Reviewerly/exHarmony huggingface dataset:
Fuzzy Electromagnetism Optimization (FEMO) and its application in biomedical image segmentation

In this work, a new unsupervised classification approach is proposed for the biomedical image segmentation. The proposed method will be known as Fuzzy Electromagnetism Optimization (FEMO). As the name suggests, the proposed approach is based on the electromagnetism-like optimization (EMO) method. The EMO method is extended, modified, and combined with the modified type 2 fuzzy C-Means algorithm to improve its efficiency especially for biomedical image segmentation. The proposed FEMO method uses fuzzy membership and the electromagnetism-like optimization method to locate the optimal positions for the cluster centers. The proposed FEMO approach does not have any dependency on the initial selection of the cluster centers. Moreover, this method is suitable for the biomedical images of different modalities. This method is compared with some standard metaheuristics and evolutionary methods (e.g. Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Electromagnetism-like optimization (EMO), Ant Colony Optimization (ACO), etc.) based image segmentation approaches. Four different indices Davies–Bouldin, Xie–Beni, Dunn and β index are used for the comparison and evaluation purpose. For the GA, PSO, ACO, EMO and the proposed FEMO approach, the optimal average value of the Davies–Bouldin index is 1.833578359 (8 clusters), 1.669359475 (3 clusters), 1.623119284 (3 clusters), 1.647743907 (4 clusters) and 1.456889343 (3 clusters) respectively. It shows that the proposed approach can efficiently determine the optimal clusters. Moreover, the results of the other quantitative indices are quite promising for the proposed approach compared to the other approaches The detailed comparison is performed in both qualitative and quantitative manner and it is found that the proposed method outperforms some of the existing methods concerning some standard evaluation parameters.
Linearly Converging Error Compensated SGD

In this paper, we propose a unified analysis of variants of distributed SGD with arbitrary compressions and delayed updates. Our framework is general enough to cover different variants of quantized SGD, Error-Compensated SGD (EC-SGD) and SGD with delayed updates (D-SGD). Via a single theorem, we derive the complexity results for all the methods that fit our framework. For the existing methods, this theorem gives the best-known complexity results. Moreover, using our general scheme, we develop new variants of SGD that combine variance reduction or arbitrary sampling with error feedback and quantization and derive the convergence rates for these methods beating the state-of-the-art results. In order to illustrate the strength of our framework, we develop 16 new methods that fit this. In particular, we propose the first method called EC-SGD-DIANA that is based on error-feedback for biased compression operator and quantization of gradient differences and prove the convergence guarantees showing that EC-SGD-DIANA converges to the exact optimum asymptotically in expectation with constant learning rate for both convex and strongly convex objectives when workers compute full gradients of their loss functions. Moreover, for the case when the loss function of the worker has the form of finite sum, we modified the method and got a new one called EC-LSVRG-DIANA which is the first distributed stochastic method with error feedback and variance reduction that converges to the exact optimum asymptotically in expectation with a constant learning rate.
On Losses for Modern Language Models

BERT set many state-of-the-art results over varied NLU benchmarks by pre-training over two tasks: masked language modelling (MLM) and next sentence prediction (NSP), the latter of which has been highly criticized. In this paper, we 1) clarify NSP’s effect on BERT pre-training, 2) explore fourteen possible auxiliary pre-training tasks, of which seven are novel to modern language models, and 3) investigate different ways to include multiple tasks into pre-training. We show that NSP is detrimental to training due to its context splitting and shallow semantic signal. We also identify six auxiliary pre-training tasks – sentence ordering, adjacent sentence prediction, TF prediction, TF-IDF prediction, a FastSent variant, and a Quick Thoughts variant – that outperform a pure MLM baseline. Finally, we demonstrate that using multiple tasks in a multi-task pre-training framework provides better results than using any single auxiliary task. Using these methods, we outperform BERTBase on the GLUE benchmark using fewer than a quarter of the training tokens.
PSConv: Squeezing Feature Pyramid into One Compact Poly-Scale Convolutional Layer

Despite their strong modeling capacities, Convolutional Neural Networks (CNNs) are often scale-sensitive. For enhancing the robustness of CNNs to scale variance, multi-scale feature fusion from different layers or filters attracts great attention among existing solutions, while the more granular kernel space is overlooked. We bridge this regret by exploiting multi-scale features in a finer granularity. The proposed convolution operation, named Poly-Scale Convolution (PSConv), mixes up a spectrum of dilation rates and tactfully allocates them in the individual convolutional kernels of each filter regarding a single convolutional layer. Specifically, dilation rates vary cyclically along the axes of input and output channels of the filters, aggregating features over a wide range of scales in a neat style. PSConv could be a drop-in replacement of the vanilla convolution in many prevailing CNN backbones, allowing better representation learning without introducing additional parameters and computational complexities. Comprehensive experiments on the ImageNet and MS COCO benchmarks validate the superior performance of PSConv. Code and models are available at https://github.com/d-li14/PSConv .
Hybrid Path Planning Combining Potential Field with Sigmoid Curve for Autonomous Driving

The traditional potential field-based path planning is likely to generate unexpected path by strictly following the minimum potential field, especially in the driving scenarios with multiple obstacles closely distributed. A hybrid path planning is proposed to avoid the unsatisfying path generation and to improve the performance of autonomous driving by combining the potential field with the sigmoid curve. The repulsive and attractive potential fields are redesigned by considering the safety and the feasibility. Based on the objective of the shortest path generation, the optimized trajectory is obtained to improve the vehicle stability and driving safety by considering the constraints of collision avoidance and vehicle dynamics. The effectiveness is examined by simulations in multiobstacle dynamic and static scenarios. The simulation results indicate that the proposed method shows better performance on vehicle stability and ride comfortability than that of the traditional potential field-based method in all the examined scenarios during the autonomous driving.
Towards A Friendly Online Community: An Unsupervised Style Transfer Framework for Profanity Redaction

Offensive and abusive language is a pressing problem on social media platforms. In this work, we propose a method for transforming offensive comments, statements containing profanity or offensive language, into non-offensive ones. We design a Retrieve, Generate and Edit unsupervised style transfer pipeline to redact the offensive comments in a word-restricted manner while maintaining a high level of fluency and preserving the content of the original text. We extensively evaluate our method’s performance and compare it to previous style transfer models using both automatic metrics and human evaluations. Experimental results show that our method outperforms other models on human evaluations and is the only approach that consistently performs well on all automatic evaluation metrics.
Cybersecurity and the Digital-Health: The Challenge of This Millennium

The problem of computer security is as old as computers themselves and dates back decades [...]
Designing Personas for Expressive Robots

Imbuing robots with personality has been shown to be an effective design approach in HRI, promoting user trust and acceptance. We explore personality design in a non-anthropomorphic voice-assisted home robot. Our design approach developed three distinct robot personas: Butler, Buddy, and Sidekick, intended to differ in proactivity and emotional impact. Persona differences were signaled to users by a combination of humanoid (speech, intonation), and indirect cues (colors and movement). We use Big Five personality theory to evaluate perceived differences between personas in an exploratory Wizard of Oz study. Participants were largely able to recognize underlying personality traits expressed through these cue combinations in ways that were consistent with our design goals. The proactive Buddy persona was judged as more Extravert than the more passive Sidekick persona, and the Butler persona was perceived as more Conscientious and less Neurotic than either Buddy or Butler personas. Users also had clear preferences between different personas; they wanted robots that mimicked but accentuated their own personality. Results suggest that future designs might exploit abstract cues to signal personality traits.
Inference of dynamic systems from noisy and sparse data via manifold-constrained Gaussian processes

Significance Ordinary differential equations are a ubiquitous tool for modeling behaviors in science, such as gene regulation, biological rhythms, epidemics, and ecology. An important problem is to infer and characterize the uncertainty of parameters that govern equations. We present an accurate and fast inference method using manifold-constrained Gaussian processes, such that derivatives of the Gaussian process must satisfy the dynamics of the differential equations. Our method completely avoids the use of numerical integration and is thus fast to compute. Our construction is embedded in a principled statistical framework and is demonstrated to yield fast and reliable inference in a variety of practical problems. Our method works even when some system components are unobserved, which is a significant challenge for previous methods.
CIL: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction

The journey of reducing noise from distant supervision (DS) generated training data has been started since the DS was first introduced into the relation extraction (RE) task.For the past decade, researchers apply the multiinstance learning (MIL) framework to find the most reliable feature from a bag of sentences.Although the pattern of MIL bags can greatly reduce DS noise, it fails to represent many other useful sentence features in the datasets.In many cases, these sentence features can only be acquired by extra sentence-level human annotation with heavy costs.Therefore, the performance of distantly supervised RE models is bounded.In this paper, we go beyond typical MIL framework and propose a novel Contrastive Instance Learning (CIL) framework.Specifically, we regard the initial MIL as the relational triple encoder and constraint positive pairs against negative pairs for each instance.Experiments demonstrate the effectiveness of our proposed framework, with significant improvements over the previous methods on NYT10, GDS and KBP.
Domain Generalization under Conditional and Label Shifts via Variational Bayesian Inference

In this work, we propose a domain generalization (DG) approach to learn on several labeled source domains and transfer knowledge to a target domain that is inaccessible in training. Considering the inherent conditional and label shifts, we would expect the alignment of p(x|y) and p(y). However, the widely used domain invariant feature learning (IFL) methods relies on aligning the marginal concept shift w.r.t. p(x), which rests on an unrealistic assumption that p(y) is invariant across domains. We thereby propose a novel variational Bayesian inference framework to enforce the conditional distribution alignment w.r.t. p(x|y) via the prior distribution matching in a latent space, which also takes the marginal label shift w.r.t. p(y) into consideration with the posterior alignment. Extensive experiments on various benchmarks demonstrate that our framework is robust to the label shift and the cross-domain accuracy is significantly improved, thereby achieving superior performance over the conventional IFL counterparts.
A Survey of Researchers’ Needs and Priorities for Data Sharing

One of the ways in which the publisher PLOS supports open science is via a stringent data availability policy established in 2014. Despite this policy, and more data sharing policies being introduced by other organizations, best practices for data sharing are adopted by a minority of researchers in their publications. Problems with effective research data sharing persist and these problems have been quantified by previous research as a lack of time, resources, incentives, and/or skills to share data. In this study we built on this research by investigating the importance of tasks associated with data sharing, and researchers’ satisfaction with their ability to complete these tasks. By investigating these factors we aimed to better understand opportunities for new or improved solutions for sharing data. In May-June 2020 we surveyed researchers from Europe and North America to rate tasks associated with data sharing on (i) their importance and (ii) their satisfaction with their ability to complete them. We received 617 completed responses. We calculated mean importance and satisfaction scores to highlight potential opportunities for new solutions to and compare different cohorts. Tasks relating to research impact, funder compliance, and credit had the highest importance scores. 52% of respondents reuse research data but the average satisfaction score for obtaining data for reuse was relatively low. Tasks associated with sharing data were rated somewhat important and respondents were reasonably well satisfied in their ability to accomplish them. Notably, this included tasks associated with best data sharing practice, such as use of data repositories. However, the most common method for sharing data was in fact via supplemental files with articles, which is not considered to be best practice. We presume that researchers are unlikely to seek <em>new</em> solutions to a problem or task that they are satisfied in their <em>ability</em> to accomplish, even if many do not attempt this task. This implies there are few opportunities for <em>new solutions or tools</em> to meet these researcher needs. Publishers can likely meet these needs for data sharing by working to seamlessly integrate <em>existing</em> solutions that reduce the effort or behaviour change involved in some tasks, and focusing on advocacy and education around the benefits of sharing data. There may however be opportunities – unmet researcher needs – in relation to better supporting data reuse, which could be met in part by strengthening data sharing policies of journals and publishers, and improving the discoverability of data associated with published articles.
A survey on epistemic (model) uncertainty in supervised learning: Recent advances and applications

Quantifying the uncertainty of supervised learning models plays an important role in making more reliable predictions. Epistemic uncertainty, which usually is due to insufficient knowledge about the model, can be reduced by collecting more data or refining the learning models. Over the last few years, scholars have proposed many epistemic uncertainty handling techniques which can be roughly grouped into two categories, i.e., Bayesian and ensemble. This paper provides a comprehensive review of epistemic uncertainty learning techniques in supervised learning over the last five years. As such, we, first, decompose the epistemic uncertainty into bias and variance terms. Then, a hierarchical categorization of epistemic uncertainty learning techniques along with their representative models is introduced. In addition, several applications such as computer vision (CV) and natural language processing (NLP) are presented, followed by a discussion on research gaps and possible future research directions.
Minerals Yearbook, volume I, Metals and Minerals

For additional information, contact: Director, National Minerals Information Center U.S. Geological Survey 12201 Sunrise Valley Drive 988 National Center Reston, VA 20192 Email: nmicrecordsmgt@usgs.gov The U.S. Geological Survey (USGS) Minerals Yearbook discusses the performance of the worldwide minerals and materials industries and provides background information to assist in interpreting that performance. Content of the individual Minerals Yearbook volumes follows:Volume I, Metals and Minerals, contains chapters about virtually all metallic and industrial mineral commodities important to the U.S. economy. Chapters on survey methods, summary statistics for domestic nonfuel minerals, and trends in mining and quarrying in the metals and industrial mineral industries in the United States are also included.Volume II, Area Reports: Domestic, contains a chapter on the mineral industry of each of the 50 States and Puerto Rico and the Administered Islands. This volume also has chapters on survey methods and summary statistics of domestic nonfuel minerals.Volume III, Area Reports: International, is published as four separate reports. These regional reports contain the latest available minerals data on more than 180 foreign countries and discuss the importance of minerals to the economies of these nations and the United States. Each report begins with an overview of the region’s mineral industries during the year. It continues with individual country chapters that examine the mining, refining, processing, and use of minerals in each country of the region and how each country’s mineral industry relates to U.S. industry. Most chapters include production tables and industry structure tables, information about Government policies and programs that affect the country’s mineral industry, and an outlook section.The USGS continually strives to improve the value of its publications to users. Constructive comments and suggestions by readers of the Minerals Yearbook are welcome.
Hiscox Cyber Readiness Report 2019

More than half of organisations have suffered some form of cyber attack in the past year, according to the annual ‘Cyber Readiness Report’ from insurance firm Hiscox. This is the third such report and marks the first time that the proportion of companies that have come under attack is a significant majority of those studied, strongly suggesting that the security situation is getting worse.
YOLOX-SAR: High-Precision Object Detection System Based on Visible and Infrared Sensors for SAR Remote Sensing

The object detection technology for Synthetic Aperture Radar (SAR) image generation is of great significance in signal processing, radar imaging and other fields. SAR image is the image data obtained from the electromagnetic wave echo from the radar to the object after processing the range and the azimuth respectively. However, the sizes of the objects to be detected change dramatically and the detection difficulty increases, because the scattering characteristics of electromagnetic waves have a great influence on SAR images. At the same time, the large areas of background information will contain confused geographical elements, which seriously affect the detection performance. Therefore, we propose the YOLOX-SAR algorithm to solve these problems. Based on YOLOX, we combine the adaptive activation function Meta-ACON with the SPP module in Backbone to improve the feature extraction ability. We also integrate the Convolutional Block Attention Model (CBAM) at FPN to find the attention area in scenes with dense objects. Furthermore, many useful strategies, such as data enhancement and MS-testing, are provided to improve our proposed YOLOX-SAR. Apart from that, in order to objectively demonstrate the competitive performance of YOLOX-SAR, we compare it with other object detection algorithms on the sensor acquisition dataset of NWPU VHR-10. There remain a large amount of research results demonstrating that YOLOX-SAR possesses good performance as well as interpretation ability in SAR image object detection. The mAP of YOLOX-SAR reaches 89.56%, which is 4.04% higher than that of the previous YOLOX algorithm. YOLOX-SAR, as an improved version of YOLO series, can still be detected in real-time and its FPS reaches 67, keeping the characteristic of fast detection speed of the YOLOX series. Experimental results and analysis show that under the complex electromagnetic scattering background, YOLOX-SAR can ensure the authenticity and effectiveness of the dataset collected by the sensors. Meanwhile, YOLOX-SAR ensures a good balance between the accuracy and running time of object detection. Therefore, the algorithm has good detection performance for objects of various sizes in SAR images.
Into the Metaverse – Perspectives on a New Reality

As virtual and augmented reality technologies continue to advance, a new concept has risen above the rest that aims to advance the virtual experience to create a complex, interactive and interconnected world - known as the metaverse. Offering a near limitless potential, the metaverse would provide new 3-D environments for people to communicate, shop, learn, work, and do anything that would normally be done on the internet. This column will introduce the metaverse, provide current examples of development, and consider a few takeaways for medical librarians.
DualPrompt: Complementary Prompting for Rehearsal-Free Continual Learning

Continual learning aims to enable a single model to learn a sequence of tasks without catastrophic forgetting. Top-performing methods usually require a rehearsal buffer to store past pristine examples for experience replay, which, however, limits their practical value due to privacy and memory constraints. In this work, we present a simple yet effective framework, DualPrompt, which learns a tiny set of parameters, called prompts, to properly instruct a pre-trained model to learn tasks arriving sequentially without buffering past examples. DualPrompt presents a novel approach to attach complementary prompts to the pre-trained backbone, and then formulates the objective as learning task-invariant and task-specific “instructions”. With extensive experimental validation, DualPrompt consistently sets state-of-the-art performance under the challenging class-incremental setting. In particular, DualPrompt outperforms recent advanced continual learning methods with relatively large buffer sizes. We also introduce a more challenging benchmark, Split ImageNet-R, to help generalize rehearsal-free continual learning research. Source code is available at https://github.com/google-research/l2p .