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

Authors:
raw_author_name: Guiying Li
raw_affiliation_string: Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China
raw_author_name: Peng Yang
raw_affiliation_string: Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China
raw_author_name: Chao Qian
raw_affiliation_string: State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
raw_author_name: Richang Hong
raw_affiliation_string: School of Computer and Information, Hefei University of Technology, Hefei, China

Abstract: 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

Authors:
raw_author_name: YaoChong Li
raw_affiliation_string: Research Center of Intelligent Information Processing and Quantum Intelligent Computing and the College of Information Engineering, Shanghai Maritime University, Shanghai, China
raw_author_name: Ri-Gui Zhou
raw_affiliation_string: Research Center of Intelligent Information Processing and Quantum Intelligent Computing and the College of Information Engineering, Shanghai Maritime University, Shanghai, China
raw_author_name: RuiQing Xu
raw_affiliation_string: Research Center of Intelligent Information Processing and Quantum Intelligent Computing and the College of Information Engineering, Shanghai Maritime University, Shanghai, China
raw_author_name: Jia Luo
raw_affiliation_string: Research Center of Intelligent Information Processing and Quantum Intelligent Computing and the College of Information Engineering, Shanghai Maritime University, Shanghai, China
raw_author_name: WenWen Hu
raw_affiliation_string: Research Center of Intelligent Information Processing and Quantum Intelligent Computing and the College of Information Engineering, Shanghai Maritime University, Shanghai, China
raw_author_name: Ping Fan
raw_affiliation_string: College of Information Engineering, East China Jiaotong University, Nanchang, China

Abstract: 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.
Nonlinear topological symmetry protection in a dissipative system

Authors:
raw_author_name: Stéphane Coen
raw_affiliation_string: Physics Department, The University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand
raw_author_name: Bruno Garbin
raw_affiliation_string: Physics Department, The University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand
raw_author_name: Gang Xu
raw_affiliation_string: Physics Department, The University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand
raw_author_name: Liam Quinn
raw_affiliation_string: Physics Department, The University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand
raw_author_name: Nathan Goldman
raw_affiliation_string: Center for Nonlinear Phenomena and Complex Systems, Université Libre de Bruxelles, CP 231, B-1050, Brussels, Belgium
raw_author_name: Gian-Luca Oppo
raw_affiliation_string: SUPA and Department of Physics, University of Strathclyde, Glasgow, G4 0NG, Scotland
raw_author_name: Miro Erkintalo
raw_affiliation_string: Physics Department, The University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand
raw_author_name: Stuart G Murdoch
raw_affiliation_string: The Dodd-Walls Centre for Photonic and Quantum Technologies, Dunedin, New Zealand
raw_author_name: Julien Fatome
raw_affiliation_string: ICB - Laboratoire Interdisciplinaire Carnot de Bourgogne (Université de Bourgogne - 9 avenue Alain Savary - BP 47870 - 21078 Dijon Cedex - France)

Abstract: 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

Authors:
raw_author_name: Jin, Qiao
raw_affiliation_string: National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health , Bethesda, MD 20894, USA
raw_author_name: Yang, Yifan
raw_affiliation_string: National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health , Bethesda, MD 20894, USA
raw_author_name: Chen, Qingyu
raw_affiliation_string: National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health , Bethesda, MD 20894, USA
raw_author_name: Lu, Zhiyong
raw_affiliation_string: National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health , Bethesda, MD 20894, USA

Abstract: 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

Authors:
raw_author_name: Fangda Li
raw_affiliation_string: Department of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA
raw_author_name: Zhiqiang Hu
raw_affiliation_string: SenseTime Research, Beijing, China
raw_author_name: Wen Chen
raw_affiliation_string: SenseTime Research, Beijing, China
raw_author_name: Avinash Kak
raw_affiliation_string: Department of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA

Abstract: 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

Authors:
raw_author_name: Adel Alaeddini
raw_affiliation_string: School of Data Science, The University of Texas at San Antonio (UTSA), San Antonio, Texas, United States of America

Abstract: 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.
Discriminative Regression With Adaptive Graph Diffusion

Authors:
raw_author_name: Jie Wen
raw_affiliation_string: Shenzhen Key Laboratory of Visual Object Detection and Recognition, Harbin Institute of Technology, Shenzhen, Shenzhen, China
raw_author_name: Shijie Deng
raw_affiliation_string: Shenzhen Key Laboratory of Visual Object Detection and Recognition, Harbin Institute of Technology, Shenzhen, Shenzhen, China
raw_author_name: Lunke Fei
raw_affiliation_string: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China
raw_author_name: Zheng Zhang
raw_affiliation_string: Shenzhen Key Laboratory of Visual Object Detection and Recognition, Harbin Institute of Technology, Shenzhen, Shenzhen, China
raw_author_name: Bob Zhang
raw_affiliation_string: Department of Computer and Information Science, PAMI Research Group, University of Macau, Taipa, Macau
raw_author_name: Zhao Zhang
raw_affiliation_string: School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China

Abstract: 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

Authors:
raw_author_name: Changqing Zhang
raw_affiliation_string: Tianjin Key Laboratory of Machine Learning and the College of Intelligence and Computing, Tianjin University, Tianjin, China
raw_author_name: Yu Geng
raw_affiliation_string: College of Intelligence and Computing, Tianjin University, Tianjin, China
raw_author_name: Zongbo Han
raw_affiliation_string: College of Intelligence and Computing, Tianjin University, Tianjin, China
raw_author_name: Yeqing Liu
raw_affiliation_string: College of Intelligence and Computing, Tianjin University, Tianjin, China
raw_author_name: Huazhu Fu
raw_affiliation_string: Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A&#x002A;STAR), Fusionopolis, Singapore
raw_author_name: Qinghua Hu
raw_affiliation_string: Tianjin Key Laboratory of Machine Learning and the College of Intelligence and Computing, Tianjin University, Tianjin, China

Abstract: 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

Authors:
raw_author_name: Yunfeng Zhao
raw_affiliation_string: School of Software, Shandong University, Jinan, China
raw_author_name: Guoxian Yu
raw_affiliation_string: School of Software, Shandong University, Jinan, China
raw_author_name: Jun Wang
raw_affiliation_string: School of Software, Shandong University, Jinan, China
raw_author_name: Carlotta Domeniconi
raw_affiliation_string: Department of Computer Science, George Mason University, Fairfax, VA, USA
raw_author_name: Maozu Guo
raw_affiliation_string: Department of Computer Science, Beijing University of Civil Engineering and Architecture, Beijing, China
raw_author_name: Xiangliang Zhang
raw_affiliation_string: Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA
raw_author_name: Lizhen Cui
raw_affiliation_string: School of Software, Shandong University, Jinan, China

Abstract: 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

Authors:
raw_author_name: Julia Ziyatdinova
raw_affiliation_string:  American Society for Engineering Education, 2016; College of Technology of Purdue University.; Kazan State Pedagogical University in 1999.; Purdue University, West Lafayette
raw_author_name: Artem Bezrukov
raw_author_name: Phillip Albert Sanger
raw_affiliation_string:  American Society for Engineering Education, 2016; College of Technology of Purdue University.; Kazan State Pedagogical University in 1999.; Purdue University, West Lafayette
raw_author_name: Petr Osipov
raw_affiliation_string:  American Society for Engineering Education, 2016; College of Technology of Purdue University.; Kazan State Pedagogical University in 1999.; Purdue University, West Lafayette

Abstract: 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

Authors:
raw_author_name: Abdourrahmane Mahamane Atto
raw_affiliation_string: LISTIC - Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance (BP 80439 74944 ANNECY LE VIEUX Cedex - France)

Abstract: 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.