Miracle-dz/UniFAR-FTUs huggingface dataset:
An overview of the MILAGRO 2006 Campaign: Mexico City emissions and their transport and transformation

Abstract. MILAGRO (Megacity Initiative: Local And Global Research Observations) is an international collaborative project to examine the behavior and the export of atmospheric emissions from a megacity. The Mexico City Metropolitan Area (MCMA) – one of the world's largest megacities and North America's most populous city – was selected as the case study to characterize the sources, concentrations, transport, and transformation processes of the gases and fine particles emitted to the MCMA atmosphere and to evaluate the regional and global impacts of these emissions. The findings of this study are relevant to the evolution and impacts of pollution from many other megacities. The measurement phase consisted of a month-long series of carefully coordinated observations of the chemistry and physics of the atmosphere in and near Mexico City during March 2006, using a wide range of instruments at ground sites, on aircraft and satellites, and enlisting over 450 scientists from 150 institutions in 30 countries. Three ground supersites were set up to examine the evolution of the primary emitted gases and fine particles. Additional platforms in or near Mexico City included mobile vans containing scientific laboratories and mobile and stationary upward-looking lidars. Seven instrumented research aircraft provided information about the atmosphere over a large region and at various altitudes. Satellite-based instruments peered down into the atmosphere, providing even larger geographical coverage. The overall campaign was complemented by meteorological forecasting and numerical simulations, satellite observations and surface networks. Together, these research observations have provided the most comprehensive characterization of the MCMA's urban and regional atmospheric composition and chemistry that will take years to analyze and evaluate fully. In this paper we review over 120 papers resulting from the MILAGRO/INTEX-B Campaign that have been published or submitted, as well as relevant papers from the earlier MCMA-2003 Campaign, with the aim of providing a road map for the scientific community interested in understanding the emissions from a megacity such as the MCMA and their impacts on air quality and climate. This paper describes the measurements performed during MILAGRO and the results obtained on MCMA's atmospheric meteorology and dynamics, emissions of gases and fine particles, sources and concentrations of volatile organic compounds, urban and regional photochemistry, ambient particulate matter, aerosol radiative properties, urban plume characterization, and health studies. A summary of key findings from the field study is presented.
Estimating the Ecosystem Services Value of Edinburgh's Trees

Trees and woodlands provide a range of social, environmental and economic benefits. There is strong evidence that trees influence our lives in many ways from improving health and well-being, improved learning, increasing property values, providing a focal point to improve social cohesion, improved air quality, offsetting carbon emissions, promoting biodiversity, limiting the risk of flooding, cooling our towns and cities, promoting inward investment and job creation through to making us drive more safely (Forest Research, 2010 1). The i-Tree Eco model was developed by the US Forest Service to quantify a selection of these ecosystem services at the town and city scale. This summary provides an overview of the results from a study which was undertaken using the i-Tree Eco model to estimate some of the major environmental benefits delivered by Edinburgh's trees. The model is still being developed for use in the UK and therefore only a limited number of benefits were able to be quantified from the full range of ecosystem services that trees can provide. The benefits quantified in this study were carbon storage and sequestration and air pollution removal. A survey of 200 field plots located across Edinburgh was carried out in the summer of 2011. Data from trees and shrubs were recorded from these plots and used to estimate the amount of carbon stored and that sequestered each year by each tree, as well as the amount of gaseous and particulate air pollutants removed by a tree. The following list of findings is not exhaustive but provides an overview of the study's main results. For detail on how theses results were derived please read the Results and Discussion section of the main report (page 9).  Edinburgh has over 600,000 trees and their canopies are estimated to cover 17.0% of the total land area.  Over half of Edinburgh's trees are native to Scotland. The ten most common tree species make up over 65% of the total population are: sycamore, holly, silver birch, Leyland cypress, ash, beech, rowan, Scots pine, Wych elm and cherry.
Early and recurrent cerebral vasospasms after aneurysmal subarachnoid hemorrhage: The impact of age

Introduction: Cerebral vasospasms remain a strong predictor of poor outcome after aneurysmal SAH. The aim of this study was to describe the time course of relevant vasospasms after aneurysmal SAH and to determine the variables associated with early-onset or prolonged and recurrent vasospasms. Patients and methods: We conducted a retrospective, single-center study of consecutive adult patients with aneurysmal SAH admitted between 2016 and 2022 at our tertiary stroke center. Relevant vasospasms, defined as vessel narrowing detected in DSA in combination with clinical deterioration or new perfusion deficit, were detected according to our in-house algorithm and eventually treated endovascularly. The primary endpoint was the diagnosis of relevant vasospasms. As secondary endpoints, the time from hemorrhage to the onset of vasospasms and the time from the first to the last endovascular intervention were measured. Results: Of 368 patients with aneurysmal SAH, 135 (41.0%) developed relevant vasospasms. The median time between ictus and detection of vasospasms was 8 days (IQR: 6–10). Patients with early-onset vasospasms were significantly younger (mean 52.7 ± 11.2 years vs 58.7 ± 11.5 years, p = 0.003) and presented more frequently vasospasm-related infarctions at discharge (58.8% vs 38.7%, p = 0.03). In 74 patients (54.8%), recurrent relevant vasospasms were observed despite endovascular treatment. Younger age and early onset were significantly associated with longer duration of relevant vasospasms (both p < 0.05). Discussion and conclusion: Younger age was associated with early-onset and longer duration of relevant vasospasms in this study. More frequent clinical and diagnostic follow-up should be considered in this subgroup of patients that are at risk for poor outcomes.
Optimization of Cement Spacer Rheology Model Using Genetic Algorithm (RESEARCH NOTE)

The primary cement job is a critical step in successful well completion. To achieve effective cementing job, complete mud removal from the annular is recommended. Spacer and flushers are used widely to achieve this goal. This study is about weighted cement spacer systems containing a surfactant package, weighting agent and rheological modifiers. Weighted spacer systems are utilized when a high formation fluid pressure is expected inside the wellbore. A testing program is conducted in laboratory to determine the spacer fluid rheological properties in different temperatures. The measured rheological properties are estimated using the known rheological models. Each model is firstly optimized using Genetic Algorithm as an optimization tool and then the rheological properties are modeled. The performance of the genetic algorithm is then tested by comparing the real laboratory data and modeled data.
Net primary productivity, allocation pattern and carbon use efficiency in an apple orchard assessed by integrating eddy covariance, biometric and continuous soil chamber measurements

Carbon use efficiency (CUE), the ratio of net primary production (NPP) over gross primary production (GPP), is a functional parameter that could possibly link the current increasingly accurate global GPP estimates with those of net ecosystem exchange, for which global predictors are still unavailable. Nevertheless, CUE estimates are actually available for only a few ecosystem types, while information regarding agro-ecosystems is scarce, in spite of the simplified spatial structure of these ecosystems that facilitates studies on allocation patterns and temporal growth dynamics. We combined three largely deployed methods, eddy covariance, soil respiration and biometric measurements, to assess monthly values of CUE, NPP and allocation patterns in different plant organs in an apple orchard during a complete year (2010). We applied a measurement protocol optimized for quantifying monthly values of carbon fluxes in this ecosystem type, which allows for a cross check between estimates obtained from different methods. We also attributed NPP components to standing biomass increments, detritus cycle feeding and lateral exports. We found that in the apple orchard, both net ecosystem production and gross primary production on a yearly basis, 380 ± 30 g C m −2 and 1263 ± 189 g C m −2 respectively, were of a magnitude comparable to those of natural forests growing in similar climate conditions. The largest differences with respect to forests are in the allocation pattern and in the fate of produced biomass. The carbon sequestered from the atmosphere was largely allocated to production of fruit: 49% of annual NPP was taken away from the ecosystem through apple production. Organic material (leaves, fine root litter, pruned wood and early fruit falls) contributing to the detritus cycle was 46% of the NPP. Only 5% was attributable to standing biomass increment, while this NPP component is generally the largest in forests. The CUE, with an annual average of 0.71 ± 0.12, was higher than the previously suggested constant values of 0.47–0.50. Low nitrogen investment in fruit, the limited root apparatus, and the optimal growth temperature and nutritional condition observed at the site are suggested to be explanatory variables for the high CUE observed.
Analytical Stress Modeling for Mine related Microseismicity

Summary Microseismicity is recorded in an underground mine by a network of 7 boreholes each having 4 geophones during January 2011. The inverted event locations are confined in a narrow band between depths of 0.4 to 0.5 km around the tunnels. We propose a simplified geomechanical model for the tunnels as an oblate spheroidal cavity and compute stress concentration around it using Eshelby's equivalent inclusion method. We use Coulomb's failure function as a stability criterion to identify regions most susceptible to shear slippages which show good match with the observed microseismic cloud. The same methodology is in principle applicable to microseismic data recorded during hydraulic fracturing treatments and may give more insight into the possible shape of the induced fracture network, resulting stress changes and the recorded microseismic cloud.
Incidence of muscle symptoms in placebo arm among statin-intolerant patients: a systematic review with meta-analysis

ABSTRACT Introduction Statins are highly used in cardiovascular prevention. Statin intolerance is the most significant cause of decreased adherence, translating into a higher cardiovascular risk. This systematic review aims to estimate the incidence of muscle adverse events in patients with a history of statin intolerance receiving placebo. Methods Database search was performed in CENTRAL, MEDLINE, and EMBASE until March 2023. This systematic review included blinded randomized control trials enrolling patients with a history of statin intolerance who received a placebo. A random-effects meta-analysis was performed. Results were presented in percentages, with 95% confidence intervals (95% CI). Results Overall, eight studies with 8095 patients with a history of statin intolerance receiving placebo were included. The muscle adverse events incidence rate was 21.34% (95% CI 13.26–30.63%, 8 studies), and discontinuation due to adverse muscle events was 6.12% (95% CI 1.22–13.70%, 3 studies). The incidence was higher in subcutaneous placebo/sham (41.67%, 1 study) compared to oral placebo studies (22.95%, 6 studies). Conclusion In patients previously labeled as statin-intolerant, about a fifth of the patients exhibited muscle symptoms when receiving a placebo. This highlights the importance of ruling out non-statin-related symptoms to further optimize statin therapy for cardiovascular risk improvement.
Construction Artifacts in Metaphor Identification Datasets

Metaphor identification aims at understanding whether a given expression is used figuratively in context. However, in this paper we show how existing metaphor identification datasets can be gamed by fully ignoring the potential metaphorical expression or the context in which it occurs. We test this hypothesis in a variety of datasets and settings, and show that metaphor identification systems based on language models without complete information can be competitive with those using the full context. This is due to the construction procedures to build such datasets, which introduce unwanted biases for positive and negative classes. Finally, we test the same hypothesis on datasets that are carefully sampled from natural corpora and where this bias is not present, making these datasets more challenging and reliable.
ABC-based Forecasting in State Space Models

Approximate Bayesian Computation (ABC) has gained popularity as a method for conducting inference and forecasting in complex models, most notably those which are intractable in some sense. In this paper we use ABC to produce probabilistic forecasts in state space models (SSMs). Whilst ABC-based forecasting in correctly-specified SSMs has been studied, the misspecified case has not been investigated, and it is that case which we emphasize. We invoke recent principles of 'focused' Bayesian prediction, whereby Bayesian updates are driven by a scoring rule that rewards predictive accuracy; the aim being to produce predictives that perform well in that rule, despite misspecification. Two methods are investigated for producing the focused predictions. In a simulation setting, 'coherent' predictions are in evidence for both methods: the predictive constructed via the use of a particular scoring rule predicts best according to that rule. Importantly, both focused methods typically produce more accurate forecasts than an exact, but misspecified, predictive. An empirical application to a truly intractable SSM completes the paper.
The Operator Product Expansion for Radial Lattice Quantization of 3D $\phi^4$ Theory

At its critical point, the three-dimensional lattice Ising model is described by a conformal field theory (CFT), the 3d Ising CFT. Instead of carrying out simulations on Euclidean lattices, we use the Quantum Finite Elements method to implement radially quantized critical $\phi^4$ theory on simplicial lattices approaching $\mathbb{R} \times S^2$. Computing the four-point function of identical scalars, we demonstrate the power of radial quantization by the accurate determination of the scaling dimensions $\Delta_{\epsilon}$ and $\Delta_{T}$ as well as ratios of the operator product expansion (OPE) coefficients $f_{\sigma \sigma \epsilon}$ and $f_{\sigma \sigma T}$ of the first spin-0 and spin-2 primary operators $\epsilon$ and $T$ of the 3d Ising CFT.
Computing local minimizers in polynomial optimization under genericity conditions

In this paper, we aim at computing all local minimizers of a polynomial optimization problem under genericity conditions. By using a technique in computer algebra, we provide a univariate representation for the set of local minimizers. In particular, for an unconstrained problem, the coordinates of all local minimizers can be represented by several univariate polynomial equalities and one univariate polynomial matrix inequality. We also develop the technique for constrained problems having equality constraints. Based on the above technique, we design algorithms to enumerate the local minimizers. At the end of the paper, we provide some experimental examples.
Collaborative Multi-task Learning for Multi-Object Tracking and Segmentation

The advancement of computer vision has pushed visual analysis tasks from still images to the video domain. In recent years, video instance segmentation, which aims to track and segment multiple objects in video frames, has drawn much attention for its potential applications in various emerging areas such as autonomous driving, intelligent transportation, and smart retail. In this paper, we propose an effective framework for instance-level visual analysis on video frames, which can simultaneously conduct object detection, instance segmentation, and multi-object tracking. The core idea of our method is collaborative multi-task learning which is achieved by a novel structure, named associative connections among detection, segmentation, and tracking task heads in an end-to-end learnable CNN. These additional connections allow information propagation across multiple related tasks, so as to benefit these tasks simultaneously. We evaluate the proposed method extensively on KITTI MOTS and MOTS Challenge datasets and obtain quite encouraging results.
Topological Waveguide Quantum Sensors

We present an efficient and robust protocol for quantum-enhanced sensing using a single-spin qubit in the topological waveguide system. Our method relies on the topological-paired bound states, which are localized near the spin and can be effectively regarded as a two-level system. Through the lens of Bayesian inference theory, we show the sensitivity can reach the Heisenberg limit across a large field range. Inheriting from the topological robustness of the waveguide, our sensing protocol is robust against local perturbations. The advantages of our protocol are multifold as it allows for sensing various parameters and uses a product initial state, which can be easily prepared in experiments. We expect this approach would pave the way towards robust topological quantum sensors based on near term quantum platforms such as topological photonics and Rydberg arrays.
Physically constrained eigenspace perturbation for turbulence model uncertainty estimation

Aerospace design is increasingly incorporating design under uncertainty-based approaches to lead to more robust and reliable optimal designs. These approaches require dependable estimates of uncertainty in simulations for their success. The key contributor of predictive uncertainty in computational fluid dynamics (CFD) simulations of turbulent flows are the structural limitations of Reynolds-averaged Navier–Stokes models, termed model-form uncertainty. Currently, the common procedure to estimate turbulence model-form uncertainty is the eigenspace perturbation framework (EPF), involving perturbations to the modeled Reynolds stress tensor within physical limits. The EPF has been applied with success in design and analysis tasks in numerous prior works from the industry and academia. Owing to its rapid success and adoption in several commercial and open-source CFD solvers, in-depth verification and validation of the EPF is critical. In this work, we show that under certain conditions, the perturbations in the EPF can lead to Reynolds stress dynamics that are not physically realizable. This analysis enables us to propose a set of necessary physics-based constraints, leading to a realizable EPF. We apply this constrained procedure to the illustrative test case of a converging-diverging channel, and we demonstrate that these constraints limit physically implausible dynamics of the Reynolds stress tensor, while enhancing the accuracy and stability of the uncertainty estimation procedure.
Towards Safe Propofol Dosing during General Anesthesia Using Deep Offline Reinforcement Learning

—Automated anesthesia promises to enable more precise and personalized anesthetic administration and free anesthesiologists from repetitive tasks, allowing them to focus on the most critical aspects of a patient’s surgical care. Current research has typically focused on creating simulated environments from which agents can learn. These approaches have demonstrated good experimental results, but are still far from clinical application. In this paper, Policy Constraint Q-Learning (PCQL), a data-driven reinforcement learning algorithm for solving the problem of learning anesthesia strategies on real clinical datasets, is proposed. Conservative Q-Learning was ﬁrst introduced to alleviate the problem of Q function overestimation in an ofﬂine context. A policy constraint term is added to agent training to keep the policy distribution of the agent and the anesthesiologist consistent to ensure safer decisions made by the agent in anesthesia scenarios. The effectiveness of PCQL was validated by extensive experiments on a real clinical anesthesia dataset. Experimental results show that PCQL is predicted to achieve higher gains than the baseline approach while maintaining good agreement with the reference dose given by the anesthesiologist, using less total dose, and being more responsive to the patient’s vital signs. In addition, the conﬁdence intervals of the agent were investigated, which were able to cover most of the clinical decisions of the anesthesiologist. Finally, an interpretable method, SHAP, was used to analyze the contributing components of the model predictions to increase the transparency of the model.
High-dimensional Linear Bandits with Knapsacks

We study the contextual bandits with knapsack (CBwK) problem under the high-dimensional setting where the dimension of the feature is large. The reward of pulling each arm equals the multiplication of a sparse high-dimensional weight vector and the feature of the current arrival, with additional random noise. In this paper, we investigate how to exploit this sparsity structure to achieve improved regret for the CBwK problem. To this end, we first develop an online variant of the hard thresholding algorithm that performs the sparse estimation in an online manner. We further combine our online estimator with a primal-dual framework, where we assign a dual variable to each knapsack constraint and utilize an online learning algorithm to update the dual variable, thereby controlling the consumption of the knapsack capacity. We show that this integrated approach allows us to achieve a sublinear regret that depends logarithmically on the feature dimension, thus improving the polynomial dependency established in the previous literature. We also apply our framework to the high-dimension contextual bandit problem without the knapsack constraint and achieve optimal regret in both the data-poor regime and the data-rich regime. We finally conduct numerical experiments to show the efficient empirical performance of our algorithms under the high dimensional setting.
Simpler Distribution Testing with Little Memory

We consider the question of distribution testing (specifically, uniformity and closeness testing) in the streaming setting, \ie under stringent memory constraints. We improve on the results of Diakonikolas, Gouleakis, Kane, and Rao (2019) by providing considerably simpler algorithms, which remove some restrictions on the range of parameters and match their lower bounds.