Miracle-dz/UniFAR-FTUs huggingface dataset:
Xylem embolism and bubble formation during freezing suggest complex dynamics of pressure in Betula pendula stems.

Freeze-thaw-induced embolism is a key limiting factor for perennial plants in frost-exposed environments. Gas bubbles are formed during freezing and expand during thawing resulting in xylem embolism. However, when water freezes, its volume increases by 9%, generating local pressures, which can affect the formation of bubbles. To characterize local dynamic of pressure-tension and physical state of the sap during freeze-thaw cycles, we simultaneously used ultrasonic acoustic emissions analysis and synchrotron-based high resolution computed tomography on the diffuse-porous species Betula pendula. Visualization of individual air-filled vessels was performed to measure freeze-thaw induced embolism after successive freeze-thaw cycles down to -10°C or -20°C during the leafy and the leafless periods. We also measured the distribution of gas bubbles in frozen xylem of Betula pendula, and made additional continuous monitoring of embolism spreading during one freeze-thaw cycle using a dedicated cooling system that allowed X-ray scanning during freezing and thawing. Experiments confirmed that ultrasonic emissions occurred after the onset of ice formation, together with bubble formation, whereas the development of embolism took place after thawing in all cases. The pictures of frozen tissues indicated that the positive pressure induced by the volumetric increase of ice can provoke inward flow from the cell wall toward the lumen of the vessels. We found no evidence that wider vessels within a tissue were more prone to embolism although the occurrence of gas bubbles in larger conduits would make them prone to earlier embolism. These results highlight the need to monitor local pressure as well as ice and air distribution during xylem freezing to understand the mechanism leading to frost-induced embolism.
What do Vision Transformers Learn? A Visual Exploration

Vision transformers (ViTs) are quickly becoming the de-facto architecture for computer vision, yet we understand very little about why they work and what they learn. While existing studies visually analyze the mechanisms of convolutional neural networks, an analogous exploration of ViTs remains challenging. In this paper, we first address the obstacles to performing visualizations on ViTs. Assisted by these solutions, we observe that neurons in ViTs trained with language model supervision (e.g., CLIP) are activated by semantic concepts rather than visual features. We also explore the underlying differences between ViTs and CNNs, and we find that transformers detect image background features, just like their convolutional counterparts, but their predictions depend far less on high-frequency information. On the other hand, both architecture types behave similarly in the way features progress from abstract patterns in early layers to concrete objects in late layers. In addition, we show that ViTs maintain spatial information in all layers except the final layer. In contrast to previous works, we show that the last layer most likely discards the spatial information and behaves as a learned global pooling operation. Finally, we conduct large-scale visualizations on a wide range of ViT variants, including DeiT, CoaT, ConViT, PiT, Swin, and Twin, to validate the effectiveness of our method.
Modelling Local Bubble analogs: synthetic dust polarization maps

We present a study of synthetic observations of polarized dust emission at 353 GHz as seen by an observer within a cavity in the interstellar medium (ISM). The cavity is selected from a magnetohydrodynamic simulation of the local ISM with time-dependent chemistry, star formation, and stellar feedback in form of supernova explosions with physical properties comparable to the Local Bubble ones. We find that the local density enhancement together with the coherent magnetic field in the cavity walls makes the selected candidate a translucent polarization filter to the emission coming from beyond its domains. This underlines the importance of studying the Local Bubble in further detail. The magnetic field lines inferred from synthetic dust polarization data are qualitatively in agreement with the all-sky maps of polarized emission at 353 GHz from the Planck satellite in the latitudes interval 15° ≲ |b| ≲ 65°. As our numerical simulation allows us to track the galactic mid-plane only out to distances of $250\,$ pc, we exclude the region |b| ≲ 15° from our analysis. At large galactic latitudes, our model exhibits a high degree of small-scale structures. On the contrary, the observed polarization pattern around the Galactic Poles is relatively coherent and regular, and we argue that the global toroidal magnetic field of the Milky Way is important for explaining the data at |b| ≳ 65°. We show that from our synthetic polarization maps, it is difficult to distinguish between an open and a closed galactic cap using the inferred magnetic field morphology alone.
The Gateway from Centaurs to Jupiter-family Comets: Thermal and Dynamical Evolution

It was recently proposed that there exists a “gateway” in the orbital parameter space through which Centaurs transition to Jupiter-family comets (JFCs). Further studies have implied that the majority of objects that eventually evolve into JFCs should leave the Centaur population through this gateway. This may be naively interpreted as gateway Centaurs being pristine progenitors of JFCs. This is the point we want to address in this work. We show that the opposite is true: gateway Centaurs are, on average, more thermally processed than the rest of the population of Centaurs crossing Jupiter’s orbit. Using a dynamically validated JFC population, we find that only ∼20% of Centaurs pass through the gateway prior to becoming JFCs, in accordance with previous studies. We show that more than half of JFC dynamical clones entering the gateway for the first time have already been JFCs—they simply avoided the gateway on their first pass into the inner solar system. By coupling a thermal evolution model to the orbital evolution of JFC dynamical clones, we find a higher than 50% chance that the layer currently contributing to the observed activity of gateway objects has been physically and chemically altered, due to previously sustained thermal processing. We further illustrate this effect by examining dynamical clones that match the present-day orbits of 29P/Schwassmann-Wachmann 1, P/2019 LD2 (ATLAS), and P/2008 CL94 (Lemmon).
Subjective Sleepiness Dynamics Dataset (SSDD) Presentation: the Study of Two Scales Consistency

: While the first references to the system of sleepiness assessment are associated with medical research and the study of the effects of drugs on sleep, currently subjective sleepiness assessment is widely used across fundamental and practically oriented studies. The Stanford Sleepiness Scale (SSS) and the Karolinska Sleepiness Scale (KSS) are often used as ground truth in sleepiness research. Only a few studies applied both scales and practically none aimed at studying their consistency and specific features. The present study is devoted to analyzing the dynamics and consistency of subjective sleepiness as measured by the KSS and the SSS in the adult population. A particular task of the paper is to present the Subjective Sleepiness Dynamics Dataset (SSDD) with the evening and morning dynamics of situational subjective sleepiness. A total of 208 adults took part in the experiment. The results of the study revealed that sleepiness generally increased from evening till night and was maximal at early morning. The SSS score appeared to be more sensitive to some factors (e.g., the presence of sleep problems). The SSS and KSS scores were strongly consistent with each other. The KSS showed a generally more even distribution than the SSS. SSDD continues to be collected, we are going to equalize the sample by sex, we are actively adding older people. We plan to collect a sample of 1,000 people. Currently SSDD contains a lot of information that can be used for scientific research.
Relativistic strong-field ionization of hydrogenlike atomic systems in constant crossed electromagnetic fields

Relativistic strong-field ionization of hydrogen-like atoms or ions in a constant crossed electromagnetic field is studied. The transition amplitude is formulated within the strong-field approximation in G\"oppert-Mayer gauge, with initial and final electron states being described by the corresponding Dirac-Coulomb and Dirac-Volkov wave functions, respectively. Coulomb corrections to the electron motion during tunneling are taken into account by adjusting an established method to the present situation. Total and energy-differential ionization rates are calculated and compared with predictions from other theories in a wide range of atomic numbers and applied field strengths.
SMT Sampling via Model-Guided Approximation

We investigate the domain of satisfiable formulas in satisfiability modulo theories (SMT), in particular, automatic generation of a multitude of satisfying assignments to such formulas. Despite the long and successful history of SMT in model checking and formal verification, this aspect is relatively under-explored. Prior work exists for generating such assignments, or samples, for Boolean formulas and for quantifier-free first-order formulas involving bit-vectors, arrays, and uninterpreted functions (QF_AUFBV). We propose a new approach that is suitable for a theory T of integer arithmetic and to T with arrays and uninterpreted functions. The approach involves reducing the general sampling problem to a simpler instance of sampling from a set of independent intervals, which can be done efficiently. Such reduction is carried out by expanding a single model - a seed - using top-down propagation of constraints along the original first-order formula.
U2Net: A General Framework with Spatial-Spectral-Integrated Double U-Net for Image Fusion

In image fusion tasks, images obtained from different sources exhibit distinct properties. Consequently, treating them uniformly with a single-branch network can lead to inadequate feature extraction. Additionally, numerous works have demonstrated that multi-scaled networks capture information more sufficiently than single-scaled models in pixel-level computer vision problems. Considering these factors, we propose U2Net, a spatial-spectral-integrated double U-shape network for image fusion. The U2Net utilizes a spatial U-Net and a spectral U-Net to extract spatial details and spectral characteristics, which allows for the discriminative and hierarchical learning of features from diverse images. In contrast to most previous works that merely employ concatenation to merge spatial and spectral information, this paper introduces a novel spatial-spectral integration structure called S2Block, which combines feature maps from different sources in a logical and effective way. We conduct a series of experiments on two image fusion tasks, including remote sensing pansharpening and hyperspectral image super-resolution (HISR). The U2Net outperforms representative state-of-the-art (SOTA) approaches in both quantitative and qualitative evaluations, demonstrating the superiority of our method. The code is available at https://github.com/PSRben/U2Net.
Emergent grain boundary phases in stressed polycrystalline thin films

The grain boundary (GB) microstructure influences and is influenced by the development of residual stresses during synthesis of polycrystalline thin films. Recent studies have shown that the frustration between the preferred growth direction and rotations of abutting crystals to local cusps in GB energies leads to internal stresses localized within nanoscopic surface layers around the valleys and ridges that form at emergent boundaries (eGBs). Using a combination of continuum frameworks, numerical analyses and all-atom simulations of bicrystal $\langle 111\rangle$ copper films, we show that eGBs tune their surface morphology and rotation extent in response to external strains. Compression favors rotation to and growth of low energy GB phases (complexions) at eGB valleys while tension favors the transitions at eGB ridges, a reflection of the stress-induced mass efflux/influx that changes the energetic balance between interfacial and deformation energies. Molecular dynamics simulations of strained and growing bicrystal films reveal that the eGB phase transition is coupled to island formation at the surface triple junctions, providing a direct link between eGB phases and surface step flow. The interplay between eGB structure, morphology and mechanics emerges as a crucial ingredient for predictive understanding of stress and morphological evolution during film growth, with broad implications for multifunctional response of polycrystalline surfaces in a diverse range of surface phenomena such as surface mediated deformation, interfacial embrittlement, thermal grooving, stress corrosion, surface catalysis and topological conduction.
Gradient flow in the gaussian covariate model: exact solution of learning curves and multiple descent structures

A recent line of work has shown remarkable behaviors of the generalization error curves in simple learning models. Even the least-squares regression has shown atypical features such as the model-wise double descent, and further works have observed triple or multiple descents. Another important characteristic are the epoch-wise descent structures which emerge during training. The observations of model-wise and epoch-wise descents have been analytically derived in limited theoretical settings (such as the random feature model) and are otherwise experimental. In this work, we provide a full and unified analysis of the whole time-evolution of the generalization curve, in the asymptotic large-dimensional regime and under gradient-flow, within a wider theoretical setting stemming from a gaussian covariate model. In particular, we cover most cases already disparately observed in the literature, and also provide examples of the existence of multiple descent structures as a function of a model parameter or time. Furthermore, we show that our theoretical predictions adequately match the learning curves obtained by gradient descent over realistic datasets. Technically we compute averages of rational expressions involving random matrices using recent developments in random matrix theory based on"linear pencils". Another contribution, which is also of independent interest in random matrix theory, is a new derivation of related fixed point equations (and an extension there-off) using Dyson brownian motions.
Regularized Optimal Transport Layers for Generalized Global Pooling Operations

Global pooling is one of the most significant operations in many machine learning models and tasks, which works for information fusion and structured data (like sets and graphs) representation. However, without solid mathematical fundamentals, its practical implementations often depend on empirical mechanisms and thus lead to sub-optimal, even unsatisfactory performance. In this work, we develop a novel and generalized global pooling framework through the lens of optimal transport. The proposed framework is interpretable from the perspective of expectation-maximization. Essentially, it aims at learning an optimal transport across sample indices and feature dimensions, making the corresponding pooling operation maximize the conditional expectation of input data. We demonstrate that most existing pooling methods are equivalent to solving a regularized optimal transport (ROT) problem with different specializations, and more sophisticated pooling operations can be implemented by hierarchically solving multiple ROT problems. Making the parameters of the ROT problem learnable, we develop a family of regularized optimal transport pooling (ROTP) layers. We implement the ROTP layers as a new kind of deep implicit layer. Their model architectures correspond to different optimization algorithms. We test our ROTP layers in several representative set-level machine learning scenarios, including multi-instance learning (MIL), graph classification, graph set representation, and image classification. Experimental results show that applying our ROTP layers can reduce the difficulty of the design and selection of global pooling — our ROTP layers may either imitate some existing global pooling methods or lead to some new pooling layers fitting data better.
Tracking exceptional points above laser threshold

Recent studies on non-Hermitian optical systems having exceptional points (EPs) have revealed a host of unique characteristics associated with these singularities, including unidirectional invisibility, chiral mode switching and laser self-termination, to mention just a few examples. The vast majority of these works focused either on passive systems or active structures where the EPs were accessed below the lasing threshold, i.e. when the system description is inherently linear. In this work, we experimentally demonstrate that EP singularities in coupled semiconductor nanolasers can be accessed and tracked above the lasing threshold, where they become branch points of a nonlinear dynamical system. Contrary to the common belief that unavoidable cavity detuning will impede the formation of an EP, here we demonstrate that this same detuning is necessary for compensating the carrier-induced frequency shift, hence restoring the nonlinear EP in the lasing regime. Furthermore, unlike linear non-Hermitian systems, we find that the spectral location of EPs above laser threshold varies as a function of total pump power and can therefore be continuously tracked. Our work is a first step towards the realization of lasing EPs in more complex laser geometries, and enabling the enhancement of photonic local density of states through non-Hermitian symmetries combined with nonlinear interactions in coupled laser arrays.
Probing the Cold Neutral Medium through H I Emission Morphology with the Scattering Transform

Neutral hydrogen (H I) emission exhibits complex morphology that encodes rich information about the physics of the interstellar medium. We apply the scattering transform (ST) to characterize the H I emission structure via a set of compact and interpretable coefficients, and find a connection between the H I emission morphology and H I cold neutral medium (CNM) phase content. Where H I absorption measurements are unavailable, the H I phase structure is typically estimated from the emission via spectral line decomposition. Here, we present a new probe of the CNM content using measures that are solely derived from H I emission spatial information. We apply the ST to GALFA-H I data at high Galactic latitudes ( b>30° ), and compare the resulting coefficients to CNM fraction measurements derived from archival H I emission and absorption spectra. We quantify the correlation between the ST coefficients and the measured CNM fraction (f CNM), finding that the H I emission morphology encodes substantial f CNM-correlating information and that ST-based metrics for small-scale linearity are particularly predictive of f CNM. This is further corroborated by the enhancement of the I 857/N HI ratio with larger ST measures of small-scale linearity. These results are consistent with the picture of regions with higher CNM content being more populated by small-scale filamentary H I structures. Our work illustrates a physical connection between the H I morphology and phase content, and suggests that future phase decomposition methods can be improved by making use of both H I spectral and spatial information.
Flow past a square cylinder at low Reynolds numbers

Results are presented for the flow past a stationary square cylinder at zero incidence for Reynolds number, Re ⩽ 150. A stabilized finite‐element formulation is employed to discretize the equations of incompressible fluid flow in two‐dimensions. For the first time, values of the laminar separation Reynolds number, Res, and separation angle, θs, at Res are predicted. Also, the variation of θs with Re is presented. It is found that the steady separation initiates at Re = 1.15. Contrary to the popular belief that separation originates at the rear sharp corners, it is found to originate from the base point, i.e. θs=180∘ at Re = Res. For Re > 5, θs approaches the limit of 135 ∘. The length of the separation bubble increases approximately linearly with increasing Re. The drag coefficient varies as Re−0.66. Flow characteristics at Re ⩽ 40 are also presented for elliptical cylinders of aspect ratios 0.2, 0.5, 0.8 and 1 (circle) having the same characteristic dimension as the square and major axis oriented normal to the free‐stream. Compared with a circular cylinder, the flow separates at a much lower Re from a square cylinder leading to the formation of a bigger wake (larger bubble length and width). Consequently, at a given Re, the drag on a square cylinder is more than the drag of a circular cylinder. This suggests that a cylinder with square section is more bluff than the one with circular section. Among all the cylinder shapes studied, the square cylinder with sharp corners generates the largest amount of drag. Copyright © 2010 John Wiley & Sons, Ltd.
DNA damage and mitochondria dysfunction in cell apoptosis induced by nonthermal air plasma

Nonthermal plasma is known to induce animal cell death but the mechanism is not yet clear. Here, cellular and biochemical regulation of cell apoptosis is demonstrated for plasma treated cells. Surface type nonthermal air plasma triggered apoptosis of B16F10 mouse melanoma cancer cells causing DNA damage and mitochondria dysfunction. Plasma treatment activated caspase-3, apoptosis executioner. The plasma treated cells also accumulated gamma-H2A.X, marker for DNA double strand breaks, and p53 tumor suppressor gene as a response to DNA damage. Interestingly, cytochrome C was released from mitochondria and its membrane potential was changed significantly.
GPViT: A High Resolution Non-Hierarchical Vision Transformer with Group Propagation

We present the Group Propagation Vision Transformer (GPViT): a novel nonhierarchical (i.e. non-pyramidal) transformer model designed for general visual recognition with high-resolution features. High-resolution features (or tokens) are a natural fit for tasks that involve perceiving fine-grained details such as detection and segmentation, but exchanging global information between these features is expensive in memory and computation because of the way self-attention scales. We provide a highly efficient alternative Group Propagation Block (GP Block) to exchange global information. In each GP Block, features are first grouped together by a fixed number of learnable group tokens; we then perform Group Propagation where global information is exchanged between the grouped features; finally, global information in the updated grouped features is returned back to the image features through a transformer decoder. We evaluate GPViT on a variety of visual recognition tasks including image classification, semantic segmentation, object detection, and instance segmentation. Our method achieves significant performance gains over previous works across all tasks, especially on tasks that require highresolution outputs, for example, our GPViT-L3 outperforms Swin Transformer-B by 2.0 mIoU on ADE20K semantic segmentation with only half as many parameters. Project page: chenhongyiyang.com/projects/GPViT/GPViT