Miracle-dz/UniFAR-FTUs huggingface dataset:
Error bound analysis of the stochastic parareal algorithm

Stochastic parareal (SParareal) is a probabilistic variant of the popular parallel-in-time algorithm known as parareal. Similarly to parareal, it combines fine- and coarse-grained solutions to an ordinary differential equation (ODE) using a predictor-corrector (PC) scheme. The key difference is that carefully chosen random perturbations are added to the PC to try to accelerate the location of a stochastic solution to the ODE. In this paper, we derive superlinear and linear mean-square error bounds for SParareal applied to nonlinear systems of ODEs using different types of perturbations. We illustrate these bounds numerically on a linear system of ODEs and a scalar nonlinear ODE, showing a good match between theory and numerics.
Wiedemann-Franz law in graphene

We analyze a well-known experimental work [J. Crossno et al., Science 351, 1058 (2016)] which reported on the failure of the Wiedemann-Franz law in graphene at $T\sim 10$-$100\,\mathrm{K}$, attributing this failure to the non-Fermi liquid nature of the Dirac fluid associated with undoped intrinsic graphene. In spite of serious theoretical efforts, the reported observations remain unexplained. Our detailed quantitative analysis based on Fermi liquid considerations, which apply to extrinsic doped graphene, establishes that one possible explanation for the reported observations is the opening of a gap at the Dirac point, induced perhaps by the boron nitride substrate. We suggest that more experiments are necessary to resolve the issue, and we believe that the experiment may not actually have anything to do with Dirac fluid hydrodynamics, but relates to finite-temperature low-density bipolar diffusive transport by electrons and holes in the presence of short- and long-range disorder, and phonons.
Covariant effective action for an antisymmetric tensor field

Covariant quantization of rank-2 antisymmetric fields is non-trivial due to additional symmetries of the gauge parameters. We present an intuitive way to deal with this additional symmetry of gauge parameters in terms of geometrical understanding of field space. We generalize the DeWitt-Vilkovisky covariant effective action formalism for quantization of such theories. As an application, we quantize a massive rank-2 antisymmetric field using the covariant effective action approach and reproduce previously obtained results.
The influence of metallicity on a combined stellar and disk evolution

The effects of an accretion disk are crucial to understanding the evolution of young stars. During the combined evolution, stellar and disk parameters influence each other, motivating a combined stellar and disk model. This makes a combined numerical model, evolving the disk alongside the star, the next logical step in the progress of studying early stellar evolution. We aim to understand the effects of metallicity on the accretion disk and the stellar spin evolution during the T~Tauri phase. We combine the numerical treatment of a hydrodynamic disk with stellar evolution, including a stellar spin model, allowing a self-consistent calculation of the back-reactions between the individual components. We present the self-consistent theoretical evolution of T-Tauri stars coupled to a stellar disk. We find that disks in low metallicity environments are heated differently and have shorter lifetimes, compared to their solar metallicity counterparts. Differences in stellar radii, the contraction rate of the stellar radius, and the shorter disk lifetimes result in faster rotation of low metallicity stars. We present an additional explanation for the observed short disk lifetimes in low metallicity clusters. A combination of our model with previous studies (e.g., a metallicity-based photo-evaporation) could help to understand disk evolution and dispersal at different metallicities. Furthermore, the stellar spin evolution model includes several important effects, previously ignored (e.g., the stellar magnetic field strength and a realistic calculation of the disk lifetime) and we motivate to include our results as initial or input parameters for further spin evolution models, covering the stellar evolution towards and during the main sequence.
Deep Learning for Time Series Anomaly Detection: A Survey

Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare. The presence of anomalies can indicate novel or unexpected events, such as production faults, system defects, or heart fluttering, and is therefore of particular interest. The large size and complex patterns of time series have led researchers to develop specialised deep learning models for detecting anomalous patterns. This survey focuses on providing structured and comprehensive state-of-the-art time series anomaly detection models through the use of deep learning. It providing a taxonomy based on the factors that divide anomaly detection models into different categories. Aside from describing the basic anomaly detection technique for each category, the advantages and limitations are also discussed. Furthermore, this study includes examples of deep anomaly detection in time series across various application domains in recent years. It finally summarises open issues in research and challenges faced while adopting deep anomaly detection models.
A comparison of several AI techniques for authorship attribution on Romanian texts

Determining the author of a text is a difficult task. Here, we compare multiple Artificial Intelligence techniques for classifying literary texts written by multiple authors by taking into account a limited number of speech parts (prepositions, adverbs, and conjunctions). We also introduce a new dataset composed of texts written in the Romanian language on which we have run the algorithms. The compared methods are artificial neural networks, multi-expression programming, k-nearest neighbour, support vector machines, and decision trees with C5.0. Numerical experiments show, first of all, that the problem is difficult, but some algorithms are able to generate acceptable error rates on the test set.
onlineFGO: Online Continuous-Time Factor Graph Optimization with Time-Centric Multi-Sensor Fusion for Robust Localization in Large-Scale Environments

Accurate and consistent vehicle localization in urban areas is challenging due to the large-scale and complicated environments. In this paper, we propose onlineFGO, a novel time-centric graph-optimization-based localization method that fuses multiple sensor measurements with the continuous-time trajectory representation for vehicle localization tasks. We generalize the graph construction independent of any spatial sensor measurements by creating the states deterministically on time. As the trajectory representation in continuous-time enables querying states at arbitrary times, incoming sensor measurements can be factorized on the graph without requiring state alignment. We integrate different GNSS observations: pseudorange, deltarange, and time-differenced carrier phase (TDCP) to ensure global reference and fuse the relative motion from a LiDAR-odometry to improve the localization consistency while GNSS observations are not available. Experiments on general performance, effects of different factors, and hyper-parameter settings are conducted in a real-world measurement campaign in Aachen city that contains different urban scenarios. Our results show an average 2D error of 0.99m and consistent state estimation in urban scenarios.
Automatic Number Plate Recognition (ANPR) with YOLOv3-CNN

We present a YOLOv3-CNN pipeline for detecting vehicles, segregation of number plates, and local storage of final recognized characters. Vehicle identification is performed under various image correction schemes to determine the effect of environmental factors (angle of perception, luminosity, motion-blurring, and multi-line custom font etc.). A YOLOv3 object detection model was trained to identify vehicles from a dataset of traffic images. A second YOLOv3 layer was trained to identify number plates from vehicle images. Based upon correction schemes, individual characters were segregated and verified against real-time data to calculate accuracy of this approach. While characters under direct view were recognized accurately, some numberplates affected by environmental factors had reduced levels of accuracy. We summarize the results under various environmental factors against real-time data and produce an overall accuracy of the pipeline model.
Optimal exact quantum algorithm for the promised element distinctness problem

The element distinctness problem is to determine whether a string x = ( x 1 , . . . , x N ) of N elements contains two elements of the same value (a.k.a colliding pair), for which Ambainis proposed an optimal quantum algorithm. The idea behind Ambainis’ algorithm is to ﬁrst reduce the problem to the promised version in which x is promised to contain at most one colliding pair, and then design an algorithm A requiring O ( N 2 / 3 ) queries based on quantum walk search for the promise problem. However, A is probabilistic and may fail to give the right answer. We thus, in this work, design an exact quantum algorithm for the promise problem which never errs and requires O ( N 2 / 3 ) queries. This algorithm is proved optimal. Technically, we modify the quantum walk search operator on quasi-Johnson graph to have arbitrary phases, and then use Jordan’s lemma as the analyzing tool to reduce the quantum walk search operator to the generalized Grover’s operator. This allows us to utilize the recently proposed ﬁxed-axis-rotation (FXR) method for exact quantum search, and hence achieve 100% success.
Adaptive Real Time Exploration and Optimization for Safety-Critical Systems

We consider the problem of decision-making under uncertainty in an environment with safety constraints. Many business and industrial applications rely on real-time optimization with changing inputs to improve key performance indicators. In the case of unknown environmental characteristics, real-time optimization becomes challenging, particularly for the satisfaction of safety constraints. We propose the ARTEO al-gorithm, where we cast multi-armed bandits as a mathematical programming problem subject to safety constraints and learn the environmental characteristics through changes in optimization inputs and through exploration. We quantify the uncertainty in unknown characteristics by using Gaussian processes and incorporate it into the utility function as a contribution which drives exploration. We adaptively control the size of this contribution using a heuristic in accordance with the requirements of the environment. We guarantee the safety of our algorithm with a high probability through conﬁdence bounds constructed under the regularity assumptions of Gaussian processes. Compared to existing safe-learning approaches, our algorithm does not require an exclusive exploration phase and follows the optimization goals even in the explored points, which makes it suitable for safety-critical systems. We demonstrate the safety and efﬁciency of our approach with two experiments: an industrial process and an online bid optimization benchmark problem.
Robust Model Selection of Non Tree-Structured Gaussian Graphical Models

We consider the problem of learning the structure underlying a Gaussian graphical model when the variables (or subsets thereof) are corrupted by independent noise. A recent line of work establishes that even for tree-structured graphical models, only partial structure recovery is possible and goes on to devise algorithms to identify the structure up to an (unavoidable) equivalence class of trees. We extend these results beyond trees and consider the model selection problem under noise for non tree-structured graphs, as tree graphs cannot model several real-world scenarios. Although unidentifiable, we show that, like the tree-structured graphs, the ambiguity is limited to an equivalence class. This limited ambiguity can help provide meaningful clustering information (even with noise), which is helpful in computer and social networks, protein-protein interaction networks, and power networks. Furthermore, we devise an algorithm based on a novel ancestral testing method for recovering the equivalence class. We complement these results with finite sample guarantees for the algorithm in the high-dimensional regime.
Electroadhesive Auxetics as Programmable Layer Jamming Skins for Formable Crust Shape Displays

Shape displays are a class of haptic devices that enable whole-hand haptic exploration of 3D surfaces. However, their scalability is limited by the mechanical complexity and high cost of traditional actuator arrays. In this paper, we propose using electroadhesive auxetic skins as a strain-limiting layer to create programmable shape change in a continuous (“formable crust”) shape display. Auxetic skins are manufactured as flexible printed circuit boards with dielectric-laminated electrodes on each auxetic unit cell (AUC), using monolithic fabrication to lower cost and assembly time. By layering multiple sheets and applying a voltage between electrodes on subsequent layers, electroadhesion locks individual AUCs, achieving a maximum in-plane stiffness variation of 7.6x with a power consumption of 50 $\boldsymbol{\mu \mathrm{W}/\text{AUC}. }$ We first characterize an individual AUC and compare results to a kinematic model. We then validate the ability of a 5x5 AUC array to actively modify its own axial and transverse stiffness. Finally, we demonstrate this array in a continuous shape display as a strain-limiting skin to programmatically modulate the shape output of an inflatable LDPE pouch. Integrating electroadhesion with auxetics enables new capabilities for scalable, low-profile, and low-power control of flexible robotic systems.
NaCo/SAM observations of sources at the Galactic Center

Sparse aperture masking (SAM) interferometry combined with Adaptive Optics (AO) is a technique that is uniquely suited to investigate structures near the diffraction limit of large telescopes. The strengths of the technique are a robust calibration of the Point Spread Function (PSF) while maintaining a relatively high dynamic range. We used SAM+AO observations to investigate the circumstellar environment of several bright sources with infrared excess in the central parsec of the Galaxy. For our observations, unstable atmospheric conditions as well as significant residuals after the background subtraction presented serious problems for the standard approach of calibrating SAM data via interspersed observations of reference stars. We circumvented these difficulties by constructing a synthesized calibrator directly from sources within the field-of-view. When observing crowded fields, this novel method can boost the efficiency of SAM observations because it renders interspersed calibrator observations unnecessary. Here, we presented the first NaCo/SAM images reconstructed using this method.
BLUE STRAGGLER MASSES FROM PULSATION PROPERTIES. I. THE CASE OF NGC 6541

We used high spatial resolution images acquired with the Wide Field Camera 3 on board Hubble Space Telescope to probe the population of variable blue straggler stars (BSSs) in the central region of the poorly studied Galactic globular cluster NGC 6541. The time sampling of the acquired multiwavelength (F390W, F555W, and F814W) data allowed us to discover three WUMa stars and nine SX Phoenicis. Periods, mean magnitudes, and pulsation modes have been derived for the nine SX Phoenicis, and their masses have been estimated by using pulsation equations obtained from linear nonadiabatic models. We found masses in the range 1.0–1.1 M☉, with an average value of 1.06 ± 0.09 M☉ (σ = 0.04), significantly in excess of the cluster main-sequence turn-off mass (∼0.75 M☉). A mild trend between mass and luminosity seems also to be present. The computed pulsation masses turn out to be in very good agreement with the predictions of evolutionary tracks for single stars, indicating values in the range ∼1.0–1.2 M☉ for most of the BSS population, in agreement with what was discussed in a number of previous studies.
Job Scheduling in Datacenters using Constraint Controlled RL

—This paper studies a model for online job scheduling in green datacenters. In green datacenters, resource availability depends on the power supply from the renewables. Intermittent power supply from renewables leads to intermittent resource availability, inducing job delays (and associated costs). Green datacenter operators must intelligently manage their workloads and available power supply to extract maximum beneﬁts. The scheduler’s objective is to schedule jobs on a set of resources to maximize the total value (revenue) while minimizing the overall job delay. A trade-off exists between achieving high job value on the one hand and low expected delays on the other. Hence, the aims of achieving high rewards and low costs are in opposi-tion. In addition, datacenter operators often prioritize multiple objectives, including high system utilization and job completion. To accomplish the opposing goals of maximizing total job value and minimizing job delays, we apply the Proportional-Integral-Derivative (PID) Lagrangian methods in Deep Reinforcement Learning to job scheduling problem in the green datacenter environment. Lagrangian methods are widely used algorithms for constrained optimization problems. We adopt a controls perspective to learn the Lagrange multiplier with proportional, integral, and derivative control, achieving favorable learning dynamics. Feedback control deﬁnes cost terms for the learning agent, monitors the cost limits during training, and continuously adjusts the learning parameters to achieve stable performance. Our experiments demonstrate improved performance compared to scheduling policies without the PID Lagrangian methods. Experimental results illustrate the effectiveness of the Constraint Controlled Reinforcement Learning (CoCoRL) scheduler that simultaneously satisﬁes multiple objectives.
IK-Geo: Unified Robot Inverse Kinematics Using Subproblem Decomposition

This paper presents the open-source robot inverse kinematics (IK) solver IK-Geo, the fastest general IK solver based on published literature. In this unifying approach, IK for any 6-DOF all-revolute (6R) manipulator is decomposed into six canonical geometric subproblems solved by intersecting circles with other geometric objects. We present new efficient and singularity-robust solutions to these subproblems using geometric and linear algebra methods. IK-Geo finds all IK solutions including singular solutions and sometimes least-squares solutions by solving for subproblem solutions in all cases, including in a continuous and sometimes least-squares sense when a solution does not exist. Robots are classified into kinematic families based on cases of intersecting or parallel joint axes, and robots in the same family use the same IK algorithm. 6R robots with three intersecting or parallel axes are solved in closed form, and all solutions are found exactly without iteration. Other 6R robots are efficiently solved by searching for zeros of an error function of one or two joint angles. The subproblem and IK solutions are easy to understand, implement, test, and modify, meaning this method is readily ported to new languages and environments. We connect our geometric method with less efficient but more robust polynomial-based methods: rather than using search, subproblems and error functions may be written in terms of the tangent half-angle of one joint. This results in a system of multivariate polynomial equations from which the univariate polynomial with zeros corresponding to IK solutions is readily derived.
Deep observations of Cas A with MAGIC indicate it is no PeVatron

It is widely believed that Galactic Cosmic Rays (CR) are accelerated in Supernova Remnants (SNRs) through the process of diffusive shock acceleration. In this scenario, particles should be accelerated up to energies around 1 PeV (the so-called knee) and emit gamma rays. To test this hypothesis precise measurements of the gamma-ray spectra of young SNRs at TeV energies are needed. Among the already known SNRs, Cassiopea A (Cas A) appears as one of the best candidates for such studies, because it is relatively young (about 300 years) and it has been largely studied in radio and X-ray bands, which constrains essential parameters for testing emission models. Here we present the results of a multi-year campaign of Cas A observations with the MAGIC Imaging Atmospheric Cherenkov Telescopes between December 2014 and October 2016, for a total of 158 hours of good-quality data. We obtained a spectrum of the source from $\sim$100GeV to $\sim$10TeV and fit it assuming it follows a power-law distribution, with and without exponential cut-off. We found, for the first time in the Very High Energy regime (VHE, $\gtrsim$50GeV), observational evidence for the present of a cut-off in Cas A: an exponential cut-off at about 3.5 TeV is preferred with 4.6 $\sigma$ significance over a pure power-law scenario. Assuming that TeV gamma rays are produced by hadronic processes and that there is no significant cosmic ray diffusion, this indicates that Cas A is not a PeVatron (PeV accelerator) at its present age.
Set based velocity shaping for robotic manipulators

—We develop a new framework for trajectory plan- ning on predeﬁned paths, for general N-link manipulators. Different from previous approaches generating open-loop min- imum time controllers or pre-tuned motion proﬁles by time-scaling, we establish analytic algorithms that recover all ini- tial conditions that can be driven to the desirable target set while adhering to environment constraints. More technologically relevant, we characterise families of corresponding safe state-feedback controllers with several desirable properties. A key enabler in our framework is the introduction of a state feedback template, that induces ordering properties between trajectories of the resulting closed-loop system. The proposed structure allows working on the nonlinear system directly in both the analysis and synthesis problems. Both ofﬂine computations and online implementation are scalable with respect to the number of links of the manipulator. The results can potentially be used in a series of challenging problems: Numerical experiments on a commercial robotic manipulator demonstrate that efﬁcient online implementation is possible.
Variational Quantum Kernels with Task-Specific Quantum Metric Learning

: Quantum kernel methods, i.e., kernel methods with quantum kernels, offer distinct advantages as a hybrid quantum-classical approach to quantum machine learning (QML), including applicability to Noisy Intermediate-Scale Quantum (NISQ) devices and usage for solving all types of machine learning problems. Kernel methods rely on the notion of similarity between points in a higher (possibly infinite) dimensional feature space. For machine learning, the notion of similarity assumes that points close in the feature space should be close in the machine learning task space. In this paper, we discuss the use of variational quantum kernels with task-specific quantum metric learning to generate optimal quantum embeddings (a.k.a. quantum feature encodings) that are specific to machine learning tasks. Such task-specific optimal quantum embeddings, implicitly supporting feature selection, are valuable not only to quantum kernel methods in improving the latter’s performance, but they can also be valuable to non-kernel QML methods based on parameterized quantum circuits (PQCs) as pretrained embeddings and for transfer learning. This further demonstrates the quantum utility, and quantum advantage (with classically-intractable quantum embeddings), of quantum kernel methods.