Miracle-dz/UniFAR-FTUs huggingface dataset:
Distant Reading how much distance can we bear?

Close Reading The Close-Reading Self-Test – the framework conditions The Close-Reading Self Test – the comparison category Quick Reading The Quick Reading Self-Test A question of interpretation Advantages of Quick Reading A disadvantage of Quick Reading Distant Reading The great advantage of Distant Reading – the time saving The big disadvantage of Distant Reading – the incorrect recognition Scalable Reading – the solution? How much distance to the text can a literary analysis cope with? Bibliografie In his book “Distant Reading” (Moretti, 2013), which I recently presented here, Franco Moretti deliberately and surely a bit provocatively claims that it would be useless to read more and more. Instead, literary scholars should finally learn the art of not reading. In fact, we now have many digital methods at our disposal with which one can distance oneself from the text, i.e. one does not have to read it oneself, but can use a computer to help. But what inaccuracies must one actually accept if one wants to do Distant Reading? And how can the method be used sensibly? In a self-test, I compared the methods of Close and Distant Reading (here with the help of an “out of the box” named Entity Recognition Tool) with a third variant, casual, not very close reading, let’s call it Quick Reading.
GEOMORPHOLOGY, SOIL TEXTURE AND TREE DENSITY IN A SEASONAL SAVANNA IN EASTERN VENEZUELA GEOMORFOLOGIA, TEXTURA DEL SUELO Y DENSIDAD DE LEÑOSAS EN UNA SABANA ESTACIONAL EN EL ORIENTE DE VENEZUELA

We studied the relationship between geomorphology, tree density, and soil texture in a seasonal savanna from eastern Venezuela. Six landforms were identified and mapped. Using a regular design, we sampled soils at depths of 0-20 and 20-40 cm, and determined textural fractions. Alternatively, using a stratified procedure, we measured tree densities by counting and identifying all woody individuals disregarding their size in 5x5 m plots. Contrary to our expectations, results showed that total tree density was positively linked to sand content that assures better drainage but lower water retention capacity. Moreover, tree density was also linked to the more stable landforms, suggesting that surface stability is more influential on tree population growth than water availability in the topsoil. Although most species are present in all landforms, the results also showed that three species (Curatella americana, Byrsonima coccolobifolia and Casearia sylvestris) were associated to the more stable depositional surfaces with sandy soils and other three (Byrsonima crassifolia, Roupala complicata and Bowdichia virgilioides) were associated to surfaces undergoing active morphogenesis with clayish top soil.
Accuracy Evaluation of EPL and ETAR Algorithms in the Treatment Planning Systems using CIRS Thorax Phantom

Background: It is recommended for each set of radiation data and algorithm that subtle deliberation is done regarding dose calculation accuracy. Knowing the errors in dose calculation for each treatment plan will result in an accurate estimate of the actual dose achieved by the tumor. Objective: This study aims to evaluate the equivalent path length (EPL) and equivalent tissue air ratio (ETAR) algorithms in radiation dose calculation. Material and Methods: In this experimental study, the TEC-DOC 1583 guideline was used. Measurements and calculations were obtained for each algorithm at specific points in thorax CIRS phantom for 6 and 18 MVs and results were compared. Results: In the EPL, calculations were in agreement with measurements for 27 points and differences between them ranged from 0.1% to 10.4% at 6 MV. The calculations were in agreement with measurements for 21 points and differences between them ranged from 0.4% to 13% at 18 MV. In ETAR, calculations were also in consistent with measurements for 21 points, and differences between them ranged from 0.1% to 9% at 6 MV. Moreover, for 18 MV, the calculations were in agreement with measurements for 17 points and differences between them ranged from 0% to 11%. Conclusion: For the EPL algorithm, more dose points were in consistent with acceptance criteria. The errors in the ETAR were 1% to 2% less than the EPL. The greatest calculation error occurs in low-density lung tissue with inhomogeneities or in high-density bone. Errors were larger in shallow depths. The error in higher energy was more than low energy beam.
IFT 6085-Lecture 14 The Numerics of GANs Scribes

Proposition 3 (Prop. A.26 Bertsekas [1]). [Contruction Mapping Theorem] Suppose that g : X → X is a contraction mapping with c ∈ [0, 1) and X is a closed subset ofR. Then: (a) (Existence and Uniqueness of a Fixed Point) The mapping g has a unique fixed point x∗ ∈ X . (b) (Convergence Rate) For every initial vector x ∈ X , the sequence {x} generated by x = g(x) converges to x∗. In particular ||x − x∗|| ≤ c||x − x∗||,∀k ≥ 0.
Metal Removal from Water Discharges by a Constructed Treatment Wetland

A full‐scale constructed wetland treatment system consisting of four pairs of wetland cells (3.2 ha total area) with water flowing through a pair of cells in series prior to discharge was investigated. A retention basin provided stable water flow to the system. Water retention time in the wetland system was approximately 48 hours, and the wetland cells operated at circumneutral pH. Vegetation development within the cells has been excellent. Copper removal efficiency was greater than 75 % from the start‐up of the system, while mercury efficiency improved with maturation of the treatment cells. Sampling of the water course through the wetlands conducted during the fourth year of operation validated continued performance, and assessed the fate of a larger suite of metals present in the water. Copper and mercury removal efficiencies were still very high, both in excess of 80 % removal from the water after passage through the wetland system. Mercury removal continued along the entire water course through the system, while copper was removed almost immediately upon entering the wetland cells. Lead removal from the water by the system was 83 %, zinc removal was 60 %, and nickel was generally unaffected. Organic carbon in the water was also increased by the system and reduced the bioavailability of some metals. Operation and maintenance of the system continued to be minimal, and mainly consisted of checking for growth of the vegetation and free flow of the water through the system. The system was entirely passive, relying on gravity as the power source of water flow. No reportable permit exceedances have been experienced since the wetland began treating an outfall discharge.
Safe and Efficient Model-free Adaptive Control via Bayesian Optimization

Adaptive control approaches yield high-performance controllers when a precise system model or suitable parametrizations of the controller are available. Existing data-driven approaches for adaptive control mostly augment standard model-based methods with additional information about uncertainties in the dynamics or about disturbances. In this work, we propose a purely data-driven, model-free approach for adaptive control. Tuning low-level controllers based solely on system data raises concerns on the underlying algorithm safety and computational performance. Thus, our approach builds on GOOSE, an algorithm for safe and sample-efficient Bayesian optimization. We introduce several computational and algorithmic modifications in GOOSE that enable its practical use on a rotational motion system. We numerically demonstrate for several types of disturbances that our approach is sample efficient, outperforms constrained Bayesian optimization in terms of safety, and achieves the performance optima computed by grid evaluation. We further demonstrate the proposed adaptive control approach experimentally on a rotational motion system.
Tangent infinity-categories and Goodwillie calculus

We make precise the analogy between Goodwillie's calculus of functors in homotopy theory and the differential calculus of smooth manifolds by introducing a higher-categorical framework of which both theories are examples. That framework is an extension to infinity-categories of the tangent categories of Cockett and Cruttwell (introduced originally by Rosick\'y). The basic data of a tangent infinity-category consist of an endofunctor, that plays the role of the tangent bundle construction, together with various natural transformations that mimic structure possessed by the ordinary tangent bundles of smooth manifolds. The role of the tangent bundle functor in Goodwillie calculus is played by Lurie's tangent bundle for infinity-categories, introduced to generalize the cotangent complexes of Andr\'e, Quillen and Illusie. We show that Lurie's construction admits the additional structure maps and satisfies the conditions needed to form a tangent infinity-category which we refer to as the Goodwillie tangent structure. Cockett and Cruttwell (and others) have started to develop various aspects of differential geometry in the abstract context of tangent categories, and we begin to apply those ideas to Goodwillie calculus. For example, we show that the role of Euclidean spaces in the calculus of manifolds is played in Goodwillie calculus by the stable infinity-categories. We also show that Goodwillie's n-excisive functors are the direct analogues of n-jets of smooth maps between manifolds; to state that connection precisely, we develop a notion of tangent (infinity,2)-category and show that Goodwillie calculus is best understood in that context.
Learning Abstract Task Representations

A proper form of data characterization can guide the process of learning-algorithm selection and model-performance estimation. The field of meta-learning has provided a rich body of work describing effective forms of data characterization using different families of meta-features (statistical, model-based, information-theoretic, topological, etc.). In this paper, we start with the abundant set of existing meta-features and propose a method to induce new abstract meta-features as latent variables in a deep neural network. We discuss the pitfalls of using traditional meta-features directly and argue for the importance of learning high-level task properties. We demonstrate our methodology using a deep neural network as a feature extractor. We demonstrate that 1) induced meta-models mapping abstract meta-features to generalization performance outperform other methods by ~18% on average, and 2) abstract meta-features attain high feature-relevance scores.
Electric dipole moment of the tau lepton revisited

We reconsider the issue of the search for a nonzero electric dipole form factor (EDM) d τ ð s Þ using optimal observables in τ þ τ − production by e þ e − collisions in the center-of-mass energy range from the τ -pair threshold to about ﬃﬃﬃ s p ∼ 15 GeV. We discuss the general formalism of optimal observables and apply it to two CP -odd observables that are sensitive to the real and imaginary part of d τ ð s Þ , respectively. We compute the expectation values and covariances of these optimal CP observables for τ -pair production at ﬃﬃﬃ s p ¼ 10 . 58 GeV with subsequent decays of τ (cid:2) into major leptonic or semihadronic modes. For the τ decays to two pions and three charged pions we take the full kinematic information of the hadronic system into account. Assuming that the Belle II experiment at the KEKB accelerator will eventually analyze data corresponding to an integrated luminosity of 50 ab − 1 and applying acceptance cuts on the final-state pions, we find that 1 s.d. sensitivities δ Re d τ ¼ 6 . 8 × 10 − 20 e cm and δ Im d τ ¼ 4 . 0 × 10 − 20 e cm can be obtained with events where both τ ’ s decay semihadronically. We consider also the ideal case that no cuts on the final-state particles are applied. With 50 ab − 1 at ﬃﬃﬃ s p ¼ 10 . 58 GeV corresponding to 4 . 5 × 10 10 τ þ τ − events we find the 1 s.d. sensitivities δ Re d τ ¼ 5 . 8 × 10 − 20 e cm and δ Im d τ ¼ 3 . 2 × 10 − 20 e cm, again for events where both τ leptons decay semihadronically. Furthermore, we analyze the potential magnitude of the τ EDM form factor in the type-II two-Higgs doublet extension and in two scalar leptoquark extensions of the Standard Model, taking into account phenomenological constraints.
Improved Signed Distance Function for 2D Real-time SLAM and Accurate Localization

Accurate mapping and localization are very important for many industrial robotics applications. In this paper, we propose an improved Signed Distance Function (SDF) for both 2D SLAM and pure localization to improve the accuracy of mapping and localization. To achieve this goal, firstly we improved the back-end mapping to build a more accurate SDF map by extending the update range and building free space, etc. Secondly, to get more accurate pose estimation for the front-end, we proposed a new iterative registration method to align the current scan to the SDF submap by removing random outliers of laser scanners. Thirdly, we merged all the SDF submaps to produce an integrated SDF map for highly accurate pure localization. Experimental results show that based on the merged SDF map, a localization accuracy of a few millimeters (5mm) can be achieved globally within the map. We believe that this method is important for mobile robots working in scenarios where high localization accuracy matters.
Sub-damped Lyman α systems in the XQ-100 survey – II. Chemical evolution at 2.4 ≤ z ≤ 4.3

We present the measured gas-phase metal column densities in 155 sub-damped Ly α systems (subDLAs) with the aim to investigate the contribution of subDLAs to the chemical evolution of the Universe. The sample was identified within the absorber-blind XQ-100 quasar spectroscopic survey over the redshift range 2.4 ≤ zabs ≤ 4.3. Using all available column densities of the ionic species investigated (mainly C iv, Si ii, Mg ii, Si iv, Al ii, Fe ii, C ii, and O i; in order of decreasing detection frequency), we estimate the ionization-corrected gas-phase metallicity of each system using Markov chain Monte Carlo techniques to explore a large grid of cloudy ionization models. Without accounting for ionization and dust depletion effects, we find that the H i-weighted gas-phase metallicity evolution of subDLAs is consistent with damped Ly α systems (DLAs). When ionization corrections are included, subDLAs are systematically more metal poor than DLAs (between ≈0.5σ and ≈3σ significance) by up to ≈1.0 dex over the redshift range 3 ≤ zabs ≤ 4.3. The correlation of gas phase [Si/Fe] with metallicity in subDLAs appears to be consistent with that of DLAs, suggesting that the two classes of absorbers have a similar relative dust depletion pattern. As previously seen for Lyman limit systems, the gas phase [C/O] in subDLAs remains constantly solar for all metallicities indicating that both subDLAs and Lyman limit systems could trace carbon-rich ejecta, potentially in circumgalactic environments.
On the thermodynamics of the Hayward black hole

In light of the growing interest in the Hayward black hole solution, a detailed study on the corresponding lapse function and its roots is presented. The lapse function is expressed in terms of the classical Schwarzschild radius r s and the Hayward’s parameter l. Both of these quantities are used as thermodynamic variables to find related thermodynamic quantities. In this context, the variable l is associated with a canonical conjugate variable FH , and a free energy Ξ. Moreover, a second order phase transition is found to appears at l ≈ 0.333r s.
The effects of q-statistics on black holes and cosmology

Considering Verlinde’s entropic gravity proposal, we focus the effects of fermionic q deformation on Einstein field equations and Friedmann equations. For this purpose, we represent the high temperature thermodynamical properties of the deformed fermion gas model in two-dimensional space. To describe the behavior of the quantum black holes, deformed Einstein field equations derive with the help of deformed entropy function. Moreover, deformed Friedmann equations investigate by using Friedmann-Robertson-Walker (FRW) metric. We also present the effective energy density, the effective pressure, and the effective equation of state for the dark energy. Lastly, we derive an analytical expression of the effective density parameter of dark energy.
A note on Reidemeister Torsion of G-Anosov Representations

This article considers G-Anosov representations of a fixed closed oriented Riemann surface Σ of genus at least 2. Here, G is the Lie group PSp(2n,R), PSO(n, n) or PSO(n, n + 1). It proves that Reidemeister torsion (R-torsion) associated to Σ with coefficients in the adjoint bundle representations of such representations is well-defined. Moreover, by using symplectic chain complex method, it establishes a novel formula for R-torsion of such representations in terms of the Atiyah-Bott-Goldman symplectic form corresponding to the Lie group G. Furtermore, it applies the results to Hitchin components, in particular, Teichmüller space.
Neural-based Modeling for Performance Tuning of Spark Data Analytics

Cloud data analytics has become an integral part of enterprise business operations for data-driven insight discovery. Performance modeling of cloud data analytics is crucial for performance tuning and other critical operations in the cloud. Traditional modeling techniques fail to adapt to the high degree of diversity in workloads and system behaviors in this domain. In this paper, we bring recent Deep Learning techniques to bear on the process of automated performance modeling of cloud data analytics, with a focus on Spark data analytics as representative workloads. At the core of our work is the notion of learning workload embeddings (with a set of desired properties) to represent fundamental computational characteristics of different jobs, which enable performance prediction when used together with job configurations that control resource allocation and other system knobs. Our work provides an in-depth study of different modeling choices that suit our requirements. Results of extensive experiments reveal the strengths and limitations of different modeling methods, as well as superior performance of our best performing method over a state-of-the-art modeling tool for cloud analytics.
How baryons can significantly bias cluster count cosmology

We quantify two main pathways through which baryonic physics biases cluster count cosmology. We create mock cluster samples that reproduce the baryon content inferred from X-ray observations. We link clusters to their counterparts in a dark matter-only universe, whose abundances can be predicted robustly, by assuming the dark matter density profile is not significantly affected by baryons. We derive weak lensing halo masses and infer the best-fitting cosmological parameters Ωm, S8 = σ8(Ωm/0.3)0.2, and w0 from the mock cluster sample. We find that because of the need to accommodate the change in the density profile due to the ejection of baryons, weak lensing mass calibrations are only unbiased if the concentration is left free when fitting the reduced shear with NFW profiles. However, even unbiased total mass estimates give rise to biased cosmological parameters if the measured mass functions are compared with predictions from dark matter-only simulations. This bias dominates for haloes with $m_\mathrm{500c} \lt 10^{14.5} \, \rm h^{-1} \, \mathrm{M_\odot }$. For a stage IV-like cluster survey without mass estimation uncertainties, an area $\approx 15\,000 \, \mathrm{deg^2}$ and a constant mass cut of $m_\mathrm{200m,min} = 10^{14} \,\rm h^{-1} \, \mathrm{M_\odot }$, the biases are $-11 \pm 1 \, \mathrm{per\, cent}$ in Ωm, $-3.29 \pm 0.04 \, \mathrm{per\, cent}$ in S8, and $9 \pm 1.5 \, \mathrm{per\, cent}$ in w0. The statistical significance of the baryonic bias depends on how accurately the actual uncertainty on individual cluster mass estimates is known. We suggest that rather than the total halo mass, the (re-scaled) dark matter mass inferred from the combination of weak lensing and observations of the hot gas, should be used for cluster count cosmology.
The role of the nominal iron content in the structural, compositional and physical properties of BaFe$_{2+{\delta}}$S$_3$

M. L. Amigó, Q. Stahl, A. Maljuk, A. U. B. Wolter, C. Hess, ∗ J. Geck, 3 S. Wurmehl, S. Seiro, and B. Büchner 2, 3 Leibniz IFW Dresden, Helmholtzstr. 20, 01069 Dresden, Germany Institut für Festkörperund Materialphysik, Technische Universität Dresden, 01069 Dresden, Germany Würzburg-Dresden Cluster of Excellence ct.qmat, Technische Universität Dresden, 01062 Dresden, Germany (Dated: January 21, 2021)
Learning to Augment for Data-Scarce Domain BERT Knowledge Distillation

Despite pre-trained language models such as BERT have achieved appealing performance in a wide range of Natural Language Processing (NLP) tasks, they are computationally expensive to be deployed in real-time applications. A typical method is to adopt knowledge distillation to compress these large pre-trained models (teacher models) to small student models. However, for a target domain with scarce training data, the teacher can hardly pass useful knowledge to the student, which yields performance degradation for the student models. To tackle this problem, we propose a method to learn to augment data for BERT Knowledge Distillation in target domains with scarce labeled data, by learning a cross-domain manipulation scheme that automatically augments the target domain with the help of resource-rich source domains. Specifically, the proposed method generates samples acquired from a stationary distribution near the target data and adopts a reinforced controller to automatically refine the augmentation strategy according to the performance of the student. Extensive experiments demonstrate that the proposed method significantly outperforms state-of-the-art baselines on different NLP tasks, and for the data-scarce domains, the compressed student models even perform better than the original large teacher model, with much fewer parameters (only ~13.3%) when only a few labeled examples available.
Primordial black holes in the excursion set theory

We study primordial black holes (PBHs) formation in the excursion set theory (EST) in a vast range of PBHs masses with and without confirmed constraints on their abundance. In this work, a new concept of the first touch in the EST is introduced for PBHs formation which takes into account the earlier horizon reentry of smaller masses. Our study shows that in the EST, it is possible to produce PBHs in different mass ranges which could make up all dark matter. We also show that in a broad blue-tilted power spectrum, the production of PBHs is dominated towards a smaller mass. Our analysis put upper limit ∼ 0.1 on the amplitude of the curvature power spectrum at length scales relevant for PBHs formation. ∗erfani@iasbs.ac. ir †baghram@sharif.edu 1 ar X iv :2 10 1. 07 81 2v 1 [ as tr oph . C O ] 1 9 Ja n 20 21