Miracle-dz/UniFAR-FTUs huggingface dataset:
A coupling strategy based on anisotropic mesh adaptation for solving two-fuid fl ows

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. A coupling strategy based on anisotropic mesh adaptation for solving twofuid fl ows Thi Thu Cuc Bui, Pascal Frey, Bertrand Maury
Complementation in Japhug Gyalrong

This article provides a detailed survey of complement clauses and complementation strategies in Japhug. It shows the bewildering diversity of constructions attested in these languages, which are largely unpredictable and need to be specified for each complement-taking verb. Special focus is given to typologically unusual constructions, in particular Hybrid Indirect Speech.
The detection and parameter estimation of binary black hole mergers

In this dissertation we study gravitational-wave data analysis techniques for binary neutron star and black hole mergers. During its first observing run, the Advanced Laser Interferometer Gravitational-wave Observatory (Advanced LIGO) reported the first, direct observations of gravitational waves from two binary black hole mergers. We present the results from the search for binary black hole mergers which unambiguously detected the binary black hole mergers. We determine the effect of calibration errors on the detection statistic of the search. Since the search is not designed to precisely measure the astrophysical parameters of the binary neutron star and black hole mergers, we use Bayesian methods to develop a new parameter estimation analysis. We demonstrate the performance of the analysis on the binary black hole mergers detected during Advanced LIGO’s first observing run. We use the parameter estimation analysis to assess the ability of gravitational-wave observatories to observe a gap in the black hole mass distribution between 52 M and 133 M due to pair-instability supernovae. Finally, we use simulated signals added to the Advanced LIGO detectors to validate the search and parameter estimation analyses used to publish the detection of the astrophysical events. THE DETECTION AND PARAMETER ESTIMATION OF BINARY BLACK HOLE MERGERS
Heat transfer in fully developed turbulent channel flow with streamwise traveling wave-like wall deformation

The dissimilarity between the momentum and heat transfer due to streamwise traveling wave-like wall deformation in turbulent channel flows is investigated through direct numerical simulations. The flow rate is kept constant, and the bulk Reynolds number is Re b = 5600. A constant temperature difference condition is imposed on the channel walls. The parametric study shows that the heat transfer is enhanced when the wave travels in the upstream direction. The maximum analogy factor is found to be 1.13, i.e., 13% enhancement of heat transfer under a given pressure gradient, when the wall deformation amplitude is large and the wall deformation period is short. An analysis using the identity equations for the drag and the heat transfer with a three component decomposition reveals that the random component plays an important role in the enhancement of the heat transfer.
Two new end to end verifiable voting schemes based on mixnet based helios architecture

Fair conduct of elections are essential for smooth existence of democratic societies. Voting systems are the method and infrastructure we use to conduct elections. Paper ballot based voting schemes, electronic voting machines based schemes, etc., are the some of the traditional voting schemes. These schemes full fills most of the basic requirements, and simplicity of these schemes makes them attractive even today. But there have a major drawback for these schemes, that is, for a voter it is nearly impossible to verify the correctness of election results. Voters left with no choice other than trusting on election administrators and party representatives for the correctness of election result. To address this issue, researchers come up with end to end verifiable voting schemes, where voter can verify that her vote is cast-as-intended, recorded-ascast and counted-as-recorded, and anybody can verify correctness of each and every step, all the while preserving voter privacy. From the last three decades, researchers have proposed many such schemes, many of them make use of mixnet for anonymization. Pr̂et á voter, Helios are the example for such mixnet based voting schemes. Here the mixnet work as a black box, and used for removing the mapping between voters and the encrypted votes. In the voting schemes case, mixnet receives a set of encrypted votes as input and output another set of ciphertexts. This makes proof of integrity is essential, to ensure that mixnet haven’t added or removed any vote ciphertext. In this dissertation, we propose two new schemes, both of them follows same structure of Helios but facilitate new ways for proof of mixnet integrity. The idea of first one is based on adding tweak values in ElGamal ciphertext. And the second one make use of dummy votes to provide proof of mixnet integrity. While the tweak based scheme is only applicable to ElGamal encryption scheme based mixnets, the dummy vote based idea can be extended for proof of integrity in any other mixnet based schemes.
Overleaf Example

Recent methods for embodied instruction following are typically trained end-toend using imitation learning. This requires the use of expert trajectories and lowlevel language instructions. Such approaches assume learned hidden states will simultaneously integrate semantics from the language and vision to perform state tracking, spatial memory, exploration, and long-term planning. In contrast, we propose a modular method with structured representations that (1) builds a semantic map of the scene, and (2) performs exploration with a semantic search policy, to achieve the natural language goal. Our modular method achieves SOTA performance (24.46%) with a substantial (8.17 % absolute) gap from previous work while using less data by eschewing both expert trajectories and low-level instructions. Leveraging low-level language, however, can further increase our performance (26.49%). 1 Our findings suggest that an explicit spatial memory and a semantic search policy can provide a stronger and more general representation for state-tracking and guidance, even in the absence of expert trajectories or lowlevel instructions.2
fastball: A fast algorithm to sample bipartite graphs with ﬁxed degree sequences

Many applications require randomly sampling bipartite graphs with ﬁxed degrees, or randomly sampling incidence matrices with ﬁxed row and column sums. Although several algorithms to perform this sampling have been proposed, the “curveball” algorithm is the most eﬃcient, running in O ( n log n ) time, and has been proven to sample uniformly at random. In this paper, we introduce the “fastball” algorithm, which adopts a similar approach but runs in O ( n ) time. We show that fastball allows the random sampling of large bipartite graphs with ﬁxed degrees roughly ﬁve times faster than curveball.
The influence of velocity shear on magnetic buoyancy instabilities

Most dynamo models agree that large-scale, weak poloidal magnetic field is wound up into strong toroidal field through differential rotation. Therefore, the strong radial gradients of angular velocity present in the solar tachocline are extremely likely to play a crucial role in the solar dynamo. Motivated by this, we focus on the instability mechanism thought to be responsible for the break up of a horizontal field in a stably stratified layer, namely magnetic buoyancy instability, and the effect a velocity shear has upon said instability. To study this interaction, we derive a new set of equations incorporating velocity shear and magnetic buoyancy into the Boussinesq approximation. These equations not only provide us with the ability to study the effects of a velocity shear on magnetic buoyancy instability, but also allow us to study magnetic buoyancy instability in the presence of a magnetic field varying on a short O(d) length scale, compared to the equations of Spiegel & Weiss (1982), which are restricted to field variations on a longer O(Hp) scale. Stability criteria for this new system is obtained through a linear analysis on the ideal (diffusionless) system. Motivated by the work of Mizerski et al. (2013) we use the newly derived equations to study the short-wavelength linear magnetic buoyancy instability. We first study this problem in the absence of a velocity shear and dissipation, deriving asymptotic results analogous to Mizerski et al. The governing set of equations are then solved numerically to verify the asymptotic results. A velocity shear is then added into the analysis; we derive new asymptotic results and use them to comment on the influence the velocity shear has on the instability. Upholding the short-wavelength limit, we individually introduce each diffusive parameter into the analysis, observing the role each has on the instability. Finally, we solve the newly derived system of linearised equations numerically, whilst including all diffusive parameters. We comment on the role of the diffusive parameters and investigate how they influence the instability.
Continuous-time quantum walks: simulation and application

Quantum walks, the quantum analogy of the classical random walk, exhibit markedly different properties compared to their classical counterpart; taking advantage of quantum superposition and quantum correlations, they propagate across graph structures quadratically faster. They have since become an important tool in the field of quantum computation and quantum information theory, finding use in modelling complex quantum dynamical systems and providing a universal method of quantum computation. In this thesis, we consider continuous-time quantum walks across two areas of interest: efficient numerical simulation of the continuous-time quantum walk, and applications of continuous-time quantum walks to graph isomorphism and network centrality. In Part I, we introduce the classical and quantum walks, as well as key graph theory concepts that will underpin later results. Then, in Part II, we begin by considering methods of efficiently simulating the matrix exponential using distributed memory computation. The results of our analysis leads to the presentation of the software package pyCTQW, which allows us to simulate multi-particle continuous-time quantum walks on arbitrary graphs more efficiently through the use of high performance computation. We follow with an analysis of fermionic continuous-time quantum walks, and show that by considering the set of symmetrized vertex states, the multi-particle fermionic quantum walk can be implemented by considering a single-particle quantum walk on a much reduced-vertex weighted graph. We then consider quantum walk-based algorithms and applications in Part III, beginning with graph isomorphism; using the Bose-Mesner algebra, we verify that the single-particle continuous-time quantum walk cannot distinguish non-isomorphic strongly regular graphs. Further, we show that modifying the quantum walk to include a vertex-dependent phase factor does not provide any additional distinguishing power. Finally, we turn our attention to network centrality algorithms, and propose a centrality measure based on the continuous-time quantum walk. Through a statistical analysis on randomly generated graphs, we show that our proposed measure is highly correlated with the classical eigenvector centrality, and suggest that it provides an extension of the eigenvector centrality to the quantum realm. We then detail an experimental implementation of this centrality scheme on a 4-vertex star graph; to our knowledge, the first physical quantum centrality implementation. Next, we extend the continuous-time quantum walk to select directed networks via the use of the PT-symmetry framework, without any increase in the state space or resources required — unlike approaches such as the quantum stochastic walk or the Szegedy quantum walk. Lastly, we use this framework to augment our quantum centrality scheme to include directed networks, and show via statistical analysis that the centrality scheme continues to correlate well with classical measures.
A dynamically bi-orthogonal method for time-dependent stochastic partial differential equations I: Derivation and algorithms

We propose a dynamically bi-orthogonal method (DyBO) to solve time dependent stochastic partial differential equations (SPDEs). The objective of our method is to exploit some intrinsic sparse structure in the stochastic solution by constructing the sparsest representation of the stochastic solution via a bi-orthogonal basis. It is well-known that the Karhunen–Loeve expansion (KLE) minimizes the total mean squared error and gives the sparsest representation of stochastic solutions. However, the computation of the KL expansion could be quite expensive since we need to form a covariance matrix and solve a large-scale eigenvalue problem. The main contribution of this paper is that we derive an equivalent system that governs the evolution of the spatial and stochastic basis in the KL expansion. Unlike other reduced model methods, our method constructs the reduced basis on-the-fly without the need to form the covariance matrix or to compute its eigendecomposition. In the first part of our paper, we introduce the derivation of the dynamically bi-orthogonal formulation for SPDEs, discuss several theoretical issues, such as the dynamic bi-orthogonality preservation and some preliminary error analysis of the DyBO method. We also give some numerical implementation details of the DyBO methods, including the representation of stochastic basis and techniques to deal with eigenvalue crossing. In the second part of our paper [11], we will present an adaptive strategy to dynamically remove or add modes, perform a detailed complexity analysis, and discuss various generalizations of this approach. An extensive range of numerical experiments will be provided in both parts to demonstrate the effectiveness of the DyBO method. 2013 Elsevier Inc. All rights reserved.
Enhancing synchronizability of dynamical networks using the connection graph stability method

Dynamical networks with diffusive couplings are investigated from the point of view of synchronizability. Arbitrary connection graphs are admitted but the coupling is symmetric. Networks with equal interaction coefficients for all edges of the interaction graph are compared with networks where the interaction coefficients vary from edge to edge according to the bounds for global synchronization obtained by the connection graph stability method. Synchronizability is tested numerically by establishing the time to decrease the synchronization error from 1 to 10−5 in the case of networks of identical Lorenz or Rössler systems. Synchronizability from the point of view of phase synchronization is also tested for networks of non‐identical Lorenz or Rössler systems. In this case the phase‐order parameters are compared, as a function of the mean interaction strength. Throughout, as network structures, scale‐free and Watts–Strogatz small‐world networks are used. Copyright © 2007 John Wiley & Sons, Ltd.
Overlay Networks: A Scalable Alternative for P2P

Overlay networks create a structured virtual topology above the basic transport protocol level that facilitates deterministic search and guarantees convergence. Overlay networks are evolving into a critical component for self-organizing systems. Here we outline the differences between flooding-style and overlay networks, and offer specific examples of how researchers are applying the latter to problems requiring high-speed, self-organizing network topologies.
Simultaneous Localization and Mapping - Exactly Sparse Information Filters

Simultaneous localization and mapping (SLAM) is a process where an autonomous vehicle builds a map of an unknown environment while concurrently generating an estimate for its location. This book is concerned with computationally efficient solutions to the large scale SLAM problems using exactly sparse Extended Information Filters (EIF). The invaluable book also provides a comprehensive theoretical analysis of the properties of the information matrix in EIF-based algorithms for SLAM. Three exactly sparse information filters for SLAM are described in detail, together with two efficient and exact methods for recovering the state vector and the covariance matrix. Proposed algorithms are extensively evaluated both in simulation and through experiments.
Combinatorial algorithms for nearest neighbors, near-duplicates and small-world design

We study the so called combinatorial framework for algorithmic problems in similarity spaces. Namely, the input dataset is represented by a comparison oracle that given three points x, y, y' answers whether y or y' is closer to x. We assume that the similarity order of the dataset satisfies the four variations of the following disorder inequality: if x is the a'th most similar object to y and y is the b'th most similar object to z, then x is among the D(a + b) most similar objects to z, where D is a relatively small disorder constant. Though the oracle gives much less information compared to the standard general metric space model where distance values are given, one can still design very efficient algorithms for various fundamental computational tasks. For nearest neighbor search we present deterministic and exact algorithm with almost linear time and space complexity of preprocessing, and near-logarithmic time complexity of search. Then, for near-duplicate detection we present the first known deterministic algorithm that requires just near-linear time + time proportional to the size of output. Finally, we show that for any dataset satisfying the disorder inequality a visibility graph can be constructed: all outdegrees are near-logarithmic and greedy routing deterministically converges to the nearest neighbor of a target in logarithmic number of steps. The later result is the first known work-around for Navarro's impossibility of generalizing Delaunay graphs. The technical contribution of the paper consists of handling "false positives" in data structures and an algorithmic technique up-aside-down-filter.
Maximizing Cascades in Social Networks : An Overview

Cascades are a network phenomenon in which small local shocks can result in wide-spread fads, strikes, innovations, or power failures. Determining which initial shocks will result in the largest cascades is of interest to marketing, epidemiology, and computer networking. This paper surveys work in maximizing the size of cascades in social networks.
Effects of important parameters variations on computing eigenspace-based minimum variance weights for ultrasound tissue harmonic imaging

In recent years, the minimum variance (MV) beamforming has been widely studied due to its high resolution and contrast in B-mode Ultrasound imaging (USI). However, the performance of the MV beamformer is degraded at the presence of noise, as a result of the inaccurate covariance matrix estimation which leads to a low quality image. Second harmonic imaging (SHI) provides many advantages over the conventional pulse-echo USI, such as enhanced axial and lateral resolutions. However, the low signal-to-noise ratio (SNR) is a major problem in SHI. In this paper, Eigenspace-based minimum variance (EIBMV) beamformer has been employed for second harmonic USI. The Tissue Harmonic Imaging (THI) is achieved by Pulse Inversion (PI) technique. Using the EIBMV weights, instead of the MV ones, would lead to reduced sidelobes and improved contrast, without compromising the high resolution of the MV beamformer (even at the presence of a strong noise). In addition, we have investigated the effects of variations of the important parameters in computing EIBMV weights, i.e., K, L, and δ, on the resolution and contrast obtained in SHI. The results are evaluated using numerical data (using point target and cyst phantoms), and the proper parameters of EIBMV are indicated for THI.
Small-scale structures in planetary nebulae

Two planetary nebulae (PNe) were studied in order to investigate small scale molecular structures in planetary nebulae, the survival of which affects the structure and composition of the interstellar medium (ISM) from which the next generation of stars is born. Molecular hydrogen (H2) was used a tracer for molecular emission and its excitation properties used as a probe of shock physics. Previous studies of several planetary nebulae have indicated a relationship between molecular hydrogen and cometary knots as a shielding mechanism allowing for continued survival and formation of the molecule. Our first PN studied, the Dumbbell nebula (NGC 6853), revealed not only H2 bearing knots (similar to the Helix and Ring nebulae), but also regions of H2 emission which were poorly sculpted or unstructured diffuse regions, unlike previously studied nebulae. The second PN, NGC 2392 has well documented cometary knots, however we find no detection of molecular hydrogen emission from the knots or otherwise making it an outlier among cometary knot bearing planetary nebulae. Both nebula studied in this research challenge current models of H2 bearing small scale structures in PNe and indicate a need to expand our rather small sample of such objects so that we may begin to understand the processes behind H2 formation, excitation, and its role in cometary knots of the PN as a whole.
DeepZipper: A Novel Deep Learning Architecture for Lensed Supernovae Identiﬁcation

The identiﬁcation of gravitationally lensed supernovae in modern astronomical datasets is a needle-in-a-haystack problem with dramatic scientiﬁc implications: discovered systems can be used to directly measure and resolve the current tension on the value of the expansion rate of the Universe today. We hypothesize that the image-based features of the gravitational lensing and the temporal-based features of the time-varying brightness are equally important in classiﬁcations. We therefore develop a deep learning technique that utilizes long short-term memory cells for the time-varying brightness of astronomical systems and convolutional layers for the raw images of astronomical systems simultaneously, and then concatenates the feature maps with multiple fully connected layers. This novel approach achieves a receiver operating characteristic area under curve of 0.97 on simulated astronomical data and more importantly outperforms standalone versions of its recurrent and convolutional constituents. We ﬁnd that combining recurrent and convolutional layers within one coherent network architecture allows the network to optimally weight and aggregate the temporal and image features to yield a promising tool for lensed supernovae identiﬁcation.