide a formal basis for decision making in terms of interrupting the learning process. As opposed to previous approaches focused on a single criterion, we take advantage of subsidiarities between independent assessments, thus seeking both a wider operating range and greater diagnostic reliability. With a view to illustrating the effectiveness of the halting condition described, we choose to work in the sphere of natural language processing, an operational continuum increasingly based on machine learning. As a case study, we focus on parser generation, one of the most demanding and complex tasks in the domain. The selection of cross-validation as a canary function enables an actual comparison with the most representative early stopping conditions based on overfitting identification, pointing to a promising start toward an optimal bias and variance control.
Domain generalization via adversarial out-domain augmentation for remaining useful life prediction of bearings under unseen conditions

Since classical deep learning (DL) techniques are hungry for massive data and suffer from domain shift, domain adaptation (DA) methods are broadly adopted in prognostics and health management (PHM) to align source and target domains. However, DA relies on target datasets collected in advance, which are not always available in practice. In this paper, a domain generalization (DG) approach, which learns from multiple source domains and generalizes well to unseen domains, is introduced for remaining useful life (RUL) prediction of bearings under unseen operating conditions. Specifically, we propose an adversarial out-domain augmentation (AOA) framework to generate pseudo-domains, thereby increasing the diversity of available samples. Hence, a generator is trained in an adversarial manner to generate augmented pseudo-domains by maximizing the domain discrepancy of the latent representations. In addition, we add manifold and semantic regularization to its objective function to ensure the consistency of the pseudo-domains. Trained with these available domains, a task predictor can improve the generalization in inaccessible target domain. Based on this, we provide a specific implementation of AOA-based RUL prediction for DG and validate its effectiveness and superiority using experimental datasets.
Incorporating Surprisingly Popular Algorithm and Euclidean distance-based adaptive topology into PSO

While many Particle Swarm Optimization (PSO) algorithms only use fitness to assess the performance of particles, in this work, we adopt Surprisingly Popular Algorithm (SPA) as a complementary metric in addition to fitness. Consequently, particles that are not widely known also have the opportunity to be selected as the learning exemplars. In addition, we propose a Euclidean distance-based adaptive topology to cooperate with SPA, where each particle only connects to k number of particles with the shortest Euclidean distance during each iteration. We also introduce the adaptive topology into heterogeneous populations to better solve large-scale problems. Specifically, the exploration sub-population better preserves the diversity of the population while the exploitation sub-population achieves fast convergence. Therefore, large-scale problems can be solved in a collaborative manner to elevate the overall performance. To evaluate the performance of our method, we conduct extensive experiments on various optimization problems, including three benchmark suites and two real-world optimization problems. The results demonstrate that our Euclidean distance-based adaptive topology outperforms the other widely adopted topologies and further suggest that our method performs significantly better than state-of-the-art PSO variants on small, medium, and large-scale problems.
Vaccine supply chain coordination using blockchain and artificial intelligence technologies

Currently, the global spread of COVID-19 is taking a heavy toll on the lives of the global population. There is an urgent need to improve and strengthen the coordination of vaccine supply chains in response to this severe pandemic. In this study, we consider a vaccine supply chain based on a combination of artificial intelligence and blockchain technologies and model the supply chain as a two-player dynamic game with inventory level as the dynamic equation of the system. The study focuses on the applicability and effectiveness of the two technologies in the vaccine supply chain and provides management insights. The impact of the application of the technologies on environmental performance is also considered in the model. We also examine factors such as the number of people vaccinated, positive and side effects of vaccines, vaccine decay rate, revenue-sharing/cost-sharing ratio, and commission ratio. The results are as follows: the correlation between the difficulty in obtaining certified vaccines and the profit of a vaccine manufacturer is not monotonous; the vaccine manufacturer is more sensitive to changes in the vaccine attenuation rate. The study's major conclusions are as follows: First, the vaccine supply chain should estimate the level of consumers' difficulty in obtaining a certified vaccine source and the magnitude of the production planning and demand forecasting error terms before adopting the two technologies. Second, the application of artificial intelligence (AI) technology is meaningful in the vaccine supply chain when the error terms satisfy a particular interval condition.
Revisiting Consistency Regularization for Semi-Supervised Learning

Abstract Consistency regularization is one of the most widely-used techniques for semi-supervised learning (SSL). Generally, the aim is to train a model that is invariant to various data augmentations. In this paper, we revisit this idea and find that enforcing invariance by decreasing distances between features from differently augmented images leads to improved performance. However, encouraging equivariance instead, by increasing the feature distance, further improves performance. To this end, we propose an improved consistency regularization framework by a simple yet effective technique, FeatDistLoss, that imposes consistency and equivariance on the classifier and the feature level, respectively. Experimental results show that our model defines a new state of the art across a variety of standard semi-supervised learning benchmarks as well as imbalanced semi-supervised learning benchmarks. Particularly, we outperform previous work by a significant margin in low data regimes and at large imbalance ratios. Extensive experiments are conducted to analyze the method, and the code will be published.
CHSMOTE: Convex hull-based synthetic minority oversampling technique for alleviating the class imbalance problem

The class imbalance problem, caused by skewed class distributions, is prevalent in real-world datasets. Generally, classifiers tend to bias toward the majority class, which may cause enormous misclassification costs. SMOTE is a powerful tool for this problem, which achieves a balanced class distribution by generating synthetic minority samples on the line segment between two minority samples. However, it still has some weaknesses, including 1) oversampling less-informative minority samples, 2) generating over-dense synthetic samples, and 3) generating noise. In this paper, we propose a novel and simple convex hull-based SMOTE (CHSMOTE) algorithm to overcome the three weaknesses of SMOTE simultaneously and further alleviate the class imbalance problem. Specifically, CHSMOTE selects the border minority samples as the initial minority samples for oversampling and identifies a sample synthesis area based on the convex hull by checking whether constructed convex hulls contain border majority samples. CHSMOTE can generate more samples with effective information by properly enlarging the generation range of synthetic samples. Extensive experiment results demonstrate the effectiveness and superiority of the proposed algorithm for alleviating the class imbalance problem.
Intermittent solar power hybrid forecasting system based on pattern recognition and feature extraction

Solar energy, with its abundance and accessibility, occupies an irreplaceable position in the shift in global energy consumption patterns. The difficulties of managing solar energy on the grid, caused by its highly volatile and intermittent nature, necessitate an accurate and stable forecasting system. However, existing studies have focused more on the accuracy of prediction without considering the reactive power caused by intermittency, whose prevalence leads to the solar power series having both discrete and continuous characteristics, making the prediction problem more challenging. To fill this gap, a multistep ahead hybrid forecasting system has been constructed in this study that contains feature extraction, pattern recognition, forecasting, and integrated optimization modules. The system integrates pattern recognition algorithms into the prediction models to appropriately deal with the inconsistent data features caused by intermittency and introduces a data decomposition strategy to achieve feature extraction, such that it could be adapted to forecast situations where relevant weather information is not available. Finally, the multi-objective optimization algorithm allows selecting, weighting, and integrating all submodels and yields a hybrid model with excellent accuracy and stability. The results showed that the hybrid forecasting system achieved the best performance in all aspects of the four forecast scenarios and also performed well in the multistep advance forecast. Taking Site 1 as an example, the mean absolute scale errors of the one-step, two-step, and three-step advance forecasts are 2.9953%, 3.7095%, and 3.4945%, respectively; and the standard deviations of the errors are 3.1086, 3.6210, and 3.2133, respectively. Furthermore, the hybrid forecasting system achieves a performance improvement in accuracy and stability of more than 90%, and this performance improvement is significant at the 10% significance level.
TreEnhance: A tree search method for low-light image enhancement

In this paper we present TreEnhance, an automatic method for low-light image enhancement capable of improving the quality of digital images. The method combines tree search theory, and in particular the Monte Carlo Tree Search (MCTS) algorithm, with deep reinforcement learning. Given as input a low-light image, TreEnhance produces as output its enhanced version together with the sequence of image editing operations used to obtain it. During the training phase, the method repeatedly alternates two main phases: a generation phase, where a modified version of MCTS explores the space of image editing operations and selects the most promising sequence, and an optimization phase, where the parameters of a neural network, implementing the enhancement policy, are updated. Two different inference solutions are proposed for the enhancement of new images: one is based on MCTS and is more accurate but more time and memory consuming; the other directly applies the learned policy and is faster but slightly less precise. As a further contribution, we propose a guided search strategy that "reverses" the enhancement procedure that a photo editor applied to a given input image. Unlike other methods from the state of the art, TreEnhance does not pose any constraint on the image resolution and can be used in a variety of scenarios with minimal tuning. We tested the method on two datasets: the Low-Light dataset and the Adobe Five-K dataset obtaining good results from both a qualitative and a quantitative point of view.
UIR-Net: A Simple and Effective Baseline for Underwater Image Restoration and Enhancement

Because of the unique physical and chemical properties of water, obtaining high-quality underwater images directly is not an easy thing. Hence, recovery and enhancement are indispensable steps in underwater image processing and have therefore become research hotspots. Nevertheless, existing image-processing methods generally have high complexity and are difficult to deploy on underwater platforms with limited computing resources. To tackle this issue, this paper proposes a simple and effective baseline named UIR-Net that can recover and enhance underwater images simultaneously. This network uses a channel residual prior to extract the channel of the image to be recovered as a prior, combined with a gradient strategy to reduce parameters and training time to make the operation more lightweight. This method can improve the color performance while maintaining the style and spatial texture of the contents. Through experiments on three datasets (MSRB, MSIRB and UIEBD-Snow), we confirm that UIR-Net can recover clear underwater images from original images with large particle impurities and ocean light spots. Compared to other state-of-the-art methods, UIR-Net can recover underwater images at a similar or higher quality with a significantly lower number of parameters, which is valuable in real-world applications.
Distributed Approaches to Butterfly Analysis on Large Dynamic Bipartite Graphs

Tip decomposition has a pivotal role in mining cohesive subgraphs in bipartite graphs by computing the tip number of each vertex in accordance with the non-trivial motif butterfly ((2,2)-biclique). It has been a popular research topic with applications in document clustering, spam group detection, and analysis of affiliation networks. In such applications, the graphs are not only large, but they evolve quickly with new edges being continuously added/deleted. While existing centralized techniques could solve the tip decomposition problem for static bipartite graphs, they are not efficient for maintaining the tip numbers of vertices on large-scale graphs. In this paper, we study butterfly analysis problems on bipartite graphs in a distributed environment with the vertex-centric model. We first extend a centralized butterfly counting algorithm to a distributed version, called DBCA. An ingenious message aggregation strategy is designed to reduce massive redundant messaging and avoid the memory overflow problem while processing large-scale graphs. Based on the results of DBCA, we develop a distributed tip decomposition algorithm (DTDA) to get the tip number of each vertex in parallel. Finally, to maintain the tip numbers of vertices efficiently while graphs evolve over time, we explore a distributed tip maintenance algorithm (DTMA) along with a novel task-split strategy. Specifically, for an updated edge (insertion/deletion), several sub-tasks will be generated in line with the topology structure of the original bipartite graph. To our best knowledge, this is the first study to process the butterfly analysis problems in a distributed environment. In addition, comprehensive experiments have been conducted on real-world bipartite graphs. The experiment results demonstrate that our proposed algorithms are efficient and scalable.
Financial reporting for cryptocurrency

Abstract This study compares and contrasts US and international accounting and financial reporting practices for cryptocurrency. We analyze the financial statements of 40 global companies that have exposure to cryptocurrencies, including cryptocurrency purchases, mining, payments, trading, and investments in ICOs and early-stage blockchain ventures. We document inconsistency between Generally Accepted Accounting Principles (GAAP) and International Financial Reporting Standards (IFRS), as well as distortions that can mislead users in assessing asset value, liquidity, profitability, and cash-generating abilities across firms. In particular, firms receiving cryptocurrencies in revenue-generating activities account for cryptocurrencies as intangibles using different measurement bases and classify the associated cash inflows differently. Some firms place cryptocurrencies in the usual long-term location of intangibles, while others consider intangibles as liquid, short-term assets. Limited guidance about crypto-assets from both IFRS and GAAP lets companies choose which existing standard to apply and how to apply it. Understanding the financial and valuation implications of these new virtual assets is vital for future accounting research and professional practice.
TWIST: Two-Way Inter-label Self-Training for Semi-supervised 3D Instance Segmentation

We explore the way to alleviate the label-hungry problem in a semi-supervised setting for 3D instance segmentation. To leverage the unlabeled data to boost model performance, we present a novel Two-Way Inter-label Self-Training framework named TWIST. It exploits inherent correlations between semantic understanding and instance information of a scene. Specifically, we consider two kinds of pseudo labels for semantic- and instance-level supervision. Our key design is to provide object-level information for denoising pseudo labels and make use of their correlation for two-way mutual enhancement, thereby iteratively promoting the pseudo-label qualities. TWIST attains leading performance on both ScanNet and S3DIS, compared to recent 3D pre-training approaches, and can cooperate with them to further enhance performance, e.g., +4.4% AP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</inf> on 1%-label ScanNet data-efficient benchmark. Code is available at https://github.com/dvlab-research/TWIST.
k-means Mask Transformer

The rise of transformers in vision tasks not only advances network backbone designs, but also starts a brand-new page to achieve end-to-end image recognition (e.g., object detection and panoptic segmentation). Originated from Natural Language Processing (NLP), transformer architectures, consisting of self-attention and cross-attention, effectively learn long-range interactions between elements in a sequence. However, we observe that most existing transformer-based vision models simply borrow the idea from NLP, neglecting the crucial difference between languages and images, particularly the extremely large sequence length of spatially flattened pixel features. This subsequently impedes the learning in cross-attention between pixel features and object queries. In this paper, we rethink the relationship between pixels and object queries, and propose to reformulate the cross-attention learning as a clustering process. Inspired by the traditional k-means clustering algorithm, we develop a $$\textbf{k}$$ -means Mask Xformer (kMaX-DeepLab) for segmentation tasks, which not only improves the state-of-the-art, but also enjoys a simple and elegant design. As a result, our kMaX-DeepLab achieves a new state-of-the-art performance on COCO val set with 58.0% PQ, and Cityscapes val set with 68.4% PQ, 44.0% AP, and 83.5% mIoU without test-time augmentation or external dataset. We hope our work can shed some light on designing transformers tailored for vision tasks. Code and models are available at https://github.com/google-research/deeplab2 .
Long-tail Recognition via Compositional Knowledge Transfer

In this work, we introduce a novel strategy for long-tail recognition that addresses the tail classes’ few-shot problem via training-free knowledge transfer. Our objective is to transfer knowledge acquired from information-rich common classes to semantically similar, and yet data-hungry, rare classes in order to obtain stronger tail class representations. We leverage the fact that class prototypes and learned cosine classifiers provide two different, complementary representations of class cluster centres in feature space, and use an attention mechanism to select and recompose learned classifier features from common classes to obtain higher quality rare class representations. Our knowledge transfer process is training free, reducing overfitting risks, and can afford continual extension of classifiers to new classes. Experiments show that our approach can achieve significant performance boosts on rare classes while maintaining robust common class performance, outperforming directly comparable state-of-the-art models
Bi-level Alignment for Cross-Domain Crowd Counting

Recently, crowd density estimation has received increasing attention. The main challenge for this task is to achieve high-quality manual annotations on a large amount of training data. To avoid reliance on such annotations, previous works apply unsupervised domain adaptation (UDA) techniques by transferring knowledge learned from easily accessible synthetic data to real-world datasets. However, current state-of-the-art methods either rely on external data for training an auxiliary task or apply an expensive coarse-to-fine estimation. In this work, we aim to develop a new adversarial learning based method, which is simple and efficient to apply. To reduce the domain gap between the synthetic and real data, we design a bi-level alignment framework (BLA) consisting of (1) task-driven data alignment and (2) fine-grained feature alignment. In contrast to previous domain augmentation methods, we introduce AutoML to search for an optimal transform on source, which well serves for the downstream task. On the other hand, we do fine-grained alignment for foreground and background separately to alleviate the alignment difficulty. We evaluate our approach on five real-world crowd counting benchmarks, where we outperform existing approaches by a large margin. Also, our approach is simple, easy to implement and efficient to apply. The code is publicly available at https://github.com/Yankeegsj/BLA.