Reviewerly/exHarmony huggingface dataset:
Classical symmetries and the Quantum Approximate Optimization Algorithm

We study the relationship between the Quantum Approximate Optimization Algorithm (QAOA) and the underlying symmetries of the objective function to be optimized. Our approach formalizes the connection between quantum symmetry properties of the QAOA dynamics and the group of classical symmetries of the objective function. The connection is general and includes but is not limited to problems defined on graphs. We show a series of results exploring the connection and highlight examples of hard problem classes where a nontrivial symmetry subgroup can be obtained efficiently. In particular, we show how classical objective function symmetries lead to invariant measurement outcome probabilities across states connected by such symmetries, independent of the choice of algorithm parameters or number of layers. To illustrate the power of the developed connection, we apply machine learning techniques toward predicting QAOA performance based on symmetry considerations. We provide numerical evidence that a small set of graph symmetry properties suffices to predict the minimum QAOA depth required to achieve a target approximation ratio on the MaxCut problem, in a practically important setting where QAOA parameter schedules are constrained to be linear and hence easier to optimize.
A novel ResNet101 model based on dense dilated convolution for image classification

Abstract Image classification plays an important role in computer vision. The existing convolutional neural network methods have some problems during image classification process, such as low accuracy of tumor classification and poor ability of feature expression and feature extraction. Therefore, we propose a novel ResNet101 model based on dense dilated convolution for medical liver tumors classification. The multi-scale feature extraction module is used to extract multi-scale features of images, and the receptive field of the network is increased. The depth feature extraction module is used to reduce background noise information and focus on effective features of the focal region. To obtain broader and deeper semantic information, a dense dilated convolution module is deployed in the network. This module combines the advantages of Inception, residual structure, and multi-scale dilated convolution to obtain a deeper level of feature information without causing gradient explosion and gradient disappearance. To solve the common feature loss problems in the classification network, the up- down-sampling module in the network is improved, and multiple convolution kernels with different scales are cascaded to widen the network, which can effectively avoid feature loss. Finally, experiments are carried out on the proposed method. Compared with the existing mainstream classification networks, the proposed method can improve the classification performance, and finally achieve accurate classification of liver tumors. The effectiveness of the proposed method is further verified by ablation experiments. Highlights The multi-scale feature extraction module is introduced to extract multi-scale features of images, it can extract deep context information of the lesion region and surrounding tissues to enhance the feature extraction ability of the network. The depth feature extraction module is used to focus on the local features of the lesion region from both channel and space, weaken the influence of irrelevant information, and strengthen the recognition ability of the lesion region. The feature extraction module is enhanced by the parallel structure of dense dilated convolution, and the deeper feature information is obtained without losing the image feature information to improve the classification accuracy.
Promises, threats, and the foundations of speech act theory

I suggest that promises and threats are similar speech acts and pose analogous problems for Speech Act Theory. After showing that they share the same formal types, I argue against there being purportedly fundamental differences between them in regard to explicitability, deontics, and illocution/perlocution. I conclude that the joint analysis of promises and threats suggests the propriety of a holistic theory of illocutionary acts.
Improved Wavelet Denoising by Non-Convex Sparse Regularization Under Double Wavelet Domains

This paper presents a double wavelet denoising (DWAD) method, which can preserve more details of an original signal.Although the noise removal method based on wavelet transform has been widely used, it still performs poorly for the signals with a low signal-to-noise ratio (SNR) or frequency overlap.Different from the wavelet denoising methods based on a single basis function, the DWAD considers filtering the wavelet coefficients of the noisy signal by threshold functions under two different wavelet domains, simultaneously.It considers using the difference of wavelet coefficient distribution and forcing the denoised signals under two wavelet domains to be the same to achieve more retention of details.In addition, the arctangent function is employed as a penalty function for wavelet coefficients to induce strong sparse wavelet coefficients.The DWAD is applied to one-dimensional signals and it is found that some wavelet coefficients which are smaller than the threshold could be retained during noise removal.The experiment results show that the average SNR of different noise levels is improved by at least 4.2 and 2.1 dB compared with the classical soft threshold method for the one-dimensional and image signals, respectively.Besides, the DWAD tends to obtain better performance on the details of original signals.
A 3.0 TFLOPS 0.62V Scalable Processor Core for High Compute Utilization AI Training and Inference

A processor core is presented for AI training and inference products. Leading-edge compute efficiency is achieved for robust fp16 training via efficient heterogeneous 2-D systolic array-SIMD compute engines leveraging compact DLFloat16 FPUs. Architectural flexibility is maintained for very high compute utilization across neural network topologies. A modular dual-corelet architecture with a shared scratchpad and a software-controlled network/memory interface enables scalability to many-core SoCs and large-scale systems. The 14nm AI core achieves fp16 peak performance of 3.0 TFLOPS at 0.62V and 1.4 TFLOPS/W at 0.54V.
Counterfactual Inference for Text Classification Debiasing

Today's text classifiers inevitably suffer from unintended dataset biases, especially the document-level label bias and word-level keyword bias, which may hurt models' generalization.Many previous studies employed datalevel manipulations or model-level balancing mechanisms to recover unbiased distributions and thus prevent models from capturing the two types of biases.Unfortunately, they either suffer from the extra cost of data collection/selection/annotation or need an elaborate design of balancing strategies.Different from traditional factual inference in which debiasing occurs before or during training, counterfactual inference mitigates the influence brought by unintended confounders after training, which can make unbiased decisions with biased observations.Inspired by this, we propose a model-agnostic text classification debiasing framework -CORSAIR, which can effectively avoid employing data manipulations or designing balancing mechanisms.Concretely, CORSAIR first trains a base model on a training set directly, allowing the dataset biases "poison" the trained model.In inference, given a factual input document, COR-SAIR imagines its two counterfactual counterparts to distill and mitigate the two biases captured by the poisonous model.Extensive experiments demonstrate CORSAIR's effectiveness, generalizability and fairness.
Voxel-Based Morphometry: Improving the Diagnosis of Alzheimer’s Disease Based on an Extreme Learning Machine Method from the ADNI cohort

Computer-aided diagnosis has become a widely-used auxiliary tool for the diagnosis of Alzheimer's disease (AD). In this study, we developed an extreme learning machine (ELM) model to discriminate between patients with AD and normal controls (NCs) using voxel-based morphometry (VBM) obtained from magnetic resonance imaging. Support vector machine (SVM), Gaussian process regression (GPR), and partial least squares (PLS) regression were compared with the ELM model. The calculated characteristics, i.e., texture features, VBM parameters, and clinical information, were adopted as the classification features. A 10-fold cross validation was used to evaluate the performance of ELM, SVM, GPR, and PLS models. We applied the proposed methods to data from 58 patients with AD and 94 NCs, and achieved a classification accuracy of up to 0.96 with all classification features of the ELM model, while the results of the other three models were 0.82 (PLS), 0.79 (GPR), and 0.75 (SVM). Furthermore, the effect of VBM parameter modeling is better than texture parameter. Thus, our method was optimal in distinguishing patients with AD from NCs, and may therefore be useful for the diagnosis of AD.
Testing and Quality Validation for AI Software–Perspectives, Issues, and Practices

With the fast growth of artificial intelligence and big data computing technologies, more and more software service systems have been developed using diverse machine learning models and technologies to make business and intelligent decisions based on their multimedia input to achieve intelligent features, such as image recognition, recommendation, decision making, prediction, etc. Nevertheless, there are increasing quality problems resulting in erroneous testing costs in enterprises and businesses. Existing work seldom discusses how to perform testing and quality validation for AI software. This paper focuses on quality validation for AI software function features. The paper provides our understanding of AI software testing for new features and requirements. In addition, current AI software testing categories are presented and different testing approaches are discussed. Moreover, test quality assessment and criteria analysis are illustrated. Furthermore, a practical study on quality validation for an image recognition system is performed through a metamorphic testing method. Study results show the feasibility and effectiveness of the approach.
FastBERT: a Self-distilling BERT with Adaptive Inference Time

Pre-trained language models like BERT have proven to be highly performant. However, they are often computationally expensive in many practical scenarios, for such heavy models can hardly be readily implemented with limited resources. To improve their efficiency with an assured model performance, we propose a novel speed-tunable FastBERT with adaptive inference time. The speed at inference can be flexibly adjusted under varying demands, while redundant calculation of samples is avoided. Moreover, this model adopts a unique self-distillation mechanism at fine-tuning, further enabling a greater computational efficacy with minimal loss in performance. Our model achieves promising results in twelve English and Chinese datasets. It is able to speed up by a wide range from 1 to 12 times than BERT if given different speedup thresholds to make a speed-performance tradeoff.
The SQALE of CSIDH: sublinear Vélu quantum-resistant isogeny action with low exponents

Abstract Recent independent analyses by Bonnetain–Schrottenloher and Peikert in Eurocrypt 2020 significantly reduced the estimated quantum security of the isogeny-based commutative group action key-exchange protocol CSIDH. This paper refines the estimates of a resource-constrained quantum collimation sieve attack to give a precise quantum security to CSIDH. Furthermore, we optimize large CSIDH parameters for performance while still achieving the NIST security levels 1, 2, and 3. Finally, we provide a C-code constant-time implementation of those CSIDH large instantiations using the square-root-complexity Vélu’s formulas recently proposed by Bernstein, De Feo, Leroux and Smith.
Renyi’s Entropy Based Multilevel Thresholding Using a Novel Meta-Heuristics Algorithm

Multi-level image thresholding is the most direct and effective method for image segmentation, which is a key step for image analysis and computer vision, however, as the number of threshold values increases, exhaustive search does not work efficiently and effectively and evolutionary algorithms often fall into a local optimal solution. In the paper, a meta-heuristics algorithm based on the breeding mechanism of Chinese hybrid rice is proposed to seek the optimal multi-level thresholds for image segmentation and Renyi’s entropy is utilized as the fitness function. Experiments have been run on four scanning electron microscope images of cement and four standard images, moreover, it is compared with other six classical and novel evolutionary algorithms: genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm, ant lion optimization algorithm, whale optimization algorithm, and salp swarm algorithm. Meanwhile, some indicators, including the average fitness values, standard deviation, peak signal to noise ratio, and structural similarity index are used as evaluation criteria in the experiments. The experimental results show that the proposed method prevails over the other algorithms involved in the paper on most indicators and it can segment cement scanning electron microscope image effectively.
Augmented reality technology in the libraries of universities of medical sciences: identifying the application, advantages and challenges and presenting a model

Purpose This study aimed to present a model for the use of augmented reality (AR) in the libraries of universities of medical sciences. The goal was to introduce the applications, advantages, opportunities and challenges of AR. Design/methodology/approach This study adopted a qualitative approach, had an applied goal and was based on data theory. The statistical population comprised 20 experts in the field of AR, and the data were collected based on in-depth semi-structured interviews until achieving theoretical saturation. A model was proposed after open coding and the formation of the main categories, and the use of AR in the development of libraries of medical universities was discussed. Findings The category of application consisted of strengthening education, promoting users' information literacy, finding resources, user guidance, gamification, educational justice, helping management, enriching resources, providing new services and economic savings. The advantages were library services, sociocultural excellence, educational level, software potential and helping the librarian. The challenges were technical, economic and cultural barriers. Libraries can attract many users by enacting effective policies, using technology and enriching the content of resources. AR can greatly assist library management and improve the librarians' and users' professional activities. Research limitations/implications The limitation of this study was that some experts could not participate in the interviews. Originality/value The results of this study are beneficial for managers, librarians, students and researchers. The use of AR in libraries is essential for achieving fourth-generation libraries. AR will be a necessity for the libraries of medical universities.
Generating synthetic CT from low-dose cone-beam CT by using generative adversarial networks for adaptive radiotherapy

To develop high-quality synthetic CT (sCT) generation method from low-dose cone-beam CT (CBCT) images by using attention-guided generative adversarial networks (AGGAN) and apply these images to dose calculations in radiotherapy.The CBCT/planning CT images of 170 patients undergoing thoracic radiotherapy were used for training and testing. The CBCT images were scanned under a fast protocol with 50% less clinical projection frames compared with standard chest M20 protocol. Training with aligned paired images was performed using conditional adversarial networks (so-called pix2pix), and training with unpaired images was carried out with cycle-consistent adversarial networks (cycleGAN) and AGGAN, through which sCT images were generated. The image quality and Hounsfield unit (HU) value of the sCT images generated by the three neural networks were compared. The treatment plan was designed on CT and copied to sCT images to calculated dose distribution.The image quality of sCT images by all the three methods are significantly improved compared with original CBCT images. The AGGAN achieves the best image quality in the testing patients with the smallest mean absolute error (MAE, 43.5 ± 6.69), largest structural similarity (SSIM, 93.7 ± 3.88) and peak signal-to-noise ratio (PSNR, 29.5 ± 2.36). The sCT images generated by all the three methods showed superior dose calculation accuracy with higher gamma passing rates compared with original CBCT image. The AGGAN offered the highest gamma passing rates (91.4 ± 3.26) under the strictest criteria of 1 mm/1% compared with other methods. In the phantom study, the sCT images generated by AGGAN demonstrated the best image quality and the highest dose calculation accuracy.High-quality sCT images were generated from low-dose thoracic CBCT images by using the proposed AGGAN through unpaired CBCT and CT images. The dose distribution could be calculated accurately based on sCT images in radiotherapy.
Depth-Aware Multi-Grid Deep Homography Estimation With Contextual Correlation

Homography estimation is an important task in computer vision applications, such as image stitching, video stabilization, and camera calibration. Traditional homography estimation methods heavily depend on the quantity and distribution of feature correspondences, leading to poor robustness in low-texture scenes. The learning solutions, on the contrary, try to learn robust deep features but demonstrate unsatisfying performance in the scenes with low overlap rates. In this paper, we address these two problems simultaneously by designing a contextual correlation layer (CCL). The CCL can efficiently capture the long-range correlation within feature maps and can be flexibly used in a learning framework. In addition, considering that a single homography can not represent the complex spatial transformation in depth-varying images with parallax, we propose to predict multi-grid homography from global to local. Moreover, we equip our network with a depth perception capability, by introducing a novel depth-aware shape-preserved loss. Extensive experiments demonstrate the superiority of our method over state-of-the-art solutions in the synthetic benchmark dataset and real-world dataset. The codes and models will be available at https://github.com/nie-lang/Multi-Grid-Deep-Homography.
Hierarchical Photo-Scene Encoder for Album Storytelling

In this paper, we propose a novel model with a hierarchical photo-scene encoder and a reconstructor for the task of album storytelling. The photo-scene encoder contains two subencoders, namely the photo and scene encoders, which are stacked together and behave hierarchically to fully exploit the structure information of the photos within an album. Specifically, the photo encoder generates semantic representation for each photo while exploiting temporal relationships among them. The scene encoder, relying on the obtained photo representations, is responsible for detecting the scene changes and generating scene representations. Subsequently, the decoder dynamically and attentively summarizes the encoded photo and scene representations to generate a sequence of album representations, based on which a story consisting of multiple coherent sentences is generated. In order to fully extract the useful semantic information from an album, a reconstructor is employed to reproduce the summarized album representations based on the hidden states of the decoder. The proposed model can be trained in an end-to-end manner, which results in an improved performance over the state-of-the-arts on the public visual storytelling (VIST) dataset. Ablation studies further demonstrate the effectiveness of the proposed hierarchical photo-scene encoder and reconstructor.
DFR-TSD: A Deep Learning Based Framework for Robust Traffic Sign Detection Under Challenging Weather Conditions

Robust traffic sign detection and recognition (TSDR) is of paramount importance for the successful realization of autonomous vehicle technology. The importance of this task has led to vast amount of research efforts and many promising methods have been proposed in the existing literature. However, most of these methods have been evaluated on clean and challenge-free datasets and overlooked the performance deterioration associated with different challenging conditions (CCs) that obscure the traffic-sign images captured in the wild. In this paper, we look at the TSDR problem under CCs and focus on the performance degradation associated with them. To this end, we propose a Convolutional Neural Network (CNN) based prior enhancement focused TSDR framework. Our modular approach consists of a CNN-based challenge classifier, Enhance-Net–an encoder-decoder CNN architecture for image enhancement, and two separate CNN architectures for sign-detection and classification. We propose a novel training pipeline for Enhance-Net that focuses on the enhancement of the traffic sign regions (instead of the whole image) in the challenging images subject to their accurate detection. We used CURE-TSD dataset consisting of traffic videos captured under different CCs to evaluate the efficacy of our approach. We experimentally show that our method obtains an overall precision and recall of 91.1% and 70.71% that is 7.58% and 35.90% improvement in precision and recall, respectively, compared to the current benchmark. Furthermore, we compare our approach with different CNN-based TSDR methods and show that our approach outperforms them by a large margin.
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing

TextFlint is a multilingual robustness evaluation toolkit for NLP tasks that incorporates universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses.This enables practitioners to automatically evaluate their models from various aspects or to customize their evaluations as desired with just a few lines of code.TextFlint also generates complete analytical reports as well as targeted augmented data to address the shortcomings of the model in terms of its robustness.To guarantee acceptability, all the text transformations are linguistically based and all the transformed data selected (up to 100,000 texts) scored highly under human evaluation.To validate the utility, we performed large-scale empirical evaluations (over 67,000) on state-of-the-art deep learning models, classic supervised methods, and real-world systems.The toolkit is already available at https://github.com/textflint,with all the evaluation results demonstrated at textflint.io.
Interference in a Prototype of a Two-Dimensional Ion Trap Array Quantum Simulator

Trapped ions are a promising platform for envisioned quantum simulators, with outstanding results in one-dimensional ion crystals. However, theory requires not only interactions at long range, but also higher dimensionality. We operate a basic triangular array of three individually trapped ions based on scalable microfabrication technology. We demonstrate coherent coupling, tunable in real time and enabling interference in 2D, an essential building block for a reconfigurable quantum simulator. Mitigating motional heating will permit accessing the quantum regime and 2D experimental quantum simulations.