Reviewerly/exHarmony huggingface dataset:
Artificial intelligence in neuropathology: deep learning-based assessment of tauopathy

Accumulation of abnormal tau in neurofibrillary tangles (NFT) occurs in Alzheimer disease (AD) and a spectrum of tauopathies. These tauopathies have diverse and overlapping morphological phenotypes that obscure classification and quantitative assessments. Recently, powerful machine learning-based approaches have emerged, allowing the recognition and quantification of pathological changes from digital images. Here, we applied deep learning to the neuropathological assessment of NFT in postmortem human brain tissue to develop a classifier capable of recognizing and quantifying tau burden. The histopathological material was derived from 22 autopsy brains from patients with tauopathies. We used a custom web-based informatics platform integrated with an in-house information management system to manage whole slide images (WSI) and human expert annotations as ground truth. We utilized fully annotated regions to train a deep learning fully convolutional neural network (FCN) implemented in PyTorch against the human expert annotations. We found that the deep learning framework is capable of identifying and quantifying NFT with a range of staining intensities and diverse morphologies. With our FCN model, we achieved high precision and recall in naive WSI semantic segmentation, correctly identifying tangle objects using a SegNet model trained for 200 epochs. Our FCN is efficient and well suited for the practical application of WSIs with average processing times of 45 min per WSI per GPU, enabling reliable and reproducible large-scale detection of tangles. We measured performance on test data of 50 pre-annotated regions on eight naive WSI across various tauopathies, resulting in the recall, precision, and an F1 score of 0.92, 0.72, and 0.81, respectively. Machine learning is a useful tool for complex pathological assessment of AD and other tauopathies. Using deep learning classifiers, we have the potential to integrate cell- and region-specific annotations with clinical, genetic, and molecular data, providing unbiased data for clinicopathological correlations that will enhance our knowledge of the neurodegeneration.
Light-Weight Hybrid Convolutional Network for Liver Tumor Segmentation

Automated segmentation of liver tumors in contrast-enhanced abdominal computed tomography (CT) scans is essential in assisting medical professionals to evaluate tumor development and make fast therapeutic schedule. Although deep convolutional neural networks (DCNNs) have contributed many breakthroughs in image segmentation, this task remains challenging, since 2D DCNNs are incapable of exploring the inter-slice information and 3D DCNNs are too complex to be trained with the available small dataset. In this paper, we propose the light-weight hybrid convolutional network (LW-HCN) to segment the liver and its tumors in CT volumes. Instead of combining a 2D and a 3D networks for coarse-to-fine segmentation, LW-HCN has a encoder-decoder structure, in which 2D convolutions used at the bottom of the encoder decreases the complexity and 3D convolutions used in other layers explore both spatial and temporal information. To further reduce the complexity, we design the depthwise and spatiotemporal separate (DSTS) factorization for 3D convolutions, which not only reduces parameters dramatically but also improves the performance. We evaluated the proposed LW-HCN model against several recent methods on the LiTS and 3D-IRCADb datasets and achieved, respectively, the Dice per case of 73.0% and 94.1% for tumor segmentation, setting a new state of the art.
On NMT Search Errors and Model Errors: Cat Got Your Tongue?

We report on search errors and model errors in neural machine translation (NMT).We present an exact inference procedure for neural sequence models based on a combination of beam search and depth-first search.We use our exact search to find the global best model scores under a Transformer base model for the entire WMT15 English-German test set.Surprisingly, beam search fails to find these global best model scores in most cases, even with a very large beam size of 100.For more than 50% of the sentences, the model in fact assigns its global best score to the empty translation, revealing a massive failure of neural models in properly accounting for adequacy.We show by constraining search with a minimum translation length that at the root of the problem of empty translations lies an inherent bias towards shorter translations.We conclude that vanilla NMT in its current form requires just the right amount of beam search errors, which, from a modelling perspective, is a highly unsatisfactory conclusion indeed, as the model often prefers an empty translation.
Cross-Platform Verification of Intermediate Scale Quantum Devices

We describe a protocol for cross-platform verification of quantum simulators and quantum computers. We show how to measure directly the overlap Tr[ρ1ρ2] and the purities Tr[ρ1,22], and thus a fidelity of two, possibly mixed, quantum states ρ1 and ρ2 prepared in separate experimental platforms. We require only local measurements in randomized product bases, which are communicated classically. As a proof of principle, we present the measurement of experiment-theory fidelities for entangled 10-qubit quantum states in a trapped ion quantum simulator.Received 3 September 2019DOI:https://doi.org/10.1103/PhysRevLett.124.010504© 2020 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum benchmarkingQuantum computationQuantum simulationPhysical SystemsTrapped ionsQuantum InformationAtomic, Molecular & Optical
A Framework for the Testing and Validation of Simulated Environments in Experimentation and Training

New computer technologies, like virtual reality (VR), have created opportunities to study human behavior and train skills in novel ways. VR holds significant promise for maximizing the efficiency and effectiveness of skill learning in a variety of settings (e.g., sport, medicine, safety-critical industries) through immersive learning and augmentation of existing training methods. In many cases the adoption of VR for training has, however, preceded rigorous testing and validation of the simulation tool. In order for VR to be implemented successfully for both training and psychological experimentation it is necessary to first establish whether the simulation captures fundamental features of the real task and environment, and elicits realistic behaviors. Unfortunately evaluation of VR environments too often confuses presentation and function, and relies on superficial visual features that are not the key determinants of successful training outcomes. Therefore evidence-based methods of establishing the fidelity and validity of VR environments are required. To this end, we outline a taxonomy of the subtypes of fidelity and validity, and propose a variety of practical methods for testing and validating VR training simulations. Ultimately, a successful VR environment is one that enables transfer of learning to the real-world. We propose that key elements of psychological, affective and ergonomic fidelity, are the real determinants of successful transfer. By adopting an evidence-based approach to VR simulation design and testing it is possible to develop valid environments that allow the potential of VR training to be maximized.
LSTM-Based VAE-GAN for Time-Series Anomaly Detection

Time series anomaly detection is widely used to monitor the equipment sates through the data collected in the form of time series. At present, the deep learning method based on generative adversarial networks (GAN) has emerged for time series anomaly detection. However, this method needs to find the best mapping from real-time space to the latent space at the anomaly detection stage, which brings new errors and takes a long time. In this paper, we propose a long short-term memory-based variational autoencoder generation adversarial networks (LSTM-based VAE-GAN) method for time series anomaly detection, which effectively solves the above problems. Our method jointly trains the encoder, the generator and the discriminator to take advantage of the mapping ability of the encoder and the discrimination ability of the discriminator simultaneously. The long short-term memory (LSTM) networks are used as the encoder, the generator and the discriminator. At the anomaly detection stage, anomalies are detected based on reconstruction difference and discrimination results. Experimental results show that the proposed method can quickly and accurately detect anomalies.
Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor Learning

We present a simple few-shot named entity recognition (NER) system based on nearest neighbor learning and structured inference.Our system uses a supervised NER model trained on the source domain, as a feature extractor.Across several test domains, we show that a nearest neighbor classifier in this featurespace is far more effective than the standard meta-learning approaches.We further propose a cheap but effective method to capture the label dependencies between entity tags without expensive CRF training.We show that our method of combining structured decoding with nearest neighbor learning achieves stateof-the-art performance on standard few-shot NER evaluation tasks, improving F1 scores by 6% to 16% absolute points over prior metalearning based systems.
A quantum-logic gate between distant quantum-network modules

Quantum gating at a distance The processing of quantum information is reliant on the encoding and manipulation of quantum states of a qubit. Superconducting circuits are the most advanced platform at present, but there is an issue with cross-talk between the qubits and the challenge of error correction as the systems are scaled up. Another approach being pursued is a modular platform in which the qubits are spatially separated. Daiss et al. demonstrate the operation of a quantum gate in which one qubit conditionally controls the state of another qubit spatially separated by 60 meters (see the Perspective by Hunger). Because the approach is platform independent, it could be extended from the demonstrated neutral atoms to ions, impurity vacancy centers, or even a combination of these qubits. Science , this issue p. 614 ; see also p. 576
Observation of a quantum phase transition in the quantum Rabi model with a single trapped ion

Abstract Quantum phase transitions (QPTs) are usually associated with many-body systems in the thermodynamic limit when their ground states show abrupt changes at zero temperature with variation of a parameter in the Hamiltonian. Recently it has been realized that a QPT can also occur in a system composed of only a two-level atom and a single-mode bosonic field, described by the quantum Rabi model (QRM). Here we report an experimental demonstration of a QPT in the QRM using a 171 Yb + ion in a Paul trap. We measure the spin-up state population and the average phonon number of the ion as two order parameters and observe clear evidence of the phase transition via adiabatic tuning of the coupling between the ion and its spatial motion. An experimental probe of the phase transition in a fundamental quantum optics model without imposing the thermodynamic limit opens up a window for controlled study of QPTs and quantum critical phenomena.
CLIPScore: A Reference-free Evaluation Metric for Image Captioning

Image captioning has conventionally relied on reference-based automatic evaluations, where machine captions are compared against captions written by humans.This is in contrast to the reference-free manner in which humans assess caption quality.In this paper, we report the surprising empirical finding that CLIP (Radford et al., 2021), a cross-modal model pretrained on 400M im-age+caption pairs from the web, can be used for robust automatic evaluation of image captioning without the need for references.Experiments spanning several corpora demonstrate that our new reference-free metric, CLIPScore, achieves the highest correlation with human judgements, outperforming existing reference-based metrics like CIDEr and SPICE.Information gain experiments demonstrate that CLIPScore, with its tight focus on image-text compatibility, is complementary to existing reference-based metrics that emphasize text-text similarities.Thus, we also present a reference-augmented version, RefCLIPScore, which achieves even higher correlation.Beyond literal description tasks, several case studies reveal domains where CLIPScore performs well (clip-art images, alt-text rating), but also where it is relatively weaker in comparison to reference-based metrics, e.g., news captions that require richer contextual knowledge.
Hardware-efficient variational quantum algorithms for time evolution

Parameterized quantum circuits are a promising technology for achieving a quantum advantage. An important application is the variational simulation of time evolution of quantum systems. To make the most of quantum hardware, variational algorithms need to be as hardware-efficient as possible. Here we present alternatives to the time-dependent variational principle that are hardware-efficient and do not require matrix inversion. In relation to imaginary time evolution, our approach significantly reduces the hardware requirements. With regards to real time evolution, where high precision can be important, we present algorithms of systematically increasing accuracy and hardware requirements. We numerically analyze the performance of our algorithms using quantum Hamiltonians with local interactions.
MLCM: Multi-Label Confusion Matrix

Concise and unambiguous assessment of a machine learning algorithm is key to classifier design and performance improvement. In the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-class</i> classification task, where each instance can only be labeled as one class, the confusion matrix is a powerful tool for performance assessment by quantifying the classification overlap. However, in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-label</i> classification task, where each instance can be labeled with more than one class, the confusion matrix is undefined. Performance assessment of the multi-label classifier is currently based on calculating performance averages, such as hamming loss, precision, recall, and F-score. While the current assessment techniques present a reasonable representation of each class and overall performance, their aggregate nature results in ambiguity when identifying false negative ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FN</i> ) and false positive ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FP</i> ) results. To address this gap, we define a method of creating the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-label confusion matrix</i> (MLCM) based on three proposed categories of multi-label problems. After establishing the shortcomings of current methods for identifying <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FN</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FP</i> , we demonstrate the usage of the MLCM with the classification of two publicly available multi-label data sets: i) a 12-lead ECG data set with nine classes, and ii) a movie poster data set with eighteen classes. A comparison of the MLCM results against statistics from the current techniques is presented to show the effectiveness in providing a concise and unambiguous understanding of a multi-label classifier behavior.
TTHRESH: Tensor Compression for Multidimensional Visual Data

Memory and network bandwidth are decisive bottlenecks when handling high-resolution multidimensional data sets in visualization applications, and they increasingly demand suitable data compression strategies. We introduce a novel lossy compression algorithm for multidimensional data over regular grids. It leverages the higher-order singular value decomposition (HOSVD), a generalization of the SVD to three dimensions and higher, together with bit-plane, run-length and arithmetic coding to compress the HOSVD transform coefficients. Our scheme degrades the data particularly smoothly and achieves lower mean squared error than other state-of-the-art algorithms at low-to-medium bit rates, as it is required in data archiving and management for visualization purposes. Further advantages of the proposed algorithm include very fine bit rate selection granularity and the ability to manipulate data at very small cost in the compression domain, for example to reconstruct filtered and/or subsampled versions of all (or selected parts) of the data set.
Interactive Anomaly Detection on Attributed Networks

Performing anomaly detection on attributed networks concerns with finding nodes whose patterns or behaviors deviate significantly from the majority of reference nodes. Its success can be easily found in many real-world applications such as network intrusion detection, opinion spam detection and system fault diagnosis, to name a few. Despite their empirical success, a vast majority of existing efforts are overwhelmingly performed in an unsupervised scenario due to the expensive labeling costs of ground truth anomalies. In fact, in many scenarios, a small amount of prior human knowledge of the data is often effortless to obtain, and getting it involved in the learning process has shown to be effective in advancing many important learning tasks. Additionally, since new types of anomalies may constantly arise over time especially in an adversarial environment, the interests of human expert could also change accordingly regarding to the detected anomaly types. It brings further challenges to conventional anomaly detection algorithms as they are often applied in a batch setting and are incapable to interact with the environment. To tackle the above issues, in this paper, we investigate the problem of anomaly detection on attributed networks in an interactive setting by allowing the system to proactively communicate with the human expert in making a limited number of queries about ground truth anomalies. Our objective is to maximize the true anomalies presented to the human expert after a given budget is used up. Along with this line, we formulate the problem through the principled multi-armed bandit framework and develop a novel collaborative contextual bandit algorithm, named GraphUCB. In particular, our developed algorithm: (1) explicitly models the nodal attributes and node dependencies seamlessly in a joint framework; and (2) handles the exploration-exploitation dilemma when querying anomalies of different types. Extensive experiments on real-world datasets show the improvement of the proposed algorithm over the state-of-the-art algorithms.
Potential of Augmented Reality and Virtual Reality Technologies to Promote Wellbeing in Older Adults

Older adults face significant loss and limitations in terms of mobility, cognitive ability, and socialization. By using augmented reality and virtual reality technologies they have the potential to overcome such loss and limitations, and to eventually improve their quality of life. However, this group is often excluded in augmented reality and virtual reality deployment. Further, limited studies address their challenges when using augmented reality and virtual reality. Therefore, for a critical review of augmented reality and virtual reality for older adults, we developed a framework to evaluate related factors, including physical, social, and psychological wellbeing. Through the critical review, we identified that most augmented reality and virtual reality studies focus on physical wellbeing of older adults but also make substantial efforts to increase their psychological wellbeing. Fun factors that would motivate them are also extensively considered. Further, social isolation continues to be a significant issue for older adults, but the appropriate content to increase their social wellbeing is insufficient, although many commercial products have been developed. The contribution of the present study is to provide a contextual framework and an evaluation framework for the critical review of augmented reality and virtual reality technologies to promote wellbeing in older adults. This study also suggests the augmented reality and virtual reality research direction for studies on this group by identifying the research gap through the critical review process. Lastly, this study investigates design directions of augmented reality and virtual reality for older adults by introducing challenges and design issues that emerged through the critical review.
Word Sense Disambiguation: A comprehensive knowledge exploitation framework

Word Sense Disambiguation (WSD) has been a basic and on-going issue since its introduction in natural language processing (NLP) community. Its application lies in many different areas including sentiment analysis, Information Retrieval (IR), machine translation and knowledge graph construction. Solutions to WSD are mostly categorized into supervised and knowledge-based approaches. In this paper, a knowledge-based method is proposed, modeling the problem with semantic space and semantic path hidden behind a given sentence. The approach relies on the well-known Knowledge Base (KB) named WordNet and models the semantic space and semantic path by Latent Semantic Analysis (LSA) and PageRank respectively. Experiments has proven the method’s effectiveness, achieving state-of-the-art performance in several WSD datasets.
Dragonfly Algorithm and Its Applications in Applied Science Survey

One of the most recently developed heuristic optimization algorithms is dragonfly by Mirjalili. Dragonfly algorithm has shown its ability to optimizing different real-world problems. It has three variants. In this work, an overview of the algorithm and its variants is presented. Moreover, the hybridization versions of the algorithm are discussed. Furthermore, the results of the applications that utilized the dragonfly algorithm in applied science are offered in the following area: machine learning, image processing, wireless, and networking. It is then compared with some other metaheuristic algorithms. In addition, the algorithm is tested on the CEC-C06 2019 benchmark functions. The results prove that the algorithm has great exploration ability and its convergence rate is better than the other algorithms in the literature, such as PSO and GA. In general, in this survey, the strong and weak points of the algorithm are discussed. Furthermore, some future works that will help in improving the algorithm’s weak points are recommended. This study is conducted with the hope of offering beneficial information about dragonfly algorithm to the researchers who want to study the algorithm.