Reviewerly/exHarmony huggingface dataset:
Generalized Weak Supervision for Neural Information Retrieval

Authors:
raw_author_name: Yen-Chieh Lien
raw_affiliation_string: University of Massachusetts Amherst, Amherst, MA, United States
raw_author_name: Hamed Zamani
raw_affiliation_string: University of Massachusetts Amherst, Amherst, MA, United States
raw_author_name: W. Bruce Croft
raw_affiliation_string: University of Massachusetts Amherst, Amherst, MA, United States

Abstract: Neural ranking models (NRMs) have demonstrated effective performance in several information retrieval (IR) tasks. However, training NRMs often requires large-scale training data, which is difficult and expensive to obtain. To address this issue, one can train NRMs via weak supervision, where a large dataset is automatically generated using an existing ranking model (called the weak labeler) for training NRMs. Weakly supervised NRMs can generalize from the observed data and significantly outperform the weak labeler. This paper generalizes this idea through an iterative re-labeling process, demonstrating that weakly supervised models can iteratively play the role of weak labeler and significantly improve ranking performance without using manually labeled data. The proposed Generalized Weak Supervision (GWS) solution is generic and orthogonal to the ranking model architecture. This paper offers four implementations of GWS: self-labeling, cross-labeling, joint cross- and self-labeling, and greedy multi-labeling. GWS also benefits from a query importance weighting mechanism based on query performance prediction methods to reduce noise in the generated training data. We further draw a theoretical connection between self-labeling and Expectation-Maximization. Our experiments on four retrieval benchmarks suggest that our implementations of GWS lead to substantial improvements compared to weak supervision if the weak labeler is sufficiently reliable.
Stabilizer Formalism for Operator Algebra Quantum Error Correction

Authors:
raw_author_name: Guillaume Dauphinais
raw_affiliation_string: Xanadu, Toronto, ON M5G 2C8, Canada
raw_author_name: David W. Kribs
raw_affiliation_string: Department of Mathematics & Statistics, University of Guelph, Guelph, ON N1G 2W1, Canada; Xanadu, Toronto, ON M5G 2C8, Canada
raw_author_name: Michael Vasmer
raw_affiliation_string: Institute for Quantum Computing, University of Waterloo, Waterloo, ON N2L 3G1, Canada; Perimeter Institute for Theoretical Physics, Waterloo, ON N2L 2Y5, Canada; Xanadu, Toronto, ON M5G 2C8, Canada

Abstract: We introduce a stabilizer formalism for the general quantum error correction framework called operator algebra quantum error correction (OAQEC), which generalizes Gottesman&amp;apos;s formulation for traditional quantum error correcting codes (QEC) and Poulin&amp;apos;s for operator quantum error correction and subsystem codes (OQEC). The construction generates hybrid classical-quantum stabilizer codes and we formulate a theorem that fully characterizes the Pauli errors that are correctable for a given code, generalizing the fundamental theorems for the QEC and OQEC stabilizer formalisms. We discover hybrid versions of the Bacon-Shor subsystem codes motivated by the formalism, and we apply the theorem to derive a result that gives the distance of such codes. We show how some recent hybrid subspace code constructions are captured by the formalism, and we also indicate how it extends to qudits.
On the validity of descriptors in <i>ACTFL Language Proficiency Guidelines</i>

Authors:
raw_author_name: Hao Feng
raw_affiliation_string: Capital Normal University

Abstract: Abstract ACTFL Language Proficiency Guidelines (hereinafter called Guidelines) are a multi-lingual framework mainly based on the language proficiency development of several European languages. Little consideration is given to the peculiarity of Chinese. Efforts are made to assess the validity of the descriptors in Guidelines in comparison with Chinese Proficiency Grading Standards for International Chinese Language Education (hereinafter called Standards) . A total of five parameters from Guidelines that are not compatible with Standards have been selected and two questionnaires compiled by the authors are answered by local American Chinese language teachers to evaluate the validity of these descriptors. The study shows that the descriptors in Standards are more valid in differentiating language proficiency levels of Chinese. In accordance with this study, some revisions and amendments should be made to Guidelines .
Anomaly Detection on Small Wind Turbine Blades Using Deep Learning Algorithms

Authors:
raw_author_name: Mohammad Shekaramiz
raw_affiliation_string: Machine Learning and Drone Lab., Engineering Department, Utah Valley University, Orem, UT 84097, USA
raw_author_name: Todd K. Moon
raw_affiliation_string: Information Dynamics Lab., Electrical and Computer Engineering Department, Utah State University, Logan, UT 84322, USA

Abstract: Wind turbine blade maintenance is expensive, dangerous, time-consuming, and prone to misdiagnosis. A potential solution to aid preventative maintenance is using deep learning and drones for inspection and early fault detection. In this research, five base deep learning architectures are investigated for anomaly detection on wind turbine blades, including Xception, Resnet-50, AlexNet, and VGG-19, along with a custom convolutional neural network. For further analysis, transfer learning approaches were also proposed and developed, utilizing these architectures as the feature extraction layers. In order to investigate model performance, a new dataset containing 6000 RGB images was created, making use of indoor and outdoor images of a small wind turbine with healthy and damaged blades. Each model was tuned using different layers, image augmentations, and hyperparameter tuning to achieve optimal performance. The results showed that the proposed Transfer Xception outperformed other architectures by attaining 99.92% accuracy on the test data of this dataset. Furthermore, the performance of the investigated models was compared on a dataset containing faulty and healthy images of large-scale wind turbine blades. In this case, our results indicated that the best-performing model was also the proposed Transfer Xception, which achieved 100% accuracy on the test data. These accuracies show promising results in the adoption of machine learning for wind turbine blade fault identification.
Few-Shot Metering Anomaly Diagnosis with Variable Relation Mining

Authors:
raw_author_name: Jianqiao Sun
raw_affiliation_string: State Grid Zhejiang Marketing Service Center, Hangzhou 310007, China
raw_author_name: Wei Zhang
raw_affiliation_string: State Grid Zhejiang Marketing Service Center, Hangzhou 310007, China
raw_author_name: Peng Guo
raw_affiliation_string: State Grid Zhejiang Marketing Service Center, Hangzhou 310007, China
raw_author_name: Chaohui Wang
raw_affiliation_string: School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China

Abstract: Metering anomalies not only mean huge economic losses but also indicate the faults of equipment and power lines, especially within the substation. As a result, metering anomaly diagnosis is becoming one of the most important missions in smart grids. However, due to the insufficient and imbalanced anomaly cases, identifying the anomalies in smart meter data accurately and efficiently remains challenging. Existing methods usually employ few-shot learning models in computer vision directly, which requires the rich experience of human experts and sufficient abnormal cases for training. It blocks model generalizing to various application scenarios. To address these shortcomings, we propose a novel framework for metering anomaly diagnosis based on few-shot learning, named FSMAD. Firstly, we design a fault data injection model to emulate anomalies, so that no abnormal samples are required in the training phase. Secondly, we provide a learnable variable transformation to reveal inherent relationships among various smart meter data and help FSMAD extract more efficient features. Finally, the deeper metric network is equipped to support FSMAD in obtaining powerful comparison capability. Extensive experiments on a real-world dataset demonstrate the advantages of our FSMAD over state-of-the-art methods.
Boosting Noise Reduction Effect via Unsupervised Fine-Tuning Strategy

Authors:
raw_author_name: Shaoping Xu
raw_affiliation_string: School of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, China
raw_author_name: Junyun Wu
raw_affiliation_string: School of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, China
raw_author_name: Changfei Zhou
raw_affiliation_string: School of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, China
raw_author_name: Shuichen Ji
raw_affiliation_string: School of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, China

Abstract: Over the last decade, supervised denoising models, trained on extensive datasets, have exhibited remarkable performance in image denoising, owing to their superior denoising effects. However, these models exhibit limited flexibility and manifest varying degrees of degradation in noise reduction capability when applied in practical scenarios, particularly when the noise distribution of a given noisy image deviates from that of the training images. To tackle this problem, we put forward a two-stage denoising model that is actualized by attaching an unsupervised fine-tuning phase after a supervised denoising model processes the input noisy image and secures a denoised image (regarded as a preprocessed image). More specifically, in the first stage we replace the convolution block adopted by the U-shaped network framework (utilized in the deep image prior method) with the Transformer module, and the resultant model is referred to as a U-Transformer. The U-Transformer model is trained to preprocess the input noisy images using noisy images and their labels. As for the second stage, we condense the supervised U-Transformer model into a simplified version, incorporating only one Transformer module with fewer parameters. Additionally, we shift its training mode to unsupervised training, following a similar approach as employed in the deep image prior method. This stage aims to further eliminate minor residual noise and artifacts present in the preprocessed image, resulting in clearer and more realistic output images. Experimental results illustrate that the proposed method achieves significant noise reduction in both synthetic and real images, surpassing state-of-the-art methods. This superiority stems from the supervised model’s ability to rapidly process given noisy images, while the unsupervised model leverages its flexibility to generate a fine-tuned network, enhancing noise reduction capability. Moreover, with support from the supervised model providing higher-quality preprocessed images, the proposed unsupervised fine-tuning model requires fewer parameters, facilitating rapid training and convergence, resulting in overall high execution efficiency.
Light field image coding using a residual channel attention network–based view synthesis

Authors:
raw_author_name: Faguo Liu
raw_author_name: Qian Zhang
raw_author_name: Tao Yan
raw_author_name: Bin Wang
raw_author_name: Ying Gao
raw_author_name: Jiaqi Hou
raw_author_name: Feiniu Yuan

Abstract: Purpose Light field images (LFIs) have gained popularity as a technology to increase the field of view (FoV) of plenoptic cameras since they can capture information about light rays with a large FoV. Wide FoV causes light field (LF) data to increase rapidly, which restricts the use of LF imaging in image processing, visual analysis and user interface. Effective LFI coding methods become of paramount importance. This paper aims to eliminate more redundancy by exploring sparsity and correlation in the angular domain of LFIs, as well as mitigate the loss of perceptual quality of LFIs caused by encoding. Design/methodology/approach This work proposes a new efficient LF coding framework. On the coding side, a new sampling scheme and a hierarchical prediction structure are used to eliminate redundancy in the LFI's angular and spatial domains. At the decoding side, high-quality dense LF is reconstructed using a view synthesis method based on the residual channel attention network (RCAN). Findings In three different LF datasets, our proposed coding framework not only reduces the transmitted bit rate but also maintains a higher view quality than the current more advanced methods. Originality/value (1) A new sampling scheme is designed to synthesize high-quality LFIs while better ensuring LF angular domain sparsity. (2) To further eliminate redundancy in the spatial domain, new ranking schemes and hierarchical prediction structures are designed. (3) A synthetic network based on RCAN and a novel loss function is designed to mitigate the perceptual quality loss due to the coding process.
Exploring academic influence of algorithms by co-occurrence network based on full-text of academic papers

Authors:
raw_author_name: Chengzhi Zhang
raw_author_name: Min Song
raw_author_name: Juhee Lee

Abstract: Purpose In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network. Design/methodology/approach We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms. Findings The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms. Originality/value To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.
Seeing is Learning in High Dimensions: The Synergy Between Dimensionality Reduction and Machine Learning

Authors:
raw_author_name: Alexandru Telea
raw_affiliation_string: Department of Information and Computing Science, Utrecht University, Princetonplein 5, Utrecht, 3584CC, The Netherlands
raw_author_name: Alister Machado
raw_affiliation_string: Department of Information and Computing Science, Utrecht University, Princetonplein 5, Utrecht, 3584CC, The Netherlands
raw_author_name: Yu Wang
raw_affiliation_string: Department of Information and Computing Science, Utrecht University, Princetonplein 5, Utrecht, 3584CC, The Netherlands

Abstract: Abstract High-dimensional data are a key study object for both machine learning (ML) and information visualization. On the visualization side, dimensionality reduction (DR) methods, also called projections, are the most suited techniques for visual exploration of large and high-dimensional datasets. On the ML side, high-dimensional data are generated and processed by classifiers and regressors, and these techniques increasingly require visualization for explanation and exploration. In this paper, we explore how both fields can help each other in achieving their respective aims. In more detail, we present both examples that show how DR can be used to understand and engineer better ML models (seeing helps learning) and also applications of DL for improving the computation of direct and inverse projections (learning helps seeing). We also identify existing limitations of DR methods used to assist ML and of ML techniques applied to improve DR. Based on the above, we propose several high-impact directions for future work that exploit the analyzed ML-DR synergy.
Quantum Complexity of the Kronecker Coefficients

Authors:
raw_author_name: Sergey Bravyi
raw_affiliation_string: IBM Quantum, IBM T.J. Watson Research Center
raw_author_name: Anirban Chowdhury
raw_affiliation_string: Department of Combinatorics and Optimization, University of Waterloo
raw_author_name: David Gosset
raw_affiliation_string: Department of Combinatorics and Optimization, University of Waterloo; Perimeter Institute for Theoretical Physics, Waterloo
raw_author_name: Vojtěch Havlíček
raw_affiliation_string: IBM Quantum, IBM T.J. Watson Research Center
raw_author_name: Guanyu Zhu
raw_affiliation_string: IBM Quantum, IBM T.J. Watson Research Center

Abstract: Whether or not the Kronecker coefficients of the symmetric group count some set of combinatorial objects is a longstanding open question. In this work we show that a given Kronecker coefficient is proportional to the rank of a projector that can be measured efficiently using a quantum computer. In other words a Kronecker coefficient counts the dimension of the vector space spanned by the accepting witnesses of a QMA verifier, where QMA is the quantum analogue of NP. This implies that approximating the Kronecker coefficients to within a given relative error is not harder than a certain natural class of quantum approximate counting problems that captures the complexity of estimating thermal properties of quantum many-body systems. A second consequence is that deciding positivity of Kronecker coefficients is contained in QMA, complementing a recent NP-hardness result of Ikenmeyer, Mulmuley, and Walter. We obtain similar results for the related problem of approximating row sums of the character table of the symmetric group. Finally, we discuss an efficient quantum algorithm that approximates normalized Kronecker coefficients to inverse-polynomial additive error.Received 28 April 2023Accepted 17 January 2024DOI:https://doi.org/10.1103/PRXQuantum.5.010329Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum algorithms & computationQuantum computationQuantum information theoryTechniquesComputational complexityQuantum Information, Science & Technology
Training of Deep Joint Transmitter-Receiver Optimized Communication System without Auxiliary Tools

Authors:
raw_author_name: Wenhao Sun
raw_affiliation_string: The School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China; The School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
raw_author_name: Yuchen He
raw_affiliation_string: The Silk Road Fantian (Gansu) Communication Technology Co., Ltd., Lanzhou 730046, China
raw_author_name: Tianfeng Yan
raw_affiliation_string: The School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
raw_author_name: Zhongdong Wu
raw_affiliation_string: The School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
raw_author_name: Yide Ma
raw_affiliation_string: The School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China

Abstract: Deep Joint transmitter-receiver optimized communication system (Deep JTROCS) is a new physical layer communication system. It integrates the functions of various signal processing blocks into deep neural networks in the transmitter and receiver. Therefore, Deep JTROCS can approach the optimal state at the system level by the joint training of these neural networks. However, due to the non-differentiable feature of the channel, the back-propagation of Deep JTROCS training gradients is hindered which hinders the training of the neural networks in the transmitter. Although researchers have proposed methods to train transmitters using auxiliary tools such as channel models or feedback links, these tools are not available in many real-world communication scenarios, limiting the application of Deep JTROCS. In this paper, we propose a new method to use undertrained Deep JTROCS to transmit the training signals and use these signals to reconstruct the training gradient of the neural networks in the transmitter, thus avoiding the use of an additional reliable link. The experimental results show that the proposed method outperforms the additional link-based approach in different tasks and channels. In addition, experiments conducted on real wireless channels validate the practical feasibility of the method.