Miracle-dz/UniFAR-FTUs huggingface dataset:
ION DYNAMICS DURING THE PARAMETRIC INSTABILITIES OF A LEFT-HAND POLARIZED ALFVÉN WAVE IN A PROTON–ELECTRON–ALPHA PLASMA

The parametric instabilities of an Alfvén wave in a proton–electron plasma system are found to have great influence on proton dynamics, where part of the protons can be accelerated through the Landau resonance with the excited ion acoustic waves, and a beam component along the background magnetic field is formed. In this paper, with a one-dimensional hybrid simulation model, we investigate the evolution of the parametric instabilities of a monochromatic left-hand polarized Alfvén wave in a proton–electron–alpha plasma with a low beta. When the drift velocity between the protons and alpha particles is sufficiently large, the wave numbers of the backward daughter Alfvén waves can be cascaded toward higher values due to the modulational instability during the nonlinear evolution of the parametric instabilities, and the alpha particles are resonantly heated in both the parallel and perpendicular direction by the backward waves. On the other hand, when the drift velocity of alpha particles is small, the alpha particles are heated in the linear growth stage of the parametric instabilities due to the Landau resonance with the excited ion acoustic waves. Therefore, the heating occurs only in the parallel direction, and there is no obvious heating in the perpendicular direction. The relevance of our results to the preferential heating of heavy ions observed in the solar wind within 0.3 AU is also discussed in this paper.
Exact integrability in quantum field theory and statistical systems

The properties of exactly integrable two-dimensional quantum systems are reviewed and discussed. The nature of exact integrability as a physical phenomenon and various aspects of the mathematical formalism are explored by discussing several examples, including detailed treatments of the nonlinear Schroedinger (delta-function gas) model, the massive Thirring model, and the six-vertex (ice) model. The diagonalization of a Hamiltonian by Bethe's Ansatz is illustrated for the nonlinear Schroedinger model, and the integral equation method of Lieb for obtaining the spectrum of the many-body system from periodic boundary conditions is reviewed. Similar methods are applied to the massive Thirring model, where the fermion-antifermion and bound-state spectrum are obtained explicitly by the integral equation method. After a brief review of the classical inverse scattering method, the quantum inverse method for the nonlinear Schroedinger model is introduced and shown to be an algebraization of the Bethe Ansatz technique. In the quantum inverse method, an auxiliary linear problem is used to define nonlocal operators which are functionals of the original local field on a fixed-time string of arbitrary length. The particular operators for which the string is infinitely long (free boundary conditions) or forms a closed loop around a cylinder (periodic boundary conditions) correspond tomore » the quantized scattering data and have a special significance. One of them creates the Bethe eigenstates, while the other is the generating function for an infinite number of conservation laws. The analogous operators on a lattice are constructed for the symmetric six-vertex model, where the object which corresponds to a solution of the auxiliary linear problem is a string of vertices contracted over horizontal links (arrows). The relationship between the quantum inverse method and the transfer matrix formalism is exhibited. « less
Efficient Mixed-Type Wafer Defect Pattern Recognition Using Compact Deformable Convolutional Transformers

Manufacturing wafers is an intricate task involving thousands of steps. Defect Pattern Recognition (DPR) of wafer maps is crucial to find the root cause of the issue and further improving the yield in the wafer foundry. Mixed-type DPR is much more complicated compared to single-type DPR due to varied spatial features, the uncertainty of defects, and the number of defects present. To accurately predict the number of defects as well as the types of defects, we propose a novel compact deformable convolutional transformer (DC Transformer). Specifically, DC Transformer focuses on the global features present in the wafer map by virtue of learnable deformable kernels and multi-head attention to the global features. The proposed method succinctly models the internal relationship between the wafer maps and the defects. DC Transformer is evaluated on a real dataset containing 38 defect patterns. Experimental results show that DC Transformer performs exceptionally well in recognizing both single and mixed-type defects. The proposed method outperforms the current state of the models by a considerable margin
The Hole Argument and Beyond: Part I: The Story so Far

In this two-part paper, we review, and then develop, the assessment of the hole argument for general relativity. This first Part reviews the literature hitherto, focussing on the philosophical aspects. It also introduces two main ideas we will need in Part II: which will propose a framework for making comparisons of non-isomorphic spacetimes. In Section 1 of this paper, we recall Einstein’s original argument. Section 2 recalls the argument’s revival by philosophers in the 1980s and 1990s. This includes the first main idea we will need in Part II: namely, that two spacetime points in different possible situations are never strictly identical—they are merely counterparts. In Section 3, we report—and rebut—more recent claims to “dissolve” the argument. Our rebuttal is based on the fact that in differential geometry, and its applications in physics such as general relativity, points are in some cases identified, or correspond with each other, between one context and another, by means other than isometry (or isomorphism). We call such a correspondence a threading of points. This is the second main idea we shall use in Part II.
High-energy neutrino deeply inelastic scattering cross sections from 100 GeV to 1000 EeV

We present a state-of-the-art prediction for cross sections of neutrino deeply inelastic scattering (DIS) from nucleon at high neutrino energies, $E_\nu$, from 100 GeV to 1000 EeV ($10^{12}$ GeV). Our calculations are based on the latest CT18 NNLO parton distribution functions (PDFs) and their associated uncertainties. In order to make predictions for the highest energies, we extrapolate the PDFs to small $x$ according to several procedures and assumptions, thus affecting the uncertainties at ultra-high $E_\nu$; we quantify the uncertainties corresponding to these choices. Similarly, we quantify the uncertainties introduced by the nuclear corrections which are required to evaluate neutrino-nuclear cross sections for neutrino telescopes. These results can be applied to currently-running astrophysical neutrino observatories, such as IceCube, as well as various future experiments which have been proposed.
Boosted quantum and semi-quantum communication protocols

Secure quantum communication protocols based on a prepare-and-measure scheme employ mutually unbiased bases. In these protocols, many runs, in which different participants measure in different bases, simply go wasted. In this paper, we show that it is possible to reduce the number of such runs by a suitable design of the key generation rule. This results in a significant increase in the key generation rate (KGR). We illustrate this advantage by proposing quantum and semi-quantum key distribution protocols by employing effective qubits encoded in higher dimensional quantum systems. None of them demands the preparation of entangled states as resources and a relatively large amount of information can be transferred. For this reason, we believe that our proposals are worth pursuing experimentally.
Elemental abundance analyses with DAO spectrograms - XXIV. The Mercury Manganese stars $\upsilon$ Her, $\phi$ Her, and HR 7018

Elemental abundances analyses are performed for the Mercury-Manganese stars Her, Her, and HR 7018 consistent with previous studies of this series using spectrograms obtained with Reticon and CCD detectors. Comparisons of the rst two analyses with those performed using coadded photographic plates show the general consistency of the derived elemental abundances. For Her and for Her, abundances were newly found for O, and for Al, V, Zn, and Ce, respectively. HR 7018 is discovered to be a single-lined spectroscopic binary. Its Sc abundance is the smallest of any class member with derived abundances and its Sr abundance the largest of any known HgMn star. A correlation analysis of the most complete abundance sets for 20 HgMn stars shows that the abundances of some elements are correlated with one another and some are functions of the stellar eective temperature.
Fermi-GBM Discovery of GRB 221009A: An Extraordinarily Bright GRB from Onset to Afterglow

We report the discovery of GRB 221009A, the highest flux gamma-ray burst (GRB) ever observed by the Fermi Gamma-ray Burst Monitor (Fermi-GBM). This GRB has continuous prompt emission lasting more than 600 s, which smoothly transitions to afterglow emission visible in the Fermi-GBM energy range (8 keV–40 MeV), and total energetics higher than any other burst in the Fermi-GBM sample. By using a variety of new and existing analysis techniques we probe the spectral and temporal evolution of GRB 221009A. We find no emission prior to the Fermi-GBM trigger time (t 0; 2022 October 9 at 13:16:59.99 UTC), indicating that this is the time of prompt emission onset. The triggering pulse exhibits distinct spectral and temporal properties suggestive of the thermal, photospheric emission of shock breakout, with significant emission up to ∼15 MeV. We characterize the onset of external shock at t 0 + 600 s and find evidence of a plateau region in the early-afterglow phase, which transitions to a slope consistent with Swift-XRT afterglow measurements. We place the total energetics of GRB 221009A in context with the rest of the Fermi-GBM sample and find that this GRB has the highest total isotropic-equivalent energy (E γ,iso = 1.0 × 1055 erg) and second highest isotropic-equivalent luminosity (L γ,iso = 9.9 × 1053 erg s–1) based on its redshift of z = 0.151. These extreme energetics are what allowed us to observe the continuously emitting central engine of Fermi-GBM from the beginning of the prompt emission phase through the onset of early afterglow.
EdgeTran: Co-designing Transformers for Efficient Inference on Mobile Edge Platforms

Automated design of efficient transformer models has recently attracted significant attention from industry and academia. However, most works only focus on certain metrics while searching for the best-performing transformer architecture. Furthermore, running traditional, complex, and large transformer models on low-compute edge platforms is a challenging problem. In this work, we propose a framework, called ProTran, to profile the hardware performance measures for a design space of transformer architectures and a diverse set of edge devices. We use this profiler in conjunction with the proposed co-design technique to obtain the best-performing models that have high accuracy on the given task and minimize latency, energy consumption, and peak power draw to enable edge deployment. We refer to our framework for co-optimizing accuracy and hardware performance measures as EdgeTran. It searches for the best transformer model and edge device pair. Finally, we propose GPTran, a multi-stage block-level grow-and-prune post-processing step that further improves accuracy in a hardware-aware manner. The obtained transformer model is 2.8$\times$ smaller and has a 0.8% higher GLUE score than the baseline (BERT-Base). Inference with it on the selected edge device enables 15.0% lower latency, 10.0$\times$ lower energy, and 10.8$\times$ lower peak power draw compared to an off-the-shelf GPU.
The status of the human gene catalogue

Scientists have been trying to identify all of the genes in the human genome since the initial draft of the genome was published in 2001. Over the intervening years, much progress has been made in identifying protein-coding genes, and the estimated number has shrunk to fewer than 20,000, although the number of distinct protein-coding isoforms has expanded dramatically. The invention of high-throughput RNA sequencing and other technological breakthroughs have led to an explosion in the number of reported non-coding RNA genes, although most of them do not yet have any known function. A combination of recent advances offers a path forward to identifying these functions and towards eventually completing the human gene catalogue. However, much work remains to be done before we have a universal annotation standard that includes all medically significant genes, maintains their relationships with different reference genomes, and describes clinically relevant genetic variants.
MoWE: Mixture of Weather Experts for Multiple Adverse Weather Removal

Currently, most adverse weather removal tasks are handled independently, such as deraining, desnowing, and dehazing. However, in autonomous driving scenarios, the type, intensity, and mixing degree of the weather are unknown, so the separated task setting cannot deal with these complex conditions well. Besides, the vision applications in autonomous driving often aim at high-level tasks, but existing weather removal methods neglect the connection between performance on perceptual tasks and signal fidelity. To this end, in upstream task, we propose a novel \textbf{Mixture of Weather Experts(MoWE)} Transformer framework to handle complex weather removal in a perception-aware fashion. We design a \textbf{Weather-aware Router} to make the experts targeted more relevant to weather types while without the need for weather type labels during inference. To handle diverse weather conditions, we propose \textbf{Multi-scale Experts} to fuse information among neighbor tokens. In downstream task, we propose a \textbf{Label-free Perception-aware Metric} to measure whether the outputs of image processing models are suitable for high level perception tasks without the demand for semantic labels. We collect a syntactic dataset \textbf{MAW-Sim} towards autonomous driving scenarios to benchmark the multiple weather removal performance of existing methods. Our MoWE achieves SOTA performance in upstream task on the proposed dataset and two public datasets, i.e. All-Weather and Rain/Fog-Cityscapes, and also have better perceptual results in downstream segmentation task compared to other methods. Our codes and datasets will be released after acceptance.
Optical Character Recognition and Transcription of Berber Signs from Images in a Low-Resource Language Amazigh

The Berber, or Amazigh language family is a low-resource North African vernacular language spoken by the indigenous Berber ethnic group. It has its own unique alphabet called Tifinagh used across Berber communities in Morocco, Algeria, and others. The Afroasiatic language Berber is spoken by 14 million people, yet lacks adequate representation in education, research, web applications etc. For instance, there is no option of translation to or from Amazigh / Berber on Google Translate, which hosts over 100 languages today. Consequently, we do not find specialized educational apps, L2 (2nd language learner) acquisition, automated language translation, and remote-access facilities enabled in Berber. Motivated by this background, we propose a supervised approach called DaToBS for Detection and Transcription of Berber Signs. The DaToBS approach entails the automatic recognition and transcription of Tifinagh characters from signs in photographs of natural environments. This is achieved by self-creating a corpus of 1862 pre-processed character images; curating the corpus with human-guided annotation; and feeding it into an OCR model via the deployment of CNN for deep learning based on computer vision models. We deploy computer vision modeling (rather than language models) because there are pictorial symbols in this alphabet, this deployment being a novel aspect of our work. The DaToBS experimentation and analyses yield over 92 percent accuracy in our research. To the best of our knowledge, ours is among the first few works in the automated transcription of Berber signs from roadside images with deep learning, yielding high accuracy. This can pave the way for developing pedagogical applications in the Berber language, thereby addressing an important goal of outreach to underrepresented communities via AI in education.
Statistically equivalent models with different causal structures: An example from physics identity

Structural equation modeling (SEM) is a statistical method widely used in educational research to investigate relationships between variables. SEM models are typically constructed based on theoretical foundations and assessed through fit indices. However, a well-fitting SEM model alone is not sufficient to verify the causal inferences underlying the proposed model, as there are statistically equivalent models with distinct causal structures that equally well fit the data. Therefore, it is crucial for researchers using SEM to consider statistically equivalent models and to clarify why the proposed model is more accurate than the equivalent ones. However, many SEM studies did not explicitly address this important step, and no prior study in physics education research has delved into potential methods for distinguishing statistically equivalent models with differing causal structures. In this study, we use physics identity model as an example to discuss the importance of considering statistically equivalent models and how other data can help to distinguish them. Previous research has identified three dimensions of physics identity: perceived recognition, self-efficacy, and interest. However, the relationships between these dimensions have not been thoroughly understood. In this paper, we specify a model with perceived recognition predicting self-efficacy and interest, which is inspired by individual interviews with students in physics courses to make physics learning environments equitable and inclusive. We test our model with fit indices and discuss its statistically equivalent models with different causal inferences among perceived recognition, self-efficacy, and interest. We then discuss potential experiments that could further empirically test the causal inferences underlying the models, aiding the refinement to a more accurate causal model for guiding educational improvements.
Stochastic homogenization of functionals defined on finite partitions

We prove a stochastic homogenization result for integral functionals defined on finite partitions assuming the surface tension to be stationary and possibly ergodic. We also consider the convergence of boundary value problems when we impose a boundary value just on part of the boundary. As a consequence, we show that if the homogenized surface tension is isotropic, then one can obtain it by a multi-cell problem where Dirichlet boundary conditions are imposed only at the bottom and the top of the cube. We also show that this result fails in the general anisotropic case.
A note on the renormalization group approach to the Central Limit Theorem

Two proofs of the Central Limit Theorem using a renormalization group approach are presented. The first proof is conducted under a third moment assumption and shows that a suitable renormalization group map is a contraction over the space of probability measures with a third moment. The second proof uses Lyapunov stability and works under a second moment condition. These are by far not the most optimal proofs of the CLT, and the main interest of the proofs are their existence, the CLT being the simplest case in which a renormalization group argument should apply. None of the tools used in this note are new. Similar proofs are known amongst expert in limit theorems, but explicit references are not so easy to come by for non-experts in the field.
Masked Scene Contrast: A Scalable Framework for Unsupervised 3D Representation Learning

As a pioneering work, PointContrast conducts unsupervised 3D representation learning via leveraging contrastive learning over raw RGB-D frames and proves its effectiveness on various downstream tasks. However, the trend of large-scale unsupervised learning in 3D has yet to emerge due to two stumbling blocks: the inefficiency of matching RGB-D frames as contrastive views and the annoying mode collapse phenomenon mentioned in previous works. Turning the two stumbling blocks into empirical stepping stones, we first propose an efficient and effective contrastive learning framework, which generates contrastive views directly on scene-level point clouds by a well-curated data augmentation pipeline and a practical view mixing strategy. Second, we introduce reconstructive learning on the contrastive learning framework with an exquisite design of contrastive cross masks, which targets the reconstruction of point color and surfel normal. Our Masked Scene Contrast (MSC) framework is capable of extracting comprehensive 3D representations more efficiently and effectively. It accelerates the pretraining procedure by at least 3 x and still achieves an uncompromised performance compared with previous work. Besides, MSC also enables large-scale 3D pre-training across multiple datasets, which further boosts the performance and achieves state-of-the-art fine-tuning results on several downstream tasks, e.g., 75.5% mIoU on ScanNet semantic segmentation validation set.
Extragalactic Magnetism with SOFIA (SALSA Legacy Program). V. First Results on the Magnetic Field Orientation of Galaxies

We present the analysis of the magnetic field (B-field) structure of galaxies measured with far-infrared (FIR) and radio (3 and 6 cm) polarimetric observations. We use the first data release of the Survey of extragALactic magnetiSm with SOFIA of 14 nearby ( <20 Mpc) galaxies with resolved ( 5″–18″; 90 pc–1 kpc) imaging polarimetric observations using SOFIA/HAWC+ from 53 to 214 μm. We compute the magnetic pitch-angle ( ΨB ) profiles as a function of the galactocentric radius. We introduce a new magnetic alignment parameter (ζ) to estimate the disordered-to-ordered ratio of spiral B-fields. We find FIR and radio wavelengths to not generally trace the same B-field morphology in galaxies. The ΨB profiles tend to be more ordered across all galactocentric radii in radio ( ζ6cm=0.93±0.03 ) than in FIR ( ζ154μm=0.84±0.14 ). For spiral galaxies, FIR B-fields are 2%–75% more turbulent than the radio B-fields. For starburst galaxies, we find that FIR polarization is a better tracer of the B-fields along the galactic outflows than radio polarization. Our results suggest that the B-fields associated with dense, dusty, turbulent star-forming regions (those traced at FIR) are less ordered than warmer, less dense regions (those traced at radio) of the interstellar medium. The FIR B-fields seem to be more sensitive to the activity of the star-forming regions and molecular clouds within a vertical height of a few hundred parsecs in the disk of spiral galaxies than the radio B-fields.