Miracle-dz/UniFAR-FTUs huggingface dataset:
A comparison of rational and neural network based approximations

Rational and neural network based approximations are efficient tools in modern approximation. These approaches are able to produce accurate approximations to nonsmooth and non-Lipschitz functions, including multivariate domain functions. In this paper we compare the efficiency of function approximation using rational approximation, neural network and their combinations. It was found that rational approximation is superior to neural network based approaches with the same number of decision variables. Our numerical experiments demonstrate the efficiency of rational approximation, even when the number of approximation parameters (that is, the dimension of the corresponding optimisation problems) is small. Another important contribution of this paper lies in the improvement of rational approximation algorithms. Namely, the optimisation based algorithms for rational approximation can be adjusted to in such a way that the conditioning number of the constraint matrices are controlled. This simple adjustment enables us to work with high dimension optimisation problems and improve the design of the neural network. The main strength of neural networks is in their ability to handle models with a large number of variables: complex models are decomposed in several simple optimisation problems. Therefore the the large number of decision variables is in the nature of neural networks.
A Description of the Air Force Real-Time Nephanalysis Model

Abstract The Air Force Global Weather Central (AFGWC) Real-Time Nephanalysis (RTNEPH) is an automated cloud model that produces a 48-km gridded analysis of cloud amount, cloud type, and cloud height. Its primary input is imagery from polar-orbiting satellites. Six main programs make up the RTNEPH. These are the satellite data mapper, the surface temperature analysis and forecast model, the satellite data processor, the conventional data processor, the merge processor, and the bogus processor. The satellite data mapper remaps incoming polar-orbiter imagery to a polar-stereographic database. The surface temperature model produces an analysis and forecast of shelter and skin temperatures for comparison to satellite-measured infrared (IR) brightness temperatures. The satellite data processor reads in the new satellite data and produces a satellite-derived cloud analysis. The conventional data processor retrieves and reformats cloud information from airport observations. The merge processor combines the satell.
Creep response of athermal amorphous solids under imposed shear stress

Yield stress materials fail when the imposed stress crosses a critical threshold. A well-known dynamical response to the applied stress is the phenomenon of creep where the cumulative deformation grows sublinearly with time, prior to failure or arrest. Using extensive molecular dynamics simulations, we study such response for a model amorphous system, in the athermal limit, and probe how the annealing history of the initial state determines the observed behaviour to an applied shear stress. Further, we analyze the microscopic dynamics in the vicinity of the yield threshold, using large systems, and characterize the spatiotemporal signatures towards arrest or flow, at different scales.
FastFill: Efficient Compatible Model Update

In many retrieval systems the original high dimensional data (e.g., images) is mapped to a lower dimensional feature through a learned embedding model. The task of retrieving the most similar data from a gallery set to a given query data is performed through a similarity comparison on features. When the embedding model is updated, it might produce features that are not comparable/compatible with features already in the gallery computed with the old model. Subsequently, all features in the gallery need to be re-computed using the new embedding model -- a computationally expensive process called backfilling. Recently, compatible representation learning methods have been proposed to avoid backfilling. Despite their relative success, there is an inherent trade-off between the new model performance and its compatibility with the old model. In this work, we introduce FastFill: a compatible model update process using feature alignment and policy based partial backfilling to promptly elevate retrieval performance. We show that previous backfilling strategies suffer from decreased performance and demonstrate the importance of both the training objective and the ordering in online partial backfilling. We propose a new training method for feature alignment between old and new embedding models using uncertainty estimation. Compared to previous works, we obtain significantly improved backfilling results on a variety of datasets: mAP on ImageNet (+4.4\%), Places-365 (+2.7\%), and VGG-Face2 (+1.3\%). Further, we demonstrate that when updating a biased model with FastFill, the minority subgroup accuracy gap promptly vanishes with a small fraction of partial backfilling.
Continuous Gravitational Waves from Galactic Neutron Stars: Demography, Detectability, and Prospects

We study the prospects for the detection of continuous gravitational signals from normal Galactic neutron stars, i.e., nonrecycled stars. We use a synthetic population generated by evolving stellar remnants in time, according to several models. We consider the most recent constraints set by all-sky searches for continuous gravitational waves and use them for our detectability criteria. We discuss the detection prospects for the current and the next generation of gravitational-wave detectors. We find that neutron stars whose ellipticity is solely caused by magnetic deformations cannot produce any detectable signal, not even by third-generation detectors. The currently detectable sources all have B ≲ 1012 G and deformations that are not solely due to the magnetic field. For these, we find in fact that the larger the magnetic field, the higher the ellipticity required for the signal to be detectable, and this ellipticity is well above the value induced by the magnetic field. Third-generation detectors such as the Einstein Telescope and Cosmic Explorer will be able to detect up to ≈250 more sources than current detectors. We briefly treat the case of recycled neutron stars with a simplified model. We find that continuous gravitational waves from these objects will likely remain elusive to detection by current detectors, but should be detectable with the next generation of detectors.
Vulnerability Mimicking Mutants

With the increasing release of powerful language models trained on large code corpus (e.g. CodeBERT was trained on 6.4 million programs), a new family of mutation testing tools has arisen with the promise to generate more"natural"mutants in the sense that the mutated code aims at following the implicit rules and coding conventions typically produced by programmers. In this paper, we study to what extent the mutants produced by language models can semantically mimic the observable behavior of security-related vulnerabilities (a.k.a. Vulnerability-mimicking Mutants), so that designing test cases that are failed by these mutants will help in tackling mimicked vulnerabilities. Since analyzing and running mutants is computationally expensive, it is important to prioritize those mutants that are more likely to be vulnerability mimicking prior to any analysis or test execution. Taking this into account, we introduce VMMS, a machine learning based approach that automatically extracts the features from mutants and predicts the ones that mimic vulnerabilities. We conducted our experiments on a dataset of 45 vulnerabilities and found that 16.6% of the mutants fail one or more tests that are failed by 88.9% of the respective vulnerabilities. More precisely, 3.9% of the mutants from the entire mutant set are vulnerability-mimicking mutants that mimic 55.6% of the vulnerabilities. Despite the scarcity, VMMS predicts vulnerability-mimicking mutants with 0.63 MCC, 0.80 Precision, and 0.51 Recall, demonstrating that the features of vulnerability-mimicking mutants can be automatically learned by machine learning models to statically predict these without the need of investing effort in defining such features.
Precision Measurement of M1 Optical Clock Transition in Ni12+

Highly charged ions (HCIs) have drawn significant interest in quantum metrology and in search for new physics. Among these, Ni12+ is considered as one of the most promising candidates for the next generation of HCI optical clocks, due to its two E1-forbidden transitions M1 and E2, which occur in the visible spectral range. In this work, we used the Shanghai-Wuhan Electron Beam Ion Trap to perform a high-precision measurement of the M1 transition wavelength. Our approach involved an improved calibration scheme for the spectra, utilizing auxiliary Ar+ lines for calibration and correction. Our final measured result of the M1 transition wavelength demonstrates a five-fold improvement in accuracy compared to our previous findings, reaching the sub-picometer level accuracy. In combination with our rigorous atomic-structure calculations to capture the electron correlations and relativistic effects, the quantum electrodynamic (QED) corrections were extracted. Moreover, comparing with an estimate of the one-electron QED contributions by using the GRASP2018 package, we found that the present experimental accuracy is high enough for testing the higher-order QED corrections for such a complex system with four electrons in the p subshell.
Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems

Causal representation learning is the task of identifying the underlying causal variables and their relations from high-dimensional observations, such as images. Recent work has shown that one can reconstruct the causal variables from temporal sequences of observations under the assumption that there are no instantaneous causal relations between them. In practical applications, however, our measurement or frame rate might be slower than many of the causal effects. This effectively creates"instantaneous"effects and invalidates previous identifiability results. To address this issue, we propose iCITRIS, a causal representation learning method that allows for instantaneous effects in intervened temporal sequences when intervention targets can be observed, e.g., as actions of an agent. iCITRIS identifies the potentially multidimensional causal variables from temporal observations, while simultaneously using a differentiable causal discovery method to learn their causal graph. In experiments on three datasets of interactive systems, iCITRIS accurately identifies the causal variables and their causal graph.
PASHA: Efficient HPO and NAS with Progressive Resource Allocation

Hyperparameter optimization (HPO) and neural architecture search (NAS) are methods of choice to obtain the best-in-class machine learning models, but in practice they can be costly to run. When models are trained on large datasets, tuning them with HPO or NAS rapidly becomes prohibitively expensive for practitioners, even when efficient multi-fidelity methods are employed. We propose an approach to tackle the challenge of tuning machine learning models trained on large datasets with limited computational resources. Our approach, named PASHA, extends ASHA and is able to dynamically allocate maximum resources for the tuning procedure depending on the need. The experimental comparison shows that PASHA identifies well-performing hyperparameter configurations and architectures while consuming significantly fewer computational resources than ASHA.
Classical analogue of quantum superdense coding and communication advantage of a single quantum

We analyze utility of communication channels in absence of any short of quantum or classical correlation shared between the sender and the receiver. To this aim, we propose a class of two-party communication games, and show that the games cannot be won given a noiseless 1-bit classical channel from the sender to the receiver. Interestingly, the goal can be perfectly achieved if the channel is assisted with classical shared randomness. This resembles an advantage similar to the quantum superdense coding phenomenon where pre-shared entanglement can enhance the communication utility of a perfect quantum communication line. Quite surprisingly, we show that a qubit communication without any assistance of classical shared randomness can achieve the goal, and hence establishes a novel quantum advantage in the simplest communication scenario. In pursuit of a deeper origin of this advantage, we show that an advantageous quantum strategy must invoke quantum interference both at the encoding step by the sender and at the decoding step by the receiver. We also study communication utility of a class of non-classical toy systems described by symmetric polygonal state spaces. We come up with communication tasks that can be achieved neither with 1-bit of classical communication nor by communicating a polygon system, whereas 1-qubit communication yields a perfect strategy, establishing a strict quantum nature of the advantage. To this end, we show that the quantum advantages are robust against imperfect encodings-decodings, making the protocols implementable with presently available quantum technologies.
NUMERICAL PREDICTION OF HEAT TRANSFER IN A CHANNEL WITH A BUILT-IN OVAL TUBE AND VARIOUS ARRANGEMENTS OF THE VORTEX GENERATORS

Three-dimensional flow structure in a rectangular channel with a built-in oval tube is quite complex due to the formation of horseshoe vortices near the front stagnation region of the tube and the Karman vortices behind it. The presence of a winglet pair in conjunction with the oval tube makes the flow structure even more complex. The interaction of the horseshoe vortices with the longitudinal vortices generated by the winglets and the interaction of the longitudinal vortices with the transverse Karman vortices culminate as a flow field dominated by the tangle of vortices. The existence of more than one pair of longitudinal vortices would further complicate the flow structure and the heat transfer behavior in the channel. The interaction between the secondary flows generated by the tube and by the winglets, as well as the interaction of the longitudinal vortices due to different arrangement of the winglets, play an important role in heat transfer enhancement. In the present article, effect of the streamwise as well as cross-stream location of the winglet pairs on heat transfer is investigated. For two pairs of winglets, possible configurations have been envisaged. All the possible arrangements have been investigated, and the optimal configuration has emerged.
The mean field games system: Carleman estimates, Lipschitz stability and uniqueness

Abstract An overdetermination is introduced in an initial condition for the second order mean field games system (MFGS). This makes the resulting problem close to the classical ill-posed Cauchy problems for PDEs. Indeed, in such a problem an overdetermination in boundary conditions usually takes place. A Lipschitz stability estimate is obtained. This estimate implies uniqueness. A new Carleman estimate is derived. This latter estimate is called “quasi-Carleman estimate”, since it contains two test functions rather than a single one in conventional Carleman estimates. These two estimates play the key role. Carleman estimates were not applied to the MFGS prior to the recent work of Klibanov and Averboukh in [M. V. Klibanov and Y. Averboukh, Lipschitz stability estimate and uniqueness in the retrospective analysis for the mean field games system via two Carleman estimates, preprint 2023, https://arxiv.org/abs/2302.10709].
On the Stability of Plane and Curved Flames

Theoretical studies on flame stability have generally been based on one of two approaches: (i) the hydrodynamic model which accounts for thermal expansion due to combustion, but ignores flame structure, and (ii) the diffusional-thermal model which considers flame structure in a prescribed constant density flow, thus ignoring thermal expansion. The present study is based on a model we derived, in which both thermal expansion and flame structure are accounted for. The model describes the dynamics of flame fronts including their stability. We discuss the stability of plane and curved flames. In particular we determine the effect of flame stretch, thermal expansion, Lewis number and Prandtl number, on stability.
A NEAR-INFRARED SURVEY OF OLD NOVAE--II. CK VULPECULAE AND V605 AQUILAE

We present near-infrared photometry of two objects that have been classified as clasical novae: CK Vulpeculae and V605 Aquilae. We were surprised to find that CK Vul was a moderately bright near-infrared source (mK = 14.13). Infrared images conclusively show that the near-infrared source is positionally coincident with the optical candidate for CK Vul. We are able to model the near-infrared + IRAS spectral energy distributions of both objects with a two-component model composed of a heavily extinguished central source, and a cooler blackbody source consistent with a cool dust shell. The combination of the model results and the outburst parameters suggest that these two objects might be of a similar nature. If we are to make this association, however, the central source luminosity of CK Vul object must have declined dramatically since the eruption in 1670. Because of its brightness, near-infrared spectroscopy of the CK Vul counterpart should be easily achievable with current instrumentation. Such observations are needed to determine the true nature of this source.
A staggered grid, Lagrangian-Eulerian remap code for 3-D MHD simulations

Abstract In this paper an approach to multidimensional magnetohydrodynamics (MHD) which correctly handles shocks but does not use an approximate Riemann solver is proposed. This approach is simple and is based on control volume averaging with a staggered grid. The method builds on the older and often overlooked technique of on each step taking a fully 3-D Lagrangian step and then conservatively remapping onto the original grid. At the remap step gradient limiters are applied so that the scheme is monotonicity-preserving. For Euler's equations this technique, combined with an appropriately staggered grid and Wilkins artificial viscosity, can give results comparable to those from approximate Riemann solvers. We show how this can be extended to include a magnetic field, maintaining the divergence-free condition and pressure positivity and then present numerical test results. Where possible a comparison with other shock capturing techniques is presented and the advantages and disadvantages of the proposed scheme are clearly explained.
Early-Life Dietary Restriction and Diet Type Affect Juvenile Brain Morphology in Spadefoot Toads (Spea bombifrons)

Abstract: Across a breadth of animal taxa, early-life environmental variation has been demonstrated to have lasting effects on later-life traits, including brain morphology. Here, we use Plains Spadefoot Toads (Spea bombifrons) to evaluate how larval diet type and amount influence later-stage, juvenile brain size and the relative sizes of brain regions. We specifically investigate whether developmental plasticity in brain morphology mirrors previously documented interspecific variation with relation to nutritional restriction and carnivory. Our findings demonstrate, contrary to expectation, that exposure to dietary restriction during the larval stage causes an increase in relative juvenile brain size. However, consistent with our predictions, consuming a prey-based shrimp diet during the larval stage results in relatively larger juvenile telencephalons, an intraspecific response that parallels an interspecific pattern in frogs where more-carnivorous species possess relatively larger telencephalons. Our results demonstrate that early-life dietary restriction and early-life diet type can generate changes in juvenile brain size and morphology in ways that may influence later-life behaviors and fitness. Further, our study suggests that intraspecific and environmentally induced changes in brain morphology can mirror interspecific divergence in brain morphology, supporting a role for developmental plasticity in promoting evolutionary change.
Annual Variation in Reproductive Traits and Skipped Breeding Opportunities in a Cool-Climate Lizard (Elgaria coerulea)

Abstract: Enforced winter inactivity affects life histories of cool-climate squamates in two ways: (1) a trade-off between oviparity and viviparity favoring the predominance of viviparity in cool climates; (2) reduction in frequency of reproduction in females. In this study we determined annual variation in reproductive traits in a cool-climate population of a viviparous lizard (Elgaria coerulea), as well as year-to-year patterns of breeding (versus skipping reproduction) of individual adult females. We collected reproductive data during a mark–recapture study in southeastern British Columbia, Canada. Gravid females were captured in the field and subsequently gave birth under seminatural conditions in the laboratory, whereupon the resulting newborns were measured. There were no differences in snout–vent length (SVL), relative tail length, or age of gravid females among years. Only head width, mass, and condition of newborns varied among years. Most litters consisted of >80% live young, and the proportion of live young in a litter increased with newborn condition and SVL. Litter size increased with maternal SVL, and mean newborn SVL was highest with medium-sized maternal relative tail length. Most females (87%) did not reproduce annually but had one or more years between reproductive events. There was no effect of year on whether a given female was reproductive, but larger females and females with intact tails were more likely to be pregnant in any year. The possible consequences of skipping reproduction in a given year include trading off immediate investment in offspring against increased growth in the current year and higher potential future reproductive output. Testing such hypotheses will be challenging but would contribute significantly to our understanding of the evolution of reproductive effort in both squamates in particular, and animals in general.
High-Power Analogues of the Turán-Kubilius Inequality, and an Application to Number Theory

An arithmetic function ƒ(n) is said to be additive if it satisfies ƒ(ab) = ƒ(a) + ƒ(b) whenever a and b are coprime integers. For such a function we define A standard form of the Turán-Kubilius inequality states that (1) holds for some absolute constant c 1, uniformly for all complex-valued additive arithmetic functions ƒ (n), and real x ≧ 2. An inequality of this type was first established by Turán [11], [12] subject to some side conditions upon the size of │ƒ(pm)│. For the general inequality we refer to [10]. This inequality, and more recently its dual, have been applied many times to the study of arithmetic functions. For an overview of some applications we refer to [2]; a complete catalogue of the applications of the inequality (1) would already be very large. For some applications of the dual of (1) see [3], [4], and [1].