Miracle-dz/UniFAR-FTUs huggingface dataset:
Corrections to the hadron resonance gas from lattice QCD and their effect on fluctuation-ratios at finite density

The hadron resonance gas (HRG) model is often believed to correctly describe the confined phase of QCD. This assumption is the basis of many phenomenological works on QCD thermodynamics and of the analysis of hadron yields in relativistic heavy ion collisions. We use first-principle lattice simulations to calculate corrections to the ideal HRG. Namely, we determine the sub-leading fugacity expansion coefficients of the grand canonical free energy, receiving contributions from processes like kaon-kaon or baryon-baryon scattering. We achieve this goal by performing a two dimensional scan on the imaginary baryon number chemical potential (μB) strangeness chemical potential (μS) plane, where the fugacity expansion coefficients become Fourier coefficients. We carry out a continuum limit estimation of these coefficients by performing lattice simulations with temporal extents of Nτ = 8, 10, 12 using the 4stout-improved staggered action. We then use the truncated fugacity expansion to extrapolate ratios of baryon number and strangeness fluctuations and correlations to finite chemical potentials. Evaluating the fugacity expansion along the crossover line, we reproduce the trend seen in the experimental data on net-proton fluctuations by the STAR collaboration.
Urn models with random multiple drawing and random addition

We consider an urn model with multiple drawing and random time-dependent addition matrix. The model is very general with respect to previous literature: the number of sampled balls at each time-step is random, the addition matrix has general random entries. For the proportion of balls of a given color, we prove almost sure convergence results and fluctuation theorems (through CLTs in the sense of stable convergence and of almost sure conditional convergence, which are stronger than convergence in distribution). Asymptotic confidence intervals are given for the limit proportion, whose distribution is generally unknown.
Adaptive Sampling for Fast Constrained Maximization of Submodular Function

Several large-scale machine learning tasks, such as data summarization, can be approached by maximizing functions that satisfy submodularity. These optimization problems often involve complex side constraints, imposed by the underlying application. In this paper, we develop an algorithm with poly-logarithmic adaptivity for non-monotone submodular maximization under general side constraints. The adaptive complexity of a problem is the minimal number of sequential rounds required to achieve the objective. Our algorithm is suitable to maximize a non-monotone submodular function under a $p$-system side constraint, and it achieves a $(p + O(\sqrt{p}))$-approximation for this problem, after only poly-logarithmic adaptive rounds and polynomial queries to the valuation oracle function. Furthermore, our algorithm achieves a $(p + O(1))$-approximation when the given side constraint is a $p$-extendible system. This algorithm yields an exponential speed-up, with respect to the adaptivity, over any other known constant-factor approximation algorithm for this problem. It also competes with previous known results in terms of the query complexity. We perform various experiments on various real-world applications. We find that, in comparison with commonly used heuristics, our algorithm performs better on these instances.
Well-posedness theory for nonlinear scalar conservation laws on networks

We consider nonlinear scalar conservation laws posed on a network. We define an entropy condition for scalar conservation laws on networks and establish $L^1$ stability, and thus uniqueness, for weak solutions satisfying the entropy condition. We apply standard finite volume methods and show stability and convergence to the unique entropy solution, thus establishing existence of a solution in the process. Both our existence and stability/uniqueness theory is centred around families of stationary states for the equation. In one important case – for monotone fluxes with an upwind difference scheme – we show that the set of (discrete) stationary solutions is indeed sufficiently large to suit our general theory. We demonstrate the method's properties through several numerical experiments.
Fuzzing Symbolic Expressions

Recent years have witnessed a wide array of results in software testing, exploring different approaches and methodologies ranging from fuzzers to symbolic engines, with a full spectrum of instances in between such as concolic execution and hybrid fuzzing. A key ingredient of many of these tools is Satisfiability Modulo Theories (SMT) solvers, which are used to reason over symbolic expressions collected during the analysis. In this paper, we investigate whether techniques borrowed from the fuzzing domain can be applied to check whether symbolic formulas are satisfiable in the context of concolic and hybrid fuzzing engines, providing a viable alternative to classic SMT solving techniques. We devise a new approximate solver, FUZZY-SAT, and show that it is both competitive with and complementary to state-of-the-art solvers such as Z3 with respect to handling queries generated by hybrid fuzzers.
Uncertainty spill-overs: when policy and financial realms overlap

No matter its source, financial- or policy-related, uncertainty can feed onto itself, inflicting the real economic sector, altering expectations and behaviours, and leading to identification challenges in empirical applications. The strong intertwining between policy and financial realms prevailing in Europe, and in Euro Area in particular, might complicate the problem and create amplification mechanisms difficult to pin down. To reveal the complex transmission of country-specific uncertainty shocks in a multi-country setting, and to properly account for cross-country interdependencies, we employ a global VAR specification for which we adapt an identification approach based on magnitude restrictions. Once we separate policy uncertainty from financial uncertainty shocks, we find evidence of important cross-border uncertainty spill-overs. We also uncover a new amplification mechanism for domestic uncertainty shocks, whose true nature becomes more blurred once they cross the national boundaries and spill over to other countries. With respect to ECB policy reactions, we reveal stronger but less persistent responses to financial uncertainty shocks compared to policy uncertainty shocks. This points to ECB adopting a more (passive or) accommodative stance towards the former, but a more pro-active stance towards the latter shocks, possibly as an attempt to tame policy uncertainty spill-overs and prevent the fragmentation of the Euro Area financial markets.
A Survey on Ransomware: Evolution, Taxonomy, and Defense Solutions

In recent years, ransomware has been one of the most notorious malware targeting end-users, governments, and business organizations. It has become a very profitable business for cybercriminals with revenues of millions of dollars, and a very serious threat to organizations with financial losses of billions of dollars. Numerous studies were proposed to address the ransomware threat, including surveys that cover certain aspects of ransomware research. However, no study exists in the literature that gives the complete picture on ransomware and ransomware defense research with respect to the diversity of targeted platforms. Since ransomware is already prevalent in PCs/workstations/desktops/laptops, and is becoming more prevalent in mobile devices, and has already hit IoT/CPS recently, and will likely grow further in the IoT/CPS domain very soon, understanding ransomware and analyzing defense mechanisms with respect to target platforms is becoming more imperative. In order to fill this gap and motivate further research, in this paper, we present a comprehensive survey on ransomware and ransomware defense research with respect to PCs/workstations, mobile devices, and IoT/CPS platforms. Specifically, covering 137 studies over the period of 1990-2020, we give a detailed overview of ransomware evolution, comprehensively analyze the key building blocks of ransomware, present a taxonomy of notable ransomware families, and provide an extensive overview of ransomware defense research (i.e., analysis, detection, and recovery) with respect to platforms of PCs/workstations, mobile devices, and IoT/CPS. Moreover, we derive an extensive list of open issues for future ransomware research. We believe this survey will motivate further research by giving a complete picture on state-of-the-art ransomware research.
Kinetic Analysis of Photochemical Upconversion by Triplet−Triplet Annihilation: Beyond Any Spin Statistical Limit

Upconversion (UC) via triplet−triplet annihilation (TTA) is a promising concept to improve the energy conversion efficiency of solar cells by harvesting photons below the energy threshold. Here, we present a kinetic study of the delayed fluorescence induced by TTA to explore the maximum efficiency of this process. In our model system we find that more than 60% of the triplet molecules that decay by TTA produce emitters in their first excited singlet state, so that the observed TTA effiency exceeds 40% at the point of the highest triplet emitter concentration. This result thoroughly disproves any spin-statistical limitation for the annihilation efficiency and thus has crucial consequences for the applicability of an upconvertor based on TTA, which are discussed.
I Know What You Imported Last Summer: A study of security threats in thePython ecosystem

The popularity of Python has risen rapidly over the past 15 years. It is a major language in some of the most exciting technologies today. This popularity has led to a large ecosystem of third-party packages available via the pip package registry which hosts more than 200,000 packages. These third-party packages can be reused by simply importing the package after installing using package managers like pip. The ease of reuse of third-party software comes with security risks putting millions of users in danger. In this project, we study the ecosystem to analyze this threat. The mature ecosystem of Python has multiple weak spots that we highlight in our project. First, we demonstrate how trivial it is to exploit the Python ecosystem. Then, we systematically analyze dependencies amongst packages, maintainers, and publicly reported security issues. Most attacks are possible only if users install malicious packages. We thus try to analyze and evaluate different methods used by attackers to force incorrect downloads. We quantify your ideas by estimating the potential threat that can be caused by exploiting a popular Python package. We also discuss methods used in the industry to defend against such attacks
Volumetric, Ultrasonic and Transport Properties of Binary Liquid Mixtures Containing Dimethyl Formamide at 303.15 K

Excess volumes (VE), ultrasonic velocities (u), isentropic compressibility (ΔKs) and viscosities (η) for the binary mixtures of dimethyl formamide (DMF) with 1,2-dichlorobenzene, 1,3-dichlorobenzene, 1,2,4-trichlorobenzene, o-chlorotoluene, m-chlorotoluene, p-chlorotoluene, o-nitrotoluene and m-nitrotoluene at 303.15 K were studied. Excess volume data exhibit an inversion in sign for the mixtures of dimethyl formamide with 1,2- and 1,3-dichlorobenzenes and the property is completely positive over the entire composition range for the mixtures of dimethyl formamide with 1,2,4-trichlorobenzene, o-nitrotoluene and m-nitrotoluene. On the other hand, the quantity is negative for the mixtures of dimethyl formamide with chlorotoluenes. Isentropic compressibility (Ks) has been computed for the same systems from precise sound velocity and density data. Further, deviation of isentropic compressibility (ΔKs) from ideal behavior was also calculated. ΔKs values are negative over the entire volume fraction range in all the binary mixtures. The experimental sound velocity data were analysed in terms of Free Length Theory (FLT) and Collision Factor Theory (CFT). The viscosity data were analysed on the basis of corresponding state approach. The measured data were discussed on the basis of intermolecular interactions between unlike molecules.
Complex counterpart of variance in quantum measurements for pre- and postselected systems

The variance of an observable in a pre-selected quantum system, which is always real and nonnegative, appears as an increase in the probe wave packet width in indirect measurements. Extending this framework to preand post-selected systems, we formulate a complex-valued counterpart of the variance called “weak variance. ” In our formulation, the real and imaginary parts of the weak variance appear as changes in the probe wave packet width in the vertical–horizontal and diagonal– antidiagonal directions, respectively, on the quadrature phase plane. Using an optical system, we experimentally demonstrate these changes in the probe wave packet width caused by the real negative and purely imaginary weak variances. Furthermore, we show that the weak variance can be expressed as the variance of the weak-valued probability distribution in preand post-selected systems. These operational and statistical interpretations support the rationality of formulating the weak variance as a complex counterpart of the variance in preand post-selected systems.
Sequential Neural Posterior and Likelihood Approximation

We introduce the sequential neural posterior and likelihood approximation (SNPLA) algorithm. SNPLA is a normalizing flows-based algorithm for inference in implicit models, and therefore is a simulation-based inference method that only requires simulations from a generative model. SNPLA avoids Markov chain Monte Carlo sampling and correction-steps of the parameter proposal function that are introduced in similar methods, but that can be numerically unstable or restrictive. By utilizing the reverse KL divergence, SNPLA manages to learn both the likelihood and the posterior in a sequential manner. Over four experiments, we show that SNPLA performs competitively when utilizing the same number of model simulations as used in other methods, even though the inference problem for SNPLA is more complex due to the joint learning of posterior and likelihood function. Due to utilizing normalizing flows SNPLA generates posterior draws much faster (4 orders of magnitude) than MCMC-based methods.
Transition to turbulence in nonuniform coronal loops driven by torsional Alfvén waves. II. Extended analysis and effect of magnetic twist

It has been shown in a previous work that torsional Alfv\'en waves can drive turbulence in nonuniform coronal loops with a purely axial magnetic field. Here we explore the role of the magnetic twist. We model a coronal loop as a transversely nonuniform straight flux tube, anchored in the photosphere, and embedded in a uniform coronal environment. We consider that the magnetic field is twisted and control the strength of magnetic twist by a free parameter of the model. We excite the longitudinally fundamental mode of standing torsional Alfv\'en waves, whose temporal evolution is obtained by means of high-resolution three-dimensional ideal magnetohydrodynamic numerical simulations. We find that phase mixing of torsional Alfv\'en waves creates velocity shear in the direction perpendicular to the magnetic field lines. The velocity shear eventually triggers the Kelvin-Helmholtz instability (KHi). In weakly twisted magnetic tubes, the KHi is able to grow nonlinearly and, subsequently, turbulence is driven in the coronal loop in a similar manner as in the untwisted case. Provided that magnetic twist remains weak, the effect of magnetic twist is to delay the onset of the KHi and to slow down the development of turbulence. In contrast, magnetic tension can suppress the nonlinear growth of the KHi when magnetic twist is strong enough, even if the KHi has locally been excited by the phase-mixing shear. Thus, turbulence is not generated in strongly twisted loops
Negative correlation of adjacent Busemann increments

We consider i.i. d. last-passage percolation on $\mathbb{Z}^2$ with weights having distribution $F$ and time-constant $g_F$. We provide an explicit condition on the large deviation rate function for independent sums of $F$ that determines when some adjacent Busemann function increments are negatively correlated. As an example, we prove that $\operatorname{Bernoulli}(p)$ weights for $p>p^*$, ($p^* \approx 0.6504$) satisfy this condition. We prove this condition by establishing a direct relationship between the negative correlations of adjacent Busemann increments and the dominance of the time-constant $g_F$ by the function describing the time-constant of last-passage percolation with exponential or geometric weights.
A more accurate view of the Flat Wall Theorem

We introduce a supporting combinatorial framework for the Flat Wall Theorem. In particular, we suggest two variants of the theorem and we introduce a new, more versatile, concept of wall homogeneity as well as the notion of regularity in flat walls. All proposed concepts and results aim at facilitating the use of the irrelevant vertex technique in future algorithmic applications.
Exploiting Spline Models for the Training of Fully Connected Layers in Neural Network

The fully connected (FC) layer, one of the most fundamental modules in artificial neural networks (ANN), is often considered difficult and inefficient to train due to issues including the risk of overfitting caused by its large amount of parameters. Based on previous work studying ANN from linear spline perspectives, we propose a spline-based approach that eases the difficulty of training FC layers. Given some dataset, we first obtain a continuous piece-wise linear (CPWL) fit through spline methods such as multivariate adaptive regression spline (MARS). Next, we construct an ANN model from the linear spline model and continue to train the ANN model on the dataset using gradient descent optimization algorithms. Our experimental results and theoretical analysis show that our approach reduces the computational cost, accelerates the convergence of FC layers, and significantly increases the interpretability of the resulting model (FC layers) compared with standard ANN training with random parameter initialization followed by gradient descent optimizations.
A Little Pretraining Goes a Long Way: A Case Study on Dependency Parsing Task for Low-resource Morphologically Rich Languages

Neural dependency parsing has achieved remarkable performance for many domains and languages. The bottleneck of massive labelled data limits the effectiveness of these approaches for low resource languages. In this work, we focus on dependency parsing for morphological rich languages (MRLs) in a low-resource setting. Although morphological information is essential for the dependency parsing task, the morphological disambiguation and lack of powerful analyzers pose challenges to get this information for MRLs. To address these challenges, we propose simple auxiliary tasks for pretraining. We perform experiments on 10 MRLs in low-resource settings to measure the efficacy of our proposed pretraining method and observe an average absolute gain of 2 points (UAS) and 3.6 points (LAS).
Early estimation of the epidemiological parameters of novel coronavirus disease (COVID-2019) outbreak in Iran: 19 Feb-15 March, 2020

Aim: To estimate the epidemiological parameters related to the Covid-19 outbreak in Iran. Background: Estimating the epidemiological parameters of new public health threat (COVID-19) is essential to support and inform public health decision-making in different communities including Iran. Methods: We established a mathematical model to estimate the epidemiological parameters from 19 Feb to 15 March based on daily COVID-19 confirmed cases in Iran. Then, we estimated the effect of early traffic restriction on our estimation. Results: We estimated the R0 at 2.11 (95% CI, 1.87-2.50) and the infected number at 92,260 (95% CI: 59,263 -152,212) by 15 March. Our estimate for the ascertainment rate was about 1.2% (95% CI: 1.1-1.4). The latent period estimation was 4.24 (95% CI: 2.84-6.65). We observed a decline in our estimate after considering the traffic restriction. Conclusion: Our results suggest that health authorities in Iran must take impactful strategies to control the COVID-19 outbreak to reach R0<1. Therefore, the establishment of complementary, multilateral, and cost-effective measures for the treatment of symptomatic and early diagnosis and isolation of asymptomatic cases/contacts are strongly recommended because of low ascertainment rate and large number of infected cases. We additionally recommend that traffic restriction be combined with other controlling measures.
Chemically bonded silica-polymer composites from linear and branched polyamides in a sol-gel process

A new type of polymer–silica composite based on aramids of either linear or non-linear structure has been prepared successfullyvia the sol–gel process. The linear polyamide chains were prepared by the reaction of a mixture of m-phenylene and p-phenylene diamines and terephthaloyl chloride in dimethylacetamide. The non-linear (branched) polyamide chains (having an increased number of reactive ends) were synthesized using 1,3,5-benzenetricarbonyl chloride and terephthaloyl chloride to increase the mass average functionality of the monomer. A slight excess of acid chloride was added in both cases to produce amide chains with carbonyl chloride end groups, which permitted the chains to be end-capped with aminophenyltrimethoxysilane. Addition of tetramethoxysilane to a solution of the polymer, and its subsequent hydrolysis and condensation produced a silica network phase chemically bonded to the aramid chains. Films thus produced were yellow, and transparent for concentrations of silica of up to 25 mass%. Tensile strengths increased gradually with increasing silica content up to this same concentration, but then decreased significantly. The overall values of the strength were found to be smaller for the non-linear aramid chains, relative to those of the linear, possibly because of the branches interfering with interchain interactions. Nonetheless, the increases in the tensile strength with increasing inorganic network phase were larger for the non-linear polymer, consistent with the goal of increasing the bonding between the organic and inorganic phases by increasing the number of amidophenyltrimethoxysilane chain ends. Increasing these strengths to values greater than those for the linear chains can probably be achieved by having the branched regions only near the ends of the chains. Since these transparent ceramers were found to withstand tensile stresses of the order of 175 MPa and had thermal decomposition temperatures around 460–475 °C, they may be very useful as matrices for fibre-reinforced composites.
The relationship between barchan size and barchan morphology: a case study from Northern Namibia

ABSTRACT Landform allometry has been a topic of inquiry since at least the 1970s. In this study, the presence of allometry is investigated for a barchan dunefield in northern Namibia. Using a combination of traditional morphometric parameters and techniques borrowed from geometric morphometrics it is shown that barchan allometry is present. This allometry is a combination of positive and negative allometry. Barchans show a definite change in mean shape as the size of the dune increases becoming more asymmetric. Differences in horn length, along with dune width, show positive allometry indicating that it changes faster than the shape changes. Barchan bilateral asymmetry and stoss length show negative allometry indicating that changes in these variables lag behind changes in size. Together, these results hint at the possible presence of threshold size differences beyond which distinct shape changes can be observed.