Reviewerly/exHarmony huggingface dataset:
Development of An Individualized Risk Prediction Model for COVID-19 Using Electronic Health Record Data

Developing an accurate and interpretable model to predict an individual's risk for Coronavirus Disease 2019 (COVID-19) is a critical step to efficiently triage testing and other scarce preventative resources. To aid in this effort, we have developed an interpretable risk calculator that utilized de-identified electronic health records (EHR) from the University of Alabama at Birmingham Informatics for Integrating Biology and the Bedside (UAB-i2b2) COVID-19 repository under the U-BRITE framework. The generated risk scores are analogous to commonly used credit scores where higher scores indicate higher risks for COVID-19 infection. By design, these risk scores can easily be calculated in spreadsheets or even with pen and paper. To predict risk, we implemented a Credit Scorecard modeling approach on longitudinal EHR data from 7,262 patients enrolled in the UAB Health System who were evaluated and/or tested for COVID-19 between January and June 2020. In this cohort, 912 patients were positive for COVID-19. Our workflow considered the timing of symptoms and medical conditions and tested the effects by applying different variable selection techniques such as LASSO and Elastic-Net. Within the two weeks before a COVID-19 diagnosis, the most predictive features were respiratory symptoms such as cough, abnormalities of breathing, pain in the throat and chest as well as other chronic conditions including nicotine dependence and major depressive disorder. When extending the timeframe to include all medical conditions across all time, our models also uncovered several chronic conditions impacting the respiratory, cardiovascular, central nervous and urinary organ systems. The whole pipeline of data processing, risk modeling and web-based risk calculator can be applied to any EHR data following the OMOP common data format. The results can be employed to generate questionnaires to estimate COVID-19 risk for screening in building entries or to optimize hospital resources.
Sentiment Analysis of Covid-19 Tweets Using Evolutionary Classification-Based LSTM Model

As the Covid-19 outbreaks rapidly all over the world day by day and also affects the lives of million, a number of countries declared complete lockdown to check its intensity. During this lockdown period, social media platforms have played an important role to spread information about this pandemic across the world, as people used to express their feelings through the social networks. Considering this catastrophic situation, we developed an experimental approach to analyze the reactions of people on Twitter taking into account the popular words either directly or indirectly based on this pandemic. This paper represents the sentiment analysis on collected large number of tweets on Coronavirus or Covid-19. At first, we analyze the trend of public sentiment on the topics related to Covid-19 epidemic using an evolutionary classification followed by the n-gram analysis. Then we calculated the sentiment ratings on collected tweet based on their class. Finally, we trained the long-short term network using two types of rated tweets to predict sentiment on Covid-19 data and obtained an overall accuracy of 84.46%.
ESeismic-GAN: A Generative Model for Seismic Events From Cotopaxi Volcano

With the growing ability to collect large volumes of volcano seismic data, the detection and labeling process of these records is increasingly challenging. Clearly, analyzing all available data through manual inspection is no longer a viable option. Supervised machine learning models might be considered to automatize the analysis of data acquired by in situ monitoring stations. However, the direct application of such algorithms is defiant, given the high complexity of waveforms and the scarce and often imbalanced amount of labeled data. In light of this and motivated by the wide success that generative adversarial networks (GANs) have seen at generating images, we present ESeismic-GAN, a GAN model to generate the magnitude frequency response of volcanic events. Our experiments demonstrate that ESeismic-GAN learns to generate the frequency components that characterize long-period and volcano-tectonic events from Cotopaxi volcano. We evaluate the performance of ESeismic-GAN during the training stage using Fréchet distance, and, later on, we reconstruct the signals into time-domain to be finally evaluated with Frechet inception distance.
On Combining Feature Selection and Over-Sampling Techniques for Breast Cancer Prediction

Breast cancer prediction datasets are usually class imbalanced, where the number of data samples in the malignant and benign patient classes are significantly different. Over-sampling techniques can be used to re-balance the datasets to construct more effective prediction models. Moreover, some related studies have considered feature selection to remove irrelevant features from the datasets for further performance improvement. However, since the order of combining feature selection and over-sampling can result in different training sets to construct the prediction model, it is unknown which order performs better. In this paper, the information gain (IG) and genetic algorithm (GA) feature selection methods and the synthetic minority over-sampling technique (SMOTE) are used for different combinations. The experimental results based on two breast cancer datasets show that the combination of feature selection and over-sampling outperform the single usage of either feature selection and over-sampling for the highly class imbalanced datasets. In particular, performing IG first and SMOTE second is the better choice. For other datasets with a small class imbalance ratio and a smaller number of features, performing SMOTE is enough to construct an effective prediction model.
Agile elicitation of scalability requirements for open systems: A case study

Eliciting scalability requirements during agile software development is complicated and poorly described in previous research. This article presents a lightweight artifact for eliciting scalability requirements during agile software development: the ScrumScale model. The ScrumScale model is a simple spreadsheet. The scalability concepts underlying the ScrumScale model are clarified in this design science research, which also utilizes coordination theory. This paper describes the open banking case study, where a legacy banking system becomes open. This challenges the scalability of this legacy system. The first step in understanding this challenge is to elicit the new scalability requirements. In the open banking case study, key stakeholders from TietoEVRY spent 55 hours eliciting TietoEVRY's open banking project's scalability requirements. According to TietoEVRY, the ScrumScale model provided a systematic way of producing scalability requirements. For TietoEVRY, the scalability concepts behind the ScrumScale model also offered significant advantages in dialogues with other stakeholders.
Ultrafast Holonomic Quantum Gates

Quantum computation based on geometric phase is generally believed to be more robust against certain errors or noises than the conventional dynamical strategy. However, the gate error caused by the decoherence effect is inevitable, and thus faster gate operations are highly desired. Here, we propose a nonadiabatic holonomic quantum-computation (NHQC) scheme with detuned interactions on a $\mathrm{\ensuremath{\Delta}}$-type three-level system, which combines the time-optimal control technique with the time-independent detuning adjustment to further accelerate universal gate operations, so that the gate time can be greatly shortened within the hardware limitation, and thus high-fidelity gates can be obtained. Meanwhile, our numerical simulations show that the gate robustness is also stronger than previous schemes. Finally, we present an implementation of our proposal on superconducting quantum circuits, with a decoherence-free subspace encoding, based on the experimentally demonstrated parametrically tunable coupling technique, which simplifies previous investigations. Therefore, our protocol provides a more promising alternative for future fault-tolerant quantum computation.
Tracing app technology: an ethical review in the COVID-19 era and directions for post-COVID-19

We conducted a systematic literature review on the ethical considerations of the use of contact tracing app technology, which was extensively implemented during the COVID-19 pandemic. The rapid and extensive use of this technology during the COVID-19 pandemic, while benefiting the public well-being by providing information about people’s mobility and movements to control the spread of the virus, raised several ethical concerns for the post-COVID-19 era. To investigate these concerns for the post-pandemic situation and provide direction for future events, we analyzed the current ethical frameworks, research, and case studies about the ethical usage of tracing app technology. The results suggest there are seven essential ethical considerations—privacy, security, acceptability, government surveillance, transparency, justice, and voluntariness—in the ethical use of contact tracing technology. In this paper, we explain and discuss these considerations and how they are needed for the ethical usage of this technology. The findings also highlight the importance of developing integrated guidelines and frameworks for implementation of such technology in the post- COVID-19 world.
Towards Fair Knowledge Transfer for Imbalanced Domain Adaptation

Domain adaptation (DA) becomes an up-and-coming technique to address the insufficient or no annotation issue by exploiting external source knowledge. Existing DA algorithms mainly focus on practical knowledge transfer through domain alignment. Unfortunately, they ignore the fairness issue when the auxiliary source is extremely imbalanced across different categories, which results in severe under-presented knowledge adaptation of minority source set. To this end, we propose a Towards Fair Knowledge Transfer (TFKT) framework to handle the fairness challenge in imbalanced cross-domain learning. Specifically, a novel cross-domain mixup generation is exploited to augment the minority source set with target information to enhance fairness. Moreover, dual distinct classifiers and cross-domain prototype alignment are developed to seek a more robust classifier boundary and mitigate the domain shift. Such three strategies are formulated into a unified framework to address the fairness issue and domain shift challenge. Extensive experiments over two popular benchmarks have verified the effectiveness of our proposed model by comparing to existing state-of-the-art DA models, and especially our model significantly improves over 20% on two benchmarks in terms of the overall accuracy.
Automatic maneuver detection and tracking of space objects in optical survey scenarios based on stochastic hybrid systems formulation

The state space representation of active resident space objects can be posed in the form of a stochastic hybrid system. Satellite maneuvers may be accounted for according to control cost or heuristical considerations, yet it is possible to jointly consider a combination of both. In this work, Sequential Monte Carlo filtering techniques are applied to the maneuvering target tracking problem in an optical survey scenario, where the maneuver control inputs are characterized in a Bayesian inference process. Due to the scarcity of data inherent to space surveillance and tracking, model switching probabilities are not estimated but derived from the ability of the state representation to fit incoming measurements. A Markov Chain Monte Carlo sampling scheme is used to explore the region assumed accessible to the object in terms of the hypothesized post-maneuver observation and a novel and efficient control distance metric. Results are obtained for a simulated optical survey scenario, and comparisons are drawn with respect to a moving horizon least-squares estimator. The proposed framework is proved to allow for a capable implementation of an automated online maneuver detection algorithm, thus contributing to the reduction of uncertainty in the state of active space objects.
Quantum speed limit and geometric measure of entanglement

Using the approach offered by quantum speed limit, we show that geometric measure of multipartite entanglement for pure states [Phys. Rev. A 68, 042307(2003)] can be interpreted as the minimal time necessary to unitarily evolve a given quantum state to a separable one.
Quantifying non-Gaussianity of a quantum state by the negative entropy of quadrature distributions

We propose a non-Gaussianity measure of a multimode quantum state based on the negentropy of quadrature distributions. Our measure satisfies desirable properties as a non-Gaussianity measure, i.e., faithfulness, invariance under Gaussian unitary operations, and monotonicity under Gaussian channels. Furthermore, we find a quantitative relation between our measure and the previously proposed non-Gaussianity measures defined via quantum relative entropy and the quantum Hilbert-Schmidt distance. This allows us to estimate the non-Gaussianity measures readily by homodyne detection, which would otherwise require a full quantum-state tomography.
LeViT: A Vision Transformer in ConvNet's Clothing for Faster Inference

We design a family of image classification architectures that optimize the trade-off between accuracy and efficiency in a high-speed regime. Our work exploits recent findings in attention-based architectures, which are competitive on highly parallel processing hardware. We revisit principles from the extensive literature on convolutional neural networks to apply them to transformers, in particular activation maps with decreasing resolutions. We also introduce the attention bias, a new way to integrate positional information in vision transformers. As a result, we propose LeVIT: a hybrid neural network for fast inference image classification. We consider different measures of efficiency on different hardware platforms, so as to best reflect a wide range of application scenarios. Our extensive experiments empirically validate our technical choices and show they are suitable to most architectures. Overall, LeViT significantly outperforms existing convnets and vision transformers with respect to the speed/accuracy tradeoff. For example, at 80% ImageNet top-1 accuracy, LeViT is 5 times faster than EfficientNet on CPU. We release the code at this https URL
Set of holonomic and protected gates on topological qubits for a realistic quantum computer

In recent years qubit designs such as transmons approached the fidelities of up to 0.999. However, even these devices are still insufficient for realizing quantum error correction requiring better than 0.9999 fidelity. Topologically protected superconducting qubits are arguably most prospective for building a realistic quantum computer as they are intrinsically protected from noise and leakage errors that occur in transmons. We propose a topologically protected qubit design based on a $\pi$-periodic Josephson element and a universal set of gates: protected Clifford group and highly robust (with infidelity $\sim 10^{-4}$) non-discrete holonomic phase gate. The qubit is controlled via charge($Q$) and flux($\Phi$)-biases. The holonomic gate is realized by quickly, but adiabatically, going along a particular closed path in the two-dimensional $\{\Phi,Q\}$-space -- a path where computational states are always degenerate, but Berry curvature is localized inside the path. This gate is robust against currently achievable noise levels. This qubit architecture allows building a realistic scalable superconducting quantum computer with leakage and noise-induced errors as low as $10^{-4}$, which allows performing realistic error correction codes with currently available fabrication techniques.
PIREN: Prediction of Intermediary Readers’ Emotion from News-Articles

Stimuli like text narratives from news articles and editorials trigger numerous emotions as a response in the readers. As seen in previous works of news documents classification, the attention has been centralized more towards writer’s perspective rather than how a particular article affects its readers. This work focuses on a reader’s stance, one forms after reading a certain content. Ontology driven knowledge base is used for semantic matching to further calculate the coherence of the words to distinct intermediary emotions. On reception of a new document, frequency of terms are calculated which are then matched with ontologies and hence classification is done using deep learning-based classifier. A series of experiments are taken up on these news documents, and hence an inference for the proposed method is marked out for it being much reliable than other existing systems for emotion detection of a text document.
Time-aware evidence ranking for fact-checking

Truth can vary over time. Fact-checking decisions on claim veracity should therefore take into account temporal information of both the claim and supporting or refuting evidence. In this work, we investigate the hypothesis that the timestamp of a Web page is crucial to how it should be ranked for a given claim. We delineate four temporal ranking methods that constrain evidence ranking differently and simulate hypothesis-specific evidence rankings given the evidence timestamps as gold standard. Evidence ranking in three fact-checking models is ultimately optimized using a learning-to-rank loss function. Our study reveals that time-aware evidence ranking not only surpasses relevance assumptions based purely on semantic similarity or position in a search results list, but also improves veracity predictions of time-sensitive claims in particular.
The Fermionic Quantum Emulator

The fermionic quantum emulator (FQE) is a collection of protocols for emulating quantum dynamics of fermions efficiently taking advantage of common symmetries present in chemical, materials, and condensed-matter systems. The library is fully integrated with the OpenFermion software package and serves as the simulation backend. The FQE reduces memory footprint by exploiting number and spin symmetry along with custom evolution routines for sparse and dense Hamiltonians, allowing us to study significantly larger quantum circuits at modest computational cost when compared against qubit state vector simulators. This release paper outlines the technical details of the simulation methods and key advantages.
Establishment of a research workflow for occurrence state of critical metal in ore concentrate powder: A case study of the cobalt-rich sulfur ore concentrate powder from the Middle-Lower Yangtze River Valley Metallogenic Belt, China

长江中下游成矿带作为我国矿床学研究程度和矿产利用技术水平最高的成矿带之一，大部分矿床中除了主要成矿元素铜、金、铁和硫等，还伴生潜在可利用的关键金属资源镉、钴、硒、碲和铼等。由于目前矿山主要选矿回收铜、铁、硫、铅、锌、金和银等主要矿种，在不改变选矿工艺的条件下，矿山产品矿精粉中关键金属的分布特征和可利用性是当前关键金属资源利用的核心问题之一。本文对成矿带内玢岩型铁矿床、矽卡岩型铁矿床、矽卡岩型铁铜矿床、矽卡岩型铜金矿床、斑岩型铜金矿床和角砾岩筒型铜金矿床等六类矿床的矿山产品（铜金粉、硫精粉、铁精粉和金精粉）进行了系统化学成分分析，发现目前的矿山产品中关键金属元素含量普遍不高，但矽卡岩型铁矿床中的龙桥铁矿床硫精粉中富集钴，具有综合回收利用钴资源的潜力。在此基础上，本次工作利用综合矿物分析技术（TIMA），扫描电镜（SEM）和激光剥蚀-等离子体质谱仪（LA-ICP-MS）三种测试方法，系统开展了富钴硫精粉中钴的赋存状态研究。结果表明，龙桥硫精粉中钴矿物为辉砷钴矿、铁硫砷钴矿和硫铜钴矿，分别占硫精粉中总钴含量的9.93%、0.64%和0.01%。黄铁矿晶格中类质同象形式产出的钴占总量的81.97%，此外还有7.46%的钴赋存在其他矿物中。通过对比研究，初步建立了矿精粉中关键金属元素赋存状态的研究方法流程，为矿精粉中钴的回收利用提供了依据。;The Middle-Lower Yangtze River Valley Metallogenic Belt is one of the metallogenic belts with well-studied economic geology research and comprehensive mineral utilization technology in China. In addition of the main ore-forming elements Cu, Au, Fe and S, most deposits are associated with potentially available critical element resources, such as Cd, Co, Se, Te and Re. At present, the mines processed and recovered Cu, Fe, S, Pb, Zn, Au, Ag and other major products. Without changing the mineral process, the key problems in the utilization of critical element resources are the understanding of the occurrence of critical metals in ore concentrations. We have conducted a systematic chemical composition analysis of the mine products (i.e., copper concentrate powder, sulfur concentrate powder, iron concentrate powder and gold concentrate powder) of six types of deposits in the metallogenic belt, including apatite-magnetite Fe deposit, skarn Fe deposit, skarn Fe-Cu deposit, skarn Cu-Au deposit, porphyry Cu-Au deposit and breccia-hosted Au deposit. It is found that the contents of critical elements (Cd, Co, Se, Te and Re) in those mine products are generally low, except Co enriched in sulfur concentrate powder of Longqiao skarn iron deposit, which has the possibility of recovery and utilization of cobalt resources. In this study, the occurrence of cobalt in Co-rich sulfur concentrate powder was analyzed by Tescan Integrated Mineral Analyzer (TIMA), Scanning Electron Microscope (SEM) and Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS). The analyses results show that the cobalt independent minerals in Longqiao sulfur concentrate powder are cobaltite, glaueodot and carrollite, which account for 9.93%, 0.64% and 0.01% of the total cobalt in sulfur concentrate powder, respectively. The amount of cobalt occurs as isomorphic form in pyrite lattice accounts for 81.97% of the total. In addition, 7.46% of cobalt exists in other unknown minerals. Research workflow of critical elements deportments in ore concentrate powder is established initially, which provides a theoretical basis for the recovery and utilization of cobalt in ore concentrate powder.
Semi-Siamese Bi-encoder Neural Ranking Model Using Lightweight Fine-Tuning

A BERT-based Neural Ranking Model (NRM) can be either a crossencoder or a bi-encoder. Between the two, bi-encoder is highly efficient because all the documents can be pre-processed before the actual query time. In this work, we show two approaches for improving the performance of BERT-based bi-encoders. The first approach is to replace the full fine-tuning step with a lightweight fine-tuning. We examine lightweight fine-tuning methods that are adapter-based, prompt-based, and hybrid of the two. The second approach is to develop semi-Siamese models where queries and documents are handled with a limited amount of difference. The limited difference is realized by learning two lightweight fine-tuning modules, where the main language model of BERT is kept common for both query and document. We provide extensive experiment results for monoBERT, TwinBERT, and ColBERT where three performance metrics are evaluated over Robust04, ClueWeb09b, and MS-MARCO datasets. The results confirm that both lightweight fine-tuning and semi-Siamese are considerably helpful for improving BERT-based bi-encoders. In fact, lightweight fine-tuning is helpful for crossencoder, too
Photonic Kernel Machine Learning for Ultrafast Spectral Analysis

We introduce photonic kernel machines, a scheme for ultrafast spectral analysis of noisy radio-frequency signals from single-shot optical intensity measurements. The approach combines the versatility of machine learning and the speed of photonic hardware to reach unprecedented throughput rates. We theoretically describe some of the key underlying principles, and then numerically illustrate the performance achieved in a photonic lattice-based implementation. We apply the technique both to picosecond pulsed radio-frequency signals, on energy-spectral-density estimation and a shape-classification task, and to continuous signals, on a frequency-tracking task. The optical computing scheme presented is resilient to noise while requiring minimal control on the photonic-lattice parameters, making it readily implementable in realistic state-of-the-art photonic platforms.