Reviewerly/exHarmony huggingface dataset:
Automatically Segmenting the Left Atrium from Cardiac Images Using Successive 3D U-Nets and a Contour Loss

Radiological imaging offers effective measurement of anatomy, which is useful in disease diagnosis and assessment. Previous study [1] has shown that the left atrial wall remodeling can provide information to predict treatment outcome in atrial fibrillation. Nevertheless, the segmentation of the left atrial structures from medical images is still very time-consuming. Current advances in neural network may help creating automatic segmentation models that reduce the workload for clinicians. In this preliminary study, we propose automated, two-stage, three-dimensional U-Nets with convolutional neural network, for the challenging task of left atrial segmentation. Unlike previous two-dimensional image segmentation methods, we use 3D U-Nets to obtain the heart cavity directly in 3D. The dual 3D U-Net structure consists of, a first U-Net to coarsely segment and locate the left atrium, and a second U-Net to accurately segment the left atrium under higher resolution. In addition, we introduce a Contour loss based on additional distance information to adjust the final segmentation. We randomly split the data into training datasets (80 subjects) and validation datasets (20 subjects) to train multiple models, with different augmentation setting. Experiments show that the average Dice coefficients for validation datasets are around 0.91–0.92, the sensitivity around 0.90–0.94 and the specificity 0.99. Compared with traditional Dice loss, models trained with Contour loss in general offer smaller Hausdorff distance with similar Dice coefficient, and have less connected components in predictions. Finally, we integrate several trained models in an ensemble prediction to segment testing datasets.
Robust cubature Kalman filter based on variational Bayesian and transformed posterior sigma points error

An improved robust cubature Kalman filter (RCKF) based on variational Bayesian (VB) and transformed posterior sigma points error is proposed in this paper, which not only retains the robustness of RCKF, but also exhibits adaptivity in the presence of time-varying noise. First, a novel sigma-point update framework with uncertainties reduction is developed by employing the transformed posterior sigma points error. Then the VB is used to estimate the time-varying measurement noise, where the state-dependent noise is addressed in the iteratively parameter estimation. The new filter not only reduces the uncertainty on sigma points generation but also accelerates the convergence of VB-based noise estimation. The effectiveness of the proposed filter is verified on integrated navigation, and numerical simulations demonstrate that VB-RCKF outperforms VB-CKF and RCKF.
Densely-Connected Multi-Magnification Hashing for Histopathological Image Retrieval

Content-based medical image retrieval is an important computer-aided diagnosis technique providing the clinicians with interpretative references based on visual similarity. In this paper, we focus on the tasks of histopathological image retrieval for breast cancer diagnosis. The densely-connected multi-magnification (DCMMH) framework is proposed to generate the discriminative binary codes by exploiting the histopathological images with multiple magnification factors. The low-magnification images are boosted by the accumulated similarity based on local patches that also regularize the feature learning of high-magnification images. In order to fully utilize the information across different magnification levels, a densely-connected architecture is finally deployed for high-low magnification pairs of datasets. Experiments on BreakHis dataset demonstrate that, DCMMH outperforms the previous hashing methods on histopathological image retrieval.
Experimentally feasible semi-device-independent certification of four-outcome positive-operator-valued measurements

Recently the quantum information science community devoted a lot of attention to the theoretical and practical aspects of generalized measurements, the formalism of all possible quantum operations leading to acquisition of classical information. On the other hand, due to imperfections present in quantum devices, and limited thrust to them, a trend of formulating quantum information tasks in a semi-device-independent manner emerged. In this Rapid Communication we use the concept of quantum random access codes to construct a protocol able to certify the presence of the generalized measurements in a semi-device-independent way without employing quantum entanglement. We use semidefinite programming methods to show robustness of the protocol and characterize its statistical properties. We conclude that it allows for experimental realizations using technology currently available in laboratories.
Square wave excitability in quantum dot lasers under optical injection

Quantum dot lasers display many unique dynamic phenomena when optically injected. Bistability has been predicted in a region of high injection strength. Experimentally, we show that a square wave phenomenon, rather than a phase-locked bistability, is observed in this region. The squares can manifest as a periodic train but also as noise-driven Type II excitable events. We interpret the appearance of the square waves as a thermally induced breaking of the bistability. Indeed, we find experimentally that over the duration of a square, the relative detuning between the master and the slave evolves deterministically. A relatively simple, physically motivated, rate equation model is presented and displays excellent agreement with the experiment.
Investigating Syntactic Complexity in EFL Learners' Writing across Common European Framework of Reference Levels A1, A2, and B1

Abstract The study investigates the linguistic basis of Common European Framework of Reference (CEFR) levels in English as a foreign language (EFL) learners’ writing. Specifically, it examines whether CEFR levels can be distinguished with reference to syntactic complexity (SC) and whether the results differ between two groups of EFL learners with different first languages (Sindhi and Finnish). This sheds light on the linguistic comparability of the CEFR levels across L1 groups. Informants were teenagers from Pakistan (N = 868) and Finland (N = 287) who wrote the same argumentative essay that was rated on a CEFR-based scale. The essays were analysed for 28 SC indices with the L2 Syntactic Complexity Analyzer and Coh-Metrix. Most indices were found to distinguish CEFR levels A1, A2, and B1 in both language groups: the clearest separators were the length of production units, subordination, and phrasal density indices. The learner groups differed most in the length measures and phrasal density when their CEFR level was controlled for. However, some indices remained the same, and the A1 level was more similar than A2 and B2 in terms of SC across the two groups.
Activity Recognition Method for Home-Based Elderly Care Service Based on Random Forest and Activity Similarity

In home-based elderly care service, how to precisely recognize activities is a key issue in the design and implementation of context-aware service for elderly people. Existing research works reveal that those approaches ignore the characteristics of activity diversity, and similarity and the features of activities of elderly people at home, so recognition accuracy of those approaches are not high enough for real-life applications. Thus, in this paper, we first study the types of activities in home-based elderly care service. Then, we propose a two-stage elderly home activity recognition method based on random forest and activity similarity. The method uses improved random forest to obtain a preliminary result in the first stage. Then, the correlation between activity, location, and time is employed to judge the rationality of the result. The similarity of activities is further used to correct the results in the second stage. We set up a series of experiments to evaluate the effectiveness and efficiency of our approach.
Augment your batch: better training with larger batches

Large-batch SGD is important for scaling training of deep neural networks. However, without fine-tuning hyperparameter schedules, the generalization of the model may be hampered. We propose to use batch augmentation: replicating instances of samples within the same batch with different data augmentations. Batch augmentation acts as a regularizer and an accelerator, increasing both generalization and performance scaling. We analyze the effect of batch augmentation on gradient variance and show that it empirically improves convergence for a wide variety of deep neural networks and datasets. Our results show that batch augmentation reduces the number of necessary SGD updates to achieve the same accuracy as the state-of-the-art. Overall, this simple yet effective method enables faster training and better generalization by allowing more computational resources to be used concurrently.
Novel Color Normalization Method for Hematoxylin &amp; Eosin Stained Histopathology Images

With the advent of computer-assisted diagnosis (CAD), the accuracy of cancer detection from histopathology images is significantly increased.However, color variation in the CAD system is inevitable due to the variability of stain concentration and manual tissue sectioning.The small variation in color may lead to the misclassification of cancer cells.Therefore, color normalization is a very much essential step prior to segmentation and classification in order to reduce the inter-variability of background color among a set of source images.In this paper, a novel color normalization method is proposed for Hematoxylin and Eosin stained histopathology images.Conventional Reinhard algorithm is modified in our proposed method by incorporating fuzzy logic.Moreover, mathematically, it is proved that our proposed method satisfies all three hypotheses of color normalization.Furthermore, several quality metrics are estimated locally for evaluating the performance of various color normalization methods.The experimental result reveals that our proposed method has outperformed all other benchmark methods.
Linguistic Knowledge and Transferability of Contextual Representations

Contextual word representations derived from large-scale neural language models are successful across a diverse set of NLP tasks, suggesting that they encode useful and transferable features of language. To shed light on the linguistic knowledge they capture, we study the representations produced by several recent pretrained contextualizers (variants of ELMo, the OpenAI transformer language model, and BERT) with a suite of seventeen diverse probing tasks. We find that linear models trained on top of frozen contextual representations are competitive with state-of-the-art task-specific models in many cases, but fail on tasks requiring fine-grained linguistic knowledge (e.g., conjunct identification). To investigate the transferability of contextual word representations, we quantify differences in the transferability of individual layers within contextualizers, especially between recurrent neural networks (RNNs) and transformers. For instance, higher layers of RNNs are more task-specific, while transformer layers do not exhibit the same monotonic trend. In addition, to better understand what makes contextual word representations transferable, we compare language model pretraining with eleven supervised pretraining tasks. For any given task, pretraining on a closely related task yields better performance than language model pretraining (which is better on average) when the pretraining dataset is fixed. However, language model pretraining on more data gives the best results.
Unsupervised Dialog Structure Learning

Weiyan Shi, Tiancheng Zhao, Zhou Yu. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Spaces and Traces

Smart home technologies are beginning to become more widespread and common, even as their deployment and implementation remain complex and spread across different competing commercial ecosystems. Looking beyond the middle-class, single-family home often at the center of the smart home narrative, we report on a series of participatory design workshops held with residents and building managers to better understand the role of smart home technologies in the context of public housing in the U.S. The design workshops enabled us to gather insight into the specific challenges and opportunities of deploying smart home technologies in a setting where issues of privacy, data collection and ownership, and autonomy collide with diverse living arrangements, where income, age, and the consequences of monitoring and data aggregation setup an expanding collection of design implications in the ecosystems of smart home technologies.
Rapid assessment of heavy metal pollution using ion-exchange resin sachets and micro-XRF core-scanning

Abstract Conventional pollution monitoring strategies for heavy metals are often costly and unpractical. Innovative sampling and analytical approaches are therefore needed to efficiently monitor large areas. This study presents a novel, simple, fast, and inexpensive method to monitor heavy metal pollution that uses cation-exchange resin sachets and the micro-XRF core-scanning technique (XRF-CS). The resin passive samplers act as concentrators of cationic species and can be readily deployed spatially and temporally to record pollution signals. The large number of analytical tasks are then overcome by the fast and non-destructive XRF-CS to precisely assess elemental concentrations. Quantifying element loading involves direct comparison with a set of identically prepared and scanned resin reference standards containing Ca, Ti, Cr, Mn, Ni, Cu, Zn, Pb. The results show that within the test range (from 0–1000 s mg kg −1 ), the calibration lines have excellent regressions (R 2 ≥ 0.97), even at the shortest exposure time (1 s). A pilot field survey of a suspected polluted area in central Taiwan, where 30 resin sachets had been deployed, identified a pollution hot spot in a rapid and economical manner. Therefore, this approach has the potential to become a valuable tool in environmental monitoring and forensics.
New Kid on the Web: A Study on the Prevalence of WebAssembly in the Wild

WebAssembly, or Wasm for short, is a new, low-level language that allows for near-native execution performance and is supported by all major browsers as of today. In comparison to JavaScript it offers faster transmission, parsing, and execution times. Up until now it has, however, been largely unclear what WebAssembly is used for in the wild. In this paper, we thus conduct the first large-scale study on the Web. For this, we examine the prevalence of WebAssembly in the Alexa Top 1 million websites and find that as many as 1 out of 600 sites execute Wasm code. Moreover, we perform several secondary analyses, including an evaluation of code characteristics and the assessment of a Wasm module’s field of application. Based on this, we find that over 50 % of all sites using WebAssembly apply it for malicious deeds, such as mining and obfuscation.
AlpacaTag: An Active Learning-based Crowd Annotation Framework for Sequence Tagging

We introduce an open-source web-based data annotation framework (AlpacaTag) for sequence tagging tasks such as named-entity recognition (NER).The distinctive advantages of AlpacaTag are three-fold.1) Active intelligent recommendation: dynamically suggesting annotations and sampling the most informative unlabeled instances with a back-end active learned model; 2) Automatic crowd consolidation: enhancing real-time interannotator agreement by merging inconsistent labels from multiple annotators; 3) Real-time model deployment: users can deploy their models in downstream systems while new annotations are being made.AlpacaTag is a comprehensive solution for sequence labeling tasks, ranging from rapid tagging with recommendations powered by active learning and auto-consolidation of crowd annotations to real-time model deployment.
Putting Words in Context: LSTM Language Models and Lexical Ambiguity

In neural network models of language, words are commonly represented using contextinvariant representations (word embeddings) which are then put in context in the hidden layers.Since words are often ambiguous, representing the contextually relevant information is not trivial.We investigate how an LSTM language model deals with lexical ambiguity in English, designing a method to probe its hidden representations for lexical and contextual information about words.We find that both types of information are represented to a large extent, but also that there is room for improvement for contextual information.
ENT Rank

Related work has demonstrated the helpfulness of utilizing information about entities in text retrieval; here we explore the converse: Utilizing information about text in entity retrieval. We model the relevance of Entity-Neighbor-Text (ENT) relations to derive a learning-to-rank-entities model.
A Self-Attentive model for Knowledge Tracing

Knowledge tracing is the task of modeling each student's mastery of knowledge concepts (KCs) as (s)he engages with a sequence of learning activities. Each student's knowledge is modeled by estimating the performance of the student on the learning activities. It is an important research area for providing a personalized learning platform to students. In recent years, methods based on Recurrent Neural Networks (RNN) such as Deep Knowledge Tracing (DKT) and Dynamic Key-Value Memory Network (DKVMN) outperformed all the traditional methods because of their ability to capture complex representation of human learning. However, these methods face the issue of not generalizing well while dealing with sparse data which is the case with real-world data as students interact with few KCs. In order to address this issue, we develop an approach that identifies the KCs from the student's past activities that are \textit{relevant} to the given KC and predicts his/her mastery based on the relatively few KCs that it picked. Since predictions are made based on relatively few past activities, it handles the data sparsity problem better than the methods based on RNN. For identifying the relevance between the KCs, we propose a self-attention based approach, Self Attentive Knowledge Tracing (SAKT). Extensive experimentation on a variety of real-world dataset shows that our model outperforms the state-of-the-art models for knowledge tracing, improving AUC by 4.43% on average.
A Dialogue-Act Taxonomy for a Virtual Coach Designed to Improve the Life of Elderly

This paper presents a dialogue act taxonomy designed for the developmentof a conversational agent for elderly. The main goal of this conversational agent is to improvelife quality of the user by means of coaching sessions in different topics. In contrast to otherapproaches such as task-oriented dialogue systems and chit-chat implementations, the agent shoulddisplay a pro-active attitude, driving the conversation to reach a number of diverse coachinggoals. Therefore, the main characteristic of the introduced dialogue act taxonomy is its capacityfor supporting a communication based on the GROW model for coaching. In addition, the taxonomyhas a hierarchical structure between the tags and it is multimodal. We use the taxonomy toannotate a Spanish dialogue corpus collected from a group of elder people. We also present apreliminary examination of the annotated corpus and discuss on the multiple possibilities it presentsfor further research.
Oblivious DNS: Practical Privacy for DNS Queries

Abstract Virtually every Internet communication typically involves a Domain Name System (DNS) lookup for the destination server that the client wants to communicate with. Operators of DNS recursive resolvers—the machines that receive a client’s query for a domain name and resolve it to a corresponding IP address—can learn significant information about client activity. Past work, for example, indicates that DNS queries reveal information ranging from web browsing activity to the types of devices that a user has in their home. Recognizing the privacy vulnerabilities associated with DNS queries, various third parties have created alternate DNS services that obscure a user’s DNS queries from his or her Internet service provider. Yet, these systems merely transfer trust to a different third party. We argue that no single party ought to be able to associate DNS queries with a client IP address that issues those queries. To this end, we present Oblivious DNS (ODNS), which introduces an additional layer of obfuscation between clients and their queries. To do so, ODNS uses its own authoritative namespace; the authoritative servers for the ODNS namespace act as recursive resolvers for the DNS queries that they receive, but they never see the IP addresses for the clients that initiated these queries. We present an initial deployment of ODNS; our experiments show that ODNS introduces minimal performance overhead, both for individual queries and for web page loads. We design ODNS to be compatible with existing DNS protocols and infrastructure, and we are actively working on an open standard with the IETF.
Pareto Monte Carlo Tree Search for Multi-Objective Informative Planning

In many environmental monitoring scenarios, the sampling robot needs to simultaneously explore the environment and exploit features of interest with limited time. We present an anytime multi-objective informative planning method called Pareto Monte Carlo tree search which allows the robot to handle potentially competing objectives such as exploration versus exploitation. The method produces optimized decision solutions for the robot based on its knowledge (estimation) of the environment state, leading to better adaptation to environmental dynamics. We provide algorithmic analysis on the critical tree node selection step and show that the number of times choosing sub-optimal nodes is logarithmically bounded and the search result converges to the optimal choices at a polynomial rate.
Quantum-Inspired Acromyrmex Evolutionary Algorithm

Abstract Obtaining efficient optimisation algorithms has become the focus of much research interest since current developing trends in machine learning, traffic management, and other cutting-edge applications require complex optimised models containing a huge number of parameters. At present, computers based on the classical Turing-machine are inefficient when intent to solve optimisation tasks in complex and wicked problems. As a solution, quantum computers that should satisfy the Deutsch-Church-Turing principle have been proposed but this technology is still at an early stage. quantum-inspired algorithms (QIA) have emerged trying to fill-up an existing gap between the theoretical advances in quantum computation and real quantum computers. QIA use classical computers to simulate some physical phenomena such as superposition and entanglement to perform quantum computations. This paper proposes the quantum-inspired Acromyrmex evolutionary algorithm (QIAEA) as a highly efficient global optimisation method for complex systems. We present comparative statistical analyses that demonstrate how this nature-inspired proposal outperforms existing outstanding quantum-inspired evolutionary algorithms when testing benchmark functions.
Zero-shot Reading Comprehension by Cross-lingual Transfer Learning with Multi-lingual Language Representation Model

Because it is not feasible to collect training data for every language, there is a growing interest in cross-lingual transfer learning.In this paper, we systematically explore zeroshot cross-lingual transfer learning on reading comprehension tasks with a language representation model pre-trained on multi-lingual corpus.The experimental results show that with pre-trained language representation zeroshot learning is feasible, and translating the source data into the target language is not necessary and even degrades the performance.We further explore what does the model learn in zero-shot setting 0 .