Miracle-dz/UniFAR-FTUs huggingface dataset:
Constructing multipartite Bell inequalities from stabilizers

Bell inequality with self-testing property has played an important role in quantum information field with both fundamental and practical applications. However, it is generally challenging to find Bell inequalities with self-testing property for multipartite states and actually there are not many known candidates. In this work, we propose a systematical framework to construct Bell inequalities from stabilizers which are maximally violated by general stabilizer states, with two observables for each local party. We show that the constructed Bell inequalities can self-test any stabilizer state which is essentially device-independent, if and only if these stabilizers can uniquely determine the state in a device-dependent manner. This bridges the gap between device-independent and device-dependent verification methods. Our framework can provide plenty of Bell inequalities for self-testing stabilizer states. Among them, we give two families of Bell inequalities with different advantages: (1) a family of Bell inequalities with a constant ratio of quantum and classical bounds using 2N correlations, (2) Single pair inequalities improving on all previous robustness self-testing bounds using N+1 correlations, which are both efficient and suitable for realizations in multipartite systems. Our framework can not only inspire more fruitful multipartite Bell inequalities from conventional verification methods, but also pave the way for their practical applications.
Magnetized filament models for diverging plasma lenses

Spherical plasma lens models are known to suffer from a severe over-pressure problem, with some observations requiring lenses with central pressures up to millions of times in excess of the ambient ISM. There are two ways that lens models can solve the over-pressure problem: a confinement mechanism exists to counter the internal pressure of the lens, or the lens has a unique geometry, such that the projected column-density appears large to an observer. This occurs with highly asymmetric models, such as edge-on sheets or filaments, with potentially low volume-density. In the first part of this work we investigate the ability of non-magnetized plasma filaments to mimic the magnification of sources seen behind spherical lenses and we extend a theorem from gravitational lens studies regarding this model degeneracy. We find that for plasma lenses, the theorem produces unphysical charge density distributions. In the second part of the work, we consider the plasma lens over-pressure problem. Using magnetohydrodynamics, we develop a non self-gravitating model filament confined by a helical magnetic field. We use toy models in the force-free limit to illustrate novel lensing properties. Generally, magnetized filaments may act as lenses in any orientation with respect to the observer, with the most high density events produced from filaments with axes near the line of sight. We focus on filaments that are perpendicular to the line of sight that show the toroidal magnetic field component may be observed via the lens rotation measure.
Detection of Strong Epicyclic Density Spikes in the GD-1 Stellar Stream: An Absence of Evidence for the Influence of Dark Matter Subhalos?

The density variations in thin stellar streams may encode important information on the nature of dark matter. For instance, if dark matter aggregates into massive subhalos, these perturbers are expected to scatter stars out of dynamically cold stellar streams, possibly leading to detectable gaps in those structures. Here, we reexamine the density variations in the GD-1 stream, using Gaia Data Release 2 (DR2) astrometry and Panoramic Survey Telescope and Rapid Response System photometry, together with high-precision radial velocities measured with the Canada–France–Hawaii Telescope/ESPaDOnS and Very Large Telescope/Ultraviolet and Visual Echelle Spectrograph instruments and complemented with public radial velocity catalogs. We show that after correcting for projection effects, the density profile exhibits high contrast periodic peaks, separated by 2.64 ± 0.18 kpc. An N-body simulation is presented that reproduces this striking morphology with simple epicyclic motion in a smooth Galactic potential. We also discuss the reliability of measuring density variations using ground-based photometric surveys, and for the particular case of GD-1 we highlight some of the artifacts present in the Gaia DR2 catalog along its track. Massive dark subhalos do not appear to be required to explain the density clumping along GD-1.
Fast and Robust Comparison of Probability Measures in Heterogeneous Spaces

The problem of comparing distributions endowed with their own geometry appears in various settings, e.g., when comparing graphs, high-dimensional point clouds, shapes, and generative models. Although the Gromov Wasserstein (GW) distance is usually presented as the natural geometry to handle such comparisons, computing it involves solving a NP-hard problem. In this paper, we propose the Anchor Energy (AE) and Anchor Wasserstein (AW) distances, simpler alternatives to GW built upon the representation of each point in each distribution as the 1D distribution of its distances to all other points. We propose a sweep line algorithm to compute AE \emph{exactly} in $O(n^2 \log n)$ time, where $n$ is the size of each measure, compared to a naive implementation of AE requires $O(n^3)$ efforts. This is quasi-linear w.r. t. the description of the problem itself. AW can be pending a single $O(n^3)$ effort, in addition to the $O(n^2)$ cost of running the Sinkhorn algorithm. We also propose robust versions of AE and AW using rank-based criteria rather than cost values. We show in our experiments that the AE and AW distances perform well in 3D shape comparison and graph matching, at a fraction of the computational cost of popular GW approximations.
Quality Assurance Technologies of Big Data Applications: A Systematic Literature Review

Big data applications are currently used in many application domains, ranging from statistical applications to prediction systems and smart cities. However, the quality of these applications is far from perfect, such as functional error, failure and low performance. Consequently, assuring the overall quality for big data applications plays an increasingly important role. This paper aims at summarizing and assessing existing quality assurance (QA) technologies addressing quality issues in big data applications. We have conducted a systematic literature review (SLR) by searching major scientific databases, resulting in 83 primary and relevant studies on QA technologies for big data applications. The SLR results reveal the following main findings: (1) the quality attributes that are focused for the quality of big data applications, including correctness, performance, availability, scalability and reliability, and the factors influencing them; (2) the existing implementation-specific QA technologies, including specification, architectural choice and fault tolerance, and the process-specific QA technologies, including analysis, verification, testing, monitoring and fault and failure prediction; (3) existing strengths and limitations of each kind of QA technology; (4) the existing empirical evidence of each QA technology. This study provides a solid foundation for research on QA technologies of big data applications and can help developers of big data applications apply suitable QA technologies.
A family of virtual contraction based controllers for tracking of flexible‐joints port‐Hamiltonian robots: Theory and experiments

In this work, we present a constructive method to design a family of virtual contraction based controllers that solve the standard trajectory tracking problem of flexible‐joint robots in the port‐Hamiltonian framework. The proposed design method, called virtual contraction based control, combines the concepts of virtual control systems and contraction analysis. It is shown that under potential energy matching conditions, the closed‐loop virtual system is contractive and exponential convergence to a predefined trajectory is guaranteed. Moreover, the closed‐loop virtual system exhibits properties such as structure preservation, differential passivity, and the existence of (incrementally) passive maps. The method is later applied to a planar RR robot, and two nonlinear tracking control schemes in the developed controllers family are designed using different contraction analysis approaches. Experiments confirm the theoretical results for each controller.
Learning to Seek: Deep Reinforcement Learning for Phototaxis of a Nano Drone in an Obstacle Field

Nano drones are uniquely equipped for fully autonomous applications due to their agility, low cost, and small size. However, their constrained form factor limits flight time, sensor payload, and compute capability. While visual servoing of nano drones can achieve complex tasks, state of the art solutions have significant impact on endurance and cost. The primary goal of our work is to demonstrate phototaxis in an obstacle field, by adding only a lightweight and low-cost light sensor to a nano drone. We deploy a deep reinforcement learning model, capable of direct paths even with noisy sensor readings. By carefully designing the network input, we feed features relevant to the agent in finding the source, while reducing computational cost and enabling inference up to 100 Hz onboard the nano drone. We verify our approach with simulation and in-field testing on a Bitcraze CrazyFlie, achieving 94% success rate in cluttered and randomized test environments. The policy demonstrates efficient light seeking by reaching the goal in simulation in 65% fewer steps and with 60% shorter paths, compared to a baseline random walker algorithm.
MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding

A large number of real-world graphs or networks are inherently heterogeneous, involving a diversity of node types and relation types. Heterogeneous graph embedding is to embed rich structural and semantic information of a heterogeneous graph into low-dimensional node representations. Existing models usually define multiple metapaths in a heterogeneous graph to capture the composite relations and guide neighbor selection. However, these models either omit node content features, discard intermediate nodes along the metapath, or only consider one metapath. To address these three limitations, we propose a new model named Metapath Aggregated Graph Neural Network (MAGNN) to boost the final performance. Specifically, MAGNN employs three major components, i.e., the node content transformation to encapsulate input node attributes, the intra-metapath aggregation to incorporate intermediate semantic nodes, and the inter-metapath aggregation to combine messages from multiple metapaths. Extensive experiments on three real-world heterogeneous graph datasets for node classification, node clustering, and link prediction show that MAGNN achieves more accurate prediction results than state-of-the-art baselines.
Identification of Indian Languages using Ghost-VLAD pooling

In this work, we propose a new pooling strategy for language identification by considering Indian languages. The idea is to obtain utterance level features for any variable length audio for robust language recognition. We use the GhostVLAD approach to generate an utterance level feature vector for any variable length input audio by aggregating the local frame level features across time. The generated feature vector is shown to have very good language discriminative features and helps in getting state of the art results for language identification task. We conduct our experiments on 635Hrs of audio data for 7 Indian languages. Our method outperforms the previous state of the art x-vector [11] method by an absolute improvement of 1.88% in F1-score and achieves 98.43% F1-score on the held-out test data. We compare our system with various pooling approaches and show that GhostVLAD is the best pooling approach for this task. We also provide visualization of the utterance level embeddings generated using Ghost-VLAD pooling and show that this method creates embeddings which has very good language discriminative features.
Uncertainty Quantification for Bayesian Optimization

Bayesian optimization is a class of global optimization techniques. It regards the underlying objective function as a realization of a Gaussian process. Although the outputs of Bayesian optimization are random according to the Gaussian process assumption, quantification of this uncertainty is rarely studied in the literature. In this work, we propose a novel approach to assess the output uncertainty of Bayesian optimization algorithms, in terms of constructing confidence regions of the maximum point or value of the objective function. These regions can be computed efficiently, and their confidence levels are guaranteed by newly developed uniform error bounds for sequential Gaussian process regression. Our theory provides a unified uncertainty quantification framework for all existing sequential sampling policies and stopping criteria.
Impact of convective boundary mixing on the TP-AGB

The treatment of convective boundaries remains an important source of uncertainty within stellar evolution, with drastic implications for the thermally-pulsing stars on the Asymptotic Giant Branch (AGB). Various sources are taken as motivation for the incorporation of convective boundary mixing during this phase, from s-process nucleosynthesis to hydrodynamical models. In spite of the considerable evidence in favour of the existence of convective boundary mixing on the pre-AGB evolution, this mixing is not universally included in models of TP-AGB stars. The aim of this investigation is to ascertain the extent of convective boundary mixing, which is compatible with observations when considering full evolutionary models. Additionally, we investigate a theoretical argument that has been made that momentum-driven overshooting at the base of the pulse-driven convection zone should be negligible. We show that, while the argument holds,estimations based on the picture of turbulent entrainment suggest that mixing is possible at both convective boundaries. We demonstrate that additional mixing at convective boundaries during core-burning phases prior to the thermally-pulsing Asymptotic Giant Branch has an impact on the later evolution, changing the mass range at which the third dredge-up and hot-bottom burning occur, and thus also the final surface composition. In addition, an effort has been made to constrain the efficiency of convective boundary mixing at the different convective boundaries, using observational constraints. Our study suggests a strong tension between different constraints that makes it impossible to reproduce all observables simultaneously within the framework of an exponentially decaying overshooting. This result calls for a reassessment of both the models of convective boundary mixing and the observational constraints.
Rapid Adaptation of BERT for Information Extraction on Domain-Specific Business Documents

Techniques for automatically extracting important content elements from business documents such as contracts, statements, and filings have the potential to make business operations more efficient. This problem can be formulated as a sequence labeling task, and we demonstrate the adaption of BERT to two types of business documents: regulatory filings and property lease agreements. There are aspects of this problem that make it easier than "standard" information extraction tasks and other aspects that make it more difficult, but on balance we find that modest amounts of annotated data (less than 100 documents) are sufficient to achieve reasonable accuracy. We integrate our models into an end-to-end cloud platform that provides both an easy-to-use annotation interface as well as an inference interface that allows users to upload documents and inspect model outputs.
The Vulnerability of Cyber-Physical System Under Stealthy Attacks

In this article, we study the impact of stealthy attacks on the cyber-physical system (CPS) modeled as a stochastic linear system. An attack is characterised by a malicious injection into the system through input, output or both, and it is called stealthy (resp. strictly stealthy) if it produces bounded changes (resp. no changes) in the detection residue. Correspondingly, a CPS is called vulnerable (resp. strictly vulnerable) if it can be destabilized by a stealthy attack (resp. strictly stealthy attack). We provide necessary and sufficient conditions for the vulnerability and strictly vulnerability. For the invulnerable case, we also provide a performance bound for the difference between healthy and attacked system. Numerical examples are provided to illustrate the theoretical results.
Energetics of Large Water Clusters [up to (H2O)20] by means of Explicitly Correlated, Localized Coupled Cluster Methods

We consider the performance of combined PNO-F12 approaches for the dissociation energy of water clusters as large as (H2O)20 by comparison to canonical CCSD(T)/CBS reference values obtained through n-body decomposition of post-MP2 corrections. We find that PNO-LCCSD(T)-F12b approaches with "Tight" cutoffs are generally capable of reproducing canonical CCSD(T) results to within ~0.25%, while requiring only a fraction of the computational cost associated with the latter. However, basis set convergence patterns and effect of counterpoise corrections are more erratic than for canonical calculations, highlighting the need for canonical benchmarks on closely related systems.
Adhesive and non-adhesive contact of a rigid indenter and a thin elastic layer with surface tension

We consider an adhesive contact between a thin soft layer on a rigid substrate and a rigid cylindrical indenter ("line contact") with account of the surface tension of the layer. First, it is shown that the boundary condition for the surface outside the contact area is given by the constant contact angle - as in the case of fluids in contact with solid surfaces. In the approximation of thin layer and under usual assumptions of small indentation and small inclination angles of the surface, the problem is solved analytically.
Lipschitz stability at the boundary for time-harmonic diffuse optical tomography

ABSTRACT We study the inverse problem in Optical Tomography of determining the optical properties of a medium , with , under the so-called diffusion approximation. We consider the time-harmonic case where Ω is probed with an input field that is modulated with a fixed harmonic frequency , where c is the speed of light and k is the wave number. We prove a result of Lipschitz stability of the absorption coefficient at the boundary in terms of the measurements in the case when the scattering coefficient is assumed to be known and k belongs to certain intervals depending on some a-priori bounds on , .
Discovery of Self-Assembling π-Conjugated Peptides by Active Learning-Directed Coarse-Grained Molecular Simulation

Electronically-active organic molecules have demonstrated great promise as novel soft materials for energy harvesting and transport. Self-assembled nanoaggregates formed from π-conjugated oligopeptides composed of an aromatic core flanked by oligopeptide wings offer emergent optoelectronic properties within a water soluble and biocompatible substrate. Nanoaggregate properties can be controlled by tuning core chemistry and peptide composition, but the sequence-structure-function relations remain poorly characterized. In this work, we employ coarse-grained molecular dynamics simulations within an active learning protocol employing deep representational learning and Bayesian optimization to efficiently identify molecules capable of assembling pseudo-1D nanoaggregates with good stacking of the electronically-active π-cores. We consider the DXXX-OPV3-XXXD oligopeptide family, where D is an Asp residue and OPV3 is an oligophenylene vinylene oligomer (1,4-distyrylbenzene), to identify the top performing XXX tripeptides within all 203 = 8,000 possible sequences. By direct simulation of only 2.3% of this space, we identify molecules predicted to exhibit superior assembly relative to those reported in prior work. Spectral clustering of the top candidates reveals new design rules governing assembly. This work establishes new understanding of DXXX-OPV3-XXXD assembly, identifies promising new candidates for experimental testing, and presents a computational design platform that can be generically extended to other peptide-based and peptide-like systems.
Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors

The goal of standard 1-bit compressive sensing is to accurately recover an unknown sparse vector from binary-valued measurements, each indicating the sign of a linear function of the vector. Motivated by recent advances in compressive sensing with generative models, where a generative modeling assumption replaces the usual sparsity assumption, we study the problem of 1-bit compressive sensing with generative models. We first consider noiseless 1-bit measurements, and provide sample complexity bounds for approximate recovery under i.i. d. ~Gaussian measurements and a Lipschitz continuous generative prior, as well as a near-matching algorithm-independent lower bound. Moreover, we demonstrate that the Binary $\epsilon$-Stable Embedding property, which characterizes the robustness of the reconstruction to measurement errors and noise, also holds for 1-bit compressive sensing with Lipschitz continuous generative models with sufficiently many Gaussian measurements. In addition, we apply our results to neural network generative models, and provide a proof-of-concept numerical experiment demonstrating significant improvements over sparsity-based approaches.
Polygon Extraction from Triangular Meshes

This paper presents Polylidar, an efficient algorithm to extract non-convex polygons from 2D point sets. Polylidar is able to extract multiple disjoint polygons and capture interior holes. The algorithm begins by triangulating the point set and filtering triangles by user configurable parameters such as triangle edge length. Next, connected triangles are extracted into triangular meshes representing the shape of the point set. The key to Polylidar's speed, and the main contribution of this paper, is in efficiently transforming each triangular mesh into a concave polygon. This paper describes Polylidar and provides benchmarks to comparatively evaluate its speed and accuracy. Results show good accuracy and $\sim$ 4 times speedup compared to other concave polygon extraction methods.
Divergence-preserving reconstructions on polygons and a really pressure-robust virtual element method for the Stokes problem

Nondivergence-free discretizations for the incompressible Stokes problem may suffer from a lack of pressure-robustness characterized by large discretizations errors due to irrotational forces in the momentum balance. This paper argues that also divergence-free virtual element methods on polygonal meshes are not really pressure-robust as long as the right-hand side is not discretized in a careful manner. To be able to evaluate the right-hand side for the test functions, some explicit interpolation of the virtual test functions is needed that can be evaluated pointwise everywhere. The standard discretization via an $L^2$-best approximation does not preserve the divergence, and so destroys the orthogonality between divergence-free test functions and possibly eminent gradient forces in the right-hand side. To repair this orthogonality and restore pressure-robustness, another divergence-preserving reconstruction is suggested based on Raviart–Thomas approximations on local subtriangulations of the polygons. All findings are proven theoretically and are demonstrated numerically in two dimensions. The construction is also interesting for hybrid high-order methods on polygonal or polyhedral meshes.