Miracle-dz/UniFAR-FTUs huggingface dataset:
Intense solar near‐relativistic electron events at 0.3 AU

We study the two most intense electron events observed by the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) spacecraft during the rising phase of solar cycle 24. Both events occurred during intense periods of solar activity when sequences of coronal mass ejections (CMEs) emanated from the Sun. The first event occurred on 4 June 2011, when two successive CMEs, spaced by 15 h, were released from the same active region. A possible interpretation for this event is that the first CME, located beyond MESSENGER's heliocentric distance (R = 0.33 AU) at the time of the second CME, acted as a magnetic barrier for solar energetic particles (SEPs) accelerated during the second CME. Elevated electron intensities at MESSENGER resulted from particle confinement between the two CME‐driven shocks. This scenario is consistent with that proposed by Kallenrode and Cliver [2001a] to explain intense SEP events. The second event, observed by MESSENGER at R = 0.31 AU on 7 March 2012, belonged to a series of events generated by a single active region. A fast (>1500 km s−1) CME occurred 45 h before the onset of the main electron event. That implies that the prior CME‐driven shock was at such a distance that particle reflection from behind that shock might have not been sufficient to account for the initial onset of sunward‐directed electrons at MESSENGER, and additional mirroring and/or scattering processes closer to MESSENGER were necessary. Elevated intensities in this event are consistent with strong injection of SEPs from close to the Sun and particle reflection at some point in interplanetary space.
Superiorization as a novel strategy for linearly constrained inverse radiotherapy treatment planning

Objective: We apply the superiorization methodology to the intensity-modulated radiation therapy (IMRT) treatment planning problem. In superiorization, linear voxel dose inequality constraints are the fundamental modeling tool within which a feasibility-seeking projection algorithm will seek a feasible point. This algorithm is then perturbed with gradient descent steps to reduce a nonlinear objective function. Approach: Within the open-source inverse planning toolkit matRad, we implement a prototypical algorithmic framework for superiorization using the well-established Agmon, Motzkin, and Schoenberg (AMS) feasibility-seeking projection algorithm and common nonlinear dose optimization objective functions. Based on this prototype, we apply superiorization to intensity-modulated radiation therapy treatment planning and compare its performance with feasibility-seeking and nonlinear constrained optimization. For these comparisons, we use the TG119 water phantom and a head-and-neck patient of the CORT dataset. Main Results: Bare feasibility-seeking with AMS conﬁrms previous studies, showing it can ﬁnd solutions that are nearly equivalent to those found by the established piece-wise least-squares optimization approach. The superiorization prototype solved the linearly constrained planning problem with similar performance to that of a general-purpose nonlinear constrained optimizer while showing smooth convergence in both constraint proximity and objective feasibility-seeking, e. g. , with dose-volume constraints and more sophisticated perturbations, may unlock its full potential for high-performant inverse treatment planning.
Learned Label Aggregation for Weak Supervision

The lack of labeled training data is the bottleneck of machine learning in many applications. To resolve the bottleneck, one promising direction is the data programming approach that aggregates different sources of weak supervision signals to generate labeled data easily. Data programming encodes each weak supervision source with a labeling function (LF), a user-provided program that predicts noisy labels. The quality of the generated labels depends on a label aggregation model that aggregates all noisy labels from all LFs to infer the ground-truth labels. Existing label aggregation methods typically rely on various assumptions and are not robust across datasets, as we will show empirically. We for the ﬁrst time provide an analytical label aggregation method that makes minimum assumption and is optimal in minimizing a certain form of the averaged prediction error. Since the complexity of the analytical form is exponential, we train a model that learns to be the analytical method. Once trained, the model can be used for any unseen datasets and the model predicts the ground-truth labels for each dataset in a single forward pass in linear time. We show the model can be trained using synthetically generated data and design an effective architecture for the model. On 14 real-world datasets, our model signiﬁcantly outperforms the best existing methods in both accuracy (by 3.5 points on average) and efﬁciency (by six times on average).
INFRARED AND X-RAY EVIDENCE OF AN AGN IN THE NGC 3256 SOUTHERN NUCLEUS

We investigate signs of an active galactic nucleus (AGN) in the luminous infrared (IR) galaxy NGC 3256 at both IR and X-ray wavelengths. NGC 3256 has double nuclei: the northern and southern (hereafter, N and S nuclei, respectively). We show that the Spitzer IRAC colors extracted at the S nucleus are AGN-like, and the Spitzer IRS spectrum is bluer at < 6 ? > μm than at the N nucleus. We built for the S nucleus an AGN–starburst composite model with a heavily absorbed AGN to successfully reproduce not only the IRAC and IRS specrophotometries at ≃ 3 ? > ″, but also the very deep silicate 9.7 μm absorption observed at a 0. ″ 36 scale by Díaz-Santos et al. We found a 2.2 μm compact source at the S nucleus in an HST NICMOS image and identified its unresolved core (at 0.″ 26 resolution) with the compact core in previous mid-infrared observations at comparable resolution. The flux of the 2.2 μm core is consistent with our AGN spectral energy distribution model. We also analyzed a deeper than ever Chandra X-ray spectrum of the unresolved (at 0.″ 5 resolution) source at the S nucleus. We found that a dual-component power-law model (for primary and scattered ones) fits an apparently very hard spectrum with a moderately large absorption on the primary component. Together with a limit on equivalent width of a fluorescent Fe–K emission line at 6.4 keV, the X-ray spectrum is consistent with a typical Compton-thin Seyfert 2. We therefore suggest that the S nucleus hosts a heavily absorbed low-luminosity AGN.
Resonant tides in binary neutron star mergers: Analytical-numerical relativity study

Resonant excitations of $f$-modes in binary neutron star coalescences influence the gravitational waves (GWs) emission in both quasicircular and highly eccentric mergers and can deliver information on the star interior. Most models of resonant tides are built using approximate, perturbative approaches and thus require to be carefully validated against numerical relativity (NR) simulations in the high-frequency regime. We perform detailed comparisons between a set of high-resolution NR simulations and the state of the art effective one body (EOB) model ${\tt TEOBResumS}$ with various tidal potentials and including a model for resonant tides. For circular mergers, we find that $f$-mode resonances can improve the agreement between EOB and NR, but there is no clear evidence that the tidal enhancement after contact is due to a resonant mechanism. Tidal models with $f$-mode resonances do not consistently reproduce, at the same time, the NR waveforms and the energetics within the errors, and their performances is comparable to resummed tidal models without resonances. For highly eccentric mergers, we show for the first time that our EOB model reproduces the bursty NR waveform to a high degree of accuracy. However, the considered resonant model does not capture the $f$-mode oscillations excited during the encounters and present in the NR waveform. Finally, we analyze GW170817 with both adiabatic and dynamical tides models and find that the data shows no evidence in favor of models including dynamical tides. This is in agreement with the fact that resonant tides are measured at very high frequencies, which are not available for GW170817 but might be tested with next generation detectors.
Self-Managing DRAM: A Low-Cost Framework for Enabling Autonomous and Efficient in-DRAM Operations

The memory controller is in charge of managing DRAM maintenance operations (e.g., refresh, RowHammer protection, memory scrubbing) in current DRAM chips. Implementing new maintenance operations often necessitates modifications in the DRAM interface, memory controller, and potentially other system components. Such modifications are only possible with a new DRAM standard, which takes a long time to develop, leading to slow progress in DRAM systems. In this paper, our goal is to 1) ease, and thus accelerate, the process of enabling new DRAM maintenance operations and 2) enable more efficient in-DRAM maintenance operations. Our idea is to set the memory controller free from managing DRAM maintenance. To this end, we propose Self-Managing DRAM (SMD), a new low-cost DRAM architecture that enables implementing new in-DRAM maintenance mechanisms (or modifying old ones) with no further changes in the DRAM interface, memory controller, or other system components. We use SMD to implement new in-DRAM maintenance mechanisms for three use cases: 1) periodic refresh, 2) RowHammer protection, and 3) memory scrubbing. We show that SMD enables easy adoption of efficient maintenance mechanisms that significantly improve the system performance and energy efficiency while providing higher reliability compared to conventional DDR4 DRAM. A combination of SMD-based maintenance mechanisms that perform refresh, RowHammer protection, and memory scrubbing achieve 7.6% speedup and consume 5.2% less DRAM energy on average across 20 memory-intensive four-core workloads. We make SMD source code openly and freely available at https://github.com/CMU-SAFARI/SelfManagingDRAM.
Deep Clustering with Features from Self-Supervised Pretraining

A deep clustering model conceptually consists of a feature extractor that maps data points to a latent space, and a clustering head that groups data points into clusters in the latent space. Although the two components used to be trained jointly in an end-to-end fashion, recent works have proved it beneficial to train them separately in two stages. In the first stage, the feature extractor is trained via self-supervised learning, which enables the preservation of the cluster structures among the data points. To preserve the cluster structures even better, we propose to replace the first stage with another model that is pretrained on a much larger dataset via self-supervised learning. The method is simple and might suffer from domain shift. Nonetheless, we have empirically shown that it can achieve superior clustering performance. When a vision transformer (ViT) architecture is used for feature extraction, our method has achieved clustering accuracy 94.0%, 55.6% and 97.9% on CIFAR-10, CIFAR-100 and STL-10 respectively. The corresponding previous state-of-the-art results are 84.3%, 47.7% and 80.8%. Our code will be available online with the publication of the paper.
Fast optical refocusing through multimode fiber bend using Cake-Cutting Hadamard encoding algorithm to improve robustness

: Multimode fibres offer the advantages of high resolution and miniaturization over single-mode fibers in the field of optical imaging. However, multimode fibre’s imaging is susceptible to perturbations of MMF that can lead to secondary spatial distortions in the transmitted image. Perturbations include random disturbances in the fiber as well as environmental noise. Here, we exploit the fast focusing capability of the Cake-Cutting Hadamard coding algorithm to counteract the effects of perturbations and improve the system's robustness. Simulation shows that it can approach the theoretical enhancement at 2000 measurements. Experimental results show that the algorithm can help the system to refocus in a short time when MMFs are perturbed. This research will further contribute to using multimode fibres in medicine, communication, and detection.
Passive vector turbulence

The existence of a dynamo effect in a simplified magnetohydrodynamic model of turbulence is considered when the magnetic Prandtl number approaches zero or infinity. The magnetic field is interacting with an incompressible Kraichnan-Kazantsev model velocity field which incorporates also a viscous cutoff scale. An approximate system of equations in the different scaling ranges can be formulated and solved, so that the solution tends to the exact one when the viscous and magnetic-diffusive cutoffs approach zero. In this approximation we are able to determine analytically the conditions for the existence of a dynamo effect and give an estimate of the dynamo growth rate. Among other things we show that in the large magnetic Prandtl number case the dynamo effect is always present. Our analytical estimates are in good agreement with previous numerical studies of the Kraichnan-Kazantsev dynamo by Vincenzi (J. Stat. Phys. 106:1073–1091, 2002).
Diagnosing FU Ori-like Sources: The Parameter Space of Viscously Heated Disks in the Optical and Near-infrared

FU Ori-type objects (FUors) are decades-long outbursts of accretion onto young stars that are strong enough to viscously heat disks so that the disk outshines the central star. We construct models for FUor objects by calculating emission components from a steady-state viscous accretion disk, a passively-heated dusty disk, magnetospheric accretion columns, and the stellar photosphere. We explore the parameter space of the accretion rate Ṁ and stellar mass M * to investigate implications on the optical and near-infrared spectral energy distribution and spectral lines. The models are validated by fitting to multiwavelength photometry of three confirmed FUor objects, FU Ori, V883 Ori, and HBC 722, and then comparing the predicted spectra to the observed optical and infrared spectra. The brightness ratio between the viscous disk and the stellar photosphere, η, provides an important guide for identifying viscous accretion disks, with η = 1 (“transition line”) and η = 5 (“sufficient dominance line”) marking turning points in diagnostics, evaluated here in the near-infrared. These turning points indicate the emergence and complete development of FUor-characteristic strong CO absorption, weak metallic absorption, the triangular spectral continuum shape in the H band, and location in color–magnitude diagrams. Lower M * and higher Ṁ imply larger η; for M * = 0.3 M ⊙, η = 1 corresponds to Ṁ=2×10−7M⊙ yr−1 and η = 5 to Ṁ=6×10−7M⊙ yr−1. The “sufficient dominance line” also coincides with the expected accretion rate where accreting material directly reaches the star. We discuss implications of the models on extinction diagnostics, FUor brightening timescales, viscous disks during initial protostellar growth, and eruptive young stellar objects associated with FUors.
Unsupervised Learning under Latent Label Shift

What sorts of structure might enable a learner to discover classes from unlabeled data? Traditional approaches rely on feature-space similarity and heroic assumptions on the data. In this paper, we introduce unsupervised learning under Latent Label Shift (LLS), where we have access to unlabeled data from multiple domains such that the label marginals $p_d(y)$ can shift across domains but the class conditionals $p(\mathbf{x}|y)$ do not. This work instantiates a new principle for identifying classes: elements that shift together group together. For finite input spaces, we establish an isomorphism between LLS and topic modeling: inputs correspond to words, domains to documents, and labels to topics. Addressing continuous data, we prove that when each label's support contains a separable region, analogous to an anchor word, oracle access to $p(d|\mathbf{x})$ suffices to identify $p_d(y)$ and $p_d(y|\mathbf{x})$ up to permutation. Thus motivated, we introduce a practical algorithm that leverages domain-discriminative models as follows: (i) push examples through domain discriminator $p(d|\mathbf{x})$; (ii) discretize the data by clustering examples in $p(d|\mathbf{x})$ space; (iii) perform non-negative matrix factorization on the discrete data; (iv) combine the recovered $p(y|d)$ with the discriminator outputs $p(d|\mathbf{x})$ to compute $p_d(y|x) \; \forall d$. With semi-synthetic experiments, we show that our algorithm can leverage domain information to improve upon competitive unsupervised classification methods. We reveal a failure mode of standard unsupervised classification methods when feature-space similarity does not indicate true groupings, and show empirically that our method better handles this case. Our results establish a deep connection between distribution shift and topic modeling, opening promising lines for future work.
TINKER: A framework for Open source Cyberthreat Intelligence

Threat intelligence on malware attacks and campaigns is increasingly being shared with other security experts for a cost or for free. Other security analysts use this intelligence to inform them of indicators of compromise, attack techniques, and preventative actions. Security analysts prepare threat analysis reports after investigating an attack, an emerging cyber threat, or a recently discovered vulnerability. Collectively known as cyber threat intelligence (CTI), the reports are typically in an unstructured format and, therefore, challenging to integrate seamlessly into existing intrusion detection systems. This paper proposes a framework that uses the aggregated CTI for analysis and defense at scale. The information is extracted and stored in a structured format using knowledge graphs such that the semantics of the threat intelligence can be preserved and shared at scale with other security analysts. Specifically, we propose the first semi-supervised open-source knowledge graph-based framework, TINKER, to capture cyber threat information and its context. Following TINKER, we generate a Cyberthreat Intelligence Knowledge Graph (CTI-KG) and demonstrate the usage using different use cases.
On the exact number of primes in the arithmetic progression 4n ± 1 and 6n ± 1.

Let n(x) be the number of primes ^x, and let φ(χ, r) be the number of positive integers ^x which are relatively prime to each of the first r primes. Setting n(x) = m, n(x) = n, n — m = s, and letting (/>(*,/:) be the number of integers ^x relatively prime to the first k primes, E. Meissel [8] gave the following formula which enables one to compute the exact value of n(x) when each of the primes q ̂ x are individually known:
Open-Source Radiative Modeling Tools for Extragalactic VHE Gamma-ray Sources

In this review, we discuss various open-source software for modeling the broadband emission of extragalactic sources from radio up to the highest gamma-ray energies. As we provide an overview of the different tools available, we discuss the physical processes that such tools implement and detail the computations they can perform. We also examine their conformity with modern good software practices. After considering the currently available software as a first generation of open-source modeling tools, we outline some desirable characteristics for the next generation.
Efficient Video Deblurring Guided by Motion Magnitude

Video deblurring is a highly under-constrained problem due to the spatially and temporally varying blur. An intuitive approach for video deblurring includes two steps: a) detecting the blurry region in the current frame; b) utilizing the information from clear regions in adjacent frames for current frame deblurring. To realize this process, our idea is to detect the pixel-wise blur level of each frame and combine it with video deblurring. To this end, we propose a novel framework that utilizes the motion magnitude prior (MMP) as guidance for eﬃcient deep video deblurring. Speciﬁcally, as the pixel movement along its trajectory during the exposure time is positively correlated to the level of motion blur, we ﬁrst use the average magnitude of optical ﬂow from the high-frequency sharp frames to generate the synthetic blurry frames and their corresponding pixel-wise motion magnitude maps. We then build a dataset including the blurry frame and MMP pairs. The MMP is then learned by a compact CNN by regression. The MMP consists of both spatial and temporal blur level information, which can be further integrated into an eﬃcient recurrent neural network (RNN) for video deblurring. We conduct intensive experiments to validate the eﬀectiveness of the proposed methods on the public datasets. Our codes are available at https://github.com/sollynoay/MMP-RNN .
Gupta-Bleuler quantization of optical fibers in weak gravitational fields

The theory of gauge-fixed Maxwell equations in linear isotropic dielectrics is developed using a generalisation of the standard $R_\xi$ gauge-fixing term. In static space-times, the theory can be quantised using the Gupta-Bleuler method, which is worked out explicitly for optical fibres either in flat space-time or at a constant gravitational potential. This yields a consistent first-principles description of gravitational fibre-optic interferometry at the single-photon level within the framework of quantum field theory in curved space-times.
D3C2-Net: Dual-Domain Deep Convolutional Coding Network for Compressive Sensing

Mapping optimization algorithms into neural networks, deep unfolding networks (DUNs) have achieved impressive success in compressive sensing (CS). From the perspective of optimization, DUNs inherit a well-deﬁned and interpretable structure from iterative steps. However, from the viewpoint of neural network design, most existing DUNs are inherently established based on traditional image-domain unfolding, which takes one-channel images as inputs and outputs between adjacent stages, resulting in insufﬁcient information transmission capability and inevitable loss of the image details. In this paper, to break the above bottleneck, we ﬁrst propose a generalized dual-domain optimization framework, which is general for inverse imaging and integrates the merits of both (1) image-domain and (2) convolutional-coding-domain priors to constrain the feasible region in the solution space. By unfolding the proposed framework into deep neural networks, we further design a novel D ual- D omain D eep C onvolutional C oding Net work ( D 3 C 2 -Net) 1 for CS imaging with the capability of transmitting high-throughput feature-level image representation through all the unfolded stages. Experiments on natural and MR images demonstrate that our D 3 C 2 -Net achieves higher performance and better accuracy-complexity trade-offs than other state-of-the-arts. -Net) for general CS reconstruction based on our proposed framework. Our D 3 C 2 -Net can transmit high-throughput feature-level image representation through all unfolded stages to capture sufﬁcient features adaptively, thus recovering more details and textures. (3) Experiments on natural and MR image CS tasks show that our D 3 C 2 -Net outperforms existing state-of-the-art networks by large margins.