Miracle-dz/UniFAR-FTUs huggingface dataset:
Structure and properties of nanocomposites prepared from ball milled 6061aluminium alloy with ZrO2 nanoparticles

Ball milling of 6061 aluminum alloy and nanocrystalline ZrO2 powder allowed to obtain homogeneous mixture of nanocrystalline aluminum solid solution of grain size near 50 nm and ZrO2 particles. The ZrO2 particles are partially transformed after milling from tetragonal to the monoclinic crystal structure. After hot pressing in vacuum at temperature of 380 °C bulk nanocomposites were obtained of porosity below 1% consisting of aluminum solid solution of the average grain size near 80 nm and nano‐size ZrO2 particles. Partial recrystallization occurred after hot pressing particularly near particles boundaries, where elongated recrystallized grains free from ZrO2 nanoparticles of thickness near 1 μm and length of a few μm were identified. Estimated fraction of recrystallized grains was below 10%. The compression strength approaching 1000 MPa was obtained in composites containing 20% ZrO2 nanoparticles, what is more than reported for ultra and nano‐grain 6XXX aluminum alloys. (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
COLREGs-Informed RRT * for Collision Avoidance of Marine Crafts

The paper proposes novel sampling strategies to compute the optimal path alteration of a surface vessel sailing in close quarters. Such strategy directly encodes the rules for safe navigation at sea, by exploiting the concept of minimal ship domain to determine the compliant region where the path deviation is to be generated. The sampling strategy is integrated within the optimal rapidly-exploring random tree algorithm, which minimizes the length of the path deviation. Further, the feasibility of the path with respect to the steering characteristics of own ship is verified by ensuring that the position of the new waypoints respects the minimum turning radius of the vessel. The proposed sampling strategy brings a significant performance improvement both in terms of optimal cost, computational speed and convergence rate.
MagDR: Mask-guided Detection and Reconstruction for Defending Deepfakes

1 Deepfakes raised serious concerns on the authenticity of visual contents. Prior works revealed the possibility to disrupt deepfakes by adding adversarial perturbations to the source data, but we argue that the threat has not been eliminated yet. This paper presents MagDR, a mask-guided detection and reconstruction pipeline for defending deepfakes from adversarial attacks. MagDR starts with a detection module that defines a few criteria to judge the abnormality of the output of deepfakes, and then uses it to guide a learnable reconstruction procedure. Adaptive masks are extracted to capture the change in local facial regions. In experiments, MagDR defends three main tasks of deepfakes, and the learned reconstruction pipeline transfers across input data, showing promising performance in defending both black-box and white-box attacks.
Gauge-symmetrization method for energy-momentum tensors in high-order electromagnetic field theories

For electromagnetic field theories, canonical energy-momentum conservation laws can be derived from the underpinning spacetime translation symmetry according to the Noether procedure. However, the canonical Energy-Momentum Tensors (EMTs) are neither symmetric nor gauge-symmetric (gauge invariant). The Belinfante-Rosenfeld (BR) method is a well-known procedure to symmetrize the EMTs, which also renders them gauge symmetric for first-order field theories. High-order electromagnetic field theories appear in the study of gyrokinetic systems for magnetized plasmas and the Podolsky system for the radiation reaction of classical charged particles. For these high-order field theories, gauge-symmetric EMTs are not necessarily symmetric and vice versa. In the present study, we develop a new gauge-symmetrization method for EMTs in high-order electromagnetic field theories. The Noether procedure is carried out using the Faraday tensor F, instead of the 4-potential A, to derive a canonical EMT T_N. We show that the gauge-dependent part of T_N can be removed using the displacement-potential tensor \mathcal{F}=\mathcal{D}*A/4\pi, where \mathcal{D} is the anti-symmetric electric displacement tensor. This method gauge-symmetrize the EMT without necessarily making it symmetric, which is adequate for applications not involving general relativity. For first-order electromagnetic field theories, such as the standard Maxwell system, \mathcal{F} reduces to the familiar BR super-potential \mathcal{S}, and the method developed can be used as a simpler procedure to calculate \mathcal{S} without employing the angular momentum tensor in 4D spacetime. When the electromagnetic system is coupled to classical charged particles, the gauge-symmetrization method for EMTs is shown to be effective as well.
Machine Learning the Sixth Dimension: Stellar Radial Velocities from 5D Phase-space Correlations

The Gaia satellite will observe the positions and velocities of over a billion Milky Way stars. In the early data releases, the majority of observed stars do not have complete 6D phase-space information. In this Letter, we demonstrate the ability to infer the missing line-of-sight velocities until more spectroscopic observations become available. We utilize a novel neural network architecture that, after being trained on a subset of data with complete phase-space information, takes in a star’s 5D astrometry (angular coordinates, proper motions, and parallax) and outputs a predicted line-of-sight velocity with an associated uncertainty. Working with a mock Gaia catalog, we show that the network can successfully recover the distributions and correlations of each velocity component for stars that fall within ∼5 kpc of the Sun. We also demonstrate that the network can accurately reconstruct the velocity distribution of a kinematic substructure in the stellar halo that is spatially uniform, even when it comprises a small fraction of the total star count.
Materials with exotic magnetic interactions: First principles computer simulations

The aim of the present work is to study the magnetic properties of seminal materials such as magnetic semiconductors (part II) and weak itinerant ferromagnets (part III). Recently magnetic semiconductors are very much in the center of attention, in particular in the emerging field of spin-based electronics. Researchers try to exploit the electrons’ magnetic properties (e.g. their spin) to transport information. Weak itinerant ferromagnets are also very promising materials because their ferromagnetism is not very stable and can easily be tuned by external parameters like pressure or stoichiometry. Weak itinerant ferromagnets can be used for example in magneto-caloric applications like refrigerators. The investigations are done by applying density functional theory (DFT), one of the most powerful techniques for gaining a detailled understanding of real materials. In general, computer simulations have several advantages if done supplementary to experiment. For example one can study ”hypothetical” materials with crystal structures or magnetic states usually not found in nature. In such an approach questions can be answered beyond the scope of experiment, e.g. why certain structures are found in nature and others are not. A thorough understanding of the relation between atomic structure and macroscopic, physical properties is the key requirement for the success in the design of so-called functional materials. In part I of this work a short introduction into the theory of electronic structure methods and magnetism is given. In part II two completely different magnetic semiconductors are discussed. The binary I/II-V compounds (prototype CaAs) in the hypothetical zinc-blende structure belong to the so-called concentrated magnetic semiconductors (CMS). CaAs and related I/II-V compounds might be prepared as thin films and in that form were very promising materials for spintronics [A]. The second class of materials investigated, Cu2O doped with Mn, Fe, Co, and Ni, belongs to the dilute magnetic semiconductors (DMS). The compounds derived from Cu2O have already been prepared in experiment. They yield very diverse physical properties ranging from room-temperature ferromagnetism to spin-glass behavior [D]. In part III metals in the perovskite structure are in the center of attention. First the series TCu3N with T=Pd, Rh, and Ru is investigated [B]. It is demonstrated that electron localization and magnetism can occur also for a material having 4d electrons, which is a very rare phenomenon. Subsequently AlCNi3 and GaCNi3 serve as examples demonstrating how tightly carbon stoichiometry and magnetism are connected [C]. Mo-cluster compounds (prototype GaMo4S8) studied in [E] owe their interesting magnetic properties to well-separated Mo4 clusters and narrow, partially filled Mo 4d cluster orbitals. Finally non-collinear magnetism, a highly challenging field, is touched by studying TNMn3 with T=Cu, Zn, Ga, and Ge.
First prototype of a biaxially textured YBa$_{2}$Cu$_{3}$O$_{7-x}$ microwave cavity in a high magnetic field for dark matter axion search

Danho Ahn, 1 Ohjoon Kwon, Woohyun Chung, ∗ Wonjun Jang, † Doyu Lee, ‡ Jhinhwan Lee, Sung Woo Youn, HeeSu Byun, Dojun Youm, and Yannis K. Semertzidis 2 Center for Axion and Precision Physics Research, Institute for Basic Science, Daejeon 34051, Republic of Korea Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea Center for Quantum Nanoscience, Institute for Basic Science, Seoul 33760, Republic of Korea Center for Artificial Low Dimensional Electronic Systems, Institute for Basic Science, Pohang 37673, Republic of Korea (Dated: March 29, 2021)
Selective Production of Hydrogen by Partial Oxidation of Methanol over ZnO-Supported Palladium Catalysts

Hydrogen production by partial oxidation of methanol (CH3OH+1/2O2↔2H2+CO2) was studied over zinc oxide-supported Pd catalysts. Catalyst performance was investigated as a function of the Pd loading and pretreatment in hydrogen environment under feed ratios O2/CH3OH (molar) of 0.3 and 0.5 at 503–543 K. High yields of hydrogen were obtained under integral operation regime. The hydrogen selectivity showed a strong dependence on the methanol conversion, which suggests that oxidation and reforming steps take place consecutively. Catalyst characterization by temperature-programmed reduction, X-ray diffraction, and X-ray photoelectron spectroscopy revealed that PdZn alloys can be formed upon reduction at moderate temperatures. A shift of +0.7 eV has been observed in the binding energy of Pd 3d5/2core level spectrum of catalyst 1% Pd/ZnO prereduced at temperatures as low as 373 K and catalyst 5% Pd/ZnO reduced at 573 K and above. The relative ease with which PdZn alloy is formed in 1% Pd/ZnO catalyst is explained in terms of a stronger metal–metal oxide interactions of the smaller Pd particles. PdZn alloys were also detected by X-ray diffraction on the 2% Pd/ZnO catalyst after on-stream operation. Pretreatments of the catalysts in hydrogen at high temperatures led to sintering of metallic particles with the subsequent drop in methanol conversion.
Improved stability conditions for systems under aperiodic sampling: model- and data-based analysis*

Discrete-time systems under aperiodic sampling may serve as a modeling abstraction for a multitude of problems arising in cyber-physical and networked control systems. Recently, model- and data-based stability conditions for such systems were obtained by rewriting them as an interconnection of a linear time-invariant system and a delay operator, and subsequently, performing a robust stability analysis using a known bound on the gain of this operator. In this paper, we refine this approach: First, we show that the delay operator is input-feedforward passive and second, we compute its gain exactly. Based on these findings, we derive improved stability conditions both in case of full model knowledge and in case only data are available. In the latter, we require only a finite-length and potentially noisy state-input trajectory of the unknown system. In two examples, we illustrate the reduced conservativeness of the proposed stability conditions over existing ones.
Equivariant Point Network for 3D Point Cloud Analysis

Features that are equivariant to a larger group of symmetries have been shown to be more discriminative and powerful in recent studies [4], [40], [5]. However, higher-order equivariant features often come with an exponentially-growing computational cost. Furthermore, it remains relatively less explored how rotation-equivariant features can be leveraged to tackle 3D shape alignment tasks. While many past approaches have been based on either non-equivariant or invariant descriptors to align 3D shapes, we argue that such tasks may benefit greatly from an equivariant framework. In this paper, we propose an effective and practical SE(3) (3D translation and rotation) equivariant network for point cloud analysis that addresses both problems. First, we present SE(3) separable point convolution, a novel framework that breaks down the 6D convolution into two separable convolutional operators alternatively performed in the 3D Euclidean and SO(3) spaces respectively. This significantly reduces the computational cost without compromising the performance. Second, we introduce an attention layer to effectively harness the expressiveness of the equivariant features. While jointly trained with the network, the attention layer implicitly derives the intrinsic local frame in the feature space and generates attention vectors that can be integrated with different alignment tasks. We evaluate our approach through extensive studies and visual interpretations. The empirical results demonstrate that our proposed model outperforms strong baselines in a variety of benchmarks. Code is available at https://github.com/nintendops/EPN_PointCloud.
Minimum Scan Cover and Variants: Theory and Experiments

We consider a spectrum of geometric optimization problems motivated by contexts such as satellite communication and astrophysics. In the problem Minimum Scan Cover with Angular Costs, we are given a graph G that is embedded in Euclidean space. The edges of G need to be scanned, i.e., probed from both of their vertices. To scan their edge, two vertices need to face each other; changing the heading of a vertex incurs some cost in terms of energy or rotation time that is proportional to the corresponding rotation angle. Our goal is to compute schedules that minimize the following objective functions: (i) in Minimum Makespan Scan Cover (MSC-MS), this is the time until all edges are scanned; (ii) in Minimum Total Energy Scan Cover (MSC-TE), the sum of all rotation angles; and (iii) in Minimum Bottleneck Energy Scan Cover (MSC-BE), the maximum total rotation angle at one vertex. Previous theoretical work on MSC-MS revealed a close connection to graph coloring and the cut cover problem, leading to hardness and approximability results. In this article, we present polynomial-time algorithms for one-dimensional (1D) instances of MSC-TE and MSC-BE but NP-hardness proofs for bipartite 2D instances. For bipartite graphs in 2D, we also give 2-approximation algorithms for both MSC-TE and MSC-BE. Most importantly, we provide a comprehensive study of practical methods for all three problems. We compare three different mixed-integer programming and two constraint programming approaches and show how to compute provably optimal solutions for geometric instances with up to 300 edges. Additionally, we compare the performance of different meta-heuristics for even larger instances.
Detection, growth quantification and malignancy prediction of pulmonary nodules using deep convolutional networks in follow-up CT scans

The use of computed tomography (CT) scan images has increased dramatically over the last decades, becoming a crucial tool for the diagnosis and follow-up of malignant lung tumours [1,2]. Radiologists are able to detect, measure and monitor the evolution of abnormal tissues in their lungs by visually inspecting CT scans of the patient’s chest. However, tumours, specially at early stages, are complex to detect and diagnose due to large heterogeneities in their morphology, size, texture, localization and growth rates [3]. Moreover, spatial resolution in computerized axial tomography images is often limited by the acquisition protocol [4]. This leads to some ambiguities and conflicts for radiologists when having to determine the next study, whether to discharge the patient from the follow-up, or whether to resolve a clinical intervention for the patient [5]. Therefore, the experience and expertise of physicians is fundamental for the early diagnosis and prognosis of lung cancer. Unfortunately, the aggressive nature of this disease, its important incidence in the adult population, and the constant need of specialized professionals, make it necessary to have accurate and efficient tools to reduce the workload of clinicians as well as to help them in making critical decisions.
The muon Smasher’s guide

We lay out a comprehensive physics case for a future high-energy muon collider, exploring a range of collision energies (from 1 to 100 TeV) and luminosities. We highlight the advantages of such a collider over proposed alternatives. We show how one can leverage both the point-like nature of the muons themselves as well as the cloud of electroweak radiation that surrounds the beam to blur the dichotomy between energy and precision in the search for new physics. The physics case is buttressed by a range of studies with applications to electroweak symmetry breaking, dark matter, and the naturalness of the weak scale. Furthermore, we make sharp connections with complementary experiments that are probing new physics effects using electric dipole moments, flavor violation, and gravitational waves. An extensive appendix provides cross section predictions as a function of the center-of-mass energy for many canonical simplified models.
Structural and magnetic properties of Ce(Fe,M)12Nx interstitial compounds, M=Ti, V, Cr, and Mo

Stabilizing the 1‐12 phase with cerium was possible using a special heat treatment. The structural and magnetic properties of Ce(Fe,M)12 and their nitrides, for M=Ti, V, Cr, Mo, etc., were investigated using x‐ray‐diffraction and magnetic measurements. The effects of interstitial nitrogen atoms on lattice parameters, Curie temperature, saturation magnetization, and magnetocrystalline anisotropy are reported. A profound change was found on the magnetocrystalline anisotropy. As an example, in the case of CeMo2Fe10Nx, the easy magnetization direction changes from c axis to the basal plane depending on the nitrogen content x.
Andreev Molecule in Parallel InAs Nanowires

Coupling individual atoms fundamentally changes the state of matter: electrons bound to atomic cores become delocalized turning an insulating state to a metallic one. A chain of atoms could lead to more exotic states if the tunneling takes place via the superconducting vacuum and can induce topologically protected excitations like Majorana or parafermions. Although coupling a single atom to a superconductor is well studied, the hybridization of two sites with individual tunability was not reported yet. The peculiar vacuum of the Bardeen–Cooper–Schrieffer (BCS) condensate opens the way to annihilate or generate two electrons from the bulk resulting in a so-called Andreev molecular state. By employing parallel nanowires with an Al shell, two artificial atoms were created at a minimal distance with an epitaxial superconducting link between. Hybridization via the BCS vacuum was observed and the spectrum of an Andreev molecule as a function of level positions was explored for the first time.
Heterogeneous Graph Neural Networks for Multi-label Text Classification

Multi-label text classiﬁcation (MLTC) is an attractive and challenging task in natural language processing (NLP). Compared with single-label text classiﬁcation, MLTC has a wider range of application in practice. In this paper, we propose a heterogeneous graph convolutional network model to solve the MLTC problem by modeling tokens and labels as nodes in a heterogeneous graph. In this way, we are able to take into account multiple relationships including token-level relationships. Besides, the model allows a good explain-ability as the token-label edges are exposed. We evaluate our method on three real-world datasets and the experimental results show that it achieves signiﬁcant improvements and out-performs state-of-the-art comparison methods.
Revisiting the Color–Color Selection: Submillimeter and AGN Properties of NUV–r–J Selected Quiescent Galaxies

We examine the robustness of the color–color selection of quiescent galaxies (QGs) against contamination of dusty star-forming galaxies using the latest submillimeter data. We selected 18,304 QG candidates out to z ∼ 3 using the commonly adopted NUV–r–J selection based on the high-quality multiwavelength COSMOS2015 catalog. Using extremely deep 450 and 850 μm catalogs from the latest JCMT SCUBA-2 Large Programs, S2COSMOS and STUDIES, as well as the Atacama Large Millimeter/submillimeter Array submillimeter, VLA 3 GHz, and Spitzer MIPS 24 μm catalogs, we identified luminous, dusty, star-forming galaxies among the QG candidates. We also conducted stacking analyses in the SCUBA-2 450 and 850 μm images to look for less-luminous dusty galaxies among the QG candidates. By cross matching to the 24 μm and 3 GHz data, we were able to identify a subgroup of “IR-radio-bright” QGs that possess strong 450 and 850 μm stacking signals. The potential contamination of these luminous and less-luminous dusty galaxies accounts for approximately 10% of the color-selected QG candidates. In addition, there exists a spatial correlation between the luminous star-forming galaxies and the QGs at a ≲60 kpc scale. Finally, we found a high QG fraction among radio active galactic nuclei (AGNs) at z < 1.5. Our data show a strong correlation between QGs and radio AGNs, which may suggest a connection between the quenching process and the radio-mode AGN feedback.
Continual Speaker Adaptation for Text-to-Speech Synthesis

Training a multi-speaker Text-to-Speech (TTS) model from scratch is computationally expensive and adding new speakers to the dataset requires the model to be re-trained. The naive solution of sequential fine-tuning of a model for new speakers can lead to poor performance of older speakers. This phenomenon is known as catastrophic forgetting. In this paper, we look at TTS modeling from a continual learning perspective, where the goal is to add new speakers without forgetting previous speakers. Therefore, we first propose an experimental setup and show that serial fine-tuning for new speakers can cause the forgetting of the earlier speakers. Then we exploit two well-known techniques for continual learning, namely experience replay and weight regularization. We reveal how one can mitigate the effect of degradation in speech synthesis diversity in sequential training of new speakers using these methods. Finally, we present a simple extension to experience replay to improve the results in extreme setups where we have access to very small buffers.