Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum Hall effect and Landau levels without spatial long-range correlations

The spectrum of charged particles in translation-invariant systems in a magnetic field is characterized by the Landau levels, which play a fundamental role in the thermodynamic and transport properties of solids. The topological nature and the approximate degeneracy of the Landau levels are known to also survive on crystalline lattices with discrete translation symmetry when the magnetic flux through a primitive cell is small compared to the flux quantum. Here we show that the notion of Landau levels and the quantum Hall effect can be generalized to 2d non-crystalline lattices without spatial long-range order. Remarkably, even when the spatial correlations decay over microscopic distances, 2d systems can exhibit a number of well-resolved Landau-like bands. The existence of these bands imply that non-crystalline systems in magnetic fields can support the hallmark quantum effects which have been typically associated with crystalline solids.
Connecting transonic buffet with incompressible low-frequency oscillations on aerofoils

Self-sustained, low-frequency, coherent flow unsteadiness over rigid, stationary aerofoils in the transonic regime is referred to as transonic buffet. This study examines the role of shock waves in sustaining this transonic phenomenon and its relation to low-frequency oscillations (LFO) that occur in flow over aerofoils in the incompressible regime (Zaman et al., J. Fluid Mech., vol. 202, 1989, pp. 403–442). This is investigated by performing large-eddy simulations of the flow over a NACA0012 profile for a wide range of flow conditions under free-transition conditions. At low Reynolds numbers, zero incidence angle and sufficiently high free-stream Mach numbers, $M$ , transonic buffet occurs with shock waves present in the flow. However, when $M$ alone is lowered, self-sustained, periodic oscillations at a low frequency are observed even though shock waves are absent and the entire flow field remains subsonic at all times. At higher incidence angles, the oscillations are sustained at progressively lower $M$ and are present even at $M=0.3$ , where compressibility effects are low. A spectral proper orthogonal decomposition (SPOD) shows that the spatial structure of these oscillations is consistent for all cases. The SPOD modes are topologically similar, suggesting a connection between transonic buffet and LFO in the incompressible regime. Comparisons with other studies examining transonic buffet on various aerofoils, under forced-transition and fully turbulent conditions support this hypothesis. Future studies using tools of global linear stability analysis, especially at high free-stream Reynolds numbers are required to examine whether the underlying mechanisms of transonic buffet and incompressible LFO are the same.
Empirical Bayes Estimation of a Distribution (Survival) Function from Right Censored Observations

Abstract : This paper provides an empirical Bayes approach to the problem of nonparametric estimation of a distribution (or survival) function when the observations are censored on the right. The results use the notion of a Dirichlet process prior (Ferguson, 1973, Ann. Stat., 2, 209-230). The paper presents a generalization to the case of right censored observations of the rate result of an empirical Bayes nonparametric estimator of a distribution function of Korwar and Hollander ((1974, Tech. Rept. No. 288, Dept. of Statistics, Florida State University) in the uncensored case. The rate of asymptotic convergence to optimality is shown to be the best obtainable for the problem considered.
Deep Reinforcement Learning for Power Trading

The Dutch power market includes a day-ahead market and an auction-like intraday balancing market. The varying supply and demand of power and its uncertainty induces an imbalance, which causes differing power prices in these two markets and creates an opportunity for arbitrage. In this paper, we present collaborative dual-agent reinforcement learning (RL) for bi-level simulation and optimization of European power arbitrage trading. Moreover, we propose two novel practical implementations specifically addressing the electricity power market. Leveraging the concept of imitation learning, the RL agent’s reward is reformed by taking into account prior domain knowledge results in better convergence during training and, moreover, improves and generalizes performance. In addition, tranching of orders improves the bidding success rate and significantly raises the P&L. We show that each method contributes significantly to the overall performance uplifting, and the integrated methodology achieves about three-fold improvement in cumulative P&L over the original agent, as well as outperforms the highest benchmark policy by around 50% while exhibits efficient computational performance.
Spectral Embedding of Weighted Graphs

When analyzing weighted networks using spectral embedding, a judicious transformation of the edge weights may produce better results. To formalize this idea, we consider the asymptotic behavior of spectral embedding for different edge-weight representations, under a generic low rank model. We measure the quality of different embeddings -- which can be on entirely different scales -- by how easy it is to distinguish communities, in an information-theoretic sense. For common types of weighted graphs, such as count networks or p-value networks, we find that transformations such as tempering or thresholding can be highly beneficial, both in theory and in practice.
Counterexample Guided Abstraction Refinement with Non-Refined Abstractions for Multi-Agent Path Finding

Counterexample guided abstraction refinement (CEGAR) represents a powerful symbolic technique for various tasks such as model checking and reachability analysis. Recently, CEGAR combined with Boolean satisfiability (SAT) has been applied for multi-agent path finding (MAPF), a problem where the task is to navigate agents from their start positions to given individual goal positions so that the agents do not collide with each other. The recent CEGAR approach used the initial abstraction of the MAPF problem where collisions between agents were omitted and were eliminated in subsequent abstraction refinements. We propose in this work a novel CEGAR-style solver for MAPF based on SAT in which some abstractions are deliberately left non-refined. This adds the necessity to post-process the answers obtained from the underlying SAT solver as these answers slightly differ from the correct MAPF solutions. Non-refining however yields order-of-magnitude smaller SAT encodings than those of the previous approach and speeds up the overall solving process making the SAT-based solver for MAPF competitive again in relevant benchmarks.
Testing the order of a model using locally conic parametrization : population mixtures and stationary ARMA processes

In this paper, we address the problem of testing hypotheses using the likelihood ratio test statistic in nonidentifiable models, with application to model selection in situations where the parametrization for the larger model leads to nonidentifiability in the smaller model. We give two major applications: the case where the number of populations has to be tested in a mixture and the case of stationary ARMA(p, q) processes where the order (p,q) has to be tested. We give the asymptotic distribution for the likelihood ratio test statistic when testing the order of the model. In the case of order selection for ARMAs, the asymptotic distribution is invariant with respect to the parameters generating the process. A locally conic parametrization is a key tool in deriving the limiting distributions; it allows one to discover the deep similarity between the two problems.
How an exact discrete symmetry can preserve black hole information or Turning a black hole inside out

To apply the laws of General Relativity to quantum black holes, one first needs to remove the horizon singularity by means of Kruskal-Szekeres coordinates. This however doubles spacetime, which thereby is equipped with an exact binary symmetry. All particles near a black hole share the same symmetry, and conservation of this symmetry may completely remove the information paradox: the quantum black hole has no interior, or equivalently, the black hole interior is a quantum clone of the exterior region. These observations, totally overlooked in most of the literature on quantum black holes, resolve some issues concerning conservation of information. Some other problems do remain.
CHARACTERIZATION OF MULTILAYERS BY FOURIER ANALYSIS OF X-RAY REFLECTIVITY

We discuss a new method to characterize multilayer structures with grazing-incidence reflectivity measurements using hard x-ray radiation, such as Cu-Kα or Mo-Kα radiation. The method is based on the analysis of the reverse Fourier transforms of the reflectivity at the Bragg peaks in q-space, the reflectivity data being obtained from an angular scan (θ−2θ). This method is faster than curve fitting of the reflectivity data, results in an accurate value of the density and thickness of both materials, and needs no pre-assumptions about the material composition and the parameters of the multilayer. The method makes a distinction between interface roughness and layer thickness errors, and is independent of measurement of the critical angle. A minor disadvantage is that only an average value of the layer thickness is determined, rather than the individual layer thicknesses. As an example our method is used to analyze small-angle reflectivity measurements of Mo/Si and Co/C multilayers. The parameters thus obtain.
Complexity of linearized augmented Lagrangian for optimization with nonlinear equality constraints

In this paper, we consider a nonconvex optimization problem with nonlinear equality constraints. We assume that both, the objective function and the functional constraints are locally smooth. For solving this problem, we propose a linearized augmented Lagrangian method, i.e., we linearize the functional constraints in the augmented Lagrangian at the current iterate and add a quadratic regularization, yielding a subproblem that is easy to solve, and whose solution is the next iterate. Under a dynamic regularization parameter choice, we prove global asymptotic convergence of the iterates to a critical point of the problem. We also derive convergence guarantees for the iterates of our method to an (cid:15) ﬁrst-order optimal solution in O (1 /(cid:15) 2 ) outer iterations. Finally, we show that, when the problem data are e.g., semialgebraic, the sequence generated by our algorithm converges and we derive convergence rates. We validate the theory and the performance of the proposed algorithm by numerically comparing it with the existing methods from the literature.
When Source-Free Domain Adaptation Meets Label Propagation

Source-free domain adaptation, where only a pre-trained source model is used to adapt to the target distribution, is a more general approach to achieving domain adaptation. However, it can be challenging to capture the inherent structure of the target features accurately due to the lack of supervised information on the target domain. To tackle this problem, we propose a novel approach called Adaptive Local Transfer (ALT) that tries to achieve efﬁcient feature clustering from the perspective of label propagation. ALT divides the target data into inner and outlier samples based on the adaptive threshold of the learning state, and applies a customized learning strategy to best ﬁts the data property. Speciﬁcally, inner samples are utilized for learning intra-class structure thanks to their relatively well-clustered properties. The low-density outlier samples are regularized by input consistency to achieve high accuracy with respect to the ground truth labels. In this way, local clustering can be prevented from forming spurious clusters while effectively propagating label information among sub-populations. Empirical evidence demonstrates that ALT outperforms the state of the arts on three public benchmarks: Ofﬁce-31, Ofﬁce-Home, and VisDA.
The Formation of Hard Very High Energy Spectra from Gamma-ray Burst Afterglows via Two-zone Synchrotron Self-Compton Emission

Electron Compton scattering of target photons into the gamma-ray energy band (inverse Compton scattering; IC) is commonly expected to dominate the very high energy (VHE) spectra in gamma-ray bursts (GRBs) especially during the afterglow phase. For sufficiently large center-of-mass energies in these collisions, the effect of the electron recoil starts reducing the scattering cross-section (the Klein–Nishina regime). The IC spectra generated in the Klein–Nishina regime is softer and has a smaller flux level compared to the synchrotron spectra produced by the same electrons. The detection of afterglow emission from nearby GRB190829A in the VHE domain with H.E. S.S. has revealed an unexpected feature: the slope of the VHE spectrum matches well the slope of the X-ray spectra, despite expectations that, for the IC production process, the impact of the Klein–Nishina effect should be strong. The multi-wavelength spectral energy distribution appears to be inconsistent with predictions of one-zone synchrotron–self-Compton models. We study the possible impact of two-zone configuration on the properties of IC emission when the magnetic field strength differs considerably between the two zones. Synchrotron photons from the strong magnetic field zone provide the dominant target for cooling of the electrons in the weak magnetic field zone, which results in a formation of hard electron distribution and consequently of a hard IC emission. We show that the two-zone model can provide a good description of the Swift's X-ray Telescope and VHE H.E. S.S. data.
Analysis of Data Augmentation Methods for Low-Resource Maltese ASR

Recent years have seen an increased interest in the computational speech processing of Maltese, but resources remain sparse. In this paper, we consider data augmentation techniques for improving speech recognition for low-resource languages, focusing on Maltese as a test case. We consider three different types of data augmentation: unsupervised training, multilingual training and the use of synthesized speech as training data. The goal is to determine which of these techniques, or combination of them, is the most effective to improve speech recognition for languages where the starting point is a small corpus of approximately 7 hours of transcribed speech. Our results show that combining the data augmentation techniques studied here lead us to an absolute WER improvement of 15% without the use of a language model.
Spectral Gap-Based Seismic Survey Design

Seismic imaging in challenging sedimentary basins and reservoirs requires acquiring, processing, and imaging very large volumes of data (tens of terabytes). To reduce the cost of acquisition and the time from acquiring the data to producing a subsurface image, novel acquisition systems based on compressive sensing (CS), low-rank matrix recovery (LRMR), and randomized sampling have been developed and implemented. These approaches allow practitioners to achieve dense wavefield reconstruction from a substantially reduced number of field samples. However, designing acquisition surveys suited for this new sampling paradigm remains a critical and challenging role in oil, gas, and geothermal exploration. Typical random designs studied in the LRMR and CS literature are difficult to achieve by standard industry hardware. For practical purposes, a compromise between stochastic and realizable samples is needed. In this article, we propose a deterministic and computationally cheap tool to alleviate randomized acquisition design, prior to survey deployment and large-scale optimization. We consider universal and deterministic matrix completion results in the context of seismology, where a bipartite graph representation of the source–receiver layout allows for the respective spectral gap (SG) to act as a quality metric for wavefield reconstruction. We provide realistic scenarios to demonstrate the utility of the SG as a flexible tool that can be incorporated into existing survey design workflows for successful seismic data acquisition via low-rank and sparse signal recovery.
Evolution in Bayesian Supermodular Population Games

We study evolution in Bayesian supermodular population games. We define such games in the context of large populations of agents and establish the existence of a maximum and a minimum pure strategy Nash equilibria which are monotone in types. To study evolution in such games, we introduce Bayesian perturbed best response dynamics and the corresponding aggregate perturbed best response dynamic. Using the theory of cooperative dynamical systems, we show that solution trajectories of the aggregate perturbed best response dynamic converge to the set of perturbed equilibrium distributions. Using results from Ely and Sandholm (2005), we then conclude that the L1 solution trajectories of the Bayesian perturbed best response dynamic also converge to the set of Bayesian perturbed equilibria.
First-Principles Study of Orthorhombic Perovskites MgSiO3 up to 120 GPa and Its Geophysical Implications

High-pressure behaviour of orthorhombic MgSiO3 perovskite crystal is simulated by using the density functional theory and plane-wave pseudopotentials approach up to 120 GPa pressure at zero temperature. The lattice constants and mass density of the MgSiO3 crystal as functions of pressure are computed, and the corresponding bulk modulus and bulk velocity are evaluated. Our theoretical results agree well with the high-pressure experimental data. A thermodynamic method is introduced to correct the temperature effect on the 0-K first-principles results of bulk wave velocity, bulk modulus and mass density in lower mantle P/T range. Taking into account the temperature corrections, the corrected mass density, bulk modulus and bulk wave velocity of MgSiO3-perovskite are estimated from the first-principles results to be 2%, 4%, and 1% lower than the preliminary reference Earth model (PREM) profile, respectively, supporting the possibility of a pure perovskite lower mantle model.
Conservation of the surface-nesting Kermadec Petrel Pterodroma neglecta neglecta in the South Pacific: clarifying breeding ecology and the threat of avian ground predators

Summary Understanding population dynamics and impacts on island ecology remain top priorities for the conservation management of seabirds, particularly when attempting species-recovery for island restoration. Identifying suitable nesting habitat is a keystone detail in seabird restoration which can be complicated when predator pressures impact colony productivity and population viability. The surface-nesting Kermadec Petrel Pterodroma neglecta neglecta is dispersed on remote islands through the tropics and subtropics. We examined their breeding ecology on Phillip Island, Norfolk Group in the South Pacific and considered the limitations to re-establishment at their previous breeding location on Lord Howe Island (900 km south-east). On Phillip Island, the petrels were mostly monogamous with nesting locations generally within the vicinity of the previous season’s attempts. Breeding sites were limited to sloping terrain 182–228 m above the shoreline and up to 85 m from the coast in small sub-colonies under low scrubby woodland. Based on observations of 56 pairs and data gleaned from Global Light Sensing devices, we determined that breeding (incubation and provisioning) occurs in all calendar months of the year, with a laying peak in the late austral spring. Breeding success in the first year of study was 25% but improved to 56% once management of Purple Swamphens Porphyrio melanotus was implemented. Successful nests were located at sites with naturally limited egress from cleared or lightly vegetated areas. Prudent management of the petrel population on Phillip Island could aid in the regional recovery of the species and, in the process, assist the island’s restoration by significantly increasing transfers of marine-derived nutrient. Their reintroduction to Lord Howe Island is also possible but will likely rely upon consistent productivity of the Phillip Island population for founding immigrants. Moreover, successful establishment will require adaptive management of selected sites to ensure native avian predators do not overly impact breeding.
Structural properties of disk galaxies: The intrinsic equatorial ellipticity of bulges

The structural parameters of a magnitude-limited sample of 148 unbarred S0-Sb galaxies were derived to study the correlations between bulge and disk parameters as well as the probability distribution function (PDF) of the intrinsic equatorial ellipticity of bulges. A new algorithm (GASP2D) was used to perform the bidimensional bulge-disk decomposition of the J-band galaxy images extracted from the archive of the 2MASS survey. The PDF of intrinsic ellipticities was derived from the distribution of the observed ellipticities of the bulges and misalignments between the the bulges and disks. About 80% of the observed bulges are not oblate but triaxial ellipsoids. Their mean axial ratio in the equatorial plane is =0.85. There is not significant dependence of their PDF on morphology, light concentration or luminosity. This has to be explained by the different scenarios of bulge formation.
Brain Oscillations during Visuomotor Processing in Children with ASD

Autism Spectrum disorder (ASD) has been reported to often involve significant motor dysfunction in addition to other core traits. While individual differences in visual processing have also been noted in individuals with ASD. However, the brain oscillations between visual and motor processing in ASD are poorly understood due to a lack of research surrounding the topic. To elucidate this, we focused on the power-power correlation between visual- and motor-related oscillations. We recorded magnetoencephalography in 18 children with ASD and 19 IQ-matched typically developing children aged 4-7 while they pressed a button during a visual-targeted motor task. We estimated visual- and motor-related brain oscillations and focused on the gamma oscillations. We observed gamma oscillatory power changes during the visual-targeted motor task in both groups. We further found statistically significant differences in motor-related gamma power in the right primary motor cortex, but not in the left primary motor cortex between the two groups. We finally found a significant correlation between the left visual gamma power (50-60 Hz, 150-450ms) and the left motor gamma power (70-90 Hz, 0-100 ms) in the TD group. The current findings revealed differences in visuomotor processing in individuals with ASD compared to TD children. These results may be helpful in understanding the neural mechanisms underlying visuomotor processing in ASD, supporting the development of a potential biomarker for ASD.