Miracle-dz/UniFAR-FTUs huggingface dataset:
Spin-lattice couplings in a skyrmion multilayers of Pd-Fe/Ir(111)

Pd-Fe/Ir(111) has attracted tremendous attention for next-generation spintronics devices due to existence of magnetic skyrmions with the external magnetic field. Our density functional theoretical calculations in combination with spin dynamics simulation suggest that the spin spiral phase in fcc stacked Pd-Fe/Ir(111) flips into the skyrmion lattice phase around B$_{ext} \sim$ 6 T. This leads to the microscopic understanding of the thermodynamic and kinetic behaviours affected by the intrinsic spin-lattice couplings (SLCs) in this skyrmion material for magneto-mechanical properties. Here we calculate fully relativistic SLC parameters from first principle computations and investigate the effect of SLC on dynamical magnetic interactions in skyrmion multilayers Pd-Fe/Ir(111). The exchange interactions arising from next nearest-neighbors (NN) in this material are highly frustrated and responsible for enhancing skyrmion stability. We report the larger spin-lattice effect on both dynamical Heisenberg exchanges and Dzyaloshinskii-Moriya interactions for next NN compared to NN which is in contrast with recently observed spin-lattice effect in bulk bcc Fe and CrI$_3$ monolayer. Based on our analysis, we find that the effective measures of SLCs in fcc (hcp) stacking of Pd-Fe/Ir(111) are $\sim 2.71 ( \sim 2.36)$ and $\sim 14.71 ( \sim21.89)$ times stronger for NN and next NN respectively, compared to bcc Fe. The linear regime of displacement for SLC parameters is $\leq$ 0.02 {\AA} which is 0.72\% of the lattice constant for Pd-FeIr(111). The microscopic understanding of SLCs provided by our current study could help in designing spintronic devices based on thermodynamic properties of skyrmion multilayers.
Observational Causality Testing

In prior work we have introduced an asymptotic threshold of sufficient randomness for causal inference from observational data. In this paper we extend that prior work in three main ways. First, we show how to empirically estimate a lower bound for the randomness from measures of concordance transported from studies of monozygotic twins. Second, we generalize our methodology for application on a finite population and we introduce methods to implement finite population corrections. Third, we generalize our methodology in another direction by incorporating measured covariate data into the analysis. The first extension represents a proof of concept that observational causality testing is possible. The second and third extensions help to make observational causality testing more practical. As a theoretical and indirect consequence of the third extension we formulate and introduce a novel criterion for covariate selection. We demonstrate our proposed methodology for observational causality testing with numerous example applications.
TSO Games - On the decidability of safety games under the total store order semantics (extended version with appendix)

We consider an extension of the classical Total Store Order (TSO) semantics by expanding it to turn-based 2-player safety games. During her turn, a player can select any of the communicating processes and perform its next transition. We consider different formulations of the safety game problem depending on whether one player or both of them transfer messages from the process buffers to the shared memory. We give the complete decidability picture for all the possible alternatives.
Advanced Underwater Image Restoration in Complex Illumination Conditions

Underwater image restoration has been a challenging problem for decades since the advent of underwater photography. Most solutions focus on shallow water scenarios, where the scene is uniformly illuminated by the sunlight. However, the vast majority of uncharted underwater terrain is located beyond 200 meters depth where natural light is scarce and artificial illumination is needed. In such cases, light sources co-moving with the camera, dynamically change the scene appearance, which make shallow water restoration methods inadequate. In particular for multi-light source systems (composed of dozens of LEDs nowadays), calibrating each light is time-consuming, error-prone and tedious, and we observe that only the integrated illumination within the viewing volume of the camera is critical, rather than the individual light sources. The key idea of this paper is therefore to exploit the appearance changes of objects or the seafloor, when traversing the viewing frustum of the camera. Through new constraints assuming Lambertian surfaces, corresponding image pixels constrain the light field in front of the camera, and for each voxel a signal factor and a backscatter value are stored in a volumetric grid that can be used for very efficient image restoration of camera-light platforms, which facilitates consistently texturing large 3D models and maps that would otherwise be dominated by lighting and medium artifacts. To validate the effectiveness of our approach, we conducted extensive experiments on simulated and real-world datasets. The results of these experiments demonstrate the robustness of our approach in restoring the true albedo of objects, while mitigating the influence of lighting and medium effects. Furthermore, we demonstrate our approach can be readily extended to other scenarios, including in-air imaging with artificial illumination or other similar cases.
Recurrence-Free Survival Prediction for Anal Squamous Cell Carcinoma Chemoradiotherapy using Planning CT-based Radiomics Model

Objectives: Approximately 30% of non-metastatic anal squamous cell carcinoma (ASCC) patients will experience recurrence after chemoradiotherapy (CRT), and currently available clinical variables are poor predictors of treatment response. We aimed to develop a model leveraging information extracted from radiation pretreatment planning CT to predict recurrence-free survival (RFS) in ASCC patients after CRT. Methods: Radiomics features were extracted from planning CT images of 96 ASCC patients. Following pre-feature selection, the optimal feature set was selected via step-forward feature selection with a multivariate Cox proportional hazard model. The RFS prediction was generated from a radiomics-clinical combined model based on an optimal feature set with five repeats of five-fold cross validation. The risk stratification ability of the proposed model was evaluated with Kaplan-Meier analysis. Results: Shape- and texture-based radiomics features significantly predicted RFS. Compared to a clinical-only model, radiomics-clinical combined model achieves better performance in the testing cohort with higher C-index (0.80 vs 0.73) and AUC (0.84 vs 0.79 for 1-year RFS, 0.84 vs 0.78 for 2-year RFS, and 0.86 vs 0.83 for 3-year RFS), leading to distinctive high- and low-risk of recurrence groups (p<0.001). Conclusions: A treatment planning CT based radiomics and clinical combined model had improved prognostic performance in predicting RFS for ASCC patients treated with CRT as compared to a model using clinical features only.
Identifying frequency decorrelated dust residuals in B-mode maps by exploiting the spectral capability of bolometric interferometry

Astrophysical polarized foregrounds represent the most critical challenge in Cosmic Microwave Background (CMB) B-mode experiments. Multi-frequency observations can be used to constrain astrophysical foregrounds to isolate the CMB contribution. However, recent observations indicate that foreground emission may be more complex than anticipated. We investigate how the increased spectral resolution provided by band splitting in Bolometric Interferometry (BI) through a technique called spectral imaging can help control the foreground contamination in the case of unaccounted Galactic dust frequency decorrelation along the line-of-sight. We focus on the next generation ground-based CMB experiment CMB-S4, and compare its anticipated sensitivities, frequency and sky coverage with a hypothetical version of the same experiment based on BI. We perform a Monte-Carlo analysis based on parametric component separation methods (FGBuster and Commander) and compute the likelihood on the recovered tensor-to-scalar ratio. The main result of this analysis is that spectral imaging allows us to detect systematic uncertainties on r from frequency decorrelation when this effect is not accounted for in component separation. Conversely, an imager would detect a biased value of r and would be unable to spot the presence of a systematic effect. We find a similar result in the reconstruction of the dust spectral index, where we show that with BI we can measure more precisely the dust spectral index also when frequency decorrelation is present. The in-band frequency resolution provided by BI allows us to identify dust LOS frequency decorrelation residuals where an imager of similar performance would fail. This opens the prospect to exploit this potential in the context of future CMB polarization experiments that will be challenged by complex foregrounds in their quest for B-modes detection.
The Detection of Higher-order Millimeter Hydrogen Recombination Lines in the Large Magellanic Cloud

We report the first extragalactic detection of the higher-order millimeter hydrogen recombination lines (Δn > 2). The γ-, ϵ-, and η-transitions have been detected toward the millimeter continuum source N 105–1 A in the star-forming region N 105 in the Large Magellanic Cloud with the Atacama Large Millimeter/submillimeter Array. We use the H40α line, the brightest of the detected recombination lines (H40α, H36β, H50β, H41γ, H57γ, H49ϵ, H53η, and H54η), to determine the electron temperature and study ionized gas kinematics in the region, and the 3 mm free–free continuum emission to determine the physical parameters: the size, emission measure, and electron density. We compare the physical properties of N 105–1 A to a large sample of Galactic compact and ultracompact (UC) H ii regions and conclude that N 105–1 A is similar to the most luminous (L > 105 L ⊙) UC H ii regions in the Galaxy. N 105–1 A is ionized by an O5.5 V star; it is deeply embedded in its natal molecular clump, and likely associated with a (proto)cluster. We incorporate high-resolution molecular line data including CS, SO, SO2, and CH3OH (∼0.12 pc), and HCO+ and CO (∼0.087 pc) to explore the molecular environment of N 105–1 A. Based on the CO data, we find evidence for a cloud–cloud collision that likely triggered star formation in the region. We find no clear outflow signatures, but the presence of filaments and streamers indicates ongoing accretion onto the clump hosting the UC H ii region. Sulfur chemistry in N 105–1 A is consistent with the accretion shock model predictions.
Prediction of Giant Tunneling Magnetoresistance in RuO$_{2}$/TiO$_{2}$/RuO$_{2}$ (110) Antiferromagnetic Tunnel Junctions

Using first-principles quantum-transport calculations, we investigate spin-dependent electronic and transport properties of antiferromagnetic tunnel junctions (AFMTJs) that consist of (110)-oriented antiferromagnetic (AFM) metal RuO$_{2}$ electrodes and an insulating TiO$_{2}$ tunneling barrier. We predict the emergence of a giant tunneling magnetoresistance (TMR) effect in a wide energy window, a series of barrier layer thicknesses, and different interface terminations, indicating the robustness of this effect. We show that the predicted TMR cannot be explained in terms of the global transport spin-polarization of RuO$_{2}$ (110) but is well understood based on matching the momentum-dependent spin-polarized conduction channels of the two RuO$_{2}$ (110) electrodes. We predict oscillations of TMR with increasing barrier thickness, indicating a non-negligible contribution from the perfectly epitaxial interfaces. Our work helps the understanding of the physics of TMR in AFMTJs and aids in realizing efficient AFM spintronic devices.
Sistem Pendukung Keputusan Pemilihan Daerah Prioritas Penanganan Stunting pada Balita Menggunakan Metode TOPSIS (Studi Kasus : Kota Pontianak)

Stunting adalah kondisi gagal tumbuh pada anak balita akibat dari kekurangan gizi kronis sehingga tinggi atau panjang badan anak terlalu pendek untuk usianya. Pada tahun 2017 dari hasil Pantauan Status Gizi (PSG) yang dilakukan oleh pemerintah pusat di Kota Pontianak tercatat sebanyak 28% balita dinyatakan terkena stunting. Hasil PSG 2017 tersebut mengalami perbaikan dengan angka persentase penurunan stunting sebanyak 4% dari tahun sebelumnya, yaitu 32% di tahun 2016. Untuk dapat mendukung dalam mengurangi angka persentase stunting di Kota Pontianak, dibuatlah sebuah aplikasi sistem pendukung keputusan pemilihan daerah prioritas penanganan stunting pada balita. Sistem Pendukung Keputusan (SPK) merupakan suatu sistem berbasis komputer yang menghasilkan berbagai alternatif keputusan untuk membantu pihak pengambil keputusan. Sistem pendukung keputusan bukan merupakan alat pengambilan keputusan, melainkan sebagai sistem yang membantu pengambil keputusan yang dilengkapi informasi dari data yang telah diolah dengan relevan dan diperlukan untuk membuat keputusan tentang suatu masalah dengan lebih cepat dan akurat. Metode TOPSIS merupakan metode yang digunakan pada aplikasi ini. Metode TOPSIS adalah suatu metode alternatif terbaik yang memiliki jarak terpendek dari solusi ideal positif dan jarak terjauh dari solusi ideal negatif. Hasil keluaran dari aplikasi ini adalah urutan nilai preferensi dari nilai terbesar hingga terkecil berdasarkan alternatif yang menjadi pilihan. Adapun hasil pengujian kuesioner yang sudah dilakukan responden memberi tanggapan dengan rata-rata nilai 80% dari tiga aspek penilaian yaitu aspek rekayasa perangkat lunak, aspek fungsionalitas perangkat lunak, dan aspek visual perangkat lunak.
Offensive Hebrew Corpus and Detection using BERT

Offensive language detection has been well studied in many languages, but it is lagging behind in low-resource languages, such as Hebrew. In this paper, we present a new offensive language corpus in Hebrew. A total of 15,881 tweets were retrieved from Twitter. Each was labeled with one or more of five classes (abusive, hate, violence, pornographic, or none offensive) by Arabic-Hebrew bilingual speakers. The annotation process was challenging as each annotator is expected to be familiar with the Israeli culture, politics, and practices to understand the context of each tweet. We fine-tuned two Hebrew BERT models, HeBERT and AlephBERT, using our proposed dataset and another published dataset. We observed that our data boosts HeBERT performance by 2% when combined with D_OLaH. Fine-tuning AlephBERT on our data and testing on D_OLaH yields 69% accuracy, while fine-tuning on D_OLaH and testing on our data yields 57% accuracy, which may be an indication to the generalizability our data offers. Our dataset and fine-tuned models are available on GitHub and Huggingface.
A Bayesian Nonparametric Method to Adjust for Unmeasured Confounding with Negative Controls

Unmeasured confounding bias is among the largest threats to the validity of observational studies. Although sensitivity analyses and various study designs have been proposed to address this issue, they do not leverage the growing availability of auxiliary data accessible through open data platforms. Using negative controls has been introduced in the causal inference literature as a promising approach to account for unmeasured confounding bias. In this paper, we develop a Bayesian nonparametric method to estimate a causal exposure-response function (CERF). This estimation method effectively utilizes auxiliary information from negative control variables to adjust for unmeasured confounding completely. We model the CERF as a mixture of linear models. This strategy offers the dual advantage of capturing the potential nonlinear shape of CERFs while maintaining computational efficiency. Additionally, it leverages closed-form results that hold under the linear model assumption. We assess the performance of our method through simulation studies. The results demonstrate the method's ability to accurately recover the true shape of the CERF in the presence of unmeasured confounding. To showcase the practical utility of our approach, we apply it to adjust for a potential unmeasured confounder when evaluating the relationship between long-term exposure to ambient $PM_{2.5}$ and cardiovascular hospitalization rates among the elderly in the continental U.S. We implement our estimation procedure in open-source software to ensure transparency and reproducibility and make our code publicly available.
The Case for Asymmetric Systolic Array Floorplanning

The widespread proliferation of deep learning applications has triggered the need to accelerate them directly in hardware. General Matrix Multiplication (GEMM) kernels are elemental deep-learning constructs and they inherently map onto Systolic Arrays (SAs). SAs are regular structures that are well-suited for accelerating matrix multiplications. Typical SAs use a pipelined array of Processing Elements (PEs), which communicate with local connections and pre-orchestrated data movements. In this work, we show that the physical layout of SAs should be asymmetric to minimize wirelength and improve energy efficiency. The floorplan of the SA adjusts better to the asymmetric widths of the horizontal and vertical data buses and their switching activity profiles. It is demonstrated that such physically asymmetric SAs reduce interconnect power by 9.1% when executing state-of-the-art Convolutional Neural Network (CNN) layers, as compared to SAs of the same size but with a square (i.e., symmetric) layout. The savings in interconnect power translate, in turn, to 2.1% overall power savings.
Fluctuation covariance-based study of roll-streak dynamics in Poiseuille flow turbulence

Although the roll-streak (R-S) is fundamentally involved in the dynamics of wall-turbulence, the physical mechanism responsible for its formation and maintenance remains controversial. In this work we investigate the dynamics maintaining the R-S in turbulent Poiseuille flow at $R=1650$. Spanwise collocation is used to remove spanwise displacement of the streaks and associated flow components, which isolates the streamwise-mean flow R-S component and the second-order statistics of the streamwise-varying fluctuations that are collocated with the R-S. This streamwise-mean/fluctuation partition of the dynamics facilitates exploiting insights gained from the analytic characterization of turbulence in the second-order statistical state dynamics (SSD), referred to as S3T, and its closely associated restricted nonlinear dynamics (RNL) approximation. Symmetry of the statistics about the streak centerline permits separation of the fluctuations into sinuous and varicose components. The Reynolds stress forcing induced by the sinuous and varicose fluctuations acting on the R-S is shown to reinforce low- and high-speed streaks respectively. This targeted reinforcement of streaks by the Reynolds stresses occurs continuously as the fluctuation field is strained by the streamwise-mean streak and not intermittently as would be associated with streak-breakdown events. The Reynolds stresses maintaining the streamwise-mean roll arise primarily from the dominant POD modes of the fluctuations, which can be identified with the time average structure of optimal perturbations growing on the streak. These results are consistent with a universal process of R-S growth and maintenance in turbulent shear flow arising from torques generated by straining turbulent fluctuations, which was identified using the S3T SSD.
Effective Description of the Quantum Damped Harmonic Oscillator: Revisiting the Bateman Dual System

In this work, we present a quantization scheme for the damped harmonic oscillator (QDHO) using a framework known as momentous quantum mechanics. Our method relies on a semiclassical dynamical system derived from an extended classical Hamiltonian, where the phase-space variables are given by expectation values of observables and quantum dispersions. The significance of our study lies in its potential to serve as a foundational basis for the effective description of open quantum systems (OQS), and the description of dissipation in quantum mechanics. By employing the Bateman's dual model as the initial classical framework, and undergoing quantization, we demonstrate that our description aligns exceptionally well with the well-established Lindblad master equation. Furthermore, our approach exhibits robustness and broad applicability in the context of OQS, rendering it a versatile and powerful tool for studying various phenomena. We intend to contribute to the advancement of quantum physics by providing an effective means of quantizing the damped harmonic oscillator and shedding light on the behavior of open quantum systems.
Parameter Dependent Robust Control Invariant Sets for LPV Systems with Bounded Parameter Variation Rate

Real-time measurements of the scheduling parameter of linear parameter-varying (LPV) systems enables the synthesis of robust control invariant (RCI) sets and parameter dependent controllers inducing invariance. We present a method to synthesize parameter-dependent robust control invariant (PD-RCI) sets for LPV systems with bounded parameter variation, in which invariance is induced using PD-vertex control laws. The PD-RCI sets are parameterized as configuration-constrained polytopes that admit a joint parameterization of their facets and vertices. The proposed sets and associated control laws are computed by solving a single semidefinite programing (SDP) problem. Through numerical examples, we demonstrate that the proposed method outperforms state-of-the-art methods for synthesizing PD-RCI sets, both with respect to conservativeness and computational load.
3D simulations of positive streamers in air in a strong external magnetic ﬁeld

We study how external magnetic fields from 0 to 40~T influence positive streamers in atmospheric pressure air, using 3D PIC-MCC (particle-in-cell, Monte Carlo collision) simulations. When a magnetic field B is applied perpendicular to the background electric field E, the streamers deflect towards the +B and -B direction which results in a branching into two main channels. With a stronger magnetic field the angle between the branches increases, and for the 40 T case the branches grow almost parallel to the magnetic field. Due to the ExB drift of electrons we also observe a streamer deviation in the opposite -ExB direction, where the minus sign appears because positive streamers propagate opposite to the electron drift velocity. The deviation due to this ExB effect is smaller than the deviation parallel to B. In both cases of B perpendicular and parallel to E, the streamer radius decreases with the magnetic field strength. We relate our observations to the effects of electric and magnetic fields on electron transport and reaction coefficients.
Algorithm Selection for Software Verification using Graph Neural Networks

The field of software verification has produced a wide array of algorithmic techniques that can prove a variety of properties of a given program. It has been demonstrated that the performance of these techniques can vary up to 4 orders of magnitude on the same verification problem. Even for verification experts, it is difficult to decide which tool will perform best on a given problem. For general users, deciding the best tool for their verification problem is effectively impossible. In this work, we present Graves, a selection strategy based on graph neural networks (GNNs). Graves generates a graph representation of a program from which a GNN predicts a score for a verifier that indicates its performance on the program. We evaluate Graves on a set of 10 verification tools and over 8000 verification problems and find that it improves the state-of-the-art in verification algorithm selection by 12%, or 8 percentage points. Further, it is able to verify 9% more problems than any existing verifier on our test set. Through a qualitative study on model interpretability, we find strong evidence that the Graves’ model learns to base its predictions on factors that relate to the unique features of the algorithmic techniques.