Reviewerly/exHarmony huggingface dataset:
Richer fusion network for breast cancer classification based on multimodal data

Abstract Background Deep learning algorithms significantly improve the accuracy of pathological image classification, but the accuracy of breast cancer classification using only single-mode pathological images still cannot meet the needs of clinical practice. Inspired by the real scenario of pathologists reading pathological images for diagnosis, we integrate pathological images and structured data extracted from clinical electronic medical record (EMR) to further improve the accuracy of breast cancer classification. Methods In this paper, we propose a new richer fusion network for the classification of benign and malignant breast cancer based on multimodal data. To make pathological image can be integrated more sufficient with structured EMR data, we proposed a method to extract richer multilevel feature representation of the pathological image from multiple convolutional layers. Meanwhile, to minimize the information loss for each modality before data fusion, we use the denoising autoencoder as a way to increase the low-dimensional structured EMR data to high-dimensional, instead of reducing the high-dimensional image data to low-dimensional before data fusion. In addition, denoising autoencoder naturally generalizes our method to make the accurate prediction with partially missing structured EMR data. Results The experimental results show that the proposed method is superior to the most advanced method in terms of the average classification accuracy (92.9%). In addition, we have released a dataset containing structured data from 185 patients that were extracted from EMR and 3764 paired pathological images of breast cancer, which can be publicly downloaded from http://ear.ict.ac.cn/?page_id=1663 . Conclusions We utilized a new richer fusion network to integrate highly heterogeneous data to leverage the structured EMR data to improve the accuracy of pathological image classification. Therefore, the application of automatic breast cancer classification algorithms in clinical practice becomes possible. Due to the generality of the proposed fusion method, it can be straightforwardly extended to the fusion of other structured data and unstructured data.
SpanEmo: Casting Multi-label Emotion Classification as Span-prediction

Emotion recognition (ER) is an important task in Natural Language Processing (NLP), due to its high impact in real-world applications from health and well-being to author profiling, consumer analysis and security.Current approaches to ER, mainly classify emotions independently without considering that emotions can co-exist.Such approaches overlook potential ambiguities, in which multiple emotions overlap.We propose a new model "SpanEmo" casting multi-label emotion classification as span-prediction, which can aid ER models to learn associations between labels and words in a sentence.Furthermore, we introduce a loss function focused on modelling multiple coexisting emotions in the input sentence.Experiments performed on the SemEval2018 multilabel emotion data over three language sets (i.e., English, Arabic and Spanish) demonstrate our method's effectiveness.Finally, we present different analyses that illustrate the benefits of our method in terms of improving the model performance and learning meaningful associations between emotion classes and words in the sentence 1 .
Realising and compressing quantum circuits with quantum reservoir computing

Abstract Quantum computers require precise control over parameters and careful engineering of the underlying physical system. In contrast, neural networks have evolved to tolerate imprecision and inhomogeneity. Here, using a reservoir computing architecture we show how a random network of quantum nodes can be used as a robust hardware for quantum computing. Our network architecture induces quantum operations by optimising only a single layer of quantum nodes, a key advantage over the traditional neural networks where many layers of neurons have to be optimised. We demonstrate how a single network can induce different quantum gates, including a universal gate set. Moreover, in the few-qubit regime, we show that sequences of multiple quantum gates in quantum circuits can be compressed with a single operation, potentially reducing the operation time and complexity. As the key resource is a random network of nodes, with no specific topology or structure, this architecture is a hardware friendly alternative paradigm for quantum computation.
Neuromorphic computation with a single magnetic domain wall

Abstract Machine learning techniques are commonly used to model complex relationships but implementations on digital hardware are relatively inefficient due to poor matching between conventional computer architectures and the structures of the algorithms they are required to simulate. Neuromorphic devices, and in particular reservoir computing architectures, utilize the inherent properties of physical systems to implement machine learning algorithms and so have the potential to be much more efficient. In this work, we demonstrate that the dynamics of individual domain walls in magnetic nanowires are suitable for implementing the reservoir computing paradigm in hardware. We modelled the dynamics of a domain wall placed between two anti-notches in a nickel nanowire using both a 1D collective coordinates model and micromagnetic simulations. When driven by an oscillating magnetic field, the domain exhibits non-linear dynamics within the potential well created by the anti-notches that are analogous to those of the Duffing oscillator. We exploit the domain wall dynamics for reservoir computing by modulating the amplitude of the applied magnetic field to inject time-multiplexed input signals into the reservoir, and show how this allows us to perform machine learning tasks including: the classification of (1) sine and square waves; (2) spoken digits; and (3) non-temporal 2D toy data and hand written digits. Our work lays the foundation for the creation of nanoscale neuromorphic devices in which individual magnetic domain walls are used to perform complex data analysis tasks.
News keyword extraction algorithm based on semantic clustering and word graph model

The internet is an abundant source of news every day. Thus, efficient algorithms to extract keywords from the text are important to obtain information quickly. However, the precision and recall of mature keyword extraction algorithms need improvement. TextRank, which is derived from the PageRank algorithm, uses word graphs to spread the weight of words. The keyword weight propagation in TextRank focuses only on word frequency. To improve the performance of the algorithm, we propose Semantic Clustering TextRank (SCTR), a semantic clustering news keyword extraction algorithm based on TextRank. Firstly, the word vectors generated by the Bidirectional Encoder Representation from Transformers (BERT) model are used to perform k-means clustering to represent semantic clustering. Then, the clustering results are used to construct a TextRank weight transfer probability matrix. Finally, iterative calculation of word graphs and extraction of keywords are performed. The test target of this experiment is a Chinese news library. The results of the experiment conducted on this text set show that the SCTR algorithm has greater precision, recall, and F1 value than the traditional TextRank and Term Frequency-Inverse Document Frequency (TF-IDF) algorithms.
Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue StateTracking

Zero-shot cross-domain dialogue state tracking (DST) enables us to handle task-oriented dialogue in unseen domains without the expense of collecting in-domain data.In this paper, we propose a slot description enhanced generative approach for zero-shot cross-domain DST.Specifically, our model first encodes dialogue context and slots with a pre-trained self-attentive encoder, and generates slot values in an auto-regressive manner.In addition, we incorporate Slot Type Informed Descriptions that capture the shared information across slots to facilitate cross-domain knowledge transfer.Experimental results on the MultiWOZ dataset show that our proposed method significantly improves existing stateof-the-art results in the zero-shot cross-domain setting.
An Improved Sparrow Search Algorithm

The sparrow search algorithm (SSA) is a relatively new swarm intelligence heuristic algorithm. It has fast convergence speed, strong optimization ability and more extensive application scenarios compared with traditional heuristic search methods. And thus, the SSA is attracting the attention of researchers in different fields. However, there are deficiencies of initial population quality, search ability, and population diversity in the SSA. Therefore, this paper proposes an improved sparrow search algorithm (ISSA). The ISSA uses skew tent map-based chaotic method to produce initial population for a higher quality of convergence. For the location update of the producer sparrows during the iterations, the ISSA introduces a non-linear decreasing weight, promoting both exploration and exploitation of the search space, to improve the convergence and search precision. And the mutation strategy is employed to update the location of the scrounger sparrows with lower energy and the chaotic search is combined with the local exploitation for the scroungers with higher energy, which can enhance the diversity and avoid trapping in local optimum. Simulation experiments are carried out on 26 benchmark test functions. And the results show that the ISSA is superior to or at least competitive to the SSA in the convergence properties of accuracy, speed, and stability.
Mineral Resource Science in China: Review and perspective

Abstract Mineral resources are essential to prosperity and security of modern societies. How mineral resources can guarantee sustainable development of economy in countries, especially those developing countries, has long been a focus of attention of international communities. This paper provides a comprehensive summary for major advance of the research on mineral resources in past decades, and proposes some key issues regarding ore-forming mechanism, exploration and utilization of major and critical mineral resources. On the basis of these aspects, we also identify four priority science issues to be addressed in the future, including (1) mechanism of both metal circulation and extremely high concentration, (2) theories and technologies of prospecting deep-earth resources, (3) investigation of mineral resources in seafloor and polar regions, and (4) efficient, clean and recycling utilization of mineral resources. It can be expected that new advances in these four issues would tremendously promote the innovation of mineral resource science, and provide scientific and technologic support to meet the demand of mineral resources for human activities and the harmonious development of both mineral-resource exploration and ecological restoration.
Data Anomaly Detection of Bridge Structures Using Convolutional Neural Network Based on Structural Vibration Signals

Structural monitoring provides valuable information on the state of structural health, which is helpful for structural damage detection and structural state assessment. However, when the sensors are exposed to harsh environmental conditions, various anomalies caused by sensor failure or damage lead to abnormalities of the monitoring data. It is inefficient to remove abnormal data by manual elimination because of the massive number of data obtained by monitoring systems. In this paper, a data anomaly detection method based on structural vibration signals and a convolutional neural network (CNN) is proposed, which can automatically identify and eliminate abnormal data. First, the anomaly detection problem is modeled as a time series classification problem. Data preprocessing and data augmentation, including data expansion and down-sampling to construct new samples, are employed to process the original time series. For a small number of samples in the data set, randomly increase outliers, symmetrical flipping, and noise addition methods are used for data expansion, and samples with the same label are added without increasing the original samples. The down-sampling method of symmetrically extracting the maximum value and the minimum value at the same time can effectively reduce the dimensionality of the input sample, while retaining the characteristics of the data to the greatest extent. Using hyperparameter tuning of the classification weights, CNN is more effective in dealing with unbalanced training sets. Finally, the effectiveness of the proposed method is proved by the anomaly detection of acceleration data on a long-span bridge. For the anomaly detection problem modeled as a time series classification problem, the proposed method can effectively identify various abnormal patterns.
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Superconducting qubits on a circuit exhibit an always-on state-dependent phase error. This error is due to sub-MHz parasitic interaction that repels computational levels from non-computational ones. We study a general theory to evaluate the `static' repulsion between seemingly idle qubits as well as the `dynamical' repulsion between entangled qubits under microwave driving gate. By combining qubits of either the same or opposite anharmonicity signs we find the characteristics of static and dynamical ZZ freedoms. The latter universally eliminate the parasitic repulsion, leading us to mitigate high fidelity gate operation. Our theory introduces new opportunities for making perfect entangled and unentangled states which is extremely useful for quantum technology.
ShipYOLO: An Enhanced Model for Ship Detection

The application of ship detection for assistant intelligent ship navigation has stringent requirements for the model’s detection speed and accuracy. In response to this problem, this study uses an improved YOLO-V4 detection model (ShipYOLO) to detect ships. Compared to YOLO-V4, the model has three main improvements. Firstly, the backbone network (CSPDarknet) of YOLO-V4 is optimized. In the training process, the 3 <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"><mo>×</mo></math> 3 convolution, 1 <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"><mo>×</mo></math> 1 convolution, and identity parallel mode are used to replace the original feature extraction component (ResUnit) and more features are extracted. In the inference process, the branch parameters are combined to form a new backbone network named RCSPDarknet, which improves the inference speed of the model while improving the accuracy. Secondly, in order to solve the problem of missed detection of the small-scale ships, we designed a new amplified receptive field module named DSPP with dilated convolution and Max-Pooling, which improves the model’s acquisition of small-scale ship spatial information and robustness of ship target space displacement. Finally, we use the attention mechanism and Resnet’s shortcut idea to improve the feature pyramid structure (PAFPN) of YOLO-V4 and get a new feature pyramid structure named AtFPN. The structure effectively improves the model’s feature extraction effect for ships of different scales and reduces the number of model parameters, further improving the model’s inference speed and detection accuracy. In addition, we have created a ship dataset with a total of 2238 images, which is a single-category dataset. The experimental results show that ShipYOLO has the advantage of faster speed and higher accuracy even in different input sizes. Considering the input size of 320 <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"><mo>×</mo></math> 320 on the PC equipped with NVIDIA 1080Ti GPU, the FPS and mAP@5 : 5:95 (mAP90) of ShipYOLO are increased by 23.7% and 13.6% (10.6%), respectively, with an input size of 320 <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"><mo>×</mo></math> 320, ShipYOLO, compared to YOLO-V4.
Scheduled Sampling in Vision-Language Pretraining with Decoupled Encoder-Decoder Network

Despite having impressive vision-language (VL) pretraining with BERT-based encoder for VL understanding, the pretraining of a universal encoder-decoder for both VL understanding and generation remains challenging. The difficulty originates from the inherently different peculiarities of the two disciplines, e.g., VL understanding tasks capitalize on the unrestricted message passing across modalities, while generation tasks only employ visual-to-textual message passing. In this paper, we start with a two-stream decoupled design of encoder-decoder structure, in which two decoupled cross-modal encoder and decoder are involved to separately perform each type of proxy tasks, for simultaneous VL understanding and generation pretraining. Moreover, for VL pretraining, the dominant way is to replace some input visual/word tokens with mask tokens and enforce the multi-modal encoder/decoder to reconstruct the original tokens, but no mask token is involved when fine-tuning on downstream tasks. As an alternative, we propose a primary scheduled sampling strategy that elegantly mitigates such discrepancy via pretraining encoder-decoder in a two-pass manner. Extensive experiments demonstrate the compelling generalizability of our pretrained encoder-decoder by fine-tuning on four VL understanding and generation downstream tasks. Source code is available at https://github.com/YehLi/TDEN.
On the Convergence of Communication-Efficient Local SGD for Federated Learning

Federated Learning (FL) has attracted increasing attention in recent years. A leading training algorithm in FL is local SGD, which updates the model parameter on each worker and averages model parameters across different workers only once in a while. Although it has fewer communication rounds than the classical parallel SGD, local SGD still has large communication overhead in each communication round for large machine learning models, such as deep neural networks. To address this issue, we propose a new communication-efficient distributed SGD method, which can significantly reduce the communication cost by the error-compensated double compression mechanism. Under the non-convex setting, our theoretical results show that our approach has better communication complexity than existing methods and enjoys the same linear speedup regarding the number of workers as the full-precision local SGD. Moreover, we propose a communication-efficient distributed SGD with momentum, which also has better communication complexity than existing methods and enjoys a linear speedup with respect to the number of workers. At last, extensive experiments are conducted to verify the performance of our proposed two methods. Moreover, we propose a communication-efficient distributed SGD with momentum to accelerate the convergence, which also has better communication complexity than existing methods and enjoys a linear speedup with respect to the number of workers. At last, extensive experiments are conducted to verify the performance of our proposed methods.
Can Generative Pre-trained Language Models Serve As Knowledge Bases for Closed-book QA?

Recent work has investigated the interesting question using pre-trained language models (PLMs) as knowledge bases for answering open questions.However, existing work is limited in using small benchmarks with high test-train overlaps.We construct a new dataset of closed-book QA using SQuAD, and investigate the performance of BART.Experiments show that it is challenging for BART to remember training facts in high precision, and also challenging to answer closed-book questions even if relevant knowledge is retained.Some promising directions are found, including decoupling the knowledge memorizing process and the QA finetune process, forcing the model to recall relevant knowledge when question answering.
Attacking and Defending Masked Polynomial Comparison for Lattice-Based Cryptography

In this work, we are concerned with the hardening of post-quantum key encapsulation mechanisms (KEM) against side-channel attacks, with a focus on the comparison operation required for the Fujisaki-Okamoto (FO) transform. We identify critical vulnerabilities in two proposals for masked comparison and successfully attack the masked comparison algorithms from TCHES 2018 and TCHES 2020. To do so, we use first-order side-channel attacks and show that the advertised security properties do not hold. Additionally, we break the higher-order secured masked comparison from TCHES 2020 using a collision attack, which does not require side-channel information. To enable implementers to spot such flaws in the implementation or underlying algorithms, we propose a framework that is designed to test the re-encryption step of the FO transform for information leakage. Our framework relies on a specifically parametrized t-test and would have identified the previously mentioned flaws in the masked comparison. Our framework can be used to test both the comparison itself and the full decapsulation implementation.
Country-Specific Interests towards Fall Detection from 2004–2021: An Open Access Dataset and Research Questions

Falls, which are increasing at an unprecedented rate in the global elderly population, are associated with a multitude of needs such as healthcare, medical, caregiver, and economic, and they are posing various forms of burden on different countries across the world, specifically in the low- and middle-income countries. For these respective countries to anticipate, respond, address, and remedy these diverse needs either by using their existing resources, or by developing new policies and initiatives, or by seeking support from other countries or international organizations dedicated to global public health, the timely identification of these needs and their associated trends is highly necessary. This paper addresses this challenge by presenting a study that uses the potential of the modern Internet of Everything lifestyle, where relevant Google Search data originating from different geographic regions can be interpreted to understand the underlining region-specific user interests towards a specific topic, which further demonstrates the public health need towards the same. The scientific contributions of this study are two-fold. First, it presents an open-access dataset that consists of the user interests towards fall detection for all the 193 countries of the world studied from 2004–2021. In the dataset, the user interest data is available for each month for all these countries in this time range. Second, based on the analysis of potential and emerging research directions in the interrelated fields of Big Data, Data Mining, Information Retrieval, Natural Language Processing, Data Science, and Pattern Recognition, in the context of fall detection research, this paper presents 22 research questions that may be studied, evaluated, and investigated by researchers using this dataset.