Miracle-dz/UniFAR-FTUs huggingface dataset:
Stable rotating regular black holes

We present a rotating regular black hole whose inner horizon has zero surface gravity for any value of the spin parameter, and is therefore stable against mass inflation. Our metric is built by combining two successful strategies for regularizing singularities, i.e. by replacing the mass parameter with a function of $r$ and by introducing a conformal factor. The mass function controls the properties of the inner horizon, whose displacement away from the Kerr geometry's inner horizon is quantified in terms of a parameter $e$; while the conformal factor regularizes the singularity in a way that is parametrized by the dimensionful quantity $b$. The resulting line element not only avoids the stability issues that are common to regular black hole models endowed with inner horizons, but is also free of problematic properties of the Kerr geometry, such as the existence of closed timelike curves. While the proposed metric has all the phenomenological relevant features of singular rotating black holes -- such as ergospheres, light ring and innermost stable circular orbit -- showing a remarkable similarity to a Kerr black hole in its exterior, it allows nonetheless sizable deviations, especially for large values of the spin parameter $a$. In this sense, the proposed rotating"inner-degenarate"regular black hole solution is not only amenable to further theoretical investigations but most of all can represent a viable geometry to contrast to the Kerr one in future phenomenological tests.
Online Dynamics Learning for Predictive Control with an Application to Aerial Robots

In this work, we consider the task of improving the accuracy of dynamic models for model predictive control (MPC) in an online setting. Although prediction models can be learned and applied to model-based controllers, these models are often learned offline. In this offline setting, training data is first collected and a prediction model is learned through an elaborated training procedure. However, since the model is learned offline, it does not adapt to disturbances or model errors observed during deployment. To improve the adaptiveness of the model and the controller, we propose an online dynamics learning framework that continually improves the accuracy of the dynamic model during deployment. We adopt knowledge-based neural ordinary differential equations (KNODE) as the dynamic models, and use techniques inspired by transfer learning to continually improve the model accuracy. We demonstrate the efficacy of our framework with a quadrotor, and verify the framework in both simulations and physical experiments. Results show that our approach can account for disturbances that are possibly time-varying, while maintaining good trajectory tracking performance.
LOTUS: A (Non-) LTE Optimization Tool for Uniform Derivation of Stellar Atmospheric Parameters

Precise fundamental atmospheric stellar parameters and abundance determination of individual elements in stars are important for all stellar population studies. Non–local thermodynamic equilibrium (non-LTE; hereafter NLTE) models are often important for such high precision, however, can be computationally complex and expensive, which renders the models less utilized in spectroscopic analyses. To alleviate the computational burden of such models, we developed a robust 1D, NLTE fundamental atmospheric stellar parameter derivation tool, LOTUS, to determine the effective temperature T eff, surface gravity logg , metallicity [Fe/H], and microturbulent velocity v mic for FGK-type stars, from equivalent width (EW) measurements of Fe i and Fe ii lines. We utilize a generalized curve of growth method to take into account the EW dependencies of each Fe i and Fe ii line on the corresponding atmospheric stellar parameters. A global differential evolution optimization algorithm is then used to derive the fundamental parameters. Additionally, LOTUS can determine precise uncertainties for each stellar parameter using a Markov Chain Monte Carlo algorithm. We test and apply LOTUS on a sample of benchmark stars, as well as stars with available asteroseismic surface gravities from the K2 survey, and metal-poor stars from the Gaia-ESO and R-Process Alliance surveys. We find very good agreement between our NLTE-derived parameters in LOTUS to nonspectroscopic values on average within T eff = ±30 K, and logg = ±0.10 dex for benchmark stars. We provide open access of our code, as well as of the interpolated precomputed NLTE EW grids available on Github (the software is available on GitHub 3 3 https://github.com/Li-Yangyang/LOTUS under an MIT License, and version 0.1.1 (as the persistent version) is archived in Zenodo) and documentation with working examples on the Readthedocs book. https://github.com/Li-Yangyang/LOTUS
Conditional DETR V2: Efficient Detection Transformer with Box Queries

In this paper, we are interested in Detection Transformer (DETR), an end-to-end object detection approach based on a transformer encoder-decoder architecture without hand-crafted postprocessing, such as NMS. Inspired by Conditional DETR, an improved DETR with fast training convergence, that presented box queries (originally called spatial queries) for internal decoder layers, we reformulate the object query into the format of the box query that is a composition of the embeddings of the reference point and the transformation of the box with respect to the reference point. This reformulation indicates the connection between the object query in DETR and the anchor box that is widely studied in Faster R-CNN. Furthermore, we learn the box queries from the image content, further improving the detection quality of Conditional DETR still with fast training convergence. In addition, we adopt the idea of axial self-attention to save the memory cost and accelerate the encoder. The resulting detector, called Conditional DETR V2, achieves better results than Conditional DETR, saves the memory cost and runs more efficiently. For example, for the DC$5$-ResNet-$50$ backbone, our approach achieves $44.8$ AP with $16.4$ FPS on the COCO $val$ set and compared to Conditional DETR, it runs $1.6\times$ faster, saves $74$\% of the overall memory cost, and improves $1.0$ AP score.
Is Integer Arithmetic Enough for Deep Learning Training?

The ever-increasing computational complexity of deep learning models makes their training and deployment difficult on various cloud and edge platforms. Replacing floating-point arithmetic with low-bit integer arithmetic is a promising approach to save energy, memory footprint, and latency of deep learning models. As such, quantization has attracted the attention of researchers in recent years. However, using integer numbers to form a fully functional integer training pipeline including forward pass, back-propagation, and stochastic gradient descent is not studied in detail. Our empirical and mathematical results reveal that integer arithmetic seems to be enough to train deep learning models. Unlike recent proposals, instead of quantization, we directly switch the number representation of computations. Our novel training method forms a fully integer training pipeline that does not change the trajectory of the loss and accuracy compared to floating-point, nor does it need any special hyper-parameter tuning, distribution adjustment, or gradient clipping. Our experimental results show that our proposed method is effective in a wide variety of tasks such as classification (including vision transformers), object detection, and semantic segmentation.
SCARA: Scalable Graph Neural Networks with Feature-Oriented Optimization

Recent advances in data processing have stimulated the demand for learning graphs of very large scales. Graph Neural Networks (GNNs), being an emerging and powerful approach in solving graph learning tasks, are known to be difficult to scale up. Most scalable models apply node-based techniques in simplifying the expensive graph message-passing propagation procedure of GNN. However, we find such acceleration insufficient when applied to million- or even billion-scale graphs. In this work, we propose SCARA, a scalable GNN with feature-oriented optimization for graph computation. SCARA efficiently computes graph embedding from node features, and further selects and reuses feature computation results to reduce overhead. Theoretical analysis indicates that our model achieves sub-linear time complexity with a guaranteed precision in propagation process as well as GNN training and inference. We conduct extensive experiments on various datasets to evaluate the efficacy and efficiency of SCARA. Performance comparison with baselines shows that SCARA can reach up to 100x graph propagation acceleration than current state-of-the-art methods with fast convergence and comparable accuracy. Most notably, it is efficient to process precomputation on the largest available billion-scale GNN dataset Papers100M (111M nodes, 1.6B edges) in 100 seconds.
Dynamics of SARS-CoV-2 infection for distinct immune functions and coexistence space of SARS-CoV-2 endemic and cytotoxic T lymphocytes response-free equilibria (preprint)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly contagious virus responsible for coronavirus disease 2019 (CoViD-19). The symptoms of CoViD-19 are essentially reflected in the respiratory system, although other organs are also affected. More than 6.5 million people have died worldwide due to this disease. Despite CoViD-19 vaccines are already being administered, alternative treatments that address the immunopathology of the infection are still needed. For this end, a deeper knowledge on how human immune system responds to SARS-CoV-2 is required. In this study, we propose a non-integer order model to understand the dynamics of cytotoxic T lymphocytes (CTL) in the presence of SARS-CoV-2. We have found a space of parameters for which a SARS-CoV-2 endemic and a CTL response-free equilibria can coexist. Also, we computed the basic reproduction number and analysed the values of the model parameters in order to understand which ones inhibit or enhance the progression of the infection. Numerical simulations were performed for different values of the order of the fractional derivative and for four different proliferation functions of cytotoxic T lymphocytes.
Reconfigurable Plug-and-play Distributed Model Predictive Control for Reference Tracking

A plug-and-play model predictive control (PnP MPC) scheme is proposed for varying-topology networks to track piecewise constant references. The proposed scheme al-lows subsystems to occasionally join and leave the network while preserving asymptotic stability and recursive feasibility and comprises two main phases. In the redesign phase, passivity-based control is used to ensure that asymptotic stability of the network is preserved. In the transition phase, reconfigurable terminal ingredients are used to ensure that the distributed MPC problem is initially feasible after the PnP operation. The efficacy of the proposed scheme is evaluated by applying it to a network of mass-spring-damper systems and comparing it to a benchmark scheme. It is found that the novel redesign phase results in faster PnP operations, whereas the novel transition phase increases flexibility by accepting more requests.
Resolution in the Calculus of Structures

The relation between the sequent calculus and resolution has been mainly studied by Mints in [GM]; the textbook [TS] presents an overview. The correspondence is sufficiently clear and clean to allow several applications, especially in proof complexity. I don’t know of any application in proof search, intended as the proof theoretical framework for designing logic programming languages advocated by Miller et al. in [MNPS].
Pareto Optimal Strategies for Event-triggered Estimation

This work considers the problem of resource-performance trade-off analysis for a system consisting of an active agent and a sensor network. Specifically, we focus on Event-triggered (ET) estimation, which allows communication of measurements only when deemed useful, e.g., when Kalman filter innovations exceed some threshold. We introduce a framework for ET-threshold strategy synthesis that optimally trades off resource consumption with task performance. Our approach is based on a novel belief space discretization technique that abstracts a continuous-space dynamics model for ET estimation to a finite Markov decision process. We show this abstraction method is more efficient than alternatives and also benefits from interpretability. Then, we leverage off-the-shelf tools to compute the set of all optimal trade-offs between resource consumption and task performance. Given a choice from this set, we synthesize an ET-threshold strategy that achieves the desirable behavior. Simulated results show our approach identifies nontrivial trade-offs between performance and energy savings, with only modest computational effort.
$N$-quandles of spatial graphs

The fundamental quandle is a powerful invariant of knots, links and spatial graphs, but it is often difficult to determine whether two quandles are isomorphic. One approach is to look at quotients of the quandle, such as the $n$-quandle defined by Joyce \cite{JO}; in particular, Hoste and Shanahan \cite{HS2} classified the knots and links with finite $n$-quandles. Mellor and Smith \cite{MS} introduced the $N$-quandle of a link as a generalization of Joyce's $n$-quandle, and proposed a classification of the links with finite $N$-quandles. We generalize the $N$-quandle to spatial graphs, and investigate which spatial graphs have finite $N$-quandles. We prove basic results about $N$-quandles for spatial graphs, and conjecture a classification of spatial graphs with finite $N$-quandles, extending the conjecture for links in \cite{MS}. We verify the conjecture in several cases, and also present a possible counterexample.
Verifying raytracing/Fokker–Planck lower-hybrid current drive predictions with self-consistent full-wave/Fokker–Planck simulations

Raytracing/Fokker–Planck (FP) simulations used to model lower-hybrid current drive (LHCD) often fail to reproduce experimental results, particularly when LHCD is weakly damped. A proposed reason for this discrepancy is the lack of ‘full-wave’ effects, such as diffraction and interference, in raytracing simulations and the breakdown of the raytracing approximation. Previous studies of LHCD using non-Maxwellian full-wave/FP simulations have been performed, but these simulations were not self-consistent and enforced power conservation between the FP and full-wave code using a numerical rescaling factor. Here, we have created a fully self-consistent full-wave/FP model for LHCD that is automatically power conserving. This was accomplished by coupling an overhauled version of the non-Maxwellian TORLH full-wave solver and the CQL3D FP code using the Integrated Plasma Simulator. We performed converged full-wave/FP simulations of Alcator C-Mod discharges and compared them with raytracing. We found that excellent agreement in the power deposition profiles from raytracing and TORLH could be obtained, however, TORLH had somewhat lower current drive efficiency and broader power deposition profiles in some cases. This discrepancy appears to be a result of numerical limitations present in the TORLH model and a small amount of diffractional broadening of the TORLH wave spectrum. Our results suggest full-wave simulation of LHCD is likely not necessary as diffraction and interference represented only a small correction that could not account for the differences between simulations and experiment.
Assaying Out-Of-Distribution Generalization in Transfer Learning

Since out-of-distribution generalization is a generally ill-posed problem, various proxy targets (e.g., calibration, adversarial robustness, algorithmic corruptions, invariance across shifts) were studied across different research programs resulting in different recommendations. While sharing the same aspirational goal, these approaches have never been tested under the same experimental conditions on real data. In this paper, we take a unified view of previous work, highlighting message discrepancies that we address empirically, and providing recommendations on how to measure the robustness of a model and how to improve it. To this end, we collect 172 publicly available dataset pairs for training and out-of-distribution evaluation of accuracy, calibration error, adversarial attacks, environment invariance, and synthetic corruptions. We fine-tune over 31k networks, from nine different architectures in the many- and few-shot setting. Our findings confirm that in- and out-of-distribution accuracies tend to increase jointly, but show that their relation is largely dataset-dependent, and in general more nuanced and more complex than posited by previous, smaller scale studies.
Deeply-Learned Generalized Linear Models with Missing Data

Deep Learning (DL) methods have dramatically increased in popularity in recent years, with significant growth in their application to supervised learning problems in the biomedical sciences. However, the greater prevalence and complexity of missing data in modern biomedical datasets present significant challenges for DL methods. Here, we provide a formal treatment of missing data in the context of deeply learned generalized linear models, a supervised DL architecture for regression and classification problems. We propose a new architecture, \textit{dlglm}, that is one of the first to be able to flexibly account for both ignorable and non-ignorable patterns of missingness in input features and response at training time. We demonstrate through statistical simulation that our method outperforms existing approaches for supervised learning tasks in the presence of missing not at random (MNAR) missingness. We conclude with a case study of a Bank Marketing dataset from the UCI Machine Learning Repository, in which we predict whether clients subscribed to a product based on phone survey data. Supplementary materials for this article are available online.
Universality classes of the Anderson transitions driven by quasiperiodic potential in the three-dimensional Wigner-Dyson symmetry classes

Quasiperiodic system is an intermediate state between periodic and disordered systems with unique delocalization-localization transition driven by the quasiperiodic potential (QP). One of the intriguing questions is whether the universality class of the Anderson transition (AT) driven by QP is similar to that of the AT driven by the random potential in the same symmetry class. Here, we study the critical behavior of the ATs driven by QP in the three-dimensional (3D) Anderson model, Peierls phase model, and Ando model, which belong to the Wigner-Dyson symmetry classes. The localization length and two-terminal conductance have been calculated by the transfer matrix method, and we argue that their error estimations in statistics suffer from the correlation of QP. With the correlation under control, the critical exponents $\nu$ of the ATs driven by QP are estimated by the finite size scaling analysis of conductance, which are consistent with $\nu$'s of the ATs driven by the random potential. Moreover, the critical conductance distribution and the level spacing ratio distribution have been studied. We also find that a convolutional neural network trained by the localized/delocalized wavefunctions in a disordered system predicts the localized/delocalized wavefunctions in quasiperiodic systems. Our numerical results strongly support that the universality classes of the ATs driven by QP and random potential are similar in the 3D Wigner-Dyson symmetry classes.
Sensorless Speed Control of Brushless Doubly-Fed Reluctance Motor Drive using Secondary Flux based MRAS

Abstract Different MRAS based approaches are gaining ample importance for the speed estimation of AC machine drives because of their simplicity and direct physical interpretation. In this work, MRAS is introduced for the sensorless control of promising low-cost brushless doubly-fed reluctance motor drive under primary field orientation and maximum torque per inverter ampere (MTPIA) conditions. In this regard, secondary flux vectors are employed as the control quantity in the formulation of the proposed MRAS. All the analytical studies, in this context, are carried out in MATLAB/Simulink. The scheme is further validated by a real-time experimentation using dSPACE-1103 based brushless doubly-fed reluctance machine (BDFRM) prototype. The simulation study and test results confirm the robust performance of the proposed controller around the synchronous speed. The investigation decisively affirms the feasibility of MRAS based approach for sensorless speed control of BDFRM drive as well.
Interstellar Complex Organic Molecules in SiO-traced Massive Outflows

The interstellar medium contains dust and gas, in which molecules can proliferate at high densities and in cold conditions. Interstellar complex organic molecules (iCOMs) are C-bearing species that contain at least six atoms. As they are detected in young stellar objects, iCOMs are expected to inhabit early stages of star formation evolution. In this study, we try to determine which iCOMs are present in the outflow component of massive protostars. To do this, we analyzed the morphological extension of blue- and redshifted iCOM emission in a sample of 11 massive protostars employing mapping observations at 1 mm within a ∼1 GHz bandwidth for both the IRAM-30 m and APEX telescopes. We modeled the iCOM emission of the central pointing spectra of our objects using the XCLASS local thermal equilibrium radiative transfer code. We detected the presence of several iCOMs such as CH3OH, 13CH3OH, CH3OCHO, C2H5C15N, and (c-C3H2)CH2. In G034.41+0.24, G327.29-0.58, G328.81+0.63, G333.13-0.43, G340.97-1.02, G351.45+0.66, and G351.77-0.54, the iCOM lines show a faint broad-line profile. Due to the offset peak positions of the blue- and redshifted emission, covering from ∼0.1 to 0.5 pc, these wings are possibly related to movements external to the compact core, such as large-scale low-velocity outflows. We have also established a correlation between the parent iCOM molecule CH3OH and the shock tracer SiO, reinforcing the hypothesis that shock environments provide the conditions to boost the formation of iCOMs via gas-phase reactions.
Group Validation in Recommender Systems: Framework for Multi-layer Performance Evaluation

Evaluation of recommendation systems continues evolving, especially in recent years. There have been several attempts to standardize the assessment processes and propose replacement metrics better oriented toward measuring effective personalization. However, standard evaluation tools merely possess the capacity to provide a general overview of a system’s performance; they lack consistency and effectiveness in their use, as evidenced by most recent studies on the topic. Furthermore, traditional evaluation techniques fail to detect potentially harmful data on small subsets. Moreover, they generally lack explainable features to interpret how such minor variations could affect the system’s performance. This proposal focuses on data clustering for recommender evaluation and applies a cluster assessment technique to locate such performance issues. Our new approach, named group validation , aids in spotting critical performance variability in compact subsets of the system’s data and unravels hidden weaknesses in predictions where such unfavorable variations generally go unnoticed with typical assessment methods. Group validation for recommenders is a modular evaluation layer that complements regular evaluation and includes a new unique perspective to the evaluation process. Additionally, it allows several applications to the recommender ecosystem, such as model evolution tests, fraud/attack detection, and the capacity for hosting a hybrid model setup.