Reviewerly/exHarmony huggingface dataset:
Automatic liver segmentation by integrating fully convolutional networks into active contour models

Purpose Automatic and accurate three‐dimensional (3D) segmentation of liver with severe diseases from computed tomography (CT) images is a challenging task. Fully convolutional networks (FCNs) have emerged as powerful tools for automatic semantic segmentation, with multiple potential applications in medical imaging. However, the use of a large receptive field and multiple pooling layers in the network leads to poor localization around object boundaries. The network usually makes pixel‐wise prediction independently, making it difficult to respect local label consistency and enforce the smoothness of the object boundary. Methods We have developed an automatic liver segmentation method based on a novel framework that integrates fully convolutional network predictions into active contour models (ACM). We use only a single network architecture to generate a pixel label map containing spatial regional information (foreground and background) as well as layered boundary information. We exploit the structured network outcome to define an external constraint force of active contour models. A unique property of the designed force is that both its strength and direction are adaptive to its position and relative distance to the object boundary. The resulting integrated active contour models have the advantages of incorporating both high‐level and low‐level image information simultaneously, while enforcing the smoothness of the contour. Because the external constraint force can push the evolving contour to the liver boundary and exists everywhere in the image domain, it allows us to place the initial contour far away from the liver boundary. It potentially allows us to control the evolution of the contour in order to preserve the topology of the liver. Results We have trained and evaluated our model on 73 liver CT scans from a clinic study. The integrated ACM model yields mean dice coefficients (DICE) 95.8 ± 1.4 (%). Without further fine‐tuning the network weights for two independent datasets, it yields mean DICE 96.2 ± 0.9 (%) for the SLIVER07 training dataset, and mean DICE 94.3 ± 2.7 (%) for the LiTS training dataset. In comparison with FCN alone model, the integrated ACM model yields improvements in terms of surface distance and DICE values for almost all the cases. Furthermore, the initialization of the active contour can be very far away from the liver boundary. Conclusions Experimental results for segmenting livers (with severe diseases on CT images resulting in shape and density abnormalities) have revealed that our proposed model improves segmentation results in comparison with FCN alone. Without further fine‐tuning the network weights for two independent datasets, the model is capable of handling image variations from different datasets due to its inherent deformable nature. It is relatively easy to integrate more advanced (either existing or future) FCN architecture into our framework to further improve the segmentation performance.
Unsupervised Spectral Feature Selection with Dynamic Hyper-graph Learning

Unsupervised spectral feature selection (USFS) methods could output interpretable and discriminative results by embedding a Laplacian regularizer in the framework of sparse feature selection to keep the local similarity of the training samples. To do this, USFS methods usually construct the Laplacian matrix using either a general-graph or a hyper-graph on the original data. Usually, a general-graph could measure the relationship between two samples while a hyper-graph could measure the relationship among no less than two samples. Obviously, the general-graph is a special case of the hyper-graph and the hyper-graph may capture more complex structure of samples than the general graph. However, in previous USFS methods, the construction of the Laplacian matrix is separated from the process of feature selection. Moreover, the original data usually contain noise. Each of them makes difficult to output reliable feature selection models. In this paper, we propose a novel feature selection method by dynamically constructing a hyper-graph based Laplacian matrix in the framework of sparse feature selection. Experimental results on real datasets showed that our proposed method outperformed the state-of-the-art methods in terms of both clustering and segmentation tasks.
More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence

Artificial Intelligence (AI) has attracted a great deal of attention in recent years. However, alongside all its advancements, problems have also emerged, such as privacy violations, security issues and model fairness. Differential privacy, as a promising mathematical model, has several attractive properties that can help solve these problems, making it quite a valuable tool. For this reason, differential privacy has been broadly applied in AI but to date, no study has documented which differential privacy mechanisms can or have been leveraged to overcome its issues or the properties that make this possible. In this paper, we show that differential privacy can do more than just privacy preservation. It can also be used to improve security, stabilize learning, build fair models, and impose composition in selected areas of AI. With a focus on regular machine learning, distributed machine learning, deep learning, and multi-agent systems, the purpose of this article is to deliver a new view on many possibilities for improving AI performance with differential privacy techniques.
Does Gender Matter? Towards Fairness in Dialogue Systems

Recently there are increasing concerns about the fairness of Artificial Intelligence (AI) in realworld applications such as computer vision and recommendations.For example, recognition algorithms in computer vision are unfair to black people such as poorly detecting their faces and inappropriately identifying them as "gorillas".As one crucial application of AI, dialogue systems have been extensively applied in our society.They are usually built with real human conversational data; thus they could inherit some fairness issues which are held in the real world.However, the fairness of dialogue systems has not been well investigated.In this paper, we perform a pioneering study about the fairness issues in dialogue systems.In particular, we construct a benchmark dataset and propose quantitative measures to understand fairness in dialogue models.Our studies demonstrate that popular dialogue models show significant prejudice towards different genders and races.Besides, to mitigate the bias in dialogue systems, we propose two simple but effective debiasing methods.Experiments show that our methods can reduce the bias in dialogue systems significantly.The dataset and the implementation are released to foster fairness research in dialogue systems 1 .
Optical observation of single spins in silicon

The global quantum internet will require long-lived, telecommunications-band photon-matter interfaces manufactured at scale1. Preliminary quantum networks based on photon-matter interfaces that meet a subset of these demands are encouraging efforts to identify new high-performance alternatives2. Silicon is an ideal host for commercial-scale solid-state quantum technologies. It is already an advanced platform within the global integrated photonics and microelectronics industries, as well as host to record-setting long-lived spin qubits3. Despite the overwhelming potential of the silicon quantum platform, the optical detection of individually addressable photon-spin interfaces in silicon has remained elusive. In this work, we integrate individually addressable 'T centre' photon-spin qubits in silicon photonic structures and characterize their spin-dependent telecommunications-band optical transitions. These results unlock immediate opportunities to construct silicon-integrated, telecommunications-band quantum information networks.
Domain General Face Forgery Detection by Learning to Weight

In this paper, we propose a domain-general model, termed learning-to-weight (LTW), that guarantees face detection performance across multiple domains, particularly the target domains that are never seen before. However, various face forgery methods cause complex and biased data distributions, making it challenging to detect fake faces in unseen domains. We argue that different faces contribute differently to a detection model trained on multiple domains, making the model likely to fit domain-specific biases. As such, we propose the LTW approach based on the meta-weight learning algorithm, which configures different weights for face images from different domains. The LTW network can balance the model's generalizability across multiple domains. Then, the meta-optimization calibrates the source domain's gradient enabling more discriminative features to be learned. The detection ability of the network is further improved by introducing an intra-class compact loss. Extensive experiments on several commonly used deepfake datasets to demonstrate the effectiveness of our method in detecting synthetic faces. Code and supplemental material are available at https://github.com/skJack/LTW.
Investigation of Microwave Loss Induced by Oxide Regrowth in High- <i>Q</i> Niobium Resonators

To improve the performance of state-of-the-art superconducting quantum devices, microwave loss due to defects at amorphous interfacial layers must be reduced, via proper surface treatment. The authors study niobium resonators after removing native oxides by HF etching, which reduces losses about tenfold and yields a quality factor of $7\ifmmode\times\else\texttimes\fi{}{10}^{6}$ in the single-photon limit. Losses reappear as oxides form upon exposure to air; Nb${}_{2}$O${}_{5}$ is the only surface oxide that grows significantly in the first week. These findings are of interest for a panoply of devices, inluding superconducting qubits, quantum-limited amplifiers, microwave kinetic-inductance detectors, and single-photon detectors.
Universal Approximation with Deep Narrow Networks.

The classical Universal Approximation Theorem holds for neural networks of arbitrary width and bounded depth. Here we consider the natural `dual' scenario for networks of bounded width and arbitrary depth. Precisely, let $n$ be the number of inputs neurons, $m$ be the number of output neurons, and let $\rho$ be any nonaffine continuous function, with a continuous nonzero derivative at some point. Then we show that the class of neural networks of arbitrary depth, width $n + m + 2$, and activation function $\rho$, is dense in $C(K; \mathbb{R}^m)$ for $K \subseteq \mathbb{R}^n$ with $K$ compact. This covers every activation function possible to use in practice, and also includes polynomial activation functions, which is unlike the classical version of the theorem, and provides a qualitative difference between deep narrow networks and shallow wide networks. We then consider several extensions of this result. In particular we consider nowhere differentiable activation functions, density in noncompact domains with respect to the $L^p$-norm, and how the width may be reduced to just $n + m + 1$ for `most' activation functions.
End2End Occluded Face Recognition by Masking Corrupted Features

With the recent advancement of deep convolutional neural networks, significant progress has been made in general face recognition. However, the state-of-the-art general face recognition models do not generalize well to occluded face images, which are exactly the common cases in real-world scenarios. The potential reasons are the absences of large-scale occluded face data for training and specific designs for tackling corrupted features brought by occlusions. This article presents a novel face recognition method that is robust to occlusions based on a single end-to-end deep neural network. Our approach, named FROM (Face Recognition with Occlusion Masks), learns to discover the corrupted features from the deep convolutional neural networks, and clean them by the dynamically learned masks. In addition, we construct massive occluded face images to train FROM effectively and efficiently. FROM is simple yet powerful compared to the existing methods that either rely on external detectors to discover the occlusions or employ shallow models which are less discriminative. Experimental results on the LFW, Megaface challenge 1, RMF2, AR dataset and other simulated occluded/masked datasets confirm that FROM dramatically improves the accuracy under occlusions, and generalizes well on general face recognition.
Federated-Learning Based Privacy Preservation and Fraud-Enabled Blockchain IoMT System for Healthcare

These days, the usage of machine-learning-enabled dynamic Internet of Medical Things (IoMT) systems with multiple technologies for digital healthcare applications has been growing progressively in practice. Machine learning plays a vital role in the IoMT system to balance the load between delay and energy. However, the traditional learning models fraud on the data in the distributed IoMT system for healthcare applications are still a critical research problem in practice. The study devises a federated learning-based blockchain-enabled task scheduling (FL-BETS) framework with different dynamic heuristics. The study considers the different healthcare applications that have both hard constraint (e.g., deadline) and resource energy consumption (e.g., soft constraint) during execution on the distributed fog and cloud nodes. The goal of FL-BETS is to identify and ensure the privacy preservation and fraud of data at various levels, such as local fog nodes and remote clouds, with minimum energy consumption and delay, and to satisfy the deadlines of healthcare workloads. The study introduces the mathematical model. In the performance evaluation, FL-BETS outperforms all existing machine learning and blockchain mechanisms in fraud analysis, data validation, energy and delay constraints for healthcare applications.
Connecting data and expertise: a new alliance for biodiversity knowledge

There has been major progress over the last two decades in digitising historical knowledge of biodiversity and in making biodiversity data freely and openly accessible. Interlocking efforts bring together international partnerships and networks, national, regional and institutional projects and investments and countless individual contributors, spanning diverse biological and environmental research domains, government agencies and non-governmental organisations, citizen science and commercial enterprise. However, current efforts remain inefficient and inadequate to address the global need for accurate data on the world's species and on changing patterns and trends in biodiversity. Significant challenges include imbalances in regional engagement in biodiversity informatics activity, uneven progress in data mobilisation and sharing, the lack of stable persistent identifiers for data records, redundant and incompatible processes for cleaning and interpreting data and the absence of functional mechanisms for knowledgeable experts to curate and improve data. Recognising the need for greater alignment between efforts at all scales, the Global Biodiversity Information Facility (GBIF) convened the second Global Biodiversity Informatics Conference (GBIC2) in July 2018 to propose a coordination mechanism for developing shared roadmaps for biodiversity informatics. GBIC2 attendees reached consensus on the need for a global alliance for biodiversity knowledge, learning from examples such as the Global Alliance for Genomics and Health (GA4GH) and the open software communities under the Apache Software Foundation. These initiatives provide models for multiple stakeholders with decentralised funding and independent governance to combine resources and develop sustainable solutions that address common needs. This paper summarises the GBIC2 discussions and presents a set of 23 complementary ambitions to be addressed by the global community in the context of the proposed alliance. The authors call on all who are responsible for describing and monitoring natural systems, all who depend on biodiversity data for research, policy or sustainable environmental management and all who are involved in developing biodiversity informatics solutions to register interest at https://biodiversityinformatics.org/ and to participate in the next steps to establishing a collaborative alliance. The supplementary materials include brochures in a number of languages (English, Arabic, Spanish, Basque, French, Japanese, Dutch, Portuguese, Russian, Traditional Chinese and Simplified Chinese). These summarise the need for an alliance for biodiversity knowledge and call for collaboration in its establishment.
Data-Driven Discovery in Mineralogy: Recent Advances in Data Resources, Analysis, and Visualization

Large and growing data resources on the diversity, distribution, and properties of minerals are ushering in a new era of data-driven discovery in mineralogy. The most comprehensive international mineral database is the IMA database, which includes information on more than 5400 approved mineral species and their properties, and the mindat.org data source, which contains more than 1 million species/locality data on minerals found at more than 300 000 localities. Analysis and visualization of these data with diverse techniques—including chord diagrams, cluster diagrams, Klee diagrams, skyline diagrams, and varied methods of network analysis—are leading to a greater understanding of the co-evolving geosphere and biosphere. New data-driven approaches include mineral evolution, mineral ecology, and mineral network analysis—methods that collectively consider the distribution and diversity of minerals through space and time. These strategies are fostering a deeper understanding of mineral co-occurrences and, for the first time, facilitating predictions of mineral species that occur on Earth but have yet to be discovered and described.
Descriptive statistics and visualization of data from the R datasets package with implications for clusterability

The manuscript describes and visualizes datasets from the datasets package in the R statistical software, focusing on descriptive statistics and visualizations that provide insights into the clusterability of these datasets. These publicly available datasets are contained in the R software system, and can be downloaded at https://www.r-project.org/, with documentation provided at https://stat.ethz.ch/R-manual/R-devel/library/datasets/html/00Index.html. Further information on clusterability is found in the companion to this article, To Cluster or Not to Cluster: An Analysis of Clusterability Methods? (https://doi.org/10.1016/j.patcog.2018.10.026). Brief descriptions and graphs of the variables contained in each dataset are provided in the form of means, extrema, quartiles, standard deviation and standard error. Two-dimensional plots for each pair of variables are provided. Original references to the data sets are included when available. Further, each dataset is reduced to a single dimension by each of two different methods: pairwise distances and principal component analysis. For the latter, only the first component is used. Histograms of the reduced data are included for every dataset using both methods.
How character limit affects language usage in tweets

Abstract In November 2017 Twitter doubled the available character space from 140 to 280 characters. This provided an opportunity for researchers to investigate the linguistic effects of length constraints in online communication. We asked whether the character limit change (CLC) affected language usage in Dutch tweets and hypothesized that there would be a reduction in the need for character-conserving writing styles. Pre-CLC tweets were compared with post-CLC tweets. Three separate analyses were performed: (I) general analysis: the number of characters, words, and sentences per tweet, as well as the average word and sentence length. (II) Token analysis: the relative frequency of tokens and bigrams; (III) part-of-speech analysis: the grammatical structure of the sentences in tweets (i.e., adjectives, adverbs, articles, conjunctives, interjections, nouns, prepositions, pronouns, and verbs); pre-CLC tweets showed relatively more textisms, which are used to abbreviate and conserve character space. Consequently, they represent more informal language usage (e.g., internet slang); in turn, post-CLC tweets contained relatively more articles, conjunctions, and prepositions. The results show that online language producers adapt their texts to overcome limit constraints.
hULMonA: The Universal Language Model in Arabic

Arabic is a complex language with limited resources which makes it challenging to produce accurate text classification tasks such as sentiment analysis.The utilization of transfer learning (TL) has recently shown promising results for advancing accuracy of text classification in English.TL models are pre-trained on large corpora, and then fine-tuned on taskspecific datasets.In particular, universal language models (ULMs), such as recently developed BERT, have achieved state-of-the-art results in various NLP tasks in English.In this paper, we hypothesize that similar success can be achieved for Arabic.The work aims at supporting the hypothesis by developing the first Universal Language Model in Arabic (hUL-MonA -meaning our dream), demonstrating its use for Arabic classifications tasks, and demonstrating how a pre-trained multilingual BERT can also be used for Arabic.We then conduct a benchmark study to evaluate both ULM successes with Arabic sentiment analysis.Experiment results show that the developed hULMonA and multi-lingual ULM are able to generalize well to multiple Arabic data sets and achieve new state of the art results in Arabic Sentiment Analysis for some of the tested sets.
What Can ResNet Learn Efficiently, Going Beyond Kernels?

How can neural networks such as ResNet \emph{efficiently} learn CIFAR-10 with test accuracy more than $96 \%$, while other methods, especially kernel methods, fall relatively behind? Can we more provide theoretical justifications for this gap? Recently, there is an influential line of work relating neural networks to kernels in the over-parameterized regime, proving they can learn certain concept class that is also learnable by kernels with similar test error. Yet, can neural networks provably learn some concept class \emph{better} than kernels? We answer this positively in the distribution-free setting. We prove neural networks can efficiently learn a notable class of functions, including those defined by three-layer residual networks with smooth activations, without any distributional assumption. At the same time, we prove there are simple functions in this class such that with the same number of training examples, the test error obtained by neural networks can be \emph{much smaller} than \emph{any} kernel method, including neural tangent kernels (NTK). The main intuition is that \emph{multi-layer} neural networks can implicitly perform hierarchal learning using different layers, which reduces the sample complexity comparing to ``one-shot'' learning algorithms such as kernel methods. In the end, we also prove a computation complexity advantage of ResNet with respect to other learning methods including linear regression over arbitrary feature mappings.