Miracle-dz/UniFAR-FTUs huggingface dataset:
On the lowest-frequency bandgap of 1D phononic crystals

This manuscript puts forward and verifies an analytical approach to design phononic crystals that feature a bandgap at the lowest possible frequencies. This new approach is verified against numerical optimization. It rests on the exact form of the trace of the cumulative transfer matrix. This matrix arises from the product of N elementary transfer matrices, where N represents the number of layers in the unit cell of the crystal. The paper presents first a new proof of the harmonic decomposition of the trace (which, unlike the original derivation, does not resort to group-theoretical concepts), and then goes to demonstrate that the long-wavelength asymptotics of the function that governs the dispersion relation can be described in closed form for any layering, plus that it possesses a simple and explicit form that facilitates its study. Using this asymptotic result for low frequencies, we then propose an analytical curvature-minimization approach to devise layerings that yield an opening of the first frequency bandgap at the lowest possible frequency value. Finally, we also show that minimizing the frequency at which the first gap opens can be at odds with obtaining broadband attenuation, i.e., maximizing the width of the first frequency bandgap.
Skeleton Ground Truth Extraction: Methodology, Annotation Tool and Benchmarks

Skeleton Ground Truth (GT) is critical to the success of supervised skeleton extraction methods, especially with the popularity of deep learning techniques. Furthermore, we see skeleton GTs used not only for training skeleton detectors with Convolutional Neural Networks (CNN), but also for evaluating skeleton-related pruning and matching algorithms. However, most existing shape and image datasets suffer from the lack of skeleton GT and inconsistency of GT standards. As a result, it is difficult to evaluate and reproduce CNN-based skeleton detectors and algorithms on a fair basis. In this paper, we present a heuristic strategy for object skeleton GT extraction in binary shapes and natural images. Our strategy is built on an extended theory of diagnosticity hypothesis, which enables encoding human-in-the-loop GT extraction based on clues from the target’s context, simplicity, and completeness. Using this strategy, we developed a tool, SkeView, to generate skeleton GT of 17 existing shape and image datasets. The GTs are then structurally evaluated with representative methods to build viable baselines for fair comparisons. Experiments demonstrate that GTs generated by our strategy yield promising quality with respect to standard consistency, and also provide a balance between simplicity and completeness.
Unleashing the power of Neural Collapse for Transferability Estimation

Transferability estimation aims to provide heuristics for quantifying how suitable a pre-trained model is for a specific downstream task, without fine-tuning them all. Prior studies have revealed that well-trained models exhibit the phenomenon of Neural Collapse. Based on a widely used neural collapse metric in existing literature, we observe a strong correlation between the neural collapse of pre-trained models and their corresponding fine-tuned models. Inspired by this observation, we propose a novel method termed Fair Collapse (FaCe) for transferability estimation by comprehensively measuring the degree of neural collapse in the pre-trained model. Typically, FaCe comprises two different terms: the variance collapse term, which assesses the class separation and within-class compactness, and the class fairness term, which quantifies the fairness of the pre-trained model towards each class. We investigate FaCe on a variety of pre-trained classification models across different network architectures, source datasets, and training loss functions. Results show that FaCe yields state-of-the-art performance on different tasks including image classification, semantic segmentation, and text classification, which demonstrate the effectiveness and generalization of our method.
A Semantic Invariant Robust Watermark for Large Language Models

Watermark algorithms for large language models (LLMs) have achieved extremely high accuracy in detecting text generated by LLMs. Such algorithms typically involve adding extra watermark logits to the LLM's logits at each generation step. However, prior algorithms face a trade-off between attack robustness and security robustness. This is because the watermark logits for a token are determined by a certain number of preceding tokens; a small number leads to low security robustness, while a large number results in insufficient attack robustness. In this work, we propose a semantic invariant watermarking method for LLMs that provides both attack robustness and security robustness. The watermark logits in our work are determined by the semantics of all preceding tokens. Specifically, we utilize another embedding LLM to generate semantic embeddings for all preceding tokens, and then these semantic embeddings are transformed into the watermark logits through our trained watermark model. Subsequent analyses and experiments demonstrated the attack robustness of our method in semantically invariant settings: synonym substitution and text paraphrasing settings. Finally, we also show that our watermark possesses adequate security robustness. Our code and data are available at https://github.com/THU-BPM/Robust_Watermark.
Accretion disk around Reissner-Nordstr\"{o}m black hole coupled with a nonlinear electrodynamics field

The phenomenon by which matter accumulates in the vicinity of a huge celestial object is known as accretion. The gravitational energy is excreted as a consequence of infalling matter onto compact objects. The accretion procedure around celestial bodies like neutron stars, white dwarfs, and black holes has considerable importance because of its ability to transform gravitational energy into radiation. This study investigates the particle's geodesic motion and accretion around the spherically symmetric Reissner-Nordstr\"{o}m black hole coupled with a nonlinear electrodynamics field utilizing isothermal fluid. The formation of the disc-like structure in the accretion process arises from the geodesic motion exhibited by particles near the black hole. The circular orbits, radiant flux energy, radioactive efficiency, and radiant temperature, can be determined. Our study focuses on the examination of particles exhibiting stable circular orbits within the equatorial plane. We analyze the perturbations experienced by particles throughout employing restoring forces and the oscillatory behavior of the particles around a compact object. We conduct an analysis of the fluid's critical flow and maximum accretion rate. Our results show how the black hole parameter $\zeta$ and charge $q$ affect the circular geodesic of particles and the maximum accretion rate of the Reissner-Nordstr\"{o}m black hole coupled with nonlinear electrodynamics.
Temporal Convolutional Explorer Helps Understand 1D-CNN's Learning Behavior in Time Series Classification from Frequency Domain

While one-dimensional convolutional neural networks (1D-CNNs) have been empirically proven effective in time series classification tasks, we find that there remain undesirable outcomes that could arise in their application, motivating us to further investigate and understand their underlying mechanisms. In this work, we propose a Temporal Convolutional Explorer (TCE) to empirically explore the learning behavior of 1D-CNNs from the perspective of the frequency domain. Our TCE analysis highlights that deeper 1D-CNNs tend to distract the focus from the low-frequency components leading to the accuracy degradation phenomenon, and the disturbing convolution is the driving factor. Then, we leverage our findings to the practical application and propose a regulatory framework, which can easily be integrated into existing 1D-CNNs. It aims to rectify the suboptimal learning behavior by enabling the network to selectively bypass the specified disturbing convolutions. Finally, through comprehensive experiments on widely-used UCR, UEA, and UCI benchmarks, we demonstrate that 1) TCE's insight into 1D-CNN's learning behavior; 2) our regulatory framework enables state-of-the-art 1D-CNNs to get improved performances with less consumption of memory and computational overhead.
Differentiation of the South Pole–Aitken basin impact melt sheet: Implications for lunar exploration

We modeled the differentiation of the South Pole–Aitken (SPA) impact melt sheet to determine whether noritic lithologies observed within SPA formed as a result of the impact. Results indicate differentiation of SPA impact melt can produce noritic layers that may accommodate observed surface compositions but only in specific scenarios. One of nine modeled impact melt compositions yielded layers of noritic materials that account for observations of noritic lithologies at depths of ~6 km. In this scenario, impact occurred before a hypothesized lunar magma ocean cumulate overturn. The 50 km deep melt sheet would have formed an insulating quenched layer at the surface before differentiating. The uppermost differentiated layers in this scenario have FeO and TiO2 contents consistent with orbital observations if they were subsequently mixed with the uppermost quenched melt layer and with less FeO‐ and TiO2‐enriched materials such as ejecta emplaced during younger impacts. These results verify that noritic lithologies observed within SPA could have formed as a direct result of the impact. Therefore, locations within SPA that contain noritic materials represent potential destinations for collecting samples that can be analyzed to determine the age of the SPA impact. Potential destinations include central peaks of Bhabha, Bose, Finsen, and Antoniadi craters, as well as walls of Leibnitz and Schrödinger basins. Additionally, potential remnants of the uppermost quenched melt may be preserved in gabbroic material exposed in “Mafic Mound. ” Exploring and sampling these locations can constrain the absolute age of SPA, a task that ranks among the highest priorities in lunar science.
Neural ring homomorphism preserves mandatory sets required for open convexity

It has been studied by Curto et al. (SIAM J. on App. Alg. and Geom., 1(1) : 222 $\unicode{x2013}$ 238, 2017) that a neural code that has an open convex realization does not have any local obstruction relative to the neural code. Further, a neural code $ \mathcal{C} $ has no local obstructions if and only if it contains the set of mandatory codewords, $ \mathcal{C}_{\min}(\Delta),$ which depends only on the simplicial complex $\Delta=\Delta(\mathcal{C})$. Thus if $\mathcal{C} \not \supseteq \mathcal{C}_{\min}(\Delta)$, then $\mathcal{C}$ cannot be open convex. However, the problem of constructing $ \mathcal{C}_{\min}(\Delta) $ for any given code $ \mathcal{C} $ is undecidable. There is yet another way to capture the local obstructions via the homological mandatory set, $ \mathcal{M}_H(\Delta). $ The significance of $ \mathcal{M}_H(\Delta) $ for a given code $ \mathcal{C} $ is that $ \mathcal{M}_H(\Delta) \subseteq \mathcal{C}_{\min}(\Delta)$ and so $ \mathcal{C} $ will have local obstructions if $ \mathcal{C}\not\supseteq\mathcal{M}_H(\Delta). $ In this paper we study the affect on the sets $\mathcal{C}_{\min}(\Delta) $ and $\mathcal{M}_H(\Delta)$ under the action of various surjective elementary code maps. Further, we study the relationship between Stanley-Reisner rings of the simplicial complexes associated with neural codes of the elementary code maps. Moreover, using this relationship, we give an alternative proof to show that $ \mathcal{M}_H(\Delta) $ is preserved under the elementary code maps.
A case study of ionospheric storm effects during long‐lasting southward IMF Bz‐driven geomagnetic storm

Multiple instrumental observations including GPS total electron content (TEC), foF2 and hmF2 from ionosondes, vertical ion drift measurements from Communication/Navigation Outage Forecasting System, magnetometer data, and far ultraviolet airglow measured by Thermosphere, Ionosphere, Mesosphere Energetics and Dynamics/Global Ultraviolet Imager (TIMED/GUVI) are used to investigate the profound ionospheric disturbances at midlatitude and low latitude during the 14–17 July 2012 geomagnetic storm event, which was featured by prolonged southward interplanetary geomagnetic field component for about 30 h below −10 nT. In the East Asian/Australian sector, latitudinal profile of TEC variations in the main phase were characterized by three bands of increments and separated by weak depressions in the equatorial ionospheric anomaly (EIA) crest regions, which were caused by the combined effects of disturbance dynamo electric fields (DDEF) and equatorward neutral winds. In the recovery phase, strong inhibition of EIA occurred and the summer crest of EIA disappeared on 16 July due to the combined effects of intrusion of neutral composition disturbance zone as shown by the TIMED/GUVI O/N2 measurements and long‐lasting daytime westward DDEF inferred from the equatorial electrojet observations. The transit time of DDEF over the dip equator from westward to eastward is around 2200 LT. In the American longitude, the salient ionospheric disturbances in the summer hemisphere were characterized by daytime periodical intrusion of negative phase for three consecutive days in the recovery phase, preceded by storm‐enhanced density plume in the initial phase. In addition, multiple short‐lived prompt penetration electric fields appeared during stable southward interplanetary magnetic field (IMF) Bz in the recovery phase and were responsible for enhanced the EIA and equatorial ionospheric uplift around sunset.
Magnetostratigraphy Susceptibility used for High-Resolution Correlation Among Paleocene–Eocene Boundary Sequences in Egypt, Spain, and the U.S.A.

The magnetostratigraphy susceptibility technique is used to establish high-resolution correlation among Paleocene–Eocene boundary sequences in Egypt, Spain, and the U.S.A. This work initially focuses on the Global boundary Stratotype Section and Point (GSSP), defining the base of the Ypresian Stage (lowest Eocene), located in the Dababiya Quarry near Luxor in Upper Egypt. The base of the Eocene represents the beginning of the Paleocene–Eocene Thermal Maximum (PETM) identified by a negative carbon isotope (δ13C) excursion. While onset of the CIE is somewhat gradual in most reported Paleocene–Eocene (P–E) sections, at the GSSP it is very abrupt and begins immediately after an unusual lithologic change that magnetic susceptibility (MS) and other data indicate represents a short erosional or nondepositional hiatus. Comparison of MS zones from five well-studied marine sequences (the Dababiya Quarry GSSP, Jebal El Qreiya, also in Upper Egypt, Zumaia in northern Spain, Alamedilla in southern Spain, and the MGS-1 Harrell Core from southeastern Mississippi, U.S.A.) with that from the GSSP site shows a period of reduced sedimentation and nondeposition through the boundary interval in the GSSP. This interval, estimated to have lasted for ~ 10,000 years, is less than the biostratigraphic resolution for the site. Due to the hiatus in the GSSP, we have chosen the P–E section in Zumaia as the MS reference section for the P–E boundary interval. Because the correlation between the Zumaia section in Spain and the MGS-1 Core from the U.S.A. is excellent, and because the MGS-1 data set represents a longer interval of time than does the Zumaia data set, we use the MS data from the MGS-1 Core to extend the MS zones from Zumaia and establish a MS composite reference section (MS CRS) for the P–E boundary interval sampled. Orbital-forcing frequencies for the Zumaia reference section are then identified, via spectral analysis. Extending the MS zones into the MS CRS allows age assignment to MS zones for all five sections with a resolution of ~ 26,000 years.
Probing spin hydrodynamics on a superconducting quantum simulator

Characterizing the nature of hydrodynamical transport properties in quantum dynamics provides valuable insights into the fundamental understanding of exotic non-equilibrium phases of matter. Simulating infinite-temperature transport on large-scale complex quantum systems remains an outstanding challenge. Here, using a controllable and coherent superconducting quantum simulator, we experimentally realize the analog quantum circuit, which can efficiently prepare the Haar-random states, and probe spin transport at infinite temperature. We observe diffusive spin transport during the unitary evolution of the ladder-type quantum simulator with ergodic dynamics. Moreover, we explore the transport properties of the systems subjected to strong disorder or a titled potential, revealing signatures of anomalous subdiffusion in accompany with the breakdown of thermalization. Our work demonstrates a scalable method of probing infinite-temperature spin transport on analog quantum simulators, which paves the way to study other intriguing out-of-equilibrium phenomena from the perspective of transport.
A Landsat-based study of black rock coatings proximal to base metal smelters, Sudbury, Ontario, Canada

Past emission of metal-bearing particulate matter, sulphur dioxide (SO2), and sulphuric acid by base metal smelters in the Sudbury region led to widespread loss of vegetation, contamination of soils, and formation of black coatings on rock surfaces. These black coatings formed through the incorporation of smelter-borne particulate matter into the partly dissolved uppermost layers of siliceous minerals on exposed rock, and are characterized by high heavy-metal content. This study involved assessment of the reflectance properties of black coatings in the Sudbury region, and determination of the geographic distribution of coatings through supervised classification of reflectance data derived from a Landsat Enhanced Thematic Mapper Plus (ETM+) image. Classifications involved the use of the Spectral Angle Mapper (SAM), Maximum Likelihood, and Feedforward Backpropagation Neural Network algorithms. The reflectance spectra of black coatings in the Sudbury region are relatively flat and featureless, and are characterized by reflectance values less than ~13% across the visible, near-infrared, and short-wave infrared. Spectral properties are similar to those of magnetite, a spinel-group mineral known to be present in Sudbury coatings. The presence of carbon-rich soot particles may be an important influence on the reflectance properties of coatings. SAM classification results are characterized by the widespread mislabelling of uncoated urban and open-pit sites as mantled by black coatings, and neural network results problematically mislabel some uncoated wetland sites as coated. Results generated by the Maximum Likelihood algorithm most usefully depict the distribution of exposed black coatings in the Sudbury region. The mapping of black coatings using remote-sensing methods can provide useful information on the spatial character of environmental degradation in the vicinity of smelters, and should be helpful in the monitoring of environmental recovery where emissions have been reduced or eliminated.
An Initial Investigation of Neural Replay Simulator for Over-the-Air Adversarial Perturbations to Automatic Speaker Verification

Deep Learning has advanced Automatic Speaker Verification (ASV) in the past few years. Although it is known that deep learning-based ASV systems are vulnerable to adversarial examples in digital access, there are few studies on adversarial attacks in the context of physical access, where a replay process (i.e., over the air) is involved. An over-the-air attack involves a loudspeaker, a microphone, and a replaying environment that impacts the movement of the sound wave. Our initial experiment confirms that the replay process impacts the effectiveness of the over-the-air attack performance. This study performs an initial investigation towards utilizing a neural replay simulator to improve over-the-air adversarial attack robustness. This is achieved by using a neural waveform synthesizer to simulate the replay process when estimating the adversarial perturbations. Experiments conducted on the ASVspoof2019 dataset confirm that the neural replay simulator can considerably increase the success rates of over-the-air adversarial attacks. This raises the concern for adversarial attacks on speaker verification in physical access applications.
Data-driven modeling and forecasting of chaotic dynamics on inertial manifolds constructed as spectral submanifolds.

We present a data-driven and interpretable approach for reducing the dimensionality of chaotic systems using spectral submanifolds (SSMs). Emanating from fixed points or periodic orbits, these SSMs are low-dimensional inertial manifolds containing the chaotic attractor of the underlying high-dimensional system. The reduced dynamics on the SSMs turn out to predict chaotic dynamics accurately over a few Lyapunov times and also reproduce long-term statistical features, such as the largest Lyapunov exponents and probability distributions, of the chaotic attractor. We illustrate this methodology on numerical data sets including delay-embedded Lorenz and Rössler attractors, a nine-dimensional Lorenz model, a periodically forced Duffing oscillator chain, and the Kuramoto-Sivashinsky equation. We also demonstrate the predictive power of our approach by constructing an SSM-reduced model from unforced trajectories of a buckling beam and then predicting its periodically forced chaotic response without using data from the forced beam.
The Supersonic Project: Lighting Up the Faint End of the JWST UV Luminosity Function

The James Webb Space Telescope (JWST) is capable of probing extremely early eras of our Universe, when the supersonic relative motions between dark matter and baryonic overdensities modulate structure formation (z ≳ 10). We study low-mass galaxy formation, including this “stream velocity,” using high-resolution AREPO hydrodynamics simulations and present theoretical predictions of the UV luminosity function (UVLF) and galaxy stellar mass function down to extremely faint and low-mass galaxies (M UV ≳ −15, 104 M ⊙ ≤ M * ≤ 108 M ⊙). We show that, although the stream velocity suppresses early star formation overall, it induces a short period of rapid star formation in some larger dwarfs, leading to an enhancement in the faint end of the UVLF at z = 12. We demonstrate that JWST observations are close to this enhanced regime and propose that the UVLF may constitute an important probe of the stream velocity at high redshift for JWST and future observatories.
Three-dimensional characterization of the steel-concrete interface by FIB-SEM nanotomography

While it is widely accepted that the steel-concrete interface (SCI) plays an important role in governing the long-term durability of reinforced concrete structures, understanding about the primary features of the SCI that influence corrosion degradation mechanisms has remained elusive. This lack of knowledge can be attributed, on the one hand, to the complex heterogeneous nature of the SCI, and, on the other hand, the absence of experimental techniques suitable for studying the relevant features of the SCI. Here, we use focused ion beam - scanning electron microscopy (FIB-SEM) nanotomography to obtain high resolution 3D tomograms of the steel-concrete interfacial zone. Five tomograms, spanning volumes ranging from 8,000 to 200,000 cubic micrometer, were acquired for situations representative of both non-corroded and corroded SCIs. The achieved voxel size falls within the range of 30-50 nm, thus providing a resolution clearly surpassing the capabilities of computed X-ray tomography. This resolution enables the 3D characterization of the microstructure at the capillary scale, which is the scale at which relevant corrosion and related mass transport processes occur. Thus, FIB-SEM nanotomography is capable of yielding datasets of the SCI that serve as basis for the generation of digital twins of the interfacial microstructure, thereby enabling future studies about durability and corrosion of reinforced concrete at the pore scale.