Miracle-dz/UniFAR-FTUs huggingface dataset:
Industrial transformation in the aftermath of the crisis:an empirical analysis of industrialpolicies in France, Germany, Spain andthe United Kingdom

This paper gauges the forces and structures that shape economic transformation through an analysis of industrial polices in four European economies since the 2008 crisis: France, Germany, Spain and the United Kingdom. We argue that differences across recent European industrial policies respond to variations in national historical and institutional legacies; the characteristics of productive structures and the capabilities of the state. Pathdependency shapes views regarding the acceptable role of the state, although institutional legacies need to be balanced against historical institutional consistency and the intensity of the crisis. The characteristics of industry in terms of size, specialization, and position in the global division of labor affect preferences for framework versus sector-specific policies and the ambitiousness of goals. Finally, the state’s coordination capacity is essential to the design and efficient implementation of interrelated actions across multiple areas whereas financial capacity establishes commitment, signals priorities, and determines the feasibility of forward-looking projects.
Internet and Family

This project aims to evaluate the Internet use profile of Turkish families with a reference to issues which have become easier or widespread through Internet use. Such an approach will also include problems experienced in Internet use and potential solutions to those problems. The current situation of Internet in Turkish families’ lives is evaluated through investigating family members’ perspectives within four subtitles namely, communication, health, safety and ethics, and location. In addition, a section regarding Internet cafes is provided since these locations have been quite popular for children recently.
Nieuwsbrief van de Nederlandse Vereniging voor Theoretische Informatica

On the Design and Implementation of Static Analysis Tools (Thomas Ball) At Microsoft, we now regularly apply a new generation of static analysis tools that can automatically identify serious defects in programs. These tools examine millions of lines of code every day, long before the software is released for general use. With these tools, we catch more defects earlier in the software process, enabling Microsoft to deliver more reliable systems. A number of these tools have been released for general use through Microsoft’s Visual Studio integrated development environment as well as freely available development kits. In this talk I will address the question: ”How does one design and implement a static analysis tool chain to help people effectively address a software reliability problem? ” In particular, I will identify a set of basic techniques that have proven very useful in constructing static analysis tools and have shown their worth through numerous applications. Experience with these techniques suggests we are approaching an exciting time when more people can contribute to the design and implementation of static analysis tools.
The solar wind from a stellar perspective

Context.Due to the effects that they can have on the atmospheres of exoplanets, stellar winds have recently received significant attention in the literature. Alfvén-wave-driven 3D magnetohydrodynamic models, which are increasingly used to predict stellar wind properties, contain unconstrained parameters and rely on low-resolution stellar magnetograms.Aims. In this paper, we explore the effects of the input Alfvén wave energy flux and the surface magnetogram on the wind properties predicted by the Alfvén Wave Solar Model (AWSoM) model for both the solar and stellar winds.Methods. We lowered the resolution of two solar magnetograms during solar cycle maximum and minimum using spherical harmonic decomposition. The Alfvén wave energy was altered based on non-thermal velocities determined from a far ultraviolet spectrum of the solar twin 18 Sco. Additionally, low-resolution magnetograms of three solar analogues, 18 Sco, HD 76151, and HN Peg, were obtained using Zeeman Doppler imaging and used as a proxy for the solar magnetogram. Finally, the simulated wind properties were compared to Advanced Composition Explorer (ACE) observations.Results. AWSoM simulations using well constrained input parameters taken from solar observations can reproduce the observed solar wind mass loss and angular momentum loss rates. The simulated wind velocity, proton density, and ram pressure differ from ACE observations by a factor of approximately two. The resolution of the magnetogram has a small impact on the wind properties and only during cycle maximum. However, variation in Alfvén wave energy influences the wind properties irrespective of the solar cycle activity level. Furthermore, solar wind simulations carried out using the low-resolution magnetogram of the three stars instead of the solar magnetogram could lead to an order of a magnitude difference in the simulated solar wind properties.Conclusions. The choice in Alfvén energy has a stronger influence on the wind output compared to the magnetogram resolution. The influence could be even stronger for stars whose input boundary conditions are not as well constrained as those of the Sun. Unsurprisingly, replacing the solar magnetogram with a stellar magnetogram could lead to completely inaccurate solar wind properties, and should be avoided in solar and stellar wind simulations. Further observational and theoretical work is needed to fully understand the complexity of solar and stellar winds.
Variational Hyper RNN for Sequence Modeling

In this work, we propose a novel probabilistic sequence model that excels at capturing high variability in time series data, both across sequences and within an individual sequence. Our method uses temporal latent variables to capture information about the underlying data pattern and dynamically decodes the latent information into modifications of weights of the base decoder and recurrent model. The efficacy of the proposed method is demonstrated on a range of synthetic and real-world sequential data that exhibit large scale variations, regime shifts, and complex dynamics.
A systematic search for galaxy proto-cluster cores at z ∼ 2

A proto-cluster core is the most massive dark matter halo (DMH) in a given proto-cluster. To reveal the galaxy formation in core regions, we search for proto-cluster cores at z ∼ 2 in ${\sim}1.5\, \mathrm{deg}^{2}$ of the COSMOS field. Using pairs of massive galaxies [log (M*/M⊙) ≥ 11] as tracers of cores, we find 75 candidate cores, among which 54 per cent are estimated to be real. A clustering analysis finds that these cores have an average DMH mass of $2. 6_{-0.8}^{+0.9}\times 10^{13}\, \mathrm{M}_{\odot }$, or $4. 0_{-1.5}^{+1.8}\, \times 10^{13} \, \mathrm{M}_{\odot }$ after contamination correction. The extended Press–Schechter model shows that their descendant mass at z = 0 is consistent with Fornax-like or Virgo-like clusters. Moreover, using the IllustrisTNG simulation, we confirm that pairs of massive galaxies are good tracers of DMHs massive enough to be regarded as proto-cluster cores. We then derive the stellar mass function (SMF) and the quiescent fraction for member galaxies of the 75 candidate cores. We find that the core galaxies have a more top-heavy SMF than field galaxies at the same redshift, showing an excess at log (M*/M⊙) ≳ 10.5. The quiescent fraction, $0. 17_{-0.04}^{+0.04}$ in the mass range 9.0 ≤ log (M*/M⊙) ≤ 11.0, is about three times higher than that of field counterparts, giving an environmental quenching efficiency of $0. 13_{-0.04}^{+0.04}$. These results suggest that stellar mass assembly and quenching are accelerated as early as z ∼ 2 in proto-cluster cores.
Stochastic encoding of graphs in deep learning allows for complex analysis of gender classification in resting-state and task functional brain networks from the UK Biobank

Classification of whole-brain functional connectivity MRI data with convolutional neural networks (CNNs) has shown promise, but the complexity of these models impedes understanding of which aspects of brain activity contribute to classification. While visualization techniques have been developed to interpret CNNs, bias inherent in the method of encoding abstract input data, as well as the natural variance of deep learning models, detract from the accuracy of these techniques. We introduce a stochastic encoding method in an ensemble of CNNs to classify functional connectomes by gender. We applied our method to resting-state and task data from the UK BioBank, using two visualization techniques to measure the salience of three brain networks involved in task- and resting-states, and their interaction. To regress confounding factors such as head motion, age, and intracranial volume, we introduced a multivariate balancing algorithm to ensure equal distributions of such covariates between classes in our data. We achieved a final AUROC of 0.8459. We found that resting-state data classifies more accurately than task data, with the inner salience network playing the most important role of the three networks overall in classification of resting-state data and connections to the central executive network in task data.
Semantic Relatedness for Keyword Disambiguation: Exploiting Different Embeddings

Understanding the meaning of words is crucial for many tasks that involve human-machine interaction. This has been tackled by research in Word Sense Disambiguation (WSD) in the Natural Language Processing (NLP) field. Recently, WSD and many other NLP tasks have taken advantage of embeddings-based representation of words, sentences, and documents. However, when it comes to WSD, most embeddings models suffer from ambiguity as they do not capture the different possible meanings of the words. Even when they do, the list of possible meanings for a word (sense inventory) has to be known in advance at training time to be included in the embeddings space. Unfortunately, there are situations in which such a sense inventory is not known in advance (e.g., an ontology selected at run-time), or it evolves with time and its status diverges from the one at training time. This hampers the use of embeddings models for WSD. Furthermore, traditional WSD techniques do not perform well in situations in which the available linguistic information is very scarce, such as the case of keyword-based queries. In this paper, we propose an approach to keyword disambiguation which grounds on a semantic relatedness between words and senses provided by an external inventory (ontology) that is not known at training time. Building on previous works, we present a semantic relatedness measure that uses word embeddings, and explore different disambiguation algorithms to also exploit both word and sentence representations. Experimental results show that this approach achieves results comparable with the state of the art when applied for WSD, without training for a particular domain.
Estimating Human Teleoperator Posture Using Only a Haptic-Input Device

Ergonomic analysis of human posture plays a vital role in understanding long-term, work-related safety and health. Current analysis is often hindered due to difficulties in estimating human posture. We introduce a new approach to the problem of human posture estimation for teleoperation tasks which relies solely on a haptic-input device for generating observations. We model the human upper body using a redundant, partially observable dynamical system. This allows us to naturally formulate the estimation problem as probabilistic inference and solve the inference problem using a standard particle filter. We show that our approach accurately estimates the posture of different human users without knowing their specific segment lengths. We evaluate our posture estimation approach from a haptic-input device by comparing it with the human posture estimates from a commercial motion capture system. Our results show that the proposed algorithm successfully estimates human posture based only on the trajectory of the haptic-input device stylus. We additionally show that ergonomic risk estimates derived from our posture estimation approach are comparable to those estimates from gold-standard, motion-capture based pose estimates.
Abstractive Snippet Generation

An abstractive snippet is an originally created piece of text to summarize a web page on a search engine results page. Compared to the conventional extractive snippets, which are generated by extracting phrases and sentences verbatim from a web page, abstractive snippets circumvent copyright issues; even more interesting is the fact that they open the door for personalization. Abstractive snippets have been evaluated as equally powerful in terms of user acceptance and expressiveness—but the key question remains: Can abstractive snippets be automatically generated with sufficient quality? This paper introduces a new approach to abstractive snippet generation: We identify the first two large-scale sources for distant supervision, namely anchor contexts and web directories. By mining the entire ClueWeb09 and ClueWeb12 for anchor contexts and by utilizing the DMOZ Open Directory Project, we compile the Webis Abstractive Snippet Corpus 2020, comprising more than 3.5 million triples of the form ⟨query, snippet, document⟩ as training examples, where the snippet is either an anchor context or a web directory description in lieu of a genuine query-biased abstractive snippet of the web document. We propose a bidirectional abstractive snippet generation model and assess the quality of both our corpus and the generated abstractive snippets with standard measures, crowdsourcing, and in comparison to the state of the art. The evaluation shows that our novel data sources along with the proposed model allow for producing usable query-biased abstractive snippets while minimizing text reuse. Code, data, and slides: https://webis.de/publications.html#? q=WWW+2020
Towards scalable bosonic quantum error correction

We review some of the recent efforts in devising and engineering bosonic qubits for superconducting devices, with emphasis on the Gottesman–Kitaev–Preskill (GKP) qubit. We present some new results on decoding repeated GKP error correction using finitely-squeezed GKP ancilla qubits, exhibiting differences with previously studied stochastic error models. We discuss circuit-QED ways to realize CZ gates between GKP qubits and we discuss different scenarios for using GKP and regular qubits as building blocks in a scalable superconducting surface code architecture.
Feedback game on 3-chromatic Eulerian triangulations of surfaces

In this paper, we study the feedback game on $3$-chromatic Eulerian triangulations of surfaces. We prove that the winner of the game on every $3$-chromatic Eulerian triangulation of a surface all of whose vertices have degree $0$ modulo $4$ is always fixed. Moreover, we also study the case of $3$-chromatic Eulerian triangulations of surfaces which have at least two vertices whose degrees are $2$ modulo $4$, and in particular, we determine the winner of the game on a concrete class of such graphs, called an octahedral path.
Efficient exploration of zero-sum stochastic games

We investigate the increasingly important and common game-solving setting where we do not have an explicit description of the game but only oracle access to it through gameplay, such as in financial or military simulations and computer games. During a limited-duration learning phase, the algorithm can control the actions of both players in order to try to learn the game and how to play it well. After that, the algorithm has to produce a strategy that has low exploitability. Our motivation is to quickly learn strategies that have low exploitability in situations where evaluating the payoffs of a queried strategy profile is costly. For the stochastic game setting, we propose using the distribution of state-action value functions induced by a belief distribution over possible environments. We compare the performance of various exploration strategies for this task, including generalizations of Thompson sampling and Bayes-UCB to this new setting. These two consistently outperform other strategies.
Brauer groups of moduli of hyperelliptic curves via cohomological invariants

Abstract Using the theory of cohomological invariants for algebraic stacks, we compute the Brauer group of the moduli stack of hyperelliptic curves ${\mathcal {H}}_g$ over any field of characteristic $0$. In positive characteristic, we compute the part of the Brauer group whose order is prime to the characteristic of the base field.
Defining Smart Contract Defects on Ethereum

Smart contracts are programs running on a blockchain. They are immutable to change, and hence can not be patched for bugs once deployed. Thus it is critical to ensure they are bug-free and well-designed before deployment. A Contract defect is an error, flaw or fault in a smart contract that causes it to produce an incorrect or unexpected result, or to behave in unintended ways. The detection of contract defects is a method to avoid potential bugs and improve the design of existing code. Since smart contracts contain numerous distinctive features, such as the gas system. decentralized, it is important to find smart contract specified defects. To fill this gap, we collected smart-contract-related posts from Ethereum StackExchange, as well as real-world smart contracts. We manually analyzed these posts and contracts; using them to define 20 kinds of contract defects. We categorized them into indicating potential security, availability, performance, maintainability and reusability problems. To validate if practitioners consider these contract as harmful, we created an online survey and received 138 responses from 32 different countries. Feedback showed these contract defects are harmful and removing them would improve the quality and robustness of smart contracts. We manually identified our defined contract defects in 587 real world smart contract and publicly released our dataset. Finally, we summarized 5 impacts caused by contract defects. These help developers better understand the symptoms of the defects and removal priority.
Second generalized Hamming weight of Projective Toric Code over Hypersimplices

The $d$-th hypersimplex of $\mathbb{R}^{s}$ is the convex hull in $\mathbb{R}^{s}$ of all integral points $e_{i_1}+e_{i_2}+\cdots+e_{i_d}$ such that $1 \leq i_{1} <\cdots < i_{d} \leq s$ where $e_{i}$ is the $i$-th unit vector in $\mathbb{R}^{s}$. In [1], the authors have defined projective toric code of $\mathcal{P}$ of degree $d$ denoted by $C_{\mathcal{P}}(d)$ and computed its dimension and minimum distance. In this note, we compute the second generalized Hamming weight of these codes.
Minimal volume entropy of simplicial complexes

This article deals with topological assumptions under which the minimal volume entropy of a closed manifold, and more generally of a finite simplicial complex, vanishes or is positive. In the first part of the article, we present complementing topological conditions expressed in terms of the growth of the fundamental group of the fibers of maps from a given finite simplicial complex to simplicial complexes of lower dimension which ensure that the minimal volume entropy of the simplicial complex either vanishes or is positive. We also give examples of finite simplicial complexes with zero simplicial volume and arbitrarily large minimal volume entropy. In the second part of the article, we present topological assumptions related to the exponential growth of certain subgroups in the fundamental group of a finite simplicial complex and to the topology of the loop space of its classifying space under which the minimal volume entropy is positive. Several examples are presented throughout the text.
Optimisation of Body-ground Contact for Augmenting the Whole-Body Loco-manipulation of Quadruped Robots

Legged robots have great potential to perform complex loco-manipulation tasks, yet it is challenging to keep the robot balanced while it interacts with the environment. In this paper we investigated the use of additional contact points for maximising the robustness of loco-manipulation motions. Specifically, body-ground contact was studied for its ability to enhance robustness and manipulation capabilities of quadrupedal robots. We proposed equipping the robot with prongs: small legs rigidly attached to the body which create body-ground contact at controllable point-contacts. The effect of these prongs on robustness was quantified by computing the Smallest Unrejectable Force (SUF), a measure of robustness related to Feasible Wrench Polytopes. We applied the SUF to evaluate the robustness of the system, and proposed an effective approximation of the SUF that can be computed at near-real-time speed. We developed a hierarchical quadratic programming based whole-body controller that can control stable interaction when the prongs are in contact with the ground. This novel prong concept and complementary control framework were implemented on hardware to validate their effectiveness by showing increased robustness and newly enabled loco-manipulation tasks, such as obstacle clearance and manipulation of a large object.
Hot Gas in Galaxy Halos Traced by Coronal Broad Lyα Absorbers

We explore the possibility to systematically study the extended, hot gaseous halos of low-redshift galaxies with coronal broad Lyα absorbers (CBLAs). These are weak, thermally broadenend H i absorption lines arising from the tiny fraction of neutral hydrogen that resides in the collisionally ionized, million-degree halo gas in these galaxies. Using a semi-analytic approach, we model the spatial density and temperature distribution of hot coronal gas to predict strength, spectral shape, and cross section of CBLAs as a function of galaxy-halo mass and line-of-sight impact parameter. For virial halo masses in the range log , the characteristic logarithmic CBLA H i column densities and Doppler parameters are log N(H i) = 12.4–13.4 and b(H i) = 70–200 km s−1, indicating that CBLAs represent weak, shallow spectral features that are difficult to detect. Yet, the expected number density of CBLAs per unit redshift in the above given mass range is (CBLA) ≈ 3, implying that CBLAs have a substantial absorption cross section. We compare the model predictions with a combined set of UV absorption-line spectra from the Hubble Space Telescope (HST)/Cosmic Origins Spectrograph and HST/Space Telescope Imaging Spectrograph that trace the halos of four low-redshift galaxies. We demonstrate that CBLAs might already have been detected in these spectra, but the complex multi-component structure and the limited signal-to-noise ratio complicate the interpretation of these CBLA candidate systems. Our study suggests that CBLAs represent a very interesting absorber class that potentially will allow us to further explore the hot coronae of galaxies with UV spectral data.