Miracle-dz/UniFAR-FTUs huggingface dataset:
Vibronic Effects on the Quantum Tunnelling of Magnetisation in Single-Molecule Magnets

Single-molecule magnets are among the most promising platforms for achieving molecular-scale data storage and processing. Their magnetisation dynamics are determined by the interplay between electronic and vibrational degrees of freedom, which can couple coherently, leading to complex vibronic dynamics. Building on an ab initio description of the electronic and vibrational Hamiltonians, we formulate a non-perturbative vibronic model of the low-energy magnetic degrees of freedom in a single-molecule magnet, which we benchmark against ﬁeld-dependent magnetisation measurements. Describing the low-temperature magnetism of the complex in terms of magnetic polarons, we are able to quantify the vibronic contribution to the quantum tunnelling of the magnetisation. Despite collectively enhancing magnetic relaxation, we observe that speciﬁc vibrations suppress quantum tunnelling by enhancing the magnetic axiality of the complex. Finally, we discuss how this observation might impact the current paradigm to chemical design of new high-performance single-molecule magnets, promoting vibrations to an active role rather than just regarding them as sources of noise and decoher-ence.
Motion Classification Based on Harmonic Micro-Doppler Signatures Using a Convolutional Neural Network

We demonstrate the classification of common motions of held objects using the harmonic micro-Doppler signatures scattered from harmonic radio-frequency tags. Harmonic tags capture incident signals and retransmit at harmonic frequencies, making them easier to distinguish from clutter. We characterize the motion of tagged handheld objects via the time-varying frequency shift of the harmonic signals (harmonic Doppler). With complex micromotions of held objects, the time-frequency response manifests complex micro-Doppler signatures that can be used to classify the motions. We developed narrow-band harmonic tags at 2.4/4.8 GHz that support frequency scalability for multi-tag operation, and a harmonic radar system to transmit a 2.4 GHz continuous-wave signal and receive the scattered 4.8 GHz harmonic signal. Experiments were conducted to mimic four common motions of held objects from 35 subjects in a cluttered indoor environment. A 7-layer convolutional neural network (CNN) multi-label classifier was developed and obtained a real time classification accuracy of 94.24%, with a response time of 2 seconds per sample with a data processing latency of less than 0.5 seconds.
Coevolution of Dust and Chemistry in Galaxy Simulations with a Resolved Interstellar Medium

Nearby dwarf irregular galaxies are ideal laboratories for studying the interstellar medium (ISM) at low metallicity, which is expected to be common for galaxies at very high redshift being observed by JWST. We present the first high-resolution (∼0.2 pc) hydrodynamical simulations of an isolated low-metallicity (0.1 Z ⊙) dwarf galaxy coupled with a time-dependent chemistry network and a dust evolution model where dust is locally produced and destroyed by various processes. To accurately model carbon monoxide (CO), we post-process the simulations with a detailed chemistry network including the time-dependent effect of molecular hydrogen (H2). Our model successfully reproduces the observed star formation rate and CO(1–0) luminosity (L CO). We find that dust growth in dense gas is required to reproduce the observed L CO otherwise CO would be completely photodissociated. In contrast, the H2 abundance is extremely small and is insensitive to dust growth, leading to a CO-to-H2 conversion factor that is only slightly higher than the Milky Way value despite the low metallicity. An observationally inferred dust-to-gas ratio is thus underestimated if adopting the metallicity-dependent CO-to-H2 conversion factor. The newly produced dust in dense gas mixes with the ISM through supernova feedback without being completely destroyed by sputtering, which leads to galactic outflows 20%–50% dustier than the ISM, providing a possible source for intergalactic dust.
Do the Findings of Document and Passage Retrieval Generalize to the Retrieval of Responses for Dialogues?

A number of learned sparse and dense retrieval approaches have recently been proposed and proven effective in tasks such as passage retrieval and document retrieval. In this paper we analyze with a replicability study if the lessons learned generalize to the retrieval of responses for dialogues, an important task for the increasingly popular field of conversational search. Unlike passage and document retrieval where documents are usually longer than queries, in response ranking for dialogues the queries (dialogue contexts) are often longer than the documents (responses). Additionally, dialogues have a particular structure, i.e. multiple utterances by different users. With these differences in mind, we here evaluate how generalizable the following major findings from previous works are: (F1) query expansion outperforms a no-expansion baseline; (F2) document expansion outperforms a no-expansion baseline; (F3) zero-shot dense retrieval underperforms sparse baselines; (F4) dense retrieval outperforms sparse baselines; (F5) hard negative sampling is better than random sampling for training dense models. Our experiments -- based on three different information-seeking dialogue datasets -- reveal that four out of five findings (F2-F5) generalize to our domain
The influence of shear flow on the α- and γ-effects in helical MHD turbulence

We investigate the α-effect and the turbulent transport of magnetic flux (γ-effect) by considering a turbulence driven by an isotropic helical forcing in the presence of a background large-scale shear flow. Due to shear flow, the turbulence is highly anisotropic. The effect of this shear-induced anisotropy on the structure of the electromotive force is computed self-consistently. We show that for weak shear, magnetic pumping (γ-effect) appears, and the α-effect becomes highly anisotropic: the component of the α-effect in the direction of the shear is smaller than the one in the direction perpendicular to the shear with non-vanishing non-diagonal components. However, a sufficiently strong shear leads to severe quenching of the γ- and α-effects due to shear stabilisation.
A localized approach to generalized Tur\'an problems

Generalized Tur\'an problems ask for the maximum number of copies of a graph $H$ in an $n$-vertex, $F$-free graph, denoted by ex$(n,H,F)$. We show how to extend the new, localized approach of Brada\v{c}, Malec, and Tompkins to generalized Tur\'{a}n problems. We weight the copies of $H$ (typically taking $H=K_t$), instead of the edges, based on the size of the largest clique, path, or star containing the vertices of the copy of $H$, and in each case prove a tight upper bound on the sum of the weights. A consequence of our new localized theorems is an asymptotic determination of ex$(n,H,K_{1,r})$ for every $H$ having at least one dominating vertex and mex$(m,H,K_{1,r})$ for every $H$ having at least two dominating vertices.
Shock Corrugation to the Rescue of the Internal Shock Model in Microquasars: The Single-scale Magnetohydrodynamic View

Questions regarding the energy dissipation in astrophysical jets remain open to date, despite numerous attempts to limit the diversity of the models. Some of the most popular models assume that energy is transferred to particles via internal shocks, which develop as a consequence of the nonuniform velocity of the jet matter. In this context, we study the structure and energy deposition of colliding plasma shells, focusing our attention on the case of initially inhomogeneous shells. This leads to the formation of distorted (corrugated) shock fronts—a setup that has recently been shown to revive particle acceleration in relativistic magnetized perpendicular shocks. Our study shows that the radiative power of the far downstream of nonrelativistic magnetized perpendicular shocks is moderately enhanced with respect to the flat-shock cases. Based on the decay rate of the downstream magnetic field, we make predictions for multiwavelength polarization properties.
Guidelines for the use of spatially varying coefficients in species distribution models

Species distribution models (SDMs) are increasingly applied across macroscales using detection‐nondetection data. These models typically assume that a single set of regression coefficients can adequately describe species–environment relationships and/or population trends. However, such relationships often show nonlinear and/or spatially varying patterns that arise from complex interactions with abiotic and biotic processes that operate at different scales. Spatially varying coefficient (SVC) models can readily account for variability in the effects of environmental covariates. Yet, their use in ecology is relatively scarce due to gaps in understanding the inferential benefits that SVC models can provide compared to simpler frameworks.Here we demonstrate the inferential benefits of SVC SDMs, with a particular focus on how this approach can be used to generate and test ecological hypotheses regarding the drivers of spatial variability in population trends and species–environment relationships. We illustrate the inferential benefits of SVC SDMs with simulations and two case studies: one that assesses spatially varying trends of 51 forest bird species in the eastern United States over two decades and a second that evaluates spatial variability in the effects of five decades of land cover change on grasshopper sparrow (Ammodramus savannarum) occurrence across the continental United States.We found strong support for SVC SDMs compared to simpler alternatives in both empirical case studies. Factors operating at fine spatial scales, accounted for by the SVCs, were the primary divers of spatial variability in forest bird occurrence trends. Additionally, SVCs revealed complex species–habitat relationships with grassland and cropland area for grasshopper sparrow, providing nuanced insights into how future land use change may shape its distribution. These applications display the utility of SVC SDMs to help reveal the environmental factors that drive species distributions across both local and broad scales. We conclude by discussing the potential applications of SVC SDMs in ecology and conservation.
Self-Training Guided Disentangled Adaptation for Cross-Domain Remote Sensing Image Semantic Segmentation

Deep convolutional neural networks (DCNNs) based remote sensing (RS) image semantic segmentation technology has achieved great success used in many real-world applications such as geographic element analysis. However, strong dependency on annotated data of specific scene makes it hard for DCNNs to fit different RS scenes. To solve this problem, recent works gradually focus on cross-domain RS image semantic segmentation task. In this task, different ground sampling distance, remote sensing sensor variation and different geographical landscapes are three main factors causing dramatic domain shift between source and target images. To decrease the negative influence of domain shift, we propose a self-training guided disentangled adaptation network (ST-DASegNet). We first propose source student backbone and target student backbone to respectively extract the source-style and target-style feature for both source and target images. Towards the intermediate output feature maps of each backbone, we adopt adversarial learning for alignment. Then, we propose a domain disentangled module to extract the universal feature and purify the distinct feature of source-style and target-style features. Finally, these two features are fused and served as input of source student decoder and target student decoder to generate final predictions. Based on our proposed domain disentangled module, we further propose exponential moving average (EMA) based cross-domain separated self-training mechanism to ease the instability and disadvantageous effect during adversarial optimization. Extensive experiments and analysis on benchmark RS datasets show that ST-DASegNet outperforms previous methods on cross-domain RS image semantic segmentation task and achieves state-of-the-art (SOTA) results. Our code is available at https://github.com/cv516Buaa/ST-DASegNet.
Globular Clusters in NGC 4839 Falling into Coma: Evidence for the Second Infall?

NGC 4839 is the brightest galaxy (cD) of the NGC 4839 group at R ≈ 1 Mpc in the southwest of the Coma cluster, which is known to be falling into Coma. However, it has been controversial whether it is in the first phase of infall or in the second phase of infall after passing the Coma center. We present a wide field study of globular clusters (GCs) in NGC 4839 and its environment based on Hyper Suprime-Cam gr images in the Subaru archive. We compare the GC system of NGC 4839 with that of NGC 4816, which is the brightest member (S0) of the nearby group and lies at a similar distance in the west from the Coma center. Interestingly the spatial distribution of the GCs in NGC 4839 is significantly more compact than that of the GCs in NGC 4816. In addition, the radial number density profile of the GCs in NGC 4839 shows an abrupt drop at R N4839 ≈ 80 kpc, while that of the GCs in NGC 4816 shows a continuous slow decline even in the outer region at 80 < R N4816 < 500 kpc. The effective radius of the NGC 4839 GC system is about 3 times smaller than that of the NGC 4816 GC system. This striking difference can be explained if NGC 4839 lost a significant fraction of the GCs in its outskirt when it passed through Coma. This supports strongly the second-infall scenario where the NGC 4839 passed the Coma center about 1.6 Gyr ago, and began the second infall after reaching the apocenter in the southwest recently.
Data assimilation finite element method for the linearized Navier-Stokes equations with higher order polynomial approximation

In this article, we design and analyze an arbitrary-order stabilized finite element method to approximate the unique continuation problem for laminar steady flow described by the linearized incompressible Navier--Stokes equation. We derive quantitative local error estimates for the velocity, which account for noise level and polynomial degree, using the stability of the continuous problem in the form of a conditional stability estimate. Numerical examples illustrate the performances of the method with respect to the polynomial order and perturbations in the data. We observe that the higher order polynomials may be efficient for ill-posed problems, but are also more sensitive for problems with poor stability due to the ill-conditioning of the system.
LVRNet: Lightweight Image Restoration for Aerial Images under Low Visibility

Learning to recover clear images from images having a combination of degrading factors is a challenging task. That being said, autonomous surveillance in low visibility conditions caused by high pollution/smoke, poor air quality index, low light, atmospheric scattering, and haze during a blizzard, etc, becomes even more important to prevent accidents. It is thus crucial to form a solution that can not only result in a high-quality image but also which is efficient enough to be deployed for everyday use. However, the lack of proper datasets available to tackle this task limits the performance of the previous methods proposed. To this end, we generate the LowVis-AFO dataset, containing 3647 paired dark-hazy and clear images. We also introduce a new lightweight deep learning model called Low-Visibility Restoration Network (LVRNet). It outperforms previous image restoration methods with low latency, achieving a PSNR value of 25.744 and an SSIM of 0.905, hence making our approach scalable and ready for practical use.
Single Flux Quantum-Based Digital Control of Superconducting Qubits in a Multichip Module

The single flux quantum (SFQ) digital superconducting logic family has been proposed for the scalable control of next-generation superconducting qubit arrays. In the initial implementation, SFQ-based gate fidelity was limited by quasiparticle (QP) poisoning induced by the dissipative on-chip SFQ driver circuit. In this work, we introduce a multi-chip module architecture to suppress phonon-mediated QP poisoning. Here, the SFQ elements and qubits are fabricated on separate chips that are joined with In bump bonds. We use interleaved randomized benchmarking to characterize the fidelity of SFQ-based gates, and we demonstrate an error per Clifford gate of 1.2(1)%, an order-of-magnitude reduction over the gate error achieved in the initial realization of SFQ-based qubit control. We use purity benchmarking to quantify the contribution of incoherent error at 0.96(2)%; we attribute this error to photon-mediated QP poisoning mediated by the resonant mm-wave antenna modes of the qubit and SFQ-qubit coupler. We anticipate that a straightforward redesign of the SFQ driver circuit to limit the bandwidth of the SFQ pulses will eliminate this source of infidelity, allowing SFQ-based gates with fidelity approaching theoretical limits, namely 99.9% for resonant sequences and 99.99% for more complex pulse sequences involving variable pulse-to-pulse separation.
Probing Ganymede’s Atmosphere with HST Lyα Images in Transit of Jupiter

We report results from far-ultraviolet observations by the Hubble Space Telescope of Jupiter’s largest moon, Ganymede, transiting across the planet’s dayside hemisphere. Within a targeted campaign on 2021 September 9 two exposures were taken during one transit passage to probe for attenuation of Jupiter's hydrogen Lyα dayglow above the moon limb. The background dayglow is slightly attenuated over an extended region around Ganymede, with stronger attenuation in the second exposure, when Ganymede was near the planet’s center. In the first exposure, when the moon was closer to Jupiter’s limb, the effects from the Ganymede corona are hardly detectable, likely because the Jovian Lyα dayglow is spectrally broader and less intense at this viewing geometry. The obtained vertical H column densities of around (1–2) × 1012 cm−2 are consistent with previous results. Constraining angular variability around Ganymede’s disk, we derive an upper limit on a local H2O column density of (2–3) × 1016 cm−2, such as could arise from outgassing plumes in regions near the observed moon limb.
Understanding Concept Identification as Consistent Data Clustering Across Multiple Feature Spaces

Identifying meaningful concepts in large data sets can provide valuable insights into engineering design problems. Concept identification aims at identifying non-overlapping groups of design instances that are similar in a joint space of all features, but which are also similar when considering only subsets of features. These subsets usually comprise features that characterize a design with respect to one specific context, for example, constructive design parameters, performance values, or operation modes. It is desirable to evaluate the quality of design concepts by considering several of these feature subsets in isolation. In particular, meaningful concepts should not only identify dense, well separated groups of data instances, but also provide non-overlapping groups of data that persist when considering pre-defined feature subsets separately. In this work, we propose to view concept identification as a special form of clustering algorithm with a broad range of potential applications beyond engineering design. To illustrate the differences between concept identification and classical clustering algorithms, we apply a recently proposed concept identification algorithm to two synthetic data sets and show the differences in identified solutions. In addition, we introduce the mutual information measure as a metric to evaluate whether solutions return consistent clusters across relevant subsets. To support the novel understanding of concept identification, we consider a simulated data set from a decision-making problem in the energy management domain and show that the identified clusters are more interpretable with respect to relevant feature subsets than clusters found by common clustering algorithms and are thus more suitable to support a decision maker.
Exploring Magnetic Loops and Serpentine Fields in the Quiet Sun with the GRIS-IFU

Synthetic observations produced from radiative magnetohydrodynamic simulations have predicted that higher polarization fractions in the quiet solar photosphere would be revealed by increasing the total integration time of observations at GREGOR resolutions. We present recently acquired disk center observations of the Fe i 15648.5 Å line obtained with the GREGOR telescope equipped with the GRIS-IFU during excellent seeing conditions, showing exceptionally high polarization fractions. Our observations reveal an internetwork region with a majority (>60%) of magnetized pixels displaying a clear transverse component of the magnetic field. This result is in stark contrast to previous disk center GRIS-IFU observations in this spectral line, which had predominantly vertical magnetic fields in the deep photosphere. At the same time, the median magnetic field strength is weaker than previous GRIS-IFU observations, indicating that the larger fraction of polarization signals cannot be explained by a more active target. We use the Stokes Inversion based on Response functions (SIR) code to analyze the data, performing over 45 million inversions, and interrogate the impact of two conflicting approaches to the treatment of noise on the retrieval of the magnetic inclination and azimuth. We present several case studies of the zoo of magnetic features present in these data, including small-scale magnetic loops that seem to be embedded in a sea of magnetism, and serpentine fields, focusing on regions where full-vector spectropolarimetry has been achieved. We also present a new open-source Python 3 analysis tool, SIR Explorer, which we use to examine the dynamics of these small-scale magnetic features.
A Mixed Stirring Mechanism for Debris Discs with Giant and Dwarf Planetary Perturbations

Debris discs consist of belts of bodies ranging in size from dust grains to planetesimals; these belts are visible markers of planetary systems around other stars that can reveal the influence of extrasolar planets through their shape and structure. Two key stirring mechanisms — self-stirring by planetesimals and secular perturbation by an external giant planet — have been identified to explain the dynamics of planetesimal belts; their relative importance has been studied independently, but are yet to be considered in combination. In this work we perform a suite of 286 N-body simulations exploring the evolution of debris discs over 1 Gyr, combining the gravitational perturbations of both dwarf planets embedded in the discs, and an interior giant planet. Our systems were somewhat modeled after the architecture of the outer Solar system: a Solar mass star, a single massive giant planet at 30 au (MGP = 10 to 316 M⊕), and a debris disc formed by 100 massive dwarf planets and 1 000 massless particles (MDD = 3.16 to 31.6 M⊕). We present the evolution of both the disc and the giant planet after 1 Gyr. The time evolution of the average eccentricity and inclination of the disc is strongly dependent on the giant planet mass as well as on the remaining disc mass. We also found that efficient stirring is achieved even with small disc masses. In general, we find that a mixed mechanism is more efficient in the stirring of cold debris discs than either mechanism acting in isolation.