Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple photodetachment of oxygen anions via 
K
-shell excitation and ionization: Direct double-detachment processes and subsequent deexcitation cascades

Experimental cross sections for m -fold photodetachment ( m = 2 − 5) of oxygen anions via K shell excitation and ionization were measured in the photon-energy range of 525–1500 eV using the photon-ion merged-beams technique at a synchrotron light source. The measured cross sections exhibit clear signatures of direct double detachment, including double K -hole creation. The shapes of the double-detachment cross sections as a function of photon energy are in accord with Pattard’s [J. Phys. B 35, L207 (2002)] empirical scaling law. We have also followed the complex deexcitation cascades that evolve subsequently to the initial double-detachment events by systematic large-scale cascade calculations. The resulting theoretical product charge-state distributions are in good agreement with the experimental ﬁndings.
Dynamic Algorithms for Maximum Matching Size

We study fully dynamic algorithms for maximum matching. This is a well-studied problem, known to admit several update-time/approximation trade-offs. For instance, it is known how to maintain a 1/2-approximate matching in $\log^{O(1)} n$ update time or a $2/3$-approximate matching in $O(\sqrt{n})$ update time, where $n$ is the number of vertices. It has been a long-standing open problem to determine whether either of these bounds can be improved. In this paper, we show that when the goal is to maintain just the size of the matching (and not its edge-set), then these bounds can indeed be improved. First, we give an algorithm that takes $\log^{O(1)} n$ update-time and maintains a $. 501$-approximation ($.585$-approximation if the graph is bipartite). Second, we give an algorithm that maintains a $(2/3 + \Omega(1))$-approximation in $O(\sqrt{n})$ time for bipartite graphs. Our results build on new connections to sublinear time algorithms. In particular, a key tool for both is an algorithm of the author for estimating the size of maximal matchings in $\widetilde{O}(n)$ time [Behnezhad; FOCS 2021]. Our second result also builds on the edge-degree constrained subgraph (EDCS) of Bernstein and Stein [ICALP'15, SODA'16]. In particular, while it has been known that EDCS may not include a better than 2/3-approximation, we give a new characterization of such tight instances which allows us to break it. We believe this characterization might be of independent interest.
Non-Separability of Physical Systems as a Foundation of Consciousness

A hypothesis is presented that non-separability of degrees of freedom is the fundamental property underlying consciousness in physical systems. The amount of consciousness in a system is determined by the extent of non-separability and the number of degrees of freedom involved. Non-interacting and feedforward systems have zero consciousness, whereas most systems of interacting particles appear to have low non-separability and consciousness. By contrast, brain circuits exhibit high complexity and weak but tightly coordinated interactions, which appear to support high non-separability and therefore high amount of consciousness. The hypothesis applies to both classical and quantum cases, and we highlight the formalism employing the Wigner function (which in the classical limit becomes the Liouville density function) as a potentially fruitful framework for characterizing non-separability and, thus, the amount of consciousness in a system. The hypothesis appears to be consistent with both the Integrated Information Theory and the Orchestrated Objective Reduction Theory and may help reconcile the two. It offers a natural explanation for the physical properties underlying the amount of consciousness and points to methods of estimating the amount of non-separability as promising ways of characterizing the amount of consciousness.
Feasibility of Inconspicuous GAN-generated Adversarial Patches against Object Detection

Standard approaches for adversarial patch generation lead to noisy conspicuous patterns, which are easily recognizable by humans. Recent research has proposed several approaches to generate naturalistic patches using generative adversarial networks (GANs), yet only a few of them were evaluated on the object detection use case. Moreover, the state of the art mostly focuses on suppressing a single large bounding box in input by overlapping it with the patch directly. Suppressing objects near the patch is a different, more complex task. In this work, we have evaluated the existing approaches to generate inconspicuous patches. We have adapted methods, originally developed for different computer vision tasks, to the object detection use case with YOLOv3 and the COCO dataset. We have evaluated two approaches to generate naturalistic patches: by incorporating patch generation into the GAN training process and by using the pretrained GAN. For both cases, we have assessed a trade-off between performance and naturalistic patch appearance. Our experiments have shown, that using a pre-trained GAN helps to gain realistic-looking patches while preserving the performance similar to conventional adversarial patches.
Riemannian Natural Gradient Methods

This paper studies large-scale optimization problems on Riemannian manifolds whose objective function is a finite sum of negative log-probability losses. Such problems arise in various machine learning and signal processing applications. By introducing the notion of Fisher information matrix in the manifold setting, we propose a novel Riemannian natural gradient method, which can be viewed as a natural extension of the natural gradient method from the Euclidean setting to the manifold setting. We establish the almost-sure global convergence of our proposed method under standard assumptions. Moreover, we show that if the loss function satisfies certain convexity and smoothness conditions and the input-output map satisfies a Riemannian Jacobian stability condition, then our proposed method enjoys a local linear -- or, under the Lipschitz continuity of the Riemannian Jacobian of the input-output map, even quadratic -- rate of convergence. We then prove that the Riemannian Jacobian stability condition will be satisfied by a two-layer fully connected neural network with batch normalization with high probability, provided that the width of the network is sufficiently large. This demonstrates the practical relevance of our convergence rate result. Numerical experiments on applications arising from machine learning demonstrate the advantages of the proposed method over state-of-the-art ones.
Towards Privacy-Preserving Person Re-identification via Person Identify Shift

Recently privacy concerns of person re-identification (ReID) raise more and more attention and preserving the privacy of the pedestrian images used by ReID methods become essential. De-identification (DeID) methods alleviate privacy issues by removing the identity-related of the ReID data. However, most of the existing DeID methods tend to remove all personal identity-related information and compromise the usability of de-identified data on the ReID task. In this paper, we aim to develop a technique that can achieve a good trade-off between privacy protection and data usability for person ReID. To achieve this, we propose a novel de-identification method designed explicitly for person ReID, named Person Identify Shift (PIS). PIS removes the absolute identity in a pedestrian image while preserving the identity relationship between image pairs. By exploiting the interpolation property of variational auto-encoder, PIS shifts each pedestrian image from the current identity to another with a new identity, resulting in images still preserving the relative identities. Experimental results show that our method has a better trade-off between privacy-preserving and model performance than existing de-identification methods and can defend against human and model attacks for data privacy.
WaveGAN: Frequency-aware GAN for High-Fidelity Few-shot Image Generation

Existing few-shot image generation approaches typically employ fusion-based strategies, either on the image or the feature level, to produce new images. However, previous approaches struggle to synthesize high-frequency signals with fine details, deteriorating the synthesis quality. To address this, we propose WaveGAN, a frequency-aware model for few-shot image generation. Concretely, we disentangle encoded features into multiple frequency components and perform low-frequency skip connections to preserve outline and structural information. Then we alleviate the generator's struggles of synthesizing fine details by employing high-frequency skip connections, thus providing informative frequency information to the generator. Moreover, we utilize a frequency L1-loss on the generated and real images to further impede frequency information loss. Extensive experiments demonstrate the effectiveness and advancement of our method on three datasets. Noticeably, we achieve new state-of-the-art with FID 42.17, LPIPS 0.3868, FID 30.35, LPIPS 0.5076, and FID 4.96, LPIPS 0.3822 respectively on Flower, Animal Faces, and VGGFace. GitHub: https://github.com/kobeshegu/ECCV2022_WaveGAN
Relationship between Successive Flares in the Same Active Region and SHARP Parameters

A solar active region (AR) may produce multiple notable flares during its passage across the solar disk. We investigate successive flares from flare-eruptive ARs, and explore their relationship with solar magnetic parameters. We examine six ARs in this study, each with at least one major flare above X1.0. The Space-weather HMI Active Region Patch (SHARP) is employed in this study to parameterize the ARs. We aim to identify the most flare-related SHARP parameters and lay foundation for future practical flare forecasts. We first evaluate the correlation coefficients between the SHARP parameters and the successive flare production. Then we adopt a Natural Gradient Boost (NGBoost) method to analyze the relationship between the SHARP parameters and the successive flare bursts. Based on the correlation analysis and the importance distribution returned from NGBoost, we select the eight most flare-related SHARP parameters. Finally, we discuss the physical meanings of the eight selected parameters and their relationship with flare production.
Parameterization of Cross-token Relations with Relative Positional Encoding for Vision MLP

Vision multi-layer perceptrons (MLPs) have shown promising performance in computer vision tasks, and become the main competitor of CNNs and vision Transformers. They use token-mixing layers to capture cross-token interactions, as opposed to the multi-head self-attention mechanism used by Transformers. However, the heavily parameterized token-mixing layers naturally lack mechanisms to capture local information and multi-granular non-local relations, thus their discriminative power is restrained. To tackle this issue, we propose a new positional spacial gating unit (PoSGU). It exploits the attention formulations used in the classical relative positional encoding (RPE), to efficiently encode the cross-token relations for token mixing. It can successfully reduce the current quadratic parameter complexity O(N2) of vision MLPs to $O(N)$ and O(1). We experiment with two RPE mechanisms, and further propose a group-wise extension to improve their expressive power with the accomplishment of multi-granular contexts. These then serve as the key building blocks of a new type of vision MLP, referred to as PosMLP. We evaluate the effectiveness of the proposed approach by conducting thorough experiments, demonstrating an improved or comparable performance with reduced parameter complexity. For instance, for a model trained on ImageNet1K, we achieve a performance improvement from 72.14% to 74.02% and a learnable parameter reduction from 19.4M to 18.2M. Code could be found at https://github.com/Zhicaiwww/PosMLP https://github.com/Zhicaiwww/PosMLP.
A primal-dual approach for solving conservation laws with implicit in time approximations

In this work, we propose a novel framework for the numerical solution of time-dependent conservation laws with implicit schemes via primal-dual hybrid gradient methods. We solve an initial value problem (IVP) for the partial differential equation (PDE) by casting it as a saddle point of a min-max problem and using iterative optimization methods to find the saddle point. Our approach is flexible with the choice of both time and spatial discretization schemes. It benefits from the implicit structure and gains large regions of stability, and overcomes the restriction on the mesh size in time by explicit schemes from Courant--Friedrichs--Lewy (CFL) conditions (really via von Neumann stability analysis). Nevertheless, it is highly parallelizable and easy-to-implement. In particular, no nonlinear inversions are required! Specifically, we illustrate our approach using the finite difference scheme and discontinuous Galerkin method for the spatial scheme; backward Euler and backward differentiation formulas for implicit discretization in time. Numerical experiments illustrate the effectiveness and robustness of the approach. In future work, we will demonstrate that our idea of replacing an initial-value evolution equation with this primal-dual hybrid gradient approach has great advantages in many other situations.
Lattice QCD and Particle Physics

Richard Brower, ∗ Vincenzo Cirigliano, ∗ Zohreh Davoudi, ∗ Bálint Jóo, ∗ Chulwoo Jung, ∗ Christoph Lehner, 17, ∗ Stefan Meinel, ∗ Ethan T. Neil, ∗ Peter Petreczky, ∗ David G. Richards, ∗ Alexei Bazavov, 19, † Simon Catterall, † Jozef J. Dudek, † Aida X. El-Khadra, † Michael Engelhardt, † George Fleming, † Joel Giedt, † Rajan Gupta, † Maxwell T. Hansen, † Taku Izubuchi, † Frithjof Karsch, 26, † Jack Laiho, † Keh-Fei Liu, † Aaron S. Meyer, † Enrico Rinaldi, † Martin Savage, † David Schaich, † Phiala E. Shanahan, † Stephen R. Sharpe, † Raza Sufian, † Sergey Syritsyn, † Ruth S. Van de Water, † Michael L. Wagman, † Evan Weinberg, † Oliver Witzel, † Christopher Aubin, Shailesh Chandrasekharan, Ian C. Cloët, Martha Constantinou, George T. Fleming, Zoltan Fodor, 41, 42, 43 Sam Foreman, 45 Steven Gottlieb, Daniel Hoying, William I. Jay, Xiao-Yong Jin, 45 Henry Lamm, Meifeng Lin, Yin Lin, Andrew T. Lytle, Yannick Meurice, Christopher Monahan, Ethan T. Neil, Konstantinos Orginos, 8 James C. Osborn, 45 Sungwoo Park, James N. Simone, 1 Walter Wilcox, Boram Yoon, and Yong Zhao
Exploring Subtropical Stratocumulus Multiple Equilibria using a Mixed-Layer Model

Stratocumulus clouds cover about a fifth of Earth’s surface, and due to their albedo and low-latitude location, they have a strong effect on Earth’s radiation budget. Previous studies using Large Eddy Simulations have shown that multiple equilibria (both stratocumulus-covered and cloud-free/scattered cumulus states) exist as a function of fixed-SST, with relevance to equatorward advected air masses. Multiple equilibria have also been found as a function of atmospheric CO2, with a subtropical SST nearly 10 K higher in the cloud-free state, and with suggested relevance to warm-climate dynamics. In this study, we use a mixed-layer model with an added surface energy balance, and the ability to simulate both the stratocumulus (coupled) and cloud-free/scattered cumulus (decoupled) states using a “stacked” mixed-layer approach to study both types of multiple equilibria and the corresponding hysteresis. The model’s simplicity and computational efficiency allow us to qualitatively explore the mechanisms critical to the stratocumulus cloud instability and hysteresis as well as isolate key processes that allow for multiple equilibria via mechanism denial experiments not possible with a full-complexity model. For the hysteresis in fixed-SST, we find that decoupling can occur due to either enhanced entrainment warming or a reduction in cloud-top longwave cooling. The critical SST at which decoupling occurs is highly sensitive to precipitation and entrainment parameterizations. In the CO2 hysteresis, decoupling occurs in the simple model used even without the inclusion of SST-cloud cover feedbacks, and the width of the hysteresis displays the same sensitivities as the fixed-SST case. Overall, the simple model analysis and results motivate further studies using higher complexity models.
An ALMA 1.3 millimeter Search for Debris Disks around Solar-type Stars in the Pleiades

Millimeter emission from debris disks around stars of different ages provides constraints on the collisional evolution of planetesimals. We present ALMA 1.3 millimeter observations of a sample of 76 Solar-type stars in the ∼115 Myr old Pleiades star cluster. These ALMA observations complement previous infrared observations of this sample by providing sensitivity to emission from circumstellar dust at lower temperatures, corresponding to debris at radii comparable to the Kuiper Belt and beyond. The observations obtain a beam size of 1. ″5 (200 au) and a median rms noise of 54 μJy beam−1, which corresponds to a fractional luminosity L dust/L star ∼ 10−4 for 40 K dust for a typical star in the sample. The ALMA images show no significant detections of the targeted stars. We interpret these limits in the context of a steady-state collisional cascade model for debris disk evolution that provides a good description of observations of the field population near the Sun but is not well-calibrated on younger populations. The ALMA nondetections of the Pleiades systems are compatible with the disk flux predictions of this model. We find no high fractional luminosity outliers from these ALMA data that could be associated with enhanced collisions resulting from activity not accounted for by steady-state evolution. However, we note that two systems (H ii 1132 and HD 22680) show 24 μm excess much higher than the predictions of this model, perhaps due to unusually high dust production from dynamical events involving planets.
A Comparison of Source Distribution and Result Overlap in Web Search Engines

When it comes to search engines, users generally prefer Google. Our study aims to find the differences between the results found in Google compared to other search engines. We compared the top 10 results from Google, Bing, DuckDuckGo, and Metager, using 3,537 queries generated from Google Trends from Germany and the US. Google displays more unique domains in the top results than its competitors. Wikipedia and news websites are the most popular sources overall. With some top sources dominating search results, the distribution of domains is also consistent across all search engines. The overlap between Google and Bing is always under 32%, while Metager has a higher overlap with Bing than DuckDuckGo, going up to 78%. This study shows that the use of another search engine, especially in addition to Google, provides a wider variety in sources and might lead the user to find new perspectives.
The Effects of Radiative Feedback and Supernova-induced Turbulence on Early Galaxies

The recently launched James Webb Space Telescope promises unparalleled advances in our understanding of the first stars and galaxies, but realizing this potential requires cosmological simulations that capture the key physical processes that affected these objects. Here, we show that radiative transfer and subgrid turbulent mixing are two such processes. By comparing simulations with and without radiative transfer but with exactly the same physical parameters and subgrid turbulent mixing model, we show that tracking radiative transfer suppresses the Population III star formation density by a factor ≈4. In both simulations, ≳90% of Population III stars are found in the unresolved pristine regions tracked by our subgrid model, which does a better job at modeling the regions surrounding proto-galaxy cores where metals from supernovae take tens of megayears to mix thoroughly. At the same time, radiative transfer suppresses Population III star formation, via the development of ionized bubbles that slow gas accretion in these regions, and it results in compact high-redshift galaxies that are surrounded by isolated low-mass satellites. Thus, turbulent mixing and radiative transfer are both essential processes that must be included to accurately model the morphology, composition, and growth of primordial galaxies.
High-energy limit of $2\to 2$ scattering amplitudes at NNLL

The high-energy limit of $2\to 2$ scattering amplitudes offers an excellent setting to explore the universal features of gauge theories. At Leading Logarithmic (LL) accuracy the partonic amplitude is governed by Regge poles in the complex angular momentum plane. Beyond LL, Regge cuts in this plane begin to play an important role. Specifically, the real part of the amplitude at Next-to-Next-to-Leading Logarithmic (NNLL) accuracy presents for the first time both a Regge pole and a Regge cut. Analysing this tower of logarithms and computing it explicitly through four loops we are able to systematically separate between the Regge pole and the Regge cut. The former involves two fundamental parameters, namely the gluon Regge trajectory and impact factors. We explain how to consistently define the impact factors at two loops and the Regge trajectory at three loops. We confirm that the singularities of the trajectory are given by the cusp anomalous dimension. We also show that the Regge-cut contribution at four loop is nonplanar.
Glucose Control, Sleep, Obesity, and Real-World Driver Safety at Stop Intersections in Type 1 Diabetes

Background : Diabetes is associated with obesity, poor glucose control and sleep dysfunction which impair cognitive and psychomotor functions, and, in turn, increase driver risk. How this risk plays out in the real-world driving settings is terra incognita. Addressing this knowledge gap requires comprehensive observations of diabetes driver behavior and physiology in challenging settings where crashes are more likely to occur, such as stop-controlled traﬃc intersections, as in the current study of drivers with Type 1 Diabetes (T1DM). Methods : 32 legally licensed active drivers (18 T1DM + 14 controls) from around Omaha, NE participated in 4-week, real-world study. Each participant’s own vehicle was instrumented with an advanced telematics and camera system collecting driving sensor data (e.g., speed, acceleration, GPS) and video (forward road and cabin). Videos were analyzed using computer vision models detecting traﬃc elements to identify stop signs. Stop sign detections and driver stopping trajectories were clustered to geolocate and extract driver-visited stop intersections. Driver videos were then annotated to record stopping behavior (full stop, rolling stop, no stop) and key traﬃc characteristics (e.g., presence/absence of lead/crossing vehicles). Stops were categorized as safe or unsafe based on traﬃc law. Results : Mixed eﬀects logistic regression models examined how stopping behavior (safe vs. unsafe) in T1DM drivers was aﬀected by 1) abnormal sleep (above or below 7–9 hours), 2) obesity, and 3) poor glucose control (greater standard deviation [SD], coeﬃcient of variation [CV], low blood glucose index [LBGI], and high blood glucose index [HBGI]). Model results indicate that one standard deviation increase in BMI ( ∼ 7 points) in T1DM drivers associated with a 14.96 increase in unsafe stopping odds compared to similar controls. Abnormal sleep and glucose control were not associated with increased unsafe stopping. Conclusion : This study links chronic patterns of abnormal T1DM driver physiology, sleep, and health to driver safety risk at intersections, advancing models to identify real-world safety risk in diabetes drivers for clinical intervention and development of in-vehicle safety assistance technology.