Miracle-dz/UniFAR-FTUs huggingface dataset:
What makes decisions about urban water infrastructure forward looking? A fuzzy-set qualitative comparative analysis of investment decisions in 40 Dutch municipalities

Municipalities worldwide are confronted with the need to take long-term decisions about ageing water infrastructure in the face of unpredictable future developments. Previous studies on long-term decision making have proposed solutions targeted at the domain of either politics or planning. This study combines insights from the domains of policy, politics, and planning by using the Multiple Streams Framework to explain what enables municipalities to take forward-looking investment decisions. We combine the configurational MSF perspective with an explicitly configurational method namely fuzzy-set qualitative comparative analysis and apply this to 40 cases of Dutch municipalities. We conclude that enabling conditions differ for small versus medium-to-large municipalities. Furthermore, forward-looking investment decisions can be achieved regardless of the municipalities’ organizational analytical capacity. In fact, and contrasting to the requirement of the MSF, not all streams necessarily have to be present for forward-looking decisions to occur. For medium-to-large municipalities, forward-looking investment decisions are stimulated by: (1) the presence of organizational analytical capacity, (2) transactional/networking political leadership in situations without focusing events, or (3) entrepreneurial/ transformative political leadership in situations with focusing events. For small municipalities, forward-looking investment decisions are stimulated by networking/interpersonal political leadership combined with the occurrence of focusing events.
Influence of turbulence on transfer of heat from cylinders

Note presenting an investigation of the problem of the influence of free-stream turbulence on the transfer of heat from a cylinder in forced convection at very low Mach numbers but at large Reynolds numbers. An attempt is made to determine whether the sole influence of turbulence is to shift the point of laminar separation in subcritical flow, or the point of transition in supercritical flow, and thus effect a change in the rate of heat transfer.
The crossroads of magic and wellbeing: A review of wellbeing-focused magic programs, empirical studies, and conceivable theories

In recent years, magicians and scientists have begun collaborating to gain insight into various psychological functions. However, one underexplored area is the use of magic tricks to enhance wellbeing. Several past and current magic programs have been used to enhance cognitive, emotional, social, and physical wellbeing. The application of these wellbeing-focused magic programs ranges from physical and psychological therapies to gang prevention, hospitals and classrooms. A few have been empirically investigated and additional studies have also explored the wellbeing value of magic. These studies are reviewed in light of a proposed hierarchical model based on how magic was applied. Overall, methodologies need improvement but distinctions between levels of the hierarchy can still be observed and are discussed. Furthermore, the positive effects on wellbeing can also be organised into interrelated physical, cognitive, social, and affective components that reflect existing theoretical frameworks on wellbeing. To conclude, possible mechanisms and theoretical frameworks based on more established psychological theories are discussed in order to help guide future research.
A Hydrodynamical Atmosphere/Ocean Coupled Modeling System for Multiple Tropical Cyclones

The goal of this paper is to introduce a new multi-storm atmosphere/ocean coupling scheme that was implemented and tested in the Basin-Scale Hurricane Weather Research and Forecasting (HWRF-B) model. HWRF-B, an experimental model developed at the National Oceanic and Atmospheric Administration (NOAA) and supported by the Hurricane Forecast Improvement Program, is configured with multiple storm-following nested domains to produce high-resolution predictions for several tropical cyclones (TCs) within the same forecast integration. The new coupling scheme parallelizes atmosphere/ocean interactions for each nested domain in HWRF-B, and it may be applied to any atmosphere/ocean coupled system. TC forecasts from this new hydrodynamical modeling system were produced in the North Atlantic and eastern North Pacific from 2017–2019. The performance of HWRF-B was evaluated, including forecasts of TC track, intensity, structure (e.g., surface wind radii), and intensity change, and simulated sea-surface temperatures were compared with satellite observations. Median forecast skill scores showed significant improvement over the operational HWRF at most forecast lead times for track, intensity, and structure. Sea-surface temperatures cooled by 1–8 °C for the five HWRF-B case studies, demonstrating the utility of the model to study the impact of the ocean on TC intensity forecasting. These results show the value of a multi-storm modeling system and provide confidence that the multi-storm coupling scheme was implemented correctly. Future TC models within NOAA, especially the Unified Forecast System’s Hurricane Analysis and Forecast System, would benefit from the multi-storm coupling scheme whose utility and performance are demonstrated in HWRF-B here.
Gender Fairness within the Force Concept Inventory

Research on the test structure of the Force Concept Inventory (FCI) has largely ignored gender, and research on FCI gender effects (often reported as "gender gaps") has seldom interrogated the structure of the test. These rarely-crossed streams of research leave open the possibility that the FCI may not be structurally valid across genders, particularly since many reported results come from calculus-based courses where 75% or more of the students are men. We examine the FCI considering both psychometrics and gender disaggregation (while acknowledging this as a binary simplification), and find several problematic questions whose removal decreases the apparent gender gap. We analyze three samples (total $N_{pre}=5,391$, $N_{post}=5,769$) looking for gender asymmetries using Classical Test Theory, Item Response Theory, and Differential Item Functioning. The combination of these methods highlights six items that appear substantially unfair to women and two items biased in favor of women. No single physical concept or prior experience unifies these questions, but they are broadly consistent with problematic items identified in previous research. Removing all significantly gender-unfair items halves the gender gap in the main sample in this study. We recommend that instructors using the FCI report the reduced-instrument score as well as the 30-item score, and that credit or other benefits to students not be assigned using the biased items.
Exploiting Textual Content in Academic Citation Networks

An academic citation network can be viewed as an information graph where individual nodes contain rich textual information. With the current trend of open-access to most scientific literature, we presume that the full text of a scientific article (or paper) is a vital source of information which aids in various recommendation and prediction tasks concerning this domain. The textual content of a paper serves as a rich source of information about a node in the citation network graph. The first task we focus on is competing algorithm mining from research papers. As an example, consider the association rule mining problem in data mining or the object recognition problem in computer vision. There has been a lot of research on these particular ideas and several authors and research groups have come up with their novel solutions to tackle these problems. The authors would measure their proposed algorithms against current state-of-the-art. Given the textual content of a paper we propose to mine all the algorithms which are being compared against inside the paper of interest. As our first contribution, we develop an unsupervised mechanism leveraging natural language processing techniques to solve this problem. Recently representation learning using neural networks has been receiving much attention from both industrial and academic communities. Thanks to the advent of powerful GPUs and advanced techniques like dropout and rectified linear units, the interest in neural networks has been revived. The current state-of-the-art solution for the applications in the field of Natural Language Processing (NLP) and Information Retrieval (IR) is powered by neural network based representation learning models. We explore the similar theme of using text content from research papers and propose ways of improving search, retrieval and recommendation in academic citation networks. For example, two papers published between a short timespan would not neccessarily cite each other even if they cater to very similar ideas. As our second contribution we introduce Paper2vec: a novel two step neural network based approach to obtain vector representaions of scientific papers from an academic citation network by exploiting both its textual content and network (graph) properties. Our third contribution is on similar lines, where instead of papers we focus on authors inside the whole network. We specifically explore how we can exploit the content of papers written by an author to aid in possible collaboration prediction and propose Author2vec: a novel joint training approach using neural networks for the same. In the growing body of open access to research papers and journals, the set of algorithms and frameworks proposed in this thesis could act as a first step towards the use of full textual content for the aid of various prediction and recommendation tasks in academic citation networks.
Degenerations of Cn and Calabi–Yau metrics

We construct infinitely many complete Calabi-Yau metrics on $\mathbf{C}^n$ for $n \geq 3$, with maximal volume growth, and singular tangent cones at infinity. In addition we construct Calabi-Yau metrics in neighborhoods of certain isolated singularities whose tangent cones have singular cross section, generalizing work of Hein-Naber.
DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-To-Image Synthesis

In this paper, we focus on generating realistic images from text descriptions. Current methods first generate an initial image with rough shape and color, and then refine the initial image to a high-resolution one. Most existing text-to-image synthesis methods have two main problems. (1) These methods depend heavily on the quality of the initial images. If the initial image is not well initialized, the following processes can hardly refine the image to a satisfactory quality. (2) Each word contributes a different level of importance when depicting different image contents, however, unchanged text representation is used in existing image refinement processes. In this paper, we propose the Dynamic Memory Generative Adversarial Network (DM-GAN) to generate high-quality images. The proposed method introduces a dynamic memory module to refine fuzzy image contents, when the initial images are not well generated. A memory writing gate is designed to select the important text information based on the initial image content, which enables our method to accurately generate images from the text description. We also utilize a response gate to adaptively fuse the information read from the memories and the image features. We evaluate the DM-GAN model on the Caltech-UCSD Birds 200 dataset and the Microsoft Common Objects in Context dataset. Experimental results demonstrate that our DM-GAN model performs favorably against the state-of-the-art approaches.
Luminosity Ratio between [O iv] 25.89 μm Line and Nuclear Continuum 12 μm as a Diagnostic for “Buried” AGNs

We propose new diagnostics that utilize the [O iv] 25.89 μm and nuclear (subarcsecond scale) 12 μm luminosity ratios for identifying whether an active galactic nucleus (AGN) is deeply “buried” in their surrounding materials. Utilizing a sample of 16 absorbed AGNs at redshifts lower than 0.03 in the Swift/BAT catalog observed with Suzaku, we find that AGNs with small scattering fractions (<0.5%) tend to show weaker [O iv]-to-12 μm luminosity ratios than the average of Seyfert 2 galaxies. This suggests that this ratio is a good indicator for identifying buried AGNs. Then, we apply this criterion to 23 local luminous and ultraluminous infrared galaxies (U/LIRGs) in various merger stages hosting AGNs. We find that AGNs in most mid- to late-stage mergers are buried, while those in earlier-stage ones (including nonmerger) are not. This result suggests that the fraction of buried AGNs in U/LIRGs increases as the galaxy–galaxy interaction becomes more significant.
Aiming Low Is Harder - Inductive Proof Rules for Lower Bounds on Weakest Preexpectations in Probabilistic Program Verification

We present a new inductive proof rule for reasoning about lower bounds on weakest preexpectations, i.e., expected values of random variables after execution of a probabilistic loop. Our rule is simple in the sense that the semantics of the loop needs to be applied to a candidate lower bound only a finite number of times in order to verify that the candidate is indeed a lower bound. We do not require finding the limit of a sequence as many previous rules did. Furthermore, and also in contrast to existing rules, we do not require the random variables to be bounded.
Influence of general-relativity effects, dynamical tides, and collisions on planet–planet scattering close to the star

Context. Planet–planet (P–P) scattering is an efficient and robust dynamical mechanism for producing eccentric exoplanets. Coupled to tidal interactions with the central star, this phenomenon can also explain close-in giant planets on circularized and potentially misaligned orbits. Aims. We explore scattering events occurring close to the star and test if they can reproduce the main features of the observed orbital distribution of giant exoplanets on tight orbits. Methods. In our modeling we exploited a numerical integration code based on the Hermite algorithm and including the effects of general relativity, dynamical tides, and two-body collisions. Results. We find that P–P scattering events occurring in systems with three giant planets initially moving on circular orbits close to their star produce a population of planets similar to that presently observed, including eccentric and misaligned close-in planets. The contribution of tides and general relativity is relevant in determining the final outcome of the chaotic phase. Conclusions. Even if two-body collisions dominate the chaotic evolution of three planets in crossing orbits close to their star, the final distribution shows a significant number of planets on eccentric orbits. The highly misaligned close-in giant planets are instead produced by systems where the initial semimajor axis of the inner planet was around 0.2 au or beyond.
Cutoff for the Swendsen–Wang dynamics on the lattice

We study the Swendsen-Wang dynamics for the $q$-state Potts model on the lattice. Introduced as an alternative algorithm of the classical single-site Glauber dynamics, the Swendsen-Wang dynamics is a non-local Markov chain that recolors many vertices at once based on the random-cluster representation of the Potts model. In this work we derive strong enough bounds on the mixing time, proving that the Swendsen-Wang dynamics on the lattice at sufficiently high temperatures exhibits a sharp transition from ``unmixed" to ``well-mixed," which is called the cutoff phenomenon. In particular, we establish that at high enough temperatures the Swendsen-Wang dynamics on the torus $(\mathbb{Z}/n\mathbb{Z})^d$ has cutoff at time $\frac{d}{2} \left( -\log (1-\gamma) \right)^{-1} \log n$, where $\gamma(\beta)$ is the spectral gap of the infinite-volume dynamics.
Temporal and Aspectual Entailment

Inferences regarding “Jane’s arrival in London” from predications such as “Jane is going to London” or “Jane has gone to London” depend on tense and aspect of the predications. Tense determines the temporal location of the predication in the past, present or future of the time of utterance. The aspectual auxiliaries on the other hand specify the internal constituency of the event, i.e. whether the event of “going to London” is completed and whether its consequences hold at that time or not. While tense and aspect are among the most important factors for determining natural language inference, there has been very little work to show whether modern embedding models capture these semantic concepts. In this paper we propose a novel entailment dataset and analyse the ability of contextualised word representations to perform inference on predications across aspectual types and tenses. We show that they encode a substantial amount of information relating to tense and aspect, but fail to consistently model inferences that require reasoning with these semantic properties.
Filling Factors of Sunspots in SODISM Images

The calculated filling factors (FFs) for a feature reflect the fraction of the solar disc covered by that feature, and the assignment of reference synthetic spectra. In this paper, the FFs, specified as a function of radial position on the solar disc, are computed for each image in a tabular form. The filling factor (FF) is an important parameter and is defined as the fraction of area in a pixel covered with the magnetic field, whereas the rest of the area in the pixel is field-free. However, this does not provide extensive information about the experiments conducted on tens or hundreds of such images. This is the first time that filling factors for SODISM images have been catalogued in tabular formation. This paper presents a new method that provides the means to detect sunspots on full-disk solar images recorded by the Solar Diameter Imager and Surface Mapper (SODISM) on the PICARD satellite. The method is a totally automated detection process that achieves a sunspot recognition rate of 97.6%. The number of sunspots detected by this method strongly agrees with the NOAA catalogue. The sunspot areas calculated by this method have a 99% correlation with SOHO over the same period, and thus help to calculate the filling factor for wavelength (W.L.) 607nm.
Towards Intuitive Reasoning in Axiomatic Geometry

Proving lemmas in synthetic geometry is often a time-consuming endeavour since many intermediate lemmas need to be proven before interesting results can be obtained. Improvements in automated theorem provers (ATP) in recent years now mean they can prove many of these intermediate lemmas. The interactive theorem prover Elfe accepts mathematical texts written in fair English and verifies them with the help of ATP. Geometrical texts can thereby easily be formalized in Elfe, leaving only the cornerstones of a proof to be derived by the user. This allows for teaching axiomatic geometry to students without prior experience in formalized mathematics.
Spontaneous Facial Micro-Expression Recognition using 3D Spatiotemporal Convolutional Neural Networks

Facial expression recognition in videos is an active area of research in computer vision. However, fake facial expressions are difficult to be recognized even by humans. On the other hand, facial micro-expressions generally represent the actual emotion of a person, as it is a spontaneous reaction expressed through human face. Despite of a few attempts made for recognizing micro-expressions, still the problem is far from being a solved problem, which is depicted by the poor rate of accuracy shown by the state-of-the-art methods. A few CNN based approaches are found in the literature to recognize micro-facial expressions from still images. Whereas, a spontaneous microexpression video contains multiple frames that have to be processed together to encode both spatial and temporal information. This paper proposes two 3D-CNN methods: MicroExpSTCNN and MicroExpFuseNet, for spontaneous facial micro-expression recognition by exploiting the spatiotemporal information in CNN framework. The MicroExpSTCNN considers the full spatial information, whereas the MicroExpFuseNet is based on the 3D-CNN feature fusion of the eyes and mouth regions. The experiments are performed over CAS(ME)2 and SMIC microb expression databases. The proposed MicroExpSTCNN model outperforms the state-of-the-art methods.
An efficient technique for fractional modified Boussinesq and approximate long wave equations.

In this paper, an efficient technique is employed to study the modified Boussinesq and approximate long wave equations of the Caputo fractional time derivative, namely q-homotopy analysis transform method. These equations are playing a vital rule in describing the properties of shallow water waves through distinct dispersion relation. The convergence analysis and error analysis has been presented in the present investigation for the future scheme. We illustrate two examples to demonstrate the leverage and effectiveness of the proposed scheme, and the error analysis has been discussed to verify the accuracy. The numerical simulation has been conducted to ensure the exactness of the future technique. The obtained numerical and graphical results are divulge, the proposed scheme is computationally very accurate and straightforward to study and find the solution for fractional coupled nonlinear complex phenomena arised in science and technology.
Dynamically optimal treatment allocation using Reinforcement Learning

Devising guidance on how to assign individuals to treatment is an important goal of empirical research. In practice individuals often arrive sequentially, and the planner faces various constraints such as limited budget/capacity, or borrowing constraints, or the need to place people in a queue. For instance, a governmental body may receive a budget outlay at the beginning of an year, and it may need to decide how best to allocate resources within the year to individuals who arrive sequentially. In this and other examples involving inter-temporal trade-offs, previous work on devising optimal policy rules in a static context is either not applicable, or is sub-optimal. Here we show how one can use offline observational data to estimate an optimal policy rule that maximizes ex-ante expected welfare in this dynamic context. We allow the class of policy rules to be restricted for computational, legal or incentive compatibility reasons. The problem is equivalent to one of optimal control under a constrained policy class, and we exploit recent developments in Reinforcement Learning (RL) to propose an algorithm to solve this. The algorithm is easily implementable and computationally efficient, with speedups achieved through multiple RL agents learning in parallel processes. We also characterize the statistical regret from using our estimated policy rule. To do this, we show that a Partial Differential Equation (PDE) characterizes the evolution of the value function under each policy. The data enables us to obtain a sample version of the PDE that provides estimates of these value functions. The estimated policy rule is the one with the maximal estimated value function. Using the theory of viscosity solutions to PDEs we show that the policy regret decays at a $n^{-1/2}$ rate in most examples; this is the same rate as that obtained in the static case.