Reviewerly/exHarmony huggingface dataset:
Stage-Wise Magnitude-Based Pruning for Recurrent Neural Networks

A recurrent neural network (RNN) has shown powerful performance in tackling various natural language processing (NLP) tasks, resulting in numerous powerful models containing both RNN neurons and feedforward neurons. On the other hand, the deep structure of RNN has heavily restricted its implementation on mobile devices, where quite a few applications involve NLP tasks. Magnitude-based pruning (MP) is a promising way to address such a challenge. However, the existing MP methods are mostly designed for feedforward neural networks that do not involve a recurrent structure, and, thus, have performed less satisfactorily on pruning models containing RNN layers. In this article, a novel stage-wise MP method is proposed by explicitly taking the featured recurrent structure of RNN into account, which can effectively prune feedforward layers and RNN layers, simultaneously. The connections of neural networks are first grouped into three types according to how they are intersected with recurrent neurons. Then, an optimization-based pruning method is applied to compress each group of connections, respectively. Empirical studies show that the proposed method performs significantly better than the commonly used RNN pruning methods; i.e., up to 96.84% connections are pruned with little or even no degradation of precision indicators on the testing datasets.
Implementing Graph-Theoretic Feature Selection by Quantum Approximate Optimization Algorithm

Feature selection plays a significant role in computer science; nevertheless, this task is intractable since its search space scales exponentially with the number of dimensions. Motivated by the potential advantages of near-term quantum computing, three graph-theoretic feature selection (GTFS) methods, including minimum cut (MinCut)-based, densest k -subgraph (DkS)-based, and maximal-independent set/minimal vertex cover (MIS/MVC)-based, are investigated in this article, where the original graph-theoretic problems are naturally formulated as the quadratic problems in binary variables and then solved using the quantum approximate optimization algorithm (QAOA). Specifically, three separate graphs are created from the raw feature set, where the vertex set consists of individual features and pairwise measure describes the edge. The corresponding feature subset is generated by deriving a subgraph from the established graph using QAOA. For the above three GTFS approaches, the solving procedure and quantum circuit for the corresponding graph-theoretic problems are formulated with the framework of QAOA. In addition, those proposals could be employed as a local solver and integrated with the Tabu search algorithm for solving large-scale GTFS problems utilizing limited quantum bit resource. Finally, extensive numerical experiments are conducted with 20 publicly available datasets and the results demonstrate that each model is superior to its classical scheme. In addition, the complexity of each model is only O(p n2) even in the worst cases, where p is the number of layers in QAOA and n is the number of features.
ReCNAS: Resource-Constrained Neural Architecture Search Based on Differentiable Annealing and Dynamic Pruning

The differentiable neural architecture search (NAS) framework has obtained extensive attention and achieved remarkable performance due to its search efficiency. However, most existing differentiable NAS methods still suffer from issues of model collapse, degenerated search-evaluation correlation, and inefficient hardware deployment, which causes the searched architectures to be suboptimal in accuracy and cannot meet different computation resource constraints (e.g., FLOPs and latency). In this article, we propose a novel resource-constrained NAS (ReCNAS) method, which can efficiently search high-performance architectures that satisfy the given constraints, and deal with the issues observed in previous differentiable NAS methods from three aspects: search space, search strategy, and resource adaptability. First, we introduce an elastic densely connected layerwise search space, which decouples the architecture depth representation from the search of candidate operations to alleviate the aggregation of skip connections and architecture redundancies. Second, a scheme of group annealing and progressive pruning is proposed to improve the efficiency and bridge the search-evaluation gap, which steadily forces the architecture parameters close to binary distribution and progressively prunes the inferior operations. Third, we present a novel resource-constrained architecture generation method, which prunes the redundant channel throughout the search based on dynamic programming, making the searched architecture scalable to different devices and requirements. Extensive experimental results demonstrate the efficiency and search stability of our ReCNAS, which is capable of discovering high-performance architectures on different datasets and tasks, surpassing other NAS methods, while tightly meeting the target resource constraints without any tuning required. Besides, the searched architectures show strong generalizability to other complex vision tasks.
Nonlinear topological symmetry protection in a dissipative system

We report an experimental and theoretical investigation of a system whose dynamics is dominated by an intricate interplay between three key concepts of modern physics: topology, nonlinearity, and spontaneous symmetry breaking. The experiment is based on a two-mode coherently-driven optical resonator in which photons interact through the Kerr nonlinearity. In presence of a phase defect between the modes, a nonlinear attractor develops, which confers a synthetic M\"obius topology to the modal structure of the system. That topology is associated with an inherently protected exchange symmetry between the modes, enabling the realization of spontaneous symmetry breaking in ideal, bias-free, conditions without any fine tuning of parameters. The dynamic manifests itself by a periodic alternation of the modes from one resonator roundtrip to the next reminiscent of period-doubling. This extends to a range of localized structures in the form of domain walls, bright solitons, and breathers, which have all been observed with remarkable long term stability. A rigorous testing of the randomness of the symmetry-broken state selection statistics has also confirmed the robustness of the exchange symmetry in our experiment. Our results and conclusions are supported by an effective Hamiltonian model explaining the symmetry protection in our system. The model also shows that our work has relevance to other systems of interacting bosons and to the Floquet engineering of quantum matter. Our work could also be beneficial to the implementation of coherent Ising machines.
GeneGPT: Augmenting large language models with domain tools for improved access to biomedical information

Abstract Motivation While large language models (LLMs) have been successfully applied to various tasks, they still face challenges with hallucinations. Augmenting LLMs with domain-specific tools such as database utilities can facilitate easier and more precise access to specialized knowledge. In this paper, we present GeneGPT, a novel method for teaching LLMs to use the Web APIs of the National Center for Biotechnology Information (NCBI) for answering genomics questions. Specifically, we prompt Codex to solve the GeneTuring tests with NCBI Web APIs by in-context learning and an augmented decoding algorithm that can detect and execute API calls. Results Experimental results show that GeneGPT achieves state-of-the-art performance on eight tasks in the GeneTuring benchmark with an average score of 0.83, largely surpassing retrieval-augmented LLMs such as the new Bing (0.44), biomedical LLMs such as BioMedLM (0.08) and BioGPT (0.04), as well as GPT-3 (0.16) and ChatGPT (0.12). Our further analyses suggest that: First, API demonstrations have good cross-task generalizability and are more useful than documentations for in-context learning; Second, GeneGPT can generalize to longer chains of API calls and answer multi-hop questions in GeneHop, a novel dataset introduced in this work; Finally, different types of errors are enriched in different tasks, providing valuable insights for future improvements. Availability The GeneGPT code and data are publicly available at https://github.com/ncbi/GeneGPT.
A Laplacian Pyramid Based Generative H&amp;E Stain Augmentation Network

Hematoxylin and Eosin (H&E) staining is a widely used sample preparation procedure for enhancing the saturation of tissue sections and the contrast between nuclei and cytoplasm in histology images for medical diagnostics. However, various factors, such as the differences in the reagents used, result in high variability in the colors of the stains actually recorded. This variability poses a challenge in achieving generalization for machine-learning based computer-aided diagnostic tools. To desensitize the learned models to stain variations, we propose the Generative Stain Augmentation Network (G-SAN) - a GAN-based framework that augments a collection of cell images with simulated yet realistic stain variations. At its core, G-SAN uses a novel and highly computationally efficient Laplacian Pyramid (LP) based generator architecture, that is capable of disentangling stain from cell morphology. Through the task of patch classification and nucleus segmentation, we show that using G-SAN-augmented training data provides on average 15.7% improvement in F1 score and 7.3% improvement in panoptic quality, respectively. Our code is available at https://github.com/lifangda01/GSAN-Demo.
Discovering Datasets in Unstructured Corpora: Discovering Use and Identifying New Opportunities

Federal statistical agencies are keenly aware that scientific research is not the only way in which their data assets are used for evidence building. Entities around the world have been building services on top of curated corpuses of scientific research, to help provide insight as to the importance of the works in the collection. This also provides a much-needed framework for statistical agencies and other dataset creators to search and find the usages and impacts of those datasets. While the other papers in this special issue largely apply machine learning models to find datasets in an extensively curated corpus, this paper starts with a much less structured framework and examines the potential to discover how datasets are used in writing thatâs targeted at a broader base of users than scientific researchers, and describes the challenges and lessons learned from the exercise.
Bioframe: Operations on genomic intervals in pandas dataframes

Genomic intervals are one of the most prevalent data structures in computational genome biology, and used to represent features ranging from genes, to DNA binding sites, to disease variants. Operations on genomic intervals provide a language for asking questions about relationships between features. While there are excellent interval arithmetic tools for the command line, they are not smoothly integrated into Python, one of the most popular general-purpose computational and visualization environments.Bioframe is a library to enable flexible and performant operations on genomic interval dataframes in Python. Bioframe extends the Python data science stack to use cases for computational genome biology by building directly on top of two of the most commonly-used Python libraries, NumPy and Pandas. The bioframe API enables flexible name and column orders, and decouples operations from data formats to avoid unnecessary conversions, a common scourge for bioinformaticians. Bioframe achieves these goals while maintaining high performance and a rich set of features.Bioframe is open-source under MIT license, cross-platform, and can be installed from the Python Package Index. The source code is maintained by Open2C on GitHub at https://github.com/open2c/bioframe.
Discriminative Regression With Adaptive Graph Diffusion

In this article, we propose a new linear regression (LR)-based multiclass classification method, called discriminative regression with adaptive graph diffusion (DRAGD). Different from existing graph embedding-based LR methods, DRAGD introduces a new graph learning and embedding term, which explores the high-order structure information between four tuples, rather than conventional sample pairs to learn an intrinsic graph. Moreover, DRAGD provides a new way to simultaneously capture the local geometric structure and representation structure of data in one term. To enhance the discriminability of the transformation matrix, a retargeted learning approach is introduced. As a result of combining the above-mentioned techniques, DRAGD can flexibly explore more unsupervised information underlying the data and the label information to obtain the most discriminative transformation matrix for multiclass classification tasks. Experimental results on six well-known real-world databases and a synthetic database demonstrate that DRAGD is superior to the state-of-the-art LR methods.
Autoencoder in Autoencoder Networks

Modeling complex correlations on multiview data is still challenging, especially for high-dimensional features with possible noise. To address this issue, we propose a novel unsupervised multiview representation learning (UMRL) algorithm, termed autoencoder in autoencoder networks (AE2-Nets). The proposed framework effectively encodes information from high-dimensional heterogeneous data into a compact and informative representation with the proposed bidirectional encoding strategy. Specifically, the proposed AE2-Nets conduct encoding in two directions: the inner-AE-networks extract view-specific intrinsic information (forward encoding), while the outer-AE-networks integrate this view-specific intrinsic information from different views into a latent representation (backward encoding). For the nested architecture, we further provide a probabilistic explanation and extension from hierarchical variational autoencoder. The forward-backward strategy flexibly addresses high-dimensional (noisy) features within each view and encodes complementarity across multiple views in a unified framework. Extensive results on benchmark datasets validate the advantages compared to the state-of-the-art algorithms.
Personalized Federated Few-Shot Learning

Personalized federated learning (PFL) learns a personalized model for each client in a decentralized manner, where each client owns private data that are not shared and data among clients are non-independent and identically distributed (i.i.d.) However, existing PFL solutions assume that clients have sufficient training samples to jointly induce personalized models. Thus, existing PFL solutions cannot perform well in a few-shot scenario, where most or all clients only have a handful of samples for training. Furthermore, existing few-shot learning (FSL) approaches typically need centralized training data; as such, these FSL methods are not applicable in decentralized scenarios. How to enable PFL with limited training samples per client is a practical but understudied problem. In this article, we propose a solution called personalized federated few-shot learning (pFedFSL) to tackle this problem. Specifically, pFedFSL learns a personalized and discriminative feature space for each client by identifying which models perform well on which clients, without exposing local data of clients to the server and other clients, and which clients should be selected for collaboration with the target client. In the learned feature spaces, each sample is made closer to samples of the same category and farther away from samples of different categories. Experimental results on four benchmark datasets demonstrate that pFedFSL outperforms competitive baselines across different settings.
Best Practices of Engineering Education Internationalization in a Russian Top-20 University

Top world universities apply complex internationalization strategies to keep a sustainable leadership confirmed by the global rankings such as QS, THE, ARWU, or to improve their position in these rankings.Russian universities are not an exception and the analysis of the websites of Russian Top-20 universities (according to the National Ranking of Russian Universities, 9 universities of this Top-20 list are engineering universities) demonstrates their strong internationalization infrastructure.Tracking success stories and best practices of such universities using accurate statistics is a useful tool for sharing their internationalization experiences, finding matching points for cooperation and predicting of their competitiveness potential in the global higher education community.
Altruistic Collaborative Learning

This article proposes a new learning paradigm based on the concept of concordant gradients for ensemble learning strategies. In this paradigm, learners update their weights if and only if the gradients of their cost functions are mutually concordant in a sense given by paper. The objective of the proposed concordant optimization framework is robustness against uncertainties by postponing to a later epoch, the consideration of examples associated with discordant directions during a training phase. Concordance constrained collaboration is shown to be relevant, especially in intricate classification issues where exclusive class labeling involves information bias due to correlated disturbances affecting almost all training examples. The first learning paradigm applies on a gradient descent strategy based on allied agents, subjected to concordance checking before moving forward in training epochs. The second learning paradigm is related to multivariate dense neural matrix fusion, where the fusion operator is itself a learnable neural operator. In addition to these paradigms, this article proposes a new categorical probability transform to enrich the existing collection and propose an alternative scenario for integrating penalized SoftMax information. Finally, this article assesses the relevance of the above contributions with respect to several deep learning frameworks and a collaborative classification involving dependent classes.
SSFG: Stochastically Scaling Features and Gradients for Regularizing Graph Convolutional Networks

Graph convolutional networks (GCNs) have been successfully applied in various graph-based tasks. In a typical graph convolutional layer, node features are updated by aggregating neighborhood information. Repeatedly applying graph convolutions can cause the oversmoothing issue, i.e., node features at deep layers converge to similar values. Previous studies have suggested that oversmoothing is one of the major issues that restrict the performance of GCNs. In this article, we propose a stochastic regularization method to tackle the oversmoothing problem. In the proposed method, we stochastically scale features and gradients (SSFG) by a factor sampled from a probability distribution in the training procedure. By explicitly applying a scaling factor to break feature convergence, the oversmoothing issue is alleviated. We show that applying stochastic scaling at the gradient level is complementary to that applied at the feature level to improve the overall performance. Our method does not increase the number of trainable parameters. When used together with ReLU, our SSFG can be seen as a stochastic ReLU activation function. We experimentally validate our SSFG regularization method on three commonly used types of graph networks. Extensive experimental results on seven benchmark datasets for four graph-based tasks demonstrate that our SSFG regularization is effective in improving the overall performance of the baseline graph networks. The code is available at https://github.com/vailatuts/SSFG-regularization.
Biased Complementary-Label Learning Without True Labels

In complementary-label learning (CLL), the complementary transition matrix, denoting the probabilities that true labels flip into complementary labels (CLs) which specify classes observations do not belong to, is crucial to building statistically consistent classifiers. Most existing works implicitly assume that the transition probabilities are identical, which is not true in practice and may lead to undesirable bias in solutions. Few recent works have extended the problem to a biased setting but limit their explorations to modeling the transition matrix by exploiting the complementary class posteriors of anchor points (i.e., instances that almost certainly belong to a specific class). However, due to the severe corruption and unevenness of biased CLs, both anchor points and complementary class posteriors are difficult to predict accurately in the absence of true labels. In this article, rather than directly predicting these two error-prone items, we instead propose a divided-T estimator as an alternative to effectively learn transition matrices from only biased CLs. Specifically, we exploit semantic clustering to mitigate the adverse effects arising from CLs. By introducing the learned semantic clusters as an intermediate class, we factorize the original transition matrix into the product of two easy-to-estimate matrices that are not reliant on the two error-prone items. Both theoretical analyses and empirical results justify the effectiveness of the divided- T estimator for estimating transition matrices under a mild assumption. Experimental results on benchmark datasets further demonstrate that the divided- T estimator outperforms state-of-the-art (SOTA) methods by a substantial margin.
Riemannian Manifold-Based Feature Space and Corresponding Image Clustering Algorithms

Image feature representation is a key factor influencing the accuracy of clustering. Traditional point-based feature spaces represent spectral features of an image independently and introduce spatial relationships of pixels in the image domain to enhance the contextual information expression ability. Mapping-based feature spaces aim to preserve the structure information, but the complex computation and the unexplainability of image features have a great impact on their applications. To this end, we propose an explicit feature space called Riemannian manifold feature space (RMFS) to present the contextual information in a unified way. First, the Gaussian probability distribution function (pdf) is introduced to characterize the features of a pixel in its neighborhood system in the image domain. Then, the feature-related pdfs are mapped to a Riemannian manifold, which constructs the proposed RMFS. In RMFS, a point can express the complex contextual information of corresponding pixel in the image domain, and pixels representing the same object are linearly distributed. This gives us a chance to convert nonlinear image segmentation problems to linear computation. To verify the superiority of the expression ability of the proposed RMFS, a linear clustering algorithm and a fuzzy linear clustering algorithm are proposed. Experimental results show that the proposed RMFS-based algorithms outperform their counterparts in the spectral feature space and the RMFS-based ones without the linear distribution characteristics. This indicates that the RMFS can better express features of an image than spectral feature space, and the expressed features can be easily used to construct linear segmentation models.