Miracle-dz/UniFAR-FTUs huggingface dataset:
Public policy and economic dynamics of COVID-19 spread: A mathematical modeling study

Summary Background: With the COVID-19 pandemic infecting millions of people, largescale quarantine policies have been enacted across the globe. Understanding how to maximize the effectiveness of these policies while mitigating economic damage is essential. We develop a mathematical model with different levels of population health and economic risk to assess the impact of quarantine measures on deaths, hospitalizations, and economic output. Methods: We combine an extended SEIR model that has multiple levels of health risk and economic vulnerability with disease parameters for the COVID19 pandemic to study the effects of quarantine policies. We examine the dynamics of infectious disease transmission and its economic impact for one year. We populate our model with a baseline of 95.6 % of high-risk people and 85.6 % of low-risk people in quarantine. We use our model to simulate leaving the current quarantine restrictions in place indefinitely. We then assess a policy where quarantine restrictions for lower-risk groups are gradually relaxed. Finally, we simulate relaxing quarantine restrictions more quickly. To compare outbreak outcomes under the same quarantine policies using different disease parameters, we also perform a sensitivity analysis on the contagiousness of infected people, the strictness of social isolation measures, and the amount that public policymakers value the economy in setting quarantine restrictions. Findings: We first consider a baseline scenario with constant quarantine level, finding that in a population of 330,000,000 million, there are 32,023 deaths among those under 60 and 174,682 deaths among those over 60, there is a peak of 189,136 hospitalizations, and the economy relative to the beginning of the quarantine state is 1.4 % smaller. In the scenario where higher-risk people are strictly quarantined, and for others the quarantine is slowly relaxed, there are 63,776 deaths among those under 60, 85,880 deaths among those over 60, a peak of 341,321 hospitalizations, and the economy relative to the beginning of the quarantine state is 6% larger. In the scenario where quarantine is more quickly lifted, there are 25,720 deaths among those under 60, 214,718 deaths among those over 60, a peak of 341,321 hospitalizations, and economic output relative to the beginning of the quarantine state is 4.5 % smaller. Interpretation: Strict restrictions on high-risk patients and a gradual lifting of quarantine for low-risk patients results in a limited number of deaths and lesser economic damage. We recommend this kind of strategy, combined with policies lowering the contagiousness of the disease and making quarantine more tolerable and economically viable for individuals.
Analysis of the SARS-Cov-2 epidemic in Lombardy (Italy) in its early phase. Are we going in the right direction?

BACKGROUND We described the epidemiological features of the codiv-19 outbreak, and evaluated the impact of interventions measures on the epidemic in the Lombardy region, Italy. METHODS Laboratory-confirmed covid-19 cases reported through the beginning of April were extracted from the Italian Civil Protection database. Based on key events and interventions, we divided the epidemic into three periods: before February 21, from February 22 to early March, after early March. We compared epidemiological characteristics across periods and developed a modified susceptible-exposed-infectious-recovered model to study the epidemic and evaluate the impact of interventions. We explicitly took into account for unascertained cases (positive cases with no symptoms or mild symptoms that have not been accounted for in official statistics). RESULTS Currently, the number of positive active cases has increased to around 30,000 in the Lombardy region. Due to restriction measures, the effective reproduction number dropped from 3.33 (95% CI: 2.03-3.69) during the first period, to 2.36 (95% CI: 2.21-2.70) during the second period. In the third period, the effective reproduction number is estimated to have dropped to 1.49 (95% CI: 1.35-1.62). The model estimates a great proportion of unascertained cases, about 90% of infected people has not been accounted for in official statistics. CONCLUSIONS Considerable countermeasures have slowed down the covid-19 outbreak in the Lombardy region. However, notwithstanding the long-lasting lockdown period, the epidemic is still not under control. The effective reproduction number, according to the model used in this work, is still greater than 1.0. Estimation of unascertained cases has important implications on continuing surveillance and interventions.
Projected geographic disparities in healthcare worker absenteeism from COVID-19 school closures and the economic feasibility of child care subsidies: a simulation study

Background: School closures have been enacted as a measure of mitigation during the ongoing COVID-19 pandemic. It has been shown that school closures could cause absenteeism amongst healthcare workers with dependent children, but there remains a need for spatially granular analyses of the relationship between school closures and healthcare worker absenteeism to inform local community preparedness. Methods: We provide national- and county-level simulations of school closures and unmet child care needs across the United States. We develop individual simulations using county-level demographic and occupational data, and model school closure effectiveness with age-structured compartmental models. We perform multivariate quasi-Poisson ecological regressions to find associations between unmet child care needs and COVID-19 vulnerability factors. Results: At the national level, we estimate the projected rate of unmet child care needs for healthcare worker households to range from 7.5% to 8.6%, and the effectiveness of school closures to range from 3.2% to 7.2% reduction in fewer ICU beds at peak demand. At the county-level, we find substantial variations of projected unmet child care needs and school closure effects, ranging from 1.9% to 18.3% of healthcare worker households and 5.7% to 8.8% reduction in fewer ICU beds at peak demand. We find significant positive associations between estimated levels of unmet child care needs and diabetes prevalence, county rurality, and race (p < 0.05). We estimate costs of absenteeism and child care and observe from our models that an estimated 71.1% to 98.8% of counties would find it less expensive to provide child care to all healthcare workers with children than to bear the costs of healthcare worker absenteeism during school closures. Conclusions: School closures are projected to reduce peak ICU bed demand, but could disrupt healthcare systems through absenteeism, especially in counties that are already particularly vulnerable to COVID-19. Child care subsidies could help circumvent the ostensible tradeoff between school closures and healthcare worker absenteeism.
Use of Skin Grafting to Demonstrate Tolerance before Kidney Transplantation without Immunosuppression in the Recipient of a Previous Bone Marrow Transplant

had doubled and a soft-tissue ultrasound and a skull CT scan showed the presence of a 7 5-cm osteolytic mass, expanding through the left parietal bone without involving the dura mater (Fig. 1). A total-body CT scan disclosed the presence of multiple HCC nodules located in the right hepatic lobe, two 1-cm pulmonary nodules located in the right lung, and a bone metastasis in the inferior aspect of the sternum. The patient was referred to surgery for excision of the parietal bone mass. Histology of the resected specimen was consistent with HCC metastasis. Cyclosporine was reduced and chemotherapy with oral capecitabin 1000 mg/m bid was initiated. Tumor seeding and growth is influenced by interactions between the tumor cells and the extracellular matrix (ECM) through the action of metalloproteases and plasmin (2). These enzymes remodel the ECM under the control of molecular mediators, such as the tumor growth factor(TGF), the vascular endothelial growth factor (VEGF) and the lysophosphatic acid (LPA) (2– 4). The reported propensity of tumor cells to infiltrate wound sites may be accounted for by the fact that either the composition of tumor stroma is similar to that found in a healing wound or that several processes involved in wound healing, when aberrantly regulated, may play a part in tumor progression (1). TGF, VEGF, and LPA are involved in both processes and are highly expressed during wound healing and in many tumor types (2– 4). We may speculate that in the present case the granulation tissue at the trauma site acted as a substrate for tumor cell seeding, accounting for the manifestation of the skull metastasis in advance of the other locations.
Annual Scientific Meeting of the Neuroanaesthesia Society of Great Britain and Ireland, Edinburgh, United Kingdom, April 19–20th, 2007

s published in the Journal of Neurosurgical Anesthesiology have been reviewed by the organizations or JNA Affiliate Societies at whose meetings the abstracts have been accepted for presentation. These abstracts have not undergone review by the Editorial Board of the Journal of Neurosurgical Anesthesiology. Annual Scientific Meeting of the Neuroanaesthesia Society of Great Britain and Ireland, Edinburgh, United Kingdom, April 19–20th, 2007 Audit of Acute Pain and Analgesia Following Craniotomy Dr Neil Stewart, Dr Karen Cranfield. Senior House Officer, Department of Anaesthesia; and Consultant, Department of Anaesthesia, Aberdeen Royal Infirmary, UK. Introduction: In our institution Paracetamol and Codeine Phosphate remain the most commonly prescribed analgesics given following craniotomy. However, moderate to severe pain is encountered commonly following craniotomy and strong opioids such as Morphine and Oxycodone Patient Controlled Analgesia (PCA) have been used safely. We have examined postoperative pain control and analgesia in patients undergoing craniotomy in our institution. Method: We recorded pain, nausea, and sedation scores for 48 hours postoperatively and prescribed intraoperative and postoperative analgesia in patients undergoing craniotomy in Aberdeen Royal Infirmary between September 2006 and January 2007. Data were available for 20 patients. Results: All patients received 1-g Paracetamol 4 times daily. 16/20 patients were prescribed regular oral Codeine Phosphate, 5 in addition received IM or S/C Morphine. 1/20 received regular oral Dihydrocodeine and 3/20 patients received intravenous Morphine via PCA. 1 patient had opioids withheld due to confusion. Of those not receiving PCA 7/17 were judged not capable due to confusion or weakness.
Wurtzite InP microdisks: from epitaxy to room-temperature lasing

Metastable wurtzite crystal phases of conventional semiconductors comprise enormous potential for high-performance electro-optical devices, owed to their extended tunable direct band gap range. However, synthesizing these materials in good quality and beyond nanowire size constraints has remained elusive. In this work, the epitaxy of wurtzite InP microdisks and related geometries on insulator for advanced optical applications is explored. This is achieved by an elaborate combination of selective area growth of fins and a zipper-induced epitaxial lateral overgrowth, which enables co-integration of diversely shaped crystals at precise position. The grown material possesses high phase purity and excellent optical quality characterized by STEM and µ-PL. Optically pumped lasing at room temperature is achieved in microdisks with a lasing threshold of 365 µJ cm−2. Our platform could provide novel geometries for photonic applications.
Automotive Collision Risk Estimation Under Cooperative Sensing

This paper offers a technique for estimating collision risk for automated ground vehicles engaged in cooperative sensing. The technique allows quantification of (i) risk reduced due to cooperation, and (ii) the increased accuracy of risk assessment due to cooperation. If either is significant, cooperation can be viewed as a desirable practice for meeting the stringent risk budget of increasingly automated vehicles; if not, then cooperation—with its various drawbacks—need not be pursued. Collision risk is evaluated over an ego vehicle’s trajectory based on a dynamic probabilistic occupancy map and a loss function that maps collision-relevant state information to a cost metric. The risk evaluation framework is demonstrated using real data captured from two cooperating vehicles traversing an urban intersection.
Light-Matter Interaction and Hybrid Vector Breather

The nonlinear coherent interaction of light with the dispersive and Kerr-type third-order susceptibility medium containing optical impurity atoms or semiconductor quantum dots is considered. Using the generalized perturbation reduction method, the nonlinear wave equation is reduced to the coupled nonlinear Schrodinger equations. It is shown that the second-order derivatives play a key role in the description of the process of formation of the bound state of two breathers oscillating with the sum and the difference of frequencies and wave numbers. The resonant, nonresonant and hybrid mechanisms of the formation of the two-component nonlinear pulse -- the vector breather are realized depending on the light and medium parameters. Explicit analytical expressions for the profile and parameters of the nonlinear pulse are presented. The conditions of the excitation of resonant, nonresonant and hybrid nonlinear waves are discussed. In the particular case, the resonant vector breather coincides with the vector $0\pi$ pulse of self-induced transparency.
Digging Into MUD With Python: mudpy, bdata, and bfit

Used to store the results of $\mu$SR measurements at TRIUMF, the Muon Data (MUD) file format serves as a useful and flexible scheme that is both lightweight and self-describing. The application programming interface (API) for these files is written in C and FORTRAN, languages not known for their ease of use. In contrast, Python is a language which emphasizes rapid prototyping and readability. This work describes three Python 3 packages to interface with MUD files and analyze their contents: mudpy, bdata, and bfit. The first enables easy access to the contents of any MUD file. The latter two are implemented specifically for the implanted-ion $\beta$-detected NMR ($\beta$-NMR) experiment at TRIUMF. These tools provide both an API and graphical user interface (GUI) to help users extract and fit $\beta$-NMR data.
Synthetic vs. Real Reference Strings for Citation Parsing, and the Importance of Re-training and Out-Of-Sample Data for Meaningful Evaluations: Experiments with GROBID, GIANT and CORA

Citation parsing, particularly with deep neural networks, suffers from a lack of training data as available datasets typically contain only a few thousand training instances. Manually labelling citation strings is very time-consuming, hence synthetically created training data could be a solution. However, as of now, it is unknown if synthetically created reference-strings are suitable to train machine learning algorithms for citation parsing. To find out, we train Grobid, which uses Conditional Random Fields, with a) human-labelled reference strings from ‘real’ bibliographies and b) synthetically created reference strings from the GIANT dataset. We find that both synthetic and organic reference strings are equally suited for training Grobid (F1 = 0.74). We additionally find that retraining Grobid has a notable impact on its performance, for both synthetic and real data (+30% in F1). Having as many types of labelled fields as possible during training also improves effectiveness, even if these fields are not available in the evaluation data (+13.5% F1). We conclude that synthetic data is suitable for training (deep) citation parsing models. We further suggest that in future evaluations of reference parsers both evaluation data similar and dissimilar to the training data should be used for more meaningful evaluations.
Semantic Entity Enrichment by Leveraging Multilingual Descriptions for Link Prediction

Most Knowledge Graphs (KGs) contain textual descriptions of entities in various natural languages. These descriptions of entities provide valuable information that may not be explicitly represented in the structured part of the KG. Based on this fact, some link prediction methods which make use of the information presented in the textual descriptions of entities have been proposed to learn representations of (monolingual) KGs. However, these methods use entity descriptions in only one language and ignore the fact that descriptions given in different languages may provide complementary information and thereby also additional semantics. In this position paper, the problem of effectively leveraging multilingual entity descriptions for the purpose of link prediction in KGs will be discussed along with potential solutions to the problem.
Exploring Trade-offs in Dynamic Task Triggering for Loosely Coupled Scientific Workflows

In order to achieve near-time insights, scientific workflows tend to be organized in a flexible and dynamic way. Data-driven triggering of tasks has been explored as a way to support workflows that evolve based on the data. However, the overhead introduced by such dynamic triggering of tasks is an under-studied topic. This paper discusses different facets of dynamic task triggers. Particularly, we explore different ways of constructing a data-driven dynamic workflow and then evaluate the overheads introduced by such design decisions. We evaluate workflows with varying data size, percentage of interesting data, temporal data distribution, and number of tasks triggered. Finally, we provide advice based upon analysis of the evaluation results for users looking to construct data-driven scientific workflows.
Formal Verification of Flow Equivalence in Desynchronized Designs

Seminal work by Cortadella, Kondratyev, Lavagno, and Sotiriou includes a hand-written proof that a particular handshaking protocol preserves flow equivalence, a notion of equivalence between synchronous latchbased specifications and their desynchronized bundled-data asynchronous implementations. In this work we identify a counterexample to Cortadella et al.’s proof illustrating how their protocol can in fact lead to a violation of flow equivalence. However, two of the less concurrent protocols identified in their paper do preserve flow equivalence. To verify this fact, we formalize flow equivalence in the Coq proof assistant and provide mechanized, machinecheckable proofs of our results.
Discovering Imperfectly Observable Adversarial Actions using Anomaly Detection

Anomaly detection is a method for discovering unusual and suspicious behavior. In many real-world scenarios, the examined events can be directly linked to the actions of an adversary, such as attacks on computer networks or frauds in financial operations. While the defender wants to discover such malicious behavior, the attacker seeks to accomplish their goal (e.g., exfiltrating data) while avoiding the detection. To this end, anomaly detectors have been used in a game-theoretic framework that captures these goals of a two-player competition. We extend the existing models to more realistic settings by (1) allowing both players to have continuous action spaces and by assuming that (2) the defender cannot perfectly observe the action of the attacker. We propose two algorithms for solving such games -- a direct extension of existing algorithms based on discretizing the feature space and linear programming and the second algorithm based on constrained learning. Experiments show that both algorithms are applicable for cases with low feature space dimensions but the learning-based method produces less exploitable strategies and it is scalable to higher dimensions. Moreover, we use real-world data to compare our approaches with existing classifiers in a data-exfiltration scenario via the DNS channel. The results show that our models are significantly less exploitable by an informed attacker.
Hormone replacement therapy and age-related brain shrinkage: regional e¡ects

Neuroprotective properties of estrogen have been established in animal models, but clinical trials of hormone replacement therapy (HRT) produced contradictory results.We examined the impact of HRTon age-related regional changes in human brain volume. Six brain regions were measured twice, ¢ve years apart, in12 healthy women who took HRTand in matched controls who did not.The controls showed a typical pattern of di¡erential brain shrinkage in the association cortices and the hippocampus with no change in the primary visual cortex. In contrast, women who took HRT showed comparable shrinkage of the hippocampus but no signi¢cant shrinkage of the neocortex. Future large scale studies may bene¢t from applying regional rather than global measures in assessment of brain integrity. NeuroReport 15:2531^2534 c 2004 LippincottWilliams &Wilkins.
A modified Euler–Maclaurin formula in 1D and 2D with applications in statistical physics

The Euler–Maclaurin summation formula is generalized to a modified form by expanding the periodic Bernoulli polynomials as its Fourier series and taking cuts, which includes both the Euler–Maclaurin summation formula and the Poisson summation formula as special cases. By making use of the modified formula, a possible numerical summation method is obtained and the remainder can be controlled. The modified formula is also generalized from one dimension to two dimensions. Approximate expressions of partition functions of a classical particle in square well in 1D and 2D and that of a quantum rotator are obtained with error estimation.
Nanoelectromechanical Position-Sensitive Detector with Picometer Resolution

Subnanometer displacement detection lays the solid foundation for critical applications in modern metrology. In-plane displacement sensing, however, is mainly dominated by the detection of differential photocurrent signals from photodiodes, with resolution in the nanometer range. Here, we present an integrated nanoelectromechanical in-plane displacement sensor based on a nanoelectromechanical trampoline resonator. With a position resolution of 4 pm/ for a low laser power of 85 μW and a repeatability of 2 nm after five cycles of operation as well as good long-term stability, this new detection principle provides a reliable alternative for overcoming the current position detection limit in a wide variety of research and application fields.
Machine Learning for Causal Inference: On the Use of Cross-fit Estimators

Supplemental Digital Content is available in the text. Background: Modern causal inference methods allow machine learning to be used to weaken parametric modeling assumptions. However, the use of machine learning may result in complications for inference. Doubly robust cross-fit estimators have been proposed to yield better statistical properties. Methods: We conducted a simulation study to assess the performance of several different estimators for the average causal effect. The data generating mechanisms for the simulated treatment and outcome included log-transforms, polynomial terms, and discontinuities. We compared singly robust estimators (g-computation, inverse probability weighting) and doubly robust estimators (augmented inverse probability weighting, targeted maximum likelihood estimation). We estimated nuisance functions with parametric models and ensemble machine learning separately. We further assessed doubly robust cross-fit estimators. Results: With correctly specified parametric models, all of the estimators were unbiased and confidence intervals achieved nominal coverage. When used with machine learning, the doubly robust cross-fit estimators substantially outperformed all of the other estimators in terms of bias, variance, and confidence interval coverage. Conclusions: Due to the difficulty of properly specifying parametric models in high-dimensional data, doubly robust estimators with ensemble learning and cross-fitting may be the preferred approach for estimation of the average causal effect in most epidemiologic studies. However, these approaches may require larger sample sizes to avoid finite-sample issues.