Miracle-dz/UniFAR-FTUs huggingface dataset:
Survey of Sustainable Regeneration of Historic and Cultural Cores of Cities

The united nations educational, scientific and cultural organization (UNESCO) considers the historic urban landscapes as the world heritages. Managing historic city centers and maintaining historic cores are the emerging challenges for sustainable urban planning. Today, the historic cores form an important part of the economic, social, environmental, and physical assets and capacities of contemporary cities, and play a strategic role in their development. One of the most important approaches to the development of central textures, especially in historical and cultural cities, is the sustainable urban regeneration approach, which encompasses all aspects of sustainability, such as the economic, social, cultural and environmental aspects. To maintain sustainability and regeneration of historic cores of cities, it is necessary to provide insight into the underlying characteristics of the local urbanization. Furthermore, the fundamental assets are to be investigated as indicators of sustainable regeneration and drivers of urban development. In the meantime, a variety of research and experience has taken place around the world, all of which has provided different criteria and indicators for the development of strategies for the historic cores of cities. The present study, through a meta-analytic and survey method, analyzing the experience and research reported in 139 theoretical and empirical papers in the last twenty years, seeks to provide a comprehensive conceptual model taking into account the criteria and indices of sustainable regeneration in historic cores of cities. The quality of the survey has been ensured using the preferred reporting items for systematic reviews and meta-analysis (PRISMA).
Quantification of market efficiency based on informational-entropy

Since the 1960s, the question whether markets are efficient or not is controversially discussed. One reason for the difficulty to overcome the controversy is the lack of a universal, but also precise, quantitative definition of efficiency that is able to graduate between different states of efficiency. The main purpose of this article is to fill this gap by developing a measure for the efficiency of markets that fulfill all the stated requirements. It is shown that the new definition of efficiency, based on informational-entropy, is equivalent to the two most used definitions of efficiency from Fama and Jensen. The new measure therefore enables steps to settle the dispute over the state of efficiency in markets. Moreover, it is shown that inefficiency in a market can either arise from the possibility to use information to predict an event with higher than chance level, or can emerge from wrong pricing/ quotes that do not reflect the right probabilities of possible events. Finally, the calculation of efficiency is demonstrated on a simple game (of coin tossing), to show how one could exactly quantify the efficiency in any market-like system, if all probabilities are known.
Studying the morphology of reionization with the triangle correlation function of phases

We present a new statistical tool, called the triangle correlation function (TCF), inspired by the earlier work of Obreschkow et al. It is derived from the three-point correlation function and aims to probe the characteristic scale of ionized regions during the epoch of reionization from 21cm interferometric observations. Unlike most works, which focus on power spectrum, i.e. amplitude information, our statistic is based on the information we can extract from the phases of the Fourier transform of the ionization field. In this perspective, it may benefit from the well-known interferometric concept of closure phases. We find that this statistical estimator performs very well on simple ionization fields. For example, with well-defined fully ionized discs, there is a peaking scale, which we can relate to the radius of the ionized bubbles. We explore the robustness of the TCF when observational effects such as angular resolution and noise are considered. We also get interesting results on fields generated by more elaborate simulations such as 21CMFAST. Although the variety of sources and ionized morphologies in the early stages of the process make its interpretation more challenging, the nature of the signal can tell us about the stage of reionization. Finally, and in contrast to other bubble size distribution algorithms, we show that the TCF can resolve two different characteristic scales in a given map.
Analysis of temperature-sensitive hydrogel microvalves in a T-junction flow sorter using full scale fluid–structure interaction

Hydrogels have attracted attention in microfluidic applications as sensors and actuators due to their ability to undergo huge volume changes when subjected to environmental stimuli. In this study, a T-junction flow sorter is numerically investigated. Each of the branches involves one hydrogel microvalve with reverse sensitivity to temperature changes. The valve’s functionality is studied with and without considering fluid–structure interaction for various inlet pressures. The results of fluid–structure interaction and non-fluid–structure interaction solutions, such as fluid flow rate and valves close temperature, are presented and compared. In order to reduce hydrogel’s response time, the solution is employed for multiple valves patterns. It can be concluded that the hydrogel deformation due to the fluid pressure has a significant effect on the valves’ operational parameters which cannot be ignored in design and analysis.
Scheduling Algorithms for 5G Networks with Mid-haul Capacity Constraints

We consider a virtualized RAN architecture for 5G networks where the Remote Units are connected to a central unit via a mid-haul. To support high data rates, the mid-haul is realized with a Passive Optical Network (PON). In this architecture, the data are stored at the central unit until the scheduler decides to transmit it through the mid-haul to an appropriate remote unit, and then over the air at the same slot. We study an optimal scheduling problem that arises in this context. This problem has two key features. First, multiple cells must be scheduled simultaneously for efficient operation. Second, the interplay between the time-varying wireless interface rates and the fixed capacity PON needs to be handled efficiently. In this paper, we take a comprehensive look at this resource allocation problem by formulating it as a utility-maximization problem. Using combinatorial techniques, we derive useful structural properties of the optimal allocation and utilize these results to design polynomial-time approximation algorithms and a pseudo- polynomial-time optimal algorithm. Finally, we numerically compare the performance of the proposed algorithms to heuristics which are natural generalizations of the ubiquitous Proportional Fair algorithm.
Short‐term building load forecast based on a data‐mining feature selection and LSTM‐RNN method

Short‐term load forecast for individual electric customers is becoming increasingly important in the grid operation, since the power system is becoming a more interactive and intelligent system. Accurate short‐term load forecast for industrial or commercial electric buildings is more challenging due to the complicated load characteristics and numerous influence variables. In this paper, we consider maximizing the relevancy and minimizing the redundancy criterion to select effectively feature variables, which influence the building load consumption, and then a deep learning technique—long‐short memory recurrent neural network is proposed to predict the load consumption. This novel strategy captures distinct load characteristics, choosing accurate input variables, and shows a great forecasting performance as demonstrated by three different types of city building load in China. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Soft bremsstrahlung

Simple analytic formulas are considered for the energy radiated in low frequency bremsstrahlung from fully ionized gases. A formula that has been frequently cited over many years turns out to have only a limited range of validity, more narrow than for a formula derived using the Born approximation. In an attempt to find a more widely valid simple formula, a soft photon theorem is employed, which in this context implies that the differential rate of photon emission in an electron-ion collision with definite initial and final electron momenta is correctly given for sufficiently soft photons by the Born approximation, to all orders in the Coulomb potential. Corrections to the Born approximation arise because the upper limit on photon energy for this theorem to apply to a given collision becomes increasingly stringent as the scattering approaches the forward direction. A general formula is suggested that takes this into account.
Dense Intrinsic Appearance Flow for Human Pose Transfer

We present a novel approach for the task of human pose transfer, which aims at synthesizing a new image of a person from an input image of that person and a target pose. We address the issues of limited correspondences identified between keypoints only and invisible pixels due to self-occlusion. Unlike existing methods, we propose to estimate dense and intrinsic 3D appearance flow to better guide the transfer of pixels between poses. In particular, we wish to generate the 3D flow from just the reference and target poses. Training a network for this purpose is non-trivial, especially when the annotations for 3D appearance flow are scarce by nature. We address this problem through a flow synthesis stage. This is achieved by fitting a 3D model to the given pose pair and project them back to the 2D plane to compute the dense appearance flow for training. The synthesized ground-truths are then used to train a feedforward network for efficient mapping from the input and target skeleton poses to the 3D appearance flow. With the appearance flow, we perform feature warping on the input image and generate a photorealistic image of the target pose. Extensive results on DeepFashion and Market-1501 datasets demonstrate the effectiveness of our approach over existing methods. Our code is available at http://mmlab.ie. cuhk.edu. hk/projects/pose-transfer
Asymptotic Theory for Clustered Samples

We provide a complete asymptotic distribution theory for clustered data with a large number of groups, generalizing the classic laws of large numbers, uniform laws, central limit theory, and clustered covariance matrix estimation. Our theory allows for clustered observations with heterogeneous and unbounded cluster sizes. Our conditions cleanly nest the classical results for i.n. i.d. observations, in the sense that our conditions specialize to the classical conditions under independent sampling. We use this theory to develop a full asymptotic distribution theory for estimation based on linear least-squares, 2SLS, nonlinear MLE, and nonlinear GMM.
Single-Pixel Imaging with Origami Pattern Construction

Single-pixel compressive imaging can recover images from fewer measurements, offering many benefits especially for the imaging modalities where array detection is unavailable. However, the widely used random projections fail to explore internal relations between coding patterns and image reconstruction. Here, we propose a single-pixel imaging method based on a deterministic origami pattern construction that can lead to a more accurate pattern ordering sequence and better imaging quality. It can decrease the sampling ratio, closer to the upper bounds. The experimental realization of this approach is a big step forward towards practical applications.
Prediction of Shared Bicycle Demand with Wavelet Thresholding

Consumers are creatures of habit, often periodic, tied to work, shopping and other schedules. We analyzed one month of data from the world's largest bike-sharing company to elicit demand behavioral cycles, initially using models from animal tracking that showed large customers fit an Ornstein-Uhlenbeck model with demand peaks at periodicities of 7, 12, 24 hour and 7-days. Lorenz curves of bicycle demand showed that the majority of customer usage was infrequent, and demand cycles from time-series models would strongly overfit the data yielding unreliable models. Analysis of thresholded wavelets for the space-time tensor of bike-sharing contracts was able to compress the data into a 56-coefficient model with little loss of information, suggesting that bike-sharing demand behavior is exceptionally strong and regular. Improvements to predicted demand could be made by adjusting for 'noise' filtered by our model from air quality and weather information and demand from infrequent riders.
Electroconvulsive Therapy Practice in the Kingdom of Denmark: A Nationwide Register- and Questionnaire-Based Study.

OBJECTIVES The aim of this study was to survey and describe the contemporary practice of electroconvulsive therapy (ECT) in the Kingdom of Denmark (Denmark, Greenland, and the Faroe Islands). METHODS Data regarding number of ECTs and number of patients with different diagnoses treated with ECT were retrieved from the Danish National Patient Registry. In addition, a 45-item questionnaire was sent to all psychiatric departments practicing ECT in Denmark (n = 26), Greenland (n = 1), and the Faroe Islands (n = 1). RESULTS According to the Danish National Patient Registry, a total of 21,730 ECTs were administered to 1891 unique patients in 2017. All departments responded to the survey. The psychiatric departments' attitude toward ECT was generally favorable and in accord with official guidelines. Maintenance ECT was used in all departments but one. Bilateral electrode placement was preferred. All departments used a preselected age-based dosing strategy. Involuntary ECT was performed in 96% of the psychiatric departments, but infrequently (3% of all treatments). All departments used a Thymatron (brief pulse) device, and in 71% of the departments, ECT was given in a specialized ECT unit and preanesthetic evaluation was carried out in all departments. The departments reported several different practices regarding documentation and monitoring of treatment effect, patient consent, screening for side effects (including cognitive side effects), and guidelines for the discharge of ECT patients. CONCLUSIONS Electroconvulsive therapy is frequently used in Denmark, Greenland, and the Faroe Islands in a relatively uniform way in adherence with clinical guidelines.
Investigating Wheat Price with a Multi-Agent Model

In this paper, we build a computational model for the analysis of international wheat spot price formation, its dynamics and the dynamics of internationally exchanged quantities. The model has been calibrated using FAOSTAT data to evaluate its in-sample predictive power. The model is able to generate wheat prices in twelve international markets and wheat used quantities in twenty-four world regions. The time span considered goes from 1992 to 2013. In our study, a particular attention was paid to the impact of Russian Federation's 2010 grain export ban on wheat price and internationally traded quantities. Among other results, we find that wheat average weighted world price in 2013 would have been 3.55\% lower than the observed one if the Russian Federation would not have imposed the export ban in 2010.
Dynamic Control of Stochastic Evolution: A Deep Reinforcement Learning Approach to Adaptively Targeting Emergent Drug Resistance

The challenge in controlling stochastic systems in which random events can set the system on catastrophic trajectories is to develop a robust ability to respond to such events without significantly compromising the optimality of the baseline control policy. Drug resistance can emerge from random and variable mutations in targeted cell populations; in the absence of an appropriate dosing policy, emergent resistant subpopulations can proliferate and lead to treatment failure. Dynamic feedback dosage control holds promise in combatting this phenomenon, but cell population evolutionary dynamics are complex, stochastic, and often high-dimensional, posing significant challenges to system control. This paper presents CelluDose, a deep reinforcement learning closed-loop dynamic control prototype for automated precision drug dosing targeting stochastic and heterogeneous cell proliferation. Developing optimal dosing schedules for preventing therapy-induced drug resistance involves a tradeoff between the effective suppression of emergent resistant cell subpopulations and the use of conservative dosages and a preference for first-line drugs. CelluDose is trained on model simulations of cell population evolutionary dynamics that combine a system of stochastic differential equations and the additional occurrence of random perturbing events. Both the single-drug and combination therapy policies obtained in training exhibit a 100% success rate at suppressing simulated heterogeneous harmful cell growth and responding to diverse system fluctuations and perturbations within the alloted time and using conservative dosing. The policies obtained were found to be highly robust to model parameter changes and fluctuations not introduced during training.
Cervical Muscular Endurance Performance in Women With and Without Migraine

•BACKGROUND: Despite previous evidence, the association between migraines and cervical muscular performance is unclear. •OBJECTIVE: To compare the differences in neck flexor and extensor muscle endurance between women with and without migraine. •METHODS: In this cross‐sectional, controlled laboratory study, 26 women with migraine and 26 age‐matched women without migraine or headache were assessed using clinical tests of neck flexor and extensor muscle endurance. Holding times were compared between groups using the Mann‐Whitney U test for independent samples. •RESULTS: Patients with migraine exhibited a lower holding time for both neck extensor endurance (P = .001) and neck flexor endurance (P<.001) than did the controls. The median neck flexor holding time was 35.0 seconds for the migraine group and 60.5 seconds for the control group. The migraine group held the neck extensor endurance test position for a median of 166.5 seconds compared to 290.5 seconds held by the control group. Both groups reported a similar level of neck pain during the endurance tests (P>.05); however, only individuals in the migraine group reported pain referred to the head during testing. •CONCLUSION: Women with migraine demonstrated decreased neck flexor and extensor endurance compared to women without migraine, which may indicate an association between migraine and reduced performance of the neck muscles.
Improvement of Atmospheric Objective Analysis Over Sloping Terrain and Its Impact on Shallow‐Cumulus Clouds in Large‐Eddy Simulations

Surface topography strongly impacts the regional atmospheric circulation dynamically and thermodynamically. Over the Great Plains in the United States, the gently tilted slope is an important factor that impacts clouds, convection, and regional circulations. This study enhances an atmospheric constrained variational analysis by using a terrain‐following sigma vertical coordinate and applies to the data collected at the Southern Great Plain (SGP) site of the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program. Sensitivity studies are performed to examine the impact of the terrain effects on the derived large‐scale atmospheric forcing fields and the simulated shallow‐cumulus clouds in large‐eddy simulation (LES) driven by the forcing. We found that the terrain impacts on the large‐scale forcing fields are mainly at lower levels and are strongly controlled by up/downslope winds. The response of the derived forcing to the slope of the terrain is monotonic, but the response of the simulated shallow‐cumulus clouds is more complex and depends on several factors. Overall, the terrain impact is small over SGP due to the small slope angle. However, the flat‐surface assumption may cause larger biases in the large‐scale forcing fields at locations with steeper terrain. The new sigma coordinate algorithm, with its consideration of surface slope, should be more suitable to derive large‐scale objective analysis over regions with steep terrain for application to force single‐column models, cloud resolving models, and LES models.
Performance-Optimal Filtering: Bloom overtakes Cuckoo at High-Throughput

We define the concept of performance-optimal filtering to indicate the Bloom or Cuckoo filter configuration that best accelerates a particular task. While the space-precision tradeoff of these filters has been well studied, we show how to pick a filter that maximizes the performance for a given workload. This choice might be "suboptimal" relative to traditional space-precision metrics, but it will lead to better performance in practice. In this paper, we focus on high-throughput filter use cases, aimed at avoiding CPU work, e.g., a cache miss, a network message, or a local disk I/O - events that can happen at rates of millions to hundreds per second. Besides the false-positive rate and memory footprint of the filter, performance optimality has to take into account the absolute cost of the filter lookup as well as the saved work per lookup that filtering avoids; while the actual rate of negative lookups in the workload determines whether using a filter improves overall performance at all. In the course of the paper, we introduce new filter variants, namely the register-blocked and cache-sectorized Bloom filters. We present new implementation techniques and perform an extensive evaluation on modern hardware platforms, including the wide-SIMD Skylake-X and Knights Landing. This experimentation shows that in high-throughput situations, the lower lookup cost of blocked Bloom filters allows them to overtake Cuckoo filters.
How to appraise mixed methods research

Using a combination of quantitative and qualitative research methods in a study can provide more robust answers to the research question. Nurses should be able to confidently and competently appraise research papers to be able to offer evidence-based care. While nurses may be able to appraise quantitative and qualitative research individually, this paper provides guidance on how to appraise a mixed methods (MM) research paper. To make it easier to understand MM research, we suggest that you read the two first papers (of this series), on how to appraise quantitative research and how to appraise qualitative research.1 2 ### What is MM research? MM are often described as the third methodological movement,3 the first two being quantitative and qualitative. As a refresher quantitative methodology aims to address research questions about causality, generalisability or size of effects. On the other hand, qualitative methodologies are useful in research questions that explore why or how a phenomenon occurs. Qualitative research can also be used to develop a theory or to describe the nature of an individual’s experience. MM studies collect, analyse and interpret both quantitative and qualitative data within a single or series of studies.4 The distinctiveness of MM is that the research design and methodology bring both sets of data together, which allows interpretations to be made as a single study. …