Reviewerly/exHarmony huggingface dataset:
Deep Learning Approaches for Classification of Breast Cancer in Ultrasound (US) Images

Breast cancer is one of the deadliest cancer types affecting women worldwide. As with all types of cancer, early detection of breast cancer is of vital importance. Early diagnosis plays an important role in reducing deaths and fighting cancer. Ultrasound (US) imaging is a painless and common technique used in the early detection of breast cancer. In this article, deep learning-based approaches for the classification of breast US images have been extensively reviewed. Classification performance of breast US images of architectures such as AlexNet, VGG, ResNet, GoogleNet and EfficientNet, which are among the most basic CNN architectures, has been compared. Then, transformer models, which are one of the most popular deep learning architectures these days and show similar performance to the performance of CNN' architectures in medical images, are examined. BUSI, the only publicly available dataset, was used in experimental studies. Experimental studies have shown that the transformer and CNN models successfully classify US images of the breast. It has been observed that vision transformer model outperforms other models with 88.6% accuracy, 90.1% precison, 87.4% recall and 88.7% F1-score. This study shows that deep learning architectures are successful in classification of US images and can be used in the clinic experiments in the near future.
Blockchain-based privacy and security preserving in electronic health: a systematic review

In today's world, health and medicine play an undeniable role in human life. Traditional and current Electronic Health Records (EHR) systems that are used to exchange information between medical stakeholders (patients, physicians, insurance companies, pharmaceuticals, medical researchers, etc.) suffer weaknesses in terms of security and privacy due to having centralized architecture. Blockchain technology ensures the privacy and security of EHR systems thanks to the use of encryption. Moreover, due to its decentralized nature, this technology prevents central failure and central attack points. In this paper, a systematic literature review (SLR) is proposed to analyze the existing Blockchain-based approaches for improving privacy and security in electronic health systems. The research methodology, paper selection process, and the search query are explained. 51 papers returned from our search criteria published between 2018 and Dec 2022 are reviewed. The main ideas, type of Blockchain, evaluation metrics, and used tools of each selected paper are discussed in detail. Finally, future research directions, open challenges, and some issues are discussed.
Coupling Single Atoms to a Nanophotonic Whispering-Gallery-Mode Resonator via Optical Guiding

We demonstrate an efficient optical guiding technique for coupling cold atoms in the near field of a planar nanophotonic circuit, and realize large atom-photon coupling to a whispering-gallery mode in a microring resonator with a single-atom cooperativity C≳8. The guiding potential is created by diffracted light on a nanophotonic waveguide that smoothly connects to a dipole trap in the far field for atom guiding with subwavelength precision. We observe atom-induced transparency for light coupled to a microring, characterize the atom-photon coupling rate, extract guided atom flux, and demonstrate on-chip photon routing by single atoms. Our demonstration promises new applications with cold atoms on a nanophotonic circuit for chiral quantum optics and quantum technologies.
Overcoming the challenges to implementation of artificial intelligence in pathology

Abstract Pathologists worldwide are facing remarkable challenges with increasing workloads and lack of time to provide consistently high-quality patient care. The application of artificial intelligence (AI) to digital whole-slide images has the potential of democratizing the access to expert pathology and affordable biomarkers by supporting pathologists in the provision of timely and accurate diagnosis as well as supporting oncologists by directly extracting prognostic and predictive biomarkers from tissue slides. The long-awaited adoption of AI in pathology, however, has not materialized, and the transformation of pathology is happening at a much slower pace than that observed in other fields (eg, radiology). Here, we provide a critical summary of the developments in digital and computational pathology in the last 10 years, outline key hurdles and ways to overcome them, and provide a perspective for AI-supported precision oncology in the future.
Simultaneous single-qubit driving of semiconductor spin qubits at the fault-tolerant threshold

Abstract Practical Quantum computing hinges on the ability to control large numbers of qubits with high fidelity. Quantum dots define a promising platform due to their compatibility with semiconductor manufacturing. Moreover, high-fidelity operations above 99.9% have been realized with individual qubits, though their performance has been limited to 98.67% when driving two qubits simultaneously. Here we present single-qubit randomized benchmarking in a two-dimensional array of spin qubits, finding native gate fidelities as high as 99.992(1)%. Furthermore, we benchmark single qubit gate performance while simultaneously driving two and four qubits, utilizing a novel benchmarking technique called N -copy randomized benchmarking, designed for simple experimental implementation and accurate simultaneous gate fidelity estimation. We find two- and four-copy randomized benchmarking fidelities of 99.905(8)% and 99.34(4)% respectively, and that next-nearest neighbor pairs are highly robust to cross-talk errors. These characterizations of single-qubit gate quality are crucial for scaling up quantum information technology.
CycleMLP: A MLP-Like Architecture for Dense Visual Predictions

This article presents a simple yet effective multilayer perceptron (MLP) architecture, namely CycleMLP, which is a versatile neural backbone network capable of solving various tasks of dense visual predictions such as object detection, segmentation, and human pose estimation. Compared to recent advanced MLP architectures such as MLP-Mixer (Tolstikhin et al. 2021), ResMLP (Touvron et al. 2021), and gMLP (Liu et al. 2021), whose architectures are sensitive to image size and are infeasible in dense prediction tasks, CycleMLP has two appealing advantages: 1) CycleMLP can cope with various spatial sizes of images; 2) CycleMLP achieves linear computational complexity with respect to the image size by using local windows. In contrast, previous MLPs have <inline-formula><tex-math notation="LaTeX">$O(N^{2})$</tex-math></inline-formula> computational complexity due to their full connections in space. 3) The relationship between convolution, multi-head self-attention in Transformer, and CycleMLP are discussed through an intuitive theoretical analysis. We build a family of models that can surpass state-of-the-art MLP and Transformer models e.g., Swin Transformer (Liu et al. 2021), while using fewer parameters and FLOPs. CycleMLP expands the MLP-like models’ applicability, making them versatile backbone networks that achieve competitive results on dense prediction tasks For example, CycleMLP-Tiny outperforms Swin-Tiny by 1.3% mIoU on ADE20 K dataset with fewer FLOPs. Moreover, CycleMLP also shows excellent zero-shot robustness on ImageNet-C dataset.
Current Status and Outlook of Higher Education Digital Transformation in China

The requirements of technological advancement, trends in the development of higher education, including the epidemic and other features of the times have prompted the digital transformation of education to become inevitable. Digital transformation can help higher education to create new types of governance capacity and provide quality higher education resources to more students and the public. This article points out that the digital transformation of China’s higher education is characterized by multi-dimensional, multi-level, and multi-regional development, as well as the lack of top-level design and overall planning, and a low level of digital application. Digital governance, digital information platform building and institutional research are key components of the digital transformation. Cooperation between educational administrations, professional societies and universities can accelerate the construction of digital platforms for higher education and enhance the sharing of digital resources. Also, strengthening the research function of institutions to achieve digital strategic goals can help to enhance the practical value of digital transformation in the transformation of higher education.
Workshop on Web Archiving and Digital Libraries (WADL)

The 2019 edition of the Workshop on Web Archiving and Digital Libraries (WADL) will explore the integration of web archiving and digital libraries. The workshop aims at addressing aspects covering the entire life cycle of digital resources and will also explore areas such as archiving processes and tools for “non-traditional” resources such as scholarly and government data, 3D objects, and digital online art.
Machine learning on sequential data using a recurrent weighted average

Recurrent Neural Networks (RNN) are a type of statistical model designed to handle sequential data. The model reads a sequence one symbol at a time. Each symbol is processed based on information collected from the previous symbols. With existing RNN architectures, each symbol is processed using only information from the previous processing step. To overcome this limitation, we propose a new kind of RNN model that computes a recurrent weighted average (RWA) over every past processing step. Because the RWA can be computed as a running average, the computational overhead scales like that of any other RNN architecture. The approach essentially reformulates the attention mechanism into a stand-alone model. The performance of the RWA model is assessed on the variable copy problem, the adding problem, classification of artificial grammar, classification of sequences by length, and classification of the MNIST images (where the pixels are read sequentially one at a time). On almost every task, the RWA model is found to fit the data significantly faster than a standard LSTM model.
Qualitative Measurements of Policy Discrepancy for Return-Based Deep Q-Network

The deep Q-network (DQN) and return-based reinforcement learning are two promising algorithms proposed in recent years. DQN brings advances to complex sequential decision problems, while return-based algorithms have advantages in making use of sample trajectories. In this paper, we propose a general framework to combine DQN and most of the return-based reinforcement learning algorithms, named R-DQN. We show the performance of traditional DQN can be improved effectively by introducing return-based reinforcement learning. In order to further improve the R-DQN, we design a strategy with two measurements which can qualitatively measure the policy discrepancy. Moreover, we give the two measurements' bounds in the proposed R-DQN framework. We show that algorithms with our strategy can accurately express the trace coefficient and achieve a better approximation to return. The experiments, conducted on several representative tasks from the OpenAI Gym library, validate the effectiveness of the proposed measurements. The results also show that the algorithms with our strategy outperform the state-of-the-art methods.
From aging to early-stage Alzheimer's: Uncovering handwriting multimodal behaviors by semi-supervised learning and sequential representation learning

We present, in this paper, a novel paradigm for assessing Alzheimer’s disease and aging by analyzing impairment of handwriting (HW) on tablets, a challenging problem that is still in its infancy. The state of the art is dominated by methods that assume a unique behavioral trend for each cognitive profile or age group, and that extract global kinematic parameters, assessed by standard statistical tests or classification models, for discriminating the neuropathological disorders (Alzheimer’s (AD), Mild Cognitive Impairment (MCI)) from Healthy Controls (HC), or HC age groups from each other. Our work tackles these two major limitations as follows. First, instead of considering a unique behavioral pattern for each cognitive profile or age group, we relax this heavy constraint by allowing the emergence of multimodal behavioral patterns. We achieve this by performing semi or unsupervised learning to uncover homogeneous clusters of subjects, and then we analyze how much information these clusters carry on the cognitive profiles (or age groups). Second, instead of relying on global kinematic parameters, mostly consisting of their average, we refine the encoding either by a semi-global parameterization, or by modeling the full dynamics of each parameter, harnessing thereby the rich temporal information inherently characterizing online HW. To illustrate the power of our paradigm, we present three studies, one regarding age, and two regarding Alzheimer’s. Thanks to our modeling, we obtain new findings that are the first of their kind on this research field. On aging, unlike previous works reporting only one pattern of HW change with age, our study, based on a semiglobal parametrization scheme, uncovers three major aging HW styles, one specific to aged subjects and two shared with other age groups. On Alzheimer’s, a striking finding is revealed: two major clusters are unveiled, one dominated by HC and MCI subjects, and one by MCI and ES-AD, thus revealing that MCI patients have fine motor skills leaning towards either HC’s or ES-AD’s. Our paper introduces also a new temporal representation learning from HW trajectories that uncovers a rich set of features simultaneously like the full velocity profile, size and slant, fluidity, and shakiness, and reveals, in a naturally explainable way, how these HW features conjointly characterize, with fine and subtle details, the cognitive profiles.
COGL: Coefficient Graph Laplacians for Optimized JPEG Image Decoding

We address the problem of decoding joint photographic experts group (JPEG)-encoded images with less visual artifacts. We view the decoding task as an ill-posed inverse problem and find a regularized solution using a convex, graph Laplacian-regularized model. Since the resulting problem is non-smooth and entails non-local regularization, we use fast high-dimensional Gaussian filtering techniques with the proximal gradient descent method to solve our convex problem efficiently. Our patch-based ”coefficient graph” is better suited than the traditional pixel-based ones for regularizing smooth non-stationary signals such as natural images and relates directly to classic non-local means de-noising of images. We also extend our graph along the temporal dimension to handle the decoding of M-JPEG-encoded video. Despite the minimalistic nature of our convex problem, it produces decoded images with similar quality to other more complex, state-of-the-art methods while being up to five times faster. We also expound on the relationship between our method and the classic ANCE method, reinterpreting ANCE from a graph-based regularization perspective.
Reimagining Health Communication: A Noninferiority Randomized Controlled Trial of Crowdsourced Intervention in China

Background Crowdsourcing, the process of shifting individual tasks to a large group, may be useful for health communication, making it more people-centered. We aimed to evaluate whether a crowdsourced video is noninferior to a social marketing video in promoting condom use. Methods Men who have sex with men (≥16 years old, had condomless sex within 3 months) were recruited and randomly assigned to watch 1 of the 2 videos in 2015. The crowdsourced video was developed through an open contest, and the social marketing video was designed by using social marketing principles. Participants completed a baseline survey and follow-up surveys at 3 weeks and 3 months postintervention. The outcome was compared with a noninferiority margin of +10%. Results Among the 1173 participants, 907 (77%) and 791 (67%) completed the 3-week and 3-month follow-ups. At 3 weeks, condomless sex was reported by 146 (33.6%) of 434 participants and 153 (32.3%) 473 participants in the crowdsourced and social marketing arms, respectively. The crowdsourced intervention achieved noninferiority (estimated difference, +1.3%; 95% confidence interval, −4.8% to 7.4%). At 3 months, 196 (52.1%) of 376 individuals and 206 (49.6%) of 415 individuals reported condomless sex in the crowdsourced and social-marketing arms (estimated difference: +2.5%, 95% confidence interval, −4.5 to 9.5%). The 2 arms also had similar human immunodeficiency virus testing rates and other condom-related secondary outcomes. Conclusions Our study demonstrates that crowdsourced message is noninferior to a social marketing intervention in promoting condom use among Chinese men who have sex with men. Crowdsourcing contests could have a wider reach than other approaches and create more people-centered intervention tools for human immunodeficiency virus control.
Re-Evaluating ADEM: A Deeper Look at Scoring Dialogue Responses

Automatically evaluating the quality of dialogue responses for unstructured domains is a challenging problem. ADEM (Lowe et al. 2017) formulated the automatic evaluation of dialogue systems as a learning problem and showed that such a model was able to predict responses which correlate significantly with human judgements, both at utterance and system level. Their system was shown to have beaten word-overlap metrics such as BLEU with large margins. We start with the question of whether an adversary can game the ADEM model. We design a battery of targeted attacks at the neural network based ADEM evaluation system and show that automatic evaluation of dialogue systems still has a long way to go. ADEM can get confused with a variation as simple as reversing the word order in the text! We report experiments on several such adversarial scenarios that draw out counterintuitive scores on the dialogue responses. We take a systematic look at the scoring function proposed by ADEM and connect it to linear system theory to predict the shortcomings evident in the system. We also devise an attack that can fool such a system to rate a response generation system as favorable. Finally, we allude to future research directions of using the adversarial attacks to design a truly automated dialogue evaluation system.
A Declarative Rendering Model for Multiclass Density Maps

Fig. 1: Design alternatives for a four-class density map. 1 shows small multiples where each density map is individually presented with a unique color; 2 stacks the density maps and blends the color at each pixel; 3 shows the color of the pixel with the highest density; 4 -6 use regular and irregular weaving patterns; 7 shows a contour plot for each class; and 8 -14 use rebinning (binning and aggregation over the density maps) with tiles produced by a random Voronoï tessellation.The aggregated values are rendered in 8 with a flat color showing the highest density, 9 with hatching, 10 with proportional bars, 11 with regular weaving, 12 with a dot density plot, 13 with bar-chart glyphs, and 14 with circle sizes.Abstract-Multiclass maps are scatterplots, multidimensional projections, or thematic geographic maps where data points have a categorical attribute in addition to two quantitative attributes.This categorical attribute is often rendered using shape or color, which does not scale when overplotting occurs.When the number of data points increases, multiclass maps must resort to data aggregation to remain readable.We present multiclass density maps: multiple 2D histograms computed for each of the category values.Multiclass density maps are meant as a building block to improve the expressiveness and scalability of multiclass map visualization.In this article, we first present a short survey of aggregated multiclass maps, mainly from cartography.We then introduce a declarative model-a simple yet expressive JSON grammar associated with visual semantics-that specifies a wide design space of visualizations for multiclass density maps.Our declarative model is expressive and can be efficiently implemented in visualization front-ends such as modern web browsers.Furthermore, it can be reconfigured dynamically to support data exploration tasks without recomputing the raw data.Finally, we demonstrate how our model can be used to reproduce examples from the past and support exploring data at scale.
Semantic Prior Analysis for Salient Object Detection

Salient object detection aims to detect the main objects in the given image. In this paper, we proposed an approach that integrates semantic priors into the salient object detection process. The method first obtains an explicit saliency map that is refined by the explicit semantic priors learned from data. Then an implicit saliency map is constructed using a trained model that maps the implicit semantic priors embedded into superpixel features with the saliency values. Next, the fusion saliency map is computed by adaptively fusing both the explicit and implicit semantic maps. The final saliency map is eventually computed via the post-processing refinement step. Experimental results have demonstrated the effectiveness of the proposed method, particularly, it achieves competitive performance with the state-of-the-art baselines on three challenging datasets, namely, ECSSD, HKUIS, and iCoSeg.
Geo-Graph-Indistinguishability: Protecting Location Privacy for LBS over Road Networks

In recent years, Geo-Indistinguishability (GeoI) has been increasingly explored for protecting location privacy in location-based services (LBSs). GeoI is considered a theoretically rigorous location privacy notion since it extends differential privacy to the setting of location privacy. However, GeoI does not consider the road network, which may cause insufficiencies in terms of both privacy and utility for LBSs over a road network. In this paper, we first empirically evaluate the privacy guarantee and the utility loss of GeoI for LBSs over road networks. We identify an extra privacy loss when adversaries have the knowledge of road networks and the degradation of LBS quality of service. Second, we propose a new privacy notion, Geo-Graph-Indistinguishability (GeoGI), for protecting location privacy for LBSs over a road network and design a Graph-Exponential mechanism (GEM) satisfying GeoGI. We also show the relationship between GeoI and GeoGI to explain theoretically why GeoGI is a more suitable privacy notion over road networks. Finally, we evaluate the empirical privacy and utility of the proposed mechanism in real-world road networks. Our experiments confirm that GEM achieves higher utility for LBSs over a road network than the planar Laplace mechanism for GeoI under the same empirical privacy level.
Experimental proof of quantum Zeno-assisted noise sensing

Abstract In the ideal quantum Zeno (QZ) effect, repeated quantum projective measurements can freeze the coherent dynamics of a quantum system. However, in the weak QZ regime, measurement back-actions can allow the sensing of semi-classical field fluctuations. In this regard, we theoretically show how to combine the controlled manipulation of a quantum two-level system, used as a probe, with a sequence of projective measurements to have direct access to the noise correlation function. We experimentally test the effectiveness of the proposed noise sensing method on a properly engineered Bose–Einstein condensate of <?CDATA ${}^{87}{\rm{Rb}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mrow /> <mml:mrow> <mml:mn>87</mml:mn> </mml:mrow> </mml:msup> <mml:mi mathvariant="normal">Rb</mml:mi> </mml:math> atoms realized on an atom chip. We believe that our QZ-based approach can open a new path towards novel quantum sensing devices.