nils-herrmann/clean_scirepeval huggingface dataset:
Climate extreme event attribution using multivariate peaks-over-thresholds modeling and counterfactual theory

Numerical climate models are complex and combine a large number of physical processes. They are key tools in quantifying the relative contribution of potential anthropogenic causes (e.g., the current increase in greenhouse gases) on high impact atmospheric variables like heavy rainfall. These so-called climate extreme event attribution problems are particularly challenging in a multivariate context, that is, when the atmospheric variables are measured on a possibly high-dimensional grid. 
In this paper, we leverage two statistical theories to assess causality in the context of multivariate extreme event attribution. As we consider an event to be extreme when at least one of the components of the vector of interest is large, extreme-value theory justifies, in an asymptotical sense, a multivariate generalized Pareto distribution to model joint extremes. Under this class of distributions, we derive and study probabilities of necessary and sufficient causation as defined by the counterfactual theory of Pearl. To increase causal evidence, we propose a dimension reduction strategy based on the optimal linear projection that maximizes such causation probabilities. Our approach is tested on simulated examples and applied to weekly winter maxima precipitation outputs of the French CNRM from the recent CMIP6 experiment.
FAIRY: A Framework for Understanding Relationships Between Users' Actions and their Social Feeds

Users increasingly rely on social media feeds for consuming daily information. The items in a feed, such as news, questions, songs, etc., usually result from the complex interplay of a user's social contacts, her interests and her actions on the platform. The relationship of the user's own behavior and the received feed is often puzzling, and many users would like to have a clear explanation on why certain items were shown to them. Transparency and explainability are key concerns in the modern world of cognitive overload, filter bubbles, user tracking, and privacy risks. This paper presents FAIRY, a framework that systematically discovers, ranks, and explains relationships between users' actions and items in their social media feeds. We model the user's local neighborhood on the platform as an interaction graph, a form of heterogeneous information network constructed solely from information that is easily accessible to the concerned user. We posit that paths in this interaction graph connecting the user and her feed items can act as pertinent explanations for the user. These paths are scored with a learning-to-rank model that captures relevance and surprisal. User studies on two social platforms demonstrate the practical viability and user benefits of the FAIRY method.
Efficient 3D Reconstruction and Streaming for Group-Scale Multi-client Live Telepresence

Sharing live telepresence experiences for teleconferencing or remote collaboration receives increasing interest with the recent progress in capturing and AR/VR technology. Whereas impressive telepresence systems have been proposed on top of on-the-fly scene capture, data transmission and visualization, these systems are restricted to the immersion of single or up to a low number of users into the respective scenarios. In this paper, we direct our attention on immersing significantly larger groups of people into live-captured scenes as required in education, entertainment or collaboration scenarios. For this purpose, rather than abandoning previous approaches, we present a range of optimizations of the involved reconstruction and streaming components that allow the immersion of a group of more than 24 users within the same scene - which is about a factor of 6 higher than in previous work - without introducing further latency or changing the involved consumer hardware setup. We demonstrate that our optimized system is capable of generating high-quality scene reconstructions as well as providing an immersive viewing experience to a large group of people within these live-captured scenes.
Some properties of a Brauer class

Let $X$ be a smooth proper curve defined over a field $k$. The representability of the relative Picard functor is obstructed by a class $\alpha\in\mathrm{Br}(\mathrm{Pic}_{X/k})$. We show the associated division algebra on $\mathrm{Pic}^0_{X/k}$ has natural involutions. We show the class $\alpha$ splits at some height one points in $\mathrm{Pic}_{X/k}$.
Fast Pricing of Energy Derivatives with Mean-reverting Jump Processes

The law of a mean-reverting (Ornstein-Uhlenbeck) process driven by a compound Poisson with exponential jumps is investigated in the context of the energy derivatives pricing. The said distribution turns out to be related to the self-decomposable gamma laws, and its density and characteristic function are here given in closed-form. Algorithms for the exact simulation of such a process are accordingly derived with the advantage of being significantly faster (at least 30 times) than those available in the literature. They are also extended to more general cases (bilateral exponential jumps, and time-dependent intensity of the Poisson process). These results are finally applied to the pricing of gas storages and swings under jump-diffusion market models, and the apparent computational advantages of the proposed procedures are emphasized
The Hitchhiker's Guide to LDA

Latent Dirichlet Allocation (LDA) model is a famous model in the topic model field, it has been studied for years due to its extensive application value in industry and academia. However, the mathematical derivation of LDA model is challenging and difficult, which makes it difficult for the beginners to learn. To help the beginners in learning LDA, this book analyzes the mathematical derivation of LDA in detail, and it also introduces all the knowledge background to make it easy for beginners to understand. Thus, this book contains the author's unique insights. It should be noted that this book is written in Chinese.
Viterbi Extraction tutorial with Hidden Markov Toolkit

An algorithm used to extract HMM parameters is revisited. Most parts of the extraction process are taken from implemented Hidden Markov Toolkit (HTK) program under name HInit. The algorithm itself shows a few variations compared to another domain of implementations. The HMM model is introduced briefly based on the theory of Discrete Time Markov Chain. We schematically outline the Viterbi method implemented in HTK. Iterative definition of the method which is ready to be implemented in computer programs is reviewed. We also illustrate the method calculation precisely using manual calculation and extensive graphical illustration. The distribution of observation probability used is simply independent Gaussians r.v.s. The purpose of the content is not to justify the performance or accuracy of the method applied in a specific area. This writing merely to describe how the algorithm is performed. The whole content should enlighten the audience the insight of the Viterbi Extraction method used by HTK.
Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media

To what extent user's stance towards a given topic could be inferred? Most of the studies on stance detection have focused on analysing user's posts on a given topic to predict the stance. However, the stance in social media can be inferred from a mixture of signals that might reflect user's beliefs including posts and online interactions. This paper examines various online features of users to detect their stance towards different topics. We compare multiple set of features, including on-topic content, network interactions, user's preferences, and online network connections. Our objective is to understand the online signals that can reveal the users' stance. Experimentation is applied on tweets dataset from the SemEval stance detection task, which covers five topics. Results show that stance of a user can be detected with multiple signals of user's online activity, including their posts on the topic, the network they interact with or follow, the websites they visit, and the content they like. The performance of the stance modelling using different network features are comparable with the state-of-the-art reported model that used textual content only. In addition, combining network and content features leads to the highest reported performance to date on the SemEval dataset with F-measure of 72.49%. We further present an extensive analysis to show how these different set of features can reveal stance. Our findings have distinct privacy implications, where they highlight that stance is strongly embedded in user's online social network that, in principle, individuals can be profiled from their interactions and connections even when they do not post about the topic.
Analysis of Networks via the Sparse $\beta$-Model

Data in the form of networks are increasingly available in a variety of areas, yet statistical models allowing for parameter estimates with desirable statistical properties for sparse networks remain scarce. To address this, we propose the Sparse $\beta$-Model (S$\beta$M), a new network model that interpolates the celebrated Erdős-Renyi model and the $\beta$-model that assigns one different parameter to each node. By a novel reparameterization of the $\beta$-model to distinguish global and local parameters, our S$\beta$M can drastically reduce the dimensionality of the $\beta$-model by requiring some of the local parameters to be zero. We derive the asymptotic distribution of the maximum likelihood estimator of the S$\beta$M when the support of the parameter vector is known. When the support is unknown, we formulate a penalized likelihood approach with the $\ell_0$-penalty. Remarkably, we show via a monotonicity lemma that the seemingly combinatorial computational problem due to the $\ell_0$-penalty can be overcome by assigning nonzero parameters to those nodes with the largest degrees. We further show that a $\beta$-min condition guarantees our method to identify the true model and provide excess risk bounds for the estimated parameters. The estimation procedure enjoys good finite sample properties as shown by simulation studies. The usefulness of the S$\beta$M is further illustrated via the analysis of a microfinance take up example.
Time-periodic solutions of Hamiltonian PDEs using pseudoholomorphic curves.

We prove the existence of a forced time-periodic solution to general nonlinear Hamiltonian PDEs by using pseudoholomorphic curves as in symplectic homology theory. When the nonlinearity is sufficiently regularizing, bounded and time-periodic we prove that for a generic time period a solution exists, thereby complementing the result by Rabinowitz from 1978. In order to extend the pseudoholomorphic curve methods from Floer theory to our infinite-dimensional setting and prove a type of Gromov-Floer compactness, we use ideas from the theory of Diophantine approximations to overcome the small divisor problem.
Completing and Debugging Ontologies: state of the art and challenges

As semantically-enabled applications require high-quality ontologies, developing and maintaining as correct and complete as possible ontologies is an important, although difficult task in ontology engineering. A key step is ontology debugging and completion. In general, there are two steps: detecting defects and repairing defects. In this paper we formalize the repairing step as an abduction problem and situate the state of the art with respect to this framework. We show that there still are many open research problems and show opportunities for further work and advancing the field.
Universal Adversarial Audio Perturbations

We demonstrate the existence of universal adversarial perturbations, which can fool a family of audio classification architectures, for both targeted and untargeted attack scenarios. We propose two methods for finding such perturbations. The first method is based on an iterative, greedy approach that is well-known in computer vision: it aggregates small perturbations to the input so as to push it to the decision boundary. The second method, which is the main contribution of this work, is a novel penalty formulation, which finds targeted and untargeted universal adversarial perturbations. Differently from the greedy approach, the penalty method minimizes an appropriate objective function on a batch of samples. Therefore, it produces more successful attacks when the number of training samples is limited. Moreover, we provide a proof that the proposed penalty method theoretically converges to a solution that corresponds to universal adversarial perturbations. We also demonstrate that it is possible to provide successful attacks using the penalty method when only one sample from the target dataset is available for the attacker. Experimental results on attacking five 1D CNN architectures have shown attack success rates higher than 85.4% and 83.1% for targeted and untargeted attacks, respectively using the proposed penalty method.
Center foliation rigidity for partially hyperbolic toral diffeomorphisms.

We study perturbations of a partially hyperbolic toral automorphism L which is diagonalizable over C and has a dense center foliation. For a small perturbation of L with a smooth center foliation we establish existence of a smooth leaf conjugacy to L. We also show that if a small perturbation of an ergodic irreducible L has smooth center foliation and is bi-Holder conjugate to L, then the conjugacy is smooth. As a corollary, we show that for any symplectic perturbation of such an L any bi-Holder conjugacy must be smooth. For a totally irreducible L with two-dimensional center, we establish a number of equivalent conditions on the perturbation that ensure smooth conjugacy to L.
Dynamic Scale Inference by Entropy Minimization

Given the variety of the visual world there is not one true scale for recognition: objects may appear at drastically different sizes across the visual field. Rather than enumerate variations across filter channels or pyramid levels, dynamic models locally predict scale and adapt receptive fields accordingly. The degree of variation and diversity of inputs makes this a difficult task. Existing methods either learn a feedforward predictor, which is not itself totally immune to the scale variation it is meant to counter, or select scales by a fixed algorithm, which cannot learn from the given task and data. We extend dynamic scale inference from feedforward prediction to iterative optimization for further adaptivity. We propose a novel entropy minimization objective for inference and optimize over task and structure parameters to tune the model to each input. Optimization during inference improves semantic segmentation accuracy and generalizes better to extreme scale variations that cause feedforward dynamic inference to falter.
Hyperluminous starburst gives up its secrets

HATLAS J084933.4 + 021443 was identified as a dusty starburst via its rest-frame far-infrared (far-IR) emission. Multifrequency imaging and spectroscopy revealed a cluster of four dusty galaxies at z = 2.41, covering 80 kpc. Here, we use Atacama Large Millimetre Array to confirm a more distant, fifth protocluster member, and present X-ray and rest-frame optical imaging spectroscopy of the brightest, an unlensed hyperluminous IR galaxy (HyLIRG). The data reveal broad Hα and bright [N ii] lines, and bright X-ray emission, characteristics that betray a Type-1 active galactic nucleus (AGN), strengthening evidence that AGN are ubiquitous amongst HyLIRGs. The accreting black hole is supermassive, Mbh ≈ 2 × 109 M⊙, with little intrinsic absorption, NH ≈ 5 × 1021 cm−2. The X-ray properties suggest the accretion luminosity rivals that of the starburst, yet it is not obvious where this emerges in its panchromatic spectral energy distribution. We outline three scenarios that could give rise to the observed characteristics, and how we might distinguish between them. In the first, we see the AGN through the host galaxy because of the cavity it excavates. In the others, the AGN is not cospatial with the starburst, having been ejected via asymmetric gravitational radiation, or having evolved towards the naked quasar phase in an unseen companion.
Multi Scale Supervised 3D U-Net for Kidney and Tumor Segmentation

U-Net has achieved huge success in various medical image segmentation challenges. Kinds of new architectures with bells and whistles might succeed in certain dataset when employed with optimal hyper-parameter, but their generalization always can't be guaranteed. Here, we focused on the basic U-Net architecture and proposed a multi scale supervised 3D U-Net for the segmentation task in KiTS19 challenge. To enhance the performance, our work can be summarized as three folds: first, we used multi scale supervision in the decoder pathway, which could encourage the network to predict right results from the deep layers; second, with the aim to alleviate the bad effect from the sample imbalance of kidney and tumor, we adopted exponential logarithmic loss; third, a connected-component based post processing method was designed to remove the obviously wrong voxels. In the published KiTS19 training dataset (totally 210 patients), we divided 42 patients to be test dataset and finally obtained DICE scores of 0.969 and 0.805 for the kidney and tumor respectively. In the challenge, we finally achieved the 7th place among 106 teams with the Composite Dice of 0.8961, namely 0.9741 for kidney and 0.8181 for tumor.
Gravitational waves from holographic neutron star mergers

We simulate the merger of binary neutron stars and analyze the spectral properties of their gravitational waveforms. For the stars we construct hybrid equations of state (EoSs) with a standard nuclear matter EoS at low densities, transitioning to a state-of-the-art holographic EoS in the otherwise intractable high density regime. Depending on the transition density the characteristic frequencies in the spectrum produced from the hybrid EoSs are shifted to significantly lower values as compared to the pure nuclear matter EoS. The highest rest-mass density reached outside a possible black hole horizon is approximately $1.1 \cdot 10^{15}$ g/cm$^3$, which for the holographic model is below the density of the deconfined quark matter phase.
Liouville theory, AdS2 string, and three-point functions

This is a write-up of the lectures given in Young Researchers Integrability School 2017. The main goal is to explain the connection between the ODE/IM correspondence and the classical integrability of strings in AdS. As a warm up, we first discuss the classical three-point function of the Liouville theory. Our starting point is the well-known fact that the classical solutions to the Liouville equation can be constructed by solving a certain Schrödinger-like differential equation. We then convert it into a set of functional equations using the idea akin to the ODE/IM correspondence. The classical three-point functions can be computed directly from these functional equations and the result matches with the classical limit of the celebrated DOZZ formula. We then proceed to discuss the semi-classical three-point function of strings in AdS2 and show that one can apply a similar idea by making use of the classical integrability of the string sigma model on AdS2.
Analytic Planetary Transit Light Curves and Derivatives for Stars with Polynomial Limb Darkening

We derive analytic, closed-form solutions for the light curve of a planet transiting a star with a limb-darkening profile that is a polynomial function of the stellar elevation, up to an arbitrary integer order. We provide improved analytic expressions for the uniform, linear, and quadratic limb-darkened cases, as well as novel expressions for higher-order integer powers of limb darkening. The formulae are crafted to be numerically stable over the expected range of usage. We additionally present analytic formulae for the partial derivatives of instantaneous flux with respect to the radius ratio, impact parameter, and limb-darkening coefficients. These expressions are rapid to evaluate and compare quite favorably in speed and accuracy to existing transit light-curve codes. We also use these expressions to numerically compute the first partial derivatives of exposure-time-averaged transit light curves with respect to all model parameters. An additional application is modeling eclipsing binary or eclipsing multiple star systems in cases where the stars may be treated as spherically symmetric. We provide code which implements these formulae in C++, Python, IDL, and Julia, with tests and examples of usage (https://github.com/rodluger/Limbdark.jl).
Turbulence Driven by Stellar Jets, the Possibility and the Efficiency

We investigate the feedback of the stellar jets on the surrounding interstellar gas based on 2D and 3D simulations applying HD and MHD modules of the PLUTO 4.2 code. The main question we address is whether the stellar jet can be considered as a turbulence driver into the interstellar gas. In addition, we investigate the most effective circumstances in which the driven turbulence is larger and can survive for a longer timescale in the ambient gas. We present a case study of different parameter runs including the jet Mach number, the initial jet velocity field, the background magnetic field geometries and the interacting jets. We also study the environmental effects on the jet-gas interaction by considering the non-homogeneous surrounding gas containing the clumps in the model setup. Among different setups, we find (1) a higher jet Mach number, (2) a rotating jet, (3) a jet propagating in a magnetized environment, (4) a jet propagating in a non-homogeneous environment, and (5) the interacting jets produce more fluctuations and random motions in the entrained gas, which can survive for a longer timescale. In addition, we perform the 3D simulations of jet-ambient gas interaction and we find that the amount of (subsonic–supersonic) fluctuation increases compared to the axisymmetric run, and the entrained gas gains higher velocities in a 3D run. In total, we confirm the previous finding that the stellar jets can transfer the turbulence on neighboring regions and are not sufficient drivers of the large-scale supersonic turbulence in molecular clouds.
Skew braces of size pq

We construct all skew braces of size pq (where p > q are primes) by using Byott’s classification of Hopf–Galois extensions of the same degree. For there exists only one skew brace which is the trivial one. When we have skew braces, two of which are of cyclic type (so, contained in Rump’s classification) and 2q of non-abelian type.
Sparse Coding of Shape Trajectories for Facial Expression and Action Recognition

The detection and tracking of human landmarks in video streams has gained in reliability partly due to the availability of affordable RGB-D sensors. The analysis of such time-varying geometric data is playing an important role in the automatic human behavior understanding. However, suitable shape representations as well as their temporal evolution, termed trajectories, often lie to nonlinear manifolds. This puts an additional constraint (i.e., nonlinearity) in using conventional Machine Learning techniques. As a solution, this paper accommodates the well-known Sparse Coding and Dictionary Learning approach to study time-varying shapes on the Kendall shape spaces of 2D and 3D landmarks. We illustrate effective coding of 3D skeletal sequences for action recognition and 2D facial landmark sequences for macro- and micro-expression recognition. To overcome the inherent nonlinearity of the shape spaces, intrinsic and extrinsic solutions were explored. As main results, shape trajectories give rise to more discriminative time-series with suitable computational properties, including sparsity and vector space structure. Extensive experiments conducted on commonly-used datasets demonstrate the competitiveness of the proposed approaches with respect to state-of-the-art.