Reviewerly/exHarmony huggingface dataset:
Deep Learning-Based Knowledge Graph Generation for COVID-19

Many attempts have been made to construct new domain-specific knowledge graphs using the existing knowledge base of various domains. However, traditional “dictionary-based” or “supervised” knowledge graph building methods rely on predefined human-annotated resources of entities and their relationships. The cost of creating human-annotated resources is high in terms of both time and effort. This means that relying on human-annotated resources will not allow rapid adaptability in describing new knowledge when domain-specific information is added or updated very frequently, such as with the recent coronavirus disease-19 (COVID-19) pandemic situation. Therefore, in this study, we propose an Open Information Extraction (OpenIE) system based on unsupervised learning without a pre-built dataset. The proposed method obtains knowledge from a vast amount of text documents about COVID-19 rather than a general knowledge base and add this to the existing knowledge graph. First, we constructed a COVID-19 entity dictionary, and then we scraped a large text dataset related to COVID-19. Next, we constructed a COVID-19 perspective language model by fine-tuning the bidirectional encoder representations from transformer (BERT) pre-trained language model. Finally, we defined a new COVID-19-specific knowledge base by extracting connecting words between COVID-19 entities using the BERT self-attention weight from COVID-19 sentences. Experimental results demonstrated that the proposed Co-BERT model outperforms the original BERT in terms of mask prediction accuracy and metric for evaluation of translation with explicit ordering (METEOR) score.
Reservoir Computing as a Tool for Climate Predictability Studies

Reduced-order dynamical models play a central role in developing our understanding of predictability of climate irrespective of whether we are dealing with the actual climate system or surrogate climate-models. In this context, the Linear-Inverse-Modeling (LIM) approach, by capturing a few essential interactions between dynamical components of the full system, has proven valuable in providing insights into predictability of the full system. We demonstrate that Reservoir Computing (RC), a form of learning suitable for systems with chaotic dynamics, provides an alternative nonlinear approach that improves on the predictive skill of the LIM approach. We do this in the example setting of predicting sea-surface-temperature in the North Atlantic in the pre-industrial control simulation of a popular earth system model, the Community-Earth-System-Model so that we can compare the performance of the new RC based approach with the traditional LIM approach both when learning data is plentiful and when such data is more limited. The improved predictive skill of the RC approach over a wide range of conditions -- larger number of retained EOF coefficients, extending well into the limited data regime, etc. -- suggests that this machine-learning technique may have a use in climate predictability studies. While the possibility of developing a climate emulator -- the ability to continue the evolution of the system on the attractor long after failing to be able to track the reference trajectory -- is demonstrated in the Lorenz-63 system, it is suggested that further development of the RC approach may permit such uses of the new approach in more realistic predictability studies.
Practical Quantum Key Distribution That is Secure Against Side Channels

There is a big gap between theory and practice in quantum key distribution (QKD) because real devices do not satisfy the assumptions required by the security proofs. Here, we close this gap by introducing a simple and practical measurement-device-independent (MDI) QKD type of protocol, based on the transmission of coherent light, for which we prove its security against any possible device imperfection and/or side-channel at the transmitters' side. Besides using a much simpler experimental set-up and source characterization with only one single parameter, we show that the performance of the protocol is comparable to other MDI-QKD type of protocols which disregard the effect of several side-channels.
Application of deep and machine learning techniques for multi-label classification performance on psychotic disorder diseases

Electronic Health Records (EHRs) hold symptoms of many diverse diseases and it is imperative to build models to recognise these problems early and classify the diseases appropriately. This classification task could be presented as a single or multi-label problem. Thus, this study presents Psychotic Disorder Diseases (PDD) dataset with five labels: bipolar disorder, vascular dementia, attention-deficit/hyperactivity disorder (ADHD), insomnia, and schizophrenia as a multi-label classification problem. The study also investigates the use of deep neural network and machine learning techniques such as multilayer perceptron (MLP), support vector machine (SVM), random forest (RF) and Decision tree (DT), for identifying hidden patterns in patients' data. The study furthermore investigates the symptoms associated with certain types of psychotic diseases and addresses class imbalance from a multi-label classification perspective. The performances of these models were assessed and compared based on an accuracy metric. The result obtained revealed that deep neural network gave a superior performance of 75.17% with class imbalance accuracy, while the MLP model accuracy is 58.44%. Conversely, the best performance in the machine learning techniques was exhibited by the random forest model, using the dataset without class imbalance and its result, compared with deep learning techniques, is 64.1% and 55.87%, respectively. It was also observed that patient's age is the most contributing feature to the performance of the model while divorce is the least. Likewise, the study reveals that there is a high tendency for a patient with bipolar disorder to have insomnia; these diseases are strongly correlated with an R-value of 0.98. Our concluding remark shows that applying the deep and machine learning model to PDD dataset not only offers improved clinical classification of the diseases but also provides a framework for augmenting clinical decision systems by eliminating the class imbalance and unravelling the attributes that influence PDD in patients.
ResKD: Residual-Guided Knowledge Distillation

Knowledge distillation, aimed at transferring the knowledge from a heavy teacher network to a lightweight student network, has emerged as a promising technique for compressing neural networks. However, due to the capacity gap between the heavy teacher and the lightweight student, there still exists a significant performance gap between them. In this article, we see knowledge distillation in a fresh light, using the knowledge gap, or the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">residual</i> , between a teacher and a student as guidance to train a much more lightweight student, called a res-student. We combine the student and the res-student into a new student, where the res-student rectifies the errors of the former student. Such a residual-guided process can be repeated until the user strikes the balance between accuracy and cost. At inference time, we propose a sample-adaptive strategy to decide which res-students are not necessary for each sample, which can save computational cost. Experimental results show that we achieve competitive performance with 18.04%, 23.14%, 53.59%, and 56.86% of the teachers’ computational costs on the CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet datasets. Finally, we do thorough theoretical and empirical analysis for our method.
Circuit complexity as a novel probe of quantum entanglement: A study with black hole gas in arbitrary dimensions

In this article, we investigate the quantum circuit complexity and entanglement entropy in the recently studied black hole gas framework using the two-mode squeezed states formalism written in arbitrary dimensional spatially flat cosmological Friedmann-Lema\^{\i}tre-Robertson-Walker background space-time. We compute the various complexity measures and study the evolution of these complexities by following two different prescriptions viz the covariant matrix method and Nielsen's method. Independently, using the two-mode squeezed states formalism we also compute the R\'enyi and von-Neumann entanglement entropy, which show an inherent connection between the entanglement entropy and quantum circuit complexity. We study the behavior of the complexity measures and entanglement entropy separately for three different spatial dimensions and observe various significant different features in three spatial dimensions on the evolution of these quantities with respect to the scale factor. Furthermore, we also study the underlying behavior of the equilibrium temperature with two of the most essential quantities i.e., rate of change of complexity with scale factor and the entanglement entropy. We observe that irrespective of the spatial dimension, the equilibrium temperature depends quartically on entanglement entropy.
Flexplot: Graphically-based data analysis.

The human visual processing system has enormous bandwidth, able to interpret vast amounts of data in fractions of a second (Otten et al., 2015). Despite this amazing ability, there is a troubling lack of graphics in scientific literature (Healy & Moody, 2014), and the graphics most traditionally used tend to bias perception in unintentional ways (Weissgerber et al., 2015). I suspect the reason for the underuse and misuse of graphics is because sound graphs are difficult to produce with existing software (Wainer, 2010). While ggplot2 allows immense flexibility in creating graphics, its learning curve is quite steep, and even basic graphics require multiple lines of code. flexplot is an R package that aims to address these issues by providing a formula-based suite of tools that simplifies and automates much of the graphical decision-making. Additionally, flexplot pairs well with statistical modeling, making it easy for researchers to produce graphs that map onto statistical procedures. With one-line functions, users can visualize bivariate statistical models (e.g., scatterplots for regression, beeswarm plots for ANOVA/t-tests), multivariate statistical models (e.g., ANCOVA and multiple regression), and even more sophisticated models like multilevel models and logistic regressions. Further, this package utilizes old tools (e.g., added variable plots and coplots) as well as introduces new tools for complex visualizations, including ghost lines and point sampling. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Unified learning approach for egocentric hand gesture recognition and fingertip detection

Head-mounted device-based human-computer interaction often requires egocentric recognition of hand gestures and fingertips detection. In this paper, a unified approach of egocentric hand gesture recognition and fingertip detection is introduced. The proposed algorithm uses a single convolutional neural network to predict the probabilities of finger class and positions of fingertips in one forward propagation. Instead of directly regressing the positions of fingertips from the fully connected layer, the ensemble of the position of fingertips is regressed from the fully convolutional network. Subsequently, the ensemble average is taken to regress the final position of fingertips. Since the whole pipeline uses a single network, it is significantly fast in computation. Experimental results show that the proposed method outperforms the existing fingertip detection approaches including the Direct Regression and the Heatmap-based framework. The effectiveness of the proposed method is also shown in-the-wild scenario as well as in a use-case of virtual reality.
Deterministic preparation of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>W</mml:mi></mml:math> states via spin-photon interactions

Spin systems such as silicon or nitrogen vacancy centers in diamond, quantum dots and quantum dot molecules coupled to optical cavities appear as key elements for creating quantum networks as not only constituting the nodes of the network, but also assisting the creation of photonic networks. Here we study deterministic preparation of arbitrary size $W$ states with spin systems. We present an efficient operation on three qubits, two being the logical qubits and one being the ancillary qubit, where no interaction between the logical qubits are required. The proposed operation can create a $W$-type Einstein-Podolsky-Rosen (EPR) pair from two separable qubits, and expand that EPR pair or an arbitrary size $W$ state by one, creating a $W$-like state. Taking this operation as the fundamental building block, we show how to create a large scale $W$ state out of separable qubits, or double the size of a $W$ state. Based on this operation and focusing on nitrogen vacancy (NV) centers in diamond as an exemplary spin system, we propose a setup for preparing $W$ states of circularly polarized photons, assisted by a single spin qubit, where no photon-photon interactions are required. Next, we propose a setup for preparing $W$ states of spin qubits of spatially separated systems, assisted by a single photon. We also analyze the effects of possible imperfections in implementing the gates on the fidelity of the generated $W$ states. In our setups, neither post-measurement, nor post-processing on the states of spin or photonic qubit is required. Our setups can be implemented with current technology, and we anticipate that they contribute to quantum science and technologies.
Nanoscale continuous quantum light sources based on driven dipole emitter arrays

Regular arrays of two-level emitters at distances smaller that the transition wavelength collectively scatter, absorb and emit photons. The strong inter-particle dipole coupling creates large energy shifts of the collective delocalized excitations, which generates a highly nonlinear response at the single and few photon level. This should allow to implement nanoscale non-classical light sources via weak coherent illumination. At the generic tailored examples of regular chains or polygons we show that the fields emitted perpendicular to the illumination direction exhibit a strong directional confinement with genuine quantum properties as antibunching. For short interparticle distances superradiant directional emission can enhance the radiated intensity by an order of magnitude compared to a single atom focused to a strongly confined solid angle but still keeping the anti-bunching parameter at the level of $g^{(2)}(0) \approx 10^{-2}$.
Global-Local Enhancement Network for NMF-Aware Sign Language Recognition

Sign language recognition (SLR) is a challenging problem, involving complex manual features, i.e., hand gestures, and fine-grained non-manual features (NMFs), i.e., facial expression, mouth shapes, etc. Although manual features are dominant, non-manual features also play an important role in the expression of a sign word. Specifically, many sign words convey different meanings due to non-manual features, even though they share the same hand gestures. This ambiguity introduces great challenges in the recognition of sign words. To tackle the above issue, we propose a simple yet effective architecture called Global-local Enhancement Network (GLE-Net), including two mutually promoted streams towards different crucial aspects of SLR. Of the two streams, one captures the global contextual relationship, while the other stream captures the discriminative fine-grained cues. Moreover, due to the lack of datasets explicitly focusing on this kind of features, we introduce the first non-manual-features-aware isolated Chinese sign language dataset~(NMFs-CSL) with a total vocabulary size of 1,067 sign words in daily life. Extensive experiments on NMFs-CSL and SLR500 datasets demonstrate the effectiveness of our method.
Finite-key analysis for quantum conference key agreement with asymmetric channels

As an essential ingredient of quantum networks, quantum conference key agreement (QCKA) provides unconditional secret keys among multiple parties, which enables only legitimate users to decrypt the encrypted message. Recently, some QCKA protocols employing twin-field was proposed to promote transmission distance. These protocols, however, suffer from relatively low conference key rate and short transmission distance over asymmetric channels, which demands a prompt solution in practice. Here, we consider a tripartite QCKA protocol utilizing the idea of sending-or-not-sending twin-field scheme and propose a high-efficiency QCKA over asymmetric channels by removing the symmetry parameters condition. Besides, we provide a composable finite-key analysis with rigorous security proof against general attacks by exploiting the entropic uncertainty relation for multiparty system. Our protocol greatly improves the feasibility to establish conference keys over asymmetric channels.
Hi-Fi Multi-Speaker English TTS Dataset

This paper introduces a new multi-speaker English dataset for training text-to-speech models. The dataset is based on LibriVox audiobooks and Project Gutenberg texts, both in the public domain. The new dataset contains about 292 hours of speech from 10 speakers with at least 17 hours per speaker sampled at 44.1 kHz. To select speech samples with high quality, we considered audio recordings with a signal bandwidth of at least 13 kHz and a signal-to-noise ratio (SNR) of at least 32 dB. The dataset is publicly released at this http URL .
Federated Learning in Robotic and Autonomous Systems

Autonomous systems are becoming inherently ubiquitous with the advancements of computing and communication solutions enabling low-latency offloading and real-time collaboration of distributed devices. Decentralized technologies with blockchain and distributed ledger technologies (DLTs) are playing a key role. At the same time, advances in deep learning (DL) have significantly raised the degree of autonomy and level of intelligence of robotic and autonomous systems. While these technological revolutions were taking place, raising concerns in terms of data security and end-user privacy has become an inescapable research consideration. Federated learning (FL) is a promising solution to privacy-preserving DL at the edge, with an inherently distributed nature by learning on isolated data islands and communicating only model updates. However, FL by itself does not provide the levels of security and robustness required by today's standards in distributed autonomous systems. This survey covers applications of FL to autonomous robots, analyzes the role of DLT and FL for these systems, and introduces the key background concepts and considerations in current research.
Multi-view Analysis of Unregistered Medical Images Using Cross-View Transformers

Multi-view medical image analysis often depends on the combination of information from multiple views. However, differences in perspective or other forms of misalignment can make it difficult to combine views effectively, as registration is not always possible. Without registration, views can only be combined at a global feature level, by joining feature vectors after global pooling. We present a novel cross-view transformer method to transfer information between unregistered views at the level of spatial feature maps. We demonstrate this method on multi-view mammography and chest X-ray datasets. On both datasets, we find that a cross-view transformer that links spatial feature maps can outperform a baseline model that joins feature vectors after global pooling.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi>k</mml:mi></mml:math>-Anonymity in practice: How generalisation and suppression affect machine learning classifiers

The protection of private information is a crucial issue in data-driven research and business contexts. Typically, techniques like anonymisation or (selective) deletion are introduced in order to allow data sharing, e. g. in the case of collaborative research endeavours. For use with anonymisation techniques, the $k$-anonymity criterion is one of the most popular, with numerous scientific publications on different algorithms and metrics. Anonymisation techniques often require changing the data and thus necessarily affect the results of machine learning models trained on the underlying data. In this work, we conduct a systematic comparison and detailed investigation into the effects of different $k$-anonymisation algorithms on the results of machine learning models. We investigate a set of popular $k$-anonymisation algorithms with different classifiers and evaluate them on different real-world datasets. Our systematic evaluation shows that with an increasingly strong $k$-anonymity constraint, the classification performance generally degrades, but to varying degrees and strongly depending on the dataset and anonymisation method. Furthermore, Mondrian can be considered as the method with the most appealing properties for subsequent classification.
Exploring deep neural networks via layer-peeled model: Minority collapse in imbalanced training

In this paper, we introduce the Layer-Peeled Model, a nonconvex, yet analytically tractable, optimization program, in a quest to better understand deep neural networks that are trained for a sufficiently long time. As the name suggests, this model is derived by isolating the topmost layer from the remainder of the neural network, followed by imposing certain constraints separately on the two parts of the network. We demonstrate that the Layer-Peeled Model, albeit simple, inherits many characteristics of well-trained neural networks, thereby offering an effective tool for explaining and predicting common empirical patterns of deep-learning training. First, when working on class-balanced datasets, we prove that any solution to this model forms a simplex equiangular tight frame, which, in part, explains the recently discovered phenomenon of neural collapse [V. Papyan, X. Y. Han, D. L. Donoho, Proc. Natl. Acad. Sci. U.S.A. 117, 24652-24663 (2020)]. More importantly, when moving to the imbalanced case, our analysis of the Layer-Peeled Model reveals a hitherto-unknown phenomenon that we term Minority Collapse, which fundamentally limits the performance of deep-learning models on the minority classes. In addition, we use the Layer-Peeled Model to gain insights into how to mitigate Minority Collapse. Interestingly, this phenomenon is first predicted by the Layer-Peeled Model before being confirmed by our computational experiments.
A weighted patient network-based framework for predicting chronic diseases using graph neural networks

Abstract Chronic disease prediction is a critical task in healthcare. Existing studies fulfil this requirement by employing machine learning techniques based on patient features, but they suffer from high dimensional data problems and a high level of bias. We propose a framework for predicting chronic disease based on Graph Neural Networks (GNNs) to address these issues. We begin by projecting a patient-disease bipartite graph to create a weighted patient network (WPN) that extracts the latent relationship among patients. We then use GNN-based techniques to build prediction models. These models use features extracted from WPN to create robust patient representations for chronic disease prediction. We compare the output of GNN-based models to machine learning methods by using cardiovascular disease and chronic pulmonary disease. The results show that our framework enhances the accuracy of chronic disease prediction. The model with attention mechanisms achieves an accuracy of 93.49% for cardiovascular disease prediction and 89.15% for chronic pulmonary disease prediction. Furthermore, the visualisation of the last hidden layers of GNN-based models shows the pattern for the two cohorts, demonstrating the discriminative strength of the framework. The proposed framework can help stakeholders improve health management systems for patients at risk of developing chronic diseases and conditions.