Miracle-dz/UniFAR-FTUs huggingface dataset:
Journal of Homeland Security and Emergency Management At Risk : Natural Hazards , People ' s Vulnerability , and Disasters

Presently in its second edition, "At Risk" stands as a primary text in risk and vulnerability studies. The authors focus on the political and economic causes of disaster, arguing that vulnerability is at the foundation of risks and recovery practices. Through focusing on natural hazards, such as floods, earthquakes, and volcanoes, "At Risk" demonstrates how social groups with little economic or political power are the most at risk during times of disaster. The author's "Pressure and Release" model for risk and hazard progression is an interesting addition to disaster scholarship.
Crystal structure of 2-[(2-ethyl)phenylamino]pyridine, C13H14N2

Abstract C13H14N2, monoclinic, P121/c1 (no. 14), a = 11.892(6) Å, b = 7.570(7) Å, c = 13.309(7) Å, β = 112.65(3)°, V = 1105.7 Å3, Z = 4, Rgt(F) = 0.055, wRref(F2) = 0.151, T = 193 K.
Pollution Taxes for Monopolistically Competitive Firms

This paper examines ways of taxing monopolistically competitive firms that pollute the environment. There are two sources of welfare loss: production externalities and product selection bias associated with fixed costs. We argue that the optimal firm specific tax is always, strictly speaking, less than the Pigouvian tax and that furthermore, under some circumstances, both Pigouvian and optimal taxes leave the monopolistically competitive industry with too few firms. Under either regime, however, the tax targets the right firms.
The Importance of Mathematics Background and Student Performance in A Property Degree

Abstract This paper investigates the relative importance of a wide range of academic and personal variables that may impact on student performance in a property degree. In particular, the importance of the property students’ mathematics background and prior knowledge is assessed. Using a multi-year data set over 2006–2010, regression analysis (OLS) and quantile regression analysis are used to quantify the marginal learning effects of specific variables, including mathematics background. This issue is assessed at the overall property student performance level ( Grade Point Average (GPA) on completion of property degree) and at the individual property subject level (3 valuation subjects). Mathematics background is seen to be an important determinant of success in the property degree; particularly in the more advanced valuation subjects requiring cashflow analysis. The property education implications are also highlighted.
Comparative analysis of core heat transport of JET high density H-mode plasmas in carbon wall and ITER-like wall

A consistent deterioration of global confinement in H-mode experiments has been observed in JET [] following the replacement of all carbon plasma facing components (PFCs) with an all metal (‘ITER-like’) wall (ILW). This has been correlated to the observed degradation of the pedestal confinement, as lower electron temperature (Te) values are routinely measured at the top of the edge barrier region. A comparative investigation of core heat transport in JET-ILW and JET-CW (carbon wall) discharges has been performed, to assess whether core confinement has also been affected by the wall change. The results presented here have been obtained by analysing a set of discharges consisting of high density JET-ILW H-mode plasmas and comparing them against their counterpart discharges in JET-CW having similar global operational parameters. The set contains 10 baseline (βN=1.5∼2 ? >) discharge-pairs with 2.7 T toroidal magnetic field, 2.5 MA plasma current, and 14 to 17 MW of neutral beam injection (NBI) heating. Based on a Te profile analysis using high resolution Thomson scattering (HRTS) data, the Te profile peaking (i.e. core Te (ρ = 0.3) / edge Te (ρ = 0.7)) is found to be similar, and weakly dependent on edge Te, for both JET-ILW and JET-CW discharges. When ILW discharges are seeded with N2, core and edge Te both increase to maintain a similar peaking factor. The change in core confinement is addressed with interpretative TRANSP simulations. It is found that JET-ILW H-mode plasmas have higher NBI power deposition to electrons and lower NBI power deposition to ions as compared to the JET-CW counterparts. This is an effect of the lower electron temperature at the top of the pedestal. As a result, the core electron energy confinement time is reduced in JET-ILW discharges, but the core ion energy confinement time is not decreased. Overall, the core energy confinement is found to be the same in the JET-ILW discharges compared to the JET-CW counterparts.
Natural linearization for the identification of nonlinear heat transfer laws

A fast algorithm for numerical identification of nonlinear heat transfer laws based on a natural linearization of the corresponding inverse problem is introduced. Its theoretical background is discussed, and the numerical tests show that it is suitable for problems with perturbed data. The proposed approach can be also used for other parameter identification problems, where one wants to recover an unknown nonlinear parameter β(u) from distributed noisy observations of the state u.
MoEL: Mixture of Empathetic Listeners

Previous research on empathetic dialogue systems has mostly focused on generating responses given certain emotions. However, being empathetic not only requires the ability of generating emotional responses, but more importantly, requires the understanding of user emotions and replying appropriately. In this paper, we propose a novel end-to-end approach for modeling empathy in dialogue systems: Mixture of Empathetic Listeners (MoEL). Our model first captures the user emotions and outputs an emotion distribution. Based on this, MoEL will softly combine the output states of the appropriate Listener(s), which are each optimized to react to certain emotions, and generate an empathetic response. Human evaluations on EMPATHETIC-DIALOGUES dataset confirm that MoEL outperforms multitask training baseline in terms of empathy, relevance, and fluency. Furthermore, the case study on generated responses of different Listeners shows high interpretability of our model.
Deep Neural Networks, Generic Universal Interpolation, and Controlled ODEs

A recent paradigm views deep neural networks as discretizations of certain controlled ordinary differential equations, sometimes called neural ordinary differential equations. We make use of this perspective to link expressiveness of deep networks to the notion of controllability of dynamical systems. Using this connection, we study an expressiveness property that we call universal interpolation, and show that it is generic in a certain sense. The universal interpolation property is slightly weaker than universal approximation, and disentangles supervised learning on finite training sets from generalization properties. We also show that universal interpolation holds for certain deep neural networks even if large numbers of parameters are left untrained, and are instead chosen randomly. This lends theoretical support to the observation that training with random initialization can be successful even when most parameters are largely unchanged through the training. Our results also explore what a minimal amount of trainable parameters in neural ordinary differential equations could be without giving up on expressiveness.
Deep High-Resolution Representation Learning for Visual Recognition

High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection. Existing state-of-the-art frameworks first encode the input image as a low-resolution representation through a subnetwork that is formed by connecting high-to-low resolution convolutions in series (e.g., ResNet, VGGNet), and then recover the high-resolution representation from the encoded low-resolution representation. Instead, our proposed network, named as High-Resolution Network (HRNet), maintains high-resolution representations through the whole process. There are two key characteristics: (i) Connect the high-to-low resolution convolution streams in parallel and (ii) repeatedly exchange the information across resolutions. The benefit is that the resulting representation is semantically richer and spatially more precise. We show the superiority of the proposed HRNet in a wide range of applications, including human pose estimation, semantic segmentation, and object detection, suggesting that the HRNet is a stronger backbone for computer vision problems. All the codes are available at https://github.com/HRNet.
Two-dimensional ferromagnetic van der Waals 
CrCl3
 monolayer with enhanced anisotropy and Curie temperature

Among the recently widely studied van der Waals layered magnets $\mathrm{Cr}{X}_{3}$ ($X=\mathrm{Cl}$, Br, I), $\mathrm{CrC}{\mathrm{l}}_{3}$ monolayer (ML) is particularly puzzling: it is shown by experiments to have only an in-plane magnetic easy axis and, furthermore, all previous first-principles calculation results contradict this. Through systematic first-principles calculations, we unveil that the in-plane shape anisotropy that dominates over the weak perpendicular magnetocrystalline anisotropy is responsible for the in-plane magnetic easy axis of $\mathrm{CrC}{\mathrm{l}}_{3}$ ML. To tune the in-plane ferromagnetism of $\mathrm{CrC}{\mathrm{l}}_{3}$ ML into the desirable perpendicular one, we propose substituting Cr with isovalent tungsten (W). We find that $\mathrm{CrWC}{\mathrm{l}}_{6}$ has a strong perpendicular magnetic anisotropy and a high Curie temperature up to 76 K. Our work not only gives insight into understanding the two-dimensional ferromagnetism of van der Waals MLs but also sheds new light on engineering their performances for nanodevices.
Sparse sampling approach to efficient 
ab initio
 calculations at finite temperature

Efficient ab initio calculations of correlated materials at finite temperature require compact representations of the Green's functions both in imaginary time and Matsubara frequency. In this paper, we introduce a general procedure which generates sparse sampling points in time and frequency from compact orthogonal basis representations, such as Chebyshev polynomials and intermediate representation (IR) basis functions. These sampling points accurately resolve the information contained in the Green's function, and efficient transforms between different representations are formulated with minimal loss of information. As a demonstration, we apply the sparse sampling scheme to diagrammatic $GW$ and GF2 calculations of a hydrogen chain, of noble gas atoms and of a silicon crystal.
Blast Waves from Magnetar Flares and Fast Radio Bursts

Magnetars younger than one century are expected to be hyperactive: besides winds powered by rotation, they can generate frequent magnetic flares, which launch powerful blast waves into the wind. The resulting magnetized shocks act as masers producing bright radio emission. This theoretical picture predicts radio bursts with the following properties. (1) GHz radio emission occurs at radii and lasts ms in the observer's time. (2) Induced scattering in the surrounding wind does not suppress the radio burst. (3) The emission has linear polarization set by the magnetar rotation axis. (4) The emission drifts to lower frequencies during the burst, and its duration broadens at lower frequencies. (5) Blast waves in inhomogeneous winds may emit variable radio bursts; periodicity might appear on submillisecond timescales if the magnetar rotates with a ∼1 s period. However, the observed burst structure is likely changed by lensing effects during propagation through the host galaxy. (6) The magnetar bursts should repeat, with rare ultrastrong events (possibly up to ∼1043 erg in radio waves) or more frequent weak bursts. (7) When a repeating magnetic flare strikes the wind bubble in the tail of a previous flare, the radio burst turns into a bright optical burst lasting . Locations of hyperactive magnetars in their host galaxies depend on how they form: magnetars created in supernova explosions will trace star formation regions, and magnetars formed in mergers of compact objects will be offset. The merger magnetars are expected to be particularly hyperactive.
Transmission of quantum information through quantum fields

We investigate the quantum channel consisting of two localized quantum systems that communicate through a scalar quantum field. We choose a scalar field rather than a tensor or vector field, such as the electromagnetic field, in order to isolate the situation where the qubits are carried by the field amplitudes themselves rather than, for example, by encoding qubits in the polarization of photons. We find that suitable protocols for this type of quantum channel require the careful navigation of several constraints, such as the no-cloning principle, the strong Huygens principle and the tendency of field-matter couplings that are short to be entanglement breaking. We non-perturbatively construct a protocol for such a quantum channel that possesses maximal quantum capacity.
The effects of clinically‐derived parametric data uncertainty in patient‐specific coronary simulations with deformable walls

Cardiovascular simulations are increasingly used for noninvasive diagnosis of cardiovascular disease, to guide treatment decisions, and in the design of medical devices. Quantitative assessment of the variability of simulation outputs due to input uncertainty is a key step toward further integration of cardiovascular simulations in the clinical workflow. In this study, we present uncertainty quantification in computational models of the coronary circulation to investigate the effect of uncertain parameters, including coronary pressure waveform, intramyocardial pressure, morphometry exponent, and the vascular wall Young's modulus. We employ a left coronary artery model with deformable vessel walls, simulated via an Arbitrary‐Lagrangian‐Eulerian framework for fluid‐structure interaction, with a prescribed inlet pressure and open‐loop lumped parameter network outlet boundary conditions. Stochastic modeling of the uncertain inputs is determined from intra‐coronary catheterization data or gathered from the literature. Uncertainty propagation is performed using several approaches including Monte Carlo, Quasi Monte Carlo sampling, stochastic collocation, and multi‐wavelet stochastic expansion. Variabilities in the quantities of interest, including branch pressure, flow, wall shear stress, and wall deformation are assessed. We find that uncertainty in inlet pressures and intramyocardial pressures significantly affect all resulting QoIs, while uncertainty in elastic modulus only affects the mechanical response of the vascular wall. Variability in the morphometry exponent used to distribute the total downstream vascular resistance to the single outlets, has little effect on coronary hemodynamics or wall mechanics. Finally, we compare convergence behaviors of statistics of QoIs using several uncertainty propagation methods on three model benchmark problems and the left coronary simulations. From the simulation results, we conclude that the multi‐wavelet stochastic expansion shows superior accuracy and performance against Quasi Monte Carlo and stochastic collocation methods.
The negativity contour: a quasi-local measure of entanglement for mixed states

In this paper, we study the entanglement structure of mixed states in quantum many-body systems using the negativity contour, a local measure of entanglement that determines which real-space degrees of freedom in a subregion are contributing to the logarithmic negativity and with what magnitude. We construct an explicit contour function for Gaussian states using the fermionic partial-transpose. We generalize this contour function to generic many-body systems using a natural combination of derivatives of the logarithmic negativity. Though the latter negativity contour function is not strictly positive for all quantum systems, it is simple to compute and produces reasonable and interesting results. In particular, it rigorously satisfies the positivity condition for all holographic states and those obeying the quasi-particle picture. We apply this formalism to quantum field theories with a Fermi surface, contrasting the entanglement structure of Fermi liquids and holographic (hyperscale violating) non-Fermi liquids. The analysis of non-Fermi liquids show anomalous temperature dependence of the negativity depending on the dynamical critical exponent. We further compute the negativity contour following a quantum quench and discuss how this may clarify certain aspects of thermalization.
Bounds On $(t,r)$ Broadcast Domination of $n$-Dimensional Grids

In this paper, we study a variant of graph domination known as $(t, r)$ broadcast domination, first defined in Blessing, Insko, Johnson, and Mauretour in 2015. In this variant, each broadcast provides $t-d$ reception to each vertex a distance $d < t$ from the broadcast. If $d \ge t$ then no reception is provided. A vertex is considered dominated if it receives $r$ total reception from all broadcasts. Our main results provide some upper and lower bounds on the density of a $(t, r)$ dominating pattern of an infinite grid, as well as methods of computing them. Also, when $r \ge 2$ we describe a family of counterexamples to a generalization of Vizing's Conjecture to $(t,r)$ broadcast domination.
On the Robustness of Unsupervised and Semi-supervised Cross-lingual Word Embedding Learning

Cross-lingual word embeddings are vector representations of words in different languages where words with similar meaning are represented by similar vectors, regardless of the language. Recent developments which construct these embeddings by aligning monolingual spaces have shown that accurate alignments can be obtained with little or no supervision, which usually comes in the form of bilingual dictionaries. However, the focus has been on a particular controlled scenario for evaluation, and there is no strong evidence on how current state-of-the-art systems would fare with noisy text or for language pairs with major linguistic differences. In this paper we present an extensive evaluation over multiple cross-lingual embedding models, analyzing their strengths and limitations with respect to different variables such as target language, training corpora and amount of supervision. Our conclusions put in doubt the view that high-quality cross-lingual embeddings can always be learned without much supervision.
Microstructure-determined Pulsar Dispersion Measures and the Problem of Profile Alignment

Time-aligned profile measurements for two pulsars, combining data from the Arecibo, Puerto Rico, and Pushchino, USSR, observatories over a seven-octave frequency interval between 25 and 5000 MHZ are analyzed along with several new microstructure dispersion values. DMA(A) values of 2.9701 + or {minus} 0.0003, and 4.8470 + or {minus} 0.0003 pc/cu cm for PSR 0950+08 and 1113 + 16, respectively are obtained, with small departures from alignment that appear traceable to changes in a profile shape with frequency. These subtle changes in the profile form are noticeable only by virtue of the high time resolution and broad frequency coverage. It is noted that small low-frequency delays result when the sets of profiles are aligned optimally according to the best available microstructure dispersion values. 19 refs.
Semisimplicial spaces

This is an exposition of homotopical results on the geometric realization of semisimplicial spaces. We then use these to derive basic foundational results about classifying spaces of topological categories, possibly without units. The topics considered include: fibrancy conditions on topological categories; the effect on classifying spaces of freely adjoining units; approximate notions of units; Quillen’s Theorems A and B for non-unital topological categories; the effect on classifying spaces of changing the topology on the space of objects; the GroupCompletion Theorem.
Testing Robustness Against Unforeseen Adversaries

Considerable work on adversarial defense has studied robustness to a ﬁxed, known family of adversarial distortions, most frequently L p -bounded distortions. In reality, the speciﬁc form of attack will rarely be known and adversaries are free to employ distortions outside of any ﬁxed set. The present work advocates measuring robustness against this much broader range of unforeseen attacks —attacks whose precise form is not known when designing a defense. We propose a methodology for evaluating a defense against a diverse range of distortion types together with a summary metric UAR that measures the Unforeseen Attack Robustness against a distortion. We construct novel JPEG, Fog, Gabor, and Snow adversarial attacks to simulate unforeseen adversaries and perform a careful study of adversarial robustness against these and existing distortion types. We ﬁnd that evaluation against existing L p attacks yields highly correlated information that may not generalize to other attacks and identify a set of 4 attacks that yields more diverse information. We further ﬁnd that adversarial training against either one or multiple distortions, including our novel ones, does not confer robustness to unforeseen distortions. These results underscore the need to study robustness against unforeseen distortions and provide a starting point for doing so.
Assessing the Impact of a User-Item Collaborative Attack on Class of Users

Collaborative Filtering (CF) models lie at the core of most recommendation systems due to their state-of-the-art accuracy. They are commonly adopted in e-commerce and online services for their impact on sales volume and/or diversity, and their impact on companies' outcome. However, CF models are only as good as the interaction data they work with. As these models rely on outside sources of information, counterfeit data such as user ratings or reviews can be injected by attackers to manipulate the underlying data and alter the impact of resulting recommendations, thus implementing a so-called shilling attack. While previous works have focused on evaluating shilling attack strategies from a global perspective paying particular attention to the effect of the size of attacks and attacker's knowledge, in this work we explore the effectiveness of shilling attacks under novel aspects. First, we investigate the effect of attack strategies crafted on a target user in order to push the recommendation of a low-ranking item to a higher position, referred to as user-item attack. Second, we evaluate the effectiveness of attacks in altering the impact of different CF models by contemplating the class of the target user, from the perspective of the richness of her profile (i.e., cold v.s. warm user). Finally, similar to previous work we contemplate the size of attack (i.e., the amount of fake profiles injected) in examining their success. The results of experiments on two widely used datasets in business and movie domains, namely Yelp and MovieLens, suggest that warm and cold users exhibit contrasting behaviors in datasets with different characteristics.
Tree builder random walk: Recurrence, transience and ballisticity

The Tree Builder Random Walk is a special random walk that evolves on trees whose size increases with time, randomly and depending upon the walker. After every s steps of the walker, a random number of vertices are added to the tree and attached to the current position of the walker. These processes share similarities with other important classes of markovian and non-markovian random walks presenting a large variety of behaviors according to parameters specifications. We show that for a large and most significant class of tree builder random walks, the process is either null recurrent or transient. If s is odd, the walker is ballistic and thus transient. If s is even, the walker's behavior can be explained from local properties of the growing tree and it can be either null recurrent or it gets trapped on some limited part of the growing tree.