Miracle-dz/UniFAR-FTUs huggingface dataset:
SMT-based Robot Transition Repair

State machines are a common model for robot behaviors. Transition functions often rely on parameterized conditions to model preconditions for the controllers, where the correct values of the parameters depend on factors relating to the environment or the specific robot. In the absence of specific calibration procedures a roboticist must painstakingly adjust the parameters through a series of trial and error experiments. In this process, identifying when the robot has taken an incorrect action, and what should be done is straightforward, but finding the right parameter values can be difficult. We present an alternative approach that we call, interactive SMT-based Robot Transition Repair. During execution we record an execution trace of the transition function, and we ask the roboticist to identify a few instances where the robot has transitioned incorrectly, and what the correct transition should have been. A user supplies these corrections based on the type of error to repair, and an automated analysis of the traces partially evaluates the transition function for each correction. This system of constraints is then formulated as a MaxSMT problem, where the solution is a minimal adjustment to the parameters that satisfies the maximum number of constraints. In order to identify a repair that accurately captures user intentions and generalizes to novel scenarios, solutions are explored by iteratively adding constraints to the MaxSMT problem to yield sets of alternative repairs. We test with state machines from multiple domains including robot soccer and autonomous driving, and we evaluate solver based repair with respect to solver choice and optimization hyperparameters. Our results demonstrate that SRTR can repair a variety of states machines and error types 1) quickly, 2) with small numbers of corrections, while 3) not overcorrecting state machines and harming generalized performance.
Finite-Level Quantization Procedures for Construction and Decoding of Polar Codes

We consider finite-level, symmetric quantization procedures for construction and decoding of polar codes. Whether polarization occurs in the presence of quantization is not known in general. In [1], it is shown that a simple three-level quantization procedure polarizes and a calculation method is proposed to obtain a lower bound for achievable rates. We find an improved calculation method for achievable rates and also the exact asymptotic behavior of the block error probability for the simple case. We then prove that certain D-level quantization schemes polarize and give a lower bound on achievable rates. Furthermore, we show that a broad class of quantization procedures result in a weaker form of the polarization phenomenon.
Deep Image Translation With an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection

Image translation with convolutional neural networks has recently been used as an approach to multimodal change detection. Existing approaches train the networks by exploiting supervised information of the change areas, which, however, is not always available. A main challenge in the unsupervised problem setting is to avoid that change pixels affect the learning of the translation function. We propose two new network architectures trained with loss functions weighted by priors that reduce the impact of change pixels on the learning objective. The change prior is derived in an unsupervised fashion from relational pixel information captured by domain-specific affinity matrices. Specifically, we use the vertex degrees associated with an absolute affinity difference matrix and demonstrate their utility in combination with cycle consistency and adversarial training. The proposed neural networks are compared with the state-of-the-art algorithms. Experiments conducted on three real data sets show the effectiveness of our methodology.
Pantheon update on a model-independent analysis of cosmological supernova data

We present an update of our previous work, necessitated by availability of a significantly improved dataset. The work is a model-independent analysis of the cosmological supernova (type Ia) data, where function families are fit to the data in form of luminosity distance as function of redshift, that is, d L(z); and subsequently time-derivatives of the scale function a(t) are analytically derived, but as functions of z, without making assumptions about the theory of gravity or the contents of the Universe. This gives, e.g. the redshift value at which the Universe goes over from deceleration to acceleration, as z t = 0.54 ± 0.04 for a flat Universe. In the update, we switch to a more modern fit criterion and also take into account the uncertainty in the calibration of the SNIa luminosities. If a theory of gravity is assumed, our results allow determination of the density of the Universe as function of z, from which conclusions about the contents of the Universe can be drawn. We update the previous work’s result where this was done for Einstein gravity, finding a lower-limit on the dark energy (DE) fraction, ΩDE > 0.46; and here we do this also for Starobinsky gravity, where we can find a Starobinsky parameter that can eliminate the need for DE.
A Universal Attractor Decomposition Algorithm for Parity Games

An attractor decomposition meta-algorithm for solving parity games is given that generalizes the classic McNaughton-Zielonka algorithm and its recent quasi-polynomial variants due to Parys (2019), and to Lehtinen, Schewe, and Wojtczak (2019). The central concepts studied and exploited are attractor decompositions of dominia in parity games and the ordered trees that describe the inductive structure of attractor decompositions. The main technical results include the embeddable decomposition theorem and the dominion separation theorem that together help establish a precise structural condition for the correctness of the universal algorithm: it suffices that the two ordered trees given to the algorithm as inputs embed the trees of some attractor decompositions of the largest dominia for each of the two players, respectively. The universal algorithm yields McNaughton-Zielonka, Parys's, and Lehtinen-Schewe-Wojtczak algorithms as special cases when suitable universal trees are given to it as inputs. The main technical results provide a unified proof of correctness and deep structural insights into those algorithms. A symbolic implementation of the universal algorithm is also given that improves the symbolic space complexity of solving parity games in quasi-polynomial time from $O(d \lg n)$---achieved by Chatterjee, Dvořak, Henzinger, and Svozil (2018)---down to $O(\lg d)$, where $n$ is the number of vertices and $d$ is the number of distinct priorities in a parity game. This not only exponentially improves the dependence on $d$, but it also entirely removes the dependence on $n$.
Dispersal of protoplanetary discs by the combination of magnetically driven and photoevaporative winds

We investigate the roles of magnetically driven disc wind (MDW) and thermally driven photoevaporative wind (PEW) in the long-time evolution of protoplanetary discs. We start simulations from the early phase in which the disc mass is $0.118\, \rm M_{\odot }$ around a $1\, \rm M_{\odot }$ star and track the evolution until the disc is completely dispersed. We incorporate the mass-loss by PEW and the mass-loss and magnetic braking (wind torque) by MDW, in addition to the viscous accretion, viscous heating, and stellar irradiation. We find that MDW and PEW, respectively, have different roles: magnetically driven wind ejects materials from an inner disc in the early phase, whereas photoevaporation has a dominant role in the late phase in the outer (≳1 au) disc. The disc lifetime, which depends on the combination of MDW, PEW, and viscous accretion, shows a large variation of ∼1–20 Myr; the gas is dispersed mainly by the MDW and the PEW in the cases with a low viscosity and the lifetime is sensitive to the mass-loss rate and torque of the MDW, whereas the lifetime is insensitive to these parameters when the viscosity is high. Even in discs with very weak turbulence, the cooperation of MDW and PEW enables the disc dispersal within a few Myr.
ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training

This paper presents a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of optimizing one-step-ahead prediction in the traditional sequence-to-sequence model, the ProphetNet is optimized by n-step ahead prediction that predicts the next n tokens simultaneously based on previous context tokens at each time step. The future n-gram prediction explicitly encourages the model to plan for the future tokens and prevent overfitting on strong local correlations. We pre-train ProphetNet using a base scale dataset (16GB) and a large-scale dataset (160GB), respectively. Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for abstractive summarization and question generation tasks. Experimental results show that ProphetNet achieves new state-of-the-art results on all these datasets compared to the models using the same scale pre-training corpus.
On the Resilience of Biometric Authentication Systems against Random Inputs

We assess the security of machine learning based biometric authentication systems against an attacker who submits uniform random inputs, either as feature vectors or raw inputs, in order to find an accepting sample of a target user. The average false positive rate (FPR) of the system, i.e., the rate at which an impostor is incorrectly accepted as the legitimate user, may be interpreted as a measure of the success probability of such an attack. However, we show that the success rate is often higher than the FPR. In particular, for one reconstructed biometric system with an average FPR of 0.03, the success rate was as high as 0.78. This has implications for the security of the system, as an attacker with only the knowledge of the length of the feature space can impersonate the user with less than 2 attempts on average. We provide detailed analysis of why the attack is successful, and validate our results using four different biometric modalities and four different machine learning classifiers. Finally, we propose mitigation techniques that render such attacks ineffective, with little to no effect on the accuracy of the system.
A hydrodynamical study of outflows in starburst galaxies with different driving mechanisms

Outflows from starburst galaxies can be driven by thermal pressure, radiation, and cosmic rays. We present an analytic phenomenological model that accounts for these contributions simultaneously to investigate their effects on the hydrodynamical properties of outflows. We assess the impact of energy injection, wind opacity, magnetic field strength, and the mass of the host galaxy on flow velocity, temperature, density, and pressure profiles. For an M82-like wind, a thermally dominated driving mechanism is found to deliver the fastest and hottest wind. Radiation-driven winds in typical starburst-galaxy configurations are unable to attain the higher flow velocities and temperatures associated with thermal and cosmic ray-driven systems, leading to higher wind densities which would be more susceptible to cooling and fragmentation at lower altitudes. High opacity winds are more sensitive to radiative driving, but terminal flow velocities are still lower than those achieved by other driving mechanisms at realistic opacities. We demonstrate that variations in the outflow magnetic field can influence its coupling with cosmic rays, where stronger fields enable greater streaming but less driving near the base of the flow, instead with cosmic rays redirecting their driving impact to higher altitudes. The gravitational potential is less important in M82-like wind configurations, and substantial variations in the flow profiles only emerge at high altitude in massive haloes. This model offers a more generalized approach to examine the large-scale hydrodynamical properties for a wide variety of starburst galaxies.
Sense, Connect, Facilitate: Nurse Educator Experiences of Caring Online Through Watson's Lens

Twenty-four nurse educators spontaneously provided rich narratives in response to an online survey that asked only for brief answers to 4 questions: Is it possible to demonstrate caring online? What cues prompt you to initiate caring interventions with students? What caring interventions do you initiate in response to those cues? How do students respond to your caring efforts? Themes encompassed within these remarkable narratives include sense, connect, and facilitate. These themes correspond to key elements in Watson's human caring theory and provide a window into the philosophies and practices of 24 wise, intuitive, creative, and deeply caring nurse educators.
The Swift Bulge Survey: optical and near-IR follow-up featuring a likely symbiotic X-ray binary and a focused wind CV

The nature of very faint X-ray transients (VFXTs) – transient X-ray sources that peak at luminosities $L_X\lesssim 10^{36} {\rm \, erg \, s^{-1}}$ – is poorly understood. The faint and often short-lived outbursts make characterizing VFXTs and their multiwavelength counterparts difficult. In 2017 April we initiated the Swift Bulge Survey, a shallow X-ray survey of ∼16 square degrees around the Galactic centre with the Neil Gehrels Swift Observatory. The survey has been designed to detect new and known VFXTs, with follow-up programmes arranged to study their multiwavelength counterparts. Here we detail the optical and near-infrared follow-up of four sources detected in the first year of the Swift Bulge Survey. The known neutron star binary IGR J17445-2747 has a K4III donor, indicating a potential symbiotic X-ray binary nature and the first such source to show X-ray bursts. We also find one nearby M-dwarf (1SXPS J174215.0-291453) and one system without a clear near-IR counterpart (Swift J175233.9-290952). Finally, 3XMM J174417.2-293944 has a subgiant donor, an 8.7 d orbital period, and a likely white dwarf accretor; we argue that this is the first detection of a white dwarf accreting from a gravitationally focused wind. A key finding of our follow-up campaign is that binaries containing (sub)giant stars may make a substantial contribution to the VFXT population.
Photometric, kinematic, and variability study in the young open cluster NGC 1960

We present a comprehensive photometric analysis of a young open cluster NGC 1960 (M36) along with the long-term variability study of this cluster. Based on the kinematic data of Gaia DR2, the membership probabilities of 3871 stars are ascertained in the cluster field among which 262 stars are found to be cluster members. Considering the kinematic and trigonometric measurements of the cluster members, we estimate a mean cluster parallax of 0.86+/-0.05 mas and mean proper motions of mu_RA = -0.143+/-0.008 mas/yr, mu_Dec = -3.395+/-0.008 mas/yr. We obtain basic parameters of the cluster such as E(B-V) = 0.24+/-0.02 mag, log(Age/yr)=7.44+/-0.02, and distance = 1.17+/-0.06 kpc. The mass function slope in the cluster for the stars in the mass range of 0.72-7.32 M_solar is found to be \gamma = -1.26+/-0.19. We find that mass segregation is still taking place in the cluster which is yet to be dynamically relaxed. This work also presents first high-precision variability survey in the central 13'x13' among which 72 are periodic variables. Among them, 59 are short-period (P 1 day). The variable stars have V magnitudes ranging between 9.1 to 19.4 mag and periods between 41 minutes to 10.74 days. On the basis of their locations in the H-R diagram, periods and characteristic light curves, the 20 periodic variables belong to the cluster. We classified them as 2 delta-Scuti, 3 gamma-Dor, 2 slowly pulsating B stars, 5 rotational variables, 2 non-pulsating B stars and 6 as miscellaneous variables.
Tight bound on finite-resolution quantum thermometry at low temperatures

Precise thermometry is of wide importance in science and technology in general and in quantum systems in particular. Here, we investigate fundamental precision limits for thermometry on cold quantum systems, taking into account constraints due to finite measurement resolution. We derive a tight bound on the optimal precision scaling with temperature, as the temperature approaches zero. The bound can be saturated by monitoring the non-equilibrium dynamics of a single-qubit probe. We support this finding by accurate numerical simulations of a spin-boson model. Our results are relevant both fundamentally, as they illuminate the ultimate limits to quantum thermometry, and practically, in guiding the development of sensitive thermometric techniques applicable at ultracold temperatures.
Attitude Determination and Estimation using Vector Observations: Review, Challenges and Comparative Results

This paper concerns the problem of attitude determination and estimation. The early applications considered algebraic methods of attitude determination. Attitude determination algorithms were supplanted by the Gaussian attitude estimation filters (which continue to be widely used in commercial applications). However, the sensitivity of the Gaussian attitude filter to the measurement noise prompted the introduction of the nonlinear attitude filters which account for the nonlinear nature of the attitude dynamics problem and allow for a simpler filter derivation. This paper presents a survey of several types of attitude determination and estimation algorithms. Each category is detailed and illustrated with literature examples in both continuous and discrete form. A comparison between these algorithms is demonstrated in terms of transient and steady-state error through simulation results. The comparison is supplemented by statistical analysis of the error-related mean, infinity norm, and standard deviation of each algorithm in the steady-state. Keywords: Comparative Study, Attitude, Determination, Estimation, Filter, Adaptive Filter, Gaussian Filter, Nonlinear Filter, Overview, Review, Rodrigues Vector, Special Orthogonal Group, Unit-quaternion, Angle-axis, Determinstic, Stochastic, Continuous, Discrete, Multiplicative extended kalman filter, KF, EKF, MEKF, white noise, colored noise.
CLUENER2020: Fine-grained Name Entity Recognition for Chinese

In this paper, we introduce the NER dataset from CLUE organization (CLUENER2020), a well-deﬁned ﬁne-grained dataset for name entity recognition in Chinese. CLUENER2020 contains 10 categories. Apart from common labels like person, organization and location, it contains more diverse categories. It is more challenging than current other Chinese NER datasets and could better reﬂect real-world applications. For comparison, we implement several state-of-the-art baselines as sequence labelling tasks and report human performance, as well as its analysis. To facilitate future work on ﬁne-grained NER for Chinese, we release our dataset, baselines and leader-board. 1
Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems

We consider nonconvex-concave minimax problems of the form $\min_{\bf x}\max_{\bf y} f({\bf x},{\bf y})$, where $f$ is strongly-concave in $\bf y$ but possibly nonconvex in $\bf x$. We focus on the stochastic setting, where we can only access an unbiased stochastic gradient estimate of $f$ at each iteration. This formulation includes many machine learning applications as special cases such as adversary training and certifying robustness in deep learning. We are interested in finding an ${\mathcal O}(\varepsilon)$-stationary point of the function $\Phi(\cdot)=\max_{\bf y} f(\cdot, {\bf y})$. The most popular algorithm to solve this problem is stochastic gradient decent ascent, which requires $\mathcal O(\kappa^3\varepsilon^{-4})$ stochastic gradient evaluations, where $\kappa$ is the condition number. In this paper, we propose a novel method called Stochastic Recursive gradiEnt Descent Ascent (SREDA), which estimates gradients more efficiently using variance reduction. This method achieves the best known stochastic gradient complexity of ${\mathcal O}(\kappa^3\varepsilon^{-3})$, and its dependency on $\varepsilon$ is optimal for this problem.
Generation of quasiparticles by flavor mixing and CP violation: heavy Majorana neutrinos, part I.

The phenomenology of flavor mixing of heavy Majorana neutrinos is studied. The physical degrees of freedom, which propagate like free particles until they decay in the presence of flavor mixing, are identified by diagonalizing the resummed propagator. It is shown that they should be interpreted as quasiparticles which lose Majorana nature.
Gravitational-wave Detection and Parameter Estimation for Accreting Black-hole Binaries and Their Electromagnetic Counterpart

We study the impact of gas accretion on the orbital evolution of black-hole binaries initially at large separation in the band of the planned Laser Interferometer Space Antenna (LISA). We focus on two sources: (i) stellar-origin black-hole binaries (SOBHBs) that can migrate from the LISA band to the band of ground-based gravitational-wave (GW) observatories within weeks/months; and (ii) intermediate-mass black-hole binaries (IMBHBs) in the LISA band only. Because of the large number of observable GW cycles, the phase evolution of these systems needs to be modeled to great accuracy to avoid biasing the estimation of the source parameters. Accretion affects the GW phase at negative (−4) post-Newtonian order, being thus dominant for binaries at large separations. Accretion at the Eddington or at super-Eddington rate will leave a detectable imprint on the dynamics of SOBHBs. For super-Eddington rates and a 10 yr mission, a multiwavelength strategy with LISA and a ground-based interferometer can detect about 10 (a few) SOBHB events for which the accretion rate can be measured at 50% (10%) level. In all cases, the sky position can be identified within much less than 0.4 deg2 uncertainty. Likewise, accretion at ≳100% of the Eddington rate can be measured in IMBHBs up to redshift z ≈ 0.1, and the position of these sources can be identified within less than uncertainty. Altogether, a detection of SOBHBs or IMBHBs would allow for targeted searches of electromagnetic counterparts to black-hole mergers in gas-rich environments with future X-ray detectors (such as Athena) and/or radio observatories (such as SKA).
AdS Robin solitons and their stability

We consider the four-dimensional Einstein–Klein–Gordon–AdS system with the conformal mass subject to the Robin boundary conditions at infinity. Above a critical value of the Robin parameter, at which the AdS spacetime goes linearly unstable, we prove existence of a family of globally regular static solutions (that we call AdS Robin solitons) and discuss their properties.